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Research Project for 5th/6th Semester A.S 2022-23

Research project guidelines 2022-23, tracking sheet : research project tracking sheet 2021-22.

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Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about:

Your overall aims and approach
The type of research design you’ll use
Your sampling methods or criteria for selecting subjects
Your data collection methods
The procedures you’ll follow to collect data
Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

How much time do you have to collect data and write up the research?
Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

How many participants do you need for an adequate sample size?
What inclusion and exclusion criteria will you use to identify eligible participants?
How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

The distribution of the data (e.g., the frequency of each score on a test)
The central tendency of the data (e.g., the mean to describe the average score)
The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

Make estimates about the population based on your sample data.
Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

What is the golden thread
What are research aims ( examples )
What are research objectives ( examples )
What are research questions ( examples )
The importance of alignment in the golden thread

What is the “golden thread”?

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

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Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.” “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

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Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

The research objectives detail the specific steps that you, as the researcher, will take to achieve the research aims you laid out.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples

Again, we’ll stick with the research aims and research objectives we mentioned previously.

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

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39 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

Thanks so much. This was really helpful.

I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

i found this document so useful towards my study in research methods. thanks so much.

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

This is quite helpful. I really appreciate!

The article made it simple for researcher students to differentiate between three concepts.

Very innovative and educational in approach to conducting research.

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

A very helpful piece. thanks, I really appreciate it .

Very well explained, and it might be helpful to many people like me.

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Exactly what I need in this research journey, I look forward to more of your coaching videos.

This helped a lot. Thanks so much for the effort put into explaining it.

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

I’m excited and thankful. I got so much value which will help me progress in my thesis.

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

I really found these tips helpful. Thank you very much Grad Coach.

I found this article helpful. Thanks for sharing this.

thank you so much, the explanation and examples are really helpful

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

Provide an overview of the research problem and the need for a research methodology section
Outline the main research questions and objectives

II. Research Design

Explain the research design chosen and why it is appropriate for the research question(s) and objectives
Discuss any alternative research designs considered and why they were not chosen
Describe the research setting and participants (if applicable)

III. Data Collection Methods

Describe the methods used to collect data (e.g., surveys, interviews, observations)
Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
Detail any procedures or software used for data analysis

V. Ethical Considerations

Discuss any ethical issues that may arise from the research and how they were addressed
Explain how informed consent was obtained (if applicable)
Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

Summarize the key aspects of the research methodology section
Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

How to Cite Research Paper – All Formats and...

Data Collection – Methods Types and Examples

Delimitations in Research – Types, Examples and...

Research Paper Format – Types, Examples and...

Research Process – Steps, Examples and Tips

Research Design – Types, Methods and Examples

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1st Sem B.Sc HHA Notes (hidden)

Food Pro Th
F & B Service Th
Acc. Opr Th
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Hotel Engg.

1st Sem B.Sc HHA Notes

2nd sem b.sc hha notes.

Acc. Op. Th
Accountancy
Food Science
Communication

3/4th Sem B.Sc HHA Notes

Food Pro. Th
F & B Opr. Th
F & B Control
H Accountancy

5th Sem B.Sc HHA Notes

F & B Oper. Th
Acc. Mgt. Th
F.O. Mgt. Th
Financial Mgt.
Strategic Mgt.

6th Sem B.Sc HHA Notes

F & B Mgt.
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M.Sc HA – IHM Notes

Hospitality Mgt.
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International Marketing
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The following are individual research projects completed in the NRES online M.S. program that may be of use to current students preparing for this phase of their degrees. These alumni have granted permission for their project to be posted. These works may not be reproduced or redistributed without the author's explicit consent.

Danielle McLaughlin (2016) Using Q-Methodology to Understand Environmental Opposition to Fracking Research Director: Dr. Bethany Cutts
Christin Crutchfield (2016) Effect of Land Management Practices on Soil Moisture Retention Research Director: Dr. Michelle Wander
Ann Devine (2016) Peregrine Falcon Dispersal and Habitat Imprinting (1994-2013) Research Director: Dr. Michael Ward
Danielle Hilbrich (2015) Determining the Effectiveness of the Clean Boats Crew: An Education and Outreach Program Aimed at Preventing the Spread of Aquatic Invasive Species in Lake County, Illinois Research Director: Dr. Craig Miller
Joshua Yellin (2014) Evaluating the Efficacy of an Artificial Floating Island as Fish Habitat in the Chicago River: A Pilot Study Research Director: Dr. Christopher Taylor
Jeffrey Weiss (2014) Virtual Buffalo Creek: A Tool to Educate Citizens and Students on Issues in a Watershed with Symptoms of Urban Stream Syndrome in Northeastern Illinois Research Director: Dr. Robert Hudson
Margaret Edwards (2014) Sources of Variation in Height to Dry Weight Ratios of the Eastern Oyster (Crassostrea verginica) Research Director: Dr. Ruth Carmichael
Rebecca Grill (2013) Can Lake Michigan Ravine Streams Support Potadromous Fish? Research Director: Dr. Leon Hinz
Samantha Rich (2013) Distribution and Functional Linkages of Large Woody Debris in the Nearshore-Elwha River, Olympic Peninsula Research Director: Anne Shaffer
Christie Wortz (2012) Using Infrared Cameras to Create Population Indices for White-Tailed Deer on the Islands of Lake Sidney Lanier, Georgia Research Director: Dr. Robert Schooley
Jason Hargrove (2011) Assessing the Potential Hazards at O'Hare International Airport Wetlands Using Field Observations and Avian Radar Research Director: Dr. Edwin Herricks
Mark Melka (2011) Classroom in Nature Research Director: Dr. Andrea Faber Taylor
Russell Brandenburg (2010) A Post Project Analysis: An Assessment of an Urban Riparian Ecosystem Restoration in Central Ohio Research Director: Dr. Anton Endress
Janet Backs (2008) Urban greening: low maintenance planters as a means for urban residents to to participate in greening initiatives Research Director: Dr. Wes Jarrell.
Anne-Marie Eischen (2008) Project-based Learning in an UIUC Horticultural Capstone Course Research Director: Dr. Daniel Warnock.
Kathleen Frazier (2007) Effects of Juglone on Juglans Nigra Seedling Growth Research Director: Dr. Jeff Dawson.

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Biomechanical Engineering Research Samples

Student: Narek Akopyan Professor/Sponsor: Professor Liwei Lin Mentor: Dr. Ryan Sochol Research Project Title: Micropost Traction Force Quantification

Abstract: Microfabricated posts were designed to advance cell handling techniques, which is useful for research in biology. By creating stiffness and interpost spacing gradients along the micropost array, bovine aortic endothelial cells (BAECs) were observed to unidirectionally migrate. The cells migrated in directions of increasing micropost stiffness and decreasing interpost spacing. The goal was to quantify the forces that the cell pushed or pulled on the microposts in order to move in one direction. These forces were calculated by taking microscopic images of the immovable bottom of the micropost array which was stuck to the substrate and the top of the micropost array which moved due to the forces applied by the cell. By applying the general Hooke’s Law, forces were related by the displacement each micropost moved since each cantilever could be approximated as a spring. With aid of image processing software, micropost traction forces were quantified, and the edges of the cells were found to pull more strongly on the microposts compared to the center of the cell. The forces were found to pull inwards towards the center of the cell causing unidirectional cellular migration due to the variable stiffness and spacing gradients.

Student: Leela Amladi Professor/Sponsor: Professor Liwei Lin & Professor Ken Goldberg Mentor: Sanjay Krishnan Research Project Title: Trend Searching for Surgical Robotics with Low-Cost, Low-Resolution Force and Capacitive Sensors

Student: Alex Belinski Professor/Sponsor: Professor Lisa Pruitt Mentor: Farzana Ansari and Hannah Gramling Research Project Title: Investigating the possibility of cell-induced corrosion on metallic bearing surfaces in total shoulder arthroplasty devices

Abstract: Introduction: Evidence of direct cellular attack on orthopedic implants has been observed in knee and hip implants [1], but has not been reported in shoulder implants. This work documents signs of direct cellular attack on cobalt chromium humeral heads retrieved from shoulder arthroplasty patients. These findings may provide evidence that cells initiate corrosive sites on metal bearing surfaces in the shoulder.

Methods: Visual evidence of cellular attack was observed macroscopically on eighteen humeral head implants (Figure 1). Ten of the samples showing possible cellular attack (Depuy Global) were further examined using scanning electron microscopy (FEI Quanta, Hillsboro, OR). Following Gilbert et al., samples were imaged at 5 kV and 20 kV to highlight possible biological material. Sites were characterized using EDS line scans at 20 kV to determine whether changes in element concentrations spatially correlated with boundaries of possible cellular attack (“attack sites”).

Results: Two types of attack sites were observed. The first showed increased concentrations of carbon overlapping areas previously identified as possible biological material (Figure 2), and the second showed increased concentrations of iron (Figure 3). Both instances were coupled with significant dips in concentrations of constituent materials cobalt, chromium, and molybdenum. Due to the low excitation energy of carbon, lower energy electron beams scattered by biological material resulted in brighter regions within the SEM image, while using higher energy beams produced comparatively darker regions. Carbon-rich regions were found on a total of 7 samples and iron-rich regions were found on 3.

Discussion: The presence of carbon may be indicative of biological material remnant from direct cellular attack. As shown previously [1], increased levels of iron may be evidence of a Fenton reaction. These observations have been observed in hip and knee retrievals; this is the first reported observation in shoulder retrievals. Future work will examine additional samples and reverse shoulder arthroplasty retrievals. Reference: [1] Gilbert, J., et al. (2014). Journal of Biomedical Materials Research Part A, 103(1), pp.211-223.

Student: Bita Behziz Professor/Sponsor: Professor Masayoshi Tomizuka Mentor: Daisuke Kaneishi, Robert Matthew Research Project Title: Torsional Stiffness Characterisation for the APEX Gamma Exoskeleton Research Areas: Biomechanical Engineering, Controls

Abstract: Weakness or paralysis of body muscles are one of the common side effects for stroke survivors. Assistive devices can be to support these individuals, aiding them in functional daily tasks. Developing devices to assist upper limb movement would depend on precise characterization and control of the assistive device and on the needs of the user. This paper investigates the mechanical impedance of a pneumatic cylinder under several air pressure conditions for use in assistive devices. The characterization is based on fixing the number of moles of gas on each side of the cylinder and measuring the associated torque. This static torque is evaluated at different angular positions, corresponding to the flexion/extension of the elbow. It was shown experimentally that the maximum stiffness that can be applied to the user is 2.18Nm/rad when the air pressure is initialized at 50 psi in both chambers and the minimum stiffness is below 0.01 Nm/rad when the system is initialized at atmospheric pressure. This study offers deeper insight into how linear pneumatic cylinders can be used to semi-passively provide assistance to individuals with limb weakness, and supports previous publications which tested different assistive pressures on human subjects without a model of the associated stiffness’s response.

Student: Connor Benton Professor/Sponsor: Professor Tony Keaveny Mentors: Megan Pendleton and Alex Baker Research Project Title: Design and Implementation of an Apparatus for Flexural Testing of Trabeculae

Abstract: The purpose of this research is to identify the most efficient and precise method of measuring the strain-stress properties of trabecular bone tissue at a very small scale. The heterogeneity inherent in bone tissue across different people suggests that there is value in large-scale testing in order to better understand how factors such as age, disease, and disease treatment impact the material properties of the bone itself. We first evaluated the differences between strain tests and their feasibility in regard to testing specimens of our ideal size, eventually coming to the conclusion that three-point flexural testing was the best approach. We designed a test bed for this research, using high-resolution micrometers and actuators in order to give us the control we desired, and fabricated the apparatus in the student machine shop. In order to verify the accuracy of our solution, we conducted material tests of aluminum samples with known strain-stress properties. Our test bed is a compact, accurate, easy-to-use platform that provides the means to test large quantities of specimens and establish a better understanding of their material properties. This research will be built upon by our team in future testing and evaluation of different impacts on the strength of trabecular bone tissue.

Student: Christopher Berthelet Professor/Sponsor: Professor Lisa Pruitt Mentors: Farzana Ansari and Louis Malito Research Project Title: Design Considerations for Total Shoulder Replacements: An Analysis of Glenoid Contact Stresses

Abstract: INTRODUCTION The total shoulder replacement (TSR) is the third leading reconstructive orthopedic procedure behind hip and knee replacements, and the fastest growing arthroplasty device in the market today [1]. The ball-and-socket design typically consists of a cobalt chrome (CoCr) humeral head that articulates against an ultrahigh molecular weight polyethylene (UHMWPE) glenoid. While hip and knee replacements can last 10-15 and 18-20 years respectively, TSR’s only last around 5-13 years. This may be related to the fact that TSRs encounter complex rotational and translational joint kinematics; for example, superior-inferior motion of the humeral head along the glenoid occurs through abduction-adduction. These complex loading scenarios can lead to failure via implant loosening, joint instability, infection, implant wear, and device fracture. Fracture along the glenoid rim is a particularly unique failure, where excessive translational motion results in eccentric loading that can cause subsurface cracking and eventual fracture along the circumference [2]. While the effect of conformity and glenoid thickness on contact stresses has been documented, the interplay of these design factors with differing material properties and eccentric loading scenarios has not been thoroughly examined. This research seeks to utilize computational analysis and Finite Element Analysis (FEA) methods to assess the contact stresses that develop in the glenoid as a function of device geometry, material properties, and translational motion.

METHODS The glenoid contact stresses that develop due to translation of the humeral head were computationally investigated using MATLAB. A standard elasticity solution (SES) developed by Bartel et al. [3] was used to calculate contact stress. Following Sweiszkowski et al. [4] , the translation of the humeral head from the central axis was modeled as a rotation of the head about the origin of the glenoid. Maximum glenoid contact stress data was generated by the MATLAB code at each degree of angular translation. In addition to testing the effects of humeral head translation on contact stresses in the glenoid component, the effects of material composition (elastic modulus), glenoid thickness, component conformity, and specific humeral-glenoid radii combinations were investigated.

RESULTS & DISCUSSION For all variables evaluated, higher contact stresses were seen farther away from the central axis (i.e. greater translation). Contact stresses near the rim of the glenoid were on average 11.3% greater than those at the center of the component for all conformities, material compositions, and thicknesses evaluated. This has important clinical implications considering that neutral positioning results in stresses ranging from 12 to 40 MPa. Combined with translation, these elevated stresses can easily exceed the yield stress of UHMWPE and contribute to crack initiation that under cyclic loading can propagate to fracture.

Decreasing conformity (increasing radial mismatch) caused an overall increase in contact stress and enhanced the effect of humeral head translation. Contact stresses were greatest for highly crosslinked and sub-melt annealed formulations. Contact stress also increased as glenoid thickness decreased or as device size decreased. Overall, the findings demonstrate that TSRs experience a complex stress state that subjects the UHMWPE to both yield and fracture.

ONGOING WORK Current work is focusing on developing an FEA model of a humeral-head/glenoid system (ABAQUS) to confirm and enhance the above computational analysis.

ACKNOWLEDGEMENTS I would like to also acknowledge Robin Parrish for her assistance with code development and analysis for this project.

Student: Matt Cameron Professor/Sponsor: Professor Lisa Pruitt Mentor: Cynthia Cruz Research Project Title: Improved Method for Specified Motion Monitoring While Conducting Wear Testing on Biomedical Thermoplastic Polycarbonate-Urethane

Abstract: In current medical devices, cobalt chromium (CoCr) and ultra-high molecular weight polyethylene (UHMWPE) materials are used to minimize fatigue and wear of joint replacements while in vivo. However due to the nature of the polymer, UHMWPE particulates can wear off and be released into the body and subsequently loosen the implant with the potential to fail. A new polycarbonate-urethane material, Bionate®, is considered to have little to no wear compared to UHMWPE [1]. Studies have only focused on Bionate® 90A and 55D. DSM, Inc. is looking at the application of Bionate® 80A and 75D in the shoulder, yet no studies focused on the wear characteristics of these specific Bionate® chemistries. Hence, the Medical Polymer Group has a Multi-Directional Tribo-System (ELVIS), which will conduct wear testing on the 80A and 75D Bionate® material.

ELVIS is a converted CNC mill controlled by a custom a National Instruments LabVIEW Virtual Instrument (VI). The VI controls the type of motion, speed, and amount of translation and rotation to conduct wear tests as well as mimic motions within a joint. The VI simultaneously monitors the position of the point of contact, the temperate and electric current of the CNC motors, and cycle information. In preparation for the Bionate® wear testing, improvements were made to how the VI monitors cycle count and cycle time. This was achieved by adding new features to the motion code in addition to timing functions that execute when motion parameters change. This allows for more accurate testing and estimation for how long the test will take. The revamped VI in combination with other adjustments will allow ELVIS to accommodate samples of the new material and then the wear characteristics of Bionate® 80A and 75D can be established. Future studies will use ELVIS to mimic the gait cycle in a shoulder joint, which has translation, abduction/adduction, and elevation all at varying rates within one motion cycle. This would be a better model at predicting the wear in the shoulder joint.

Student: Jiayang Cao, Joyce Huang, Tatiana Jansen, and Rohan Konnur Professor/Sponsor: Professor Grace O’Connell Research Project Title: Modifying the existing Patient Controlled Analgesia (PCA) pump to explore non-chemical pain relief

Abstract: Following research conducted at UCSF, under Ben Alter and Walter German, our group had the task of designing and building a device to attach to the existing PCA pump, in order to assess the effect of audiovisual cues on pain relief. Currently, the commonly-used PCA pump includes a handset that only the patient has control over. When the button on the handset is pressed, a very small dose of pain relieving medicine is dispensed into an IV in the patient’s arm. There is a specified lockout time as well that varies patient to patient. Our device is comprised of a Raspberry Pi Model 3B as our computer, enclosed in an acrylic box that we designed and made to attach to the IV pole. We also had to add a second button to the existing handset to interact with our device. Our attachment to the pump reliably plays an audiovisual cue, which researchers have made, on a monitor screen any time the button is pressed outside the lockout period. In addition, our device stores the patient data associated with these button presses into a csv file that can later be analyzed.

Student: Wan Fung Chui Professor/Sponsor: Professor Grace O’Connell Mentor: Megan Pendleton Research Project Title: Data Processing for MTS Fatigue Testing Research Areas: Biomechanical Engineering, Mechanics

Abstract: As ever more ambitious projects for human spaceflight are planned by both governmental and commercial organizations, the effects of long-term exposure to ionizing radiation on the mechanical properties of bone has emerged as an active area of research. Our team in the O’Connell lab strives to test the hypothesis that ionizing radiation encountered in space leads has an embrittling effect on bone tissue.

The central source of data on the experimental side of our study this semester involved conducting fatigue tests on L5 bones extracted from irradiated and non-irradiated rats. These tests, carried out using an MTS testing system, resulted in many large, unwieldy raw data files. In an attempt to automate and streamline our data collection process across the multiple samples we tested, I was tasked with writing several computer programs in MATLAB which accepted raw data as input and produced processed numbers and graphs as output. Metrics of interest included number of cycles to failure, strain to failure, slope of the secondary region in the fatigue process, and stiffness within the secondary region, all of which shed light into how, and to what extent, ionizing radiation affects the mechanical properties of bone.

The process of writing efficient, generalizable and user-friendly code involved navigating a series of challenges including lengthy file-parsing times, accounting for formatting difficulties in the raw data input, and recognizing the secondary and tertiary regions in the strains-cycles graphs, among other issues. Ultimately, this culminated in three polished, well-commented MATLAB programs which can now be easily used by members of our team and, potentially, other members of our lab conducting fatigue studies, to produce clean graphs and extract desired metrics in a user-friendly and timely manner.

Student: Lace Co Ting Keh Professor/Sponsor: Professor Homayoon Kazerooni Research Project Title: Exoskeleton For Stroke Rehabilitation

Student: Joe Felipe Professor/Sponsor: Professor Grace O’Connell Research Project Title: Design of Large Scale Waterbath For Mechanical Testing of Soft Tissue

Abstract: Mechanical testing of the intervertebral disc requires that the soft tissue maintain hydration for accurate and meaningful results. The aim of this project was to build a large scale waterbath compatible with the lab’s MTS machine for usage in soft tissue biomechanics. Prior to using a bath for mechanical testing, experiments that were performed could only be executed for 2-3 hours before samples of bone-disc-bone segments no longer maintained proper hydration. A new testing configuration was to be implemented in which the load cell was to be moved from the bottom to the top of the MTS machine. Grips were prepared to hold samples of bone-disc- bone segments in place for testing. With this new configuration, the bath and grips could be used for all sorts of mechanical testing such as compression, tension, and torsion. A 3-D model of the bath was made in Solidworks prior the machining of the bath. The implementation of a waterbath has greatly improved the accuracy of our results. Studies in the O’Connell lab have been focused on understanding the mechanical function of the healthy, injured and degenerated disc with the goal of developing viable repairment strategies. It is essential to have accurate and repeatable data to meet this goal. For example, a recent study “Osmotic loading environment alters intervertebral disc mechanical function” focused on comparing the mechanical properties of the intervertebral disc when soaked in a 1X vs 20X (.1M or 2M) saline solution. The difference in salinity would mimic two different states of hydration experienced in diurnal loading. Prior to the bath, bone-disc-bone segments were soaked overnight and then tested for only 2.5 hours. A prediction model was used in MATLAB which determined the samples would take about 16 hours until they reached equilibrium (no longer displacing during creep). With the implementation of the waterbath, results will no longer need to be “predicted” based off the limited data that could be collected. The next study using the bath will be on understanding the effects of space flight on spine biomechanics.

Student: Benjamin Glaser Professor/Sponsor: Professor Lisa Pruitt Mentor: Noah Bonnheim Research Project Title: Machining Methods for Carbon Fiber PEEK Composite Research Areas: Biomechanical Engineering, Materials

Abstract: Carbon fiber reinforced polyether ether ketone (PEEK) is a polymer composite used in orthopedic implants and screws because it offers benefits over metals in some cases. Because there are multiple manufacturers of carbon fiber reinforced PEEK, it is important to understand their relative material properties, and also to compare those samples to a non-carbon-fiber PEEK for a control. A way to identify important material properties is through monotonic tensile testing, which requires material samples of specified sizes, as per ASTM documentation. In preparing for the machining of the PEEK sample testing “dogbones”, three manufacturing methods were researched for their benefits and effectiveness. Water jet cutting, CO2 laser cutting, and CNC milling were looked at. Water jet cutting was discounted because of machine limitations for handling polymer waste. CO2 laser cutting was not chosen because of the impact of high temperatures on the material properties of PEEK, specifically samples containing carbon fiber. Traditional machining was found to be more expensive for both cost and time, but was chosen for machining dogbones as the remaining viable option. Samples containing carbon fiber need are, however, more difficult to machine because of an increased wear on machining tools, and also because of the potential health hazards from carbon fiber dust being released into the air during the subtractive manufacturing process. A machine shop was chosen based on the capability of handling carbon fiber PEEK, as well as cost per specimen. ASTM testing will be performed following the ASTM document D638, which specifies the geometric shape of the dogbone specimens as well as the strain rate and method for testing. Because the strain rate is expressed in terms of time to failure, some dogbones must be sacrificed at different strain rates to find the appropriate rate for the remaining samples.

Student: Benjamin Glaser Professor/Sponsor: Professor Lisa Pruitt Mentor: Noah Bonnheim Research Project Title: Finite element modeling of vertebral bodies for total disc replacements

Student: Aditya Goel Professor/Sponsor: Professor Grace O’Connell Mentor: Shannon Emerzian Research Project Title: The Effect of Ribose on Mechanical Properties of Bones

Student: Landon Henson Professor/Sponsor: Professor Lisa Pruitt Mentor: Cynthia Cruz Research Project Title: Polishing UHMWPE for use in experiments

Abstract: The purpose of our current research is focused on various aspects of ultra high molecular weight polyethylene (UHMWPE) and Bionate® as it relates to wear, life and early failure in orthopedic implants. It is well known in the orthopaedic community that UHMWPE in combination with cobalt chromium (CoCr) are good counter bearing materials for joint replacements. However, UHMWPE does have a finite life expectancy. Historically wear and damage of UHMWPE has affected the longevity of orthopaedic implants. Thus, it is our goal to gain a better understanding of how the wear characteristics of Bionate® compares with the wear characteristics of UHMWPE. It has been proposed that Bionate® 80A and 75D should be used as a counter bearing material for CoCr in the shoulder joint by DSM, inc.

In order to run experiments and thus get a better understanding of how the wear characteristics of Bionate® and UHMWPE effect the life of implants, we must reproduce as close as possible a medical grade finish that manufacturers achieve on their implants. One such property is the “smoothness” or roughness average (Ra) number of the sample to be used in experiments. Ra is the measure of the texture of a surface. To quantify the surface roughness we use a profilometer to measure the profile of the UHMWPE surface.

Previously in our lab, UHMWPE samples were polished to an Ra number of approximately 0.2~.4 µm. To achieve this finish with repeatability a new SOP for polishing was needed. The new process is a two-part wet sanding procedure. Once the appropriate geometric tolerances are obtained from machining the samples, they are polished. Optimal conditions for polishing show that abrading the sample with 800-grade sandpaper followed by 1200-grade result in consistent Ra of .2~.3 µm.

Student: Patrick Holmes Professor/Sponsor: Professor Tony Keaveny Research Project Title: Effects of space-relevant levels of ionizing radiation on rat trabecular bone

Abstract: Ionizing radiation is often used to treat cancer by applying a large dose of radiation locally to targeted tissue. This causes a number of destructive effects on bone in the affected area, which have been fairly well studied. The effects of very small doses of radiation on bone is less well documented. On a deep space mission, astronauts will be constantly exposed to radiation that is blocked for the rest of us by Earth’s magnetosphere. The cumulative whole body dose they are likely to receive is very small; below what is used locally on a cancer patient in a single sitting. However, in conjunction with musculoskeletal disuse, this small amount of radiation could have a significant effect on the astronaut’s bone. After helping with a literature review last semester, this semester I aided in the setup of an experiment to quantify changes to the material properties of rat vertebrae exposed to low doses of radiation. Individual vertebra will be tested in compression and at the same time simulated in a finite element model. In order to receive accurate compression results, the vertebrae must be prepared such that they have parallel ends. Otherwise, bending can occur and skew our data. To do this, several jigs were employed to first cement the vertebra in PMMA (bone cement) and then to saw off the ends of the vertebra with a slow moving ISOMET saw, yielding parallel sides. Physical tests will determine the parameters of our finite element model, with which we hope to explore the changes to the post yield properties of rat trabecular bone. We expect low doses of radiation to embrittle the trabecular bone, and to cause it to fracture earlier.

Student: Naomi Kibrya Professor/Sponsor: Professor Grace O’Connell Research Project Title: Effect of Injury and Axial Compression Preload on Intervertebral Disc Torsional Mechanics

Student: Divya Kulkarni Professor/Sponsor: Professor Tony Keaveny Mentor: Shashank Nawathe Research Project Title: Influence Of Typical Population-Variations In Tissue-Level Ductility On The Femoral Strength

Abstract: The strength of the whole bone is widely known to have a direct correlation with aging, disease and treatment. However there is not much work on the effect of tissue level ductility on whole bone strength. It makes sense that a change in individual tissue ductility would affect the overall failure of the bone whether it be the femur or the vertebrae. There have been studies in the past for which the tissue level ductility is manipulated to be either fully ductile or fully brittle and the effect of these cases on the strength of the whole bone are studied. In the real world case such extreme behaviors would most likely not be seen. In our study, we focus on human proximal femurs to study the whole bone strength and varying values of ultimate strain for the bone tissue ductility. The distinction between cortical and trabecular bone is made to find a deeper correlation between tissue level ductility and femoral strength. Relating the tissue level ductility on a micro scale with whole bone strength will be vital in understanding the cause of hip fractures and its risk-assessment.

Four cadavers are chosen to test various values of ultimate strain for both trabecular and cortical tissues of these bones. The values used are based on the previous studies of general ultimate strain values in the human population. It was assumed that ultimate strain values in tension and compression were equal. We performed our non-linear finite element analyses using the iterative quasi-nonlinear technique that has also been previously used in our fully brittle analyses.

The femoral strength was determined from each set of ultimate strains on both the cortical and the trabecular bone. This strength was determined using the force strain curve for a structure-level and calculating the 0.2% offset. Tissue level failure included both yielding and fracture. It seems as though during a sideways fall, only about 10% to 12% of the femoral strength is actually affected by the changing tissue ductility. The trabecular bone seems to have a larger effect on the entire bone strength. It seems the cortical bone ductility only plays a large role when the trabecular bone ductility is already low.

Student: Siyang Liu Professor/Sponsor: Professor Liwei Lin Mentor: Eric Sweet Research Project Title: 3D Printed Three-flow Microfluidic Concentration Gradient Generator for Clinical E.Coli Antibiotic Drug Screening Research Areas: Biomechanical Engineering, Design, Fluids, MEMS/Nano

Abstract: In the spring 2017 semester, I worked as an undergraduate researcher in Micro-Mechanical Method for Biology (M3B) program in Lin Lab of University of California, Berkeley under the supervision of Ph.D Student Eric Sweet. The project I conducted research on is the 3D Printed Three-flow Micro-fluidic Concentration Gradient Generator for Clinical E.Coli Antibiotic Drug Screening.

Specifically, I am to develop a device, through means of 3D printing, to mix three species of bio-fluids and obtain flow outputs with various concentration compositions. The design research process is comprised of CAD designing, 3D printing and cleaning, and Testing with Fluigent micro-fluidic system. The primary goal is to obtain equal flow and linear concentration gradient from the outputs. Through the semester, I went through multiple mixer designs with different design parameters, and reached an agreement with Eric that the design with equilateral tetrahedrons units will produce the ideal outcome. For the final testing design, we have three layers of tetrahedron units, which mixes 3 inlets into 15 outlets.

We used the Projet 3500 HDMax printer to print the mixer and went through a series of cleaning process including hot mineral oil bath, hot water cleaning and room temperature water cleaning. Then we used the Fluigent Micro-fluidic system to input fluid with uniform pressure into the mixer and try to obtain even flow rate out of all outlets. Surprisingly, it turns out the task is more difficult than expected due to uncontrollable disturbances coming from gravity and flow resistance in micro-channels. By varying fluid supply volume and refining cleaning process we were able to obtain even flow out of the mixer ultimately.

Student: Ruben Maldonado Professor/Sponsor: Professor Tony Keaveny Mentor: Arnav Sanyal Research Project Title: Multi-Axial Strength Testing of Human Femoral Trabecular Bone

Abstract: Since multiaxial stresses can develop in trabecular bone during falls and at bone-implant interfaces, multiaxial strength behavior is of fundamental relevance to a number of orthopaedic problems. Building on the work of other student researchers in the lab who developed a 3D multiaxial failure criterion for human trabecular bone, the goal of this research is to extend the work to low-density trabecular bone and subsequently validate it using experiments. The experimental results will be used to validate the finite element models.

Student: Audrey Martin Professor/Sponsor: Professor Lisa Pruitt Mentor: Farzana Ansari Research Project Title: Evaluation of Damage on Retrieved Humeral Head Prostheses

Abstract: Introduction: Over 53,000 patients in the United States each year receive a Total Shoulder Replacement (TSR), a synthetic metal-polymer bearing system that serves to reproduce the function of a diseased or injured glenohumeral joint [1]. On average, 10 percent of these patients will undergo a risky and costly revision due to premature wear, loosening, and fracture of the ultrahigh molecular weight polyethylene (UHMWPE) glenoid component [2]. Studies have shown that there is a strong correlation between the presence of UHMWPE-wear debris and bone loss (osteolysis) which can induce loosening of the glenoid component [3, 4]. The purpose of this ongoing study is to analyze the relationship between damage on Cobalt-Chrome (CoCr) humeral head prostheses and glenoid component wear. The goal for this term was to collect more scoring data for and prepare for a counter-bearing wear analysis.

Methods: The Medical Polymers Group (MPG) houses a collection of retrieved humeral head prostheses, many with matching glenoid components. Samples were prioritized for scoring based on the presence of (1) a matching glenoid component, (2) a damage evaluation for the glenoid component, and (3) an orientation marking on the CoCr component. Three undergraduates were trained in a previously developed, detail-oriented scoring methodology to evaluate damage on the retrieved humeral heads. The scoring methodology segregates damage modes into six categories: hairline scratching, curvilinear abrasion, pitting, dimpling, striated scratching, and linear abrasion. The data was analyzed by determining the percent of samples exhibiting each damage mode and the percentage of identification variation between scorers as compared to previously collected scores. Preparations were also made for a counter-bearing wear analysis by evaluating the capabilities of MPG’s custom multidirectional tribological-system and designing fixtures for testing.

Results: In total, seven new scores were collected. Striated scratching continues to be the most commonly found damage mode with 100% of samples exhibiting this damage mode followed by curvilinear abrasion at 94.1%. Dimpling was found to be the least common at 61.8%. At least 88% of scorers per sample showed agreement on the presence of a particular damage mode for n > 2 scorers. The test parameters for the counter bearing analysis were determined. Given the current capabilities of the test frame, a 200/20N load profile was deemed appropriate for preliminary testing.

Discussion and Conclusions:MPG’s scoring methodology continues to yield consistent results. With striated scratching and curvilinear abrasion being the most commonly found damage modes, these would be appropriate parameters to isolate for upcoming counter-bearing analyses. Future work will include performing these wear analysis using a sample with an isolated region of striated scratching, and abrasion as compared to an unused sample with no damage.

Student: Ariana Moini Professor/Sponsor: Professor Tony Keaveny Mentor: Saghi Sadoughi

Research Project Title: Structure-Function Relations for Calcaneal Trabecular Bone – Comparison with other Sites

Abstract: There are various methods used to detect osteoporosis, a growing disease that leads to low bone density and an increased risk of bone fracture. A common modality used to detect osteoporosis is Dual Energy X-ray Absorptiometry (DXA), which measures the bone mineral density of the patient. DXA is most often performed on the lower spine and hips. However, it is expensive and exposes patients to small doses of ionizing radiation. An alternative to DXA is calcaneal ultrasound, which is non-ionizing and inexpensive. It is easier to use and is widely accessible to the public. However, it is not clear how well the calcaneal trabecular bone relates to the mechanical behavior of the trabecular bone in the hip and spine. In this experiment, we want to understand the structure-function relations of the calcaneal trabecular bone and compare them to previously measured trabecular bone properties from other anatomic sites. The trabecular bone in the calcaneus specimens is not oriented the same way as in the vertebral body. Therefore, uniform compression loading configuration will not be along the main axis of trabeculae and as a result will be off-axis. Therefore, to be able to have a reasonable comparison between the structure-function relations of trabecular bone from different anatomic sites, we need to account for this on-axis versus off-axis loading. For that, the orientation of each trabecula in calcaneal specimens must be found. Individual trabeculae segmentation (ITS) software was utilized to categorize each individual trabecula within each specimen as a plate or rod and return its corresponding coordinates as a vector of its orientation. The data was then imported into Matlab to calculate the angle of the rod and plates with the horizontal axis to then compare the structure-function relations of calcaneal trabecular bone with that of the vertebral trabecular bone. This research is currently in the process of comparing the data to the vertebral body.

Student: Robin Parrish Professor/Sponsor: Professor Lisa Pruitt Mentor: Farzana Ansari Research Project Title: Analysis of Stresses in Glenoids

Abstract: Introduction: Ultra high molecular weight polyethylene (UHMWPE) is the most commonly used bearing surface in total joint arthroplasties. However, failure of the UHMWPE component is a common cause of device failure. Therefore, novel materials are being developed in an attempt to increase the life of these devices. This study set out to determine stresses in the bearing surface used in total joint arthroplasty as a function of material, geometry, and loading condition.

Methods: This study was carried out computationally using the simplified elasticity solution and focused on the glenoid component of total shoulder arthroplasties. The following parameters were varied to determine their effects on stresses: elastic modulus of material used, backing material, and radial mismatch. Glenoid radius of curvature was also investigated for consideration of its effects on stresses in the glenoid.

Results and Discussion: It was shown that stresses in the glenoid increase as the modulus of elasticity of the glenoid increases. Glenoid stresses also increase with decreasing radial mismatch between the glenoid and humeral components. However, due to the increased contact area associated with lower moduli, effects of conformity are minimized in systems containing glenoids with lower moduli. Finally, it was shown that for any given geometric configuration, there is a polynomial relationship between modulus and maximum stress. This relationship was used to isolate the effects of backing thickness and humeral geometry and to demonstrate that increasing backing thickness increases the effective modulus and maximum stress in the glenoid. Finally, our findings suggest that biomaterials with lower moduli may be able to decrease stresses in the glenoid, subsequently reducing wear rates and leading to lower device failure rates.

Future Work: Finite element analysis (FEA) will be performed, and results from the simplified elasticity solution will be compared to the results of the simulation. Following the validation of this FEA model, a glenoid with variable radius of curvature will be investigated. Finally, conclusions will be drawn concerning the efficacy of novel biomaterials.

Student: Robin Parrish Professor/Sponsor: Professor Lisa Pruitt Mentor: Farzana Ansari Research Project Title: Finite Element Analysis of Crack Propagation in UHMWPE

Abstract: Introduction: Ultra high molecular weight polyethylene (UHMWPE) is commonly used as a bearing surface in total join arthroplasties. However, failure of the UHMWPE component is a common cause of device failure. Several material modifications can be made to increase wear resistance, fracture resistance, and oxidative resistance. However, each compositional change has trade-offs. We are interested in characterizing the structure-property relationships that govern crack propagation because fracture is a common cause of catastrophic device failure.

Methods: This study was carried out computationally in conjunction with mechanical crack-propagation tests. A crack test specimen was modeled in Abaqus FEA software (Dassault Systèmes Simulia Corp), and various loading conditions were applied. The radius of the notch tip was varied, and a side-groove was added to the model. Complementary mechanical tests were carried out with the same set-up as the Abaqus model.

Results and Discussion: The conclusions that were drawn from the results of the simulations are as follows: (1) Stresses near the notch tip increase with decreasing notch radius. (2) Stresses near the notch tip increase with movement through the depth of the sample into the center. (3) Sharper notch radius results in lower stresses away from the notch tip. (4) Stresses at the surface and in the center do not change proportionally with movement away from the notch tip.

Future Work: Material data is being collected on the specific formulations of UHMWPE that are of interest to us. Connections are being drawn between the FEA model and the mechanical tests. We will calculate the size of the plastic zone in front of the notch tip to better design the mechanical tests to result in cracking rather than yielding.

Student: Sam Pliska Professor/Sponsor: Professor Grace O’Connell Mentor: Ben Werbner Research Project Title: Effects of chABC Treatment on Annulus Fibrosus Biochemical Composition

Abstract: The integrity of the intervertebral disc (IVD) is dependent on many structural factors. Failure occurring in and around the disc can manifest in many different forms, from fractures to tears and herniation [1]. The annulus fibrosus (AF) specifically is susceptible to multiple forms of tears that can increase in frequency and severity with age [1]. These tears can allow for the herniation of the nucleus resulting in pinching of spinal nerves, causing pain throughout the lower back and leg [2]. Due to its increased prevalence with age and it being genetically inherited [1], IVD degeneration is a large and growing problem. From what is known, a key contributor to deterioration is a loss of proteoglycans or glycosaminoglycan (GAG) chains [3].

GAGs are hydrophilic meaning they attract water which assists in absorbing and distributing compressive loads [3]. One method of characterizing IVD degeneration consists of analyzing the biochemical composition such as GAG and water content [4]. At this point in time, however, there is still a gap in understanding regarding the relationship between composition and mechanics. Analysis of the impact of GAG content on the mechanical properties of IVDs could help reveal some of the mechanisms associated with deterioration.

In the past, chondroitinase ABC (chABC) has been used in studies to enzymatically digest GAG in the AF to simulate the natural degeneration of the IVD [1, 3, 5]. To validate the efficacy of this degeneration process, comparisons will be made between the GAG content of chABC digested and non-digested specimens.

Through the utilization of chABC to enzymatically digest GAG, the exact impact to the biochemical composition of the AF was calculated. Along with the value of percent GAG content by dry weight, the weight percent of the samples that were water was also discovered. Due to GAG’s ability to attract water, it makes sense that the chABC digestion protocol would impact water content as well.

Our study shows that with a very high statistical significance, the samples treated with chABC had a reduced GAG content. This initial result makes sense as it matches results of past studies. The statistical significance between the control water content and dGAG water content makes sense as well as it also matches past works [4]. This decrease in water content associated with the chABC digestion process is also intuitive. Water is retained in the matrix of the AF by the attraction of the GAG’s. When these GAG’s are digested out, the ability of the AF to keep the same hydration levels decreases.

Knowing the degree to which the GAG was digested by the chABC is important for future works in being able to know the exact changes in GAG content. By knowing the impact of GAG loss on the mechanical properties of the IVD, and knowing the specific GAG loss generated by the chABC digestion protocol, a model can be generated to describe how the IVD’s mechanical properties will degenerate based on the GAG content.

Seeing how the water content of the AF is positively correlated to the GAG concentration, it becomes apparent why the chABC process has such a large impact on the mechanical properties of the IVD. Water is a key factor in protecting the disc. The water being held assists in absorbing compressive loads and distributes the load more evenly around the circumference of the annulus [3].

Student: Steven Roth Professor/Sponsor: Professor Shawn Shadden Mentor: Jessica Oakes Research Project Title: Particle Deposition in Human Lungs due to Varying Cross-Sectional Ellipticity of Left and Right Main Bronchi

Abstract: Particle deposition in the human lungs can occur with every breath. Airborne particles can range from toxic constituents (e.g. tobacco smoke and air pollution) to aerosolized particles designed for drug treatment (e.g. insulin to treat diabetes). The effect of various realistic airway geometries on complex f ow structures, and thus particle deposition sites, has yet to be extensively investigated using computational fluid dynamics (CFD). In this work, we created an image-based geometric airway model of the human lung and performed CFD simulations by employing multi-domain methods (Oakes et al. (2014), Annals of Biomedical Engineering, 42: 899-914). Following the flow simulations, Lagrangian particle tracking was used to study the effect of cross-sectional shape on deposition sites in the conducting airways. From a single human lung model, the cross-sectional ellipticity (the ratio of major and minor diameters) of the left and right main bronchi was varied systematically from 2:1 to 1:1. The inf uence of the airway ellipticity on the surrounding flow field and particle deposition was determined.

Student: Gerald Santos Professor/Sponsor: Professor Grace O’Connell Mentor: Megan Pendleton Research Project Title: Understanding Spine Biomechanics When Exposed to Spaceflight Radiation

Abstract: Examining the changes in bone quality after exposure to spaceflight radiation is the interest of this research. Bone quality properties of Young’s modulus, fracture and yield stresses, and number of cycles to failure are studied through mechanical testing methods. Rat spines were obtained in the lab, with certain specimens having an exposed radiation rating, while others served as controls. Proper care and dissection measures were taken to remove all non-bone tissue from the rat spines, without imposing any cuts or fractures on the bone. This tissue removing process averaged 2.5 hours to complete. The following step involved separating the L3, L4, and L5 vertebrae sections by cutting through the vertebral discs. The vertebrae were then secured with PMMA in fixtures to allow parallel cuts on each end with an Isomet Diamond Saw. After being cut, the samples to be used in the data analysis were Micro CT scanned to allow finite element analysis. Mechanical testing was performed on multiple samples, with a combination of three test methods. One test method obtained the Young’s modulus value of the bone, the second executes a compression to failure test, and the third is a cycles to failure test. The modulus obtaining method was successful in repeating Young’s modulus values through cyclic compression testing at stresses lower than 20% of the fracture stress. The compression to failure test provided the fracture stress of roughly 100 N. The cycles to failure test was not run successfully due to the modifications of the modulus obtaining method, which is a prerequisite test. Further modifications to the modulus obtaining method and cycles to failure test will be done, while the data thus far will serve as base values for future tests.

Student: Joanna Scheffelin Professor/Sponsor: Professor Tony Keaveny Mentor: Arnav Sanyal Research Project Title: Multiaxial Failure Criterion of Trabecular Bone

Abstract: This semester I worked with Arnav Sanyal on the “Multi-axial Strength Criterion” project in which micro-CT scans of trabecular bone cube specimens were crushed in FE simulations by applying displacements in the x, y, and z directions. Data was collected for failure (Principal stress at failure) for all 3 directions. The ultimate goal is to fit this data to a closed ellipsoid in which the failure stresses in each direction are superimposed to create a super ellipsoid to show failure criteria of the bone specimen. I wrote various algorithms in MATLAB to fit this code to a closed surface. The best fit is a quartic ellipsoid translated and rotated by 3 Euler angles and with an additional variable term to alter the fit. The fit is done using the “fmincon” function in MATLAB with 10 variables.

Student: Colin Shanahan Professor/Sponsor: Professor Lisa Pruitt Mentor: Farzana Ansari Research Project Title: Compression Testing of Cross-linked Vitamin E Enriched Ultra High Molecular Weight Polyethylene

Abstract: Vitamin E enriched Ultra-high molecular weight polyethylene (UHMWPE) is growing in popularity as a material for knee and other joint replacements due to its anti-oxidation properties. However, there have not previously been any studies done on its compressive properties which greatly determine its quality as a material in joints such as the knee. Using a methodology developed in previous tests which was based off of ASTM standard D695 for compressive testing of rigid plastics a series of tests were performed using an Instron machine. Conventional GUR 1020 UHMWPE was tested for baseline comparison purposes, both cross linked and non-cross linked. The same was performed for GUR 1020 UHMWPE enriched with Vitamin E, both cross linked and non-cross linked. Also of interest was the orientation of samples cut from stock material to confirm isotropy. Results so far have not shown any clear correlations and so further testing is required.

Student: Gregory Slatton Professor/Sponsor: Professor Liwei Lin Mentor: Dr. Ryan Sochol Sub Area: Microfluidics Research Project Title: Kidney-on-a-Chip: Biophysical Biomimicry via Micro/Nanoscale 3D Printing

Abstract: With nephrotoxicity, or kidney failure, accounting for nearly 20% of pharmaceutical drug development failures during clinical trials, in vivo kidney systems could render costly, time-consuming (and sporadically inaccurate) animal testing obsolete. Current state-of- the-art platforms are typically fabricated with multi-layer soft lithography and contain two planar channels separated by a permeable membrane. In contrast to their in vivo counterparts, which include complex architectural geometries, state-of-the-art kidney-on- a-chip platforms have overly simplified geometries. Additionally, biophysical stimuli, including micro-environmental geometric cues, have been shown to greatly influence a wide array of cellular functions, thus necessitating a better model of biomimetic architecture to enhance the predictive capabilities of kidney-on-a-chip technologies. Current micro-and-nanoscale 3D printing-based methodologies are uniquely suited for mimicking the complex geometries of in vivo kidney structures, making an artificial kidney-on-a-chip substitute more attainable than ever before. Utilizing multi-jet 3D printing, we have set out to demonstrate this process by fabricating microscale fluidic channels that are lined with kidney cells and permeable membranes to mimic tubules in the kidney. Once simple geometries are successfully demonstrated with our process, the next step is to achieve the functions of a permeable membrane and cell lining in complex geometric architectures to create an artificial kidney-on-a-chip structure functional enough to replace its in vivo counterpart in clinical drug trials.

Student: Nisha Subramanian Professor/Sponsor: Professor Tony Keaveny Mentor: Megan Pendleton & Shannon Emerzian Research Project Title: The Effects of Ionizing Radiation on Bone Biomechanics

Student: Amelia Swan Professor/Sponsor: Professor Lisa Pruitt Mentor: Farzana Ansari Research Project Title: Comparison of Scratching and Abrasion Damage on Retrieved Cobalt Chrome Humeral Heads

Abstract: The in vivo damage observed on the counterbearing cobalt chrome (CoCr) surface of total joint replacements (TJR) can increase the volume of wear debris released from the ultra-high molecular weight polyethylene (UHMPWE) glenoid surface. Consequently, osteolysis and implant loosening can occur [1]. The previous study investigated metallic damage on a microscale, scanning retrievals for striated and hairline scratches with a Phaseshift 3D Optical Profilometer. MapVue and Vision 32 software were used to retrieve 2D profiles of the surface. Matlab uses this data to gain values for average roughness (Ra), minimum valley depth (Rv), maximum peak height (Rp), skewness (Rsk), and kurtosis (Rku) [2]. This investigation applies the same methodology to scratches within abrasion patches found on the CoCr surface. The abrasion data will be compared to scratch data to determine if damage modes have different severities. Additionally, after testing different profiling methods in Vision 32, a more global abrasion analysis has also been developed. This analyzes a whole patch of abrasion as opposed to just one of its components. Future studies will include a comparison of the damage found on shoulder retrievals with that of hips and knees using the same procedures, as well as examining damage trends on CoCr surfaces of total arthroplasties versus hemiarthroplasties. Thanks to the principle investigator Lisa Pruitt, graduate mentor Farzana Ansari, and the Biomedical Nanotechnology Center for the use of their optical profilometer.

Student: Amelia Swan Professor/Sponsor: Professor Lisa Pruitt Mentor: Farzana Ansari Research Project Title: Development of Roughness Parameter Analysis for Retrieved Humeral Heads

Abstract: Once retrieved, total shoulder replacements display damaged counterbearing cobalt chrome (Co-Cr) humeral heads. This damage varies in geometry and severity, from hairline and striated scratching to curvilinear and linear abrasion. It is theorized that this damage accelerates the wear of the bearing ultra-high molecular weight polyethylene (UHMWPE) surface in vivo. These wear particles can lead to implant loosening and an inflammatory response called osteolysis [1]. Previous studies in the lab have developed a macroscale damage scoring system, as well as a damage analysis method that determines roughness parameters over 2D profiles of the microscale surface. [2] The study shows that scratching has a higher peak height, kurtosis (peak sharpness), and average roughness compared to abrasion, although abrasion has higher skewness. It is likely that the third-body wear mechanisms differ between the damage modes. The large peaks from scratch profiles likely generate larger UHMWPE particles, but their negative skewness indicates that some peak material may be worn away over time. Abrasion with a linear geometry commonly occurs at the center of the humeral head, which experiences the largest contact stresses; this explains the reduced peak heights and kurtosis values found for this damage type. Meanwhile, curvilinear abrasion has blunter, shorter peaks and positive skewness but covers a large portion of the head’s surface area. This could generate smaller wear particles but a larger overall volume of wear debris, which can worsen the immune response. [3] The study is currently ongoing. Future steps include increasing the sample size for statistical analysis; comparing damaged surfaces from different fixation methods, implant geometries, and causes of failure; coupling the damage between UHMPWE and metal surface; expanding the roughness analysis to hips and knees; and testing retrieved implants to see how scratch morphologies change after wear testing. Thank you to Professor Lisa Pruitt, graduate mentor Farzana Ansari, the employees of the Mechanical Engineering Student Machine Shop, and the Medical Polymers and Biomaterials Group for their advisement and facilities, as well as the Biomedical Nanotechnology Center for the use of their optical profilometer.

Student: Amelia Swan Professor/Sponsor: Professor Lisa Pruitt Mentor: Farzana Ansari Research Project Title: Damage Analysis of Cobalt Chrome Humeral Head Retrievals using 3D Profilometry

Abstract: After a total shoulder replacement is retrieved, damage is observed on both the bearing glenoid surface and the counterbearing humeral head. These are made of ultra-high molecular weight polyethylene (UHMWPE) and cobalt chrome (Co-Cr), respectively. The damage accelerates UHMPWE wear debris generation when the two surfaces articulate in vivo. This can lead to implant loosening and a painful immune response called osteolysis [1]. Previous studies have used a Phaseshift Optical Profilometer to scan the surface of the Co-Cr component, and MapVue and Vision 32 software to collect 2D surface profiles. Matlab imports this data and calculates average roughness (Ra), minimum valley depth (Rv), maximum peak height (Rp), skewness (Rsk), and kurtosis (Rku) [2]. This methodology has been applied to different severity levels of hairline scratching, striated scratching, and linear abrasion patches. It has been shown that certain damage modes and severities have some significant differences in roughness parameter values. Logically, it follows that the differing roughness values on the Co-Cr surface generate variably-sized UHMWPE wear particles [3]. However, a macroscale analysis should also be considered, as the damaged area’s size and density will also affect the volume of wear debris. A multi-directional tribotester will be used for preliminary wear testing of retrieved Co-Cr humeral heads against UHMWPE disks. The tests will focus on comparing results from contact areas covered by varying damage modes and severities. This will illuminate the volume of UHMWPE debris that is worn away based on damage mode, and how metallic damage modes change after articulation against UHMWPE disks. Future studies will include additional roughness parameter statistics, the continuation of wear testing, and the expansion of this analysis to hip and knee retrievals.

Student: Albert Wang Professor/Sponsor: Professor Tony Keaveny Mentor: Shannon Emerzian Research Project Title: Effects of Ribose on Bone Bending Mechanical Properties

Abstract: Introduction Previously our team had concluded our project investigating the effects of ribose on rat vertebrae, and the goal of this study is to expand upon the scope and look into how bone quality is affected by ribose. The clinical motivation behind this study is based on the many papers exploring the link between diabetes and an increased risk of bone fractures. Our study aims to further explore this and determine whether an increase in non-enzymatic crosslinks can negatively affect the bending mechanical properties of murine femurs. The focus of this semester was performing literature review and coming up with a sound experimental procedure and SOP. We also sample prepped all the femurs that are needed in the study, which included both the left and right femurs of 25 mice. Next semester will be focused on conducting microCT scans for computational analysis before performing 3-point bending tests on our femur samples with the Instron.

Research and Proposed Method Advanced glycation end products (AGE) are very important in the study of bone material properties. Different from naturally-occurring, healthy enzymatic crosslinks, AGEs effectively bind collagen fibers together, thus increasing the brittleness of bones and the risk of bone fractures.In our literature review, many papers pointed to a correlation between diabetic patients and increased fracture incidents, while others indicated that ribose significantly increased collagen crosslinking in bones. We hypothesized that an increase in non-enzymatic crosslinks as a result of ribose or sugar treatment negatively affects the bending mechanical properties of long bones.

Since we are interested in long bones, we extracted both the left and right femurs of 25 mice, giving us 25 sets of mice femurs to work with. We randomly assigned the left and right femurs to the control and experimental treatments, meaning that one bone will serve as the control sample while the other from the same animal will receive ribose treatment and serve as our experimental sample. This experimental design will make our future data analysis much more powerful as we are limiting the potential of external factors such as mice behavior or fitness from impacting our data. Over winter break, we will begin our bone treatment so as to ensure that we can immediately begin scanning once the semester begins. The control group will be treated with PBS for 14 days while the experimental group will be treated with ribose for 14 days. The ribose solution will be made with 35mL of PBS, 3.5g of ribose (0.6M), and 35 mg of sodium azide (0.1%). We will be changing the solutions for both treatment groups once every 2 days.

Future Steps We did not have the chance to begin mechanical testing this semester. The whole of next semester will be dedicated to computational analysis through microCT scanning and performing bending mechanical tests with the Instron. Upon finishing testing, we will be analyzing the data and determining whether the presence of non-enzymatic crosslinks as a result of our ribose treatment did indeed cause bone bending mechanical properties to change.

Student: Minhao Zhou Professor/Sponsor: Professor Grace O’Connell Mentor: Bo Yang Research Project Title: Study On A Novel Hip Joint Replacement Surgical Technique

Student: Shan Zhu Professor/Sponsor: Professor Tony Keaveny Mentor: Saghi Sadoughi Research Project Title: Micromechanics of the Human Calcaneus Bone

Abstract: One of the highest priorities of osteoporosis research is to define measures of bone quality that are better predictors of clinical fracture risk than bone mineral density (BMD) measurements. Within osteoporosis research, osteoporotic fractures represent a biomechanical breakdown of the bone, therefore, a detailed understanding of the biomechanical mechanisms of such fractures is required in order to move beyond BMD in fracture risk assessment. To better understand additional fracture risk predictors, we sought to determine the dependence of bone strength on bone volume fraction by performing high-resolution micro–computed tomography (micro-CT), and micro–finite-element analysis on a heterogeneous cohort of 25 human calcaneus bones. Although, elastic modulus used in linear studies has been reported to be well correlated with strength over a range of bone densities, we also conducted non-linear analysis because yield stress may be a superior indicator of strength since failure behavior generally involves nonlinear phenomena. High resolution images were acquired for each sample. The resulting images were segmented using a global threshold. Using these high-resolution scans, a 3D voxel-type finite element model was generated for each sample. All elements were cube-shaped. Displacement-type boundary conditions were applied to simulate the loading configuration. Individual finite element models were solved using an implicit, parallel finite element framework. The calcaneus stiffness was calculated from the linear analysis resultant force and calcaneus strength was determined using the nonlinear analysis force strain curve with 0.2% offset. The results showed that both stiffness and yield stress scale reasonably with bone volume fraction. Additionally, nonlinear analysis visualizations showed local failure starting in the most porous regions. Finally, it was observed that tensile failure is the dominant failure mechanism in bone since bone tissue is stronger in compression.

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AI Is Everybody’s Business

This briefing presents three principles to guide business leaders when making AI investments: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects. The principles are supported by the MIT CISR data monetization research, and the briefing illustrates them using examples from the Australia Taxation Office and CarMax. The three principles apply to any kind of AI, defined as technology that performs human-like cognitive tasks; subsequent briefings will present management advice distinct to machine learning and generative tools, respectively.

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Today, everybody across the organization is hungry to know more about AI. What is it good for? Should I trust it? Will it take my job? Business leaders are investing in massive training programs, partnering with promising vendors and consultants, and collaborating with peers to identify ways to benefit from AI and avoid the risk of AI missteps. They are trying to understand how to manage AI responsibly and at scale.

Our book Data Is Everybody’s Business: The Fundamentals of Data Monetization describes how organizations make money using their data.[foot]Barbara H. Wixom, Cynthia M. Beath, and Leslie Owens, Data Is Everybody's Business: The Fundamentals of Data Monetization , (Cambridge: The MIT Press, 2023), https://mitpress.mit.edu/9780262048217/data-is-everybodys-business/ .[/foot] We wrote the book to clarify what data monetization is (the conversion of data into financial returns) and how to do it (by using data to improve work, wrap products and experiences, and sell informational solutions). AI technology’s role in this is to help data monetization project teams use data in ways that humans cannot, usually because of big complexity or scope or required speed. In our data monetization research, we have regularly seen leaders use AI effectively to realize extraordinary business goals. In this briefing, we explain how such leaders achieve big AI wins and maximize financial returns.

Using AI in Data Monetization

AI refers to the ability of machines to perform human-like cognitive tasks.[foot]See Hind Benbya, Thomas H. Davenport, and Stella Pachidi, “Special Issue Editorial: Artificial Intelligence in Organizations: Current State and Future Opportunities , ” MIS Quarterly Executive 19, no. 4 (December 2020), https://aisel.aisnet.org/misqe/vol19/iss4/4 .[/foot] Since 2019, MIT CISR researchers have been studying deployed data monetization initiatives that rely on machine learning and predictive algorithms, commonly referred to as predictive AI.[foot]This research draws on a Q1 to Q2 2019 asynchronous discussion about AI-related challenges with fifty-three data executives from the MIT CISR Data Research Advisory Board; more than one hundred structured interviews with AI professionals regarding fifty-two AI projects from Q3 2019 to Q2 2020; and ten AI project narratives published by MIT CISR between 2020 and 2023.[/foot] Such initiatives use large data repositories to recognize patterns across time, draw inferences, and predict outcomes and future trends. For example, the Australian Taxation Office (ATO) used machine learning, neural nets, and decision trees to understand citizen tax-filing behaviors and produce respectful nudges that helped citizens abide by Australia’s work-related expense policies. In 2018, the nudging resulted in AUD$113 million in changed claim amounts.[foot]I. A. Someh, B. H. Wixom, and R. W. Gregory, “The Australian Taxation Office: Creating Value with Advanced Analytics,” MIT CISR Working Paper No. 447, November 2020, https://cisr.mit.edu/publication/MIT_CISRwp447_ATOAdvancedAnalytics_SomehWixomGregory .[/foot]

In 2023, we began exploring data monetization initiatives that rely on generative AI.[foot]This research draws on two asynchronous generative AI discussions (Q3 2023, N=35; Q1 2024, N=34) regarding investments and capabilities and roles and skills, respectively, with data executives from the MIT CISR Data Research Advisory Board. It also draws on in-progress case studies with large organizations in the publishing, building materials, and equipment manufacturing industries.[/foot] This type of AI analyzes vast amounts of text or image data to discern patterns in them. Using these patterns, generative AI can create new text, software code, images, or videos, usually in response to user prompts. Organizations are now beginning to openly discuss data monetization initiative deployments that include generative AI technologies. For example, used vehicle retailer CarMax reported using OpenAI’s ChatGPT chatbot to help aggregate customer reviews and other car information from multiple data sets to create helpful, easy-to-read summaries about individual used cars for its online shoppers. At any point in time, CarMax has on average 50,000 cars on its website, so to produce such content without AI the company would require hundreds of content writers and years of time; using ChatGPT, the company’s content team can generate summaries in hours.[foot]Paula Rooney, “CarMax drives business value with GPT-3.5,” CIO , May 5, 2023, https://www.cio.com/article/475487/carmax-drives-business-value-with-gpt-3-5.html ; Hayete Gallot and Shamim Mohammad, “Taking the car-buying experience to the max with AI,” January 2, 2024, in Pivotal with Hayete Gallot, produced by Larj Media, podcast, MP3 audio, https://podcasts.apple.com/us/podcast/taking-the-car-buying-experience-to-the-max-with-ai/id1667013760?i=1000640365455 .[/foot]

Big advancements in machine learning, generative tools, and other AI technologies inspire big investments when leaders believe the technologies can help satisfy pent-up demand for solutions that previously seemed out of reach. However, there is a lot to learn about novel technologies before we can properly manage them. In this year’s MIT CISR research, we are studying predictive and generative AI from several angles. This briefing is the first in a series; in future briefings we will present management advice specific to machine learning and generative tools. For now, we present three principles supported by our data monetization research to guide business leaders when making AI investments of any kind: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects.

Principle 1: Invest in Practices That Build Capabilities Required for AI

Succeeding with AI depends on having deep data science skills that help teams successfully build and validate effective models. In fact, organizations need deep data science skills even when the models they are using are embedded in tools and partner solutions, including to evaluate their risks; only then can their teams make informed decisions about how to incorporate AI effectively into work practices. We worry that some leaders view buying AI products from providers as an opportunity to use AI without deep data science skills; we do not advise this.

But deep data science skills are not enough. Leaders often hire new talent and offer AI literacy training without making adequate investments in building complementary skills that are just as important. Our research shows that an organization’s progress in AI is dependent on having not only an advanced data science capability, but on having equally advanced capabilities in data management, data platform, acceptable data use, and customer understanding.[foot]In the June 2022 MIT CISR research briefing, we described why and how organizations build the five advanced data monetization capabilities for AI. See B. H. Wixom, I. A. Someh, and C. M. Beath, “Building Advanced Data Monetization Capabilities for the AI-Powered Organization,” MIT CISR Research Briefing, Vol. XXII, No. 6, June 2022, https://cisr.mit.edu/publication/2022_0601_AdvancedAICapabilities_WixomSomehBeath .[/foot] Think about it. Without the ability to curate data (an advanced data management capability), teams cannot effectively incorporate a diverse set of features into their models. Without the ability to oversee the legality and ethics of partners’ data use (an advanced acceptable data use capability), teams cannot responsibly deploy AI solutions into production.

It’s no surprise that ATO’s AI journey evolved in conjunction with the organization’s Smarter Data Program, which ATO established to build world-class data analytics capabilities, and that CarMax emphasizes that its governance, talent, and other data investments have been core to its generative AI progress.

Capabilities come mainly from learning by doing, so they are shaped by new practices in the form of training programs, policies, processes, or tools. As organizations undertake more and more sophisticated practices, their capabilities get more robust. Do invest in AI training—but also invest in practices that will boost the organization’s ability to manage data (such as adopting a data cataloging tool), make data accessible cost effectively (such as adopting cloud policies), improve data governance (such as establishing an ethical oversight committee), and solidify your customer understanding (such as mapping customer journeys). In particular, adopt policies and processes that will improve your data governance, so that data is only used in AI initiatives in ways that are consonant with your organization's values and its regulatory environment.

Principle 2: Involve All Your People in Your AI Journey

Data monetization initiatives require a variety of stakeholders—people doing the work, developing products, and offering solutions—to inform project requirements and to ensure the adoption and confident use of new data tools and behaviors.[foot]Ida Someh, Barbara Wixom, Michael Davern, and Graeme Shanks, “Configuring Relationships between Analytics and Business Domain Groups for Knowledge Integration, ” Journal of the Association for Information Systems 24, no. 2 (2023): 592-618, https://cisr.mit.edu/publication/configuring-relationships-between-analytics-and-business-domain-groups-knowledge .[/foot] With AI, involving a variety of stakeholders in initiatives helps non-data scientists become knowledgeable about what AI can and cannot do, how long it takes to deliver certain kinds of functionality, and what AI solutions cost. This, in turn, helps organizations in building trustworthy models, an important AI capability we call AI explanation (AIX).[foot]Ida Someh, Barbara H. Wixom, Cynthia M. Beath, and Angela Zutavern, “Building an Artificial Intelligence Explanation Capability,” MIS Quarterly Executive 21, no. 2 (2022), https://cisr.mit.edu/publication/building-artificial-intelligence-explanation-capability .[/foot]

For example, at ATO, data scientists educated business colleagues on the mechanics and results of models they created. Business colleagues provided feedback on the logic used in the models and helped to fine-tune them, and this interaction helped everyone understand how the AI made decisions. The data scientists provided their model results to ATO auditors, who also served as a feedback loop to the data scientists for improving the model. The data scientists regularly reported on initiative progress to senior management, regulators, and other stakeholders, which ensured that the AI team was proactively creating positive benefits without neglecting negative external factors that might surface.

Given the consumerization of generative AI tools, we believe that pervasive worker involvement in ideating, building, refining, using, and testing AI models and tools will become even more crucial to deploying fruitful AI projects—and building trust that AI will do the right thing in the right way at the right time.

Principle 3: Focus on Realizing Value From Your AI Projects

AI is costly—just add up your organization’s expenses in tools, talent, and training. AI needs to pay off, yet some organizations become distracted with endless experimentation. Others get caught up in finding the sweet spot of the technology, ignoring the sweet spot of their business model. For example, it is easy to become enamored of using generative AI to improve worker productivity, rolling out tools for employees to write better emails and capture what happened in meetings. But unless those activities materially impact how your organization makes money, there likely are better ways to spend your time and money.

Leaders with data monetization experience will make sure their AI projects realize value in the form of increased revenues or reduced expenses by backing initiatives that are clearly aligned with real challenges and opportunities. That is step one. In our research, the leaders that realize value from their data monetization initiatives measure and track their outcomes, especially their financial outcomes, and they hold someone accountable for achieving the desired financial returns. At CarMax, a cross-functional team owned the mission to provide better website information for used car shoppers, a mission important to the company’s sales goals. Starting with sales goals in mind, the team experimented with and then chose a generative AI solution that would enhance the shopper experience and increase sales.

Figure 1: Three Principles for Getting Value from AI Investments

The three principles are based on the following concepts from MIT CISR data research: 1. Data liquidity: the ease of data asset recombination and reuse 2. Data democracy: an organization that empowers employees in the access and use of data 3. Data monetization: the generation of financial returns from data assets

Managing AI Using a Data Monetization Mindset

AI has and always will play a big role in data monetization. It’s not a matter of whether to incorporate AI, but a matter of how to best use it. To figure this out, quantify the outcomes of some of your organization’s recent AI projects. How much money has the organization realized from them? If the answer disappoints, then make sure the AI technology value proposition is a fit for your organization’s most important goals. Then assign accountability for ensuring that AI technology is applied in use cases that impact your income statements. If the AI technology is not a fit for your organization, then don’t be distracted by media reports of the AI du jour.

Understanding your AI technology investments can be hard if your organization is using AI tools that are bundled in software you purchase or are built for you by a consultant. To set yourself up for success, ask your partners to be transparent with you about the quality of data they used to train their AI models and the data practices they relied on. Do their answers persuade you that their tools are trustworthy? Is it obvious that your partner is using data compliantly and is safeguarding the model from producing bad or undesired outcomes? If so, make sure this good news is shared with the people in your organization and those your organization serves. If not, rethink whether to break with your partner and find another way to incorporate the AI technology into your organization, such as by hiring people to build it in-house.

To paraphrase our book’s conclusion: When people actively engage in data monetization initiatives using AI , they learn, and they help their organization learn. Their engagement creates momentum that initiates a virtuous cycle in which people’s engagement leads to better data and more bottom-line value, which in turn leads to new ideas and more engagement, which further improves data and delivers more value, and so on. Imagine this happening across your organization as all people everywhere make it their business to find ways to use AI to monetize data.

This is why AI, like data, is everybody’s business.

Related Publications

Talking Points

Ai, like data, is everybody's business.

Working Paper: Vignette

The australian taxation office: creating value with advanced analytics.

Research Briefing

Building advanced data monetization capabilities for the ai-powered organization.

Building AI Explanation Capability for the AI-Powered Organization

What is Data Monetization?

About the researchers.

Barbara H. Wixom, Principal Research Scientist, MIT Center for Information Systems Research (CISR)

Cynthia M. Beath, Professor Emerita, University of Texas and Academic Research Fellow, MIT CISR

Mit center for information systems research (cisr).

Founded in 1974 and grounded in MIT's tradition of combining academic knowledge and practical purpose, MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

MIT CISR Associate Members

MIT CISR wishes to thank all of our associate members for their support and contributions.

MIT CISR's Mission Expand

MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We provide insights on how organizations effectively realize value from approaches such as digital business transformation, data monetization, business ecosystems, and the digital workplace. Founded in 1974 and grounded in MIT’s tradition of combining academic knowledge and practical purpose, we work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

Evaluation of Hi-C sequencing for the detection of gene fusions in hematologic and solid pediatric cancer samples

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HiC sequencing is a DNA-based next-generation sequencing method that preserves the 3D conformation of the genome and has shown promise in detecting genomic rearrangements in translational research studies. To evaluate HiC as a potential clinical diagnostic platform, analytical concordance with routine laboratory testing was assessed using primary pediatric leukemia and sarcoma specimens previously positive for clinically significant genomic rearrangements. Archived specimen types tested included viable and nonviable frozen leukemic cells, as well as formalin-fixed paraffin-embedded (FFPE) tumor tissues. Initially, pediatric acute myeloid leukemia (AML) and alveolar rhabdomyosarcoma (A-RMS) specimens with known genomic rearrangements were subjected to HiC analysis to assess analytical concordance. Subsequently, a discovery cohort consisting of AML and acute lymphoblastic leukemia (ALL) cases with no known genomic rearrangements based on prior clinical diagnostic testing were evaluated to determine whether HiC could detect rearrangements. Using a standard sequencing depth of 50 million raw read-pairs per sample, or approximately 5X raw genomic coverage, 100% concordance was observed between HiC and previous clinical cytogenetic and molecular testing. In the discovery cohort, a clinically relevant gene fusion was detected in 45% of leukemia cases (5/11). This study demonstrates the value of HiC sequencing to medical diagnostic testing as it identified several clinically significant rearrangements, including those that might have been missed by current clinical testing workflows.

HiC sequencing is a DNA-based next-generation sequencing method that preserves the 3D conformation of the genome, facilitating detection of genomic rearrangements.

HiC was 100% concordant with clinical diagnostic testing workflows for detecting clinically significant genomic rearrangements in pediatric leukemia and rhabdomyosarcoma specimens.

HiC detected clinically significant genomic rearrangements not previously detected by prior clinical cytogenetic and molecular testing.

HiC performed well with archived non-viable and viable frozen leukemic cell samples, as well as archived formalin-fixed paraffin-embedded tumor tissue specimens.

Competing Interest Statement

ADS, KS, and SM are current employees of Arima Genomics at the time of manuscript preparation. DR and LVM are former employees of Arima Genomics. EG reports consulting fees from Jazz Pharmaceuticals, Syndax Pharmaceuticals, and honoraria from Jazz Pharmaceuticals, Speakers Bureau, stock or stock options from Moderna. TP reports consulting fees from Hitachi High Tech America, honoraria from University of Nebraska, University of Toronto, TD Cowen, travel support from University of Nebraska, University of Toronto, TD Cowen, Hitachi HTA, University of Washington, Pacific Biosciences AAA and BY report no conflict of interest MSF is a current employee of Children's Mercy Hospital and reports honoraria from Bayer Speakers Bureau, PacBio Speakers Bureau

Funding Statement

Arima Genomics received support from NIH NCI grant # R44CA247185. E.G. reports grants from the Alex Lemonade Stand Foundation, Braden's Hope for Childhood Cancer, Noah's Bandage Project, Children's Cancer Research Fund, American Cancer Society. T.P. reports grants from the Helmsley Foundation and NIH (R44HG011897, 1UC4DK117483-01, 5UL1TR002366-04). M.S.F. reports grants from Braden's Hope for Childhood Cancer, Black & Veatch Foundation, Big Slick, Masonic Cancer Alliance, Noah's Bandage Project, and the Department of Defense (W81XWH-20-1-0358).

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The Institutional Review Board (IRB)of the Children's Mercy Hospital (CMH, Kansas City, MO, USA) gave ethical approval for this work.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

↵ # D.R. is currently an employee of Neurocrine Biosciences

↵ $ L.V.M. is currently an employee of Zafrens

Grant numbers/sources of support:

Arima Genomics received support from NIH NCI grant # R44CA247185.

E.G. reports grants from the Alex Lemonade Stand Foundation, Braden’s Hope for Childhood Cancer, Noah’s Bandage Project, Children’s Cancer Research Fund, American Cancer Society. T.P. reports grants from the Helmsley Foundation and NIH (R44HG011897, 1UC4DK117483-01, 5UL1TR002366-04).

M.S.F. reports grants from Braden’s Hope for Childhood Cancer, Black & Veatch Foundation, Big Slick, Masonic Cancer Alliance, Noah’s Bandage Project, and the Department of Defense (W81XWH-20-1-0358).

Disclosures:

A.D.S, K.S., and S.M. are current employees of Arima Genomics at the time of manuscript preparation. D.R. and L.V.M. are former employees of Arima Genomics. E.G. reports consulting fees from Jazz Pharmaceuticals, Syndax Pharmaceuticals; and honoraria from Jazz Pharmaceuticals, Speakers Bureau; stock or stock options from Moderna. T.P. reports consulting fees from Hitachi High Tech America; honoraria from University of Nebraska, University of Toronto, TD Cowen; travel support from University of Nebraska, University of Toronto, TD Cowen, Hitachi HTA, University of Washington, Pacific Biosciences. A.A.A. and B.Y. report no conflict of interest. M.S.F. is a current employee of Children’s Mercy Hospital and reports honoraria from Bayer Speakers Bureau, PacBio Speakers Bureau.

Data Availability

Select non-identifying data in the present study are available upon reasonable request to the authors.

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