AGE

 promoter methylation is associate social factor in dogs

Yuan 2011 longevity between mouse and human Framingham study. 

George Williams, evolutionary theory of aging. antagnistic pleotropic genes. 

Hongjie

Lu Wang, UT San Antonio

Alex UW Seattle

=>single cell aging: 

worm

https://c.elegans.aging.atlas.research.calicolabs.com/

fly 

=> Hevolution

GPT for few-shot genomics analysis across species

=> women in Japan and immigrant in US have different breast-cancer risks, suggesting environmental factors on age-dependent risks, city of hope presentation. 

=> 

Karen Guerrero Vazquez

Authors: Karen Guerrero Vazquez, Pilib Ó Broin, Katarzyna Goljanek-Whysall

Selection of miRNA candidates for the treatment of sarcopenia using network-based analysis and differential expression scoring. Karen Guerrero Vazquez 1, Pilib Ó Broin 1, Katarzyna Goljanek-Whysall 2. 1 School of Mathematical & Statistical Sciences, National University of Ireland Galway, 2 School of Medicine, National University of Ireland Galway. Sarcopenia is a natural consequence of aging and leads to progressive muscle wasting. Currently, there is no cure for this condition, and target identification and validation remain pressing challenges. Many potential therapeutic targets have failed in clinical trials or shown poor association with the disease. This project aims to address these challenges by creating a model of microRNA:target interactions for more efficient in silico selection of potential therapeutic targets for sarcopenia. We conducted a novel network-based analysis of microRNA involvement in aging using RNAseq and microarray data from five studies, with a total of 246 samples of skeletal muscle from healthy participants with ages ranging from 19 to 85 years old. We analyzed young, middle age and older adults and determined interactions between genes coming from co-expression, colocalization, genetic interaction, physical interaction, predicted and shared protein domain calculated with GeneMania. Next, we predicted microRNAs that are putative regulators of shortlisted genes using target prediction tools, such as TargetScan, mirDB and mirTarbase. Finally, we add tissue expression from Diana-miTed and miRNATissue Atlas2. After identifying the network of gene:gene and microRNA:gene interactions, the relevance of each node was calculated using multiple scoring metrics and measures of centrality in order to identify the most likely key regulatory microRNAs and their targets during aging. Our model of microRNA-target interactions is specifically tailored to the context of muscle aging and offers a more comprehensive and accurate representation of the complex regulatory mechanisms involved in muscle aging than existing tools, as it integrates multiple layers of biological information. By identifying a few tens of microRNAs and genes with potential therapeutic power, our model offers a valuable and efficient approach to target identification and validation for the treatment of sarcopenia. This abstract has emanated from research supported in part by a research grant from Science Foundation Ireland under Grant number [18/CRT/6214]

=> 

Explaining the asynchrony of aging through cell population dynamicsMing Yang1, Benjamin R. Harrison1, Daniel E.L. Promislow1,21 Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195 USA 2 Department of Biology, University of Washington, Seattle, WA 98195 USA Different tissues age at different rates within a single individual. Such asynchrony in aging has been widely observed at multiple levels, from functional hallmarks, such as anatomical structures and physiological processes, to molecular endophenotypes, such as the transcriptome and metabolome. However, we lack a conceptual framework to understand why some components age faster than others. Just as demographic models explain why aging evolves, here we test the hypothesis that demographic differences among cell types, determined by cell-specific differences in turnover rate, can explain why the transcriptome shows signs of aging in some cell types but not others. Through analysis of mouse single-cell transcriptome data across diverse tissues and ages, we find that cell lifespan explains a large proportion of the variation in the age-related increase in transcriptome variance. We further show that long-lived cells are characterized by relatively high expression of genes associated with proteostasis, and that the transcriptome of long-lived cells shows greater evolutionary constraint than short-lived cells. In contrast, in short-lived cell types the transcriptome is enriched for genes associated with DNA repair. Based on these observations, we develop a novel heuristic model that explains how and why aging rates differ among cell types.This work was supported by NIH R01 AG063371 (to D. Promislow and S. Pletcher) and Norn Group Impetus Grant (to D. Promislow).

https://www.biorxiv.org/content/10.1101/2023.05.31.543091v1

fair AI high school camp

 

https://www.bloomberg.com/graphics/2023-generative-ai-bias/

greek letters in math with latex input

 Greek letters in Math can be pronounced quite differently than the way in Greek languages. 

https://www.youtube.com/watch?v=CnotqG06t_A

algebra, Fibonecci series

 

https://www.youtube.com/watch?v=z4cP8aDYt0g

SHAP tutorial from BING

 

There are several popular tutorials on using SHAP for ML. Some of them include:

1. "SHAP: How to Interpret Machine Learning Models With Python" by Dario Radečić on Better Data Science ¹.

2. "An introduction to explainable AI with Shapley values" on the SHAP documentation website ².

3. "Interpretable Machine Learning using SHAP — theory and applications" by Towards Data Science ³.

4. "Tutorial: Explainable Machine Learning with Python and SHAP" by Natalie Beyer on ML Conference ⁴.

These tutorials provide a step-by-step guide on how to use the SHAP package in Python to interpret machine learning models.

Is there anything else you would like to know?

Source: Conversation with Bing, 6/1/2023

(1) SHAP: How to Interpret Machine Learning Models With Python. https://betterdatascience.com/shap/ .

(2) An introduction to explainable AI with Shapley values — SHAP latest .... https://shap.readthedocs.io/en/latest/example_notebooks/overviews/An%20introduction%20to%20explainable%20AI%20with%20Shapley%20values.html.

(3) Interpretable Machine Learning using SHAP — theory and applications .... https://towardsdatascience.com/interpretable-machine-learning-using-shap-theory-and-applications-26c12f7a7f1a.

(4) Tutorial: Explainable Machine Learning with Python and SHAP. https://mlconference.ai/blog/tutorial-explainable-machine-learning-with-python-and-shap/.

SHAP tutorial

 

KernelSHAP vs TreeSHAP

Comparing SHAP approximation methods based on speed, complexity and other considerations

Conor O'Sullivan

·

Follow

medium-articles/kernelSHAP_vs_treeSHAP.ipynb at master · conorosully/medium-articles · GitHub

Fairness AI materials

 

Fairness AI materials

https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8

knowledge graph neural network and transformer

 for instrusion detection, knowledge graph model can train on intrusion data that are often much smaller than normal data. 

for transformer models, we need large amount of intrusion data which is unlikely. 

ECSI, student loans

 

ECSI stands for **Educational Computer Systems, Inc.** ¹. It is a company approved by the government to service federal Perkins student loans. It manages the loans, including collecting and tracking payments, but doesn’t own them. The company is sometimes referred to as Heartland ECSI ³.

Source: Conversation with Bing, 5/23/2023

(1) ECSI. https://home.ecsi.net/.

(2) ECSI Customer Service: What It Can Do and How to Contact. https://www.nerdwallet.com/article/loans/student-loans/ecsi-customer-service-what-it-can-do-and-how-to-contact.

(3) ECSI. http://heartland.ecsi.net/.

(4) ECSI Student Loan Review: What to Know About This Servicer - LendingTree. https://www.lendingtree.com/student/ecsi-student-loans-review/.

(5) ECSI Student Loan Servicing | Student Debt Warriors. https://studentdebtwarriors.com/ecsi-student-loan-servicing/.

UTC Travel card can be used for local events

 

I have checked with our AP/Travel staff and have learned it IS allowable to use your travel card for meals/refreshments for local events. You’ll submit in Concur and itemized receipts are required. If it is for fewer than 15 people, a list of participants will be required. Tip should not exceed 20%. Please let me know if you have any additional questions.

Python Basics with ChatGPT

 

### Summary of the Python Coding Session

1. **Introduction to Recording Setup**:

   - Clarified the recording process and privacy measures.

   - Example given on Python syntax and explaining key elements like lists and functions.

2. **Basic Python Examples**:

   - Demonstrated a simple Python script to calculate the average of a list of numbers.

   - Discussed the importance of syntax elements such as square brackets for lists and the sum function.

3. **Explaining Code in Detail**:

   - Importance of indentation in Python was highlighted.

   - Function definition and arithmetic operations were explained.

   - Addressed the need for better explanations of commas and other syntax elements.

4. **Loop and Indentation**:

   - Provided an example of a loop and discussed the concept of indentation in Python.

   - Introduced the concept of defining functions and their use.

5. **Object-Oriented Programming (OOP)**:

   - Explained OOP concepts using classes like Rectangle, Square, and Circle.

   - Demonstrated inheritance and method overriding with practical examples.

6. **Coding Exercise with Google Colab**:

   - Encouraged using Google Colab for running Python code.

   - Showcased copying code from ChatGPT into Colab and executing it.

   - Addressed potential logical errors in code and the importance of verifying outputs.

7. **Interactive Q&A with ChatGPT**:

   - Examples included sorting algorithms (bubble sort, selection sort, insertion sort).

   - Discussed the importance of comments for code explanation.

   - Covered variable data types and list operations in Python.

8. **Weather and Air Quality Example**:

   - Attempted to use an API to get weather and air quality data.

   - Faced issues with API keys and registration requirements.

   - Showcased alternative methods like web scraping using BeautifulSoup.

9. **Challenges with Drawing and Visualization**:

   - Asked ChatGPT to draw a bunny using Python, encountered issues with libraries and runtime errors.

   - Highlighted the iterative process of debugging and refining code.

10. **Reflections and Future Directions**:

    - Discussed the implications of using AI tools for learning and completing homework assignments.

    - Emphasized the importance of creativity and critical thinking in coding.

11. **Wrap-up and Next Steps**:

    - Encouraged participants to explore basic Python concepts using provided GitHub resources.

    - Suggested taking a break and reconvening to continue the session.

### Suggested Next Steps

**a.** If you need a deeper dive into any specific concept covered during the session, let me know, and I can provide more detailed explanations and examples.

**b.** If you have specific code you want to run or debug, share it here, and I'll help you refine and troubleshoot it.

What would you like to explore or discuss next?

UTC CSE JUMP program

 

 

XYZ is a current Computer Science Data Science major in her last few semesters. She is thinking about going into grad school after completing her degree and would like to take advantage of the JUMP program. However, I am getting familiar with the approved courses. 

 

What courses are part of the Jump program at UTC?

There are three courses that have been approved to be part of the JUMP program. Students admitted to JUMP could take one, two, or all three of the following:

CPSC 5100 (in place of CPSC 4100)

CPSC 5590 (in place of CPSC 4550)

CPSC 5700 (in place of CPEN 4700)

Students in the Data Science concentration already take 4100 and 4700, so those would have direct substitutions as outlined above. For students (such as Data Science) not required to take CPSC 4550, if they were to take CPSC 5590 they could count it as an upper-level elective, just as if they had chosen 4550 as an elective.

Fall 2023 PhD application

To apply for Fall 2023 enrollment of PhD program, please apply at https://www.utc.edu/research/graduate-school/prospective-students/graduate-school-admissions

https://www.utc.edu/enrollment-management-and-student-affairs/center-for-global-education/international-student-and-scholar-services/prospective-students

https://www.utc.edu/sites/default/files/2021-03/how-to-apply-f12.pdf

 

The deadline for fall  application is July 1 (for students interested in graduate assistantship)

https://www.utc.edu/engineering-and-computer-science/academic-programs/electrical-engineering/graduate-curriculum/graduate-admission

 

For spring application, the website says Nov 1 as the deadline, but for assistantship, it is advised to complete application as earlier as possible. 

Our graduate school application requires GRE and TOEFL/IELTS scores.  For students with US degrees, please contact Lora Cook to verify whether foreign language exams might be waived. 

 

Thanks again for your interest. Please let me know if I can answer some of your questions,

 

Dear XYZ

 

Currently you have not submitted an application for our university.  Our PhD programs have a firm deadline for our PhD programs as noted below.  Degrees received from with the U.S. in the last two years will be waived from the English requirements.

Thank you for your interest in the University of Tennessee at Chattanooga. My name is Lora, and I will assist you in the application process. 

 

The following requirements must be fulfilled by February 1^st for the fall semestersand by September 1^st for spring semesters. We do not begin new students in the summer semester.

 

 

• Pay a nonrefundable application fee of $40.
• Submit the program-specific graduate admissions form. 
• Submit official transcripts from each college or university you have attended.  
• Report satisfactory English test scores from one of the three test scores (institution code 1831). TOEFL IBT minimum score: 79.  IELTS minimum score: 6. Duolingo minimum score 100.
• Submit a GRE score sheet, our institutional code is 1831.

• Submit a clear copy of your current passport.

 

 

Submit official proof of funding. may be submitted after acceptance by the program.   

 

PhD Program information.

 

Visit www.utc.edu/international for more information regarding housing, orientation, student fees, health insurance, and the admissions process.

 

If you have any questions, please don’t hesitate to ask.

Sincerely,

 

 

UTC independent research contract

 

You can submit the form from here

https://www.utc.edu/enrollment-management-and-student-affairs/registrar/individual-studies-contracts

Cloud computing, fog computing, and edge computing

 

Cloud computing, fog computing, and edge computing are three different approaches to processing and storing data from edge devices.

The main difference between cloud, fog and edge computing is defined by where data from edge devices is processed and stored. Cloud servers are placed away from the edge, while fog is pulled closer to reduce the time needed to process data and respond to events faster¹.

In cloud computing, data is processed on a central cloud server, which is usually located far away from the source of information³. Edge computing brings processing and storage systems as close as possible to the application, device, or component that generates and collects data. This helps minimize processing time by removing the need for transferring data to a central processing system and back to the endpoint².

Fog computing addresses this problem by inserting a processing layer between the edge and the cloud. This way, the ‘fog computer’ receives the data gathered at the edge and processes it before it reaches the cloud. Fog computing also differentiates between relevant and irrelevant data. While relevant data is sent to the cloud for storage, irrelevant data is either deleted or transmitted to the appropriate local platform².

Source: Conversation with Bing, 5/1/2023

(1) Differences Between Cloud, Fog and Edge Computing | Digiteum. https://www.digiteum.com/cloud-fog-edge-computing-iot/.

(2) Cloud, Fog, and Edge Computing: 3 Differences That Matter. https://dzone.com/articles/cloud-vs-fog-vs-edge-computing-3-differences-that.

(3) Edge Computing vs. Fog Computing: 10 Key Comparisons. https://www.spiceworks.com/tech/cloud/articles/edge-vs-fog-computing/.

(4) What's the Difference Between Cloud, Edge, and Fog Computing?. https://www.xenonstack.com/blog/cloud-edge-fog-computing.

(5) Edge & Cloud & Fog Computing: What Is The Difference Between Them. https://www.knowledgenile.com/blogs/edge-cloud-fog-computing-what-is-the-difference-between-them/.