coding style

 

https://github.com/trekhleb/state-of-the-art-shitcode

ADVANCE

 

there 4.8 million patient

SQL database 

There are demographic information, income information. 

FDA medical device cybersecurity

 

Cybersecurity is a serious concern for medical device safety and effectiveness. Without protection, software running on a medical device could cause severe injury or death to a patient.

There are many forms of cybersecurity and many remedies for thwarting attempts to penetrate medical device software. Most of these are based on physical and logical security practices that are becoming best industry practices.

This webinar will detail some of the threats and ways to mitigate them to protect consumers from harm. This webinar will also focus on IEC 62304. Medical devices can use very complex software applications, and any failure to function properly could lead to the potential injury or death of a consumer or patient.

There is a need to improve overall standards for medical device software to account for this high-risk potential. The majority of software recalls in the 1990s were due to software defects that were a result of software being upgraded.

There is a need to restructure medical device software development processes, and adopting IEC 62304 provides a standard for a design that is accepted in the United States (US) and European Union (EU).

IEC 62304 is a risk-based approach to compliance that ensures the standards followed are appropriate for their potential assessed risk.

IEC 62304 is a lifecycle approach that defines the activities and tasks required to ensure software for medical devices will be safe and reliable.

Applying IEC 62304 will reduce your overall rate of software failure and improve your bottom line.

Why you should Attend:

Providing safe and effective medical devices is in the best interests of all those involved in the development, manufacturing, testing, and distribution of these products. One of the largest current threats to these devices working safely and effectively is cyberattacks that can wreak havoc on code and device functionality. Preventing these attacks by identifying sources of threats and rooting them out before they can take effect is of the utmost concern.

In this webinar, you will learn just how cyberattacks threaten medical devices and how the industry is currently responding to them. We will discuss the many ways of preventing and mitigating the cybersecurity risk, and the industry best practices that can help your company do the same.

This webinar is intended for those working in the FDA-regulated industries, including pharmaceutical, medical devices, biological, animal health, and tobacco. Functions that are applicable include research and development, manufacturing, Quality Control, distribution, clinical testing and management, adverse events management, and post-marketing surveillance.

You should attend this webinar if you are responsible for planning, executing, or managing the development or implementation of any system governed by FDA medical device or software regulations, or if you are maintaining or supporting such a system.

Areas Covered in the Session:

• Understand cybersecurity and guidance on-device software
• Learn about the most common problems faced by the industry in terms of medical device security, efficacy, and safety
• Understand the best practices and industry standards to meet the challenges of cybersecurity and other threats to devices and software
• Learn how safe and effective medical devices are in the best interests of all those involved in developing software for these products, and for those involved in developing medical devices that use software
• Gain insight into the IEC 62304 standard as it is applied to medical device software
• Learn how to apply this standard to your own work processes
• Gain insight into the current industry best practices that will help you with IEC 62304 compliance

Who Will Benefit:

• Medical Device manufacturing, Testing, Packaging, and Distribution companies
• In some cases, the medical device software will be provided by a software development firm vs. the medical device company itself
• In some cases, the medical device will deliver a drug to a patient, and so those working in the pharmaceutical and/or biotechnology industries may have some interest in this topic
• 
  

Seurat scRNA normalisation

 

The default normalisation in Seurat is pretty simple - it simply scales the counts by the total counts in each cell, multiplies by 10,000 and then log transforms.

https://www.bioinformatics.babraham.ac.uk/training/10XRNASeq/seurat_workflow.html#Normalisation,_Selection_and_Scaling

UTC PhD students, continuous enrollment

 

I would like to thank everyone for all your help with advising PhD students. Our program has grown (we started from zero) and students are doing great. Couple of quick reminders if you are advising PhD students with their course work:

Continuous Enrollment: 

All PhD students have to be enrolled continuously. If they want to take a break (even for a semester) and not register, they need to talk to me and Kim. We would need to file a form to the graduate school. This is a requirement from the Graduate School. Please note that this applies to ALL PhD students regardless of their funding status or their immigration status. The minimum credit hours for students in different categories are provided in the next list. 

Credit Hours: 

There are minimum credit hours students have to take every semester. 

International Students:

• Fall and Spring semesters: They have to take at least 9 credit hours
• Summer semester: They have to take at least 6 credit hours 

US Citizens and Green Card Holders that have GRA: 

• Fall and Spring semesters: They have to take at least 9 credit hours
• Summer semester: They have to take at least 6 credit hours 

US Citizens and Green Card Holders without funding: 

• Fall and Spring semesters: They have to register for at least one course
• Summer semester: They have to register for at least one course

AFTER they pass the qualification exam, the minimum is dropped to 1 credit hour for all students, with the exception of their last semester that has to be 2 credit hours. 

I have cc’ed Kim on this email. Majority of the students go to Kim, if they have any questions. She knows all the details and provided the list above for me. Please let Kim or I know if you have any questions. 

AI key words

 

derivative of sigmoid function

 

https://towardsdatascience.com/derivative-of-the-sigmoid-function-536880cf918e

ADVNACE

 

ADVANCE is working to improve the health of vulnerable populations

https://ochin.org/advance-network

http://advancecollaborative.org/

AI aspects

 

parse yeast PPI from biogrid 4.4.208

code at  https://github.com/QinLab/Biogrid-Qin2022 

download BIOGRID-ALL-4.4.208.tab3.zip 

write a python code to parse out yeast entries

myfile ='data-large-unsynced/BIOGRID-ALL-4.4.208.tab3.txt'
df = pd.read_csv(myfile,sep='\t', header=(0))
df = df[df['Organism Name Interactor A'].str.contains('Saccharomyces cerevisiae') ]
df = df[df['Organism Name Interactor B'].str.contains('Saccharomyces cerevisiae') ]

Remove duplicated  interactions

def alphabetic_ordered_tag(in_tag1, in_tag2):
    tmp = [str(in_tag1), str(in_tag2)]
    tmp.sort()
    return( str(in_tag1) + "_" + str(in_tag2))
df['alphabetic_ordered_tag'] = df.apply(lambda x: alphabetic_ordered_tag(x['Systematic Name Interactor A'], x['Systematic Name Interactor B']), axis=1)
df2 = df.drop_duplicates(subset=['alphabetic_ordered_tag'])

Output a lean version form small file size

df3 = df2[['Systematic Name Interactor A', 'Systematic Name Interactor B', 'Official Symbol Interactor A', 'Official Symbol Interactor B', 'alphabetic_ordered_tag' ]]
df3.to_csv("biogrid_s288c_4.4.208.lean.csv")

Output a dictionary from systematic names to symbols. 

dicA = df3[['Systematic Name Interactor A', 'Official Symbol Interactor A']]
dicB = df3[['Systematic Name Interactor B', 'Official Symbol Interactor B']]
dicA.columns = ['Name', 'Symbol']
dicB.columns = ['Name', 'Symbol']
dic = pd.concat([dicA, dicB])
dic2 = dic.drop_duplicates(subset=['Name', 'Symbol'])
dic2.to_csv("Sce_Name2Symbol.csv")

A total 627732 interactions and 6155 unique names/symbols were found for s288c biogrid data set. 

Note: Self-interactions were included. 

MC learning job position in PF

Machine Learning Research Scientist

• Formal training in Computer Science, Statistics, Applied Mathematics, Computational Biology , Computational Chemistry, Physics, related technical discipline, or relevant practical experience
• 2+ years’ programming experience in Python
• 2+ years’ experience in software design, development, and algorithm-related solutions for production-grade systems using machine learning
• Applied experience with major ML algorithms in either of natural language processing, computer vision, and information retrieval (CNNs, transformers, etc.)
• Working knowledge of one or more scientific data types (e.g. biomedical images, biomedical text, large-scale, multidimensional 'omics, large- or small- molecule therapeutics, clinical or Real World Data, etc.)

Preferred Qualifications

• MS/PhD + 2 years of relevant research experience
• Experience with high performance computing (HPC) environments (SLURM/LSF/SGE schedulers)
• Familiarity with cloud computing platforms (AWS, Google Cloud, etc.)
• Practical experience with deep learning-based solutions
• Strong publication record and demonstrated contributions to the field, e.g. NeurIPS, ICML, ICLR, etc.
• Passion and curiosity for data and proven ability to take ideas from prototype to production.

Technologies We Use:

Python, Java, C++, Slurm-based on-premise compute clusters, Google Cloud Platform, AWS, Docker, Singularity, Kubernetes, Python (Numpy, Pandas, Dask, PyTorch, TensorFlow, sci-kit learn, RDKit, Weights and Biases etc.

 

REU student netID (networkd ID) and mocs cards, housing

 

Affiliate & Guest Accounts

https://utc.teamdynamix.com/TDClient/2717/Portal/KB/?CategoryID=21656

https://utc.teamdynamix.com/TDClient/2717/Portal/Requests/ServiceDet?ID=49847

  If you fill out that form for each user, we will create the accounts that will grant them network access.  It is important to get information such as their birthdays and phone numbers, so that we may verify them properly if they call us with password issues.  As filling out the form will create new tickets, I am going to close this one for now.

 I completed 5 rather simple steps to get this completed for our REU.  I’ve outlined my steps below for your convenience.

Step 1: 

https://utc.teamdynamix.com/TDClient/2717/Portal/Requests/ServiceDet?ID=49847

#1 click Request Service

An email form iam@utc.edu will be sent for each student netID. 

Step 2: Email the students with the following rules:

Photo Submission Criteria

Your photo must meet the criteria outlined to be accepted. All photos are subject to approval.

1. Take a new photo.
2. Use good lighting. Photos that are dark, overexposed, or show glare on glasses will not be accepted.
3. Face forward and look at the camera.
4. The photo should be a centered and front-facing headshot that doesn't need to go below the shoulders.

DO:

• Use a color photo in jpg format
• Make sure your eyes are open and visible
• Crop the image to just above the top of the head to the collarbone

DON'T:

• Use pictures with social media filters
• Wear sunglasses or other item that will obscure your face
• Use pictures that are blurry or have a glare
• Use scenic backgrounds
• Use overexposed or underexposed photos
• Group photos
• Inappropriate expressions
• Use Senior portraits or Graduation photos
• Use professional pictures

Step 3:

            Once the UTC IDs have been created and you have all of the photos, save the photos onto a jump drive named as such:  Last_First_UTCID

Step 4:

            Make an appointment to meet with Kathleen Metcalf in the MocsCard office to print the cards (I waited and took them immediately).

Step 5:

            Take the cards to Housing for activation and delivery upon check-in.

Housing: 

Housing and Residence Life is excited to share that we are accepting applications for summer camp, conference, and intern housing requests. The summer season is just around the corner, and it is our goal to host summer groups and interns as we have in previous years. 

 

Please complete the Inquiry form on our website and select Summer Guest Housing so we can review your groups date, location and counts. 

 

·          Conference season is June 1, 2022- July 31, 2022. 

·          Email communication will come from and should be sent to guesthousing@utc.edu

 

Additional information available here.

reading notes, linear systems control

 Brunton book: 

matrix exponential

 

For diagonal matrix with eigen values: 

UTC international student requirement

The GRE score and percentage is also required for Computational Science PhD

https://www.utc.edu/engineering-and-computer-science/academic-programs/computer-science-and-engineering/masters-and-phd-programs/phd-computer-science 

 

2022-2023 catalog

https://catalog.utc.edu/content.php?catoid=42&navoid=1596#International_Students

Exam	Minimum Score
TOEFL iBT	79
IELTS	6.0
Cambridge English Qualifications C1 Advanced*	177
Duolingo English Test **	100
Pearson Test of English Academic	53
International Test of English Proficiency (iTEP) Academic	4.0

predicting mutation effect on protein

 

quantum computing for computer scientist, microsoft research

 

 

Information geometry and divergences

https://franknielsen.github.io/IG/

tech symposium notes

 

FlutterFlow , buliding apps cross platform. 

EEG smart move, http://www.eegsmart.com/en/udroneIndex.html

Very few white faculty show up. Mostly black and asian faculty. I saw Mengjun Xie and Yingfeng Wang. Chemistry Department head Dungey. 

multi-stage fatigue model

multi-stage fatigue model 

 

Microstructure-based Multistage Fatigue Modeling of Aluminum Alloy 7075-T651

https://icme.hpc.msstate.edu/mediawiki/index.php/Microstructure-based_Multistage_Fatigue_Modeling_of_Aluminum_Alloy_7075-T651.html

Fatigues models uses the 3D print microstructures features to predict how many cycles of of specific strains that a materials can withstand. This is very similar to the replicative aging model of yeast cells. 

network attack simulation

 

https://geekflare.com/cyberattack-simulation-tools/

SNORT

 

Snort is the foremost Open Source Intrusion Prevention System (IPS) in the world. Snort IPS uses a series of rules that help define malicious network activity and uses those rules to find packets that match against them and generates alerts for users.

https://www.snort.org/

algorithm online practice

 

https://dev.to/tadea/5-websites-for-practicing-algorithms-376

HackerRank

LeetCode (good for practice for interviews)

CodeWars

HackerEarth

CoderBryte

UTC Graduate student summer support and courses

 

As the end of the semester is upon us, a reminder that if you are funding Master Graduate Assistants and  they are planning to work with you this summer but they do not plan to register for classes, now is the time to work with your home department administrative assistant to make sure the students are switched to bi-weekly pay status. 

Master GAs are not required to take classes during the summer however if you want them to work for you as a Graduate Assistant over the summer (versus hourly), they must register for six credit hours for the summer. In this case, they will continue to receive a monthly stipend for May-August as well as tuition/fee waivers. Please provide your administrative assistant the appropriate accounts to charge.

All PhD Graduate Assistants MUST register for summer classes. If they have passed their exams, they only need to register for two credit hours. If they have not taken their exams yet, they must be registered for six credit hours. If your funding source for summer is different than the spring, please let me know as soon as possible.

As always, please let me know if you have any questions.

 

Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer’s disease neuropathologies

https://www.nature.com/articles/s41467-021-25680-7

All code for our study, including code to train the MD-AD model and to generate all figures included in the manuscript, are available at https://github.com/suinleelab/MD-AD (archived at https://doi.org/10.5281/zenodo.5043447).

Thomas More

 Thomas More

https://en.wikipedia.org/wiki/Thomas_More#Indictment,_trial_and_execution

naked emperor syndrome

 

https://larryjohnsonspeaker.com/speak-truth-to-power/

Spurious Regression With Stationary Time Series

 

Spurious Regression With Stationary Time Series

Ref: 

https://blog.ms-researchhub.com/2019/10/26/spurious-regression-with-stationary-time-series/#:~:text=The%20spurious%20relationship%20is%20said,theoretical%20relationship%20between%20two%20variables.

cpsc4900 Ch9 Testing, part 1

 

4/5 Testing part 1. Q1-20. 

vaccine zombie disinformation

 

https://www.tiktok.com/@bryceamorosi/video/7082142088439156010?is_copy_url=1&is_from_webapp=v1&q=ukraine%20battle&t=1648953977371

https://www.tiktok.com/@bryceamorosiv