Genome-wide analysis of SARS-CoV-2 virus strains circulating worldwide implicates heterogeneity

https://www.nature.com/articles/s41598-020-70812-6#Sec2 Article Open Access Published: 19 August 2020 Genome-wide analysis of SARS-CoV-2 virus strains circulating worldwide implicates heterogeneity

Oct 8, Hamilton county to allow big party in restaurant? https://newschannel9.com/news/local/gov-lee-lifts-restrictions-bradley-co-restaurant-calls-executive-order-a-god-send Marl 11, 2020 WHO pandemic declaired.

ARHGAP11B

https://en.wikipedia.org/wiki/ARHGAP11B

computational genomics with R

http://compgenomr.github.io/book/

worldmet

Functions to import data from more than 30,000 surface meteorological sites around the world managed by the National Oceanic and Atmospheric Administration (NOAA) Integrated Surface Database (ISD, see ). https://cran.r-project.org/web/packages/worldmet/index.html https://github.com/davidcarslaw/worldmet Hong note: it seems worldmet only provide pre-defined sites. In Bukhari et al, 2020, effects of weather on coronavirus pandmic, USA weather were provided at the state level. For many other countries, weather were estiamted from the busiest airports.

EpiEstim cran pacakge

https://cran.r-project.org/web/packages/EpiEstim/index.html

EpiNow2 package

https://github.com/epiforecasts/EpiNow2 https://epiforecasts.io/EpiNow2/reference/estimate_infections.html#examples https://github.com/epiforecasts/covid-rt-estimates

These worked

reporting_delay <- bootstrapped_dist_fit(rlnorm(100, log(4), 1), max_value = 30)

generation_time <- get_generation_time(disease = "SARS-CoV-2", source = "ganyani")

incubation_period <- get_incubation_period(disease = "SARS-CoV-2", source = "lauer")

estimates <- epinow(reported_cases = local_cases, samples=100, output='samples',

                    generation_time = generation_time,

                    delays = list(incubation_period, reporting_delay))

localRtTb = estimates$estimates$summarised[ estimates$estimates$summarised$variable=='R' , ]

It seems the default setting is recommended. 

# Note: all examples below have been tuned to reduce the runtimes of examples # these settings are not suggesed for real world use. # run model with default setting

It seems that by default, Rt varies. 

# run model with stationary Rt assumption (likely to provide biased real-time estimates) stat <- estimate_infections(reported_cases, generation_time = generation_time, delays = list(incubation_period, reporting_delay), stan_args = list(warmup = 200, cores = ifelse(interactive(), 4, 1), control = list(adapt_delta = 0.9)), stationary = TRUE)

Rt D3 package

https://epiforecasts.io/RtD3/

possible criteria of a head.

Ability and experience evaluating faculty and staff work performance in an equitable and clearly documented way Ability to communicate with faculty in an open and transparent way Ability to foster an environment in which everyone is comfortable contributing, and in which all voices are considered Ability to unify and support a large, complex, multidisciplinary department that maintains and promotes its disciplinary identities, strengths, and resources Ability to resolve conflict to the advantage of the department as a whole Accessibility and approachability to faculty, staff, and students Commitment and ability to attract and support a diverse and inclusive faculty, staff, and student body Effective advocacy of department needs and promotion of department strengths to upper administration Experience in an administrative role that includes program, budget, and personnel management Motivation and dedication to develop and maintain positive relationships with donors, alumni, and the professional community Support of faculty excellence in teaching across all modalities Support of faculty excellence in research including dissemination and funding Understanding that the department head position is complex, time consuming, and requires total commitment and dedication to the role Other (Please describe any other qualification(s), ability(s), and/or skill(s) that you think should be prioritized in our Department Chair search. Please only describe priorities here that would be among your top five priorities. In other words, priorities that you describe here plus those that you choose from the priorities listed above should amount to no more than five total.)

down load ERA5 land hourly data

 

ERA5 land hours data was last updated on July 12. 

I selected all 2020, all day, and all hours, it is 45.8 GB, which will take 12 hours to download at home.

I ssh into my simcenter workstation, 

wget url, 

which runs with an estimated time of a little over 2 hours. 

Kalman filter versus smoothing splines

 

https://stats.stackexchange.com/questions/15070/kalman-filter-vs-smoothing-splines

beautiful soup

 Beautiful Soup is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work.

https://www.crummy.com/software/BeautifulSoup/bs4/doc/

rvest, websracpping

 

https://blog.rstudio.com/2014/11/24/rvest-easy-web-scraping-with-r/

color in hex values

 

https://www.colorhexa.com/

notes, NSF AI institute meeting


Dear Panelists: Please be advised that you are in Listen Mode Only. Thank you.
Link to FAQ: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf20123
Link to FAQ: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf20123



Link to FAQ: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf20123


From MEM AV TECH-Angel Ntumy to All panelists and other attendees: (15:47)


https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf20123
https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf20123



https://www.captionedtext.com/client/event.aspx?EventID=4573120&CustomerID=321



Dear attendees, more information of the Theme 5 - AI Institute in Dynamic Systems Webinar is available at https://www.nsf.gov/events/event_summ.jsp?cntn_id=301241&WT.mc_id=USNSF_13&WT.mc_ev=click


The DOD NDSEG Fellowship program

The DOD NDSEG Fellowship program is now open for applications. Please pass this opportunity along to your eligible students! https://ndseg.sysplus.com/NDSEG/About/

 

The fellowship lasts for 3 years and pays for full tuition and all mandatory fees; a monthly stipend ($38,400 annually); a $5,000 travel budget over the Fellow’s tenure for professional development; and up to $1,200 a year in health insurance. Applications are due November 2, 2020, and students pursuing any graduate degree in an appropriate field may apply (the field are what you might expect—math, physics, CS, pretty much any kind of engineering).

 

Eligibility

• The NDSEG Fellowship Program is open only to U.S. citizens or U.S. nationals. Dual citizens may apply.
• Eligible applicants are required to be enrolled in their final year of undergraduate studies through the second year of a traditional PhD program. See below for a breakout of eligibility criteria:
• Undergraduate Students (degree must be received prior to the Fellowship Start date)
• Recent Bachelor's Degree Recipients
• Graduate Students (1st year, 2nd year)
• Recent Master's Degree Recipients
• Pre-Doctoral Students (1st year, 2nd year)
• Dual MD-PHD Students
• Dual PSYD-PHD Students
• Applicants must plan to enroll in a full-time program at an accredited U.S. institution of higher education leading to graduate degrees in fields specified in the DoD services Broad Agency Announcements (BAAs) research and development discipline

 

tidyverse, ggplot, FAQ

 

# Count the number of full duplicates

sum(duplicated(bike_share_rides))

# Remove duplicates

bike_share_rides_unique <- distinct(bike_share_rides)

# Count the full duplicates in bike_share_rides_unique

sum(duplicated(bike_share_rides_unique))

# Find duplicated ride_ids

bike_share_rides %>% 

  count(ride_id) %>% 

  filter(n > 1)

# Remove full and partial duplicates

bike_share_rides_unique <- bike_share_rides %>%

  # Only based on ride_id instead of all cols

  distinct(ride_id, .keep_all = TRUE)

bike_share_rides %>%

  # Group by ride_id and date

  group_by(ride_id, date) %>%

  # Add duration_min_avg column

  mutate(duration_min_avg = mean(duration_min)) %>%

  # Remove duplicates based on ride_id and date, keep all cols

  distinct(ride_id, date, .keep_all = TRUE) %>%

  # Remove duration_min column

  select(-duration_min)

# Find bad dest_size rows

sfo_survey %>% 

  # Join with dest_sizes data frame to get bad dest_size rows

  anti_join(dest_sizes, by = "dest_size") %>%

  # Select id, airline, destination, and dest_size cols

  select(id, airline, destination, dest_size)

# Add new columns to sfo_survey

sfo_survey <- sfo_survey %>%

  # dest_size_trimmed: dest_size without whitespace

  mutate(dest_size_trimmed = str_trim(dest_size),

         # cleanliness_lower: cleanliness converted to lowercase

         cleanliness_lower = str_to_lower(cleanliness))

# Count values of dest_size_trimmed

sfo_survey %>%

  count(dest_size_trimmed)

# Count values of cleanliness_lower

sfo_survey %>%

  count(cleanliness_lower)

# Count categories of dest_region

sfo_survey %>%

  count(dest_region)

# Categories to map to Europe

europe_categories <- c("EU", "eur", "Europ")

# Add a new col dest_region_collapsed

sfo_survey %>%

  # Map all categories in europe_categories to Europe

  mutate(dest_region_collapsed = fct_collapse(dest_region, 

                                              Europe = europe_categories)) %>%

  # Count categories of dest_region_collapsed

  count(dest_region_collapsed)

sfo_survey %>%

  filter(str_detect(phone, "-"))

Outside Interest Disclosure Form, UTC

 

Outside Interest Disclosure Form, UTC

The form and instructions can be found here: https://new.utc.edu/finance-and-administration/human-resources/employment-services/conflict-of-interest

*** government response stringency index, COVID19

 university of oxford

COVID19 

https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker

https://github.com/OxCGRT/covid-policy-tracker/raw/master/data/timeseries/OxCGRT_timeseries_all.xlsx

https://github.com/OxCGRT/covid-policy-tracker

this is related to Twitter sentiment analysis

cell vision .org

 yeast data resource

https://thecellvision.org/

17 Eqs

 

mass spec data base

MassIVE.quant: a community resource of quantitative mass spectrometry–based proteomics datasets

x

https://www.nature.com/articles/s41592-020-0955-0

resampling white-box to examine deep learning

 

Deep learning for gravitational-wave data analysis: A resampling white-box approach
Manuel D. Morales, Javier M. Antelis, Claudia Moreno, Alexander I. Nesterov
x

https://arxiv.org/abs/2009.04088?fbclid=IwAR3INBZBVRi7JL0tyeoHc2yM2W0LhSRssaErdODwhfA664ht-vGS9OZGM-4

Calculation of R0, Rt

 

Rt live: https://github.com/rtcovidlive/covid-model

zhang genome wide SARS- cov2 protein structure modeling

 

https://zhanglab.ccmb.med.umich.edu/COVID-19/

Quantum programming with Qiskit

 

Quantum programming with Qiskit 

https://youtu.be/aPCZcv-5qfA sentdex

mask rcnn, Okinawa group, github

 https://github.com/oist/Usiigaci

Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning

ETL, extract, transform, load

 

https://en.wikipedia.org/wiki/Extract,_transform,_load

TVM apache

 

https://tvm.apache.ohttps://tvm.apache.org/

Google AI blog

 

https://ai.googleblog.com/2020/05/announcing-7th-fine-grained-visual.html

Chattanooga open data

 

Chattanooga open data

https://www.chattadata.org/ 

policing and racial equipty

https://www.chattadata.org/stories/s/26bg-ejs3

Liu et al, Genome biology, 2020, Genome-wide studies reveal the essential and opposite roles of ARID1A in controlling human cardiogenesis and neurogenesis from pluripotent stem cells

 in virto human embryonic stem cell study. hESC

ARID1A in H9 hESC were deleted using dual guided-RNA mediated CRISP-Cas9 method. 

" Mutations in 4 different SWI/SNF subunits including ARID1A/B were identified in three congenital syndromes that include both neural and cardiac defects: Coffin-Siris syndrome (CSS), Nicolaides- Baraitser syndrome (NCBRS), and ARID1B-related intellectual disability (ID) syndrome  Patients with these syndromes show severe intellectual deficits as well as cardiac defects such as atrial/ventricular septal defects, patent ductus

arteriosus (PDA), mitral and pulmonary atresia, aortic stenosis, and single right ventricle. These data indicate that abnormal ARID1A activity can lead to defective  formation of both the heart and brain in humans. However, the molecular mechanisms  by which ARID1A controls human cardiogenesis and neurogenesis still remain. elusive." 

in hESC, Surprisingly, knockout-of-ARID1A in hESCs (ARID1A−/−) led to spontaneous neural. differentiation even under pluripotent stem cell culture conditions. Additionally, under conditions of targeted cardiac differentiation, ARID1A−/− hESCs gave rise to robustly increased numbers of neural cells, including neural stem cells and neurons, whereas cardiac differentiation was significantly suppressed

single-cell RNA reveals spontaneous differentiation neural differentiation in ARIA-/- cells. 

So, scRNA data are available for WT and KO in cardiac differential and neural differentiation. Based on my understanding of Liu, GB, 2020, there are WT and ARID1A-/- hESC cells, and the hESC cells are induced for cardiac differentation in CDM3 and neural differentation in N2B27 medium. The scRNA results show ARIDA-/- lead to different clusters of ScRNA in WT and KO in both differentation conditions. So, it seems to me that these data sets can be used to contruct weight single-cell gene network to study network control.

The noise levels seem to be good testing data sets on noises and weight in controllability analysis. 

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02082-4 

Shor's factorizing quantum computing algorithm

 Shor's factorizing quantum computing algorithm 

https://en.wikipedia.org/wiki/Shor%27s_algorithm 

A good video: https://youtu.be/lvTqbM5Dq4Q 

Shor's video (confusing)  https://youtu.be/hOlOY7NyMfs

IBM https://youtu.be/yy6TV9Dntlw 

Shor code, quantaum error correction

https://en.wikipedia.org/wiki/Quantum_error_correction#The_Shor_code 

Climate at a Glance

 

provide monthly average temperature

https://www.ncdc.noaa.gov/cag/national/time-series/110/tavg/1/7/1990-2020?base_prd=true&begbaseyear=1901&endbaseyear=2000&trend=true&trend_base=10&begtrendyear=1895&endtrendyear=2020

student created videos

 Learning through videos involved trials and errors,  lessons and failure. 

https://www.edutopia.org/article/student-created-videos-classroom 

making thinking visible

visible learning (this is different from my SLVideo concept). 

 

https://www.teachervision.com/blog/morning-announcements/tips-and-strategies-for-making-student-thinking-visible 

covid19 tweets

 https://www.kaggle.com/gpreda/covid19-tweets 

covid policy data set

 https://healthdata.gov/dataset/covid-19-state-and-county-policy-orders

yeast aging, somatic mutation, Dr. Dang

Dr. Dang Modulus aging model discussion

  DNA double strand break. Increased in old cells. Gamma H2A, phophlylation. A double strand breaker marker. Conserved in mammalian cells. It does mark single strand break. 

 https://www.nature.com/articles/leu20106#:~:text=Phosphorylation%20of%20the%20Ser%2D139,DNA%20damage%20initiation%20and%20resolution.

Yeast only has H2A (not H2AX). 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3889172/#:~:text=In%20Saccharomyces%20cerevisiae%2C%20histone%20H2A,about%2050%20kb%20in%20yeast).

  Mitosis crisis, short lived. Otherwise, long-lived. G1-death long0-lived. G2-death short lived. 

  SIR2-OE give third mode. 

  Rapamycin can be absolved by microfluidic device matrix, so it has been studied by microfluidics, according to Dang's conversation with N Hao

  TOR1Delta and SIR2-OE are additive in RLS, based on Dang’s own data.  Hong needs to check Kaberlein lab data sets. 

covid19 open data,

 https://ourworldindata.org/coronavirus-source-data

 

https://datascience.nih.gov/covid-19-open-access-resources

 

https://ncats.nih.gov/n3c/about/program-faq

 

suggested by Dr. Koo

domain adaptation

 

domain shift in machine shift

https://en.wikipedia.org/wiki/Domain_adaptation