COVID Vaccines not being tested to work, CBD a COVID Lung Saver?, Shoes thee COVID carrier and Data.

This week we review disturbing vaccine study requirements, CBD an incredible gem if possibly protecting the lungs and restoring oxygen levels, and a strong correlation as to shoes being an unrecognized major disease vector. In addition to looking at COVID data correlations to which countries are locking down in response Sars-COV-2 to those which have not or have done little. #covidvaccine #covidvector #covidnews Data Sources API for DataFrames: The COVID Tracking Project Our wold in Data (Oxford) Links: https://www.eurekalert.org/pub_releases/2020-10/uoo-ecw102220.php#.X5N_7_DuPM0.wordpress https://www.eurekalert.org/pub_releases/2020-10/b-cvt102020.php#.X5OGbCHAYR8.wordpress https://www.eurekalert.org/pub_releases/2020-10/mcog-chr101620.php#.X45lOsCeu4k.wordpress https://wwwnc.cdc.gov/eid/article/26/7/20-0885_article

COVID19 Analytics – Mask Trash and Shoes a Major Spreader, Newsom & Fauci Being Odd, Florida Wins

Our weekly review of the current COVID data and country comparisons as well as other oddities such as Mask Litter, Trash Cans, and Shoes being unintended spreaders. All this under the guise of Amateur Python Analytics. Brief CSV File Request Code below (Pandas). That will allow you to pull Oxford University Data up to the current date. Enjoy 😉

This is a long one, next week I will make it A LOT shorter.

#covid19 #sarscov2 #data

Code Snippet:
import pandas as pd
import csv
import requests
younameit = pd.read_csv(‘https://covid.ourworldindata.org/data/owid-covid-data.csv’)

https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/deathsduetocoronaviruscovid19comparedwithdeathsfrominfluenzaandpneumoniaenglandandwales/deathsoccurringbetween1januaryand31august2020

https://wwwnc.cdc.gov/eid/article/26/7/20-0885_article

https://www.eurekalert.org/pub_releases/2020-10/uoh-rci100120.php#.X3fUGZsAGM0.wordpress

https://www.cidrap.umn.edu/news-perspective/2020/04/commentary-masks-all-covid-19-not-based-sound-data

Pandemic Charting – Weaponizing Uncertainty – Countries Do better with a Light touch – Python Data

COVID-19 Made worse By Social Distancing?

We are led to question whether the recommended social distancing measures to prevent SARS-CoV-2 transmission could increase the number of other serious instabilities. The breaking of the contagion pathways reduces the sharing of microorganisms between people, thus favoring dysbiosis, which, in turn, may increase the poor prognosis of the disease. #covid #microbiome #dysbiosis Célia P. F. Domingues, João S. Rebelo, Francisco Dionisio, Ana Botelho, Teresa Nogueira. The Social Distancing Imposed To Contain COVID-19 Can Affect Our Microbiome: a Double-Edged Sword in Human Health. mSphere, 2020; 5 (5) DOI: 10.1128/mSphere.00716-20 https://msphere.asm.org/content/5/5/e00716-20

Lockdowns a Complete Failure compared to controls – Countries that did not? Python Analysis Part 2

Part 2 as promised. We compare cases and death per million from industrialized countries which did little to nothing to Great Britain and the United States. The Data extrapolated is from: https://ourworldindata.org/coronaviru…
#covid19 #lockdown #socialdistancing

(Volume is kind of Choppy midpoint)
Additional Code: From Part 1:
datasw = data.loc[data.iso_code==’SWE’, :]
datagb = data.loc[data.iso_code==’GBR’, :]
dataus = data.loc[data.iso_code==’USA’, :]
datasg = data.loc[data.iso_code==’SGP’, :]
datajp = data.loc[data.iso_code==’JPN’, :]
datako = data.loc[data.iso_code==’KOR’, :]
datatw = data.loc[data.iso_code==’TWN’, :]
dataall = [datagb,dataus,datasw,datasg,datajp,datako, datatw]
dataall = pd.concat(dataall)
dataall
dataall.datetime = pd.to_datetime(dataall.date)
dataall.set_index(‘date’, inplace=True)
fig, ax = plt.subplots(figsize=(50,25))
dataall.groupby(‘iso_code’)[‘new_cases_smoothed_per_million’].plot(legend=True,fontsize = 20, linewidth=7.0)
ax.legend([‘Great Britain = Lockdown’,’Japan = No LD’, ‘South Korea = No LD’, ‘Singapore = LD JUNE -Migrant LD HIghest POP Density’, ‘Sweden = No LD’, ‘Taiwan = No LD’,’USA = Lockdown’],prop=dict(size=50))
comp = dataall.loc[‘2020-09-18’]
comp.set_index(“iso_code”, inplace=True)
comp= pd.DataFrame(comp[[‘total_cases_per_million’,’total_deaths_per_million’]])
plt.rc(‘legend’, fontsize=50)
comp.plot.bar(rot=0, figsize=(20,20),fontsize=30)

Pandemic Over? COVID-19 World data Amateur Python Analysis

From an educational perspective, we review current COVID-19 data and arrive look at lockdowns and population density appears to have no numerical effect currently on COVID-19. In any case, this is more about exploring the code from a beginner’s standpoint with Python and DataFrames.
#covid19 #pandemicover #coviddata
CSV files found here:
https://ourworldindata.org/coronaviru…
Code: (Had to remove the angle brackets)
import numpy as np
import pandas as pd
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
import pandas as pd
from scipy.stats import spearmanr
from scipy.stats import kendalltau
from scipy.stats import pearsonr
from scipy import stats
import seaborn as sns
import warnings
warnings.filterwarnings(“ignore”)
#Pandemic
Claim Currently Invalid —Ralph Turchiano
data = pd.read_csv(‘owid-covid-data-19SEP2020.csv’)
data.info()
pd.set_option(‘max_columns’, None)
data.tail(5)
data[‘date’] = pd.to_datetime(data[‘date’])
data.info()
data_18SEP = data[data[‘date’]==’2020-09-18′]
data_ind = data_18SEP[data_18SEP[‘human_development_index’]=.8]
data_ind.head(10)
data_ind.drop([‘iso_code’,’continent’,’handwashing_facilities’,’stringency_index’,], axis=1, inplace=True)
data_ind.columns
data_ind[‘extreme_poverty’].fillna(0, inplace=True)
data_compare = pd.DataFrame([data.loc[37991],data.loc[41736]])
data_compare
data_compare.set_index(‘location’,inplace=True)
data_compare[‘total_cases_per_million’]
data_Swe_USA=pd.DataFrame(data_compare[[‘total_cases_per_million’,’new_cases_per_million’,’new_deaths_per_million’]])
data_Swe_USApd.DataFrame(data_compare[[‘total_cases_per_million’,’new_cases_per_million’,’new_deaths_per_million’]])
data_Swe_USA
data_ind.drop([‘date’,’new_cases’,’new_deaths’,’total_tests’, ‘total_tests_per_thousand’,
‘new_tests_per_thousand’, ‘new_tests_smoothed’, ‘new_tests’,
‘new_tests_smoothed_per_thousand’, ‘tests_per_case’,’tests_units’,’new_deaths_per_million’,’positive_rate’ ], axis=1, inplace=True)
data_ind.tail()
data_ind.dropna(inplace=True)
data_ind.corr(“kendall”)
data_18SEP.tail()
data_18SEP.loc[44310]
data_18SEP.loc[44310,[‘new_cases_smoothed_per_million’,’new_deaths_smoothed_per_million’]]
New =pd.DataFrame(data[[‘new_cases_smoothed_per_million’,’new_deaths_smoothed_per_million’]])
New.corr(‘kendall’)
dataw = data.loc[data[‘iso_code’] == ‘OWID_WRL’]
dataw
dataw.datetime = pd.to_datetime(data.date)
dataw.set_index(‘date’, inplace=True)
data_cl = pd.DataFrame(dataw[[‘new_deaths_smoothed’,’new_cases_smoothed’]])
data_cl.dropna(inplace=True)
data_cl.plot(figsize=(30,12))
data_cl.tail(20)

COVID-19 Tracking Data API and Data Anomalies (No Correlations? Cases to Hospitalizations Increases)

Is there a correlation between Positive cases and Hospitalizations? Below is the API for python access, open to all who desire to filter the data. I want to just give easy access to all the beginner students data scientists out there, such as myself..Explore and Discover: **My Apologies It says High Def, but does not look High Def on video here**

Code: import matplotlib.pyplot as plt import pandas as pd from scipy import stats import statsmodels.api as sm import requests import time from IPython.display import clear_output response = requests.get(“https://covidtracking.com/api/v1/us/daily.csv”) covid = response.content ccc = open(“daily.csv”,”wb”) ccc.write(covid) ccc.close() df = pd.read_csv(“daily.csv”, index_col = ‘date’) df.head() data = df[[‘positiveIncrease’,’hospitalizedIncrease’]] dataT = df[[‘positiveIncrease’,’hospitalizedIncrease’,’hospitalizedCurrently’]] dataD = df[[‘hospitalizedIncrease’,’deathIncrease’]] dataT.head(20) plt.figure(figsize=(20,10)) Y = data[‘positiveIncrease’] X = data[‘hospitalizedIncrease’] plt.scatter(X,Y) plt.ylabel(“Tested Positive Increase”) plt.xlabel(“Hospitalization Increase”) plt.show() Y1 = sm.add_constant(Y) reg = sm.OLS(X, Y1).fit() reg.summary() data.plot(y=[‘hospitalizedIncrease’,’positiveIncrease’],xticks=data.index[0:len(data):30], rot=90, figsize=(20,10) ) for x in range(len(data)): plt.figure(figsize=(20,10)) plt.xticks( data.index.values[0:len(data):30], rotation = 90, fontsize=20 ) plt.plot(data.tail(x))

Honeysuckle Decoction Inhibits SARS-CoV-2

In a new study in Cell Discovery, Chen-Yu Zhang’s group at Nanjing University and two other groups from Wuhan Institute of Virology and the Second Hospital of Nanjing present a novel finding that absorbed miRNA MIR2911 in honeysuckle decoction (HD) can directly target SARS-CoV-2 genes and inhibit viral replication. Drinking of HD accelerate the negative conversion of COVID-19 patients.

#mir2911 #sarcov2 #honeysuckle

Zhou, L., Zhou, Z., Jiang, X. et al. Absorbed plant MIR2911 in honeysuckle decoction inhibits SARS-CoV-2 replication and accelerates the negative conversion of infected patients. Cell Discov 6, 54 (2020). https://doi.org/10.1038/s41421-020-00197-3

https://www.nature.com/articles/s41421-020-00197-3#ethics

An easier way to go vegan, Vitamin B12 CAN be produced during grain fermentation

The highest production was found in the rice bran (ca. 742 ng/g dw), followed by the buckwheat bran (ca. 631 ng/g dw), after fermentation. Meanwhile, the addition of L. brevis was able to dominate indigenous microbes during fermentation and thus greatly improve microbial safety during the fermentation of different grain materials. #b12 #vegan #fermentation https://helda.helsinki.fi/bitstream/handle/10138/317682/insitufo.pdf?sequence=1&isAllowed=y In situ fortification of vitamin B12 in grain materials by fermentation withPropionibacterium freudenreichii, Chong Xie ISBN 978-951-51-6355-4 (PAPERBACK) ISBN 978-951-51-6356-1 (PDF, http://ETHESIS.HELSINKI.FI) ISSN 0355-1180 UNIGRAFIA HELSINKI 2020

Black raspberries show promise for reducing skin inflammation, allergies

More COVID Research Information Censored

More COVID Research Censored CDC and the WHO, to my dismay, are either directly or indirectly controlling the flow of information and research, possibly creating an echo chamber of bias. The level of censorship is getting so out of control; it is highly likely now it may be resulting in harm in a variety of societal dimensions. As well as the Freedom to Speech is becoming rapidly stratified among those in positions of wealth, power, or fame, It is becoming painfully apparent that self-proclaimed thought leaders may not be behooving us in times of crisis, manufactured, self-inflicted, or real. At the very least, by not reviewing and growing from our errors, we are, in all essence, committed to repeating them. Freedom of Speech, in its most basic form, is simply the freedom to speak. Take that right away from one, and you build a case to take it away from all, for, of course, your own protection. #censorship #freedomofspeech #covid

COVID-19 Infection and Mortality Rate Questions

COVID-19 Infection and Mortality Rate Questions

Person-to-person transmission of SARS-CoV-2 occurred between two people with prolonged, unprotected exposure while the first patient was symptomatic. Despite active monitoring and testing of 372 contacts of both cases, no further transmission was detected

The RKI added: ‘We don’t consider post-mortem tests to be a decisive factor.

‘We work on the principle that patients are tested before they die.’

But this means that if a person dies in quarantine at home and does not go to hospital, there is a high chance they will not be included in the statistics, as Giovanni Maga of Italy’s National Research Council pointed out in an interview with Euronews

First known person-to-person transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the USA. The Lancet, 2020; DOI: 10.1016/S0140-6736(20)30607-3

https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30607-3/fulltext

COVID-19 and the Risk to Health Care Workers: A Case Report

Published: Ann Intern Med. 2020. DOI: 10.7326/L20-0175

https://annals.org/aim/fullarticle/2763329/covid-19-risk-health-care-workers-case-report

cov19, covid19, sarscov2, SARS-CoV-2 test, SARS-CoV-2, infection, risk, contagion, pathogenicity, pathogen, mortality, diagnosis, data collection, covid-19, statistics, mutagenesis, data, protection, Health care workers, person-to-person transmission, transmission

The European Union will make review of clinical trials for drugs ” absolutely impossible “

2014-05-27

Just look, but don’t touch: EMA terms of use for clinical study data are impracticable

Data are only allowed to be viewed on screen / Pre-censorship by drug manufacturers

The European Medicines Agency (EMA) receives comprehensive clinical study data from drug manufacturers. These data form the basis for the decision on the approval of new drugs. To make this information available to researchers and decision-makers, EMA issued a draft policy in 2013 for the publication of clinical study data, in which extensive data transparency was planned. Continue reading “The European Union will make review of clinical trials for drugs ” absolutely impossible “”

Anonymous group claims Bank of America monitored activists

Feb 28, 2013 13:56 Moscow Time

26.01.2012 Анонимус Anonimous Бельгия Европарламент

Photo: EPA

The famous hacktivist group, Anonymous, has released data that it claims shows how Bank of America employed security firms to monitor hackers and activists.

They unveiled some 14 Gigabytes of data, code and software that allegedly “shows that Bank of America and others are contracting other companies to spy and collect information on private citizens,” adding the “overall quality of the research is poor and potentially false.”

Hacktivists said they didn’t need to break into any BofA accounts as all the documents were stored on a misconfigured server and were effectively “open for grabs.”

Leaked data reveal that TEKSystems assembled reports on Occupy Wall Street online activity and hackers in 2012.

Voice of Russia, SALON

 

http://english.ruvr.ru/2013_02_28/Anonymous-group-claims-Bank-of-America-monitored-activists/

Never lose your data again! Hitachi develops glass-based storage system that will last for 100 MILLION years

By John Hutchinson

PUBLISHED:15:13 EST, 27  September 2012| UPDATED:15:18 EST, 27 September 2012

Breakthrough: A woman holds up Hitachi's newly unveiled quartz glass plate technology, which can be used for the indefinite storage of data
Breakthrough: A woman holds up Hitachi’s newly unveiled  quartz glass plate technology, which can be used for the indefinite storage of  data

The developments in recent years of file  storage have moved from the physical to the electronic, yet the problems of  damage and loss still persist.

However, Hitachi have developed what could be  a foolproof giant in the world of file storage, with a piece of  glass.

The company unveiled a method of storing  digital information on slivers of quartz glass that can endure extreme  temperatures and hostile conditions without degrading, almost  forever.

‘The volume of data being created every day  is exploding, but  in terms of keeping it for later generations, we haven’t  necessarily  improved since the days we inscribed things on stones,’ Hitachi  researcher Kazuyoshi Torii said.

‘The possibility of losing information may  actually have increased,’  he said, noting the life of digital media currently  available — CDs and hard drives — is limited to a few decades or a century at  most.

Hitachi’s new technology stores data in  binary form by creating dots inside a thin sheet of quartz glass, which can be  read with an  ordinary optical microscope.

Provided a computer with the know-how to  understand that binary is  available — simple enough to programme, no matter  how advanced  computers become — the data will always be readable.

The chip, which is resistant to many  chemicals and unaffected by radio waves, can be exposed directly to high  temperature flames and heated to 1,000 degrees Celsius (1,832 Fahrenheit) for at  least two hours without being damaged.

It is also waterproof, meaning it could  survive natural calamities, such as fires and tsunami.

‘We believe data will survive unless this  hard glass is broken,’ said senior researcher Takao Watanabe.

Complicated? The glass stores binary data by using a laser beam to create dots which can then be read using an optical microscope connected to a monitor device with data reading softwareComplicated? The glass stores binary data by using a  laser beam to create dots which can then be read using an optical microscope  connected to a monitor device with data reading software

The material currently has four layers of  dots, which can hold 40 megabytes per square inch, approximately the density on  a music CD, researchers said, adding they believe adding more layers should not  be a problem.

Hitachi have not decided when to put the chip  to practical use but researchers said they could start with storage services for  government agencies, museums and religious organisations.

Read more: http://www.dailymail.co.uk/sciencetech/article-2209627/Hitachi-develops-glass-based-storage-100-MILLION-years.html#ixzz27i5xJUsT Follow us: @MailOnline on Twitter | DailyMail on Facebook