High-resolution global population projections dataset developed with CMIP6 RCP and SSP scenarios for year 2010 – 2100

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The viral outbreak started in late 2019 and rapidly became a serious health threat to the global population. COVID-19 was declared a pandemic by the World Health Organization in March 2020. Several therapeutic options have been adopted to prevent the spread of the virus. Although vaccines have been developed, antivirals are still needed to combat the infection of this virus. SARS-CoV-2 is an enveloped virus, and its genome encodes polyproteins that can be processed into structural and nonstructural proteins. Maturation of viral proteins requires cleavages by proteases. Therefore, the main protease (3 chymotrypsin-like protease (3CLpro) or Mpro) encoded by the viral genome is an attractive drug target because it plays an important role in cleaving viral polyproteins into functional proteins. Inhibiting this enzyme is an efficient strategy to block viral replication. Structural studies provide valuable insight into the function of this protease and structural basis for rational inhibitor design. In this review, we describe structural studies on the main protease of SARS-CoV-2. The strategies applied in developing inhibitors of the main protease of SARS-CoV-2 and currently available protein inhibitors are summarized. Due to the availability of high-resolution structures, structure-guided drug design will play an important role in developing antivirals. The availability of high-resolution structures, potent peptidic inhibitors, and diverse compound scaffolds indicate the feasibility of developing potent protease inhibitors as antivirals for COVID-19.

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Dimensions of global population projections: what do we know about future population trends and structures?

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2010.0133 ◽

2010 ◽

Vol 365 (1554) ◽

pp. 2779-2791 ◽

Cited By ~ 130

Author(s):

Wolfgang Lutz ◽

Samir KC

Keyword(s):

Population Projections ◽

World Population ◽

Mortality Trends ◽

Total Size ◽

Future Fertility ◽

Western Asia ◽

The World ◽

Global Population ◽

Population Stabilization ◽

Future Population

The total size of the world population is likely to increase from its current 7 billion to 8–10 billion by 2050. This uncertainty is because of unknown future fertility and mortality trends in different parts of the world. But the young age structure of the population and the fact that in much of Africa and Western Asia, fertility is still very high makes an increase by at least one more billion almost certain. Virtually, all the increase will happen in the developing world. For the second half of the century, population stabilization and the onset of a decline are likely. In addition to the future size of the population, its distribution by age, sex, level of educational attainment and place of residence are of specific importance for studying future food security. The paper provides a detailed discussion of different relevant dimensions in population projections and an evaluation of the methods and assumptions used in current global population projections and in particular those produced by the United Nations and by IIASA.

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Footprints to Singularity: A global population model explains late 20th century slow-down and predicts peak within ten years

10.1101/2021.02.04.429734 ◽

2021 ◽

Author(s):

Christopher Bystroff

Keyword(s):

Carrying Capacity ◽

Agricultural Production ◽

Population Data ◽

Population Projections ◽

Growth Equation ◽

Dynamics Model ◽

Late 20Th Century ◽

Life Saving ◽

Global Population ◽

Population Numbers

AbstractProjections of future global human population are traditionally made using birth/death trend extrapolations, but these methods ignore limits. Expressing humanity as a K-selected species whose numbers are limited by the global carrying capacity produces a different outlook. Population data for the second millennium up to the year 1970 was fit to a hyper-exponential growth equation, where the rate constant for growth itself grows exponentially due to growth of life-saving technology. The discrepancies between the projected growth and the actual population data since 1970 are accounted for by a decrease in the global carrying capacity due to ecosystem degradation. A system dynamics model that best fits recent population numbers suggests that the global biocapacity may already have been reduced to one-half of its historical value and global carrying capacity may be at its 1965 level and falling. Simulations suggest that population may soon peak or may have already peaked. Population projections depend strongly on the unknown fragility or robustness of the Earth’s essential ecosystem services that affect agricultural production. Numbers for the 2020 global census were not available for this study.

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Automated COVID-19 Detection from Chest X-Ray Images : A High Resolution Network (HRNet)Approach

10.1101/2020.08.26.20182311 ◽

2020 ◽

Author(s):

Sifat Ahmed ◽

Tonmoy Hossain ◽

Oishee Bintey Hoque ◽

Sujan Sarker ◽

Sejuti Rahman ◽

...

Keyword(s):

High Resolution ◽

Well Being ◽

X Rays ◽

X Ray ◽

Pcr Diagnosis ◽

Chest X Ray ◽

Novel Coronavirus ◽

And Control ◽

Learning Architectures ◽

Global Population

The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body, to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the noninvasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. In this work, we propose an automated COVID-19 classifier, utilizing available COVID and non-COVID X-Ray datasets, along with High Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed dataset, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease.

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