Illustration of amino acid changes in SARS-CoV-2 proteins for the temporal and spatial non-synonymous hotspot mutations. by Nimisha Ghosh (9637889)

Subjects:

Month wise (temporal) entropy of Indian SARS-CoV-2 genomes to show the changes in non-synonymous hotspot mutations. by Nimisha Ghosh (9637889)

Subjects:

Fig 9 - by Nimisha Ghosh (9637889)

Subjects:

Fig 6 - by Nimisha Ghosh (9637889)

Subjects:

State wise (spatial) entropy of Indian SARS-CoV-2 genomes to show the changes in non-synonymous hotspot mutations. by Nimisha Ghosh (9637889)

Subjects:

Characteristics of non-synonymous hotspot mutations for temporal analysis. by Nimisha Ghosh (9637889)

Subjects:

Fig 10 - by Nimisha Ghosh (9637889)

Subjects:

Fig 2 - by Nimisha Ghosh (9637889)

Subjects:

Plots hardness, density and volume loss data. by Eduardo Paixão (20536077)

Dissecting Biological and Synthetic Soft–Hard Interfaces for Tissue-Like Systems by Yin Fang (1499122)

Subjects:

The most frequent trigrams involving unique AOIs in the pilot group during the Control Scenario (CS), the Easy Dual-Task Scenario (EDTS), and the Hard Dual-Task Scenario (HDTS). by Christophe Lounis (10164121)

“…<p>The most frequent trigrams involving unique AOIs in the pilot group during the Control Scenario (CS), the Easy Dual-Task Scenario (EDTS), and the Hard Dual-Task Scenario (HDTS).…”

Machine Learning-Driven Optimization of Therapeutic Substance Composition for High-Hardness, Fast-Dissolving Microneedles for Androgenetic Alopecia Treatment by Peiyu Yan (14011299)

“…Direct injection causes pain, and PRP-incorporated microneedles (MNs) have low hardness and slow dissolution. To tackle this problem, we propose a machine-learning (ML)-driven strategy, which involves integrating the selection of therapeutic substances, orthogonal experiment designs, ML prediction, and Pareto front identification. …”

A breakdown of COVID-19 presence across the periods of interest. by Justin Kaashoek (14216292)

Subjects:

A summary of variables considered. by Justin Kaashoek (14216292)

Subjects:

The number of counties that have been exposed to the virus in each quantile shown in Fig 4. by Justin Kaashoek (14216292)

Subjects:

A summary of our virus spread model runs across all periods for the spatial lag model. by Justin Kaashoek (14216292)

Subjects:

A summary of virus introduction model runs in period 1. by Justin Kaashoek (14216292)

Subjects:

Variables and corresponding quantiles for Fig 4. by Justin Kaashoek (14216292)

Subjects:

Proportion of counties exposed to COVID-19 and median deaths rate for six population variables broken down by period and the stringency of governor’s interventions. by Justin Kaashoek (14216292)

Subjects:

A summary of 3-means clustering and maps of counties included in the analysis shaded by the six different population variables of interest. by Justin Kaashoek (14216292)

Subjects: