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Changes for Development of Al2O3 Coated PVA (Polyvinyl Alcohol) Composite Nonwoven Separator For Improving Thermal and Electrochemical Properties

Kim et al. | Oct 16, 2019

Changes for Development of Al2O3 Coated PVA (Polyvinyl Alcohol) Composite Nonwoven Separator For Improving Thermal and Electrochemical Properties

Lithium-ion batteries, a breakthrough in chemistry that enabled the electronic revolution we live today have become an essential part of our day-to-day life. A phone battery running out after a heavy day of use with limited opportunities for recharging is a well-known and resented experience by almost everyone. How then can we make batteries more efficient? This paper proposes the use of a different type of separator, that improves the charging and discharging capacities of lithium ions compared to the classical separator. This and similar attempts to improve Lithium-ion battery function could facilitate the development of higher-performance batteries that work longer and withstand harsher use.

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Homology modeling of clinically-relevant rilpivirine-resistant HIV-RT variants identifies novel rilpivirine analogs with retained binding affinity against NNRTI-resistant HIV mutations

Luk et al. | Jan 24, 2022

Homology modeling of clinically-relevant rilpivirine-resistant HIV-RT variants identifies novel rilpivirine analogs with retained binding affinity against NNRTI-resistant HIV mutations

Human immunodeficiency virus (HIV), which affects tens of millions of individuals worldwide, can lead to acquired immunodeficiency syndrome (AIDS). While there is currently no cure for HIV, the development of small molecule antiretroviral agents has greatly improved the prognosis of infected individuals, especially in developed countries. Here, the authors employ homology modeling and molecular docking towards the identification of novel rilpivirine analogs that retain high binding affinity to clinically relevant rilpivirine-resistant mutations of the HIV reverse transcriptase enzyme.

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A Data-Centric Analysis of “Stop and Frisk” in New York City

Bhat et al. | Apr 18, 2021

A Data-Centric Analysis of “Stop and Frisk” in New York City

The death of George Floyd has shed light on the disproportionate level of policing affecting non-Whites in the United States of America. To explore whether non-Whites were disproportionately targetted by New York City's "Stop and Frisk" policy, the authors analyze publicly available data on the practice between 2003-2019. Their results suggest African Americans were indeed more likely to be stopped by the police until 2012, after which there was some improvement.

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Dune flora can emerge from seed islands (Concon, Chile)

Farías Giusti-Bilz et al. | Dec 07, 2020

Dune flora can emerge from seed islands (Concon, Chile)

In the field of ecology, little is known about how plant communities originate. Through the process of characterizing dunes, mounds of sand formed by the wind, and their plant communities we can get to know the physiognomy and floristic composition of the territory. Based on the hypothesis that dune flora can emerge from seed islands: holes in the sand 6 cm deep containing a mixture of seeds, broken branches of shrubbery, and rabbit feces, during spring, the authors determined the composition of 20 seed islands in the sand dunes of Concon, Chile and measured how many seeds germinated in each one.

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Exponential regression analysis of the Canadian Zero Emission Vehicle market’s effects on climate emissions in 2030

Ajay et al. | Feb 25, 2023

Exponential regression analysis of the Canadian Zero Emission Vehicle market’s effects on climate emissions in 2030
Image credit: Andrew Roberts

Here, the authors explored how the sale and use of electric vehicles could reduce emissions from the transport industry in Canada. By fitting the sale of total of electric vehicles with an exponential model, the authors predicted the number of electric vehicle sales through 2030 and related that to the average emission for such vehicles. Ultimately, they found that the sale and use of electric vehicles alone would likely not meet the 45% reduction in emissions from the transport industry suggested by the Canadian government

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siRNA-dependent KCNMB2 silencing inhibits lung cancer cell proliferation and promotes cell death

Jeong et al. | Nov 01, 2022

siRNA-dependent KCNMB2 silencing inhibits lung cancer cell proliferation and promotes cell death

Here, seeking to better understand the genetic associations underlying non-small cell lung cancer, the authors screened hundreds of genes, identifying that KCNMB2 upregulation was significantly correlated with poor prognoses in lung cancer patients. Based on this, they used small interfering RNA to decrease the expression of KCNMB2 in A549 lung cancer cells, finding decreased cell proliferation and increased lung cancer cell death. They suggest this could lead to a new potential target for lung cancer therapies.

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Investigation of the correlation between trihalomethane concentrations and socioeconomic factors in NY State

Lee et al. | Aug 19, 2023

Investigation of the correlation between trihalomethane concentrations and socioeconomic factors in NY State

Trihalomethanes, probable human carcinogens, are commonly found disinfection by-products (DBPs) in public water systems (PWS). The authors investigated the correlation between trihalomethane concentrations and socioeconomic factors in New York State, finding a negative correlation between median household income and trihalomethane concentrations. The inverse association between trihalomethanes and household income may indicate socioeconomic disparity regarding drinking water quality and the need for improved efforts to assist small- and medium-sized community water systems to lower DBP levels in New York State.

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The Role of a Mask - Understanding the Performance of Deep Neural Networks to Detect, Segment, and Extract Cellular Nuclei from Microscopy Images

Dasgupta et al. | Jul 06, 2021

The Role of a Mask - Understanding the Performance of Deep Neural Networks to Detect, Segment, and Extract Cellular Nuclei from Microscopy Images

Cell segmentation is the task of identifying cell nuclei instances in fluorescence microscopy images. The goal of this paper is to benchmark the performance of representative deep learning techniques for cell nuclei segmentation using standard datasets and common evaluation criteria. This research establishes an important baseline for cell nuclei segmentation, enabling researchers to continually refine and deploy neural models for real-world clinical applications.

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