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Fingerprint patterns through genetics

O'Brien et al. | Dec 02, 2020

Fingerprint patterns through genetics

This study explores the link between fingerprints and genetics by analyzing familial fingerprints to show how the fingerprints between family members, and in particular siblings, could be very similar. The hypothesis was that the fingerprints between siblings would be very similar and the dominant fingerprint features within the family would be the same throughout the generations. Fingerprints between the siblings showed a trend of similarity, with only very small differences which makes these fingerprints unique. This work helps to support the link between fingerprints and genetics while providing a modern technological application.

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Mathematical modeling of plant community composition for urban greenery plans

Fang et al. | Jul 05, 2023

Mathematical modeling of plant community composition for urban greenery plans
Image credit: CHUTTERSNAP

Here recognizing the importance of urban green space for the health of humans and other organisms, the authors investigated if mathematical modeling can be used to develop an urban greenery management plan with high eco-sustainability by calculating the composition of a plant community. They optimized and tested their model against green fields in a Beijing city park. Although the compositions predicted by their models differed somewhat from the composition of testing fields, they conclude that by using a mathematical model such as this urban green space can be finely designed to be ecologically and economically sustainable.

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Evaluation of platelet-rich plasma vs. platelet lysate: VEGF and PDGF concentration, stability, and shelf life

Prasad et al. | Mar 30, 2022

Evaluation of platelet-rich plasma vs. platelet lysate: VEGF and PDGF concentration, stability, and shelf life

Cell-free biologicals are a novel method of treating clinical conditions which involve chronic inflammation such as tendonitis and osteoarthritis. This study compared platelet-derived growth factor (PDGF) and vascular endothelial growth factor (VEGF) in platelet-rich plasma (PRP), activated PRP (aPRP), and platelet lysate (PL). It was hypothesized that PL would contain higher concentrations of growth factors than PRP and that different storage temperatures for PL would diminish cytokine expression. Results demonstrated PL had the highest concentrations of both cytokines, with concentrations slightly diminishing at-80C. aPRP and PRP demonstrated lower concentrations of PDGF and VEGF than PL.

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Utilizing the Magnus effect to produce more downforce than a standard wing

Angiras et al. | Aug 15, 2022

Utilizing the Magnus effect to produce more downforce than a standard wing

Here, seeking a better solution to produce downforce that keeps a vehicle grounded at high speeds than wings which tend to result in degraded car performance due to increased air resistance, the authors considered using the Magnus effect as a replacement. The authors found that a spinning cylinder generated significantly more downforce through the Magnus effect than a standard wing at all wind speeds as simulated through the use of a leaf blower. They suggest that a cylinder could be a potential replacement for a wing when downforce is a priority.

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Racemic serine is less soluble than pure enantiomers due to stronger intermolecular hydrogen bonds

Ranka et al. | Nov 18, 2021

Racemic serine is less soluble than pure enantiomers due to stronger intermolecular hydrogen bonds

Seeking to develop a better understanding of the chemical and physical properties of amino acids that compose proteins, here the authors investigated the unusual relative insolubility of racemic mixtures of D- and L-serine compared to the solubility of pure D- or L-serine. The authors used a combination of microscopy and temperature measurements alongside previous X-ray diffraction studies to conclude that racemic DL-serine crystals consist of comparatively stronger hydrogen bond interactions compared to crystals of pure enantiomers. These stronger interactions were found to result in the unique release of heat during the crystallization of racemic mixtures.

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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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Expressional correlations between SERPINA6 and pancreatic ductal adenocarcinoma-linked genes

Selver et al. | Oct 06, 2021

Expressional correlations between <em>SERPINA6</em> and pancreatic ductal adenocarcinoma-linked genes

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, with early diagnosis and treatment challenges. When any of the genes KRAS, SMAD4, TP53, and BRCA2 are heavily mutated, they correlate with PDAC progression. Cellular stress, partly regulated by the gene SERPINA6, also correlates with PDAC progression. When SERPINA6 is highly expressed, corticosteroid-binding globulin inhibits the effect of the stress hormone cortisol. In this study, the authors explored whether there is an inverse correlation between the expression of SERPINA6 and PDAC-linked genes.

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High-throughput virtual screening of novel dihydropyrimidine monastrol analogs reveals robust structure-activity relationship to kinesin Eg5 binding thermodynamics

Shern et al. | Jan 20, 2021

High-throughput virtual screening of novel dihydropyrimidine monastrol analogs reveals robust structure-activity relationship to kinesin Eg5 binding thermodynamics

As cancer continues to take millions of lives worldwide, the need to create effective therapeutics for the disease persists. The kinesin Eg5 assembly motor protein is a promising target for cancer therapeutics as inhibition of this protein leads to cell cycle arrest. Monastrol, a small dihydropyrimidine-based molecule capable of inhibiting the kinesin Eg5 function, has attracted the attention of medicinal chemists with its potency, affinity, and specificity to the highly targeted loop5/α2/α3 allosteric binding pocket. In this work, we employed high-throughput virtual screening (HTVS) to identify potential small molecule Eg5 inhibitors from a designed set of novel dihydropyrimidine analogs structurally similar to monastrol.

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Utilizing meteorological data and machine learning to predict and reduce the spread of California wildfires

Bilwar et al. | Jan 15, 2024

Utilizing meteorological data and machine learning to predict and reduce the spread of California wildfires
Image credit: Pixabay

This study hypothesized that a machine learning model could accurately predict the severity of California wildfires and determine the most influential meteorological factors. It utilized a custom dataset with information from the World Weather Online API and a Kaggle dataset of wildfires in California from 2013-2020. The developed algorithms classified fires into seven categories with promising accuracy (around 55 percent). They found that higher temperatures, lower humidity, lower dew point, higher wind gusts, and higher wind speeds are the most significant contributors to the spread of a wildfire. This tool could vastly improve the efficiency and preparedness of firefighters as they deal with wildfires.

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Machine learning on crowd-sourced data to highlight coral disease

Narayan et al. | Jul 26, 2021

Machine learning on crowd-sourced data to highlight coral disease

Triggered largely by the warming and pollution of oceans, corals are experiencing bleaching and a variety of diseases caused by the spread of bacteria, fungi, and viruses. Identification of bleached/diseased corals enables implementation of measures to halt or retard disease. Benthic cover analysis, a standard metric used in large databases to assess live coral cover, as a standalone measure of reef health is insufficient for identification of coral bleaching/disease. Proposed herein is a solution that couples machine learning with crowd-sourced data – images from government archives, citizen science projects, and personal images collected by tourists – to build a model capable of identifying healthy, bleached, and/or diseased coral.

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