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Factors Influencing Muon Flux and Lifetime: An Experimental Analysis Using Cosmic Ray Detectors

Samson et al. | May 18, 2020

Factors Influencing Muon Flux and Lifetime: An Experimental Analysis Using Cosmic Ray Detectors

Muons, one of the fundamental elementary particles, originate from the collision of cosmic rays with atmospheric particles and are also generated in particle accelerator collisions. In this study, Samson et al analyze the factors that influence muon flux and lifetime using Cosmic Ray Muon Detectors (CRMDs). Overall, the study suggests that water can be used to decrease muon flux and that scintillator orientation is a potential determinant of the volume of data collected in muon decay studies.

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String analysis of exon 10 of the CFTR gene and the use of Bioinformatics in determination of the most accurate DNA indicator for CF prediction

Carroll et al. | Jul 12, 2020

String analysis of exon 10 of the CFTR gene and the use of Bioinformatics in determination of the most accurate DNA indicator for CF prediction

Cystic fibrosis is a genetic disease caused by mutations in the CFTR gene. In this paper, the authors attempt to identify variations in stretches of up to 8 nucleotides in the protein-coding portions of the CFTR gene that are associated with disease development. This would allow screening of newborns or even fetuses in utero to determine the likelihood they develop cystic fibrosis.

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Risk assessment modeling for childhood stunting using automated machine learning and demographic analysis

Sirohi et al. | Sep 25, 2022

Risk assessment modeling for childhood stunting using automated machine learning and demographic analysis

Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

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Automated classification of nebulae using deep learning & machine learning for enhanced discovery

Nair et al. | Feb 01, 2024

Automated classification of nebulae using deep learning & machine learning for enhanced discovery

There are believed to be ~20,000 nebulae in the Milky Way Galaxy. However, humans have only cataloged ~1,800 of them even though we have gathered 1.3 million nebula images. Classification of nebulae is important as it helps scientists understand the chemical composition of a nebula which in turn helps them understand the material of the original star. Our research on nebulae classification aims to make the process of classifying new nebulae faster and more accurate using a hybrid of deep learning and machine learning techniques.

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In vitro Comparison of Anticancer and Immunomodulatory Activities of Resveratrol and its Oligomers

Zhang et al. | Sep 02, 2020

<em>In vitro</em> Comparison of Anticancer and Immunomodulatory Activities of Resveratrol and its Oligomers

Resveratrol is a type of stillbenoid, a phenolic compound produced in plants, that is known for its anti-inflammatory and anticancer effects. Many oligomers of resveratrol have recently been isolated their bioactivities remain unknown. Here, authors compared the bioactivities of resveratrol with natural dimers (ε-viniferin and gnetin H) and trimers (suffruticosol B and C). Results provide preliminary evidence that resveratrol oligomers could be potential preventive or therapeutic agents for cancers and other immune-related diseases

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Wound healing properties of mesenchymal conditioned media: Analysis of PDGF, VEGF and IL-8 concentrations

Prasad et al. | Dec 15, 2021

Wound healing properties of mesenchymal conditioned media: Analysis of PDGF, VEGF and IL-8 concentrations

Regenerative medicine has become a mainstay in recent times, and employing stem cells to treat several degenerative, inflammatory conditions has resulted in very promising outcomes. These forms of cell-based therapies are novel approaches to existing treatment modalities. In this study, the authors compared the concentrations of the cytokines PDGF, IL-8, and VEGF between conditioned and spent media of mesenchymal stem cells (MSCs) to evaluate their potential therapeutic properties for wound healing in inflammatory conditions. They hypothesized that conditioned media contains higher concentrations of wound healing cytokines compared to spent media. The authors found that while IL-8 and VEGF were present in highest concentrations in conditioned media, PDGF was present in maximal amounts in spent media.

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