Gender, Race, and Regional Disparities in Leading Authorships of Gastroenterology and Hepatology Randomized Controlled Trials.

Background And Aims: To investigate gender, racial, ethnic, and regional disparities in first and senior authorship positions in gastroenterology/hepatology-related randomised controlled trials (RCT).

Method: Retrospective bibliometric analysis of PubMed-indexed RCTs published between January 2000 to December 2022 in leading journals with an impact factor of at least five.

Results: 943 RCTs met our inclusion criteria, providing a participant pool of 301 female (15.

In vitro Genebank of India for safe conservation of horticultural plant diversity: four decades of milestones.

India is a treasure trove of biological diversity with its plant genetic resources playing a crucial role in the crop improvement serving as the foundation for the country's sustainable food and nutritional security. India's in vitro genebank (IVG) is part of the National Genebank at the Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources (ICAR-NBPGR). The IVG houses distinctive multi-crop repository that utilizes several tissue culture techniques for short- to medium-term storage in in vitro active genebank (IVAG) and cryoconservation approaches for long-term storage in in vitro base genebank (IVBG).

A Systematic Review of the Attitudes, Beliefs, and Acceptance of the COVID-19 Vaccine in the Western and Eastern Hemispheres.

The availability of an effective vaccine does not equate to its use; its effectiveness primarily depends on vaccine acceptance by the targeted population. Despite the rapid development and widespread access to the COVID-19 vaccine, herd immunity is yet to be achieved, with vaccine hesitancy as a major barrier. This study sought to systematically assess the beliefs, attitudes, and acceptance towards COVID-19 vaccines, including factors contributing to vaccination hesitancy in the Eastern and Western Hemispheres.

Stacked Ensemble with Machine Learning Regressors on Optimal Features (SMOF) of hyperspectral sensor PRISMA for inland water turbidity prediction.

Leveraging hyperspectral data across various domains yields substantial benefits, yet managing many spectral bands and identifying the essential ones poses a formidable challenge. This study identifies the most relevant bands within a hyperspectral data cube for turbidity prediction in inland water. Nine machine learning regressors Cat Boost, Decision Trees, Extra Trees, Gradient Boost, Light Gradient Boost (LightGBM), Recursive Feature Elimination (RFE), Random Forest, Support Vector Regressor (SVR), and Xtreme Gradient Boost (XGBoost) have been used to compute the feature importance of the hyperspectral bands for predicting turbidity.