Comprehensive stomata image dataset of Sundarbans Mangrove and Ratargul Swamp forest tree species in Bangladesh.

Plants' leaf stomata are crucial for various scientific research, including identifying species, studying ecology, conserving ecosystems, improving agriculture, and advancing the field of deep learning. This dataset, containing 1083 images, encompasses 11 species from two distinct locations in Bangladesh: nine from the Sundarbans mangrove forest and two from the Ratargul Swamp Forest. It is a valuable tool for refining machine learning algorithms that specialize in detecting stomata and categorizing species accurately.

Strategic insights for sustainable growth of mushroom farming industry in Bangladesh: A comprehensive evaluation using SWOT-AHP and TOPSIS frameworks.

Mushroom farming using agri-waste as substrates can offer a sustainable solution to the food security challenges of inadequate and imbalanced diets. Developing strategies to exploit the potential of the mushroom industry fully is yet to be explored in Bangladesh. We, thus, conducted this study to investigate the challenges and opportunities associated with mushroom farming, as well as the characteristics of farms and employees engaged in this industry.

Uveal melanoma incidence and survival analysis in Portugal between 2013 and 2022.

Purpose: This study aims to provide a comprehensive overview of the clinical characteristics and epidemiology of uveal melanoma (UM) in the Portuguese population, evaluated at the National Reference Centre (NRC).

Methods: A prospective observational study was conducted, involving patients consecutively diagnosed with UM at the Portuguese NRC between July 2013 and December 2022. The study collected data on demographic and tumour characteristics, clinical staging according to the American Joint Committee on Cancer (AJCC), treatment approaches, local disease control, patient survival, and the occurrence of distant metastases.

Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables.

Machine learning (ML) can make use of agricultural data related to crop yield under varying soil nutrient levels, and climatic fluctuations to suggest appropriate crops or supplementary nutrients to achieve the highest possible production. The aim of this study was to evaluate the efficacy of five distinct ML models for a dataset sourced from the Kaggle repository to generate practical recommendations for crop selection or determination of required nutrient(s) in a given site. The datasets contain information on NPK, soil pH, and three climatic variables: temperature, rainfall, and humidity.

Assessing deep convolutional neural network models and their comparative performance for automated medicinal plant identification from leaf images.

Medicinal plants have got notable attention in recent years in the field of pharmaceutical and drug research. The high demand of herbal medicine in the rural areas of developing countries and drug industries necessitates correct identification of the medicinal plant species which is challenging in absence of expert taxonomic knowledge. Against this backdrop, we attempted to assess the performance of seven advanced deep learning algorithms in the automated identification of the plants from their leaf images and to suggest the best model from a comparative study of the models.

Involving victim population in environmentally sustainable strategies: An analysis for post-conflict Colombia.

Environmental objectives tend to receive a push to the background in post-conflict scenarios. On the other hand, the processes displacement and refugee's relocation in post-conflict countries have been associated with different environmental impacts. Currently, Colombia faces a post-conflict scenario, which brings about relevant socio-environmental challenges: it is a mega-biodiverse country, it has millions of displaced people and is one of the most inequitable country in the world.

Newborn Urinary Metabolic Signatures of Prematurity and Other Disorders: A Case Control Study.

This work assesses the urinary metabolite signature of prematurity in newborns by nuclear magnetic resonance (NMR) spectroscopy, while establishing the role of possible confounders and signature specificity, through comparison to other disorders. Gender and delivery mode are shown to impact importantly on newborn urine composition, their analysis pointing out at specific metabolite variations requiring consideration in unmatched subject groups. Premature newborns are, however, characterized by a stronger signature of varying metabolites, suggestive of disturbances in nucleotide metabolism, lung surfactants biosynthesis and renal function, along with enhancement of tricarboxylic acid (TCA) cycle activity, fatty acids oxidation, and oxidative stress.

Impact of fetal chromosomal disorders on maternal blood metabolome: toward new biomarkers?

Objective: This study aimed at determining the relationship between fetal chromosomal disorders (CDs), including trisomy 21 (T21), and on first- and second-trimester maternal blood plasma, to identify the time-course metabolic adaptations to the conditions and the possible new plasma biomarkers. Furthermore, a definition of a joint circulatory (plasma) and excretory (urine) metabolic description of second-trimester CDs was sought.

Study Design: Plasma was obtained for 119 pregnant women: 74 controls and 45 CD cases, including 22 T21 cases.

Prediction of Gestational Diabetes through NMR Metabolomics of Maternal Blood.

Metabolic biomarkers of pre- and postdiagnosis gestational diabetes mellitus (GDM) were sought, using nuclear magnetic resonance (NMR) metabolomics of maternal plasma and corresponding lipid extracts. Metabolite differences between controls and disease were identified through multivariate analysis of variable selected (1)H NMR spectra. For postdiagnosis GDM, partial least squares regression identified metabolites with higher dependence on normal gestational age evolution.