Malpighiaceae has undergone unprecedented changes in its traditional classification in the past two decades due to several phylogenetic studies shedding light on the non-monophyly of all subfamilies and most tribes and genera. Even though morphological characters were used to reconstruct the last molecular generic phylogeny of Malpighiaceae, a new classification system has never been proposed for this family. Based on a comprehensive review of the last twenty years of published studies for this family, we propose a new classification system and provide a taxonomic synopsis for Malpighiaceae based on molecular phylogenetics, morphology, palynology, and chemistry as a baseline for the systematics, conservation, and taxonomy of this family worldwide. Malpighiaceae currently comprises two subfamilies (Byrsonimoideae and Malpighioideae), 12 tribes ( Acmanthereae, Acridocarpeae, Barnebyeae, Bunchosieae, Byrsonimeae, Galphimieae, Gaudichaudieae, Hiptageae, Hiraeeae, Malpighieae, Mcvaughieae, and Ptilochaeteae), 72 genera (incl. ), and 1,499 accepted species (715 of which are currently under some kind of extinction threat). We present identification keys for all subfamilies, tribes, and genera, a full morphological description for the proposed new genus, the re-circumscription of ten genera alongside the needed new combinations, the proposition of several new synonyms, the typification of several names, and notes on the taxonomy, distribution, conservation, and ecology up to the genus rank. Morphological plates are also provided to illustrate the immense diversity of morphological traits used in the new classification and synopsis.
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http://dx.doi.org/10.3897/phytokeys.242.117469 | DOI Listing |
Biomed Phys Eng Express
January 2025
National School of Electronics and Telecommunication of Sfax, Sfax rte mahdia, sfax, sfax, 3012, TUNISIA.
Deep learning has emerged as a powerful tool in medical imaging, particularly for corneal topographic map classification. However, the scarcity of labeled data poses a significant challenge to achieving robust performance. This study investigates the impact of various data augmentation strategies on enhancing the performance of a customized convolutional neural network model for corneal topographic map classification.
View Article and Find Full Text PDFAnn Intern Med
January 2025
Durham VA Health Care System, Durham; and Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina (K.M.G.).
Background: Tissue-based genomic classifiers (GCs) have been developed to improve prostate cancer (PCa) risk assessment and treatment recommendations.
Purpose: To summarize the impact of the Decipher, Oncotype DX Genomic Prostate Score (GPS), and Prolaris GCs on risk stratification and patient-clinician decisions on treatment choice among patients with localized PCa considering first-line treatment.
Data Sources: MEDLINE, EMBASE, and Web of Science published from January 2010 to August 2024.
J Clin Psychiatry
January 2025
Division of Gastrointestinal and Liver Diseases, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California.
We compared substance use disorder (SUD) prevalence among adult inflammatory bowel disease (IBD) hospitalizations with non-IBD controls from the 2016-2018 National Inpatient Sample, assessing correlations with demographics, socioeconomic status, geographic regions, depression, and anxiety. The primary aim focused on SUD, defined as substance abuse or dependence (: F10-F19) excluding unspecified use or remission, among hospitalizations documenting IBD (Crohn's disease or ulcerative colitis; : K50-51) as one admitting diagnosis (IBD-D). The prevalence of SUD among hospitalizations with and without IBD was compared.
View Article and Find Full Text PDFBioinformatics
January 2025
College of Artificial Intelligence, Nankai University, Tianjin, 300350, China.
Motivation: The drug-disease, gene-disease, and drug-gene relationships, as high-frequency edge types, describe complex biological processes within the biomedical knowledge graph. The structural patterns formed by these three edges are the graph motifs of (disease, drug, gene) triplets. Among them, the triangle is a steady and important motif structure in the network, and other various motifs different from the triangle also indicate rich semantic relationships.
View Article and Find Full Text PDFJ Infect Dev Ctries
December 2024
Centro de Investigación en Alimentación y Desarrollo A.C. (CIAD). Coordinación Regional Culiacán, Culiacán, Sinaloa, México. Carretera a Eldorado km 5.5, Campo El Diez, 80110 Culiacán, Sinaloa, México.
Introduction: Salmonella is a major foodborne pathogen widely distributed in the environment. Surface water, soil, and sediments may confer a protective effect on Salmonella against non-host conditions.
Methodology: This study focused on determining the prevalence of Salmonella spp.
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