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Fast and accurate deep learning scans for signatures of natural selection in genomes using FASTER-NN.
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University of Twente, Enschede, The Netherlands.
Deep learning classification models based on Convolutional Neural Networks (CNNs) are increasingly used in population genetic inference for detecting signatures of natural selection. Prevailing detection methods treat the design of the classifier as a discrete phase, assuming that high classification accuracy is the sole prerequisite for precise detection. This frequently steers method development toward classification-driven optimizations that can inadvertently impede detection.
View Article and Find Full Text PDFValidation of mixed reality in planning orbital reconstruction with patient-specific implants.
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Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium.
This study aims to evaluate and compare the usability and performance of mixed reality (MR) technology versus conventional methods for preoperative planning of patient-specific reconstruction plates for orbital fractures. A crossover study design was used to compare MR technology with conventional three-dimensional (3D) printing approaches in the planning of maxillofacial traumatology treatments. The primary focus was on user-friendliness and the accuracy of patient-specific reconstruction planning.
View Article and Find Full Text PDFAbnormality detection in nailfold capillary images using deep learning with EfficientNet and cascade transfer learning.
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Hive AI Innovation Studio, Department of Computer Science and Engineering, University of Louisville, Louisville, KY, 40292, USA.
Nailfold Capillaroscopy (NFC) is a simple, non-invasive diagnostic tool used to detect microvascular changes in nailfold. Chronic pathological changes associated with a wide range of systemic diseases, such as diabetes, cardiovascular disorders, and rheumatological conditions like systemic sclerosis, can manifest as observable microvascular changes in the terminal capillaries of nailfolds. The current gold standard relies on experts performing manual evaluations, which is an exhaustive time-intensive, and subjective process.
View Article and Find Full Text PDFDeveloping a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength.
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Faculty of Civil Engineering, Damascus University, Damascus, Syria.
Concrete compressive strength is a critical parameter in construction and structural engineering. Destructive experimental methods that offer a reliable approach to obtaining this property involve time-consuming procedures. Recent advancements in artificial neural networks (ANNs) have shown promise in simplifying this task by estimating it with high accuracy.
View Article and Find Full Text PDFBiological characteristics, immune infiltration and drug prediction of PANoptosis related genes and possible regulatory mechanisms in inflammatory bowel disease.
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Department of Gastroenterology, The Third Xiangya Hospital, Central South University, 138 Tongzipo Road, Changsha, 410013, Hunan, China.
PANoptosis is one of several modes of programmed cell death (PCD) and plays an important role in many inflammatory and immune diseases. The role of PANoptosis in inflammatory bowel disease (IBD) is currently unknown. Differentially expressed PANoptosis-related genes (DE-PRGs) were identified, and pathway enrichment analyses were performed.
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