Leveraging deep transfer learning and explainable AI for accurate COVID-19 diagnosis: Insights from a multi-national chest CT scan study.

The COVID-19 pandemic has emerged as a global health crisis, impacting millions worldwide. Although chest computed tomography (CT) scan images are pivotal in diagnosing COVID-19, their manual interpretation by radiologists is time-consuming and potentially subjective. Automated computer-aided diagnostic (CAD) frameworks offer efficient and objective solutions.

mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations.

Anticancer peptides (ACPs), naturally occurring molecules with remarkable potential to target and kill cancer cells. However, identifying ACPs based solely from their primary amino acid sequences remains a major hurdle in immunoinformatics. In the past, several web-based machine learning (ML) tools have been proposed to assist researchers in identifying potential ACPs for further testing.

HOTGpred: Enhancing human O-linked threonine glycosylation prediction using integrated pretrained protein language model-based features and multi-stage feature selection approach.

O-linked glycosylation is a complex post-translational modification (PTM) in human proteins that plays a critical role in regulating various cellular metabolic and signaling pathways. In contrast to N-linked glycosylation, O-linked glycosylation lacks specific sequence features and maintains an unstable core structure. Identifying O-linked threonine glycosylation sites (OTGs) remains challenging, requiring extensive experimental tests.

Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2'-O-methylation sites in human RNA.

2'-O-methylation (2-OM or Nm) is a widespread RNA modification observed in various RNA types like tRNA, mRNA, rRNA, miRNA, piRNA, and snRNA, which plays a crucial role in several biological functional mechanisms and innate immunity. To comprehend its modification mechanisms and potential epigenetic regulation, it is necessary to accurately identify 2-OM sites. However, biological experiments can be tedious, time-consuming, and expensive.

SEP-AlgPro: An efficient allergen prediction tool utilizing traditional machine learning and deep learning techniques with protein language model features.

Allergy is a hypersensitive condition in which individuals develop objective symptoms when exposed to harmless substances at a dose that would cause no harm to a "normal" person. Most current computational methods for allergen identification rely on homology or conventional machine learning using limited set of feature descriptors or validation on specific datasets, making them inefficient and inaccurate. Here, we propose SEP-AlgPro for the accurate identification of allergen protein from sequence information.

ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning.

RNA N4-acetylcytidine (ac4C) is a highly conserved RNA modification that plays a crucial role in controlling mRNA stability, processing, and translation. Consequently, accurate identification of ac4C sites across the genome is critical for understanding gene expression regulation mechanisms. In this study, we have developed ac4C-AFL, a bioinformatics tool that precisely identifies ac4C sites from primary RNA sequences.

H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA.

2'-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high-throughput detection, the chemical stability of 2OM makes it difficult to detect and map in messenger RNA.

Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach.

The worldwide appearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has generated significant concern and posed a considerable challenge to global health. Phosphorylation is a common post-translational modification that affects many vital cellular functions and is closely associated with SARS-CoV-2 infection. Precise identification of phosphorylation sites could provide more in-depth insight into the processes underlying SARS-CoV-2 infection and help alleviate the continuing COVID-19 crisis.

ADP-Fuse: A novel two-layer machine learning predictor to identify antidiabetic peptides and diabetes types using multiview information.

Diabetes mellitus has become a major public health concern associated with high mortality and reduced life expectancy and can cause blindness, heart attacks, kidney failure, lower limb amputations, and strokes. A new generation of antidiabetic peptides (ADPs) that act on β-cells or T-cells to regulate insulin production is being developed to alleviate the effects of diabetes. However, the lack of effective peptide-mining tools has hampered the discovery of these promising drugs.

An exploratory simulation study and prediction model on human brain behavior and activity using an integration of deep neural network and biosensor Rabi antenna.

The plasmonic antenna probe is constructed using a silver rod embedded in a modified Mach-Zehnder interferometer (MZI) ad-drop filter. Rabi antennas are formed when space-time control reaches two levels of system oscillation and can be used as human brain sensor probes. Photonic neural networks are designed using brain-Rabi antenna communication, and transmissions are connected via neurons.

Pretoria: An effective computational approach for accurate and high-throughput identification of CD8 t-cell epitopes of eukaryotic pathogens.

T-cells recognize antigenic epitopes present on major histocompatibility complex (MHC) molecules, triggering an adaptive immune response in the host. T-cell epitope (TCE) identification is challenging because of the extensive number of undetermined proteins found in eukaryotic pathogens, as well as MHC polymorphisms. In addition, conventional experimental approaches for TCE identification are time-consuming and expensive.

Multiband Rabi antenna using nest microstrip add-drop filter (NMADF) for relativistic sensing applications.

A microstrip circuit is designed, constructed, and tested based on the nest microstrip add-drop filters (NMADF). The multi-level system oscillation is generated by the wave-particle behaviors of AC driven along the microstrip ring circular path. The continuous successive filtering is applied via the device input port.

Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.

COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly virus has spread worldwide, leading to a global pandemic since March 2020. A recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths globally.

Zoning the suitability of the western Mekong Delta for paddy rice cultivation and aquaculture under current and future environmental conditions.

Ca Mau and Kien Giang, the two provinces of the Mekong Delta bordering the Gulf of Thailand, are facing major environmental challenges affecting the agriculture and aquaculture sectors upon which many livelihoods in this region depend on. This study maps the suitability of these two provinces for paddy rice cultivation and shrimp farming according to soil characteristics and current and future environmental conditions for variables found to significantly influence the yield of those two sectors, i.e.

A deep learning approach for detecting drill bit failures from a small sound dataset.

Monitoring the conditions of machines is vital in the manufacturing industry. Early detection of faulty components in machines for stopping and repairing the failed components can minimize the downtime of the machine. In this article, we present a method for detecting failures in drill machines using drill sounds in Valmet AB, a company in Sundsvall, Sweden that supplies equipment and processes for the production of pulp, paper, and biofuels.

Improving ligand-ranking of AutoDock Vina by changing the empirical parameters.

AutoDock Vina (Vina) achieved a very high docking-success rate, , but give a rather low correlation coefficient, , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters.

Preoperative Determination of the Size of the Semitendinosus and Gracilis Tendon by Multidetector Row CT Scanner for Anterior Cruciate Ligament Reconstruction.

Accurately measuring the length and diameter of the hamstring tendon autograft preoperatively is important for planning anterior cruciate ligament (ACL) reconstructive surgery. The purpose of this study was to assess the reliability of three-dimensional computed tomography (3D CT) scanning technique to produce the actual measurement of the gracilis and semitendinosus (GT and ST, respectively) tendon grafts' length and diameter for surgery. Ninety patients were scheduled for ACL reconstruction with hamstring autograft.