Genetics

Born to Fear the Machine? Genetic and Environmental Influences on Negative Attitudes toward AI Agents.

Despite the rapid development of artificial intelligence (AI) agents, substantial individual differences in public acceptance persist. To explain the difference in attitudes toward AI agents, existing research has primarily focused on environmental factors. However, evolutionary psychology research suggests that the mechanism of outgroup rejection has a genetic basis, highlighting the need to expl...

Physiological Response of Tissue-Engineered Vascular Grafts to Vasoactive Agents in an Ovine Model.

Tissue-engineered vascular grafts (TEVGs) are emerging as promising alternatives to synthetic grafts, particularly in pediatric cardiovascular surgery. While TEVGs have demonstrated growth potential, compliance, and resistance to calcification, their functional integration into the circulation, especially their ability to respond to physiological stimuli, remains underexplored. Vasoreactivity, the...

Temporal Dynamics of APOE and TREM2 Expression in Microglial Activation of NMOSD Mouse Models.

Neuromyelitis optica spectrum disorder (NMOSD) is a severe autoimmune demyelinating disease characterized by recurrent neuroinflammation and disabilit...

Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.

BACKGROUND: Pathological complete response (pCR) to neoadjuvant systemic therapy (NAST) is an established prognostic marker in breast cancer (BC). Mul...

Classification of COVID-19, Long COVID, and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near-Real-Time Monitoring Component.

BACKGROUND: Heart rate variability (HRV) is a validated biomarker of autonomic and inflammatory regulation, and has been associated with both acute CO...

Leveraging transformers for semi-supervised pathogenicity prediction with soft labels.

The rapid advancement of Next-Generation Sequencing (NGS) technologies has revolutionized the field of genomics, producing large volumes of data that ...

Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method.

BACKGROUND: Genome-wide association studies have provided profound insights into the genetic aetiology of metabolic syndrome (MetS). However, there is...

A Case Study for Visual Detection of a Systemic Disease: Optimizing Identification of Phony Peach Disease Based on Statistical and Machine Learning Models.

Phony peach disease (PPD), caused by subsp. (Xfm), poses a significant threat to commercial peach orchards in Georgia. Early and accurate detection ...

Applying multimodal AI to physiological waveforms improves genetic prediction of cardiovascular traits.

Electronic health records, biobanks, and wearable biosensors enable the collection of multiple health modalities from many individuals. Access to mult...

Multistage nucleation pathway in LiF molten salt mirrors the crystal-melt interface structure.

Despite over a century of studies, fundamental questions remain about the processes governing crystal nucleation from melts or solutions. Research ove...

A Salient Object Detection Network Enhanced by Nonlinear Spiking Neural Systems and Transformer.

Although a variety of deep learning-based methods have been introduced for Salient Object Detection (SOD) to RGB and Depth (RGB-D) images, existing ap...

Deciphering Cell Type Abundance in Proteomics Data Through Graph Neural Networks.

Recent advancements in proteomics sequencing have significantly enhanced our ability to explore cell-type-specific signatures within complex tissues, ...

Investigating the metastability of amorphous calcium carbonate by droplet microfluidics experiments using machine learning.

Amorphous calcium carbonate (ACC) plays an important role in the crystallization pathways of calcite and its polymorphs influencing many natural and a...

Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents.

Viral genome sequencing provides valuable information for antiviral development, yet its integration with machine learning for virtual screening remai...

Design of Hopfield Neural Network Based on DNA Strand Displacement Circuits and Its Application in Sudoku Conjecture.

In recent years, biological neural networks have developed rapidly due to their advantages of fast parallel computing processing speed and strong faul...

MVHGCN: Predicting circRNA-disease associations with multi-view heterogeneous graph convolutional neural networks.

Circular RNA, a class of RNA molecules gaining widespread attentions, has been widely recognized as a potential biomarker for many diseases. In recent...

Decoding the interactions and functions of non-coding RNA with artificial intelligence.

In addition to encoding proteins, mRNAs have context-specific regulatory roles that contribute to many cellular processes. However, uncovering new mRN...

Multi-omics approaches: transforming the landscape of natural product isolation.

The field of natural product (NPs) discovery has significantly evolved with the advent of multi-omics approaches, encompassing genomics, transcriptomi...

A compact encoding of the genome suitable for machine learning prediction of traits and genetic risk scores.

Genotype to phenotype prediction is a central problem in biology and medicine. Machine learning is a natural tool to address this problem. However, a ...

Integrating AI and genomics: predictive CNN models for schizophrenia phenotypes.

This study explores the use of deep learning to analyze genetic data and predict phenotypic traits associated with schizophrenia, a complex psychiatri...