OS-SSVEP: One-shot SSVEP classification.

It is extremely challenging to classify steady-state visual evoked potentials (SSVEPs) in scenarios characterized by a huge scarcity of calibration data where only one calibration trial is available for each stimulus target. To address this challenge, we introduce a novel approach named OS-SSVEP, which combines a dual domain cross-subject fusion network (CSDuDoFN) with the task-related and task-discriminant component analysis (TRCA and TDCA) based on data augmentation. The CSDuDoFN framework is designed to comprehensively transfer information from source subjects, while TRCA and TDCA are employed to exploit the information from the single available calibration trial of the target subject.

CircaKB: a comprehensive knowledgebase of circadian genes across multiple species.

Circadian rhythms, which are the natural cycles that dictate various physiological processes over a 24-h period, have been increasingly recognized as important in the management and treatment of various human diseases. However, the lack of sufficient data and reliable analysis methods have been a major obstacle to understanding the bidirectional interaction between circadian variation and human health. We have developed CircaKB, a comprehensive knowledgebase of circadian genes across multiple species.

ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein-DNA binding site prediction.

Efficient and accurate recognition of protein-DNA interactions is vital for understanding the molecular mechanisms of related biological processes and further guiding drug discovery. Although the current experimental protocols are the most precise way to determine protein-DNA binding sites, they tend to be labor-intensive and time-consuming. There is an immediate need to design efficient computational approaches for predicting DNA-binding sites.

scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) enables researchers to reveal previously unknown cell heterogeneity and functional diversity, which is impossible with bulk RNA sequencing. Clustering approaches are widely used for analyzing scRNA-seq data and identifying cell types and states. In the past few years, various advanced computational strategies emerged.

HNSPPI: a hybrid computational model combing network and sequence information for predicting protein-protein interaction.

Most life activities in organisms are regulated through protein complexes, which are mainly controlled via Protein-Protein Interactions (PPIs). Discovering new interactions between proteins and revealing their biological functions are of great significance for understanding the molecular mechanisms of biological processes and identifying the potential targets in drug discovery. Current experimental methods only capture stable protein interactions, which lead to limited coverage.

Automated CT Pancreas Segmentation for Acute Pancreatitis Patients by combining a Novel Object Detection Approach and U-Net.

Acute pancreatitis is an inflammatory disorder of the pancreas. Medical imaging, such as computed tomography (CT), has been widely used to detect volume changes in the pancreas for acute pancreatitis diagnosis. Many pancreas segmentation methods have been proposed but no methods for pancreas segmentation from acute pancreatitis patients.

A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection.

Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern recognition. However, it has limited interpretability for deep features. With the transfer of expert knowledge, handcrafted features provide a new way for personalized diagnosis of plant diseases.

Redox integration of signaling and metabolism in a head and neck cancer model of radiation resistance using COSM.

Redox metabolism is increasingly investigated in cancer as driving regulator of tumor progression, response to therapies and long-term patients' quality of life. Well-established cancer therapies, such as radiotherapy, either directly impact redox metabolism or have redox-dependent mechanisms of action defining their clinical efficacy. However, the ability to integrate redox information across signaling and metabolic networks to facilitate discovery and broader investigation of redox-regulated pathways in cancer remains a key unmet need limiting the advancement of new cancer therapies.

ROR activation by Nobiletin enhances antitumor efficacy via suppression of IκB/NF-κB signaling in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a heterogeneous disease characterized by poor response to standard therapies and therefore unfavorable clinical outcomes. Better understanding of TNBC and new therapeutic strategies are urgently needed. ROR nuclear receptors are multifunctional transcription factors with important roles in circadian pathways and other processes including immunity and tumorigenesis.

A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion.

Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE).

Deep Transfer Learning for Cross-Species Plant Disease Diagnosis Adapting Mixed Subdomains.

A deep transfer learning framework adapting mixed subdomains is proposed for cross-species plant disease diagnosis. Most existing deep transfer learning studies focus on knowledge transfer between highly correlated domains. These methods may fail to deal with domains that are poorly correlated.

Single-cell transcriptomics reveals heterogeneous progression and EGFR activation in pancreatic adenosquamous carcinoma.

Pancreatic adenosquamous carcinoma (PASC) - a rare pathological pancreatic cancer (PC) type - has a poor prognosis due to high malignancy. To examine the heterogeneity of PASC, we performed single-cell RNA sequencing (scRNA-seq) profiling with sample tissues from a healthy donor pancreas, an intraductal papillary mucinous neoplasm, and a patient with PASC. Of 9,887 individual cells, ten cell subpopulations were identified, including myeloid, immune, ductal, fibroblast, acinar, stellate, endothelial, and cancer cells.

Evolution of the PB1 gene of human influenza A (H3N2) viruses circulating between 1968 and 2019.

One avian H3N2 influenza virus, providing its PB1 and HA segments, reassorted with one human H2N2 virus and caused a pandemic outbreak in 1968, killing over 1 million people. After its introduction to humanity, the pandemic H3N2 virus continued adapting to humans and has resulted in epidemic outbreaks every influenza season. To understand the functional roles of the originally avian PB1 gene in the circulating strains of human H3N2 influenza viruses, we analyzed the evolution of the PB1 gene in all human H3N2 isolates from 1968 to 2019.

Multi-scale modeling for systematically understanding the key roles of microglia in AD development.

Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States. Unfortunately, current therapies are largely palliative and several potential drug candidates have failed in late-stage clinical trials. Studies suggest that microglia-mediated neuroinflammation might be responsible for the failures of various therapies.

Inhibitory effect of selected hydrocolloids on 2-amino-1-methyl-6-phenylimidazo [4,5-b]pyridine (PhIP) formation in chemical models and beef patties.

2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) is a mutagen and a rodent carcinogen mainly formed in thermally processed muscle foods. Hydrocolloids are widely used as thickeners, gelling agents and stabilizers to improve food quality in the food industry. In this study, the inhibitory effects of eight hydrocolloids on the formation of PhIP were investigated in both chemical models and beef patties.

Potential Pathogenic Genes Prioritization Based on Protein Domain Interaction Network Analysis.

Pathogenicity-related studies are of great importance in understanding the pathogenesis of complex diseases and improving the level of clinical medicine. This work proposed a bioinformatics scheme to analyze cancer-related gene mutations, and try to figure out potential genes associated with diseases from the protein domain-domain interaction network. Herein, five measures of the principle of centrality lethality had been adopted to implement potential correlation analysis, and prioritize the significance of genes.

Exploring and validating the clinical risk factors for pancreatic cancer in chronic pancreatitis patients using electronic medical records datasets: three cohorts comprising 2,960 patients.

Background: Patients with chronic pancreatitis (CP) have an increased risk of developing pancreatic cancer (PC). The purpose of this study was to identify predictors of PC in CP patients.

Methods: Electronic medical records (EMRs) of CP patients from two cohorts were collected, and a logistic regression analysis was performed to investigate the risk factors for PC.

Systematically understanding the immunity leading to CRPC progression.

Prostate cancer (PCa) is the most commonly diagnosed malignancy and the second leading cause of cancer-related death in American men. Androgen deprivation therapy (ADT) has become a standard treatment strategy for advanced PCa. Although a majority of patients initially respond to ADT well, most of them will eventually develop castration-resistant PCa (CRPC).

The different responses of growth and photosynthesis to NH enrichments between Gracilariopsis lemaneiformis and its epiphytic alga Ulva lactuca grown at elevated atmospheric CO.

We investigated how elevated CO affects the responses of Gracilariopsis lemaneiformis and Ulva lactuca to NH enrichments. All algae were incubated under four nutritional conditions (zero addition, 100, 500, and 2500 μM NH), and two CO levels (390 ppm and 1000 ppm). The growth, photosynthesis, and soluble protein contents of both species increased under the eutrophication condition (100 μM NH).

Cross-situation consistency of mobile App users' psychological needs.

Previous studies showed that individuals' traits could be used to explain the similarity of behavioral patterns across different occasions. Such studies have typically focused on personality traits, and have not been extended to psychological needs. Our study used a large dataset of 1,715,078 anonymous users' App usage records to examine whether the individual's needs-based profiles of App usage were consistent across different situations (as indexed by categories of App functions).

Genome-Wide Association and Mechanistic Studies Indicate That Immune Response Contributes to Alzheimer's Disease Development.

Alzheimer's disease (AD) is the most common cause of dementia. Although genome-wide association study (GWAS) have reported hundreds of single-nucleotide polymorphisms (SNPs) and genes linked to AD, the mechanisms about how these SNPs modulate the development of AD remain largely unknown. In this study, we performed GWAS for three traits in cerebrospinal fluid (CSF) and one clinical trait in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.

The impact of elevated atmospheric CO on cadmium toxicity in Pyropia haitanensis (Rhodophyta).

Cadmium is one of the major heavy metal pollutions in coastal waters, and it is well known that cadmium at trace concentration is toxic to macroalgae. Change in marine carbonate system and ocean acidification caused by elevated atmospheric CO also alter physiological characteristics of macroalgae. However, less research is focused on the combined impacts of elevated CO and cadmium pollution on the growth and physiology in macroalgae.

Integrating MicroRNA Expression Profiling Studies to Systematically Evaluate the Diagnostic Value of MicroRNAs in Pancreatic Cancer and Validate Their Prognostic Significance with the Cancer Genome Atlas Data.

Background/aims: MicroRNAs (miRNAs) are promising biomarkers for pancreatic cancer (PaCa). However, systemic and unified evaluations of the diagnostic value of miRNAs are lacking. Therefore, we performed a systematic evaluation based on miRNA expression profiling studies.