ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI.

Functional magnetic resonance imaging (fMRI) has been increasingly employed to investigate functional brain activity. Many fMRI-related software/toolboxes have been developed, providing specialized algorithms for fMRI analysis. However, existing toolboxes seldom consider fMRI data augmentation, which is quite useful, especially in studies with limited or imbalanced data.

Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection.

Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive imaging technique to study patterns of brain activity, and is increasingly used to facilitate automated brain disorder analysis. Existing fMRI-based learning methods often rely on labeled data to construct learning models, while the data annotation process typically requires significant time and resource investment. Graph contrastive learning offers a promising solution to address the small labeled data issue, by augmenting fMRI time series for self-supervised learning.

Attention-Enhanced Fusion of Structural and Functional MRI for Analyzing HIV-Associated Asymptomatic Neurocognitive Impairment.

Asymptomatic neurocognitive impairment (ANI) is a predominant form of cognitive impairment among individuals infected with human immunodeficiency virus (HIV). The current diagnostic criteria for ANI primarily rely on subjective clinical assessments, possibly leading to different interpretations among clinicians. Some recent studies leverage structural or functional MRI containing objective biomarkers for ANI analysis, offering clinicians companion diagnostic tools.

Genome-Wide Characterization of the () Zinc Finger Gene Family in and the Functional Analysis of in Shoot Gravitropism.

The plant-specific transcription factors (TFs) are vital for regulating plant growth and developmental processes. However, the characteristics and biological roles of the gene family in tomato () are still largely unexplored. In this study, 17 genes were identified in the tomato genome and classified into seven subgroups according to the evolutionary relationships of IDD proteins.

Source-free collaborative domain adaptation via multi-perspective feature enrichment for functional MRI analysis.

Resting-state functional MRI (rs-fMRI) is increasingly employed in multi-site research to analyze neurological disorders, but there exists cross-site/domain data heterogeneity caused by site effects such as differences in scanners/protocols. Existing domain adaptation methods that reduce fMRI heterogeneity generally require accessing source domain data, which is challenging due to privacy concerns and/or data storage burdens. To this end, we propose a source-free collaborative domain adaptation (SCDA) framework using only a pretrained source model and unlabeled target data.

Water mediated redox-neutral cleavage of arylalkenes via photoredox catalysis.

Cleavage of carbon-carbon bonds remains a challenging task in organic synthesis. Traditional methods for splitting C=C bonds into two halves typically involve non-redox (metathesis) or oxidative (ozonolysis) mechanisms, limiting their synthetic potential. Disproportionative deconstruction of alkenes, which yields one reduced and one oxidized fragment, remains an unexplored area.

Investigation of cellular communication and signaling pathways in tumor microenvironment for high TP53-expressing osteosarcoma cells through single-cell RNA sequencing.

Osteosarcoma (OS) stands as the most prevalent primary bone cancer in children and adolescents, and its limited treatment options often result in unsatisfactory outcomes, particularly for metastatic cases. The tumor microenvironment (TME) has been recognized as a crucial determinant in OS progression. However, the intercellular dynamics between high TP53-expressing OS cells and neighboring cell types within the TME are yet to be thoroughly understood.

Source-free unsupervised domain adaptation: A survey.

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different domains. Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden. To tackle this issue, many source-free unsupervised domain adaptation (SFUDA) methods have been proposed recently, which perform knowledge transfer from a pre-trained source model to the unlabeled target domain with source data inaccessible.

Dissecting lysosomal viscosity fluctuations in live cells and liver tissues with an ingenious NIR fluorescent probe.

Viscosity is a pivotal component in the cell microenvironment, while lysosomal viscosity fluctuation is associated with various human diseases, such as tumors and liver diseases. Herein, a near-infrared fluorescent probe (BIMM) based on merocyanine dyes was designed and synthesized for detecting lysosomal viscosity in live cells and liver tissue. The increase in viscosity restricts the free rotation of single bonds, leading to enhanced fluorescence intensity.

Modularity-Constrained Dynamic Representation Learning for Interpretable Brain Disorder Analysis with Functional MRI.

Resting-state functional MRI (rs-fMRI) is increasingly used to detect altered functional connectivity patterns caused by brain disorders, thereby facilitating objective quantification of brain pathology. Existing studies typically extract fMRI features using various machine/deep learning methods, but the generated imaging biomarkers are often challenging to interpret. Besides, the brain operates as a modular system with many cognitive/topological modules, where each module contains subsets of densely inter-connected regions-of-interest (ROIs) that are sparsely connected to ROIs in other modules.

Rhodanine Derivatives Containing 5-Aryloxypyrazole Moiety as Anti-inflammatory and Anticancer Agents.

In this study, a series of rhodanine derivatives containing 5-aryloxypyrazole moiety were identified as potential agents with anti-inflammatory and anticancer properties. Most of the synthesized compounds demonstrated anti-inflammatory and anticancer activity. Notably, compound 7 g (94.

The Essence in Selectivity of Copper-Mediated Intermolecular Nucleophilic Substitution of a C-H Bond in 2-Methyl--methoxyaniline: A Theoretical Study.

The detailed mechanism for NHC-Cu(I)catalyzed intermolecular nucleophilic substitution of the C-H bonds at aniline (2-methyl--methoxyaniline) was studied via DFT methods to reveal the essence of the selectivity. Calculations revealed that the C-H functionalization proceeds via two nucleophilic attacks on the aromatic ring rather than a one-step C-H substitution to give the experimentally observed major product. The reaction is initiated by activation of the substrate via oxidative addition with an NHC-Cu(I) catalyst, through which an umpolung occurs at the ring.

Analysis of the duration of exclusive breastfeeding in premature infants and its influencing factors.

Purpose: The purpose of this study was to investigate the duration of breastfeeding among preterm infants within the first 12 months after birth and analyzed factors influencing the duration of exclusive breastfeeding.

Design And Methods: In this retrospective study, premature infants who were hospitalized in the Neonatal Intensive Care Unit (NICU) premature delivery area of a third-class maternal and child health hospital in Changsha City, Hunan Province, China from October 2020 to January 2021 were selected as the participants for this study. Relevant data of these infants during their hospitalization was obtained from the hospital information system, while the rate of exclusive breastfeeding among preterm infants at a corrected age of 12 months was tracked through telephone follow-up.

Addressing multi-site functional MRI heterogeneity through dual-expert collaborative learning for brain disease identification.

Several studies employ multi-site rs-fMRI data for major depressive disorder (MDD) identification, with a specific site as the to-be-analyzed target domain and other site(s) as the source domain. But they usually suffer from significant inter-site heterogeneity caused by the use of different scanners and/or scanning protocols and fail to build generalizable models that can well adapt to multiple target domains. In this article, we propose a dual-expert fMRI harmonization (DFH) framework for automated MDD diagnosis.

Detecting type 2 diabetes mellitus cognitive impairment using whole-brain functional connectivity.

Type 2 diabetes mellitus (T2DM) is closely linked to cognitive decline and alterations in brain structure and function. Resting-state functional magnetic resonance imaging (rs-fMRI) is used to diagnose neurodegenerative diseases, such as cognitive impairment (CI), Alzheimer's disease (AD), and vascular dementia (VaD). However, whether the functional connectivity (FC) of patients with T2DM and mild cognitive impairment (T2DM-MCI) is conducive to early diagnosis remains unclear.

DNA methylation entropy is associated with DNA sequence features and developmental epigenetic divergence.

Epigenetic information defines tissue identity and is largely inherited in development through DNA methylation. While studied mostly for mean differences, methylation also encodes stochastic change, defined as entropy in information theory. Analyzing allele-specific methylation in 49 human tissue sample datasets, we find that methylation entropy is associated with specific DNA binding motifs, regulatory DNA, and CpG density.

DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning.

The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field. However, a persistent issue remains unsolved during experiments: the interferential technical noises caused by systematic errors (e.g.

Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification.

Resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used for automated diagnosis of brain disorders such as major depressive disorder (MDD) to assist in timely intervention. Multi-site fMRI data have been increasingly employed to augment sample size and improve statistical power for investigating MDD. However, previous studies usually suffer from significant inter-site heterogeneity caused for instance by differences in scanners and/or scanning protocols.

Improving intestinal absorption and antibacterial effect of florfenicol via nanocrystallisation technology.

To study the effects of nanocrystallisation technology on the intestinal absorption properties and antibacterial activity of florfenicol (FF). The florfenicol nanocrystals (FF-NC) were prepared by wet grinding and spray drying. Additionally, changes in particle size, charge, morphology, and dissolution of FF-NC in the long-term stability were monitored by laser particle sizer, TEM, SEM, paddle method, and the structure of FF-NC powder was characterised by nuclear magnetic resonance (NMR) test.

Domain-Prior-Induced Structural MRI Adaptation for Clinical Progression Prediction of Subjective Cognitive Decline.

Growing evidence shows that subjective cognitive decline (SCD) among elderly individuals is the possible pre-clinical stage of Alzheimer's disease (AD). To prevent the potential disease conversion, it is critical to investigate biomarkers for SCD progression. Previous learning-based methods employ T1-weighted magnetic resonance imaging (MRI) data to aid the future progression prediction of SCD, but often fail to build reliable models due to the insufficient number of subjects and imbalanced sample classes.

Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer.

Objective: This study aimed to develop an artificial intelligence model for predicting the pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) of locally advanced rectal cancer (LARC) using digital pathological images.

Background: nCRT followed by total mesorectal excision (TME) is a standard treatment strategy for patients with LARC. Predicting the PCR to nCRT of LARC remine difficulty.

The effect of physical activity on anhedonia in individuals with depressive symptoms.

The therapeutic effect of antidepressants has been demonstrated for anhedonia in patients with depression. However, antidepressants may cause side-effects, such as cardiovascular dysfunction. Although physical activity has minor side-effects, it may serve as an alternative for improving anhedonia and depression.

A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images.

Hepatocellular carcinoma (HCC), as the most common type of primary malignant liver cancer, has become a leading cause of cancer deaths in recent years. Accurate segmentation of HCC lesions is critical for tumor load assessment, surgery planning, and postoperative examination. As the appearance of HCC lesions varies greatly across patients, traditional manual segmentation is a very tedious and time-consuming process, the accuracy of which is also difficult to ensure.