Comprehensive evaluation of pipelines for classification of psychiatric disorders using multi-site resting-state fMRI datasets.

Objective classification biomarkers that are developed using resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contribute to more effective treatment for psychiatric disorders. Unfortunately, no widely accepted biomarkers are available at present, partially because of the large variety of analysis pipelines for their development. In this study, we comprehensively evaluated analysis pipelines using a large-scale, multi-site fMRI dataset for major depressive disorder (MDD).

Beyond case-control study in neuroimaging for psychiatric disorders: Harmonizing and utilizing the brain images from multiple sites.

Recent magnetic resonance imaging (MRI) research has advanced our understanding of brain pathophysiology in psychiatric disorders. This progress necessitates re-evaluation of the diagnostic system for psychiatric disorders based on MRI-based biomarkers, with implications for precise clinical diagnosis and optimal therapeutics. To achieve this goal, large-scale multi-site studies are essential to develop a standardized MRI database, with the analysis of several thousands of images and the incorporation of new data.

Proposal for a Novel Classification of Patients With Enlarged Ventricles and Cognitive Impairment Based on Data-Driven Analysis of Neuroimaging Results in Patients With Psychiatric Disorders.

One of the challenges in diagnosing psychiatric disorders is that the results of biological and neuroscience research are not reflected in the diagnostic criteria. Thus, data-driven analyses incorporating biological and cross-disease perspectives, regardless of the diagnostic category, have recently been proposed. A data-driven clustering study based on subcortical volumes in 5604 subjects classified into four brain biotypes associated with cognitive/social functioning.

Stratifying and predicting progression to acute liver failure during the early phase of acute liver injury.

Acute liver failure (ALF) is a serious disease that progresses from acute liver injury (ALI) and that often leads to multiorgan failure and ultimately death. Currently, effective treatment strategies for ALF, aside from transplantation, remain elusive, partly because ALI is highly heterogeneous. Furthermore, clinicians lack a quantitative indicator that they can use to predict which patients hospitalized with ALI will progress to ALF and the need for liver transplantation.

First Joint Oscillation Analysis of Super-Kamiokande Atmospheric and T2K Accelerator Neutrino Data.

The Super-Kamiokande and T2K Collaborations present a joint measurement of neutrino oscillation parameters from their atmospheric and beam neutrino data. It uses a common interaction model for events overlapping in neutrino energy and correlated detector systematic uncertainties between the two datasets, which are found to be compatible. Using 3244.

One-core neuron deep learning for time series prediction.

The enormous computational requirements and unsustainable resource consumption associated with massive parameters of large language models and large vision models have given rise to challenging issues. Here, we propose an interpretable 'small model' framework characterized by only a single core-neuron, i.e.

Prediction of cccDNA dynamics in hepatitis B patients by a combination of serum surrogate markers.

Quantification of intrahepatic covalently closed circular DNA (cccDNA) is a key for evaluating an elimination of hepatitis B virus (HBV) in infected patients. However, quantifying cccDNA requires invasive methods such as a liver biopsy, which makes it impractical to access the dynamics of cccDNA in patients. Although HBV RNA and HBV core-related antigens (HBcrAg) have been proposed as surrogate markers for evaluating cccDNA activity, they do not necessarily estimate the amount of cccDNA.

Aging in a weighted ensemble of excitable and self-oscillatory neurons: The role of pairwise and higher-order interactions.

We investigate the aging transition in networks of excitable and self-oscillatory units as the fraction of inherently excitable units increases. Two network topologies are considered: a scale-free network with weighted pairwise interactions and a two-dimensional simplicial complex with weighted scale-free pairwise and triadic interactions. Without triadic interactions, the aging transition from collective oscillations to oscillation death (inhomogeneous stationary states) can occur either suddenly or through an intermediate state of partial oscillation.

Effectiveness of sequential bilateral repetitive transcranial stimulation versus bilateral theta burst stimulation for patients with treatment-resistant depression (BEAT-D): A randomized non-inferiority clinical trial.

Background: Bilateral repetitive transcranial magnetic stimulation (BL-rTMS) over the dorsolateral prefrontal cortex is effective for treatment-resistant depression (TRD). Owing to a shorter treatment time, bilateral theta burst stimulation (BL-TBS) can be more efficient protocol. The non-inferiority of BL-TBS to BL-rTMS was established in late-life TRD; however, this has not been determined in adults of other age groups.

Protective role of school climate for impacts of COVID-19 on depressive symptoms and psychotic experiences among adolescents: a population-based cohort study.

Background: Schools are central places for adolescent social lives, which is a major factor greatly affecting adolescent mental health; school climate (i.e. quality of the school social environments) can be a proximal social determinant for adolescent mental health.

High-Statistics Measurement of the Cosmic-Ray Electron Spectrum with H.E.S.S.

Owing to their rapid cooling rate and hence loss-limited propagation distance, cosmic-ray electrons and positrons (CRe) at very high energies probe local cosmic-ray accelerators and provide constraints on exotic production mechanisms such as annihilation of dark matter particles. We present a high-statistics measurement of the spectrum of CRe candidate events from 0.3 to 40 TeV with the High Energy Stereoscopic System, covering 2 orders of magnitude in energy and reaching a proton rejection power of better than 10^{4}.

Increased structural covariance of cortical measures in individuals with an at-risk mental state.

An anomalous pattern of structural covariance has been reported in schizophrenia, which has been suggested to represent connectome changes during brain maturation and neuroprogressive processes. It remains unclear whether similar differences exist in a clinical high-risk state for psychosis, and if they are associated with a prodromal phenotype and/or later psychosis onset. This multicenter magnetic resonance imaging study cross-sectionally examined structural covariance in a large at-risk mental state (ARMS) sample with different outcomes.

MCGAE: unraveling tumor invasion through integrated multimodal spatial transcriptomics.

Spatially Resolved Transcriptomics (SRT) serves as a cornerstone in biomedical research, revealing the heterogeneity of tissue microenvironments. Integrating multimodal data including gene expression, spatial coordinates, and morphological information poses significant challenges for accurate spatial domain identification. Herein, we present the Multi-view Contrastive Graph Autoencoder (MCGAE), a cutting-edge deep computational framework specifically designed for the intricate analysis of spatial transcriptomics (ST) data.

From the Laboratory to the Real-World: The Role of Mismatch Negativity in Psychosis.

Mismatch negativity (MMN) has gained attention as a biomarker for psychosis and a translational intermediate phenotype in animal models of psychosis, including rodents and non-human primates. MMN has been linked to global functioning (Global Assessment of Functioning [GAF] score) and prognosis (psychosis onset or remission), suggesting that MMN reflects activities beyond auditory processing alone. This review examines the 45-year history of MMN from the perspective of psychiatric researchers and discusses current advances in computational and translational research on MMN, summarizing the current understanding of the MMN generation mechanism.

Direct Collapse Supermassive Black Holes from Relic Particle Decay.

We investigate the formation of high-redshift supermassive black holes (SMBHs) via the direct collapse of baryonic clouds, where the unwanted formation of molecular hydrogen is successfully suppressed by a Lyman-Werner (LW) photon background from relic particle decay. We improve on existing studies by dynamically simulating the collapse, accounting for the adiabatic contraction of the DM halo, as well as the in situ production of the LW photons within the cloud which reduce the impact of the cloud's shielding. We find a viable parameter space where the decay of either some of the dark matter or all of a subdominant decaying species successfully allows direct collapse of the cloud to a SMBH.

Dynamic and Static Workout of In Vitro Skeletal Muscle Tissue through a Weight Training Device.

Enhancing muscle strength through workouts is a key factor in improving physical activity and maintaining metabolic profiles. The controversial results concerning the impacts of weight training often arise from clinical experiments that require controlled experimental conditions. In this study, a weight training system for a muscle development model is presented, which is capable of performing weight training motions with adjustable weight loads.