Low-frequency thalamocortical oscillations that underlie drowsiness and slow-wave sleep depend on rhythmic inhibition of relay cells by neurons in the reticular nucleus (RTN) under influence corticothalamic fibers branch to innervate RTN neurons. To generate oscillations, input predictably should be stronger so disynaptic overcomes direct excitation. Amplitudes excitatory postsynaptic conductances (EPSCs) evoked minimal stimulation were 2.4 times larger than neurons, quantal size EPSCs was...

Hyperspectral image (HSI) clustering is a challenging task due to the high complexity of HSI data. Subspace has been proven be powerful for exploiting intrinsic relationship between data points. Despite impressive performance in clustering, traditional subspace methods often ignore inherent structural information among In this paper, we revisit with graph convolution and present novel framework called Graph Convolutional Clustering (GCSC) robust clustering. Specifically, recasts...

Transfer learning and ensemble are the new trends for solving problem that training data test have different distributions. In this paper, we design an transfer framework to improve classification accuracy when insufficient. First, a weightedresampling method is proposed, which named TrResampling. each iteration, with heavy weights in source domain resampled, TrAdaBoost algorithm used adjust of target data. Second, three classic machine algorithms, namely, naive Bayes, decision tree, SVM, as...

As the most classical unsupervised dimension reduction algorithm, principal component analysis (PCA) has been widely used in hyperspectral images (HSIs) preprocessing and tasks. Recently proposed superpixelwise PCA (SuperPCA) shown promising accuracy where superpixels segmentation technique was first to segment an HSI various homogeneous regions then adopted each superpixel block extract local features. However, features could be ineffective due neglect of global information especially some...

The joint clustering of multimodal remote sensing (RS) data poses a critical and challenging task in Earth observation. Although recent advances multiview subspace have shown remarkable success, existing methods become computationally prohibitive when dealing with large-scale RS datasets. Moreover, they neglect intrinsic nonlinear spatial interdependencies among heterogeneous lack generalization ability for out-of-sample data, thereby restricting their applicability. This article introduces...

Objectives The global prevalence of diabetes is continuously rising, and the gut microbiota closely associated with it. Dietary Index for Gut Microbiota (DI-GM) assesses impact diet on microbiota, but its association risk remains unclear. This study aims to investigate between DI-GM analyze mediating roles phenotypic age body mass index (BMI). Methods Utilizing data from National Health nutrition examination survey (NHANES) 1999–2018, we included 17,444 adults aged 20 years older. (score...

Hyperspectral image (HSI) consists of hundreds continuous narrow bands with high redundancy, resulting in the curse dimensionality and an increased computation complexity HSI classification. Many clustering-based band selection approaches have been proposed to deal such a problem. However, few them consider spectral spatial relationship simultaneously. In this letter, we novel approach using deep subspace clustering (DSC). The combines task into convolutional autoencoder by treating it as...

Currently, deep neural networks (DNNs) are an important method for handling hyperspectral image (HSI) classification because of their good performance in processing. However, DNNs' depends on a massive number training data and hyperparameters that carefully fine-tuned, which results structural complexity time-consuming process. Deep forest is novel learning does not need much has simple structure. In this paper, we first design spectral-based HSI then propose improved algorithm, named...

Deep subspace clustering has achieved remarkable performances in the unsupervised classification of hyperspectral images. However, previous models based on pixel-level self-expressiveness data suffer from exponential growth computational complexity and access memory requirements with increasing number samples, thus leading to poor applicability large This paper presents a Neighborhood Contrastive Subspace Clustering network (NCSC), scalable robust deep approach, for Instead using...

Hyperspectral images (HSIs) consist of hundreds continuous bands with high correlation, making it contain great abundant information. Band selection is an effective idea for removing redundant and preserving the physical significance at same time. Popular sparse representation-based band commonly introduces additional coefficient to combine error term constraint term, difficult find out optimal balance coefficient. In this letter, we propose a hybrid clustering-based band-selection approach...

Hyperspectral image (HSI) consists of hundreds continuous narrow bands with high spectral correlation, which would lead to the so-called Hughes phenomenon and computational cost in processing. Band selection has been proven effective avoiding such problems by removing redundant bands. However, many existing band methods separately estimate significance for every single cannot fully consider nonlinear global interaction between In this paper, assuming that a complete HSI can be reconstructed...

Hyperspectral image (HSI) band selection (BS) is an important task for HSI dimensionality reduction, whose goal to select informative subset containing less redundancy. However, traditional BS methods basically work in the Euclidean domain, and thus, often neglect consider structural information of spectral bands. In this article, make full use information, a novel method termed as efficient graph convolutional self-representation (EGCSR) proposed by incorporating convolution into model....

Deep neural networks have gained increasing interest in hyperspectral image (HSI) processing. However, prior arts often neglect the high-order correlation among data points, failing to capture intraclass variations. In this letter, we present a unified network framework, termed as hypergraph-structured autoencoder (HyperAE), leverage relationship and learn robust deep representation for downstream tasks. Technically, proposed method adopts regularized by hypergraph structure backbone...

This study investigated the relationship between frailty index and all-cause cause-specific mortality in patients with depression. We recruited 2,669 participants depression from National Health Nutrition Examination Survey (NHANES) 2005 to 2018 quantified their status using a 53-item index. Cox proportional hazards models were used estimate hazard ratios (HR) 95% confidence intervals (CI). The median (IQR) score was 0.3 (0.2, 0.4). During follow-up of 7.1 years, 342 deaths (including 85...

Cancer survivors may experience accelerated biological aging, increasing their risk of mortality. However, the association between phenotypic age acceleration (PAA) and mortality among cancer remains unclear. This study aimed to evaluate relationship PAA all-cause mortality, cancer-specific non-cancer adult in United States. We utilized data from National Health Nutrition Examination Survey (NHANES) 1999 2018, including 2,643 (unweighted) patients aged ≥ 20 years. Phenotypic was calculated...

Objective To explore the application of a deep learning model based on lateral nasopharyngeal X-rays in diagnosing tonsillar and adenoid hypertrophy. Methods A retrospective study was conducted using DICOM images from pediatric outpatients aged 2-12 at our hospital July 2014 to 2024. The included patients exhibiting varying degrees respiratory obstruction symptoms (disease group). Initially, 1006 were collected, but after excluding low-quality standardizing imaging phase, 819 remained. These...

Bacillus spp. have emerged as pivotal sources of probiotic preparations, garnering considerable attention in recent years owing to their vigorous bacteriostatic activity and antimicrobial resistance. This study aimed investigate these characteristics depth verify the safety velezensis K12, a strain isolated from broiler intestine. The K12 was identified based on its morphology 16S rDNA sequence homology analysis. Subsequently, B. evaluated for acid resistance, bile salt gastrointestinal...

Remote sensing images present formidable classification challenges due to their complex spatial organization, high inter-class similarity, and significant intra-class variability. To address the balance between computational efficiency feature extraction capability in existing methods, this paper innovatively proposes a lightweight convolutional network, STConvNeXt. In its architectural design, model incorporates split-based mobile convolution module with hierarchical tree structure. It...