Lysosomal dysfunction and overload of nucleosides in thymidine phosphorylase deficiency of MNGIE.

Inherited deficiency of thymidine phosphorylase (TP), encoded by TYMP, leads to a rare disease with multiple mitochondrial DNA (mtDNA) abnormalities, mitochondrial neurogastrointestinal encephalomyopathy (MNGIE). However, the impact of TP deficiency on lysosomes remains unclear, which are important for mitochondrial quality control and nucleic acid metabolism. Muscle biopsy tissue and skin fibroblasts from MNGIE patients, patients with m.

Point-Based Learnable Query Generator for Human-Object Interaction Detection.

Transformer-based and interaction point-based methods have demonstrated promising performance and potential in human-object interaction detection. However, due to differences in structure and properties, direct integration of these two types of models is not feasible. Recent Transformer-based methods divide the decoder into two branches: an instance decoder for human-object pair detection and a classification decoder for interaction recognition.

A Contrastive Learning Network for Performance Metric and Assessment of Physical Rehabilitation Exercises.

Human activity analysis in the legal monitoring environment plays an important role in the physical rehabilitation field, as it helps patients with physical injuries improve their postoperative conditions and reduce their medical costs. Recently, several deep learning-based action quality assessment (AQA) frameworks have been proposed to evaluate physical rehabilitation exercises. However, most of them treat this problem as a simple regression task, which requires both the action instance and its score label as input.

Distinctive metabolic remodeling in TYMP deficiency beyond mitochondrial dysfunction.

Mitochondrial neurogastrointestinal encephalomyopathy (MNGIE) is caused by mutations in the TYMP gene, which encodes thymidine phosphorylase (TP). As a cytosolic metabolic enzyme, TP defects affect biological processes that are thought to not be limited to the abnormal replication of mitochondrial DNA. This study aimed to elucidate the characteristic metabolic alterations and associated homeostatic regulation caused by TYMP deficiency.

Online Multi-Label Streaming Feature Selection Based on Label Group Correlation and Feature Interaction.

Multi-label streaming feature selection has received widespread attention in recent years because the dynamic acquisition of features is more in line with the needs of practical application scenarios. Most previous methods either assume that the labels are independent of each other, or, although label correlation is explored, the relationship between related labels and features is difficult to understand or specify. In real applications, both situations may occur where the labels are correlated and the features may belong specifically to some labels.

The incomplete circle of Willis is associated with vulnerable intracranial plaque features and acute ischemic stroke.

Background: The circle of Willis (CoW) plays a significant role in intracranial atherosclerosis (ICAS). This study investigated the relationship between different types of CoW, atherosclerosis plaque features, and acute ischemic stroke (AIS).

Methods: We investigated 97 participants with AIS or transient ischemic attacks (TIA) underwent pre- and post-contrast 3T vessel wall cardiovascular magnetic resonance within 7 days of the onset of symptoms.

Label-reconstruction-based pseudo-subscore learning for action quality assessment in sporting events.

Most existing action quality assessment (AQA) methods provide only an overall quality score for the input video and lack an evaluation of each substage of the movement process; thus, these methods cannot provide detailed feedback for users. Moreover, the existing datasets do not provide labels for substage quality assessment. To address these problems, in this work, a new label-reconstruction-based pseudo-subscore learning (PSL) method is proposed for AQA in sporting events.

An ionic lock and a hydrophobic zipper mediate the coupling between an insect pheromone receptor BmOR3 and downstream effectors.

Pheromone receptors (PRs) recognize specific pheromone compounds to guide the behavioral outputs of insects, which are the most diverse group of animals on earth. The activation of PRs is known to couple to the calcium permeability of their coreceptor (Orco) or putatively with G proteins; however, the underlying mechanisms of this process are not yet fully understood. Moreover, whether this transverse seven transmembrane domain (7TM)-containing receptor is able to couple to arrestin, a common effector for many conventional 7TM receptors, is unknown.

Novel biallelic mutations in POLG gene: large deletion and missense variant associated with PEO.

Background: Mitochondrial disorders are clinically heterogeneous diseases associated with impaired oxidative phosphorylation (OXPHOS) activity. POLG, which encodes the DNA polymerase-γ (Polγ) catalytic subunit, is the most commonly mutated nuclear gene associated with mitochondrial disorders.

Methods: We carried out whole-exome sequencing (WES) to identify the gene associated with progressive external ophthalmoplegia (PEO).

Functional suppression of Ripk1 blocks the NF-κB signaling pathway and induces neuron autophagy after traumatic brain injury.

Traumatic brain injury (TBI), known as intracranial injury, has been a serious threat to human health. Evidence exists indicating that autophagy and inflammatory responses contribute to secondary brain injury after TBI. Notably, receptor-interacting protein kinase 1 (Ripk1) exerts an important role in cell autophagy.

A novel m.11406 T > A mutation in mitochondrial ND4 gene causes MELAS syndrome.

Pathogenic point mutations of mitochondrial DNA (mtDNA) are associated with a large number of heterogeneous diseases involving multiple systems with which patients may present with a wide range of clinical phenotypes. In this study, we describe a novel heteroplasmic missense mutation, m.11406 T > A, of the ND4 gene encoding the subunit 4 of mitochondrial complex I in a 32-year-old woman with recurrent epileptic seizure, headache and bilateral hearing loss.

A Survey of Vision-Based Human Action Evaluation Methods.

The fields of human activity analysis have recently begun to diversify. Many researchers have taken much interest in developing action recognition or action prediction methods. The research on human action evaluation differs by aiming to design computation models and evaluation approaches for automatically assessing the quality of human actions.

A Comprehensive Survey of Vision-Based Human Action Recognition Methods.

Although widely used in many applications, accurate and efficient human action recognition remains a challenging area of research in the field of computer vision. Most recent surveys have focused on narrow problems such as human action recognition methods using depth data, 3D-skeleton data, still image data, spatiotemporal interest point-based methods, and human walking motion recognition. However, there has been no systematic survey of human action recognition.

Intelligent diagnosis with Chinese electronic medical records based on convolutional neural networks.

Background: Benefiting from big data, powerful computation and new algorithmic techniques, we have been witnessing the renaissance of deep learning, particularly the combination of natural language processing (NLP) and deep neural networks. The advent of electronic medical records (EMRs) has not only changed the format of medical records but also helped users to obtain information faster. However, there are many challenges regarding researching directly using Chinese EMRs, such as low quality, huge quantity, imbalance, semi-structure and non-structure, particularly the high density of the Chinese language compared with English.

Axially Symmetric Data Clustering Through Dirichlet Process Mixture Models of Watson Distributions.

This paper proposes a Bayesian nonparametric framework for clustering axially symmetric data. Our approach is based on a Dirichlet processes mixture model with Watson distributions, which can also be considered as the infinite Watson mixture model. In this paper, first, we extend the finite Watson mixture model into its infinite counterpart based on the framework of truncated Dirichlet process mixture model with a stick-breaking representation.

MicroRNA-139-5p/Flt1/Wnt/β-catenin regulatory crosstalk modulates the progression of glioma.

Fms-related tyrosine kinase 1 (Flt1), the receptor of VEGF/PIGF, is associated with cancer angiogenesis and tumorigenesis. Although the high expression of Flt1 in glioma is identified, its regulatory mechanism remains unclear. In the present study, we demonstrate that miR‑139‑5p inhibits Flt1 expression mediated by binding its 3' untranslated region (3'UTR) to regulate the progression of human glioma.

MiR-433-3p suppresses cell growth and enhances chemosensitivity by targeting CREB in human glioma.

Previous studies reported that miR-433 exerts function widely in human tumorigenesis and development. Here, we further investigate the potential role of miR-433 in glioma. Quantitative real-time PCR demonstrated that miR-433-3p and miR-433-5p were low expressed in glioma tissues and cell lines.

Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision.

In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data.

Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision.

Tracking individual-cell/object over time is important in understanding drug treatment effects on cancer cells and video surveillance. A fundamental problem of individual-cell/object tracking is to simultaneously address the cell/object appearance variations caused by intrinsic and extrinsic factors. In this paper, inspired by the architecture of deep learning, we propose a robust feature learning method for constructing discriminative appearance models without large-scale pretraining.

Jointly Feature Learning and Selection for Robust Tracking via a Gating Mechanism.

To achieve effective visual tracking, a robust feature representation composed of two separate components (i.e., feature learning and selection) for an object is one of the key issues.

Multi-surface analysis for human action recognition in video.

The majority of methods for recognizing human actions are based on single-view video or multi-camera data. In this paper, we propose a novel multi-surface video analysis strategy. The video can be expressed as three-surface motion feature (3SMF) and spatio-temporal interest feature.

Silence of bFGF enhances chemosensitivity of glioma cells to temozolomide through the MAPK signal pathway.

Basic fibroblast growth factor (bFGF) is a multifunctional growth factor in glioma cells and has been proved to be associated with the grade malignancy of glioma and prognosis of patients. Although there is evidence showing that bFGF plays an important role in proliferation, differentiation, angiogenesis, and survival of glioma cells, the effect of bFGF on chemosensitivity of glioma has not been verified. In this study, we analyzed the relationship between bFGF and chemotherapy resistance, with the objective of offering new strategy for chemotherapy of glioma patients.

Endogenous FOXP3 inhibits cell proliferation, migration and invasion in glioma cells.

The transcription factor forkhead box P3 (FOXP3) has been demonstrated to play important roles in the development and function of regulatory T cells (Tregs). In addition, studies had recently demonstrated that FOXP3 also expressed in some tumor cells. However, the exact role and molecular mechanism of FOXP3 function in glioma's cells are still unclear.

Microarray data classification based on ensemble independent component selection.

Independent component analysis (ICA) has been widely deployed to the analysis of microarray datasets. Although it was pointed out that after ICA transformation, different independent components (ICs) are of different biological significance, the IC selection problem is still far from fully explored. In this paper, we propose a genetic algorithm (GA) based ensemble independent component selection (EICS) system.

A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks.

In this paper, a novel heuristic structure optimization methodology for radial basis probabilistic neural networks (RBPNNs) is proposed. First, a minimum volume covering hyperspheres (MVCH) algorithm is proposed to select the initial hidden-layer centers of the RBPNN, and then the recursive orthogonal least square algorithm (ROLSA) combined with the particle swarm optimization (PSO) algorithm is adopted to further optimize the initial structure of the RBPNN. The proposed algorithms are evaluated through eight benchmark classification problems and two real-world application problems, a plant species identification task involving 50 plant species and a palmprint recognition task.