Semi-Supervised Medical Image Segmentation Based on Frequency Domain Aware Stable Consistency Regularization.

With the advancement of deep learning models nowadays, they have successfully applied in the semi-supervised medical image segmentation where there are few annotated medical images and a large number of unlabeled ones. A representative approach in this regard is the semi-supervised method based on consistency regularization, which improves model training by imposing consistency constraints (perturbations) on unlabeled data. However, the perturbations in this kind of methods are often artificially designed, which may introduce biases unfavorable to the model learning in the handling of medical image segmentation.

Prediction of Freezing of Gait in Parkinson's disease based on multi-channel time-series neural network.

Freezing of Gait (FOG) is a noticeable symptom of Parkinson's disease, like being stuck in place and increasing the risk of falls. The wearable multi-channel sensor system is an efficient method to predict and monitor the FOG, thus warning the wearer to avoid falls and improving the quality of life. However, the existing approaches for the prediction of FOG mainly focus on a single sensor system and cannot handle the interference between multi-channel wearable sensors.

A Fast Attention-Guided Hierarchical Decoding Network for Real-Time Semantic Segmentation.

Semantic segmentation provides accurate scene understanding and decision support for many applications. However, many models strive for high accuracy by adopting complex structures, decreasing the inference speed, and making it challenging to meet real-time requirements. Therefore, a fast attention-guided hierarchical decoding network for real-time semantic segmentation (FAHDNet), which is an asymmetric U-shaped structure, is proposed to address this issue.

A pyroptosis-related gene expression signature predicts immune microenvironment and prognosis in head and neck squamous cell carcinoma.

Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous and aggressive malignancy with a poor prognosis. Pyroptosis triggered by gasdermins family proteins is reported vital for tumor microenvironment and cancer progression. However, pyroptosis-related gene expression and its relationship with immune infiltration and prognosis of HNSCC have not been fully defined.

A novel and independent survival prognostic model for OSCC: the functions and prognostic values of RNA-binding proteins.

Background: Oral squamous cell carcinoma (OSCC), exhibiting high morbidity and malignancy, is the most common type of oral cancer. The abnormal expression of RNA-binding proteins (RBPs) plays important roles in the occurrence and progression of cancer. The objective of the present study was to establish a prognostic assessment model of RBPs and to evaluate the prognosis of OSCC patients.

Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier.

Background: Self-interacting proteins (SIPs), two or more copies of the protein that can interact with each other expressed by one gene, play a central role in the regulation of most living cells and cellular functions. Although numerous SIPs data can be provided by using high-throughput experimental techniques, there are still several shortcomings such as in time-consuming, costly, inefficient, and inherently high in false-positive rates, for the experimental identification of SIPs even nowadays. Therefore, it is more and more significant how to develop efficient and accurate automatic approaches as a supplement of experimental methods for assisting and accelerating the study of predicting SIPs from protein sequence information.

MNMDCDA: prediction of circRNA-disease associations by learning mixed neighborhood information from multiple distances.

Emerging evidence suggests that circular RNA (circRNA) is an important regulator of a variety of pathological processes and serves as a promising biomarker for many complex human diseases. Nevertheless, there are relatively few known circRNA-disease associations, and uncovering new circRNA-disease associations by wet-lab methods is time consuming and costly. Considering the limitations of existing computational methods, we propose a novel approach named MNMDCDA, which combines high-order graph convolutional networks (high-order GCNs) and deep neural networks to infer associations between circRNAs and diseases.

Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation.

Protein-protein interactions (PPIs) play a crucial role in understanding disease pathogenesis, genetic mechanisms, guiding drug design, and other biochemical processes, thus, the identification of PPIs is of great importance. With the rapid development of high-throughput sequencing technology, a large amount of PPIs sequence data has been accumulated. Researchers have designed many experimental methods to detect PPIs by using these sequence data, hence, the prediction of PPIs has become a research hotspot in proteomics.

Potential Novel Modules and Hub Genes as Prognostic Candidates of Thyroid Cancer by Weighted Gene Co-Expression Network Analysis.

Background: Although thyroid cancer (THCA) is one of the most common type of endocrine malignancy, its highly complex molecular mechanisms of carcinogenesis are not completely known.

Materials And Methods: In this study, weighted gene co-expression network analysis (WGCNA) was utilized to construct gene co-expression networks and evaluate the relations between modules and clinical traits to identify potential prognostic biomarkers for THCA patients. RNA-seq data and clinical data were downloaded from The Cancer Genome Atlas (TCGA).

Development and validation of a novel survival model for head and neck squamous cell carcinoma based on autophagy-related genes.

Background: In view of the critical role of autophagy-related genes (ARGs) in the pathogenesis of various diseases including cancer, this study aims to identify and evaluate the potential value of ARGs in head and neck squamous cell carcinoma (HNSCC).

Methods: RNA sequencing and clinical data in The Cancer Genome Atlas (TCGA) were analyzed by univariate Cox regression analysis and Lasso Cox regression analysis model established a novel 13- autophagy related prognostic genes, which were used to build a prognostic risk model. A multivariate Cox proportional regression model and the survival analysis were used to evaluate the prognostic risk model.

Identification and validation of key modules and hub genes associated with the pathological stage of oral squamous cell carcinoma by weighted gene co-expression network analysis.

Background: Oral squamous cell carcinoma (OSCC) is a major lethal malignant cancer of the head and neck region, yet its molecular mechanisms of tumourigenesis are still unclear.

Patients And Methods: We performed weighted gene co-expression network analysis (WGCNA) on RNA-sequencing data with clinical information obtained from The Cancer Genome Atlas (TCGA) database. The relationship between co-expression modules and clinical traits was investigated by Pearson correlation analysis.

Downregulation of miR-224-5p Promotes Migration and Proliferation in Human Dental Pulp Stem Cells.

Introduction: Pulp regeneration, as a treatment for pulp necrosis, has significant advantages over root canal therapy for the preservation of living pulp. To date, research on pulp regeneration has mainly focused on the transplantation of pulp stem cells into the root canal, but there is still a lack of research on the migration of pulp cells into the root canal via cell homing. Stem cells from the apical tooth papilla (SCAP) are recognized as multidirectional stem cells, but these cells are difficult to obtain.

Involvement of miR-146a-5p/neurogenic locus notch homolog protein 1 in the proliferation and differentiation of STRO-1 human dental pulp stem cells.

Dental pulp stem cells (DPSCs) and stem cells from the apical papilla (SCAPs) are oral mesenchymal stem cells capable of self-renewal and have a potential for multilineage differentiation. Increasing evidence shows that microRNAs (miRNAs) play important roles in stem cell biology. Here, we focused on exploring miR-146a-5p and its relationship to the undifferentiated status of STRO-1 SCAPs and STRO-1 DPSCs, as well as its role during STRO-1 DPSC differentiation and proliferation.

Attribute Selection Based on Constraint Gain and Depth Optimal for a Decision Tree.

Uncertainty evaluation based on statistical probabilistic information entropy is a commonly used mechanism for a heuristic method construction of decision tree learning. The entropy kernel potentially links its deviation and decision tree classification performance. This paper presents a decision tree learning algorithm based on constrained gain and depth induction optimization.

A novel long non-coding RNA, AC012456.4, as a valuable and independent prognostic biomarker of survival in oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is a major malignant cancer of the head and neck. Long non-coding RNAs (lncRNAs) have emerged as critical regulators during the development and progression of cancers. This study aimed to identify a lncRNA-related signature with prognostic value for evaluating survival outcomes and to explore the underlying molecular mechanisms of OSCC.

Concurrent preoperative chemotherapy and three-dimensional conformal radiotherapy followed by surgery for oral squamous cell carcinoma: a retrospective analysis of 104 cases.

Objectives: The objectives of this study were to assess the clinical effects of an integrated program consisting of concurrent preoperative combined paclitaxel and nedaplatin chemotherapy and three-dimensional conformal radiotherapy followed by surgery intended to cure oral squamous cell carcinoma and to determine whether this integrated program is feasible and effective with respect to the treatment of oral squamous cell carcinoma.

Methods: A total of 104 biopsy-confirmed patients who presented with oral squamous cell carcinoma for the first time were included in this study. Concurrent preoperative combined paclitaxel and nedaplatin chemotherapy and three-dimensional conformal radiotherapy were administered to these patients.

Learning From Short Text Streams With Topic Drifts.

Short text streams such as search snippets and micro blogs have been popular on the Web with the emergence of social media. Unlike traditional normal text streams, these data present the characteristics of short length, weak signal, high volume, high velocity, topic drift, etc. Short text stream classification is hence a very challenging and significant task.

Robust face recognition via adaptive sparse representation.

Sparse representation (or coding)-based classification (SRC) has gained great success in face recognition in recent years. However, SRC emphasizes the sparsity too much and overlooks the correlation information which has been demonstrated to be critical in real-world face recognition problems. Besides, some paper considers the correlation but overlooks the discriminative ability of sparsity.

MAIL: mining sequential patterns with wildcards.

Sequential pattern mining is an important research task in many domains, such as biological science. In this paper, we study the problem of mining frequent patterns from sequences with wildcards. The user can specify the gap constraints with flexibility.

Developmental toxicity of 3,4-dichloroaniline on rare minnow (Gobiocypris rarus) embryos and larvae.

Gobiocypris rarus is a freshwater cyprinid, which possesses lots of attractive features (short life cycle, high fecundity, and especially the transparent trait during early life stage) that make it a suitable model in aquatic toxicity tests. In this study, the effects of 3,4-dichloroaniline (3,4-DCA) on the early life stages of G. rarus were measured.

Antiprotozoal activity of medicinal plants against Ichthyophthirius multifiliis in goldfish (Carassius auratus).

Ichthyophthiriasis is a widespread disease in aquaculture and causes mass mortalities of fish. The development of new antiprotozoal agents for the treatment of Ichthyophthirius multifiliis infections is of increasing interest. The aim of the present study was to investigate the efficacy of 30 medicinal plants against I.

Bioassay-guided isolation and identification of active compounds from Semen pharbitidis against Dactylogyrus intermedius (Monogenea) in goldfish (Carassius auratus).

The present study was undertaken to isolate the active compounds responsible for the anthelmintic activity of methanol extract of Semen pharbitidis against Dactylogyrus intermedius in goldfish (Carassius auratus). The active methanol extract was fractionated on silica gel column chromatography in a bioassay-guided fractionation, eventually yielding two bioactive compounds: palmitic acid and pharnilatin A by comparing spectral data (NMR and ESI-MS) with literature values. According to in vivo anthelmintic assays, they were found to be 50% effective at the concentrations (EC(50)) of 5.