Exploring Si-Ni-San's therapeutic mechanism in autoimmune thyroid diseases: A network pharmacology approach and experimental evidence.

Ethnopharmacological Relevance: Autoimmune thyroid diseases (AITD), a group of prevalent and persistent immune-mediated disorders affecting the endocrine system, can progressively result in total thyroid failure, thereby drastically impacting metabolic processes. Given the inadequacies of current clinical approaches to managing AITD, The exigency to investigate novel therapeutic strategies demands immediate attention, given the limitations and potential resistances associated with conventional approaches. Si-Ni-San (SNS), first chronicled in the esteemed Eastern Han Dynasty medical text " Treatise on Cold Damage and Miscellaneous Diseases" circa 200-210 AD, is a time-honored remedy known for its harmonizing effects on the liver and invigorating properties for the spleen.

Impact of vector richness on the risk of vector-borne disease: The role of vector competence.

A central goal of disease ecology is to identify the factors that drive the spread of infectious diseases. Changes in vector richness can have complex effects on disease risk, but little is known about the role of vector competence in the relationship between vector richness and disease risk. In this study, we firstly investigated the combined effects of vector competence, interspecific competition, and feeding interference on disease risk through a two-vector, one-host SIR-SI model, and obtained threshold conditions for the occurrence of dilution and amplification effects.

Shape recovery from fusion of polarization binocular vision and shading.

The shape from polarization can recover the fine texture of the target surface. However, the gradient field for shape recovery by polarization is ambiguous, which is caused by the multi-value of the azimuth angle. In response to the problem, a method of correcting the ambiguity by the fusion of polarization binocular vision and shading information is proposed in this paper.

Effects of controlled hypotension with restrictive transfusion on intraoperative blood loss and systemic oxygen metabolism in elderly patients who underwent lumbar fusion.

Background: The effects of restrictive fluid therapy combined with controlled hypotension in the elderly on systemic oxygen metabolism and renal function are clinical concerns. The aim of this study was to evaluate blood loss, oxygen metabolism, and renal function in different levels of controlled hypotension induced by intravenous nitroglycerin, in combination with limited infusion, in elderly patients undergoing posterior lumbar fusion.

Methods: A total of 40 patients, aged 60-75 with ASA grade II or III, who were planned for posterior lumbar fusion were randomly allocated into two groups: experimental group [target mean arterial pressure 65 mmHg (MAP 65) or control group (MAP 75)].

Improved calibration method based on the RANSAC approach and an improved gray centroid method for a laser-line-based structured light system.

This paper proposes an improved calibration method for a structured light system by using the random sample consensus (RANSAC) method with nonlinear optimization and an improved gray centroid method. The proposed method is composed of two steps: calibrating intrinsic and extrinsic parameters for the camera, exploiting the improved gray centroid method to extract the centerline, and fitting the structured light plane by the RANSAC approach with the three-dimensional (3D) points obtained from different positions. The error function caused by the extracted centerline is deduced based on the pixel error perturbation method.

Rapid identification of human-infecting viruses.

Viruses have caused much mortality and morbidity to humans and pose a serious threat to global public health. The virome with the potential of human infection is still far from complete. Novel viruses have been discovered at an unprecedented pace as the rapid development of viral metagenomics.

RAEdb: a database of enhancers identified by high-throughput reporter assays.

High-throughput reporter assays have been recently developed to directly and quantitatively assess enhancer activity for thousands of regulatory elements. However, there is still no database to collect these enhancers. We developed RAEdb, the first database to collect enhancers identified by high-throughput reporter assays.

Adaptive Local Aspect Dictionary Pair Learning for Synthetic Aperture Radar Target Image Classification.

In this paper, a new target classification algorithm based on adaptive local aspect dictionary pair learning for synthetic aperture radar (SAR) images is developed. To that end, first, the aspect sector of one testing sample is determined adaptively by a regularized non-negative sparse learning method. Second, a synthesis dictionary and an analysis dictionary are jointly learned from the corresponding training subset located in the aspect sector.

Cell membrane proteins with high N-glycosylation, high expression and multiple interaction partners are preferred by mammalian viruses as receptors.

Motivation: Receptor mediated entry is the first step for viral infection. However, the question of how viruses select receptors remains unanswered.

Results: Here, by manually curating a high-quality database of 268 pairs of mammalian virus-host receptor interaction, which included 128 unique viral species or sub-species and 119 virus receptors, we found the viral receptors are structurally and functionally diverse, yet they had several common features when compared to other cell membrane proteins: more protein domains, higher level of N-glycosylation, higher ratio of self-interaction and more interaction partners, and higher expression in most tissues of the host.

Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification.

In this paper, we propose a two-stage multi-task learning representation method for the classification of synthetic aperture radar (SAR) target images. The first stage of the proposed approach uses multi-features joint sparse representation learning, modeled as a ℓ 2 , 1 -norm regularized multi-task sparse learning problem, to find an effective subset of training samples. Then, a new dictionary is constructed based on the training subset.

Predicting the host of influenza viruses based on the word vector.

Newly emerging influenza viruses continue to threaten public health. A rapid determination of the host range of newly discovered influenza viruses would assist in early assessment of their risk. Here, we attempted to predict the host of influenza viruses using the Support Vector Machine (SVM) classifier based on the word vector, a new representation and feature extraction method for biological sequences.