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Hypothyroidism is closely related to mental disorders, mainly depression, through an as-yet-unknown mechanism. The cerebral inflammatory immune process has been implied to play a pivotal role in the onset of affective symptoms in several conditions. In order to gain insight into the mechanism underlying the depressive behaviors in hypothyroid rats, brain microglial activation was evaluated using micro positron emission tomography imaging with a translocator protein (TSPO) radioligand. Hypothyroidism was induced in adult male Wistar rats by administration of 0.05% propylthiouracil in drinking water for five weeks. Open field, forced swimming and tail suspension tests were employed to evaluate the depressive behavior in hypothyroid rats, and the relationship between the behavioral changes and brain microglial activation was evaluated using [18F] DPA-714 micro positron emission tomography imaging. The open field test revealed significantly reduced first-minute activity and rearing behavior in the hypothyroid group, as well as significantly increased immobility in the forced swimming test and the tail suspension test. Hypothyroidism induced significantly increased microglial activation in the hippocampus. The radioligand uptake in the hippocampus correlated negatively with first-minute activity in the open field test (p < 0.05), and the radioligand uptake in the hippocampus correlated positively with changes in the immobility time in the forced swimming test and the tail suspension test (p < 0.05). Immunohistochemistry also confirmed the activation of microglia and inflammatory bodies in hypothyroid rats. The results indicate that hypothyroidism can induce depressive behavior in adult Wistar rats, microglial activation in the hippocampus plays an important role in the depressive behavior in hypothyroid rats and the inflammatory immune mechanism may underlie the behavioral abnormalities in thyroid dysfunction. Furthermore, the findings in the present study suggest there might be a common mechanism underlying depressive behavior in adult-onset hypothyroidism and depression. [ABSTRACT FROM AUTHOR]

Albinism is a group of inherited disorders mainly affecting skin, hair and eyes. Here we identify a de novo point mutation, p.R210C, in the TPCN2 gene which encodes Two Pore Channel 2 (TPC2) from a patient with albinism. TPC2 is an endolysosome and melanosome localized non-selective cation channel involved in regulating pigment production. Through inside-out recording of plasma membrane targeted TPC2 and direct recording of enlarged endolysosomal vacuoles, we reveal that the R210C mutant displays constitutive channel activation and markedly increased affinity to PI(3,5)P2. Mice harboring the homologous mutation, R194C, also exhibit hypopigmentation in the fur and skin, as well as less pigment and melanosomes in the retina in a dominant inheritance manner. Moreover, mouse embryonic fibroblasts carrying the R194C mutation show enlarged endolysosomes, enhanced lysosomal Ca2+ release and hyper-acidification. Our data suggest that R210C is a pathogenic gain-of-function TPC2 variant that underlies an unusual dominant type of albinism. TPC2 is an important organellar Na+/Ca2+ release channel which regulates function of lysosomes and lysosome-related organelles. Here, Wang et al. demonstrate that a gain-of-function mutation (R210C) in TPC2 leads to hypopigmentaion, enlarged endolysosomes, enhanced lysosomal Ca2+ release and hyper-acidification. [ABSTRACT FROM AUTHOR]

The SQUAMOSA promoter binding-like protein (SPL) is a specific transcription factor that affects plant growth and development. The SPL gene family has been explored in various plants, but information about these genes in alfalfa is limited. This study, based on the whole genome data of alfalfa SPL, the fundamental physicochemical properties, phylogenetic evolution, gene structure, cis-acting elements, and gene expression of members of the MsSPL gene family were analyzed by bioinformatics methods. We identified 82 SPL sequences in the alfalfa, which were annotated into 23 genes, including 7 (30.43%) genes with four alleles, 10 (43.47%) with three, 3 (13.04%) with two, 3 (13.04%) with one allele. These SPL genes were divided into six groups, that are constructed from A. thaliana, M. truncatula and alfalfa. Chromosomal localization of the identified SPL genes showed arbitary distribution. The subcellular localization predictions showed that all MsSPL proteins were located in the nucleus. A total of 71 pairs of duplicated genes were identified, and segmental duplication mainly contributed to the expansion of the MsSPL gene family. Analysis of the Ka/Ks ratios indicated that paralogs of the MsSPL gene family principally underwent purifying selection. Protein–protein interaction analysis of MsSPL proteins were performed to predict their roles in potential regulatory networks. Twelve cis-acting elements including phytohormone and stress elements were detected in the regions of MsSPL genes. We further analyzed that the MsSPLs had apparent responses to abiotic stresses such as drought and salt and the biotic stress of methyl jasmonate. These results provide comprehensive information on the MsSPL gene family in alfalfa and lay a solid foundation for elucidating the biological functions of MsSPLs. This study also provides valuable on the regulation mechanism and function of MsSPLs in response to biotic and abiotic stresses. [ABSTRACT FROM AUTHOR]

In the field of protein engineering, the function and structure of proteins are key to understanding cellular mechanisms, biological evolution, and biodiversity. With the advancement of high-throughput sequencing technologies, we have accumulated a vast amount of protein sequence data, yet the protein properties and functional information contained within these data have not been fully deciphered. Predicting protein properties is crucial for revealing how proteins function within complex biological systems and also offers possibilities for the early diagnosis of diseases and the development of new drugs. However, due to the complexity of protein properties and functions, traditional experimental methods face significant challenges in terms of cost, time, and accuracy. In recent years, machine learning techniques have become a powerful tool for addressing these challenges due to their ability to learn patterns and relationships from large-scale data. Machine learning methods have demonstrated outstanding performance in areas such as protein structure prediction, function annotation, interaction recognition, and physicochemical property prediction. This survey reviews the application of machine learning in protein property prediction. Current research progress, challenges in the field, and future development directions have been discussed, highlighting the significance and potential of machine learning methods in advancing protein science research and applications. • Comprehensive review of machine learning methods. • Deep learning innovations in protein science, especially from 2019 to 2023. • Role of pre-trained models in protein property prediction. • Integration of advanced machine learning techniques in protein property prediction. • Representation learning from protein sequences and structures. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DRP) is commonly caused by the complications of diabetes and, if ignored and untreated, may lead to irreversible blindness in adults. Medical experts mainly diagnose DRP by its disease features, such as microangioma (MA), exudate (EX), and hemorrhage (HE). However, there are limitations in the diagnostic effect due to the small disease features lesions and inconspicuous feature areas of DRP. In this paper, we propose a self-calibrated convolutional network, BathNet, for fine-grained classification, to assist in the diagnosis of three disease features, MA, EX, and HE. In this network we replace the basic convolutional blocks with self-calibrated convolutional blocks so that the internal communication can be extended the field of view of each convolutional layer to extract feature information efficiently. Then, in the multi-branch structure, we obtain local features by using the CNN structure with self-calibrated convolution blocks, and global features by using the progressive transformer structure to obtain the remote dependencies of the feature mapping. Finally, the relatively important feature channels are filtered out by the branch fusion module. In addition, many DRP-related disease features (e.g., EX) must be observed by the fluorescein angiography fundus image (FAF) but not by the colorful fundus image (CFP), so we propose a method for label migration across modalities by corresponding feature labels on FAF images to CFP images as network inputs. The experiment showed that the prediction results of CFP images after label migration were similar to FAF images. Comparing with baseline, our study BathNet achieves the best accuracy for MA, EX, and HE disease features, from 57.01%, 58.88%, and 64.49% to 78.5%, 71.96%, and 77.57%, respectively. In the future, patients can diagnose the disease features on FAF images with only one CFP image, which largely reduces the cost of disease detection. The code is available at https://github.com/hedgehogW/BathNet/. • Proposed cross-modal label transfer method reduces the fundus diseases diagnosis cost. • Replaced the CNN blocks with self-calibrated ones to become more discriminatively. • Proposed a multi-branch structure that to extract both local and global features. [ABSTRACT FROM AUTHOR]

Train timetabling is crucial for passenger railway operation. Demand-oriented train timetable optimization by minimizing travel time plays an important role in both theory and practice. Most of the current researches of demand-oriented timetable models assume an idealized situation in which the service order is fixed and in which zero overtaking exists between trains. In order to extend the literature, this paper discusses the combinatorial effect of service order and overtaking by developing four mixedinteger quadratic programming timetabling models with different service order as well as overtaking conditions. With the objective of minimizing passengers&apos; waiting time and in-vehicle time, the models take five aspects as constraints, namely dwell time, running time, safety interval, overtaking, and capacity. All four models are solved by ILOG CPLEX; and the results, which are based on Shanghai-Hangzhou intercity high-speed rail data, show that either allowing overtaking or changing service order can effectively optimize the quality of timetable with respect to reducing the total passengers&apos; travel time. Although optimizing train overtaking and service order simultaneously can optimize the timetable more significantly, compared to overtaking, allowing the change of service order can help passengers save total travel time without extending the train travel time. Moreover, considering the computation effort, satisfying both of the conditions in the meantime, when optimizing timetable has not got a good cost benefit., 1. Introduction Railway timetabling is the basis for train operation, whose quality affects the service level and the efficiency of the whole network. In the form of timetables, the railway [...]

The WHY family is a group of plant-specific transcription factors, that can bind to single-stranded DNA molecules and play a variety of functions in plant nuclei and organelles, participating in the regulation of plant leaf senescence. It has been identified and analyzed in many species, however, the systematic identification and analysis of the WHY genes family have not yet been reported in alfalfa (Medicago sativa L.). Therefore, to explore the function of alfalfa the WHY genes, and 10 MsWHY genes were identified and further characterized their evolutionary relationship and expression patterns by analyzing the recently published genome of alfalfa. Comprehensive analysis of the chromosome location, physicochemical properties of the protein, evolutionary relationship, conserved motifs, and responses to abiotic stresses of the WHY gene family in alfalfa using bioinformatics methods. The results showed that 10 MsWHY genes were distributed on 10 chromosomes, and collinearity analysis showed that many MsWHYs might be derived from segmental duplications, and these genes are under purifying selection. Based on phylogenetic analyses, the WHY gene family of alfalfa can be divided into four subfamilies: I-IV subfamily, and approximately all the WHY genes within the same subfamily share similar gene structures. The 10 MsWHY gene family members contained 10 motifs, of which motif 2 and motif 4 are the conserved motifs shared by these genes. Furthermore, the analysis of cis-regulatory elements indicated that regulatory elements related to transcription, cell cycle, development, hormone, and stress response are abundant in the promoter sequence of the MsWHY genes. Real-time quantitative PCR demonstrated that MsWHYs gene expression is induced by drought, salt, and methyl jasmonate. The present study serves as a basic foundation for future functional studies on the alfalfa WHY family. [ABSTRACT FROM AUTHOR]

Obesity has become a health threat and hard enough to deal with. Evidences show that metformin could inhibit adipogenesis and combat obesity, while its mechanisms remain to be elucidated more comprehensively. In this study, we found that administration of metformin could combat obesity of mice induced by high-fat diet (HFD), indicated by strikingly decreased body weight and weight of inguinal white adipose tissue (iWAT) and epidydimal white adipose tissue (eWAT) compared with the control group. Mechanically, we revealed that metformin could inhibit protein expression of FTO, leading to increased m6A methylation levels of cyclin D1 (Ccnd1) and cyclin dependent kinase 2 (Cdk2), two crucial regulators in cell cycle. Ccnd1 and Cdk2 with increased m6A levels were recognised by YTH m6A RNA binding protein 2 (YTHDF2), causing an YTHDF2-dependent decay and decreased protein expressions. In consequence, mitotic clonal expansion (MCE) process was blocked and adipogenesis was inhibited. [ABSTRACT FROM AUTHOR]