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Topoisomerases introduce transient DNA breaks to relax supercoiled DNA, thereby mediating chromatin dynamics and stability, transcription, replication and DNA damage repair. Topoisomerases are targets of various anticancer drugs, and their deregulation can cause, in addition to cancer, neurodegenerative diseases and immune disorders. Topoisomerases introduce transient DNA breaks to relax supercoiled DNA, remove catenanes and enable chromosome segregation. Human cells encode six topoisomerases (TOP1, TOP1mt, TOP2α, TOP2β, TOP3α and TOP3β), which act on a broad range of DNA and RNA substrates at the nuclear and mitochondrial genomes. Their catalytic intermediates, the topoisomerase cleavage complexes (TOPcc), are therapeutic targets of various anticancer drugs. TOPcc can also form on damaged DNA during replication and transcription, and engage specific repair pathways, such as those mediated by tyrosyl-DNA phosphodiesterase 1 (TDP1) and TDP2 and by endonucleases (MRE11, XPF–ERCC1 and MUS81). Here, we review the roles of topoisomerases in mediating chromatin dynamics, transcription, replication, DNA damage repair and genomic stability, and discuss how deregulation of topoisomerases can cause neurodegenerative diseases, immune disorders and cancer.

In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may also threaten public health. This study utilizes the Internet of Things (IoT) in developing a monitoring system, particularly for water quality, to reduce the risk of contracting diseases. Water quality components data, such as water temperature, alkalinity or acidity, and contaminants, were obtained through a series of linked sensors. An Arduino microcontroller board acquired all the data and the Narrow Band-IoT (NB-IoT) transmitted them to the web server. Due to limited human resources to observe the water quality physically, the monitoring was complemented by real-time notifications alerts via a telephone text messaging application. The water quality data were monitored using Grafana in web mode, and the binary classifiers of machine learning techniques were applied to predict whether the water was drinkable or not based on the data collected, which were stored in a database. The non-decision tree, as well as the decision tree, were evaluated based on the improvements of the artificial intelligence framework. With a ratio of 60% for data training: at 20% for data validation, and 10% for data testing, the performance of the decision tree (DT) model was more prominent in comparison with the Gradient Boosting (GB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) modeling approaches. Through the monitoring and prediction of results, the authorities can sample the water sources every two weeks.

The complex interactions among different immune cells have important functions in the development of malignant pleural effusion (MPE). Here we perform single-cell RNA sequencing on 62,382 cells from MPE patients induced by non-small cell lung cancer to describe the composition, lineage, and functional states of infiltrating immune cells in MPE. Immune cells in MPE display a number of transcriptional signatures enriched for regulatory T cells, B cells, macrophages, and dendritic cells compared to corresponding counterparts in blood. Helper T, cytotoxic T, regulatory T, and T follicular helper cells express multiple immune checkpoints or costimulatory molecules. Cell-cell interaction analysis identifies regulatory B cells with more interactions with CD4 + T cells compared to CD8 + T cells. Macrophages are transcriptionally heterogeneous and conform to M2 polarization characteristics. In addition, immune cells in MPE show the general up-regulation of glycolytic pathways associated with the hypoxic microenvironment. These findings show a detailed atlas of immune cells in human MPE and enhance the understanding of potential diagnostic and therapeutic targets in advanced non-small cell lung cancer. Detailed immune cell subset identification in human lung (malignant pleural effusion) MPE is important for diagnosis and prognosis. Here the authors use single cell RNA sequencing to identify immune cell types within MPE and show differential gene expression compared to circulating immune cells in blood.

Logistics resilience is a significant representation of sustainable development ability and a necessary support for high-quality economic development. In order to explore the influencing factors and realization mechanism of the improvement of logistics resilience of the Yangtze River Economic Belt and the high-quality and sustainable development of the economy, this paper comprehensively considers factors of the supply and demand relationship of the logistics market, industrial structure and ecological environment, and evaluates the urban logistics resilience of the Yangtze River Economic Belt by using POI data and statistical data. Combined with the spatial Durbin model, the influencing factors and spatial spillover effects of inter-city logistics resilience were revealed. This study found that the urban logistics resilience in the lower reaches of the Yangtze River has been high. Except Chongqing and Shanghai, the COVID-19 epidemic happened in 2020 led to a significant decrease in logistics resilience. In the meanwhile, every 1% increase in the logistics resilience of the city will promote the logistics resilience of the adjacent cities by 0.145%. Economic condition and urban development potential have positive effects on logistics resilience of the city and its adjacent cities. The economic condition has a direct effect coefficient of 0.166 and an indirect effect coefficient of 0.181, The direct and indirect effects of urban development potential are significantly positive, and the coefficients are 0.001 and 0.006, respectively. The level of information, government support and ability of technological innovation are helpful for the improvement of urban logistics resilience while hindering the enhancement of logistics resilience in adjacent cities. The research area can be extended in the future and more influencing factors can be considered in the future.

Person re-identification (re-ID) aims to retrieve images of a given individual from different camera views. Obstacles obstructing parts of a pedestrian’s body often result in incomplete identity information, impairing recognition performance. To address the occlusion problem, a method called Multi-Branch Interaction Network (MBIN) is proposed, which exploits the information interaction between different branches to effectively characterize occluded pedestrians for person re-ID. The method consists primarily of a hard branch, a soft branch, and a view branch. The hard branch enhances feature robustness via a unified horizontal partitioning strategy. The soft branch improves the high-level feature representation via multi-head attention. The view branch fuses multi-view feature maps to form a comprehensive representation via a dual-classifier fusion mechanism. Moreover, a mutual knowledge distillation strategy is employed to promote knowledge exchange among the three branches. Extensive experiments are conducted on widely used person re-ID datasets to validate the effectiveness of our method.

Early detection is critical for effective management of Alzheimer's disease (AD) and screening for mild cognitive impairment (MCI) is common practice. Among several deep-learning techniques that have been applied to assessing structural brain changes on magnetic resonance imaging (MRI), convolutional neural network (CNN) has gained popularity due to its superb efficiency in automated feature learning with the use of a variety of multilayer perceptrons. Meanwhile, ensemble learning (EL) has shown to be beneficial in the robustness of learning-system performance via integrating multiple models. Here, we proposed a classifier ensemble developed by combining CNN and EL, i.e., the CNN-EL approach, to identify subjects with MCI or AD using MRI: i.e., classification between (1) AD and healthy cognition (HC), (2) MCIc (MCI patients who will convert to AD) and HC, and (3) MCIc and MCInc (MCI patients who will not convert to AD). For each binary classification task, a large number of CNN models were trained applying a set of sagittal, coronal, or transverse MRI slices; these CNN models were then integrated into a single ensemble. Performance of the ensemble was evaluated using stratified fivefold cross-validation method for 10 times. The number of the intersection points determined by the most discriminable slices separating two classes in a binary classification task among the sagittal, coronal, and transverse slice sets, transformed into the standard Montreal Neurological Institute (MNI) space, acted as an indicator to assess the ability of a brain region in which the points were located to classify AD. Thus, the brain regions with most intersection points were considered as those mostly contributing to the early diagnosis of AD. The result revealed an accuracy rate of 0.84 ± 0.05, 0.79 ± 0.04, and 0.62 ± 0.06, respectively, for classifying AD vs. HC, MCIc vs. HC, and MCIc vs. MCInc, comparable to previous reports and a 3D deep learning approach (3D-SENet) based on a more state-of-the-art and popular Squeeze-and-Excitation Networks model using channel attention mechanism. Notably, the intersection points accurately located the medial temporal lobe and several other structures of the limbic system, i.e., brain regions known to be struck early in AD. More interestingly, the classifiers disclosed multiple patterned MRI changes in the brain in AD and MCIc, involving these key regions. These results suggest that as a data-driven method, the combined CNN and EL approach can locate the most discriminable brain regions indicated by the trained ensemble model while the generalization ability of the ensemble model was maximized to successfully capture AD-related brain variations early in the disease process; it can also provide new insights into understanding the complex heterogeneity of whole-brain MRI changes in AD. Further research is needed to examine the clinical implication of the finding, capability of the advocated CNN-EL approach to help understand and evaluate an individual subject's disease status, symptom burden and progress, and the generalizability of the advocated CNN-EL approach to locate the most discriminable brain regions in the detection of other brain disorders such as schizophrenia, autism, and severe depression, in a data-driven way.

Vitamin D is a hormone involved in many physiological processes. Its active form, 1,25(OH)2D3, modulates serum calcium–phosphate homeostasis and skeletal homeostasis. A growing body of evidence has demonstrated the renoprotective effects of vitamin D. Vitamin D modulates endothelial function, is associated with podocyte preservation, regulates the renin–angiotensin–aldosterone system, and has anti-inflammatory effects. Diabetic kidney disease (DKD) is a leading cause of end-stage kidney disease worldwide. There are numerous studies supporting vitamin D as a renoprotector, potentially delaying the onset of DKD. This review summarizes the findings of current research on vitamin D and its role in DKD.

Drugs targeting the orthosteric, primary binding site of G protein–coupled receptors are the most common therapeutics. Allosteric binding sites, elsewhere on the receptors, are less well-defined, and so less exploited clinically. We report the crystal structure of the prototypic β₂-adrenergic receptor in complex with an orthosteric agonist and compound-6FA, a positive allosteric modulator of this receptor. It binds on the receptor’s inner surface in a pocket created by intracellular loop 2 and transmembrane segments 3 and 4, stabilizing the loop in an α-helical conformation required to engage the G protein. Structural comparison explains the selectivity of the compound for β₂- over the β₁-adrenergic receptor. Diversity in location, mechanism, and selectivity of allosteric ligands provides potential to expand the range of receptor drugs.

Objective To assess the associations of the Assessment of Spondyloarthritis International Society Health Index (ASAS HI) with gender and other factors in patients with ankylosing spondylitis (AS). Methods From November 2017 to October 2018, we measured the Ankylosing Spondylitis Disease Activity Score (ASDAS), the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), the Bath Ankylosing Spondylitis Functional Index (BASFI), the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) and the ASAS HI score for AS patients at the Taichung Veterans General Hospital. After adjusting for disease activity (ASDAS-erythrocyte sedimentation rate [ESR], ASDAS- C-reactive protein [CRP], BASDAI+ESR or BASDAI+CRP), mSASSS and other potential confounders including medications, comorbidities, and laboratory data, any associations between gender and the sum score of ASDAS HI were assessed using multiple linear regression analysis, as well as any associations between gender and an ASAS HI score >5 using multivariable logistic regression analysis. Results A total of 307 AS patients (62 [20.2%] females, mean age 46.4 years [S.D. 13.3], mean symptom duration 20.6 years [S.D. 12.1]) were included. Multiple linear regression analysis showed that the male gender was significantly associated with a lower ASAS HI (B = -1. 91, 95% confidence interval [CI], -2.82--1.00, p 5 than females (odds ratio = 0.15, 95% CI, 0.07-0.36, p <0.001). Disease activity measures, including ASDAS-ESR, ASDAS-CRP and BASDAI, had positive correlations with ASAS HI. Conclusion This single-center, cross-sectional study revealed that a higher ASAS HI score was significantly associated with female gender and higher disease activity measures.