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Grottoes are a comprehensive art treasure trove that integrate architecture, sculpture, and murals. They showcase the historical spiritual civilization of humanity and provide a solid foundation for studying the dissemination and development of Buddhist culture. Gansu Province is an important node on the transmission route of Buddhist culture, constituting a relatively complete and rich history of Buddhist art and cultural history. This article uses GIS technology to systematically analyse the spatial distribution characteristics and spatiotemporal evolution patterns of grottoes in Gansu Province from the Wei Jin to the Ming and Qing dynasties and explores the main factors affecting their distribution. The results indicate the following: (1) the grottoes in Gansu Province exhibit clustering and uneven distribution characteristics, which form the core aggregation area of Qingyang Tianshui City and the secondary aggregation area of Wuwei and Zhangye City. (2) Spatiotemporal characteristics show significant changes in the number and focus of excavation and repair of grottoes in Gansu Province over the years. The Northern and Southern Dynasties, Sui and Tang Dynasties, and Ming and Qing Dynasties had more grottoes than the Wei, Jin, and Yuan Dynasties. The overall centre of gravity shifted from northwest to southeast. Natural factors such as topography, stratigraphy, and hydrology and cultural factors such as politics and transportation significantly impacted the spatial pattern of grotto heritage in Gansu Province. Exploring and studying the spatial layout of grotto heritage from the perspective of historical geography is beneficial for understanding the cultural development and historical changes in Buddhism and is of great significance for the development of landscape environmental protection and utilization of grotto heritage.

Background Musculoskeletal tissue degeneration impairs the life quality and motor function of many people, especially seniors and athletes. Tendinopathy is one of the most common diseases associated with musculoskeletal tissue degeneration, representing a major global healthcare burden that affects both athletes and the general population, with the clinical presentation of long-term recurring chronic pain and decreased tolerance to activity. The cellular and molecular mechanisms at the basis of the disease process remain elusive. Here, we use a single-cell and spatial RNA sequencing approach to provide a further understanding of cellular heterogeneity and molecular mechanisms underlying tendinopathy progression. Results To explore the changes in tendon homeostasis during the tendinopathy process, we built a cell atlas of healthy and diseased human tendons using single-cell RNA sequencing of approximately 35,000 cells and explored the variations of cell subtypes’ spatial distributions using spatial RNA sequencing. We identified and localized different tenocyte subpopulations in normal and lesioned tendons, found different differentiation trajectories of tendon stem/progenitor cells in normal/diseased tendons, and revealed the spatial location relationship between stromal cells and diseased tenocytes. We deciphered the progression of tendinopathy at a single-cell level, which is characterized by inflammatory infiltration, followed by chondrogenesis and finally endochondral ossification. We found diseased tissue-specific endothelial cell subsets and macrophages as potential therapeutic targets. Conclusions This cell atlas provides the molecular foundation for investigating how tendon cell identities, biochemical functions, and interactions contributed to the tendinopathy process. The discoveries revealed the pathogenesis of tendinopathy at single-cell and spatial levels, which is characterized by inflammatory infiltration, followed by chondrogenesis, and finally endochondral ossification. Our results provide new insights into the control of tendinopathy and potential clues to developing novel diagnostic and therapeutic strategies.

With no current cure for mild cognitive impairment (MCI), delaying its progression could significantly reduce the disease burden and improve the quality of life for patients with MCI. Computerized cognitive training (CCT) has recently become a potential instrument for improvement of cognition. However, the evidence for its effectiveness remains limited. This systematic review aims to (1) analyze the efficacy of CCT on cognitive impairment or cognitive decline in patients with MCI and (2) analyze the relationship between the characteristics of CCT interventions and cognition-related health outcomes. A systematic search was performed using MEDLINE, Cochrane, Embase, Web of Science, and Google Scholar. Full texts of randomized controlled trials of CCT interventions in adults with MCI and published in English language journals between 2010 and 2021 were included. Overall global cognitive function and domain-specific cognition were pooled using a random-effects model. Sensitivity analyses were performed to determine the reasons for heterogeneity and to test the robustness of the results. Subgroup analyses were performed to identify the relationship between the characteristics of CCT interventions and cognition-related effectiveness. A total of 18 studies with 1059 participants were included in this review. According to the meta-analysis, CCT intervention provided a significant but small increase in global cognitive function compared to that in the global cognitive function of the control groups (standardized mean difference=0.54, 95% CI 0.35-0.73; I =38%). CCT intervention also resulted in a marginal improvement in domain-specific cognition compared to that in the control groups, with moderate heterogeneity. Subgroup analyses showed consistent improvement in global cognitive behavior in the CCT intervention groups. This systematic review suggests that CCT interventions could improve global cognitive function in patients with MCI. Considering the relatively small sample size and the short treatment duration in all the included studies, more comprehensive trials are needed to quantify both the impact of CCT on cognitive decline, especially in the longer term, and to establish whether CCT should be recommended for use in clinical practice. PROSPERO International Prospective Register of Systematic Reviews CRD42021278884; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=278884.

In this work, via engineering the conformation of cobalt active center in cobalt phthalocyanine molecular catalyst, the catalytic efficiency of electrochemical carbon monoxide reduction to methanol can be dramatically tuned. Based on a collection of experimental investigations and density functional theory calculations, it reveals that the electron rearrangement of the Co 3d orbitals of cobalt phthalocyanine from the low-spin state (S = 1/2) to the high-spin state (S = 3/2), induced by molecular conformation change, is responsible for the greatly enhanced CO reduction reaction performance. Operando attenuated total reflectance surface-enhanced infrared absorption spectroscopy measurements disclose accelerated hydrogenation of CORR intermediates, and kinetic isotope effect validates expedited proton-feeding rate over cobalt phthalocyanine with high-spin state. Further natural population analysis and density functional theory calculations demonstrate that the high spin Co 2+ can enhance the electron backdonation via the d xz / d yz −2π* bond and weaken the C-O bonding in *CO, promoting hydrogenation of CORR intermediates. Molecular catalysts provide an ideal model system to investigate the relationship between active site structure and catalytic performance. Here, the authors explore how electrochemical CO reduction to methanol can be controlled through modification of the active cobalt site in cobalt phthalocyanine.

Characterized by high recurrence rates and limited therapeutic options, hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide. Notwithstanding the fact that telomerase-related long non-coding RNAs (TRLs) have been implicated in tumorigenesis, it remains poorly understood about their prognostic and immunological roles in HCC. For the purpose of identifying telomerase-related genes (TRGs) and TRLs, we used transcriptomic data from The Cancer Genome Atlas (TCGA). We built a prognostic signature using LASSO-Cox regression. Then, we validated it with time-dependent ROC curves. We assessed the model's clinical utility with nomogram calibration and DCA. We also evaluated immune profiling, tumor mutation burden, drug sensitivity and TIDE scores to characterize the tumor microenvironment. Using a pilot cohort of clinical samples, initial experimental validation was completed with RT-qPCR. By using a 4-TRLs signature, HCC patients can be divided into Low-risk (L-R) and High-risk (H-R) groups. The signature acted as an independent prognostic factor. It provided a highly accurate prediction of patient survival at 1, 3, and 5 years (AUC: 0.744-0.770). H-R patients had more immune cells in their tumors. They also showed higher levels of checkpoint expression. Besides, their TIDE (tumor immune dysfunction and exclusion) scores were also higher. All these things mean they might not respond well to immunotherapy. Subtype-specific therapeutic vulnerabilities can be read from drug sensitivity analysis. By carrying out reverse transcription quantitative polymerase chain reaction (RT-qPCR), consistent dysregulation patterns of TRLs can be observed in HCC tissues. This providing basis supports for our bioinformatic findings. Mechanistically, lncRNA AC026356.1 linked to a telomerase-related ceRNA network. This network includes miR-126-5p and its downstream targets. The 4-TRLs signature is a tool that can be applied in HCC clinical practice. It enables prognostic stratification and helps guide treatment. These lncRNAs are linked to both immune activity and drug response. This dual role shows they affect tumor progression and the microenvironment. This finding provides new insights for precision oncology in HCC.

The battery industry has been growing fast because of strong demand from electric vehicle and power storage applications.Laser welding is a key process in battery manufacturing. To control the production quality, the industry has a great desire for defect inspection of automated laser welding. Recently, Convolutional Neural Networks (CNNs) have been applied with great success for detection, recognition, and classification. In this paper, using transfer learning theory and pre-training approach in Visual Geometry Group (VGG) model, we proposed the optimized VGG model to improve the efficiency of defect classification. Our model was applied on an industrial computer with images taken from a battery manufacturing production line and achieved a testing accuracy of 99.87%. The main contributions of this study are as follows: (1) Proved that the optimized VGG model, which was trained on a large image database, can be used for the defect classification of laser welding. (2) Demonstrated that the pre-trained VGG model has small model size, lower fault positive rate, shorter training time, and prediction time; so, it is more suitable for quality inspection in an industrial environment. Additionally, we visualized the convolutional layer and max-pooling layer to make it easy to view and optimize the model.

Hypoxia (low oxygen) is associated with many brain disorders as well as inflammation, but the lack of widely available technology has limited our ability to study hypoxia in human brain. Multiple sclerosis (MS) is a poorly understood neurological disease with a significant inflammatory component which may cause hypoxia. We hypothesized that if hypoxia were to occur, there should be reduced microvascular hemoglobin saturation (S t O 2 ). In this study, we aimed to determine if reduced S t O 2 can be detected in MS using frequency domain near-infrared spectroscopy (fdNIRS). We measured fdNIRS data in cortex and assessed disability of 3 clinical isolated syndrome (CIS), 72 MS patients and 12 controls. Control S t O 2 was 63.5 ± 3% (mean ± SD). In MS patients, 42% of S t O 2 values were more than 2 × SD lower than the control mean. There was a significant relationship between S t O 2 and clinical disability. A reduced microvascular S t O 2 is supportive (although not conclusive) that there may be hypoxic regions in MS brain. This is the first study showing how quantitative NIRS can be used to detect reduced S t O 2 in patients with MS, opening the door to understanding how microvascular oxygenation impacts neurological conditions.

Background Knee osteoarthritis is a common degenerative joint disease involving multiple pathological processes, including energy metabolism, cartilage repair, and osteogenesis. To investigate the alterations in critical metabolic pathways and differential proteins in osteoarthritis patients through metabolomic and proteomic analyses and to explore the potential mechanisms underlying synovial osteogenesis during osteoarthritis progression. Methods Metabolomics was used to analyze metabolites in the synovial fluid and synovium of osteoarthritis patients (osteoarthritis group: 10; control group: 10), whereas proteomics was used to examine differential protein expression. Alkaline phosphatase activity was assessed to evaluate osteogenesis. Results Upregulation of the tricarboxylic acid cycle: Significant upregulation of the tricarboxylic acid cycle in the synovial fluid and synovium of osteoarthritis patients indicated increased energy metabolism and cartilage repair activity. Arginine metabolism and collagen degradation: Elevated levels of ornithine, proline, and hydroxyproline in the synovial fluid reflect active collagen degradation and metabolism, contributing to joint cartilage breakdown. Abnormal Phenylalanine Metabolism: Increased phenylalanine and tyrosine metabolite levels in osteoarthritis patients suggest their involvement in cartilage destruction and osteoarthritis progression. Synovial osteogenesis: Increased expression of type I collagen in the synovium and elevated alkaline phosphatase activity confirmed the occurrence of osteogenesis, potentially driven by the differentiation of synovial fibroblasts, mesenchymal stem cells, and hypertrophic chondrocytes. Relationships between differential proteins and osteogenesis: FN1 and TGFBI are closely associated with synovial osteogenesis, while the upregulation of energy metabolism pathways provides the energy source for osteogenic transformation. Conclusions Alterations in energy metabolism, cartilage repair, and osteogenic mechanisms are critical. The related metabolites and proteins have potential as diagnostic and therapeutic targets for osteoarthritis.

Musculoskeletal tissue degeneration impairs the life quality and function of many people. Meniscus degeneration is a major origin of knee osteoarthritis and a common threat to athletic ability, but its cellular mechanism remains elusive. We built a cell atlas of 12 healthy or degenerated human meniscus samples from the inner and outer meniscal zones of 8 patients using scRNA-seq to investigate meniscal microenvironment homeostasis and its changes in the degeneration process and verified findings with immunofluorescent imaging. We identified and localized cell types in inner and outer meniscus and found new chondrocyte subtypes associated with degeneration. The observations suggested understandings on how cellular compositions, functions, and interactions participated in degeneration, and on the possible loop-like interactions among extracellular matrix disassembly, angiogenesis, and inflammation in driving the degeneration. The study provided a rich resource reflecting variations in the meniscal microenvironment during degeneration and suggested new cell subtypes as potential therapeutic targets. The hypothesized mechanism could also be a general model for other joint degenerations. The National Natural Science Foundation of China (81972123, 82172508, 62050178, 61721003), the National Key Research and Development Program of China (2021YFF1200901), Fundamental Research Funds for the Central Universities (2015SCU04A40); The Innovative Spark Project of Sichuan University (2018SCUH0034); Sichuan Science and Technology Program (2020YFH0075); Chengdu Science and Technology Bureau Project (2019-YF05-00090-SN); 1.3.5 Project for Disciplines of Excellence of West China Hospital Sichuan University (ZYJC21030, ZY2017301); 1.3.5 Project for Disciplines of Excellence - Clinical Research Incubation Project, West China Hospital, Sichuan University (2019HXFH039).

With the rapid development of advanced technologies, such as artificial intelligence and deep learning, educational informatization has entered a new era. However, the explosion of information has brought numerous challenges. Knowledge graphs, as a crucial component of artificial intelligence, can contribute to the quality of teaching. This study proposes an educational knowledge graph based on course information named CourseKG for precision teaching. Precision teaching seeks to individualize the curriculum for each learner and optimize learning efficiency. CourseKG aims to establish a correct and comprehensive curriculum knowledge system and promote personalized learning paths. CourseKG can address the issue that current general-purpose knowledge graphs are not suitable for the education field. Particularly, this study proposes a framework for educational entity recognition based on the pre-trained BERT model. This framework captures relevant information in the educational domain using the BERT model and combines it with the BiGRU and multi-head self-attention mechanism to extract multi-scale and multi-level global dependency relationships. In addition, the CRF is used for character-label decoding. Further, a relationship extraction method based on the BERT model, which integrates sentence features and educational entities and estimates the similarity between knowledge pairs using cosine similarity, is proposed. The proposed CourseKG is verified by experiments using real-world C programming course data. The experimental results demonstrate the effectiveness of CourseKG. Finally, the results show that the proposed CourseKG can significantly enhance the precision teaching quality and realize multi-directional adaptation among teachers, courses, and students.