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Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events, causing surveillance and energy loss. This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters. Initially, the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes. Subsequently, it delves into communication and data processing mechanisms, specifically focusing on central data processing (CDP), communication routers (CRs), and phasor measurement units (PMUs), to re-establish an equipment recovery model based on these data transmission methodologies. Furthermore, it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution. The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system. The findings suggest that the proposed branch- and-bound algorithm significantly augments the observational capabilities of a power system with limited resources, thereby bolstering its stability and emergency response mechanisms.

Objective: Tumor cell malignancy is indicated by histopathological differentiation and cell proliferation. Ki-67, an indicator of cellular proliferation, has been used for tumor grading and classification in breast cancer and neuroendocrine tumors. However, its prognostic significance in pancreatic ductal adenocarcinoma (PDAC) remains uncertain. Methods: Patients who underwent radical pancreatectomy for PDAC were retrospectively enrolled, and relevant prognostic factors were examined. Grade of malignancy (GOM), a novel index based on histopathological differentiation and Ki-67, is proposed, and its clinical significance was evaluated. Results: The optimal threshold for Ki-67 was determined to be 30%. Patients with a Ki-67 expression level > 30% rather than ≤ 30% had significantly shorter 5-year overall survival (OS) and recurrence-free survival (RFS). In multivariate analysis, both histopathological differentiation and Ki-67 were identified as independent prognostic factors for OS and RFS. The GOM was used to independently stratify OS and RFS into 3 tiers, regardless of TNM stage and other established prognostic factors. The tumor-node-metastasis-GOM stage was used to stratify survival into 5 distinct tiers, and surpassed the predictive performance of TNM stage for OS and RFS. Conclusions: Ki-67 is a valuable prognostic indicator for PDAC. Inclusion of the GOM in the TNM staging system may potentially enhance prognostic accuracy for PDAC.

Abstract Background Numerous studies have been conducted to investigate the relationship between ABO and Rhesus (Rh) blood groups and various health outcomes. However, a comprehensive evaluation of the robustness of these associations is still lacking. Methods We searched PubMed, Web of Science, Embase, Scopus, Cochrane, and several regional databases from their inception until Feb 16, 2024, with the aim of identifying systematic reviews with meta-analyses of observational studies exploring associations between ABO and Rh blood groups and diverse health outcomes. For each association, we calculated the summary effect sizes, corresponding 95% confidence intervals, 95% prediction interval, heterogeneity, small-study effect, and evaluation of excess significance bias. The evidence was evaluated on a grading scale that ranged from convincing (Class I) to weak (Class IV). We assessed the certainty of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation criteria (GRADE). We also evaluated the methodological quality of included studies using the A Measurement Tool to Assess Systematic Reviews (AMSTAR). AMSTAR contains 11 items, which were scored as high (8–11), moderate (4–7), and low (0–3) quality. We have gotten the registration for protocol on the PROSPERO database (CRD42023409547). Results The current umbrella review included 51 systematic reviews with meta-analysis articles with 270 associations. We re-calculated each association and found only one convincing evidence (Class I) for an association between blood group B and type 2 diabetes mellitus risk compared with the non-B blood group. It had a summary odds ratio of 1.28 (95% confidence interval: 1.17, 1.40), was supported by 6870 cases with small heterogeneity (I 2 = 13%) and 95% prediction intervals excluding the null value, and without hints of small-study effects (P for Egger’s test > 0.10, but the largest study effect was not more conservative than the summary effect size) or excess of significance (P

Abstract A novel double side friction stir Z shape lap-butt welding (DS-FSZW) process was proposed to achieve excellent mechanical properties of Al/Cu medium-thick dissimilar joints. The influence of welding parameters on weld microstructure and properties of DS-FSZW joint were systematically investigated. It indicated that defect-free medium-thick Al/Cu DS-FSZW joint could be achieved under an optimal welding parameter. DS-FSZW joint was prone to form void defects in the bottom of the second-pass weld. The recrystallization mechanisms at the top and middle of the weld nugget zone (WNZ) were continuous dynamic recrystallization (CDRX) and geometric dynamic recrystallization (GDRX). While the major recrystallization mechanism at the bottom of the WNZ was GDRX. DS-FSZW joint of the optimal welding condition with 850 r/min-400 mm/min was produced with a continuous thin and crack-free IMCs layer at the Al/Cu interface, and the maximum tensile strength of this joint is 160.57 MPa, which is equivalent to 65.54% of pure Cu base material. Moreover, the corrosion resistance of Al/Cu DS-FSZW joints also achieved its maximum value at the optimal welding parameter of 850 r/min-400 mm/min. It demonstrates that the DS-FSZW process can simultaneously produce medium-thick Al/Cu joints with excellent mechanical performance and corrosion resistance.

Abstract In generating adversarial examples, the conventional black-box attack methods rely on sufficient feedback from the to-be-attacked models by repeatedly querying until the attack is successful, which usually results in thousands of trials during an attack. This may be unacceptable in real applications since Machine Learning as a Service Platform (MLaaS) usually only returns the final result (i.e., hard-label) to the client and a system equipped with certain defense mechanisms could easily detect malicious queries. By contrast, a feasible way is a hard-label attack that simulates an attacked action being permitted to conduct a limited number of queries. To implement this idea, in this paper, we bypass the dependency on the to-be-attacked model and benefit from the characteristics of the distributions of adversarial examples to reformulate the attack problem in a distribution transform manner and propose a distribution transform-based attack (DTA). DTA builds a statistical mapping from the benign example to its adversarial counterparts by tackling the conditional likelihood under the hard-label black-box settings. In this way, it is no longer necessary to query the target model frequently. A well-trained DTA model can directly and efficiently generate a batch of adversarial examples for a certain input, which can be used to attack un-seen models based on the assumed transferability. Furthermore, we surprisingly find that the well-trained DTA model is not sensitive to the semantic spaces of the training dataset, meaning that the model yields acceptable attack performance on other datasets. Extensive experiments validate the effectiveness of the proposed idea and the superiority of DTA over the state-of-the-art.

Abstract Tumor cells must rewire nucleotide synthesis to satisfy the demands of unbridled proliferation. Meanwhile, they exhibit augmented reactive oxygen species (ROS) production which paradoxically damages DNA and free deoxy-ribonucleoside triphosphates (dNTPs). How these metabolic processes are integrated to fuel tumorigenesis remains to be investigated. MYC family oncoproteins coordinate nucleotide synthesis and ROS generation to drive the development of numerous cancers. We herein perform a Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based functional screen targeting metabolic genes and identified nudix hydrolase 1 (NUDT1) as a MYC-driven dependency. Mechanistically, MYC orchestrates the balance of two metabolic pathways that act in parallel, the NADPH oxidase 4 (NOX4)-ROS pathway and the Polo like kinase 1 (PLK1)-NUDT1 nucleotide-sanitizing pathway. We describe LC-1-40 as a potent, on-target degrader that depletes NUDT1 in vivo. Administration of LC-1-40 elicits excessive nucleotide oxidation, cytotoxicity and therapeutic responses in patient-derived xenografts. Thus, pharmacological targeting of NUDT1 represents an actionable MYC-driven metabolic liability.

Abstract Mechanical antennas have garnered considerable attention due to their ability to address the challenges posed by the significant size and high energy consumption associated with traditional electric antennas when operating at low frequencies. Here, a compact and structurally stable mechanical antenna design is presented. The proposed antenna is constructed using cylindrical piezoelectric ceramics, which have dimensions smaller than 1/1000 of the wavelength. The scrutiny of the influence exerted by the antenna feed area and material thickness on the radiation performance was undertaken, followed by an exhaustive discourse on these effects. Experimental measurements demonstrate the practical functionalities of signal coding, transmission, and reception in the low‐frequency communication system. Notably, at a frequency of 288 kHz, a single proposed antenna achieves an effective information transmission distance of 60 m using binary information coding. This remarkable outcome underscores the potential of this design in facilitating the development of portable and cost‐effective wireless communication equipment for low‐frequency applications.

Abstract Introduction Rheumatoid arthritis (RA) is an incurable autoimmune disease. The role of interleukin-38 (IL-38), an anti-inflammatory cytokine, in RA is not fully understood, and its clinical relevance in RA remains unclear. This study aims to investigate the correlation of IL-38 with disease activity and the clinical manifestation of RA. Methods In this cross-sectional study, patients with treatment-naïve RA (n = 63) and healthy controls (HC) (n = 60) were consecutively enrolled over a 15-month period. Patients with RA were categorized into three subgroups—low disease activity (LDA), moderate disease activity (MDA) and high disease activity (HDA)—using the Disease Activity Score in 28 joints based on C-reactive protein (DAS28-CRP). Circulating levels of IL-38, tumour necrosis factor (TNF), IL-6, IL-17, IL-1β, and 25(OH)D were assessed using enzyme-linked immunosorbent assay (ELISA). Clinical data, including duration, tender joints count (TJC), swollen joints count (SJC), patient global assessment (PGA), evaluator global assessment (EGA), bone mineral density (BMD), clinical disease activity index (CDAI), simplified disease activity index (SDAI), DAS28-CRP, joint musculoskeletal ultrasound (MSUS), and serological indicators were recorded. We determined the correlation between IL-38 and disease activity, as well as clinical manifestation in RA. Results At the macroscopic level, musculoskeletal ultrasonography of joints in different stages of disease activity in RA suggests that, as the disease progresses, arthritis in the hand becomes more severe, accompanied by synovial thickening and pronounced blood flow signals in the joint area. The expression of IL-38, TNF, IL-6, IL-17 and IL-1β significantly increased in patients with RA compared to HC. Noteworthy differences were observed in the blood flow signal score, synovial signal score, IL-38, TNF, IL-6, IL-17 and IL-1β among the three subgroups (LDA, MDA and HDA). As disease activity increased in patients with RA, the blood flow signal score, synovial signal score and expression of TNF, IL-6, IL-17 and IL-1β exhibited a gradual increase, while the expression of IL-38 showed the opposite pattern. Inverse correlations were identified between IL-38 and pro-inflammatory cytokines (IL-6, IL-17), as well as key clinical parameters, including disease duration, SJC, TJC and DAS28-CRP score. Conclusion IL-38, intricately linked to the pathogenesis of RA, emerges as a promising therapeutic target for the management of this debilitating disease.

Recycled aggregate concrete (RAC) has multiple interface transition zones (ITZs) due to its complex composition, providing convenient ion transfer channels. Therefore, it is significant to explore the changes of the internal pore structure in RAC under ion erosion conditions for quantifying and predicting ion transfer mechanisms. This paper innovatively utilizes the seepage effect for investigating the evolution of pore structures in RAC under sulfate attack. We conduct a systematic experimental study on the transport of sulfate ions in RAC with different fly ash (FA) substitution rates under dry-wet cycles. Combining backscattered electron imaging (BSE), permeation and fractal characteristics, this paper deeply explore the relationship between pore structure feature, sulfate percolation mechanism and the macroscopic properties of RAC incorporated with different amount of FA.

Vincristine (VCR), as a cytotoxic drug, is used clinically to treat acute lymphatic leukemia and breast cancer, and commonly used clinically as vincristine sulfate (VCRS). However, its clinical use is limited by unpredictable pharmacologic characteristics, a narrow therapeutic index, and neurotoxicity. The pH gradient method was used for active drug loading of VCRS, and the process route mainly includes the preparation of blank liposomes and drug-loaded liposomes. VCRS liposomes had suitable particle size, high encapsulation efficiency and good stability. The loading and release kinetics of VCRS liposomes were explored. By calculating the changes of encapsulation efficiency with time at different temperatures, it was confirmed that the drug-loading process of liposomes exhibited a first-order kinetic feature, and the activation energy required for the reaction was determined as 20.6 kcal/mol. The release behavior at different pH was also investigated, and it was demonstrated that the release behavior conformed to the first-order model, suggesting that the release mechanism of VCRS was simple transmembrane diffusion. VCRS liposomes also enhanced in vitro and in vivo antitumor activity. Thus, VCRS liposomes showed great potential for VCRS delivery, and the loading and release kinetics were well researched to provide a reference for investigating active drug loading liposomes.

Summary: Background: Despite numerous studies having evaluated the associations between human papillomavirus (HPV) infection and risk of specific cancers other than anogenital tract and oropharyngeal, the findings are inconsistent and the quality of evidence has not been systematically quantified. We aimed to summarise the existing evidence as well as to evaluate the strength and credibility of these associations. Methods: We conducted an umbrella review of systematic reviews and meta-analyses of observational studies. PubMed, EMBASE, and Web of Science were searched from inception to March 2024. Studies with systematic reviews and meta-analyses that examined associations between HPV or HPV-associated genotypes infection and specific cancers were eligible for this review. The quality of the methodology was evaluated using A Measurement Tool to Assess systematic Reviews (AMSTAR). The credibility of the evidence was assessed using GRADE. The protocol was preregistered with PROSPERO (CRD42023439070). Findings: The umbrella review identified 31 eligible studies reporting 87 associations with meta-analytic estimates, including 1191 individual studies with 336,195 participants. Of those, 29 (93.5%) studies were rated as over moderate quality by AMSTAR. Only one association indicating HPV-18 infection associated with an increased risk of breast cancer (odds ratio [OR] = 3.48, 95% confidence interval [CI] = 2.24–5.41) was graded as convincing evidence. There were five unique outcomes identified as highly suggestive evidence, including HPV infection increased the risk of oral squamous cell carcinoma (OR = 7.03, 95% CI = 3.87–12.76), oesophageal cancer (OR = 3.32, 95% CI = 2.54–4.34), oesophageal squamous cell carcinoma (OR = 2.69, 95% CI = 2.05–3.54), lung cancer (OR = 3.60, 95% CI = 2.59–5.01), and breast cancer (OR = 6.26, 95% CI = 4.35–9.00). According to GRADE, one association was classified as high, indicating that compared with the controls in normal tissues, HPV infection was associated with an increased risk of breast cancer. Interpretation: The umbrella review synthesised up-to-date observational evidence on HPV infection with the risk of breast cancer, oral squamous cell carcinoma, oesophageal cancer, oesophageal squamous cell carcinoma, and lung cancer. Further larger prospective cohort studies are needed to verify the associations, providing public health recommendations for prevention of disease. Funding: National Key Research and Development Program of China, Natural Science Foundation of China, Outstanding Scientific Fund of Shengjing Hospital of China Medical University, and 345 Talent Project of Shengjing Hospital of China Medical University.

Building change detection (CD) aims to detect changes in buildings from bi-temporal pairwise images obtained at different times. Typically, a deep learning-based building CD algorithm requires bi-temporal samples with significant building changes for training. However, obtaining such bi-temporal samples is challenging because building changes have a low probability of occurrence. Fortunately, it is relatively simple to obtain single-temporal samples that include a substantial number of buildings. By using these single-temporal building samples, pseudo bi-temporal building change samples can be generated, which can effectively address the problem of limited available bi-temporal building change samples. In view of that, this study proposes a metric guided single-temporal supervised learning framework that uses single-temporal building samples for building CD. In the proposed framework, patch-pairing single-temporal supervised learning (PPSL) adopts a patch-pairing method to construct pseudo bi-temporal building change samples, while equipping the network to effectively suppresses the negative impact of geometric offset and radiation difference in real samples. To further suppress the impact of radiation difference and enhance the effectiveness of our framework, a metric-guided spatial attention module (MGSAM) is designed to minimize the intra-class feature differences between temporal samples and augment the spatial context modeling ability. The proposed method is verified by experiments on different datasets, and the results demonstrate that the proposed method can outperform the existing methods and achieve superior performance.

ABSTRACTThe Annual Meeting of the American Association of Geographers (AAG) in 2023 marked a five-year milestone since the first Geospatial Artificial Intelligence (GeoAI) Symposium was held at AAG in 2018. In the past five years, progress has been made while open questions remain. In this context, we organized an AAG panel and invited five panellists to discuss the advances and limitations in GeoAI research. The panellists commended the successes, such as the development of spatially explicit models, the production of large-scale geographic datasets, and the use of GeoAI to address real-world problems. The panellists also shared their thoughts on limitations in current GeoAI research, which were considered as opportunities to engage theories in geography, enhance model explainability, quantify uncertainty, and improve model generalizability. This article summarizes the presentations from the panellists and also provides after-panel thoughts from the organizers. We hope that this article can make these thoughts more accessible to interested readers and help stimulate new ideas for future breakthroughs.

Abstract Platinum-based chemotherapy remains one of the major choices for treatment of ovarian cancer (OC). However, primary or acquired drug resistance severely impairs their efficiency, thereby causing chemotherapy failure and poor prognosis. SH3 domain containing ring finger 2 (SH3RF2) has been linked to the development of cancer. Here we find higher levels of SH3RF2 in the tumor tissues from cisplatin-resistant OC patients when compared to those from cisplatin-sensitive patients. Similarly, cisplatin-resistant OC cells also express higher levels of SH3RF2 than normal OC cells. Through in vitro and in vivo loss-of-function experiments, SH3RF2 is identified as a driver of cisplatin resistance, as evidenced by increases in cisplatin-induced cell apoptosis and DNA damage and decreases in cell proliferation induced by SH3RF2 depletion. Mechanistically, SH3RF2 can directly bind to the RNA-binding protein mRNA processing factor (RBPMS). RBPMS has been reported as an inhibitor of cisplatin resistance in OC. As a E3 ligase, SH3RF2 promotes the K48-linked ubiquitination of RBPMS to increase its proteasomal degradation and activator protein 1 (AP-1) transactivation. Impairments in RBPMS function reverse the inhibitory effect of SH3RF2 depletion on cisplatin resistance. Collectively, the SH3RF2-RBPMS-AP-1 axis is an important regulator in cisplatin resistance and inhibition of SH3RF2 may be a potential target in preventing cisplatin resistance.

To accurately characterize the dynamic deformation mechanical properties of hot forming steels 6Mn6 and 22MnB5 under collision conditions, quasi-static tensile tests (0.001s−1) and Hopkinson compression bar tests (∼2000s−1, ∼3000s−1, ∼4000s−1) were conducted to analyze the influence of strain rate on the material mechanical properties. Based on the experimental results, four constitutive models including Johnson-Cook (JC), modified JC (m-JC), Cowper-Symond (CS), and Khan-Huang (KH) models were established for both 6Mn6 and 22MnB5. Additionally, a modified model based on the Swift-Voce strain hardening model coupled with the Cowper-Symond model was proposed. A numerical simulation model for compression bar tests was developed using JC and m-JC models. The experimental results indicate that both hot forming steels exhibit strain rate sensitivity, with 6Mn6 performed a more pronounced effect than 22MnB5. The average absolute error (AARE) and correlation coefficient (R) of the m-JC and CS models were 0.99 and 0.29 %, 0.95 and 0.4 %, respectively. The dynamic deformation behavior of 6Mn6 and 22MnB5 at high strain rates was accurately predicted. Comparing with the JC model, the utilization of the m-JC user subroutine has resulted in a significant improvement in simulation accuracy by 58 % and 84 % for these two materials, which laying a solid foundation for high-precision vehicle collision simulation.

Abstract Reducing inter-spin distance can enhance magnetic interactions and allow for the realization of outstanding magnetic properties. However, achieving reduced distances is technically challenging. Here, we construct a 3d-4f metal cluster (Dy2VN) inside a C80 cage, affording a heretofore unseen metallofullerene containing both paramagnetic 3d and 4f metal ions. The significantly suppressed 3d-4f (Dy-V) distances, due to the unique cage confinement effect, were observed by crystallographic and theoretical analysis of Dy2VN@I h (7)-C80. These reduced distances result in an enhanced magnetic coupling (J total, Dy-V = 53.30 cm−1; J total, Dy-Dy = −6.25 cm−1), leading to a high magnetic blocking temperature compared to reported 3d-4f single-molecule magnets and strong coercive field of 2.73 Tesla. Our work presents a new class of single-molecule magnets with both paramagnetic 3d and 4f metals confined in a fullerene cage, offering superior and tunable magnetic properties due to the unique cage confinement effect and the diverse composition of the entrapped magnetic core.

Abstract Clear cell carcinoma (CCC), endometrioid carcinoma (EC), and serous carcinoma (SC) are the major histological subtypes of epithelial ovarian cancer (EOC), whose differences in carcinogenesis are still unclear. Here, we undertake comprehensive proteomic profiling of 80 CCC, 79 EC, 80 SC, and 30 control samples. Our analysis reveals the prognostic or diagnostic value of dysregulated proteins and phosphorylation sites in important pathways. Moreover, protein co-expression network not only provides comprehensive view of biological features of each histological subtype, but also indicates potential prognostic biomarkers and progression landmarks. Notably, EOC have strong inter-tumor heterogeneity, with significantly different clinical characteristics, proteomic patterns and signaling pathway disorders in CCC, EC, and SC. Finally, we infer MPP7 protein as potential therapeutic target for SC, whose biological functions are confirmed in SC cells. Our proteomic cohort provides valuable resources for understanding molecular mechanisms and developing treatment strategies of distinct histological subtypes.

Recycled aggregate concrete (RAC) is one of the most important ways to achieve sustainable development for concrete industry. However, the research on durability, especially on sulfate transport and erosion mechanism, of RAC needs deep investigation. Pore structure evolution is a vital parameter for sulfate transport, which is difficult to be traced. To investigate the dynamic changes of pore structure in metakaolin blended RAC under sulfate erosion, percolation theory is addressed in this study. Firstly, dry and wet cycle was applied to accelerate the sulfate transport in metakaolin (MK) blended RAC systems. Then the compressive strength of the samples after sulfate attack was measured to evaluate the sulfate erosion degree. Furthermore, backscattered electron imaging (BSE) was used to acquire the percolation parameters, such as wet site ratio, percolation probability and so forth. Finally, sulfate content and compressive strength of the sample were related to the percolation probability. Results present that MK exhibits excellent resistance to sulfate erosion and can be used to enhance the performance of RAC. There is a certain correlation between the degree of sulfate erosion, compressive strength, and percolation parameters. The influence of the number of dry–wet cycles and MK content on sulfate ion transport can be demonstrated using percolation theory, which can be employed for predicting sulfate transport and revealing erosion mechanisms. This enables a clear understanding of the ion transport mechanisms within concrete and holds significant importance on the investigation of durability issues of RAC.

Fuel cell vehicles have been paid more and more attention, while the low temperature cold start capability has become the main reason that hinders their large-scale commercialization, especially for the fuel cell bus. Generally, liquid heating (positive temperature coefficient thermistor (PTC) as the heat source) and fuel cell self-starting heating are the common cold start methods. However, these methods can bring about low start speed and high energy consumption. Therefore, a new cold start method combined with liquid heating and fuel cell self-starting is proposed to solve the problems. Besides, phase change material (PCM) is introduced to reduce energy consumption further. The PCM can absorb the waste heat of the fuel cell stack. Then the heat can be used for stack cold start by circulating liquid. In this study, a fuel cell bus cold start system simulating model is built. Based on this model, the fuel cell temperature and energy consumption during the cold start are analyzed. The results show that the method combined with liquid heating and fuel cell self-starting is superior to the liquid heating method. The cold start time and PTC consumption of the PTC-PCM-self-starting heating mode are 19.0 % and 19.2 % less than that of PTC-PCM heating mode. As for the combined heating method, dropping the self-starting temperature can not only increase the cold start speed but also reduce the proportion of electric heating. For every 1 °C decrease, the start-up time is shortened by 1 % on average, and the waste heat utilization rate is increased by 0.5 %. Although increasing the self-starting current can increase energy consumption, it will save more cold start time and reduce the proportion of electric heating. For every 1 A increase, the start-up time is shortened by 0.3 % on average, and the waste heat utilization rate is increased by 0.1 %. The PTC-PCM-self-starting heating method provides a way for fuel cell stack to start quickly and energy-efficiently in low temperature environment.

Change Detection (CD) is a vital monitoring method in Earth observation, especially pertinent for land-use analysis, city management, and disaster damage assessment. However, in the era of constellation interconnection and air-sky collaboration, the changes in the Regions Of Interest (ROI) cause many false detections due to geometric perspective rotation and temporal style difference. In response to these challenges, we introduce CDNeXt, this framework elucidates a robust and efficient method for combining Siamese networks based on the pre-trained backbone with the innovative Temporospatial Interactive Attention Module (TIAM) for remote sensing imagery. The CDNeXt can be categorized into four primary components: Encoder, Interactor, Decoder, and Detector. Notably, the Interactor, powered by TIAM, queries and rebuilds spatial perspective dependencies and temporal style correlations from binary temporal features extracted by the Encoder to enlarge the difference of ROI change. Culminating the process, the Detector integrates the hierarchical features generated by the Decoder, subsequently producing a binary change mask. We have achieved new State-Of-The-Art (SOTA) performance in change detection, with our method surpassing existing techniques on four benchmark datasets: an F1 score of 82.63% on SYSU-CD, 87.14% on LEVIR-CD+, 66.71% on S2Looking, and 71.11% BANDON. To further validate the effectiveness of the TIAM, we compared it to other attention modules in both interactive and non-interactive modes. Our code is available on GitHub: https://github.com/wjj282439449/CDNeXt.

Summary: Background: Benefits of Intermittent fasting (IF) on health-related outcomes have been found in a range of randomised controlled trials (RCTs). Our umbrella review aimed to systematically analyze and synthesize the available causal evidence on IF and its impact on specific health-related outcomes while evaluating its evidence quality. Methods: We comprehensively searched the PubMed, Embase, Web of Science, and Cochrane databases (from inception up to 8 January 2024) to identify related systematic reviews and meta-analyses of RCTs investigating the association between IF and human health outcomes. We recalculated the effect sizes for each meta-analysis as mean difference (MD) or standardized mean difference (SMD) with corresponding 95% confidence intervals (CIs). Subgroup analyses were performed for populations based on three specific status: diabetes, overweight or obesity, and metabolic syndrome. The quality of systematic reviews was evaluated using A Measurement Tool to Assess Systematic Reviews (AMSTAR), and the certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) system. This study is registered with PROSPERO (CRD42023382004). Findings: A total of 351 associations from 23 meta-analyses with 34 health outcomes were included in the study. A wide range of outcomes were investigated, including anthropometric measures (n = 155), lipid profiles (n = 83), glycemic profiles (n = 57), circulatory system index (n = 41), appetite (n = 9), and others (n = 6). Twenty-one (91%) meta-analyses with 346 associations were rated as high confidence according to the AMSTAR criteria. The summary effects estimates were significant at p

Spatial scene similarity plays a crucial role in spatial cognition, as it enables us to understand and compare different spatial scenes and their relationships. However, understanding spatial scenes is a complex task. While existing literature has contributed to spatial scene representation learning, these methods primarily focus on comprehending the spatial relationships among objects, often neglecting their semantic features. Furthermore, there is a lack of scene representation learning methods that can seamlessly handle different types of spatial objects (e.g., points, polylines, and polygons) in a scene. Moreover, since expert knowledge is required for the annotation process of spatial scene understanding, publicly available high-quality annotation data has a limited size which usually leads to suboptimal results. To address these issues, we propose a novel multi-scale spatial scene encoding model called SpatialScene2Vec. SpatialScene2Vec utilizes a point location encoder to seamlessly encode the spatial information of different types of spatial objects. A point feature encoder is employed to encode the semantic features of these objects. A spatial scene embedding is generated by integrating the spatial embeddings and feature embeddings of spatial objects within this scene. Furthermore, to address the limited labeled data problem, we propose a self-supervised learning framework to train the SpatialScene2Vec model in which a contrastive loss is used for spatial scene similarity evaluation. In addition, we introduce a novel spatial scene data augmentation method to generate positive scene augmentations by leveraging the unique characteristics of spatial scenes and random sampling points based on the shapes of polyline/polygon objects within the current spatial scenes. We conduct experiments on real-world datasets for spatial scene retrieval tasks, including vector data types of points, polylines, and polygons. Results show that SpatialScene2Vec outperforms well-established encoding methods such as Space2Vec due to the advantages of the integrated multi-scale representations and the proposed spatial scene data augmentation method, with significant improvements and robustness.

ObjectiveTo evaluate the value of a nomogram combined MRI Diffusion Weighted Imaging (DWI) and clinical features to predict the treatment response of Neoadjuvant Chemotherapy (NAC) in patients with osteosarcoma.MethodsA retrospective analysis was conducted on 209 osteosarcoma patients admitted into two bone cancer treatment centers (133 males, 76females; mean age 16.31 ± 11.42 years) from January 2016 to January 2022. Patients were classified as pathological good responders (pGRs) if postoperative histopathological examination revealed ≥90% tumor necrosis, and non-pGRs if 0.05), but pGRs had a higher ADC 0 (P

Abstract In‐depth profiling of embryogenesis‐associated endogenous and exogenous metabolic changes can reveal potential bio‐effects resulting from human‐made chemicals and underlying mechanisms. Due to the lack of potent tools for monitoring spatiotemporal distribution and bio‐transformation behavior of dynamic metabolites at single‐cell resolution, however, how and to what extent environmental chemicals may influence or interfere embryogenesis largely remain unclear. Herein, a zero‐sample‐loss micro‐biopsy‐based mass spectrometric platform is presented for quantitative, chemo‐selective, high‐coverage, and minimal‐destructive profiling of development‐associated cis‐diol metabolites, which are critical for signal transduction and epigenome regulation, at both cellular level and tissue level of Xenopus laevis. Using this platform, three extraordinary findings that are otherwise hard to achieve are revealed: 1) there are characteristically different cis‐diol metabolic signatures among oocytes, anterior and posterior part of tailbud‐stage embryos; 2) halogenated cis‐diols heavily accumulate at the posterior part of tailbud‐stage embryos of Xenopus laevis; 3) dimethachlon, a kind of exogenous fungicide that is widely used as pesticide, may be bio‐transformed and accumulated in vertebrate animals in environment. Thus, this study opens a new avenue to simultaneously monitoring intercellular and intraembryonic heterogeneity of endogenous and exogenous metabolites, providing new insights into metabolic remolding during embryogenesis and putting a warning on potential environmental risk.

In this paper, the robust !nite-time control for impact angle guidance of missile dynamic system with uncertainties is investigated by combining linear extended state observer (LESO) and adaptive non-singular fast terminal sliding mode method. Specially for dealing with existing uncertainties including time-varying parametric perturbation and nonparametric disturbances in high order line-of-sight rates and target acceleration, a robust LESO strategy is proposed for designing sliding mode-based impact angle guidance, which can guarantee that estimation error converges to the neighborhood of the origin in finite-time. Based on the proposed LESO framework, an adaptive non-singular fast terminal sliding mode guidance law is considered for realizing interception of maneuvering targets, which can guarantee asymptotically stability of the system. Simulation results are shown for confirming effectiveness of the proposed guidance strategy of this paper. Compared with former methods, accuracy of estimation is increased by nearly two times, and miss distance is reduced by nearly two times.

ABSTRACT: Objectives: Carbapenem-resistant hypermucoviscous Klebsiella pneumoniae (CR-HMKP) poses unprecedented public health challenges. However, genomic information regarding the CR-HMKP K2-ST375 strain is scarce. The aim of this study was to characterize the whole genome sequence of the CR-HMKP K2-ST375 strain Kp0179 isolated from a male patient in China. Methods: The whole genome of Kp0179 was sequenced using the DNBSEQ and Pacific Biosciences RSII platforms. The capsular serotype, multilocus sequence typing (MLST), antimicrobial resistance genes, and virulence factors were determined using available databases and bioinformatics tools. Conjugation experiments were performed using rifampicin-resistant Escherichia coli C600 as the recipient. Results: The Kp0179 strain with hypermucoviscous phenotype was resistant to almost all β-lactams, including ertapenem and imipenem. Whole genome sequencing revealed that Kp0179 belonged to K2-ST375 and contained blaNDM-IncX3 and a virulence plasmid ca. 121 kb. Kp0179 contained 5146 coding genes, 88 tRNAs, 25 rRNAs and 38 non-coding RNA genes. Among the six acquired antibiotic resistance genes, blaSHV-99, fosA, oqxAB were located on the chromosome, whereas blaNDM-1, qnrS1 and blaSHV-12 were located on the conjugative plasmid pNDM-Kp0179 (IncX3 type). Virulence gene analysis indicated that pLVPK-Kp0179 carried multiple virulence-encoding genes, such as iroBCDN, iucABCDiutA, rmpA and rmpA2. In addition to carrying a virulence plasmid, capsule formation (kvgA) and the type 3 fimbriae operon (mrkABCDFHIJ) were located on the chromosome of Kp0179. Conclusion: To our knowledge, this is the first report of a CR-HMKP K2-ST375 strain with a blaNDM-harboured conjugative IncX3 plasmid and a pLVPK-like virulence plasmid from a patient in China. Therefore, the spread of CR-HMKP K2-ST375 isolates in China should be closely monitored.

Abstract Solid-state electrolytes with high ionic conductivities are crucial for the development of all-solid-state lithium batteries, and there is a strong correlation between the ionic conductivities and underlying lattice structures of solid-state electrolytes. Here, we report a lattice manipulation method of replacing [Li2OH]+ clusters with potassium ions in antiperovskite solid-state electrolyte (Li2OH)0.99K0.01Cl, which leads to a remarkable increase in ionic conductivity (4.5 × 10‒3 mS cm‒1, 25 °C). Mechanistic analysis indicates that the lattice manipulation method leads to the stabilization of the cubic phase and lattice contraction for the antiperovskite, and causes significant changes in Li-ion transport trajectories and migration barriers. Also, the Li||LiFePO4 all-solid-state battery (excess Li and loading of 1.78 mg cm‒2 for LiFePO4) employing (Li2OH)0.99K0.01Cl electrolyte delivers a specific capacity of 116.4 mAh g‒1 at the 150th cycle with a capacity retention of 96.1% at 80 mA g‒1 and 120 °C, which indicates potential application prospects of antiperovskite electrolyte in all-solid-state lithium batteries.

Abstract Understanding metal-metal bonding involving f-block elements has been a challenging goal in chemistry. Here we report a series of mixed-valence di-metallofullerenes, ThDy@C2n (2n = 72, 76, 78, and 80) and ThY@C2n (2n = 72 and 78), which feature single electron actinide-lanthanide metal-metal bonds, characterized by structural, spectroscopic and computational methods. Crystallographic characterization unambiguously confirmed that Th and Y or Dy are encapsulated inside variably sized fullerene carbon cages. The ESR study of ThY@D 3h (5)-C78 shows a doublet as expected for an unpaired electron interacting with Y, and a SQUID magnetometric study of ThDy@D 3h (5)-C78 reveals a high-spin ground state for the whole molecule. Theoretical studies further confirm the presence of a single-electron bonding interaction between Y or Dy and Th, due to a significant overlap between hybrid spd orbitals of the two metals.

Abstract Background The fruits of Gardenia are rich in flavonoids and geniposides, which have various pharmacological effects such as antioxidant, anti-inflammatory and anticancer. In this study, we analyzed the transcriptome and metabolome of gardenia peel and kernel at different growth stages, revealed the regulatory network related to flavonoid synthesis, and identified the key regulatory genes. Results The results showed that in terms of flavonoid metabolic pathways, gardenia fruits mainly synthesized cinnamic acid through the phenylpropanoid pathway, and then synthesized flavonoids through the action of catalytic enzymes such as 4-coumaroyl-CoA ligase, chalcone synthase, chalcone isomerase and flavanol synthase, respectively. In addition, we found that the metabolomics data showed a certain spatial and temporal pattern in the expression of genes related to the flavonoid metabolism pathway and the relative content of metabolites, which was related to the development and ripening process of the fruit. Conclusions In summary, this study successfully screened out the key genes related to the biosynthesis metabolism of flavonoids in gardenia through the joint analysis of transcriptome and metabolome. This is of certain significance to the in-depth study of the formation mechanism of gardenia efficacy components and the improvement of quality.

ABSTRACTThe unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban hierarchical spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management.

After wind power is connected to the grid, because the power of wind power will fluctuate greatly, the frequency deviation of the grid will increase with access to wind power generation, then the challenge of frequency control in power grid is also increasing. The Load frequency control(LFC) is one of the important components of frequency control, the fluctuation of wind power output of the system will put forward a higher demand for load frequency control (LFC) of the power grid so as to increase the proportion of wind power in the system. In this paper, a PI controller whose parameters are set by an intelligent cloud calculation result is designed for LFC of the interconnected power system with large-scale wind power. The parameters in PI controller are obtained through the cloud model whose inputs are the control error and the corresponding rate of change so as to realize the adaptive tuning. The subordinate cloud parameters are designed considering randomness of wind power, which effectively solves the problems of poor parameter robustness in traditional PI control. The simulation of the two-area interconnection system with wind power is carried out to verify the effectiveness of the proposed method in the case of random wind power.

Objective: Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant gastrointestinal cancer with a 5-year survival rate of only 9%. Of PDAC patients, 15%-20% are eligible for radical surgery. Gemcitabine is an important chemotherapeutic agent for patients with PDAC; however, the efficacy of gemcitabine is limited due to resistance. Therefore, reducing gemcitabine resistance is essential for improving survival of patients with PDAC. Identifying the key target that determines gemcitabine resistance in PDAC and reversing gemcitabine resistance using target inhibitors in combination with gemcitabine are crucial steps in the quest to improve survival prognosis in patients with PDAC. Methods: We constructed a human genome-wide CRISPRa/dCas 9 overexpression library in PDAC cell lines to screen key targets of drug resistance based on sgRNA abundance and enrichment. Then, co-IP, ChIP, ChIP-seq, transcriptome sequencing, and qPCR were used to determine the specific mechanism by which phospholipase D1 (PLD1) confers resistance to gemcitabine. Results: PLD1 combines with nucleophosmin 1 (NPM1) and triggers NPM1 nuclear translocation, where NPM1 acts as a transcription factor to upregulate interleukin 7 receptor (IL7R) expression. Upon interleukin 7 (IL-7) binding, IL7R activates the JAK1/STAT5 signaling pathway to increase the expression of the anti-apoptotic protein, BCL-2, and induce gemcitabine resistance. The PLD1 inhibitor, Vu0155069, targets PLD1 to induce apoptosis in gemcitabine-resistant PDAC cells. Conclusions: PLD1 is an enzyme that has a critical role in PDAC-associated gemcitabine resistance through a non-enzymatic interaction with NPM1, further promoting the downstream JAK1/STAT5/Bcl-2 pathway. Inhibiting any of the participants of this pathway can increase gemcitabine sensitivity.

Abstract Understanding and exploiting the redox properties of uranium is of great importance because uranium has a wide range of possible oxidation states and holds great potential for small molecule activation and catalysis. However, it remains challenging to stabilise both low and high-valent uranium ions in a preserved ligand environment. Herein we report the synthesis and characterisation of a series of uranium(II–VI) complexes supported by a tripodal tris(amido)arene ligand. In addition, one- or two-electron redox transformations could be achieved with these compounds. Moreover, combined experimental and theoretical studies unveiled that the ambiphilic uranium–arene interactions are the key to balance the stabilisation of low and high-valent uranium, with the anchoring arene acting as a δ acceptor or a π donor. Our results reinforce the design strategy to incorporate metal–arene interactions in stabilising multiple oxidation states, and open up new avenues to explore the redox chemistry of uranium.

Background: Many phenolic C-glycosides possess nutritional benefits and pharmacological efficacies. However, the MS/MS fragmentation pattern of phenolic C-glycosides analysis is understudied. This paper aims to determine the MS/MS fragmentation patterns of phenolic C-glycosides. Method: Ten compounds with different sugar moieties, aglycones, and substitutes were analyzed to determine the impact of these structural features on MS/MS fragmentation using UPLC-QTOF-MS analysis. Results: The results showed that water loss followed by RDA reaction and alpha cleavage in the C-C bonded sugar moieties are the major fragmentation pathways. Additionally, the sugar cleavage was not affected by the skeleton and the substitute of the aglycones. These results suggested that the fragmentation patterns of phenolic C-glycosides differ from those in the O-glycosides, where the O-C glycosidic bond is the most cleavage-liable bond in MS/MS analysis. Conclusions: These MS/MS fragmentation patterns can be used for the identification of C-glycosides from dietary components and herbal medicine as well as developing robust methods using MRM methods to quantify C-glycosides.

High-molecular-weight glutenin subunits (HMW-GS) are an important component of total cereal proteins in wheat. It is closely related to the processing quality of flour. Here, we analyzed allelic variations at the Glu-1 locus in 163 wheat accessions from Hubei Province, China with SDS-PAGE. Among the 15 alleles detected, alleles 1, 7+8, and 2+12 were the major alleles, and 7, 6+8, and 2+10 were rare alleles. The breeding lines had higher genetic diversity than the commercial varieties. Alleles 7 and 6+8 significantly reduced the grain protein content and wet gluten content of wheat. The “1, 7+9, 5+10” and “1, 14+15, and 2+12” allelic combinations significantly increased the grain protein content, hardness index, test weight, and wet gluten content of wheat. Alleles 7+9, 14+15, and 5+10 were identified as alleles related to high wheat quality. The “1, 7, 5+10”, “1, 6+8, 5+10”, “null, 7+9, 2+12”, “1, 14+15, 2+12”, and “1, 7+9, 5+10” allelic combinations had greater effects on wheat grain quality traits. These results demonstrate the effects of HMW-GS on wheat grain quality traits and provide a reference for the genetic improvement of wheat quality.

Objective: The aim was to evaluate and optimize the performance of sensor monitors in measuring PM2.5 and PM10 under typical emission scenarios both indoors and outdoors. Method: Parallel measurements and comparisons of PM2.5 and PM10 were carried out between sensor monitors and standard instruments in typical indoor (2 months) and outdoor environments (1 year) in Shanghai, respectively. The optimized validation model was determined by comparing six machining learning models, adjusting for meteorological and related factors. The intra- and inter-device variation, measurement accuracy, and stability of sensor monitors were calculated and compared before and after validation. Results: Indoor particles were measured in a range of 0.8–370.7 μg/m3 and 1.9–465.2 μg/m3 for PM2.5 and PM10, respectively, while the outdoor ones were in the ranges of 1.0–211.0 μg/m3 and 0.0–493.0 μg/m3, correspondingly. Compared to machine learning models including multivariate linear model (ML), K-nearest neighbor model (KNN), support vector machine model (SVM), decision tree model (DT), and neural network model (MLP), the random forest (RF) model showed the best validation after adjusting for temperature, relative humidity (RH), PM2.5/PM10 ratios, and measurement time lengths (months) for both PM2.5 and PM10, in indoor (R2: 0.97 and 0.91, root-mean-square error (RMSE) of 1.91 μg/m3 and 4.56 μg/m3, respectively) and outdoor environments (R2: 0.90 and 0.80, RMSE of 5.61 μg/m3 and 17.54 μg/m3, respectively), respectively. Conclusions: Sensor monitors could provide reliable measurements of PM2.5 and PM10 with high accuracy and acceptable inter and intra-device consistency under typical indoor and outdoor scenarios after validation by RF model. Adjusting for both climate factors and the ratio of PM2.5/PM10 could improve the validation performance.

In recent years, many deep learning-based object detection methods have performed well in various applications, especially in large-scale object detection. However, when detecting small targets, previous object detection algorithms cannot achieve good results due to the characteristics of the small targets themselves. To address the aforementioned issues, we propose the small object algorithm model MCF-YOLOv5, which has undergone three improvements based on YOLOv5. Firstly, a data augmentation strategy combining Mixup and Mosaic is used to increase the number of small targets in the image and reduce the interference of noise and changes in detection. Secondly, in order to accurately locate the position of small targets and reduce the impact of unimportant information on small targets in the image, the attention mechanism coordinate attention is introduced in YOLOv5’s neck network. Finally, we improve the Feature Pyramid Network (FPN) structure and add a small object detection layer to enhance the feature extraction ability of small objects and improve the detection accuracy of small objects. The experimental results show that, with a small increase in computational complexity, the proposed MCF-YOLOv5 achieves better performance than the baseline on both the VisDrone2021 dataset and the Tsinghua Tencent100K dataset. Compared with YOLOv5, MCF-YOLOv5 has improved detection APsmall by 3.3% and 3.6%, respectively.

Online anomaly detection is critical for industrial safety and security monitoring but is facing challenges due to the complexity of evolving data streams from working conditions and performance degradation. Unfortunately, existing approaches fall short of such challenges, and these models may be disabled, suffering from the evolving data distribution. The paper presents a framework for online anomaly detection of data streams, of which the baseline algorithm is the incremental learning method of Growing Neural Gas (GNG). It handles complex and evolving data streams via the proposed model Bayes-Optimized Adaptive Growing Neural Gas (BOA-GNG). Firstly, novel learning rate adjustment and neuron addition strategies are designed to enhance the model convergence and data presentation capability. Then, the Bayesian algorithm is adopted to realize the fine-grained search of BOA-GNG-based hyperparameters. Finally, comprehensive studies with six data sets verify the superiority of BOA-GNG in terms of detection accuracy and computational efficiency.

Building, as an integral aspect of human life, is vital in the domains of urban management and urban analysis. To facilitate large-scale urban planning applications, the acquisition of complete and reliable building data becomes imperative. There are a few publicly available products that provide a lot of building data, such as Microsoft and Open Street Map. However, in East Asia, due to the more complex distribution of buildings and the scarcity of auxiliary data, there is a lack of building data in these regions, hindering the large-scale application in East Asia. Some studies attempt to simulate large-scale building distribution information using incomplete local buildings footprints data through regression. However, the reliance on inaccurate buildings data introduces cumulative errors, rendering this simulation data highly unreliable, leading to limitations in achieving precise research in East Asian region. Therefore, we proposed a comprehensive large-scale buildings mapping framework in view of the complexity of buildings in East Asia, and conducted buildings footprints extraction in 2,897 cities across 5 countries in East Asia and yielded a substantial dataset of 281,093,433 buildings. The evaluation shows the validity of our building product, with an average overall accuracy of 89.63% and an F1 score of 82.55%. In addition, a comparison with existing products further shows the high quality and completeness of our building data. Finally, we conduct spatial analysis of our building data, revealing its value in supporting urban-related research. The data for this article can be downloaded from https://doi.org/10.5281/zenodo.8174931.

Rotate vector (RV) reducers are typical deceleration elements that moderate and increase torsion. They are widely applied in industrial robots and automatic machinery with the superiorities of compact structure, high precision, and overload resistance performance. However, the RV reducers also have disadvantages, such as low bearing capacity and short service life. As the core drive mechanism in an RV reducer, the bearing capacity for a cycloid-pin gear drive system directly affects the performance of the entire deceleration system. Therefore, the bearing capacity of an RV reducer should be improved by increasing the capacity of a cycloid-pin gear. In this paper, the design of a cycloid-pin gear is optimized to improve its bearing capacity. The tooth profile equations for cycloid gear and the meshing gap are derived based on the gear meshing principle. The bearing capacity for cycloid-pin gear is modeled by combining the contact strength theory with the multi-tooth contact bearing analysis. The effects of eccentricity, the radius of pin tooth distribution circle, pin teeth number, pin tooth radius, cycloid thickness, modification value of moved distance and equidistance on total volume, contact stress, and torsional stiffness are systematically researched. Then, a single-, double-, and three-objective optimization model is proposed based on the load-bearing capacity for cycloid-pin gear by taking these three factors as the objective function. Moreover, the parameters are optimized with the genetic algorithm, and the analyses for three optimizations are compared and discussed. The theoretical models are confirmed by the simulation analysis through ANSYS software. The results show that the bearing capacity for the cycloid-pin gear system can be largely enhanced after optimization.

The widespread dissemination of high-fidelity fake faces created by face forgery techniques has caused serious trust concerns and ethical issues in modern society. Consequently, face forgery detection has emerged as a prominent topic of research to prevent technology abuse. Although, most existing face forgery detectors demonstrate success when evaluating high-quality faces under intra-dataset scenarios, they often overfit manipulation-specific artifacts and lack robustness to postprocessing operations. In this work, we design an innovative dual-branch collaboration framework that leverages the strengths of the transformer and CNN to thoroughly dig into the multimodal forgery artifacts from both a global and local perspective. Specifically, a novel adaptive noise trace enhancement module (ANTEM) is proposed to remove high-level face content while amplifying more generalized forgery artifacts in the noise domain. Then, the transformer-based branch can track long-range noise features. Meanwhile, considering that subtle forgery artifacts could be described in the frequency domain even in a compression scenario, a multilevel frequency-aware module (MFAM) is developed and further applied to the CNN-based branch to extract complementary frequency-aware clues. Besides, we incorporate a collaboration strategy involving cross-entropy loss and single center loss to enhance the learning of more generalized representations by optimizing the fusion features of the dual branch. Extensive experiments on various benchmark datasets substantiate the superior generalization and robustness of our framework when compared to the competing approaches.

ABSTRACT The monkeypox virus, which was declared as a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO), continues to cause infection cases worldwide. Given the risk of virus evolution, it is essential to identify monkeypox virus infection in a timely manner and isolate patients to prevent outbreaks so that the vulnerable population is protected and the emergence of dangerous monkeypox variants is restrained. In this study, a novel one-pot recombinase polymerase amplification-Clustered Regularly Interspaced Short Palindromic Repeats (RPA-CRISPR) assay for monkeypox based on a sucrose-aided multiphase aqueous system has been established with an ultra-high sensitivity of a single copy, which is 10 times higher than the existing RPA-CRISPR one-pot method. The entire reaction was completed within 60 min at 37°C. The detection method exhibited good specificity, effectively distinguishing monkeypox from other orthopoxviruses. The detection results could be observed by the naked eye under ultraviolet or blue light, making it highly suitable for home or limited healthcare settings. The assay has solved the incompatibility between the Cas12a cleavage reaction and the RPA reaction and shows good specificity, accuracy, and the rapidness and convenience important for point-of-care testing. It provides an effective tool for the early diagnosis of monkeypox and sets a technical example of using a multiphase aqueous system to establish one-pot RPA-CRISPR detection. IMPORTANCE The monkeypox virus was declared as a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO) and continues to cause infection cases worldwide. Given the risk of virus evolution, it is essential to identify monkeypox virus infection in a timely manner to prevent outbreaks. This study establishes a novel one-pot recombinase polymerase amplification-Clustered Regularly Interspaced Short Palindromic Repeats (RPA-CRISPR) assay for monkeypox virus with an ultra-high sensitivity. The assay shows good specificity, accuracy, and the rapidness and convenience important for point-of-care testing. It provides an effective tool for the early diagnosis of monkeypox, which is useful for the prevention of an epidemic.

AbstractParkinson’s disease (PD) is one of the most common neurodegenerative diseases, second only to Alzheimer’s disease. Drugs and deep brain stimulation (DBS) are the main treatments for PD. However, the long-term side effects of drugs and the risks of surgery cannot be ignored. It is therefore important to research safe and effective complementary and alternative therapies for PD. Yoga, an ancient mind-body exercise, has been widely used in health promotion. Although, yoga can address a range of health problems, little is known about its role in people with PD. This article reviews the evidence that yoga improves PD symptoms, including movement disorders, balance function and emotional disturbance. The authors analyze the role and shortcomings of the yoga intervention process, with the aim of providing a scientific basis for the application of yoga training to people with PD.

Traditional overhead imagery techniques for urban land use detection and mapping often lack the precision needed for accurate, fine-grained analysis, particularly in complex environments with multi-functional, multi-story buildings. To bridge the gap, this study introduces a novel approach, utilizing ground-level street view images geo-located at the point level, to provide more concrete, subtle, and informative visual characteristics for urban mixed land use analysis, addressing the two major limitations of overhead imagery: coarse resolution and insufficient visual information. Given that spatial context-aware land-use descriptions are commonly employed to describe urban environments, this study treats mixed land use detection as a Natural Language for Visual Reasoning (NLVR) task, i.e., classifying land use(s) in images based on the similarity of their visual characteristics and local descriptive land use contexts, by integrating street view images (vision) with spatial context-aware land use descriptions (language) through vision-language multimodal learning. The results indicate that our multimodal approach significantly outperforms traditional vision-based methods and can accurately capture the multiple functionalities of the ground features. It benefits from the incorporation of spatial context-aware prompts, whereas the geographic scale of geo-locations matters. Additionally, our approach marks a significant advancement in mixed land use mapping, achieving point-level precision. It allows for the representation of diverse land use types at point locations, offering the flexibility of mapping at various spatial resolutions, including census tracts and zoning districts. This approach is particularly effective in areas with diverse urban functionalities, facilitating a more fine-grained and detailed perspective on mixed land uses in urban settings.

Streptococcus suis (S. suis) is widely acknowledged as a significant zoonotic pathogen in Southeast Asia and China, which has led to a substantial number of fatalities in both swine and humans. Despite the prevalent use of mice as the primary animal model to study S. suis pathogenesis, the substantial differences in the major histocompatibility complex (MHC) between humans and mice underscore the ongoing exploration for a more suitable and effective animal model. In this study, humanized transgenic HLA-A11/DR1 genotypes mice were used to evaluate the differences between humanized HLA and murine H2 in S. suis infection. Following intravenous administration of S. suis suspensions, we investigated bacterial load, cytokine profiles, pathological alterations, and immune cell recruitment in both Wild-type (WT) and humanized mice across different post-infection time points. Relative to WT mice, humanized mice exhibited heightened pro-inflammatory cytokines, exacerbated tissue damage, increased granulocyte recruitment with impaired resolution, notably more pronounced during the late infection stage. Additionally, our examination of bacterial clearance rates suggests that HLA-A11/DR1 primarily influences cell recruitment and mitochondrial reactive oxygen species (ROS) production, which affects the bacterial killing capacity of macrophages in the late stage of infection. The reduced IL-10 production and lower levels of regulatory T cells in humanized mice could underlie their compromised resolution ability. Intervention with IL-10 promotes bacterial clearance and inflammatory regression in the late stages of infection in transgenic mice. Our findings underscore the heightened sensitivity of HLA-A11/DR1 mice with impaired resolution to S. suis infection, effectively mirroring the immune response seen in humans during infection. The humanized HLA-A11/DR1 mice could serve as an optimal animal model for investigating the pathogenic and therapeutic mechanisms associated with sepsis and other infectious diseases.

There are many types of coal seams in China, and the mining of protective layers will cause different rates of stress reduction in protected coal seams at different intervals. Therefore, experiments were conducted at different unloading rates to explore the strength, deformation, and energy characteristics of coal. Research findings: the AE (acoustic emission) signal of the coal body before unloading has a small range of changes and similar characteristics. After unloading begins, because of the different development rates of internal crack in the coal body under different unloading states, the AE signal of the coal body varies at different unloading rates. The maximum stress increases exponentially with the increase of unloading rate. It was found that the higher the unloading rate, the easier and earlier the coal sample is to be damaged. And it was discovered that the dissipated energy of the coal sample in the elastic stage is extremely low, and a large amount of total energy is converted into elastic energy and stored inside the coal sample. The dissipation energy increases during the plastic stage, while the trend of increasing elastic energy slows down. After the peak stage, the dissipated energy rapidly increases and the elastic energy decreases.

Abstract Background Clear cell renal cell carcinoma (ccRCC) is the most lethal renal cancer. An overwhelming increase of patients experience tumor progression and unfavorable prognosis. However, the molecular events underlying ccRCC tumorigenesis and metastasis remain unclear. Therefore, uncovering the underlying mechanisms will pave the way for developing novel therapeutic targets for ccRCC. In this study, we sought to investigate the role of mitofusin‐2 (MFN2) in supressing ccRCC tumorigenesis and metastasis. Methods The expression pattern and clinical significance of MFN2 in ccRCC were analyzed by using the Cancer Genome Atlas datasets and samples from our independent ccRCC cohort. Both in vitro and in vivo experiments, including cell proliferation, xenograft mouse models and transgenic mouse model, were used to determine the role of MFN2 in regulating the malignant behaviors of ccRCC. RNA‐sequencing, mass spectrum analysis, co‐immunoprecipitation, bio‐layer interferometry and immunofluorescence were employed to elucidate the molecular mechanisms for the tumor‐supressing role of MFN2. Results we reported a tumor‐suppressing pathway in ccRCC, characterized by mitochondria‐dependent inactivation of epidermal growth factor receptor (EGFR) signaling. This process was mediated by the outer mitochondrial membrane (OMM) protein MFN2. MFN2 was down‐regulated in ccRCC and associated with favorable prognosis of ccRCC patients. in vivo and in vitro assays demonstrated that MFN2 inhibited ccRCC tumor growth and metastasis by suppressing the EGFR signaling pathway. In a kidney‐specific knockout mouse model, loss of MFN2 led to EGFR pathway activation and malignant lesions in kidney. Mechanistically, MFN2 preferably binded small GTPase Rab21 in its GTP‐loading form, which was colocalized with endocytosed EGFR in ccRCC cells. Through this EGFR‐Rab21‐MFN2 interaction, endocytosed EGFR was docked to mitochondria and subsequently dephosphorylated by the OMM‐residing tyrosine‐protein phosphatase receptor type J (PTPRJ). Conclusions Our findings uncover an important non‐canonical mitochondria‐dependent pathway regulating EGFR signaling by the Rab21‐MFN2‐PTPRJ axis, which contributes to the development of novel therapeutic strategies for ccRCC.