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The semirigid pavement base is known for its high strength and good stability. However, it faces challenges owing to the generation of shrinkage stress during the development of its strength subsequent service. This stress arises from internal changes in relative humidity and temperature. When this shrinkage stress exceeds the bonding force of the cementitious material, the base may develop dry shrinkage cracks and temperature shrinkage cracks. Addressing this issue, cementitious material based on red mud emerges as a novel solution for roadbed construction, potentially replacing traditional cement entirely. Despite its promise, the crack resistance evolution of this material is still unclear. This study focuses on the dry shrinkage and temperature shrinkage properties of the stabilized gravel layer made from red mud-based cementitious material. It investigates how various factors, including the content of red mud-based cementitious material and duration of maintenance, affect these shrinkage properties. Additionally, it establishes a correlation between dry shrinkage, temperature shrinkage, and their maximum strain values through grey correlation degree analysis. Experimental results reveal a positive correlation between the cumulative dry shrinkage strain of stabilized crushed stone based on red mud cementitious material and the curing age, with the growth rate diminishing over time. The cumulative dry shrinkage strain first decreases and then increases as the content of red mud cementitious material rises, hitting its lowest point at 7% content. The cumulative dry shrinkage coefficient of stabilized crushed stone of red mud–based cementation material reaches the peak value at the age of 1 d and before gradually decreasing and stabilizing. Notably, at an 8% dosage, this coefficient is significantly higher than in other groups. Regarding temperature shrinkage, the coefficient first increases and then decreases with rising temperature. The average temperature shrinkage coefficient follows a similar pattern based on the content of red mud-based cementitious material, with the 7-day coefficient substantially exceeding the 28-day figure. Microcracks fill the stable gravel matrix at 7 days of age, whereas at 28 days, the matrix becomes denser owing to the increase in hydration products. Scanning electron microscope morphology analysis further elucidates the mechanisms of dry and temperature shrinkage. Employing grey relational analysis, the study identifies the dry shrinkage resistance index and temperature shrinkage coefficient as optimal indicators for assessing the cracking resistance of stabilized crushed stone using red mud-based cementitious material. Optimal cracking resistance is achieved when the content of red-mud cementitious material is 7%.

Abstract Neurogenic lower urinary tract dysfunction (NLUTD) is a frequent consequence of spinal cord injury (SCI), leading to symptoms that significantly impact quality of life. Although many life-saving techniques are available, current treatment strategies for managing NLUTD still exhibit limitations and drawbacks. Here, we introduce a new electrical neuromodulation strategy involving electrical stimulation of the major pelvic ganglion (MPG) to initiate bladder contraction, in conjunction with innovative programmable (IPG) electrical stimulation on the pudendal nerve (PN) to induce external urethral sphincter (EUS) relaxation in freely moving or anesthetized SCI mice. Furthermore, we conducted the void spot assay, and cystometry coupled with EUS electromyography (EMG) recordings to evaluate voiding function, and monitor bladder pressure and EUS activity. Our findings demonstrate that our novel electrical neuromodulation approach effectively triggers coordinated bladder muscle contraction and EUS relaxation, effectively counteracting SCI-induced NLUTD. Additionally, this electrical neuromodulation method enhances voiding efficiency, closely resembling natural reflexive urination in SCI mice. Thus, our study offers a promising electrical neurostimulation approach aimed at restoring physiological coordination and potentially offering personalized treatment for improving voiding efficiency in individuals with SCI-associated NLUTD.

Abstract The rise of object detection models has brought new breakthroughs to the development of clinical decision support systems. However, in the field of gastrointestinal polyp detection, there are still challenges such as uncertainty in polyp identification and inadequate coping with polyp scale variations. To address these challenges, this paper proposes a novel gastrointestinal polyp object detection model. The model can automatically identify polyp regions in gastrointestinal images and accurately label them. In terms of design, the model integrates multi-channel information to enhance the ability and robustness of channel feature expression, thus better coping with the complexity of polyp structures. At the same time, a hierarchical structure is constructed in the model to enhance the model’s adaptability to multi-scale targets, effectively addressing the problem of large-scale variations in polyps. Furthermore, a channel attention mechanism is designed in the model to improve the accuracy of target positioning and reduce uncertainty in diagnosis. By integrating these strategies, the proposed gastrointestinal polyp object detection model can achieve accurate polyp detection, providing clinicians with reliable and valuable references. Experimental results show that the model exhibits superior performance in gastrointestinal polyp detection, which helps improve the diagnostic level of digestive system diseases and provides useful references for related research fields.

Abstract Microbial fuel cells (MFCs) use the metabolic actions of microorganisms in an anode chamber to convert the chemical energy from wastewater into electrical energy. To improve the MFC power generation performance and chemical oxygen demand (COD) removal efficiency, Stenotrophomonas acidaminiphila was added to the anode chamber of a dual-compartment MFC. In this process, Stenotrophomonas acidaminiphila promotes the degradation of macromolecules such as bis(2-ethylhexyl) phthalate in food waste oil. Additionally, the generated electrical energy reduced Cu2+ in the copper-containing wastewater in the cathode chamber to Cu monomers. The maximum power density of the MFC was 49.5 ± 3.5 mW/m2, the maximum removal efficiencies of COD and Cu2+ were 63.5 ± 5.8% and 96.5 ± 1.0%, respectively, and Cu2+ was reduced to brick-red Cu monomers. This study provides insights into the simultaneous implementation of food waste oil treatment and metal resource recovery.

Color features carries rich information and directly influencing people’s regional perception. This exploration originates from the serial research conducted in the Huizhou area. By analyzing the color features of Hongcun, Nanping, and Xidi, this study probes into the descriptive cognition of independent dimensions, advancing to visual cognition of combined dimensions and psychological cognition of system dimensions. The study established a systematic technical route. Firstly, it sets up spatial sequence paths in typical regional built spaces, uses 3D laser scanning technology for high-quality modeling. Secondly, this study utilizes color as an independent dimension, conducting descriptive statistics on the color attributes – namely, Lightness, Chroma, and Hue – of architectural components to determine their distinct color features in absolute terms. Thirdly, exploring the combined dimension of color, the research delves into visual similarity, examining commonalities within architectural elements. Lastly, from a systemic color perspective, it focuses on the stability of psychological cognition, identifying Huizhou’s color identity through a “black-grey-white” tri-color sequence as a stable composition. Additionally, this technical system features advantages of being systematic and precise. It promotes the scientific and rationality in the color research field, guiding the integrated application of urban design.

Background and objective: Micro-ultrasound (MUS) uses a high-frequency transducer with superior resolution to conventional ultrasound, which may differentiate prostate cancer from normal tissue and thereby allow targeted biopsy. Preliminary evidence has shown comparable sensitivity to magnetic resonance imaging (MRI), but consistency between users has yet to be described. Our objective was to assess agreement of MUS interpretation across multiple readers. Methods: After institutional review board approval, we prospectively collected MUS images for 57 patients referred for prostate biopsy after multiparametric MRI from 2022 to 2023. MUS images were interpreted by six urologists at four institutions with varying experience (range 2–6 yr). Readers were blinded to MRI results and clinical data. The primary outcome was reader agreement on the locations of suspicious lesions, measured in terms of Light’s κ and positive percent agreement (PPA). Reader sensitivity for identification of grade group (GG) ≥2 prostate cancer was a secondary outcome. Key findings and limitations: Analysis revealed a κ value of 0.30 (95% confidence interval [CI] 0.21–0.39). PPA was 33% (95% CI 25–42%). The mean patient-level sensitivity for GG ≥2 cancer was 0.66 ± 0.05 overall and 0.87 ± 0.09 when cases with anterior lesions were excluded. Readers were 12 times more likely to detect higher-grade cancers (GG ≥3), with higher levels of agreement for this subgroup (κ 0.41, PPA 45%). Key limitations include the inability to prospectively biopsy reader-delineated targets and the inability of readers to perform live transducer maneuvers. Conclusions and clinical implications: Inter-reader agreement on the location of suspicious lesions on MUS is lower than rates previously reported for MRI. MUS sensitivity for cancer in the anterior gland is lacking. Patient summary: The ability to find cancer on imaging scans can vary between doctors. We found that there was frequent disagreement on the location of prostate cancer when doctors were using a new high-resolution scan method called micro-ultrasound. This suggests that the performance of micro-ultrasound is not yet consistent enough to replace MRI (magnetic resonance imaging) for diagnosis of prostate cancer.

Co-based superalloys have attracted attention to replace Ni-based superalloys in high temperature structural applications because of their higher melting temperature and corrosion resistance. Strengthening is provided by Co3(Al, X) (X = W, Cr, Mo, Ni) phases and optimization of their properties requires detailed information about the free energies of the different phases and of the phase diagram of the Co–Al system. This is achieved in this paper through first principles calculations in combination with statistical mechanics. Configurational entropic contributions to the free energy were included through Monte Carlo simulations using the cluster expansion formalism. The vibrational entropy of each phase was determined through the length-stiffness relationship while the magnetic entropy was also included through the Heisenberg Hamiltonian. The computed phase diagram is compared with the currently accepted experimental phase diagram and the different contributions to the stability of each phase are analyzed independently. The potential of this strategy to predict phase diagrams of magnetic systems is clearly established.

BackgroundRecent studies have suggested that cell death may be involved in bone loss or the resolution of inflammation in periodontitis. Immunogenic cell death (ICD), a recently identified cell death pathway, may be involved in the development of this disease.MethodsBy analyzing single-cell RNA sequencing (scRNA-seq) for periodontitis and scoring gene set activity, we identified cell populations associated with ICD, which were further verified by qPCR, enzyme linked immunosorbent assay (ELISA) and immunofluorescence (IF) staining. By combining the bulk transcriptome and applying machine learning methods, we identified several potential ICD-related hub genes, which were then used to build diagnostic models. Subsequently, consensus clustering analysis was performed to identify ICD-associated subtypes, and multiple bioinformatics algorithms were used to investigate differences in immune cells and pathways between subtypes. Finally, qPCR and immunohistochemical staining were performed to validate the accuracy of the models.ResultsSingle-cell gene set activity analysis found that in non-immune cells, fibroblasts had a higher ICD activity score, and KEGG results showed that fibroblasts were enriched in a variety of ICD-related pathways. qPCR, Elisa and IF further verified the accuracy of the results. From the bulk transcriptome, we identified 11 differentially expressed genes (DEGs) associated with ICD, and machine learning methods further identified 5 hub genes associated with ICD. Consensus cluster analysis based on these 5 genes showed that there were differences in immune cells and immune functions among subtypes associated with ICD. Finally, qPCR and immunohistochemistry confirmed the ability of these five genes as biomarkers for the diagnosis of periodontitis.ConclusionFibroblasts may be the main cell source of ICD in periodontitis. Adaptive immune responses driven by ICD may be one of the pathogenesis of periodontitis. Five key genes associated with ICD (ENTPD1, TLR4, LY96, PRF1 and P2RX7) may be diagnostic biomarkers of periodontitis and future therapeutic targets.

A green strategy that using Bacillus velezensis to degrade cellulose in bamboo forest waste was demonstrated. The cultivation conditions of microorganism, identified as Bacillus velezensis by 16S rDNA, were optimized by response surface methodology (RSM). Results showed that with a temperature of 35 °C, a pH of 6 in a Luria-Bertani (LB) medium, and a rotation speed of 140 r/min, the bamboo forest waste powder was added to the purified bacteria at a dose of 1 % (w/v), the cellulose degradation rate reached the highest value of 33.12 %. Besides, the fourier-transform infrared (FTIR), X-ray diffraction (XRD), scanning electron microscope (SEM) and liquid chromatography-mass spectrometry (LC-MS) indicated that cellulose could be effectively degraded, with possible products including cellobiose, uridine diphosphate (UDP)-glucose, D-glucuronic acid, etc. The metabolic pathways involved in cellulose degradation by this strain include the pentose phosphate, starch, sucrose, and glucuronic acid pathways.

Silicosis is an occupational disease caused by exposure to silica characterized by pulmonary inflammation and fibrosis, for which there is a lack of effective drugs. Glycyrrhetinic acid 3-O-β-D-glucuronide (GAMG) can treat silicosis due to its anti-inflammatory and anti-fibrotic properties. Here, the effect of therapeutic interventions of GAMG was evaluated in early-stage and advanced silicosis mouse models. GAMG significantly improved fibrotic pathological changes and collagen deposition in the lungs, alleviated lung inflammation in the BALF, reduced the expression of TNF-α, IL-6, NLRP3, TGF-β1, vimentin, Col-Ⅰ, N-cadherin, and inhibited epithelial-mesenchymal transition (EMT), thereby ameliorating pulmonary fibrosis. Moreover, the dose of 100 mg/kg GAMG can effectively prevent early-stage silicosis, while that of 200 mg/kg was recommended for advanced silicosis. In vitro and in vivo study verified that GAMG can suppress EMT through the NLRP3/TGF-β1/Smad2/3 signaling pathway. Therefore, GAMG could be a promising preventive (early-stage silicosis) and therapeutic (advanced silicosis) strategy, which provides a new idea for formulating prevention and treatment strategies.

Lower urinary tract dysfunction (LUTD) is a prevalent condition characterized by symptoms such as urinary frequency, urgency, incontinence, and difficulty in urination, which can significantly impair patient's quality of life and lead to severe physiological complications. Despite the availability of diverse treatment options, including pharmaceutical and behavioral therapies, these approaches are not without challenges. The objective of this study was to enhance treatment options for LUTD by developing a wireless, battery-free device for managing bladder contractions. We designed and validated a compact, fully implantable, battery-free pulse generator using the magnetic induction coupling mechanism of wireless power transmission. Weighing less than 0.2 g and with a volume of less than 0.1 cubic centimeters, this device enables precise stimulation of muscles or neurons at voltages ranging from 0 to 10 V. Wireless technology allows real-time adjustment of key stimulation parameters such as voltage, duration, frequency, pulse width, and pulse interval. Our findings demonstrate that the device effectively controlled bladder contractions in mice when used to stimulate the Major Pelvic Ganglion (MPG). Additionally, the device successfully managed micturition in mice with bilateral transection of the pudendal nerve. In conclusion, the development of this innovative wireless pulse generator provides a safer and more cost-effective alternative to conventional battery-powered neurostimulators for bladder control, addressing the limitations of such devices. We anticipate that this novel technology will play a pivotal role in the future of electrical stimulation therapies for voiding dysfunctions.

Traumatic brain injury (TBI) is a leading cause of disability and death. Thus, timely and effective secondary brain injury intervention is crucial, with potential to improve the prognosis of TBI. Oxidative stress contributes to post-traumatic secondary cognitive impairment, and the reduction of post-traumatic oxidative stress effectively enhances cognitive function. Phosphoglycerate-mutating enzyme 5 (PGAM5), a member of the phosphoglycerate transporter enzyme family, is upregulated in TBI and induces mitochondrial autophagy. This further exacerbates damage following TBI. The present study focused on the small molecule drug, LFHP-1c, which is a novel inhibitor of PGAM5. The present study used an in vivo mouse model incorporating a controlled cortical impact-induced TBI, to examine the impact of LFHP-1c on oxidative stress and cognitive function. The present study aimed to determine the impact of LFHP-1c on the PGAM5-Kelch-like ECH-associated protein 1 (KEAP1)- nuclear factor erythroid 2-related factor 2 (NRF2) ternary complex within the TBI context. Results of the present study indicated that LFHP-1c suppresses PGAM5 expression and inhibits the development of the PGAM5-KEAP1-NRF2 ternary complex, thereby promoting the release of NRF2 and KEAP1. This in turn promotes the entry of NRF2 into the nucleus following TBI, leading to increased expression of anti-oxidative stress downstream factors, such as heme oxygenase-1, glutathione peroxidase 1 and superoxide dismutase 1. In addition, LFHP-1c also released KEAP1, leading to mitochondrial Rho GTPase 2 degradation and reducing perinuclear aggregation of mitochondria in the cell, which reduced oxidative stress and ultimately improved cognitive function after TBI.

Turpan-Hami (Tuha) Basin is in an extremely arid area, surrounded by mountains and desert with sparse vegetation. It has experienced serious desertification over the past 30 years, resulting in ecological environment and socio-economic issues. To reveal the temporal and spatial evolutions of desertification degrees in Tuha Basin, we first constructed an evaluation system for the desertification degree classification using vegetation and soil-related indices; then long-time series Landsat 5 TM/ Landsat8 OLI images and a C5.0 decision tree (DT) model were used to monitor large-scale desertification from 1990 to 2020. We applied a geographic detector to discover the driving forces between desertification changes and natural and human factors. The results demonstrated that the severe desertification (SD) accounted for the largest desertification degree, and the transitions of various desertification degrees mainly occurred between extremely severe desertification (ESD) and SD, which were considered the most active degrees. Only 9.4 % of Tuha Basin showed an expansion trend in desertification degree, with the most significant expansion occurred in 1990–1995. The proportion of 13.6 % desertification showed a reversion trend, with the most significant reversion occurred in 1995–2010. The main driving factors affecting the evolution of desertification degrees were precipitation and temperature, with the average q value higher than 0.95. Human activity factors such as year-end cropland area and population also impacted the patterns of desertification degree, with the q values of 0.9 and 0.7, respectively. The interactive detection enhanced the driving forces and the interaction between precipitation and other factors played a leading role in desertification degree changes. The results provide scientific data and a basis for desertification control and ecological protection in arid areas.

Abstract The development of an effective survival prediction tool is key for reducing colorectal cancer mortality. Here, we apply a three-stage study to devise a polygenic prognostic score (PPS) for stratifying colorectal cancer overall survival. Leveraging two cohorts of 3703 patients, we first perform a genome-wide survival association analysis to develop eight candidate PPSs. Further using an independent cohort with 470 patients, we identify the 287 variants-derived PPS (i.e., PPS287) achieving an optimal prediction performance [hazard ratio (HR) per SD = 1.99, P = 1.76 × 10−8], accompanied by additional tests in two external cohorts, with HRs per SD of 1.90 (P = 3.21 × 10−14; 543 patients) and 1.80 (P = 1.11 × 10−9; 713 patients). Notably, the detrimental impact of pathologic characteristics and genetic risk could be attenuated by a healthy lifestyle, yielding a 7.62% improvement in the 5-year overall survival rate. Therefore, our findings demonstrate the integrated contribution of pathologic characteristics, germline variants, and lifestyle exposure to the prognosis of colorectal cancer patients.

Abstract Background Numerous studies have shown that Schistosoma japonicum infection correlates with an increased risk of liver hepatocellular carcinoma (LIHC). However, data regarding the role of this infection in LIHC oncogenesis are scarce. This study aimed to investigate the potential mechanisms of hepatocarcinogenesis associated with Schistosoma japonicum infection. Methods By examining chronic liver disease as a mediator, we identified the genes contributing to Schistosoma japonicum infection and LIHC. We selected 15 key differentially expressed genes (DEGs) using weighted gene co-expression network analysis (WGCNA) and random survival forest models. Consensus clustering revealed two subgroups with distinct prognoses. Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression identified six prognostic DEGs, forming an Schistosoma japonicum infection-associated signature for strong prognosis prediction. This signature, which is an independent LIHC risk factor, was significantly correlated with clinical variables. Four DEGs, including BMI1, were selected based on their protein expression levels in cancerous and normal tissues. We confirmed BMI1's role in LIHC using Schistosoma japonicum-infected mouse models and molecular experiments. Results We identified a series of DEGs that mediate schistosomiasis, the parasitic disease caused by Schistosoma japonicum infection, and hepatocarcinogenesis, and constructed a suitable prognostic model. We analyzed the mechanisms by which these DEGs regulate disease and present the differences in prognosis between the different genotypes. Finally, we verified our findings using molecular biology experiments. Conclusion Bioinformatics and molecular biology analyses confirmed a relationship between schistosomiasis and liver hepatocellular cancer. Furthermore, we validated the role of a potential oncoprotein factor that may be associated with infection and carcinogenesis. These findings enhance our understanding of Schistosoma japonicum infection's role in LIHC carcinogenesis.

ABSTRACTDeep learning (DL) models have been widely used for remote sensing-based landslide mapping due to their impressive capabilities for automatic information extraction. However, the large volumes of parameters and calculations have compromised the efficiency of DL models in extracting landslides from a large set of RS images. Lightweight convolutional neural networks (CNNs) exhibit promising feature representation abilities with fewer parameters. This study aims to introduce a new lightweight CNN called MS2LandsNet, designed to detect landslides with both high efficiency and accuracy. The MS2LandsNet consists of three down-sampling stages embedded with multi-scale feature fusion (MFF), aiming to decrease parameters while aggregating contextual features. Additionally, we incorporate multi-scale channel attention (MSCA) into MFF to improve performance. According to experimental results on three landslip datasets, MS2LandsNet obtains the highest F1 score of 85.90% and the highest IoU of 75.28%. Notably, MS2LandsNet accomplishes the resuts with the fewest parameters and the fastest inference speed, outperforming seven classical semantic segmentation models and three lightweight CNNs. The proposed lightweight model holds potential for application on a cloud computing platform for larger-scale landslide mapping tasks in future work.

Background: To explore the causal association between Helicobacter pylori (H. pylori) infection, herpesvirus infection and periodontitis (PD) from a genetic perspective using Mendelian randomization (MR). Methods: The PD data were derived from genome-wide association study (GWAS) from the Dental Endpoints (GLIDE) consortium, and the FinnGen Biobank provided data on H. pylori and herpesvirus infections. In addition, we examined GWAS data for subtypes of H. pylori and herpesvirus infection. Inverse variance weighting (IVW) was selected as a major analysis technique, and weighted median (WM), weighted model, simple model, and MR-Egger regression were added as supplementary methods. To verify the findings, the effects of pleiotropy and heterogeneity were assessed. Results: Genetically predicted H. pylori infection (OR = 0.914, 95%CI = 0.693–1.205, P = 0.523), anti-H. pylori VacA (OR = 0.973, 95%CI = 0.895–1.057, P = 0.515), anti-H. pylori CagA (OR = 1.072, 95%CI = 0.986–1.164; P = 0.102), Epstein-Barr virus (EBV) infection (OR = 1.026, 95%CI = 0.940–1.120, P = 0.567), Herpes simplex virus (HSV) infection (OR = 0.962, 95%CI = 0.883–1.048, P = 0.372), cytomegalovirus (CMV) infection (OR = 1.025, 95%CI = 0.967–1.088, P = 0.415), EBV nuclear antigen-1 (EBNA1) (OR = 1.061, 95%CI = 0.930–1.209, P = 0.378), EBV virus capsid antigen (VCA) (OR = 1.043, 95CI% = 0.890–1.222, P = 0.603), HSV-1 (OR = 1.251, 95%CI = 0.782–2.001, P = 0.351), HSV-2 (OR = 1.020, 95%CI = 0.950–1.096, P = 0.585), CMV IgG (OR = 0.990, 95CI% = 0.882–1.111, P = 0.861) were not associated with PD, indicated that H. pylori and herpesvirus infection had no causal relationship to PD. Reverse studies also found no cause effect of PD on H. pylori or herpesvirus infection. The results of the sensitivity analysis suggested the robustness of the MR results. Conclusion: This study offered preliminary proof that H. pylori and herpesvirus infections were not causally linked to PD, and vice versa. However, more robust instrumental variables (IVs) and larger samples of GWAS data were necessary for further MR analysis.

In the system-level simulations of heat transfer systems, the characteristics of heat exchangers are often considered constant or used in empirical formulas, which affect the accuracy of the system model. Herein a multi-scale simulation method to analyze the heat transfer characteristics of a thermal management system, including an intermediate loop is proposed. The system model is developed based on the heat current method and flow resistance models. By applying COMSOL LiveLink for MATLAB module, a multi-scale model is obtained, which combines three-dimensional numerical models of the heat exchangers with the system model. Compared to the experimental results, the relative errors associated with the outlet temperatures and heat transfer rate are less than 1.1% and 8.2 %, respectively, thus validating the model. Discussions on the effects of the geometric structure of the heat exchangers on the system show that a plate heat exchanger with 12 plates and a plate spacing of 8 mm is better. For achieving systematic maximum heat dissipation for a certain pumping power consumption, a multi-scale optimization method is proposed by coupling the Lagrange function and numerical model. The results show that the total heat transfer rate improves by 5.89 %, from 8314.81 W to 8804.35 W.

BackgroundTo investigate the causal relationship between major depression and functional dyspepsia using two-sample Mendelian randomization.MethodsData for major depression and functional dyspepsia were obtained from genome-wide association studies. We selected Single Nucleotide Polymorphisms (SNPs) strongly associated with severe depression. Mendelian randomization analysis was conducted using methods such as Inverse-Variance Weighted (IVW), MR-Egger, and Weighted Median Estimator (WME). Sensitivity analysis was performed to assess the robustness of the results.ResultsA total of 31 eligible SNPs were identified as instrumental variables for major depression. IVW analysis indicated a positive causal relationship between the two conditions (β = 0.328; SE = 0.137; p = 0.017), suggesting that severe depression increases the risk of functional dyspepsia (OR = 1.389; 95% CI: 1.062–1.816). Sensitivity tests showed no evidence of heterogeneity or horizontal pleiotropy (p > 0.05).ConclusionMR analysis had shown that major depressive disorder is associated with an increased risk of functional dyspepsia.

The sustainability of port cities affects the competitiveness of their hinterland in international shipping and maritime trade, however, the coordinated relationship between urbanization and ecological environment in port cities has received less attention. In this study, we quantitatively analyzed the urban expansion process and vegetation cover changes in 44 port cities along the Maritime Silk Road during 2000–2020 based on multi-source remote sensing data. Adopting the urbanization-vegetation cover index (UVCI), we measured the coordination relationship between urban expansion and vegetation cover and the dynamics during 2000–2020. The results found that all port cities experienced dramatic development from 2000 to 2020, with an average increase in artificial surface area of 127.81%. The 63.64% of port cities experienced increased vegetation cover, thus the overall UVCI in the port city moved toward a more coordinated and sustainable pattern with significant heterogeneity at the economic levels. High-economy port cities have generally become more coordinated, while most low- and medium-economy cities still face challenges of conflicting urban expansion and ecological preservation. This study contributes to the development of government policies that support green and sustainable urban development in port cities, and provides insights for rationalizing development policies in port cities worldwide.

Abstract Alzheimer's disease (AD) is the leading cause of dementia worldwide, with recent studies highlighting the potential role of immunogenic cell death (ICD) in the pathogenesis of this neurodegenerative disorder. A total of 52 healthy controls and 64 patients with AD were included. Compared to the controls, the patients with AD exhibited 2392 differentially expressed genes (DEGs), of which 1015 and 1377 were upregulated and downregulated genes, respectively. Among them, nine common genes were identified by intersecting the AD-related module genes with the DEGs and ICD-associated genes. Gene ontology (GO)analysis further revealed “positive regulation of cytokine production” as the most significant term. Moreover, the enriched molecular functions were primarily related to the inflammatory body complex, while the overlapping genes were significantly enriched in lipopolysaccharide binding. Kyoto encyclopedia of genes and genomes (KEGG) analysis also indicated that these overlapping genes were mainly enriched in immunity, inflammation, and lipid metabolism pathways. Furthermore, the following four hub genes were detected using machine learning algorithms: P2RX7, HSP90AA1, NT5E, and NLRP3. These genes demonstrated significant differences in expression between the AD and healthy control groups (P 0.7, indicating their potential diagnostic value for AD. We further validated the protein levels of these four genes in the hippocampus of 3xTg-AD and C57BL/6J mice, showing P2RX7 and HSP90AA1 expression levels consistent with the previously analyzed trends. Finally, the single-sample gene set enrichment analysis (ssGSEA) algorithm provided additional evidence by demonstrating the crucial role of immune cell infiltration and its link with the hub genes in AD progression. Our study results suggest that ICD-mediated elevation of HSP90AA1 and P2RX7 levels and the resulting induction of tau hyperphosphorylation and neuroinflammation are vital in the AD pathogenic mechanism.

Lung cancer is the leading cause of cancer-related deaths worldwide. Early diagnosis significantly improves the 5-year survival rate from 6% in patients with metastatic cancer to 60% in those with localized cancer. Histopathological examination is the gold standard for lung cancer diagnosis, but analyzing whole slide images (WSI) is time-consuming and prone to error for pathologists. This study aims to enhance the classification accuracy of non-small cell lung cancer (NSCLC) histopathological images by proposing a novel deep-learning architecture that integrates convolutional neural networks (CNNs) and vision transformers (ViTs). The model classifies NSCLC into three categories: normal, adenocarcinoma, and squamous cell carcinoma. CNNs are employed to capture local features, while ViTs are used to understand long-range relationships between image patches. We trained and validated our model on the LC25000 dataset, a benchmark dataset for NSCLC histopathology image classification. Our proposed model demonstrated superior performance, achieving an accuracy of 0.988, an F-1 score of 0.980, specificity of 0.991, recall of 0.982, and precision of 0.980, outperforming existing state-of-the-art methods. Additionally, our model achieved a low inference time of 1.816 ms, highlighting its potential for real-world applications where both accuracy and speed are critical. Our code is now publicly available at https://github.com/ImranNust/LungCancerDetection to facilitate further research and validation of our findings.

Clouds in remote sensing images inevitably affect information extraction, which hinders the following analysis of satellite images. Hence, cloud detection is a necessary preprocessing procedure. However, most existing methods have numerous calculations and parameters. In this article, a lightweight convolutional neural network (CNN)-Transformer network, CD-CTFM, is proposed to solve the problem, which is based on encoder–decoder architecture and incorporates the attention mechanism. In the encoder part, we utilize a lightweight network combing CNN and Transformer as backbone, which is conducive to extracting local and global features simultaneously. The backbone of CD-CTFM also incorporates attention gate based on dark channel extraction module. Moreover, a lightweight feature pyramid module is designed to fuse multiscale features with contextual information. In the decoder part, a lightweight channel-spatial attention module is integrated into each skip connection between encoder and decoder to extract low-level features while suppressing irrelevant information without introducing many parameters. Finally, the proposed model is evaluated on two cloud datasets, 38-Cloud and MODIS. The results demonstrate that CD-CTFM achieves comparable accuracy as the state-of-art methods and outperforms in terms of efficiency.

Objective: Epigenetic abnormalities have a critical role in breast cancer by regulating gene expression; however, the intricate interrelationships and key roles of approximately 400 epigenetic regulators in breast cancer remain elusive. It is important to decipher the comprehensive epigenetic regulatory network in breast cancer cells to identify master epigenetic regulators and potential therapeutic targets. Methods: We employed high-throughput sequencing-based high-throughput screening (HTS 2) to effectively detect changes in the expression of 2,986 genes following the knockdown of 400 epigenetic regulators. Then, bioinformatics analysis tools were used for the resulting gene expression signatures to investigate the epigenetic regulations in breast cancer. Results: Utilizing these gene expression signatures, we classified the epigenetic regulators into five distinct clusters, each characterized by specific functions. We discovered functional similarities between BAZ2B and SETMAR, as well as CLOCK and CBX3. Moreover, we observed that CLOCK functions in a manner opposite to that of HDAC8 in downstream gene regulation. Notably, we constructed an epigenetic regulatory network based on the gene expression signatures, which revealed 8 distinct modules and identified 10 master epigenetic regulators in breast cancer. Conclusions: Our work deciphered the extensive regulation among hundreds of epigenetic regulators. The identification of 10 master epigenetic regulators offers promising therapeutic targets for breast cancer treatment.

Abstract Background Although periodontitis has previously been reported to be linked with multiple sclerosis (MS), but the molecular mechanisms and pathological interactions between the two remain unclear. This study aims to explore potential crosstalk genes and pathways between periodontitis and MS. Methods Periodontitis and MS data were obtained from the Gene Expression Omnibus (GEO) database. Shared genes were identified by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Then, enrichment analysis for the shared genes was carried out by multiple methods. The least absolute shrinkage and selection operator (LASSO) regression was used to obtain potential shared diagnostic genes. Furthermore, the expression profile of 28 immune cells in periodontitis and MS was examined using single-sample GSEA (ssGSEA). Finally, real-time quantitative fluorescent PCR (qRT-PCR) and immune histochemical staining were employed to validate Hub gene expressions in periodontitis and MS samples. Results FAM46C, SLC7A7, LY96, CFI, DDIT4L, CD14, C5AR1, and IGJ genes were the shared genes between periodontitis, and MS. GO analysis revealed that the shared genes exhibited the greatest enrichment in response to molecules of bacterial origin. LASSO analysis indicated that CFI, DDIT4L, and FAM46C were the most effective shared diagnostic biomarkers for periodontitis and MS, which were further validated by qPCR and immunohistochemical staining. ssGSEA analysis revealed that T and B cells significantly influence the development of MS and periodontitis. Conclusions FAM46C, SLC7A7, LY96, CFI, DDIT4L, CD14, C5AR1, and IGJ were the most important crosstalk genes between periodontitis, and MS. Further studies found that CFI, DDIT4L, and FAM46C were potential biomarkers in periodontitis and MS.

In the present work, a power battery pack and a novel thermal management system (TMS) based on the tubular heat pipe (THP) technology are designed according to the parameters of a pure electric light truck and battery. The TMS consists of THPs, power batteries, cooling plates, fins, and equivalent thermal conductivity is used to characterize the excellent thermal conductivity of THP. The heat generation rate of the battery at different discharge rates is obtained by establishing a multi-scale electrochemical–thermal coupling model, which accuracy is verified by the experiment. A multi-physics field coupling model is built to the thermal management system designed for the study. The results show that the battery temperature is higher when the battery is far from the THP in the cooling plate, and the maximum temperature reaches 35.6 °C at 1 C. Furthermore, the temperature difference between batteries is proportional to the discharge rate, and the maximum temperature difference reaches 7 °C at 2 C. The change in inlet temperature and wind speed can only restrain the temperature rise, while the temperature difference of the battery module is always 3.2 °C. When a fin spacing of 3 mm is chosen, the best temperature uniformity of the battery module is achieved at this time. The use of the multi-scale multi-physics field coupling analysis method in BTMS with heat pipes can be used as a reference for future numerical simulations of the same type.

Carbon-fiber-reinforced polymer (CFRP) is a composite material consisting of a resin matrix reinforced with carbon fibers. This study focuses on CFRP composite pipe piles as the subject of investigation, exploring the impact of substituting steel bars with CFRP bars on the bending performance of pipe piles through rigorous three-point bending tests. The attenuation of flexural stiffness in CFRP pipe piles under a chloride salt environment was anticipated. The lateral bearing capacity of CFRP pipe piles was calculated by introducing a stiffness degradation coefficient for the piles and utilizing the finite difference method. The findings of the analysis suggest that as the CFRP reinforcement replacement rate increases, the initial bending stiffness of the composite pipe pile experiences a corresponding decrease. After serving for 28.15 years, the steel reinforcement within the pipe pile commences rusting, resulting in a nonlinear decline in the bending stiffness of the composite pipe pile. As the service time of pipe piles increases, a higher replacement rate of CFRP reinforcement results in a slower attenuation of pile stiffness. Consequently, both the horizontal displacement at the top of the pile and the bending moment along the body of the composite pipe pile gradually increase over time. During the same service period, the higher the rate of CFRP reinforcement, the less noticeable the attenuation in the horizontal bearing capacity of the pile shaft.

The global shortage of clean water is a major problem, even in water-rich regions. To solve this problem, low-cost and energy-efficient water treatment methods are needed. Membrane separation technology (MST), as a separation method with low energy consumption, low cost, and good separation effect, has been widely used to deal with seawater desalination, resource recovery, industrial wastewater treatment, and other fields. With the continuous progress of scientific and technological innovation and the increasing demand for use, NF/RO membranes based on the TFC structure are constantly being upgraded. This paper presents the recent research progress of NF and RO membranes based on TFC structures and their applications in different fields, especially the formation mechanism and regulation of selective layer structures and the modification methods of selective layers. Our summary provides fundamental insights into the understanding of NF and RO membrane processes and hopefully triggers further thinking on the development of membrane filtration process optimization.

Optimizing the structure of the polyamide (PA) layer to improve the separation performance of PA thin-film composite (TFC) membranes has always been a hot topic in the field of membrane preparation. As novel crystalline materials with high porosity, multi-functional groups, and good compatibility with membrane substrate, metal–organic frameworks (MOFs) have been introduced in the past decade for the modification of the PA structure in order to break through the separation trade-off between permeability and selectivity. This review begins by summarizing the recent progress in the control of MOF-based thin-film nanocomposite (TFN) membrane structures. The review also covers different strategies used for preparing TFN membranes. Additionally, it discusses the mechanisms behind how these strategies regulate the structure and properties of PA. Finally, the design of a competent MOF material that is suitable to reach the requirements for the fabrication of TFN membranes is also discussed. The aim of this paper is to provide key insights into the precise control of TFN-PA structures based on MOFs.

Computer aided diagnosis methods play an important role in Attention Deficit Hyperactivity Disorder (ADHD) identification. Dynamic functional connectivity (dFC) analysis has been widely used for ADHD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), which can help capture abnormalities of brain activity. However, most existing dFC-based methods only focus on dependencies between two adjacent timestamps, ignoring global dynamic evolution patterns. Furthermore, the majority of these methods fail to adaptively learn dFCs. In this paper, we propose an adaptive spatial-temporal neural network (ASTNet) comprising three modules for ADHD identification based on rs-fMRI time series. Specifically, we first partition rs-fMRI time series into multiple segments using non-overlapping sliding windows. Then, adaptive functional connectivity generation (AFCG) is used to model spatial relationships among regions-of-interest (ROIs) with adaptive dFCs as input. Finally, we employ a temporal dependency mining (TDM) module which combines local and global branches to capture global temporal dependencies from the spatially-dependent pattern sequences. Experimental results on the ADHD-200 dataset demonstrate the superiority of the proposed ASTNet over competing approaches in automated ADHD classification.

Background: In revision total knee arthroplasty, tibial cones have demonstrated improved longevity and reduced incidence of aseptic loosening. Several currently available “off-the-shelf” (OTS) cone systems may not have sizes to accommodate all patient bone morphologies. Methods: Computed tomographies from one hundred primary total knee arthroplasty patients and dimensions of 4 OTS cones were obtained. Press-fit stems were positioned in 3D tibia models to fit the diaphyseal trajectory. Cones were positioned around the stem at 1, 6, and 13 mm resections measured from the trough of the medial tibial plateau, simulating proximal tibial cuts and bone loss. Tibias were examined for cortical breaching following modeled cone preparation. Results: Increased rate of breaching was observed as size and depth of the cone increased. In 2/49 (4.1%) male and 19/46 (41.3%) female tibias, cones could not be positioned without breaching. No breaches were found in 22/49 (45.0%) male and 5/46 (10.9%) female tibias. For every 1 centimeter increase in patient height, odds of breaching decreased by 12% (odds ratio: 0.88, confidence interval: 0.84, 0.92). For every size increase in cone width, odds of breaching increased by 34% (odds ratio: 1.34, confidence interval: 1.28, 1.47). Placing cones deeper also increased breaching compared to the 1 mm cut. Conclusions: In revision total knee arthroplasty, smaller OTS or custom tibial cones may be needed to fit a patient’s proximal tibial geometry. This is especially true in patients not accommodated by the OTS cone sizes we tested, which impacted shorter patients and/or those with substantial bone loss requiring more tibial resection and deeper cone placement. Use of smaller or custom tibial cones should be considered where indicated.

Reducing the structural load and operational energy consumption of buildings is a trend in the development of the construction industry, and lightweight concrete has become a research hotspot because of its dual characteristics of low density and high strength. In this paper, a three-step method is used to prepare lightweight concrete composites. In the first step, basalt fiber powder was adhered to polystyrene foam ball expanded polystyrene spheres (EPSs) by the “ball rolling method” to obtain basalt fiber reinforced epoxy composite balls. In the second step, the epoxy resin composite ball and the configured cement matrix are filled into a prefabricated mold. Finally, the lightweight concrete specimens were overlaid, pressed into shape, and cured for 28 days. By changing the volume ratio, size, and wall thickness of the epoxy composite balls, as well as the length and content of basalt fibers, the lightweight concrete was exhibited a reduction in density and increase in strength. The results showed that the smaller the epoxy composite spheres and the larger the wall thickness are, the higher the compressive strength of the material for a certain volume ratio of epoxy composite spheres will be. The addition of appropriate hollow glass beads can reduce the number of pores in the matrix, but excessive hollow glass beads will reduce the bonding force of the concrete matrix. Basalt fibers not only improve the strength of EPSs but also serve to connect matrix–matrix and matrix–epoxy composite spheres.

China's economy has transitioned into a phase of high-quality development, with enhancing its industrial structure becoming a critical objective. We gathered panel data from 30 major provinces in China from 2010 to 2020 and employed the fixed effects model to assess the actual influence of environmental regulations on industrial structure upgrading. Our empirical findings show that the impacts of various environmental regulations on industrial structure upgrading in China are significantly different. Mandatory environmental regulation demonstrates an inverted U-shaped nonlinear correlation with the upgrading level of the entire industrial structure. When the intensity of this regulation is low, it significantly accelerates industrial structure upgrading. As the intensity of this regulation rises, its effect on industrial structure upgrading is inhibitory. In contrast, induced environmental regulation exhibits a nonlinear U-shaped relationship with industrial structure upgrading and shows a nonlinear change trend of first decreasing and then rising. When the intensity of induced environmental regulation reaches a certain critical point and continues to increase, it will change from a negative influence on the upgrading of the industrial structure to a promoting effect. The further discussion of threshold regression and the robustness test also led to similar conclusions. The above research is conducive to the Chinese government's rational use of environmental regulation tools to promote industrial structure upgrading. It is also beneficial to developing countries, allowing them to learn from China's experience to improve the effectiveness of environmental regulation and boost their industrial development.

The quality of raw camel milk is affected by its bacterial composition and diversity. However, few studies have investigated the bacterial composition and diversity of raw camel milk. In this study, we obtained 20 samples of camel milk during spring and summer in Urumqi and Hami, Xinjiang, China. Single-molecule real-time sequencing technology was used to analyze the bacterial community composition. The results revealed that there were significant seasonal differences in the bacterial composition and diversity of camel milk. Overall, Epilithonimonas was the most abundant bacterial genus in our samples. Through the annotated genes inferred by PICRUSt2 were mapped against KEGG database. Non-parametric analysis of the bacterial community prediction function revealed a strong bacterial interdependence with metabolic pathways (81.83%). There were clear regional and seasonal differences in level 3 metabolic pathways such as fat, vitamins, and amino acids in camel milk. In addition, we identified lactic acid bacteria in camel milk with antibacterial and anti-tumor activities. Our findings revealed that camel milk from Xinjiang had serious risk of contamination by psychrophilic and pathogenic bacteria. Our research established a crucial theoretical foundation for ensuring the quality and safety of camel milk, thereby contributing significantly to the robust growth of China’s camel milk industry.

Abstract Background Whether and to what extent serum uric acid (SUA) mediates the association between combined lifestyle behaviors and type 2 diabetes mellitus (T2DM) remain unclear. This study aimed to investigate the role of SUA in the relationship between healthy lifestyle scores (HLS) and the incidence of T2DM. Methods This prospective study used data from Zhejiang Metabolic Syndrome cohort. A HLS (5-point scale including healthy waist circumference (WC), never smoking, high physical activity, healthy diet and moderate alcohol intake) was estimated in 13,919 participants, who had SUA at baseline examination in 2009–2014, and were followed-up to 2021–2022 to ascertain incident of T2DM. Cox proportional hazards models and mediation analysis were used to examine the associations between HLS, SUA and T2DM. Results We included 13,919 participants aged 18 years or older without diabetes at baseline (mean age 54.6 [SD 13.9] years, 58.7% female). During a median follow-up of 9.94 years, 645 cases of T2DM occurred. Compared with participants with a poor HLS, those with 4–5 low-risk lifestyle factors showed a 60% reduction in the risk of developing T2DM (adjusted HR, 0.40; 95% CI: 0.28–0.57). Further, the population-attributable risk percent (95% CI) of T2DM for poor adherence to the overall healthy lifestyle (

Abstract Background Therapeutic ultrasound, education, and massage are the most common physical therapy interventions provided to mothers with breast symptoms. However, there is insufficient evidence on the effectiveness of the combination of these interventions. This study aimed to explore the effects of the combination of therapeutic ultrasound, education, and massage on breast symptoms in lactating women. Methods This study was a single-blind randomized controlled trial. Postpartum lactating women aged from 21 to 45 with breast symptoms were recruited and randomly allocated to one of three groups (ultrasound group, sham group, and usual care group). The severity of breast symptoms (pain, redness, lump, general malaise), breast engorgement, breast hardness, body temperature, breast temperature, and milk volume were assessed at baseline (T1), immediately post-intervention (T2), and at 3 months following baseline (T3). Results A total of 37 participants were included in the study (ultrasound group n = 12; sham group n = 12; usual care n = 13). The severity of breast symptoms (i.e., pain, lump, and general malaise) as well as breast engorgement, were significantly improved in the ultrasound group at T2 when compared to T1, and these improvements were sustained at T3. The severity of breast engorgement was significantly lower in the ultrasound group when compared to the usual care group at T2. However, no statistically significant differences were found between the ultrasound and sham groups for all outcomes at any assessment time points. Conclusions Physical therapy interventions may be beneficial in relieving breast symptoms in lactating women. Larger randomized controlled trials are needed to confirm the findings of this study. Trial registration ClinicalTrials.gov (NCT04569136); Date of registration: 29/09/2020.

The development of decision-making systems based on artificial intelligence can lead to achieving optimal solutions water-land-food nexus. In this paper, an extreme learning machine model was developed with the objective function of wheat production maximization. The constraints defined for this problem are divided into three categories: technical parameters of production in agriculture, climatic stress on water resources and land limits. The water, land and food nexus was simulated using 23 experimental farms in Henan province during the 2021–2022 cultivation year. Root-mean-square error was used as an error criterion, and Pearson's coefficient was incorporated into the decision-making system as a correlation index of variables. Harvest index, length of the growth period, cultivation costs and irrigation water were the criteria to evaluate the impact of the sustainable model. The harvest index and the length of the growth period showed the highest and lowest correlation with the production rate, respectively. Furthermore, the optimal management of irrigation water and cost had the most significant impact on increasing crop production. The method proposed in this paper can be a virtual cropping model by changing the area under cultivation of a crop in the different farms of a study area, which increases yield production. HIGHLIGHTS Extreme machine learning has been used to increase wheat production.; The harvest index, length of growth period, irrigation and cost were the four investigated factors related to the correlation between water and food.; Modeling based on information and intelligent learning could increase agricultural productivity.;

Facial emotion recognition achieves great success with the help of large neural models but also fails to be applied in practical situations due to the large model size of neural methods. To bridge this gap, in this paper, we combine two mainstream model compression methods (pruning and quantization) together, and propose a pruning-then-quantization framework to compress the neural models for facial emotion recognition tasks. Experiments on three datasets show that our model could achieve a high model compression ratio and maintain the model’s high performance well. Besides, We analyze the layer-wise compression performance of our proposed framework to explore its effect and adaptability in fine-grained modules.

Zinc (Zn) is a potential bioresorbable metal material (BMM) with a degradation rate lower than magnesium and higher than iron. However, clinical applications of pure Zn are limited due to its poor mechanical properties and localized degrading tendency. Overcoming these drawbacks is made possible by the process of alloying. The Zn-0.6Li-0.5Mg alloy samples were prepared using the laser powder bed fusion (LPBF) process in this work. The innovative technique combining fast solidification produced by LPBF and alloying with Li and Mg was presented to enhance the mechanical properties of Zn in this work. The relative density, microstructure, mechanical properties, and corrosion resistance of LPBF-built Zn-0.6Li-0.5Mg alloy were investigated. According to the results, the LPBF fabricated Zn-0.6Li-0.5Mg alloy with fine equiaxed α-Zn grains were uniformly distributed with precipitated Mg2Zn11 and LiZn4 at grain boundaries. More interestingly, the average grain size of α-Zn was recorded at 7.2 ± 3.2 μm with Li and Mg. Due to the rapid solidification process, Mg dissolved primarily in the Zn matrix and developed a supersaturated solid solution. As a result, with the addition of Li and Mg, the mechanical properties of the Zn matrix increased considerably. When combined, Zn-0.6Li-0.5Mg alloy can be an attractive candidate for bioresorbable medical implants in orthopedic applications.

With the transformation and development of the automotive industry, low-cost and seamless indoor and outdoor positioning has become a research hotspot for modern vehicles equipped with in-vehicle infotainment systems, Internet of Vehicles, or other intelligent systems (such as Telematics Box, Autopilot, etc.). This paper analyzes modern vehicles in different configurations and proposes a low-cost, versatile indoor non-visual semantic mapping and localization solution based on low-cost sensors. Firstly, the sliding window-based semantic landmark detection method is designed to identify non-visual semantic landmarks (e.g., entrance/exit, ramp entrance/exit, road node). Then, we construct an indoor non-visual semantic map that includes the vehicle trajectory waypoints, non-visual semantic landmarks, and Wi-Fi fingerprints of RSS features. Furthermore, to estimate the position of modern vehicles in the constructed semantic maps, we proposed a graph-optimized localization method based on landmark matching that exploits the correlation between non-visual semantic landmarks. Finally, field experiments are conducted in two shopping mall scenes with different underground parking layouts to verify the proposed non-visual semantic mapping and localization method. The results show that the proposed method achieves a high accuracy of 98.1% in non-visual semantic landmark detection and a low localization error of 1.31 m.

Background: Magnetic resonance imaging (MRI) underestimation of prostate cancer extent complicates the definition of focal treatment margins. Objective: To validate focal treatment margins produced by an artificial intelligence (AI) model. Design, setting, and participants: Testing was conducted retrospectively in an independent dataset of 50 consecutive patients who had radical prostatectomy for intermediate-risk cancer. An AI deep learning model incorporated multimodal imaging and biopsy data to produce three-dimensional cancer estimation maps and margins. AI margins were compared with conventional MRI regions of interest (ROIs), 10-mm margins around ROIs, and hemigland margins. The AI model also furnished predictions of negative surgical margin probability, which were assessed for accuracy. Outcome measurements and statistical analysis: Comparing AI with conventional margins, sensitivity was evaluated using Wilcoxon signed-rank tests and negative margin rates using chi-square tests. Predicted versus observed negative margin probability was assessed using linear regression. Clinically significant prostate cancer (International Society of Urological Pathology grade ≥2) delineated on whole-mount histopathology served as ground truth. Results and limitations: The mean sensitivity for cancer-bearing voxels was higher for AI margins (97%) than for conventional ROIs (37%, p

Unknown geology ahead of the tunnel boring machine (TBM) brings a large safety risk for tunnel construction. Seismic ahead-prospecting using TBM drilling noise as a source can achieve near-real-time detection, meeting the requirements of TBM rapid drilling. Seismic wavefield retrieval is the key data processing step for the efficient utilization of TBM drilling noise. The traditional solution is based on cross-correlation to extract reflected waves, but the reference waves remain in the result, disturbing the imaging and interpretation of the adverse geology. To solve this problem, the deep learning method was introduced in wavefield retrieval to improve the accuracy of geological prospecting. We trained a deep neural network (DNN) with its strong nonlinear mapping capability to transform seismic data from TBM drilling noise to data from the active source. The issue lies in its features for this specific tunnel task, including the decay of the seismic signal with time and the incomplete spatial correspondence. Thus, we improved a classical DNN with the time constraint as an additional input, and an additional pre-decoder to enlarge the receptive field. Additionally, a loss function weighted by the ground truth and time constraint is improved to achieve an accurate retrieval of the effective signal, considering the little effective information in tunnel data. Finally, the workflow of the proposed method was given, and a dataset designed with reference to the field case was employed to train the network. The proposed method accurately retrieved the reflection signal with higher dominant frequencies, which helped improve the accuracy of imaging. Numerical simulations and imaging on typical geological models show that the proposed method can suppress reference waves and get more accurate results with fewer artifacts. The proposed method has been applied in the Gaoligongshan Tunnel and imaged two abnormal zones, providing meaningful geological information for TBM drilling and tunnel construction.

Abstract The NFAT1-mediated IL6/JAK-STAT signaling pathway has been observed to contribute to malignant progression in glioma patients. To predict the overall survival (OS) rate of these patients, a prognostic model was developed based on this pathway. Two datasets, mRNAseq_325 and mRNAseq_693, were obtained from the China Glioma Genome Atlas (CGGA), excluding some patients with a lack of survival information, resulting in the inclusion of 684 glioma cases. The two groups were randomly divided into training and validation groups to analyze the differential expression of NFAT1 in pan-cancer and investigate the relationship between differential NFAT1 expression and glioma clinicopathological factors and Transcriptional subtypes. A prediction model based on the IL6/JAK/STAT signaling pathway was constructed using the LASSO-COX dimension reduction analysis to predict the OS of glioma patients. Pearson correlation analysis was utilized to identify gene sets associated with patient risk scores and to perform GO and KEGG analyses. NFAT1 is differentially expressed in a variety of cancers and is enriched in the more malignant potential glioma subtypes. It is an independent prognostic factor in glioma patients, and its expression is significantly positively correlated with the IL6/JAK/STAT signalling pathway in glioma patients. The final prediction model incorporating the seven candidate genes together with other prognostic factors showed strong predictive performance in both the training and validation groups. Risk scores of glioma patients were correlated with processes such as NF-κB and protein synthesis in glioma patients. This individualized prognostic model can be used to predict the OS rate of patients with glioma at 1, 2, 3, 5, and 10 years, providing a reference value for the treatment of glioma patients.

Abstract Ulcerative colitis is a chronic inflammatory bowel disorder with cellular heterogeneity. To understand the composition and spatial changes of the ulcerative colitis ecosystem, here we use imaging mass cytometry and single-cell RNA sequencing to depict the single-cell landscape of the human colon ecosystem. We find tissue topological changes featured with macrophage disappearance reaction in the ulcerative colitis region, occurring only for tissue-resident macrophages. Reactive oxygen species levels are higher in the ulcerative colitis region, but reactive oxygen species scavenging enzyme SOD2 is barely detected in resident macrophages, resulting in distinct reactive oxygen species vulnerability for inflammatory macrophages and resident macrophages. Inflammatory macrophages replace resident macrophages and cause a spatial shift of TNF production during ulcerative colitis via a cytokine production network formed with T and B cells. Our study suggests components of a mechanism for the observed macrophage disappearance reaction of resident macrophages, providing mechanistic hints for macrophage disappearance reaction in other inflammation or infection situations.

Abstract Imaging biomarkers can assess disease progression or prognoses and are valuable tools to help guide interventions. Particularly in lung imaging, biomarkers present an opportunity to extract regional information that is more robust to the patient’s condition prior to intervention than current gold standard pulmonary function tests (PFTs). This regional aspect has particular use in functional avoidance radiation therapy (RT) in which treatment planning is optimized to avoid regions of high function with the goal of sparing functional lung and improving patient quality of life post-RT. To execute functional avoidance, detailed dose–response models need to be developed to identify regions which should be protected. Previous studies have begun to do this, but for these models to be clinically translated, they need to be validated. This work validates two metrics that encompass the main components of lung function (ventilation and perfusion) through post-mortem histopathology performed in a novel porcine model. With these methods validated, we can use them to study the nuanced radiation-induced changes in lung function and develop more advanced models.

Soil hydraulic parameters are influenced by various inherent soil properties, such as pore structure and organic matter content, which can vary with changes in the ecosystem. However, identifying the temporal variations of soil hydraulic parameters in a co-evolving soil-vegetation system remains a challenge. This study focused on a tropical forest with significant seasonal variations in vegetation attributes, evaporation, and carbon fluxes over a five-year monitoring period. The particle batch smoother algorithm was integrated with an unsaturated flow model to identify the seasonally varied soil hydraulic parameters through assimilation of in-situ measured soil moisture. As a benchmark, the Generalized Likelihood Uncertainty Estimation method was applied to optimize soil hydraulic parameters without considering temporal variation. The results indicated that the temporally varying soil hydraulic parameters exhibited regular seasonal patterns and outperformed the unvaried soil hydraulic parameters in terms of reducing the errors in modeling of soil moisture and evaporation. Moreover, the seasonal variations in soil hydraulic parameters were closely linked to changes in the litterfall and terrestrial carbon fluxes over time. Specifically, due to the hysteresis of the transformation from litterfall to soil organic matter, the accumulated litterfall in Hot-dry season can replenish the soil organic matter, resulting in an increase in field capacity and saturated hydraulic conductivity in the Hot-rainy season. However, the intense decomposition of soil organic matter under high temperature in Hot-dry season led to a decrease in field capacity and saturated hydraulic conductivity. This study emphasizes the value of the particle batch smoother algorithm in detecting temporal variations in soil hydraulic parameters within a coevolving soil-vegetation system, thereby contributing to a more comprehensive understanding of the intricate dynamics within the ecohydrological system under a changing environment.

Abstract Background Dysbiosis of gut microbiota is causally linked to impaired host glucose metabolism. We aimed to study effects of the new method of fecal microbiota transplantation, washed microbiota transplantation (WMT), on reducing glycemic variability (GV) in unstable diabetes. Methods Fourteen eligible patients received three allogenic WMTs and were followed up at 1 week, 1 month, and 3 months. Primary outcomes were daily insulin dose, glucose excursions during meal tests, and GV indices calculated from continuous monitoring or self‐monitoring glucose values. Secondary outcomes were multiomics data, including 16S rRNA gene sequencing, metagenomics, and metabolomics to explore underlying mechanisms. Results Daily insulin dose and glucose excursions markedly dropped, whereas GV indices significantly improved up to 1 month. WMT increased gut microbial alpha diversity, beta diversity, and network complexity. Taxonomic changes featured lower abundance of genera Bacteroides and Escherichia‐Shigella, and higher abundance of genus Prevotella. Metagenomics functional annotations revealed enrichment of distinct microbial metabolic pathways, including methane biosynthesis, citrate cycle, amino acid degradation, and butyrate production. Derived metabolites correlated significantly with improved GV indices. WMT did not change circulating inflammatory cytokines, enteroendocrine hormones, or C‐peptide. Conclusions WMT showed strong ameliorating effect on GV, raising the possibility of targeting gut microbiota as an effective regimen to reduce GV in diabetes.

To achieve the high-quality economic development, China implemented the energy-consuming right trading system, aiming to leverage market-oriented environmental regulations to drive enterprises’ green transformation. The purpose of this paper is to figure out whether the energy-consuming right trading system has promoted the green technology innovation of enterprises. Using the empirical analysis method, the paper samples 718 listed industrial enterprises from 2014 to 2019 and constructs a DID model to assess the impact. The results indicate a significant promotion of green technology innovation by the trading system. Non-state-owned and non-high-energy-consuming enterprises exhibit greater sensitivity to the energy-consuming right trading system. Additionally, the institutional environment plays a regulatory role in promoting green innovation of enterprises. Furthermore, it is found that the energy-consuming right trading system influences enterprises by enhancing capital allocation efficiency. The findings are valuable for the government in shaping energy policies and fostering the green transformation of enterprises.

Cardiac dysfunction frequently emerges in the initial stages of cancer cachexia, posing a significant complication of the disease. Physical fitness is commonly recommended in these early stages of cancer cachexia due to its beneficial impacts on various aspects of the condition, including cardiac dysfunction. However, the direct functional impacts of exercise on the heart during cancer cachexia largely remain unexplored. In this study, we induced cancer cachexia in mice using a metastatic B16F10 melanoma model. Concurrently, these mice underwent a low-intensity exercise regimen to investigate its potential role in cardiac function during cachexia. Our findings indicate that exercise training can help prevent metastatic melanoma-induced muscle loss without significant alterations to body and fat weight. Moreover, exercise improved the melanoma-induced decline in left ventricular ejection fraction and fractional shortening, while also mitigating the increase in high-sensitive cardiac troponin T levels caused by metastatic melanoma in mice. Transcriptome analysis revealed that exercise significantly reversed the transcriptional alterations in the heart induced by melanoma, which were primarily enriched in pathways related to heart contraction. These results suggest that exercise can improve systolic heart function and directly influence the transcriptome of the heart during metastatic melanoma-induced cachexia.