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Cross-Regional Model Transfer (CRMT) provides a solution to crop classification challenges in target regions with limited labeled samples. However, when the source region (source domain) and the target region (target domain) are spatially distant, a shift in crop Satellite Image Time Series (SITS) occurs, resulting in suboptimal performance of SITS-based classification models in the target domain. To address this issue, we propose a novel domain adaptation method, named SITS Correction. This method mitigates the SITS shift by introducing correction factors to improve the classification performance of CRMT. The correction factors consist of source domain correction units and target domain correction units, which are from variables associated with the SITS shift. In cases where labeled samples are available in the target domain, the SITS have calculated Barycenter (i.e. SitsB) as the correction units, which then combine the source domain and the target domain’s SitsB to form a SITS-based correction factor. When no labeled samples are available, the correction units are substituted by Barycenter of the temperature variables time series (i.e. temperature indicator) influencing crop growth, and the correction factors are composed of temperature indicators from both the source and target domains. We validate SITS Correction through transfer experiments conducted in four regions, encompassing various years, crops, and classification methods. Experimental results demonstrate a significant improvement in crop classification performance with SITS Correction in both scenarios – target domain with labeled samples (accuracy increased by 39.1%) and target domain without labeled samples (accuracy increased by 14.8%). This research provides a practical option for crop classification in target regions with either limited or no labeled samples.

ObjectiveTo understand the correlation between hepatitis C virus (HCV) antibodies and HCV RNA viral load in HIV co-infected individuals and to provide a basis for predicting HCV RNA based on HCV antibody detection. MethodsThe blood samples and relevant data were collected from 979 HIV-infected individuals with HCV antibody detection at the Yunnan Provincial Infectious Disease Hospital from November 2022 to March 2023. HCV antibodies were detected by enzyme-linked immunosorbent assay (ELISA), and HCV RNA was measured by real-time reverse transcription-PCR (qRT-PCR). T-test and chi-squared test were used for data analysis. ResultsAmong all participants, 17.9% (n = 176) were HCV antibody positive, and the positive rates were 17.1% (n = 110) and 19.6% (n = 66) for male and female participants, respectively. The rate of HCV antibody positivity was associated with age (χ2 = 107.662, P < 0.001), with the highest rate of 29.6% in participants aged 41 – 60 years and the lowest rate of 0% in participants younger than 20 years. Among participants who were HCV antibody positive, the HCV RNA positivity rate was 46.6%, with positive rates of 50.0% and 40.9% in male and female participants, respectively, and the positive rate was not related to age (P > 0.05). Spearman rank correlation analysis showed a positive correlation between HCV RNA positivity and HCV antibody levels (r = 0.237, P < 0.05). Receiver operating characteristic (ROC) curve analysis showed that an HCV antibody level ≥ 16.78 (signal to cutoff ratio, S/CO) indicated HCV RNA positivity. ConclusionThe rate of HCV antibody positivity was high in the HIV-infected population and positively correlated with HCV RNA levels, with an S/CO value of 16.78 being an optimal threshold for predicting HCV RNA positivity, which could be used in the diagnosis and management of patients co-infected with HIV and HCV.

Abstract Maintaining skeletal muscle mass is important for improving muscle strength and function. Hence, maximizing lean body mass (LBM) is the primary goal for both elite athletes and fitness enthusiasts. The use of amino acids as dietary supplements is widespread among athletes and physically active individuals. Extensive literature analysis reveals that branched-chain amino acids (BCAA), creatine, glutamine and β-alanine may be beneficial in regulating skeletal muscle metabolism, enhancing LBM and mitigating exercise-induced muscle damage. This review details the mechanisms of these amino acids, offering insights into their efficacy as supplements. Recommended dosage and potential side effects are then outlined to aid athletes in making informed choices and safeguard their health. Lastly, limitations within the current literature are addressed, highlighting opportunities for future research.

Enhancing the endurance and efficiency of polymer electrolyte membrane fuel cells (PEMFCs) requires efficient thermal management. This comprehensive review examines the primary cooling techniques employed in PEMFC systems, concentrating on techniques for air and liquid cooling. Liquid cooling, which circulates a coolant through channels adjacent to the ability of the fuel cell stack to maintain ideal operating temperatures, is highlighted and significantly reduces temperature variations, thereby improving overall efficiency and lifespan. In contrast, air cooling, while simpler and more cost-effective, is less effective in high-power applications due to its reliance on ambient air for heat dissipation. The review also discusses advancements in thermal management strategies, including innovative designs for heat exchangers and the integration of thermal resistance networks, which enhance heat dissipation efficiency. Furthermore, the paper underscores the importance of developing durable materials to address catalyst and membrane degradation, and it explores the potential for integrating PEMFCs using renewable energy sources to encourage environmentally friendly transportation solutions. By identifying current challenges and proposing future research directions, this review aims to support the continuous creation of effective and reliable PEMFC technologies.

Abstract Autophagy modulates the degradation and recycling of intracellular materials and contributes to male gametophyte development and male fertility in plants. However, whether autophagy participates in seed development remains largely unknown. Here, we demonstrate that autophagy is crucial for timely programmed cell death (PCD) in the integumentary tapetum, the counterpart of anther tapetum, influencing embryo pattern formation and seed viability. Inhibition of autophagy resulted in delayed PCD of the integumentary tapetum and defects in embryo patterning. Cell-type-specific restoration of autophagic activities revealed that the integumentary tapetum plays a non-autonomous role in embryo patterning. Furthermore, high-throughput, comprehensive lipidomic analyzes uncovered an unexpected seed-developmental-stage-dependent role of autophagy in seed lipid metabolism: it contributes to triacylglycerol degradation before fertilization and to triacylglycerol biosynthesis after fertilization. This study highlights the critical role of autophagy in regulating timely integumentary tapetum PCD and reveals its significance in seed lipid metabolism and viability.

Reliable classification results are crucial for guiding agricultural production, forecasting crop yield, and ensuring food security. Generating reliable classification results is relatively simple in regions with sufficient labeled samples, but regions with limited labeled samples remain a significant challenge. In this study, we propose two new solutions that leverage the feature representation capabilities of deep learning and the sample reuse potential of transfer learning to solve the limited label problem. Specifically, we develop a Variable-dimensional Symmetric Network with Position Encoding (VPSNet) to improve the efficiency of labeled sample utilization. Additionally, we introduce a transfer strategy based on the Inter-Regional Discrepancies in Crop Time Series (IRDCTS) to expand the labeled sample reuse region. We evaluated the proposed model in three regions with limited labels between 2017 and 2018. Experimental results show that our model has superior discriminative feature extraction capabilities compared to other existing models. The feasibility of the proposed transfer strategy is tested in three pair regions, showing that IRDCTS can enhance the model adaptability by reducing inter-domain discrepancies. This study provides a comprehensive solution to the classification problem of the limited labeled samples, involving both the development of classification models and the implementation of transfer strategies.

BackgroundDiabetic retinopathy (DR) is considered one of the most severe complications of diabetes mellitus, but its pathogenesis is still unclear. We hypothesize that certain genes exert a pivotal influence on the progression of DR. This study explored biomarkers for the diagnosis and treatment of DR through bioinformatics analysis.MethodsWithin the GSE221521 and GSE189005 datasets, candidate genes were acquired from intersections of genes obtained using WGCNA and DESeq2 packages. Mendelian randomization (MR) analysis selected candidate biomarkers exhibiting causal relationships with DR. Receiver Operating Characteristic (ROC) analysis determined the diagnostic efficacy of biomarkers, the expression levels of biomarkers were verified in the GSE221521 and GSE189005 datasets, and a nomogram for diagnosing DR was constructed. Enrichment analysis delineated the roles and pathways associated with the biomarkers. Immune infiltration analysis analyzed the differences in immune cells between DR and control groups. The miRNet and networkanalyst databases were then used to predict the transcription factors (TFs) and miRNAs, respectively, of biomarkers. Finally, RT-qPCR was used to verify the expression of the biomarkers in vitro.ResultsMR analysis identified 13 candidate biomarkers that had causal relationships with DR. The ROC curve demonstrated favorable diagnostic performance of three biomarkers (OSER1, HIPK2, and DDRGK1) for DR, and their expression trends were consistent across GSE221521 and GSE189005 datasets. The calibration curves and ROC curves indicated good predictive performance of the nomogram. The biomarkers were enriched in pathways of immune, cancer, amino acid metabolism, and oxidative phosphorylation. Ten immune cell lines showed notable disparities between the DR and control groups. Among them, effector memory CD8+ T cells, plasmacytoid dendritic cells, and activated CD4+ T cells exhibited good correlation with biomarker expression. The TF-mRNA-miRNA network suggested that hsa-mir-92a-3p, GATA2, and RELA play important roles in biomarker targeting for DR. RT-qPCR results also demonstrated a notably high expression of HIPK2 in patients with DR, whereas notably low expression of OSER1.ConclusionOSER1, HIPK2, and DDRGK1 were identified as biomarkers for DR. The study findings provide novel insights into the pathogenesis of DR.

Objectives This study aimed to explore the temporal relationship between blood glucose, lipids and body mass index (BMI), and their impacts on atherosclerosis (AS).Design A prospective cohort study was designed.Setting and participants A total of 2659 subjects from Harbin Cohort Study on Diet, Nutrition and Chronic Non-communicable Diseases, and aged from 20 to 74 years were included.Primary and secondary outcome measures Body weight, height, fasting blood glucose (FBG) and 2-hour postprandial glucose (2-h PG), blood lipids including total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-c) and high-density lipoprotein cholesterol (HDL-c) were measured at baseline and follow-up. Brachial ankle pulse wave velocity (baPWV) was examined at follow-up as a marker of AS risk. Logistic regression analysis, cross-lagged path analysis and mediation analysis were performed to explore the temporal relationships between blood glucose, lipids and BMI, and their impacts on AS risk.Results Logistic regression analysis indicated that increased FBG, 2-h PG, TC, TG, LDL-c and BMI were positively associated with AS risk, while increased HDL-c was negatively associated with AS risk. The path coefficients from baseline blood parameters to the follow-up BMI were significantly greater than those from baseline BMI to the follow-up blood parameters. Mediation analysis suggested that increased FBG, 2-h PG, TC, TG and LDL-c could increase AS risk via increasing BMI, the effect intensity from strong to weak was LDL-c>TC>TG>FBG>2 h PG, while increased HDL-c could decrease AS risk via decreasing BMI.Conclusions Changes in blood glucose and lipids could cause change in BMI, which mediated the impacts of blood glucose and lipids on AS risk. These results highlight the importance and provide support for the early and comprehensive strategies of AS prevention and control.

AbstractContext Clerodendranthus spicatus Thunb. (Labiatae) (CS), a perennial traditional Chinese medicinal herb that can reduce serum uric acid (sUA) levels and ameliorate renal function is widely used to treat hyperuricaemic nephropathy (HN).Objective To investigate the molecular mechanism of action of CS in HN treatment using in vivo and in vitro experiments.Materials and methods Sprague-Dawley rats were randomly divided into control, HN, CS and positive control allopurinol groups. The HN group was intraperitoneally injected with 750 mg/kg oxonic acid potassium (OA), whereas the CS group was injected with OA along with a gavage of CS (low dose 3.125 g/kg, high dose 6.25 g/kg) for five weeks. For in vitro studies, uric acid-treated HK2 cells were used to verify the therapeutic mechanism of CS in HN.Results HN rats exhibit pathological phenotypes of elevated sUA levels and renal injury. CS significantly improved these symptoms and sUA (p

Aiming at the difficulty of measuring various costs and time-consuming elements in multimodal transport, this paper constructs a green vehicle comprehensive multimodal transport model which incorporates transportation, transit, quality damage, fuel consumption, and carbon emission costs and proposes a hybrid embedded time window to calculate the time penalty cost in order to reflect the actual transport characteristics. Furthermore, in order to better solve the model, a hybrid sand cat swarm optimization (HSCSO) algorithm is proposed by introducing Logistic–Tent chaotic mapping and an adaptive lens opposition-based learning strategy to enhance the global search capability, and inspired by the swarm intelligence scheme, a momentum–bellicose strategy and an equilibrium crossover pool are introduced to improve the search efficiency and convergence ability. Through testing nine benchmark functions, the HSCSO algorithm exhibits superior convergence accuracy and speed in dealing with complex multi-dimensional problems. Based on the excellent global performance, the HSCSO algorithm was utilized for multimodal vehicle transportation in East China, and a path with a lower comprehensive cost was successfully planned, which proved the effectiveness of the HSCSO algorithm in green intermodal transport path planning.

Abstract Background High neutrophil/lymphocyte ratio (NLR) is associated with poor prognosis in ischemic stroke. However, the role of NLR in cerebral small vessel disease (CSVD) is controversial. Herein, we evaluated the value of NLR in identifying CSVD and its relationship with the common imaging markers of CSVD. Methods A total of 667 patients were enrolled in this study, including 368 in the CSVD group and 299 in the non‐CSVD group. Clinical, laboratory, and imaging data were collected. The relationship of NLR with CSVD and common imaging markers of CSVD were analyzed with univariate and multivariate logistic regression analysis. The predictive value of NLR was assessed with the receiver operating characteristic curve. Results NLR (odds ratio [OR] = 1.929, 95% confidence interval [CI] = 1.599–2.327, p

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.

Abstract Background Owing to the advances in diagnosis and therapy, survival or remission rates for lymphoma have improved prominently. Apart from the lymphoma- and chemotherapy-related somatic symptom burden, increasing attention has been drawn to the health-related quality of life. The application of 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) has been routinely recommended for the staging and response assessment of FDG-avid lymphoma. However, up till now, only a few researches have investigated the brain metabolic impairments in patients with pre-treatment lymphoma. The determination of the lymphoma-related metabolic brain pattern would facilitate exploring the tailored therapeutic regimen to alleviate not only the physiological, but also the psychological symptoms. In this retrospective study, we aimed to establish the diffuse large B-cell lymphoma-related pattern (DLBCLRP) of metabolic brain network and investigate the correlations between DLBCLRP and several indexes of the staging and response assessment. Results The established DLBCLRP was characterized by the increased metabolic activity in bilateral cerebellum, brainstem, thalamus, striatum, hippocampus, amygdala, parahippocampal gyrus and right middle temporal gyrus and by the decreased metabolic activity in bilateral occipital lobe, parietal lobe, anterior cingulate gyrus, midcingulate cortex and medial frontal gyrus. Significant difference in the baseline expression of DLBCLRP was found among complete metabolic response (CMR), partial metabolic response (PMR) and progressive metabolic disease (PMD) groups (P 0.05), the post-treatment declines of DLBCLRP expression were significantly positively correlated with Ann Arbor staging (r s = 0.284, P

The prediction of vessel trajectories plays a crucial role in ensuring maritime safety and reducing maritime accidents. Substantial progress has been made in trajectory prediction tasks by adopting sequence modeling methods, containing recurrent neural networks (RNNs) and sequence-to-sequence networks (Seq2Seq). However, (1) most of these studies focus on the application of trajectory information, such as the longitude, latitude, course, and speed, while neglecting the impact of differing vessel features and behavioral preferences on the trajectories. (2) Challenges remain in acquiring these features and preferences, as well as enabling the model to sensibly integrate and efficiently express them. To address the issue, we introduce a novel deep framework VEPO-S2S, consisting of a Multi-level Vessel Trajectory Representation Module (Multi-Rep) and a Feature Fusion and Decoding Module (FFDM). Apart from the trajectory information, we first defined the Multi-level Vessel Characteristics in Multi-Rep, encompassing Shallow-level Attributes (vessel length, width, draft, etc.) and Deep-level Features (Sailing Location Preference, Voyage Time Preference, etc.). Subsequently, Multi-Rep was designed to obtain trajectory information and Multi-level Vessel Characteristics, applying distinct encoders for encoding. Next, the FFDM selected and integrated the above features from Multi-Rep for prediction by employing both a priori and a posteriori mechanisms, a Feature Fusion Component, and an enhanced decoder. This allows the model to efficiently leverage them and enhance overall performance. Finally, we conducted comparative experiments with several baseline models. The experimental results demonstrate that VEPO-S2S is both quantitatively and qualitatively superior to the models.

Abstract Deep learning has recently been proven to deliver excellent performance in multi-view stereo (MVS). However, it is difficult for deep learning-based MVS approaches to balance their efficiency and effectiveness. Towards this end, we propose the DSC-MVSNet, a novel coarse-to-fine and end-to-end framework for more efficient and more accurate depth estimation in MVS. In particular, we propose an attention aware 3D UNet-shape network, which first uses the depthwise separable convolutions for cost volume regularization. This mechanism enables effective aggregation of information and significantly reduces the model parameters and computation by transforming the ordinary convolution on cost volume as depthwise convolution and pointwise convolution. Besides, a 3D-Attention module is proposed to alleviate the feature mismatching problem in cost volume regularization and aggregate the important information of cost volume in three dimensions (i.e. channel, space, and depth). Moreover, we propose an efficient Feature Transfer Module to upsample the low-resolution (LR) depth map to a high-resolution (HR) depth map to achieve higher accuracy. With extensive experiments on two benchmark datasets, i.e. DTU and Tanks & Temples, we demonstrate that the parameters of our model are significantly reduced to $$25\%$$ 25 % of the state-of-the-art model MVSNet. Besides, our method outperforms or maintains on par accuracy with the state-of-the-art models. Our source code is available at https://github.com/zs670980918/DSC-MVSNet .

For difficult overlap extension PCR, a Gibson assembly process was inserted between the two PCR rounds to facilitate the formation of complete gene templates at a moderate temperature. That is, after amplifying each DNA fragment, they were preluded by a Gibson assembly process in equal proportion. Then, the assembled mixture was used as a template for the second PCR round. This idea was tested and verified by taking the cloning example of a single and a double site mutation of the retinoblastoma gene. This scheme associates overlap extension PCR with Gibson assembly exquisitely, significantly improving gene amplification efficiency, particularly in the fusion of long genes and multifragments using overlap extension PCR.

Abstract Particulate 2-methyltetrols (2-MT) and 2-methylglyceric acid (2-MG) are typically used to indicate the abundance of isoprene-derived secondary organic aerosols (SOA). However, their formation and fate are not fully understood. In this study, we showed that particulate 2-MT and 2-MG collected at multiple monitoring sites under a wide range of atmospheric and emission conditions, with concentrations spanning six orders of magnitudes, are well reproduced with an expanded isoprene-SOA scheme implemented into the Community Multiscale Air Quality (CMAQ) model. The scheme considers their three-phase (gas-aqueous-organic phase) partitioning, formation from acid-driven multiphase reactions, and degradation by OH radicals in the gas and aqueous phases. The model results reveal that a non-aqueous formation pathway or direct biogenic emission is needed to supplement the commonly assumed acid-driven multiphase reaction process to explain the observed 2-MT concentrations. This missing pathway contributes to 20–40% of 2-MT in areas with aerosol pH

Introduction: The excretion and absorption of uric acid (UA) by the kidneys helps regulate serum UA levels. GLUT9, encoded by SLC2A9, is mainly expressed in the renal tubules responsible for UA absorption. SLC2A9 polymorphisms are associated with different serum UA levels. However, the lack of proper in vitro models has stalled research on the mechanisms of single nucleotide polymorphisms (SNPs) that affect UA metabolism in human urate transporters.Methods: In this study, we constructed a gene-edited human embryonic stem cells-9 (ESC-H9) derived kidney organoid bearing rs16890979, an SLC2A9 missense mutation with undetermined associations with hyperuricemia or hypouricemia. Kidney organoids derived from ESC-H9 with genetical overexpression (OE) and low expression (shRNA) of SLC2A9 to serve as controls to study the function of SLC2A9. The function of rs16890979 on UA metabolism was evaluated after placing the organoids to urate-containing medium and following histopathological analysis.Results: The kidney organoids with heterozygous or homozygous rs16890979 mutations showed normal SLC2A9 expression levels and histological distribution, phenotypically similar to the wild-type controls. However, reduced absorption of UA by the kidney organoids with rs16890979 mutants was observed. This finding together with the observation that UA absorption is increased in organoids with SLC2A9 overexpression and decreased in those with SLC2A9 knockdown, suggest that GLUT9 is responsible for UA absorption, and the rs16890979 SNP may compromise this functionality. Moreover, epithelial-mesenchymal transition (EMT) was detected in organoids after UA treatment, especially in the kidney organoid carrying GLUT9OE, suggesting the cytobiological mechanism explaining the pathological features in hyperuricosuria-related renal injury.Discussion: This study showing the transitional value of kidney organoid modeling the function of SNPs on UA metabolism. With a defined genetic background and a confirmed UA absorption function should be useful for studies on renal histological, cellular, and molecular mechanisms with this organoid model.

The relationship between the structure characteristics and performances of coal-based hydrogenation isomeric (CTL) base oil and metallocene-catalyzed coal-based poly-alpha-olefin (mPAO) base oil is clarified in this paper. CTL and mPAO were compared with typical petroleum-based and natural gas-based commercial API III and IV base oils. Pressurized differential scanning calorimetry (PDSC), the rotary bomb oxidation test (RBOT), and a four-ball friction tester were used to evaluate the oxidation stability and lubrication performance of base oils under different working conditions. The sensitivity of different base oils to typical antioxidants and extreme-pressure antiwear agents was compared. In particular, the composition and structure of CTL base oil are clearly different from GTL and mineral base oil. The coal-based CTL and mPAO base oils exhibit commendable viscosity–temperature properties, coupled with low-temperature fluidity, fire safety, and minimal evaporation loss. The lubricating properties, oxidation stability, and sensitivity to extreme-pressure antiwear agents of CTL are close to those of similar base oils. However, the sensitivity of CTL to typical antioxidants is relatively poor. In addition, compared with commercial PAO base oil, mPAO has a lower isomerization degree and fewer isomerization types. The oxidation stability and sensitivity to typical antioxidants of mPAO base oil are comparable with those of commercial PAO base oil, while its lubrication performance and sensitivity to typical extreme-pressure antiwear agents are significantly better than those of commercial PAO base oil.

In the field of multi-view satellite photogrammetry, the neural radiance field (NeRF) method has received widespread attention due to its ability to provide continuous scene representation and realistic rendering effects. However, the satellite radiance field methods based on the NeRF are limited by the slow training speed of the original NeRF, and the scene reconstruction efficiency is low. Training for a single scene usually takes 8–10 h or even longer, which severely constrains the utilization and exploration of the NeRF approach within the domain of satellite photogrammetry. In response to the above problems, we propose an efficient neural radiance field method called SatelliteRF, which aims to quickly and efficiently reconstruct the earth’s surface through multi-view satellite images. By introducing innovative multi-resolution hash coding, SatelliteRF enables the model to greatly increase the training speed while maintaining high reconstruction quality. This approach allows for smaller multi-layer perceptron (MLP) networks, reduces the computational cost of neural rendering, and accelerates the training process. Furthermore, to overcome the challenges of illumination changes and transient objects encountered when processing multi-date satellite images, we adopt an improved irradiance model and learn transient embeddings for each image. This not only increases the adaptability of the model to illumination variations but also improves its ability to handle changing objects. We also introduce a loss function based on stochastic structural similarity (SSIM) to provide structural information of the scene for model training, which further improves the quality and detailed performance of the reconstructed scene. Through extensive experiments on the DFC 2019 dataset, we demonstrate that SatelliteRF is not only able to significantly reduce the training time for the same region from the original 8–10 h to only 5–10 min but also achieves better performance in terms of rendering and the reconstruction quality.

ObjectiveThe aim of this study was to explore the influential mechanism of the relationship between sleep quality and activities of daily living (ADL) in patients with Parkinson’s disease (PD), we hypothesized disease severity as a mediator and assumed the mediating process was regulated by cognition.Methods194 individuals with PD (95 women and 99 men) were enrolled in study. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality of PD patients. Patients’ ADL, disease severity and cognition were measured by the Unified Parkinson’s Disease Rating Scale-II (UPDRSII), Hoehn-Yahr (H-Y) Scale, and Mini-Mental State Examination (MMSE). We investigated the mediating role of disease severity and the moderating effect of cognition on the association between sleep quality and ADL in PD patients.ResultsThe score of UPDRSII was positively correlated with the score of PSQI and H-Y stage, while the score of MMSE was negatively correlated with the score of H-Y stage and UPDRSII. Sleep quality predicts disease severity, and disease severity predicts ADL. Disease severity mediated the relationship between sleep quality and ADL, and the mediating effect was 0.179. Cognition alone did not affect ADL, but the interaction between disease severity and cognition was significantly affected ADL, confirming the moderating effect of cognition in PD patients.ConclusionDisease severity mediated the association between sleep quality and ADL, good cognition significantly reduced disease severity’s mediating influence on the relationship between sleep quality and ADL. Our study indicated a close relationship between ADL and sleep and cognition in PD, and also provided new insights into the overall management of PD and a better quality of life of PD patients.

Maize is the most widely planted food crop in China, and maize inbred lines, as the basis of maize genetic breeding and seed breeding, have a significant impact on China’s seed security and food safety. Satellite remote sensing technology has been widely used for growth monitoring and yield estimation of various crops, but it is still doubtful whether the existing remote sensing monitoring means can distinguish the growth difference between maize inbred lines and hybrids and accurately estimate the yield of maize inbred lines. This paper explores a method for estimating the yield of maize inbred lines based on the assimilation of crop models and remote sensing data, initially solves the problem. At first, this paper analyzed the WOFOST(World Food Studies)model parameter sensitivity and used the MCMC(Markov Chain Monte Carlo) method to calibrate the sensitive parameters to obtain the parameter set of maize inbred lines differing from common hybrid maize; then the vegetation indices were selected to establish an empirical model with the measured LAI(Leaf Area Index) at three key development stages to obtain the remotely sensed estimated LAI; finally, the yield of maize inbred lines in the study area was estimated and mapped pixel by pixel using the EnKF(Ensemble Kalman Filter) data assimilation algorithm. Also, this paper compares a method of assimilation by setting a single parameter. Instead of the WOFOST parameter optimization process, a parameter representing the growth weakness of the inbred lines was set in WOFOST to distinguish the inbred lines from the hybrids. The results showed that the yield estimated by the two methods compared with the field measured yield data had R2: 0.56 and 0.18, and RMSE: 684.90 Kg/Ha and 949.95 Kg/Ha, respectively, which proved that the crop growth model of maize inbred lines established in this study combined with the data assimilation method could initially achieve the growth monitoring and yield estimation of maize inbred lines.

Numerous antibiotic resistance genes (ARGs) and virulence factors (VFs) found in animal manure pose significant risks to human health. However, the effects of graphene sodium selenite (GSSe), a novel chemical nano-Selenium, and biological nano-Selenium (BNSSe), a new bioaugmentation nano-Se, on bacterial Se metabolism, chemotaxis, ARGs, and VFs in animal manure remain unknown. In this study, we investigated the effects of GSSe and BNSSe on ARGs and VFs expression in broiler manure using high-throughput sequencing. Results showed that BNSSe reduced Se pressure during anaerobic fermentation by inhibiting bacterial selenocompound metabolism pathways, thereby lowering manure Selenium pollution. Additionally, the expression levels of ARGs and VFs were lower in the BNSSe group compared to the Sodium Selenite and GSSe groups, as BNSSe inhibited bacterial chemotaxis pathways. Co-occurrence network analysis identified ARGs and VFs within the following phyla Bacteroidetes (genera Butyricimonas, Odoribacter, Paraprevotella, and Rikenella), Firmicutes (genera Lactobacillus, Candidatus_Borkfalkia, Merdimonas, Oscillibacter, Intestinimonas, and Megamonas), and Proteobacteria (genera Desulfovibrio). The expression and abundance of ARGs and VFs genes were found to be associated with ARGs-VFs coexistence. Moreover, BNSSe disruption of bacterial selenocompound metabolism and chemotaxis pathways resulted in less frequent transfer of ARGs and VFs. These findings indicate that BNSSe can reduce ARGs and VFs expression in animal manure by suppressing bacterial selenocompound metabolism and chemotaxis pathways.

We introduce isQ, a new software stack for quantum programming in an imperative programming language, also named isQ. The aim of isQ is to make programmers write quantum programs as conveniently as possible. In particular, 1) the isQ language and its compiler contain many useful features, including but not limited to classical control flow, such as recursion, decomposition of self-defined unitary gates, and oracle programming and its circuit realization. 2) To make it flexible, an isQ program can be compiled into several different kinds of intermediate representation and assemblies, including QIR, eQASM, OpenQASM 3.0, and QCIS (specially tailored for the superconducting quantum hardware at the University of Science and Technology of China). 3) Besides interfacing isQ with real superconducting hardware, a QIR simulator is also developed for the demonstration and testing of isQ programs. The isQ software stack encompasses abundant compiler optimizations of high-level quantum programs. To realize it, a distinct multilevel intermediate representation (MLIR) dialect name isQ-IR is proposed.

Abstract Background Biological experiments have demonstrated that circRNA plays an essential role in various biological processes and human diseases. However, it is time-consuming and costly to merely conduct biological experiments to detect the association between circRNA and diseases. Accordingly, developing an efficient computational model to predict circRNA-disease associations is urgent. Methods In this research, we propose a multiple heterogeneous networks-based method, named XGBCDA, to predict circRNA-disease associations. The method first extracts original features, namely statistical features and graph theory features, from integrated circRNA similarity network, disease similarity network and circRNA-disease association network, and then sends these original features to the XGBoost classifier for training latent features. The method utilizes the tree learned by the XGBoost model, the index of leaf that instance finally falls into, and the 1 of K coding to represent the latent features. Finally, the method combines the latent features from the XGBoost with the original features to train the final model for predicting the association between the circRNA and diseases. Results The tenfold cross-validation results of the XGBCDA method illustrate that the area under the ROC curve reaches 0.9860. In addition, the method presents a striking performance in the case studies of colorectal cancer, gastric cancer and cervical cancer. Conclusion With fabulous performance in predicting potential circRNA-disease associations, the XGBCDA method has the promising ability to assist biomedical researchers in terms of circRNA-disease association prediction.

Abstract Background Increasing evidence shows that circRNA plays an essential regulatory role in diseases through interactions with disease-related miRNAs. Identifying circRNA-disease associations is of great significance to precise diagnosis and treatment of diseases. However, the traditional biological experiment is usually time-consuming and expensive. Hence, it is necessary to develop a computational framework to infer unknown associations between circRNA and disease. Results In this work, we propose an efficient framework called MSPCD to infer unknown circRNA-disease associations. To obtain circRNA similarity and disease similarity accurately, MSPCD first integrates more biological information such as circRNA-miRNA associations, circRNA-gene ontology associations, then extracts circRNA and disease high-order features by the neural network. Finally, MSPCD employs DNN to predict unknown circRNA-disease associations. Conclusions Experiment results show that MSPCD achieves a significantly more accurate performance compared with previous state-of-the-art methods on the circFunBase dataset. The case study also demonstrates that MSPCD is a promising tool that can effectively infer unknown circRNA-disease associations.

IntroductionOn Hainan Island, a rubber plantation that occupies a large swath of land plays an important role in the regional carbon budget. However, the carbon exchange of the rubber plantation is poorly understood.MethodsIn this study, using the eddy covariance methods we measured carbon metrics in the rubber plantation for 13 years from 2010 to 2022.ResultsWe clarified that the rubber plantation is a carbon sink and the annual net ecosystem exchange (NEE), ecosystem respiration, and gross primary production were −911.89 ± 135.37, 1,528.04 ± 253.50, and 2,439.93 ± 259.63 gC·m−2·a−1, respectively. Carbon fluxes differed between interannual years; specifically, rainy season fluxes were nearly double dry season fluxes. Radiation explained 46% of the variation for NEE in rainy season, and temperature explained 36% of the variation for NEE in the dry season. LAI explained the highest proportion of the monthly variation in NEE (R2 = 0.72, p < 0.001), indicating that when hydrothermal conditions are sufficient phenology may be the primary factor controlling carbon sequestration of rubber plantation. Due to climate change, there is an increasing probability of extreme climate events, such as typhoons, heat waves, and drought. Thus, we compared NEE before and after such events and results show extreme climate events reduce carbon uptake in the rubber plantation. We found that typhoons reduced NEE to varying degrees on different timescales. Heat waves generally decreased NEE during the day but recovered quickly and increased carbon uptake if there was sufficient precipitation. Drought reduced carbon uptake and continued to decrease even after precipitation.DiscussionEstimating the carbon sink capacity of the rubber plantation and studying the response to regional environmental changes are important for both applied research (carbon sink research and market trading, sink enhancement, and emission reduction, etc.) and basic research (land use change, phenology change, etc.).

Three-dimensional reconstruction is a key technology employed to represent virtual reality in the real world, which is valuable in computer vision. Large-scale 3D models have broad application prospects in the fields of smart cities, navigation, virtual tourism, disaster warning, and search-and-rescue missions. Unfortunately, most image-based studies currently prioritize the speed and accuracy of 3D reconstruction in indoor scenes. While there are some studies that address large-scale scenes, there has been a lack of systematic comprehensive efforts to bring together the advancements made in the field of 3D reconstruction in large-scale scenes. Hence, this paper presents a comprehensive overview of a 3D reconstruction technique that utilizes multi-view imagery from large-scale scenes. In this article, a comprehensive summary and analysis of vision-based 3D reconstruction technology for large-scale scenes are presented. The 3D reconstruction algorithms are extensively categorized into traditional and learning-based methods. Furthermore, these methods can be categorized based on whether the sensor actively illuminates objects with light sources, resulting in two categories: active and passive methods. Two active methods, namely, structured light and laser scanning, are briefly introduced. The focus then shifts to structure from motion (SfM), stereo matching, and multi-view stereo (MVS), encompassing both traditional and learning-based approaches. Additionally, a novel approach of neural-radiance-field-based 3D reconstruction is introduced. The workflow and improvements in large-scale scenes are elaborated upon. Subsequently, some well-known datasets and evaluation metrics for various 3D reconstruction tasks are introduced. Lastly, a summary of the challenges encountered in the application of 3D reconstruction technology in large-scale outdoor scenes is provided, along with predictions for future trends in development.

A 1-Pbps orbital-angular-momentum fibre-optic transmission system that combines space-division multiplexing and wavelength-division multiplexing in a 34 km 7-core ring-core fibre is demonstrated utilizing low-complexity 4 × 4 MIMO equalization.

Timely extraction of high-quality photovoltaic (PV) panels from high-resolution remote sensing imagery can contribute to a comprehensive understanding of energy production, and supports global carbon neutrality and Sustainable Development Goals (SDGs). Existing studies for extracting PV panels only focus on small-scale rooftop PV systems but not on large-scale PV systems composed of single or several PV panels or arrays. Furthermore, the currently available public datasets related to large-scale PV systems are limited by the coarse resolution of the imagery used or annotation scale and do not provide highly detailed footprints for PV panels or attributes such as the location, quantity, or area of these panels. To fill this gap, a novel semantic segmentation model (PVNet) for extracting high-quality PV panels from the densely distributed and regularly shaped PV panels in large-scale PV systems is proposed. PVNet consists of two modules, a Coarse Prediction Module (CPM) and a Fine Optimization Module (FOM). The CPM extracts complete regions of individual PV panels by fusing low-level local features with high-level global features, while the FOM optimizes the output of CPM by residual refinement to match extracted boundaries to ground truth. High-quality details from region to boundary of PV panels are obtained through joint supervision of CPM and FOM results. PVNet was trained on a newly annotated PV Panel Dataset and tested under four scenario conditions in China where the large-scale PV industry is growing rapidly. Qualitative and quantitative results show that PVNet can achieve the highest accuracy for PV panel extraction with F1socre higher than 0.88 and IoU higher than 0.79. Area-wide PV panel mapping and comparisons with existing PV footprint datasets demonstrate that PVNet is a feasible solution for obtaining high-quality geo-spatial databases of large-scale PV systems.

Background: We aimed to explore the relationship between the serum Soluble Scavenger with 5 Domains (SSC5D) levels and heart failure (HF).Methods and Results: We retrospectively enrolled 276 patients diagnosed with HF or normal during hospitalization in Shanghai General Hospital between September 2020 and December 2021. Previously published RNA sequencing data were re-analyzed to confirm the expression profile of SSC5D in failing and non-failing human and mouse heart tissues. Quantitative real-time polymerase chain reaction assay was used to quantify Ssc5d mRNA levels in murine heart tissue after myocardial infarction and transverse aortic constriction surgery. To understand the HF-induced secreted proteins profile, 1,755 secreted proteins were investigated using human dilated cardiomyopathy RNA-seq data, and the results indicated that SSC5D levels were significantly elevated in failing hearts compared to the non-failing. Using single-cell RNA sequencing data, we demonstrated that Ssc5d is predominantly expressed in cardiac fibroblasts. In a murine model of myocardial infarction or transverse aortic constriction, Ssc5d mRNA levels were markedly increased compared with those in the sham group. Similarly, serum SSC5D levels were considerably elevated in the HF group compared with the control group [15,789.35 (10,745.32–23,110.65) pg/mL, 95% CI (16,263.01–19,655.43) vs. 8,938.72 (6,154.97–12,778.81) pg/mL, 95% CI (9,337.50–11,142.93); p < 0.0001]. Moreover, serum SSC5D levels were positively correlated with N-terminal pro-B-type natriuretic peptide (R = 0.4, p = 7.9e-12) and inversely correlated with left ventricular ejection fraction (R = −0.46, p = 9.8e-16).Conclusion: We concluded that SSC5D was a specific response to HF. Serum SSC5D may function as a novel biomarker and therapeutic target for patients with HF.

ObjectivesTo investigate the effects of excessive tryptophan intake on the body and the effects of tryptophan metabolism-related aryl hydrocarbon receptor (AhR) pathway in healthy rats and chronic kidney disease rats, to study the adverse effects of excess tryptophan.DesignIn Part I Experiment, the healthy rats were fed with diet containing 0.6, 1.2 and 1.8% tryptophan for 12 weeks. After the intervention, the blood and kidney tissues were collected. Serum creatinine and blood urea nitrogen were detected. Hematoxylin–eosin (H&E) staining was used to observe renal pathological changes. Enzyme-linked immunosorbent assay was used to detect serum kynurenic acid and AhR levels. The kidney levels of AhR, CyP1A1 and CyP1B1 were detected by western-blot. In Part II Experiment, the chronic kidney disease (CKD) model was induced by intra-gastric gavage with adenine for 4 weeks. Then the CKD rats were given tryptophan at a dose of 100 mg/kg or 500 mg/kg for eight weeks. Rat survival curve, renal function, renal tissue pathology and serum AhR were detected. Tryptophan-targeted ultra-high-performance liquid chromatography coupled with multiple reaction monitoring mass spectrometry (UHPLC-MRM-MS) was employed to quantitatively access the tryptophan-targeted metabolites in two parts experiments.ResultsIn part I experiment, high tryptophan diet can increase the level of blood urea nitrogen (BUN) in healthy rats and induce focal renal tubulointerstitial injury. Tryptophan-targeted analyzes showed that high tryptophan diet feeding can significantly increase the concentration of kynurenine and indole metabolites. The serum AhR level and kidney AhR, CyP1A1 and CyP1B1 were also significantly increased in high tryptophan diet rats. In part II experiment, high tryptophan intervention induced a significant increase in mortality, serum creatinine, urea nitrogen levels, and renal pathological damage in CKD rats. The levels of tryptophan-targeted metabolites, kynurenine, xanthurenate, picolinic acid, 5-hydroxyindole-3-acetic acid, indole-3-lactic acid, indoleacetate and indoxyl sulfate, showed an upward trend in the high-dose tryptophan group (Ade + Trp-H) compared with the adenine group. The serum AhR of Ade + Trp-H rats was significantly higher than those of adenine rats.ConclusionModerate tryptophan intake may be beneficial, but excessive tryptophan can lead to accumulation of kynurenine and indole metabolites, activate AhR pathway and induce kidney injury.

Abstract Al-alloy/carbon fibre reinforced polymer (CFRP) joint systems offer exceptionally lightweight, superior fatigue behaviour and impact resistance for aerospace applications. Nevertheless, the galvanic corrosion at the joint interfaces accelerates the adhesive failure and strength damage. In this work, oxidation of Al 7075 alloy was studied by employing plasma electrolytic oxidation (PEO) and thin film sulphuric acid anodizing (TFSAA) methods, addressing their galvanic corrosion (GC) protection performance in contact with CFRP. Structural and electrochemical characterisations were carried out in tandem with varied oxidation process parameters, revealing that high voltage PEO resulted in crystallized compact ceramic coating and thus improved GC protection. A decrease in the GC current by ~ 90% has been achieved by using the PEO coating at 700 V compared with the ~ 12% current reduction of commercial TFSAA coating. Further microstructure studies revealed that the improved GC protection of the crystallized PEO coating was realized by suppressing the initiation and propagation of localized pitting due to the improved electrical isolation between the Al-alloy/CFRP interfaces. A high voltage PEO process provides sufficient energy to produce uniform and crystalline ceramic coating consisting of Al2O3 and mullite, which give rise to improved corrosion protection.

The photovoltaic (PV) industry boom has accelerated the need for accurately understanding the spatial distribution of PV energy systems. The synergy of remote sensing and artificial intelligence presents significant prospects for PV energy monitoring. Currently, numerous studies have focused on extracting rooftop PV systems from airborne or satellite imagery, but their small-scale and size-varying characteristics make the segmentation results suffer from PV internal incompleteness and small PV omission. To address these issues, this study proposed a size-aware deep learning network called Rooftop PV Segmenter (RPS) for segmenting small-scale rooftop PV systems from high-resolution imagery. In detail, the RPS network introduced a Semantic Refinement Module (SRM) to sense size variations of PV panels and reconstruct high-resolution deep semantic features. Moreover, a Feature Aggregation Module (FAM) enhanced the representation of robust features by continuously aggregating deeper features into shallower ones. In the output stage, a Deep Supervised Fusion Module (DSFM) was employed to constrain and fuse the outputs at different scales to achieve more refined segmentation. The proposed RPS network was tested and shown to outperform other models in producing segmentation results closer to the ground truth, with the F1 score and IoU reaching 0.9186 and 0.8495 on the publicly available California Distributed Solar PV Array Dataset (C-DSPV Dataset), and 0.9608 and 0.9246 on the self-annotated Heilbronn Rooftop PV System Dataset (H-RPVS Dataset). This study has provided an effective solution for obtaining a refined small-scale energy distribution database.

Emergency management agencies must address the challenges presented by frequent flooding events. Remote sensing imagery provides a means for timely monitoring of rapidly changing water bodies during flooding events; but manual analysis of remote sensing (RS) images however, is labor intensive and time consuming. Automated methods are effective, but the post-classification comparison method for flood inundation detection is subject to error accumulation, and the direct change detection method is limited by the accuracy of flood mapping and the difficulty of obtaining training samples. To overcome these challenges, a flood inundation detection network (Siam-DWENet) that achieves high-accuracy inundation detection is proposed. In Siam-DWENet, an innovative cross-task transfer learning strategy incorporates an attention mechanism and multi-scale pyramid structure based on Siamese architectures. This approach realizes a priori knowledge transfer-based flood inundation detection with a limited number of training samples. Comparative experiments on Siam-DWENet and other methods using two flooding SAR datasets to evaluate the accuracy of flood detection. The experimental results indicate that Siam-DWENet outperforms other change detection methods and makes the inundation area edge more accurate when dealing with complex backgrounds, achieving an average OA of 0.887 and F1 of 0.865 in flood inundation detection tasks.

The purpose of this study was clarify the relationship between the differential expression of cyclins CCNB1 and CCNG1 and chondrocyte damage in Kashin-Beck disease. Systematic review and high-throughput sequencing of chondrocytes derived from Kashin-Beck disease patients were combined to identify the differentially expressed cyclins and cyclin-dependent kinase genes. In parallel, weaned SD rats were treated with low selenium for 4 weeks and then T-2 toxin for 4 weeks. Knee cartilage was collected to harvest chondrocytes for gene expression profiling. Finally, the protein expression levels of CCNB1 and CCNG1 were verified in knee cartilage tissue of Kashin-Beck disease patients and normal controls by immunohistochemical staining. The systematic review found 52 cartilage disease-related cyclins and cyclin-dependent kinase genes, 23 of which were coexpressed in Kashin-Beck disease, including 15 upregulated and 8 downregulated genes. Under the intervention of a low selenium diet and T-2 toxin exposure, CCNB1 (FC = 0.36) and CCNG1 (FC = 0.73) showed a downward expression trend in rat articular cartilage. Furthermore, compared to normal controls, CCNB1 protein in Kashin-Beck disease articular cartilage was 71.98% and 66.27% downregulated in the superficial and middle zones, respectively, and 12.06% upregulated in the deep zone. CCNG1 protein was 45.66% downregulated in the superficial zone and 12.19% and 9.13% upregulated in the middle and deep zones, respectively. The differential expression of cyclins CCNB1 and CCNG1 may be related to articular cartilage damage in Kashin-Beck disease.

Abstract Recent experiments observed significant energy transfer in type-II van der Waals (vdW) heterostructures, such as WS2/MoSe2, which is surprising due to their staggered band alignment and weak spectral overlap. In this work, we carry out first-principles calculations to shed light on energy and charge transfer in WS2/MoSe2 heterostructure. Incorporating excitonic effect in nonadiabatic electronic dynamics, our first-principles calculations uncover a two-step process in competing energy and charge transfer, unravel their relative efficiencies and explore the means to control their competition. While both Dexter and Förster mechanisms can be responsible for energy transfer, they are shown to operate at different conditions. The excitonic effect is revealed to drive ultrafast energy and charge transfer in type-II WS2/MoSe2 heterostructure. Our work provides a comprehensive picture of exciton dynamics in vdW heterostructures and paves the way for rational design of novel vdW heterostructures for optoelectronic and photovoltaic applications.

Objectives This study aimed to explore the vitamin D status of children in northern China and the association between vitamin D and glucose metabolism.Design Cross-sectional study was conducted among child participants and retrospective study designs were conducted among adult participants.Setting and participants Both studies were recruited from Harbin, 326 children were included in children’s study, 8469 adults were included in adult study.Primary and secondary outcome measures Physical examination, lifestyle and dietary habit data were recorded in all the participants. Serum insulin, glucose, 25(OH)D3 concentrations in children and serum glucose and lipids levels in adults were measured. Rickets history was also investigated in adults, which was used to define vitamin D deficiency in childhood. The associations were tested by linear regression and binary logistic regression.Result In the children’s study, only 10.7% of participants were vitamin D sufficient (≥30 ng/mL). Inverse correlations between serum 25(OH)D3 concentration and fasting insulin and homeostasis model assessment - insulin resistance (HOMA-IR) were found, and children with lower serum 25(OH)D3 concentrations were likely to have insulin resistance (IR) (OR: 0.955, 95% CI: 0.917 to 0.995, p value: 0.027). In an adult study, rickets in childhood increased the risk of type 2 diabetes in male participants (OR=1.414, 95% CI=1.013 to 1.972; p value=0.042), but this result was not observed in female participants.Conclusion Our findings suggest that vitamin D deficiency is widespread in northern China. Vitamin D deficiency in childhood was associated with IR and increased the risk of type 2 diabetes in male adults.

A novel prefabricated rapid closure device is proposed to plug dike breaches through a simplified dam structured by different types of closure devices. The experiments show that, however, the closure device will be affected by the flow of water and moves longitudinally in the process of falling. This would adversely affect the arrangement of the devices and construction of stable dams. Hence, a three-dimensional (3D) calculation model is established based on computational fluid dynamics (CFD) methodology to investigate the displacements, forces and velocities of this kind of closure device in water. Having established the consistency of numerical simulations and the model test data, the device underwater movements are investigated for various water depths, current velocities and device weights. Numerical predictions and model test observations suggest that the longitudinal translation travelled by the closure device nonlinearly goes up with the increment of water depth and current velocity as well as the reduction of device mass. For the closure devices with same penetration depth underwater, its horizontal displacement in shallower water is greater than that in deeper water. For the conditions of water depth greater than 2.5 m, the flow velocity greater than 2 m/s and the device mass less than 2.13 t, the longitudinal drift of plugging device cannot be underrated.

Unmanned aerial vehicles (UAVs) are extensively employed for urban image captures and the reconstruction of large-scale 3D models due to their affordability and versatility. However, most commercial flight software lack support for the adaptive capture of multi-view images. Furthermore, the limited performance and battery capacity of a single UAV hinder efficient image capturing of large-scale scenes. To address these challenges, this paper presents a novel method for multi-UAV continuous trajectory planning aimed at the image captures and reconstructions of a scene. Our primary contribution lies in the development of a path planning framework rooted in task and search principles. Within this framework, we initially ascertain optimal task locations for capturing images by assessing scene reconstructability, thereby enhancing the overall quality of reconstructions. Furthermore, we curtail energy costs of trajectories by allocating task sequences, characterized by minimal corners and lengths, among multiple UAVs. Ultimately, we integrate considerations of energy costs, safety, and reconstructability into a unified optimization process, facilitating the search for optimal paths for multiple UAVs. Empirical evaluations demonstrate the efficacy of our approach in facilitating collaborative full-scene image captures by multiple UAVs, achieving low energy costs while attaining high-quality 3D reconstructions.

The axle box bearing is a crucial component of high-speed electric multiple units (EMU) and is exposed to harsh working conditions, making it susceptible to subsurface-induced rolling contact fatigue (RCF) under long-term alternating stress. The objective of this paper is to develop a damage-coupled elastic–plastic constitutive model that can accurately predict the RCF life of EMU axle box bearings made from AISI 52100 bearing steel. The total damage is divided into elastic damage related to the shear stress range and plastic damage associated with plastic deformation. Material parameters are determined based on experimental data from the literature, and validation is conducted to ensure the validity of the model. Finally, the RCF behavior of the EMU axle box bearing, including crack initiation, crack propagation, and spalling, is simulated, and reasonable results are obtained. This study provides valuable insights into the RCF behavior of EMU axle box bearings and contributes to the accurate prediction of the fatigue life.

Accurate classification and identification of Internet traffic are crucial for maintaining network security. However, unknown network traffic in the real world can affect the accuracy of current machine learning models, reducing the efficiency of traffic classification. Existing unknown traffic classification algorithms are unable to optimize traffic features and require the entire system to be retrained each time new traffic data are collected. This results in low recognition efficiency, making the algoritms unsuitable for real-time application detection. To solve the above issues, we suggest a multi-feature fusion-based incremental technique for detecting unknown traffic in this paper. The approach employs a multiple-channel parallel architecture to extract temporal and spatial traffic features. It then uses the mRMR algorithm to rank and fuse the features extracted from each channel to overcome the issue of redundant encrypted traffic features. In addition, we combine the density-ratio-based clustering algorithm to identify the unknown traffic features and update the model via incremental learning. The cassifier enables real-time classification of known and unknown traffic by learning newly acquired class knowledge. Our model can identify encrypted unknown Internet traffic with at least 86% accuracy in various scenarios, using the public ISCX-VPN-Tor datasets. Furthermore, it achieves 90% accuracy on the intrusion detection dataset NSL-KDD. In our self-collected dataset from a real-world environment, the accuracy of our model exceeds 96%. This work offers a novel method for identifying unknown network traffic, contributing to the security preservation of network environments.

Background. Traumatic optic neuropathy (TON) refers to damage to the optic nerve resulting from direct and indirect trauma to the head and face. One of the important pathological processes in TON is the death of retinal ganglion cells (RGCs), but the cause of RGCs death remains unclear. We aimed to explore the mechanisms of RGCs death in an experimental TON model. Methods. Optic nerve crush injury was induced in ten New Zealand white rabbits. On the 1st, 3rd, 7th, 14th, and 28th days after the operation, the retinal tissues of the rabbits were observed pathologically by hematoxylin-eosin staining. The expression of POU-homeodomain transcription factor Brn3a and glial fibrillary acidic protein (GFAP) was measured by immunofluorescence to evaluate the number of RGCs and astrocytes, respectively. miRNA expression and protein levels were assessed by RT-qPCR and western blot methods, respectively. Finally, the malondialdehyde content, superoxide dismutase activity, and proinflammatory factor levels were measured by ELISA. Western blot and dual-luciferase reporter assays were used to elucidate the relationship between miR-181d-5p and nuclear factor I-A (NFIA). Results. Blunt ocular trauma increased oxidative stress and apoptosis and reduced ganglion cell layer (GCL) density. The expression of miR-181d-5p was decreased in retinal tissues, and its overexpression relieved RGCs death, astrocyte development, oxidative stress, and inflammation of the retina, which were reversed by NFIA overexpression. Conclusion. miR-181d-5p can protect against the deterioration of TON by inhibiting RGCs death, astrocyte development, oxidative stress, and inflammation by targeting NFIA. This study provides new insight into early medical intervention in patients with TON.

Three-dimensional (3D) printing technology has developed rapidly and demonstrates great potential in biomedical applications. Although 3D printing techniques have good control over the macrostructure of metallic implants, the surface properties have superior control over the tissue response. By focusing on the types of surface treatments, the osseointegration activity of the bone–implant interface is enhanced. Therefore, this review paper aims to discuss the surface functionalities of metallic implants regarding their physical structure, chemical composition, and biological reaction through surface treatment and bioactive coating. The perspective on the current challenges and future directions for development of surface treatment on 3D-printed implants is also presented.

The production of cucumber (Cucumis sativus L.) is often harmed by biotic and abiotic stresses. Although the dehydration-responsive element-binding (DREB) transcription factors, playing vital roles in stress responses, have been characterized in several plant species, little is known about the pan-genome characteristics of DREB genes and their expression patterns under different stresses in cucumber. In this study, we identified 55 CsDREBs from the cucumber pan-genomes of 13 accessions, but only four accessions had all the genes. Most of the CsDREB proteins had sequence length and/or amino acid variations, and only four of them had no variation among different accessions. Using the 55 CsDREBs from ‘9930’, we analyzed their gene structures, conserved domains, phylogenetic relationships, gene promoter’s cis-elements and syntenic relationships, and classified them into six groups. Expression pattern analysis revealed that eight CsDREBs showed constitutive expression (FPKM > 1 in all samples), and different CsDREBs showed specifically high expression in root, stem, leaf, tendril, male-flower, female flower, and ovary, respectively, suggesting that these genes might be important for morphogenesis and development in cucumber. Additionally, a total of 31, 22, 30 and nine CsDREBs were differentially expressed in responding to the treatments of heat, NaCl and/or silicon, power mildew and downy mildew, respectively. Interestingly, CsDREB33 could respond to all the tested stresses. Our results provide a reference and basis for further investigation of the function and mechanism of the DREB genes for resistance breeding in cucumber.

Emerging evidence suggests that the reduction of ethanolamine plasmalogen (PlsEtn) is associated with in Alzheimer's disease and metabolic diseases. However, the mechanistic bases for PlsEtn on the these diseases are not well understood. Plasmalogens are primarily synthesized in the liver and enriched in brain. To this end, the present study sought to investigate the potential role of PlsEtn on steatohepatitis and memory impairments and its underlying mechanism. Here we show that peroxisome dysfunction and impairment of PlsEtn synthesis pathway occurs in both of hippocampus and liver, resulting in the decrease of PlsEtn level in APP/PS1 mice and HFD-fed mice. shGNPAT induced PlsEtn deficiency in hepatocytes induces p75NTR enhancement leading to decreased lipolysis activity, thereby exacerbating steatosis. Moreover, in the brain, PlsEtn administration appears to not only improve steatosis but also prevent Alzheimer's disease through restoration of TrkA/p75NTR balance. Together, our findings reveal a molecular mechanistic insight into the preventive role of plasmalogen modulation against steatosis and memory impairments via p75NTR inhibition.

BackgroundLethal giant larvae (Lgl), scaffolding proteins, regulate the epithelial cell apicobasal polarity in Drosophila. They play important roles in asymmetric cell division, cell migration, and progenitor cells self-renewal as tumor suppressors. One of Lgl mammalian homologues proteins, LLGL2 overexpression has been reported in ER+ breast cancer and promotes tumor proliferation through regulating leucine uptake. Nonetheless, the role of LLGL2 in hepatocellular carcinoma (HCC) is still unknown.MethodsTCGA dataset mining, qRT-PCR, Western blot along with immunohistochemistry assays were employed to explore LLGL2 expression in human HCC samples and cell lines. Moreover, the clinical value of LLGL2 was investigated in 156 HCC patients. Furthermore, the role as well as the molecular mechanism of LLGL2 in the progression of HCC was explored through a series of in vitro and in vivo experiments.ResultsLLGL2 was up-regulated in HCC tissues, which was related with certain clinicopathological features including tumor number, vascular invasion as well as advanced stage. High expression of LLGL2 predicted poor prognosis after hepatectomy. LLGL2 promoted HCC cells proliferation, migration and invasion through PI3K/ATK signaling by promoting calcium ion influx.ConclusionOur study identified that LLGL2 is a tumor promoter in HCC for the first time, which could potentially be utilized as a new biomarker and a therapeutic target for HCC.

With the wide application of Internet of things (IoT) devices in enterprises, the traditional boundary defense mechanisms are difficult to satisfy the demands of the insider threats detection. IoT insider threat detection can be more challenging, since internal employees are born with the ability to escape the deployed information security mechanism, such as firewalls and endpoint protection. In order to detect internal attacks more accurately, we can analyze users’ web browsing behaviors to identify abnormal users. The existing web browsing behavior anomaly detection methods ignore the dynamic change of the web browsing behavior of the target user and the behavior consistency of the target user in its peer group, which results in a complex modeling process, low system efficiency and low detection accuracy. Therefore, the paper respectively proposes the individual user behavior model and the peer-group behavior model to characterize the abnormal dynamic change of user browsing behavior and compare the mutual behavioral inconsistency among one peer-group. Furthermore, the fusion model is presented for insider threat detection which simultaneously considers individual behavioral abnormal dynamic changes and mutual behavioral dynamic inconsistency from peers. The experimental results show that the proposed fusion model can accurately detect insider threat based on the abnormal user web browsing behaviors in the enterprise networks.

MicroRNAs (miRNAs) are non-coding RNA molecules that make a significant contribution to diverse biological processes, and their mutations and dysregulations are closely related to the occurrence, development, and treatment of human diseases. Therefore, identification of potential miRNA–disease associations contributes to elucidating the pathogenesis of tumorigenesis and seeking the effective treatment method for diseases. Due to the expensive cost of traditional biological experiments of determining associations between miRNAs and diseases, increasing numbers of effective computational models are being used to compensate for this limitation. In this study, we propose a novel computational method, named PMDFI, which is an ensemble learning method to predict potential miRNA–disease associations based on high-order feature interactions. We initially use a stacked autoencoder to extract meaningful high-order features from the original similarity matrix, and then perform feature interactive learning, and finally utilize an integrated model composed of multiple random forests and logistic regression to make comprehensive predictions. The experimental results illustrate that PMDFI achieves excellent performance in predicting potential miRNA–disease associations, with the average area under the ROC curve scores of 0.9404 and 0.9415 in 5-fold and 10-fold cross-validation, respectively.