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Abstract To assess the safety and efficacy of fecal microbiota transplantation (FMT) as an adjunctive therapeutic intervention for IgA nephropathy (IgAN). Fifteen patients with IgA nephropathy were recruited based on inclusion and exclusion criteria and underwent FMT using enteric microbial capsules. Clinical indicators, intestinal microbiota and metabolomic profiles, as well as changes in serum immune cells and cytokines, were monitored before and after FMT. No severe adverse reactions were observed in the subjects. After FMT, there was a reduction in the 24-h urinary protein quantification in subjects. The relative abundances of Phocaeicola_vulgatus, Bacteroides_uniformis, Prevotella_copri, Phocaeicola_dorei, Bacteroides_ovatus, Bacteroides_xylanisolvens, Parabacteroides _distasonis, Bifidobacterium_pseudocatenulatum, Bacteroides_sp._HF-162, and Bifidobacterium_longum changed after FMT. In terms of intestinal metabolites, the levels of acylcarnitine18:0 (ACar.18:0), cotinine, N-arachidonoyl-L-serine, phosphatidylcholine (PC. (18:3e/22:6)), serotonin, and fumagillin showed significant changes. Flow cytometry analysis showed the absolute count of plasma B cells decreased in subjects, and this change correlated with alterations in the intestinal microbiota and metabolites. This study preliminarily evaluates the safety and efficacy of FMT in patients with IgAN. No significant adverse reactions were observed, and the administration of FMT alongside ACEI/ARB therapy was effective in reducing urinary protein levels in patients with IgAN, a process that may be associated with B-cell immunity.

Abstract From the perspective of configuration, this paper takes the region of manufacturing efficiency as the explanatory variable, selects eight antecedent conditions, and applies fuzzy set qualitative comparative analysis (fsQCA) to study the paths and methods of improving manufacturing emission efficiency. The results of the study show that there are two configuration paths of carbon emission efficiency in manufacturing industry, namely, research frontier and technological innovation level and labour force structure, R&D investment, science and technology innovation level, manufacturing output value, and environmental regulation synergistic path.

Abstract Immune checkpoint inhibitors (ICI) such as Programmed cell Death 1 (PD-1) inhibitors have improved cancer treatment by enhancing the immune system’s ability to target malignant cells. Their use is associated with immune-related adverse events (irAEs), including uveitis. The profile of pro-inflammatory cytokines underlying Anti-PD-1-induced uveitis shares significant overlap with that of non-infectious uveitis. Current corticosteroid treatments for uveitis while effective are fraught with vision threatening side effects. The cytokine profile in ICI-related uveitis has a large overlap with that of noninfectious uveitis, this overlap strongly supports the potential for therapy that activates the PD-1 axis in the eye to treat uveitis. Indeed, ICI related uveitis often resolves with cessation of the ICI, restoring the endogenous PD-1 axis. The potential benefit of targeting many pro-inflammatory cytokines via local PD-1 axis activation is mitigating ocular inflammation while minimizing adverse effects.

BackgroundChronic kidney disease (CKD) is one of the common chronic diseases, and malnutrition and inflammation play a key role in the development of CKD. The advanced lung cancer inflammation index (ALI) is a novel index of nutrition and inflammation, and its association with CKD has not yet been clarified. The aim of this study was to explore the potential association between ALI and CKD.MethodsWe conducted a cross-sectional survey using data extracted from the National Health and Nutrition Examination Survey (NHANES, 2003–2018). Weighted multivariate logistic regression was used to assess the association between ALI and CKD, and smoothed curve fitting and threshold effect analyses were used to describe the nonlinear association between ALI and CKD. Subgroup analyses were performed to further assess the influence of other covariates on the relationship between ALI and CKD.ResultsA total of 39,469 adult participants were included in the study, of whom 7,204 (18.25%) were diagnosed with CKD. After adjusting for multiple confounders, we found a significant negative correlation between ALI and CKD (OR = 0.93; 95%CI, 0.91–0.95; p 0.05).ConclusionOur findings suggest a significant correlation between ALI and CKD in the US adult population. However, more large-scale prospective studies are still needed to further confirm our findings.

IntroductionCrop height and above-ground biomass (AGB) serve as crucial indicators for monitoring crop growth and estimating grain yield. Timely and accurate acquisition of wheat crop height and AGB data is paramount for guiding agricultural production. However, traditional data acquisition methods suffer from drawbacks such as time-consuming, laborious and destructive sampling.MethodsThe current approach to estimating AGB using unmanned aerial vehicles (UAVs) remote sensing relies solely on spectral data, resulting in low accuracy in estimation. This method fails to address the ill-posed inverse problem of mapping from two-dimensional to three-dimensional and issues related to spectral saturation. To overcome these challenges, RGB and multispectral sensors mounted on UAVs were employed to acquire spectral image data. The five-directional oblique photography technique was utilized to construct the three-dimensional point cloud for extracting crop height.Results and DiscussionThis study comparatively analyzed the potential of the mean method and the Accumulated Incremental Height (AIH) method in crop height extraction. Utilizing Vegetation Indices (VIs), AIH and their feature combinations, models including Random Forest Regression (RFR), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRT), Support Vector Regression (SVR) and Ridge Regression (RR) were constructed to estimate winter wheat AGB. The research results indicated that the AIH method performed well in crop height extraction, with minimal differences between 95% AIH and measured crop height values were observed across various growth stages of wheat, yielding R2 ranging from 0.768 to 0.784. Compared to individual features, the combination of multiple features significantly improved the model’s estimate accuracy. The incorporation of AIH features helps alleviate the effects of spectral saturation. Coupling VIs with AIH features, the model’s R2 increases from 0.694-0.885 with only VIs features to 0.728-0.925. In comparing the performance of five machine learning algorithms, it was discovered that models constructed based on decision trees were superior to other machine learning algorithms. Among them, the RFR algorithm performed optimally, with R2 ranging from 0.9 to 0.93.ConclusionIn conclusion, leveraging multi-source remote sensing data from UAVs with machine learning algorithms overcomes the limitations of traditional crop monitoring methods, offering a technological reference for precision agriculture management and decision-making.

ABSTRACT: The H10 avian influenza viruses (AIV) have been detected in both birds and mammals. Recently, the cases of human infection with H10N8 and H10N3 in China pose high risk to public health. However, the antigenic sites in hemagglutinin (HA) of H10 are poorly understood. In this study, 3 monoclonal antibodies (MAb), designated as 1F4, 6B3 and 6G12, against the HA protein of the H10N3 strain A/chicken/Taizhou/498/2021(H10N3) (TZ498), were first generated. All of these MAb could effectively inhibit TZ498 in haemagglutination inhibition assay and microneutralization assay. Four novel antigenic sites at positions 135, 208, 227, and 266 (H10 numbering) were identified in the HA of TZ498 through escape mutants selected by these 3 MAb. Moreover, natural mutations at positions 135 and 227 were found in the H10 field strains. All these not only provide novel insights into the molecular markers for monitoring the antigenic variation of H10 but also be helpful for developing efficient control strategies against H10.

Background IgA nephropathy (IgAN) is intimately linked to mucosal immune responses, with nasopharyngeal and intestinal lymphoid tissues being crucial for its abnormal mucosal immunity. The specific pathogenic bacteria in these sites associated with IgAN, however, remain elusive. Our study employs 16S rRNA sequencing and machine learning (ML) approaches to identify specific pathogenic bacteria in these locations and to investigate common pathogens that may exacerbate IgAN.Methods In this cross-sectional analysis, we collected pharyngeal swabs and stool specimens from IgAN patients and healthy controls. We applied 16SrRNA sequencing to identify differential microbial populations. ML algorithms were then used to classify IgAN based on these microbial differences. Spearman correlation analysis was employed to link key bacteria with clinical parameters.Results We observed a reduced microbial diversity in IgAN patients compared to healthy controls. In the gut microbiota of IgAN patients, increases in Bacteroides, Escherichia-Shigella, and Parabacteroides, and decreases in Parasutterella, Dialister, Faecalibacterium, and Subdoligranulum were notable. In the respiratory microbiota, increases in Neisseria, Streptococcus, Fusobacterium, Porphyromonas, and Ralstonia, and decreases in Prevotella, Leptotrichia, and Veillonella were observed. Post-immunosuppressive therapy, Oxalobacter and Butyricoccus levels were significantly reduced in the gut, while Neisseria and Actinobacillus levels decreased in the respiratory tract. Veillonella and Fusobacterium appeared to influence IgAN through dual immune loci, with Fusobacterium abundance correlating with IgAN severity.Conclusions This study revealing that changes in flora structure could provide important pathological insights for identifying therapeutic targets, and ML could facilitate noninvasive diagnostic methods for IgAN.

Previous studies indicate a strong correlation between the incidence of chronic kidney disease (CKD) and lower economic status. However, these studies often struggle to delineate a clear cause–effect relationship, leaving healthcare providers uncertain about how to manage kidney disease in a way that improves patients’ financial outcomes. Our study aimed to explore and establish a causal relationship between CKD and socioeconomic status, identifying critical influencing factors. We utilized summary meta-analysis data from the CKDGen Consortium and UK Biobank. Genetic variants identified from these sources served as instrumental variables (IVs) to estimate the association between CKD and socioeconomic status. The presence or absence of CKD, estimated glomerular filtration rate (eGFR), and albuminuria were used as exposures, while income and regional deprivation were analyzed as outcomes. We employed the R packages ‘TwoSampleMR’ and ‘Mendelianrandomization’ to conduct both univariable and multivariable Mendelian randomization (MR) analyses, assessing for potential pleiotropy and heterogeneity. Our univariable MR analysis revealed a significant causal relationship between high levels of albuminuria and lower income (OR = 0.84, 95% CI: 0.73–0.96, p = 0.013), with no significant pleiotropy detected. In the multivariable MR analysis, both CKD (OR = 0.867, 95% CI: 0.786–0.957, p = 0.0045) and eGFR (OR = 0.065, 95% CI: 0.010–0.437, p = 0.0049) exhibited significant effects on income. This study underscores that higher albuminuria levels in CKD patients are associated with decreased income and emphasizes the importance of effective management and treatment of albuminuria in CKD patients to mitigate both social and personal economic burdens.

Summary: Time-restricted eating (TRE) is a promising obesity management strategy, but weight-loss efficacy varies among participants, and the underlying mechanism is unclear. The study aimed to investigate the role of gut microbiota in weight-loss response during long-term TRE intervention. We analyzed data from 51 obese adults in a 12-month TRE program, categorizing them into distinct weight loss groups (DG) and moderate weight loss groups (MG) based on their TRE responses. Shotgun metagenomic sequencing analysis revealed a significant increase in species closely associated with weight loss effectiveness and metabolic parameter changes in the DG group. Pathways related to fatty acid biosynthesis, glycogen biosynthesis, and nucleotide metabolism were reduced in the DG group and enhanced in the MG group. Next, we identified nine specific species at baseline that contributed better responses to TRE intervention and significant weight loss. Collectively, gut microbiota contributes to responsiveness heterogeneity in TRE and can predict weight-loss effectiveness.

Abstract Designing novel nonfullerene acceptors (NFAs) is of vital importance for the development of organic solar cells (OSC). Modification on the side chain and end group are two powerful tools to construct efficient NFAs. Here, based on the high‐performance L8BO, we selected 3‐ethylheptyl to substitute the inner chain of 2‐ethylhexyl, obtaining the backbone of BON3. Then we introduced different halogen atoms of fluorine and chlorine on 2‐(3‐oxo‐2,3‐dihydro‐1H‐inden‐1‐ylidene) malononitrile end group (EG) to construct efficient NFAs named BON3‐F and BON3‐Cl, respectively. Polymer donor D18 was chosen to combine with two novel NFAs to construct OSC devices. Impressively, D18:BON3‐Cl‐based device shows a remarkable power conversion efficiency (PCE) of 18.57%, with a high open‐circuit voltage (VOC) of 0.907 V and an excellent fill factor (FF) of 80.44%, which is one of the highest binary PCE of devices based on D18 as the donor. However, BON3‐F‐based device shows a relatively lower PCE of 17.79% with a decreased FF of 79.05%. The better photovoltaic performance is mainly attributed to the red‐shifted absorption, higher electron and hole mobilities, reduced charge recombination, and enhanced molecular packing in the D18:BON3‐Cl films. Also, we performed stability tests on two binary systems; the D18:BON3‐Cl and D18:BON3‐F devices maintain 88.1% and 85.5% of their initial efficiencies after 169 h of storage at 85°C in an N2‐filled glove box, respectively. Our work demonstrates the importance of selecting halogen atoms on EG and provides an efficient binary system of D18:BON3‐Cl for further improvement of PCE.

INTRODUCTION： With the continuous progress of science and technology, the monitoring and control of oil and gas pipeline networks have become more and more critical; SCADA systems, as a kind of technology widely used in industrial control, play a key role. The screen configuration of the SCADA system is the core part of its user interface, which is directly related to the operator's mastery of the status of the pipeline network. In order to improve the monitoring efficiency and reduce the operation risk, this study is devoted to exploring a new method of SCADA system screen configuration based on the oil and gas pipeline network model. PURPOSE: The purpose of this study is to develop an innovative SCADA system screen configuration method to present the operating status of the oil and gas pipeline network more intuitively and efficiently. The design based on the pipeline network model aims to enhance the operators' understanding of essential information, such as pipeline network topology, fluid flow, etc., so as to make monitoring and control more intelligent. METHODS: The study adopts a new method of SCADA system screen configuration based on the oil and gas pipeline network model. First, the topology, sensor data, and control nodes of the oil and gas pipeline network are comprehensively modelled. Then, through the design principle of human-computer interaction, the modelling results are integrated into the screen configuration of the SCADA system to realize the intuitive presentation of information. At the same time, advanced visualization technology is introduced so that the operators can understand the real-time changes in the pipe network status more clearly. RESULTS: After experimental verification, the new method shows significant advantages in oil and gas pipeline network monitoring. The operators can recognize the abnormalities of the pipeline network more quickly and accurately through the SCADA system screen configuration, which improves the efficiency of troubleshooting and treatment. The visualized interface design makes the operation more intuitive and reduces the possibility of operating errors, thus improving the safety and reliability of the pipeline network. CONCLUSION: The new method of SCADA system screen configuration based on the oil and gas pipeline network model has achieved significant results in improving monitoring efficiency and reducing operational risks. Through a more intuitive and intelligent interface design, operators can have a more comprehensive understanding of the operating status of the pipeline network, which provides practical support for rapid response and decision-making. This approach introduces new ideas to the field of oil and gas pipeline network monitoring, which is of positive significance for improving the overall performance of the system. Future work can be carried out to optimize the interface design further and expand the applicable scenarios.

Accurately assessing maize crop height (CH) and aboveground biomass (AGB) is crucial for understanding crop growth and light-use efficiency. Unmanned aerial vehicle (UAV) remote sensing, with its flexibility and high spatiotemporal resolution, has been widely applied in crop phenotyping studies. Traditional canopy height models (CHMs) are significantly influenced by image resolution and meteorological factors. In contrast, the accumulated incremental height (AIH) extracted from point cloud data offers a more accurate estimation of CH. In this study, vegetation indices and structural features were extracted from optical imagery, nadir and oblique photography, and LiDAR point cloud data. Optuna-optimized models, including random forest regression (RFR), light gradient boosting machine (LightGBM), gradient boosting decision tree (GBDT), and support vector regression (SVR), were employed to estimate maize AGB. Results show that AIH99 has higher accuracy in estimating CH. LiDAR demonstrated the highest accuracy, while oblique photography and nadir photography point clouds were slightly less accurate. Fusion of multi-source data achieved higher estimation accuracy than single-sensor data. Embedding structural features can mitigate spectral saturation, with R2 ranging from 0.704 to 0.939 and RMSE ranging from 0.338 to 1.899 t/hm2. During the entire growth cycle, the R2 for LightGBM and RFR were 0.887 and 0.878, with an RMSE of 1.75 and 1.76 t/hm2. LightGBM and RFR also performed well across different growth stages, while SVR showed the poorest performance. As the amount of nitrogen application gradually decreases, the accumulation and accumulation rate of AGB also gradually decrease. This high-throughput crop-phenotyping analysis method offers advantages, such as speed and high accuracy, providing valuable references for precision agriculture management in maize fields.

Catholicism during the late Ming and early Qing Dynasties is the most fascinating chapter in the development history of Christian culture in China. Apostolate through books emerged as a prominent method of Sino-West cultural exchange during that time. Apostolate through books by Catholicism was realized by means of translation practice, leading to a notable religious influence. Based on this perspective, this paper discusses the history and connotation of apostolate through books, particularly focusing on the characteristics of apostolate through books by Catholicism during the late Ming and early Qing Dynasties. In achieving this, this research shows the translation motivation and translation effect of apostolate through books during the late Ming and early Qing Dynasties. Furthermore, this paper emphasizes that the translation practice of apostolate through books by Catholicism has laid a good foundation for the sinicization and acceptance of Catholicism in China, highlighting the relationship between apostolate through books by Catholicism and the sinicization of Christian culture, so as to enlighten the internationalization of religious culture.

The rapid development of artificial intelligence and remote sensing technologies is indispensable for modern agriculture. In orchard environments, challenges such as varying light conditions and shading complicate the tasks of intelligent picking robots. To enhance the recognition accuracy and efficiency of apple-picking robots, this study aimed to achieve high detection accuracy in complex orchard environments while reducing model computation and time consumption. This study utilized the CenterNet neural network as the detection framework, introducing gray-centered RGB color space vertical decomposition maps and employing grouped convolutions and depth-separable convolutions to design a lightweight feature extraction network, Light-Weight Net, comprising eight bottleneck structures. Based on the recognition results, the 3D coordinates of the picking point were determined within the camera coordinate system by using the transformation relationship between the image’s physical coordinate system and the camera coordinate system, along with depth map distance information of the depth map. Experimental results obtained using a testbed with an orchard-picking robot indicated that the proposed model achieved an average precision (AP) of 96.80% on the test set, with real-time performance of 18.91 frames per second (FPS) and a model size of only 17.56 MB. In addition, the root-mean-square error of positioning accuracy in the orchard test was 4.405 mm, satisfying the high-precision positioning requirements of the picking robot vision system in complex orchard environments.

Convolution is a crucial component of convolution neural networks (CNNs). However, the standard static convolution has two primary defects: data independence and the weak ability to integrate global and local features. This paper proposes a novel and efficient dynamic convolution method with global and local attention to address these issues. A building block called the Global and Local Attention Unit (GLAU) is designed, in which a weighted fusion of global channel attention kernels and local spatial attention kernels generates the proposed dynamic convolution kernels. The GLAU is data-dependent and has better adaptability and the ability to integrate global and local features into each layer. We refer to such modified CNNs with GLAUs as “GLAUNets”. Extensive evaluation experiments for image classification compared to classical CNNs and the state-of-the-art dynamic convolution neural networks were conducted on the popular benchmark datasets. In terms of classification accuracy, the number of parameters, and computational complexity, the experimental results demonstrate the outstanding performance of GLAUNets.

Chlorophyll, as a key component of crop leaves for photosynthesis, is one significant indicator for evaluating the photosynthetic efficiency and developmental status of crops. Fractional-order differentiation (FOD) enhances the feature spectral information and reduces the background noise. In this study, we analyzed hyperspectral data from grape leaves of different varieties and fertility periods with FOD to monitor the leaves’ chlorophyll content (LCC). Firstly, through sensitive analysis, the fractional-order differential character bands were identified, which was used to construct the typical vegetation index (VI). Then, the grape LCC prediction model was built based on the random forest regression algorithm (RFR). The results showed the following: (1) FOD differential spectra had a higher sensitivity to LCC compared with the original spectra, and the constructed VIs had the best estimation performance at the 1.2th-order differential. (2) The accuracy of the FOD-RFR model was better than that of the conventional integer-order model at different fertility periods, but there were differences in the number of optimal orders. (3) The LCC prediction model for whole fertility periods achieved good prediction at order 1.3, R2 = 0.778, RMSE = 2.1, and NRMSE = 4.7%. As compared to the original reflectance spectra, R2 improved by 0.173; RMSE and NRMSE decreased, respectively, by 0.699 and 1.5%. This indicates that the combination of FOD and RFR based on hyperspectral data has great potential for the efficient monitoring of grape LCC. It can provide technical support for the rapid quantitative estimation of grape LCC and methodological reference for other physiological and biochemical indicators in hyperspectral monitoring.

While Bayesian networks (BNs) offer a promising approach to discussing factors related to many diseases, little attention has been poured into chronic kidney disease with mental illness (KDMI) using BNs. This study aimed to explore the complex network relationships between KDMI and its related factors and to apply Bayesian reasoning for KDMI, providing a scientific reference for its prevention and treatment. Data was downloaded from the online open database of CHARLS 2018, a population-based longitudinal survey. Missing values were first imputed using Random Forest, followed by propensity score matching (PSM) for class balancing regarding KDMI. Elastic Net was then employed for variable selection from 18 variables. Afterwards, the remaining variables were included in BNs model construction. Structural learning of BNs was achieved using tabu algorithm and the parameter learning was conducted using maximum likelihood estimation. After PSM, 427 non-KDMI cases and 427 KDMI cases were included in this study. Elastic Net identified 11 variables significantly associated with KDMI. The BNs model comprised 12 nodes and 24 directed edges. The results suggested that diabetes, physical activity, education levels, sleep duration, social activity, self-report on health and asset were directly related factors for KDMI, whereas sex, age, residence and Internet access represented indirect factors for KDMI. BN model not only allows for the exploration of complex network relationships between related factors and KDMI, but also could enable KDMI risk prediction through Bayesian reasoning. This study suggests that BNs model holds great prospects in risk factor detection for KDMI.

Tumors significantly impact individuals’ physical well-being and quality of life. With the ongoing advancements in optical technology, information technology, robotic technology, etc., laser technology is being increasingly utilized in the field of tumor treatment, and laser ablation (LA) of tumors remains a prominent area of research interest. This paper presents an overview of the recent progress in tumor LA therapy, with a focus on the mechanisms and biological effects of LA, commonly used ablation lasers, image-guided LA, and robotic-assisted LA. Further insights and future prospects are discussed in relation to these aspects, and the paper proposed potential future directions for the development of tumor LA techniques.

In recent years, glucagon-like peptide-1 (GLP-1) has demonstrated considerable potential in the treatment of type 2 diabetes (T2D) and obesity. However, the half-life of naturally occurring GLP-1 is quite short in vivo. Two common strategies employed for half-life extension are the use of the Albumin-binding domain (ABD) and XTEN polypeptide, which operate through different mechanisms. In this study, we designed an innovative GLP-1 receptor agonist with an extended duration of action. This new construct incorporated an albumin binding domain (ABD) and an XTEN sequence (either XTEN144 or XTEN288) as carriers. We referred to these fusion proteins as GLP-ABD-XTEN144 and GLP-ABD-XTEN288. In an E. coli system, the said constructs were efficaciously produced in substantial quantity. It was observed from in vitro studies that the fusion protein GLP-ABD-XTEN144 demonstrated a five times stronger affinity towards human serum albumin (HSA), boasting a binding affinity (Kd) of 5.50 nM. This was in contrast to GLP-ABD-XTEN288, whose Kd value was registered at 27.78 nM. Moreover, GLP-ABD-XTEN144 presented a half-life of 12.9 h in mice, thus exceeding the corresponding value for GLP-ABD-XTEN288, 7.32 h in mice. Both these fusion proteins significantly mitigated non-fasting blood sugar levels and overall food consumption for 48 h subsequent to a one-time injection in mice. Notably, GLP-ABD-XTEN144 exhibited more pronounced hypoglycemic activity and food inhibitory effects than GLP-ABD-XTEN288. The designed GLP-ABD-XTEN144 fusion protein shows promising prospects for clinical application in T2D treatment. Our findings also suggest that ABD and XTEN polypeptides synergistically contribute to half-life extension, further enhancing the pharmacokinetic characteristics of a payload.

ObjectivesSystemic Lupus Erythematosus (SLE) is a complex autoimmune disease that disproportionately affects women. Early diagnosis and prevention are crucial for women’s health, and the gut microbiota has been found to be strongly associated with SLE. This study aimed to identify potential biomarkers for SLE by characterizing the gut microbiota landscape using feature selection and exploring the use of machine learning (ML) algorithms with significantly dysregulated microbiotas (SDMs) for early identification of SLE patients. Additionally, we used the SHapley Additive exPlanations (SHAP) interpretability framework to visualize the impact of SDMs on the risk of developing SLE in females.MethodsStool samples were collected from 54 SLE patients and 55 Negative Controls (NC) for microbiota analysis using 16S rRNA sequencing. Feature selection was performed using Elastic Net and Boruta on species-level taxonomy. Subsequently, four ML algorithms, namely logistic regression (LR), Adaptive Boosting (AdaBoost), Random Forest (RF), and eXtreme gradient boosting (XGBoost), were used to achieve early identification of SLE with SDMs. Finally, the best-performing algorithm was combined with SHAP to explore how SDMs affect the risk of developing SLE in females.ResultsBoth alpha and beta diversity were found to be different in SLE group. Following feature selection, 68 and 21 microbiota were retained in Elastic Net and Boruta, respectively, with 16 microbiota overlapping between the two, i.e., SDMs for SLE. The four ML algorithms with SDMs could effectively identify SLE patients, with XGBoost performing the best, achieving Accuracy, Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, and AUC values of 0.844, 0.750, 0.938, 0.923, 0.790, and 0.930, respectively. The SHAP interpretability framework showed a complex non-linear relationship between the relative abundance of SDMs and the risk of SLE, with Escherichia_fergusonii having the largest SHAP value.ConclusionsThis study revealed dysbiosis in the gut microbiota of female SLE patients. ML classifiers combined with SDMs can facilitate early identification of female patients with SLE, particularly XGBoost. The SHAP interpretability framework provides insight into the impact of SDMs on the risk of SLE and may inform future scientific treatment for SLE.

ABSTRACT H6 influenza A viruses are enzootic and genetically diverse in both domestic poultry and wild birds, occasionally causing spillovers to mammals, including humans, posing threat to public health. However, the antigenic sites of the hemagglutinin (HA) of H6 are poorly understood. In this study, a panel of murine monoclonal antibodies (MAbs) with different reactivity spectra against HA of H6 was generated and nine key residues of antigenic epitopes in H6 were identified through escape mutants of these MAbs. Of these, residues 139 and 140 belonged to antigenic Sa, residue 89 belonged to antigenic Cb, residue 149 belonged to Ca2, while residue 221 belonged to Ca1 regarding the H1 antigenic sites. However, the other four residues 69, 120, 124, and 246 were not located in the identified antigenic regions of H1. Although most HA variants of the escape mutants had the preference for avian-like receptor, whereas escape mutants m3B9 and m4C2 increased the binding affinity for both avian and human-like receptors, and m1A5 and m6D11 decreased the binding affinity for avian-like receptors. Moreover, a single dose of MAbs 4C2 or 6E3 could fully protect mice against lethal challenge of mouse-adapted A/Eurasian teal/Jiangxi/2018WB0417(H6N2) (MA E-Teal/417) in the prophylactic treatment and also could provide partial protection in the therapeutic treatment. Overall, the generated MAbs and the identified key residues of antigenic epitopes not only provide novel insights into the key markers for monitoring the antigenic variation of H6 but also demonstrate the potential for developing MAb-based passitive treatments for H6. IMPORTANCE Since the escape immunity of influenza A viruses (IAVs) is mainly caused by the continuous antigenic variations in HA, the identification of key antigenic epitopes is crucial for better understanding of the escape immunity and vaccine development for IAVs. The antigenic sites of several HA subtypes, including H1, H3, H5, and H9, have been well characterized, whereas those of H6 subtype are poorly understood. Here, we mapped nine key residues of antigenic epitopes in H6 through escape mutants using a panel of MAbs. Moreover, MAbs 4C2 and 6E3, targeting 140 and 89 residues, respectively, could protect mice against lethal challenge of MA E-Teal/417. These key residues of antigenic epitopes identified here provide the molecular targets for further elucidating the antigenic evolution of H6 and better preparing the vaccine against H6 IAV.

The timely and precise prediction of winter wheat yield plays a critical role in understanding food supply dynamics and ensuring global food security. In recent years, the application of unmanned aerial remote sensing has significantly advanced agricultural yield prediction research. This has led to the emergence of numerous vegetation indices that are sensitive to yield variations. However, not all of these vegetation indices are universally suitable for predicting yields across different environments and crop types. Consequently, the process of feature selection for vegetation index sets becomes essential to enhance the performance of yield prediction models. This study aims to develop an integrated feature selection method known as PCRF-RFE, with a focus on vegetation index feature selection. Initially, building upon prior research, we acquired multispectral images during the flowering and grain filling stages and identified 35 yield-sensitive multispectral indices. We then applied the Pearson correlation coefficient (PC) and random forest importance (RF) methods to select relevant features for the vegetation index set. Feature filtering thresholds were set at 0.53 and 1.9 for the respective methods. The union set of features selected by both methods was used for recursive feature elimination (RFE), ultimately yielding the optimal subset of features for constructing Cubist and Recurrent Neural Network (RNN) yield prediction models. The results of this study demonstrate that the Cubist model, constructed using the optimal subset of features obtained through the integrated feature selection method (PCRF-RFE), consistently outperformed the RNN model. It exhibited the highest accuracy during both the flowering and grain filling stages, surpassing models constructed using all features or subsets derived from a single feature selection method. This confirms the efficacy of the PCRF-RFE method and offers valuable insights and references for future research in the realms of feature selection and yield prediction studies.

AbstractPurpose Renal ischemia-reperfusion injury(IRI)is a major cause of acute kidney injury(AKI), the injury and repair of renal tubular epithelial cells play an important role in the pathological process of IR-AKI. Metabolomics was used to detect cell metabolism alterations and metabolic reprogramming in the initial injury, peak injury, and recovery stage of human renal proximal tubular cells (HK-2 cells) to provide insights into clinical prevention and treatment of IRI-induced AKI.Methods An in vitro ischemia-reperfusion (H/R) injury and the recovery model of HK-2 cells were established at different times of hypoxia/reoxygenation. Comprehensive detection of metabolic alterations in HK-2 cells after H/R induction by nontarget metabolomics. Interconversion of glycolysis and fatty acid oxidation (FAO) in HK-2 cells after H/R induction was examined by western blotting and qRT-PCR.Results Multivariate data analysis found significant differences among the groups, with significant changes in metabolites such as glutamate, malate, aspartate, and L-palmitoylcarnitine. Hypoxia-reoxygenated HK-2 cells are accompanied by altered metabolisms such as disturbance of amino acid and nucleotide metabolism, dysregulation of lipid metabolism, increased glycolysis, and metabolic reprogramming, which manifests as a shift in energy metabolism from FAO to glycolysis.Conclusion The development of IRI-induced AKI in HK-2 cells is accompanied by the disturbance of amino acid, nucleotide, and tricarboxylic acid cycle metabolism and specifically metabolic reprogramming of FAO to glycolytic conversion. The timely recovery of energy metabolism in HK-2 cells is of great significance for treating and prognosis IRI-induced AKI.

Abstract Severe COVID-19 is characterized by an increase in the number and changes in the function of innate immune cells including neutrophils. However, it is not known how the metabolome of immune cells changes in patients with COVID-19. To address these questions, we analyzed the metabolome of neutrophils from patients with severe or mild COVID-19 and healthy controls. We identified widespread dysregulation of neutrophil metabolism with disease progression including in amino acid, redox, and central carbon metabolism. Metabolic changes in neutrophils from patients with severe COVID-19 were consistent with reduced activity of the glycolytic enzyme GAPDH. Inhibition of GAPDH blocked glycolysis and promoted pentose phosphate pathway activity but blunted the neutrophil respiratory burst. Inhibition of GAPDH was sufficient to cause neutrophil extracellular trap (NET) formation which required neutrophil elastase activity. GAPDH inhibition increased neutrophil pH, and blocking this increase prevented cell death and NET formation. These findings indicate that neutrophils in severe COVID-19 have an aberrant metabolism which can contribute to their dysfunction. Our work also shows that NET formation, a pathogenic feature of many inflammatory diseases, is actively suppressed in neutrophils by a cell-intrinsic mechanism controlled by GAPDH.

Abstract Background Rapidly progressive glomerulonephritis (RPGN) is clinically manifestations as a rapidly progressive renal failure and pathologically as crescentic and necrotizing lesions with infiltration of inflammatory cells in the glomeruli. Uremic encephalopathy (UE) usually develops in patients who are suffering from acute or chronic renal failure. Objective The purpose of this article is to provide reference for clinical diagnosis and treatment of renal disease complicated with seizures. Patients Two cases of anti‐glomerular basement membrane type rapidly progressive glomerulonephritis complicated with seizures were reported. Materials & Methods In case 1, a 40‐year‐old woman was hospitalized for the treatment of nausea, anorexia, and fever. On admission, she presented with elevated serum inflammatory indicators, moderate anemia, and advanced acute kidney injury requiring hemodialysis. Her anti‐glomerular basement membrane (GBM) antibody in serum and renal tissues was found to be extremely high. She was finally diagnosed with anti‐GBM disease. She was treated with a combination of corticosteroid pulse therapy, oral cyclophosphamide and prednisolone, and plasma exchange, while continued to require maintenance hemodialysis for end‐stage kidney disease. During treatment, she suddenly suffered blindness, seizure, and consciousness disturbance. She was diagnosed as posterior reversible leukoencephalopathy syndrome by magnetic resonance imaging (MRI). The posterior reversible leukoencephalopathy syndrome subsided quickly after control of her hypertension and reinforcement of immunosuppressive treatment. In case 2, the patient also developed epileptic symptoms on the basis of GBM disease, and was given treatment similar to that of Case 1, so that the epileptic symptoms were controlled. Result Reversible posterior leukoencephalopathy syndrome, especially when accompanied by cerebral hemorrhage, may lead to irreversible and lethal neurological abnormalities, and nephrologists should, therefore, be aware of the potential risk of reversible posterior leukoencephalopathy syndrome in patients with anti‐GBM disease. We can discuss the current two cases in the light of the previous literature.

Abstract Hyperhomocysteinemia (HHcy) is a condition closely associated with cardiovascular and cerebrovascular diseases. Detecting its risk factors and taking some relevant interventions still represent the top priority to lower its prevalence. Yet, in discussing risk factors, Logistic regression model is usually adopted but accompanied by some defects. In this study, a Tabu Search-based BNs was first constructed for HHcy and its risk factors, and the conditional probability between nodes was calculated using Maximum Likelihood Estimation. Besides, we tried to compare its performance with Hill Climbing-based BNs and Logistic regression model in risk factor detection and discuss its prospect in clinical practice. Our study found that Age, sex, α1-microgloblobumin to creatinine ratio, fasting plasma glucose, diet and systolic blood pressure represent direct risk factors for HHcy, and smoking, glycosylated hemoglobin and BMI constitute indirect risk factors for HHcy. Besides, the performance of Tabu Search-based BNs is better than Hill Climbing-based BNs. Accordingly, BNs with Tabu Search algorithm could be a supplement for Logistic regression, allowing for exploring the complex network relationship and the overall linkage between HHcy and its risk factors. Besides, Bayesian reasoning allows for risk prediction of HHcy, which is more reasonable in clinical practice and thus should be promoted.

Stroke is a major chronic non-communicable disease with high incidence, high mortality, and high recurrence. To comprehensively digest its risk factors and take some relevant measures to lower its prevalence is of great significance. This study aimed to employ Bayesian Network (BN) model with Max-Min Hill-Climbing (MMHC) algorithm to explore the risk factors for stroke. From April 2019 to November 2019, Shanxi Provincial People's Hospital conducted opportunistic screening for stroke in ten rural areas in Shanxi Province. First, we employed propensity score matching (PSM) for class balancing for stroke. Afterwards, we used Chi-square testing and Logistic regression model to conduct a preliminary analysis of risk factors for stroke. Statistically significant variables were incorporated into BN model construction. BN structure learning was achieved using MMHC algorithm, and its parameter learning was achieved with Maximum Likelihood Estimation. After PSM, 748 non-stroke cases and 748 stroke cases were included in this study. BN was built with 10 nodes and 12 directed edges. The results suggested that age, fasting plasma glucose, systolic blood pressure, and family history of stroke constitute direct risk factors for stroke, whereas sex, educational levels, high density lipoprotein cholesterol, diastolic blood pressure, and urinary albumin-to-creatinine ratio represent indirect risk factors for stroke. BN model with MMHC algorithm not only allows for a complicated network relationship between risk factors and stroke, but also could achieve stroke risk prediction through Bayesian reasoning, outshining traditional Logistic regression model. This study suggests that BN model boasts great prospects in risk factor detection for stroke.

Polyphenols containing styrene pyranone skeleton are unique to porous fungi. Inonotus obliquus (IO) is a medicinal and edible porous fungus. Twelve phenolic compounds containing four styryl pyranone polyphenols from IO were isolated and identified in this work. The antioxidant ability of the isolates was characterized utilizing the ferric reducing antioxidant power (FRAP) assay and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonate) (ABTS) radical scavenging test. Styrylpyranone polyphenols, especially methylinoscavin B, could capture free radicals better than other phenolic compounds, and quantum chemical calculations also confirmed this evaluation. The density functional theory (DFT) calculation data showed that Styrylpyranone polyphenols, especially methylinoscavin B, have a lower energy gap, higher softness and higher electronic chemical potential than other phenolic compounds. The bond dissociation energy values of the bond in C7ʹ O–H of the methylinoscavin B molecule are less than those in C11 and C6ʹ O–H when reacting with ∙OOH (selected as a representative free radical). On the basis of calculations, hydrogen atom transfer (HAT) is supposed to be a preferred mechanism over single electron transfer (SET) when phenolic compounds react with free radicals. Moreover, after treatment with final concentrations of 0.5, 1, 2, 3, 4 and 5 μM phelligridin E (PHE), the activity of SOD1 increased by 70.15%, 11.36%, 145.45%, 172.73%, 205.05% and 275.23%, respectively. The molecular dynamics simulation (MD) study of PHE employed SOD1 (PDB ID: 6FN8). The MD results showed that the hydrogen bonds between ASP147 of SOD1 and PHE promote GLU223-ARG224 to form a stable C coil after combining with PHE. The formation of the C Coil enhanced the stability of the electrostatic loop (EL) of SOD1 and the rate of diffusion of the superoxide anion to the active site. Styrylpyrone polyphenols of Inonotus obliquus origin have the potential to be a source of vigorous free radical scavengers and antioxidant enzyme activators.

Automated evaluation of all glomeruli throughout the whole kidney is essential for the comprehensive study of kidney function as well as understanding the mechanisms of kidney disease and development. The emerging large-volume microscopic optical imaging techniques allow for the acquisition of mouse whole-kidney 3D datasets at a high resolution. However, fast and accurate analysis of massive imaging data remains a challenge. Here, we propose a deep learning-based segmentation method called FastCellpose to efficiently segment all glomeruli in whole mouse kidneys. Our framework is based on Cellpose, with comprehensive optimization in network architecture and the mask reconstruction process. By means of visual and quantitative analysis, we demonstrate that FastCellpose can achieve superior segmentation performance compared to other state-of-the-art cellular segmentation methods, and the processing speed was 12-fold higher than before. Based on this high-performance framework, we quantitatively analyzed the development changes of mouse glomeruli from birth to maturity, which is promising in terms of providing new insights for research on kidney development and function.

Abstract Background Porcine pancreatic elastase (PPE) is successfully used to induce abdominal aortic aneurysm (AAA) in mice. However, differences between mouse strains in susceptibility to PPE induction have been reported. Kunming mouse is one of the most frequently used strains in China but whether it is suitable for induction of AAA by PPE application remains unclear. Methods PPE infusion (1.5 units/ml) in temporary controlled aorta was performed to induce AAAs in both C57BL/6J and Kunming mice. Phosphate‐buffered saline (PBS) application was used as vehicle control. The aorta diameters of all mice were measured at days 0 and 14 after surgery to evaluate the AAA formation. Results After 14 days of PPE or PBS infusion, all mice were sacrificed and aorta tissues were collected for histological staining analysis. At the 14th day after infusion, PPE successfully induced aortic dilation in Kunming mice and typical AAA in C57BL/6J mice. The aorta diameter increased by 0.23 mm in Kunming mice after PPE infusion, while it was 0.72 mm in the C57BL/6J strain. PPE induced mild elastin degradation, smooth muscle cell (SMC) depletion and mural leucocyte infiltration in Kunming mice, but in PPE‐sensitive C57BL/6J mice, it induced total loss of SMCs, elastin disappearance and diffused infiltrated leucocytes in aortic aneurysmal segments. The effects of PPE in inducing angiogenesis and upregulating matrix metalloproteinase 2 and 9 expression in Kunming mice were also weaker than that in C57BL/6J mice. Conclusion At the reported dose of PPE, Kunming mouse is not as susceptible to AAA formation as C57BL/6J mice. The failure of PPE to induce AAA formation in Kunming mice may be associated to its inability to boost a strong inflammatory response.

Characteristics of pore structure are crucial to oil exploration and development in shale reservoirs, but practical evaluation of shale pore structure remains a longstanding challenge. In this study, based on nuclear magnetic resonance transverse relaxation time (T2) spectra under water-saturated and irreducible conditions, total pore space and moveable fluid space were skillfully determined. Meanwhile, multifractal spectra were used to characterize T2 distributions of total pore space and movable fluid space. The results show characteristic parameters of high probability measure areas of multifractal spectra (multifractal parameters) have strong negative correlations with T2 geometric mean value, and average radii of pore and throat increase with a decrease in the multifractal parameters, indicating the shale reservoirs with low values of the multifractal parameters may have good pore structure. Moreover, clay mineral content has strong positive effects on the multifractal parameters. In contrast, (quartz+feldspar) mineral content negatively influences on the multifractal parameters. Finally, new accurate models for permeability estimation in core scale and well-logging scale of shales are both proposed based on the multifractal parameters, which are also applicable for the other reservoirs. Overall, this study is important and timely for evaluating pore structure and predicting permeability in shale reservoirs.

IntroductionChronic kidney disease (CKD) is a progressive disease with high incidence but early imperceptible symptoms. Since China’s rural areas are subject to inadequate medical check-ups and single disease screening programme, it could easily translate into end-stage renal failure. This study aimed to construct an early warning model for CKD tailored to impoverished areas by employing machine learning (ML) algorithms with easily accessible parameters from ten rural areas in Shanxi Province, thereby, promoting a forward shift of treatment time and improving patients’ quality of life.MethodsFrom April to November 2019, CKD opportunistic screening was carried out in 10 rural areas in Shanxi Province. First, general information, physical examination data, blood and urine specimens were collected from 13,550 subjects. Afterward, feature selection of explanatory variables was performed using LASSO regression, and target datasets were balanced using the SMOTE (synthetic minority over-sampling technique) algorithm, i.e., albuminuria-to-creatinine ratio (ACR) and α1-microglobulin-to-creatinine ratio (MCR). Next, Bagging, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were employed for classification of ACR outcomes and MCR outcomes, respectively.Results12,330 rural residents were included in this study, with 20 explanatory variables. The cases with increased ACR and increased MCR represented 1,587 (12.8%) and 1,456 (11.8%), respectively. After conducting LASSO, 14 and 15 explanatory variables remained in these two datasets, respectively. Bagging, RF, and XGBoost performed well in classification, with the AUC reaching 0.74, 0.87, 0.87, 0.89 for ACR outcomes and 0.75, 0.88, 0.89, 0.90 for MCR outcomes. The five variables contributing most to the classification of ACR outcomes and MCR outcomes constituted SBP, TG, TC, and Hcy, DBP and age, TG, SBP, Hcy and FPG, respectively. Overall, the machine learning algorithms could emerge as a warning model for CKD.ConclusionML algorithms in conjunction with rural accessible indexes boast good performance in classification, which allows for an early warning model for CKD. This model could help achieve large-scale population screening for CKD in poverty-stricken areas and should be promoted to improve the quality of life and reduce the mortality rate.

ObjectiveTo explore the common differential flora of IgAN, Kawasaki disease and IgA vasculitis by screening and analyzing the differential intestinal flora between the three disease groups of IgAN, Kawasaki disease and IgA vasculitis and their healthy controls.MethodsPapers on 16srRNA sequencing-related intestinal flora of IgAN, Kawasaki disease and IgA vasculitis were searched in databases, the literature was systematically collated and analysed, the original data was download from the relevant databases, and then the operational taxonomic unit and species classification analysis were performed. Besides, Alpha diversity analysis and Beta diversity analysis were performed to screen for IgAN, Kawasaki disease and I1gA vasculitis groups and finally compare the common intestinal differential flora among the three groups.ResultsAmong the common differential flora screened, Lachnospiracea_incertae_sedis was lower in both the IgAN and Kawasaki disease groups than in the respective healthy controls; Coprococcus was low in the IgAN group but high in the IgA vasculitis group. Fusicatenibacter was lower in both the Kawasaki disease and IgA vasculitis groups than in their respective healthy controls, and Intestinibacter was low in the Kawasaki disease group, but its expression was high in the IgA vasculitis group.ConclusionThe dysbiosis of the intestinal flora in the three groups of patients with IgAN, Kawasaki disease and IgA vasculitis, its effect on the immunity of the organism and its role in the development of each disease group remain unclear, and the presence of their common differential flora may further provide new ideas for the association of the pathogenesis of the three diseases.

Abstract Information hiding as a crucial method for multimedia security and privacy protection, has received a substantial amount of attention. However, the most existing methods focus on the resistance of steganalysis and the robustness of information, resulting in low capacity. In this paper, to further increase the capacity of information hiding, a high‐capacity adversarial image steganography model with end‐to‐end manner, termed as HCISNet is proposed. First, an enhanced Dense Atrous Spatial Pyramid Pooling module is presented to learn more semantic information in cover image, which can adaptively embed more message in redundant regions with rich textures. Then, a modified discriminator network with lightweight residual block is employed to assist the encoder network with optimal high‐capacity solution. Meanwhile, the multiple objective function with the perceptual loss and several training tricks are developed, which can improve the visual and perceptual consistency of the original and the steganographic image, further enhancing the capacity. Finally, experiments and analysis are conducted on three public datasets, where this model has better imperceptibility, higher security and greater capacity. Under same experimental conditions, the cover image can embed 5.68 bits per pixel (BPP), far exceeding the previous highest value of 4.4 BPP in state‐of‐the‐art methods in the authors' knowledge.

Phosphorylation is the most common and important post-translational modification of proteins, which plays an important role in the regulation of cell proliferation, differentiation, development and metabolism, and is closely related to the tumorigenesis and metastasis of cancer. Protein kinases and phosphatases generally regulate protein phosphorylation levels as a pair of opposite acting enzymes. Protein phosphorylation in eukaryotes occurs mainly in serine, threonine, and tyrosine residues, and their roles in tumorigenesis and development have been extensively studied. But the roles on histidine phosphorylation is less known due to the immature mass spectrometry and enrichment techniques. In recent years, with the rapid development of related technologies and the discovery of new histidine phosphatases, researchers have paid more attention to the roles of histidine phosphorylation in tumors. Therefore, we aim to review the roles of histidine kinases and phosphatases in tumor.

H6 avian influenza viruses (AIVs) not only continue to circulate in both domestic poultry and wild waterfowl, but also have occasionally caused spillovers infections in pigs and humans, posing a potential threat to public health. However, the molecular mechanism of H6 AIV adaptation to mammals remains largely unknown. In this study, two mouse-adapted (MA) H6 AIV strains, named as MA E-Teal/417 and MA GWF-Goose/740, were generated through blind passages in BALB/c mice. The two MA H6 strains replicated more efficiently and showed higher virulence than the corresponding wild type (WT) H6 strains in mice. Genome sequencing revealed that MA E-Teal/417 and MA GWF-Goose/740 carried six amino acid mutations (PB2-T224A/E627K, HA-G124R, NA-F167L/Y356H and M1-M92R), and four amino acid mutations (PB1-K577E, PA-T97I/D514E and HA-T276K), respectively, when compared to the corresponding WT virus. Receptor binding assay showed MA E-Teal/417 had stronger binding activity to α-2,3 SA than WT E-Teal/417. Moreover, the polymerase activity analysis found the RNP polymerase activity of both MA H6 viruses was significantly higher than that of the corresponding WT virus in 293T cells. All these demonstrate that H6 AIV can acquire limit amino acid substitutions to adapt to mammals and increase virulence, highlighting the significance of monitoring such mutations of H6 AIV in the field for alarming the potential of its cross-transmission and pathogenesis in mammals.

ObjectiveTo analyze the clinical manifestations and imaging features of a hospitalized patient with intermittent headache who was finally diagnosed with von Hippel–Lindau (VHL) syndrome and to perform whole-exon gene detection to improve the understanding of the diagnosis and treatment strategies of the disease.MethodsA case of suspected VHL syndrome in Shanxi Provincial People's Hospital was analyzed. Proband DNA was also extracted for whole exome sequencing and screened for causative mutation sites, which were validated by Sanger sequencing. The literature about VHL gene mutations in Chinese patients in the past 10 years were also reviewed.ResultsThere is a heterozygous mutation site c.499C > G on the VHL gene on the short arm of chromosome 3 of the patient, which is a missense mutation. The mutation results in the substitution of arginine with glycine at amino acid 167 of the encoded protein, which may be primarily responsible for the disease in the patient with VHL syndrome. However, the mutation did not occur in other family members.ConclusionEarly recognition and treatment of VHL syndrome can be available with genetic testing technology. Strengthening the understanding of this complex genetic disease and improving the diagnostic rate of VHL syndrome are helpful for the precise treatment of patients with this disease, which may help prolong the survival time of patients to a certain extent and improve their quality of life.

BackgroundDysbiosis of the gut microbiota is closely related to chronic systemic inflammation and autoimmunity, playing an essential role in the pathogenesis of primary Sjögren’s syndrome (pSS). Abnormalities in the proportions of blood T lymphocyte subtype, that is Th17/Treg, were detected in pSS patients. We aimed to determine the associations between gut microbiota and Th17/Treg in pSS.Method98 pSS patients and 105 healthy controls (NC) were enrolled between Dec 1, 2018, and Aug 31, 2019. The baseline information and clinical parameters on pSS patients and healthy controls were collected. 16S rRNA sequencing was performed to characterize the gut microbiome and identify gut microbes that are differentially abundant between patients and healthy controls. Lastly, associations between relative abundances of specific bacterial taxa in the gut and clinical outcome parameters were evaluated.ResultsPatients with pSS show decreased gut microbial diversity and richness, decreased abundance of butyrate producing bacteria, such as Roseburia and Coprococcus, and increased abundance of other taxa, such as Eubacterium rectale and Roseburia inulinivorans. These bacteria are enriched with functions related to glycolytic and lipogenic, energy, substance, galactose, pentose metabolism pathways and glucuronate interconversions, decreased with functions related to peptidoglycan biosynthesis, pyrimidine metabolism pathways. An integrative analysis identified pSS-related specific bacterial taxa in the gut, for which the abundance of Eubacterium rectale is negatively correlated with Th17/Treg. Furthermore, the pathways of biosynthesis of secondary metabolites, biosynthesis of amino acids, peptidoglycan biosynthesis and pyrimidine, galactose, pentose, microbial metabolism in diverse environments, glyoxylate and dicarboxylate metabolism are associated with Treg or Th17/Treg.ConclusionsPrimary Sjögren’s syndrome could lead to decreased gut microbial diversity and richness of intestinal flora in patients. The proportions of Th17 and Treg cells induced by microbiota were predictive pSS manifestations and accounted for the pSS severity.

Purpose: To describe a unique case of bilateral cataract formation in the setting of multisystem inflammation after the 1st dose of the BNT162b2 mRNA COVID-19 vaccination. Observations: A previously healthy 20-year-old male developed intumescent bilateral cataracts leading to visual decline from 20/20 to 20/300–20/400 in each eye, likely from systemic inflammation after vaccination. Conclusion and importance: This is the first reported case of cataract formation following a COVID-19 vaccine. While ocular adverse effects associated with COVID-19 vaccination are rare, it is important to raise awareness of these entities amongst medical providers as the COVID-19 pandemic continues and vaccinations become widespread.

Aiming at the land subsidence problem caused by multiple coal seam mining in the Ehuobulake Coal Mine, this paper, considering the geological conditions of the first and fifth layers of coal, adopts the method of combining FLAC3D numerical simulation and SBAS-InSAR technology to analyze the dynamic evolution law of land subsidence amount and range under multiple coal seam repeated mining conditions. The reliability of the technology is verified by the field GPS monitoring data. The results show that, under the mining condition of multiple coal seams in the Ehuobulake Coal Mine, the land subsidence presents obvious asymmetry, and the size and range of the land subsidence in the mining area further increase due to the mining of lower layer coal. FLAC3D simulation results show that the maximum land subsidence is −211.8 mm. The results of SBAS-InSAR monitoring show that the maximum land subsidence is −225 mm, and the land subsidence obtained by the two methods has a high degree of fitting. The method of combining FLAC3D and InSAR technology can accurately and reliably monitor and analyze the land subsidence under the repeated mining of multiple coal seams in the mining area. It can provide effective guidance for the stability analysis of mined-out areas and the prediction of the influence of repeated mining on ground deformation.

Most studies related to aspects of wellbore stability, such as wellbore breakage, block dropping, and wellbore expansion, revolve around the physicochemical interaction between drilling fluid and surrounding rock, but relevant studies show that drill string vibration during drilling also has a crucial and even decisive influence on wellbore stability. In order to thoroughly explore the influence mechanism of drill string vibration on wellbore stability, our research group established a finite element flexible simulation model of drill string dynamics and used a storage downhole vibration measurement device to collect downhole real drilling vibration data to verify the correctness of the simulation model. Then, based on the critical conditions of wellbore breakage, a wellbore stability evaluation method was established, and the wellbore stability under different drilling parameters and drilling tool combination conditions was evaluated and analyzed. The research results play an important role in revealing the influence mechanism of drill string vibration on wellbore stability and can provide theoretical guidance for engineering problems such as wellbore instability risk assessment.

Above-ground biomass (AGB) serves as an indicator of crop growth status, and acquiring timely AGB information is crucial for estimating crop yield and determining appropriate water and fertilizer inputs. Unmanned Aerial Vehicles (UAVs) equipped with RGB cameras offer an affordable and practical solution for efficiently obtaining crop AGB. However, traditional vegetation indices (VIs) alone are insufficient in capturing crop canopy structure, leading to poor estimation accuracy. Moreover, different flight heights and machine learning algorithms can impact estimation accuracy. Therefore, this study aims to enhance wheat AGB estimation accuracy by combining VIs, crop height, and texture features while investigating the influence of flight height and machine learning algorithms on estimation. During the heading and grain-filling stages of wheat, wheat AGB data and UAV RGB images were collected at flight heights of 30 m, 60 m, and 90 m. Machine learning algorithms, including Random Forest Regression (RFR), Gradient Boosting Regression Trees (GBRT), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso) and Support Vector Regression (SVR), were utilized to construct wheat AGB estimation models. The research findings are as follows: (1) Estimation accuracy using VIs alone is relatively low, with R2 values ranging from 0.519 to 0.695. However, combining VIs with crop height and texture features improves estimation accuracy, with R2 values reaching 0.845 to 0.852. (2) Estimation accuracy gradually decreases with increasing flight height, resulting in R2 values of 0.519–0.852, 0.438–0.837, and 0.445–0.827 for flight heights of 30 m, 60 m, and 90 m, respectively. (3) The choice of machine learning algorithm significantly influences estimation accuracy, with RFR outperforming other machine learnings. In conclusion, UAV RGB images contain valuable crop canopy information, and effectively utilizing this information in conjunction with machine learning algorithms enables accurate wheat AGB estimation, providing a new approach for precision agriculture management using UAV remote sensing technology.

Permissionless blockchain, as a kind of distributed ledger, has gained considerable attention because of its openness, transparency, decentralization, and immutability. Currently, permissionless blockchain has shown a good application prospect in many fields, from the initial cryptocurrency to the Internet of Things (IoT) and Vehicular Ad-Hoc Networking (VANET), which is considered as the beginning of rewriting our digital infrastructure. However, blockchain confronts some privacy risks that hinder its practical applications. Though numerous surveys reviewed the privacy preservation in blockchain, they failed to reveal the latest advances, nor have they been able to conduct a unified standard comprehensive classification of the privacy protection of permissionless blockchain. Therefore, in this paper, we analyze the specific characteristics of permissionless blockchain, summarize the potential privacy threats, and investigate the unique privacy requirements of blockchain. Existing privacy preservation technologies are carefully surveyed and evaluated based on our proposed evaluation criteria. We finally figure out open research issues as well as future research directions from the perspective of privacy issues.

Tactical Data Link (TDL) is a communication system that utilizes a particular message format and a protocol to transmit data via wireless channels in an instant, automatic, and secure way. So far, TDL has shown its excellence in military applications. Current TDL adopts a distributed architecture to enhance anti-destruction capacity. However, It still faces a problem of data inconsistency and thus cannot well support cooperation across multiple militarily domains. To tackle this problem, we propose to leverage blockchain to build an automatic and adaptive data transmission control scheme for TDL. It achieves automatic data transmission and realizes information consistency among different TDL entities. Besides, applying smart contracts based on blockchain further enables adjusting data transmission policies automatically. Security analysis and experimental results based on simulations illustrate the effectiveness and efficiency of our proposed scheme.

BackgroundIgA nephropathy (IgAN) is an autoimmune disease that affects people of any age and is an important cause of end-stage renal disease. However, the pathogenesis and pathophysiology of IgAN is not clear. This article aimed to explore the immune-mediated inflammation and genetic mechanisms in IgAN.MethodsThe transcriptome sequencing data of IgAN glomeruli in the Gene Expression Omnibus database were downloaded. Single-sample gene set enrichment analysis was used to estimate the immune microenvironment of the merged microarray data and GSE141295. IgAN samples were divided into two clusters by cluster analysis. “limma” and “DEseq2” package in R were used to identify differentially expressed genes (DEGs). The weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression modules related to inflammation in IgAN. R software package “clusterProfiler” was used for enrichment analysis, whereas Short Time-Series Expression Miner (STEM) analysis was used to identify the trend of gene expression. Machine-learn (ML) was performed using the shiny app. Finally, Drug Signatures Database (DSigDB) was used to identify potential molecules for treating IgAN.ResultsThe infiltration of macrophages in IgAN glomeruli was increased, whereas CD4+ T cells, especially inducedregulatory T cells (iTregs) were decreased. A total of 1,104 common DEGs were identified from the merged data and GSE141295. Brown module was identified to have the highest inflammatory correlation with IgAN using WGCNA, and 15 hub genes were screened from this module. Among these 15 hub genes, 14 increased with the severity of IgAN inflammation based on STEM analysis. Neural network (nnet) is considered as the best model to predict the severity of IgAN. Fucose identified from DSigDB has a potential biological activity to treat IgAN.ConclusionThe increase of macrophages and the decrease of iTregs in glomeruli represent the immune-mediated inflammation of IgAN, and fucose may be a potential therapeutic molecule against IgAN because it affects genes involved in the severe inflammation of IgAN.

Summary: Optical visualization of complex microstructures in the entire organ is essential for biomedical research. However, the existing methods fail to accurately acquire the detailed microstructures of whole organs with good morphological and biochemical preservation. This study proposes a cryo-fluorescence micro-optical sectioning tomography (cryo-fMOST) to image whole organs in three dimensions (3D) with submicron resolution. The system comprises a line-illumination microscope module, cryo-microtome, three-stage refrigeration module, and heat insulation device. To demonstrate the imaging capacity and wide applicability of the system, we imaged and reconstructed various organs of mice in 3D, including the healthy tongue, kidney, and brain, as well as the infarcted heart. More importantly, imaged brain slices were performed sugar phosphates determination and fluorescence in situ hybridization imaging to verify the compatibility of multi-omics measurements. The results demonstrated that cryo-fMOST is capable of acquiring high-resolution morphological details of various whole organs and may be potentially useful for spatial multi-omics.

ObjectivesMultimorbidity (MMD) is a medical condition that is linked with high prevalence and closely related to many adverse health outcomes and expensive medical costs. The present study aimed to construct Bayesian networks (BNs) with Max-Min Hill-Climbing algorithm (MMHC) algorithm to explore the network relationship between MMD and its related factors. We also aimed to compare the performance of BNs with traditional multivariate logistic regression model.MethodsThe data was downloaded from the Online Open Database of CHARLS 2018, a population-based longitudinal survey. In this study, we included 10 variables from data on demographic background, health status and functioning, and lifestyle. Missing value imputation was first performed using Random Forest. Afterward, the variables were included into logistic regression model construction and BNs model construction. The structural learning of BNs was achieved using MMHC algorithm and the parameter learning was conducted using maximum likelihood estimation.ResultsAmong 19,752 individuals (9,313 men and 10,439 women) aged 64.73 ± 10.32 years, there are 9,129 ones without MMD (46.2%) and 10,623 ones with MMD (53.8%). Logistic regression model suggests that physical activity, sex, age, sleep duration, nap, smoking, and alcohol consumption are associated with MMD (P < 0.05). BNs, by establishing a complicated network relationship, reveals that age, sleep duration, and physical activity have a direct connection with MMD. It also shows that education levels are indirectly connected to MMD through sleep duration and residence is indirectly linked to MMD through sleep duration.ConclusionBNs could graphically reveal the complex network relationship between MMD and its related factors, outperforming traditional logistic regression model. Besides, BNs allows for risk reasoning for MMD through Bayesian reasoning, which is more consistent with clinical practice and thus holds some application prospects.