Search

Abstract Bladder cancer (BCa), as the most common malignant tumor of the urinary system, has received significant attention in research on the clinical application of artificial intelligence algorithms. Nevertheless, it has been observed that certain investigations use data from various medical facilities to train models for BCa, which may pose a privacy risk. Given this concern, protecting patient privacy during machine learning algorithm training is a crucial aspect that requires substantial attention. One emerging machine learning paradigm that addresses this concern is federated learning (FL). FL enables multiple entities to collaboratively build machine learning models while preserving data privacy and security. In this study, we present a multicenter BCa magnetic resonance imaging (MRI) dataset. The dataset comprises 275 three-dimensional bladder T2-weighted MRI scans collected from four medical centers, and each scan includes diagnostic pathological labels for muscle invasion and pixel-level annotations of tumor contours. Four FL methods are used to assess the baseline of the dataset for both the task of diagnosing muscle-invasive bladder cancer and automatic bladder tumor lesion segmentation.

BackgroundAlthough liraglutide has established advantages in treating patients with type 2 diabetes mellitus (T2DM) and metabolic syndrome (MS), there are still some patients with lower responsiveness to liraglutide. The objective of the study was to identify the predictors of response to liraglutide in patients with T2DM and MS.MethodsThis retrospective cohort study included patients diagnosed with T2DM and MS who received liraglutide treatment as a part of their diabetes management for a minimum of six months. The participants were stratified into two groups: responders (HbA1c reduction≥1.0% and weight loss≥3%) and non-responders. The discrepancies in baseline data between the two groups were analyzed, containing comedications, test parameters, and basic profiles. The affecting factors of response to liraglutide by Logistic regression analysis were performed, and the predictive ability of the identified factors was evaluated by plotting a receiver operating characteristic (ROC) curve.ResultsA total of 417 patients with T2DM and MS were examined and followed up according to the inclusion criteria, and 206 patients completed the follow-up; 105 (50.97%) were responders and 101 (49.03%) were non-responders to liraglutide. The binary logistic regression analysis identified baseline HbA1c, baseline BMI, and the duration of T2DM as significant predictors of glycemic and weight responses to liraglutide (P

Abstract Background Since COVID-19 became a global epidemic disease in 2019, pulmonary fibrosis (PF) has become more prevalent among persons with severe infections, with IPF being the most prevalent form. In traditional Chinese medicine, various disorders are treated using Sinomenine (SIN). The SIN’s strategy for PF defense is unclear. Methods Bleomycin (BLM) was used to induce PF, after which inflammatory factors, lung histological alterations, and the TGF-/Smad signaling pathway were assessed. By administering various dosages of SIN and the TGF- receptor inhibitor SB-431,542 to human embryonic lung fibroblasts (HFL-1) and A549 cells, we were able to examine proliferation and migration as well as the signaling molecules implicated in Epithelial-Mesenchymal Transition (EMT) and Extra-Cellular Matrix (ECM). Results In vivo, SIN reduced the pathological changes in the lung tissue induced by BLM, reduced the abnormal expression of inflammatory cytokines, and improved the weight and survival rate of mice. In vitro, SIN inhibited the migration and proliferation by inhibiting TGF-β1/Smad3, PI3K/Akt, and NF-κB pathways, prevented the myofibroblasts (FMT) of HFL-1, reversed the EMT of A549 cells, restored the balance of matrix metalloenzymes, and reduced the expression of ECM proteins. Conclusion SIN attenuated PF by down-regulating TGF-β/Smad3, PI3K/Akt, and NF-κB signaling pathways, being a potential effective drug in the treatment of PF.

Intestinal dysbiosis and disrupted bile acid (BA) homeostasis are associated with obesity, but the precise mechanisms remain insufficiently explored. Hepatic protein phosphatase 1 regulatory subunit 3G (PPP1R3G) plays a pivotal role in regulating glycolipid metabolism; nevertheless, its obesity-combatting potency remains unclear. In this study, a substantial reduction was observed in serum PPP1R3G levels in high-body mass index (BMI) and high-fat diet (HFD)-exposed mice, establishing a positive correlation between PPP1R3G and non-12α-hydroxylated (non-12-OH) BA content. Additionally, hepatocyte-specific overexpression of Ppp1r3g (PPP1R3G HOE) mitigated HFD-induced obesity as evidenced by reduced weight, fat mass, and an improved serum lipid profile; hepatic steatosis alleviation was confirmed by normalized liver enzymes and histology. PPP1R3G HOE considerably impacted systemic BA homeostasis, which notably increased the non-12-OH BAs ratio, particularly lithocholic acid (LCA). 16S ribosomal DNA (16S rDNA) sequencing assay indicated that PPP1R3G HOE reversed HFD-induced gut dysbiosis by reducing the Firmicutes/Bacteroidetes ratio and Lactobacillus population, and elevating the relative abundance of Blautia, which exhibited a positive correlation with serum LCA levels. A fecal microbiome transplantation test confirmed that the anti-obesity effect of hepatic PPP1R3G was gut microbiota-dependent. Mechanistically, PPP1R3G HOE markedly suppressed hepatic cholesterol 7α-hydroxylase (CYP7A1) and sterol-12α-hydroxylase (CYP8B1), and concurrently upregulated oxysterol 7-α hydroxylase and Takeda G protein-coupled BA receptor 5 (TGR5) expression under HFD conditions. Furthermore, LCA administration significantly mitigated the HFD-induced obesity phenotype and elevated non-12-OH BA levels. These findings emphasize the significance of hepatic PPP1R3G in ameliorating diet-induced adiposity and hepatic steatosis through the gut microbiota-BA axis, which may serve as potential therapeutic targets for obesity-related disorders.

This paper presents an innovative fusion model called “CALSE-LSTM,” which integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), self-attention mechanisms, and squeeze-and-excitation attention mechanisms to optimize the estimation accuracy of the State of Charge (SoC). The model incorporates battery historical data as input and employs a dual-attention mechanism based on CNN-LSTM to extract diverse features from the input data, thereby enhancing the model's ability to learn hidden information. To further improve model performance, we fine-tune the model parameters using the Pelican algorithm. Experiments conducted under Urban Dynamometer Driving Schedule (UDDS) conditions show that the CALSE-LSTM model achieves a Root Mean Squared Error (RMSE) of only 1.73 % in lithium battery SoC estimation, significantly better than GRU, LSTM, and CNN-LSTM models, reducing errors by 31.9 %, 31.3 %, and 15 %, respectively. Ablation experiments further confirm the effectiveness of the dual-attention mechanism and its potential to improve SoC estimation performance. Additionally, we validate the learning efficiency of CALSE-LSTM by comparing model training time with the number of iterations. Finally, in the comparative experiment with the Kalman filtering method, the model in this paper significantly improved its performance by incorporating power consumption as an additional feature input. This further verifies the accuracy of CALSE-LSTM in estimating the State of Charge (SoC) of lithium batteries.

A high-sensitivity photoacoustic spectroscopy (PAS) sensor based on differential Helmholtz photoacoustic cell (DHPAC) with dense spot pattern is reported in this paper for the first time. A multi-pass cell based on two concave mirrors was designed to achieve a dense spot pattern, which realized 212 times excitation of incident laser. A finite element analysis was utilized to simulate the sound field distribution and frequency response of the designed DHPAC. An erbium-doped fiber amplifier (EDFA) was employed to amplify the output optical power of the laser to achieve strong excitation. In order to assess the designed sensor's performance, an acetylene (C2H2) detection system was established using a near infrared diode laser with a central wavelength 1530.3 nm. According to experimental results, the differential characteristics of DHPAC was verified. Compared to the sensor without dense spot pattern, the photoacoustic signal with dense spot pattern had a 44.73 times improvement. The minimum detection limit (MDL) of the designed C2H2-PAS sensor can be improved to 5 ppb when the average time of the sensor system is 200 s.

Rapid and accurate determination of pigment content is important for quality inspection of spinach leaves during storage. This study aimed to use hyperspectral imaging at two spectral ranges (visible/near-infrared, VNIR: 400–1000 nm; NIR: 900–1700 nm) to simultaneously determine the pigment (chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids) content in spinach stored at different durations and conditions (unpackaged and packaged). Partial least squares (PLS), back propagation neural network (BPNN) and convolutional neural network (CNN) were used to establish single-task and multi-task regression models. Single-task CNN (STCNN) models and multi-task CNN (MTCNN) models obtained better performances than the other models. The models using VNIR spectra were superior to those using NIR spectra. The overall results indicated that hyperspectral imaging with multi-task learning could predict the quality attributes of spinach simultaneously for spinach quality inspection under various storage conditions. This research will guide food quality inspection by simultaneously inspecting multiple quality attributes.

The advent of autonomous vehicles (AVs) poses challenges to parking guidance in mixed driving scenarios involving human-driven vehicles (HVs) and AVs. This study introduced a dynamic and coordinated strategy (DCS) to optimize parking space allocation and path guidance within a mixed driving parking lot, aiming to enhance parking-cruising efficiency. DCS considers the distinctive characteristics of HVs and AVs and dynamically formulates parking guiding schemes based on real-time conditions. The strategy encompasses four main steps: Triggering scheme formulation, identifying preoccupied parking spaces, updating the parking lot traffic network and optimizing the vehicle-path-space matching scheme. A programming model was established to minimize the total remaining cruising time, and iterative optimization was conducted with vehicle loading test based on timing. To elevate computational efficiency, the concept of parking-cruising path tree (PCPT) and its updating method were introduced based on the dynamic shortest path tree algorithm. Comparative analysis of cases and simulations demonstrated the efficacy of DCS in mitigating parking-cruising duration of different types of vehicles and minimizing forced delays arising from lane blocking. Notably, the optimization effect is particularly significant for vehicles with extended cruising durations or in parking lots with low AV penetration rates and high saturation, with an achievable optimization rate reaching up to 18%. This study addressed challenges related to drivers' noncompliance with guidance and lane blocking, thereby improving overall operational efficiency in mixed driving parking lots.

Abstract Background Osteoking has been used for fracture therapy with a satisfying clinical efficacy. However, its therapeutic properties and the underlying mechanisms remain elusive. Method A bone defect rat model was established to evaluate the pharmacological effects of Osteoking by the dynamic observation of X-ray, micro-CT and histopathologic examination. Transcriptome profiling was performed to identify bone defect-related genes and Osteoking effective targets. Then, a “disease-related gene–drug target” interaction network was constructed and a list of key network targets were screened, which were experimentally verified. Results Osteoking effectively promoted bone defect repair in rats by accelerating the repair of cortical bone and the growth of trabeculae. Histopathologically, the bone defect rats displayed lower histopathologic scores in cortical bone, cancellous bone and bone connection than normal controls. In contrast, Osteoking exerted a favorable effect with a dose-dependent manner. The abnormal serum levels of bone turnover markers, bone growth factors and bone metabolism-related biochemical indexes in bone defect rats were also reversed by Osteoking treatment. Following the transcriptome-based network investigation, we hypothesized that osteoking might attenuate the levels of ZBP1–STAT1–PKR–MLKL-mediated necroptosis involved into bone defect. Experimentally, the expression levels of ZBP1, STAT1, PKR and the hallmark inflammatory cytokines for the end of necroptosis were distinctly elevated in bone defect rats, but were all effectively reversed by Osteoking treatment, which were also suppressed the activities of RIPK1, RIPK3 and MLKL in bone tissue supernatants. Conclusions Osteoking may promote bone formation and bone defect repair by regulating ZBP1–STAT1–PKR axis, leading to inhibit RIPK1/RIPK3/MLKL activation-mediated necroptosis.

Precise water level forecasting plays a decisive role in improving the efficiency of flood prevention and disaster reduction, optimizing water resource management, enhancing the safety of waterway transportation, reducing flood risks, and promoting ecological and environmental protection, which is crucial for the sustainable development of society. This study proposes a hybrid water level forecasting model based on Time-Varying Filter-based Empirical Mode Decomposition (TVFEMD), Inverse-Free Extreme Learning Machine (IFELM), and error correction. Firstly, historical water level data are decomposed into different modes using TVFEMD; secondly, the Improved Jellyfish Search (IJS) algorithm is employed to optimize the IFELM, and subsequently, the optimized IFELM independently forecasts each sub-sequence and obtains the predictive results of each sub-sequence; thirdly, an Online Sequential Extreme Learning Machine (OSELM) model is used to correct data errors, and the initial predictive results and error prediction results are added together to obtain the final prediction for the sub-sequence; and finally, the final prediction for the sub-sequences are added to obtain the prediction results of the entire water level sequence. Taking the daily water level data from 2006 to 2018 in Taihu, China as the research object, this paper compares the proposed model with the ELM, BP, LSTM, IFELM, TVFEMD-IFELM, and TVFEMD-IFELM-OSELM models. The results show that the TVFEMD-IJS-IFELM-OSELM model established in this study has high prediction accuracy and strong stability and is suitable for water level forecasting.

With the expansion of urban scale and the popularization of multi-modal transportation, transportation hubs, as the link of multi-modal travel, are becoming increasingly important in urban development and residents’ lives. In situations of high parking demand, the increase in road traffic volume and parking search delays exacerbates the service pressure on hub parking lots and the traffic congestion on surrounding roads. Therefore, reasonable parking demand allocation is one of the key solutions to this problem. Based on the analysis of the vehicle parking search process, this paper constructs a model for estimating parking search delay on roads outside hub parking lots and proposes an optimization model for parking demand allocation aimed at minimizing the total parking search delay of vehicles. Finally, taking a major transportation hub in Nanjing as a case study, data were obtained through field investigations and simulation experiments to identify peak parking demand periods and calibrate the model parameters. The results show that the average vehicle delay was reduced by 4.5%, with a total reduction of 13,860 s in vehicle delay for parking demands at the hub within one hour. Therefore, by optimizing the allocation of parking demand, the average delay for vehicles searching for parking can be reduced to a certain extent.

Cu interconnect chemical mechanical polishing (CMP) technology has been continuously evolving, leading to increasingly stringent post-CMP cleaning requirements. To address the environmental pollution caused by traditional post-CMP cleaning solutions, we have explored the use of photocatalytic processes to remove citric acid, which is a commonly used complexing agent for CMP. In this study, CeO2 abrasives, characterized by a hardness of 5.5, are extensively employed in CMP. Importantly, CeO2 also exhibits a suitable band structure with a band gap of 2.27 eV, enabling it to photocatalytically remove citric acid, a commonly used complexing agent in Cu CMP. Additionally, the integration of H2O2, an essential oxidant in Cu CMP, enhances the photocatalytic degradation efficiency. The research indicates that the removal rate of single-phase CeO2 was 1.78 mmol/g/h and the degradation efficiency increased by 40% with the addition of H2O2, attributed to the hydroxyl radicals generated from a Fenton-like reaction between H2O2 and CeO2. These findings highlight the potential of photocatalytic processes to improve organic contaminant removal in post-CMP cleaning, offering a more sustainable alternative to conventional practices.

Redox of the transition metals is definitely the most important way of charge compensation in the conventional electrode materials. Utilization of oxygen redox (or more popularly the anionic redox) is another effective strategy to capture extra capacities in the oxide cathode materials (OCMs). Oxygen redox has been realized in oxides with diversified structures in addition to the conventional Li-rich Mn-rich layered oxide (LMR) cathode materials. However, a unified theory is still lacking about the oxygen redox mechanism in these materials. Moreover, the oxygen redox is often accompanied with undesired structural and performance degradations, which turn to hinder the application of the conventional materials at high potentials and the development of novel oxygen-redox based cathode materials. Considering that oxygen constructs the framework of OCMs and its electronic structure has a strong impact on the electrochemical performance of OCMs, it is necessary to summarize the correlation between oxygen redox and the structure of the oxides. This review recalls the recent advances in oxygen redox in different oxide materials, its impacts on the structural transformation, and strategies to suppress or promote the oxygen redox. On the basis of these, a perspective on enhancing the structural stability and performance of the oxygen-redox OCMs is proposed.

Connected and automated vehicles (CAVs) are poised to revolutionize mobility. The relocation feature of CAVs enhances parking convenience for the public. Users can instruct CAVs to arrive at their work destination, drop them off, and then assign CAVs to a cost-effective parking facility through an optimized itinerary. However, realizing the benefits of CAVs depends on user acceptance, and the impact of relocation features on CAV acceptance remains an area that is yet to be explored. This study introduces a novel acceptance model to mainly investigate the effects of relocation-related factors on CAV acceptance through 717 valid responses. The results indicate that the perceived convenience of relocation (PCOR) indirectly increases human acceptance through three determinants, initial trust, perceived usefulness (PU), and perceived ease of use (PEOU), while initial trust, PU, and PEOU directly increase human acceptance. The public expectations of saving on parking fees (EOSPF) can enhance PCOR. Additionally, a multigroup analysis revealed that PCOR exerts a more positive impact on PU or PEOU in subgroups including males, pre-Generation-Z individuals, experienced drivers, and those with autopilot riding experience. The findings on mediators are also discussed. This study provides valuable insights for further research and the practical adoption of emerging CAVs.

Accurately segmenting grape clusters and detecting grape varieties in orchards is beneficial for orchard staff to accurately understand the distribution, yield, growth information, and efficient mechanical harvesting of different grapes. However, factors, such as lighting changes, grape overlap, branch and leaf occlusion, similarity in fruit and background colors, as well as the high similarity between some different grape varieties, bring tremendous difficulties in the identification and segmentation of different varieties of grape clusters. To resolve these difficulties, this study proposed an improved Mask R-CNN model by assembling an efficient channel attention (ECA) module into the residual layer of the backbone network and a dual attention network (DANet) into the mask branch. The experimental results showed that the improved Mask R-CNN model can accurately segment clusters of eight grape varieties under various conditions. The bbox_mAP and mask_mAP on the test set were 0.905 and 0.821, respectively. The results were 1.4% and 1.5% higher than the original Mask R-CNN model, respectively. The effectiveness of the ECA module and DANet module on other instance segmentation models was explored as comparison, which provided a certain ideological reference for model improvement and optimization. The results of the improved Mask R-CNN model in this study were superior to other classic instance segmentation models. It indicated that the improved model could effectively, rapidly, and accurately segment grape clusters and detect grape varieties in orchards. This study provides technical support for orchard staff and grape-picking robots to pick grapes intelligently.

Abstract Background Malignant transformation from hepatic fibrosis to carcinogenesis may be a therapeutic target for hepatocellular carcinoma (HCC). The aim of this study was to evaluate anti-cancer efficacy of Pien-Tze-Huang (PZH), and to investigate the underlying mechanisms by integrating transcriptional regulatory network analysis and experimental validation. Methods A diethylnitrosamine (DEN)-induced HCC model in rats was established and used to evaluate the anti-cancer efficacy of PZH. After detecting a transcriptomic profiling, the “disease-related gene–drug effective target” interaction network was constructed, and the candidate targets of PZH against malignant transformation from hepatic fibrosis to HCC were identified and verified in vitro. Results PZH effectively alleviated the pathological changes of hepatic fibrosis and cirrhosis, and inhibited tumor formation and growth in DEN-induced HCC rats. Additionally, the administration of PZH reduced the levels of various hepatic function-related serological indicators significantly. Mechanically, a ferroptosis-related SLC7A11-GSH-GPX4 axis might be one of potential targets of PZH against malignant transformation from hepatic fibrosis to HCC. Especially, high SLC7A11 expression may be associated with poor prognosis of HCC patients. Experimentally, the administration of PZH markedly increased the trivalent iron and ferrous ion, suppressed the expression levels of SLC7A11 and GPX4 proteins, and reduced the GSH/GSSG ratio in the liver tissues of DEN-induced HCC rats. Conclusions Our data offer an evidence that PZH may effectively improve the hepatic fibrosis microenvironment and prevent the occurrence of HCC through promoting ferroptosis in tumor cells via inhibiting the SLC7A11-GSH-GPX4 axis, implying that PZH may be a potential candidate drug for prevention and treatment of HCC at an early stage.

AIM: To report outcomes of endoscopy-assisted vitrectomy (EAV) in patients with chronic hypotony following severe ocular trauma or vitrectomy. METHODS: This was a retrospective, noncomparative case series. Ciliary bodies were evaluated using ultrasound biomicroscopy pre-operatively and direct visualisation intraoperatively. All selected individuals (seven patients/seven eyes) underwent EAV. Removal of ciliary membrane and traction, gas/silicone oil tamponade (GT/SOT), and scleral buckling (SB) were performed in selected eyes. Outcome measurements mainly included intraocular pressure (IOP) and best-corrected visual acuity (BCVA). RESULTS: Seven eyes from 7 male aphakic patients with a mean age of 45 (range, 20-68)y were included in this study; the average follow-up time was 12 (9-15)mo. GT was performed in 2 eyes; membrane peeling (MP) and SOT in 2 eyes; and MP, SOT, and SB in 3 eyes. The mean pre- and post-operative IOP were 4.5 (range, 4.0±0.11 to 4.8±0.2) mm Hg and 9.9 (range, 5.6±0.17 to 12.1±0.2) mm Hg at 52wk (12mo), respectively. BCVA improved in six eyes; one eye still showed light perception, and no bulbi phthisis was observed. CONCLUSION: Endoscopy offers improved judgment and recognition and has an improved prognosis for chronic hypotony. Therefore, endoscopy can be an effective and promising operative technique for chronic traumatic hypotony management.

A high sensitivity and ultra-low concentration range photoacoustic spectroscopy (PAS) gas detection system, which was based on a novel trapezoid compound ellipsoid resonant photoacoustic cell (TCER-PAC) and partial least square (PLS), was proposed to detect acetylene (C2H2) gas. In the concentration range of 0.5 ppm ∼ 10.0 ppm, the limit of detection (LOD) values of TCER-PAC-based PAS system without data processing was 66.4 ppb, which was lower than that of the traditional trapezoid compound cylindrical resonant photoacoustic cell (TCCR-PAC). The experimental results indicated that the TCER-PAC had higher sensitivity than of TCCR-PAC. Within the concentration range of 12.5 ppb ∼ 125.0 ppb, the LOD and limit of quantification (LOQ) of TCER-PAC-based PAS system combined with PLS regression algorithm were 1.1 ppb and 3.7 ppb, respectively. The results showed that higher detection sensitivity and lower LOD were obtained by PAS system with TCER-PAC and PLS than that of TCCR-PAC-based PAS system.

The microthruster is generally chosen as the actuator of the drag-free satellite control system, and the layout directly affects the stability of the satellite control. Due to the extremely rigorous objectives of drag-free control, a design method for the redundant thruster layout is proposed in this paper to improve the control stability of the satellite and reduce the allocation error of the control system. First, the factors corresponding to the allocation errors of redundant thruster layouts are analyzed. And the minimum number of thrusters required for satellite control is determined through the calculation method of thruster redundancy. Then, the design criterion of reducing the allocation error and increasing the satellite control stability is put forward. To demonstrate the effectiveness and feasibility of the design method proposed in this paper, the space gravitational wave detection satellite is taken as a simulation case. Thrust distribution models corresponding to the designed thruster layouts are established, and the results show that a proper layout can not only improve stability but also reduce fuel consumption. The research is looking forward to providing a reference for designing the microthruster layout of the gravitational wave detection satellite in the future.

Watermelon seeds are a significant source of nutrition in the diet. To assess the potential of near-infrared hyperspectral imaging technology for swift and nondestructive identification of watermelon seed varieties, near-infrared hyperspectral imaging (NIR-HSI) technology was used. The Savitzky–Golay (SG) smoothing algorithm and standard normal variable (SNV) algorithm were combined to preprocess the extracted spectral data. The successive projections algorithm (SPA) was used to reduce the dimensionality of the spectral data. Subsequently, three deep learning models (LeNet, GoogLeNet, and ResNet) were used to classify 10 common watermelon seeds. SPA was used to reduce the dimensionality of hyperspectral data. In terms of full band, the ResNet model achieved a classification accuracy of 86.77% on the test set. By using characteristic bands, the GoogLeNet model achieved a classification accuracy of 83.85% on the test set. The ensemble fusion model based on a scoring mechanism achieved accuracy rates of 99.56%, 90.88%, and 87.97% on the training, validation, and test sets, respectively. The results indicated that the ensemble fusion model based on a scoring mechanism can enhance accuracy. Combining deep learning with NIR-HSI can effectively distinguish different varieties of watermelon seeds.

The quality of people’s lives is closely related to their emotional state. Positive emotions can boost confidence and help overcome difficulties, while negative emotions can harm both physical and mental health. Research has shown that people’s handwriting is associated with their emotions. In this study, audio-visual media were used to induce emotions, and a dot-matrix digital pen was used to collect neutral text data written by participants in three emotional states: calm, happy, and sad. To address the challenge of limited samples, a novel conditional table generative adversarial network called conditional tabular-generative adversarial network (CTAB-GAN) was used to increase the number of task samples, and the recognition accuracy of task samples improved by 4.18%. The TabNet (a neural network designed for tabular data) with SimAM (a simple, parameter-free attention module) was employed and compared with the original TabNet and traditional machine learning models; the incorporation of the SimAm attention mechanism led to a 1.35% improvement in classification accuracy. Experimental results revealed significant differences between negative (sad) and nonnegative (calm and happy) emotions, with a recognition accuracy of 80.67%. Overall, this study demonstrated the feasibility of emotion recognition based on handwriting with the assistance of CTAB-GAN and SimAm-TabNet. It provides guidance for further research on emotion recognition or other handwriting-based applications.

Summary: Viral inclusion bodies (VIBs) are subcellular structures required for efficient viral replication. How type II grass carp reovirus (GCRV-II), the mainly prevalent strain, forms VIBs is unknown. In this study, we found that GCRV-II infection induced punctate VIBs in grass carp ovary (GCO) cells and that non-structural protein 38 (NS38) functioned as a participant in VIB formation. Furthermore, VP56 and VP35 induced VIBs and recruited other viral proteins via the N-terminal of VP56 and the middle domain of VP35. Additionally, we found that the newly synthesized viral RNAs co-localized with VP56 and VP35 in VIBs during infection. Taken together, VP56 and VP35 induce VIB formation and recruit other viral proteins and viral RNAs to the VIBs for viral replication, which helps identify new targets for developing anti-GCRV-II drugs to disrupt viral replication.

IntroductionRecognizing wheat ears plays a crucial role in predicting wheat yield. Employing deep learning methods for wheat ears identification is the mainstream method in current research and applications. However, such methods still face challenges, such as high computational parameter volume, large model weights, and slow processing speeds, making it difficult to apply them for real-time identification tasks on limited hardware resources in the wheat field. Therefore, exploring lightweight wheat ears detection methods for real-time recognition holds significant importance.MethodsThis study proposes a lightweight method for detecting and counting wheat ears based on YOLOv5s. It utilizes the ShuffleNetV2 lightweight convolutional neural network to optimize the YOLOv5s model by reducing the number of parameters and simplifying the complexity of the calculation processes. In addition, a lightweight upsampling operator content-aware reassembly of features is introduced in the feature pyramid structure to eliminate the impact of the lightweight process on the model detection performance. This approach aims to improve the spatial resolution of the feature images, enhance the effectiveness of the perceptual field, and reduce information loss. Finally, by introducing the dynamic target detection head, the shape of the detection head and the feature extraction strategy can be dynamically adjusted, and the detection accuracy can be improved when encountering wheat ears with large-scale changes, diverse shapes, or significant orientation variations.Results and discussionThis study uses the global wheat head detection dataset and incorporates the local experimental dataset to improve the robustness and generalization of the proposed model. The weight, FLOPs and mAP of this model are 2.9 MB, 2.5 * 109 and 94.8%, respectively. The linear fitting determination coefficients R2 for the model test result and actual value of global wheat head detection dataset and local experimental Site are 0.94 and 0.97, respectively. The improved lightweight model can better meet the requirements of precision wheat ears counting and play an important role in embedded systems, mobile devices, or other hardware systems with limited computing resources.

AbstractBackground Non-invasive prenatal testing (NIPT) has been widely adopted as an approach for foetal aneuploidy screening. This study was to evaluate the performance of NIPT for foetal T21 detection in subgroups of pregnancies and the correlation between Z-score and discordant positive predictive values (PPVs).Methods We retrospectively reviewed the NIPT results among 22361 pregnancies undergoing combined second-trimester screening (cSTS) previously. Sixty-four cases with positive NIPT results for foetal T21 were validated by invasive prenatal diagnosis.Results In pregnancies with cSTS-T21 low-, intermediate-, and high-risk, the PPVs at NIPT were 14.3%, 64.3%, and 86.4%, respectively. Mean Z-scores of positive NIPT cases with cSTS-T21 high- and intermediate-risk were comparable, while were higher than that of cases with pre-test low-risk. Furthermore, PPVs for positive NIPT cases at 3

Vigor is one of the important factors that affects rice yield and quality. Rapid and accurate detection of rice seed vigor is of great importance for rice production. In this study, near-infrared hyperspectral imaging technique and transfer learning were combined to detect rice seed vigor. Four varieties of artificial-aged rice seeds (Yongyou12, Yongyou1540, Suxiangjing100, and Longjingyou1212) were studied. Different convolutional neural network (CNN) models were built to detect the vigor of the rice seeds. Two transfer strategies, fine-tuning and MixStyle, were used to transfer knowledge among different rice varieties for vigor detection. The experimental results showed that the convolutional neural network model of Yongyou12 classified the vigor of Yongyou1540, Suxiangjing100, and Longjingyou1212 through MixStyle transfer knowledge, and the accuracy reached 90.00%, 80.33%, and 85.00% in validation sets, respectively, which was better or close to the initial modeling performances of each variety. MixStyle statistics are based on probabilistic mixed instance-level features of cross-source domain training samples. When training instances, new domains can be synthesized, which increases the domain diversity of the source domain, thereby improving the generalization ability of the trained model. This study would help rapid and accurate detection of a large varieties of crop seeds.

Water distribution can affect the storage and transport of methane in coal. There are many reports involving the distribution of water and methane in coal, but the microscopic mechanism of water-methane interaction is still unclear. In this study, realistic molecular structure models of bituminous and anthracite coals were constructed based on the chemical structures of two coal samples. And the chemical structures were analyzed by solid-state nuclear magnetic resonance, Fourier-transform infrared, and X-photoelectron spectroscopy. Then slit-pore models with different pore sizes were constructed based on the molecular structures to simulate the methane-water interaction in bituminous and anthracite coals. Results show that water molecules tend to form water bridges and water films in the pores of bituminous and anthracite coals, respectively. The significant displacement of pre-adsorbed water by methane was observed through simulation. It is found that the water molecules on the pore surface and the interfaces of small-size water bridges are more easily displaced by methane. In the larger pores, methane molecules mainly drive water molecules to agglomerate larger water bridges in bituminous coal. While methane molecules mainly drive water molecules to cover the pore surface and form a thicker water film in anthracite coal. This study provides new insights into the microscopic distribution and interaction mechanism of fluids in coal.

Developing a fast and non-destructive methodology to identify the storage years of Coix seed is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), as well as the deep learning method of residual neural network (ResNet), to establish identification models for Coix seed samples from different storage years. Under the fusion-based modeling approach, the model’s classification accuracy surpasses that of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectral modeling individually. The classification accuracy of the ResNet model and SVM exceeds that of other conventional machine learning models (KNN, RF, and XGBoost). Redundant variables were further diminished through competitive adaptive reweighted sampling feature wavelength screening, which had less impact on the model’s accuracy. Upon validating the model’s performance using an external validation set, the ResNet model yielded more satisfactory outcomes, exhibiting recognition accuracy exceeding 85%. In conclusion, the comprehensive results demonstrate that the integration of deep learning with HSI techniques effectively distinguishes Coix seed samples from different storage years.

Cherry tomatoes are cultivated worldwide and favored by consumers of different ages. The soluble solid content (SSC) and pH are two of the most important quality attributes of cherry tomatoes. The rapid and non-destructive measurement of the SSC and pH of cherry tomatoes is of great significance to their production and consumption. In this research, hyperspectral imaging combined with a convolutional neural network with Transformer (CNN-Transformer) was utilized to analyze the SSC and pH of cherry tomatoes. Conventional machine learning and deep learning models were established for the determination of the SSC and pH. The findings demonstrated that CNN-Transformer yielded outstanding results in predicting the SSC, with the coefficient of determination of calibration (R2C), validation (R2V), and prediction (R2P) for the SSC being 0.83, 0.87, and 0.83, respectively. Relatively worse results were obtained for the pH value prediction, with R2C, R2V, and R2P values of 0.74, 0.68, and 0.60, respectively. Furthermore, the visualization of the CNN-Transformer model revealed the wavelength weight distributions, indicating that the 1380–1650 nm range served as the characteristic band for the SSC, while the spectral range at 945–1280 nm was the characteristic band for pH. In conclusion, integrating spectral information features with the attention mechanism of Transformer through a convolutional neural network can enhance the accuracy of predicting the SSC and pH for cherry tomatoes.

Cableways have been widely used in industrial areas, cities, and scenic spots due to their advantages, such as being a convenient mode of transportation, time-saving, labor-saving, and low cost, as well as offering environmental protection. To ensure the safe operation of a cableway, based on the characteristic that the velocity of the cableway bracket is approximately zero in a static deformation monitoring environment, a deformation monitoring method called zero velocity update (ZUPT)-based GNSS/IMU tightly coupled algorithm with the constraint of the Earth’s rotation angular velocity was proposed. The proposed method can effectively solve the problem of a single GNSS being unable to output attitude, which is directly related to the status of wire ropes and cable cars. Meanwhile, ZUPT is used to restrain the Kalman filter’s divergence when IMU is stationary. However, the improvements of ZUPT on attitude are not obvious, so the constraint of the Earth’s rotation angular velocity was applied. The performance of the proposed method was evaluated through monitoring the cableway bracket of the Yimeng Mountain Tourism area in Shandong. Compared with the ZUPT-based GNSS/IMU tightly coupled algorithm (ZUPT-TC), the proposed method can further constrain the error accumulation of IMU while stationary and, therefore, it can provide reliable position and attitude information on cableway brackets.

Abstract Background Targeted therapy has revolutionized the treatment of patients with malignancies harboring mutations in driver genes and has brought a favorable survival benefit to the population with actionable oncogenic mutations. In recent years, the MET exon14 skipping mutation has been recognized as a potentially promising therapeutic target in non-small cell lung cancer (NSCLC). These changes are mutually exclusive with molecular drivers such as EGFR, KRAS, HER-2, BRAF, ALK and ROS1. The prevalence rate of coexisting MET exon 14 mutations and EGFR sensitive mutations (L858R, exon 19 deletions) in Chinese population was reported to be 0.2% (3/1590). However, the coexistence of MET exon 14 mutations with EGFR exon 20 insertion mutations has never been reported and the management of this subtype is not identified. Case presentation A 69-year-old male with a right lung adenocarcinoma (T4N2M0, IIIB) was confirmed to be positive for MET exon 14 skipping (c.3028_3028+1delGGinsTT, 44.4%), MET amplification (copy number 4.4), and EGFR exon 20 insertion (p. N771_H773dup, 22.1%) mutations. After the progression of one cycle of chemotherapy (Pemetrexed 0.8 g d1), the patient was subsequently accepted treatment with Crizotinib (250 mg twice a day) and achieved an important clinical remission for six months until the development of brain metastases. Then, he was submitted to a cycle of anti-programmed cell death-1 (PD-1) therapy after failure of Crizotinib and eventually acquired resistance despite of the high expression of programmed death ligand-1 (PD-L1) and tumor mutational burden (TMB) status. Conclusion This case report provides treatment strategies for epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs)-untreated lung adenocarcinoma patients simultaneously carrying MET alterations and EGFR exon 20 insertion mutations. In addition, the signatures of PD-L1 or TMB expression were not the candidate for predicting the efficacy of immunotherapy in this context.

Root phenotypic parameters are the important basis for studying the growth state of plants, and root researchers obtain root phenotypic parameters mainly by analyzing root images. With the development of image processing technology, automatic analysis of root phenotypic parameters has become possible. And the automatic segmentation of roots in images is the basis for the automatic analysis of root phenotypic parameters. We collected high-resolution images of cotton roots in a real soil environment using minirhizotrons. The background noise of the minirhizotron images is extremely complex and affects the accuracy of the automatic segmentation of the roots. In order to reduce the influence of the background noise, we improved OCRNet by adding a Global Attention Mechanism (GAM) module to OCRNet to enhance the focus of the model on the root targets. The improved OCRNet model in this paper achieved automatic segmentation of roots in the soil and performed well in the root segmentation of the high-resolution minirhizotron images, achieving an accuracy of 0.9866, a recall of 0.9419, a precision of 0.8887, an F1 score of 0.9146 and an Intersection over Union (IoU) of 0.8426. The method provided a new approach to automatic and accurate root segmentation of high-resolution minirhizotron images.

In this paper, a highly sensitive photoacoustic spectroscopy (PAS) sensor based on retro-reflection-cavity-enhanced differential photoacoustic cell (DPAC) is demonstrated for the first time. Acetylene (C2H2) was selected as the analyte. The DPAC was designed to effectively suppress noise and increase signal level. The retro-reflection-cavity consisted of two right-angle prisms was designed to reflect the incident light to realize four passes. The photoacoustic response of the DPAC was simulated and investigated based on the finite element method. Wavelength modulation and second harmonic demodulation technologies were applied for sensitive trace gas detection. The first-order resonant frequency of the DPAC was found to be 1310 Hz. The differential characteristics were investigated and the 2f signal amplitude for this C2H2-PAS sensor based on retro-reflection-cavity-enhanced DPAC had a 3.55 times improvement compared to the system without the retro-reflection-cavity. An Allan deviation analysis was performed to investigate the long-term stability of the system. The minimum detection limit (MDL) was measured to be 15.81 ppb with an integration time of 100 s

Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.

Background and objectivesAlzheimer’s disease (AD) has an insidious onset, the early stages are easily overlooked, and there are no reliable, rapid, and inexpensive ancillary detection methods. This study analyzes the differences in handwriting kinematic characteristics between AD patients and normal elderly people to model handwriting characteristics. The aim is to investigate whether handwriting analysis has a promising future in AD auxiliary screening or even auxiliary diagnosis and to provide a basis for developing a handwriting-based diagnostic tool.Materials and methodsThirty-four AD patients (15 males, 77.15 ± 1.796 years) and 45 healthy controls (20 males, 74.78 ± 2.193 years) were recruited. Participants performed four writing tasks with digital dot-matrix pens which simultaneously captured their handwriting as they wrote. The writing tasks consisted of two graphics tasks and two textual tasks. The two graphics tasks are connecting fixed dots (task 1) and copying intersecting pentagons (task 2), and the two textual tasks are dictating three words (task 3) and copying a sentence (task 4). The data were analyzed by using Student’s t-test and Mann–Whitney U test to obtain statistically significant handwriting characteristics. Moreover, seven classification algorithms, such as eXtreme Gradient Boosting (XGB) and Logistic Regression (LR) were used to build classification models. Finally, the Receiver Operating Characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Area Under Curve (AUC) were used to assess whether writing scores and kinematics parameters are diagnostic.ResultsKinematic analysis showed statistically significant differences between the AD and controlled groups for most parameters (p

Abstract The toxicity of lead ions in halide perovskite absorbing materials is the main bottleneck for practical application. To replace the traditional lead halide perovskites by environmental friendly double perovskite, computational tools based on density functional theory were employed to predict the intrinsic properties of potential double perovskites to efficiently and rapidly find more double perovskites with properties suitable for optoelectronic applications. Screening homovalent alternatives for B and X‐site ions in vacancy‐ordered double perovskite Cs2BX6 for solar cell applications and photocatalyst was done using Perdew–Burke–Ernzerhof and Heyd–Scuseria–Ernzerhof functional with spin‐orbit coupling. Three empirical factors and formation enthalpy were used to evaluate the stability of 30 materials at different temperatures. Finally, the Cs‐based vacancy‐ordered double perovskites with suitable bandgap for optoelectronic applications can thus be obtained. Using computational techniques, this study can also provide theoretical guidance for the rational design of possible double perovskite materials with improved photocatalytic characteristics.

Background: Linking phenotypes and genotypes to identify genetic architectures that regulate important traits is crucial for plant breeding and the development of plant genomics. In recent years, genome-wide association studies (GWASs) have been applied extensively to interpret relationships between genes and traits. Successful GWAS application requires comprehensive genomic and phenotypic data from large populations. Although multiple high-throughput DNA sequencing approaches are available for the generation of genomics data, the capacity to generate high-quality phenotypic data is lagging far behind. Traditional methods for plant phenotyping mostly rely on manual measurements, which are laborious, inaccurate, and time-consuming, greatly impairing the acquisition of phenotypic data from large populations. In contrast, high-throughput phenotyping has unique advantages, facilitating rapid, non-destructive, and high-throughput detection, and, in turn, addressing the shortcomings of traditional methods.Aim of Review: This review summarizes the current status with regard to the integration of high-throughput phenotyping and GWAS in plants, in addition to discussing the inherent challenges and future prospects.Key Scientific Concepts of Review: High-throughput phenotyping, which facilitates non-contact and dynamic measurements, has the potential to offer high-quality trait data for GWAS and, in turn, to enhance the unraveling of genetic structures of complex plant traits. In conclusion, high-throughput phenotyping integration with GWAS could facilitate the revealing of coding information in plant genomes.

Heavy metal pollution is becoming a prominent stress on plants. Plants contaminated with heavy metals undergo changes in external morphology and internal structure, and heavy metals can accumulate through the food chain, threatening human health. Detecting heavy metal stress on plants quickly, accurately, and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant varieties. Traditional chemical reagent-based detection methods are laborious, destructive, time-consuming, and costly. The internal and external structures of plants can be altered by heavy metal contamination, which can lead to changes in plants’ absorption and reflection of light. Visible/near-infrared (V/NIR) spectroscopy can obtain plant spectral information, and hyperspectral imaging (HSI) can obtain spectral and spatial information in simple, speedy, and nondestructive ways. These 2 technologies have been the most widely used high-throughput phenotyping technologies of plants. This review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress, including unstressed and stressed identification, stress types identification, stress degrees identification, and heavy metal content estimation. The vegetation indexes, full-range spectra, and feature bands identified by different plant heavy metal stress phenotyping methods are reviewed. The advantages, limitations, challenges, and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are discussed. Further studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping.

Deep learning and computer vision have become emerging tools for diseased plant phenotyping. Most previous studies focused on image-level disease classification. In this paper, pixel-level phenotypic feature (the distribution of spot) was analyzed by deep learning. Primarily, a diseased leaf dataset was collected and the corresponding pixel-level annotation was contributed. A dataset of apple leaves samples was used for training and optimization. Another set of grape and strawberry leaf samples was used as an extra testing dataset. Then, supervised convolutional neural networks were adopted for semantic segmentation. Moreover, the possibility of weakly supervised models for disease spot segmentation was also explored. Grad-CAM combined with ResNet-50 (ResNet-CAM), and that combined with a few-shot pretrained U-Net classifier for weakly supervised leaf spot segmentation (WSLSS), was designed. They were trained using image-level annotations (healthy versus diseased) to reduce the cost of annotation work. Results showed that the supervised DeepLab achieved the best performance (IoU = 0.829) on the apple leaf dataset. The weakly supervised WSLSS achieved an IoU of 0.434. When processing the extra testing dataset, WSLSS realized the best IoU of 0.511, which was even higher than fully supervised DeepLab (IoU = 0.458). Although there was a certain gap in IoU between the supervised models and weakly supervised ones, WSLSS showed stronger generalization ability than supervised models when processing the disease types not involved in the training procedure. Furthermore, the contributed dataset in this paper could help researchers get a quick start on designing their new segmentation methods in future studies.

Anthropogenic activities have dramatically changed land uses over the last century, which has considerably increased soil disturbance. Despite considerable attention paid to the effects of land use on soil erosion, it remains controversial as to how much land use and climate contribute to erosion. In this study, we conducted a comprehensive meta-analysis of 1,380 measurements from published studies using the 137Cs method to examine the responses of erosion to land uses and climatic variables across terrestrial ecosystems in China. Our results showed that land use had significant impacts on the rate of erosion, with the lowest rates on natural and bamboo forests and the highest rates on farmland. Land use change of forest and grassland to farmland significantly increased erosion. Erosion rates respond significantly but differently to climatic variables in varying land use types. Correlations of erosion rates with mean annual temperature and precipitation were negative in forest ecosystems, positive in farmland, and nonsignificant in grassland. Variance partition analysis showed that land use types explained more of the variation in erosion rates than did environmental variables at a broad geographic scale, such as climate. This study highlights the importance of land use in affecting erosion rate. Incorporating these evidences into global erosion models may improve the simulation of long-term soil dynamics.

Abstract Intercalation provides to the host materials a means for controlled variation of many physical/chemical properties and dominates the reactions in metal‐ion batteries. Of particular interest is the graphite intercalation compounds with intriguing staging structures, which however are still unclear, especially in their nanostructure and dynamic transition mechanism. Herein, the nature of the staging structure and evolution of the lithium (Li)‐intercalated graphite was revealed by cryogenic‐transmission electron microscopy and other methods at the nanoscale. The intercalated Li‐ions distribute unevenly, generating local stress and dislocations in the graphitic structure. Each staging compound is found macroscopically ordered but microscopically inhomogeneous, exhibiting a localized‐domains structural model. Our findings uncover the correlation between the long‐range ordered structure and short‐range domains, refresh the insights on the staging structure and transition of Li‐intercalated/deintercalated graphite, and provide effective ways to enhance the reaction kinetic in rechargeable batteries by defect engineering.

Leaf nitrogen concentration (LNC) is a critical indicator of crop nutrient status. In this study, the feasibility of using visible and near-infrared spectroscopy combined with deep learning to estimate LNC in cotton leaves was explored. The samples were collected from cotton’s whole growth cycle, and the spectra were from different measurement environments. The random frog (RF), weighted partial least squares regression (WPLS), and saliency map were used for characteristic wavelength selection. Qualitative models (partial least squares discriminant analysis (PLS-DA), support vector machine for classification (SVC), convolutional neural network classification (CNNC) and quantitative models (partial least squares regression (PLSR), support vector machine for regression (SVR), convolutional neural network regression (CNNR)) were established based on the full spectra and characteristic wavelengths. Satisfactory results were obtained by models based on CNN. The classification accuracy of leaves in three different LNC ranges was up to 83.34%, and the root mean square error of prediction (RMSEP) of quantitative prediction models of cotton leaves was as low as 3.36. In addition, the identification of cotton leaves based on the predicted LNC also achieved good results. These results indicated that the nitrogen content of cotton leaves could be effectively detected by deep learning and visible and near-infrared spectroscopy, which has great potential for real-world application.

Abstract Background The optimal sequence of pulmonary vessel interruption during lung cancer resection remains controversial. This review aimed to elucidate the association of vein-first versus artery-first ligation and survival of the patients. Methods We searched PubMed, Web of Science, Scopus, Embase, Cochrane Library and Google Scholar from their inception to September 2021 for published articles that compared vein-first (the pulmonary vein was interrupted first) and artery-first procedure (the pulmonary artery was ligated first) during lung cancer surgery. Results Finally, a total of 13 full articles were obtained. First, 7 studies with survival information were included for meta-analyses. As compared with the artery-first ligation, vein-first approach did not decrease the risk of local recurrence (risk ratio [RR] 0.92 in favour of vein-first; 95% confidence interval [CI] 0.61–1.39, p = 0.68) or distant metastasis (RR 0.92; 95% CI 0.30–2.85, p = 0.89); but it was associated with better disease-free survival (RR 0.52; 95% CI 0.37–0.73, p

Long-term grazing leads to soil degradation in Inner Mongolia grassland. Based on the Hulunbeier meadow steppe, the variation characteristics of soil nitrogen content and storage in soil layers between 0–40 cm, under six different grazing intensities, and the response of vegetation and other physical and chemical properties of soil to grazing were studied. The main results were as follows: (1) Moderate grazing increased soil total nitrogen (TN), soluble total nitrogen (STN) and microbial biomass nitrogen (MBN) contents, while heavy grazing decreased MBN content. In the year with more rain, heavy grazing increased nitrate nitrogen (NO3−-N) content and storage, while less rain increased ammonium nitrogen (NH4+-N) content. (2) The proportion of 0–40 cm nitrogen components showed an upward trend in the year with more rain, and the opposite in the years with less rainfall with the increase of grazing intensity. Soil soluble organic nitrogen (SON) and NO3−-N storages decreased and MBN storage increased in rainy years. (3) Soil nitrogen component contents and storages were correlated with plant growth status, soil moisture (SM) and soil bulk density (SBD), and were significantly negatively correlated with soil temperature (ST) and pH (p < 0.05). The content and storage of soil nitrogen were affected by grazing, soil, vegetation, meteorological and other environmental factors. Moderate grazing was more conducive to the improvement of soil nitrogen storage capacity and the healthy development of grassland.

Abstract Background The dual inhibitors of receptor interacting protein kinase-1 and -3 (RIP1 and RIP3) play an important role in cell death processes and inflammatory responses. White matter injury (WMI), a leading cause of neurodevelopmental disabilities in preterm infants, which is characterized by extensive myelination disturbances and demyelination. Neuroinflammation, leads to the loss and differentiation-inhibition of oligodendrocyte precursor cells (OPCs), represents a major barrier to myelin repair. Whether the novel RIP1/RIP3 dual inhibitor ZJU-37 can promote transplanted OPCs derived from human neural stem cells (hOPCs) survival, differentiation and myelination remains unclear. In this study, we investigated the effect of ZJU-37 on myelination and neurobehavioral function in a neonatal rat WMI model induced by hypoxia and ischemia. Methods In vivo, P3 rat pups were subjected to right common carotid artery ligation and hypoxia, and then treated with ZJU-37 or/and hOPCs, then OPCs apoptosis, myelination, glial cell and NLRP3 inflammasome activation together with cognitive outcome were evaluated at 12 weeks after transplantation. In vitro, the effect of ZJU-37 on NLRP3 inflammasome activation in astrocytes induced by oxygen–glucose deprivation (OGD) were examined by western blot and immunofluorescence. The effect of ZJU-37 on OPCs apoptosis induced by the conditioned medium from OGD-injured astrocytes (OGD-astrocyte-CM) was analyzed by flow cytometry and immunofluorescence. Results ZJU-37 combined with hOPCs more effectively decreased OPC apoptosis, promoted myelination in the corpus callosum and improved behavioral function compared to ZJU-37 or hOPCs treatment. In addition, the activation of glial cells and NLRP3 inflammasome was reduced by ZJU-37 or/and hOPCs treatment in the neonatal rat WMI model. In vitro, it was also confirmed that ZJU-37 can suppress NLRP3 inflammasome activation in astrocytes induced by OGD. Not only that, the OGD-astrocyte-CM treated with ZJU-37 obviously attenuated OPC apoptosis and dysdifferentiation caused by the OGD-astrocyte-CM. Conclusions The novel RIP1/RIP3 dual inhibitor ZJU-37 may promote OPC survival, differentiation and myelination by inhibiting NLRP3 inflammasome activation in a neonatal rat model of WMI with hOPC graft.

The role of anti-programmed cell death protein-1 (PD-1) antibody camrelizumab in brain metastases (BMs) from lung adenocarcinoma is uncertain. Herein, for the first time, we report the efficacy of camrelizumab in a patient with chemotherapy-refractory BMs from lung adenocarcinoma. A 49-year-old male non-smoker was admitted with cough and back pain. Primary lung adenocarcinoma with brain and spinal metastases was diagnosed. The specimen from CT-guided lung biopsy showed positive expression of PD-L1 (~20%). The BMs were enlarged after first-line intravenous pemetrexed/cisplatin and zoledronic acid; whereas second-line camrelizumab demonstrated impressive complete remission of the BMs. The intracranial progression-free survival and overall survival of the patient since immunotherapy were more than 12 months and 20 months, respectively. In addition, we searched PubMed for relevant studies from inception to May 2020, and a total of 23 reports enrolling 1187 patients also indicated the promising efficacy of immunotherapy for BMs from lung cancer. However, more and better evidence are still needed before a definite conclusion could be drawn.

Summary: The foot-and-mouth disease virus (FMDV) 2C protein shares conserved motifs with enterovirus 2Cs despite low sequence identity. Here, we determine the crystal structure of an FMDV 2C fragment to 1.83 Å resolution, which comprises an ATPase domain, a region equivalent to the enterovirus 2C zinc-finger (ZFER), and a C-terminal domain harboring a loop (PBL) that occupies a hydrophobic cleft (Pocket) in an adjacent 2C molecule. Mutations at ZFER, PBL, and Pocket affect FMDV 2C ATPase activity and are lethal to FMDV infectious clones. Because the PBL-Pocket interaction between FMDV 2C molecules is essential for its functions, we design an anti-FMDV peptide derived from PBL (PBL-peptide). PBL-peptide inhibits FMDV 2C ATPase activity, binds FMDV 2C with nanomolar affinity, and disrupts FMDV 2C oligomerization. FMDV 2C targets lipid droplets (LDs) and induces LD clustering in cells, and PBL-peptide disrupts FMDV 2C-induced LD clustering. Finally, we demonstrate that PBL-peptide exhibits anti-FMDV activity in cells.

Software development is an iterative process from designing to implementation, and to testing, in which product development staff should be closely integrated with users. Satisfying user needs effectively is often the pain point for developers. In order to alleviate this, this paper manages to establish the quantitative connection between users' online reviews and APP (Application Program) downloads. By analyzing user online comments, companies can dig out user needs and preferences. This could benefit them by making accurate market positioning of their APP products, and therefore iteratively innovating products based on user needs, which hopefully will increase the volume of APP downloads. This paper regards WeChat APP during 47 updates periods as the research object. Based on Grounded Theory, user needs are extracted after data cleaning. Next, by using semantic analysis and word frequency analysis, we are able to obtain the implicit feedbacks such as emotion tendency, satisfaction and requirements lie under online reviews. Then, we construct a quantile regression model to study the impact of users' online reviews on downloads based on the influencing factors we extracted so as to provide a decision basis for enterprises to iteratively update their products. Results show that: (1) Generally speaking, needs of WeChat users mainly focus on performance, reliability, usability, functional deficiency, functional insufficiency, and system adaptability; (2) For those APP versions with relatively fewer downloads, user needs are mostly about functional deficiency, followed by functional insufficiency, performance, usability, and system adaptability. At this stage, it is found out that users' emotion tendency and user satisfaction significantly affect the volume of downloads; (3) When the volume of APP downloads is moderate, the user needs are functional deficiency, functional insufficiency, and system adaptability. While under this circumstances, users' star ratings have a significant impact on downloads; (4) In addition, when the volume of App downloads is high, user needs are performance, usability, and system adaptability. Our methods effectively extract users' requirements from online reviews and then successfully build up the quantitative connection between the implicit feedbacks from those requirements and APP downloads.

Abstract Pumped storage units (PSUs) are now widely used for energy storage. However, the uncertainty of the identification results of the pump‐turbine governing system (PTGS) caused by the random observation noises and the lack of prior knowledge remains an unaddressed issue. In recent years, the differential evolution adaptive Metropolis algorithm (DREAM) based on the Bayesian theory has been extensively used for parameter estimation and uncertainty analysis, but its application to the uncertainty analysis of PTGS has been rare. This study systematically evaluates the applicability of DREAM in the parameter identification and uncertainty quantification of PTGS. A real PSU in China has been employed as a case study for numerical experiments. Four groups of control experiments with different proportions of observation noises and different prior search spaces have been constructed in this study. It can be concluded from this study that: (a) accurate point identification results and effective uncertainty quantification can be obtained simultaneously using DREAM, (b) a lower proportion of observation noises can enhance the efficiency and effectiveness of the DREAM algorithm when applying to PTGS simulation. The DREAM method can narrow the prior search space of PTGS parameters effectively and thus help the hydropower engineers to make decisions.