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The grinding process is a key link in mineral processing production and a typical complex, controlled process. The steady-state model is limited by its model structure and thus difficult to applyied in a control system. A hybrid modeling method driven by multi-source compensation is proposed in this paper based on the mechanism model using key equipment in the grinding and classification process, addressing the uncertainties which affect the stability of the control systems. This method combines the relevant multi-source signals with uncertainties by using a priori knowledge, extracts the nonlinear feature vector in the signal through an unsupervised depth network, and constructs a compensation model based on dynamic radial basis function network to realize the integration of mechanism modeling and data-driven compensation modeling. The simulation results show that the model has a high degree of fit to the real physical system; the industrial validation was conducted at a gold concentrator, the grinding product quality was predicted and controlled with the model. [ABSTRACT FROM AUTHOR]

Magnetotelluric (MT) sounding is a geophysical technique widely utilized in mineral resource surveys, where conductivity and magnetic permeability serve as essential physical parameters for forward modeling and inversion. However, the effects of conductive anisotropy and non-zero magnetic susceptibility are usually ignored. In this study, we present a three-dimensional (3D) MT modeling algorithm using Coulomb-gauged electromagnetic potentials, incorporating a mixed nodal and edge-based finite element method capable of simulating MT responses for conductive anisotropic and magnetic anomalies. Subsequently, the algorithm's accuracy was validated in two steps: first, it was compared with analytical solutions for a 1D magnetic model; then, a comparison was made with previously published numerical results for a 3D generalized conductive anisotropic model. The results of two tests show that the maximum relative error is below 0.5% for both models. Furthermore, representative models were computed to comprehensively analyze the responses of MT. The findings illustrate the relationship between anisotropic parameters and electric fields and emphasize the significance of considering the impact of magnetic susceptibility in magnetite-rich regions. [ABSTRACT FROM AUTHOR]

To achieve the in situ capacity expansion of the post-denitrification biological aerated filter (BAF-DN), the integration of BAF with the anammox process (BAF/AX) was proposed. With the objective of maximizing retaining ammonia nitrogen, the operational optimization of BAF was achieved by two distinct strategies. The treatment performance of BAF demonstrated that the removal efficiencies of chemical oxygen demand (COD) and ammonia nitrogen ( NH 4 + - N ) was 66.3~67.3% and 4~12%, respectively, under conditions of low aeration intensity (0.4 m3·m−2·h−1) or a shortened empty bed residence time (EBRT) of 30 min. Residual NH 4 + - N in the BAF effluent served as the ammonia substrate for the subsequent anammox process, which was successfully launched by using ceramic particles and sponges as carriers. Notably, the sponge carrier facilitated a shorter start-up period of 41 to 44 days. Furthermore, the sponge-based anammox reactor exhibited a superior NH 4 + - N removal capacity (≥85.7%), under operations of a shorter EBRT of 40 min, low influent NH 4 + - N concentrations (≤30 mg/L), and COD levels of ≤67 mg/L. In addition, a comprehensive evaluation of the BAF/AX process was conducted, which considered performance, cost-effectiveness, and engineering feasibility. The performance results illustrated that the effluent quality met the standard well (with a COD level of ≤ 50 mg/L, and a TN of ≤3.1~10.5 mg/L). Following a comparison against the low aeration intensity operation, it was recommended to operate BAF at a low EBRT within the BAF/AX process. Consequently, the treated volume was double the volume of the standalone BAF-DN, synchronously achieving low costs (0.413 yuan/m3). [ABSTRACT FROM AUTHOR]

For geomagnetic navigation technology, taking inspiration from nature and leveraging the principle of animals' utilization of the geomagnetic field for long-distance navigation, and employing biomimetic technology to develop higher-precision geomagnetic sensors and more advanced navigation strategies, has emerged as a new trend. Based on the two widely acknowledged biological magnetic induction mechanisms, we have designed a bioinspired weak magnetic vector (BWMV) sensor and integrated it with neural networks to achieve geomagnetic matching navigation. In this paper, we assess the performance of the BWMV sensor through finite element model simulation. The result validates its high measurement accuracy and outstanding adaptability to installation errors with the assistance of specially trained neural networks. Furthermore, we have enhanced the bioinspired geomagnetic navigation algorithm and proposed a more advanced search strategy to adapt to navigation under the condition of no prior geomagnetic map. A simulated geomagnetic navigation platform was constructed based on the finite element model to simulate the navigation of the BWMV sensor in geomagnetic environments. The simulated navigation experiment verified that the proposed search strategy applied to the BWMV sensor can achieve high-precision navigation. This study proposes a novel approach for the research of bioinspired geomagnetic navigation technology, which holds great development prospects. [ABSTRACT FROM AUTHOR]

Deep learning-based object detection has become a powerful tool in dress code monitoring. However, even state-of-the-art detection models inevitably suffer from false alarms or missed detections, especially when handling small targets such as hats and masks. To overcome these limitations, this paper proposes a novel method for dress code monitoring using an improved YOLOv8n model, the DeepSORT tracking, and a new dress code judgment criterion. We improve the YOLOv8n model through three means: (1) a new neck structure named FPN-PAN-FPN (FPF) is introduced to enhance the model's feature fusion capability, (2) Receptive-Field Attention convolutional operation (RFAConv) is utilized to better capture the difference in information brought by different positions, and a (3) Focused Linear Attention (FLatten) mechanism is added to expand the model's receptive field. This improved YOLOv8n model increases mAP while reducing model size. Next, DeepSORT is integrated to obtain instance information across multi-frames. Finally, we adopt a new judgment criterion to conduct real-scene dress code monitoring. The experimental results show that our method effectively identifies instances of dress violations, reduces false alarms, and improves accuracy. [ABSTRACT FROM AUTHOR]

The CO2 sequestration capacity evaluation of reservoirs is a critical procedure for carbon capture, utilization, and storage (CCUS) techniques. However, calculating the sequestration amount for CO2 flooding in low-permeability reservoirs is challenging. Herein, a method combining numerical simulation technology with artificial intelligence is proposed. Based on the typical geological and fluid characteristics of low-permeability oil reservoirs in the Liaohe oilfield, the CMG 2020 version software GEM module is used to establish a model for CO2 flooding and sequestration. Meanwhile, a calculation method for the effective sequestration coefficient of CO2 is established. We systematically study the sequestration rules in low-permeability reservoirs under varying conditions of permeability, reservoir temperature, and initial reservoir pressure. The results indicate that, as the permeability and sequestration pressure of the reservoir increase, oil recovery gradually increases. The proportion of structurally bound sequestration volume increases from 55% to 60%. Reservoir temperature has minimal impact on both the recovery rate and the improvement in sequestration efficiency. Sequestration pressure primarily improves sequestration efficiency by increasing the dissolution of CO2 in the remaining oil and water. The calculation chart for the effective sequestration coefficient, developed using artificial intelligence algorithms under multi-factor conditions, enables accurate and rapid evaluation of the sequestration potential and the identification of favorable sequestration areas in low-permeability reservoirs. This approach provides valuable technical support for CO2 flooding and sequestration in pilot applications. [ABSTRACT FROM AUTHOR]

Observational data on ocean subsurface temperature and salinity are patently insufficient because in situ observations are complex and costly, while satellite remote-sensed measurements are abundant but mainly focus on sea surface data. To make up for the ocean interior data shortage and entirely use the abundant satellite data, we developed a data-driven deep learning model named Convformer to reconstruct ocean subsurface temperature and salinity fields from satellite-observed sea surface data. Convformer is designed by deeply optimizing Vision Transformer and ConvLSTM, consisting of alternating residual connections between multiple temporal and spatial attention blocks. The input variables consist of sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and sea surface wind (SSW). Our results demonstrate that Convformer exhibits superior performance in estimating the temperature-salinity structure of the tropical Pacific Ocean. The all-depth average root mean square error (RMSE) of the reconstructed subsurface temperature (ST)/subsurface salinity (SS) is 0.353 °C/0.0695 PSU, with correlation coefficients (R²) of 0.98663/0.99971. In the critical thermocline, although the root mean square errors of ST and SS reach 0.85 °C and 0.121 PSU, respectively, they remain smaller compared to other models. Furthermore, we assessed Convformer's performance from various perspectives. Notably, we also delved into the potential of Convformer to extract physical and dynamic information from a model mechanism perspective. Our study offers a practical approach to reconstructing the subsurface temperature and salinity fields from satellite-observed sea surface data. [ABSTRACT FROM AUTHOR]

In this study, a novel gas burner combining air swirl and an inverse diffusion flame (IDF) is designed for industrial applications. Numerical simulations using the Reynolds-averaged Navier–Stokes (RANS) method and simplified reaction mechanisms are conducted to predict the turbulent flow and combustion performance of the burner. Detailed flow structures, flame structures and effects of burner configurations are examined. The simulation results indicate that the swirl action of the burner creates a central recirculation zone and two external recirculation zones at the burner head, which stabilize combustion. The tangential velocity is minimal at the center of the burner and decreases with increasing distance from the outlet. As the distance from the exit increases, the maximum tangential velocity gradually decreases, and the peak value shifts towards the wall. This decrease in tangential velocity with axial distance signifies the gradual dissipation of the swirl effect, which disappears near the chamber outlet. The comparisons reveal that altering the number of burner fuel nozzles is more effective in reducing NO emissions than changing the inclination angle of the fuel nozzles, in the given conditions. Favorable combustion conditions are achieved when there are 16 fuel nozzles and the nozzle inclination angle is 60°, resulting in a 28.5% reduction in NO emissions at the outlet, compared to the reference condition. [ABSTRACT FROM AUTHOR]

Simple Summary: The most prominent feature of coconut oil (CO) is that it contains over 80% saturated fatty acids and about 60% medium chain fatty acids of total fatty acids. With these characteristics, CO shows many health benefits, but concerns about the risk of cardiovascular disease still remain in dispute. So far, there has been limited research on the potential use of CO in aquafeeds, being used as an alternative to fish oil (FO), and inconsistent results were presented. Therefore, we performed an experiment to investigate the effects of CO replacing FO on the growth performance of the orange-spotted grouper (Epinephelus coioides) in an attempt to determine its potential use and pertinent lipid metabolic mechanism in the fish species. Results show that CO could completely replace FO without affecting the growth performance, but high CO feeding will compromise the liver lipid deposit and long chain polyunsaturated fatty acids reduction of fish flesh in orange-spotted groupers. In this study, we conducted an 8-week feeding trial to investigate the effects of replacing fish oil (FO) with coconut oil (CO) on the growth performance, blood components, tissue fatty acid (FA) profile, and mRNA levels of genes related to lipid metabolism in the liver of the orange-spotted grouper (Epinephelus coioides). Five isolipidic and isoproteic diets were formulated through increasing the CO levels (0, 25%, 50%, 75%, and 100%, respectively). Triplicate groups of twenty-five fish (initial wet weight of about 22.4 g/fish) were fed one of the diets twice daily to apparent satiety. The 25% CO diet had the highest growth rate and feed utilization, and the 100% CO diet exhibited a comparable growth and feed utilization with that of the control diet, indicating a suitable FO substitute. Moreover, the hepatosomatic index, intraperitoneal fat rate, liver lipid content, as well as the serum HDL-C content and ALT activity had positive linear and/or quadratic responses, but the serum TC and LDL-C contents exhibited the opposite trend, with an increasing CO inclusion level. The FA profile in the liver and muscle generally mirrored the FA profile in the feed. Furthermore, the mRNA levels of the fas, acc, g6pd, srebp-1c, and δ6fad genes in the liver had positive linear and/or quadratic responses, but the mRNA levels of elovl 4 and elovl 5 had the opposite trend, with increasing dietary CO inclusion levels. When compared with the control diet, 25% and 50% CO diets up-regulated the mRNA levels of cpt 1, while the 75% and 100% CO diets down-regulated its mRNA levels. The hsl and atgl were down-regulated through the addition of dietary CO. The mRNA level of lpl was not affected by dietary treatments. Results showed that CO could completely replace FO without affecting growth performance, but high CO will lead to the significant liver lipid deposition and lower LC-PUFAs contents of fish flesh. [ABSTRACT FROM AUTHOR]

In this study, we explored the resilience of a clubroot resistance (CR) stacking model against a field population of Plasmodiophora brassicae pathotype 3H. This contrasts with our earlier work, where stacking CRaM and Crr1rutb proved only moderately resistant to pathotype X. Canola varieties carrying Rcr1/Crr1rutb and Rcr1 + Crr1rutb were repeatedly exposed to 3H at low (1 × 104/g soil) and high (1 × 107/g soil) initial resting spore concentrations over five planting cycles under controlled environments to mimic intensive canola production. Initially, all resistant varieties showed strong resistance. However, there was a gradual decline in resistance over time for varieties carrying only a single CR gene, particularly with Crr1rutb alone and at the high inoculum level, where the disease severity index (DSI) increased from 9% to 39% over five planting cycles. This suggests the presence of virulent pathotypes at initially low levels in the 3H inoculum. In contrast, the variety with stacked CR genes remained resilient, with DSI staying below 3% throughout, even at the high inoculum level. Furthermore, the use of resistant varieties, carrying either a single or stacked CR genes, reduced the total resting spore numbers in soil over time, while the inoculum level either increased or remained high in soils where susceptible Westar was continuously grown. Our study demonstrates greater resistance resilience for stacking Rcr1 and Crr1rutb against the field population of 3H. Additionally, the results suggest that resistance may persist even longer in fields with lower levels of inoculum, highlighting the value of extended crop rotation (reducing inoculum) alongside strategic CR-gene deployment to maximize resistance resilience. [ABSTRACT FROM AUTHOR]

Isoflavones are a class of natural products that exhibit a wide range of interesting biological properties, including antioxidant, hepatoprotective, antimicrobial, and anti-inflammatory activities. Scandenone (1), osajin (2), and 6,8-diprenylgenistein (3) are natural prenylated isoflavones that share the same polyphenol framework. In this research, the key intermediate 15 was used for the synthesis of the natural isoflavones 1–3, establishing a stereoselective synthetic method for both linear and angular pyran isoflavones. The antibacterial activities of 1–3 were also evaluated, and all of them displayed good antibacterial activity against Gram-positive bacteria. Among them, 2 was the most potent one against MRSA, with a MIC value of 2 μg/mL, and the SEM assay indicated that the bacterial cell membranes of both MRSA and E. faecalis could be disrupted by 2. These findings suggest that this type of isoflavone could serve as a lead for the development of novel antibacterial agents for the treatment of Gram-positive bacterial infections. [ABSTRACT FROM AUTHOR]

Gel treatment is an economical and efficient method of controlling excessive water production. The gelation of in situ gels is prone to being affected by the dilution of formation water, chromatographic during the transportation process, and thus controlling the gelation time and penetration depth is a challenging task. Therefore, a novel gel system termed preformed particle gels (PPGs) has been developed to overcome the drawbacks of in situ gels. PPGs are superabsorbent polymer gels which can swell but not dissolve in brines. Typically, PPGs are a granular gels formed based on the crosslinking of polyacrylamide, characterized by controllable particle size and strength. This work summarizes the application scenarios of PPGs and elucidates their plugging mechanisms. Additionally, several newly developed PPG systems such as high-temperature-resistant PPGs, re-crosslinkable PPGs, and delayed-swelling PPGs are also covered. This research indicates that PPGs can selectively block the formation of fractures or high-permeability channels. The performance of the novel modified PPGs was superior to in situ gels in harsh environments. Lastly, we outlined recommended improvements for the novel PPGs and suggested future research directions. [ABSTRACT FROM AUTHOR]

To address the problem of problematic spray design inside mining anchor-digging equipment, a switching seal using a permanent magnet eddy current drive is initially presented here. The layer model of the permanent magnet eddy current structure is established, the subdomain analysis model is introduced, the permanent magnet eddy current structure is divided into six regions along the axial direction, and the boundary equations are established at the interfaces of each region. The vector magnetic potential equations in each region are deduced, along with the electromagnetic torque and axial force equations. The computational results are compared and analyzed with the results of finite element simulation, verifying the accuracy of the theoretical model. The design of experiments is used to verify the feasibility of the switching seal using the permanent magnet eddy current structure. [ABSTRACT FROM AUTHOR]

Aggressive flight has become increasingly important for expanding the applications of quadrotors. The typical characteristic of large and rapid changes in commands poses stringent demands on the maneuverability of quadrotors. Ensuring flight stability alone is not enough; dynamic responses must also be selectively constrained, presenting quadcopter flight control with daunting challenges. The prescribed performance control (PPC) method is seen as having the potential to solve this problem by allowing for the constrained control of specified performance, leading to extensive research. However, its practical application still faces challenges, such as the system divergence caused by errors exceeding boundaries due to sudden command mutations. This paper presents a robust dynamic event-triggered PPC (DETPPC) method for an aggressive quadrotor flight. By assessing the direction and proximity of tracking errors approaching constraint boundaries, a dynamic event-triggered compensation mechanism for performance function boundaries is established to mitigate the divergence caused by error surpassing and to preserve preset control over the targeted metrics. Controllers were designed for both the translational and rotational subsystems of the quadrotor, and stability analysis was conducted based on Lyapunov functions. Simulation tests on agile trajectory tracking and abrupt attitude control were carried out, demonstrating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The river and lake chief system offers a valuable policy toolkit to mitigate the degradation of water ecology, thereby bolstering water resource management for sustainable water development in China. To evaluate the effects of implementation and improve policy, this study took Beijing as a typical case and conducted a quantitative assessment using multidimensional data. The findings suggest that while the river and lake chief system in Beijing is effective and has significantly contributed to the ecological management of rivers and lakes, there are also notable regional disparities and urban–rural divergences. In addition, human activities are the main sources of environmental pollution in rivers and lakes, which should be the focus of the river and lake chief system. The river and lake chief system needs to embed more public participation and cooperative governance. This research aids in better understanding China's river and lake chief system for both researchers and practitioners, facilitating the advancement of the knowledge body of global water policy and governance. [ABSTRACT FROM AUTHOR]

Estimating the intensity of tropical cyclones (TCs) is beneficial for preventing and reducing the impact of natural disasters. Most existing methods for estimating TC intensity utilize single-satellite or single-band remote sensing images, but they lack the ability to quantify the uncertainty of the estimation results. However, TC, as a typical chaotic system, often requires confidence intervals for intensity estimates in real-world emergency decision-making scenarios. Additionally, the use of multi-source image inputs contributes to the uncertainty of the model. Consequently, this study introduces a neural network (MTCIE) that utilizes multi-source satellite images to provide probabilistic estimates of TC intensity. The model utilizes infrared and microwave images from multiple satellites as inputs. It uses a dual-branch self-attention encoder to extract TC image features and provides uncertainty estimates for TC intensity. Furthermore, a dataset for estimating the intensity of multi-source TC remote sensing images (MTCID) is constructed through the registration of latitude, longitude, and time, along with data augmentation. The proposed method achieves a MAE of 7.42 kt in deterministic estimation, comparable to mainstream networks like TCIENet. In uncertain estimation, it outperforms methods like MC Dropout in the PICP metric, providing reliable probability estimates. This supports TC disaster emergency decision making, enhancing risk mitigation in real-world applications. [ABSTRACT FROM AUTHOR]

In the quest for unparalleled reliability and robustness within control systems, significant attention has been directed toward mitigating actuator faults in diverse applications, from space vehicles to sophisticated industrial systems. Despite these advances, the prevalent assumption of homogeneous actuator faults remains a stark simplification, failing to encapsulate the stochastic and unpredictable nature of real-world operational environments. The problem of finite-time fault-tolerant control for nonlinear flexible spacecraft systems with actuator faults is addressed in this paper, utilizing the T-S fuzzy framework. In a departure from conventional approaches, actuator failures are modeled as random signals following a nonhomogeneous Markov process, thus comprehensively addressing the issue of timeliness, which has previously been overlooked in the literature. To effectively manage the intricacies introduced by these factors, the nonhomogeneous Markov process is represented as a polytope set. The proposed solution involves the development of a nonhomogeneous matrix transformation, accompanied by the introduction of adaptable parameters. This innovative controller design methodology yields a stability criterion that ensures H ∞ performance in a mean-square sense. To empirically substantiate the effectiveness and advantages of the proposed approaches, a numerical example featuring a nonlinear spacecraft system is presented. [ABSTRACT FROM AUTHOR]

Phosphorylation site prediction has important application value in the field of bioinformatics. It can act as an important reference and help with protein function research, protein structure research, and drug discovery. So, it is of great significance to propose scientific and effective calculation methods to accurately predict phosphorylation sites. In this study, we propose a new method, Attenphos, based on the self-attention mechanism for predicting general phosphorylation sites in proteins. The method not only captures the long-range dependence information of proteins but also better represents the correlation between amino acids through feature vector encoding transformation. Attenphos takes advantage of the one-dimensional convolutional layer to reduce the number of model parameters, improve model efficiency and prediction accuracy, and enhance model generalization. Comparisons between our method and existing state-of-the-art prediction tools were made using balanced datasets from human proteins and unbalanced datasets from mouse proteins. We performed prediction comparisons using independent test sets. The results showed that Attenphos demonstrated the best overall performance in the prediction of Serine (S), Threonine (T), and Tyrosine (Y) sites on both balanced and unbalanced datasets. Compared to current state-of-the-art methods, Attenphos has significantly higher prediction accuracy. This proves the potential of Attenphos in accelerating the identification and functional analysis of protein phosphorylation sites and provides new tools and ideas for biological research and drug discovery. [ABSTRACT FROM AUTHOR]

The utilization and development of biomass resources is an efficient solution to mitigate the fossil energy crisis. Based on the advantages of mild reaction conditions, rapid reaction, and high conversion, the synthesis of 2,5-furandicarboxylic acid (FDCA) by the electrocatalytic oxidation of 5-hydroxymethylfurfural (HMFOR) has attracted considerable attention. This review will summarize the recent advances of HMFOR to FDCA, including the reaction pathway and mechanism, as well as the catalytic performance of various heterogeneous electrocatalysts. The challenges and prospects for HMFOR are also focused on. Finally, it is expected that this work may provide guidance for the design of high-efficiency electrocatalysts and thereby accelerate the industrialization process of biomass utilization. [ABSTRACT FROM AUTHOR]

Since the discovery of the vast Jimusaer shale oilfield in the southeastern Junggar Basin in 2012, there has been considerable interest in neighboring areas around Bogeda Mountain that have shale oil potential. The primary productive interval in the basin, the Middle Permian Lucaogou Formation (P2l), is well-developed in the areas of Qitai, Mulei, Shiqiantan, Chaiwopu, and Miquan. In this study, we conducted an assessment of the hydrocarbon generation potential of the P2l in these five areas and compared it with that of the P2l in the Jimusaer oilfield, which were determined by GC-MS, total organic carbon (TOC) and vitrinite reflectance (Ro) measurements, Rock-Eval pyrolysis, and organic petrology to investigate the type, origin, thermal maturity, hydrocarbon potential, and oil/gas proneness of organic matter in the P2l. Additionally, we applied open-system pyrolysis of hydrocarbon generation kinetics to explore differences in hydrocarbon generation and expulsion across various P2l mudstone/shale in the southeastern Junggar Basin. The findings of this study revealed that the P2l shale in Qitai and Miquan areas contains more abundant and lower thermally mature organic matter (early mature–mature stage), characterized by primarily Type II1–I kerogen, similar to that found in the P2l shale of the Jimusaer oilfield. Conversely, the P2l shale in Mulei, Shiqiantan, and Chaiwopu contains less abundant and more thermally mature organic matter (mainly mature–highly mature stage), dominated by Type II2–III kerogen. Consequently, shale in these areas is considerably less desirable for oil exploration compared to the Jimusaer shale. The semi-deep to deep lake facies in Miquan and Qitai exhibit the most promising exploration potential. This study can serve as a guide for shale oil exploration in the southeastern Junggar Basin. [ABSTRACT FROM AUTHOR]

In recent decades, differential evolution (DE) has been employed to address a diverse range of nonlinear, nondifferentiable, and nonconvex optimization problems. In this study, we introduce an enhanced adaptive differential evolution algorithm to address the inversion problem associated with magnetic data. The primary objective of the inversion process is to minimize the discrepancy between observed data and predicted data derived from the inverted model. So, the contributions of this paper include the following two points. First, a new mechanism for generating crossover rate (CR) is proposed, which tends to reduce the CR values corresponding to vectors with better objective function values. Second, a new mutation strategy with direction information is proposed to expedite convergence. Additionally, modifications were made to the adjustment of the regularization factor to prevent it from becoming too minimal, thereby preserving its efficacy. The proposed algorithm is validated through synthetic models and a field example. Results from synthetic models demonstrate that our method is superior to and competitive with the original adaptive DE in both solution quality and convergence velocity. For the field example, the Inverted models align closely with the drill-well information. [ABSTRACT FROM AUTHOR]

The mechanism parameters of the manipulator not only have a great influence on the size of the working space but also affect flexible performance distribution. Aimed at obtaining a 6 DOF modular manipulator, mechanism parameters were optimized in order to explore the effect of upper arm and forearm dimensions on the end dexterity of the manipulator. First, forward kinematic equations were derived using the DH method, and the Jacobian matrix of the manipulator was solved. Second, three indicators, including the condition number index, structural length index, and global conditioning index, were employed as optimization indicators for the mechanism parameters of the manipulator, and an orthogonal experiment was designed based on the Grey–Taguchi method and robot toolbox. Third, the grey relational analysis method was used to process the experimental results, and the grey relational grade for each group was solved. Last, the variation curve between the grey relational grade and the parameter level of each mechanism was drawn, and optimized mechanical arm mechanism parameters were derived. It was found that although the overall dimension of the manipulator was slightly decreased, as determined via comparing the original and optimized manipulator length, the performance indexes were improved. The results not only verified the correctness of the proposed optimization method but also laid a foundation for subsequent research on the dynamic performance of modular robot systems. [ABSTRACT FROM AUTHOR]

Nuclear factor-erythroid 2-related factor 2 (Nrf2) has been shown to be a negative regulator of osteoclast differentiation, but the precise mechanisms have not yet been established. We examined the precise roles of Nrf2 in regulating antioxidants and reactive oxygen species (ROS) levels, especially the cytoplasmic and mitochondrial ROS during osteoclastogenesis in vitro. In the current study, we found that the absence of Nrf2 promotes osteoclast differentiation in bone-marrow-derived macrophages (BMMs) and RAW 264.7 cells. The receptor activator of NF-κB ligand (RANKL) and macrophage colony-stimulating factor (M-CSF) significantly lowered the levels of Nrf2 and its downstream antioxidant enzymes at mRNA and/or protein levels during osteoclast differentiation in the BMMs of mice and RAW 264.7 mouse leukemic monocytes. Compared to the wild-type cells, Nrf2-deficient cells exhibited heightened sensitivity to both transient RANKL-induced cytoplasmic ROS and prolonged RANKL and M-CSF-induced cytoplasmic and mitochondrial ROS accumulation. Furthermore, exogenous antioxidant agents, including N-acetyl-cysteine (NAC), diphenyleneiodonium chloride (DPI), and mitoquinone mesylate (MitoQ), exhibited substantial capability to suppress the elevation of ROS levels during osteoclast differentiation induced by Nrf2 deficiency, and they consequently inhibited osteoclast differentiation augmented by the lack of Nrf2. The activation of phosphorylated c-FOS resulting from elevated ROS promoted osteoclast differentiation. The inhibition of c-FOS blocked osteoclast differentiation, which was elevated by Nrf2-deficiency. Taken together, these data reveal that Nrf2 effectively decreased the accumulation of intracellular ROS and the phosphorylation of c-FOS during osteoclastic differentiation by regulating antioxidant enzymes and subsequently inhibited RANKL-induced osteoclast differentiation. [ABSTRACT FROM AUTHOR]

Practical application has shown that the blown-up theory has great predictive ability for predicting transitional weather systems, especially catastrophic weather systems. This study applies the blown-up theory to analyze and predict a rainstorm case in Jiangsu Province of East China to explore the applicability of the blown-up theory. At the same time, a numerical simulation experiment is conducted using the Weather Research and Forecasting Model (WRF) v4.2. The numerical results are compared with the European Center for Medium Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) data and the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) products. It is found that there is a deviation in the simulation for the precipitation center, and further analysis indicates that it is likely related to the position of the simulated low-level shear line. On the other hand, the blown-up analyses are consistent with the actual situation and provide additional information besides the numerical simulation results. These results indicate that the blown-up charts and V-3θ diagrams are able to predict the weather system transformation, the rainfall area, and the evolution of the rainstorm, which confirms the applicability of the blown-up theory to rainstorm forecasts. This provides an auxiliary analysis method in addition to numerical simulations for rainstorm forecasts. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) image target detection is of great significance in civil surveillance and military reconnaissance. However, there are few publicly released SAR image datasets of typical non-cooperative targets. Aiming to solve this problem, a fast facet-based SAR imaging model is proposed to simulate the SAR images of non-cooperative aircraft targets under different conditions. Combining the iterative physical optics and the Kirchhoff approximation, the scattering coefficient of each facet on the target and rough surface can be obtained. Then, the radar echo signal of an aircraft target above a rough surface environment can be generated, and the SAR images can be simulated under different conditions. Finally, through the simulation experiments, a dataset of typical non-cooperative targets can be established. Combining the YOLOv5 network with the convolutional block attention module (CBAM) and another detection head, a SAR image target detection model based on the established dataset is realized. Compared with other YOLO series detectors, the simulation results show a significant improvement in precision. Moreover, the automatic target recognition system presented in this paper can provide a reference for the detection and recognition of non-cooperative aircraft targets and has great practical application in situational awareness of battlefield conditions. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for the kinematics and statics analysis of a Gough–Stewart platform. Through defining the velocity screw, the relative angular and linear velocities of a single rigid body can be expressed as a single vector. The velocity screw equations of various mechanisms are deduced in detail, the forward and inverse kinematics of a parallel mechanism can be solved through the velocity screw equation. Similarly, the definition of the force screw allows all constraint forces and torques of a single rigid body to be expressed using a single vector, and the static screw equation can be used to solve the forward and inverse statics of a parallel mechanism in one coordinate system. The advantage of this approach is that kinematics and statics modeling are unified in screw coordinates because the kinematic parameters in screw form can be directly employed in statics modeling. The results of the kinematics and statics analysis of the Gough-Stewart platform validate this method. This algorithm is easy to compute and program with high efficiency, and it can also be applied to any other spatial, complex multi-rigid-body systems. [ABSTRACT FROM AUTHOR]

The bourgeoning of e-commerce in the context of the information era has accelerated the urbanization trend by broaching a new horizon of economic and industrial boosters for rural places, epitomized by a great number of "Taobao Villages" in China. This paper has two objectives: (1) explore the process and mechanism of digitalization enabling rural in-situ urbanization represented by e-commerce; (2) nuance the specific case evidence of Daiji Town, where digitalization enabled in-situ urbanization recently. We build up a theoretical framework for digitalization-enabled in-situ urbanization from the juxtaposition of four interlinked elements: industry, talent, rural governance, and land use. It then analyzed the details and evidence of digitalization enabling rural in-situ urbanization through the case study of Daiji Town. The main conclusions of this paper are as follows: First, digitalization plugs rural areas into production and consumption networks in wider contexts, promoting the transformation and prosperity of rural economies. Secondly, the reverse migration of young generations to rural areas becomes the key to rural in-situ urbanization. Thirdly, digitization materializes the urbanization of rural spaces. Finally, digitalization enables the rural transformation and improvement of urban-rural relations in the Global South, which needs to be further explored. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG) is a promising technology that can capture muscle activation signals to control robots through novel human–machine interfaces (HMIs). This technology has already been applied in scenarios such as prosthetic design, assisted robot control, and rehabilitation training. This article provides an overview of sEMG-based robot control, covering two important aspects: (1) sEMG signal processing and classification methods and (2) robot control strategies and methods based on sEMG. First, the article outlines the general steps in sEMG signal processing and summarizes the commonly used methods for data acquisition, pre-processing, and feature extraction. In addition, machine-learning-based pattern recognition methods have been introduced for sEMG signal classification. Subsequently, user intent-based robot control strategies are classified into three categories: full-human continuous control, semi-autonomous continuous control, and discrete control, and their control methods and applicable scenarios are compared. Finally, this article discusses the advantages, disadvantages, and future development prospects of sEMG-based robot control. This review provides a comprehensive overview of sEMG-based robot control, from signal processing and classification methods to robot control strategies and methods, aiming to guide future research on selecting filters, feature sets, and pattern recognition methods and to assist in establishing sEMG-driven robot control frameworks. [ABSTRACT FROM AUTHOR]

The retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs) play a crucial role as pattern-recognition receptors within the innate immune system. These receptors, present in various cell and tissue types, serve as essential sensors for viral infections, enhancing the immune system's capacity to combat infections through the induction of type I interferons (IFN-I) and inflammatory cytokines. RLRs are involved in a variety of physiological and pathological processes, including viral infections, autoimmune disorders, and cancer. An increasing body of research has examined the possibility of RLRs or microRNAs as therapeutic targets for antiviral infections and malignancies, despite the fact that few studies have focused on the regulatory function of microRNAs on RLR signaling. Consequently, our main emphasis in this review is on elucidating the role of microRNAs in modulating the signaling pathways of RLRs in the context of cancer and viral infections. The aim is to establish a robust knowledge base that can serve as a basis for future comprehensive investigations into the interplay between microRNAs and RIG-I, while also facilitating the advancement of therapeutic drug development. [ABSTRACT FROM AUTHOR]

Mineral particle size is an important parameter in the mineral beneficiation process. In industrial processes, the grinding process produces pulp with qualified particle size for subsequent flotation processes. In this paper, a hierarchical intelligent control method for mineral particle size based on machine learning is proposed. In the machine learning layer, artificial intelligence technologies such as long and short memory neural networks (LSTM) and convolution neural networks (CNN) are used to solve the multi-source ore blending prediction and intelligent classification of dry and rainy season conditions, and then the ore-feeding intelligent expert control system and grinding process intelligent expert system are used to coordinate the production of semi-autogenous mill and Ball mill and Hydrocyclone (SAB) process and intelligently adjust the control parameters of DCS layer. This paper presents the practical application of the method in the SAB production process of an international mine to realize automation and intelligence. The process throughput is increased by 6.05%, the power consumption is reduced by 7.25%, and the annual economic benefit has been significantly improved. [ABSTRACT FROM AUTHOR]

In this paper, a two-rotational degrees of freedom parallel mechanism with five kinematic subchains (3UPS-UPU-S) (U, P, and S stand for universal joints, prismatic joints, and spherical joints) for an aerospace product is introduced, and its kinematic and dynamic characteristics are subsequently analyzed. The kinematic and dynamic analyses of this mechanism are carried out in screw coordinates. Firstly, the inverse kinematics is performed through the kinematic equations established by the velocity screws of each joint to obtain the position, posture, and velocity of each joint within the mechanism. Then, a dynamic modeling method with screw theory for multi-body systems is proposed. In this method, the momentum screws are established by the momentum and moment of momentum according to the fundamentals of screws. By using the kinematic parameters of joints, the dynamic analysis can be carried out through the dynamic equations formed by momentum screws and force screws. This method unifies the kinematic and dynamic analyses by expressing all parameters in screw form. The approach can be employed in the development of computational dynamics because of its simplified and straightforward analysis procedure and its high adaptability for different kinds of multi-body systems. [ABSTRACT FROM AUTHOR]

Mycoplasma hyopneumoniae is the etiological agent of porcine enzootic pneumonia (EP), leading to a mild and chronic pneumonia in swine. Relative control has been attained through active vaccination programs, but porcine enzootic pneumonia remains a significant economic challenge in the swine industry. Cellular immunity plays a key role in the prevention and control of porcine enzootic pneumonia. Therefore, the development of a more efficient vaccine that confers a strong immunity against M. hyopneumoniae is necessary. In this study, a multi-antigen chimera (L9m6) was constructed by combining the heat-labile enterotoxin B subunit (LTB) with three antigens of M. hyopneumoniae (P97R1, mhp390, and P46), and its immunogenic and antigenic properties were assessed in a murine model. In addition, we compared the effect of individual administration and multiple-fusion of these antigens. The chimeric multi-fusion vaccine induced significant cellular immune responses and high production of IgG and IgM antibodies against M. hyopneumoniae. Collectively, our data suggested that rL9m6 chimera exhibits potential as a viable vaccine candidate for the prevention and control of porcine enzootic pneumonia. [ABSTRACT FROM AUTHOR]

Raffinose synthase (Rafs) is an important enzyme in the synthesis pathway of raffinose from sucrose and galactinol in higher plants and is involved in the regulation of seed development and plant responses to abiotic stresses. In this study, we analyzed the Rafs families and profiled their alternative splicing patterns at the genome-wide scale from 10 grass species representing crops and grasses. A total of 73 Rafs genes were identified from grass species such as rice, maize, foxtail millet, and switchgrass. These Rafs genes were assigned to six groups based the phylogenetic analysis. We compared the gene structures, protein domains, and expression patterns of Rafs genes, and also unraveled the alternative transcripts of them. In addition, different conserved sequences were observed at these putative splice sites among grass species. The subcellular localization of PvRafs5 suggested that the Rafs gene was expressed in the cytoplasm or cell membrane. Our findings provide comprehensive knowledge of the Rafs families in terms of genes and proteins, which will facilitate further functional characterization in grass species in response to abiotic stress. [ABSTRACT FROM AUTHOR]

Total sleep deprivation (TSD) leads to cognitive decline; however, the neurophysiological mechanisms underlying resting-state electroencephalogram (EEG) changes after TSD remain unclear. In this study, 42 healthy adult participants were subjected to 36 h of sleep deprivation (36 h TSD), and resting-state EEG data were recorded at baseline, after 24 h of sleep deprivation (24 h TSD), and after 36 h TSD. The analysis of resting-state EEG at baseline, after 24 h TSD, and after 36 h TSD using source localization analysis, power spectrum analysis, and functional connectivity analysis revealed a decrease in alpha-band power and a significant increase in delta-band power after TSD and impaired functional connectivity in the default mode network, precuneus, and inferior parietal lobule. The cortical activities of the precuneus, inferior parietal lobule, and superior parietal lobule were significantly reduced, but no difference was found between the 24 h and 36 h TSD groups. This may indicate that TSD caused some damage to the participants, but this damage temporarily slowed during the 24 h to 36 h TSD period. [ABSTRACT FROM AUTHOR]

Purpose: The function of working memory (WM) is impaired by total sleep deprivation (TSD) and cognitive load. However, it is unclear whether the load modulates the effect of TSD on WM. We conducted a pilot study to investigate the effects of 36 h of TSD on WM under different load levels. Materials and methods: Twenty-two male students aged 18–25 years were enrolled, who underwent two types of sleep conditions (baseline and 36 h TSD), where they performed two N-back WM tasks (one-back task and two-back task) with simultaneous electroencephalography recordings. Results: Repeated measures analysis of variance (ANOVA) indicated that, with the increasing load, the reaction time increased and the accuracy decreased. After TSD, the correct number per unit time decreased. The significant interaction effect of the P3 amplitudes between the load level and the sleep condition showed that the reduction in the amplitude of P3 in the two-back task due to TSD was more obvious than that in the one-back task. Conclusions: Our results provided evidence for the moderation of load on the impairment of TSD on WM. The degree of TSD-induced impairment for a higher load was greater than that for a lower load. The current study provides new insights into the mechanisms by which sleep deprivation affects cognitive function. [ABSTRACT FROM AUTHOR]

This paper investigates the kinematics and dynamics of multi-rigid-body systems in screw form. The Newton–Euler dynamics equations are established in screw coordinates. All forces and torques of the multi-rigid-body system can be solved straightforwardly since they are explicit in the form of screw coordinates. The displacement and acceleration are unified in matrix form, which associates the kinematics and dynamics with variable of velocity. A one-step numerical algorithm only is needed to solve the displacements and accelerations. As a result, all absolute displacements, velocities, and accelerations are directly obtained by one kinematic equation. The kinematics and dynamics of Gough–Stewart platform validate this the method. In this paper, the kinematics and dynamics are carried out with the example of a Gough–Stewart platform, which represents the most complex multi-rigid-body system, to verify the computational dynamics method. The proposed algorithm is also fit for the kinematics and dynamics modeling of other multi-rigid-body systems. [ABSTRACT FROM AUTHOR]

There has been growing interest in applying the DEM (discrete element method) to study the charging and burden distribution in a BF (blast furnace). In practice, the real particles in a BF are non-spherical. However, spherical particles have mostly been used in previous DEM investigations. Furthermore, various particle models have been developed to describe non-spherical particles. However, the effects of using different particle models on particle behavior in a BF are still unclear. Therefore, a comparative study of how the particle shape model impacts the burden charging in a BF was conducted. Specifically, the DEM using a multi-sphere model, polyhedral model, and super-ellipsoid model was first established. Then, experiments and DEM simulations of the charging and burden distribution of non-spherical quartz sand particles in a lab-scale bell-less top BF were performed. The results indicated that the number of sub-spheres, the principle of creating the particle for multi-spheres, the number of planes for polyhedrons, and the shape indices for super-ellipsoids could all affect the accuracy and efficiency. Moreover, applying the super-ellipsoid model and multi-sphere model could achieve reasonable accuracy and efficiency, with the highest simulation accuracy for the polyhedral model but at the cost of a rather heavy computational burden. [ABSTRACT FROM AUTHOR]

Single-cell transcriptomics is rapidly advancing our understanding of the composition of complex tissues and biological cells, and single-cell RNA sequencing (scRNA-seq) holds great potential for identifying and characterizing the cell composition of complex tissues. Cell type identification by analyzing scRNA-seq data is mostly limited by time-consuming and irreproducible manual annotation. As scRNA-seq technology scales to thousands of cells per experiment, the exponential increase in the number of cell samples makes manual annotation more difficult. On the other hand, the sparsity of gene transcriptome data remains a major challenge. This paper applied the idea of the transformer to single-cell classification tasks based on scRNA-seq data. We propose scTransSort, a cell-type annotation method pretrained with single-cell transcriptomics data. The scTransSort incorporates a method of representing genes as gene expression embedding blocks to reduce the sparsity of data used for cell type identification and reduce the computational complexity. The feature of scTransSort is that its implementation of intelligent information extraction for unordered data, automatically extracting valid features of cell types without the need for manually labeled features and additional references. In experiments on cells from 35 human and 26 mouse tissues, scTransSort successfully elucidated its high accuracy and high performance for cell type identification, and demonstrated its own high robustness and generalization ability. [ABSTRACT FROM AUTHOR]

The formation of mature mRNA requires cutting introns and splicing exons. The occurrence of splicing involves the participation of the spliceosome. Common spliceosomes mainly include five snRNPs: U1, U2, U4/U6, and U5. SF3a2, an essential component of spliceosome U2 snRNP, participates in splicing a series of genes. There is no definition of SF3a2 in plants. The paper elaborated on SF3a2s from a series of plants through protein sequence similarity. We constructed the evolutionary relationship of SF3a2s in plants. Moreover, we analyzed the similarities and differences in gene structure, protein structure, the cis-element of the promoter, and expression pattern; we predicted their interacting proteins and constructed their collinearity. We have preliminarily analyzed SF3a2s in plants and clarified the evolutionary relationship between different species; these studies can better serve for in-depth research on the members of the spliceosome in plants. [ABSTRACT FROM AUTHOR]

In this paper, a parallel mechanism with two rotational degrees of freedom is proposed. It could rotate freely and continuously around the two coordinate axes at the fixed origin of the coordinate frame. The structure of the mechanism is a second-order over constraint parallel structure and the moving platform and base platform are connected by five kinematic chains. The motion characteristics of this structure are analyzed by reciprocal screw equation. Then, the kinematics and dynamics modelling are carried out systematically in a unified way. The kinematics of the mechanism is established by means of screws, the displacements and accelerations of each joint and any point on a link could be calculated by the kinematic screw equation directly. The analysis of acceleration and its mathematical expression in screw form are given, and the acceleration matrix could be applied into the dynamic analysis based on the Newton–Euler equation. All the constraint forces and torques could be obtained by a single set of Newton–Euler equations. [ABSTRACT FROM AUTHOR]

Trajectory planning and avoidance of tipping are the main keys to success in the mobile dual-arm manipulation, especially when the dual-arm or moving platform is running fast. The forces and moments between wheel-terrain and body-arm have been analyzed by kinematics and force analysis of a robot to define tip-over stability constraint. Then, an improved tip-over moment stability criterion for a wheeled mobile dual-arm robot is presented and defines tip-over stability constraint based on it. To improve the motion stability of the robot, this paper presents an optimal joint trajectory planning model based on time and energy. The quintic B-spline curve and an improved NSGA-II algorithm, which are time and energy, are applied to multi-objective optimization. The simulation results show that the motion stability of a robot is improved based on the tip-over stability constraint. This trajectory planning method based on the stability constraint can be applied to other mobile robots as well. [ABSTRACT FROM AUTHOR]

To achieve global sustainable development and actively respond to climate change, China, as the world's largest energy consumer and carbon emitter, needs to save energy and reduce emissions without delay. By constructing LEAP-China production-side and LEAP-China consumption-side models, we predict the carbon emissions of China's production side and consumption side in different scenarios from 2020 to 2050, respectively. The results show that under the current policies, neither the production side nor consumption side can achieve targeted peak carbon emissions by 2030, which is contrary to China's current carbon emission policy. Under the sustainable development scenario, China's production-side CO2 emissions would peak at 10,462.2 Mt in 2029, and China's consumption-side CO2 emissions would peak 3 years later compared to the production side at 9904.3 Mt in 2032. Therefore, to achieve the peak for both the production and consumption side, we need to coordinate various policies and actively promote industrial restructuring and energy structure optimization. In terms of trade structure, China's existing import and export trade structure should be adjusted to reduce the export of industrially manufactured goods and increase the proportion of technology-intensive products in foreign trade to realize the transformation from a high-carbon trade structure to a low-carbon trade structure. [ABSTRACT FROM AUTHOR]

Background: Previous studies revealed that sleep deprivation (SD) impairs risk perception and leads to poor decision-making efficiency. However, how risk perception is related to brain regions' communication after SD has not been elucidated. In this study, we investigated the neuropsychological mechanisms of SD-impaired risk perception. Methods: Nineteen healthy male adults were recruited and underwent resting-state functional magnetic resonance imaging during a state of rested wakefulness and after nearly 36 h of total SD. They then completed the balloon analog risk task, which was used to measure the risk perception ability of risky decision-making. Regional homogeneity (ReHo) and voxel-wise functional connectivity were used to investigate neurobiological changes caused by SD. Correlation analysis was used to investigate the relationship between changes in ReHo, function, and risk perception. Results: At the behavioral level, risk perception decreased after 36 h of SD. At the neural level, SD induced a significant increase in ReHo in the right postcentral gyrus and was positively correlated with risk perception changes. The functional connectivity between the right postcentral gyrus, left medial temporal gyrus, and right inferior temporal gyrus was enhanced. Critically, increased right postcentral gyrus and right inferior temporal gyrus connectivity positively correlated with changes in risk perception. Conclusions: SD impairs the risk perception associated with altered postcentral connectivity. The brain requires more energy to process and integrate sensory and perceptual information after SD, which may be one possible reason for decreased risk perception ability after SD. [ABSTRACT FROM AUTHOR]

In motor imagery (MI) brain–computer interface (BCI) research, some researchers have designed MI paradigms of force under a unilateral upper-limb static state. It is difficult to apply these paradigms to the dynamic force interaction process between the robot and the patient in a brain-controlled rehabilitation robot system, which needs to induce thinking states of the patient's demand for assistance. Therefore, in our research, according to the movement of wiping the table in human daily life, we designed a three-level-force MI paradigm under a unilateral upper-limb dynamic state. Based on the event-related de-synchronization (ERD) feature analysis of the electroencephalography (EEG) signals generated by the brain's force change motor imagination, we proposed a multi-scale temporal convolutional network with attention mechanism (MSTCN-AM) algorithm to recognize ERD features of MI-EEG signals. Aiming at the slight feature differences of single-trial MI-EEG signals among different levels of force, the MSTCN module was designed to extract fine-grained features of different dimensions in the time–frequency domain. The spatial convolution module was then used to learn the area differences of space domain features. Finally, the attention mechanism dynamically weighted the time–frequency–space domain features to improve the algorithm's sensitivity. The results showed that the accuracy of the algorithm was 86.4 ± 14.0% for the three-level-force MI-EEG data collected experimentally. Compared with the baseline algorithms (OVR-CSP+SVM (77.6 ± 14.5%), Deep ConvNet (75.3 ± 12.3%), Shallow ConvNet (77.6 ± 11.8%), EEGNet (82.3 ± 13.8%), and SCNN-BiLSTM (69.1 ± 16.8%)), our algorithm had higher classification accuracy with significant differences and better fitting performance. [ABSTRACT FROM AUTHOR]

Genetic resistance is a cornerstone for managing clubroot (Plasmodiophora brassicae). However, when used repeatedly, a clubroot resistance (CR) gene can be broken rapidly. In this study, canola inbred/hybrid lines carrying one or two CR genes (Rcr1/CRaM and Crr1rutb) were assessed against P. brassicae pathotype X by repeated exposure to the same inoculum source under a controlled environment. Lines carrying two CR genes, either Rcr1 + Crr1rutb or CRaM + Crr1rutb, showed partial resistance. Selected lines were inoculated with a field pathotype X population (L-G3) at 5 × 106 resting spores/g soil, and all clubs were returned to the soil they came from six weeks after inoculation. The planting was repeated for five cycles, with diseased roots being returned to the soil after each cycle. The soil inoculum was quantified using qPCR before each planting cycle. All lines with a single CR gene were consistently susceptible, maintaining high soil inoculum levels over time. The lines carrying two CR genes showed much lower clubroot severity, resulting in a 10-fold decline in soil inoculum. These results showed that the CR-gene stacking provided moderate resistance against P. brassicae pathotype X, which may also help reduce the pathogen inoculum buildup in soil. [ABSTRACT FROM AUTHOR]

This study aims to explore the value of a machine learning (ML) model based on radiomics features and clinical features in predicting the outcome of spontaneous supratentorial intracerebral hemorrhage (sICH) 90 days after surgery. A total of 348 patients with sICH underwent craniotomy evacuation of hematoma from three medical centers. One hundred and eight radiomics features were extracted from sICH lesions on baseline CT. Radiomics features were screened using 12 feature selection algorithms. Clinical features included age, gender, admission Glasgow Coma Scale (GCS), intraventricular hemorrhage (IVH), midline shift (MLS), and deep ICH. Nine ML models were constructed based on clinical feature, and clinical features + radiomics features, respectively. Grid search was performed on different combinations of feature selection and ML model for parameter tuning. The averaged receiver operating characteristics (ROC) area under curve (AUC) was calculated and the model with the largest AUC was selected. It was then tested using multicenter data. The combination of lasso regression feature selection and logistic regression model based on clinical features + radiomics features had the best performance (AUC: 0.87). The best model predicted an AUC of 0.85 (95%CI, 0.75–0.94) on the internal test set and 0.81 (95%CI, 0.64–0.99) and 0.83 (95%CI, 0.68–0.97) on the two external test sets, respectively. Twenty-two radiomics features were selected by lasso regression. The second-order feature gray level non-uniformity normalized was the most important radiomics feature. Age is the feature with the greatest contribution to prediction. The combination of clinical features and radiomics features using logistic regression models can improve the outcome prediction of patients with sICH 90 days after surgery. [ABSTRACT FROM AUTHOR]

Barley bran has potential bioactivities due to its high content of polyphenols and dietary fiber, etc. Fermentation has been considered as an effective way to promote the functional activity of food raw materials. In this study, polysaccharides from barley bran extract fermented by Lactiplantibacillus plantarum dy-1 (FBBE-PS) were analyzed, and its effects on lipid accumulation and oxidative stress in high-fat HepG2 cells induced by sodium oleate were evaluated. The results showed that the molecular weight decreased and monosaccharide composition of polysaccharides changed significantly after fermentation. In addition, 50 μg/mL FBBE-PS could reduce the triglyceride (TG) content and reaction oxygen species (ROS) level in high-fat HepG2 cells by 21.62% and 30.01%, respectively, while increasing the activities of superoxide dismutase (SOD) and catalase (CAT) represented by 64.87% and 22.93%, respectively. RT-qPCR analysis revealed that FBBE-PS could up-regulate the lipid metabolism-related genes such as ppar-α, acox-1 and cpt-1α, and oxidation-related genes such as nrf2, ho-1, nqo-1, sod1, cat, etc. The metabolomics analysis indicated that FBBE-PS could alleviate lipid deposition by inhibiting the biosynthesis of unsaturated fatty acids, which is consistent with the downregulation of scd-1 expression. It is demonstrated that fermentation can alter the properties and physiological activities of polysaccharides in barley bran, and FBBE-PS exhibited an alleviating effect on lipid deposition and oxidative stress in high-fat cells. [ABSTRACT FROM AUTHOR]

Objective: to better reconstruct the nasal shape after cleft lip repair with 3D printing assisted autologous costal cartilage augmentation rhinoplasty, especially for patients with radix augmentation needs. Method: 20 patients with nasal deformity secondary to cleft lip repair and radix augmentation needs had received surgical treatment from July 2016 to November 2021. A total of 10 cases were treated with autologous costal cartilage augmentation rhinoplasty for nasal deformity after cleft lip repair, and 10 cases were treated with the help of 3D printing. According to the characteristics of nasal deformity, autologous costal cartilage was carved and implanted into the nose back. Results: 3D printing assisted autologous costal cartilage augmentation in the treatment of nasal deformity after cleft lip repair, the incision healed well, and there were no complications in the thoracic cartilage donor area. The shape of the nose is satisfactory, the height and shape of the nose tip and the size of both nostrils are mostly symmetrical, the nasal columella is elongated, the original nose tip is flat, the collapse of the nose wing is satisfactory, and the nose lip angle is close to normal. Conclusions: 3D printing assisted autologous costal cartilage augmentation is an ideal treatment for nasal deformity after cleft lip repair. [ABSTRACT FROM AUTHOR]