Search

The rpl1001 gene encodes 60S ribosomal protein L10, which is involved in intracellular protein synthesis and cell growth. However, it is not yet known whether it is involved in the regulation of cell mitosis dynamics. This study focuses on the growth, spore production, cell morphology, the dynamics of microtubules, chromosomes, actin, myosin, and mitochondria of fission yeast (Schizosaccharomyces pombe) to investigate the impact of rpl1001 deletion on cell mitosis. RNA-Seq and bioinformatics analyses were also used to reveal key genes, such as hsp16, mfm1 and isp3, and proteasome pathways. The results showed that rpl1001 deletion resulted in slow cell growth, abnormal spore production, altered cell morphology, and abnormal microtubule number and length during interphase. The cell dynamics of the rpl1001Δ strain showed that the formation of a monopolar spindle leads to abnormal chromosome segregation with increased rate of spindle elongation in anaphase of mitosis, decreased total time of division, prolonged formation time of actin and myosin loops, and increased expression of mitochondrial proteins. Analysis of the RNA-Seq sequencing results showed that the proteasome pathway, up-regulation of isp3, and down-regulation of mfm1 and mfm2 in the rpl1001Δ strain were the main factors underpinning the increased number of spore production. Also, in the rpl1001Δ strain, down-regulation of dis1 caused the abnormal microtubule and chromosome dynamics, and down-regulation of hsp16 and pgk1 were the key genes affecting the delay of actin ring and myosin ring formation. This study reveals the effect and molecular mechanism of rpl1001 gene deletion on cell division, which provides the scientific basis for further clarifying the function of the Rpl1001 protein in cell division.

In recent years, academic research on perovskite solar cells (PSCs) has attracted remarkable attention, and one of the most crucial issues is promoting the power conversion efficiency (PCE) and operational stability of PSCs. Generally, modification of the electron or hole transport layers between the perovskite layers and electrodes via surface engineering is considered an effective strategy because the inherent structural defects between charge carrier transport layers and perovskite layers can be reshaped and modified by adopting the functional nanomaterials, and thus the charge recombination rate can be naturally decreased. At present, large amounts of available nanomaterials for surface modification of the perovskite films are extensively investigated, mainly including nanocrystals, nanorods, nanoarrays, and even colloidal quantum dots (QDs). In particular, as unique size-dependent nanomaterials, the diverse quantum properties of colloidal QDs are different from other nanomaterials, such as their quantum confinement effects, quantum-tunable effects, and quantum surface effects, which display great potential in promoting the PCE and operational stability of PSCs as the charge carriers in perovskite layers can be effectively tuned by these quantum effects. However, preparing QDs with a neat and desirable size remains a technical difficulty, even though the present chemical engineering is highly advanced. Fortunately, the rapid advances in laser technology have provided new insight into the precise preparation of QDs. In this review, we introduce a new approach for preparing the QDs, namely pulsed laser irradiation in colloids (PLIC), and briefly highlight the innovative works on PLIC-prepared QDs for the optimization of PSCs. This review not only highlights the advantages of PLIC for QD preparation but also critically points out the challenges and prospects of QD-based PSCs.

Combining machine vision and deep learning, optical detection technology can achieve intelligent inspection. To address the issues of low efficiency and poor consistency in the quality classification of cork discs used for making badminton heads, research on optimizing the YOLOv5 image-processing algorithm was conducted and applied to cork disc quality detection. Real-time images of cork discs were captured using industrial cameras, and a dataset was independently constructed. A GAN-based defect synthesis algorithm was employed to resolve the lack of defect samples. An attention mechanism was embedded in the YOLOv5 backbone network to enhance feature representation. The number of anchors in the YOLOv5 detection layer was reduced to address similar sample sizes, a center-matching strategy was designed to balance positive samples, and a shortest-distance label assignment algorithm was developed to eliminate ambiguities, improving accuracy and reducing postprocessing complexity. Detection results were integrated into quality classification. Experiments on the NVIDIA RTX3080 GPU demonstrated that the optimized algorithm improved the original YOLOv5 F1 score by 2.4% and mF1 score by 9.0%, achieving a quality classification F1 score of 95.1%, a processing speed of 178.5 FPS, and an mAP of 81.5%. Comparative experiments showed that the improved algorithm achieved the best detection accuracy on the cork disc dataset while maintaining high processing speed.

The lower limb exoskeleton (LLE) plays an important role in production activities requiring assistance and load bearing. One of the challenges is to propose a control strategy that can meet the requirements of LLE trajectory tracking in different scenes. Therefore, this study proposes a control strategy (DBO–FPID) that combines the dung beetle optimizer (DBO) with feedforward proportional–integral–derivative controller (FPID) to improve the performance of LLE trajectory tracking in different scenes. The Lagrange method is used to establish the dynamic model of the LLE rod, and it is combined with the dynamic equations of the motor to obtain the LLE transfer function model. Based on the LLE model and target trajectory compensation, the feedforward controller is designed to achieve trajectory tracking in different scenes. To obtain the best performance of the controller, the DBO is utilized to perform offline parameter tuning of the feedforward controller and PID controller. The proposed control strategy is compared with the DBO tuning PID (DBO–PID), particle swarm optimizer (PSO) tuning FPID (PSO–FPID), and PSO tuning PID (PSO–PID) in simulation and joint module experiments. The results show that DBO–FPID has the best accuracy and robustness in trajectory tracking in different scenes, which has the smallest sum of absolute error (IAE), mean absolute error (MEAE), maximum absolute error (MAE), and root mean square error (RMSE). In addition, the MEAE of DBO–FPID is lower than 1.5 degrees in unloaded tests and lower than 3.6 degrees in the hip load tests, with only a few iterations, showing great practical potential.

Ratoon rice is characterized by higher production combined with enhanced rice quality. However, the relationship between the differences in the structure and properties of the starch and variations in the ratoon season and rice quality is still unclear. In this study, we conducted a field experiment in 2023 to assess the relationship between rice quality differences and starch characteristics of four different hybrid rice varieties (Longjingyou1212: LJY1212; Taoyouxiangzhan: TYXZ; Liangyou6326: LY6326; Fengliangyouxiang1: FLYX1) in the ratoon season. In terms of appearance, the chalkiness degrees and chalky grain percentage of LJY1212 (0.6%, 2.3%) and TYXZ (1.2%, 6.0%) were 53.5% and 53.9% lower than those of LY6326 (2.4%, 10.0%) and FLYX1 (1.6%, 7.8%), indicating that the appearances of LJY1212 and TYXZ were better than those of LY6326 and FLYX1. The average amylose contents of LJY1212 (18.5%) and TYXZ (19.0%) were 10.8% higher than those of LY6326 (17.7%) and FLYX1 (15.7%), and there was no significant difference in protein content between the varieties in the ratoon season. Regarding the starch microstructure, the starch granules of LJY1212 and TYXZ in the ratoon season were arranged more closely. The average ratios of 1045/1022 cm−1 of LJY1212 (0.77) and TYXZ (0.76) were 7.0% higher than those of LY6326 (0.71) and FLYX1 (0.72), indicating that the internal orders of LJY1212 and TYXZ’s starch were better than those of LY6326 and FLYX1. The average relative crystallinities of LJY1212 (24.8%) and TYXZ (24.5%) were 5.2% lower than those of LY6326 (25.7%) and FLYX1 (26.6%). The relative crystallinity of starch decreased with the increase in amylose content, while the short-range order degree, gelatinization temperature, and gelatinization enthalpy increased with the increase in amylose content. Therefore, the LJY1212 and TYXZ varieties, with better rice qualities, are recommended for planting in southern China.

In this work, the solid solution product of [Nb][C] in the Nb-microalloyed steels with various carbon contents in the range of 0.20~1.80 wt.% was investigated by means of the extraction phase analysis method. The results showed that the Nb content in austenite tended to first decrease and then increase with the increase of carbon content in the steels. A unified solid solution product of [Nb][C] in austenite at different temperatures was obtained according to the results of the experimental steels. The Nb content in austenite of the experimental steels with high carbon contents was lower than that calculated by Ohtani’s equation. The existence of NbC precipitates in the case and the core of the specimens carburized at 930 °C and 980 °C were verified by transmission electron microscopy (TEM) observations. The pinning effect of NbC precipitates on austenite grain growth was calculated according to the size and amount of NbC precipitates in the carburized case and the core of the carburized specimens. The calculated results of prior austenite grain sizes were in good agreement with the experimental results, which indicated that the unified solid solution product of [Nb][C] in Nb-microalloyed steels with various carbon contents was applicable for the low-pressure carburizing process.

The main limitation of wireless sensor networks (WSNs) lies in their reliance on battery power. Therefore, the primary focus of the current research is to determine how to transmit data in a rational and efficient way while simultaneously extending the network’s lifespan. In this paper, a hybrid of a fuzzy logic system and a quantum annealing algorithm-based clustering and routing protocol (FQA) is proposed to improve the stability of the network and minimize energy consumption. The protocol uses a fuzzy inference system (FIS) to select appropriate cluster heads (CHs). In the routing phase, we used the quantum annealing algorithm to select the optimal route from the CHs and the base station (BS). Furthermore, we defined an energy threshold to filter candidate CHs in order to save computation time. Unlike with periodic clustering, we adopted an on-demand re-clustering mechanism to perform global maintenance of the network, thereby effectively reducing the computation and overhead. The FQA was compared with FRNSEER, BOA-ACO, OAFS-IMFO, and FC-RBAT in different scenarios from the perspective of energy consumption, alive nodes, network lifetime, and throughput. According to the simulation results, the FQA outperformed all the other methods in all scenarios.

The bacterial wilt disease caused by Ralstonia solanacearum is a significant threat to eggplant production. Breeding and promoting resistant varieties is one of the most effective methods to manage bacterial wilt. Conducting QTL (quantitative trait locus) mapping of resistant genes can substantially enhance the breeding of plant resistance to bacterial wilt. In this study, a population of 2200 F2 individuals derived from resistant and susceptible materials was utilized to establish extreme resistance and susceptibility pools. Following resequencing analysis of the parents and extreme pools, the QTL were examined using the DEEP-BSA software and QTLseqr R package (version 0.7.5.2). The results revealed that the detection of 10 QTL sites on chromosomes 5, 8, 9, and 11 by the five algorithms of the DEEP-BSA software. Additionally, the candidate region of 62 Mb–72 Mb on chromosome 5 was identified in all five algorithms of the DEEP-BSA software, as well as by the QTLseqr R package. Subsequent gene annotation uncovered 276 genes in the candidate region of 62 Mb–72 Mb on chromosome 5. Additionally, RNA-seq results indicated that only 13 genes had altered expression levels following inoculation with R. solanacearum in the resistant materials. Based on the expression levels, SMEL4_05g015980.1 and SMEL4_05g016110.1 were identified as candidate genes. Notably, SNP annotation identified a non-synonymous mutation in the exonic region of SMEL4_05g015980.1 and a variant in the promoter region of SMEL4_05g016110.1. The research findings have practical significance for the isolation of bacterial wilt resistance genes in eggplant and the development of resistance to bacterial wilt varieties in eggplant.

Broad-spectrum antibiotics are frequently used to treat bacteria-induced infections, but the overuse of antibiotics may induce the gut microbiota dysbiosis and disrupt gastrointestinal tract function. Probiotics can be applied to restore disturbed gut microbiota and repair abnormal intestinal metabolism. In the present study, two strains of Enterococcus faecium (named DC-K7 and DC-K9) were isolated and characterized from the fecal samples of infant dogs. The genomic features of E. faecium DC-K7 and DC-K9 were analyzed, the carbohydrate-active enzyme (CAZyme)-encoding genes were predicted, and their abilities to produce short-chain fatty acids (SCFAs) were investigated. The bacteriocin-encoding genes in the genome sequences of E. faecium DC-K7 and DC-K9 were analyzed, and the gene cluster of Enterolysin-A, which encoded a 401-amino-acid peptide, was predicted. Moreover, the modulating effects of E. faecium DC-K7 and DC-K9 on the gut microbiota dysbiosis induced by antibiotics were analyzed. The current results demonstrated that oral administrations of E. faecium DC-K7 and DC-K9 could enhance the relative abundances of beneficial microbes and decrease the relative abundances of harmful microbes. Therefore, the isolated E. faecium DC-K7 and DC-K9 were proven to be able to alter the gut microbiota dysbiosis induced by antibiotic treatment.

Neem leaves have long been used in traditional medicine for promoting longevity. However, the precise mechanisms underlying their anti-aging effects remain elusive. In this study, we investigated the impact of neem leaf extract (NLE) extracted from a 50% ethanol solution on the chronological lifespan of Saccharomyces cerevisiae, revealing an extension in lifespan, heightened oxidative stress resistance, and a reduction in reactive oxygen species. To discern the active compounds in NLE, LC/MS and the GNPS platform were employed. The majority of identified active compounds were found to be flavonoids. Subsequently, compound-target pharmacological networks were constructed using the STP and STITCH platforms for both S. cerevisiae and Homo sapiens. GOMF and KEGG enrichment analyses of the predicted targets revealed that “oxidoreductase activity” was among the top enriched terms in both yeast and human cells. These suggested a potential regulation of oxidative stress response (OSR) by NLE. RNA-seq analysis of NLE-treated yeast corroborated the anti-oxidative effect, with “oxidoreductase activity” and “oxidation-reduction process” ranking high in enriched GO terms. Notably, CTT1, encoding catalase, emerged as the most significantly up-regulated gene within the “oxidoreductase activity” cluster. In a ctt1 null mutant, the enhanced oxidative stress resistance and extended lifespan induced by NLE were nullified. For human cells, NLE pretreatment demonstrated a decrease in reactive oxygen species levels and senescence-associated β-galactosidase activity in HeLa cells, indicative of anti-aging and anti-oxidative effects. This study unveils the anti-aging and anti-oxidative properties of NLE while delving into their mechanisms, providing novel insights for pharmacological interventions in aging using phytochemicals.

Background: Streptococcus suis (S. suis) is a Gram-positive bacterium that causes substantial disease in pigs. S. suis is also an emerging zoonoses in humans, primarily in Asia, through the consumption of undercooked pork and the handling of infected pig meat as well as carcasses. The complexity of S. suis epidemiology, characterized by the presence of multiple bacterial serotypes and strains with diverse sequence types, identifies a critical need for a universal vaccine with the ability to confer cross-protective immunity. Highly conserved immunogenic proteins are generally considered good candidate antigens for subunit universal vaccines. Methods: In this study, the cross-protection of the sugar ABC transporter substrate-binding protein (S-ABC), a surface-associated immunogenic protein of S. suis, was examined in mice for evaluation as a universal vaccine candidate. Results: S-ABC was shown to be highly conserved, with 97% amino acid sequence identity across 31 S. suis strains deposited in GenBank. Recombinantly expressed S-ABC (rS-ABC) was recognized via rabbit sera specific to S. suis serotype 2. The immunization of mice with rS-ABC induced antigen-specific antibody responses, as well as IFN-γ and IL-4, in multiple organs, including the lungs. rS-ABC immunization conferred high (87.5% and 100%) protection against challenges with S. suis serotypes 2 and 9, demonstrating high cross-protection against these serotypes. Protection, albeit lower (50%), was also observed in mice challenged with S. suis serotype 7. Conclusions: These data identify S-ABC as a promising antigenic target within a universal subunit vaccine against S. suis.

The wavefront reconstruction is a crucial step in determining the performance of wavefront detection instruments. The wavefront reconstruction algorithm is primarily evaluated in three dimensions: accuracy, speed, and noise immunity. In this paper, we propose a hybrid zonal reconstruction algorithm that introduces slope and curvature information in the diagonal, anti-diagonal, horizontal, and vertical directions by dividing the neighbor sampling points into subregions in groups of four. By canceling the same parameters in integration equations, an algorithm using multi-directional slope–curvature information is achieved with only two sets of integration equations in each subregion, reducing the processing time. Simulation experiments show that the relative root-mean-square reconstruction error of this algorithm is improved by about 4 orders of magnitude compared with existing algorithms that use multi-directional slope information or slope–curvature information alone. Compared with the hybrid multi-directional slope–curvature algorithm, the proposed algorithm can reduce computation time by about 50% as well as provide better noise immunity and reconstruction accuracy. Finally, the validity of the proposed algorithm is verified by the null test experiment.

Lysozyme, a common antimicrobial agent, is widely used in the food, biopharmaceutical, chemical, and medicine fields. Rapid and effective isolation of lysozymes is an everlasting topic. In this work, ethylene vinyl alcohol (EVOH) copolymer nanofibrous membranes with a gradient porous structure used for lysozyme adsorption were prepared through layer-by-layer nanofiber wet-laying and a cost-efficient ultraviolet (UV)-assisted graft-modification method, where benzophenone was used as an initiator and 2-acrylamide-2-methylpropanesulfonic acid as a modifying monomer. As indicated in the Fourier Transform Infrared (FTIR) and X-ray photoelectric energy spectrometer (XPS) investigation, sulfonic acid groups were introduced on the surface of the modified nanofibrous membrane, which possessed the ability to adsorb lysozyme. Compared with membranes with homogenous porous structures, membranes with a gradient porous structure present higher static (335 mg/g) and dynamic adsorption capacities (216.3 mg/g). Meanwhile, the adsorption capacity remained high after five cycles of the adsorption–desorption process. The results can be attributed to the gradient porous structure rather than the highest porosity and specific surface area. This suggests that the membrane with comprehensive separation performance can be designed from the view of the transmembrane porous structure, which is of significance for the development of next-generation advanced chromatographic membranes.

For the development of an idyllic rural landscape, an accurate survey of rural buildings is essential. The extraction of rural structures from unmanned aerial vehicle (UAV) remote sensing imagery is prone to errors such as misclassifications, omissions, and subpar edge detailing. This study introduces a multi-scale fusion and detail enhancement network for rural building extraction, termed the Multi-Attention-Detail U-shaped Network (MAD-UNet). Initially, an atrous convolutional pyramid pooling module is integrated between the encoder and decoder to enhance the main network’s ability to identify buildings of varying sizes, thereby reducing omissions. Additionally, a Multi-scale Feature Fusion Module (MFFM) is constructed within the decoder, utilizing superficial detail features to refine the layered detail information, which improves the extraction of small-sized structures and their edges. A coordination attention mechanism and deep supervision modules are simultaneously incorporated to minimize misclassifications. MAD-UNet has been tested on a private UAV building dataset and the publicly available Wuhan University (WHU) Building Dataset and benchmarked against models such as U-Net, PSPNet, DeepLabV3+, HRNet, ISANet, and AGSCNet, achieving Intersection over Union (IoU) scores of 77.43% and 91.02%, respectively. The results demonstrate its effectiveness in extracting rural buildings from UAV remote sensing images across different regions.

Forecasting air quality plays a crucial role in preventing and controlling air pollution. It is particularly significant for improving preparedness for heavily polluted weather conditions and ensuring the health and safety of the population. In this study, a novel deep learning model for predicting air quality spatio-temporal variations is introduced. The model, named graph long short-term memory with multi-head attention (GLSTMMA), is designed to capture the temporal patterns and spatial relationships within multivariate time series data related to air quality. The GLSTMMA model utilizes a hybrid neural network architecture to effectively learn the complex dependencies and correlations present in the data. The extraction of spatial features related to air quality involves the utilization of a graph convolutional network (GCN) to collect air quality data based on the geographical distribution of monitoring sites. The resulting graph structure is imported into a long short-term memory (LSTM) network to establish a Graph LSTM unit, facilitating the extraction of temporal dependencies in air quality. Leveraging a Graph LSTM unit, an encoder-multiple-attention decoder framework is formulated to enable a more profound and efficient exploration of spatio-temporal correlation features within air quality time series data. The research utilizes the 2019–2021 multi-source air quality dataset of Qinghai Province for experimental assessment. The results indicate that the model effectively leverages the impact of multi-source data, resulting in optimal accuracy in predicting six air pollutants.

Psoriasis, a chronic inflammatory skin disease induced by various factors, including genetic factors, immune factors, environmental factors, and psychological factors, is characterized by thickening of the epidermis, excessive proliferation of keratinocytes, abnormal differentiation, and an excessive inflammatory response. Traditional treatments for psoriasis still face challenges because of limited curative effects, notable side effects, and a tendency for recurrence. In contrast, topical therapy provides a favorable option for psoriasis treatment because of its noninvasive and self-administered method. In this study, gentiopicrin (Gen) is encapsulated in the liposomes to form a nanodrug, and then chitosan is covered on the nanodrug to assemble the nanodrug delivery system (CS@Gen), which is used as a topical agent for treating psoriasis. Then M5 (a mixture of five pro-inflammatory cytokines, i.e., IL-17A, IL-22, IL-1α, oncostatin M, and TNF-α)-induced HacaT cells and imiquimod-induced psoriasis mouse models are established, whose results show that CS@Gen induces apoptosis and inhibits the proliferation and cell migration of psoriasis keratinocytes. Additionally, the application of CS@Gen cream can significantly reduce epidermal thickness, diminish skin scaling, and improve other related mechanisms in mice affected by psoriasis. Meanwhile, the prepared CS@Gen can significantly reduce the expression levels of IL-17a, Cxcl2, S100a, Mki67, and other related inflammatory factors, resulting in indirectly inhibiting the inflammation of keratinocytes. In summary, the present study provides an ideal loading for an anti-inflammatory and immunomodulatory drug delivery system for the treatment of psoriasis.

Geopolitical conflicts and power games among major nations present substantial challenges to cross-border trade and global economic development; however, the existing literature has paid limited attention to the role of geopolitical risk in agricultural exports, especially the underlying macroimpact mechanisms from the perspective of agricultural land. This paper utilizes China’s agricultural export data spanning 1995–2020 to empirically investigate the influence of geopolitical risk on China’s agricultural exports and unveil its specific internal mechanisms. The findings reveal that China’s agricultural exports are negatively affected when its trading partners are exposed to geopolitical risk. Notably, trading partners’ agricultural land is instrumental in mitigating the adverse effects of geopolitical risk on China’s agricultural exports. Moreover, a heterogeneity analysis shows that the impact of geopolitical risk on China’s agricultural exports is more significant in non-Belt and Road countries than in Belt and Road countries. Given China’s status as one of the world’s major agricultural exporting nations, the results of this study hold significant importance in proactively addressing and alleviating the impact of geopolitical risks on both Chinese and global agricultural exports.

Caprine arthritis encephalitis is an infectious disease caused by the caprine arthritis encephalitis virus that infects goats, sheep, and other small ruminants. An outbreak of CAEV could be extremely harmful to the goat farming industry and could cause severe economic losses. We designed specific primers and probes for the gag gene and established a TaqMan real-time quantitative polymerase chain reaction assay. This method’s correlation coefficient (R2) was >0.999, and the sensitivity of the assay to the plasmid-carried partial gag gene was approximately 10 copies/µL, 1000 times higher than that of conventional PCR. No specific fluorescence was detected for other sheep viruses. Using this method, we tested 776 asymptomatic sheep blood samples and 4 neurodegenerative sheep brain samples from six farms in eastern China, and the positivity rate was 0.77% (6/780). The gag gene was partially sequenced in the three positive samples and compared with the sequences from other representative strains in GenBank. The results revealed that all three strains belonged to the B1 subtype and were most closely related to the strains from Shanxi and Gansu, previously isolated in China, with their homology ranging from 97.7% to 98.9%. These results suggest that the designed RT-qPCR assay can be used to detect subclinical CAEV in sheep and that the virus is still present in eastern China.

As a growing abiotic stress, light deficient conditions seriously affect the yield and quality of rice. However, few studies focus on the effects of shading on grain quality at the booting stage and the responses of different hybrid rice cultivars to shading. Field experiments involving four representative rice (Oryza sativa L.) cultivars across no shading (CK) and 40% shading at the booting (S) and grain filling stages (SS) were conducted in 2021 and 2022. Compared with CK, S reduced grain yield by 53.0% but increased the head rice rate by 11.4% averaged across varieties and years. The chalkiness degree (CD) and chalky grain percentage (CR) were reduced by 73.0% and 61.6% in S due to its 45.3% lower total spikelets m–2, 44.0% lower grain–leaf ratio and 23.5% lower dry weight spikelet production efficiency, compared with CK. The CD and CR in SS were 49.5% and 41.0% higher and HR was 7.1% lower than that in CK. Shading significantly reduced amylose content, peak viscosity and breakdown value, but increased protein content and setback value, and the effects of SS were greater than S. Y-liangyou900 and Liangyoupeijiu showed better milling quality, while Y-liangyou900 and Chuanyou6203 obtained a better appearance and eating quality than the other varieties under both S and SS. In conclusion, shading at the booting stage significantly improved the milling, appearance and nutritional quality, and did not reduce the cooking and eating quality, but led to a significant decline in the grain yield of hybrid rice. Moreover, Y-liangyou900 exhibited better rice quality but lower yield under shading treatments. Therefore, more attention needs to be focused on screening shade-tolerant varieties using both yield and quality to cope with climate change in the future.

Multilayered reservoirs with coexisting free gas and hydrates are primary targets for commercialization, nevertheless, the extremely low permeability greatly limits their extraction efficiency. Herein, multilayer commingled production using horizontal wells stimulated by hydraulic fracturing and thermal fluid injection was proposed to enhance productivity, and the effects of key factors on co-production performance were numerically examined, with the reservoir located in the Shenhu Area as the geological background. The results indicated that due to severe interlayer contradictions, the stimulation capabilities of using fracturing or thermal fluid injection alone were limited, in particular, the extraction of hydrates severely lagged behind. However, their combination exhibited tantalizing productivity due to strengthened inter-well interaction. Reducing the fracture spacing was more effective than increasing fracture conductivity in shortening the production cycle, and intensive fractures with adequate flow capacity were suggested for gas enhancement and water control. When the fracture spacing was reduced from 30 to 5 m and the fracture conductivity increased from 10 to 100 D·cm, the horizontal section length for commercial production (average daily gas production of 50,000 m3 and recovery ratio of 0.7) was reduced from 1758 to 146 m, which is lower than the on-site horizontal section length of 250–300 m. Therefore, the proposed development mode is promising for the commingled production of gas and hydrates.

In the Qinghai-Tibet Plateau region, operational deficiencies and limited maintenance capacities often impair automatic air quality monitoring stations. This results in frequent data omissions, compromising the reliability of environmental assessment data. Therefore, an effective data imputation method is required to address the gaps in observational records. Utilizing a Sequence-to-Sequence framework, we introduce a model termed Bidirectional Recurrent Imputation for Time Series-Attention-based Long Short-Term Memory (BRITS-ALSTM). The encoder of BRITS-ALSTM applies BRITS to integrate single-station historical characteristics with multi-station correlation features. Concurrently, the decoder employs LSTM within an attention mechanism to capitalize on previously observed data, thereby generating hourly imputations for missing air quality data values. The model was trained using six types of air quality data from 16 stations across Qinghai Province. Through localized testing and parameter optimization, BRITS-ALSTM achieved a reduction in mean relative error (MRE) by 74.88% compared to the baseline mean-filling approach. Additionally, ablation studies demonstrated an improvement in the coefficient of determination R-squared (R2) from 0.67 to 0.76, outperforming the standalone BRITS. Consequently, BRITS-ALSTM enhances the accuracy of air quality data evaluations in the Tibetan Plateau and offers an efficacious strategy for data imputation in elevated terrains.

Microbial fuel cell (MFC) performance is affected by the metabolic activity of bacteria and the extracellular electron transfer (EET) process. The deficiency of nanostructures on macroporous anode obstructs the enrichment of exoelectrogens and the EET. Herein, a N-doped carbon nanowire-modified macroporous carbon foam was prepared and served as an anode in MFCs. The anode has a hierarchical porous structure, which can solve the problem of biofilm blockage, ensure mass transport, favor exoelectrogen enrichment, and enhance the metabolic activity of bacteria. The microscopic morphology, spectroscopy, and electrochemical characterization of the anode confirm that carbon nanowires can penetrate biofilm, decrease charge resistance, and enhance long-distance electron transfer efficiency. In addition, pyrrolic N can effectively reduce the binding energy and electron transfer distance of bacterial outer membrane hemin. With this hierarchical anode, a maximum power density of 5.32 W/m3 was obtained, about 2.5-fold that of bare carbon cloth. The one-dimensional nanomaterial-modified macroporous anodes in this study are a promising strategy to improve the exoelectrogen enrichment and EET for MFCs.

This paper presents a hierarchical lane-changing trajectory planner based on the least action principle for autonomous driving. Our approach aims to achieve reliable real-time avoidance of static and moving obstacles in multi-vehicle interaction scenarios on structured urban roads. Unlike previous studies that rely on subjective weight allocation and single weighting methods, we propose a novel trajectory planning strategy that decomposes the process into two stages: candidate trajectory generation and optimal trajectory decision-making. The candidate trajectory generation employs a path-velocity decomposition method, using B-spline curves to generate a multi-objective optimal lane-changing candidate path. Collision checking eliminates paths at risk of collision with static obstacles. Dynamic programming (DP) and quadratic programming (QP) are then used to plan the velocity of safe paths, generating candidate lane-changing trajectories based on curvature checking. The optimal trajectory decision-making process follows the decision mechanism of excellent drivers. We introduce a comprehensive evaluation function, the average action, which considers safety, comfort, and efficiency based on the least action principle. Feasible trajectories are ranked based on their average action, and the trajectory with the minimum average action and no collision risk with moving obstacles is selected as the tracking target. The effectiveness of the proposed method is validated through two common lane-changing scenarios. The results demonstrate that our approach enables smooth, efficient, and safe lane-changing while effectively tracking the planned velocity and path. This method offers a solution to local trajectory planning problems in complex environments and holds promising prospects in the field of autonomous driving.

Super hybrid rice with predominantly large panicle types has achieved remarkable success in enhancing crop yield. However, when compared with multi-panicle-type varieties, the yield stability of large panicle-type varieties remains a challenge, and limited information is available on the comparative advantages of multi-panicle types. Consequently, a two-year experiment was conducted to evaluate the grain yield, biomass production, leaf area index (LAI), and radiation use efficiency (RUE) of large panicle-type hybrid rice (Y-liangyou 900, YLY900) and multi-panicle-type hybrid rice (C-liangyouhuazhan, CLYHZ) under three nitrogen (N) treatments (0, 180, 270 kg N ha−1). The effects of increased N fertilization were more pronounced in the large panicle-type varieties. YLY900 outperformed CLYHZ in terms of average yield (6% higher), and its yield advantage was attributed to higher spikelets per panicle (28%). Due to YLY900’s RUE being 9% higher than CLYHZ, it results in a 12% greater accumulation of dry matter than CLYHZ. Furthermore, YLY900 exhibited significant improvements of 16%, 4%, and 14% in specific leaf weight, effective leaf area ratio, and LAI at 20 days after the heading stage (20DAH), respectively, compared with CLYHZ. YLY900 also demonstrated a stronger correlation between rice yield and intercepted photosynthetically active radiation (IPAR) compared with CLYHZ, with R2 values of 0.80 and 0.66, respectively. These findings highlight the superior performance of YLY900, resulting from higher light interception percentage (IP) and IPAR values, which consequently led to enhanced RUE and grain yield. Our research reveals that delayed leaf senescence by increasing LAI at the post-heading stage for large panicle-type hybrid rice, thereby contributing to greater RUE, led to higher biomass production and grain yield.

Currently, crop physiological responses to waterlogging are considered only in a few crop models and in a limited way. Here, we examine the process bases of seven contemporary models developed to model crop growth in waterlogged conditions. The representation of plant recovery in these models is over-simplified, while plant adaptation or phenotypic plasticity due to waterlogging is often not considered. Aeration stress conceptualisation varies from the use of simple multipliers in equations describing transpiration and biomass to complex linkages of aeration-deficit factors with root growth, transpiration and nitrogen fixation. We recommend further studies investigating more holistic impacts and multiple stresses caused by plant behaviours driven by soils and climate. A sensitivity analysis using one model (a developer version of APSIM) with default parameters showed that waterlogging has the greatest impact on photosynthesis, followed by phenology and leaf expansion, suggesting a need for improved equations linking waterlogging to carbon assimilation. Future studies should compare the ability of multiple models to simulate real and in situ effects of waterlogging stress on crop growth using consistent experimental data for initialisation, calibration and validation. We conclude that future experimental and modelling studies must focus on improving the extent to which soil porosity, texture, organic carbon and nitrogen and plant-available water affect waterlogging stress, physiological plasticity and the ensuing temporal impacts on phenology, growth and yield.

Establishing an accurate and computationally efficient model for driving risk assessment, considering the influence of vehicle motion state and kinematic characteristics on path planning, is crucial for generating safe, comfortable, and easily trackable obstacle avoidance paths. To address this topic, this paper proposes a novel dual-layered dynamic path-planning method for obstacle avoidance based on the driving safety field (DSF). The contributions of the proposed approach lie in its ability to address the challenges of accurately modeling driving risk, efficient path smoothing and adaptability to vehicle kinematic characteristics, and providing collision-free, curvature-continuous, and adaptable obstacle avoidance paths. In the upper layer, a comprehensive driving safety field is constructed, composed of a potential field generated by static obstacles, a kinetic field generated by dynamic obstacles, a potential field generated by lane boundaries, and a driving field generated by the target position. By analyzing the virtual field forces exerted on the ego vehicle within the comprehensive driving safety field, the resultant force direction is utilized as guidance for the vehicle’s forward motion. This generates an initial obstacle avoidance path that satisfies the vehicle’s kinematic and dynamic constraints. In the lower layer, the problem of path smoothing is transformed into a standard quadratic programming (QP) form. By optimizing discrete waypoints and fitting polynomial curves, a curvature-continuous and smooth path is obtained. Simulation results demonstrate that our proposed path-planning algorithm outperforms the method based on the improved artificial potential field (APF). It not only generates collision-free and curvature-continuous paths but also significantly reduces parameters such as path curvature (reduced by 62.29% to 87.32%), curvature variation rate, and heading angle (reduced by 34.11% to 72.06%). Furthermore, our algorithm dynamically adjusts the starting position of the obstacle avoidance maneuver based on the vehicle’s motion state. As the relative velocity between the ego vehicle and the obstacle vehicle increases, the starting position of the obstacle avoidance path is adjusted accordingly, enabling the proactive avoidance of stationary or moving single and multiple obstacles. The proposed method satisfies the requirements of obstacle avoidance safety, comfort, and stability for intelligent vehicles in complex environments.

Nitrogen (N) deficiency can limit rice productivity, whereas the over- and underapplication of N results in agronomic and economic losses. Process-based crop models are useful tools and could assist in optimizing N management, enhancing the production efficiency and profitability of upland rice production systems. The study evaluated the ability of CSM–CERES–Rice to determine optimal N fertilization rate for different sowing dates of upland rice. Field experimental data from two growing seasons (2018–2019 and 2019–2020) were used to simulate rice responses to four N fertilization rates (N30, N60, N90 and a control–N0) applied under three different sowing windows (SD1, SD2 and SD3). Cultivar coefficients were calibrated with data from N90 under all sowing windows in both seasons and the remaining treatments were used for model validation. Following model validation, simulations were extended up to N240 to identify the sowing date’s specific economic optimum N fertilization rate (EONFR). Results indicated that CSM–CERES–Rice performed well both in calibration and validation, in simulating rice performance under different N fertilization rates. The d-index and nRMSE values for grain yield (0.90 and 16%), aboveground dry matter (0.93 and 13%), harvest index (0.86 and 7%), grain N contents (0.95 and 18%), total crop N uptake (0.97 and 15%) and N use efficiencies (0.94–0.97 and 11–15%) during model validation indicated good agreement between simulated and observed data. Extended simulations indicated that upland rice yield was responsive to N fertilization up to 180 kg N ha−1 (N180), where the yield plateau was observed. Fertilization rates of 140, 170 and 130 kg N ha−1 were identified as the EONFR for SD1, SD2 and SD3, respectively, based on the computed profitability, marginal net returns and N utilization. The model results suggested that N fertilization rate should be adjusted for different sowing windows rather than recommending a uniform N rate across sowing windows. In summary, CSM–CERES–Rice can be used as a decision support tool for determining EONFR for seasonal sowing windows to maximize the productivity and profitability of upland rice production.

Rice–crayfish coculture systems (RCSs) have been widely promoted in China as an efficient circular agriculture model that can simultaneously improve rice quality, raise the efficiency of utilization of resources, and increase farmers’ incomes. However, the herd behavior of farmers in the adoption of the rice–crayfish coculture system warrants more attention, as the rational adoption of this system by farmers is a prerequisite for achieving the above objectives. This paper analyses the formation mechanism of farmers’ herd behavior based on information cascade theory. Using micro-survey data from 603 farmers in China and a bivariate probit model, we examine the existence of herding effects in the adoption of RCSs by farmers and the inhibitory effect of information acquisition capability on herd behavior. To address possible endogeneity issues, we also conducted a robustness test using the IV-probit model. Furthermore, we tested for between-group differences in agricultural income between farmers with herding adoption and non-herding adoption. The study reveals that the larger the number of prior adopters, the higher the probability of blind adoption behavior by farmers, indicating the existence of a herd effect. Information acquisition ability could significantly inhibit the herd behavior of farmers in the adoption of RCSs. We also found that farmers’ herd behavior in the adoption of these systems was not economically rational. Based on the research findings, we proposed several constructive suggestions for policy perfection.

A suitable nitrogen (N) application rate (NAR) and ideal planting period could improve upland rice productivity, enhance the soil water utilization, and reduce N losses. This study was conducted for the assessment and application of the EPIC model to simulate upland rice productivity, soil water, and N dynamics under different NARs and planting windows (PWs). The nitrogen treatments were 30 (N30), 60 (N60), and 90 (N90) kg N ha−1 with a control (no N applied −N0). Planting was performed as early (PW1), moderately delayed (PW2), and delayed (PW3) between September and December of each growing season. The NAR and PW impacted upland rice productivity and the EPIC model predicted grain yield, aboveground biomass, and harvest index for all NARs in all PWs with a normalized good–excellent root mean square error (RMSEn) of 7.4–9.4%, 9.9–12.2%, and 2.3–12.4% and d-index range of 0.90–0.98, 0.87–0.94, and 0.89–0.91 for the grain yield, aboveground biomass, and harvest index, respectively. For grain and total plant N uptake, RMSEn ranged fair to excellent with values ranging from 10.3 to 22.8% and from 6.9 to 28.1%, and a d-index of 0.87–0.97 and 0.73–0.99, respectively. Evapotranspiration was slightly underestimated for all NARs at all PWs in both seasons with excellent RMSEn ranging from 2.0 to 3.1% and a d-index ranging from 0.65 to 0.97. A comparison of N and water balance components indicated that PW was the major factor impacting N and water losses as compared to NAR. There was a good agreement between simulated and observed soil water contents, and the model was able to estimate fluctuations in soil water contents. An adjustment in the planting window would be necessary for improved upland rice productivity, enhanced N, and soil water utilization to reduce N and soil water losses. Our results indicated that a well-calibrated EPIC model has the potential to identify suitable N and seasonal planting management options.

Developing new drugs for emerging diseases, such as COVID-19, is crucial for promoting public health. In recent years, the application of artificial intelligence (AI) has significantly advanced drug discovery pipelines. Generative models, such as generative adversarial networks (GANs), exhibit the potential for discovering novel drug molecules by relying on a vast number of training samples. However, for new diseases, only a few samples are typically available, posing a significant challenge to learning a generative model that produces both high-quality and diverse molecules under limited supervision. To address this low-data drug generation issue, we propose a novel molecule generative domain adaptation paradigm (Mol-GenDA), which transfers a pre-trained GAN on a large-scale drug molecule dataset to a new disease domain using only a few references. Specifically, we introduce a molecule adaptor into the GAN generator during the fine tuning, allowing the generator to reuse prior knowledge learned in pre-training to the greatest extent and maintain the quality and diversity of the generated molecules. Comprehensive downstream experiments demonstrate that Mol-GenDA can produce high-quality and diverse drug candidates. In summary, the proposed approach offers a promising solution to expedite drug discovery for new diseases, which could lead to the timely development of effective drugs to combat emerging outbreaks.

Superior yields of super hybrid rice have demonstrably contributed to contemporary food security. Despite this, the extent to which intensive nitrogen fertilizer requirements of such crops have impacted on soil health and microbial communities primarily remains unchartered territory, evoking questions of sustainability. Here, we examine how four management treatments (zero fertilizer, CK; farm practice, FP; high-yield and high-efficiency, HYHE; and super-high-yield management, SHY) influenced the grain yields, soil biodiversity and community strata underpinning soil health of an elite super hybrid rice variety (Y-liangyou 900). We show that SHY treatments increased yields, altered soil physicochemical properties, and fostered greater biodiversity and soil bacteria and fungi abundance, while FP, HYHE and SHY treatments transformed community bacteria and fungi strata. Environmental regulators of bacterial and fungal communities differed widely, with bacterial communities most closely associated with soil organic carbon (SOC) and NH4+-N, and with fungal communities more related to available phosphorus. We show that alpha diversity of bacteria and fungi and community composition of fungi were positively correlated with yield, but bacterial community composition was negatively correlated with yield. Our work clearly exemplifies the nexus between appropriate farm and landscape management in enabling soil health and driving consistently high yields, of which both are required for sustainable food security.

Japanese encephalitis (JE), found in pigs, is a serious mosquito-borne zoonotic infectious disease caused by the Japanese encephalitis virus (JEV). JEV is maintained in an enzootic cycle between mosquitoes and amplifying vertebrate hosts, mainly pigs and wading birds. It is transmitted to humans through the bite of an infected mosquito, allowing the pathogen to spread and cause disease epidemics. However, there is little research on JEV genotype variation in mosquitoes and pigs in Fujian province. Previous studies have shown that the main epidemic strain of JEV in Fujian Province is genotype III. In this study, a survey of mosquito species diversity in pig farms and molecular evolutionary analyses of JEV were conducted in Fujian, China, in the summer of 2019. A total of 19,177 mosquitoes were collected at four sites by UV trap. Four genera were identified, of which the Culex tritaeniorhynchus was the most common mosquito species, accounting for 76.4% of the total (14,651/19,177). Anopheles sinensi (19.25%, 3691/19,177) was the second largest species. High mosquito infection rateswere an important factor in the outbreak. The captured mosquito samples were milled and screened with JEV-specific primers. Five viruses were isolated, FJ1901, FJ1902, FJ1903, FJ1904, and FJ1905. Genetic affinity was determined by analyzing the envelope (E) gene variants. The results showed that they are JEV gene type I and most closely related to the strains SH-53 and SD0810. In this study, it was found through genetic evolution analysis that the main epidemic strain of JE in pig farms changed from gene type III to gene type I. Compared with the SH-53 and SD0810 strains, we found no change in key sites related to antigenic activity and neurovirulence of JEV in Fujian JEV and pig mosquito strains, respectively. The results of the study provide basic data for analyzing the genotypic shift of JEV in Fujian Province and support the prevention and control of JEV.

Biopesticides are perceived as a feasible alternative to chemical pesticides, providing an effective approach to pest management while mitigating the undesirable effects of chemical pesticide overuse. Yet, due to the distinctive attributes of the two types of pesticides, farmers often adopt a combination of both. This study aimed to probe the interdependent relationship underlying the mixed use of chemical pesticides and biopesticides by farmers in the rice–crayfish integrated system (RCIS) in Hubei province, China. We also sought to identify influencing factors and associated consequences of this practice. Given that the adoption of chemical pesticides and biopesticides by farmers is not mutually exclusive, a multivariable probit model was utilized to estimate simultaneous pesticide applications. Survey data from 736 RCIS farmers revealed that 10.50% of the sample farmers employ a combination of chemical pesticides and biopesticides. A substitution relationship was observed between the adoption of chemical pesticides and biopesticides by farmers, whereas no significant complementary relationship was found in the application of the two types of pesticides. The primary motivation for the mixed use of pesticides by farmers is to achieve superior pest control efficacy. Notably, when integrating the use of pesticides, farmers pay less attention to factors such as resource endowments and multiple production objectives. Further analysis indicated that farmers display significant concern for pesticide attributes including insecticidal efficacy, toxicity, insecticidal spectrum, and validity period. The mixed use of pesticides has resulted in a decreased frequency of pesticide application but has escalated the cost. While the mixed use of pesticides elevated the selling price of rice, no significant improvement was observed in yield and output value. The insights garnered from this study offer strategic implications for policymakers on how to enhance the judicious adoption of pesticides by farmers.

The frequency synthesizer plays a crucial role in atomic clock technology. In this study, we demonstrate a direct microwave frequency synthesizer for a cesium atomic clock, employing frequency multiplication and a dual-phase-locked loop mode. A mathematical model of the frequency synthesis chain is established to estimate its performance. The phase-settling time and system stability are analyzed and studied in detail, and the obtained results are verified by experiments. An optimized realization of the frequency synthesizer shows that the phase-settling time can be adjusted within the range of 644.5 µs to 1.5 ms. Additionally, we measure the absolute phase noise values to be −63.7 dBc/Hz, −75.7 dBc/Hz, −107.1 dBc/Hz, and −122.5 dBc/Hz at 1 Hz, 10 Hz, 1 kHz, and 10 kHz offset frequencies, respectively.

The remarkable yield performance of super hybrid rice has played a crucial role in ensuring global food security. However, there is a scarcity of studies investigating the contribution of radiation use efficiency (RUE) to hybrid rice yields under different nitrogen and potassium treatments. In this three-year field experiment, we aimed to evaluate the impact of two hybrid rice varieties (Y-liangyou 900: YLY900 and Quanyouhuazhan: QYHZ) under varying nitrogen regimes (N90: 90 kg N ha−1, N120: 120 kg N ha−1, N180: 180 kg N ha−1) and potassium regimes (K120: 120 kg K2O ha−1, K160: 160 kg K2O ha−1, K210: 210 kg K2O ha−1) on grain yield and its physiological determinants, including RUE, intercepted photosynthetically active radiation (IPAR), aboveground biomass production, and harvest index (HI). Our results revealed that both rice varieties exhibited significantly higher yields when coupled with nitrogen and potassium fertilization. Compared to the N90 × K120 treatment, the N120 × K160 and N180 × K210 combinations resulted in substantial increases in grain yield (12.0% and 21.1%, respectively) and RUE (11.9% and 21.4%, respectively). The YLY900 variety showed notable yield improvement due to enhanced aboveground biomass production resulting from increased IPAR and RUE. In contrast, the QYHZ variety’s aboveground biomass accumulation was primarily influenced by RUE rather than IPAR, resulting in higher RUE and grain yields of 9.2% and 5.3%, respectively, compared to YLY900. Importantly, fertilization led to significant increases in yield, biomass, and RUE, while HI remained relatively constant. Both varieties demonstrated a positive relationship between grain yield and IPAR and RUE. Multiple regression analysis indicated that increasing RUE was the primary driver of yield improvement in hybrid rice varieties. By promoting sustainable agriculture and enhancing fertilizer management, elevating nitrogen and potassium levels from a low base would synergistically enhance rice yield and RUE, emphasizing the critical importance of RUE in hybrid rice productivity compared to HI.

Climate change and land-use change are making landscape fires worse, causing them to grow in intensity and spread in range across Earth’s ecosystems. Extreme landscape fires can be devastating to people, ecosystems, and sociology. However, most research on landscape fires has not considered their potential impact on the economy, particularly with regard to entrepreneurial activity. Entrepreneurial activity includes the entry of new markets and the creation of new products or services, thereby facilitating the creation and expansion of economic activity. This manuscript empirically analyzes the impact of landscape fires on entrepreneurial activity, based on satellite monitoring data of landscape fires in China from 2014 to 2018. Different wind direction models and instrumental variable methods are used for empirical analysis. The results of the analysis show, first, that an increase in landscape fires in a county can significantly reduce local entrepreneurial activity. We further adopt the wind direction approach and instrumental variable approach to deal with potential endogeneity issues, and the regression results are consistent. Second, compared to eastern or high-economic-development areas, central and western or low-economic-development areas are more susceptible to exogenous landscape fires. Third, landscape fires have a negative impact on entrepreneurial activity through increasing air pollution, damaging human health, increasing risk aversion, and reducing the labor supply. It is important for both the government and the public to fully recognize the potential dangers that landscape fires pose to corporate behavior. This awareness can help reduce the impact of natural disasters such as landscape fires, protect the ecological environment, and provide solid support for corporate investment and regional development.

Recently, Heptafluoroisobutyronitrile (C4F7N) has received widespread attention in replacing one of the most greenhouse-insulating gas, SF6. However, gas leakage is incredibly harmful to the health of operational personnel and the security of industry production, and developing C4F7N detection technology is of great necessity. In this work, the adsorption properties, as well as the sensing performance of C4F7N on 3d VIII atom-decorated γ-graphyne (γ-GY), were theoretically discussed. The adsorption structures, adsorption energies, electron transfer, adsorption distance, electron distribution, and electronic properties were compared. The results show that the introduction of Fe and Co atom enhance the chemisorption of C4F7N, and the adsorption of C4F7N brings the maximum electron redistribution of Fe/γ-GY among three TM/γ-GY. Only the adsorption on Fe/γ-GY leads to the vanishing of the magnetic moment and creates a band gap. For three different modifications of γ-GY, the chemical interactions are highly related to the overlapping of transition metal 3d and N 2p orbitals in the density of states. The adsorption on Co/γ-GY causes the maximum change in work function from 5.06 eV to 5.26 eV. In addition, based on the band structure, work function, and desorption properties, the sensing properties of 3d VIII atom-decorated γ-GY were evaluated in order to promote the experimental exploration and development of high-performance C4F7N gas sensors.

Cepharanthine, a natural bisbenzylisoquinoline (BBIQ) alkaloid isolated from the plant Stephania Cephalantha Hayata, is the only bisbenzylisoquinoline alkaloid approved for human use and has been used in the clinic for more than 70 years. Cepharanthine has a variety of medicinal properties, including signaling pathway inhibitory activities, immunomodulatory activities, and antiviral activities. Recently, cepharanthine has been confirmed to greatly inhibit SARS-CoV-2 infection. Therefore, we aimed to describe the pharmacological properties and mechanisms of cepharanthine, mainly including antitumor, anti-inflammatory, anti-pathogen activities, inhibition of bone resorption, treatment of alopecia, treatment of snake bite, and other activities. At the same time, we analyzed and summarized the potential antiviral mechanism of cepharanthine and concluded that one of the most important anti-viral mechanisms of cepharanthine may be the stability of plasma membrane fluidity. Additionally, we explained its safety and bioavailability, which provides evidence for cepharanthine as a potential drug for the treatment of a variety of diseases. Finally, we further discuss the potential new clinical applications of cepharanthine and provide direction for its future development.

The objective of our study is to investigate the predictive value of various combinations of radiomic features from intratumoral and different peritumoral regions of interest (ROIs) for achieving a good pathological response (pGR) following neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). This retrospective study was conducted using data from LARC patients who underwent nCRT between 2013 and 2021. Patients were divided into training and validation cohorts at a ratio of 4:1. Intratumoral ROIs (ROIITU) were segmented on T2–weighted imaging, while peritumoral ROIs were segmented using two methods: ROIPTU_2mm, ROIPTU_4mm, and ROIPTU_6mm, obtained by dilating the boundary of ROIITU by 2 mm, 4 mm, and 6 mm, respectively; and ROIMR_F and ROIMR_BVLN, obtained by separating the fat and blood vessels + lymph nodes in the mesorectum. After feature extraction and selection, 12 logistic regression models were established using radiomics features derived from different ROIs or ROI combinations, and five–fold cross–validation was performed. The average area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the models. The study included 209 patients, consisting of 118 pGR and 91 non–pGR patients. The model that integrated ROIITU and ROIMR_BVLN features demonstrated the highest predictive ability, with an AUC (95% confidence interval) of 0.936 (0.904–0.972) in the training cohort and 0.859 (0.745–0.974) in the validation cohort. This model outperformed models that utilized ROIITU alone (AUC = 0.779), ROIMR_BVLN alone (AUC = 0.758), and other models. The radscore derived from the optimal model can predict the treatment response and prognosis after nCRT. Our findings validated that the integration of intratumoral and peritumoral radiomic features, especially those associated with mesorectal blood vessels and lymph nodes, serves as a potent predictor of pGR to nCRT in patients with LARC. Pending further corroboration in future research, these insights could provide novel imaging markers for refining therapeutic strategies.

Japanese encephalitis virus (JEV) causes acute viral encephalitis in humans and reproductive disorders in pigs. JEV emerged during the 1870s in Japan, and since that time, JEV has been transmitted exclusively throughout Asia, according to known reporting and sequencing records. A recent JEV outbreak occurred in Australia, affecting commercial piggeries across different temperate southern Australian states, and causing confirmed infections in humans. A total of 47 human cases and 7 deaths were reported. The recent evolving situation of JEV needs to be reported due to its continuous circulation in endemic regions and spread to non-endemics areas. Here, we reconstructed the phylogeny and population dynamics of JEV using recent JEV isolates for the future perception of disease spread. Phylogenetic analysis shows the most recent common ancestor occurred about 2993 years ago (YA) (95% Highest posterior density (HPD), 2433 to 3569). Our results of the Bayesian skyline plot (BSP) demonstrates that JEV demography lacks fluctuations for the last two decades, but it shows that JEV genetic diversity has increased during the last ten years. This indicates the potential JEV replication in the reservoir host, which is helping it to maintain its genetic diversity and to continue its dispersal into non-endemic areas. The continuous spread in Asia and recent detection from Australia further support these findings. Therefore, an enhanced surveillance system is needed along with precautionary measures such as regular vaccination and mosquito control to avoid future JEV outbreaks.

The rising age of the population in rural China and the labor intensity of grafting have resulted in a decrease in the number of grafters and a subsequent increase in their wages. Manual grafting can no longer satisfy the increasing demand for watermelon-grafted transplanting; thus, machine grafting will be an effective alternative. In order to accelerate the implementation of machine grafting in China, a comparative analysis between the automatic grafting machine (model 2TJGQ-800) and traditional hand grafting was conducted. The reliability and feasibility of machine grafting were evaluated through a comprehensive evaluation of the production capacity and grafting seedling quality. This study focuses on the grafting application of watermelon plug-tray seedlings. The scion and rootstock seeds were sown on 9 November 2022. Grafting experiments using an automatic grafting machine, skilled workers, and ordinary workers were conducted with the root-pruned one-cotyledon grafting method on 24 November 2022. The results showed that the machine grafting had a high uniformity and grafting speed. The grafting speed of the grafting machine was 774 plant·h−1 and 1.65–2.55-fold higher than the hand grafting. With training, workers can improve their grafting speed, but it will still be slower than machine grafting. In addition, there was no significant difference in the grafting survival rate between the machine grafting and hand grafting. However, using machine grafting, the success rate decreased from 100% to 90.07% and the rootstock regrowth rate increased from 18.44% to 72.69%. Incomplete rootstock cutting, clip supply failure, and grafting drop failure are the three main factors that result in machine grafting failure. In conclusion, the grafting machine has advantages in terms of grafting speed and uniformity. Upon improving the accuracy of the cutting mechanism and grafting success rate, it will be adopted by commercial nurseries.

Streptococcus suis (S. suis) is a bacterial pathogen of pigs that has a major animal health and economic impact on the pig industry. Bovine herpesvirus-4 (BoHV-4) is a new virus-based vaccine vector that has been used for the immunogenic delivery of antigens from a variety of pathogens. In the present study, two recombinant BoHV-4-based vectors were evaluated for their ability to induce immunity and protection against S. suis in a rabbit model. The GMD protein is a fusion protein consisting of multiple dominant B-cell epitopes ((B-cell dominant epitopes of GAPDH, MRP, and DLDH antigens) (BoHV-4/GMD)) and the second suilysin (SLY) (BoHV-4/SLY) from S. suis serotype 2 (SS2). Both GMD and SLY delivered by the BoHV-4 vectors were recognized by sera from SS2-infected rabbits. The vaccination of rabbits with the BoHV-4 vectors induced antibodies against SS2, as well as against additional S. suis serotypes, SS7 and SS9. However, sera from BoHV-4/GMD-vaccinated animals promoted a significant level of phagocytic activity by pulmonary alveolar macrophages (PAMs) against SS2, SS7, and SS9. In contrast, sera from rabbits immunized with BoHV-4/SLY induced PAM phagocytic activity against only SS2. In addition, BoHV-4 vaccines differed in the associated level of protection against lethal SS2 challenge, which ranged from high (71.4%) to low (12.5%) for BoHV-4/GMD and BoHV-4/SLY, respectively. These data suggest BoHV-4/GMD as a promising vaccine candidate against S. suis disease.

Glaesserella parasuis (Gps), Gram-negative bacteria, are a universal respiratory-disease-causing pathogen in swine that colonize the upper respiratory tract. Highly Pathogenic Porcine Reproductive and Respiratory Syndrome Virus (HP-PRRSV2HP-PRRSV2) and Gps coinfections are epidemics in China, but little is known about the influence of concurrent coinfection on disease severity and inflammatory responses. Herein, we studied the effects of secondary HP-PRRS infection on clinical symptoms, pathological changes, pathogen load, and inflammatory response of Gps coinfection in the upper respiratory tract of piglets. All coinfected piglets (HP-PRRSV2 + Gps) displayed fever and severe lesions in the lungs, while fever was present in only a few animals with a single infection (HP-PRRSV2 or Gps). Additionally, HP-PRRSV2 and Gps loading in nasal swabs and blood and lung tissue samples was significantly increased in the coinfected group. Necropsy data showed that coinfected piglets suffered from severe lung damage and had significantly higher antibody titers of HP-PRRSV2 or Gps than single-infected piglets. Moreover, the serum and lung concentrations of inflammatory cytokines (TNF-α, IL-1β, IL-6, and IL-8) were also significantly higher in coinfected piglets than in those infected with HP-PRRSV2 or Gps alone. In conclusion, our results show that HP-PRRSV2 promotes the shedding and replication of Gps, and their coinfection in the upper respiratory tract aggravates the clinical symptoms and inflammatory responses, causing lung damage. Therefore, in the unavoidable situation of Gps infection in piglets, necessary measures must be made to prevent and control secondary infection with HP-PRRSV2, which can save huge economic losses to the pork industry.

The hot cracking behaviors of Mg-5Zn-xEr (x = 0.83, 1.25, 2.5, 5 wt.%) alloys are investigated by optimized hot cracking experimental apparatus, optical microscope, and scanning electron microscope, such as contraction behaviors, feeding behaviors, and permeability characteristics. It is found that the solid phase fraction at hot crack initiation and within the freezing range both increased with increasing Er contents up to 2.5 wt.% and then decreased at 5 wt.% Er content. The Mg-5Zn-5Er alloy exhibits the lowest solid phase fraction (87.4%) and a reduced freezing range (74.2 °C), which leads to more effective liquid feeding in the latter stages of solidification. Combined with the grain size, the permeability of the mushy zone, and fracture morphology, the overall permeability is optimal in the Mg-5Zn-5Er alloy, which is beneficial for feeding the cavities and micro-pores. Meanwhile, a large amount of W phase precipitated by the eutectic reaction (L→α-Mg + W phase), which facilitates healing of the incurred cracking. Conversely, the Mg-5Zn-2.5Er alloy shows inferior feeding ability due to the lowest solid phase fraction (98.3%), wide freezing range (199.5 °C), and lowest permeability. Therefore, the Mg-5Zn-2.5Er alloy exhibits maximal hot cracking susceptibility, and the Mg-5Zn-5Er alloy exhibits minimal hot cracking susceptibility. This work provides guidance for improving the hot cracking resistance of cast Mg-Zn-Er alloy and enables an understanding of the hot cracking behaviors of Mg-Zn-RE alloys.

A phytochemical investigation of the steroidal saponins from the rhizomes of Paris polyohylla var. latifolia led to the discovery and characterization of three new spirostanol saponins, papolatiosides A–C (1–3), and nine known compounds (4–12). Their structures were established via extensive spectroscopic data analysis and chemical methods. Interestingly, compounds 1 and 2 possessed a fructosyl in their oligosaccharide moiety, which is rare in natural product and was firstly reported in family Melanthiaceae. The cytotoxicity of these saponins against several human cancer cell lines was evaluated by a CCK-8 experiment. As a result, compound 1 exhibited a significant cytotoxic effect on LN229, U251, Capan-2, HeLa, and HepG2 cancer cells with IC50 values of 4.18 ± 0.31, 3.85 ± 0.44, 3.26 ± 0.34, 3.30 ± 0.38 and 4.32 ± 0.51 μM, respectively. In addition, the result of flow cytometry analysis indicated that compound 1 could induce apoptosis of glioma cells LN229. The underlying mechanism was explored by network pharmacology and western bolt experiments, which indicated that compound 1 could induce glioma cells LN229 apoptosis by regulating the EGFR/PI3K/Akt/mTOR pathway.

The study aims at investigating diffusion behaviors between virgin asphalt and recycled asphalt mastic (RAM) at an atomistic scale. Firstly, a mutual diffusion model of virgin asphalt–RAM considering the actual mass ratio of filler to asphalt binder (F/A) condition was developed by molecular dynamic (MD)simulation. Secondly, the indexes of relative concentration (RC), radial distribution function (RDF) and mean square displacement (MSD) were used to analyze the molecular arrangement characteristics of polar components in the diffusion processes at different temperatures. Then, the blending efficiency of virgin asphalt–RAM was evaluated by Fick’s second law and the binding energy. The results indicate that the reliability of the RAM model was validated by thermodynamics properties. The results of RC and RDF show that the diffusion direction of virgin asphalt–RAM is not changed by the presence of mineral fillers. However, it will inhibit the occurrence of diffusion behaviors, and the aggregation of molecules in the blending zone increases due to the adsorption of mineral fillers, which would become a barrier to molecular diffusion. The development of MSD indicates that the diffusion coefficients of molecules in both virgin–aged asphalt and virgin asphalt–RAM are on the rise with the increase in temperature. Compared with the virgin–aged asphalt, the molecular migration speed in virgin asphalt–RAM is relatively slow. According to Fick’s second law and the binding energy, diffusion behaviors are dominated by the nonpolar components. The existence of mineral fillers has the greatest effect on the nonpolar components in diffusion. It is suggested that rejuvenator containing more aromatic components should be added or the temperature controlled within 433.15–443.15 K to promote blending efficiency. The research results contribute to a deeper understanding about diffusion behaviors of virgin asphalt–RAM, serving as a benchmark for further study of rejuvenation using computational experiments.

COVID-19 opened a window of opportunity to change the green development of the hospitality industry. For many years, Chinese tourists have been the world’s largest source of outbound tourists. Therefore, this study attempted to improve built-environment strategies for green rooms at B&Bs using the empirical statistics of Chinese tourists after the end of COVID-19 control measures and different green B&B standards, combining IPA (importance-performance analysis). For the lack of a green built-environment study from a tourism perspective, this study can be used mainly for improving the green satisfaction of urban B&Bs as it attempted to fill the gaps in research on green B&B rooms. This study will significantly help improve the quality of green rooms for the B&B industry in the future, and it also provides an improved green B&B room sample for other countries and regions. Moreover, it is an optimistic attempt at hospitality and tourism recovery.

The use of beneficial microbes as biofertilizer has become fundamental in the agricultural sector for their potential role in food safety and sustainable crop production. A field trial was conducted to study the influence of beneficial microbes on the efficiency of organic and inorganic sources. The experiment was conducted in two consecutive years (2008–2009 and 2009–2010) in a farmer’s field at Dargai Malakand Division. A randomized complete block design was used with four replications. The results revealed a significantly higher straw and grain nitrogen concentrations for the treatments receiving 50% N from urea + 50% N from FYM + BM, followed by the treatments receiving 50% N from urea + 50% N from (FYM + PM) + BM and 120 kg N ha−1 from urea fertilizer, respectively. Comparing the relevant treatments with and without BM, an increasing trend in N concentrations in straw and grain was observed with BM. The results revealed the highest grain total nitrogen, straw total nitrogen and total nitrogen uptake by wheat crop for the treatments receiving 120 kg N ha−1 from urea, followed by the treatments receiving 50% N from urea + 50% N from PM + BM and 50% N from urea + 50% N from (FYM + PM) + BM. Moreover, after comparing the relevant treatments with and without BM, for the parameters mentioned, an increasing trend in nitrogen uptake was observed. Significantly higher total soil nitrogen was obtained for treatment with 50% N from urea + 50% N from FYM + BM, followed by the treatment with 50% N from urea + 50% N from (FYM + PM) + BM or 50% N from urea + 50% N from PM + BM, respectively, as compared to the control treatment plot. Markedly higher soil mineral nitrogen was obtained for the 50% N from urea + 50% N from (FYM + PM) + BM treatment, followed by the treatment with 50% N from urea + 50% N from FYM + BM and 50% N treatment from urea + 50% N from PM + BM, compared to the control treatment. Comparing the relevant treatments with and without BM, an increasing trend in total soil N (g kg−1 soil) and soil mineral N (mg kg−1 soil) was noted with BM application. From the results, a significant increase in soil organic matter status (g kg−1 soil) due to application of organic and inorganic fertilization was summarized. Significantly higher soil organic matter (g kg−1 soil) was recorded for the treatment receiving 50% N from urea + 50% N from FYM + BM compared to untreated control plots. Our study further revealed an increasing trend in soil organic matter status (g kg−1 soil) when comparing the relevant treatments with and without BM.

The mixed pixel of low-resolution remote-sensing image makes the traditional water extraction method not effective for small water body extraction. This study takes the Loess Plateau with complex terrain as the research area and develops a multi-index fusion threshold segmentation algorithm (MFTSA) for a large-scale small water body extraction algorithm based on GEE (Google Earth Engine). MFTSA uses the AWEI (automated water extraction index), MNDWI (modified normalized difference water index), NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index) for multi-index synergy to extract small water bodies. It also uses slope data generated by the SRTM (Shuttle Radar Topography Mission digital elevation model) and NIR band reflectance to eliminate suppressing high reflectivity noise and shadow noise. An MFTSA algorithm was proposed and the results showed that: (1) The overall extraction accuracy of the MFTSA algorithm on the Loess Plateau was 98.14%, and the correct extraction rate of small water bodies was 92.82%. (2) Compared with traditional water index methods and classification methods, the MFTSA algorithm could extract small water bodies with higher integrity and clearer and more accurate boundaries. (3) The MFTSA algorithm was used to extract a total of 69,900 small water bodies on the Loess Plateau, accounting for 97.63% of the total water bodies, and the area was 482.11 square kilometers, accounting for 16.50% of the total water bodies.

The 17CrNi2MoVNb alloy is widely used for manufacturing heavy-duty gears in vehicles’ transmission systems, where grinding is a significant process affecting gears’ working performance and service life. This work comprehensively analyzed the grinding force, surface morphology, and surface roughness when grinding 17CrNi2MoVNb alloy using alumina and CBN grinding wheels. Results showed that the maximum normal grinding force from the CBN wheel was only ~67% of the one from the alumina wheel. Due to the small size and limited cutting depth of CBN grains, the grinding force increased by about 20% when the grinding depth increased from 0.02 to 0.03 mm for CBN grinding wheels. Surface defects, including cavities and material tearing, were mainly found on the ground surface when using an alumina grinding wheel. The surface roughness Ra recorded from the CBN grinding wheel mainly ranged from 0.263 to 0.410 μm, accounting for less than 40% of the one from the alumina grinding wheel. The information from this work could provide benchmark data and references for optimizing grinding tools and parameters when manufacturing gears in the vehicle industry.