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Nectriaceae is a highly diverse family, and members have a worldwide distribution, particularly in warm temperate to tropical regions. During the survey of fungal diversity in different habitats in Yunnan province, China, two new species isolated from soil and air respectively, namely Atractium yunnanense sp. nov. and Nalanthamala xishuangbannaensis sp. nov., were proposed based on morphological comparisons and the multi-gene phylogenetic analyses of combined ITS, LSU, rpb2, and tub2 sequence data. Phylogenetically, both species clustered in a monophyletic clade within Nectriaceae with strong support. A. yunnanense is characterized by synnematous conidiophores, pale olivaceous-green, clavate to oblong-ellipsoidal, multi-septate conidia, and pale olivaceous-green chlamydospores. N. xishuangbannaensis has acremonium-like or penicillium-like conidiophores and either obovate or ellipsoidal, cylindrical or fusiform conidia. Full descriptions, illustrations, and a phylogenetic tree showing the phylogenetic position of the two new species were provided.

Abstract Background Precisely estimating the probability of mental health challenges among college students is pivotal for facilitating timely intervention and preventative measures. However, to date, no specific artificial intelligence (AI) models have been reported to effectively forecast severe mental distress. This study aimed to develop and validate an advanced AI tool for predicting the likelihood of severe mental distress in college students. Methods A total of 2088 college students from five universities were enrolled in this study. Participants were randomly divided into a training group (80%) and a validation group (20%). Various machine learning models, including logistic regression (LR), extreme gradient boosting machine (eXGBM), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were employed and trained in this study. Model performance was evaluated using 11 metrics, and the highest scoring model was selected. In addition, external validation was conducted on 751 participants from three universities. The AI tool was then deployed as a web-based AI application. Results Among the models developed, the eXGBM model achieved the highest area under the curve (AUC) value of 0.932 (95% CI: 0.911–0.949), closely followed by RF with an AUC of 0.927 (95% CI: 0.905–0.943). The eXGBM model demonstrated superior performance in accuracy (0.850), precision (0.824), recall (0.890), specificity (0.810), F1 score (0.856), Brier score (0.103), log loss (0.326), and discrimination slope (0.598). The eXGBM model also received the highest score of 60 based on the evaluation scoring system, while RF achieved a score of 49. The scores of LR, DT, and SVM were only 19, 32, and 36, respectively. External validation yielded an impressive AUC value of 0.918. Conclusions The AI tool demonstrates promising predictive performance for identifying college students at risk of severe mental distress. It has the potential to guide intervention strategies and support early identification and preventive measures.

Abstract Background Virtual reality (VR) and augmented reality (AR) are emerging technologies that can be used for cardiopulmonary resuscitation (CPR) training. Compared to traditional face-to-face training, VR/AR-based training has the potential to reach a wider audience, but there is debate regarding its effectiveness in improving CPR quality. Therefore, we conducted a meta-analysis to assess the effectiveness of VR/AR training compared with face-to-face training. Methods We searched PubMed, Embase, Cochrane Library, Web of Science, CINAHL, China National Knowledge Infrastructure, and Wanfang databases from the inception of these databases up until December 1, 2023, for randomized controlled trials (RCTs) comparing VR- and AR-based CPR training to traditional face-to-face training. Cochrane's tool for assessing bias in RCTs was used to assess the methodological quality of the included studies. We pooled the data using a random-effects model with Review Manager 5.4, and assessed publication bias with Stata 11.0. Results Nine RCTs (involving 855 participants) were included, of which three were of low risk of bias. Meta-analyses showed no significant differences between VR/AR-based CPR training and face-to-face CPR training in terms of chest compression depth (mean difference [MD], -0.66 mm; 95% confidence interval [CI], -6.34 to 5.02 mm; P = 0.82), chest compression rate (MD, 3.60 compressions per minute; 95% CI, -1.21 to 8.41 compressions per minute; P = 0.14), overall CPR performance score (standardized mean difference, -0.05; 95% CI, -0.93 to 0.83; P = 0.91), as well as the proportion of participants meeting CPR depth criteria (risk ratio [RR], 0.79; 95% CI, 0.53 to 1.18; P = 0.26) and rate criteria (RR, 0.99; 95% CI, 0.72 to 1.35; P = 0.93). The Egger regression test showed no evidence of publication bias. Conclusions Our study showed evidence that VR/AR-based training was as effective as traditional face-to-face CPR training. Nevertheless, there was substantial heterogeneity among the included studies, which reduced confidence in the findings. Future studies need to establish standardized VR/AR-based CPR training protocols, evaluate the cost-effectiveness of this approach, and assess its impact on actual CPR performance in real-life scenarios and patient outcomes. Trial registration CRD42023482286.

The stop-jump is a crucial skill in volleyball, and analyzing its motion can greatly benefit the training of volleyball players. This paper analyzed 30 players from the volleyball team at Chongqing Normal University. To study the impact of running speed on lower limb muscle activity during stop-jumps, the players were randomly assigned the low-speed running group, the medium-speed running group, and the high-speed running group. Surface electromyography sensors were employed to collect electromyographic signals from the rectus femoris, vastus lateralis, and gastrocnemius muscles of the players during the execution of the stop-jump. The electromyographic indicators were calculated using the surface electromyography system’s built-in program, including the root-mean-square (RMS) electromyographic value and integral electromyographic (IEMG) value. The former represents the effective discharge value of motor units, which depends on the electromyographic amplitude and is related to the number of recruited motor units. The latter represents the discharge capacity of motor units involved in muscle activity per unit of time, reflecting the degree of muscle activation during exercise. Then, statistical analysis was performed on these calculated indicators using SPSS software. It was found that the stop-jump comprised three distinct stages: stop, jump, and flight. Regardless of the stage, the results indicated that the higher the speed of running, the greater the electromyographic indexes of the muscle groups. Additionally, when comparing electromyographic indexes across different stages, it was observed that the index values gradually decreased with the progression of each stage.

Abstract Climate change leads to permafrost thawing, accelerating carbon emissions increases, challenges the goal of climate change mitigation. However, it remains unknown whether implementing ecological restoration projects in Alpine areas can offset the adverse effects of permafrost thawing locally. Here we took the Qinghai‒Tibet Plateau as an example to explore this issue based on the improved Biome-BGCMuSo model. We found future climate change-induced permafrost thawing will decrease carbon sink. Projects’ carbon sink enhancement could fully counteract the permafrost thawing-induced carbon loss. Additionally, future warmer and wetter climates will enlarge the suitable area for restoration. If these areas are taken into account, carbon sink attributable to Projects will further increase. These results indicate that ERPs have the potential to combat future permafrost thawing-induced carbon loss, and their contribution will be further amplified by future climate change.

In this paper, for vertical nonlinear complementarity problems, a two-step modulus-based matrix splitting iteration method is established by applying the two-step splitting technique to the modulus-based matrix splitting iteration method. The convergence theorems of the proposed method are given when the number of system matrices is larger than 2. Numerical results show that the convergence rate of the proposed method can be accelerated compared to the existing modulus-based matrix splitting iteration method.

Approximate Bayesian computation (ABC) relaxes the need to derive explicit likelihood functions required by formal Bayesian analysis. However, the high computational cost of evaluating models limits the application of Bayesian inference in hydrological modeling. In this paper, multivariate adaptive regression splines (MARS) are used to expedite the ABC calibration process. The MARS model is trained using 6,561 runoff simulations generated by the soil and water assessment tool (SWAT) model and subsequently replaces the SWAT model to calculate the objective functions in ABC and multi-objective evolutionary algorithm (MOEA). In experiments, MARS can successfully reproduce the runoff time series simulations of the SWAT model at a low time cost, with a runoff variance determination coefficient of 0.90 as compared to the Monte Carlo method. MARS-assisted ABC can quickly and accurately estimate the parameter distributions of the SWAT model. The comparison of ABC with non-Bayesian MOEAs helps in the selection of an appropriate calibration approach. HIGHLIGHTS Approximate Bayesian computation (ABC) was used to calibrate the complex hydrological model.; Multivariate adaptive regression splines (MARS) were used to expedite the ABC process.; The ability of MARS-assisted ABC for calibration of the SWAT model was demonstrated.; The calibration results of ABC and multi-objective evolutionary algorithms were compared.; MARS-assisted ABC has the potential to extend the use of ABC in hydrological modeling.;

Microgrid and distributed generation are more and more widely used in power systems. In this background, a control strategy based on consistency is proposed in this paper, to improve the optimal regulation ability of the AC/DC hybrid microgrid groups. The control strategy is divided into two levels: control strategy within a subnet and control between microgrid groups. At the level of subnet control, the power mapping factor and secondary adjustment term are introduced into the traditional droop control, to realize the autonomous and stable optimization in the island mode of a single sub microgrid. At the level of inter microgrid groups control strategy, the local control strategy of interlinking converter based on average power mapping factor is constructed, and the compensation term based on consistency is introduced to realize the power optimization operation between different microgrids jointly. Finally, the simulation model is established by Matlab/Simulink to prove the effectiveness of the method.

Abstract Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at the core. Since subtle changes in micro-environment can lead to a heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to model cell-to-cell interactions, spatial and metabolic heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model characterizes causal interdependencies at individual cell, aggregate, and cell population levels. It has a modular design that enables data integration and improves predictions for different monolayer and aggregate culture processes. In addition, a variance decomposition analysis is derived to quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity.

Aflatoxin B1 (AFB1) is a potent toxin in food, necessitating rapid, instant, and sensitive detection. We have engineered an electrochemical sensor to monitor AFB1 using a system composed of Fe3O4-NH4/AuNPs/apt-S1. The aptamer specifically recognizes AFB1, while ‘S1’ is functionalized with methylene blue to enhance the current. The RecJf exonuclease promotes the formation of the electrochemical strategy. The Fe3O4 component, with its magnet properties, enables a rapid separation of solids and liquids without the need for instrumentation. The sensor exhibits a linear range for AFB1 ranging from 1 ng to 10 μg. The regression equation is I(nA) = 446.8 × logc+2085 (where I and c represent the peak current and AFB1 concentration, respectively). The correlation coefficient is 0.9508, and the detection limit is 3.447 nM. The relative standard deviation of AFB1 in peanut oil ranges from 4.80% to 6.80%. These results demonstrate that the sensor has high sensitivity, stability, repeatability, and specificity for AFB1 detection.

Surface coal mining in semi-arid regions has detrimental impacts on the structure and function of surface ecosystems, thereby impeding the attainment of regional sustainable development goals. Moreover, the impact of climate change on ecological restoration in semi-arid mining areas is an inevitable consideration. To elucidate the response of ecosystem services in mining areas to regional climate change, topography, soil, vegetation and socioeconomic development, this study selected six large-scale surface coal mines located in semi-arid regions of China as research objects. In this study, we aimed to assess the main ecosystem services (carbon sequestration, soil conservation, and flow regulation) provided by these mines. In addition, we analysed the spatial and temporal evolution and interrelationships of these ecosystem services. Furthermore, we explored the underlying mechanisms between ecosystem services and environmental factors. The results showed the following: (1) Geospatially, there was a gradual decrease in carbon sequestration and flow regulation from northeast to southwest in the mining areas. However, the soil conservation exhibited an upward trend. Throughout the research period, the carbon sequestration change rate in the six mining areas displayed an initial downward and subsequent upward trend, the rate of soil conservation exhibited a gradual upward trend, and the flow regulation decline rate followed a downward trend. (2) Change rate of carbon sequestration and soil conservation in the six mining areas showed a significant positive correlation (r = 0.51, p < 0.001), with the strongest correlation observed in the Shengli Mining Area (r = 0.64, p < 0.001). (3) Ecosystem service changes in the mining areas were primarily driven by initial conditions (95.0%), followed by meteorological factors (4.2%). Three ecological restoration models were proposed for the different regions based on driver analyses. The findings of this study offer scientific evidence that can be used to inform ecological management, enhance ecological security, and promote regional sustainable development in mining areas.

In agricultural landscapes, patches containing diverse species can serve as nodes, and species diffusion among these patches can serve as edges, forming metapopulation networks. However, the impact of landscape fragmentation on these networks and consequences for keystone crop species remain unclear. Here, we investigated thrips (pests) and lacewings (natural predators) in mango orchards and surrounding habitats across 15 landscapes. Using a machine learning neural network, we built metapopulation networks for thrips and lacewings based on the relationship between patch properties and species abundance. We evaluated these networks with metrics like clustering coefficient, strength, and path length. Furthermore, we analyzed the effects of landscape fragmentation, as indicated by edge density and landscape Shannon diversity, on network structures and the subsequent effect on species abundance using piecewise structural equation modeling. The results show that the neural network effectively constructed the metapopulation network, accurately predicting lacewing and thrips abundance in various patches with an accuracy rate exceeding 80%. Positive correlations were observed between edge density and network strength for both species. However, a positive relationship between landscape Shannon diversity and network path length was identified only for lacewings. Edge density had a greater impact on lacewings and thrips compared to landscape Shannon diversity, with a negative effect on lacewings and a positive effect on thrips. Edge density affected lacewing abundance both directly and indirectly through network strength. However, no network structure mediated the effect of landscape fragmentation on thrips abundance. Our conclusion highlights that the effects of landscape fragmentation on species are primarily driven by edge effects. These effects were clarified by the metapopulation network structure, which varied depending on the species. The findings provide new perspectives on metapopulation networks to unlock the landscape fragmentation effects.

Ecological restoration policies and their implementation are influenced by ecological and socioeconomic drivers. Top-down approach-based spatial planning, emphasizing hierarchical control within government structures, and without a comprehensive consideration of social–ecological interactions may result in implementation failure and low efficiency. Although many researchers have indicated the necessity to engage social–ecological interactions between stakeholders in effective planning processes, socioeconomic drivers of ecological restoration on a large scale are difficult to quantify because of data scarcity and knowledge limitations. Here, we established a new ecological restoration planning approach linking a social–ecological system framework to large-scale ecological restoration planning. The new spatial planning approach integrates bottom-up approaches targeting stakeholder interests and provides social considerations for stakeholder behavior analysis. Based on this approach, a meta-analysis is introduced to recognize key socioeconomic and social–ecological factors influencing large-scale ecological restoration implementation, and a stochastic model is constructed to analyze the impact of socioeconomic drivers on the behavior of authorities and participants on a large scale. We used the Yangtze River Basin-based Conversion of Cropland to Forest Program (CCFP), one of the largest payments for ecosystem service programs worldwide, to quantify the socioeconomic impacts of large-scale ecological restoration programs. Current CCFP planning without socioeconomic considerations failed to achieve large-scale program goals and showed low investment efficiency, with 19.71% of the implemented area reconverting to cropland after contract expiry. In contrast, spatial matching between planned and actual restoration increased from 61.55% to 81.86% when socioeconomic drivers were included. In addition, compared to that with the current CCFP implementation, the cost effectiveness of spatial planning with social considerations improved by 46.94%. Thus, spatial optimization planning that integrates both top-down and bottom-up approaches can result in more practical and effective ecological restoration than top-down approaches alone. Our new approach incorporates socioeconomic factors into large-scale ecological restoration planning with high practicality and efficiency.

The supply of public rental housing for young households has been on the rise in South Korea. Their size and composition vary depending on residents’ characteristics. However, even within similar groups, housing satisfaction may differ according to their lifestyle; hence, diversity may exist in the demand for housing improvement. This study identified housing satisfaction and improvement demands according to housing lifestyles of young residents of “Happy House”, a representative public rental housing for young households. The method used in this study was an online survey targeting individuals younger than 39 years and living in Happy Houses. A total of 536 responses were collected, and the respondents were divided into five housing lifestyle clusters despite being from similar groups. Depending on the lifestyle, differences existed in the satisfaction of space composition and size and basic options, such as facilities/furniture/home appliances. In addition, there were also differences in the demand for housing improvements. The group actively using housing for leisure, cultural activities, and social exchanges showed high demands for improvement, such as space composition and basic options. This study can be used as a reference for establishing a housing improvement plan for customizable public rental housing, considering the lifestyles of young adults.

Abstract A facile template method was employed to synthesize Fe nanowires of different sizes, dimensions. Comprehensive analyses were conducted to explore their morphology, structure, composition, and magnetic properties. The surface of as-prepared Fe nanowires was modified with SiO2 by sol–gel method to improve the dispersion of as-prepared Fe nanowires in aqueous solution. Furthermore, the relaxation properties, biocompatibility and in vivo imaging abilities of the Fe@SiO2 nanowires were evaluated. The study revealed that the SiO2-coated Fe nanowires functioned effectively as transverse relaxation time (T2) contrast agents (CAs). Notably, as the length of the Fe@SiO2 nanowires increased, their diameter decreased, leading to a higher the transverse relaxivity (r2) value. Our study identified that among the Fe nanowires synthesized, the Fe3@SiO2 nanowires, characterized by a diameter of around 30 nm and a length of approximately 500 nm, exhibited the highest r2 value of 59.3 mM−1 s−1. These nanowires demonstrated good biocompatibility and non-toxicity. Notably, upon conducting small animal imaging a 1.5 T with Sprague–Dawley rats, we observed a discernible negative enhancement effect in the liver. These findings indicate the promising potential of Fe@SiO2 nanowires as T2 CAs, with the possibility of tuning their size for optimized results.

This study presents a novel soft-switching inverter distinguished by a simplified topology and an innovative modulation approach. The design aims to optimize the energy conversion processes commonly found in auxiliary snubber circuits. By minimizing the number of auxiliary switches, the control method is streamlined, thereby enhancing system reliability and cost-efficiency. The principles of operation and conditions for soft-switching are thoroughly analyzed using equivalent circuit models. A 3 kW/16 kHz inverter prototype was constructed, and the experimental results confirm the effectiveness and benefits of the proposed inverter.

Virtual power plants (VPPs) integrate diverse energy resources using advanced communication technologies and intelligent control strategies. This integration enhances the utilization and efficiency of distributed generation. This paper explores the incorporation of VPPs into load frequency control (LFC) systems. It includes an analysis of VPP-aggregated resources’ frequency regulation characteristics and a VPP-inclusive LFC model. Additionally, a decentralized automatic generation control strategy is proposed to distribute power outputs effectively, enabling swift grid frequency adjustments. This study uses MATLAB simulations to demonstrate the benefits and efficacy of VPPs in LFC, underscoring their role in advancing grid management and stability.

Traditional single image super-resolution (SISR) methods, which focus on integer scale super-resolution, often require separate training for each scale factor, leading to increased computational resource consumption. In this paper, we propose MambaSR, a novel arbitrary-scale super-resolution approach integrating Mamba with Fast Fourier Convolution Blocks. MambaSR leverages the strengths of the Mamba state-space model to extract long-range dependencies. In addition, Fast Fourier Convolution Blocks are proposed to capture the global information in the frequency domain. The experimental results demonstrate that MambaSR achieves superior performance compared to different methods across various benchmark datasets. Specifically, on the Urban100 dataset, MambaSR outperforms MetaSR by 0.93 dB in PSNR and 0.0203 dB in SSIM, and on the Manga109 dataset, it achieves an average PSNR improvement of 1.00 dB and an SSIM improvement of 0.0093 dB. These results highlight the efficacy of MambaSR in enhancing image quality for arbitrary-scale super-resolution.

Climate change is endangering the soil carbon stock of alpine grasslands on the Qinghai-Tibetan Plateau (QTP), but the limited comprehension regarding the mechanisms that sustain carbon storage under hydrothermal changes increases the uncertainty associated with this finding. Here, we examined the relative abundance of soil microbial keystone taxa and their functional potentials, as well as their influence on soil carbon storage with increased precipitation across alpine grasslands on the QTP, China. The findings indicate that alterations in precipitation significantly decreased the relative abundance of the carbon degradation potentials of keystone taxa, such as chemoheterotrophs. The inclusion of keystone taxa and their internal functional potentials in the two best alternative models explained 70% and 63% of the variance in soil organic carbon (SOC) density, respectively. Moreover, we found that changes in chemoheterotrophs had negative effects on SOC density as indicated by a structural equation model, suggesting that some specialized functional potentials of keystone taxa are not conducive to the accumulation of carbon sink. Our study offers valuable insights into the intricate correlation between precipitation-induced alterations in soil microbial keystone taxa and SOC storage, highlighting a rough categorization is difficult to distinguish the hidden threats and the importance of incorporating functional potentials in SOC storage prediction models in response to changing climate.

The aim of this study was to investigate the anti-Eimeria tenella mechanism of Qingchang Compound (QCC) and provide a basis for its clinical application. The active ingredients, active ingredient-disease intersection targets, and possible pathways of QCC for the treatment of chicken coccidiosis were analyzed, the binding ability of pharmacodynamic components and target proteins was determined by network pharmacology and the molecular docking, and a model of infection with coccidiosis was constructed to verify and analyze the mechanism of action of QCC against coccidiosis. Among the 57 components that met the screening conditions, the main bioactive components were quercetin, dichroine, and artemisinin, with IL-1β, IL-6, IL-10, IFN-γ, and IL-8 as the core targets. Simultaneously, the KEGG signaling pathway of QCC anti-coccidiosis in chickens was enriched, including cytokine-cytokine receptor interactions. The results showed that the main pharmacodynamic components of QCC and the core targets could bind well; artemisinin and alpine possessed the largest negative binding energies and presented the most stable binding states. In addition, in vivo studies showed that QCC reduced blood stool in chickens with coccidiosis, restored cecal injury, and significantly reduced the mRNA and protein expression levels of IL-1β, IL-10, and IFN-γ in ceca (p

Blade Tip Timing Technology (BTT) is a new type of blade vibration measurement method with non-contact measurement capabilities, which is associated with high efficiency and convenience. However, due to under-sampling feature of the BTT signal, the method can only measure part of the vibration information. Therefore, various BTT spectral reconstruction algorithms have been developed based on the under-sampling feature and sparsity of the blade vibration spectrum. Because of the harsh working environment of the blades, BTT signal typically contains high-level Gaussian noise in actual measurement, which significantly reduces frequency-domain sparsity and even aliases the vibration modals, resulting in a decrease in the performance of sparse reconstruction algorithms. In this paper, a multi-sequences BTT signal noise reduction method is proposed, which is based on sparse representation to suppress Gaussian noise. Through appropriately modifying the constraint in the ℓ0 optimization problem, the method filters out the noise elements in the sparse vector of the BTT signal. Then, denoising is completed by sparse inverse transformation. Finally, a global average-based singular value decomposition dictionary learning algorithm (GA-K-SVD) is proposed to generate an over-complete sparse dictionary which is adaptable to the original signal, to increase the effectiveness of GA-K-SVD. At last, simulation and experience are carried out to verify the performance optimization effect for sparse reconstruction algorithm and the effectiveness of proposed noise reduction method.

Structural modal estimation has consistently remained one of the most crucial areas of research in mechanical vibration analysis and signal processing. It is imperative to accurately monitor the vibration signals of rotor blades and their corresponding structural modal parameters. Blade tip timing (BTT) is a promising approach used to measure vibration and monitor the health of blades. However, most existing BTT methods focus on frequency accuracy, neglecting damping, a key physical quantity that represents the strength of the structure. This research focuses on damping modal estimation of blade vibration and proposes a sparse reconstruction algorithm based on a two-dimensional Laplace wavelet family, which addresses the inherent under-sampled problem of BTT technology while enabling estimations of both the structural modal frequency and modal damping of the blades. Firstly, the proposed algorithm is achieved by designing a Laplace wavelet dictionary based on prior information and using a convex optimization objective function with a regularization term. Secondly, Laplace wavelet dictionary matches the signal better than the traditional Fourier dictionary based on the similarity between the damped vibration signal and the Laplace wavelet, then a sparser representation vector can be obtained. Moreover, the research object is not limited to a single blade, but can be easily expanded to multiple stages and multiple rotating blades monitored simultaneously. Finally, the simulation and physical test results indicate that the proposed method exhibits high reconstruction accuracy, reliability, and anti-noise abilities.

This field experiment aimed to investigate the effects of different ratios of organic and inorganic fertilizers with maintaining equal nitrogen application rates on the yield, quality, and nitrogen uptake efficiency of Dioscorea polystachya (yam). Six treatments were set, including a control without fertilizer (CK), sole application of chemical fertilizer (CF), sole application of organic fertilizer (OM), 25% organic fertilizer + 75% chemical fertilizer (25%OM + 75%CF), 50% organic fertilizer + 50% chemical fertilizer (50%OM + 50%CF), and 75% organic fertilizer + 25% chemical fertilizer (75%OM + 25%CF). The experiment followed a randomized complete block design with three replications. Various yield parameters, morphology, quality indicators, and nitrogen utilization were analyzed to assess the differences among treatments. The results indicated that all fertilizer treatments significantly increased the yield, morphology, quality indicators, and nitrogen utilization efficiency compared to the control. Specifically, 25%OM + 75%CF achieved the highest yield of 31.96 t hm-2, which was not significantly different from CF (30.18 t hm-2). 25%OM + 75%CF exhibited the highest values at 69.23 cm in tuber length and 75.86% in commodity rate, 3.14% and 1.57% higher than CF respectively. Tuber thickness and fresh weight of 25%OM + 75%CF showed no significant differences from CF, while OM and 50%OM+50%CF exhibited varying degrees of reduction compared to CF. Applying fertilizer significantly enhanced total sugar, starch, crude protein, total amino acid, and ash contents of D. polystachya (except ash content between CK and OM). Applying organic fertilizer increased the total sugar, starch, crude protein, total amino acid, and ash contents in varying degrees when compared with CF. The treatment with 25%OM+75%CF exhibited the highest increases of 6.31%, 3.78%, 18.40%, 29.70%, and 10%, respectively. Nitrogen content in different plant parts followed the sequence of tuber > leaves > stems > aerial stem, with the highest nitrogen accumulation observed in 25%OM + 75%CF treatment. Nitrogen harvest index did not show significant differences among treatments, fluctuating between 0.69 and 0.74. The nitrogen apparent utilization efficiency was highest in 25%OM + 75%CF (9.89%), followed by CF (9.09%), both significantly higher than OM (5.32%) and 50%OM + 50%CF (6.69%). The nitrogen agronomic efficiency varied significantly among treatments, with 25%OM + 75%CF (33.93 kg kg-1) being the highest, followed by CF (29.68 kg kg-1), 50%OM + 50%CF (21.82 kg kg-1), and OM (11.85 kg kg-1). Nitrogen partial factor productivity was highest in 25%OM + 75%CF treatment (76.37 kg kg-1), followed by CF (72.11 kg kg-1), both significantly higher than 50%OM + 50%CF (64.25 kg kg-1) and OM (54.29 kg kg-1), with OM exhibiting significantly lower values compared to other treatments. In conclusion, the combined application of organic and inorganic fertilizers can effectively enhance the yield, quality, and nitrogen utilization efficiency of D. polystachya. Particularly, the treatment with 25% organic fertilizer and 75% chemical fertilizer showed the most promising results.

Endosymbiotic bacteria (ESB) have important effects on their hosts, contributing to its growth, reproduction and biological functions. Although the effects of exogenous bacteria on the trap formation of nematode-trapping fungi (NTF) have been revealed, the effects of ESB on NTF remain unknown. In this study, we investigated the species diversity of ESB in the NTF Arthrobotrys musiformis using high-throughput sequencing and culture-dependent approaches, and compared bacterial profiles to assess the effects of strain source and culture media on A. musiformis. PICRUSt2 and FAPROTAX were used to predict bacterial function. Our study revealed that bacterial communities in A. musiformis displayed high diversity and heterogeneity, with Proteobacteria, Firmicutes, Bacteroidetes and Actinobacteria as the dominant phyla. The ESB between A. musiformis groups isolated from different habitats and cultured in the same medium were more similar to each other than the other groups isolated from the same habitat but cultured in different media. Function analysis predicted a broad and diverse functional repertoire of ESB in A. musiformis, and unveiled that ESB have the potential to function in five modules of the nitrogen metabolism. We isolated nitrogen-fixing and denitrifying bacteria from the ESB and demonstrated their effects on trap formation of A. musiformis. Among seven bacteria that we tested, three bacterial species Bacillus licheniformis, Achromobacter xylosoxidans and Stenotrophomonas maltophilia were found to be efficient in inducing trap formation. In conclusion, this study revealed extensive ESB diversity within NTF and demonstrated that these bacteria likely play important roles in nitrogen cycling, including nematode trap formation.

There are few studies on the impact of postoperative pain management (such as Acute Pain Service, APS) on the prognosis of patients, especially the research on large samples, even less data on Chinese patients. It is reported that only 25.12 % of hospitals in China have established APS or similar teams, and less than 10 % of them are responsible for the whole process of postoperative analgesia services. Tongji Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology has established a professional APS team led by anesthesiologists (TJ-APS), and has a standardized workflow and management system. Based on the TJ-APS standardized postoperative pain management, the incidence and adverse effects of postoperative pain in different types of surgical patients were analyzed. In total, 107,802 patients receiving intravenous PCA from the Tongji Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology were selected between January 2016 and December 2021, which were under TJ-APS standardized postoperative analgesia process, postoperative analgesia strategy based on the principle of “low opioid, multimodal, specialization and individualization”, as well as regular ward rounds and 24-h on call on-duty system. We assessed the incidence and adverse effects of postoperative pain in different types of surgical patients. Based on the TJ-APS standardized postoperative pain management, the incidence of poor postoperative analgesia in patients with intravenous PCA is significantly lower than that reported in the current literature (20 %), and mainly occurs in biliary-pancreatic surgery, extrahepatic surgery and gastrointestinal surgery. The overall incidence of adverse effects was 5.52 %, of which nausea and vomiting was the highest, especially among gynecological tumors and gynecological patients, which were 10.75 % and 8.68 % respectively, but both were lower than the level reported in the current literature (20 %). This APS multimodal management and analgesia process can provide reference and guidance for PCA management of postoperative acute pain.

To improve the gel quality of chicken blood, a by-product of concentrated slaughter, the effects of different concentrations and formulations of xanthan gum (XG) and guar gum (GG) on the gel properties of chicken blood were investigated using the indices of water retention, texture, and rheological properties, as well as analysis of molecular interactions and sensory scores. The results showed that while individual treatment with either XG or GG improved the water-holding capacity of the chicken blood gel, the action of XG was superior. Addition of 4.0 g/L of XG resulted in a cooking loss rate of 10.02%, centrifugation loss of 12.73%, and syneresis rate of 10.96% (at 48 h), while the hardness, cohesiveness, gelation, mastication, and recovery of the gel were decreased. Addition of 6.0 g/L of GG significantly improved the textural properties of the gel, resulting in a hardness of 366.95 N, elasticity of 0.94 mm, cohesiveness of 0.77, adhesiveness of 281.94, masticatory value of 263.72 mJ, and recovery of 0.25. Analysis of dynamic rheology and intermolecular forces showed that the gelation transformation of the chicken blood gel system was mainly determined by the elastic response. The formulation of XG and GG had a synergistic effect in stabilizing the chicken blood gel, with strengthened ionic bonds (from 29.18% to 32.62%) and hydrogen bonds (from 2.48% to 6.43%), reduced hydrophobic force (from 22.68% to 16.28%), increased water-holding capacity of the gel, and enhanced gel stability. The addition of 4.0 g/L of formulated colloid (XG and GG in a ratio of 7:3) to chicken blood resulted in optimal water retention and texture, together with high sensory scores of the gel.

Abstract Background To assess the prognostic effect of plasma Epstein–Barr virus (EBV) DNA load after induction chemotherapy (postIC‐EBV DNA) on survival outcomes in locoregionally advanced nasopharyngeal carcinoma (LA‐NPC). Methods Patients who were diagnosed with LA‐NPC between August 2017 and October 2021 were included. The chi‐squared test, receiver operating characteristic, Kaplan–Meier survival analysis, and Cox proportional hazard model were used for statistical analysis. Results We included 172 patients with EBV DNA‐positive LA‐NPC in this study. There were 35.5% (n = 61) of patients had plasma residual EBV DNA after induction chemotherapy (IC). Patients with higher EBV DNA before IC (p

Vegetation primarily draws water from soil moisture (SM), with restoration in drylands often reducing SM storage (SMS). However, anomalies have been detected in the Beijing–Tianjin Sand Source Region (BTSSR) of China via the Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiment (GRACE). This study quantified the sources of increased SMS in drylands to elucidate the effects of vegetation restoration on SMS. The results indicated the following: (1) In vegetated drylands, 46.2% experienced a significant increase in SMS while 53.8% remained stable; both were positively correlated with the normalised difference vegetation index (NDVI). (2) The increase in SMS was accompanied by a decrease in groundwater storage (GWS), as indicated by the significant correlation coefficients of −0.710 and −0.569 for SMS and GWS, respectively. Furthermore, GWS served as the primary source of water for vegetation. (3) The results of the redundancy analysis (RDA) indicated that the initial vegetation, the driver of the observed trend of increased SMS and decreased GWS, accounted for 50.3% of the variability in water storage. Therefore, to sustain dryland ecosystems, we recommend that future vegetation restoration projects give due consideration to the water balance while concurrently strengthening the dynamic monitoring of SMS and GWS.

The accurate identification of forest degradation and its driving factors is a prerequisite for implementing high-quality forest management. However, distinguishing degradation patterns is often neglected in large-scale forest quality assessments. The indicators were constructed to identify typical forest degradation patterns using remote sensing indexes, followed by an analysis of the spatiotemporal dynamics of forest degradation and quantification of the contributions from various driving factors. The results indicated that the constructed indicators could effectively distinguish typical forest degradation patterns, with a fire degradation identification accuracy of 90.0% and a fitting accuracy of drought and pest degradation higher than 0.7. The cold temperate conifer forest zone had the largest proportion of fire degradation, accounting for 67.7% of the area, and totals of 99.0% of the subtropical evergreen broadleaf forest zone and 92.8% of the temperate conifer and broadleaf mixed forest zone were moderately to severely affected by drought, with long-term stability. Additionally, 0.1% of the temperate grassland region and 0.1% of the cold temperate conifer forest zone underwent severe pest infestations, with a long-term stable trend. Meteorological factors were the primary contributors to all typical degradation patterns, accounting for 81.35%, 58.70%, and 82.29%, respectively. The research developed an index for assessing forest degradation and explained the importance of natural and anthropogenic factors in forest degradation. The results are beneficial for the scientific management of forest degradation and for improving forest management efficiency.

Persistent hiccups that occur after abdominal surgery seriously affect postoperative rehabilitation. Phrenic nerve block therapy has been recommended after failure of medication or physical maneuvers. However, the phrenic nerve is often difficult to accurately identify because of its small diameter and anatomic variations. We combined ultrasound with the use of a nerve stimulator to quickly and accurately identify and block the phrenic nerve in a patient with postoperative persistent hiccups. The ongoing hiccups were immediately terminated with no adverse effects. The patient reported no recurrence during the 2-week follow-up period. We conclude that the combined use of real-time ultrasound guidance and a nerve stimulator for singular phrenic nerve block might be an effective intervention for terminating postoperative persistent hiccups, although further studies are needed to evaluate the safety and efficacy of this technique. The findings in this case suggest a potential clinical application for this technique in managing persistent hiccups, thereby contributing to improved patient care and outcomes.

The present study was performed to investigate yellow catfish manganese (Mn) requirement with Mn dioxide nanoparticles (MnO2NPs) as Mn source, and to evaluate effects of dietary Mn on growth performance, Mn metabolism and kidney health. Using MnO2NPs as the Mn sources, six experimental diets were formulated with dietary Mn levels at 1.67, 4.35, 7.37, 13.63, 26.06 and 50.37 mg Mn/kg diet, respectively, and fed to yellow catfish for 8 weeks. Yellow catfish growth performance and feed intake were the highest in the group of 13.63 mg Mn/kg diet, and the lowest in the group of 1.67 mg Mn/kg diet; and the feed conversion rate showed opposite trend with growth performance. Dietary Mn addition increased Mn content in the whole body, kidney and serum. The mRNA abundances of dmt1, zip8 and znt10 increased with the increase of dietary Mn level. Yellow catfish fed high dietary MnO2NPs level had higher mRNA abundances of dynamin1, dynamin2, cav1, cltc and eps15. Dietary Mn excess damaged the kidney histology. Yellow catfish fed the 13.63 mg Mn/kg diet had higher T-AOC, T-SOD, Mn-SOD, CAT activities, and mRNA levels of sod1, sod2 and cat (antioxidant response), but lower mRNA abundances of grp78, ire1, perk and atf4 (endoplasmic reticulum stress), tnfα, il1β and il6 (inflammatory response). The broken-line analysis of WG and the linear regression of whole-body Mn retention against dietary Mn levels showed that optimal Mn requirement was 12.57 mg Mn/kg diet and 8.33 mg Mn/kg diet, respectively, with MnO2NPs as Mn source. For the first time, we explored the effects of dietary MnO2 nanoparticles on growth performance, Mn uptake and metabolism, antioxidant responses, endoplasmic reticulum stress and inflammatory responses of the kidney, and determined the optimal dietary Mn requirement of yellow catfish fed MnO2 nanoparticles-added diet.

Water use efficiency (WUE) is an important indicator in linking the carbon and water cycles of ecosystems. Previous studies performed in alpine and cold regions have contributed greatly to understanding the WUE response of single ecosystem or specific species in small-scale regions to climatic factors; however, studies focusing on differences in the WUE response among ecosystems or species are insufficient, which has limited the understanding of the climatic adaptation of WUE in alpine and cold regions. Here, based on the 134 leaf δ13C records measured for the 46 dominant species in two main ecosystems on the Qinghai-Tibetan Plateau, variation in the response of WUE among biomes was analyzed by grouped linear regression, redundancy analysis (RDA), and structural equation models (SEMs). The results showed that (1) photosynthetically active radiation (PAR0), moisture index (MI) and vapor pressure deficit (VPD) were significantly correlated with the variation in WUE; (2) compared with the WUE of alpine grasslands, alpine desert grasslands was more sensitive to climatic factors, which presenting a steeper slope that varied with modified growing degree days (MGDD0), PAR0, MI, VPD and CO2; (3) composition of genera distributions explained 32.22% of the variation in WUE, while the WUE of Stipa was more sensitive compared to other species to most climatic factors; (4) climate (composition of genera) and their joint effects explained 58.98% (63.53%) of the variation in WUE of alpine desert grasslands (alpine grasslands), while altitude indirectly controlled the WUE variation in alpine grasslands and MI directly controlled the WUE variation in alpine desert grasslands. This study quantified the driving effects of climates and biomes on the variation in WUE and contributed to the understanding of vegetation adaptation in alpine and cold regions.

ObjectiveTo create and authenticate MRI-based radiomic signatures to identify dorsal root ganglia (DRG) lesions in post-herpetic neuralgia (PHN) patients generalizable and interpretable.MethodThis prospective diagnostic study was conducted between January 2021 and February 2022. Lesioned DRG in patients with PHN and normal DRG in age-, sex-, height-, and weight-matched healthy controls were selected for assessment and divided into two groups (8:2) randomly: training and testing sets. The least absolute shrinkage and selection operator algorithm was employed to generate feature signatures and construct a model, followed by the assessment of model efficacy using the area under the curve (AUC) of the receiver operating characteristic (ROC), as well as sensitivity and specificity metrics.ResultsThe present investigation involved 30 patients diagnosed with postherpetic neuralgia (PHN), consisting of 18 males and 12 females (mean age 60.70 ± 10.18 years), as well as 30 healthy controls, comprising 18 males and 12 females (mean age 58.13 ± 10.54 years). A total of 98 DRG were randomly divided into two groups (8:2), namely a training set (n = 78) and a testing set (n = 20). Five radiomic features were chosen to construct the models. In the training dataset, the area under the curve (AUC) was 0.847, while the sensitivity and specificity were 71.79 and 97.44%, respectively. In the test dataset, the AUC was 0.87, and the sensitivity and specificity were 80.00 and 100.00%, respectively.ConclusionAn MRI-based radiomic signatures model has the capacity to uncover the micro-change of damaged DRG in individuals afflicted with postherpetic neuralgia.

Hydrological simulation has long been a challenge because of the computationally intensive and expensive nature of complex hydrological models. In this paper, a surrogate modelling (SM) framework is presented based on the Hadoop cloud for approximating complex hydrological models. The substantial model runs required by the design of the experiment (DOE) of SM were solved using the Hadoop cloud. Polynomial chaos expansion (PCE) was fitted and verified using the high-fidelity model DOE and was then used as a case study to investigate the approximation capability in a Soil and Water Assessment Tool (SWAT) surrogate model with regard to the accuracy, fidelity, and efficiency. In experiments, the Hadoop cloud reduced the computation time by approximately 86% when used in a global sensitivity analysis. PCE achieved results equivalent to those of the standard Monte Carlo approach, with a flow variance coefficient of determination of 0.92. Moreover, PCE proved to be as reliable as the Monte Carlo approach but significantly more efficient. The proposed framework greatly decreases the computational costs through cloud computing and surrogate modelling, making it ideal for complex hydrological model simulation and optimization. HIGHLIGHTS Our surrogate modelling framework reduces the computational cost of simulations.; The design of the experiment was parallelized on a Hadoop cloud.; PCE was fitted and verified using a high-fidelity model.; The approximation ability of PCE in the SWAT surrogate model was investigated.;

Abstract Grazing intensity, characterized by high spatial heterogeneity, is a vital parameter to accurately depict human disturbance and its effects on grassland ecosystems. Grazing census data provide useful county-scale information; however, they do not accurately delineate spatial heterogeneity within counties, and a high-resolution dataset is urgently needed. Therefore, we built a methodological framework combining the cross-scale feature extraction method and a random forest model to spatialize census data after fully considering four features affecting grazing, and produced a high-resolution gridded grazing dataset on the Qinghai–Tibet Plateau in 1982–2015. The proposed method (R2 = 0.80) exhibited 35.59% higher accuracy than the traditional method. Our dataset were highly consistent with census data (R2 of spatial accuracy = 0.96, NSE of temporal accuracy = 0.96) and field data (R2 of spatial accuracy = 0.77). Compared with public datasets, our dataset featured a higher temporal resolution (1982–2015) and spatial resolution (over two times higher). Thus, it has the potential to elucidate the spatiotemporal variation in human activities and guide the sustainable management of grassland ecosystem.

The physics potential of detecting ^8 B solar neutrinos will be exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model-independent manner by using three distinct channels of the charged current (CC), neutral current (NC), and elastic scattering (ES) interactions. Due to the largest-ever mass of ^13 C nuclei in the liquid scintillator detectors and the expected low background level, ^8 B solar neutrinos are observable in the CC and NC interactions on ^13 C for the first time. By virtue of optimized event selections and muon veto strategies, backgrounds from the accidental coincidence, muon-induced isotopes, and external backgrounds can be greatly suppressed. Excellent signal-to-background ratios can be achieved in the CC, NC, and ES channels to guarantee the observation of the ^8 B solar neutrinos. From the sensitivity studies performed in this work, we show that JUNO, with 10 yr of data, can reach the 1 σ precision levels of 5%, 8%, and 20% for the ^8 B neutrino flux, ${\sin }^{2}{\theta }_{12}$ , and ${\rm{\Delta }}{m}_{21}^{2}$ , respectively. Probing the details of both solar physics and neutrino physics would be unique and helpful. In addition, when combined with the Sudbury Neutrino Observatory measurement, the world's best precision of 3% is expected for the measurement of the ^8 B neutrino flux.

The field of space science places significant emphasis on deep space exploration, with a particular focus on asteroids as a potential hazard to humanity. Inverting their physical characteristics from photometric observations is essential for uncovering their origins and evolution. This article attempts to present a solution to the challenging task of estimating the physical properties of binary asteroids, which are common in near-Earth asteroids larger than 200 meters. A novel model for binary asteroids is proposed, which integrates orbital and rotational motions to simulate brightness variations based on two Cellinoid shapes. The model combines the projection and occultation effects of the shapes to generate the simulated brightness. The inversion of determining physical properties is optimized based on the Levenberg–Marquardt algorithm through a simulation process involving several parameter corrections. Finally, the performance of the proposed model is demonstrated through numerical experiments and applications to two real binary asteroids, namely, asteroid (317) Roxane and asteroid (624) Hektor. The derived results are nearly identical to those from other publications, which confirms that the proposed model provides reliable and accurate estimations of the physical properties of binary asteroids. Additionally, this method has a potential application in supporting the development of effective strategies for the Double Asteroid Redirection Test (DART) project, the first planetary defense experiment in space undertaken by humans.

In the collision of two heavy ions, the strong repulsion coming from the Coulomb field is enough to produce e+e− pair(s) from vacuum fluctuations. The energy is provided by the kinetic energy of the ions and the Coulomb interaction at the production point. If, for instance, the electron is located at the center of mass (C.M.) of the two ions moving along the z-axis, and the positron is at a distance x from the electron, the ions can be accelerated towards each other since the Coulomb barrier is lowered by the presence of the electron. This screening results in an increase in the kinetic energy of the colliding ions and may result in an increase in the fusion probability of light ions above the adiabatic limit.

【Objective】This study is aimed to investigate the effects of bio-fertilizer (BF), Bacillus subtilis (BS), slow-released nitrogen fertilizer (SRN) on cassava growth, soil nutrients and biological activity through field experients.【Method】A field trial was carried out including 13 treatments with 2 BF application amounts (BF1, 5 t/hm2; BF2, 10 t/hm2), 2 concentrations of BS (BS100, diluted 100 times; BS200, diluted 200 times), SRN and reduced chemical fertilizer (75%CF) and urea treatment (application once, UN1; application twice, UN2).【Result】BF2+75%CF treatment lowered soil available nitrogen content (P < 0.05) compared with BF2+SRN treatment. Soil available potassium (AK) content was increased by BS100 with SRN. AK content was also increased significantly by BF2 when applied with UN1. BF2+75%CF significantly increased of AK content compared with SRN. Soil microbial biomass carbon (SMBC) was increased by BS+SRN, BF+SRN andBF2+75%CF, higher than SRN. With the application of SRN, soil acid phosphatase activity was increased by BS100 and BF2. The cassava yields were increased by 20.5% and 21.7% respectively under SRN and UN2 treatments, compared with that under UN1 treatment. Both BS and BF could increase cassava yield when applied with SRN or UN, or only 75% CF. Compared with conventional fertilization (UN1), BF and BS combined with SRN or 75%CF could increase nitrogen utilization rate by 10.9%-21.5% and potassium utilization rate by 11.4%-28.9%.【Conclusion】In conclusion, cassava yields are increased approximately by SRN or UN2. With the plication of SRN, AK, SMBC, soil acid phosphatase activity and cassava yield are increased by BF and BS, and the soil sucrase activity by BF. By reducing 25% chemical fertilizer, BF can also increase AK, SMBC and cassava yield. The nitrogen and potassium utilization rates are both increased by BF and BS.

Abstract Main goal of the JUNO experiment is to determine the neutrino mass ordering using a 20 kt liquid-scintillator detector. Its key feature is an excellent energy resolution of at least 3% at 1 MeV, for which its instruments need to meet a certain quality and thus have to be fully characterized. More than 20,000 20-inch PMTs have been received and assessed by JUNO after a detailed testing program which began in 2017 and elapsed for about four years. Based on this mass characterization and a set of specific requirements, a good quality of all accepted PMTs could be ascertained. This paper presents the performed testing procedure with the designed testing systems as well as the statistical characteristics of all 20-inch PMTs intended to be used in the JUNO experiment, covering more than fifteen performance parameters including the photocathode uniformity. This constitutes the largest sample of 20-inch PMTs ever produced and studied in detail to date, i.e. 15,000 of the newly developed 20-inch MCP-PMTs from Northern Night Vision Technology Co. (NNVT) and 5000 of dynode PMTs from Hamamatsu Photonics K. K.(HPK).

Background The chronic kidney disease-mineral and bone disorder（CKD-MBD) is known to be associated with increased mortality in dialysis patients, but whether current global guidelines for CKD-MBD, which were primarily developed from hemodialysis, are suitable for peritoneal dialysis (PD) patients practice require further investigation.Methods This is a single-center retrospective cohort study. In total 491 prevalent PD patients (median follow-ups: 34 months) from Peking Union Medical College Hospital (PUMCH) from January 2004 to December 2017 were included and followed until 30 June 2018. In the first dialysis year, the average levels of serum calcium, albumin-corrected calcium (CorCa), phosphorus, and parathyroid hormone (PTH) levels were the interested predictors in Cox proportional regression model.Results Of these PD patients (age 58 ± 17 years), 52% were male and 36% had diabetic nephropathy. In Cox regression over first-year mean parameters, PTH

To investigate the effects of marketing days and genders on the bearded chicken’s quality, in this study, bearded chickens (roosters, 80, 90, 100, and 110 days of age; hens, 100, 120, 140, and 160 days of age) were slaughtered, and their slaughter performance, chicken skin microstructure, muscles color, breast muscle pH45 min, myofiber characteristics and water retention capacities were evaluated. The results showed the live weight, feathers weight, and blood weight of the bearded chicken from the same gender groups increased along with the increase of age (P0.05) for chickens which had not reached sexual maturity (roosters of 80~90 d and hens of 100~120 d), and significant differences (P

To investigate the changes of flavor compounds of chicken liver by different deodorization methods, the volatile flavor compounds of raw and cooked chicken liver samples were analyzed by sensory evaluation, electronic nose and headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). The results showed that deodorization methods of ginger juice/alcohol immersion, β-cyclodextrin embedding and yeast fermentation could significantly reduce the odor value (P

Abstract Background Paratuberculosis is a widespread chronic infection of Mycobacterium avium subspecies paratuberculosis (MAP) that causes significant economic losses to the sheep industry. The current study investigated this disease, which causes diarrhea in sheep, particularly, in Bayannaoer, Inner Mongolia, China. Diagnosis was based on clinical symptoms, pathological autopsy, histopathological inspection, and serological and molecular methods. Results MAP was confirmed using polymerase chain reaction using DNA extracted from tissue and fecal samples. Serum samples from 472 individual sheep were obtained to detect antibodies against MAP using an enzyme-linked immunosorbent assay. MAP antibodies were separately detected in 17.86% (35/196) and 18.48% (51/276) of sheep herds at approximately 6 months and ≥ 1 year of age, respectively. The tissue lesion and pathological section results were consistent with paratuberculosis infection. Conclusions To our knowledge, this is the first report of Mycobacterium avium subspecies paratuberculosis seroprevalence in Bayannaoer sheep in Inner Mongolia. Our findings show that MAP is not only prevalent, but also a potential threat to this region. Further investigations, including long-term epidemiological surveillance and isolation are needed for the awareness and effective treatment of paratuberculosis in sheep of Inner Mongolia.

Background: Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) have limited or no response in some certain patients of non-small-cell lung cancer (NSCLC). However, real-world survival analyses comparing clinical data and EGFR-plasma mutation are still lacking. Methods: In total, 159 patients with advanced NSCLC resistant to first-generation EGFR-TKIs were included for consecutive blood sampling in this study. Super-amplification refractory mutation system (Super-ARMS) was used to detect EGFR-plasma mutations and correlations between survival and circulating tumor DNA (ctDNA) were analyzed. Results: Among 159 eligible patients, the T790M mutation was detected in 27.0% (43/159) of patients. The median progression-free survival (mPFS) was 10.7 months in all patients. Survival analysis revealed that patients with the T790M mutation had shorter progression-free survival (PFS) than those with the T790M wild-type (10.6 months vs 10.8 months, P = .038). Patients who cleared EGFR-plasma mutation had prolonged PFS compared with those with nonclearing EGFR-plasma mutation (11.6 months vs 9.0 months, P = .001). Cox multivariate analysis showed that the nonclearance of EGFR-plasma mutations was an independent risk factor for PFS (RR = 1.745, 95% CI: [1.184, 2.571], P = .005). The T790M mutation was associated with nonclearance of the EGFR-plasma mutation ( χ 2 = 10.407, P = .001). Conclusion: Patients with advanced NSCLC who were resistant to the first-generation EGFR-TKI had a prolonged PFS with clearance of EGFR-plasma mutation. Those nonclearers were more likely to harbor T790M mutations in plasma.

In the context of global climate change, temperate forests in climate-sensitive areas are inevitably affected. To deepen the understanding of the impact on precipitation changes into the relationship between key ecosystem services (ESs), this study selected net primary productivity (NPP), soil conservation (SC) and water yield (WY) of temperate forest in northern China as objects, and the Spearman correlation test and redundancy analysis were applied to analyze the response of ESs relationship to precipitation gradient. The results show that precipitation is the meteorological factor with the greatest impact (contribution 21.2%, p

In order to address the convergence issue in fuzzy power flow calculations, this paper proposes an analytical approach based on the Levenberg–Marquardt method, aiming to improve the convergence of the fuzzy Newton power flow method. Firstly, a detailed analysis is conducted on the convergence theorem and convergence behavior of the fuzzy Newton method, revealing its poor convergence when the initial values are not properly selected. The Levenberg–Marquardt method is then selected as a means to enhance the convergence of the fuzzy Newton power flow calculations, specifically to tackle the problem of initial value deviation. Since the Jacobian matrix has a significant impact on the convergence region of the power flow, this paper reconstructs the Jacobian matrix based on the Levenberg–Marquardt method, effectively enlarging the convergence region. Through validation experiments on the IEEE 118 standard nodes and simulation comparative analysis, the results confirm the method’s effectiveness in resolving the problem of initial value deviation and notably enlarging the convergence region, thereby improving the convergence of power flow calculations.

Abstract Background Mechanisms underlying ischemia/reperfusion injury-acute kidney injury (IRI-AKI) are not fully elucidated. We conducted an integrative analysis of IRI-AKI by bioinformatics methods. Methods We screened gene expression profiles of the IRI-AKI at early phase from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and enrichment pathways were conducted based on gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and Gene set enrichment analysis (GSEA). Immune cell infiltration analysis was performed to reveal the change of the microenvironment cell types. We constructed protein–protein interaction (PPI), and Cytoscape with plug-ins to find hub genes and modules. We performed robust rank aggregation (RRA) to combine DEGs and analyzed the target genes for miRNA/transcription factor (TF) and drug-gene interaction networks. Results A total of 239 and 384 DEGs were identified in GSE87024 and GSE34351 separately, with the 73 common DEGs. Enrichment analysis revealed that the significant pathways involve mitogen-activated protein kinase (MAPK) signaling, interleukin-17, and tumor necrosis factor (TNF) signaling pathway, etc. RRA analysis detected a total of 27 common DEGs. Immune cell infiltration analysis showed the plasma cells reduced and T cells increased in IRI-AKI. We identified JUN, ATF3, FOS, EGR1, HMOX1, DDIT3, JUNB, NFKBIZ, PPP1R15A, CXCL1, ATF4, and HSPA1B as hub genes. The target genes interacted with 23 miRNAs and 116 drugs or molecular compounds such as curcumin, staurosporine, and deferoxamine. Conclusion Our study first focused on the early IRI-AKI adopting RRA analysis to combine DEGs in different datasets. We identified significant biomarkers and crucial pathways involved in IRI-AKI and first construct the immune landscape and detected the potential therapeutic targets of the IRI-AKI by drug-gene network.

Abstract We study damping signatures at the Jiangmen Underground Neutrino Observatory (JUNO), a medium-baseline reactor neutrino oscillation experiment. These damping signatures are motivated by various new physics models, including quantum decoherence, ν 3 decay, neutrino absorption, and wave packet decoherence. The phenomenological effects of these models can be characterized by exponential damping factors at the probability level. We assess how well JUNO can constrain these damping parameters and how to disentangle these different damping signatures at JUNO. Compared to current experimental limits, JUNO can significantly improve the limits on τ 3/m 3 in the ν 3 decay model, the width of the neutrino wave packet σ x , and the intrinsic relative dispersion of neutrino momentum σ rel.