The heating effect of electromagnetic waves in ion cyclotron range of frequencies (ICRFs) in magnetic confinement fusion device is different in different plasma conditions. In order to evaluate the ICRF heating effect in different plasma conditions, we conducted a series of experiments and corresponding TRANSP simulations on the EAST tokamak. Both simulation and experimental results show that the effect of ICRF heating is poor at low core electron density. The decrease in electron density changes the left-handed electric field near the resonant layer, resulting in a significant decrease in the power absorbed by the hydrogen fundamental resonance. However, quite a few experiments must be performed in plasma conditions with low electron density. It is necessary to study how to make ICRF heating best in low electron density plasma. Through a series of simulation scans of the parallel refractive index (n//) of the ICRF antenna, it is concluded that the change of the ICRF antenna n// will lead to the change of the left-handed electric field, which will change the fundamental absorption of ICRF power by the hydrogen minority ions. Fully considering the coupling of ion cyclotron wave at the tokamak boundary and the absorption in the plasma core, optimizing the ICRF antenna structure and selecting appropriate parameters such as parallel refractive index, minority ion concentration, resonance layer position, plasma current and core electron temperature can ensure better heating effect in the ICRF heating experiments in the future EAST upgrade. These results have important implications for the enhancement of the auxiliary heating effect of EAST and other tokamaks.

The Chinese pangolin Manis pentadactyla is categorized as Critically Endangered on the IUCN Red List, but the development of effective conservation strategies is hindered by a lack of data on its distribution range and population dynamics. In addition, standardized survey and analysis methods are required to facilitate the sharing of results and maximize conservation effectiveness. To fill these knowledge and methodological gaps, we investigated the occurrence of pangolin burrows in the subtropical forest ecosystem of Fujian, China. We surveyed a total of 70 transects across five land-cover types within the Fujian Junzifeng National Nature Reserve and detected 87 burrows. The majority of burrows (87%) were located in mixed conifer and broadleaf forests. We used six environmental variables in a generalized linear model to examine the relationship between the occurrence of burrows and environmental factors. The average model results from the best model set showed that the distribution of burrows was significantly influenced by forest type. For effective pangolin conservation, we recommend that local conservation authorities prioritize the protection of mixed conifer and broadleaf forests. Our findings support the local conservation of the Chinese pangolin and the standardization of surveys and conservation efforts across the species’ range.

The high comorbidity of major depressive disorder (MDD), anxiety disorders (ANX), and post-traumatic stress disorder (PTSD) complicates the study of their structural neural correlates, particularly in white matter (WM) alterations. Using fractional anisotropy (FA), this meta-analysis aimed to identify both unique and shared WM characteristics for these disorders by comparing them with healthy controls (HC). The aggregated sample size across studies includes 3,661 individuals diagnosed with MDD, ANX, or PTSD and 3,140 HC participants. The whole-brain analysis revealed significant FA reductions in the corpus callosum (CC) across MDD, ANX, and PTSD, suggesting a common neurostructural alteration underlying these disorders. Further pairwise comparisons highlighted disorder-specific differences: MDD patients showed reduced FA in the middle cerebellar peduncles and bilateral superior longitudinal fasciculus II relative to ANX patients and decreased FA in the CC extending to the left anterior thalamic projections (ATPs) when compared with PTSD. In contrast, PTSD patients exhibited reduced FA in the right ATPs compared to HC. No significant FA differences were observed between ANX and PTSD or between ANX and HC. These findings provide evidence for both shared and unique WM alterations in MDD, ANX, and PTSD, reflecting the neural underpinnings of the clinical characteristics that distinguish these disorders.

Direct numerical simulations are performed to explore the impact of surface roughness on inter-scale energy transfer and interaction in a turbulent open-channel flow over differently arranged rough walls. With friction Reynolds number approximately 540, six distinct configurations of roughness arrangements are examined. The results show that the clustered roughness arrangements yield notable changes in large-scale secondary-flow structures, which manifest in the profiles of dispersive stresses, predominantly near the roughness elements. They are marked by the presence of spanwise alternating high-momentum pathways and low-momentum pathways. From the outer peak in the spanwise energy spectra, the size and intensity of turbulent secondary flows are shown to be related to the spanwise spacing of the roughness heterogeneity. The emergence of turbulent secondary flows serves to suppress the original large-scale structures in the outer region of smooth-wall turbulence, paving the way for the development of new turbulent structures at the second harmonic scale. Furthermore, the spanwise triadic interaction analysis reveals the mutual energy exchange between the secondary harmonic scale and the secondary-flow scale. These findings elucidate the underlying mechanisms behind the attenuation of large-scale structures in the outer region influenced by roughness, offering new insights into the dynamic interplay of scale interactions in rough-wall turbulence.

Persistent malnutrition is associated with poor clinical outcomes in cancer. However, assessing its reversibility can be challenging. The present study aimed to utilise machine learning (ML) to predict reversible malnutrition (RM) in patients with cancer. A multicentre cohort study including hospitalised oncology patients. Malnutrition was diagnosed using an international consensus. RM was defined as a positive diagnosis of malnutrition upon patient admission which turned negative one month later. Time-series data on body weight and skeletal muscle were modelled using a long short-term memory architecture to predict RM. The model was named as WAL-net, and its performance, explainability, clinical relevance and generalisability were evaluated. We investigated 4254 patients with cancer-associated malnutrition (discovery set = 2977, test set = 1277). There were 2783 men and 1471 women (median age = 61 years). RM was identified in 754 (17·7 %) patients. RM/non-RM groups showed distinct patterns of weight and muscle dynamics, and RM was negatively correlated to the progressive stages of cancer cachexia (r = –0·340, P < 0·001). WAL-net was the state-of-the-art model among all ML algorithms evaluated, demonstrating favourable performance to predict RM in the test set (AUC = 0·924, 95 % CI = 0·904, 0·944) and an external validation set (n 798, AUC = 0·909, 95 % CI = 0·876, 0·943). Model-predicted RM using baseline information was associated with lower future risks of underweight, sarcopenia, performance status decline and progression of malnutrition (all P < 0·05). This study presents an explainable deep learning model, the WAL-net, for early identification of RM in patients with cancer. These findings might help the management of cancer-associated malnutrition to optimise patient outcomes in multidisciplinary cancer care.

Wheel-leg composite robots exhibit robust mobility and exceptional obstacle-crossing capabilities in complex environments. This paper proposes a novel transformable wheel-leg composite structure and presents the design of a wheel-leg composite obstacle-crossing robot, fundamentally configured as a two-wheeled quadruped. The research encompasses a comprehensive analysis of the robot’s overall mechanical structure, a detailed kinematic investigation of its body and obstacle-crossing gait planning, virtual prototype dynamics simulation, and field experimentation. Utilizing advanced modeling software, a 3D model of the robot was established. The kinematic characteristics of the robot in both wheeled and legged modes were thoroughly examined. Specifically, for the legged mode, the Denavit-Hartenberg coordinate system was established, and a detailed kinematic model was analyzed. The obstacle-crossing gait was planned based on the robot’s leg action mechanism. Furthermore, the Lagrangian method was employed to develop a mathematical model for the dynamics of the robot in both wheel-foot modes, allowing for a comprehensive force analysis. To validate the feasibility and rationality of the robot’s obstacle-crossing capabilities under various conditions, extensive simulations and prototype tests were conducted across diverse terrains. The results provide valuable insights and practical guidance for the structural design of wheel-leg composite obstacle-crossing robots, contributing to advancements in this promising field.

This paper provides an overview of the current status of ultrafast and ultra-intense lasers with peak powers exceeding 100 TW and examines the research activities in high-energy-density physics within China. Currently, 10 high-intensity lasers with powers over 100 TW are operational, and about 10 additional lasers are being constructed at various institutes and universities. These facilities operate either independently or are combined with one another, thereby offering substantial support for both Chinese and international research and development efforts in high-energy-density physics.

Immunological castration can be an alternative to traditional surgical castration. The active immunization against GnRH or kisspeptin has a castrating effect. To date, the fusion protein vaccine of combination with GnRH and kisspeptin have not been studied. Thus, the present study will develop a GnRH6-kisspeptin vaccine by genetic engineering method and investigate its immunocastration effect in male rats. Twenty 20-day-old male rats were randomly divided into two groups: the control group (n=10) and the immunization group (n=10). The initial immunization took place at week 0 followed by three booster doses administered intervals. The control group received an equivalent dose of white oil adjuvant. Orbital blood samples were collected at various time points following the initial immunization, at 0, 2, 4, 6, 8, 10 and 12 weeks, respectively. The entire left testis was weighed and its volume measured at week 12. Samples from the right testis were obtained for histological analysis. Serum levels of GnRH and kisspeptin antibodies, as well as testosterone levels were determined using ELISA. The results showed that the serum levels of GnRH and kisspeptin antibody titres of the immunized rats were significantly higher compared to the control group (P<0.05). Additionally, the testosterone concentration was effectively reduced following the intensified immunization. The testes of the immunized group exhibited a reduction in size and a significant decrease in the number of spermatogonia in the testicular tissue compared to the control group (P<0.05). These data indicate that the recombinant GnRH6-kisspeptin protein effectively induced immunological castration in rats.

Systematically monitoring the baseline sensitivity of troublesome weeds to herbicides is a crucial step in the early detection of their market lifespan. Florpyrauxifen-benzyl is one of the most important herbicides used in rice production throughout the world, and has been used for 5 yr in China. Barnyardgrass is one of the main targeted weed species of florpyrauxifen-benzyl. In total, 114 barnyardgrass populations were collected from rice fields in Jiangsu Province, China, and using whole-plant bioassays they were screened for susceptibility to florpyrauxifen-benzyl. The GR50 values (representing the dose that causes a 50% reduction in fresh weight of aboveground parts) of florpyrauxifen-benzyl for all populations ranged from 1.0 to 34.5 g ai ha−1, with an average of 6.8 g ai ha−1, a baseline sensitivity dose of 3.3 g ai ha−1, and a baseline sensitivity index of 34.5. Twenty-one days after treatment with florpyrauxifen-benzyl at the labeled dose (36 g ai ha−1), 90% of the barnyardgrass populations exhibited >95% reductions in fresh weight of aboveground parts. Compared with the baseline sensitivity dose, 63, 44, and 7 populations had, respectively, no resistance (55%), low resistance (39%), and moderate resistance (6%) to florpyrauxifen-benzyl. Furthermore, the GR50 distribution of barnyardgrass populations did not show a significant correlation with collection location, planting method (direct-seeding or transplanting), or rice species (Oryza sativa L. ssp. indica or ssp. japonica) at any of rice fields where seeds had been collected (P > 0.05). In conclusion, florpyrauxifen-benzyl remains effective for barnyardgrass control in rice fields despite serious resistance challenges.

The betatron radiation source features a micrometer-scale source size, a femtosecond-scale pulse duration, milliradian-level divergence angles and a broad spectrum exceeding tens of keV. It is conducive to the high-contrast imaging of minute structures and for investigating interdisciplinary ultrafast processes. In this study, we present a betatron X-ray source derived from a high-charge, high-energy electron beam through a laser wakefield accelerator driven by the 1 PW/0.1 Hz laser system at the Shanghai Superintense Ultrafast Laser Facility (SULF). The critical energy of the betatron X-ray source is 22 ± 5 keV. The maximum X-ray flux reaches up to 4 × 109 photons for each shot in the spectral range of 5–30 keV. Correspondingly, the experiment demonstrates a peak brightness of 1.0 × 1023 photons·s−1·mm−2·mrad−2·0.1%BW−1, comparable to those demonstrated by third-generation synchrotron light sources. In addition, the imaging capability of the betatron X-ray source is validated. This study lays the foundation for future imaging applications.

The multi-robot path planning problem is an NP-hard problem. The coati optimization algorithm (COA) is a novel metaheuristic algorithm and has been successfully applied in many fields. To solve multi-robot path planning optimization problems, we embed two differential evolution (DE) strategies into COA, a self-adaptive differential evolution-based coati optimization algorithm (SDECOA) is proposed. Among these strategies, the proposed algorithm adaptively selects more suitable strategies for different problems, effectively balancing global and local search capabilities. To validate the algorithm’s effectiveness, we tested it on CEC2020 benchmark functions and 48 CEC2020 real-world constrained optimization problems. In the latter’s experiments, the algorithm proposed in this paper achieved the best overall results compared to the top five algorithms that won in the CEC2020 competition. Finally, we applied SDECOA to optimization multi-robot online path planning problem. Facing extreme environments with multiple static and dynamic obstacles of varying sizes, the SDECOA algorithm consistently outperformed some classical and state-of-the-art algorithms. Compared to DE and COA, the proposed algorithm achieved an average improvement of 46% and 50%, respectively. Through extensive experimental testing, it was confirmed that our proposed algorithm is highly competitive. The source code of the algorithm is accessible at: https://ww2.mathworks.cn/matlabcentral/fileexchange/164876-HDECOA.

Access to information via social media is one of the biggest differentiators of public health crises today. During the early stages of the Covid-19 outbreak in January 2020, we conducted an experiment in Wuhan, China to assess the impact of viral social media content on pro-social and trust behaviours and preferences towards risk taking with known and unknown probabilities. Prior to the experiment, participants viewed one of two videos that had been widely and anonymously shared on Chinese social media: a central government leader visiting a local hospital and supermarket, or health care volunteers transiting to Wuhan. In a control condition, participants watched a Neutral video, unrelated to the crisis. Viewing one of the leadership or volunteer videos leads to higher levels of pro-sociality and lesser willingness to take risks in an ambiguous situation relative to the control condition. The leadership video, however, induces lower levels of trust. We provide evidence from two post-experiment surveys that the video’s impact on pro-sociality is modulated by influencing the viewer’s affective emotional state.

Developing large-eddy simulation (LES) wall models for separated flows is challenging. We propose to leverage the significance of separated flow data, for which existing theories are not applicable, and the existing knowledge of wall-bounded flows (such as the law of the wall) along with embedded learning to address this issue. The proposed so-called features-embedded-learning (FEL) wall model comprises two submodels: one for predicting the wall shear stress and another for calculating the eddy viscosity at the first off-wall grid nodes. We train the former using the wall-resolved LES (WRLES) data of the periodic hill flow and the law of the wall. For the latter, we propose a modified mixing length model, with the model coefficient trained using the ensemble Kalman method. The proposed FEL model is assessed using the separated flows with different flow configurations, grid resolutions and Reynolds numbers. Overall good a posteriori performance is observed for predicting the statistics of the recirculation bubble, wall stresses and turbulence characteristics. The statistics of the modelled subgrid-scale (SGS) stresses at the first off-wall grids are compared with those calculated using the WRLES data. The comparison shows that the amplitude and distribution of the SGS stresses and energy transfer obtained using the proposed model agree better with the reference data when compared with the conventional SGS model.

Depression is one of the major mental disorders, which seriously endangers human health, brings a serious burden to patients’ families. In this study, we intended to further explore the antidepressant-like effect and possible molecular mechanisms of Salidroside (SAL). We built corticosterone (CORT)-induced depressive mice model and used behavioural tests to evaluate depression behaviour. To explore the molecular mechanisms of SAL, we employed a variety of methods such as immunofluorescence, western blot, pharmacological interference, etc. The results demonstrated that SAL both at 25 mg/kg and 50 mg/kg can reduce immobility time in the tail suspension test (TST). At the same time, SAL treatment could restore the reduced sugar water intake preference in the sucrose preference test (SPT) in CORT-induced depressive mice and reduce the immobility time in TST and forced swimming experiments (FST). In addition, SAL treatment reversed the reduction in the number of Ki-67, BrdU, and NeuN in the hippocampus due to CORT treatment. SAL treatment also restored the expression of SIRT1, PGC-1α, brain-derived neurotrophic factor (BDNF) and other proteins in the hippocampus. In addition, after blocking SIRT1 signalling with EX527, we found that the treatment with SAL failed to reduce the immobility time in TST and FST, the level of SIRT1 and PGC-1α activity were correspondingly downregulated, and the expression of DCX and Ki-67 in the hippocampus failed to be activated. These findings suggested that SAL exerts antidepressant-like effects by promoting hippocampal neurogenesis through the SIRT1/PGC-1α signalling pathway.

MicroRNAs (miRNAs) are endogenous, non-coding RNAs, which are functional in a variety of biological processes through post-transcriptional regulation of gene expression. However, the role of miRNAs in the interaction between Bacillus thuringiensis and insects remains unclear. In this study, small RNA libraries were constructed for B. thuringiensis-infected (Bt) and uninfected (CK) Spodoptera exigua larvae (treated with double-distilled water) using Illumina sequencing. Utilising the miRDeep2 and Randfold, a total of 233 known and 726 novel miRNAs were identified, among which 16 up-regulated and 34 down-regulated differentially expressed (DE) miRNAs were identified compared to the CK. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that potential target genes of DE miRNAs were associated with ABC transporters, fatty acid metabolism and MAPK signalling pathway which are related to the development, reproduction and immunity. Moreover, two miRNA core genes, SeDicer1 and SeAgo1 were identified. The phylogenetic tree showed that lepidopteran Dicer1 clustered into one branch, with SeDicer1 in the position closest to Spodoptera litura Dicer1. A similar phylogenetic relationship was observed in the Ago1 protein. Expression of SeDicer1 increased at 72 h post infection (hpi) with B. thuringiensis; however, expression of SeDicer1 and SeAgo1 decreased at 96 hpi. The RNAi results showed that the knockdown of SeDicer1 directly caused the down-regulation of miRNAs and promoted the mortality of S. exigua infected by B. thuringiensis GS57. In conclusion, our study is crucial to understand the relationship between miRNAs and various biological processes caused by B. thuringiensis infection, and develop an integrated pest management strategy for S. exigua via miRNAs.

Mosquito-borne diseases have emerged in North Borneo in Malaysia due to rapid changes in the forest landscape, and mosquito surveillance is key to understanding disease transmission. However, surveillance programmes involving sampling and taxonomic identification require well-trained personnel, are time-consuming and labour-intensive. In this study, we aim to use a deep leaning model (DL) to develop an application capable of automatically detecting mosquito vectors collected from urban and suburban areas in North Borneo, Malaysia. Specifically, a DL model called MobileNetV2 was developed using a total of 4880 images of Aedes aegypti, Aedes albopictus and Culex quinquefasciatus mosquitoes, which are widely distributed in Malaysia. More importantly, the model was deployed as an application that can be used in the field. The model was fine-tuned with hyperparameters of learning rate 0.0001, 0.0005, 0.001, 0.01 and the performance of the model was tested for accuracy, precision, recall and F1 score. Inference time was also considered during development to assess the feasibility of the model as an app in the real world. The model showed an accuracy of at least 97%, a precision of 96% and a recall of 97% on the test set. When used as an app in the field to detect mosquitoes with the elements of different background environments, the model was able to achieve an accuracy of 76% with an inference time of 47.33 ms. Our result demonstrates the practicality of computer vision and DL in the real world of vector and pest surveillance programmes. In the future, more image data and robust DL architecture can be explored to improve the prediction result.