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.

The knowledge of the critical periods of crop-weed competition is crucial for designing weed management strategies in cropping systems. In the Lower Yangtze Valley, China, field experiments were conducted in 2011 and 2012 to study the effect of interference from mixed natural weed populations on cotton growth and yield and determine the critical period for weed control (CPWC) in direct-seeded cotton. Two treatments were applied: allowing weeds to infest the crop or keeping plots weed-free for increasing periods (0, 1, 2, 4, 6, 8, 10, 12, 14, and 20 weeks) after crop emergence. The results showed that mixed natural weed infestations led to 35 to 55 cm shorter cotton plants with stem diameters 10 to 13 mm smaller throughout the season, fitting well with modified Gompertz and logistic models, respectively. Season-long competition with weeds reduced the number of fruit branches per plant by 65% to 82%, decreasing boll number per plant by 86% to 96% and single boll weight by approximately 24%. Weed-free seed cotton yields ranged from 2,900 to 3,130 kg ha-1, while yield loss increased with the duration of weed infestation, reaching up to 83% to 96% compared with permanent weed-free plots. Modified Gompertz and logistic models were used to analyze the impact of increasing weed control duration and weed interference on relative seed cotton yield (percentage of season-long weed-free cotton), respectively. Based on a 5% yield-loss threshold, the CPWC was found to be from 145 growing degree days (GDD) to 994 GDD, corresponding to 14 days after emergence (DAE) to 85 DAE. These findings emphasize the importance of implementing effective weed control measures from 14 DAE to 85 DAE in the Lower Yangtze Valley, China, to prevent crop losses exceeding a 5% yield-loss threshold.

The propagation of multiple ultraintense femtosecond lasers in underdense plasmas is investigated theoretically and numerically. We find that the energy merging effect between two in-phase seed lasers can be improved by using two obliquely incident guiding lasers whose initial phase is $\pi$ and $\pi /2$ ahead of the seed laser. Particle-in-cell simulations show that due to the repulsion and energy transfer of the guiding laser, the peak intensity of the merged light is amplified by more than five times compared to the seed laser. The energy conversion efficiency from all incident lasers to the merged light is up to approximately 60$\%$. The results are useful for many applications, including plasma-based optical amplification, charged particle acceleration and extremely intense magnetic field generation.

A high-energy pulsed vacuum ultraviolet (VUV) solid-state laser at 177 nm with high peak power by the sixth harmonic of a neodymium-doped yttrium aluminum garnet (Nd:YAG) amplifier in a KBe2BO3F2 prism-coupled device was demonstrated. The ultraviolet (UV) pump laser is a 352 ps pulsed, spatial top-hat super-Gaussian beam at 355 nm. A high energy of a 7.12 mJ VUV laser at 177 nm is obtained with a pulse width of 255 ps, indicating a peak power of 28 MW, and the conversion efficiency is 9.42% from 355 to 177 nm. The measured results fitted well with the theoretical prediction. It is the highest pulse energy and highest peak power ever reported in the VUV range for any solid-state lasers. The high-energy, high-peak-power, and high-spatial-uniformity VUV laser is of great interest for ultra-fine machining and particle-size measurements using UV in-line Fraunhofer holography diagnostics.

Understanding the genetic basis of porcine mental health (PMH)-related traits in intensive pig farming systems may promote genetic improvement animal welfare enhancement. However, investigations on this topic have been limited to a retrospective focus, and phenotypes have been difficult to elucidate due to an unknown genetic basis. Intensively farmed pigs, such as those of the Duroc, Landrace, and Yorkshire breeds, have undergone prolonged selection pressure in intensive farming systems. This has potentially subjected genes related to mental health in these pigs to positive selection. To identify genes undergoing positive selection under intensive farming conditions, we employed multiple selection signature detection approaches. Specifically, we integrated disease gene annotations from three human gene–disease association databases (Disease, DisGeNET, and MalaCards) to pinpoint genes potentially associated with pig mental health, revealing a total of 254 candidate genes related to PMH. In-depth functional analyses revealed that candidate PMH genes were significantly overrepresented in signaling-related pathways (e.g., the dopaminergic synapse, neuroactive ligand‒receptor interaction, and calcium signaling pathways) or Gene Ontology terms (e.g., dendritic tree and synapse). These candidate PMH genes were expressed at high levels in the porcine brain regions such as the hippocampus, amygdala, and hypothalamus, and the cell type in which they were significantly enriched was neurons in the hippocampus. Moreover, they potentially affect pork meat quality traits. Our findings make a significant contribution to elucidating the genetic basis of PMH, facilitating genetic improvements for the welfare of pigs and establishing pigs as valuable animal models for gaining insights into human psychiatric disorders.

Several novel anthropometric indices, including paediatric body adiposity index (BAIp) and triponderal mass index (TMI), have emerged as potential tools for estimating body fat in preschool children. However, their comparative validity and accuracy, particularly when compared with established indicators such as BMI, have not been thoroughly investigated. This cross-sectional study enrolled 2869 preschoolers aged 3–6 years in Wuhan, China. The non-parametric Bland–Altman analysis was employed to evaluate the agreement between BMI, BAIp and TMI with percentage of body fat (PBF), determined by bioelectrical impedance analysis (BIA), serving as the reference measure of adiposity. Additionally, receiver operating characteristic curve analysis was conducted to assess the effectiveness of BMI, BAIp and TMI in screening for obesity. BAIp demonstrated the least bias in estimating PBF, showing discrepancies of 3·64 % (95 % CI 3·40 %, 4·12 %) in boys and 3·95 % (95 % CI 3·79 %, 4·23 %) in girls. Conversely, BMI underestimated PBF by 3·89 % (95 % CI 3·70 %, 4·37 %) in boys and 4·81 % (95 % CI 4·59 %, 5·09 %) in girls, while TMI also underestimated PBF by 5·15 % (95 % CI 4·90 %, 5·52 %) in boys and 5·68 % (95 % CI 5·30 %, 5·91 %) in girls. BAIp exhibited the highest AUC values (AUC = 0·867–0·996) in boys, whereas in girls, there was no statistically significant difference between BMI (AUC = 0·936, 95 % CI 0·921, 0·948) and BAIp (AUC = 0·901, 95 % CI 0·883, 0·916) in girls (P = 0·054). In summary, when considering the identification of obesity, BAIp shows promise as a screening tool for both boys and girls.

Purple nutsedge (Cyperus rotundus L.) is one of the world’s resilient upland weeds, primarily spreading through its tubers. Its emergence in rice (Oryza sativa L.) fields has been increasing, likely due to changing paddy-farming practices. This study aimed to investigate how C. rotundus, an upland weed, can withstand soil flooding and become a problematic weed in rice fields. The first comparative analysis focused on the survival and recovery characteristics of growing and mature tubers of C. rotundus exposed to soil-flooding conditions. Notably, mature tubers exhibited significant survival and recovery abilities in these environments. Based on this observation, further investigation was carried out to explore the morphological structure, nonstructural carbohydrates, and respiratory mechanisms of mature tubers in response to prolonged soil flooding. Over time, the mature tubers did not form aerenchyma but instead gradually accumulated lignified sclerenchymal fibers, with lignin content also increasing. After 90 d, the lignified sclerenchymal fibers and lignin contents were 4.0 and 1.1 times higher than those in the no soil-flooding treatment. Concurrently, soluble sugar content decreased while starch content increased, providing energy storage, and alcohol dehydrogenase activity rose to support anaerobic respiration via alcohol fermentation. These results indicated that mature tubers survived in soil-flooding conditions by adopting a low-oxygen quiescence strategy, which involves morphological adaptations through the development of lignified sclerenchymal fibers, increased starch reserves for energy storage, and enhanced anaerobic respiration. This mechanism likely underpins the flooding tolerance of mature C. rotundus tubers, allowing them to endure unfavorable conditions and subsequently germinate and grow once flooding subsides. This study provides a preliminary explanation of the mechanism by which mature tubers of C. rotundus from the upland areas confer flooding tolerance, shedding light on the reasons behind this weed’s increasing presence in rice fields.

The importance of automating pavement maintenance tasks for highway systems has garnered interest from both industry and academia. Despite significant research efforts and promising demonstrations being devoted to reaching a level of semi-automation featuring digital sensing and inspection, site maintenance work still requires manual processes using special vehicles and equipment, reflecting a clear gap to transition to fully autonomous maintenance. This paper reviews the current progress in pavement maintenance automation in terms of inspection and repair operations, followed by a discussion of three key technical challenges related to robotic sensing, control, and actuation. To address these challenges, we propose a conceptual solution we term Autonomous Maintenance Plant (AMP), mainly consisting of five modules for sensing, actuation, control, power supply, and mobility. This AMP concept is part of the “Digital Roads” project’s cyber-physical platform where a road digital twin (DT) is created based on its physical counterpart to enable real-time condition monitoring, sensory data processing, maintenance decision making, and repair operation execution. In this platform, the AMP conducts high-resolution survey and autonomous repair operations enabled (instructed) by the road DT. This process is unmanned and completely autonomous with an expectation to create a fully robotized highway pavement maintenance system.

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.

Newborn calf diarrhea has led to widespread overuse of antibiotics. Therefore, it is crucial to find effective solutions for calf diarrhea. In this study, we aimed to evaluate the impact of the synthetic organic zinc-chelating-peptide glycine-glutamine-Zn (GQ-Zn) on the microbiota and metabolites in the gut of calves with diarrhea. The results showed that GQ-Zn alleviated diarrhea in calves. Additionally, 16S rDNA sequencing and metabolomic analysis revealed that GQ-Zn improved antioxidant capacity, relieved inflammation, altered the gut microbiota by decreasing the number of harmful bacteria Prevotella denticola, Fusobacterium necrophorum and influenced metabolomic profiles via the linoleic acid metabolic pathway in calves. In conclusion, GQ-Zn supplementation alleviated diarrhea through regulating the gut microbiota and metabolites in pre-weaning Holstein calves.

The influence of outer large-scale motions (LSMs) on near-wall structures in compressible turbulent channel flows is investigated. To separate the compressibility effects, velocity fluctuations are decomposed into solenoidal and dilatational components using the Helmholtz decomposition method. Solenoidal velocity fluctuations manifest as near-wall streaks and outer large-scale structures. The spanwise drifting of near-wall solenoidal streaks is found to be driven by the outer LSMs, while LSMs have a trivial influence on the spanwise density of solenoidal streaks, consistent with the outer LSM impacts found in incompressible flows (Zhou et al., J. Fluid Mech., vol. 940, 2022, p. A23). Dilatational motions are characterized by the near-wall small-scale travelling-wave packets and the large-scale parts in the outer region. The streamwise advection velocity of the near-wall structures remains at $16 \sim 18u_{\tau }$, hardly influenced by Mach numbers, Reynolds numbers and wall temperatures. The spanwise drifting of near-wall dilatational structures, quantified by the particle image velocimetry method, follows a mechanism distinct from solenoidal streaks. This drifting velocity is notably larger than those of the solenoidal streaks, and the influence of outer LSMs is not the primary trigger for this drifting.

The practical implementation of machine learning in flow control is limited due to its significant training expenses. In the present study the convolutional neural network (CNN) trained with the data of the restricted nonlinear (RNL) model is used to predict the normal velocity on a detection plane at $y^+=10$ in a turbulent channel flow, and the predicted velocity is used as wall blowing and suction for drag reduction. An active control test is carried out by using the well-trained CNN in direct numerical simulation (DNS). Substantial drag reduction rates up to 19 % and 16 % are obtained based on the spanwise and streamwise wall shear stresses, respectively. Furthermore, we explore the online control of wall turbulence by combining the RNL model with reinforcement learning (RL). The RL is constructed to determine the optimal wall blowing and suction based on its observation of the wall shear stresses without using the label data on the detection plane for training. The controlling and training processes are conducted synchronously in a RNL flow field. The control strategy discovered by RL has similar drag reduction rates with those obtained previously by the established method. Also, the training cost decreases by over thirty times at $Re_{\tau }=950$ compared with the DNS-RL model. The present results provide a perspective that combining the RNL model with machine learning control for drag reduction in wall turbulence can be effective and computationally economical. Also, this approach can be easily extended to flows at higher Reynolds numbers.