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Pulmonary arterial hypertension (PAH) is characterized by persistently elevated pulmonary artery pressure and vascular resistance. Sympathetic overactivity in hypertension participates in pulmonary vascular remodeling and heart failure. The present study aims to explore the efficacy of highly selective thoracic sympathectomy (HSTS) on lowering pulmonary artery pressure, reversing pulmonary vascular remodeling, and improving right ventricular function in rats. A total of 24 Sprague–Dawley rats were randomly assigned into the control group (n = 8) and experimental group (n = 16). Rats in the control group were intraperitoneally injected with 0.9% normal saline, and those in the experimental group were similarly administered with received monocrotaline (MCT) injections at 60 mg/kg. Two weeks later, rats in the experimental group were further subdivided randomly into the MCT-HSTS group (n = 8) and MCT-sham group (n = 8), and they were surgically treated with HSTS and sham operation, respectively. Two weeks later, significantly lowered mean pulmonary artery pressure (mPAP), pulmonary artery systolic pressure (sPAP), and the ratio of sPAP to femoral artery systolic pressure (sFAP) were detected in the MCT-HSTS group than those of the MCT-sham group. In addition, rats in the MCT-HSTS group presented a significantly lower ratio of vascular wall area to the total vascular area (WT%), right ventricular hypertrophy index, and degrees of right ventricular fibrosis and lung fibrosis in comparison to those of the MCT-sham group. HSTS significantly downregulated protein levels of inflammasomes in pulmonary artery smooth muscle cells (PASMCs). Collectively, HSTS effectively reduces pulmonary artery pressure, pulmonary arteriolar media hypertrophy, and right ventricular hypertrophy in MCT-induced PAH rats. It also exerts an anti-inflammatory effect on PASMCs in PAH rats by suppressing inflammasomes and the subsequent release of inflammatory cytokines.

Abstract Delayed diagnosis in patients with pulmonary tuberculosis (PTB) often leads to serious public health problems. High throughput sequencing was used to determine the expression levels of lncRNAs, mRNAs, and miRNAs in the lesions and adjacent health lung tissues of patients with PTB. Their differential expression profiles between the two groups were compared, and 146 DElncRs, 447 DEmRs, and 29 DEmiRs were obtained between lesions and adjacent health tissues in patients with PTB. Enrichment analysis for mRNAs showed that they were mainly involved in Th1, Th2, and Th17 cell differentiation. The lncRNAs, mRNAs with target relationship with miRNAs were predicted respectively, and correlation analysis was performed. The ceRNA regulatory network was obtained by comparing with the differentially expressed transcripts (DElncRs, DEmRs, DEmiRs), then 2 lncRNAs mediated ceRNA networks were established. The expression of genes within the network was verified by quantitative real-time PCR (qRT-PCR). Flow cytometric analysis revealed that the proportion of Th1 cells and Th17 cells was lower in PTB than in controls, while the proportion of Th2 cells increased. Our results provide rich transcriptome data for a deeper investigation of PTB. The ceRNA regulatory network we obtained may be instructive for the diagnosis and treatment of PTB.

Abstract Background Differentially expressed genes/proteins (DEGs/DEPs) play critical roles in pulmonary tuberculosis (PTB) diagnosis and treatment. However, there is a scarcity of reports on DEGs/DEPs in lung tissues and blood samples in PTB patients. Objective We aim to identify the DEGs/DEPs in lung tissues and blood samples of PTB patients and investigate their roles in PTB. Materials and Methods The lung granulomas and normal tissues were collected from PTB patients for proteomic and transcriptomic analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses annotated the functions of DEGs/DEPs. The GSE107994 data set was downloaded to identify the DEGs/DEPs in peripheral blood. The common DEGs and DEPs were identified. A nomogram was established. Pearson correlation analysis was conducted. Results Eighty‐three DEGs/DEPs were identified. These DEGs/DEPs were mainly enriched in the movement of cell or subcellular components, regulation of cellular component biogenesis, and actin filament‐based process as well as in the pathways of inositol phosphate metabolism, adherens junction, phosphatidylinositol signaling system, leukocyte transendothelial migration, regulation of actin cytoskeleton, and tight junction. There were eight common DEGs/DEPs (TYMP, LAP3, ADGRL2, SIL1, LMO7, SULF 1, ANXA3, and PACSIN3) between the lung tissues and blood samples. They were effective in predicting tuberculosis. Moreover, the activated dendritic cells, macrophages, monocytes, neutrophils, and regulatory T cells were significantly positively correlated with TYMP (r > .50), LAP3 (r > .50), SIL1 (r > .50), ANXA3 (r > .5), and PACSIN3 (r .05). Limitations The sample size was small. Conclusions Eight common DEGs/DEPs of lung tissues and blood samples were identified. They were correlated with immune cells and demonstrated predictive value for PTB. Our data may facilitate the diagnosis and treatment of PTB.

The bamboo is widely distributed around the world and the product process is developed well, the biomass bamboo fiber composite is potential to providing strong confinement for concrete, therefore in this paper, bamboo sheet twining tube-confined concrete columns with an inner original bamboo tube (BSTCB) is proposed. This column is a new structure that consists of an external bamboo fiber composite tube and an internal original bamboo tube filled with concrete between the outer and inner tubes. A total of 18 specimens were tested in axial compression. The effects of BCT thickness and original bamboo tube diameter on the mechanical properties of the specimens were analyzed. The experimental results suggested that BCT has a strong lateral confinement ability. The strength and deformability of concrete increased significantly when BCT thickness increased. When the number of BCT layers was in the range of 6–18 layers, the increase in peak stress ranged from 7.7 % to 12.8 % and the increase in ultimate strain ranged from 6.4 % to 24.0 %. The increase in the diameter of the original bamboo tubes had a positive effect to the deformation capacity, while limited effect on the concrete compressive strength. Within the change in diameter from 90 mm to 110 mm, the reduction in concrete strength is within 6.0 %, and the change in ultimate strain ranges from 8.5 % to 14.1 %. Finally, the peak stress and strain of the concrete were evaluated using the existing FRP-confined concrete model, and the full stress-strain curve of the concrete was obtained.

The paper proposes PCB-DETR, a novel detection network for PCB surface defect identification, which enhances the performance of small defect detection through improvements to the Deformable-DETR model. Traditional supervised learning methods demonstrate strong performance in PCB surface defect detection but require large labeled datasets, which are often costly and impractical to obtain. PCB-DETR addresses this challenge by integrating EfficientNet as the backbone and introducing a spatial attention offset module that dynamically adjusts attention maps to enhance detection accuracy for small and scattered defects.The proposed model is evaluated on the PKU-Market-PCB dataset, consisting of six common PCB defect types. Experimental results demonstrate that PCB-DETR outperforms several mainstream object detection models, including Faster R-CNN, YOLOv5, YOLOv7, and the original Deformable-DETR, achieving higher mean Average Precision (mAP) and recall. The incorporation of spatial attention offset module and efficient feature extraction techniques allows PCB-DETR to excel in detecting small and complex defects while maintaining low computational overhead, making it suitable for real-time industrial applications. The study concludes that, compared to existing methods, PCB-DETR improves accuracy and the ability to determine whether defects are present in images, providing a reliable solution for small defect detection in complex manufacturing environments.

This paper investigates integration of distributed energy resources (DERs) in microgrids (MGs) through two-stage power conversion structures consisting of DC-DC boost converter and DC-AC voltage source converter (VSC) subsystems. In contrast to existing investigations that treated DC-link voltage as an ideal constant voltage, this paper considers the non-ideal dynamic coupling between both subsystems for completeness and higher accuracy, which introduces additional DC-side dynamics to the VSC. The analysis shows parameters of the boost converter's power model that impact stability through the DC-link. Carefully selecting these parameters can mitigate this effect on stability and improve dynamic performance across the DC-link. Hence, an optimization framework is developed to facilitate in selecting adequate boost converter parameters in designing a stable voltage source converter-based microgrid (VSC-MG). The developed optimization framework, based on particle swarm optimization, considers dynamic coupling between both subsystems and is also effective in avoiding inadequate boost converter parameters capable of propagating instability through the DC-link to the VSC. Simulations are performed with MATLAB/Simulink to validate theoretical analyses.

To allow a more effective utilization of distributed energy resources (DERs) in microgrids (MGs) and avoid violating operational limits of the asset, it is important that the stability and transient behavior or response of quantities such as voltages and currents is improved and maintained at appropriate levels. This paper uses small-signal analysis to investigate the behavior of blended integral-proportional/proportional-integral (IPPI) voltage and current controllers by deriving state-space models and performing eigenvalue analysis in a voltage source converter-based microgrid (VSC-MG). The results are compared with conventional PI-based voltage and current controllers on the same VSC-MG, and show that by adjusting the blending factor for each VSC’s IPPI-based voltage and current controllers, an improvement in stability and transient behavior can be achieved. An optimization framework based on particle swarm optimization is developed to aid in selecting blending factors for stable VSC-MG operation. Simulations are performed with the aid of MATLAB/Simulink to validate the theoretical analyses.

Emergency response resources (ERRs) such as mobile energy resources (MERs) and repair crews (RCs) play a pivotal role in the efficient restoration of power distribution systems after disasters. This paper presents a computationally tractable approach to utilize ERRs and post-disaster available distributed energy resources (PDA-DERs) in the restoration of disaster-impacted distribution systems. The post-disaster restoration model is proposed to co-optimize the dispatch of pre-allocated ERRs and PDA-DERs to minimize the impact of high-impact low-frequency (HILF) events on customers, i.e., energy not served for the entire restoration window. Compared with existing restoration strategies using ERRs, the proposed approach is more tractable since, in the restoration model, a super-node approximation (SNA) of distribution networks and the convex hulls relaxation (CHR) of non-linear constraints are introduced to achieve the best trade-off between computational burden and accuracy. Tests of the proposed approach on IEEE test feeders demonstrated that a combination of SNA and CHR remarkably reduces the solution time of the post-disaster restoration model.

Calculation of static voltage stability margin (SVSM) of AC/DC power systems with lots of renewable energy sources (RESs) integration requires consideration of uncertain load growth and renewable energy generation output. This paper presents a bi-level optimal power flow (BLOPF) model to identify the worst-case SVSM of an AC/DC power system with line commutation converter-based HVDC and multi-terminal voltage sourced converter-based HVDC transmission lines. Constraints of uncertain load growth's hypercone model and control mode switching of DC converter stations are considered in the BLOPF model. Moreover, uncertain RES output fluctuations are described as intervals, and two three-level optimal power flow (TLOPF) models are established to identify interval bounds of the system worst-case SVSM. The two TLOPF models are both transformed into max-min bi-level optimization models according to independent characteristics of different uncertain variables. Then, transforming the inner level model into its dual form, max-min BLOPF models are simplified to single-level optimization models for direct solution. Calculation results on the modified IEEE-39 bus AC/DC case and an actual large-scale AC/DC case in China indicate correctness and efficiency of the proposed identification method.

Spinal cord injury (SCI) is a major clinical problem in young patients. The major hurdle in SCI regeneration is the replacement of lost nerve communication signals due to injury. Here we have prepared a biocompatible electrical conductive composite such as Collagen-Polypyrrole combined with Quercetin (Col-PPy-Qur) composite. The prepared composites are characterized for their chemical functionality and morphology by the FTIR and SEM & TEM analysis, respectively. The Col-PPy-Qur composite observed electrical conductivity at 0.0653 s/cm due to the conductive Polypyrrole polymer present in the composite. The Col-PPy-Qur composite exhibits a mechanical strength of 0.1281 mPa, similar to the native human spinal cord's mechanical strength. In order to explore the regeneration potential, the viability of the composite has been tested with human astrocyte cells (HACs). The Tuj1 and GFAF marker expression was quantized by RT-PCR analysis. Increased Tuj1 and decreased GFAF expression by the Col-PPy-Qur composite indicated the potential differentiation ability of the HACs into neuron cells. The results indicated that the Col-PPy-Qur composite could have good regeneration and differentiation ability, better biocompatibility, and suitable mechanical and conductivity properties. It can act as an excellent strategy for spinal cord regeneration in the nearer future.

The weight of live pigs is directly related to their health, nutrition management, disease prevention and control, and the overall economic benefits to livestock enterprises. Direct weighing can induce stress responses in pigs, leading to decreased productivity. Therefore, modern livestock industries are increasingly turning to non-contact techniques for estimating pig weight, such as automated monitoring systems based on computer vision. These technologies provide continuous, real-time weight-monitoring data without disrupting the pigs’ normal activities or causing stress, thereby enhancing breeding efficiency and management levels. Two methods of pig weight estimation based on image and point cloud data are comprehensively analyzed in this paper. We first analyze the advantages and disadvantages of the two methods and then discuss the main problems and challenges in the field of pig weight estimation technology. Finally, we predict the key research areas and development directions in the future.

The body size, shape, weight, and scoring of goats are crucial indicators for assessing their growth, health, and meat production. The application of computer vision technology to measure these parameters is becoming increasingly prevalent. However, in real farm environments, obstacles, such as fences, ground conditions, and dust, pose significant challenges for obtaining accurate goat point cloud data. These obstacles lead to difficulties in rapid data extraction and result in incomplete reconstructions, causing substantial measurement errors. To address these challenges, we developed a system for real-time, non-contact acquisition, extraction, and reconstruction of goat point clouds using three depth cameras. The system operates in a scenario where goats walk naturally through a designated channel, and bidirectional distributed triggering logic is employed to ensure real-time acquisition of the point cloud. We also designed a noise recognition and filtering method tailored to handle complex environmental interferences found on farms, enabling automatic extraction of the goat point cloud. Furthermore, a distributed point cloud completion algorithm was developed to reconstruct missing sections of the goat point cloud caused by unavoidable factors such as railings and dust. Measurements of body height, body slant length, and chest circumference were calculated separately with deviation of no more than 25 mm and an average error of 3.1%. The system processes each goat in an average time of 3–5 s. This method provides rapid and accurate extraction and complementary reconstruction of 3D point clouds of goats in motion on real farms, without human intervention. It offers a valuable technological solution for non-contact monitoring and evaluation of goat body size, weight, shape, and appearance.

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioral assessments, enabling a comprehensive evaluation of the hens’ health, behavior, and population counts. To address the issue of insufficient data samples in the health detection process for individual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environments, various data enhancement methods such as rotation, shearing, color enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for applying visible light images and corresponding thermal infrared images in the health detection, behavioral analysis, and counting of caged laying hens under large-scale farming.

Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we constructed the CDNER dataset and developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the multiple fine-grained feature fusion (MFGFF) module with a BiLSTM neural network and employs an efficient global pointer (EGP) to predict the entity location encoding. In the MFGFF module, we designed three encoders: the character encoder, word encoder, and sentence encoder. This design effectively captured fine-grained features and improved the recognition accuracy of nested entities. Experimental results showed that the model performed robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of a knowledge graph and intelligent question-answering system for chicken diseases, but also provides a viable solution for extracting disease information that can be applied to other livestock species.

Abstract Background Although many molecules have been investigated as biomarkers for spinal cord injury (SCI) or ischemic stroke, none of them are specifically induced in central nervous system (CNS) neurons following injuries with low baseline expression. However, neuronal injury constitutes a major pathology associated with SCI or stroke and strongly correlates with neurological outcomes. Biomarkers characterized by low baseline expression and specific induction in neurons post‐injury are likely to better correlate with injury severity and recovery, demonstrating higher sensitivity and specificity for CNS injuries compared to non‐neuronal markers or pan‐neuronal markers with constitutive expressions. Methods In animal studies, young adult wildtype and global Atf3 knockout mice underwent unilateral cervical 5 (C5) SCI or permanent distal middle cerebral artery occlusion (pMCAO). Gene expression was assessed using RNA‐sequencing and qRT‐PCR, while protein expression was detected through immunostaining. Serum ATF3 levels in animal models and clinical human samples were measured using commercially available enzyme‐linked immune‐sorbent assay (ELISA) kits. Results Activating transcription factor 3 (ATF3), a molecular marker for injured dorsal root ganglion sensory neurons in the peripheral nervous system, was not expressed in spinal cord or cortex of naïve mice but was induced specifically in neurons of the spinal cord or cortex within 1 day after SCI or ischemic stroke, respectively. Additionally, ATF3 protein levels in mouse blood significantly increased 1 day after SCI or ischemic stroke. Importantly, ATF3 protein levels in human serum were elevated in clinical patients within 24 hours after SCI or ischemic stroke. Moreover, Atf3 knockout mice, compared to the wildtype mice, exhibited worse neurological outcomes and larger damage regions after SCI or ischemic stroke, indicating that ATF3 has a neuroprotective function. Conclusions ATF3 is an easily measurable, neuron‐specific biomarker for clinical SCI and ischemic stroke, with neuroprotective properties. Highlights ATF3 was induced specifically in neurons of the spinal cord or cortex within 1 day after SCI or ischemic stroke, respectively. Serum ATF3 protein levels are elevated in clinical patients within 24 hours after SCI or ischemic stroke. ATF3 exhibits neuroprotective properties, as evidenced by the worse neurological outcomes and larger damage regions observed in Atf3 knockout mice compared to wildtype mice following SCI or ischemic stroke.

The Zn–Co double metal cyanide (DMC) complex is regarded as a promising catalyst for the copolymerization of CO2 and propylene epoxide (PO). Various methods have been adopted to improve the catalytic performance of DMC. In this study, polyvinylpyrrolidone (PVP), as a co-complexing agent, was innovatively used to modify DMC and applied to the synthesis of poly(ether–carbonate) polyols from CO2, PO, and a chain transfer agent. The modified catalysts exhibited higher catalytic activity than the catalysts prepared by conventional methods, with a nearly 10 % increase in PO conversion. The most critical breakthrough was a reduction of nearly 5 wt% in the byproduct propylene–carbonate. The results confirmed that amide groups in PVP could coordinate with active center Zn2+, providing the modified catalysts with advantages such as low-symmetry structure, high specific surface area, and strong acidity. Moreover, the dual effects of PVP coordination and dispersion led to dramatic differences in the microstructure and activity of the catalysts when added during different preparation processes of DMC. The coordination effect of PVP was dominant in the nucleation stages which affected the crystal structure and morphology, whereas, the dispersion effect was dominant in the aging stages which prevented the catalyst particles from growing and thus a higher specific surface area. This is critical for paving the way towards exploring the introduction of groups with high steric hindrance into Zn–Co complex catalysts. In addition, a detailed and in-depth insight into the structure–activity relationship of DMC for poly(ether–carbonate) polyols synthesis was summarized.

Abstract Behavior is one of the important factors reflecting the health status of dairy cows, and when dairy cows encounter health problems, they exhibit different behavioral characteristics. Therefore, identifying dairy cow behavior not only helps in assessing their physiological health and disease treatment but also improves cow welfare, which is very important for the development of animal husbandry. The method of relying on human eyes to observe the behavior of dairy cows has problems such as high labor costs, high labor intensity, and high fatigue rates. Therefore, it is necessary to explore more effective technical means to identify cow behaviors more quickly and accurately and improve the intelligence level of dairy cow farming. Automatic recognition of dairy cow behavior has become a key technology for diagnosing dairy cow diseases, improving farm economic benefits and reducing animal elimination rates. Recently, deep learning for automated dairy cow behavior identification has become a research focus. However, in complex farming environments, dairy cow behaviors are characterized by multiscale features due to large scenes and long data collection distances. Traditional behavior recognition models cannot accurately recognize similar behavior features of dairy cows, such as those with similar visual characteristics, i.e., standing and walking. The behavior recognition method based on 3D convolution solves the problem of small visual feature differences in behavior recognition. However, due to the large number of model parameters, long inference time, and simple data background, it cannot meet the demand for real-time recognition of dairy cow behaviors in complex breeding environments. To address this, we developed an effective yet lightweight model for fast and accurate dairy cow behavior feature learning from video data. We focused on four common behaviors: standing, walking, lying, and mounting. We recorded videos of dairy cow behaviors at a dairy farm containing over one hundred cows using surveillance cameras. A robust model was built using a complex background dataset. We proposed a two-pathway X3DFast model based on spatiotemporal behavior features. The X3D and fast pathways were laterally connected to integrate spatial and temporal features. The X3D pathway extracted spatial features. The fast pathway with R(2 + 1)D convolution decomposed spatiotemporal features and transferred effective spatial features to the X3D pathway. An action model further enhanced X3D spatial modeling. Experiments showed that X3DFast achieved 98.49% top-1 accuracy, outperforming similar methods in identifying the four behaviors. The method we proposed can effectively identify similar dairy cow behaviors while improving inference speed, providing technical support for subsequent dairy cow behavior recognition and daily behavior statistics.

Abstract To improve the detection speed of cow mounting behavior and the lightness of the model in dense scenes, this study proposes a lightweight rapid detection system for cow mounting behavior. Using the concept of EfficientNetV2, a lightweight backbone network is designed using an attention mechanism, inverted residual structure, and depth-wise separable convolution. Next, a feature enhancement module is designed using residual structure, efficient attention mechanism, and Ghost convolution. Finally, YOLOv5s, the lightweight backbone network, and the feature enhancement module are combined to construct a lightweight rapid recognition model for cow mounting behavior. Multiple cameras were installed in a barn with 200 cows to obtain 3343 images that formed the cow mounting behavior dataset. Based on the experimental results, the inference speed of the model put forward in this study is as high as 333.3 fps, the inference time per image is 4.1 ms, and the model mAP value is 87.7%. The mAP value of the proposed model is shown to be 2.1% higher than that of YOLOv5s, the inference speed is 0.47 times greater than that of YOLOv5s, and the model weight is 2.34 times less than that of YOLOv5s. According to the obtained results, the model proposed in the current work shows high accuracy and inference speed and acquires the automatic detection of cow mounting behavior in dense scenes, which would be beneficial for the all-weather real-time monitoring of multi-channel cameras in large cattle farms.

Background: This study explored icariin’s effects on endometrial thickness and follicle numbers in mouse models of premature ovarian failure (POF). Methods: We divided 8-week-old C57BL/6J female mice into five groups: NC (control), CTX (cyclophosphamide-induced POF), CTX-ICA (CTX-induced POF with icariin), OVX (ovariectomy-induced POF), and OVX-ICA (ovariectomy-induced POF with icariin). We monitored estrous cycles and assessed endometrial thickness and follicle counts using Hematoxylin & Eosin (H&E) staining. Results: CTX and OVX reduced estrous activity, and the CTX-ICA group exhibited higher activity. Compared with NC, the primary follicles were decreased in the CTX group (p = 0.005) but not in the CTX-ICA group (p = 0.272). Endometrial thickness was respectively thinner in the OVX and OVX-ICA vs. CTX-ICA, CTX, and NC groups (p < 0.001); the CTX vs. NC (p < 0.001) groups; and the CTX vs. CTX-ICA groups (p < 0.001). No significant differences were found between the CTX-ICA and NC groups (p = 0.972). Conclusions: In conclusion, CTX and OVX induce POF in mice, causing endometrial thinning and decreased follicles (CTX). Icariin may partly restore endometrial thickness.

Named Entity Recognition (NER) is a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question-answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges, such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries. To address these challenges, we propose PDCNER, a Pig Disease Chinese Named Entity Recognition method leveraging lexicon-enhanced BERT and contrastive learning. Firstly, we construct a domain-specific lexicon and pre-train word embeddings in the pig disease domain. Secondly, we integrate lexicon information of pig diseases into the lower layers of BERT using a Lexicon Adapter layer, which employs char–word pair sequences. Thirdly, to enhance feature representation, we propose a lexicon-enhanced contrastive loss layer on top of BERT. Finally, a Conditional Random Field (CRF) layer is employed as the model’s decoder. Experimental results show that our proposed model demonstrates superior performance over several mainstream models, achieving a precision of 87.76%, a recall of 86.97%, and an F1-score of 87.36%. The proposed model outperforms BERT-BiLSTM-CRF and LEBERT by 14.05% and 6.8%, respectively, with only 10% of the samples available, showcasing its robustness in data scarcity scenarios. Furthermore, the model exhibits generalizability across publicly available datasets. Our work provides reliable technical support for the information extraction of pig diseases in Chinese and can be easily extended to other domains, thereby facilitating seamless adaptation for named entity identification across diverse contexts.

Long-term exposure to a relatively high concentration of airborne bacteria emitted from intensive livestock houses could potentially threaten the health and welfare of animals and workers. There is a dual effect of air sterilization and promotion of vitamin D synthesis for the specific bands of ultraviolet light. This study investigated the potential use of A-band ultraviolet (UVA) tubes as a clean and safe way of reducing airborne bacteria and improving calf health. The composition and emission characteristics of airborne bacteria were investigated and used to determine the correct operating regime of UVA tubes in calf houses. Intermittent exceedances of indoor airborne bacteria were observed in closed calf houses. The measured emission intensity of airborne bacteria was 1.13 ± 0.09 × 107 CFU h−1 per calf. Proteobacteria were the dominant microbial species in the air inside and outside calf houses. After UVA radiation, the indoor culturable airborne bacteria decreased in all particle size ranges of the Anderson sampler, and it showed the highest reduction rate in the size range of 3.3–4.7 μm. The results of this study would enrich the knowledge of the source characteristics of the airborne bacteria in intensive livestock farming and contribute to the environmental control of cattle in intensive livestock production.

The research on automatic monitoring methods for greenhouse gases and hazardous gas emissions is currently a focal point in the fields of environmental science and climatology. Until 2023, the amount of greenhouse gases emitted by the livestock sector accounts for about 11–17% of total global emissions, with enteric fermentation in ruminants being the main source of the gases. With the escalating problem of global climate change, accurate and effective monitoring of gas emissions has become a top priority. Presently, the determination of gas emission indices relies on specialized instrumentation such as breathing chambers, greenfeed systems, methane laser detectors, etc., each characterized by distinct principles, applicability, and accuracy levels. This paper first explains the mechanisms and effects of gas production by ruminant production systems, focusing on the monitoring methods, principles, advantages, and disadvantages of monitoring gas concentrations, and a summary of existing methods reveals their shortcomings, such as limited applicability, low accuracy, and high cost. In response to the current challenges in the field of equipment for monitoring greenhouse and hazardous gas emissions from ruminant production systems, this paper outlines future perspectives with the aim of developing more efficient, user-friendly, and cost-effective monitoring instruments.

Group 2 innate lymphoid cells (ILC2) strongly modulate COPD pathogenesis. However, the significance of microbiota in ILC2s remains unelucidated. Herein, we investigated the immunomodulatory role of short-chain fatty acids (SCFAs) in regulating ILC2-associated airway inflammation and explores its associated mechanism in COPD. In particular, we assessed the SCFA-mediated regulation of survival, proliferation, and cytokine production in lung sorted ILC2s. To elucidate butyrate action in ILC2-driven inflammatory response in COPD models, we administered butyrate to BALB/c mice via drinking water. We revealed that SCFAs, especially butyrate, derived from dietary fiber fermentation by gut microbiota inhibited pulmonary ILC2 functions and suppressed both IL-13 and IL-5 synthesis by murine ILC2s. Using in vivo and in vitro experimentation, we validated that butyrate significantly ameliorated ILC2-induced inflammation. We further demonstrated that butyrate suppressed ILC2 proliferation and GATA3 expression. Additionally, butyrate potentially utilized histone deacetylase (HDAC) inhibition to enhance NFIL3 promoter acetylation, thereby augmenting its expression, which eventually inhibited cytokine production in ILC2s. Taken together, the aforementioned evidences demonstrated a previously unrecognized role of microbial-derived SCFAs on pulmonary ILC2s in COPD. Moreover, our evidences suggest that metabolomics and gut microbiota modulation may prevent lung inflammation of COPD.

Spiking neural networks (SNNs), as brain-inspired neural network models based on spikes, have the advantage of processing information with low complexity and efficient energy consumption. Currently, there is a growing trend to design hardware accelerators for dedicated SNNs to overcome the limitation of running under the traditional von Neumann architecture. Probabilistic sampling is an effective modeling approach for implementing SNNs to simulate the brain to achieve Bayesian inference. However, sampling consumes considerable time. It is highly demanding for specific hardware implementation of SNN sampling models to accelerate inference operations. Hereby, we design a hardware accelerator based on FPGA to speed up the execution of SNN algorithms by parallelization. We use streaming pipelining and array partitioning operations to achieve model operation acceleration with the least possible resource consumption, and combine the Python productivity for Zynq (PYNQ) framework to implement the model migration to the FPGA while increasing the speed of model operations. We verify the functionality and performance of the hardware architecture on the Xilinx Zynq ZCU104. The experimental results show that the hardware accelerator of the SNN sampling model proposed can significantly improve the computing speed while ensuring the accuracy of inference. In addition, Bayesian inference for spiking neural networks through the PYNQ framework can fully optimize the high performance and low power consumption of FPGAs in embedded applications. Taken together, our proposed FPGA implementation of Bayesian inference with SNNs has great potential for a wide range of applications, it can be ideal for implementing complex probabilistic model inference in embedded systems.

Within the current process of large-scale dairy-cattle breeding, to address the problems of low recognition-accuracy and significant recognition-error associated with existing visual methods, we propose a method for recognizing the feeding behavior of dairy cows, one based on an improved RefineMask instance-segmentation model, and using high-quality detection and segmentation results to realize the recognition of the feeding behavior of dairy cows. Firstly, the input features are better extracted by incorporating the convolutional block attention module into the residual module of the feature extraction network. Secondly, an efficient channel attention module is incorporated into the neck design to achieve efficient integration of feature extraction while avoiding the surge of parameter volume computation. Subsequently, the GIoU loss function is used to increase the area of the prediction frame to optimize the convergence speed of the loss function, thus improving the regression accuracy. Finally, the logic of using mask information to recognize foraging behavior was designed, and the accurate recognition of foraging behavior was achieved according to the segmentation results of the model. We constructed, trained, and tested a cow dataset consisting of 1000 images from 50 different individual cows at peak feeding times. The method’s effectiveness, robustness, and accuracy were verified by comparing it with example segmentation algorithms such as MSRCNN, Point_Rend, Cascade_Mask, and ConvNet_V2. The experimental results show that the accuracy of the improved RefineMask algorithm in recognizing the bounding box and accurately determining the segmentation mask is 98.3%, which is higher than that of the benchmark model by 0.7 percentage points; for this, the model parameter count size was 49.96 M, which meets the practical needs of local deployment. In addition, the technologies under study performed well in a variety of scenarios and adapted to various light environments; this research can provide technical support for the analysis of the relationship between cow feeding behavior and feed intake during peak feeding periods.

In animal husbandry applications, segmenting live pigs in complex farming environments faces many challenges, such as when pigs lick railings and defecate within the acquisition environment. The pig’s behavior makes point cloud segmentation more complex because dynamic animal behaviors and environmental changes must be considered. This further requires point cloud segmentation algorithms to improve the feature capture capability. In order to tackle the challenges associated with accurately segmenting point cloud data collected in complex real-world scenarios, such as pig occlusion and posture changes, this study utilizes PointNet++. The SoftPool pooling method is employed to implement a PointNet++ model that can achieve accurate point cloud segmentation for live pigs in complex environments. Firstly, the PointNet++ model is modified to make it more suitable for pigs by adjusting its parameters related to feature extraction and sensory fields. Then, the model’s ability to capture the details of point cloud features is further improved by using SoftPool as the point cloud feature pooling method. Finally, registration, filtering, and extraction are used to preprocess the point clouds before integrating them into a dataset for manual annotation. The improved PointNet++ model’s segmentation ability was validated and redefined with the pig point cloud dataset. Through experiments, it was shown that the improved model has better learning ability across 529 pig point cloud data sets. The optimal mean Intersection over Union (mIoU) was recorded at 96.52% and the accuracy at 98.33%. This study has achieved the automatic segmentation of highly overlapping pigs and pen point clouds. This advancement enables future animal husbandry applications, such as estimating body weight and size based on 3D point clouds.

Soil salinization is one of the main problems faced by modern agricultural production, especially cucumber production. This study screened the salt–alkali-tolerant cultivar ‘D1909’ and the salt–alkali-sensitive cultivar ‘D1604’ from 32 different cucumber ecological types on the basis of morphological indicators and salt–alkali indices combined with relevant physiological and biochemical indices. By performing a transcriptome metabolome analysis, the key gene CsSRG1, which is responsive to salt–alkali stress in cucumber, was screened, and its function was verified. The role of CsSRG1 in reducing salt–alkali stress in cucumber was clarified, and the mechanism of salt–alkali tolerance in cucumber was preliminarily explored. This study provides germplasm resources for cucumber salt–alkali tolerance breeding and a theoretical basis for the effective use of saline alkali soil to achieve high quality and high yield in other crops.

A good non-viral vector is the key to the success of gene delivery and therapy; hence, modified exosomes may overcome the physiological barrier in the delivery in vivo. This study aims to design a novel non-viral vector and verify its gene delivery function in vitro and in vivo for dapper homolog 2 (DACT2) genes. In this study, amphipathicity cationic exosomes with receptor targeted function and DACT2 gene loading function were constructed by exosomes, PEG, glycidyl hexadecyl dimethylammonium chloride, and recombinant VAR2CSA malaria protein (RVP) antibody. Characterizations of RVP antibody and PEG modified cationic lipid exosome (RVP-CL) nanocomplexes were evaluated by dynamic light scattering, atomic force microscopy, FTIR, and so on. The DNA condensation ability and stability were assessed by agarose gel electrophoresis. Cellular uptake efficiency and cytotoxicity in glioma cells were investigated. Furthermore, the tumor suppressive effect was evaluated in vitro and in vivo. The results indicated that RVP-CL had a uniform size of 100–200 nm and positive zeta potential. With high uptake efficiency, RVP-CL can rapidly target, recognize, and enter the glioma cell (KNS-42 and U118 mg) and release the gene. Moreover, RVP-CL/DACT2 can effectively inhibit the growth of glioma cells in vitro and in vivo. The modified cationic exosomes may offer a promising strategy for gene delivery in the treatment of RVP-positive gliomas or other tumors.

The imbalanced distribution of intrusion detection dataset will usually result in low detection rates for the minority categories and more unreported attacks. To reduce the impact of imbalanced dataset on classification performance, a fusional intrusion detection method based on the hierarchical filtering and progressive detection model (HFPD-IDS) is proposed. This method first uses the idea of binary classification to preliminarily filter and identify the normal and abnormal data, and then uses a convolutional neural network model for progressive dimensionality reduction detection for the initially filtered abnormal data. The learning process during the progressive detection, which is entirely based on abnormal data, avoids the interference of normal data and integrates detection method different from the initial filtering process to improve the detection rate for minority categories. Finally, the normal dataset identified in the initial filtering process will be corrected to further identify the abnormal data that has been misjudged as normal data. The experimental results show that this method can significantly improve the detection rate of minority categories data without affecting the detection performance of majority categories data.

This paper introduces a data-driven optimization (DDO) method based on novel strategic sampling (SS) considering data correlations for multiperiod optimal power flow (OPF) considering energy storage devices under uncertainty (OPF-ESDUU) of uncertain renewable energy and power loads (UREPL). This DDO method depends only on the uncertainty samples to yield an optimal solution that satisfies a specific confidence level, which is effective because of two resounding learning algorithms: Bayesian hierarchical modeling (BHM) and determinantal point process (DPP). Considering both the local bus information and spatial correlations over all buses, BHM learns the convex approximation of AC power flow (CAACPF) more accurately than the existing learning methods, converting the originally non-convex OPF-ESDUU to a convex optimization problem. DPP considers the correlations between samples to find a small set of significant samples by measuring the relative weight of each sample using the random matrix theory, significantly decreasing the data samples required by the existing SS. The experimental analysis in IEEE test cases shows that after considering data correlations, 1) BHM learns CAACPF better with 13–90% accuracy improvement, compared with the existing learning methods, and 2) the proposed DDO performs more efficiently than the existing DDO as DPP-based SS boosts the sampling efficiency by 50% at least.

Objective A stable animal model was needed to study bone non‐union caused by insufficient blood supply, the main object of this paper is to develop a medial malleolar fracture model with controllable arterial vascular injuries in rats for revealing the biochemical mechanism of non‐union by insufficient blood supply. Methods A total of 18 rats were randomly divided into three equal groups: the Sham group, the Fracture group, and the Fracture + Vascular group. The animals were subjected to unilateral medial malleolar bone fracture and vascular injury using customized molding equipment. The fracture site was scanned by micro‐CT, and vascular injury was evaluated by laser Doppler flowmetry (LDF) 24 h after modeling. Histological examination (HE), alkaline phosphatase (ALP) and tartrate‐resistant acid phosphatase (TRAP) staining, immunohistochemistry and immunofluorescence were conducted on the medial malleolar fracture tissues of three rats randomly selected from each group 24 h after modeling. Subsequently, to further confirm the success of fracture modeling, the fracture sites of three other rats in each group underwent micro‐CT scanning again 6 weeks after surgery. Results The results of a 24 h micro‐CT showed that all rats used to create the fracture models showed controlled injury of the medial malleolus. The model was stable, and the satisfaction of the homemade equipment agreed with the expectation. LDF showed that the blood flow of rats in the Fracture + Vascular group decreased significantly 24 h after fracture injury, while collateral blood flow perfusion increased by 50% on average. The results of HE, ALP and TRAP staining in the medial malleolus showed that the number of osteoblasts (OBs) and osteoclasts (OCs) in the Fracture group increased significantly, but the number of OBs and OCs in the Fracture + Vascular group decreased sharply relative to the number in the Sham group 24 h later. Furthermore, immunohistochemistry and immunofluorescence results showed that the number of neovessels in the Fracture group was significantly increased, while the number of neovessels in the Fracture + Vascular group was significantly decreased, which was consistent with the above results. After 6 weeks of modeling, the micro‐CT results showed that the fractures in the Fracture group had healed substantially, while those in the Fracture + Vascular group had not. Conclusion This study provided a reproducible and stable experimental animal model for medial malleolar fractures with arterial injury.

Recently, severe plastic deformation of alloys under cold conditions has been experimentally investigated to develop refined microstructures. The present study proposed to demonstrate the feasibility of applying cold deformation to a β-solution treated near β-Ti alloy. To be specific, the evolution of microstructure and mechanical properties of a β-solution treated near β-Ti alloy subjected to unidirectional 20% cold rolling were investigated. Results showed that long cracks formed in the 20% cold rolled sample. Meanwhile, two types of deformation-inducted α′ martensite have been observed in cold-rolled β matrix, namely, acicular α′ martensite with an orientation relationship of (12¯10)α′//(111)β with β matrix, and deformed (irregular shape) α′ martensite without coordinating orientation relationship. The formation of cracks was mainly related to the pre-existing α′ plate, and the micro-crack was prone to occur in α′ plates and propagate to β matrix. The microstructure characteristics result in poor ductility and low ultimate strength in the cold-rolled sample. Lastly, the lattice distortion and {101¯2}1¯011 α′-twinning were also characterized in the deformed α′ plates.

Livestock’s live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock.

Acquiring phenotypic data from livestock constitutes a crucial yet cumbersome phase in the breeding process. Traditionally, obtaining livestock phenotypic data primarily involves manual, on-body measurement methods. This approach not only requires extensive labor but also induces stress on animals, which leads to potential economic losses. Presently, the integration of next-generation Artificial Intelligence (AI), visual processing, intelligent sensing, multimodal fusion processing, and robotic technology is increasingly prevalent in livestock farming. The advantages of these technologies lie in their rapidity and efficiency, coupled with their capability to acquire livestock data in a non-contact manner. Based on this, we provide a comprehensive summary and analysis of the primary advanced technologies employed in the non-contact acquisition of livestock phenotypic data. This review focuses on visual and AI-related techniques, including 3D reconstruction technology, body dimension acquisition techniques, and live animal weight estimation. We introduce the development of livestock 3D reconstruction technology and compare the methods of obtaining 3D point cloud data of livestock through RGB cameras, laser scanning, and 3D cameras. Subsequently, we explore body size calculation methods and compare the advantages and disadvantages of RGB image calculation methods and 3D point cloud body size calculation methods. Furthermore, we also compare and analyze weight estimation methods of linear regression and neural networks. Finally, we discuss the challenges and future trends of non-contact livestock phenotypic data acquisition. Through emerging technologies like next-generation AI and computer vision, the acquisition, analysis, and management of livestock phenotypic data are poised for rapid advancement.

To improve the classification of pig vocalization using vocal signals and improve recognition accuracy, a pig vocalization classification method based on multi-feature fusion is proposed in this study. With the typical vocalization of pigs in large-scale breeding houses as the research object, short-time energy, frequency centroid, formant frequency and first-order difference, and Mel frequency cepstral coefficient and first-order difference were extracted as the fusion features. These fusion features were improved using principal component analysis. A pig vocalization classification model with a BP neural network optimized based on the genetic algorithm was constructed. The results showed that using the improved features to recognize pig grunting, squealing, and coughing, the average recognition accuracy was 93.2%; the recognition precisions were 87.9%, 98.1%, and 92.7%, respectively, with an average of 92.9%; and the recognition recalls were 92.0%, 99.1%, and 87.4%, respectively, with an average of 92.8%, which indicated that the proposed pig vocalization classification method had good recognition precision and recall, and could provide a reference for pig vocalization information feedback and automatic recognition.

A clear understanding of the number of pigs plays a crucial role in breeding management. Computer vision technology possesses several advantages, as it is harmless and labour-saving compared to traditional counting methods. Nevertheless, the existing methods still face some challenges, such as: (1) the lack of a substantial high-precision pig-counting dataset; (2) creating a dataset for instance segmentation can be time-consuming and labor-intensive; (3) interactive occlusion and overlapping always lead to incorrect recognition of pigs; (4) existing methods for counting such as object detection have limited accuracy. To address the issues of dataset scarcity and labor-intensive manual labeling, we make a semi-auto instance labeling tool (SAI) to help us to produce a high-precision pig counting dataset named Count1200 including 1220 images and 25,762 instances. The speed at which we make labels far exceeds the speed of manual annotation. A concise and efficient instance segmentation model built upon several novel modules, referred to as the Instances Counting Network (ICNet), is proposed in this paper for pig counting. ICNet is a dual-branch model ingeniously formed of a combination of several layers, which is named the Parallel Deformable Convolutions Layer (PDCL), which is trained from scratch and primarily composed of a couple of parallel deformable convolution blocks (PDCBs). We effectively leverage the characteristic of modeling long-range sequences to build our basic block and compute layer. Along with the benefits of a large effective receptive field, PDCL achieves a better performance for multi-scale objects. In the trade-off between computational resources and performance, ICNet demonstrates excellent performance and surpasses other models in Count1200, AP of 71.4% and AP50 of 95.7% are obtained in our experiments. This work provides inspiration for the rapid creation of high-precision datasets and proposes an accurate approach to pig counting.

Chronic obstructive pulmonary disease (COPD), a common respiratory disease, can be divided into stable phase and acute exacerbation phase (AECOPD) and is characterized by inflammation and hyper-immunity. Methylation of N6-methyladenosine (m6A) is an epigenetic modification that regulates the expression and functions of genes by influencing post-transcriptional RNA modifications. Its influence on the immune regulation mechanism has attracted great attention. Herein, we present the m6Amethylomic landscape and observe how the methylation of m6A participates in the pathological process of COPD. The m6A modification of 430 genes increased and that of 3995 genes decreased in the lung tissues of mice with stable COPD. The lung tissues of mice with AECOPD exhibited 740 genes with hypermethylated m6A peak and 1373 genes with low m6A peak. These differentially methylated genes participated in signaling pathways related to immune functions. To further clarify the expression levels of differentially methylated genes, RNA immunoprecipitation sequencing (MeRIP-seq) and RNA-sequencing data were jointly analyzed. In the stable COPD group, 119 hypermethylated mRNAs (82 upregulated and 37 downregulated mRNAs) and 867 hypomethylated mRNAs (419 upregulated and 448 downregulated mRNAs) were differentially expressed. In the AECOPD group, 87 hypermethylated mRNAs (71 upregulated and 16 downregulated mRNAs) and 358 hypomethylated mRNAs (115 upregulated and 243 downregulated mRNAs) showed differential expression. Many mRNAs were related to immune function and inflammation. Together, this study provides important evidence on the role of RNA methylation of m6A in COPD.

Studying the distribution characteristics of bioaerosols and their interaction with the environment is crucial for dairy farms. The distribution of aerosols differs in dairy farming from farming of other livestock, and their sensitivity to environmental factors varies across sites. Field experiments were conducted in an intensive commercial dairy farm in Northern China to investigate the horizontal and vertical distribution of culturable bacterial bioaerosols. Concentration levels and particle size ranges were analyzed, and the impact of multiple environmental factors on culturable bacterial bioaerosols was assessed. Significant variations in culturable bacterial bioaerosol concentrations were observed across eight functional zones, ranging from 1.14 × 103 to 7.35 × 103 CFU/m3. Culturable bacterial bioaerosols exhibited consistent carrier distribution patterns across six different size ranges. Vertical analysis revealed significantly higher culturable bacterial bioaerosol concentrations at a 1 m height compared to 4 m (p < 0.05), while similar size distributions were observed at different heights of the same sampling location. The top three environmental factors influencing culturable bacterial bioaerosol concentrations were PM100 concentration, wind direction, and air temperature. This study provides insights into the distribution characteristics of culturable bacterial bioaerosols on dairy farms and their response to environmental factors. The findings serve as a reference for evaluating bioaerosol emissions and establishing daily disinfection management measures on dairy farms.

The procoagulant effect of microparticles (MPs) contributes to hypercoagulability-induced thrombosis. We provide preliminary findings of the MPs-Activated Clotting Time (MPs-ACT) assay to determine the procoagulant activity of MPs. MPs-rich plasma was obtained and recalcified. Changes in plasma viscoelasticity were evaluated and the time to the peak viscoelastic changes was defined as the MPs-ACT. MPs concentration was measured by flow cytometry. Coagulation products produced during plasma clotting were identified by fibrin and fibrinopeptide A. MPs were prepared in vitro and added to standard plasma to simulate pathological samples. In addition, reproducibility and sensitivity were evaluated. We confirmed the linear relationship between MPs-ACT and MP concentrations. Dynamic changes in fibrin production were depicted. We simulated the correlation between MPs-ACT and standard plasma containing MPs prepared in vitro. The reproducibility of high-value and low-value samples was 6.0% and 10.8%, respectively. MPs-ACT sensitively detected hypercoagulable samples from patients with pre-eclampsia, hip fractures, and lung tumors. MPs-ACT largely reflects the procoagulant effect of MPs. MPs-ACT sensitively and rapidly detects hypercoagulability with MPs-rich plasma. It may be promising for the diagnosis of hypercoagulable states induced by MPs.

This research focuses on the research problem of eliminating COVID-19 vaccine hesitancy through web search. A dynamic model of eliminating COVID-19 vaccine hesitancy through web search is constructed based on the Logistic model, the elimination degree is quantified, the elimination function is defined to analyze the dynamic elimination effect, and the model parameter estimation method is proposed. The numerical solution, process parameters, initial value parameters and stationary point parameters of the model are simulated, respectively, and the mechanism of elimination is deeply analyzed to determine the key time period. Based on the real data of web search and COVID-19 vaccination, data modeling is carried out from two aspects: full sample and segmented sample, and the rationality of the model is verified. On this basis, the model is used to carry out dynamic prediction and verified to have certain medium-term prediction ability. Through this research, the methods of eliminating vaccine hesitancy are enriched, and a new practical idea is provided for eliminating vaccine hesitancy. It also provides a method to predict the quantity of COVID-19 vaccination, provides theoretical guidance for dynamically adjusting the public health policy of the COVID-19, and can provide reference for the vaccination of other vaccines.

Near β-Ti alloys, a perfect candidate material for numerous applications in the aerospace, are expected to exhibit high temperature endurability. Here, in order to evaluate the high temperature oxidation mechanism of the near β-Ti alloy, detailed microstructural characterization was performed on a near β-Ti alloy, Ti-55511 alloy after isothermal heat treatment at 600 °C in a tube furnace. Parabolic oxidation kinetics was observed. The oxide layer consisted of Al2O3, TiO2, VO2, and Magnéli phases. Polymorphic phase transformations and Magnéli phases played critical roles in the oxide layer thickening. The formation of defects was highly correlated with the incorporation of oxygen into Al2O3 and VO2, which resulted in lattice distortion of the oxides. Continuous oxidation led to the formation of the higher-valence oxide V2O5 in VO2 particles.

Cerebral vasospasm is a frequently encountered clinical problem, especially in patients with traumatic brain injury and subarachnoid hemorrhage. Continued cerebral vasospasm can cause cerebral ischemia, even infarction and delayed ischemic neurologic deficits. It significantly affects the course of the disease and the outcome of the patient. However, the underlying mechanism of cerebral vasospasm is still unclear. Recently, increasing studies focus on the pathogenic mechanism of microparticles. It has been found that microparticles have a non-negligible role in promoting vasospasm. This research aims to summarize the dynamics of microparticles in vivo and identify a causal role of microparticles in the occurrence and development of cerebral vasospasm. We found that these various microparticles showed dynamic characteristics in body fluids and directly or indirectly affect the cerebral vasospasm or prompt it. Due to the different materials carried by microparticles from different cells, there are also differences in the mechanisms that lead to abnormal vasomotor. We suggest that microparticle scavengers might be a promising therapeutic target against microparticles associated complications.

Objective: To analyze the clinicopathological characteristics of skin basal cell carcinoma, so as to improve the coincidence rate of clinical diagnosis and provide basis for early diagnosis, treatment and prognosis. Methods: The clinical and pathological features of 611 patients with basal cell carcinoma diagnosed by primary biopsy in Dermatology Hospital, Southern Medical University from March 2014 to February 2021 were retrospectively analyzed. Results: There were 268 males and 343 females in 611 patients, the ratio of male to female was 1 ∶1.28; the onset age was 19~92 (64.18±13.90) years old. There were 296 cases (48.45%) with mass, nodule and tumor, 248 cases (40.59%) with patch and papule, and 67 cases (10.97%) with erosion or ulcer, scar and cyst. Pathological classification: nodular type 377 cases (61.71%), infiltrative type 85 cases (13.92%), superficial type 69 cases (11.30%), other types 80 cases (13.10%). Clinical misdiagnosis rate was 16.86%. Conclusions: The main clinical manifestation is mass, and the main pathological type is nodule. It is important to improve the diagnosis, treatment and prognosis of BCC by improving the rate of skin biopsy and strengthening the understanding of easily misdiagnosed lesions.

The snapshot hyperspectral imaging is an emerging technique with numerous applications. However, the hyperspectral imaging reconstruction is often time-consuming, which is placing a limit on the development of snapshot hyperspectral imaging. We present an efficient reconstruction algorithm based on the tensor analysis and the low-rank constraint. The hyperspectral data cube is regarded as a low rank three-order tensor, which can jointly treat both spatial and spectral modes. The 3D-LRC method can greatly decrease the computation time without unfolding the hyperspectral data cube into 2D patches. Compared with the-state-of-the-art method, the proposed method has a great improvement in the reconstruction speed and quality. The method has been implemented on two typical snapshot hyperspectral imaging systems.

Broiler chickens are traditionally weighed by steelyard or platform scale, which is time-consuming and labor-intensive. Broiler chickens usually exhibit stress-related behavior during weighing. The 3D camera-based weighing system for broiler chickens can only weigh the broiler chicken in the monitoring area. Usually, it makes poor weight prediction due to poor segmentation especially when the broiler chicken is flapping its wings. To solve these issues, we developed one simple and low-cost weighing system with high stability and accuracy. A validity value extraction method from dynamic weighing was proposed. Then, an improved amplitude-limiting filtering algorithm and a BP neural networks model were developed to avoid accidental interference. The BP neural networks model used daily weight gain, day-age, average velocity, and the weight data after filtering algorithm as the input layer. The weighing system was tested in a commercial Beijing Fatty Chickens house with Beijing Fatty Chickens. We tested thirteen groups of Beijing Fatty Chickens of different weights, from 500 g to 1800 g in intervals of 100 g, using the three different methods: no filtering algorithm or BP neural networks, only the improved amplitude-limiting filtering algorithm and a hybrid of the improved amplitude-limiting filtering algorithm and BP neural networks. The results showed that the hybrid algorithm had a better performance in minimizing the error, lowering from the original 6% down to 3%. The accurate weight data was transmitted to the remote service platform for further decision-making, such as activity analysis, feeding management, and health alerts.

Earthen sites are a kind of constructure with significant historical and cultural value. However, the destruction of earthen sites caused by erosion occurs frequently. The solidification of calcined ginger nuts can improve the strength of the soil so that it can be used to protect the earthen sites. However, the strength degradation of solidified soil by calcined ginger nuts after dry–wet and freeze–thaw cycles is unclear. To reveal the deterioration pattern of solidified soil strength, the effects of its dosage and cycle number on the strength of solidified soil were analyzed through shear strength, dry–wet cycle, and freeze–thaw cycle tests. The results showed that the solidified soil strength decreased first and increased with dosage increase. With the number of dry–wet cycles increasing, the strength of the plain loess decreased rapidly and gradually turned flat. The strength loss of solidified soil was small in the dry–wet process. With freeze–thaw cycle numbers increasing, the strength of the plain loess decreased first and then tended to be flat, the strength of solidified soil decreased first and then increased slightly, and the change in the strength had a clear inflection point. With the increasing dosage, freeze–thaw cycle numbers corresponding to the inflection point were significantly reduced. These results indicate that calcined ginger nuts could enhance the resistance of earthen sites loess to dry–wet and freeze–thaw cycles.

The H9N2 avian influenza virus has become one of the dominant subtypes of avian influenza virus in poultry and has been significantly harmful to chickens in China, with great economic losses in terms of reduced egg production or high mortality by co-infection with other pathogens. A prediction of H9N2 status based on easily available production data with high accuracy would be important and essential to prevent and control H9N2 outbreaks in advance. This study developed a machine learning framework based on the XGBoost classification algorithm using 3 months’ laying rates and mortalities collected from three H9N2-infected laying hen houses with complete onset cycles. A framework was developed to automatically predict the H9N2 status of individual house for future 3 days (H9N2 status + 0, H9N2 status + 1, H9N2 status + 2) with five time frames (day + 0, day − 1, day − 2, day − 3, day − 4). It had been proven that a high accuracy rate > 90%, a recall rate > 90%, a precision rate of >80%, and an area under the curve of the receiver operator characteristic ≥ 0.85 could be achieved with the prediction models. Models with day + 0 and day − 1 were highly recommended to predict H9N2 status + 0 and H9N2 status + 1 for the direct or auxiliary monitoring of its occurrence and development. Such a framework could provide new insights into predicting H9N2 outbreaks, and other practical potential applications to assist in disease monitor were also considerable.

Compact and low‐cost spectropolarimeters are desirable in many fields for probing the wavelength‐dependent polarization state of light. Conventional spectropolarimetry systems use bulky, free‐space optical components or require at least four spectrometers. Herein, a chip‐scale spectropolarimeter capable of reconstructing input Stokes spectrum with a single spectrometer is demonstrated. All the components, including a surface polarization splitter, a channeled spectrum generator, and a Vernier spectrometer, are monolithically integrated on a footprint of 0.3 mm × 1.5 mm. The device is implemented using a complementary metal oxide semiconductor (CMOS)‐compatible fabrication process and achieves a resolution of 0.1 nm across a wavelength range of 56 nm near 1550 nm. This work provides a path toward on‐chip, low‐cost Stokes spectroscopy.