A Sheep Identification Method Based on Three-Dimensional Sheep Face Reconstruction and Feature Point Matching.

As the sheep industry rapidly moves towards modernization, digitization, and intelligence, there is a need to build breeding farms integrated with big data. By collecting individual information on sheep, precision breeding can be conducted to improve breeding efficiency, reduce costs, and promote healthy breeding practices. In this context, the accurate identification of individual sheep is essential for establishing digitized sheep farms and precision animal husbandry. Currently, scholars utilize deep learning technology to construct recognition models, learning the biological features of sheep faces to achieve accurate identification. However, existing research methods are limited to pattern recognition at the image level, leading to a lack of diversity in recognition methods. Therefore, this study focuses on the small-tailed Han sheep and develops a sheep face recognition method based on three-dimensional reconstruction technology and feature point matching, aiming to enrich the theoretical research of sheep face recognition technology. The specific recognition approach is as follows: full-angle sheep face images of experimental sheep are collected, and corresponding three-dimensional sheep face models are generated using three-dimensional reconstruction technology, further obtaining three-dimensional sheep face images from three different perspectives. Additionally, this study developed a sheep face orientation recognition algorithm called the sheep face orientation recognition algorithm (SFORA). The SFORA incorporates the ECA mechanism to further enhance recognition performance. Ultimately, the SFORA has a model size of only 5.3 MB, with accuracy and F1 score reaching 99.6% and 99.5%, respectively. During the recognition task, the SFORA is first used for sheep face orientation recognition, followed by matching the recognition image with the corresponding three-dimensional sheep face image based on the established SuperGlue feature-matching algorithm, ultimately outputting the recognition result. Experimental results indicate that when the confidence threshold is set to 0.4, SuperGlue achieves the best matching performance, with matching accuracies for the front, left, and right faces reaching 96.0%, 94.2%, and 96.3%, respectively. This study enriches the theoretical research on sheep face recognition technology and provides technical support.

Evaluation of the immunogenicity and protective efficacy of an inactivated vaccine candidate for sheep infected with ovine parainfluenza virus type 3.

Respiratory diseases constitute a major health problem for ruminants, resulting in considerable economic losses throughout the world. Parainfluenza type 3 virus (PIV3) is one of the most important respiratory pathogens of ruminants. The pathogenicity and phylogenetic analyses of PIV3 virus have been reported in sheep and goats. However, there are no recent studies of the vaccination of sheep or goats against PIV3. Here, we developed a purified inactivated ovine parainfluenza virus type 3 (OPIV3) vaccine candidate. In addition, we immunized sheep with the inactivated OPIV3 vaccine and evaluated the immune response and pathological outcomes associated with OPIV3 TX01 infection. The vaccinated sheep demonstrated no obvious symptoms of respiratory tract infection, and there were no gross lesions or pathological changes in the lungs. The average body weight gain significantly differed between the vaccinated group and the control group (P < 0.01). The serum neutralization antibody levels rapidly increased in sheep post-vaccination and post-challenge with OPIV3. Furthermore, viral shedding in nasal swabs and viral loads in the lungs were reduced. The results of this study suggest that vaccination with this candidate vaccine induces the production of neutralizing antibodies and provides significant protection against OPIV3 infection. These results may be helpful for further studies on prevention and control strategies for OPIV3 infections.

Genetic characterization of bovine coronavirus strain isolated in Inner Mongolia of China.

BACKGROUND: Bovine coronavirus (BCoV) is implicated in severe diarrhea in calves and contributes to the bovine respiratory disease complex; it shares a close relationship with human coronavirus. Similar to other coronaviruses, remarkable variability was found in the genome and biology of the BCoV. In 2022, samples of feces were collected from a cattle farm. A virus was isolated from 7-day-old newborn calves. In this study, we present the genetic characteristics of a new BCoV isolate. The complete genomic, spike protein, and nucleocapsid protein gene sequences of the BCoV strain, along with those of other coronaviruses, were obtained from the GenBank database. Genetic analysis was conducted using MEGA7.0 and the Neighbor-Joining (NJ) method. The reference strains' related genes were retrieved from GenBank for comparison and analysis using DNAMAN. RESULTS: The phylogenetic tree and whole genome consistency analysis showed that it belonged to the GIIb subgroup, which is epidemic in Asia and America, and was quite similar to the Chinese strains in the same cluster. Significantly, the S gene was highly consistent with QH1 (MH810151.1) isolated from yak. This suggests that the strain may have originated from interspecies transmission involving mutations of wild strains. The N gene was conserved and showed high sequence identity with the epidemic strains in China and the USA. CONCLUSIONS: Genetic characterization suggests that the isolated strain could be a new mutant from a wild-type lineage, which is in the same cluster as most Chinese epidemic strains but on a new branch.

Lightweight model-based sheep face recognition via face image recording channel.

The accurate identification of individual sheep is a crucial prerequisite for establishing digital sheep farms and precision livestock farming. Currently, deep learning technology provides an efficient and non-contact method for sheep identity recognition. In particular, convolutional neural networks can be used to learn features of sheep faces to determine their corresponding identities. However, the existing sheep face recognition models face problems such as large model size, and high computational costs, making it difficult to meet the requirements of practical applications. In response to these issues, we introduce a lightweight sheep face recognition model called YOLOv7-Sheep Face Recognition (YOLOv7-SFR). Considering the labor-intensive nature associated with manually capturing sheep face images, we developed a face image recording channel to streamline the process and improve efficiency. This study collected facial images of 50 Small-tailed Han sheep through a recording channel. The experimental sheep ranged in age from 1 to 3 yr, with an average weight of 63.1 kg. Employing data augmentation methods further enhanced the original images, resulting in a total of 22,000 sheep face images. Ultimately, a sheep face dataset was established. To achieve lightweight improvement and improve the performance of the recognition model, a variety of improvement strategies were adopted. Specifically, we introduced the shuffle attention module into the backbone and fused the Dyhead module with the model's detection head. By combining multiple attention mechanisms, we improved the model's ability to learn target features. Additionally, the traditional convolutions in the backbone and neck were replaced with depthwise separable convolutions. Finally, leveraging knowledge distillation, we enhanced its performance further by employing You Only Look Once version 7 (YOLOv7) as the teacher model and YOLOv7-SFR as the student model. The training results indicate that our proposed approach achieved the best performance on the sheep face dataset, with a mean average precision@0.5 of 96.9%. The model size and average recognition time were 11.3 MB and 3.6 ms, respectively. Compared to YOLOv7-tiny, YOLOv7-SFR showed a 2.1% improvement in mean average precision@0.5, along with a 5.8% reduction in model size and a 42.9% reduction in average recognition time. The research results are expected to drive the practical applications of sheep face recognition technology.

Accurate identification of individual sheep is a crucial prerequisite for establishing digital sheep farms and precision livestock farming. In this study, we developed a lightweight sheep face recognition model, YOLOv7-SFR. Utilizing a face image recording channel, we efficiently collected facial images from 50 experimental sheep, resulting in a comprehensive sheep face dataset. Training results demonstrated that YOLOv7-SFR surpassed state-of-the-art lightweight sheep face recognition models, achieving a mean average precision@0.5 of 96.9%. Notably, the model size and average recognition time of YOLOv7-SFR were merely 11.3 MB and 3.6 ms, respectively. In summary, YOLOv7-SFR strikes an optimal balance between performance, model size, and recognition speed, offering promising practical applications for sheep face recognition technology. This study employs deep learning for sheep face recognition tasks, ensuring the welfare of sheep in the realm of digital agriculture and automation practices.

Physiological Mechanisms Underlying Tassel Symptom Formation in Maize Infected with Sporisorium reilianum.

Head smut is a soil-borne fungal disease caused by Sporisorium reilianum that infects maize tassels and ears. This disease poses a tremendous threat to global maize production. A previous study found markedly different and stably heritable tassel symptoms in some maize inbred lines with Sipingtou blood after infection with S. reilianum. In the present study, 55 maize inbred lines with Sipingtou blood were inoculated with S. reilianum and classified into three tassel symptom types (A, B, and C). Three maize inbred lines representing these classes (Huangzao4, Jing7, and Chang7-2, respectively) were used as test materials to investigate the physiological mechanisms of tassel formation in infected plants. Changes in enzyme activity, hormone content, and protein expression were analyzed in all three lines after infection and in control plants. The activities of peroxidase (POD), superoxide dismutase (SOD), and phenylalanine-ammonia-lyase (PAL) were increased in the three typical inbred lines after inoculation. POD and SOD activities showed similar trends between lines, with the increase percentage peaking at the V12 stage (POD: 57.06%, 63.19%, and 70.28% increases in Huangzao4, Jing7, and Chang7-2, respectively; SOD: 27.01%, 29.62%, and 47.07% in Huangzao4, Jing7, and Chang7-2, respectively. These were all higher than in the disease-resistant inbred line Mo17 at the same growth stage); this stage was found to be key in tassel symptom formation. Levels of gibberellic acid (GA3), indole-3-acetic acid (IAA), and abscisic acid (ABA) were also altered in the three typical maize inbred lines after inoculation, with changes in GA3 and IAA contents tightly correlated with tassel symptoms after S. reilianum infection. The differentially expressed proteins A5H8G4, P09233, and Q8VXG7 were associated with changes in enzyme activity, whereas P49353, P13689, and P10979 were associated with changes in hormone contents. Fungal infection caused reactive oxygen species (ROS) and nitric oxide (NO) bursts in the three typical inbred lines. This ROS accumulation caused biofilm disruption and altered host signaling pathways, whereas NO signaling triggered strong secondary metabolic responses in the host and altered the activities of defense-related enzymes. These factors together resulted in the formation of varying tassel symptoms. Thus, interactions between S. reilianum and susceptible maize materials were influenced by a variety of signals, enzymes, hormones, and metabolic cycles, encompassing a very complex regulatory network. This study preliminarily identified the physiological mechanisms leading to differences in tassel symptoms, deepening our understanding of S. reilianum-maize interactions.

Renal safety of zoledronic acid in patients with osteoporosis: a retrospective study.

INTRODUCTION: This study aimed to investigate the renal safety of Zoledronic Acid (ZOL) in patients with osteoporosis (OP). METHODS: A total of 1379 patients (baseline estimated glomerular filtration rate, eGFR ≥ 60 mL/min/1.73 m2) with primary OP who received ZOL from January 2008 to October 2020 at our hospital were retrospectively analyzed. Baseline and the changes in renal function within 1 year following infusions were collected, the incidence of renal impairment (eGFR < 60 mL/min/1.73 m2 or a > 25% reduction in the eGFR from baseline) was noted and the risk factors were analyzed. Furthermore, the changes in renal function between a 3-year consecutive infusion and a single infusion of ZOL were compared. RESULTS: Renal impairment occurred in 8.05% of patients, who with a significantly higher age, Charlson Comorbidity Index (CCI), smoking history, combination of hypertension or diabetes mellitus and worse renal function indicators (all P < 0.05). Binary logistic regression analysis showed that higher CCI (≥5) or smoking history or the baseline eGFR <90 mL/min/1.73 m2 were the risk factors for renal impairment (all P < 0.05). Patients of 3-year continuous infusion group had a significantly greater drop in the eGFR levels than the single infusion group after 1 year of infusion(s) (P < 0.05). CONCLUSION: Attention should be given to possible potential renal impairment following ZOL infusion in clinical practice for the management of OP, particularly in patients with higher CCI (≥5) or smoking history or the baseline eGFR <90 mL/min/1.73 m2. Continuous infusion of ZOL exerts a significant impact on renal function when compared to single infusion and intensive monitoring of renal function is necessary.

One-step enrichment of phenolics from Chaenomeles speciosa (Sweet) Nakai fruit using macroporous resin: Adsorption/desorption characteristics, process optimization and UPLC-QqQ-MS/MS-based quantification.

Chaenomeles speciosa (Sweet) Nakai fruit is a good source of phenolics with many health benefits. In this work, the enrichment of C. speciosa fruit total phenolics (CSFTP) using macroporous resins was studied. NKA-Ⅱ resin was selected for enriching CSFTP due to its highest adsorption/desorption quantity. Adsorption characteristics of CSFTP on NKA-Ⅱ resin exhibited a good fit with the Langmuir isotherm model and pseudo-second order kinetics model. This adsorption was spontaneous, exothermic, and entropy-decreasing through a physisorption mechanism. The breakthrough-elution curves were studied to optimize CSFTP enrichment conditions. One-step enrichment increased CSFTP content in the extracts from 26.51 % to 78.63 %, with a recovery of 81.03 %. A UPLC-QqQ-MS/MS method in multiple reaction monitoring (MRM) mode was established and validated for the simultaneous quantification of seven phenolic compounds. This study demonstrates the feasibility of industrial enrichment of CSFTP using NKA-Ⅱ resin and proposes a reliable method for quality control of CSFTP-rich products.

MXene/PPy@PDMS sponge-based flexible pressure sensor for human posture recognition with the assistance of a convolutional neural network in deep learning.

The combination of flexible sensors and deep learning has attracted much attention as an efficient method for the recognition of human postures. In this paper, an in situ polymerized MXene/polypyrrole (PPy) composite is dip-coated on a polydimethylsiloxane (PDMS) sponge to fabricate an MXene/PPy@PDMS (MPP) piezoresistive sensor. The sponge sensor achieves ultrahigh sensitivity (6.8925 kPa-1) at 0-15 kPa, a short response/recovery time (100/110 ms), excellent stability (5000 cycles) and wash resistance. The synergistic effect of PPy and MXene improves the performance of the composite materials and facilitates the transfer of electrons, making the MPP sponge at least five times more sensitive than sponges based on each of the individual single materials. The large-area conductive network allows the MPP sensor to maintain excellent electrical performance over a large-scale pressure range. The MPP sensor can detect a variety of human body activity signals, such as radial artery pulse and different joint movements. The detection and analysis of human motion data, which is assisted by convolutional neural network (CNN) deep learning algorithms, enable the recognition and judgment of 16 types of human postures. The MXene/PPy flexible pressure sensor based on a PDMS sponge has broad application prospects in human motion detection, intelligent sensing and wearable devices.

LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End.

The accurate identification of sheep is crucial for breeding, behavioral research, food quality tracking, and disease prevention on modern farms. As a result of the time-consuming, expensive, and unreliable problems of traditional sheep-identification methods, relevant studies have built sheep face recognition models to recognize sheep through facial images. However, the existing sheep face recognition models face problems such as high computational costs, large model sizes, and weak practicality. In response to the above issues, this study proposes a lightweight sheep face recognition model named LSR-YOLO. Specifically, the ShuffleNetv2 module and Ghost module were used to replace the feature extraction module in the backbone and neck of YOLOv5s to reduce floating-point operations per second (FLOPs) and parameters. In addition, the coordinated attention (CA) module was introduced into the backbone to suppress non-critical information and improve the feature extraction ability of the recognition model. We collected facial images of 63 small-tailed Han sheep to construct a sheep face dataset and further evaluate the proposed method. Compared to YOLOv5s, the FLOPs and parameters of LSR-YOLO decreased by 25.5% and 33.4%, respectively. LSR-YOLO achieved the best performance on the sheep face dataset, and the mAP@0.5 reached 97.8% when the model size was only 9.5 MB. The experimental results show that LSR-YOLO has significant advantages in recognition accuracy and model size. Finally, we integrated LSR-YOLO into mobile devices and further developed a recognition system to achieve real-time recognition. The results show that LSR-YOLO is an effective method for identifying sheep. The method has high recognition accuracy and fast recognition speed, which gives it a high application value in mobile recognition and welfare breeding.

Data-driven subgroups of newly diagnosed type 2 diabetes and the relationship with cardiovascular diseases at genetic and clinical levels in Chinese adults.

BACKGROUND: To subgroup Chinese patients with newly diagnosed type 2 diabetes (T2D) by K-means cluster analysis on clinical indicators, and to explore whether these subgroups represent different genetic features and calculated cardiovascular risks. METHODS: The K-means clustering analysis was performed on two cohorts (n = 590 and 392), both consisting of Chinese participants with newly diagnosed T2D. To assess genetic risks, multiple polygenic risk scores (PRSs) and mitochondrial DNA copy numbers (mtDNA-CN) were calculated for all participants. Furthermore, Framingham risk scores (FRS) of cardiovascular diseases in two cohorts were also calculated to verify the genetic risks. RESULTS: Four clusters were identified including the mild age-related diabetes (MARD)(35.08%), mild obesity-related diabetes (MOD) (34.41%), severe autoimmune diabetes (SAID) 19.15%, and severe insulin-resistant diabetes (SIRD) 11.36% subgroups in the MARCH (metformin, and acarbose in Chinese patients as the initial hypoglycemic treatment) cohort. There was a significant difference in PRS for cardiovascular diseases (CVD) across four subgroups in the MARCH cohort (p < 0.05). Compared with the SIDD and SIRD subgroups, patients in the MOD subgroup had a relatively lower PRS for CVD (p < 0.05) in the MARCH cohort. Females had a higher PRS compared to males, with no significant difference in FRS across the four clusters. The MOD subgroup had a significantly lower FRS which was consistent with the results of PRS. Similar results of PRS and FRS were also replicated in the CONFIDENCE (comparison of glycemic control and b-cell function among newly diagnosed patients with type 2 diabetes treated with exenatide, insulin or pioglitazone) cohort. CONCLUSION: There are different CVD risks in diabetic subgroups based on clinical and genetic evidence which may promote precision medicine.

A high-precision facial recognition method for small-tailed Han sheep based on an optimised Vision Transformer.

Accurate identification of individual animals plays a pivotal role in enhancing animal welfare and optimising farm production. Although Radio Frequency Identification technology has been widely applied in animal identification, this method still exhibits several limitations that make it difficult to meet current practical application requirements. In this study, we proposed ViT-Sheep, a sheep face recognition model based on the Vision Transformer (ViT) architecture, to facilitate precise animal management and enhance livestock welfare. Compared to Convolutional Neural Network (CNN), ViT is renowned for its competitive performance. The experimental procedure of this study consisted of three main steps. Firstly, we collected face images of 160 experimental sheep to construct the sheep face image dataset. Secondly, we developed two sets of sheep face recognition models based on CNN and ViT, respectively. To enhance the ability to learn sheep face biological features, we proposed targeted improvement strategies for the sheep face recognition model. Specifically, we introduced the LayerScale module into the encoder of the ViT-Base-16 model and employed transfer learning to improve recognition accuracy. Finally, we compared the training results of different recognition models and the ViT-Sheep model. The results demonstrated that our proposed method achieved the highest performance on the sheep face image dataset, with a recognition accuracy of 97.9%. This study demonstrates that ViT can successfully achieve sheep face recognition tasks with good robustness. Furthermore, the findings of this research will promote the practical application of artificial intelligence animal recognition technology in sheep production.

Self-Adhesive, Anti-Freezing MXene-Based Hydrogel Strain Sensor for Motion Monitoring and Handwriting Recognition with Deep Learning.

Flexible strain sensors based on self-adhesive, high-tensile, super-sensitive conductive hydrogels have promising application in human-computer interaction and motion monitoring. Traditional strain sensors have difficulty in balancing mechanical strength, detection function, and sensitivity, which brings challenges to their practical applications. In this work, the double network hydrogel composed of polyacrylamide (PAM) and sodium alginate (SA) was prepared, and MXene and sucrose were used as conductive materials and network reinforcing materials, respectively. Sucrose can effectively enhance the mechanical performance of the hydrogels and improve the ability to withstand harsh conditions. The hydrogel strain sensor has excellent tensile properties (strain >2500%), high sensitivity with a gauge factor of 3.76 at 1400% strain, reliable repeatability, self-adhesion, and anti-freezing ability. Highly sensitive hydrogels can be assembled into motion detection sensors that can distinguish between various strong or subtle movements of the human body, such as joint flexion and throat vibration. In addition, the sensor can be applied in handwriting recognition of English letters by using the fully convolutional network (FCN) algorithm and achieved the high accuracy of 98.1% for handwriting recognition. The as-prepared hydrogel strain sensor has broad prospect in motion detection and human-machine interaction, which provides great potential application of flexible wearable devices.

Biological activity of recombinant bovine IFN-α and inhibitory effect on BVDV in vitro.

Type I interferon has great broad-spectrum antiviral ability and immunomodulatory function, and its receptors are expressed in almost all types of cells. Bovine viral diarrhea virus (BVDV) is an important pathogen causing significant economic losses in cattle. In this study, a recombinant expression plasmid carrying bovine interferon-α(BoIFN-α)gene was constructed and transformed into E. coli BL21 (DE3) competent cells. SDS-PAGE and Westernblotting analysis showed that the recombinant BoIFN-α protein (rBoIFN-α) was successfully expressed. It is about 36KD and exists in the form of inclusion body. When denatured, purified and renatured rBoIFN-α protein stimulated MDBK cells, the expression of interferon stimulating genes (ISGs) such as ISG15, OAS1, IFIT1, Mx1 and IFITM1 were significantly up-regulated, and reached the peak at 12 h (P< 0.001). MDBK cells were infected with BVDV with moi of 0.1 and 1.0, respectively. The virus proliferation was observed after pretreatment with rBoIFN-α protein and post-infection treatment. The results showed that the denatured, purified and renatured BoIFN-α protein had good biological activity and could inhibit the replication of BVDV in MDBK cells in vitro, which provided a basis for BoIFN-α as an antiviral drug, immune enhancer and clinical application of BVDV.

Improved Differential Evolution Algorithm for Sensitivity Enhancement of Surface Plasmon Resonance Biosensor Based on Two-Dimensional Material for Detection of Waterborne Bacteria.

Due to the large number of waterborne bacteria presenting in drinking water, their rapid and accurate identification has become a global priority. The surface plasmon resonance (SPR) biosensor with prism (BK7)-silver(Ag)-MXene(Ti3T2Cx)-graphene- affinity-sensing medium is examined in this paper, in which the sensing medium includes pure water, vibrio cholera (V. cholera), and escherichia coli (E. coli). For the Ag-affinity-sensing medium, the maximum sensitivity is obtained by E. coli, followed by V. cholera, and the minimum is pure water. Based on the fixed-parameter scanning (FPS) method, the highest sensitivity is 246.2 °/RIU by the MXene and graphene with monolayer, and with E. coli sensing medium. Therefore, the algorithm of improved differential evolution (IDE) is obtained. By the IDE algorithm, after three iterations, the maximum fitness value (sensitivity) of the SPR biosensor achieves 246.6 °/RIU by using the structure of Ag (61 nm)-MXene (monolayer)-graphene (monolayer)-affinity (4 nm)-E. coli. Compared with the FPS and differential evolution (DE) algorithm, the highest sensitivity is more accurate and efficient, and with fewer iterations. The performance optimization of multilayer SPR biosensors provides an efficient platform.

Brønsted acid-mediated tandem cyclization of triarylphosphines and in situ generated ortho-alkynyl quinone methides: access to heterocyclic quaternary phosphonium salts.

Heterocyclic Quaternary Phosphonium Salts (HQPS) have emerged as promising chemicals for organic synthesis and medicinal chemistry. However, the present synthetic methodology of this type of compound is still limited. Here, we report a deconstructive reorganization strategy based on Brønsted acid-mediated tandem 1,4 addition/intramolecular cyclization of triphenylphosphine derivatives and in situ generated o-AQMs for the first time. This protocol provides a novel approach to heterocyclic quaternary phosphonium salts. The method also features a non-metal catalyst, mild reaction conditions, high efficiency and wide substrate scope. Moreover, a series of obtained heterocyclic phosphonium salts can be converted to isotopically labelled 2-benzofuran compounds directly by simple deuteration reactions.

Ultrastretchable, Self-Healing Conductive Hydrogel-Based Triboelectric Nanogenerators for Human-Computer Interaction.

The rapid development of wearable electronic devices and virtual reality technology has revived interest in flexible sensing and control devices. Here, we report an ionic hydrogel (PTSM) prepared from polypropylene amine (PAM), tannic acid (TA), sodium alginate (SA), and MXene. Based on the multiple weak H-bonds, this hydrogel exhibits excellent stretchability (strain >4600%), adhesion, and self-healing. The introduction of MXene nanosheets endows the hydrogel sensor with a high gauge factor (GF) of 6.6. Meanwhile, it also enables triboelectric nanogenerators (PTSM-TENGs) fabricated from silicone rubber-encapsulated hydrogels to have excellent energy harvesting efficiency, with an instantaneous output power density of 54.24 mW/m2. We build a glove-based human-computer interaction (HMI) system using PTSM-TENGs. The multidimensional signal features of PTSM-TENG are extracted and analyzed by the HMI system, and the functions of gesture visualization and robot hand control are realized. In addition, triboelectric signals can be used for object recognition with the help of machine learning techniques. The glove based on PTSM-TENG achieves the classification and recognition of five objects through contact, with an accuracy rate of 98.7%. Therefore, strain sensors and triboelectric nanogenerators based on hydrogels have broad application prospects in man-machine interface, intelligent recognition systems, auxiliary control systems, and other fields due to their excellent stretchable and high self-healing performance.

The Effects of Oncolytic Pseudorabies Virus Vaccine Strain Inhibited the Growth of Colorectal Cancer HCT-8 Cells In Vitro and In Vivo.

Oncolytic viral therapy is a promising treatment approach for a variety of tumor forms. Although a number of studies have demonstrated that the pseudorabies virus (PRV) may be applied as an oncolytic carrier, the anti-colorectal cancer impact of the virus and the mechanism of its cytotoxic effect remain elusive. In this study, the replication capacity and cell activity of PRV attenuated live vaccines Bartha K61 and HB98 in HCT-8 cells in vitro were investigated. Next, the antitumor ability and safety were evaluated in a mouse model of HCT-8 tumor transplantation. Both PRV strains were able to suppress tumor growth and HB98 showed higher safety and efficiency than the Bartha K61 strain. Finally, flow cytometry and immunohistochemistry examination were performed to investigate its possible cytotoxic mechanism. The results showed that PRV inhibited tumor proliferation both in vitro and in vivo by inducing apoptosis. In summary, our study discovered for the first time that the live attenuated PRV has an oncolytic effect on HCT-8 cells with high efficacy and safety.

Fe/Mg/Fe Multilayer Composite Sheet Fabricated by Roll Cladding.

A new multilayer composite sheet consisting of Fe/Mg/Fe was fabricated from galvanized steels and Mg alloy sheets via roll cladding. The clad steel improved the Mg surface hardness from HV 65 to HV 132. Bonding occurred as the reduction ratios increased up to over 10%. Investigation of the microstructure of the Mg/steel interface revealed a 5 µm- to 10 µm-thick transition layer between Mg and each steel sheet, consisting of Zn and an intermetallic compound (0.97Mg-0.03Zn). Zinc coating from the galvanized steel sheet improved the metallurgical bonding between Mg and Fe by forming new intermetallic phases.

Molecular characterization of a novel subgenotype of lumpy skin disease virus strain isolated in Inner Mongolia of China.

BACKGROUND: The outbreak of Lumpy skin disease (LSD) in cattle caused by LSD virus (LSDV) was first reported in August 2019 in China. Since then, several LSD outbreaks have been reported in seven different provinces of China. Until now, several Lumpy skin disease virus (LSDV) strains from China have been reported and sequenced including LSDV/Xinjiang/2019 (MN598005.1), China/GD01/2020 (MW355944.1), and LSDV/Hongkong/2021 (MW732649.1). In October 2020, more than 1,700 cattle imported from Chile arrived in Xilingol, Inner Mongolia, and were diagnosed with LSD. Currently, limited data on the origin of the virus is available. METHODS: Nucleotide sequences of the ORF11, ORF36, ORF74, ORF117, ORF126 genes and the complete genome of LSDV strains and isolates were downloaded from NCBI database. MEGA7.0 was used to perform phylogenetic analysis with Neighbor-Joining (NJ). DNASTAR software is used to analyze homologous comparison analysis with related genes of reference strains included in Genbank. RESULTS: Compared with other strains isolated from China, the results of full genome sequence analysis showed the LSDV/NMG/2020 strain belonged to the recombinant strains. The LSDV/NMG/2020 strain is different from the current LSDV field isolates in Africa, the Middle East, Europe, and the newly emerged LSDV Russia variants. Based on the identities of P32, RPO30, EEV, GPCR and LSDV117 genes (99.8%, 99%, 99.8%, 99% and 98.7%), the sub-cluster recombinant containing LSDV/NMG/2020 strain is phylogenetically closer to the Russia strain (Saratov/2017). CONCLUSIONS: In this study, we reported a new isolated LSDV strain named LSDV/NMG/2020. The results of genomic characterization and phylogenetic analysis demonstrated that the LSDV/NMG/2020 isolate was a vaccine-like recombinant strain.