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Abstract This study introduces a novel approach in the realm of liquid biopsies, employing a 3D Mueller-matrix (MM) image reconstruction technique to analyze dehydrated blood smear polycrystalline structures. Our research centers on exploiting the unique optical anisotropy properties of blood proteins, which undergo structural alterations at the quaternary and tertiary levels in the early stages of diseases such as cancer. These alterations manifest as distinct patterns in the polycrystalline microstructure of dried blood droplets, offering a minimally invasive yet highly effective method for early disease detection. We utilized a groundbreaking 3D MM mapping technique, integrated with digital holographic reconstruction, to perform a detailed layer-by-layer analysis of partially depolarizing dry blood smears. This method allows us to extract critical optical anisotropy parameters, enabling the differentiation of blood films from healthy individuals and prostate cancer patients. Our technique uniquely combines polarization-holographic and differential MM methodologies to spatially characterize the 3D polycrystalline structures within blood films. A key advancement in our study is the quantitative evaluation of optical anisotropy maps using statistical moments (first to fourth orders) of linear and circular birefringence and dichroism distributions. This analysis provides a comprehensive characterization of the mean, variance, skewness, and kurtosis of these distributions, crucial for identifying significant differences between healthy and cancerous samples. Our findings demonstrate an exceptional accuracy rate of over $$90\%$$ 90 % for the early diagnosis and staging of cancer, surpassing existing screening methods. This high level of precision and the non-invasive nature of our technique mark a significant advancement in the field of liquid biopsies. It holds immense potential for revolutionizing cancer diagnosis, early detection, patient stratification, and monitoring, thereby greatly enhancing patient care and treatment outcomes. In conclusion, our study contributes a pioneering technique to the liquid biopsy domain, aligning with the ongoing quest for non-invasive, reliable, and efficient diagnostic methods. It opens new avenues for cancer diagnosis and monitoring, representing a substantial leap forward in personalized medicine and oncology.

Abstract Primary open-angle glaucoma (POAG), characterized by retinal ganglion cell death, is a leading cause of irreversible blindness worldwide. However, its molecular and cellular causes are not well understood. Elevated intraocular pressure (IOP) is a major risk factor, but many patients have normal IOP. Colocalization and Mendelian randomization analysis of >240 POAG and IOP genome-wide association study (GWAS) loci and overlapping expression and splicing quantitative trait loci (e/sQTLs) in 49 GTEx tissues and retina prioritizes causal genes for 60% of loci. These genes are enriched in pathways implicated in extracellular matrix organization, cell adhesion, and vascular development. Analysis of single-nucleus RNA-seq of glaucoma-relevant eye tissues reveals that the POAG and IOP colocalizing genes and genome-wide associations are enriched in specific cell types in the aqueous outflow pathways, retina, optic nerve head, peripapillary sclera, and choroid. This study nominates IOP-dependent and independent regulatory mechanisms, genes, and cell types that may contribute to POAG pathogenesis.

AR integrates virtual elements with the real world in real-time to enhance interactivity and vividness, which may influence consumers’ perceptions and payment intentions. This study explores the impact of Augmented Reality (AR) on consumer willingness to pay for cultural heritage products, utilizing the Technology Acceptance Model (TAM) and flow theory. This study analyzes 603 responses (quantitative data) to understand consumer perceptions of AR in the purchasing process of cultural heritage products. The findings reveal that perceived usefulness, ease of use, and flow experience significantly enhance consumer purchase intentions, with ease of use also amplifying the effects of perceived usefulness and flow experience. Additionally, the immersive, interactive, and aesthetic aspects of AR contribute positively to ease of use and flow experiences, with immersion notably impacting perceived usefulness. The results support the research model with robust explanatory power, offering practical insights for employing AR to improve marketability and consumer engagement with cultural heritage products. This paper contributes to the existing literature by bridging the gap in understanding the role of AR in enhancing consumer experiences and financial outcomes in the cultural heritage domain.

(R, S)- and (S)-ketamine have made significant progress in the treatment of treatment-resistant depression (TRD) and have become a research focus in recent years. However, they both have risks of psychomimetic effects, dissociative effects, and abuse liability, which limit their clinical use. Recent preclinical and clinical studies have shown that (R)-ketamine has a more efficient and lasting antidepressant effect with fewer side effects compared to (R, S)- and (S)-ketamine. However, a recent small-sample randomized controlled trial found that although (R)-ketamine has a lower incidence of adverse reactions in adult TRD treatment, its antidepressant efficacy is not superior to the placebo group, indicating its antidepressant advantage still needs further verification and clarification. Moreover, an increasing body of research suggests that (R)-ketamine might also have significant applications in the prevention and treatment of medical fields or diseases such as cognitive disorders, perioperative anesthesia, ischemic stroke, Parkinson’s disease, multiple sclerosis, osteoporosis, substance use disorders, inflammatory diseases, COVID-19, and organophosphate poisoning. This article briefly reviews the mechanism of action and research on antidepressants related to (R)-ketamine, fully revealing its application potential and development prospects, and providing some references and assistance for subsequent expanded research.

ABSTRACTInfectious bronchitis virus (IBV), the causative agent of infectious bronchitis, is responsible for major economic losses in the poultry industry worldwide. While IBVs can usually be passaged in primary chicken embryonic fibroblasts (CEFs), most of the wild ones cannot adapt to passaged cell lines. In this study, the wild strain CK/CH/MY/2020 was used to infect primary CEF and immortalize DF-1 CEF cells. Results indicated that IBV was able to cause lesions and pass onto CEF, but not DF-1. Indeed, the virus could enter DF-1 cells and synthesize the associated structural gene but could not assemble into complete viral particles for release. Furthermore, transcriptome sequencing analysis showed significant differences in gene expression between CEF and DF-1 cells after viral infection, although the corresponding antiviral responses could be activated in both cell types. The biggest difference was in terms of the amino acid biosynthesis pathway and the cytokine receptor interaction pathway, which were significantly and specifically activated in CEF. This could actually explain why intact viruses can be assembled but not in DF-1. In addition, SLBP and P2RX7 affect the replication of IBV’s structural genes to some extent. Overall, IBV can enter CEF and DF-1 cells, but the complex intracellular cytokine interactions affect the assembly and release of viral particles. The insight will be useful for the study of IBV through in vitro transmission and pathogenesis.IMPORTANCEInfectious bronchitis virus (IBV) is responsible for high morbidity and mortality as well as substantial economic losses worldwide. Transcriptome sequencing of IBV-infected chicken embryonic fibroblast and DF-1 cells revealed that the virus elicits antiviral immunity in cells after viral infection, but IBV cannot activate DF-1 cells to produce sufficient amounts of viral structures to assemble into complete virions, which may be caused by the interactions between cytokines. The study of IBV cellular adaptations is important for vaccine development and investigation of the pathogenesis of IBV.

Abstract Designing next‐generation molecular devices typically necessitates plentiful oxygen‐bearing sites to facilitate multiple‐electron transfers. However, the theoretical limits of existing materials for energy conversion and information storage devices make it inevitable to hunt for new competitors. Polyoxometalates (POMs), a unique class of metal‐oxide clusters, have been investigated exponentially due to their structural diversity and tunable redox properties. POMs behave as electron‐sponges owing to their intrinsic ability of reversible uptake‐release of multiple electrons. In this review, numerous POM‐frameworks together with desired features of a contender material and inherited properties of POMs are systematically discussed to demonstrate how and why the electron‐sponge‐like nature of POMs is beneficial to design next‐generation water oxidation/reduction electrocatalysts, and neuromorphic nonvolatile resistance‐switching random‐access memory devices. The aim is to converge the attention of scientists who are working separately on electrocatalysts and memory devices, on a point that, although the application types are different, they all hunt for a material that could exhibit electron‐sponge‐like feature to realize boosted performances and thus, encouraging the scientists of two completely different fields to explore POMs as imperious contenders to design next‐generation nanodevices. Finally, challenges and promising prospects in this research field are also highlighted.

Abstract Background This project aimed to research the significance of THRIL in the diagnosis of benign and malignant solitary pulmonary nodules (SPNs) and to investigate the role of THRIL/miR-99a in malignant SPNs. Methods The study groups consisted of 169 patients with SPN and 74 healthy subjects. The differences in THRIL levels were compared between the two groups and the healthy group. The receiver operating characteristic curve (ROC) was utilized to analyze the THRIL’s significance in detecting benign and malignant SPN. Pearson correlation and binary regression coefficients represented the association between THRIL and SPN. CCK-8 assay, Transwell assay, and flow cytometry were utilized to detect the regulatory effect of THRIL silencing. The interaction between THRIL, miR-99a, and IGF1R was confirmed by the double luciferase reporter gene. Results There were differences in THRIL expression in the healthy group, benign SPN group, and malignant SPN group. High accuracy of THRIL in the diagnosis of benign SPN and malignant SPN was observed. THRIL was associated with the development of SPN. The expression of THRIL was upregulated and miR-99a was downregulated in lung cancer cells. The double luciferase report experiment confirmed the connections between THRIL/miR-99a/IGF1R. Silencing THRIL could suppress cell proliferation, migration, and invasion and promote cell apoptosis by binding miR-99a. Conclusion The detection of THRIL in serum is useful for the assessment of malignant SPN. THRIL can regulate the expression of IGF1R through miR-99a, thereby promoting the growth of lung cancer cells and inhibiting apoptosis.

Herein, we prepared two different MOS-based gas sensors with integrated micro-hotplates. The two sensors were employed to detect various fragrances (cedar, mandarin orange, rose A, and rose B), exhibiting similarly great sensing performances. The gas sensing properties of the MOS-based sensor depend on the sensor’s operating temperature. In addition to isothermal operation, various pulse heating modes were applied to investigate the gas sensing performances with respect to the four fragrances. Multivariate gas sensing features of the four fragrances were obtained under different operating modes, which were utilized for the recognition of fragrance odors successfully, based on the long short-term memory (LSTM) algorithm.

Abstract Specific emitter identification (SEI) is an emerging device authentication technology, which depends on the inherent hardware characteristics of wireless devices. By analysing the received signal, the hardware characteristics of a specific emitter can be extracted at the receiver and used for device authentication and association. Most of the existing SEI schemes focus on the identification under closed sets. In view of the explosive growth of the number of IoT devices, based on generative adversarial networks, this study proposes a method that considers the identification of unknown emitters. The reconstruction network to reconstruct the signals of the known class and fully train the feature space of the signals of the known class is designed. In the discriminator, two channels are specifically designed to perform anomaly detection for unknown signals and end‐to‐end closed‐set classification for known signals. In order to better reject unknown signals and accept known signals, Receiver operating characteristic curve and Youden index are used to determine the optimal threshold for anomaly detection. Under the given optimal threshold conditions, the identified threshold points have larger True Positive Rate. In addition, the time‐frequency feature combination vector is designed to reveal the essential characteristics of emitters. The experimental results on the real‐world datasets collected from universal software radio peripherals in short‐range communication scenario show that compared with the existing open‐set recognition methods such as C2AE, Openmax and SoftMax, the average recognition accuracy of the proposed framework improved by 0.08, 0.15, and 0.2 respectively, and the Marco‐F1 score improved by 0.05, 0.1, and 0.16, respectively, which proves the superiority of the proposed framework. In addition to identifying unknown and known Universal software radio peripherals in this article, some potential and widespread applications of the proposed framework include access user security authentication in IoT, such as Hack RF and Blue tooth devices. In addition, as a relatively general signal processing framework, the model can be used for air safety management and maritime ship identity authentication in civil aspects. In the military aspect, it can be used in electronic support and various signal reconnaissance scenarios, such as automatic signal modulation recognition in open set scenarios, individual identity determination of radar emitter and unknown working state identification of transmitters, which proves reference for subsequent research on open‐set signal recognition.

Probing the direct effect of grain boundaries as active catalytic sites is very challenging. Here, the authors reveal that the d z 2 orbital energy level of Mo atoms in grain boundaries exhibits an intrinsic relationship with the hydrogen evolution activity.

Abstract The ‘mismatch losses’ problem is commonly encountered in distributed photovoltaic (PV) power generation systems. It can directly reduce power generation. Hence, PV array reconfiguration techniques have become highly popular to minimize the mismatch losses. In this paper, a dynamical array reconfiguration method for Total-Cross-Ties (TCT) and Series–Parallel (SP) interconnected PV arrays is proposed. The method aims to improve the maximum power output generation of a distributed PV array in different mismatch conditions through a set of inverters and a switching matrix that is controlled by a dynamic and scalable reconfiguration optimization algorithm. The structures of the switching matrix for both TCT-based and SP-based PV arrays are designed to enable flexible alteration of the electrical connections between PV strings and inverters. Also, the proposed reconfiguration solution is scalable, because the size of the switching matrix deployed in the proposed solution is only determined by the numbers of the PV strings and the inverters, and is not related to the number of PV modules in a string. The performance of the proposed method is assessed for PV arrays with both TCT and SP interconnections in different mismatch conditions, including different partial shading and random PV module failure. The average optimization time for TCT and SP interconnected PV arrays is 0.02 and 3 s, respectively. The effectiveness of the proposed dynamical reconfiguration is confirmed, with the average maximum power generation improved by 8.56% for the TCT-based PV array and 6.43% for the SP-based PV array compared to a fixed topology scheme.

Abstract. Background:. Hypertrophic cardiomyopathy (HCM) is an underdiagnosed genetic heart disease worldwide. The management and prognosis of obstructive HCM (HOCM) and non-obstructive HCM (HNCM) are quite different, but it also remains challenging to discriminate these two subtypes. HCM is characterized by dysmetabolism, and myocardial amino acid (AA) metabolism is robustly changed. The present study aimed to delineate plasma AA and derivatives profiles, and identify potential biomarkers for HCM. Methods:. Plasma samples from 166 participants, including 57 cases of HOCM, 52 cases of HNCM, and 57 normal controls (NCs), who first visited the International Cooperation Center for HCM, Xijing Hospital between December 2019 and September 2020, were collected and analyzed by high-performance liquid chromatography–mass spectrometry based on targeted AA metabolomics. Three separate classification algorithms, including random forest, support vector machine, and logistic regression, were applied for the identification of specific AA and derivatives compositions for HCM and the development of screening models to discriminate HCM from NC as well as HOCM from HNCM. Results:. The univariate analysis showed that the serine, glycine, proline, citrulline, glutamine, cystine, creatinine, cysteine, choline, and aminoadipic acid levels in the HCM group were significantly different from those in the NC group. Four AAs and derivatives (Panel A; proline, glycine, cysteine, and choline) were screened out by multiple feature selection algorithms for discriminating HCM patients from NCs. The receiver operating characteristic (ROC) analysis in Panel A yielded an area under the ROC curve (AUC) of 0.83 (0.75–0.91) in the training set and 0.79 (0.65–0.94) in the validation set. Moreover, among 10 AAs and derivatives (arginine, phenylalanine, tyrosine, proline, alanine, asparagine, creatine, tryptophan, ornithine, and choline) with statistical significance between HOCM and HNCM, 3 AAs (Panel B; arginine, proline, and ornithine) were selected to differentiate the two subgroups. The AUC values in the training and validation sets for Panel B were 0.83 (0.74–0.93) and 0.82 (0.66–0.98), respectively. Conclusions:. The plasma AA and derivatives profiles were distinct between the HCM and NC groups. Based on the differential profiles, the two established screening models have potential value in assisting HCM screening and identifying whether it is obstructive.

This paper releases a rotated SAR ship detection dataset, named Rotated Ship Detection Dataset in SAR Images (RSDD-SAR), to address the problem that the existing rotated SAR ship detection datasets are not enough to meet the requirements of algorithm development and practical application. This dataset consists of 84 scenes of GF-3 data slices, 41 scenes of TerraSAR-X data slices, and 2 scenes of large uncropped images, including 7,000 slices and 10,263 ship instances of multi-observing modes, multi-polarization modes, and multi-resolutions. This dataset is effectively annotated by automatic annotation with manual correction. Meanwhile, experiments were conducted for several popular rotated object detection algorithms in optical remote sensing images and rotated ship detection algorithms in SAR images, and the one-stage algorithm S2ANet achieved the highest average precision of 90.06%. When using this dataset, scholars can reference the experimental results, and corresponding analysis can be used. Finally, this paper conducts generalization ability testing experiments on other datasets and large uncropped images to analyze and discuss the performance of the model trained on RSDD-SAR. The experimental results show that the model trained on RSDD-SAR has decent performance and confirms the application value of this dataset. The RSDD-SAR dataset is available at https://radars.ac.cn/web/data/getData?dataType=SDD-SAR.

This paper proposes an SEI method based on cost-sensitive learning and semisupervised generative adversarial networks to address the problem of incomplete sample labels and imbalanced data category distribution in Specific Emitter Identification (SEI), which leads to a decline in inaccuracy. Through semisupervised training, the method optimizes the network parameters of the generator and discriminator, adds a multiscale topological block to ResNet to fuse the multi-dimensional resolution features of the time-domain signal, and attributes additional labels to the generated samples to directly use the discriminator to complete the classification. Simultaneously, a cost-sensitive loss is designed to alleviate the imbalance of gradient propagation caused by the dominant samples and improve the recognition performance of the classifier on the class-imbalanced dataset. The experimental results on four types of imbalanced datasets show that in the presence of 40% unlabeled samples, the average recognition accuracy for five emitters is improved by 5.34% and 2.69%, respectively, compared with the cross-entropy loss and focus loss. This provides a new idea for solving the problem of SEI under the conditions of insufficient data labels and an unbalanced distribution of data.

ABSTRACT: Infectious bronchitis virus (IBV) causes respiratory diseases in chickens, incurring great losses to the poultry industry worldwide. In this study, we isolated an IBV strain, designated as AH-2020, from the chickens vaccinated with H120 and 4/91 in Anhui, China. The sequence homology analysis based on the S1 gene revealed that AH-2020 shares low similarities with the 3 vaccine strains, namely, H120, LDT3-A, and 4/91 (78.19, 80.84, and 81.6%, respectively). Phylogenetic analysis based on the S1 gene revealed that AH-2020 clustered with the GI-19 type. Furthermore, protein modeling revealed that the mutations in the amino acids in AH-2020 were mainly located in the N-terminal domain of S1 (S1-NTD), and the pattern of deletion and insertion mutations in the S1 protein may have influenced the structural changes on the surface of S1. Further, approximately 7-day-old SPF chickens were inoculated with AH-2020 at 106.0 EID50. These chickens exhibited clinical signs of the infection such as listlessness, huddling, and head-shaking, accompanied by depression and 40% mortality. Serum antibody test demonstrated that in response to the AH-2020 infection, the antibody level increased the fastest at 7 dpi, with virus shedding rate of cloaca being 100% at 14 dpi. The viral titer in various tissues was detected using hematoxylin and eosin staining and immunohistochemistry, which revealed that AH-2020 infection can damage the kidney, trachea, lung, cecal tonsil, and bursa of Fabricius. Our study provided evidence that the GI-19-type IBV is undergoing more complex mutations, and effective measures are urgently needed to prevent the spread of these variant strains.

Designing biomimetic olfactory neuroepithelium (BONe) with subnanosized active domains as artificial olfactory receptors (ORs) is highly desirable to sense various colorless and odorless hazardous odorants which find no appropriate ORs in the human olfactory neuroepithelium (ONe), yet challenging because of the unsuitability of biomolecules for a design that requires effective electronic features and stability. Herein, a DNA‐incorporated 3D BONe is introduced, where DNA facilitates optimal tuning of d‐band center, and in situ anchoring of PdO2 subnanoscale clusters (PdO2‐sNCs) on the exfoliated single‐layer reduced graphene oxide (SL rGO), to mimic wrinkled morphology of natural ONe. Unprecedentedly, BONe demonstrates benchmarked H2‐sensing performance (small recovery time of ≈30 s with a limit of detection of 50 ppb) at room temperature with yearlong durability, satisfying prerequisites of safe adoption of H2 clean energy. The great recovery is innovatively illustrated by the downshift of d‐band center of PdO2‐sNCs and strong electron transport of SL‐rGO network. An adsorption/desorption model is proposed to clarify the sensing mechanism. BONe design may eventually be integrated with artificial intelligent electronics for ppb‐level sensing of harmful gases to ensure accident prevention in modern public and military environments.

Abstract Mesenchymal stromal cell (MSC) implantation is a promising option for liver repair, but their poor retention in the injured liver milieu critically blunts therapeutic effects. The aim is to clarify the mechanisms underlying massive MSC loss post‐implantation and establish corresponding improvement strategies. MSC loss primarily occurs within the initial hours after implantation into the injured liver milieu or under reactive oxygen species (ROS) stress. Surprisingly, ferroptosis is identified as the culprit for rapid depletion. In ferroptosis‐ or ROS‐provoking MSCs, branched‐chain amino acid transaminase‐1 (BCAT1) is dramatically decreased, and its downregulation renders MSC susceptible to ferroptosis via suppressing the transcription of glutathione peroxidase‐4 (GPX4), a vital ferroptosis defensing enzyme. BCAT1 downregulation impedes GPX4 transcription via a rapid‐responsive metabolism‐epigenetics coordinating mechanism, involving α‐ketoglutarate accumulation, histone 3 lysine 9 trimethylation loss, and early growth response protein‐1 upregulation. Approaches to suppress ferroptosis (e.g., incorporating ferroptosis inhibitors in injection solvent and overexpressing BCAT1) significantly improve MSC retention and liver‐protective effects post‐implantation. This study provides the first evidence indicating that excessive MSC ferroptosis is the nonnegligible culprit for their rapid depletion and insufficient therapeutic efficacy after implantation into the injured liver milieu. Strategies suppressing MSC ferroptosis are conducive to optimizing MSC‐based therapy.

Abstract Mesenchymal stem cells (MSCs) delivered into the post-ischemic heart milieu have a low survival and retention rate, thus restricting the cardioreparative efficacy of MSC-based therapy. Chronic ischemia results in metabolic reprogramming in the heart, but little is known about how these metabolic changes influence implanted MSCs. Here, we found that excessive branched-chain amino acid (BCAA) accumulation, a metabolic signature seen in the post-ischemic heart, was disadvantageous to the retention and cardioprotection of intramyocardially injected MSCs. Discovery-driven experiments revealed that BCAA at pathological levels sensitized MSCs to stress-induced cell death and premature senescence via accelerating the loss of histone 3 lysine 9 trimethylation (H3K9me3). A novel mTORC1/DUX4/KDM4E axis was identified as the cause of BCAA-induced H3K9me3 loss and adverse phenotype acquisition. Enhancing BCAA catabolic capability in MSCs via genetic/pharmacological approaches greatly improved their adaptation to the high BCAA milieu and strengthened their cardioprotective efficacy. We conclude that aberrant BCAA accumulation is detrimental to implanted MSCs via a previously unknown metabolite-signaling-epigenetic mechanism, emphasizing that the metabolic changes of the post-ischemic heart crucially influence the fate of implanted MSCs and their therapeutic benefits.

ObjectivesTo investigate whether combining radiomics extracted from ultrafast dynamic contrast-enhanced MRI (DCE-MRI) with an artificial neural network enables differentiation of MR BI-RADS 4 breast lesions and thereby avoids false-positive biopsies.MethodsThis retrospective study consecutively included patients with MR BI-RADS 4 lesions. The ultrafast imaging was performed using Differential sub-sampling with cartesian ordering (DISCO) technique and the tenth and fifteenth postcontrast DISCO images (DISCO-10 and DISCO-15) were selected for further analysis. An experienced radiologist used freely available software (FAE) to perform radiomics extraction. After principal component analysis (PCA), a multilayer perceptron artificial neural network (ANN) to distinguish between malignant and benign lesions was developed and tested using a random allocation approach. ROC analysis was performed to evaluate the diagnostic performance.Results173 patients (mean age 43.1 years, range 18–69 years) with 182 lesions (95 benign, 87 malignant) were included. Three types of independent principal components were obtained from the radiomics based on DISCO-10, DISCO-15, and their combination, respectively. In the testing dataset, ANN models showed excellent diagnostic performance with AUC values of 0.915-0.956. Applying the high-sensitivity cutoffs identified in the training dataset demonstrated the potential to reduce the number of unnecessary biopsies by 63.33%-83.33% at the price of one false-negative diagnosis within the testing dataset.ConclusionsThe ultrafast DCE-MRI radiomics-based machine learning model could classify MR BI-RADS category 4 lesions into benign or malignant, highlighting its potential for future application as a new tool for clinical diagnosis.

Abstract Background Salmonella Enteritidis (S. Enteritidis) being one of the most prevalent foodborne pathogens worldwide poses a serious threat to public safety. Prevention of zoonotic infectious disease and controlling the risk of transmission of S. Enteriditidis critically requires the evolution of rapid and sensitive detection methods. The detection methods based on nucleic acid and conventional antibodies are fraught with limitations. Many of these limitations of the conventional antibodies can be circumvented using natural nanobodies which are endowed with characteristics, such as high affinity, thermal stability, easy production, especially higher diversity. This study aimed to select the special nanobodies against S. Enteriditidis for developing an improved nanobody-horseradish peroxidase-based sandwich ELISA to detect S. Enteritidis in the practical sample. The nanobody-horseradish peroxidase fusions can help in eliminating the use of secondary antibodies labeled with horseradish peroxidase, which can reduce the time of the experiment. Moreover, the novel sandwich ELISA developed in this study can be used to detect S. Enteriditidis specifically and rapidly with improved sensitivity. Results This study screened four nanobodies from an immunized nanobody library, after four rounds of screening, using the phage display technology. Subsequently, the screened nanobodies were successfully expressed with the prokaryotic and eukaryotic expression systems, respectively. A sandwich ELISA employing the SE-Nb9 and horseradish peroxidase-Nb1 pair to capture and to detect S. Enteritidis, respectively, was developed and found to possess a detection limit of 5 × 104 colony forming units (CFU)/mL. In the established immunoassay, the 8 h-enrichment enabled the detection of up to approximately 10 CFU/mL of S. Enteriditidis in milk samples. Furthermore, we investigated the colonization distribution of S. Enteriditidis in infected chicken using the established assay, showing that the S. Enteriditidis could subsist in almost all parts of the intestinal tract. These results were in agreement with the results obtained from the real-time PCR and plate culture. The liver was specifically identified to be colonized with quite a several S. Enteriditidis, indicating the risk of S. Enteriditidis infection outside of intestinal tract. Conclusions This newly developed a sandwich ELISA that used the SE-Nb9 as capture antibody and horseradish peroxidase-Nb1 to detect S. Enteriditidis in the spike milk sample and to analyze the colonization distribution of S. Enteriditidis in the infected chicken. These results demonstrated that the developed assay is to be applicable for detecting S. Enteriditidis in the spiked milk in the rapid, specific, and sensitive way. Meanwhile, the developed assay can analyze the colonization distribution of S. Enteriditidis in the challenged chicken to indicate it as a promising tool for monitoring S. Enteriditidis in poultry products. Importantly, the SE-Nb1-vHRP as detection antibody can directly bind S. Enteritidis captured by SE-Nb9, reducing the use of commercial secondary antibodies and shortening the detection time. In short, the developed sandwich ELISA ushers great prospects for monitoring S. Enteritidis in food safety control and further commercial production. Graphic Abstract

Deep-learning-based SAR ship classification has become a research hotspot in the military and civilian fields and achieved remarkable performance. However, the volume of available SAR ship classification data is relatively small, meaning that previous deep-learning-based methods have usually struggled with overfitting problems. Moreover, due to the limitation of the SAR imaging mechanism, the large intraclass diversity and small interclass similarity further degrade the classification performance. To address these issues, we propose a label smoothing auxiliary classifier generative adversarial network with triplet loss (LST-ACGAN) for SAR ship classification. In our method, an ACGAN is introduced to generate SAR ship samples with category labels. To address the model collapse problem in the ACGAN, the smooth category labels are assigned to generated samples. Moreover, triplet loss is integrated into the ACGAN for discriminative feature learning to enhance the margin of different classes. Extensive experiments on the OpenSARShip dataset demonstrate the superior performance of our method compared to the previous methods.

Abstract Background The Chinese government has worked out the “Rural Oriented Medical Students Training Project” to address physician maldistribution, which attempted to train physicians for rural areas. The present study attempted to evaluate the job satisfaction of the graduates of this project in Jiangsu Province, China. Methods Online questionnaires were sent to the graduates of the “Rural Oriented Medical Students Training Project” (group A) and their colleagues, who were rural physicians recruited from different sources (group B). The study was approved by the Ethics Committee of Xuzhou Medical University, and the approval number was 2,018,057. Information on demographic characteristics, work conditions, and self-reported satisfaction was collected to compare the satisfaction differences between the two recruited rural physicians using the Chi-square test and Mann–Whitney U test. Additionally, factors correlated to the satisfaction of group A were assessed using multivariate linear regression. Statistical analysis was performed using SPSS 23.0 (SPSS Inc., Chicago, IL, USA). P

Species are facing strong selection pressures to adapt to inhospitable high-altitude environments. Yaks are a valuable species and an iconic symbol of the Qinghai-Tibet Plateau. Extensive studies of high-altitude adaptation have been conducted, but few have focused on metabolism. In the present study, we determined the differences in the serum metabolomics between yaks and the closely related species of low-altitude yellow cattle and dairy cows. We generated high-quality metabolite profiling data for 36 samples derived from the three species, and a clear separation trend was obtained between yaks and the other animals from principal component analysis. In addition, we identified a total of 63 differentially expressed metabolites among the three species. Functional analysis revealed that differentially expressed metabolites were related to the innate immune activation, oxidative stress-related metabolism, and energy metabolism in yaks, which indicates the important roles of metabolites in high-altitude adaptation in yaks. The results provide new insights into the mechanism of adaptation or acclimatization to high-altitude environments in yaks and hypoxia-related diseases in humans.

Recently, unsupervised domain adaptation (UDA) has attracted increasing attention to address the domain shift problem in the semantic segmentation task. Although previous UDA methods have achieved promising performance, they still suffer from the distribution gaps between source and target domains, especially the resolution discrepancy in the remote sensing images. To address this problem, this study designs a novel end-to-end semantic segmentation network, namely, Super-Resolution Domain Adaptation Network (SRDA-Net). SRDA-Net can simultaneously achieve the super-resolution task and the domain adaptation task, thus satisfying the requirement of semantic segmentation for remote sensing images, which usually involve various resolution images. The proposed SRDA-Net includes three parts: a super-resolution and segmentation (SRS) model, which focuses on recovering high-resolution image and predicting segmentation map, a pixel-level domain classifier (PDC) for determining which domain the pixel belongs to, and an output-space domain classifier (ODC) for distinguishing which domain the pixel contribution is from. By jointly optimizing SRS with two classifiers, the proposed method can not only eliminate the resolution difference between source and target domains but also improve the performance of the semantic segmentation task. Experimental results on two remote sensing datasets with different resolutions demonstrate that SRDA-Net performs favorably against some state-of-the-art methods in terms of accuracy and visual quality. Code and models are available at https://github.com/tangzhenjie/SRDA-Net.

Abstract Bile acid metabolites have been increasingly recognized as pleiotropic signaling molecules that regulate cardiovascular functions, but their role in mesenchymal stromal cells (MSC)‐based therapy has never been investigated. It is found that overexpression of farnesoid X receptor (FXR), a main receptor for bile acids, improves the retention and cardioprotection of adipose tissue‐derived MSC (ADSC) administered by intramyocardial injection in mice with myocardial infarction (MI), which shows enhanced antiapoptotic, proangiogenic, and antifibrotic effects. RNA sequencing, LC‐MS/MS, and loss‐of‐function studies reveal that FXR overexpression promotes ADSC paracrine angiogenesis via Angptl4. FXR overexpression improves ADSC survival in vivo but fails in vitro. By performing bile acid‐targeted metabolomics using ischemic heart tissue, 19 bile acids are identified. Among them, cholic acid and deoxycholic acid significantly increase Angptl4 secretion from ADSC overexpressing FXR and further improve their proangiogenic capability. Moreover, ADSC overexpressing FXR shows significantly lower apoptosis by upregulating Nqo‐1 expression only in the presence of FXR ligands. Retinoid X receptor α is identified as a coactivator of FXR. It is first demonstrated that there is a bile acid pool in the myocardial microenvironment. Targeting the bile acid‐FXR axis may be a novel strategy for improving the curative effect of MSC‐based therapy for MI.

Lithium-ion batteries (LIBs) have become one of the most competitive energy storage technologies. However, the “thermal runaway” of LIBs leads to serious safety issues. Early safety warning of LIBs is a prerequisite for the widely applications of power battery and large-scale energy storage systems. As reported, hydrogen (H2) could be generated due to the reaction of lithium metal and polymers inside the battery. The generation of H2 is some time earlier than the “thermal runaway”. Therefore, the rapid detection of trace hydrogen is the most effective method for early safety warning of LIBs. Resistive hydrogen sensors have attracted attention in recent years. In addition, they could be placed inside the LIB package for the initial hydrogen detection. Here, we overview the recent key advances of resistive room temperature (RT) H2 sensors, and explore possible applications inside LIB. We explored the underlying sensing mechanisms for each type of H2 sensor. Additionally, we highlight the approaches to develop the H2 sensors in large scale. Finally, the present review presents a brief conclusion and perspectives about the resistive RT H2 sensors for early safety warning of LIBs.

Whether it is the Summer Olympics or the Winter Olympics, in order to resolve potential sports disputes, the Court of Arbitration for Sport (CAS) sets up a special ad hoc tribunal in the host city of the Olympic Games. Although CAS ad hoc rules have many similarities with ordinary procedures, they are different in terms of the legal basis, legal remedies, and certain procedural rules. During the Beijing 2022 Winter Olympics (Beijing 2022), the CAS Ad Hoc Division and the CAS Anti-Doping Division heard a total of seven cases. These cases involve issues such as provisional suspension of games, protection of minors, unreasonable delays in test results, cancellation of award ceremonies, and the timing of disputes. the CAS Ad Hoc Division decisions on the above issues can be regarded as the latest developments in the application of international sports arbitration rules.

Abstract Background Avian infectious bronchitis virus (IBV) is a gamma coronavirus that severely affects the poultry industry worldwide. Long non-coding RNAs (lncRNAs), a subset of non-coding RNAs with a length of more than 200 nucleotides, have been recently recognized as pivotal factors in the pathogenesis of viral infections. However, little is known about the function of lncRNAs in host cultured cells in response to IBV infection. Results We used next-generation high throughput sequencing to reveal the expression profiles of mRNAs and lncRNAs in IBV-infected HD11 cells. Compared with the uninfected cells, we identified 153 differentially expressed (DE) mRNAs (106 up-regulated mRNAs, 47 down-regulated mRNAs) and 181 DE lncRNAs (59 up-regulated lncRNAs, 122 down-regulated lncRNAs) in IBV-infected HD11 cells. Moreover, gene ontology (GO) and pathway enrichment analyses indicated that DE mRNAs and lncRNAs were mainly involved in cellular innate immunity, amino acid metabolism, and nucleic acid metabolism. In addition, 2640 novel chicken lncRNAs were identified, and a competing endogenous RNA (ceRNAs) network centered on gga-miR-30d and miR-146a-5p was established. Conclusions We identified expression profiles of mRNAs and lncRNAs during IBV infection that provided new insights into the pathogenesis of IBV.

Automatic signal recognition (ASR) is becoming increasingly important in spectrum identification and cognitive radio, but most existing space-time block code (STBC) recognition algorithms are traditional ones and do not account for the complementarities between different features. To overcome these deficiencies, a multi-delay features fusion scheme for ASR of STBC using a convolutional neural network (CNN) is proposed in this study. The proposed scheme tries to fuse different time-delay features of received STBC signals to obtain more discriminating features. The dilated convolution of different dilation rates is applied to realize automated multi-delay feature extraction. Then, two fusion methods, i.e., max-correlation (MC) fusion and multi-delay average (MDA) fusion, are proposed to combine the features of different time delays, and a residual block is applied to achieve better representation of signals. Finally, the simulation results demonstrate the superior performance of the proposed method. It is notable that the recognition accuracy can reach 97.4% with a signal-to-noise ratio (SNR) of −5 dB. In addition, the proposed multi-delay features fusion CNN (MDFCNN) scheme does not need a priori information, i.e., modulation type, channel coefficients, and noise power, which is well suited to non-collaborative communication.

Specific emitter identification (SEI) uses the unintentional modulation information carried by the emitter waveform, i.e., radio frequency fingerprints, to realize the matching identification of the received signal and its corresponding emitter. We propose an emitter identification scheme based on deep learning (DL). The received signal is subjected to time-varying filtered empirical mode decomposition (tvf-EMD). The obtained intrinsic mode functions (IMFs) are subjected to Hilbert transformation to obtain a 3D-Hilbert spectrum, using amplitude frequency aggregation characteristics obtained by projection to express the nonstationary information of the signal and calculate its bispectral diagonal slice as the second feature. A squeeze-and-excitation block (SEB) is introduced to a convolutional neural network (CNN) to focus on the effective area in the feature map, and support vector machine (SVM) is used to fuse the decision information of the two types of features. We collect four types of steady-state signals on a software-defined radio (SDR) sensor platform based on GNU Radio and universal software radio peripherals (USRP), and study the algorithm performance. The results show that our scheme has strong generalization, high recognition accuracy, and broad application prospects compared with other SEI methods.

Abstract Few intravenously administered mesenchymal stromal cells (MSCs) engraft to the injured myocardium, thereby limiting their therapeutic efficacy for the treatment of ischemic heart injury. Here, it is found that irisin pretreatment increases the cardiac homing of adipose tissue‐derived MSCs (ADSCs) administered by single and multiple intravenous injections to mice with MI/R by more than fivefold, which subsequently increases their antiapoptotic, proangiogenic, and antifibrotic effects in rats and mice that underwent MI/R. RNA sequencing, Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis, and loss‐of‐function studies identified CSF2RB as a cytokine receptor that facilitates the chemotaxis of irisin‐treated ADSCs in the presence of CSF2, a chemokine that is significantly upregulated in the ischemic heart. Cardiac‐specific CSF2 knockdown blocked the cardiac homing and cardioprotection abilities of intravenously injected irisin‐treated ADSCs in mice subjected to MI/R. Moreover, irisin pretreatment reduced the apoptosis of hydrogen peroxide‐induced ADSCs and increased the paracrine proangiogenic effect of ADSCs. ERK1/2‐SOD2, and ERK1/2‐ANGPTL4 are responsible for the antiapoptotic and paracrine angiogenic effects of irisin‐treated ADSCs, respectively. Integrin αV/β5 is identified as the irisin receptor in ADSCs. These results provide compelling evidence that irisin pretreatment can be an effective means to optimize intravenously delivered MSCs as therapy for ischemic heart injury.

BackgroundChildhood experiences can exert a huge impact on adult psychological conditions. Previous studies have confirmed the effects of adverse childhood experiences (ACEs) and benevolent childhood experiences (BCEs) on psychological distress (e.g., stress, depression, and suicidal ideation) separately, but few studies explored a combined effect of ACEs and BCEs on psychological distress. The aim of this study was to explore a combined effect of ACEs and BCEs on psychological distress among Chinese undergraduates.MethodsParticipants were undergraduates aged 17–24 years (N = 1,816) and completed a self-reported questionnaire. A series of regression analyses were conducted to examine the association between childhood experiences and psychological distress.ResultsA total of 65.7% of undergraduates had BCEs, 27.1% of undergraduates had ACEs, and 12.9% of undergraduates had ACEs and BCEs simultaneously. Logistic regression analysis indicated that undergraduates who experienced high ACEs were more likely to have a high risk of psychological distress [odds ratio (ORs) = 1.46, 1.84, and 3.15 for uncertainty stress, depressive symptoms, and suicidal ideation, respectively], while undergraduates who experienced High BCEs were less likely to have psychological distress (ORs = 0.33, 0.22, and 0.32 for uncertainty stress, depressive symptoms, and suicidal ideation, respectively) compared with Low-Both group. The combined effect of ACEs and BCEs (High-Both group) could also play as a protective factor in uncertainty stress (OR = 0.56) and depressive symptoms (OR = 0.47).ConclusionOur findings suggested that ACEs and BCEs could not only predict the psychological distress independently, but also BCEs could counteract the negative effect of ACEs in psychological problems. There is an even greater need to identify and support the victims of ACEs and to increase BCEs in early childhood.

In situ tuning of the electronic structure of active sites is a long-standing challenge. Here, the authors report an approach to tune the electronic structure of cobalt species during the styrene epoxidation reaction by the introduction of controllable hydrogen spillover for enhanced selectivity.

Communication signal modulation recognition has important research value in the fields of cognitive electronic warfare, communication countermeasures and non-collaborative communication. However, traditional signal recognition methods usually suffer some drawbacks, such as low accuracy, poor scalability, dependence on expert characteristics, and poor applicability to real-world environments. Therefore, in this article, a real-time modulation recognition system based on deep learning and software-defined radio (SDR) technology is designed. In the first step, an improved residual neural network is designed. A multi-skip residual stack (MRS) is designed to preserve more initial residuals information on the multi-scale feature map, which can simultaneously learn the deep and shallow characteristics of the signal. Then, a multi-skip residual network is designed with the MRS as the basic unit, and the network is trained using an adaptive moment estimation optimization algorithm. Finally, the network is tested on public datasets. In the second step, the network is embedded in a SDR platform composed of a GNU Radio and an universal software radio peripheral to realize real-time recognition of the input signal. Experiments show that this system has strong real-time capabilities, high recognition accuracy and considerable robustness.

Background: This study aimed to evaluate clinical features and prognosis and therapy option of patients with different risk ranks based on antibody against the M-type phospholipase-A2-receptor (PLA2Rab) level in seropositive M-type phospholipase-A2-receptor (PLA2R)-associated membranous nephropathy (MN) in a large sample size, multi-center study.Method: Based on the unvalidated cut-off value of PLA2Rab above 150 RU/ml as one of the clinical criteria for high risk of progressive kidney function loss in MN according to 2020 Kidney Disease: Improving Global Outcomes (KDIGO) draft guidelines recommendation, a total of 447 patients who received cyclophosphamide (CTX) or tacrolimus (TAC) combined with corticosteroids treatment for 12 months were divided into high titer (>150 RU/ml) group and non-high titer (20–150 RU/ml) group, which were subdivided into CTX subgroup and TAC subgroup. The overall cohort was classified into CTX group and TAC group as well. Clinical parameters levels and remission rates were recorded at 3, 6, and 12 months follow-up. PLA2Rab was tested by enzyme-linked immunosorbent assay.Results: Patients with high titer PLA2Rab were associated with more severe proteinuria and hypoalbuminemia compared to those with non-high titer antibody, accompanied by lower complete remission (CR) and total remission (TR) rates at 3, 6, and 12 months, which even took longer to remission. Similar remission rates differences between the two titer groups were observed in the CTX and TAC groups, respectively. PLA2Rab level at baseline was an independent predictive factor for CR and TR. In the high titer group, CR and TR rates in the CTX subgroup were significantly higher than those in the TAC subgroup at 12 months, although serious adverse events were more frequent in the former.Conclusion: High-risk rank patients with PLA2Rab level above 150 RU/ml have higher disease activity and worse prognosis among patients with seropositive PLA2R-associated MN, even under different immunosuppressive therapeutic models; moreover, CTX combined with corticosteroids was preferred compared to TAC plus corticosteroids, although serious adverse events were more frequent in the former. Additionally, baseline PLA2Rab level was an independent predictive factor for clinical remission.

The widespread nature and economic importance of Infectious bronchitis virus (IBV) and interactions between IBV and the host immune response remain poorly understood. Understanding the mechanism of virus recognition via innate immunity can help resist IBV invasion. Retinoic acid-induced gene I-like receptor (RLRs) recognize virus RNA in virus infection, and LGP2 is a member of RLRs. According to the current studies, LGP2 exhibited certain inhibition in the virus, and there is a lack of investigation for chicken’s LGP2. It is important to figure out the role of chLGP2 in host immune recognition of IBV. Our results showed that chLGP2 inhibited the proliferation of IBV Beaudette in cells. Also, chLGP2 can identify and combine with IBV RNA. The domains of chLGP2 were separately expressed and inspired by related literature, and the chLGP2 K30A mutant was constructed. Our results suggested its structural integrity and the adenosine triphosphatase (ATPase) activity are critical for IBV inhibiting activity. chTRBP was selected after CO-IP and Mass spectrometry test. We found chTRBP and chLGP2 are the interacting partners and promote mutual expression. Our study showed that chTRBP could also suppress IBV infections via chLGP2, which provided a basis for future innate immunity research for IBV.

Background: The incidence of cerebral ischemia disease leading cause of death in human population worldwide. Treatment of cerebral ischemia remains a clinical challenge for researchers and mechanisms of cerebral ischemia remain unknown. During the cerebral ischemia, inflammatory reaction and oxidative stress plays an important role. The current investigation scrutinized the neuroprotective and anti-inflammatory role of pterostilbene against cerebral ischemia in middle cerebral artery occlusion (MCAO) rodent model and explore the underlying mechanism.Methods: The rats were divided into following groups viz., normal, sham, MCAO and MCAO + pterostilbene (25 mg/kg) group, respectively. The groups received the oral administration of pterostilbene for 30 days followed by MCAO induction. The neurological score, brain water content, infarct volume and Evan blue leakage were estimated. Hepatic, renal, heart, inflammatory cytokines and inflammatory mediators were estimated.Results: Pterostilbene treatment significantly (p < 0.001) improved the body weight and suppressed the glucose level and brain weight. Pterostilbene significantly (p < 0.001) reduced the hepatic, renal and heart parameters. Pterostilbene significantly (p < 0.001) decreased the level of glutathione (GSH), glutathione peroxidase (GPx), superoxide dismutase (SOD) and decreased the level of malonaldehyde (MDA), 8-Hydroxy-2′-deoxyguanosine (8-OHdG). Pterostilbene significantly (p < 0.001) inflammatory cytokines and inflammatory parameters such as cyclooxygenase-2 (COX-2), inducible nitric oxidase synthase (iNOS) and prostaglandin (PGE2). Pterostilbene significantly (p < 0.001) down-regulated the level of metalloproteinases (MMP) such as MMP-2 and MMP-9. Pterostilbene suppressed the cellular swelling, cellular disintegration, macrophage infiltration, monocyte infiltration and polymorphonuclear leucocyte degranulation in the brain.Conclusion: In conclusion, Pterostilbene exhibited the neuroprotective effect against cerebral ischemia in rats via anti-inflammatory mechanism.

The traditional fast successive-cancellation (SC) decoding algorithm can effectively reduce the decoding steps, but the decoding adopts a sub-optimal algorithm, so it cannot improve the bit error performance. In order to improve the bit error performance while maintaining low decoding steps, we introduce a neural network subcode that can achieve optimal decoding performance and combine it with the traditional fast SC decoding algorithm. While exploring how to combine neural network node (NNN) with R1, R0, single-parity checks (SPC), and Rep, we find that the decoding failed sometimes when the NNN was not the last subcode. To solve the problem, we propose two neural network-assisted decoding schemes: a key-bit-based subcode NN-assisted decoding (KSNNAD) scheme and a last subcode NN-assisted decoding (LSNNAD) scheme. The LSNNAD scheme recognizes the last subcode as an NNN, and the NNN with nearly optimal decoding performance gives rise to some performance improvements. To further improve performance, the KSNNAD scheme recognizes the subcode with a key bit as an NNN and changes the training data and label accordingly. Computer simulation results confirm that the two schemes can effectively reduce the decoding steps, and their bit error rates (BERs) are lower than those of the successive-cancellation decoder (SCD).

A low reproductive rate coupled with human activities has endangered the giant panda, a species endemic to southwest China. Although giant pandas feed almost exclusively on bamboo, they retain carnivorous traits and suffer from carnivorous diseases. Additionally, their immune system is susceptible to aging, resulting in a reduced ability to respond to diseases. This study aimed to determine the genes and pathways expressed differentially with age in blood tissues. The differentially expressed genes in different age groups of giant pandas were identified by RNA-seq. The elderly giant pandas had many differentially expressed genes compared with the young group (3 years old), including 548 upregulated genes and 401 downregulated genes. Further, functional enrichment revealed that innate immune upregulation and adaptive immune downregulation were observed in the elderly giant pandas compared with the young giant pandas. Meanwhile, the immune genes in the elderly giant pandas changed considerably, including genes involved in innate immunity and adaptive immunity such as PLSCR1, CLEC7A, CCL5, CCR9, and EPAS1. Time series analysis found that giant pandas store glycogen by prioritizing fat metabolism at age 11, verifying changes in the immune system. The results reported in this study will provide a foundation for further research on disease prevention and the energy metabolism of giant pandas.

Humidity effects on resistive gas sensors operating at room temperature remain a serious bottleneck. In this work, we introduce a resistive gas sensor based on a zeolitic imidazolate framework-8/carbon nanotube (ZIF-8/CNT) composite for the detection of ammonia gas at room temperature. The composite was prepared using a facile solution method. In this sensor, the basic mechanism was the charge transfer between ammonia molecules and CNTs; meanwhile, the ZIF-8 facilitated the adsorption of ammonia molecules as a preconcentrator, and prevented the adsorption of H2O molecules due to its hydrophobicity; CNTs were threaded through the ZIF-8 to form a great conductive network for charge transfer. The obtained sensor showed good ammonia sensing, especially at room temperature, with great selectivity and immunity to humidity under moderately humid conditions (45–70 % RH). However, the ammonia response was reduced at very high humidity (90% RH) due to the competitive adsorption of H2O molecules. This proved that the NH3 sensor based on ZIF-8/CNT could be suitable for practical applications under moderately humid conditions.

ABSTRACT: Avian infectious bronchitis (IB), caused by avian infectious bronchitis virus (IBV), is an acute and highly contagious disease that is extremely harmful to the poultry industry throughout the world. The cross-using of different attenuated live vaccine strains has led to the occurrence of diverse IBV serotypes. In this study, we isolated an IBV strain from a chicken farm in southwest China and designated it CK/CH/SCMY/160315. Construction of a phylogenetic tree based on full S1 gene sequence analysis suggested that CK/CH/SCMY/160315 bears similarity to GI-28, and further comparison of S1 amino acid residues revealed that CK/CH/SCMY/160315 showed mutations and deletions in many key positions between LDT3-A and other GI-28 reference strains. Importantly, CK/CH/SCMY/160315 was identified as a novel recombinant virus derived from live attenuated vaccine strains H120 (GI-1), 4/91 (GI-13), LDT3-A (GI-28), and the field strain LJL/08-1 (GI-19), identifying at least 5 recombination sites in both structural and accessory genes. Pathogenicity analysis indicated that CK/CH/SCMY/160315 caused listlessness, sneezing, huddling, head shaking, and increased antibody levels in the inoculated chickens. To further describe pathogenicity of this novel strain, we assessed viral load in different tissues and conducted hematoxylin and eosin (HE) staining of the trachea, lungs and kidneys. Our results provide evidence for the continuing evolution of IBV field strains via genetic recombination and mutation, leading to outbreaks in the vaccinated chicken populations in China.

Background: Mesenchymal stem cell therapy improves ischemic heart failure via incompletely understood mechanisms. C1q-TNFα related protein-9 (CTRP9) is a novel anti-oxidative cardiokine capable of improving the local microenvironment and cell survival by its c-terminal active globular domain (gCTRP9). The current study attempted to: 1) identify active gCTRP9 c-terminal polypeptides with stem cell protective function; 2) determine whether a lead polypeptide may enable/enhance cortical bone-derived mesenchymal stem cell (CBSC) cardioprotection against post-myocardial infarction (post-MI) remodeling; and 3) define the responsible underlying cellular/molecular mechanisms. Methods and results: Utilizing I-TASSER structure prediction and 3-D active site modeling, we cloned and purified 3 gCTRP9 fragments (CTRP9-237, CTRP9-277, and CTRP9-281). Their activation of cell salvage kinase was compared against gCTRP9. Among the three fragments, CTRP9-281 (a 45 residue-containing polypeptide) exerted comparable or greater ERK1/2 activation compared to gCTRP9. Treatment with CTRP9-281 or gCTRP9 significantly increased CBSC proliferation and migration, and attenuated oxidative stress-induced CBSC apoptosis. CTRP9-281 and gCTRP9 comparably upregulated SOD2 and SOD3 expression. However, CTRP9-281, not gCTRP9, upregulated FGF2 and VEGFA expression/secretion in an ERK1/2 dependent manner. Administration of gCTRP9 or CTRP9-281 alone attenuated post-MI cardiac dysfunction and improved CBSC retention in the infarcted heart in similar fashion. However, CTRP9-281 exerted greater synergistic effect with CBSC than gCTRP9 related to pro-angiogenic, anti-fibrotic, and anti-remodeling effects. Mechanistically, CTRP9-281 significantly increased SOD2-rich and VEGFA-rich exosome production by CBSC. Exosomes from CTRP9-281 treated CBSC significantly attenuated oxidative stress-induced cardiomyocyte apoptosis in vitro. An exosome generation inhibitor attenuated CTRP9-281 enhancement of CBSC cardioprotection in vivo. Conclusion: We identified a CTRP9 polypeptide that upregulates SOD2/SOD3 expression and improves CBSC survival/retention, similar to gCTRP9. Moreover, CTRP9-281 stimulates VEGFA-rich exosome production by CBSC, exerting superior pro-angiogenic, anti-fibrotic, and cardioprotective actions.

Retinal structure and function have been studied in many vertebrate orders, but molecular characterization has been largely confined to mammals. We used single-cell RNA sequencing (scRNA-seq) to generate a cell atlas of the chick retina. We identified 136 cell types plus 14 positional or developmental intermediates distributed among the six classes conserved across vertebrates – photoreceptor, horizontal, bipolar, amacrine, retinal ganglion, and glial cells. To assess morphology of molecularly defined types, we adapted a method for CRISPR-based integration of reporters into selectively expressed genes. For Müller glia, we found that transcriptionally distinct cells were regionally localized along the anterior-posterior, dorsal-ventral, and central-peripheral retinal axes. We also identified immature photoreceptor, horizontal cell, and oligodendrocyte types that persist into late embryonic stages. Finally, we analyzed relationships among chick, mouse, and primate retinal cell classes and types. Our results provide a foundation for anatomical, physiological, evolutionary, and developmental studies of the avian visual system.

There is a gap in understanding the effect of the essential ω-3 and ω-6 long-chain polyunsaturated fatty acids (LCPUFA) on Phase I retinopathy of prematurity (ROP), which precipitates proliferative ROP. Postnatal hyperglycemia contributes to Phase I ROP by delaying retinal vascularization. In mouse neonates with hyperglycemia-associated Phase I retinopathy, dietary ω-3 (vs. ω-6 LCPUFA) supplementation promoted retinal vessel development. However, ω-6 (vs. ω-3 LCPUFA) was also developmentally essential, promoting neuronal growth and metabolism as suggested by a strong metabolic shift in almost all types of retinal neuronal and glial cells identified with single-cell transcriptomics. Loss of adiponectin (APN) in mice (mimicking the low APN levels in Phase I ROP) decreased LCPUFA levels (including ω-3 and ω-6) in retinas under normoglycemic and hyperglycemic conditions. ω-3 (vs. ω-6) LCPUFA activated the APN pathway by increasing the circulating APN levels and inducing expression of the retinal APN receptor. Our findings suggested that both ω-3 and ω-6 LCPUFA are crucial in protecting against retinal neurovascular dysfunction in a Phase I ROP model; adequate ω-6 LCPUFA levels must be maintained in addition to ω-3 supplementation to prevent retinopathy. Activation of the APN pathway may further enhance the ω-3 and ω-6 LCPUFA’s protection against ROP.

Yaks provide necessities such as meat and milk for Tibetans living at high altitudes on and around the Qinghai-Tibetan Plateau. Enterococci are ubiquitous members of the animal gut microbiota that can cause biofilm-associated opportunistic infections. Meanwhile, multidrug-resistant Enterococcus also poses a serious threat to public health. This study aims to characterize antibiotic resistance, virulence genes, and biofilm formation of enterococci from yaks. From April 2018 to July 2019, we collected 395 fecal samples of yaks in Aba Tibetan Autonomous Prefecture, China. Enterococci isolated from the samples were identified and classified according to the 16S rDNA sequence. The antibiotic resistance of each isolate was detected according to the Kirby-Bauer disk diffusion method, and antibiotic resistance genes were detected by polymerase chain reaction (PCR) and sequencing. Enterococcal biofilms were assessed using standard procedures. Different virulence genes were detected by PCR and sequencing. In total, 381 enterococci strains were recovered, with Enterococcus faecalis (41.99%) and Enterococcus faecium (37.80%) being the predominant species. Many isolates were multidrug- resistant (60.37%) and showed a high resistance rate to rifampicin (64.30%) and tetracycline (61.54%). We also detected various antimicrobial resistance (AMR) genes in the tested strains. The E. faecalis strains had higher frequency of biofilm formation and virulence genes than other enterococcal species. This is the first report that shows yaks are repositories for drug-resistant enterococci with virulent determinants and biofilms that may spread into humans and to environment. This study also provides useful data suggesting that enterococci may pose a potential health risk to yaks. Therefore, active surveillance of AMR and pathogenesis in enterococci from yaks is urgently warranted.

Based on the principal-agent theory, we give a theoretical interpretation on evolution of the complex partnerships between the online SCF (supply chain finance) providers in China. First, we describe the principal-agent relationships and analyze the optimal profit-sharing contracts between the banks and the B2B platforms. Then, from a dual perspective of leadership transfer and absolute benefit change, we explain the behavioral choices of the banks in the cooperation. Results show that, at the initial stage of growth of the platforms’ abilities to rate online borrowers, the leadership and the absolute benefit of the banks will suffer a “double decline,” which explains why the leading banks in China “divorced” the B2B platforms during 2011 to 2013. However, as the platforms’ rating abilities grow to “maturity,” the absolute benefit of the banks will finally exceed its original level, and then the rational banks would cooperate with the platforms again even at the expense of losing a portion of their leadership, which answers why the banks in China have come back to “remarry” the B2B platforms since 2014.

Background Poor engraftment of intramyocardial stem cells limits their therapeutic efficiency against myocardial infarction (MI)‐induced cardiac injury. Transglutaminase cross‐linked Gelatin (Col‐Tgel) is a tailorable collagen‐based hydrogel that is becoming an excellent biomaterial scaffold for cellular delivery in vivo. Here, we tested the hypothesis that Col‐Tgel increases retention of intramyocardially‐injected stem cells, and thereby reduces post‐MI cardiac injury. Methods and Results Adipose‐derived mesenchymal stem cells (ADSCs) were co‐cultured with Col‐Tgel in a 3‐dimensional system in vitro, and Col‐Tgel encapsulated ADSCs were observed using scanning electron microscopy and confocal microscopy. Vitality, proliferation, and migration of co‐cultured ADSCs were evaluated. In addition, mice were subjected to MI and were intramyocardially injected with ADSCs, Col‐Tgel, or a combination thereof. ADSCs engraftment, survival, cardiac function, and fibrosis were assessed. In vitro MTT and Cell Counting Kit‐8 assays demonstrated that ADSCs survive and proliferate up to 4 weeks in the Col‐Tgel. In addition, MTT and transwell assays showed that ADSCs migrate outside the edge of the Col‐Tgel sphere. Furthermore, when compared with ADSCs alone, Col‐Tgel‐encapsulated ADSCs significantly enhanced the long‐term retention and cardioprotective effect of ADSCs against MI‐induced cardiac injury. Conclusions In the current study, we successfully established a 3‐dimensional co‐culture system using ADSCs and Col‐Tgel. The Col‐Tgel creates a suitable microenvironment for long‐term retention of ADSCs in an ischemic area, and thereby enhances their cardioprotective effects. Taken together, this study may provide an alternative biomaterial for stem cell‐based therapy to treat ischemic heart diseases.

The rise of photovoltaic industry has raised the difficulty of the operation and maintenance. Nowadays, the growing interest in the application of unmanned aerial vehicles (UAV) in civil monitoring and diagnostic applications has been observed. Such UAV-based inspection system can significantly improve the efficiency of system monitoring and fault detections. This paper presents an intelligent UAV-based inspection system for asset assessment and defect classification for large-scale PV systems. The aerial imagery data of PV modules increase the complexity of the detection by traditional pattern recognition, a novel method based on the deep learning and supervision is proposed, which could solve the low quality and distortion flexibly and reliably. A convolutional neural network (CNN) is adopted to address the defects classification. Extracting features by the pre-trained architecture Vgg16, the suggested solution added a full-connected layer and a SVM decision layer to classify the defects. Such pre-trained learning-based algorithm can meet the demand of the small datasets, and carry out a variety of deep features and condition classification in PV system, which can supervise with significantly promoted efficiency in comparison with the conventional methods. The proposed solution is evaluated through numerical experiments and the result confirms its improved performance.