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Porcine sapelovirus (PSV) is an emerging swine enteric virus that can cause various disorders including acute diarrhea, respiratory distress, reproductive failure, and polioencephalomyelitis in pigs. In this study, we isolated a PSV strain HNHB-01 from a clinical porcine deltacoronavirus- (PDCoV-) positive intestinal content of a diarrheic piglet. PSV was first identified using the small RNA deep sequencing and assembly, and further identified by the electron microscopic observation and the immunofluorescence assay. Subsequently, this virus was serially passaged in swine testis (ST) cells, and the complete genomics of PSV HNHB-01 passage 5 (P5), P30, P60, and P100 were sequenced and analyzed. 9 nucleotide mutations and 7 amino acid changes occurred in the PSV HNHB-01 P100 strain when compared with the PSV HNHB-01 P5. Pathogenicity investigation showed that orally inoculation of PSV HNHB-01 P30 could cause obvious clinical symptoms and had broad tissue tropism in 5-day-old piglets. Epidemiological investigation revealed that PSV infections and the coinfections of diarrhea coronaviruses were highly prevalent in swine herds. The complete genomes of 8 representative PSV epidemic strains were sequenced and analyzed. Phylogenetic analysis revealed that the PSV epidemic strains were closely related to other PSV reference strains that located in the Chinese clade. Furthermore, recombination analysis revealed that the recombination events were occurred in downstream of the 2C region in our sequenced PSV HNNY-02/CHN/2018 strain. Our results provided theoretical basis for future research studies of the pathogenic mechanism, evolutionary characteristics, and the development of vaccines against PSV.

This paper studied deixis in cognitive linguistics, and mainly introduced three types of deixis: spatio-temporal; knowledge-related; empathy. On the basis, the literature on this topic in recent years was reviewed relying on China National Knowledge Infrastructure (CNKI) and Web of Science. The findings are: the deixis represented by "这" and "那" and "this" and "that" are related to the distance of psychological space rather than only physical space; the emerging study like children's deixis and the combination of deixis with sign language has been developing gradually. More cross-field or other empirical studies will attract more researchers to conduct in the future.

Single-label facial expression recognition (FER), which aims to classify single expression for facial images, usually suffers from the label noisy and incomplete problem, where manual annotations for partial training images exist wrong or incomplete labels, resulting in performance decline. Although prior work has attempted to leverage external sources or manual annotations to handle this problem, it usually requires extra costs. This article explores a simple yet effective three-phase paradigm (“warm-up,” “selection,” and “relabeling”) for FER task. First, the warm-up phase attempts to build an initial recognition network based on noisy samples for discriminative feature extractions and facial expression predictions. Then, the second selection phase defines several rules to choose high confident samples according to prediction scores, and the third relabeling phase assigns two potential labels to those samples for network updating according to a composite two-label loss. Compared with the previous studies, the three-phase learning could effectively correct noisy labels in the ground truth without extra information and automatically assign two potential labels to single-label samples without manual annotations. As a result, the label information is purified and supplemented with few cost, yielding significant performance improvement. Extensive experiments are conducted on three datasets, and the experimental results demonstrate that our approach is robust to noisy training samples and outperforms several state-of-the-art methods.

Multi-label classification aims to recognize multiple objects or attributes from images. The key to solving this issue relies on effectively characterizing the inter-label correlations or dependencies, which bring the prevailing graph neural network. However, current methods often use the co-occurrence probability of labels based on the training set as the adjacency matrix to model this correlation, which is greatly limited by the dataset and affects the model’s generalization ability. This article proposes a Graph Attention Transformer Network, a general framework for multi-label image classification by mining rich and effective label correlation. First, we use the cosine similarity value of the pre-trained label word embedding as the initial correlation matrix, which can represent richer semantic information than the co-occurrence one. Subsequently, we propose the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain. Our extensive experiments have demonstrated that our proposed methods can achieve highly competitive performance on three datasets.

Purpose To investigate the value of pre-operative gadoxetate disodium (Gd-EOB-DTPA) enhanced MRI predicting early post-operative recurrence ( Methods Retrospective analysis of pre-operative MR imaging features of 177 patients diagnosed as suffering from HCC and that underwent radical resection. Multivariate logistic regression assessment was adopted to assess predictors for HCC recurrence with different degrees of pathological differentiation. The area under the curve (AUC) of receiver operating characteristics (ROC) was utilized to assess the diagnostic efficacy of the predictors. Results Among the 177 patients, 155 (87.5%) were males, 22 (12.5%) were females; the mean age was 49.97 ± 10.71 years. Among the predictors of early post-operative recurrence of highly-differentiated HCC were an unsmooth tumor margin and an incomplete/without tumor capsule (p = 0.037 and 0.033, respectively) whereas those of early post-operative recurrence of moderately-differentiated HCC were incomplete/without tumor capsule, peritumoral enhancement along with peritumoral hypointensity (p = 0.006, 0.046 and 0.004, respectively). The predictors of early post-operative recurrence of poorly-differentiated HCC were peritumoral enhancement, peritumoral hypointensity, and tumor thrombosis (p = 0.033, 0.006 and 0.021, respectively). The AUCs of the multi-predictor diagnosis of early post-operative recurrence of highly-, moderately-, and poorly-differentiated HCC were 0.841, 0.873, and 0.875, respectively. The AUCs of the multi-predictor diagnosis were each higher than for those predicted separately. Conclusions The imaging parameters for predicting early post-operative recurrence of HCC with different degrees of pathological differentiation were different and combining these predictors can improve the diagnostic efficacy of early post-operative HCC recurrence.

In the last few years, enormous strides have been made for object detection and data association, which are vital subtasks for one-stage online multi-object tracking (MOT). However, the two separated submodules involved in the whole MOT pipeline are processed or optimized separately, resulting in a complex method design and requiring manual settings. In addition, few works integrate the two subtasks into a single end-to-end network to optimize the overall task. In this study, we propose an end-to-end MOT network called joint detection and association network (JDAN) that is trained and inferred in a single network. All layers in JDAN are differentiable, and can be optimized jointly to detect targets and output an association matrix for robust multi-object tracking. What’s more, we generate suitable pseudo-labels to address the data inconsistency between object detection and association. The detection and association submodules could be optimized by the composite loss function that is derived from the detection results and the generated pseudo association labels, respectively. The proposed approach is evaluated on two MOT challenge datasets, and achieves promising performance compared with classic and latest methods.

Urinary tract infections (UTIs) are the most common bacterial infection in young children. This study aimed to formulate nomogram plots for clinicians to predict UTIs in children aged3 years by evaluating the risk factors for UTIs in these children.This retrospective study was conducted at a tertiary medical center from December 2017 to November 2020. Children less than three years of age were eligible for the study if they had undergone both urine culture and urinalysis during the study period. Mixed-effects logistic regression models with a stepwise procedure were used to determine the relationship between outcome (positive/negative UTI) and covariates of interest (e.g., weight percentile, laboratory) for each patient. Nomogram plots were constructed on the basis of significant factors. We repeated the analysis thrice to adapt it to three different medical settings: medical centers, regional hospitals, and local clinics.In the medical center setting, the two most significant factors were urine leukocyte count ≥100 (OR =8.87; 95% CI (Confidence Interval), 4.135-19.027) and urine nitrite level (OR =8.809; 95% CI, 5.009-15.489). The two factors showed similar significance at the regional hospital and local clinic settings. Abnormal renal echo findings were positively correlated with UTI in the medical center setting (OR =2.534; 95% CI 1.757-3.655). Three nomogram plots for the prediction of UTIs were drawn for medical centers, regional hospitals, and local clinics.Using the three nomogram plots, frontline doctors can formulate the probabilities of pediatric UTIs for better decision-making.

Interaction between optical and mechanical waves can be strengthened by a compact ring resonator consisting of a few silicon nanoparticles.

The results obtained by our simulations can provide effective guidance for the design of nanoparticle-grafted bimodal bidisperse polymer chains and provide a theoretical basis for experimentation and production of polymer nanocomposites with better performance.

From the perspective of liquor brewing technology, the quality of liquor yeast undoubtedly determines the quality of liquor products, but the problems such as inconvenient storage, difficult transportation and easy deterioration of liquid liquor yeast greatly restrict the development of liquor industry. Aiming at this problem, the author firstly summarized the research status of drying technology, damage mechanism and protection strategy of white spirit yeast. Then, on the basis of studying the damage mechanism of yeast in the drying process of yeast, the optimization of drying process and the formulation of protective strategies of yeast were discussed. Finally, new research methods are proposed from three perspectives: optimal design of drying process, damage mechanism and protection strategy

The association of toll-like receptors (TLRs) and matrix metalloproteinases (MMPs) single-nucleotide polymorphisms (SNPs) among latent tuberculosis (TB) infection and active TB remained less studied.We recruited participants with TB disease (active TB) (n = 400) and TB infection (latent TB infection) (n = 203) in this study. We genotyped SNPs in TLR1, TLR2, TLR4, MMP1, MMP8, MMP9, MMP12, and tissue inhibitor of MMP2. Single-variant analysis and haplotype analysis were performed, and a polygenic risk score (PRS) was created.We found that SNPs in TLR1 (rs5743580, rs5743551), TLR2 (rs3804100), and MMP8 (rs2508383) were associated with different TB disease status risks. TLR1 rs5743580 was associated with a higher risk of TB disease status in genotypic, recessive, and additive models. TLR2 rs3804100 polymorphisms demonstrated significant association with TB disease status in genotypic, dominant, and additive models. In the haplotype analysis, the TLR1 haplotype was associated with a higher risk of TB disease, and the MMP12 haplotype was associated with a lower risk of TB disease. A PRS using 3 SNPs was associated with a higher risk of TB disease.This study revealed that SNP variants in TLR1, TLR2, and MMP8 differed among TB infection and disease. Haplotypes and PRS could potentially help predict TB disease status.

Cyclosporin A is a calcineurin inhibitor which has a narrow therapeutic window and high interindividual variability. Various population pharmacokinetic models have been reported; however, professional software and technical personnel were needed and the variables of the models were limited. Therefore, the aim of this study was to establish a model based on machine learning to predict CsA trough concentrations in Chinese allo-HSCT patients. A total of 7874 cases of CsA therapeutic drug monitoring data from 2069 allo-HSCT patients were retrospectively included. Sequential forward selection was used to select variable subsets, and eight different algorithms were applied to establish the prediction model. XGBoost exhibited the highest prediction ability. Except for the variables that were identified by previous studies, some rarely reported variables were found, such as norethindrone, WBC, PAB, and hCRP. The prediction accuracy within ±30% of the actual trough concentration was above 0.80, and the predictive ability of the models was demonstrated to be effective in external validation. In this study, models based on machine learning technology were established to predict CsA levels 3–4 days in advance during the early inpatient phase after HSCT. A new perspective for CsA clinical application is provided.

The conjunctiva covers the largest area of ocular surface and is responsible for tear balance and clear vision. After trauma or surgery, the conjunctiva is prone to scarring and contracture. Transplantation with suture often implies numerous complications, such as inflammation, suture erosion, granuloma. And the suture needs to be removed, which means a secondary trauma. In this study, a bioadhesive hydrogel (GMO) for sutureless conjunctival transplantation was developed based on a semi-interpenetrating polymer network (sIPN) consisting of gelatin methacrylate (GelMA) and oxidized hyaluronic acid (OHA). The maximum adhesion strength was 157 ± 17 kPa, and the burst pressure was 357 ± 29 kPa, which was 15 times higher than the human intraocular pressure (IOP). GMO bioadhesive hydrogel significantly improved surgical efficiency and secured the collagen scaffold firmly to a rabbit conjunctival defect. The sutureless transplantation approach revealed the promoted tissue repair without scar. In conclusion, GMO bioadhesive may be an attractive alternative to suture for ocular surface reconstruction by avoiding suture-related complications and improving clinical outcome. STATEMENT OF SIGNIFICANCE: Conjunctival tissue is prone to scarring and contracture after trauma, and surgery with sutures often implies numerous complications. In this study, the ocular surface reconstruction was achieved by sutureless transplantation of conjunctival scaffold using bioadhesive hydrogel. The prepared GMO bioadhesive based on the semi-interpenetrating network of gelatin methacrylate (GelMA) and oxidized hyaluronic acid (OHA) had favorable adhesion and mechanical properties. The sutureless transplantation approach significantly improved the operation efficiency, avoided suture-related complications, and promoted the regeneration of conjunctiva. This study highlights the great potential of the sutureless repair strategy for clinical application in ocular surface reconstruction.

Conventional administration of eye drops often requires high dosages and/or repetitive treatments to achieve therapeutic efficacy. This is inefficient and may result in side effects or even toxicity. Although many delivery systems of ophthalmic drugs have been reported, most of them work in a fixed format in which both the type and dose of the loaded drugs cannot be changed upon demand. To overcome this limitation, a hybrid double network hydrogel system composed of methacryloyl gelatin, pluronic F127 diacrylate, and β-cyclodextrin-modified oxidized dextran was developed. The hydrogels presented good mechanical strength and biocompatibility. In vitro assessments demonstrated that the hydrogels loaded with commonly used ophthalmic drugs could sustain the drug release for more than 21 days. This hydrogel system exhibited features of mechanoresponsive drug loading, and the capacity of drug loading could be significantly enhanced by macroscopically mechanical compression. Further in vivo evaluation of the drug delivery capacity showed that a dexamethasone-loaded hydrogel as a fornix insert effectively suppressed upregulation of proangiogenic factors and suture-induced corneal neovascularization in rats. This novel hydrogel system represents a promising drug delivery platform, which could potentially improve the treatments of ocular surface and other diseases.

Nuclear cataract (NC) is a priority ocular disease of blindness and vision impairment globally. Early intervention and cataract surgery can improve the vision and life quality of NC patients. Anterior segment coherence tomography (AS-OCT) imaging is a non-invasive way to capture the NC opacity objectively and quantitatively. Recent clinical research has shown that there exists a strong opacity correlation relationship between NC severity levels and the mean density on AS-OCT images. In this paper, we present an effective NC classification framework on AS-OCT images, based on feature extraction and feature importance analysis. Motivated by previous clinical knowledge, our method extracts the clinical global–local features, and then applies Pearson’s correlation coefficient and recursive feature elimination methods to analyze the feature importance. Finally, an ensemble logistic regression is employed to distinguish NC, which considers different optimization methods’ characteristics. A dataset with 11,442 AS-OCT images is collected to evaluate the method. The results show that the proposed method achieves 86.96% accuracy and 88.70% macro-sensitivity, respectively. The performance comparison analysis also demonstrates that the global–local feature extraction method improves about 2% accuracy than the single region-based feature extraction method.

This study aimed to assess the feasibility of utilizing geopolymer for repairing reinforced concrete beams. Three types of beam specimens were fabricated: benchmark specimens without any grooves, rectangular-grooved beams, and square-grooved beams. The repair materials employed included geopolymer material, and epoxy resin mortar, while carbon fiber sheets were used as reinforcement in select cases. The repair materials were applied to the rectangular and square-grooved specimens, with the carbon fiber sheets attached to the tension side of the specimens. To evaluate the flexural strength of the concrete specimens, a third-point loading test was conducted. The test results indicated that the geopolymer exhibited higher compressive strength and shrinkage rate compared to the epoxy resin mortar. Furthermore, the specimens reinforced with carbon fiber sheets demonstrated even greater strength than the benchmark specimens. In terms of flexural strength under cyclic third-point loading tests, the carbon fiber-reinforced specimens exhibited the ability to withstand over 200 cycles of repeated loading at 0.8 times the ultimate load. In contrast, the benchmark specimens could only withstand seven cycles. These findings highlight that the use of carbon fiber sheets not only enhances compressive strength but also improves resistance to cyclic loading.

Background and Objective: High variability in vancomycin exposure in neonates requires advanced individualized dosing regimens. Achieving steady-state trough concentration (C 0) and steady-state area-under-curve (AUC0–24) targets is important to optimize treatment. The objective was to evaluate whether machine learning (ML) can be used to predict these treatment targets to calculate optimal individual dosing regimens under intermittent administration conditions. Methods: C 0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0–24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C 0 and AUC0–24. An external dataset was used for predictive performance evaluation. Results: Before starting treatment, C 0 can be predicted a priori using the Catboost-based C 0-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C 0 in the range of 10–20 mg/L), much higher than the international standard dose (37.7–61.5%). Once therapeutic drug monitoring (TDM) measurements (C 0) in patients have been obtained, AUC0–24 can be further predicted using the Catboost-based AUC-ML model combined with C 0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%. Conclusion: C 0-based and AUC0–24-based ML models were developed accurately and precisely. These can be used for individual dose recommendations of vancomycin in neonates before treatment and dose revision after the first TDM result is obtained, respectively.

There is an important role for hepatic stellate cells (HSCs) in liver fibrosis. As it stands, many traditional Chinese medicine formulations can effectively improve liver fibrosis, whether it is clinically used or in animal studies; however, the efficacy and mechanism of the main formulations remain unclear, including the peach kernel, which contains numerous phytochemicals with a wide range of biological activities. The purpose of this study was to investigate peach kernel’s anti-liver fibrosis effects. In this study, peach kernel extracts inhibited lipopolysaccharide (LPS) activation in HSC-T6 cells and the expression of α-smooth muscle actin and connective tissue growth factor induced by LPS in HSC-T6 cells. Furthermore, peach kernel extracts inhibited signal transducers involving protein kinase B and mitogen-activated protein kinase, which regulate downstream genes associated with inflammation. As a result, peach kernel extracts inhibited inflammatory responses and subsequently inhibited LPS-induced transformation of activated HSC-T6 cells.

Porcine deltacoronavirus (PDCoV) is a novel coronavirus that causes diarrhea in suckling piglets and has the potential for cross-species transmission, posing a threat to animal and human health. However, the susceptibility profile of different species of mice to PDCoV infection and its evolutionary characteristics are still unclear. In the current study, we found that BALB/c and Kunming mice are susceptible to PDCoV. Our results showed that there were obvious lesions in intestinal and lung tissues from the infected mice. PDCoV RNAs were detected in the lung, kidney, and intestinal tissues from the infected mice of both strains, and there existed wider tissue tropism in the PDCoV-infected BALB/c mice. The RNA and protein levels of aminopeptidase N from mice were relatively high in the kidney and intestinal tissues and obviously increased after PDCoV infection. The viral-specific IgG and neutralizing antibodies against PDCoV were detected in the serum of infected mice. An interesting finding was that two key amino acid mutations, D138H and Q641K, in the S protein were identified in the PDCoV-infected mice. The essential roles of these two mutations for PDCoV-adaptive evolution were confirmed by cryo-electron microscope structure model analysis. The evolutionary characteristics of PDCoV among Deltacoronaviruses (δ-CoVs) were further analyzed. δ-CoVs from multiple mammals are closely related based on the phylogenetic analysis. The codon usage analysis demonstrated that similar codon usage patterns were used by most of the mammalian δ-CoVs at the global codon, synonymous codon, and amino acid usage levels. These results may provide more insights into the evolution, host ranges, and cross-species potential of PDCoV.