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Abstract Human noroviruses (HuNoVs) are a leading cause of acute viral gastroenteritis worldwide. Infectious outbreaks due to recombinant NoV genotype called GII.P16-GII.2 have been frequently reported since 2016. In this study, we expressed the major capsid protein VP1 from three GII.2 NoV strains using the recombinant baculovirus expression system. The assembly, histo-blood group antigen (HBGA)-binding patterns, and cross-blocking abilities of VP1 proteins were investigated. All the three NoV VP1 proteins successfully assembled into virus-like particles (VLPs). The HBGA-binding assay demonstrated a temporal binding pattern. The latest isolate bound to saliva samples of all blood types. Sequence alignment suggested that the observed gain in HBGA-binding ability was attributed to a limited number of amino acid mutations. Using chimeric VP1 proteins, we demonstrated that synergistic effects resulted in enhanced binding ability. Bile salts increased GII.2 VLP avidity for HBGAs except GII.2-2011/M1. In vitro blockade assay of salivary HBGA-VLP binding demonstrated the presence of cross-blocking effects among different strains. This study provides insight into the evolutionary binding characteristics and cross-blocking effects of GII.2 NoVs to facilitate the development of measures to control this type of viruses.

Abstract A hallmark of unconventional superconductors is a complex electronic phase diagram where intertwined orders of charge-spin-lattice degrees of freedom compete and coexist. While the kagome metals such as CsV3Sb5 also exhibit complex behavior, involving coexisting charge density wave order and superconductivity, much is unclear about the microscopic origin of the superconducting pairing. We study the vortex lattice in the superconducting state of Cs(V0.86Ta0.14)3Sb5, where the Ta-doping suppresses charge order and enhances superconductivity. Using small-angle neutron scattering, a strictly bulk probe, we show that the vortex lattice exhibits a strikingly conventional behavior. This includes a triangular symmetry with a period consistent with 2e-pairing, a field dependent scattering intensity that follows a London model, and a temperature dependence consistent with a uniform superconducting gap. Our results suggest that optimal bulk superconductivity in Cs(V1−x Ta x )3Sb5 arises from a conventional Bardeen-Cooper-Schrieffer electron-lattice coupling, different from spin fluctuation mediated unconventional copper- and iron-based superconductors.

Background Both diabetes and osteoporosis have developed into major global public health problems due to the increasing aging population. It is crucial to screen populations at higher risk of developing osteoporosis for disease prevention and management in postmenopausal women with type 2 diabetes (T2D). This study aims to quantitatively investigate the association between risk factors and bone mineral density (BMD) and develop a self-assessment tool for early osteoporosis screening in postmenopausal women with T2D. Methods We retrospectively enrolled 1,309 postmenopausal women with T2D. Linear regression methods were used to assess the association between risk factors and BMD. Additionally, a multivariate logistic regression analysis was performed to identify independent risk factors associated with osteoporosis. Utilizing the logistic regression machine learning algorithm, we developed an osteoporosis screening tool that categorizes the population into three risk regions based on age and body mass index (BMI), indicating low, moderate, and high prevalence of osteoporosis in the age-BMI plane. Results Older age and lower BMI were independently associated with decreased BMD. The BMD at the total hip, femur neck, and lumbar spine differed by 12.9, 10.9, and 15.5 mg/cm2 for each 1 unit increase in BMI, respectively. Both age and BMI were identified as independent predictors of osteoporosis. The osteoporosis screening tool was developed by using two straight lines with equations of BMI = 0.56 * age−4.12 and BMI = 0.56 * age−10.88; there were no significant differences in the prevalence of osteoporosis among the training, internal test, and external test datasets in the low-, moderate-, and high-risk regions. Conclusion We have successfully developed and validated a self-assessment tool for early osteoporosis screening in postmenopausal women with T2D for the first time. BMI was identified as a significant modifiable risk factor. Our study may improve awareness of osteoporosis and is valuable for disease prevention and management for postmenopausal women with T2D.

Background With the development of steganography technology, lawbreakers can implement covert communication in social networks more easily, exacerbating network security risks. Sterilization of image steganography methods can eliminate secret messages to block the transmission of illegal covert communication. However, existing methods overly rely on cover-stego image pairs and are unable to sanitize unknown image, which reduces stego image blocking rate in social networks. Methods To address the above problems, this paper proposes an effective sterilization of image steganography method using self-supervised convolutional neural network (SS-Net), which does not require any prior knowledge of image steganography schemes. SS-Net includes a purification module and a refinement module. Firstly, the pixel-shuffle down-sampling in purification module is adopted to reduce the spatial correlation of pixels in the stgeo image, and improve the learning mode from supervised learning to self-supervised learning. Secondly, centrally masked convolutions and dilated convolution residual blocks are merged to eliminate secret messages and avoid image quality degradation. Finally, a refinement module is employed to improve image texture details and boundaries. Results A series of experiments show that SS-Net from BOSSbase test sets is able to balance the destruction of secret messages with image quality, achieving 100% blocking rate of stego image. Meanwhile, our method outperforms the state-of-the-art methods in secret messages elimination ability and image quality preserving ability.

Abstract Although most people living with HIV (PLWH) receiving antiretroviral therapy (ART) achieve continuous viral suppression, some show detectable HIV RNA as low-level viremia (LLV) (50–999 copies/mL). Drug resistance mutations (DRMs) in PLWH with LLV is of particular concern as which may lead to treatment failure. In this study, we investigated the prevalence of LLV and LLV-associated DRMs in PLWH in Zhengzhou City, China. Of 3616 ART-experienced PLWH in a long-term follow-up cohort from Jan 2022 to Aug 2023, 120 were identified as having LLV. Of these PLWH with LLV, we obtained partial pol and integrase sequences from 104 (70 from HIV-1 RNA and 34 from proviral DNA) individuals. DRMs were identified in 44 individuals. Subtyping analysis indicated that the top three subtypes were B (48.08%, 50/104), CRF07_BC (31.73%, 33/104), and CRF01_AE (15.38%, 16/104). The proportions of nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), and integrase strand transfer inhibitors (INSTIs) associated DRMs were 23.83% (24/104), 35.58% (37/104), 5.77% (6/104), and 3.85% (4/104), respectively, which contributed to an overall prevalence of 42.31% (44/104). When analyzed by individual DRMs, the most common mutation(s) were V184 (18.27%, 19/104), followed by V179 (11.54%, 12/104), K103 (9.62%, 10/104), Y181 (9.62%, 10/104), M41 (7.69%, 8/104), and K65R (7.69%, 8/104). The prevalence of DRMs in ART-experienced PLWH with LLV is high in Zhengzhou City and continuous surveillance can facilitate early intervention and provision of effective treatment.

Abstract Kagome superconductors AV3Sb5 (A = K, Rb, and Cs) have attracted enormous interest due to the coexistence of charge density wave (CDW) order, unconventional superconductivity (SC) and anomalous Hall effect (AHE). In this paper, we reported an intensive investigation on Cs(V1−x Ta x )3Sb5 single crystals with systematic Ta doping. Ta was confirmed to be doped into V-site in the Kagome layer from both single crystal X-ray diffraction structural refinement and scanning transmission electron microscopy observation. The highest Ta doping level was found to be about 16%, which is more than twice as much as 7% in Nb-doped CsV3Sb5. With the increase of Ta doping, CDW order was gradually suppressed and finally vanished when the doping level reached to more than 8%. Meanwhile, superconductivity was enhanced with a maximum critical temperature (Tc) of 5.3 K, which is the highest Tc in the bulk crystal of this Kagome system at ambient pressure so far. The μ 0 H c2(T) behavior demonstrates that the system is still a two-band superconductor after Ta doping. Based on the electrical transport measurement, a phase diagram was set up to exhibit the evolution of CDW and SC in the Cs(V1−x Ta x )3Sb5 system. These findings pave a new way to search for new superconductors with higher Tc in the AV3Sb5 family and establish a new platform for tuning and controlling the multiple orders and superconducting states.

BackgroundSeveral factors, such as diverse serotypes, vaccination methods, weak biosecurity, and animal movements, contribute to recurrent Foot-and-Mouth Disease Virus (FMDV) outbreaks in Africa, establishing endemicity. These outbreaks cost over $2 billion annually, prompting a high-priority focus on FMDV vaccination. Despite extensive efforts, vaccine efficacy varies. This study aims to evaluate routine foot and mouth disease (FMD) vaccines in Africa via systematic review and meta-analysis.MethodsA systematic review and meta-analysis were carried out following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Meta-analysis was conducted to assess the efficacy of FMDV vaccination using the meta for package of R.ResultsVaccinated animals have roughly a 69.3% lower chance of FMDV infection compared to unvaccinated animals, as indicated by the pooled results from the random-effects model, which showed a risk ratio (RR) of 0.3073. There was a statistically significant heterogeneity (p

Abstract To investigate the prognostic value of lymph node status in patients with cervical cancer (CC) patients who underwent neoadjuvant chemotherapy (NACT) and followed hysterectomy. Patients in two referral centers were retrospectively analyzed. The baseline tumor size and radiological lymph node status (LNr) were evaluated on pre-NACT MRI. Tumor histology, differentiation and pathological lymph node status (LNp) were obtained from post-operative specimen. The log-rank test was used to compare survival between patient groups. Cox proportional hazards regression models were employed to estimate the hazard ratio (HR) of various factors with progression-free survival (PFS) and overall survival (OS). A total of 266 patients were included. Patients with 2018 FIGO IIIC showed worse PFS compared to those with FIGO IB-IIB (p

Abstract Background Node Reporting and Data System (Node-RADS) was proposed and can be applied to lymph nodes (LNs) across all anatomical sites. This study aimed to investigate the diagnostic performance of Node-RADS in cervical cancer patients. Methods A total of 81 cervical cancer patients treated with radical hysterectomy and LN dissection were retrospectively enrolled. Node-RADS evaluations were performed by two radiologists on preoperative MRI scans for all patients, both at the LN level and patient level. Chi-square and Fisher’s exact tests were employed to evaluate the distribution differences in size and configuration between patients with and without LN metastasis (LNM) in various regions. The receiver operating characteristic (ROC) and the area under the curve (AUC) were used to explore the diagnostic performance of the Node-RADS score for LNM. Results The rates of LNM in the para-aortic, common iliac, internal iliac, external iliac, and inguinal regions were 7.4%, 9.3%, 19.8%, 21.0%, and 2.5%, respectively. At the patient level, as the NODE-RADS score increased, the rate of LNM also increased, with rates of 26.1%, 29.2%, 42.9%, 80.0%, and 90.9% for Node-RADS scores 1, 2, 3, 4, and 5, respectively. At the patient level, the AUCs for Node-RADS scores > 1, >2, > 3, and > 4 were 0.632, 0.752, 0.763, and 0.726, respectively. Both at the patient level and LN level, a Node-RADS score > 3 could be considered the optimal cut-off value with the best AUC and accuracy. Conclusions Node-RADS is effective in predicting LNM for scores 4 to 5. However, the proportions of LNM were more than 25% at the patient level for scores 1 and 2, which does not align with the expected very low and low probability of LNM for these scores.

Abstract Background Asthma is a chronic airway inflammatory disease characterized by airway inflammation, mucus hypersecretion, airway hyper-reactivity. Sanzi Yangqin Decoction (SZYQD) is widely prescribed for asthma treatment. Its anti-asthma activities have been reported in animal model, but the exact mechanism and targets of SZYQD in asthma treatment have not been fully elucidated. Methods A network pharmacological approach was used to predict the active components, targets, and signalling pathways of SZYQD in asthma, including potential target prediction, protein‒protein interaction (PPI) network construction and analysis, and Gene Ont (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The active ingredients were identified from the SZYQD, and were molecular docked according to the results of network pharmacology. A mouse model of asthma induced by ovalbumin (OVA) and lipopolysaccharide (LPS) was constructed to evaluate the therapeutic effect of SZYQD. Furthermore, the effects of SZYQD and its active ingredients were tested in vitro for regulating inflammation and MUC5AC expression (two main pathophysiologic abnormalities of asthma) in macrophages and airway epithelial cells by using Real-time PCR and western blotting. Results A total of 28 active ingredients and 111 HUB genes were screened in the relevant databases, including three key ingredients (luteolin, β-carotene, and Sinapine) and nine core target genes (JUN, CTNNB1, IL10, TP53, AKT1, STAT3, TNF, IL6 and EGFR). KEGG and GO analysis indicated that the potential anti-asthmatic mechanisms of SZYQD were related to PI3K-Akt signalling pathway and response to lipopolysaccharide, etc. In the in vivo asthmatic model, our findings demonstrated that SZYQD exerted a protective effect against asthmatic mice induced by OVA and LPS through the inhibition of inflammation and mucus overproduction. Consistently, cell experiments showed that the SZYQD extract or the key active ingredients luteolin significantly decreased lipopolysaccharide (LPS)-induced IL-6 expression and activation of the NF-κB pathway in macrophages. In addition, SZYQD extract or luteolin inhibited activation of the AKT pathway and expression of MUC5AC induced by EGF in airway epithelial cells. Conclusion The anti-asthmatic mechanism of SZYQD might be associated with inhibiting inflammation and airway mucus hypersecretion by regulating the NF-κB and AKT signalling pathways as predicted by network pharmacology, which provides more evidence for the application of SZYQD in asthma treatment.

In the paradigm of mobile edge computing (MEC), providing low-latency and high-reliability services for users is garnering increasing attention. Appropriate edge-server placement is the crucial first step to realizing such services, as it can meet computation requirements and enhance resource utilization. This study delves into efficient and intelligent dynamic edge-server placement by taking into account time-varying network scenarios and deployment costs. Firstly, edge servers are classified into static and dynamic ones. Subsequently, an improved snake optimization algorithm is proposed to determine the number and placement locations of dynamic servers while adhering to delay requirements. Finally, a minimum placement-cost algorithm is put forward to further reduce the service cost. Experimental results demonstrate that compared to classic algorithms, the proposed algorithms can achieve a reduction in latency of 5% to 12%. And compared to the state-of-the-art methods, they can reduce service costs by 20% to 43%. This research offers an effective solution for dynamic edge-server placement and holds great theoretical and practical significance.

Abstract Designing autonomously oscillating materials is highly desirable for emerging smart material fields but challenging. Herein, a type of hypercrosslinked metal–organic polyhedra (HCMOPs) membranes formed by covalent crosslinking of boronic acid‐modified Zr‐based MOPs with polyvinyl alcohol (PVA) are rationally designed. In these membranes, MOPs serve as high‐connectivity nodes and provide dynamic borate bonds with PVA in hypercrosslinked networks, which can be broken/formed reversibly upon the stimulus of water vapor. The humidity response characteristic of HCMOPs promotes their self‐oscillating and self‐healing properties. HCMOP membranes can realize a self‐oscillating property above the water surface even after loading a cargo that is 1.5 times the weight of the membrane due to the fast adsorption and desorption kinetics. Finally, the HCMOP actuator can realize energy conversion from mechanical energy into electricity when coupled with a piezoelectric membrane. This work not only paves a new avenue to construct MOP‐polymer hybrid materials but also expands the application scopes of MOPs for smart actuation devices.

BackgroundThis study aims to develop and validate a pretreatment MRI-based radiomics model to predict lymph node metastasis (LNM) following neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC).MethodsPatients with LACC who underwent NACT from two centers between 2013 and 2022 were enrolled retrospectively. Based on the lymph node (LN) status determined in the pathology reports after radical hysterectomy, patients were categorized as LN positive or negative. The patients from center 1 were assigned as the training set while those from center 2 formed the validation set. Radiomics features were extracted from pretreatment sagittal T2-weighted imaging (Sag-T2WI), axial diffusion-weighted imaging (Ax-DWI), and the delayed phase of dynamic contrast-enhanced sagittal T1-weighted imaging (Sag-T1C) for each patient. The K-best and least absolute shrinkage and selection operator (LASSO) methods were employed to reduce dimensionality, and the radiomics features strongly associated with LNM were selected and used to construct three single-sequence models. Furthermore, clinical variables were incorporated through multivariate regression analysis and fused with the selected radiomics features to construct the clinical-radiomics combined model. The diagnostic performance of the models was assessed using receiver operating characteristic (ROC) curve analysis. The clinical utility of the models was evaluated by the area under the ROC curve (AUC) and decision curve analysis (DCA).ResultsA total of 282 patients were included, comprising 171 patients in the training set, and 111 patients in the validation set. Compared to the Sag-T2WI model (AUC, 95%CI, training set, 0.797, 0.722-0.782; validation set, 0.648, 0.521-0.776) and the Sag-T1C model (AUC, 95%CI, training set, 0.802, 0.723-0.882; validation set, 0.630, 0.505-0.756), the Ax-DWI model exhibited the highest diagnostic performance with AUCs of 0.855 (95%CI, 0.791-0.919) in training set, and 0.753 (95%CI, 0.638-0.867) in validation set, respectively. The combined model, integrating selected features from three sequences and FIGO stage, surpassed predictive ability compared to the single-sequence models, with AUC of 0.889 (95%CI, 0.833-0.945) and 0.859 (95%CI, 0.781-0.936) in the training and validation sets, respectively.ConclusionsThe pretreatment MRI-based radiomics model, integrating radiomics features from three sequences and clinical variables, exhibited superior performance in predicting LNM following NACT in patients with LACC.

Four experimental diets containing 45% crude protein and 11% crude lipid were formulated by replacing total fish oil (FO) with poultry oil (PO), beef tallow (BT) and lard oil (LO) to evaluate the effects of terrestrial animal fats (TAFs) replacing dietary FO in juvenile green mud crabs (Scylla paramamosain). A total of 120 female juveniles (initial body weight 43.23 ± 0.21 g) was randomly assigned to four diet (three replicates per diet) and stocked into single crab cells (0.23 m × 0.15 m × 0.33 m, width × height × length) with circulating seawater (25–29 °C) for 8 weeks. Compared to FO, crabs fed BT and LO group had about 20% lower weight gain, and significantly lower specific growth rate, feed efficiency and molting ratio, hepatopancreas and muscle lipid, hemolymph total triglyceride contents, and lipase activity, but had approximately 400% higher hepatopancreas aspartate aminotransferase and 100% higher alanine aminotransferase activities. There was no difference in growth performance and feed utilization between PO and other groups. The contents of linoleic acids (18:2n-6) in hepatopancreas and muscle were higher in PO group. TAFs groups had significantly lower docosahexaenoic acids (22:6n-3) and total n-3 polyunsaturated fatty acids contents, as well as lower elongase of very long-chain fatty acids 4, acyl-CoA oxidase 3 and fatty acid transport protein 4 mRNA levels. BT and LO groups had lower mRNA levels of delta-6 fatty acyl desaturase, sterol regulatory element binding protein-1, fatty acid synthase, 6-phosphogluconate dehydrogenase and acetyl-CoA carboxylase. Therefore, dietary BT and LO could reduce the health status and lipid content via decreasing lipase activity, upregulating fatty acid β-oxidation, downregulating lipogenesis and inhibiting the transporting and metabolizing of long chain polyunsaturated fatty acids, thus decrease the growth performance of mud crab. Moreover, PO could be a potential FO substitute for juvenile S. paramamosain.

Abstract As emerging alternatives to natural enzymes, nanoscale materials featuring enzyme‐like catalytic behaviors (nanozymes) exhibit some attractive merits including robust activity, low cost, and easy‐to‐regulate performance. These merits have enabled them to be intensively used in the biomedical field in recent years. To remedy the lack of catalytic selectivity in most nanozymes, deoxyribonucleic acid (DNA) chains with specific recognition functions are utilized to integrate with nanozymes to produce various nanozyme–DNA combinations via adsorption/desorption. In the formed combinations, the DNA component provides the molecular/ionic recognition role, and the nanozyme part offers response with catalytically amplified signals, enabling them to detect analytes and biomarkers selectively and sensitively. To highlight this interesting topic, here we made a critical review of the interactions between nanozymes and DNA and their applications in biosensing and disease diagnosis. First, strategies for the conjugation of DNA chains onto nanozyme surface were introduced briefly. Then, the interactions between DNA and nanozymes were summarized in detail, where flexible modulations of nanozyme activity by DNA adsorption/desorption as well as various factors were analyzed, and potential impacts caused by nanozymes on the recognition characteristics of DNA chains were pointed out. After that, typical applications of DNA‐mediated nanozyme modulation in toxic ion sensing, health risk factor monitoring, and biomedical diagnosis were introduced. In the end, prospects of the combination of nanozymes and DNA chains were presented, and future challenges of the emerging field were also discussed, to attract more interest and effort to advance this promising area.

Ever since the discovery of the charge density wave (CDW) transition in the kagome metal CsV_{3}Sb_{5}, the nature of its symmetry breaking has been under intense debate. While evidence suggests that the rotational symmetry is already broken at the CDW transition temperature (T_{CDW}), an additional electronic nematic instability well below T_{CDW} has been reported based on the diverging elastoresistivity coefficient in the anisotropic channel (m_{E_{2g}}). Verifying the existence of a nematic transition below T_{CDW} is not only critical for establishing the correct description of the CDW order parameter, but also important for understanding low-temperature superconductivity. Here, we report elastoresistivity measurements of CsV_{3}Sb_{5} using three different techniques probing both isotropic and anisotropic symmetry channels. Contrary to previous reports, we find the anisotropic elastoresistivity coefficient m_{E_{2g}} is temperature independent, except for a step jump at T_{CDW}. The absence of nematic fluctuations is further substantiated by measurements of the elastocaloric effect, which show no enhancement associated with nematic susceptibility. On the other hand, the symmetric elastoresistivity coefficient m_{A_{1g}} increases below T_{CDW}, reaching a peak value of 90 at T^{*}=20 K. Our results strongly indicate that the phase transition at T^{*} is not nematic in nature and the previously reported diverging elastoresistivity is due to the contamination from the A_{1g} channel.

Abstract Van Hove singularity (VHS) has been considered as a driving source for unconventional superconductivity. A VHS in two-dimensional (2D) materials consists of a saddle point connecting electron-like and hole-like bands. In a rare case, when a VHS appears at Fermi level, both electron-like and hole-like conduction can coexist, giving rise to an enhanced density of states as well as an attractive component of Coulomb interaction for unconventional electronic pairing. However, this van Hove scenario is often destroyed by an incorrect chemical potential or competing instabilities. Here, by using angle-resolved photoemission measurements, we report the observation of a VHS perfectly aligned with the Fermi level in a kagome superconductor CsV3-x Ta x Sb5 (x ~ 0.4), in which a record-high superconducting transition temperature is achieved among all the current variants of AV3Sb5 (A = Cs, Rb, K) at ambient pressure. Doping dependent measurements reveal the important role of van Hove scenario in boosting superconductivity, and spectroscopic-imaging scanning tunneling microscopy measurements indicate a distinct superconducting state in this system.

Abstract Background Stage I lung adenocarcinoma is a heterogeneous group. Previous studies have shown the prognostic evaluation value of PET/CT in this cohort; however, few studies focused on stage I invasive adenocarcinoma manifesting as solid nodules. This study aimed to evaluate the recurrence risk for patients with stage I invasive lung adenocarcinoma manifesting as solid nodules based on 18F-FDG PET/CT, CT imaging signs, and clinicopathological parameters. Methods We retrospectively enrolled 230 patients who underwent 18F-FDG PET/CT examination between January 2013 and July 2019. Metabolic parameters: maximum standard uptake value (SUVmax), mean standard uptake value, tumor metabolic volume (MTV), and total tumor glucose digestion were collected. Kaplan–Meier method was used to evaluate recurrence-free survival (RFS), and the multivariate Cox proportional hazards model was used to determine the independent risk factors associated with RFS. The time-dependent receiver operating characteristic curve (ROC) method was used to calculate the optimal cutoff value of metabolic parameters. Results The 5-year RFS rate for all patients was 71.7%. Multivariate Cox analysis revealed that the International Association for the Study of Lung Cancer Pathology Committee (IASLC) pathologic grade 3 [Hazard ratio (HR), 3.96; 95% Confidence interval (CI), 1.11–14.09], the presence of cavity sign (HR 5.38; 95% CI 2.23–12.96), SUVmax (HR 1.23; 95% CI 1.13–1.33), and MTV (HR 1.05; 95% CI 1.01–1.08) were potential independent prognostic factors for RFS. Patients with IASLC grade 3, the presence of cavity sign, SUVmax > 3.9, or MTV > 5.4 cm3 were classified as high risk, while others were classified as low risk. There was a significant difference in RFS between the high-risk and low-risk groups (HR 6.04; 95% CI 2.17–16.82, P

The study explores the strategic pricing and quality improvement decisions under uncertain demand in a three-layer textile and garment supply chain. According to whether the fabric manufacturer (FM) invests in quality or not and whether the garment manufacturer (GM) or garment retailer (GR) is willing to share the costs or not, five game models are constructed to investigate the impact of different members' cost sharing on the optimal decisions and profits. By conducting a theoretical and numerical analysis, we find that: (1) The GM's or GR's cost sharing plays a positive effect on the quality improvement, as for whose cost sharing performs better in improving the quality depending on the proportion of cost sharing, and the quality improvement is highest with both members share the costs simultaneously. (2) The FM receives the highest profit when both members share the costs simultaneously, however, whose cost sharing is more profitable for the FM is also related to the proportion of cost sharing; in short, the FM always benefits from the cost sharing, no matter one member does this or two members do this. (3) The GM (GR) gains the highest profit when only the GR (GM) shares the costs, and the results indicate that if one member has shared the costs, whether the other member engaging in cost sharing could benefit the former depending on their proportions. Specifically, when the GM (GR) chooses to share the costs and the proportion is relatively low, the GR(GM) joining in cost sharing is beneficial to the former; otherwise, is harmful.

IntroductionGenotypic drug resistance testing is cursrently recommended by the World Health Organization for all patients infected with human immunodeficiency virus type 1 (HIV-1) undergoing care or switching regimes due to failure with previous antiretroviral therapy (ART). Patients with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) who meet the criteria for free testing for genotypic drug resistance due to poor adherence in Henan Province may resume their previous regimens before resampling. Therefore, resistance testing based on plasma RNA can fail in a proportion of patients. Resistance testing based on peripheral blood mononuclear cells (PBMCs) is an alternative option. In this study, we investigated the differences in drug-resistant mutations (DRMs) between plasma HIV RNA and proviral DNA in treatment-experienced and treatment-naïve patients.MethodsMatched plasma RNA and proviral DNA samples of 66 HIV-1 infected treatment-naïve and 78 treatment-experienced patients were selected for DRM analysis and comparison.ResultsDRMs were detected in 27.3% (18/66) of treatment-naïve and 80.8% (63/78) of treatment-experienced samples. Resistance to at least one drug was detected based on analysis of plasma RNA and proviral DNA in 7.6% (5/66) and 9.1% (6/66) of treatment-naïve patients and in 79.5% (62/78) and 78.2% (61/78) of treatment-experienced patients, respectively. Furthermore, 61/66 (92.4%) of treatment-naïve patients showed concordant RNA and DNA drug resistance. When drug resistance was defined as intermediate and high, the concordance of drug resistance profiles of paired RNA and proviral DNA samples derived from treatment-naïve patients were up to 97.0% compared with only 80.8% (63/78) in treatment-experienced patients.DiscussionOur data indicate that drug resistance testing based on plasma RNA or proviral DNA might be interchangeable in treatment-naïve patients, whereas plasma RNA-based testing remains the best choice for drug resistance analysis in patients with ART failure in clinical practice.

Introduction: Qi-Xian Decoction (QXD), a traditional Chinese medicine (TCM) formula consisting of eight herbs, has been clinically used to treat asthma. However, the underlying mechanisms have not been completely elucidated. This study aimed to combine metabolomics and network pharmacology to reveal the mechanism of action of QXD in asthma treatment.Methods: An ovalbumin (OVA)-induced asthma mouse model was constructed to evaluate the therapeutic effects of QXD. Serum metabolomics and network pharmacology were combined to study the mechanism of anti-asthma action as well as the potential target, and related biological functions were validated.Results: The QXD treatment has demonstrated significant protective effects in OVA-induced asthmatic mice, as evidenced by its ability to inhibit inflammation, IgE, mucus overproduction, and airway hyperreactivity (AHR). Metabolomic analysis has revealed a total of 140 differential metabolites associated with QXD treatment. In addition, network pharmacology has identified 126 genes that are linked to the effects of QXD, including TNF, IL-6, IL1β, STAT3, MMP9, EGFR, JUN, CCL2, TLR4, MAPK3 and MAPK8. Through comprehensive gene-metabolite interaction network analysis, seven key metabolites have been identified and associated with the potential anti-asthmatic effect of QXD, with palmitic acid (PA) being the most notable among them. In vitro validation studies have confirmed the gene-metabolite interaction involving PA, IL-6, and MAPK8. Furthermore, our research has demonstrated that QXD treatment can effectively inhibit PA-promoted IL-6 expression in MH-S cells and reduce PA concentration in OVA-induced asthmatic mice.Conclusion: The regulation of metabolic pathways by QXD was found to be associated with its anti-asthmatic action, which provides insight into the mechanism of QXD in treating asthma.

BackgroundHashimoto thyroiditis (HT), a prevalent autoimmune disorder, is not yet thoroughly understood, especially when it comes to the influence of epigenetics in its pathogenesis. The primary goal of this research was to probe the DNAm profile across the genome in the whole blood derived from patients suffering from HT.MethodUsing the Illumina 850K BeadChip, we conducted a genome-wide DNAm assessment on 10 matched pairs of HT sufferers and healthy individuals. Genes with differential methylation (DMGs) were identified and underwent functional annotation via the databases of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. The transcriptional significance of potential epigenetic biomarker genes was corroborated through qRT-PCR.ResultsThe DNAm profiling across the genome indicated an overall reduction in methylation in HT subjects in comparison with their healthy counterparts. We detected 283 DMPs (adjusted P < 0.05 and |Δβ| > 0.1), among which 152 exhibited hypomethylation and 131 demonstrated hypermethylation. Further analysis exposed a noteworthy concentration of hypermethylated DMPs in the 3´UTR, North Shore, and CpG islands, while there was a significant decrease in the Open Sea (all P < 0.001). The 283 DMPs were broadly distributed from chromosome 1 to 22, with chromosome 6 harboring the most DMPs (n = 51) and chromosome 12 carrying the most DMGs (n = 15). The SLFN12 gene, which presented with extreme hypomethylation in its promoter DMPs among HT patients, was identified as the epigenetic marker gene. Consequently, the SLFN12 mRNA expression was markedly upregulated in HT, displaying a negative relationship with its methylation levels. The area under curve (AUC) value for the SLFN12 gene among HT patients was 0.85 (sensitivity: 0.7, specificity: 0.7), a significant difference compared with healthy controls. The methylation levels of all DMPs in SLFN12 gene were negatively correlated with TSH and one CpG site (cg24470734) was positively assocciated with FT4.ConclusionThis investigation presents an initial comprehensive DNAm blueprint for individuals with HT, which permits clear differentiation between HT subjects and normal controls through an epigenetic lens. The SLFN12 gene plays a pivotal role in the onset of HT, suggesting that the methylation status of this gene could serve as a potential epigenetic indicator for HT.

Noroviruses (NoVs) are the leading agent that causes acute viral gastroenteritis worldwide. Sporadic cases of GII.6 NoV have been reported primarily in addition to occasional outbreaks. Using the major capsid protein VP1 of GII.6 NoV derived from three distinct clusters, we demonstrated three blockade monoclonal antibodies (mAbs, 1F7, 1F11, and 2B6) generated previously exhibited cluster-specific binding effects. Combining sequence alignment and blocking immune epitopes, we sequentially designed a total of 18 mutant proteins containing one, two, or three mutations, or swapped regions. Indirect enzyme-linked immunosorbent assay (ELISA) demonstrated that the three blocking mAbs lost or showed significantly reduced binding for H383Y, D387N, V390D, and T391D mutant proteins. Combining data from mutant proteins with swapping regions and point mutations, the binding region of the three mAbs was mapped to residues 380–395. Sequence alignment of this region showed within-cluster conservation and between-cluster variations, further strengthening the idea of blockade epitope-mediated evolution of NoV.

Solid polymer electrolytes have demonstrated high promise to solve the safety problems caused by conventional liquid electrolytes in lithium ion batteries. However, the inherent flammability of most polymer electrolyte materials remains unresolved, hence hindering their further industrial application. Addressing this challenge, we designed and constructed a thermal-responsive imide-linked covalent organic framework (COF) bearing ortho-positioned hydroxy groups as precursors, which can conduct a thermal rearrangement to transform into a highly crystalline and robust benzoxazole-linked COF upon heating. Benefiting from the release of carbon dioxide through thermal rearrangement reaction, this COF platform exhibited excellent flame retardant properties. By contrast, classic COFs (e.g., boronate ester, imine, olefin, imide linked) were all flammable. Moreover, incorporating polyethylene glycol and Li salt into the COF channels can produce solid polymer electrolytes with outstanding flame retardancy, high ionic conductivity (6.42 ​× ​10−4 ​S ​cm−1) and a high lithium-ion transference number of 0.95. This thermal rearrangement strategy not only opens a new route for the fabrication of ultrastable COFs, but also provides promising perspectives to designing flame-retardant materials for energy-related applications.

ObjectiveTo develop and validate a radiomics nomogram that could incorporate clinicopathological characteristics and ultrasound (US)-based radiomics signature to non-invasively predict Ki-67 expression level in patients with breast cancer (BC) preoperatively.MethodsA total of 328 breast lesions from 324 patients with BC who were pathologically confirmed in our hospital from June 2019 to October 2020 were included, and they were divided into high Ki-67 expression level group and low Ki-67 expression level group. Routine US and shear wave elastography (SWE) were performed for each lesion, and the ipsilateral axillary lymph nodes (ALNs) were scanned for abnormal changes. The datasets were randomly divided into training and validation cohorts with a ratio of 7:3. Correlation analysis and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomics features obtained from gray-scale US images of BC patients, and each radiomics score (Rad-score) was calculated. Afterwards, multivariate logistic regression analysis was used to establish a radiomics nomogram based on the radiomics signature and clinicopathological characteristics. The prediction performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), the calibration curve, and decision curve analysis (DCA) using the results of immunohistochemistry as the gold standard.ResultsThe radiomics signature, consisted of eight selected radiomics features, achieved a nearly moderate prediction efficacy with AUC of 0.821 (95% CI:0.764-0.880) and 0.713 (95% CI:0.612-0.814) in the training and validation cohorts, respectively. The radiomics nomogram, incorporating maximum diameter of lesions, stiff rim sign, US-reported ALN status, and radiomics signature showed a promising performance for prediction of Ki-67 expression level, with AUC of 0.904 (95% CI:0.860-0.948) and 0.890 (95% CI:0.817-0.964) in the training and validation cohorts, respectively. The calibration curve and DCA indicated promising consistency and clinical applicability.ConclusionThe proposed US-based radiomics nomogram could be used to non-invasively predict Ki-67 expression level in BC patients preoperatively, and to assist clinicians in making reliable clinical decisions.

Target identification is essential for developing novel therapeutic strategies in diseases. Thioredoxin-interacting protein (TXNIP), also known as thioredoxin-binding protein-2, is a member of the α-arrestin protein family and is regulated by several cellular stress factors. TXNIP overexpression coupled with thioredoxin inhibits its antioxidant functions, thereby increasing oxidative stress. TXNIP is directly involved in inflammatory activation by interacting with Nod-like receptor protein 3 inflammasome. Bone metabolic disorders are associated with aging, oxidative stress, and inflammation. They are characterized by an imbalance between bone formation involving osteoblasts and bone resorption by osteoclasts, and by chondrocyte destruction. The role of TXNIP in bone metabolic diseases has been extensively investigated. Here, we discuss the roles of TXNIP in the regulatory mechanisms of transcription and protein levels and summarize its involvement in bone metabolic disorders such as osteoporosis, osteoarthritis, and rheumatoid arthritis. TXNIP is expressed in osteoblasts, osteoclasts, and chondrocytes and affects the differentiation and functioning of skeletal cells through both redox-dependent and -independent regulatory mechanisms. Therefore, TXNIP is a potential regulatory and functional factor in bone metabolism and a possible new target for the treatment of bone metabolism-related diseases.

Objects: Benzo(a)pyrene (BaP), an environmental pollutant, is present in high concentrations in urban smog and cigarette smoke and has been reported to promote high mucin 5AC (MUC5AC) expression. Epithelium-derived inflammatory cytokines are considered an important modulator of mucus oversecretion and MUC5AC overexpression. Here, we investigated whether the effect of BaP on MUC5AC overexpression was associated with cytokine autocrine activity in vivo and in vitro. Methods: In vivo, BALB/c mice were treated with ovalbumin (OVA) in the presence or absence of BaP. Allergy-induced mucus production was assessed by Alcian Blue Periodic acid Schiff (AB-PAS) staining. The human airway epithelial cell line NCI-H292 was used in vitro. MUC5AC and transforming growth factor (TGF)-α mRNA levels were assessed with real-time quantitative PCR. The concentration of cytokines was measured by ELISA. The MUC5AC, p-ERK, ERK, p-EGFR and EGFR proteins were detected by Western blotting in cells or by immunohistochemistry in mouse lungs. Small-interfering RNAs were used for gene silencing. Results: TGF-α was overproduced in the supernatant of NCI-H292 cells treated with BaP. Knockdown of TGF-α expression inhibited the BaP-induced increase in MUC5AC expression and subsequent activation of the EGFR-ERK signalling pathway. Knocking down aryl hydrocarbon receptor (AhR) expression or treatment with an ROS inhibitor (N-acetyl-L-cysteine) could relieve the TGF-α secretion induced by BaP in epithelial cells. In an animal study, coexposure to BaP with OVA increased mucus production, MUC5AC expression and ROS-EGFR-ERK activation in the lung as well as TGF-α levels in bronchoalveolar lavage fluid (BALF). Furthermore, the concentration of TGF-α in BALF was correlated with MUC5AC mRNA levels. Additionally, TGF-α expression was found to be positively correlated with MUC5AC expression in the airway epithelial cells of smokers. Compared with non-smoker asthma patients, TGF-α serum levels were also elevated in smoker asthma patients. Conclusion: Autocrine TGF-α was associated with BaP-induced MUC5AC expression in vitro and in vivo. BaP induced TGF-α secretion by activating AhR and producing ROS, which led to activation of the EGFR-ERK pathway

ObjectiveSmall intracranial aneurysms are increasingly being detected; however, a prediction model for their rupture is rare. Random forest modeling was used to predict the rupture status of small middle cerebral artery (MCA) aneurysms with morphological features.MethodsFrom January 2009 to June 2020, we retrospectively reviewed patients with small MCA aneurysms (

Abstract Green synthesis of crystalline porous materials for energy-related applications is of great significance but very challenging. Here, we create a green strategy to fabricate a highly crystalline olefin-linked pyrazine-based covalent organic framework (COF) with high robustness and porosity under solvent-free conditions. The abundant nitrogen sites, high hydrophilicity, and well-defined one-dimensional nanochannels make the resulting COF an ideal platform to confine and stabilize the H3PO4 network in the pores through hydrogen-bonding interactions. The resulting material exhibits low activation energy (Ea) of 0.06 eV, and ultrahigh proton conductivity across a wide relative humidity (10–90 %) and temperature range (25–80 °C). A realistic proton exchange membrane fuel cell using the olefin-linked COF as the solid electrolyte achieve a maximum power of 135 mW cm−2 and a current density of 676 mA cm−2, which exceeds all reported COF materials.

This work established a numerical model to investigate the heat transfer characteristics and wall film formation of spray impinging on the wall in the selective catalytic reduction (SCR) system. The model is developed by the Eulerian–Lagrangian approach, where the Lagrangian approach is used to represent the spray generated by a commercial non-air-assisted pressure-driven injector and the Eulerian approach is adopted to represent exhaust gas. The Stochastic Kuhnke Model is applied to spray/wall interaction. The model considers relevant processes, which include mass transfer, momentum transfer, heat transfer, droplet phase change, spray/wall interaction, and wall film formation. The numerical results compared with that of the experiment indicate that the model can accurately estimate the heat transfer characteristics of the wall surface during the spray impingement. Based on the numerical results, the causes of the spray local cooling effect and the rapid cooling effect are analyzed. The correlation between the critical transition temperature and the critical heat flux temperature for wall film formation is derived from the trends of wall temperature and heat flux. In this work, the Stochastic Kuhnke Model is applied and compared with the Kuhnke Model, which proves that it can improve the disadvantage of sudden change during the wall film formation. When the wall temperature is below the critical transition temperature, the wall film mass is sensitive to the wall temperature and increases as the wall temperature decreases.

The swelling pressure of bentonite changes dramatically due to diffused nuclear radiation heat and underground osmosis, causing the failure of the buffer isolation layer in deep geological repositories for the disposal of high-level radioactive waste. A detailed overview of the relevant research results on the swelling pressure variation of bentonite under thermo-chemical effects is presented in this paper. The results showed that the values of the swelling pressure obtained by different test methods are dissimilar. The swelling pressure of bentonite decreased with the increasing pore solution concentration; nevertheless, the effect of temperature on the swelling pressure is still controversial. At the micro-level, crystal layer swelling and double- layer swelling are generally considered to be the main factors affecting the swelling pressure; the pore structure and water distribution of bentonite will change owing to thermo-chemical effects. At the macro-level, involving intergranular stress, a mechanical parameter was proposed to explain the mechanism of the changes in the swelling pressure of bentonite. Finally, future research directions for the study of the evolution of bentonite swelling properties under thermo-chemical effects are proposed, based on the current research results.

An optimization method of structure parameters based on the quadratic regression orthogonal combination (QROC) and Genetic Algorithm (GA) is proposed in this work. The following work has been conducted to improve the performance of the coal pyrolysis filtration system and prolong the service life of the filter tubes based on QROC-GA method. Firstly, a simulation model is established and two factors always are chosen as optimization objectives. Then one single factor regression prediction algorithm is used to optimize each factor separately while the result was not satisfactory. Secondly, QROC is introduced to achieve the optimization of two factors in the filtration system. The regression relationship is obtained proved to be effective by statistical test and back propagation neural network (BPNN). Finally, a QROC-GA method is established to find the optimization points. Then a verification calculation is done with CFD again. The optimal result has the parameters that φ=40° and ψ=25°. From the simulation results, the mean square error is 0.401. The mean square error is 0.4992 by the QROC-GA results. The errors are within 0.1 between CFD and QROC-GA. The QROC-GA model has a good effect in the prediction of models under changing parameters, and the significance is also confirmed.

The twin-arginine translocation (Tat) system is involved in a variety of important bacterial physiological processes. Conserved among bacteria and crucial for virulence, the Tat system is deemed as a promising anti-microbial drug target. However, the mechanism of how the Tat system functions in bacterial pathogenesis has not been fully understood. In this study, we showed that the Tat system was critical for the virulence of an extra-intestinal pathogenic E. coli (ExPEC) strain PCN033. A total of 20 Tat-related mutant strains were constructed, and competitive infection assays were performed to evaluate the relative virulence of these mutants. The results demonstrated that several Tat substrate mutants, including the ΔsufI, ΔamiAΔamiC double mutant as well as each single mutant, ΔyahJ, ΔcueO, and ΔnapG, were significantly outcompeted by the WT strain, among which the ΔsufI and ΔamiAΔamiC strains showed the lowest competitive index (CI) value. Results of individual mouse infection assay, in vitro cell adhesion assay, whole blood bactericidal assay, and serum bactericidal assay further confirmed the virulence attenuation phenotype of the ΔsufI and ΔamiAΔamiC strains. Moreover, the two mutants displayed chained morphology in the log phase resembling the Δtat and were defective in stress response. Our results suggest that the Tat system and its dependent cell division proteins SufI, AmiA, and AmiC play critical roles during ExPEC pathogenesis.

BackgroundAlthough anterior communicating artery (ACoA) aneurysms have a higher risk of rupture than aneurysms in other locations, whether to treat unruptured ACoA aneurysms incidentally found is a dilemma because of treatment-related complications. Machine learning models have been widely used in the prediction of clinical medicine. In this study, we aimed to develop an easy-to-use decision tree model to assess the rupture risk of ACoA aneurysms.MethodsThis is a retrospective analysis of rupture risk for patients with ACoA aneurysms from two medical centers. Morphologic parameters of these aneurysms were measured and evaluated. Univariate analysis and multivariate logistic regression analysis were performed to investigate the risk factors of aneurysm rupture. A decision tree model was developed to assess the rupture risk of ACoA aneurysms based on significant risk factors.ResultsIn this study, 285 patients were included, among which 67 had unruptured aneurysms and 218 had ruptured aneurysms. Aneurysm irregularity and vessel angle were independent predictors of rupture of ACoA aneurysms. There were five features, including size ratio, aneurysm irregularity, flow angle, vessel angle, and aneurysm size, selected for decision tree modeling. The model provided a visual representation of a decision tree and achieved a good prediction performance with an area under the receiver operating characteristic curve of 0.864 in the training dataset and 0.787 in the test dataset.ConclusionThe decision tree model is a simple tool to assess the rupture risk of ACoA aneurysms and may be considered for treatment decision-making of unruptured intracranial aneurysms.

PurposeTo standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard.Materials and MethodsA dataset comprising anteroposterior, lateral, and oblique position lumbar spine x-ray images from 1,389 patients was analyzed in this study. The training set consisted of digital radiography images of 1,070 patients (800, 798, and 623 images of the anteroposterior, lateral, and oblique position, respectively) and the validation set included 319 patients (200, 205, and 156 images of the anteroposterior, lateral, and oblique position, respectively). The quality control standard for lumbar spine x-ray radiography in this study was defined using textbook guidelines of as a reference. An enhanced encoder-decoder fully convolutional network with U-net as the backbone was implemented to segment the anatomical structures in the x-ray images. The segmentations were used to build an automatic assessment method to detect unqualified images. The dice similarity coefficient was used to evaluate segmentation performance.ResultsThe dice similarity coefficient of the anteroposterior position images ranged from 0.82 to 0.96 (mean 0.91 ± 0.06); the dice similarity coefficient of the lateral position images ranged from 0.71 to 0.95 (mean 0.87 ± 0.10); the dice similarity coefficient of the oblique position images ranged from 0.66 to 0.93 (mean 0.80 ± 0.14). The accuracy, sensitivity, and specificity of the assessment method on the validation set were 0.971–0.990 (mean 0.98 ± 0.10), 0.714–0.933 (mean 0.86 ± 0.13), and 0.995–1.000 (mean 0.99 ± 0.12) for the three positions, respectively.ConclusionThis deep learning-based algorithm achieves accurate segmentation of lumbar spine x-ray images. It provides a reliable and efficient method to identify the shape of the lumbar spine while automatically determining the radiographic image quality.

Abstract Background Increasing evidence has indicated the important role of long non-coding RNAs (lncRNAs) in regulating the development and progression of cancers, including triple-negative breast cancer (TNBC). Small nucleolar RNA host gene 22 (SNHG22) is a novel lncRNA that has been identified as tumor-contributor in ovarian carcinoma. However, its function has not been explored in TNBC. Methods qRT-PCR was used to identify gene expression at mRNA level while western blot was utilized to analyze the protein level. Functional assays were implemented to identify changes on the proliferation, apoptosis and motility of TNBC cells under different conditions. Additionally, mechanistic assays, such as RIP assay, RNA pull down assay and luciferase reporter assay, were applied to assess relationships between molecules. Results SNHG22 represented a high expression level in TNBC tissues and cells. Besides, SNHG22 silencing restrained the proliferation, migration and invasion of TNBC cells. Furthermore, miR-324-3p that was lowly expressed in TNBC cells was conformed to be sponged by SNHG22. Moreover, upregulated miR-324-3p inhibited cell proliferation and motility in TNBC. Subsequently, we identified that SUDS3, a tumor-facilitator with elevated expression in TNBC, was the downstream target of SNHG22/miR-324-3p axis. Of note, miR-324-3p repression or SUDS3 overexpression could rescue the anti-tumor effect of SNHG22 silencing on the malignant phenotypes of TNBC cells. Conclusion LncRNA SNHG22 facilitated cell growth and motility in TNBC via sponging miR-324-3p and upregulating SUDS3, highlighting a new promising road for TNBC treatment development.

This paper investigates the double switchings reliable control of positive switched systems with actuator faults. Different from existing fixed actuator faults, a class of unfixed actuator faults is introduced, where each subsystem can have several faults rather than only a fault. First, a periodic occurring fault is considered for the subsystems. Using linear Lyapunov functions with linear programming, a set of reliable controllers is designed for positive switched systems. Then, an average dwell time based switching law between the normal and faulty modes of subsystems is established. In detail, the considered systems consist of several switched subsystems, where the subsystems have normal and faulty modes. In such a case, two classes of multiple linear Lyapunov functions are constructed for the subsystems and the whole switched systems, respectively. Accordingly, two classes of reliable controllers are designed for the normal and faulty modes, respectively. Meanwhile, two classes of average dwell time switching laws, named double switchings laws, are designed for the subsystems and the whole switched systems, respectively. Finally, two examples are provided to illustrate the effectiveness of the proposed design.

Face recognition is one of the hottest issues in the field of computer vision and pattern recognition. Deep learning-based recognition models already have more exceptional recognition ability than the human being on open datasets, but still cannot fully undertake the identity recognition task in real scenarios without human assistance. In this paper, we mainly analyze two obstacles, i.e., domain gap and training data shortage. We propose the unpaired Domain Transfer Generative Adversarial Network (DT-GAN) to relieve these two obstacles. We improve the GAN baseline to bridge the domain gap among datasets by generating images conforming to the style of a target domain by learning the mapping between the source domain and target domain. The generator could synthesize face with an arbitrary viewpoint at the same time. The model is trained with a combination of style transfer loss, identity loss, and pose loss, which ensures the successive domain transfer and data augment. We conduct experiments to testify the effectiveness and reasonability of DT-GAN. Experimental results demonstrate the recognition performance is dramatically boosted after domain transfer and data augment.

Face recognition in unconstraint surveillance is a complicated problem on account of motion blur, expression variations and low resolution. Recent works have demonstrated that patch-attention is strictly more powerful than convolution in recognition models. In this study, we investigate the task of unconstraint surveillance face recognition. First, a Patch-Attention Generative Adversarial Network (PA-GAN) model is devised to aggregate some robust features on behalf of a set of raw surveillance frames, which not only increases the recognition accuracy but also reduces the computational costs of face matching. Second, an improved center loss function combined with abundant unlabeled surveillance faces is utilized to accurately classify the known identities. With the proposed method, the discriminativeness of the face representations is largely enhanced. Finally, the proposed method is verified in two widely used datasets, IJB-A dataset and QMUL-SurvFace dataset to demonstrate the effectiveness. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed design can achieve competitive accuracy on both the verification and identification protocols.

Pose variant or self-occlusion is one of the open issues which severely degrades the performance of pose-invariant face recognition (PIFR). Existing solutions to PIFR either have undesirable generalization based on challenging pose normalization or are complicated for implement on account of deep neural network. To relieve the impact of ill-pose on PIFR, we have proposed Cross-Pose Generative Adversarial Networks(CP-GAN) to frontalize the profile face with unaltered identity by learning the mapping between the profile and frontal faces in image space. The generator is an encoder-decoder U-net, and generate frontal face image by fusing multiple profile images to achieve a better performance in PIFR. The siamese discriminative network attends to extract the deep representations of the generated frontal face and the ground truth without introducing extra networks in verification and recognition. Besides the implementable architecture, this problem is well alleviated by introducing a combination of adversarial loss for both the generator and the discriminator, symmetry loss, patch-wise loss, and identity loss guiding an identity reserving property of the generated frontal view. Quantitative and qualitative evaluation on both controlled and in-the-wild datasets attest that the solution we proposed to PIFR presents satisfactory perceptual results and transcends state-of-the-art methods on ill-pose face recognition.

Background. Few studies have investigated the epidemiological metabolic (dysfunction) associated with fatty liver disease (MAFLD) in China, especially among those with type 2 diabetes. Methods. We recruited 3553 patients aged 18-75 years with type 2 diabetes who underwent abdominal ultrasound and serum biochemical analyses. Patient information including demographic and anthropometric parameters was also collected. Results. Overall, 63.2% of type 2 diabetic patients had MAFLD. Among the MAFLD patients, the proportions of lean, nonobese, and obese MAFLD were 23.1%, 75.7%, and 24.3%, respectively, and the percentage of previously undiagnosed MAFLD was 42.2%. MAFLD patients were younger, had shorter diabetic duration, and had greater BMI, aspartate aminotransferase (AST), alanine aminotransferase (ALT), fasting insulin, postprandial insulin, total cholesterol, and insulin resistance levels (HOMA-IR and TyG index). Liver fibrosis diagnostic panels revealed that the proportions of elevated AST (≥40 U/L) and ALT (≥40 U/L) were 7.3% and 18.5%, respectively. The distributions of AST-to-platelet ratio index (APRI), fibrosis-4 (FIB-4) index, and nonalcoholic fatty liver disease fibrosis score (NFS) per stage were as follows: APRI—low 55.1%, indeterminate 35.3%, and high 9.5%; FIB-4—low 48.2%, indeterminate 45.3%, and high 6.5%; and NFS—low 15.0%, indeterminate 70.0%, and high 13.0%. Conclusions. MAFLD is a very common condition and generally had greater frequency of metabolic characteristics among type 2 diabetics in China. Many MAFLD patients were in the “indeterminate” or “high” stage when APRI, FIB-4, and NFS were assessed. Assessment of MAFLD should be included in the management of type 2 diabetes.

Porcine circovirus type 2 (PCV2) is one of the smallest known animal viruses and is the main pathogen of PCV-associated diseases (PCVAD). Epidemiological surveillance results have shown that the PCV2 infection rate is on the rise in China, thus, PCV2 disease prevention and control has become a huge challenge for the Chinese swine industry. We collected clinical samples from multiple different provinces in China from 2018 to 2020 and found that the positive rate of PCV2 was 53% (3619/6872), identity between the cloned 62 ORF2 genes was 84.4–100% and identity between the cloned 62 ORF2 sequences and reference sequence was 72.9–99.8%. Genetic evolution analysis found that PCV2d accounted for 79% (49/62 samples), PCV2a for 12.9% (8/62 samples), PCV2b for 8% (5/62 samples), and PCV2c and PCV2e genotypes were not found. However, most commercial PCV2 subunit vaccines are based on the PCV2a genotype, and there are very few vaccines based on PCV2b or PCV2d. Therefore, the homologous and heterologous protection ability of PCV2b and PCV2d Cap proteins based on the baculovirus against the PCV2b and PCV2d infections was evaluated, which is expected to design and develop excellent PCV2 protein vaccine candidates. This study found that both PCV2b and PCV2d Cap proteins can increase the level of humoral immunity and cellular immune response in mice. Importantly, both PCV2b and PCV2d cap proteins can provide homologous and heterologous protection against the PCV2b and PCV2d viruses. Overall, this study provides a reference for the prevention and control of PCVAD in mainland China and the development of PCV2 vaccines.

Background: Several studies have investigated the role of off-label non-vitamin K antagonist oral anticoagulants (NOACs) in patients with atrial fibrillation (AF). We aimed to compare the effectiveness and safety outcomes between off-label underdose or overdose vs. on-label dose of NOACs in AF patients.Methods: The PubMed database was systematically searched until August 2021. Observational cohorts were included if they compared the outcomes of off-label underdose or overdose with on-label dose of NOACs in AF patients. The risk ratios (RRs) and 95% confidence intervals (CIs) were pooled using a fixed-effects model (I2 ≤ 50%) or a random-effects model (I2 > 50%).Results: A total of 15 observational studies were included. Compared with on-label dose of NOACs, off-label underdose of NOACs was associated with increased risks of stroke or systemic embolism (RR = 1.09, 95% CI 1.02–1.16), and all-cause death (RR = 1.29, 95% CI 1.10–1.52) but not ischemic stroke (RR = 1.34, 95% CI 0.76–2.36), myocardial infarction (RR = 1.08, 95% CI 0.92–1.28), major bleeding (RR = 0.97, 95% CI 0.89–1.05), intracranial hemorrhage (RR = 1.12, 95% CI 0.90–1.40), and gastrointestinal bleeding (RR = 0.96, 95% CI 0.85–1.07), whereas off-label overdose of NOACs was associated with increased risks of SSE (RR = 1.20, 95% CI 1.05–1.36), all-cause death (RR = 1.22, 95% CI 1.06–1.39), and major bleeding (RR = 1.33, 95% CI 1.16–1.52) but not gastrointestinal bleeding (RR = 1.18, 95% CI 0.99–1.42) and myocardial infarction (RR = 0.98, 95% CI 0.75–1.30).Conclusion: Compared with on-label dose of NOACs, off-label underdose was associated with increased risks of stroke or systemic embolism and all-cause death, whereas off-label overdose of NOACs was associated with increased risks of stroke or systemic embolism, all-cause death, and major bleeding.

Osteoporosis is a systemic bone metabolism disease that often causes complications, such as fractures, and increases the risk of death. The nucleotide-binding oligomerization domain-like-receptor family pyrin domain-containing 3 (NLRP3) inflammasome is an intracellular multiprotein complex that regulates the maturation and secretion of Caspase-1 dependent proinflammatory cytokines interleukin (IL)-1β and IL-18, mediates inflammation, and induces pyroptosis. The chronic inflammatory microenvironment induced by aging or estrogen deficiency activates the NLRP3 inflammasome, promotes inflammatory factor production, and enhances the inflammatory response. We summarize the related research and demonstrate that the NLRP3 inflammasome plays a vital role in the pathogenesis of osteoporosis by affecting the differentiation of osteoblasts and osteoclasts. IL-1β and IL-18 can accelerate osteoclast differentiation by expanding inflammatory response, and can also inhibit the expression of osteogenic related proteins or transcription factors. In vivo and in vitro experiments showed that the overexpression of NLRP3 protein was closely related to aggravated bone resorption and osteogenesis deficiency. In addition, abnormal activation of NLRP3 inflammasome can not only produce inflammation, but also lead to pyroptosis and dysfunction of osteoblasts by upregulating the expression of Caspase-1 and gasdermin D (GSDMD). In conclusion, NLRP3 inflammasome overall not only accelerates bone resorption, but also inhibits bone formation, thus increasing the risk of osteoporosis. Thus, this review highlights the recent studies on the function of NLRP3 inflammasome in osteoporosis, provides information on new strategies for managing osteoporosis, and investigates the ideal therapeutic target to treat osteoporosis.

In comparison with bioenzymes, nanozymes exhibit excellent robustness against extreme conditions, a low production cost, and easy-to-adjust properties, as well as potential versatility. These superiorities have attracted abundant interest in the last 15 years, to develop various nanozymes for applications including analytical sensing, environmental engineering, and biomedicine. In particular, for analytical sensing, a lot of nanozyme-involved principles and methods have been explored and applied to clinical diagnosis, environmental monitoring, food safety detection, and forensic analysis. Moreover, rational exploitation and use of nanozyme materials promote the performance of analytical methods. To highlight the latest progress in this attractive field, recent design concepts of nanozymes for advanced biochemical sensing are summarized. The development of single-atom nanozymes, self-cascade nanozymes, structurally biomimetic nanozymes, molecularly imprinted nanozymes, nanozymes breaking the pH limit, and multifunctional nanozymes is discussed in detail, to enhance detection sensitivity and selectivity, as well as expand application scenarios. Finally, some challenges and trends related to nanozyme-based sensors are reported, to satisfy the increasing needs of biochemical analysis with nanozymes.

Selective catalytic reduction (SCR) is currently one of the most efficient denitration technologies to reduce nitrogen oxide (NOx) emissions of diesel engines. AdBlue (urea water solution, UWS) is the carrier of the reducing agent of SCR, and the spray process of UWS is one of the critical factors affecting denitration efficiency. In this paper, a non-air-assisted pressure-driven full process spray (NPFPS) model is proposed to illustrate the breakup mechanism and the spray distribution properties of UWS through computational fluid dynamics (CFD). In the NPFPS model, the mechanism of the primary breakup is described by the volume of fluid (VOF) approach, which realizes the quantitative study of the critical parameters determining spray characteristics such as the breakup length, inclination angle, droplet size of the primary breakup, and primary velocity. The distribution of the spray after the primary breakup is depicted by the discrete phase model (DPM) coupled with the Taylor analogy breakup (TAB) model, through which the degree of secondary breakup can be obtained including quantitative studies of the droplet size distribution and velocity distribution in the different cross-sections. To verify the accuracy and feasibility of the NPFPS model, the experimental data are employed to compare with the simulation data. The results are in good agreement, which indicate the practical value of the model.

Background: Current evidence regarding the application of direct oral anticoagulants (DOACs) vs. vitamin K antagonists (VKAs) on the fracture risk is inconsistent. Therefore, we conducted a meta-analysis to evaluate the fracture risk of DOACs vs. VKAs in patients with atrial fibrillation (AF).Methods: The PubMed and Embase databases were systematically searched until June 2021 for all the studies that reported oral anticoagulants in AF patients. The random-effect model with an inverse variance method was selected to pool the risk ratios (RRs) and 95% confidence intervals (CIs).Results: A total of 10 studies were included in this meta-analysis. Among AF patients receiving anticoagulants, DOAC users showed a reduced risk of any fracture compared to those with VKAs (RR = 0.80; 95% CI: 0.70–0.91) regardless of gender [males (RR = 0.79; 95% CI: 0.67–0.92) and females (RR = 0.71; 95% CI: 0.57–0.89)]. Apixaban (RR = 0.75; 95% CI: 0.60–0.92) and rivaroxaban (RR = 0.73; 95% CI: 0.61–0.88), but not dabigatran and edoxaban, were associated with a decreased risk of any fracture compared with VKAs. DOAC users had decreased risks of osteoporotic fractures (RR = 0.63; 95% CI: 0.47–0.84) and hip/pelvic fractures (RR = 0.88; 95% CI: 0.79–0.97) compared to those treated with VKAs.Conclusions: Our meta-analysis suggested that the use of DOACs was associated with a reduced risk of any fracture compared with VKAs. Further studies should confirm our findings.