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Abstract Background Bronchopulmonary dysplasia (BPD), a chronic lung disease prevalent among premature infants, significantly impacts lifelong respiratory health. Macrophages, as key components of the innate immune system, play a role in lung tissue inflammation and injury, exhibiting diverse and dynamic functionalities. The M4 macrophage, a distinctive subtype primarily triggered by chemokine (C-X-C motif) ligand 4 (CXCL4), has been implicated in pulmonary inflammatory and fibrotic processes. Nonetheless, its contribution to the pathophysiology of BPD remains uncertain. Objective This study aimed to elucidate the involvement of CXCL4 in hyperoxia-induced neonatal lung injury and fibrosis, with a particular focus on its influence on M4 macrophages. Methods A BPD model in neonatal mice was established through continuous exposure to 95% O2 for 7 days. Comparative analyses of lung damage and subsequent regeneration were conducted between wild-type (WT) and CXCL4 knockout (KO) mice. Lung tissue inflammation and fibrosis were assessed using histological and immunofluorescence staining, enzyme-linked immunosorbent assay, Western blot, and real-time quantitative polymerase chain reaction. Differentiation of M0 and M4 macrophages was performed in vitro using macrophage colony-stimulating factor and CXCL4, while expressions of S100A8 and MMP7, along with migration assays, were evaluated. Results Elevated CXCL4 levels and M4 macrophage activation were identified in the lung tissue of BPD model mice. CXCL4 deficiency conferred protection to alveolar type 2 epithelial cells, reduced sphingosine-1-phosphate metabolic activity, mitigated pulmonary fibrosis, and limited M4 macrophage progression. This deletion further enhanced lung matrix remodeling during recovery. In vitro, CXCL4 promoted M4 macrophage differentiation and increased macrophage migration via chemokine (C-C motif) receptor 1. Conclusion CXCL4 contributes to hyperoxia-induced lung injury and fibrosis through modulation of cytokine release, alveolar cell proliferation, lipid metabolism, and the regulation of macrophage phenotype and function.

Abstract Background Peer support is a promising and significant way for people living with HIV (PLWH) to improve their health outcomes. Matching PLWH with HIV peer volunteers can effectively stimulate and mobilize the function of peer support in health management. We develop the online platform Aspark, which features a precision matching function for PLWH and volunteers. This protocol provides a comprehensive overview of the randomized controlled trial design based on Aspark. Method A parallel randomized controlled trial using block randomization will be conducted at the Beijing YouAn Home of Loving Care. The intervention group (n = 70) will have access to all 12 features of Aspark, including precise volunteer matching, health management tools, drug interaction queries, and medication reminders. The control group (n = 70) will have access to two features of informational support on Aspark. The outcomes of both groups will be evaluated at baseline and at 3, 6, 9, and 12 months after platform registration. The primary outcome is perceived social support. The secondary outcomes include mental symptoms, quality of life, self-efficacy, medication adherence, and health behaviors. Linear mixed models or generalized linear mixed models will be applied to test the intervention effects. Discussion The Aspark trial is a randomized controlled trial designed to examine the influence of a mobile health (mHealth) application that integrates peer support and precision matching to strengthen social support for PLWH in China. This trial has the potential to significantly enhance social support for PLWH while also optimizing the delivery of peer support services and fully mobilizing the role of peer support in the overall health management of PLWH. Trial registration Clinical registration number: ChiCTR2300077454.

Abstract Background The value of quantitatively analyzing peripapillary capillary volume (PPCV) distribution was explored in normal and diabetic retinopathy (DR) eyes using dense B-scan optical coherence tomography angiography (DB OCTA). Methods This was a cross-sectional observational study followed by prospective follow-up for those with DR, which enrolled 101 healthy subjects and 140 DR patients. Dense, automatic, real-time (DART) volume scans of DB OCTA were performed using a Spectralis HRA + OCT2. ImageJ and MATLAB were used to process and calculate PPCV distribution detected by DB OCTA. Results In normal subjects, PPCV distribution were significantly correlated with the age and quadrant location (all P 0.05). The PPCV distribution is significantly correlated with post-treatment best-corrected visual acuity in both the pan-retinal photocoagulation treatment and surgery groups (all P

IntroductionIn the context of the growing global trend toward the deep integration of free trade agreements (FTAs), enhanced regional agricultural collaboration has significantly impacted the agricultural global value chains (AGVCs). Clarifying how FTA depth affects a country’s AGVC participation is crucial for promoting high-quality agricultural development and deepening international agricultural cooperation.Methods and goalsThis paper constructs and calculates indicators for FTA depth and the AGVC index, employing fixed effects models, PPML models, and other methods, aiming to empirically analyze how the depth of FTAs influences a country’s participation in AGVC and the mechanisms involved.ResultsThe findings indicate that an increase in FTA depth enhances a country’s degree of participation and position within the AGVC. Both the ‘WTO+’ and ‘WTO-X’ provision depth indices exert a significant positive influence on increasing participation and position within the AGVC, with the ‘WTO-X’ provision depth index demonstrating a more pronounced effect than the ‘WTO+’ provision. Furthermore, the positive effects of increased FTA depth on the integration of developed countries into the AGVC are greater than those on developing countries. Additional analysis reveals that FTA depth promotes trade liberalization and investment facilitation, thereby enhancing countries’ participation and position in the AGVC.DiscussionThe findings of this paper provide reliable empirical evidence for understanding the influence of FTA depth on AGVC and offer valuable policy insights for countries actively pursuing deeper FTAs.Policy recommendationsTo further advance the evolution of AGVC, it is recommended that countries actively promote the signing of deeper FTAs to accelerate trade liberalization and investment facilitation. At the same time, developed countries should strengthen agricultural technology research and development, assisting developing countries through technology transfer to jointly build a sustainable GVC; developing countries should enhance agricultural cooperation and improve their negotiating power in FTA discussions.

The viruses of the Parvoviridae family can infect both vertebrate and invertebrate animals. Recently, pigeon parvovirus (PiPV) was detected in the feces of wild urban pigeons. Owing to no specific detection platform for PiPV, studies on the epidemiology of PiPV are still a research gap. To achieve this goal, in this study, a TaqMan-based fluorescence quantitative PCR (TaqMan‒PCR) technique was established. The specific primers and probes used were designed on the basis of the NS gene characterization of PiPV downloaded from GenBank. After optimization, the established TaqMan‒PCR assay provides a sensitive, accurate, reliable and cost-effective platform for PiPV detection. We found that both YDPS and healthy birds can be found to have PiPV infection through field sample investigations, and we also investigated the presence of PiPV in Fujian, mainland China. Owing to the failure to propagate PiPV in embryos and cells, knowledge of PiPV replication mechanisms in birds still needs further study. In conclusion, a TaqMan-based fluorescence quantitative PCR method was developed, with the advantages of sensitivity, specificity, and reproducibility. This method can be used for further epidemiological monitoring of PiPV infection.

Urban green space can improve the air quality of urban human settlements. This study aimed to investigate the spatial differences of air quality among the different plant community structures and types of urban park green spaces. We select 17 sample sites in Beijing Olympic Forest Park, and they are located in different areas of plant community structures and types. The study entailed an analysis of the interrelationships between the plant community structures, types, and PM2.5, O3, and PM2.5–O3 compound data. The results showed that PM2.5 was lower in tree-shrub-grass, tree-shrub, and tree-grass than in shrub-grass and grass plant community areas; PM2.5 was lower in evergreen coniferous, mixed coniferous and broadleaved, and deciduous broadleaved plant communities than that in grass or shrub ones. In different plant community structures, types areas, O3 was higher than 100 μg·m–3, and there were no significant differences among the plant community areas. The air quality index with PM2.5–O3 composite pollution value as the main parameter reached the level of “moderate pollution”, and the result that deserves further attention. The research results provide a basic scientific basis for the planning, design, and updating optimization of functional urban green spaces based on evidence-based design.

Fowl adenovirus type 4 (FAdV-4) and duck adenovirus type 3 (DAdV-3) are the causative agents of clinical diseases in poultry and have caused considerable economic losses to the waterfowl industry in China. Both FAdV-4 and DAdV-3 are classified into the genus Aviadenovirus under the family Adenoviridae. The high-resolution melting (HRM) assay has become a useful method for virus genotyping, which offers the possibility of rapidly developing a differentiation technique in which the melting profile depends on the GC content of the product in the qPCR platform. The aim of this study was to develop a qPCR-HRM assay for sensitive FAdV-4 and DAdV-3 detection and differentiation. Here, specific primers were designed on the basis of the 100 K genes of FAdV-4 and DAdV-3, and a qPCR-HRM assay was established through optimization of the reaction conditions. A specificity test revealed that this method could detect only FAdV-4 and DAdV-3, with no cross-reaction with other common duck-derived viruses. A sensitivity test revealed that the lowest detection limits of FAdV-4 and DAdV-3 were 2.84 copies/µL and 2.85 copies/µL, respectively. A repeatability test demonstrated that the coefficient of variation was less than 2.5 % in both the intragroup and the intergroup analyses. Field sample distributions of FAdV-4 and DAdV-3 were investigated, and the percentages of DAdV-3-positive, FAdV-4-positive and coinfection-positive in Muscovy ducks were 27.78 %, 16.67 % and 11.11 %, respectively. Further studies are needed to provide more insight into the pathogenesis of FAdV-4 and DAdV-3 coinfection in ducks. In conclusion, the qPCR-HRM assay provides an accurate, sensitive, reliable and cost-effective alternative method for detecting and distinguishing FAdV-4 and DAdV-3.

Abstract Chronic obstructive pulmonary disease (COPD) stands as the prevailing chronic airway ailment, characterized by chronic bronchitis and emphysema. Current medications fall short in treatment of these diseases, underscoring the urgent need for effective therapy. Prior research indicated immunoproteasome inhibition alleviated various inflammatory diseases by modulating immune cell functions. However, its therapeutic potential in COPD remains largely unexplored. Here, an elevated expression of immunoproteasome subunits LMP2 and LMP7 in the macrophages isolated from mouse with LPS/Elastase‐induced emphysema and polarized macrophages in vitro is observed. Subsequently, intranasal administration of the immunoproteasome‐specific inhibitor ONX‐0914 significantly mitigated COPD‐associated airway inflammation and improved lung function in mice by suppressing macrophage polarization. Additionally, ONX‐0914 capsulated in PLGA nanoparticles exhibited more pronounced therapeutic effect on COPD than naked ONX‐0914 by targeting immunoproteasome in polarized macrophages. Mechanistically, ONX‐0914 activated autophagy and endoplasmic reticulum (ER) stress are not attribute to the ONX‐0914 mediated suppression of macrophage polarization. Intriguingly, ONX‐0914 inhibited M1 polarization through the nuclear factor erythroid 2‐related factor‐1 (NRF1) and NRF2‐P62 axis, while the suppression of M2 polarization is regulated by inhibiting the transcription of interferon regulatory factor 4 (IRF4). In summary, the findings suggest that targeting immunoproteasome in macrophages holds promise as a therapeutic strategy for COPD.

Duck adeno-associated Virus (DAAV) is a novel pathogen that was recently discovered in ducks. To establish a molecular detection assay for DAAV for further epidemiological investigation and pathogenic mechanism. Here, we designed specific primers and probes according to the sequence characteristics of the newly discovered DAAV and then established a TaqMan real-time PCR method (TaqMan-qPCR) for the detection of DAAV. Our data showed that the established TaqMan-qPCR for detecting DAAV had high sensitivity, with the lowest detection limit of 29.1 copies/μL. No cross reaction was found with duck circovirus (DuCV), H9N2 subtype avian influenza virus (AIV), avian Tembusu virus (ATmV). duck hepatitis A virus 1 and 3 (DHAV-1 and DHAV-3), duck adenovirus A (DAdV-A), duck adenovirus 3 (DAdV-3), or duck enteritis virus (DEV). The repeatability was excellent, with the coefficients of variation of repeated intragroup and intergroup tests ranging from 0.12–0.21% and 0.62–1.42%, respectively. Seventy-eight clinical samples collected from diseased or deceased ducklings were tested. The results showed that the DAAV positive rate was 21.79%, and a triple infection (DAAV+MDPV+GPV) was found. These data provide technical support for further molecular epidemiological surveillance and pathogenic mechanism studies of DAAV infection.

Recommendation systems offer an effective solution to information overload, finding widespread application across e-commerce, news platforms, and beyond. By analyzing interaction histories, these systems automatically filter and recommend items that are most likely to resonate with users. Recently, with the swift advancement of social networking, group recommendation has emerged as a compelling research area, enabling personalized recommendations for groups of users. Unlike individual recommendation, group recommendation must consider both individual preferences and group dynamics, thereby enhancing decision-making efficiency for groups. One of the key challenges facing recommendation algorithms is data sparsity, a limitation that is even more severe in group recommendation than in traditional recommendation tasks. While various group recommendation methods attempt to address this issue, many of them still rely on single-view modeling or fail to sufficiently account for individual user preferences within a group, limiting their effectiveness. This paper addresses the data sparsity issue to improve group recommendation performance, overcoming the limitations of overlooking individual user recommendation tasks and depending on single-view modeling. We propose MCSS (multi-view collaborative training and self-supervised learning), a novel framework that harnesses both multi-view collaborative training and self-supervised learning specifically for group recommendations. By incorporating both group and individual recommendation tasks, MCSS leverages graph convolution and attention mechanisms to generate three sets of embeddings, enhancing the model’s representational power. Additionally, we design self-supervised auxiliary tasks to maximize the data utility, further enhancing performance. Through multi-task joint training, the model generates refined recommendation lists tailored to each group and individual user. Extensive validation and comparison demonstrate the method’s robustness and effectiveness, underscoring the potential of MCSS to advance state-of-the-art group recommendation.

Abstract The structural diversity of biological macromolecules in different environments contributes complexity to enzymological processes vital for cellular functions. Fluorescence resonance energy transfer and electron microscopy are used to investigate the enzymatic reaction of T4 DNA ligase catalyzing the ligation of nicked DNA. The data show that both the ligase–AMP complex and the ligase–AMP–DNA complex can have four conformations. This finding suggests the parallel occurrence of four ligation reaction pathways, each characterized by specific conformations of the ligase–AMP complex that persist in the ligase–AMP–DNA complex. Notably, these complexes have DNA bending angles of ≈0°, 20°, 60°, or 100°. The mechanism of parallel reactions challenges the conventional notion of simple sequential reaction steps occurring among multiple conformations. The results provide insights into the dynamic conformational changes and the versatile attributes of T4 DNA ligase and suggest that the parallel multiple reaction pathways may correspond to diverse T4 DNA ligase functions. This mechanism may potentially have evolved as an adaptive strategy across evolutionary history to navigate complex environments.

Rice allelopathy is a natural method of weed control that is regarded as an eco-friendly practice in agroecology. The root growth of allelopathic rice at the seedling stage plays an important role in its weed control. Our study characterizes a plant hormone that promotes root growth, abscisic acid (ABA), to explore its role in the induction of rice allelopathy. Increasing the root morphology traits (root length, root tip number, and root biomass) in rice using different concentrations of exogenous ABA resulted in increased inhibitory ratios against barnyard grass (Echinochloa crus-galli), both in a hydroponic experiment and pot test. In particular, the relative proportion of induced allelopathy to total allelopathy in non-allelopathic rice Lemont (Le) was higher than that in allelopathic rice PI31277 (PI). The total content of phenolic acid, which is an important allelochemical in rice, as previously reported, was significantly elevated in the root exudates of both PI and LE. The gene expression levels of OsPAL, OsC4H, and OsCOL related to phenolic acid synthesis were also up-regulated, with a higher regulatory fold in PI. ABA also increased the expression of OsKSL4 and CYP75B4 involved in the biosynthesis of momilactone B and tricin. Moreover, low concentrations of exogenous ABA mainly positively regulate the expression of OsIAA11, an AUX/IAA transcription factor gene, in the root of PI and Le. These findings suggest that the application of ABA could significantly enhance the weed-suppressive activity of both rice cultivars through regulating root growth and the synthesis of allelochemicals secreted by rice roots, providing an option for the improvement of rice allelopathy through chemical induction.

ABSTRACT: The aim of this study was to develop an efficient and accurate platform for the detection of the newly identified goose megrivirus (GoMV). To achieve this goal, we developed a TaqMan real-time PCR technology for the rapid detection and identification of GoMV. Our data showed that the established TaqMan real-time PCR assay had high sensitivity, with the lowest detection limit of 67.3 copies/μL. No positive signal can be observed from other goose origin viruses (including AIV, GPV, GoCV, GHPyV, and GoAstV), with strong specificity. The coefficients of variation of repeated intragroup and intergroup tests were all less than 1.5%, with excellent repeatability. Clinical sample investigation data from domestic Minbei White geese firstly provided evidence that GoMV can be transmitted both horizontally and vertically. In conclusion, since the TaqMan real-time PCR method has high sensitivity, specificity, and reproducibility, it can be a useful candidate tool for GoMV epidemiological investigation.

BackgroundAcute myeloid leukemia (AML) is a highly aggressive and pathogenic hematologic malignancy with consistently high mortality. Lysosomes are organelles involved in cell growth and metabolism that fuse to form specialized Auer rods in AML, and their role in AML has not been elucidated. This study aimed to identify AML subtypes centered on lysosome-related genes and to construct a prognostic model to guide individualized treatment of AML.MethodsGene expression data and clinical data from AML patients were downloaded from two high-throughput sequencing platforms. The 191 lysosomal signature genes were obtained from the database MsigDB. Lysosomal clusters were identified by unsupervised consensus clustering. The differences in molecular expression, biological processes, and the immune microenvironment among lysosomal clusters were subsequently analyzed. Based on the molecular expression differences between lysosomal clusters, lysosomal-related genes affecting AML prognosis were screened by univariate cox regression and multivariate cox regression analyses. Algorithms for LASSO regression analyses were employed to construct prognostic models. The risk factor distribution, KM survival curve, was applied to evaluate the survival distribution of the model. Time-dependent ROC curves, nomograms and calibration curves were used to evaluate the predictive performance of the prognostic models. TIDE scores and drug sensitivity analyses were used to explore the implication of the model for AML treatment.ResultsOur study identified two lysosomal clusters, cluster1 has longer survival time and stronger immune infiltration compared to cluster2. The differences in biological processes between the two lysosomal clusters are mainly manifested in the lysosomes, vesicles, immune cell function, and apoptosis. The prognostic model consisting of six prognosis-related genes was constructed. The prognostic model showed good predictive performance in all three data sets. Patients in the low-risk group survived significantly longer than those in the high-risk group and had higher immune infiltration and stronger response to immunotherapy. Patients in the high-risk group showed greater sensitivity to cytarabine, imatinib, and bortezomib, but lower sensitivity to ATRA compared to low -risk patients.ConclusionOur prognostic model based on lysosome-related genes can effectively predict the prognosis of AML patients and provide reference evidence for individualized immunotherapy and pharmacological chemotherapy for AML.

ABSTRACT: Birds infected with duck circovirus (DuCV) can potentially cause immunosuppression by damaging lymphoid tissues, causing great losses in the duck breeding industry. Duck circovirus can be divided into two genotypes (DuCV-1 and DuCV-2), but simultaneous detection and differentiation of DuCV-1 and DuCV-2 by high-resolution melting (HRM) analysis is still lacking. Here, we designed specific primers according to the sequence characteristics of the newly identified ORF3 gene and then established a PCR-HRM method for the simultaneous detection and differentiation of DuCV-1 and DuCV-2 via high-resolution melting analysis. Our data showed that the established PCR-HRM assay had the advantages of specificity, with the lowest detection limits of 61.9 copies/μL (for DuCV-1) and 60.6 copies/μL (for DuCV-2). The melting curve of the PCR-HRM results indicated that the amplification product was specific, with no cross-reaction with common waterfowl origin pathogens and a low coefficient of variation less than 1.50% in both intra-batch and inter-batch repetitions, indicating the advantages of repeatability. We found that the percentage of DuCV-2-positive ducks was higher than that of DuCV-1-positive ducks, with 8.62% rate of DuCV-1 and DuCV-2 coinfection. In addition, we found DuCV-2-positive in geese firstly. In conclusion, this study provides a candidate PCR-HRM assay for the detection and accurate differentiation of DuCV-1 and DuCV-2 infection, which will help us for further epidemiological surveillance of DuCVs.

With the continuous improvement of wind power penetration in the power system, the volatility and unpredictability of wind power generation have increased the burden of system frequency regulation. With its flexible control mode and fast power adjustment speed, energy storage has obvious advantages in participating in power grid frequency regulation. Therefore, this paper studies the control strategy of wind energy storage combined with primary frequency regulation and proposes a control method of wind energy storage combined with primary frequency regulation based on the operation state of wind turbines. This paper analyzes the reserve power and rotor reserve kinetic energy of wind turbines operating in different wind speed ranges, introduces the fan frequency regulation operation state coefficient to quantify the real-time frequency regulation ability of the unit, regards the fan and energy storage system as independent frequency regulation sources, and designs the control strategy of joint frequency regulation of wind and storage in different wind speed ranges. The energy storage system is employed to participate in frequency control in the low-wind-speed range, thereby addressing the “blind spot” issue of wind turbine unit frequency control alone. In the medium- and high-wind-speed ranges, the real-time complementary output of wind and energy storage power is achieved by assigning weights based on the frequency control operation status coefficient. Finally, the effectiveness of the joint frequency modulation control strategy of wind storage in low-, medium-, and high-wind-speed regions is verified in the simulation model.

Abstract Background DNA methyltransferase 3A (DNMT3A) often mutate on arginine 882 (DNMT3AR882) in acute myeloid leukemia (AML). AML patients with DNMT3A R882 mutation are usually resistant to daunorubicin treatment; however, the associated mechanism is still unclear. Therefore, it is urgent to investigate daunorubicin resistance in AML patients with DNMT3A R882 mutant. Method AML cell lines with DNMT3A-wild type (DNMT3A-WT), and DNMT3A-Arg882His (DNMT3A-R882H) mutation were constructed to investigate the role of DNMT3A R882H mutation on cell proliferation, apoptosis and cells’ sensitivity to Danunorubin. Bioinformatics was used to analyze the role of nuclear factor-E2-related factor (NRF2) in AML patients with DNMT3A R882 mutation. The regulatory mechanism of DNMT3A R882H mutation on NRF2 was studied by Bisulfite Sequencing and CO-IP. NRF2 inhibitor Brusatol (Bru) was used to explore the role of NRF2 in AML cells carried DNMT3A R882H mutation. Results AML cells with a DNMT3A R882H mutation showed high proliferative and anti-apoptotic activities. In addition, mutant cells were less sensitive to daunorubicin and had a higher NRF2 expression compared with those in WT cells. Furthermore, the NRF2/NQO1 pathway was activated in mutant cells in response to daunorubicin treatment. DNMT3A R882H mutation regulated the expression of NRF2 via influencing protein stability rather than decreasing methylation of NRF2 promoter. Also, NRF2/NQO1 pathway inhibition improved mutant cells’ sensitivity to daunorubicin significantly. Conclusion Our findings identified NRF2 as an important player in the regulation of cell apoptosis through which helps mediate chemoresistance to daunorubicin in AML cells with DNMT3A R882H mutation. Targeting NRF2 might be a novel therapeutic approach to treat AML patients with a DNMT3A R882H mutation. Video abstract

Abstract This study proposes a primary node election method based on probabilistic linguistic term set (PLTS) for the practical Byzantine fault tolerance (PBFT) consensus mechanism to effectively enhance the efficiency of reaching consensus. Specifically, a novel concept of the probabilistic linguistic term set with a confidence interval (PLTS-CI) is presented to express the uncertain complex voting information of nodes during primary node election. Then, a novel score function based on the exponential semantic value and confidence approximation value for the PLTS-CI, called Score-ESCA, is used to solve the problems of comparing different nodes with various voting attitudes. This method helps select the node with the highest score by utilizing complex decision attitudes, making it an accurate primary node election solution. Furthermore, the feasibility of our proposed method is proved by both theoretical analysis and experimental evaluations.

Abstract Chronic diseases are one of the most severe health issues in the world, due to their terrible clinical presentations such as long onset cycle, insidious symptoms, and various complications. Recently, machine learning has become a promising technique to assist the early diagnosis of chronic diseases. However, existing works ignore the problems of feature hiding and imbalanced class distribution in chronic disease datasets. In this paper, we present a universal and efficient diagnostic framework to alleviate the above two problems for diagnosing chronic diseases timely and accurately. Specifically, we first propose a network-limited polynomial neural network (NLPNN) algorithm to efficiently capture high-level features hidden in chronic disease datasets, which is data augmentation in terms of its feature space and can also avoid over-fitting. Then, to alleviate the class imbalance problem, we further propose an attention-empowered NLPNN algorithm to improve the diagnostic accuracy for sick cases, which is also data augmentation in terms of its sample space. We evaluate the proposed framework on nine public and two real chronic disease datasets (partly with class imbalance). Extensive experiment results demonstrate that the proposed diagnostic algorithms outperform state-of-the-art machine learning algorithms, and can achieve superior performances in terms of accuracy, recall, F1, and G_mean. The proposed framework can help to diagnose chronic diseases timely and accurately at an early stage.

Background: Accumulating evidence indicated the crucial role for interleukin 6 (IL-6) signaling in the development of allergic asthma. Yet, the role of IL-6 signaling in toluene diisocyanate (TDI)-induced mixed granulocytic airway inflammation still remains unclear. Thus, the aims of this study were to dissect the role of IL-6 signaling and to evaluate the effect of tocilizumab on TDI-induced steroid-resistant asthma. Methods: TDI-induced asthma model was prepared and asthmatic mice were respectively given IL-6 monoclonal antibody, IL-6R monoclonal antibody (tocilizumab, 5 mg/kg, i.p. after each challenge) for therapeutic purposes or isotype IgG as control. Results: TDI exposure just elevated IL-6R expression in the infiltrated inflammatory cells around the airway, but increased glycoprotein 130 expression in the whole lung, especially in bronchial epithelium. Moreover, TDI inhalation increased airway hyperresponsiveness (AHR) to methacholine, coupled with mixed granulocytic inflammation, exaggerated epithelial denudation, airway smooth muscle thickening, goblet cell metaplasia, extensive submucosal collagen deposition, dysregulated Th2/Th17 responses, as well as innate immune responses and raised serum IgE. And almost all these responses except for raised serum IgE were markedly ameliorated by the administration of IL-6 neutralizing antibody or tocilizumab, but exhibited poor response to systemic steroid treatment. Also, TDI challenge induced nucleocytoplasm translocation of HMGB1 and promoted its release in the BALF, as well as elevated lung level of STAT3 phosphorylation, which were inhibited by anti-IL-6 and anti-IL-6R treatment. Conclusions: Our data suggested that IL-6 monoclonal antibody and tocilizumab might effectively abrogate TDI-induced airway inflammation and remodeling, which could be used as a clinical potential therapy for patients with severe asthma.

Pullulan has many potential applications in the food, pharmaceutical, cosmetic and environmental industries. However, the yield and molecular properties of pullulan produced by various strains still need to be promoted to fit the application needs. A novel yeast-like strain Aureobasidium pullulans BL06 producing high molecular weight (Mw) pullulan (3.3 × 106 Da) was isolated and identified in this study. The remarkable Mw of pullulan produced by A. pullulans BL06 was the highest level ever reported thus far. To further regulate the biosynthesis of pullulan in A. pullulans BL06, three gene knockout strains A. pullulans BL06 ΔPMAs, A. pullulans BL06 Δmel, and A. pullulans BL06 ΔPMAsΔmel, were constructed. The results showed that A. pullulans BL06 ΔPMAs could produce 140.2 g/L of moderate Mw (1.3 × 105 Da) pullulan after 120 h of fermentation. The highest yield level of pullulan to date could vastly reduce its production cost and expand its application scope and potential. The application experiments in food preservation showed that the moderate-Mw pullulan obtained in this work could reduce the weight loss of celery cabbages and mangos by 12.5% and 22%, respectively. Thus, the novel strains A. pullulans BL06 and A. pullulans BL06 ΔPMAs possessed unlimited development prospects in pullulan production at various Mw ranges and pullulan applications in multiple fields.

Glutamine is the most abundant amino acid in blood and tissues, and the most important nutrient except for glucose in cancer cells. Over the past years, most studies have focused on the role of Gln metabolism in supporting energy metabolism rather than maintaining oxidative homeostasis. In fact, Gln is an important factor in maintaining oxidative homeostasis of cancer cells, especially in “Glutamine addicted” cancer cells. Here, this paper will review the recent scientific literature about the link between Gln metabolism and oxidative homeostasis, with an emphasis on the potential role of Gln metabolism in different cancers. Given that oxidative homeostasis is of critical importance in cancer, understanding the impacts of a Gln metabolism on oxidative homeostasis, gaining great insights into underlying molecular mechanisms, and developing effective therapeutic strategies are of great importance.

In order to improve the effect of agricultural disaster management, this paper uses environmental big data technology to analyse historical agricultural disaster data and analyse the role and impact of this technology in agricultural disaster management. How to efficiently use the effective resources in the meteorological big data to optimise the mining effect is an important issue in the assessment of agrometeorological disasters. Parallel information entropy attribute reduction algorithm can effectively reduce the scale of research data and increase the rate of data preprocessing. The classification assessment and early warning of agrometeorological disasters involve data mining classification prediction technology. Unlike most classification prediction algorithms, KNN classification is particularly suitable for application to meteorological data sets with cross-class domains and can accelerate the classification and prediction speed of big data in the parallel processing architecture. Finally, this paper designs experiments to verify the method proposed in this paper. The results of data analysis show that the model constructed in this paper has a certain effect.

The next generation of lidar systems needs to adapt to variable environments with broadened bandwidth for increased resolution. Due to their digital components, conventional lidar systems, especially imaging lidar systems, suffer from limited detector bandwidth and sampling frequency. However, photonics devices can provide a reliable technical solution with high precision and ultra-broad bandwidth. This paper presents a photonic signal processing structure for a phase-coded lidar system. Two acousto-optic modulators (AOMs) are adopted in the proposed architecture. One is used for phase-coded laser signal modulation, and the other is used for demodulation. The echo laser signal is directed to the AOM performing demodulation before the sampling of the detector, accomplishing the multiplication of the echo laser signal and the electric reference signal. The detector is controlled to accumulate the demodulated laser signal. The AOM and detector transfer the correlation calculation from electrical signals processing to photonic signals processing. This photonics-based structure greatly decreases the sampling frequency of the detector without extending the width of the laser pulses, which achieves high resolution with low sampling speed. Photonic signal processing has the promising potential of simultaneously processing signals of multiple pixels. It is going to be an effective solution for imaging lidar systems to increase resolution with available low-cost devices.

Image dehazing has always been one of the main areas of research in image processing. The traditional dark channel prior algorithm (DCP) has some shortcomings, such as incomplete fog removal and excessively dark images. In order to obtain haze-free images with high quality, a hybrid dark channel prior (HDCP) algorithm is proposed in this paper. HDCP first employs Retinex to remove the interference of the illumination component. The variant genetic algorithm (VGA) is then used to obtain the guidance image required by the guided filter to optimize the atmospheric transmittance. Finally, the modified dark channel prior algorithm is used to obtain the dehazed image. Compared with three other modified DCP algorithms, HDCP has the best subjective visual effects of haze removal and color fidelity. HDCP also shows superior objective indexes in the mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and information entropy (E) for different haze degrees.

Classifications based on deep learning have been widely applied in the estimation of the direction of arrival (DOA) of signal. Due to the limited number of classes, the classification of DOA cannot satisfy the required prediction accuracy of signals from random azimuth in real applications. This paper presents a Centroid Optimization of deep neural network classification (CO-DNNC) to improve the estimation accuracy of DOA. CO-DNNC includes signal preprocessing, classification network, and Centroid Optimization. The DNN classification network adopts a convolutional neural network, including convolutional layers and fully connected layers. The Centroid Optimization takes the classified labels as the coordinates and calculates the azimuth of received signal according to the probabilities of the Softmax output. The experimental results show that CO-DNNC is capable of acquiring precise and accurate estimation of DOA, especially in the cases of low SNRs. In addition, CO-DNNC requires lower numbers of classes under the same condition of prediction accuracy and SNR, which reduces the complexity of the DNN network and saves training and processing time.

Robustness is a key factor for real-time positioning and navigation, especially for high-speed vehicles. While visible light positioning (VLP) based on LED illumination and image sensors is widely studied, most of the VLP systems still suffer from the high positioning latency and the image blurs caused by high-speed movements. In this paper, a robust VLP system for high-speed vehicles is proposed based on a deep learning and data-driven approach. The proposed system can significantly increase the success rate of decoding VLP-LED user identifications (UID) from blurred images and reduce the computational latency for detecting and extracting VLP-LED stripe image regions from captured images. Experimental results show that the success rate of UID decoding using the proposed BN-CNN model could be higher than 98% when that of the traditional Zbar-based decoder falls to 0, while the computational time for positioning is decreased to 9.19 ms and the supported moving speed of our scheme can achieve 38.5 km/h. Therefore, the proposed VLP system can enhance the robustness against high-speed movement and guarantee the real-time response for positioning and navigation for high-speed vehicles.

The microalga Euglena gracilis is utilized in the food, medicinal, and supplement industries. However, its mass production is currently limited by its low production efficiency and high risk of microbial contamination. In this study, physiological and biochemical parameters of E. gracilis co-cultivated with the bacteria Vibrio natriegens were investigated. A previous study reports the benefits of E. gracilis and V. natriegens co-cultivation; however, no bacterium growth and molecular mechanisms were further investigated. Our results show that this co-cultivation positively increased total chlorophyll, microalgal growth, dry weight, and storage sugar paramylon content of E. gracilis compared to the pure culture without V. natriegens. This analysis represents the first comprehensive metabolomic study of microalgae-bacterial co-cultivation, with 339 metabolites identified. This co-cultivation system was shown to have synergistic metabolic interactions between microalgal and bacterial cells, with a significant increase in methyl carbamate, ectoine, choline, methyl N-methylanthranilate, gentiatibetine, 4R-aminopentanoic acid, and glu-val compared to the cultivation of E. gracilis alone. Taken together, these results fill significant gaps in the current understanding of microalgae-bacteria co-cultivation systems and provide novel insights into potential improvements for mass production and industrial applications of E. gracilis.

In confined nanoscale structures, such as nanopores or nanotubes, the polar characteristic of water can be evident; while in bulk or droplet form, it is masked by the statistical molecular arrangements dictated by free energy minimization. In this paper, we show that, under an applied electric field, the polar character of water molecules enables the formation of macroscopic scale columns of water vapor penetrating through a silicone oil bath. These columns are up to 60 μm in length and tens of microns in cross-sectional dimension. We have directly visualized such columns by using water-soluble fluorescent molecules dispersed in silicone oil. The polar nature of columns is evidenced by the measured giant electrorheological effect that correlates with the application of an electric field. We present a phenomenological theory to support our observations and show that the experimental data are consistent with a state of highly aligned water molecular dipoles within each column.

Sparganosis is a zoonotic parasitic disease caused by the larvae (spargana) of the genus Spirometra, which is widely distributed globally and threatens human health. More than 60 species of Spirometra have already been identified, and over 2000 cases have been reported. This review summarizes the prevalence of humans, frogs, snakes, and other animals with spargana. Furthermore, the infection mode, distribution, and site are summarized and analyzed. We also describe the epidemiology, molecular diagnosis, and other aspects which are of considerable significance to preventing sparganum.

Recurrent neural networks are efficient ways of training language models, and various RNN networks have been proposed to improve performance. However, with the increase of network scales, the overfitting problem becomes more urgent. In this paper, we propose a framework-G2Basy-to speed up the training process and ease the overfitting problem. Instead of using predefined hyperparameters, we devise a gradient increasing and decreasing technique that changes the parameters training batch size and input dropout simultaneously by a user-defined step size. Together with a pretrained word embedding initialization procedure and the introduction of different optimizers at different learning rates, our framework speeds up the training process dramatically and improves performance compared with a benchmark model of the same scale. For the word embedding initialization, we propose the concept of "artificial features" to describe the characteristics of the obtained word embeddings. We experiment on two of the most often used corpora-the Penn Treebank and WikiText-2 datasets-and both outperform the benchmark results and show potential towards further improvement. Furthermore, our framework shows better results with the larger and more complicated WikiText-2 corpus than with the Penn Treebank. Compared with other state-of-the-art results, we achieve comparable results with network scales hundreds of times smaller and within fewer training epochs.

Sparganosis, caused by the plerocercoid larvae of Spirometra tapeworms, is a public health hazard worldwide. The prevalence and genetics of sparganum from snakes remain unclear. In this study, we investigated the prevalence of sparganum infection in wild snakes in Hunan province and compared the prevalence of Spirometra tapeworms in snakes worldwide. Furthermore, the genetic diversity of collected isolates was analyzed using mitochondrial cytb and cox1 genes. The result shows that the sparganum infection rate in wild snakes (89.50%, 402/449) was higher in Hunan than in other regions. Genetic diversity analysis based on concatenated sequences revealed high genetic diversity but no distinct genetic structure among Spirometra populations. Phylogenetic analysis supported the division of European and Chinese Spirometra isolates and a single species in Chinese Spirometra isolates. The prevalence of Spirometra tapeworms in snakes is serious, and the risk of sparganosis should be further publicized.

There has been substantial progress in small object detection in aerial images in recent years, due to the extensive applications and improved performances of convolutional neural networks (CNNs). Typically, traditional machine learning algorithms tend to prioritize inference speed over accuracy. Insufficient samples can cause problems for convolutional neural networks, such as instability, non-convergence, and overfitting. Additionally, detecting aerial images has inherent challenges, such as varying altitudes and illuminance situations, and blurred and dense objects, resulting in low detection accuracy. As a result, this paper adds a transformer backbone attention mechanism as a branch network, using the region-wide feature information. This paper also employs a generative model to expand the input aerial images ahead of the backbone. The respective advantages of the generative model and transformer network are incorporated. On the dataset presented in this study, the model achieves 96.77% precision, 98.83% recall, and 97.91% mAP by adding the Multi-GANs module to the one-stage detection network. These three indices are enhanced by 13.9%, 20.54%, and 10.27%, respectively, when compared to the other detection networks. Furthermore, this study provides an auto-pruning technique that may achieve 32.2 FPS inference speed with a minor performance loss while responding to the real-time detection task’s usage environment. This research also develops a macOS application for the proposed algorithm using Swift development technology.

Aerosol optical depth (AOD) is an important atmospheric correction parameter in remote sensing. In order to obtain AOD accurately, the surface-based automatic sun photometer needs to carry out calibration regularly. The normally used Langley method can be effective only when the AOD and the calibration coefficients of the instrument remain unchanged throughout the day. However, when observing the AOD with CE318 sun photometer in field environment, it was found that the AOD of silicon (Si) detector at 1020 nm and indium gallium arsenide (InGaAs) detector at 1639 nm was strongly influenced by temperature due to the large temperature difference at the Dunhuang site. Based on the corresponding relationship between AOD and wavelength, the model of the calibration coefficients varying with temperature was established by nonlinear regression method in field environment. By comparing the AOD before and after temperature correction with the theoretical one, the ratio of data with relative error (RE) less than 5% increased from 0.195 and 0.14 to 0.894 and 0.355, respectively. By this method, calibration can be carried out without the limit of constant AOD. In addition, it is simpler, more convenient, and less costly to perform temperature correction in a field environment than in a laboratory.

In view of the actual problems existing in life-cycle health monitoring and diagnosis of large complex equipment, the machine-learning algorithm is applied to data mining of the equipment operation big data, the expert knowledge base is established, the diagnosis rules related to the fault are obtained, the intelligent online monitoring and remote diagnosis of the equipment health condition are realised. The system uses uncertain fault prediction method and hybrid intelligent algorithm to discover the hierarchical association between operation feature big data and operation faults, the feature extraction of operation faults, and the intelligent diagnosis of operation faults. It effectively improved the sensitivity, robustness, and accuracy of monitoring and diagnosis. In the cloud service platform based on the Internet of things, the system realises the intelligent fault prediction and diagnosis, establishes a proactive maintenance system, improves the production efficiency, and ensures the production safety.

Membrane fusion is one of the most fundamental physiological processes in eukaryotes for triggering the fusion of lipid and content, as well as the neurotransmission. However, the architecture features of neurotransmitter release machinery and interdependent mechanism of synaptic membrane fusion have not been extensively studied. This review article expounds the neuronal membrane fusion processes, discusses the fundamental steps in all fusion reactions (membrane aggregation, membrane association, lipid rearrangement and lipid and content mixing) and the probable mechanism coupling to the delivery of neurotransmitters. Subsequently, this work summarizes the research on the fusion process in synaptic transmission, using electron microscopy (EM) and molecular simulation approaches. Finally, we propose the future outlook for more exciting applications of membrane fusion involved in synaptic transmission, with the aid of stochastic optical reconstruction microscopy (STORM), cryo-EM (cryo-EM), and molecular simulations.

Ophiopogonis Radix, also known as Mai-dong in Chinese, was a commonly used traditional Chinese medicine (TCM) and functional health food. Two products of Ophiopogonis Radix are largely produced in the Sichuan and Zhejiang province, which are called “Chuan maidong (CMD)” and “Zhe maidong (ZMD)” respectively. To distinguish and evaluate the quality of CMD and ZMD, an analytical method based on ultra-fast performance liquid chromatography coupled with triple quadrupole-linear ion trap mass spectrometry (UFLC-QTRAP-MS/MS) was established for simultaneous determination of 32 constituents including 4 steroidal saponins, 3 homisoflavonoids, 15 amino acids, and 10 nucleosides in 27 Mai-dong samples from Sichuan and Zhejiang. Furthermore, principal components analysis (PCA), partial least squares discriminant analysis (PLS-DA), t-test, and grey relational analysis (GRA) were applied to discriminate and evaluate the samples from Sichuan and Zhejiang based on the contents of 32 constituents. The results demonstrated that the bioactive constituents in CMD and ZMD were significantly different, and CMD performed better in the quality assessment than ZMD. This study not only provides a basic information for differentiating CMD and ZMD, but offers a new insight into comprehensive evaluation and quality control of Ophiopogonis Radix from two different producing areas.

Schisandrae Chinensis Fructus, also called wuweizi in China, was a widely used folk medicine in China, Korea, and Russia. Due to the limited natural resources and huge demand of wuweizi, people tend to cultivate wuweizi to protect this species. However, the quality of wild and cultivated herbs of the same species may change. Little attention has been paid to comparing wild and cultivated wuweizi based on simultaneous determination of its active components, such as lignans and organic acids. An analytical method based on UFLC-QTRAP-MS/MS was used for the simultaneous determination of 15 components, including 11 lignans (schisandrin, gomisin D, gomisin J, schisandrol B, angeloylgomisin H, schizantherin B, schisanhenol, deoxyschizandrin, γ-schisandrin, schizandrin C, and schisantherin) and 4 organic acids (quinic acid, d(−)-tartaric acid, l-(−)-malic acid, and protocatechuic acid) in wuweizi under different ecological environments. Principal components analysis (PCA), partial least squares discrimination analysis (PLS-DA), independent sample t-test, and gray relational analysis (GRA) have been applied to classify and evaluate samples from different ecological environments according to the content of 15 components. The results showed that the differential compounds (i.e., quinic acid, l-(−)-malic acid, protocatechuic acid, schisandrol B) were significantly related to the classification of wild and cultivated wuweizi. GRA results demonstrated that the quality of cultivated wuweizi was not as good as wild wuweizi. The protocol not just provided a new method for the comprehensive evaluation and quality control of wild and cultivated wuweizi, but paved the way to differentiate them at the chemistry level.

This study investigated the relationship between perfectionism and relative deprivation among nursing students in China as well as the mediating role of interpersonal sensitivity and the moderating role of resilience in this relationship. A cross-sectional study design involved 682 nursing students participants from two universities in Henan Province, China, using questionnaires about demographics, perfectionism, relative deprivation, interpersonal sensitivity, and resilience. Descriptive statistics, Pearson's correlation analysis and the PROCESS macro in regression analysis were used for data analysis. Adaptive perfectionism was not significantly correlated with relative deprivation, while maladaptive perfectionism was significantly positively correlated with interpersonal sensitivity, relative deprivation, and resilience. Interpersonal sensitivity mediated the relationship between maladaptive perfectionism and relative deprivation, accounting for 41.07% of the total effect. Moreover, the first half path and direct path of this process were moderated by resilience. These results show that targeted interventions to reduce maladaptive perfectionism, reduce interpersonal sensitivity and improve resilience of nursing students could help reduce their relative deprivation.

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.

Eucommia ulmoides Oilv. (EU), also called Du-zhong, is a classical traditional Chinese medicine. Its bark, leaf, and male flower are all used for medicinal purposes, called Eucommiae Cortex (EC), Eucommiae Folium (EF), and Eucommiae Flos Male (EFM). In order to study the difference in synthesis and the accumulation of metabolites in different parts of EU, a reliable method based on ultra-fast liquid chromatography tandem triple quadrupole mass spectrometry (UFLC-QTRAP-MS/MS) was developed for the simultaneous determination of a total of 21 constituents, including two lignans, 6 iridoids, 6 penylpropanoids, 6 flavonoids, and one phenol in the samples (EC, EF, and EFM). Furthermore, principal component analysis (PCA) was performed to evaluate and classify the samples according to the contents of these 21 constituents. All of the results demonstrated that the chemical compositions in EC, EF, and EFM were significantly different and the differential constituents (i.e., aucubin, geniposidic acid, chlorogenic acid, pinoresinol-di-O-β-d-glucopyranoside, geniposide, cryptochlorogenic acid, rutin, and quercetin) were remarkably associated with sample classifications. The research will provide the basic information for revealing the laws of metabolite accumulation in EC, EF, and EFM from the same origin.