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Abstract Sprint interval training (SIT) is a potent exercise strategy to enhance athletes’ anaerobic capacity in a time-efficient manner. This study aimed to investigate the impact of low-volume, court-based SIT on the anaerobic capacity and sport-specific performance in competitive tennis players. Twenty-four competitive collegiate tennis players were randomly assigned to either the SIT group (n = 12; three sessions per week of court-based repeated-sprint training) or the traditional endurance training (ET) group (n = 12; three sessions per week of 45-min continuous treadmill running, n = 12) for a 6-weeks intervention. Baseline and post-intervention assessments included the Wingate Anaerobic Test, elimination rate of blood lactate (BLAer), tennis-specific repeated sprint ability (RSA), and the Yo-Yo Intermittent Recovery Test Level 2 (YoYo-IR2). The results showed that SIT group demonstrated significant improvements in peak and average power during the Wingate test (p = 0.07; p

Accurate estimation of turbulence field is of great importance in aerospace, and existing means of obtaining turbulence field is inadequate in terms of resolution or accuracy. Experimental measurements are accurate but often have a limited number of observation points, and numerical computations can obtain full-field data but the accuracy is difficult to guarantee. The data assimilation method integrates experimental observation and numerical simulation, which is an effective tool for flow field reconstruction. This paper explored the effectiveness of data assimilation method based on ensemble transform Kalman filter(ETKF) in spatial flow field reconstruction, and also discussed the reconstruction accuracy and computational efficiency of different iterative updates, namely ETKF-M and ETKF-D, which update state variables based on turbulence model and flow field data respectively. Using the ONERA M6 airfoil as a numerical example, the algorithm was experimented by combining the pressure measurement data from 271 pressure holes on the airfoil surface from the wind tunnel test. The results show that different iterative updates of ETKF method can effectively modify the prediction of the turbulence model, and the ETKF-D improves the computational efficiency by 83% compared with the ETKF-M. In addition, two groups of 1/4 experimental observation points at different locations were selected for assimilation experiments, and results with different accuracies were obtained. It indicates that the accuracy of reconstruction is closely related to the location and number of assimilated observation points.

Abstract Bacteria-mediated cancer therapeutic strategies have attracted increasing interest due to their intrinsic tumor tropism. However, bacteria-based drugs face several challenges including the large size of bacteria and dense extracellular matrix, limiting their intratumoral delivery efficiency. In this study, we find that hyperbaric oxygen (HBO), a noninvasive therapeutic method, can effectively deplete the dense extracellular matrix and thus enhance the bacterial accumulation within tumors. Inspired by this finding, we modify Escherichia coli Nissle 1917 (EcN) with cypate molecules to yield EcN-cypate for photothermal therapy, which can subsequently induce immunogenic cell death (ICD). Importantly, HBO treatment significantly increases the intratumoral accumulation of EcN-cypate and facilitates the intratumoral infiltration of immune cells to realize desirable tumor eradication through photothermal therapy and ICD-induced immunotherapy. Our work provides a facile and noninvasive strategy to enhance the intratumoral delivery efficiency of natural/engineered bacteria, and may promote the clinical translation of bacteria-mediated synergistic cancer therapy.

Abstract Efficient thrombolysis in time is crucial for prognostic improvement of patients with acute arterial thromboembolic disease, while limitations and complications still exist in conventional thrombolytic treatment methods. Herein, our study sought to investigate a novel dual-mode strategy that integrated ultrasound (US) and near-infrared light (NIR) with establishment of hollow mesoporous silica nanoprobe (HMSN) which contains Arginine-glycine-aspartate (RGD) peptide (thrombus targeting), perfluoropentane (PFP) (thrombolysis with phase-change and stable cavitation) and indocyanine green (ICG) (thrombolysis with photothermal conversion). HMSN is used as the carrier, the surface is coupled with targeted RGD to achieve high targeting and permeability of thrombus, PFP and ICG are loaded to achieve the collaborative diagnosis and treatment of thrombus by US and NIR, so as to provide a new strategy for the integration of diagnosis and treatment of arterial thrombus. From the in vitro and in vivo evaluation, RGD/ICG/PFP@HMSN can aggregate and penetrate at the site of thrombus, and finally establish the dual-mode directional development and thrombolytic treatment under the synergistic effect of US and NIR, providing strong technical support for the accurate diagnosis and treatment of arterial thrombosis. Graphical Abstract

Maternal exposure to nanoparticles during gestation poses potential risks to fetal development. The placenta, serving as a vital interface for maternal-fetal interaction, plays a pivotal role in shielding the fetus from direct nanoparticle exposure. However, the impact of nanoparticles on placental function is still poorly understood, primarily due to the absence of proper human placental models. In this study, we established a placenta-on-a-chip model capable of recapitulating nanoparticle exposure to assess potential nanotoxicity. The model was assembled by coculturing human trophoblast stem cells (hTSCs) and endothelial cells within a dynamic microsystem. hTSCs exhibited progressive differentiation into syncytiotrophoblasts under continuous fluid flow, forming a bilayered trophoblastic epithelium that mimicking both structural and functional aspects of human placental villi. Copper oxide nanoparticles (CuO NPs) were introduced into the trophoblastic side to simulate maternal blood exposure. Our findings revealed that CuO NPs hindered hTSCs differentiation, leading to diminished hormone secretion and impaired glucose transport. Subsequent analysis indicated that CuO NPs disrupted the autophagic flux in trophoblasts and induced apoptosis. Furthermore, the placenta-on-a-chip model exhibited inflammatory responses to CuO NP exposure, including maternal macrophage activation, inflammatory cytokine secretion, and endothelial barrier disruption. Dysfunction of the placental barrier and the ensuing inflammatory cascades may contribute to aberrant fetal development. Overall, our placenta-on-a-chip model offers a promising platform for assessing nanoparticle exposure-related risks and conducting toxicology studies.

BackgroundAdolescent tennis players encounter critical physical demands, but the lack of comprehensive analysis of training types hampers the selection of optimal training programs. This study aims to conduct a systematic literature review to analyze the effectiveness and limitations of various training types on the physical demands of adolescent tennis players, summarizing the optimal training methods to enhance these physical qualities.MethodsFrom March 2024, a comprehensive search was conducted across four electronic databases: SCOPUS, PubMed, EBSCOhost (SPORTDiscus), and Web of Science. Additionally, Google Scholar and other sources of gray literature were referenced. Original research articles with an experimental design were included. The methodological quality of the included studies was evaluated using the Physiotherapy Evidence Database (PEDro) scale, and the overall scientific evidence was determined through the best evidence synthesis (BES).ResultsEighteen articles on exercise training met all inclusion criteria and were included in this systematic review. These studies maintained a high standard of quality, making their findings relatively credible. Among them, five studies investigated plyometric training, five focused on neuromuscular training, three explored functional training, two examined traditional strength training, and three assessed High-Intensity Interval Training.ConclusionTo enhance speed, strength, power, agility, and dynamic balance, it is recommended to prioritize plyometric training, neuromuscular training, and functional training over traditional tennis training. Functional training is particularly effective for improving flexibility and balance, while plyometric training is more suited for increasing power and speed. Neuromuscular training, when performed before routine workouts, is beneficial for enhancing speed, flexibility, and strength. Hard surface training is ideal for boosting power, whereas sand training excels in improving strength, speed, and balance. Combining HIIT with strength training is especially advantageous for enhancing short-distance sprinting, repeated sprint ability, and power. By appropriately combining and utilizing these training methods, the physical capabilities and sports performance of adolescent tennis players can be comprehensively optimized.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42024578147.

Abstract Background During the pandemic, a notable increase in diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS), conditions that warrant emergent management, was reported. We aimed to investigate the trend of DKA‐ and HHS‐related mortality and excess deaths during the pandemic. Methods Annual age‐standardized mortality rates related to DKA and HHS between 2006 and 2021 were estimated using a nationwide database. Forecast analyses based on prepandemic data were conducted to predict the mortality rates during the pandemic. Excess mortality rates were calculated by comparing the observed versus predicted mortality rates. Subgroup analyses of demographic factors were performed. Results There were 71 575 DKA‐related deaths and 8618 HHS‐related deaths documented during 2006–2021. DKA, which showed a steady increase before the pandemic, demonstrated a pronounced excess mortality during the pandemic (36.91% in 2020 and 46.58% in 2021) with an annual percentage change (APC) of 29.4% (95% CI: 16.0%–44.0%). Although HHS incurred a downward trend during 2006–2019, the excess deaths in 2020 (40.60%) and 2021 (56.64%) were profound. Pediatric decedents exhibited the highest excess mortality. More than half of the excess deaths due to DKA were coronavirus disease 2019 (COVID‐19) related (51.3% in 2020 and 63.4% in 2021), whereas only less than a quarter of excess deaths due to HHS were COVID‐19 related. A widened racial/ethnic disparity was observed, and females exhibited higher excess mortality than males. Conclusions The DKA‐ and HHS‐related excess mortality during the pandemic and relevant disparities emphasize the urgent need for targeted strategies to mitigate the escalated risk in these populations during public health crises.

Summary: Background: Drug overdose is an escalating public health crisis in the United States (U.S.). This study evaluated the temporal trend of drug overdose-related deaths in the U.S., with an emphasis on identifying disparities across subpopulations and the contributing drugs. Methods: Using the nationwide death dataset from the National Vital Statistics System (NVSS), we estimated the drug overdose-related age-standardized mortality rate (ASMR) and temporal trends for individuals aged 12 and older from 2012 to 2022. Excess mortality during the Coronavirus disease 2019 (COVID-19) pandemic was evaluated based on the pre-pandemic trends using predictive modeling analysis. Findings: Among 809,967 overdose-related deaths during 2012–2022, ASMR increased by 8.9% [95% confidence interval (CI): 6.0%–11.9%] per year from 2012 to 2019 and increased to 12.9% (95% CI: 2.1%–24.8%) from 2019 to 2022, with the excess ASMR of 16.9% in 2020, increased to 26.4% in 2021 and then decreased to 19.3% in 2022. Significant age, sex, racial/ethnic and geographic disparities were demonstrated, with adolescents (annual percentage change [APC]:21.6%) and males (APC:13.6%) having the most pronounced increase during the pandemic. Ethnic minorities especially the non-Hispanic American Indian/Alaska Native experienced the highest excess ASMR (33.1% in 2020). Illicit fentanyl and synthetics starting with the lowest ASMR in 2012 (1.0 per 100,000), marked the most dramatic increase and became the leading cause of overdose-related death since 2016 (7.5 per 100,000), leading to the highest ASMR by 2022 (27.4 per 100,000). All drug types except heroin experienced varying degrees of excess ASMRs, with prescription opioid pain relievers (23.5%–55.1%), benzodiazepines (27.4%–40.9%) and antidepressants (10.4%–17.8%) exhibiting consistent increases from 2020 to 2022, while the excess ASMRs for illicit fentanyl and synthetics (25.3%–10.0%), psychostimulants (32.8%–14.6%) and methadone (35.0%–33.3%) decreased between 2021 and 2022. Interpretation: Overdose-related mortality is increasing at an alarming rate, and the stark differences point to the need for targeted interventions to reduce the burden of drug overdose deaths. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Abstract Objectives Our aim was to assess the trend in gynaecologic cancer (GC) mortality in the period from 2010 to 2022 in the United States, with focus on the impact of the pandemic on increased deaths. Methods GC mortality data were extracted from the Center for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) platform. We analysed mortality trends and evaluated observed vs. predicted mortality for the period from 2020 to 2022 with joinpoint regression and prediction modelling analyses. Results A total of 334,382 deaths among adults aged 25 years and older with gynaecologic cancer were documented from 2010 to 2022. The overall age-standardised mortality rate (ASMR, per 100,000 persons) for ovarian cancer-related death decreased gradually from 7.189 in 2010 to 5.517 in 2019, yielding an APC (annual percentage change) of -2.8%. However, the decrease in ovarian cancer-related mortality slowed down by more than 4-fold during the pandemic. Cervical cancer -related mortality decreased slightly prior to the pandemic and increased during the pandemic with an APC of 0.6%, resulting in excess mortality of 4.92%, 9.73% and 2.03% in 2020, 2021 and 2022, respectively. For uterine corpus cancer, the ASMR increased from 1.905 in 2010 to 2.787 in 2019, and increased sharply to 3.079 in 2021 and 3.211 in 2022. The ASMR rose steadily between 2013 and 2022, yielding an APC of 6.9%. Conclusions Overall, we found that GC-related mortality increased during the COVID-19 pandemic, and this increase was not specific to age, race, or ethnicity.

Abstract Salivary effectors of piercing-sucking insects can suppress plant defense to promote insect feeding, but it remains largely elusive how they facilitate plant virus transmission. Leafhopper Nephotettix cincticeps transmits important rice reovirus via virus-packaging exosomes released from salivary glands and then entering the rice phloem. Here, we report that intact salivary vitellogenin of N. cincticeps (NcVg) is associated with the GTPase Rab5 of N. cincticeps (NcRab5) for release from salivary glands. In virus-infected salivary glands, NcVg is upregulated and packaged into exosomes mediated by virus-induced NcRab5, subsequently entering the rice phloem. The released NcVg inherently suppresses H2O2 burst of rice plants by interacting with rice glutathione S-transferase F12, an enzyme catalyzing glutathione-dependent oxidation, thus facilitating leafhoppers feeding. When leafhoppers transmit virus, virus-upregulated NcVg thus promotes leafhoppers feeding and enhances viral transmission. Taken together, the findings provide evidence that viruses exploit insect exosomes to deliver virus-hijacked effectors for efficient transmission.

To address the problem of autonomous lane-changing maneuvers for automated vehicle platoons on highways, a novel platoon lane-changing (PLC) tracking control framework based on the multi-agent Deep Deterministic Policy Gradient (MA-DDPG) and adaptive model predictive control (AMPC) is presented. Currently, the classic platoon cooperative control method is complex in structure and necessitates the establishment of an accurate vehicle dynamics model, so a fully decentralized MA-DDPG algorithm is proposed to realize the longitudinal following control, handling nonlinear systems and continuous state space. The agents associated with each following vehicle stay in communication via the predecessor-leader following (PLF) or predecessor following (PF) communication topology, training locally. Secondly, the traditional MPC is prone to large fluctuations in the early stage of solving and its tracking performance deteriorates with varied longitudinal speed. Therefore, an AMPC controller whose prediction time domain changes with the longitudinal speed is combined with the quintic polynomial curve to complete the lateral control, and a fuzzy controller is introduced for compensation of front wheel steering angles. The results of the joint Carsim/Simulink simulation demonstrate that the AMPC controller can achieve lateral tracking control under working conditions of 50 km/h, 100 km/h, and variable speed, and the front wheel steering angles are also stable. The MA-DDPG algorithm can achieve effective following control when the longitudinal expected speed is 25 m/s. The framework proposed in this study can both track effectively and smooth the longitudinal velocity profile compared with algorithm combinations 1: CACC with AMPC and 2: CACC with PID.

Abstract The tumour‐targeting efficiency of systemically delivered chemodrugs largely dictates the therapeutic outcome of anticancer treatment. Major challenges lie in the complexity of diverse biological barriers that drug delivery systems must hierarchically overcome to reach their cellular/subcellular targets. Herein, an “all‐in‐one” red blood cell (RBC)‐derived microrobot that can hierarchically adapt to five critical stages during systemic drug delivery, that is, circulation, accumulation, release, extravasation, and penetration, is developed. The microrobots behave like natural RBCs in blood circulation, due to their almost identical surface properties, but can be magnetically manipulated to accumulate at regions of interest such as tumours. Next, the microrobots are “immolated” under laser irradiation to release their therapeutic cargoes and, by generating heat, to enhance drug extravasation through vascular barriers. As a coloaded agent, pirfenidone (PFD) can inhibit the formation of extracellular matrix and increase the penetration depth of chemodrugs in the solid tumour. It is demonstrated that this system effectively suppresses both primary and metastatic tumours in mouse models without evident side effects, and may represent a new class of intelligent biomimicking robots for biomedical applications.

The interaction between influenza A virus (IAV) and host proteins is an important process that greatly influences viral replication and pathogenicity. PB2 protein is a subunit of viral ribonucleoprotein (vRNP) complex playing distinct roles in viral transcription and replication. BAG6 (BCL2-associated athanogene 6) as a multifunctional host protein participates in physiological and pathological processes. Here, we identify BAG6 as a new restriction factor for IAV replication through targeting PB2. For both avian and human influenza viruses, overexpression of BAG6 reduced viral protein expression and virus titers, whereas deletion of BAG6 significantly enhanced virus replication. Moreover, BAG6-knockdown mice developed more severe clinical symptoms and higher viral loads upon IAV infection. Mechanistically, BAG6 restricted IAV transcription and replication by inhibiting the activity of viral RNA-dependent RNA polymerase (RdRp). The co-immunoprecipitation assays showed BAG6 specifically interacted with the N-terminus of PB2 and competed with PB1 for RdRp complex assembly. The ubiquitination assay indicated that BAG6 promoted PB2 ubiquitination at K189 residue and targeted PB2 for K48-linked ubiquitination degradation. The antiviral effect of BAG6 necessitated its N-terminal region containing a ubiquitin-like (UBL) domain (17-92aa) and a PB2-binding domain (124-186aa), which are synergistically responsible for viral polymerase subunit PB2 degradation and perturbing RdRp complex assembly. These findings unravel a novel antiviral mechanism via the interaction of viral PB2 and host protein BAG6 during avian or human influenza virus infection and highlight a potential application of BAG6 for antiviral drug development.

In the present study, we demonstrated that the presence of cysteine could remarkably enhance the degradation of atrazine by Fe3O4/persulfate system. The results of electron paramagnetic resonance (EPR) spectra confirmed the combination of cysteine and Fe3O4 exhibited much higher activity on activation of persulfate to generate more SO4•- and •OH than Fe3O4 alone. At pH of 3.0, SO4•- and •OH contributed to about 58.2 % and 41.8 % of atrazine removal respectively, while •OH gradually dominated the oxidation of atrazine from neutral condition to alkaline condition. The co-existing Cl− and HCO3− could quench SO4•-, resulting in the inhibition of atrazine degradation. The presence of low natural organic matters (NOM) concentration (0–2 mg L−1) could enhance the atrazine removal, and high concentration (>5 mg L−1) of NOM restrained the atrazine degradation. During the Cysteine/Fe3O4/Persulfate process, cysteine served as a complexing reagent and reductant. Through acidolysis and complexation, Fe3O4 could release dissolved and surface bound Fe2+, both of which contributed to the activation of persulfate together. Meanwhile, cysteine was not rapidly consumed due to a regeneration process, which was beneficial for maintaining Fe2+/Fe3+ cycle and constantly accelerating the activation of persulfate for atrazine degradation. The reused Fe3O4 and cysteine in the Cysteine/Fe3O4/Persulfate process exhibited high stability for the atrazine degradation after three cycles. The degradation pathway of atrazine included alkylic-oxidation, dealkylation, dechlorination-hydroxylation processes. The present study indicates the novel Cysteine/Fe3O4/Persulfate process might be a high potential for treatment of organic polluted water.

Hemorrhagic stroke (HS) is associated with severe morbidity and high mortality. Identifying the trigger factors for HS is critical for disease prevention. This study aimed to assess the associations between short-term environmental triggers (STETs) and HS risk. We systematically searched six databases for articles published up to September 9, 2022. Pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using random-effect models to evaluate the associations between STETs and the risk of HS. Heterogeneity was assessed using Cochran Q and I2 tests. A total of 63 studies were included for analysis. Of these, 40 focused on air pollutants and 23 on meteorological factors. Pooling results showed that exposure to particulate matter 2.5 (PM2.5; OR, 1.003 per 10 μg/m3; 95% CI, 1.001–1.007), sulfur dioxide (SO2; OR, 1.022 per 10 ppb; 95% CI, 1.005–1.040), and nitrogen dioxide (NO2; OR, 1.026 per 10 ppb; 95% CI, 1.004–1.047) was associated with an increase in HS risk. Moreover, exposure to PM2.5 (OR, 1.018 per 10 μg/m3; 95% CI, 1.009–1.027) and SO2 (OR, 1.102 per 10 ppb; 95% CI, 1.010–1.204) was positively associated with the risk of intracerebral hemorrhage. In addition, extreme temperature, high pressures, high and low relative humidity were potentially associated with HS risk. Targeted preventive measures to limit the effect of these air pollutants and extreme meteorological factors should be taken to reduce the HS disease burden. Further studies are warranted to verify these findings.

BackgroundsDiabetes nephropathy (DN) is a growing public health concern worldwide. Renal dysfunction impairment in DN is intimately linked to ER stress and its related signaling pathways. Nonetheless, the underlying mechanism and biomarkers for this function of ER stress in the DN remain unknown.MethodsMicroarray datasets were retrieved from the Gene Expression Omnibus (GEO) database, and ER stress-related genes (ERSRGs) were downloaded from the MSigDB and GeneCards database. We identified hub ERSRGs for DN progression by intersecting ERSRGs with differentially expressed genes and significant genes in WGCNA, followed by a functional analysis. After analyzing hub ERSRGs with three machine learning techniques and taking the intersection, we did external validation as well as developed a DN diagnostic model based on the characteristic genes. Immune infiltration was performed using CIBERSORT. Moreover, patients with DN were then categorized using a consensus clustering approach. Eventually, the candidate ERSRGs-specific small-molecule compounds were defined by CMap.ResultsSeveral biological pathways driving pathological injury of DN and disordered levels of immune infiltration were revealed in the DN microarray datasets and strongly related to deregulated ERSRGs by bioinformatics multi-chip integration. Moreover, CDKN1B, EGR1, FKBP5, GDF15, and MARCKS were identified as ER stress signature genes associated with DN by machine learning algorithms, demonstrating their potential as DN biomarkers.ConclusionsOur research sheds fresh light on the function of ER stress in DN pathophysiology and the development of early diagnostic and ER stress-related treatment targets in patients with DN.

Context: As the increasing number of pet canines, the identification of canine has attracted much attentions in the forensic field, however, the genetic diversities of pet canines still remained unknown. Aims: To explore genetic polymorphisms of 19 short tandem repeat (STR) loci and genetic relationships between the two studied canine groups and reference group. Subjects and Methods: In the present study, genetic polymorphisms of 19 STR loci and a sex-linked zinc finger locus were analyzed in a total of 594 canines in Standard Poodle and Bichon Frise groups from China. Results: A total of 166, 159 alleles were observed in the Standard Poodle, Bichon Frise groups with the corresponding allelic frequencies ranging from 0.0030–0.6108 to 0.0012–0.6148, respectively. The combined discrimination power and probability of exclusion of 19 STR loci in Standard Poodle and Bichon Frise groups were 0.9999999999999497, 0.999962884; and 0.99999999999999995, 0.999965955, respectively. Furthermore, the genetic distances between the two canine groups and Labrador retriever group were calculated, and the results indicated that Standard Poodle and Bichon Frise groups showed a closer genetic relationship, while the two canine groups had distant genetic relationships with Labrador retriever group. The result of population genetic structure revealed that genetic component distributions in the three canine groups were different. The predicted accuracies of the constructed random forest prediction model for three validation sets (25% individuals randomly selected from three populations with 808 individuals) were higher than 0.9, especially for the individuals in validation set from the Bichon Frise group is 1. Conclusions: The 19 STR loci could be used for individual identification, canine breed identification and paternity testing in the two canine groups.

Abstract Non-invasive and cost-effective diagnosis of gastric cancer is essential to improve outcomes. Aim of the study was to establish a neural network model based on patient demographic data and serum biomarker panels to aid gastric cancer diagnosis. A total of 295 patients hospitalized in Nanjing Drum Tower hospital diagnosed with gastric cancer based on tissue biopsy, and 423 healthy volunteers were included in the study. Demographical information and tumor biomarkers were obtained from Hospital Information System (HIS) as original data. Pearson's correlation analysis was applied on 574 individuals’ data (training set, 229 patients and 345 healthy volunteers) to analyze the relationship between each variable and the final diagnostic result. And independent sample t test was used to detect the differences of the variables. Finally, a neural network model based on 14 relevant variables was constructed. The model was tested on the validation set (144 individuals including 66 patients and 78 healthy volunteers). The predictive ability of the proposed model was compared with other common machine learning models including logistic regression and random forest. Tumor markers contributing significantly to gastric cancer screening included CA199, CA125, AFP, and CA242 were identified, which might be considered as important inspection items for gastric cancer screening. The accuracy of the model on validation set was 86.8% and the F1-score was 85.0%, which were better than the performance of other models under the same condition. A non-invasive and low-cost artificial neural network model was developed and proved to be a valuable tool to assist gastric cancer diagnosis.

Autologous tumor cell vaccines can elicit anti-tumor immune responses. Here, the authors report the design of dendritic polymer-based nanoparticles loaded with doxorubicin and the tyrosine kinase inhibitor apatinib that can induce immunogenic cell death and the in situ production of immuno-stimulatory micrometer-sized vesicles, promoting anti-tumor immune responses in preclinical models.

The interaction between migratory birds and domestic waterfowl facilitates viral co-infections, leading to viral reassortment and the emergence of novel viruses. In 2022, samples were collected from duck farms around Poyang Lake in Jiangxi Province, China, which is located within the East Asia–Australasia flyway. Three strains of H4N6 avian influenza virus (AIV) were isolated. Genetic and phylogenetic analyses showed that the isolated H4N6 avian influenza viruses (AIVs) belonged to new genotypes, G23 and G24. All isolated strains demonstrated dual receptor binding properties. Additionally, the isolated strains were able to replicate efficiently not only in avian cells but also in mammalian cells. Furthermore, the H4N6 AIV isolates could infect chickens, with viral replication detected in the lungs and extrapulmonary organs, and could transmit within chicken flocks through contact, with viral shedding detected only in oropharyngeal swabs from chickens in the contact group. Notably, the H4N6 AIV could infect mice without prior adaptation and replicate in the lungs with high viral titers, suggesting that it is a potential threat to humans. In conclusion, this study provides valuable insight into the characteristics of H4N6 strains currently circulating in China.

Due to their working environments, limited resources and communication characteristics, wireless sensor networks face some challenges including energy optimization and security enhancement to extend the network lifetime and guarantee the network security. Therefore, an energy-aware and trust-based routing protocol for wireless sensor networks using adaptive genetic algorithm called TAGA is proposed to not only resist common routing attacks and special trust attacks, but also minimize the energy consumption caused by data transmission. To this end, TAGA constructs the nodes’ comprehensive trust values based on their direct trust values considering the volatilization and adaptive penalty factors, and indirect trust values with the filtering mechanisms. In addition, a novel threshold function is presented to select the optimal cluster heads, which considers the dynamic changes of the nodes’ comprehensive trust values and residual energy. Finally, a genetic algorithm with adaptive crossover probability and mutation probability is applied to find the optimal secure routing for the cluster heads. The simulation results show that TAGA can reduce the number of packets discarded by malicious nodes when facing common attacks and special trust attacks, and effectively improve the energy efficiency compared to the relative secure routing protocols EOSR and IASR.

As a naturally occurring cytolytic peptide, melittin (Mel) has strong cytolytic activity and is a potent therapeutic peptide for cancer therapy. However, the serious hemolytic activity of Mel largely impedes its clinical applications. In this work, based on the strong interactions between proteins/peptides and polyphenols, we develop a tannic acid–Fe3+ metal–phenolic network (MPN)-based strategy that can convert Mel from foe to friend via shielding its positive charges and reducing its hemolytic activity. Besides, an immune adjuvant resiquimod (R848) is also introduced for immunostimulation, affording the final Mel- and R848-coloaded nanodrug. The Mel-caused membrane disruption can induce immunogenic cell death for immunostimulation, R848 can act as an immune adjuvant to further facilitate the immunostimulatory effect, and the tannic acid–Fe3+ MPN-mediated Fenton reaction can produce reactive oxygen species for cancer treatment. Further experiments reveal that the nanodrug can effectively cause immunogenic cell death of tumor cells and arouse robust intratumoral and systemic antitumor immunostimulation. In the bilateral tumor-bearing mouse models, the nanodrug considerably destroys the primary tumor and also boosts the abscopal effect to ablate the distant tumor. Collectively, the MPN-facilitated “foe-to-friend” strategy may promote the practical applications of Mel and foster the development of cancer immunotherapeutics.

BackgroundThe intestinal tract serves as a critical regulator for nutrient absorption and overall health. However, its involvement in anti-parasitic infection and immunity has been largely neglected, especially when a parasite is not transmitted orally. The present study investigated the colonic histopathology and functional reprogramming in mice with intraperitoneal infection of the larval Echinococcus granulosus (E. granulosus).ResultsCompared with the control group, the E. granulosus–infected mice exhibited deteriorated secreted mucus, shortened length, decreased expression of tight junction proteins zonula occludens-1 (ZO-1), and occludin in the colon. Moreover, RNA sequencing was employed to characterize colonic gene expression after infection. In total, 3,019 differentially expressed genes (1,346 upregulated and 1,673 downregulated genes) were identified in the colon of infected mice. KEGG pathway and GO enrichment analysis revealed that differentially expressed genes involved in intestinal immune responses, infectious disease-associated pathways, metabolism, or focal adhesion were significantly enriched. Among these, 18 tight junction-relative genes, 44 immune response-associated genes, and 23 metabolic genes were annotated. Furthermore, mebendazole treatment could reverse the colonic histopathology induced by E. granulosus infection.ConclusionsIntraperitoneal infection with E. granulosus induced the pathological changes and functional reprogramming in the colon of mice, and mebendazole administration alleviated above alternations, highlighting the significance of the colon as a protective barrier against parasitic infection. The findings provide a novel perspective on host-parasite interplay and propose intestine as a possible target for treating parasitic diseases that are not transmitted orally.

In this paper, the whole genome of the multidrug-resistant Aeromonas hydrophila MX16A was comprehensively analyzed and compared after sequencing by PacBio RS II. To shed light on the drug resistance mechanism of A. hydrophila MX16A, a Kirby-Bauer disk diffusion method was used to assess the phenotypic drug susceptibility. Importantly, resistance against β-lactam, sulfonamides, rifamycins, macrolides, tetracyclines and chloramphenicols was largely consistent with the prediction analysis results of drug resistance genes in the CARD database. The varied types of resistance genes identified from A. hydrophila MX16A revealed multiple resistance mechanisms, including enzyme inactivation, gene mutation and active effusion. The publicly available complete genomes of 35 Aeromonas hydrophila strains on NCBI, including MX16A, were downloaded for genomic comparison and analysis. The analysis of 33 genomes with ANI greater than 95% showed that the pan-genome consisted of 9556 genes, and the core genes converged to 3485 genes. In summary, the obtained results showed that A. hydrophila exhibited a great genomic diversity as well as diverse metabolic function and it is believed that frequent exchanges between strains lead to the horizontal transfer of drug resistance genes.

Abstract Chemically manipulating bacterial surface structures, a cutting‐edge research direction in the biomedical field, predominantly relies on metabolic labeling by now. However, this method may involve daunting precursor synthesis and only labels nascent surface structures. Here, we report a facile and rapid modification strategy based on a tyrosinase‐catalyzed oxidative coupling reaction (TyOCR) for bacterial surface engineering. This strategy employs phenol‐tagged small molecules and tyrosinase to initiate direct chemical modification of Gram‐positive bacterial cell walls with high labeling efficiency, while Gram‐negative bacteria are inert to this modification due to the hindrance of an outer membrane. By using the biotin‒avidin system, we further present the selective deposition of various materials, including photosensitizer, magnetic nanoparticle, and horseradish peroxidase, on Gram‐positive bacterial surfaces, and realize the purification/isolation/enrichment and naked‐eye detection of bacterial strains. This work demonstrates that TyOCR is a promising strategy for engineering live bacterial cells.

To satisfy the increasing demand for soybeans, identifying and sorting high-vigor seeds before sowing is an effective way to improve the yield. Polarized hyperspectral imaging (PHI) technology is here proposed as a rapid, non-destructive method for detecting the vigor of naturally aged soybean seeds. First, the spectrum of 396.1–1044.1 nm was collected to automatically extract the region of interest (ROI). Then, first derivative (FD), Savitzky–Golay (SG), multiplicative scatter correction (MSC), and standard normal variate (SNV) preprocessed hyperspectral and polarized hyperspectral data (0°, 45°, 90°, and 135°) for the soybean seeds was obtained. Finally, the seed vigor prediction model based on polarized hyperspectral components such as I, Q, and U was constructed, and partial least squares regression (PLSR), back-propagation neural network (BPNN), generalized regression neural network (GRNN), support vector regression (SVR), random forest (RF), and blending ensemble learning were applied for modeling analysis. The results showed that the prediction accuracy when using PHI was improved to 93.36%, higher than that for the hyperspectral technique, with a prediction accuracy up to 97.17%, 98.25%, and 97.55% when using the polarization component of I, Q, and U, respectively.

Nitrogen deposition can change the soil in N and C cycling processes. However, a general understanding of how N deposition changes C and N mineralization has not yet been reached. Soil organic C and N mineralization beneath the dominant shrubs of Haloxylon ammodendron and between the shrubs in response to two levels of N addition (2.5 gN m−2 and 5 gN m−2 per year) were investigated in the 1st, 4th, and 9th year of N addition in a temperate desert of northern China. N addition promoted soil N mineralization (RmN), and the nitrification rate (RNN) increased C mineralization in the interplant and decreased it beneath shrubs. N addition increased soil microbial biomass C (Cmic), N (Nmic), and PLFAs in the interplant, and decreased it beneath shrubs. RmN and RNN were related to Nmic, and RCM was related to Cmic and the total PLFAs. N addition increased the fungal biomass alongside the ratio of fungal to bacterial PLFAs in the interplants while decreasing them beneath shrubs. Our results support how N addition can increase soil N mineralization and nitrification, but the effects on soil C mineralization are dependent on the amount of nitrogen addition, the soil’s available carbon content, and water. Finally, the divergent responses of microbial communities to N addition between microsites suggest that the “fertile islands” effects on nutrients and microbial biomass are important when estimating feedbacks of C and N cycling to projected N deposition in the desert ecosystem.

A neurotoxin is essentially a protein that mainly acts on the nervous system; it has a selective toxic effect on the central nervous system and neuromuscular nodes, can cause muscle paralysis and respiratory paralysis, and has strong lethality. According to their principle of action, neurotoxins are divided into presynaptic neurotoxins and postsynaptic neurotoxins. Correctly identifying presynaptic and postsynaptic nerve toxins provides important clues for future drug development and the discovery of drug targets. Therefore, a predictive model, Neu_LR, was constructed in this paper. The monoMonokGap method was used to extract the frequency characteristics of presynaptic and postsynaptic neurotoxin sequences and carry out feature selection, then, based on the important features obtained after dimensionality reduction, the prediction model Neu_LR was constructed using a logistic regression algorithm, and ten-fold cross-validation and independent test set validation were used. The final accuracy rates were 99.6078 and 94.1176%, respectively, which proved that the Neu_LR model had good predictive performance and robustness, and could meet the prediction requirements of presynaptic and postsynaptic neurotoxins. The data and source code of the model can be freely download from https://github.com/gyx123681/.

Abstract How to efficiently deliver therapeutic agents into cancer cells is a knotty problem for researchers. Over the past 30 years, the use of cell‐penetrating peptides (CPPs) to facilitate intracellular cargo delivery has received extensive attention. Encouraged by their robust membrane permeability and satisfactory biocompatibility, various CPPs have been applied to construct anticancer drugs. However, owing to some unsolved problems, such as lack of cell selectively, short circulation time, and poor in vivo stability, no CPP has been approved by the Food and Drug Administration so far. Thus, further efforts are urgently needed to solve these problems. In this commentary, we discuss the current state and future directions of CPPs in the field of cancer therapy.

The primary premise of autonomous railway inspection using unmanned aerial vehicles is achieving autonomous flight along the railway. In our previous work, fitted centerline-based unmanned aerial vehicle (UAV) navigation is proven to be an effective method to guide UAV autonomous flying. However, the empirical parameters utilized in the fitting procedure lacked a theoretical basis and the fitted curves were also not coherent nor smooth. To address these problems, this paper proposes a skeleton detection method, called the dynamic-weight parallel instance and skeleton network, to directly extract the centerlines that can be viewed as skeletons. This multi-task branch network for skeleton detection and instance segmentation can be trained end to end. Our method reformulates a fused loss function with dynamic weights to control the dominant branch. During training, the sum of the weights always remains constant and the branch with a higher weight changes from instance to skeleton gradually. Experiments show that our model yields 93.98% mean average precision (mAP) for instance segmentation, a 51.9% F-measure score (F-score) for skeleton detection, and 60.32% weighted mean metrics for the entire network based on our own railway skeleton and instance dataset which comprises 3235 labeled overhead-view images taken in various environments. Our method can achieve more accurate railway skeletons and is useful to guide the autonomous flight of a UAV in railway inspection.

Heatmap-based traditional approaches for estimating human pose usually suffer from drawbacks such as high network complexity or suboptimal accuracy. Focusing on the issue of multi-person pose estimation without heatmaps, this paper proposes an end-to-end, lightweight human pose estimation network using a multi-scale coordinate attention mechanism based on the Yolo-Pose network to improve the overall network performance while ensuring the network is lightweight. Specifically, the lightweight network GhostNet was first integrated into the backbone to alleviate the problem of model redundancy and produce a significant number of effective feature maps. Then, by combining the coordinate attention mechanism, the sensitivity of our proposed network to direction and location perception was enhanced. Finally, the BiFPN module was fused to balance the feature information of different scales and further improve the expression ability of convolutional features. Experiments on the COCO 2017 dataset showed that, compared with the baseline method YOLO-Pose, the average accuracy of the proposed network on the COCO 2017 validation dataset was improved by 4.8% while minimizing the amount of network parameters and calculations. The experimental results demonstrated that our proposed method can improve the detection accuracy of human pose estimation while ensuring that the model is lightweight.

Photocatalysis technology is a potential solution to solve the problem of environmental pollution and energy shortage, but its wide application is limited by the low efficiency of solar energy conversion. As a non-toxic and inexpensive n-type semiconductor, WO3 can absorb approximately 12% of sunlight which is considered one of the most attractive photocatalytic candidates. However, the narrow light absorption range and the high recombination rate of photogenerated electrons and holes restrict the further development of WO3-based catalysts. Herein, the studies on preparation and modification methods such as doping element, regulating defects and constructing heterojunctions to enlarge the range of excitation light to the visible region and slow down the recombination of carriers on WO3-based catalysts so as to improve their photocatalytic performance are reviewed. The mechanism and application of WO3-based catalysts in the dissociation of water, the degradation of organic pollutants, as well as the hydrogen reduction of N2 and CO2 are emphatically investigated and discussed. It is clear that WO3-based catalysts will play a positive role in the field of future photocatalysis. This paper could also provide guidance for the rational design of other metallic oxide (MOx) catalysts for the increasing conversion efficiency of solar energy.

BackgroundWe previously reported that the larval Echinococcus granulosus (E. granulosus) infection can expand the population of regulatory B cells in mice, thereby inhibiting the anti-infective immunity. However, the underlying mechanism is still largely unknown. This study further investigated the holistic transcriptomic profiles of total splenic B cells following the chronic infection of the parasite.MethodsThe infection model of larval E. granulosus was established by intraperitoneal inoculation with 2000 protoscolexes. Magnetic-Activated Cell Separation (MACS) was used to isolate the total splenic B cells. RNA sequencing was performed to screen the differentially expressed genes (DEGs) after infection. The expression of selected DEGs was verified using qRT-PCR. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Co-expression network analysis were applied to predict these DEGs' underlying biological processes, pathways, and interactions respectively.ResultsA total of 413 DEGs were identified in larval E. granulosus infected B cells, including 303 up- and 110 down-regulated genes. Notably, most DEGs related to inflammation and chemotaxis were significantly upregulated after infection. In line with these changes, significant expression upregulation of DEGs associated with fatty acid oxidation, lipid synthesis, lipolysis, lipid transport, and cholesterol biosynthesis, were observed in infected B cells. Co-expression network analysis showed an intimate interaction between these DEGs associated with immune and metabolism.ConclusionsThe present study revealed that the larval E. granulosus infection induces metabolic reprogramming of B cells, which provides a novel clue to clarify the immunoregulatory mechanism of B cells in parasitic infection.

The clinical application of magnetic hyperthermia therapy (MHT) is limited by the poor magnetic-to-thermal conversion efficiency of MHT agents. Here, the authors develop encapsulin-produced magnetic iron oxide nanocomposites (eMIONs) with excellent magnetic-heat capability and catalysis-triggered tumor suppression ability to overcome the critical issues of MHT.

Short tandem repeats (STRs) play an essential role in forensic genetics due to their high degree of polymorphisms, wide distributions and easy detection method. In this study, allelic frequencies and forensic statistical parameters of the 19 autosomal STR loci in a Kazak ethnic group were calculated, and its genetic relationships with reference populations were assessed in order to understand population structure better and enrich population genetic data for forensic practice in Chinese Kazak ethnic group. There were 226 identified alleles with the corresponding allelic frequencies ranging from 0.0008 to 0.5295 in the 628 unrelated healthy Kazak individuals in Xinjiang Uygur Autonomous Region. All autosomal STRs were conformed to the Hardy-Weinberg equilibrium after Bonferroni’s correction. The cumulative power of discrimination and the combined probability of exclusion of all the 19 autosomal STRs were 0.999 999 999 999 999 999 999 997 162 and 0.999 999 994 484, respectively. Furthermore, the DA distances and Fixation index values of pairwise populations, principal component analysis, multidimensional scaling analysis, phylogenetic tree analysis and structure analysis were conducted to probe the genetic relationships between the Kazak group and other reference populations. The population genetic results showed that these 19 autosomal STR loci were characterised by high genetic diversities in the Kazak group. Furthermore, the studied Kazak group had close genetic relationships with the Uyghur group and the Uzbek group. The present results may facilitate understanding the genetic background of the Chinese Xinjiang Kazak group.

Roadway maintenance of the gob-side entry driving with narrow pillars is a key issue that needs to be considered in underground mining. In this paper, a thick-anchored dual-layer locking (TADL) supporting technique is proposed, in which, the flexible bolt and the cable are used as the main bodies of the foundation supporting layer and the deep reinforcement layer, respectively. In the instantaneous deformation stage after excavation, TADL is applied to form a thick-anchored bearing structure in time, aiming to achieve the following effects: (1) prevent the delamination of the roadway at the initial stage of excavation, (2) avoid the reduction of the bearing capacity of the surrounding rock structure, and (3) effectively control the plastic zone development and surrounding rock deformation of roof strata. Finally, the mining conditions of gob-side entry driving with narrow pillars are selected for engineering application and verification. The results of numerical simulation and field industrial tests show that the TADL support technique can effectively control the deformation of surrounding rock and drive away tensile stress. Compared with the original support scheme, the roof displacement of the roadway is significantly reduced by more than 16% after TADL support. This study provides a reference for the stability control of roadway roofs under similar conditions.

The gob-side roadway technology is widely used in coal mining in China, as it can improves the recovery rate of coal resources and alleviate the shortage of mining replacement. However, it is still difficult to control the stability of surrounding rock in gob-side roadway under deeper mining depth and more complex stress environment. With the auxiliary transportation roadway 30207 (ATR 30207) of the Muduchaideng Coal Mine as engineering background, this paper introduces the engineering case of gob-side roadway characterized by deep buried, long-distance, and large section. Through comprehensive methods, we studied and put forward the efficient excavation-support cooperation technology for surrounding rock of gob-side roadway. The research shows that the head-on cutting of gob-side roadway causes the stress to transfer and gradually adjust in the heading face to form a steady-state advanced abutment stress field. The coupling effect of the advanced abutment stress and the lateral abutment stress of goaf are the main reasons for repeated hole collapse of anchor cable drilling in solid coal rib, development and expansion of the roof separation fracture, and the low excavation speed. Supposed that upon the heading face support is completed, the next excavation-support cycle is quickly started during the early stage of stress adjustment, and the time effect can be fully utilized to alleviate the disturbance intensity of the advanced abutment stress in the heading face, as well as reduce the damage of rock mass. This paper puts forward the time-effect collaborative control countermeasures of the roof and rib in gob-side roadway of “hole collapse anchor cable of solid coal rib lag construction and waits for pressure relief and rapid excavation-support cycle in the heading face.” Engineering practice shows that the mine pressure behavior of roadway roof and solid coal rib is effectively controlled, and the roadway forming speed is greatly improved, which can provide reference for the efficient excavation-support design of gob-side roadway under the conditions of deep buried, long-distance, and large section in similar deep mines.

Genes, the nucleotide sequences that encode a polypeptide chain or functional RNA, are the basic genetic unit controlling biological traits. They are the guarantee of the basic structures and functions in organisms, and they store information related to biological factors and processes such as blood type, gestation, growth, and apoptosis. The environment and genetics jointly affect important physiological processes such as reproduction, cell division, and protein synthesis. Genes are related to a wide range of phenomena including growth, decline, illness, aging, and death. During the evolution of organisms, there is a class of genes that exist in a conserved form in multiple species. These genes are often located on the dominant strand of DNA and tend to have higher expression levels. The protein encoded by it usually either performs very important functions or is responsible for maintaining and repairing these essential functions. Such genes are called persistent genes. Among them, the irreplaceable part of the body’s life activities is the essential gene. For example, when starch is the only source of energy, the genes related to starch digestion are essential genes. Without them, the organism will die because it cannot obtain enough energy to maintain basic functions. The function of the proteins encoded by these genes is thought to be fundamental to life. Nowadays, DNA can be extracted from blood, saliva, or tissue cells for genetic testing, and detailed genetic information can be obtained using the most advanced scientific instruments and technologies. The information gained from genetic testing is useful to assess the potential risks of disease, and to help determine the prognosis and development of diseases. Such information is also useful for developing personalized medication and providing targeted health guidance to improve the quality of life. Therefore, it is of great theoretical and practical significance to identify important and essential genes. In this paper, the research status of essential genes and the essential genome database of bacteria are reviewed, the computational prediction method of essential genes based on communication coding theory is expounded, and the significance and practical application value of essential genes are discussed.

Inspired by L1-norm minimization methods, such as basis pursuit, compressed sensing, and Lasso feature selection, in recent years, sparse representation shows up as a novel and potent data processing method and displays powerful superiority. Researchers have not only extended the sparse representation of a signal to image presentation, but also applied the sparsity of vectors to that of matrices. Moreover, sparse representation has been applied to pattern recognition with good results. Because of its multiple advantages, such as insensitivity to noise, strong robustness, less sensitivity to selected features, and no “overfitting” phenomenon, the application of sparse representation in bioinformatics should be studied further. This article reviews the development of sparse representation, and explains its applications in bioinformatics, namely the use of low-rank representation matrices to identify and study cancer molecules, low-rank sparse representations to analyze and process gene expression profiles, and an introduction to related cancers and gene expression profile database.

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases with irreversible damage of the brain and a continuous pathophysiological process. Early detection and accurate diagnosis are essential for the early intervention of AD. Precise detection of blood biomarkers related to AD could provide a shortcut to identifying early-stage patients before symptoms. In recent years, targeting peptides or peptoids have been chosen as recognition elements in nano-sensors or fluorescence detection to increase the targeting specificity, while peptide-based probes were also developed considering their specific advantages. Peptide-based sensors and probes have been developed according to different strategies, such as natural receptors, high-throughput screening, or artificial design for AD detection. This review will briefly summarize the recent developments and trends of AD diagnosis platforms based on peptide and peptoid as recognition elements and provide insights into the application of peptide and peptoid with different sources and characteristics in the diagnosis of AD biomarkers.

Hormone binding protein (HBP) is a soluble carrier protein that interacts selectively with different types of hormones and has various effects on the body’s life activities. HBPs play an important role in the growth process of organisms, but their specific role is still unclear. Therefore, correctly identifying HBPs is the first step towards understanding and studying their biological function. However, due to their high cost and long experimental period, it is difficult for traditional biochemical experiments to correctly identify HBPs from an increasing number of proteins, so the real characterization of HBPs has become a challenging task for researchers. To measure the effectiveness of HBPs, an accurate and reliable prediction model for their identification is desirable. In this paper, we construct the prediction model HBP_NB. First, HBPs data were collected from the UniProt database, and a dataset was established. Then, based on the established high-quality dataset, the k-mer (K = 3) feature representation method was used to extract features. Second, the feature selection algorithm was used to reduce the dimensionality of the extracted features and select the appropriate optimal feature set. Finally, the selected features are input into Naive Bayes to construct the prediction model, and the model is evaluated by using 10-fold cross-validation. The final results were 95.45% accuracy, 94.17% sensitivity and 96.73% specificity. These results indicate that our model is feasible and effective.

The Hui minority is predominantly composed of Chinese-speaking Islamic adherents distributed throughout China, of which the individuals are mainly concentrated in Northwest China. In the present study, we employed the length and sequence polymorphisms-based typing system of 231 molecular markers, i.e., amelogenin, 22 phenotypic-informative single nucleotide polymorphisms (PISNPs), 94 identity-informative single nucleotide polymorphisms (IISNPs), 24 Y-chromosomal short tandem repeats (Y-STRs), 56 ancestry-informative single nucleotide polymorphisms (AISNPs), 7 X-chromosomal short tandem repeats (X-STRs), and 27 autosomal short tandem repeats (A-STRs), into 90 unrelated male individuals from the Chinese Northwest Hui group to comprehensively explore its forensic characteristics and genetic background. Total of 451 length-based and 652 sequence-based distinct alleles were identified from 58 short tandem repeats (STRs) in 90 unrelated Northwest Hui individuals, denoting that the sequence-based genetic markers could pronouncedly provide more genetic information than length-based markers. The forensic characteristics and efficiencies of STRs and IISNPs were estimated, both of which externalized high polymorphisms in the Northwest Hui group and could be further utilized in forensic investigations. No significant departure from the Hardy–Weinberg equilibrium (HWE) expectation was observed after the Bonferroni correction. Additionally, four group sets of reference population data were exploited to dissect the genetic background of the Northwest Hui group separately from different perspectives, which contained 26 populations for 93 IISNPs, 58 populations for 17 Y-STRs, 26 populations for 55 AISNPs (raw data), and 109 populations for 55 AISNPs (allele frequencies). As a result, the analyses based on the Y-STRs indicated that the Northwest Hui group primarily exhibited intimate genetic relationships with reference Hui groups from Chinese different regions except for the Sichuan Hui group and secondarily displayed close genetic relationships with populations from Central and West Asia, as well as several Chinese groups. However, the AISNP analyses demonstrated that the Northwest Hui group shared more intimate relationships with current East Asian populations apart from reference Hui group, harboring the large proportion of ancestral component contributed by East Asia.

Background: The larval stages of Echinococcus granulosus sensu lato (E. granulosus s.l) infection can alter B cell function and affect host anti-infective immunity, but the underlying mechanism remains unclear. The newly emerging immunometabolism highlights that several metabolites are key factors in determining the fate of immune cells, which provides a new insight for exploring how larval E. granulosus s.l. infection remodels B cell function. This study investigated the metabolomic profiles of B cells in mice infected with E. granulosus s.l. protoscoleces (PSC).Results:Total CD19+ B cells, purified from the spleen of infected mice, showed significantly increased production of IL-6, TNF-α, and IL-10 after exposure to LPS in vitro. Moreover, the mRNA expression of metabolism related enzymes in B cells was remarkably disordered post infection. In addition, differential metabolites were identified in B cells after infection. There were 340 differential metabolites (83 upregulated and 257 downregulated metabolites) identified in the positive ion model, and 216 differential metabolites (97 upregulated and 119 downregulated metabolites) identified in the negative ion mode. Among these, 64 differential metabolites were annotated and involved in 68 metabolic pathways, including thyroid hormone synthesis, the metabolic processes of glutathione, fructose, mannose, and glycerophospholipid. Furthermore, several differential metabolites such as glutathione, taurine, and inosine were validated to regulate the cytokine production in LPS stimulated B cells.Conclusion:Infection with the larval E. granulosus s.l. causes metabolic reprogramming in the intrinsic B cells of mice, which provides the first evidence for understanding the role and mechanism of B cells in parasite anti-infective immunity from the viewpoint of immunometabolism.

Identifying and locating track areas in images through machine vision technology is the primary task of autonomous UAV inspection. Aiming at the problems that railway track images are greatly affected by light and perspective, the background environment is complex and easy to misidentify, and existing methods are difficult to reason correctly about the obscured track area, this paper proposes a generative adversarial network (GAN)-based railway track precision segmentation framework, RT-GAN. RT-GAN consists of an encoder–decoder generator (named RT-seg) and a patch-based track discriminator. For the generator design, a linear span unit (LSU) and linear extension pyramid (LSP) are used to concatenate network features with different resolutions. In addition, a loss function containing gradient information is designed, and the gradient image of the segmentation result is added into the input of the track discriminator, aiming to guide the generator, RT-seg, to focus on the linear features of the railway tracks faster and more accurately. Experiments on the railway track dataset proposed in this paper show that with the improved loss function and adversarial training, RT-GAN provides a more accurate segmentation of rail tracks than the state-of-the-art techniques and has stronger occlusion inference capabilities, achieving 88.07% and 81.34% IoU in unaugmented and augmented datasets.

Winter wheat and summer maize rotation is the main cropping pattern in the North China Plain (NCP). There are still problems with farmers’ production modes, including shallow tillage layers, single application of chemical fertilizer causing plow bottom layer thickening and soil pH decrease. A two-factor location experiment was conducted to investigate the effects of different tillage and fertilization patterns on the soil physical properties, soil organic carbon (SOC), nitrogen-use efficiency, and crop yield of wheat–maize rotation systems during the years 2018–2020. The different treatments were deep tillage + organic fertilizer (DTF), shallow tillage + organic fertilizer (STF), no tillage + organic fertilizer (NTF), deep tillage + nitrogen fertilizer (DT), shallow tillage + nitrogen fertilizer (ST), and no tillage + nitrogen fertilizer (NT). The results showed that STF treatment could effectively improve the physical properties of soil and, SOC content, and increase both the crop yield and revenue of farmers. In the STF treatment, soil water content was highest in the 0–20 cm layer (2018), which was 4.89–11.31% higher than that of the other treatments; additional organic fertilizer application reduced soil bulk and increased the proportion of −1, which were 1.02–12.94% and 2.29–9.87% higher than those of the other treatments. Therefore, the suggested planting method in the NCP is tillage of over 20 cm and additional organic fertilizer.

The H9N2 avian influenza virus (AIV) is currently widespread worldwide, posing a severe threat to the poultry industry and public health. Reassortment is an important way for influenza viruses to adapt to a new host. In 2007, the PB2 gene of H9N2 AIV in China was reassorted, and the DK1-like lineage replaced the F/98-like lineage, forming a dominant genotype of G57. This genotype and its reassortants (such as H7N9, H10N8 and H5N6) showed higher mammalian adaptation, and caused increased human infections. However, the adaptive mechanisms of the DK1-like lineage PB2 gene remain unclear. Here, we confirmed that the PB2 lineage of the H9N2 AIV currently prevalent in China still belongs to the DK1-like lineage and, compared with the previously predominant F/98-like lineage, the DK1-like lineage PB2 gene significantly enhances H9N2 AIV to mammalian adaptation. Through transcriptomic analysis and qRT–PCR and western blot experiments, we identified a host factor, sphingosine kinase 1 (SphK1), that is closely related to viral replication. SphK1 inhibits the replication of DK1-like PB2 gene H9N2 AIV, but the ability of SphK1 protein to bind DK1-like PB2 protein is weaker than that of F/98-like PB2 protein, which may contribute to H9N2 AIV containing the DK1-like PB2 gene to escape the inhibitory effect of host factor SphK1 for efficient infection. This study broadens our understanding of the adaptive evolution of H9N2 AIV and highlights the necessity to pay close attention to the AIV that contains the adaptive PB2 protein in animals and humans.

Insertion/deletion polymorphism is a promising genetic marker in the forensic genetic fields, especially in the forensic application of degraded sample at crime scene. In this research, a novel five-dye multiplex amplification panel containing 43 highly polymorphic Insertion/deletion (InDel) loci and one Amelogenin gene locus is designed and constructed in-house for the individual identification in East Asian populations. The amplicon sizes of 43 InDel loci are less than 200 bp, which help to ensure that full allele profiles can be obtained from degraded DNA sample. A series of optimizations and developmental validations including optimization of PCR conditions, detection efficiency of the degraded and casework samples, sensitivity, reproducibility, precision, tolerance for inhibitors, species specificity and DNA mixtures are performed according to the Scientific Working Group on DNA Analysis Methods (SWGDAM) guideline. The results of the internal validation demonstrated that this novel InDel panel was a reliable, sensitive and accurate system with good tolerances to different inhibitors, and performed the considerable detection efficiency for the degraded or mixed samples, which could be used in the forensic applications.

Abstract As a basic innate immune response to the disordered tissue homeostasis, inflammation is related to the pathogenesis of multiple diseases, including bacterial infections, atherosclerosis, neurodegenerative diseases, and cancers. It is also a pivotal feature of some metabolic disorders such as diabetes and obesity. The visualization of in vivo inflammations can help us to comprehend the pathogenesis of these diseases and develop new solutions to diagnose them. Over the past few decades, a variety of strategies (eg, computed tomography, magnetic resonance imaging [MRI], and ultrasound [US] imaging) have been utilized for visualizing inflammations by imaging the structural changes of inflammatory tissues. Moreover, many recent studies have focused on some probes that can target or localize the inflammatory sites by specific binding to inflammation‐related molecules, being internalized by inflammatory cells, or becoming detectable only under inflammatory conditions. These probes can also be applied to visualize inflammations by MRI, positron emission tomography, single‐photon emission computed tomography, photoacoustic imaging, optical imaging (eg, fluorescence imaging, bioluminescence imaging, and chemiluminescence imaging), contrast‐enhanced US imaging, and the combined use of the abovementioned methods. This review not only summarizes the existing strategies for visualizing inflammations, but also discusses the limitations of the present strategies and the future directions on the development of new strategies for realizing the in vivo inflammation visualization.

Species identification of unknown biological samples is of fundamental importance for forensic applications, especially in crime detection, poaching, and illegal trade of endangered animals as well as meat fraud. In this study, a novel panel was developed to simultaneously identify 10 different animal species (Gallus domesticus, Anas platyrhynchos domesticus, Ovis aries, Sus scrofa domesticus, Bos taurus, Equus caballus, Columba livia domestica, Rattus norvegicus, Mus musculus, and Canis lupus familiaris) and human beings by amplifying 22 short tandem repeat (STR) loci in a multiplex PCR using a set of five fluorescently labeled dyes. This novel 22-STR panel was validated by optimization of PCR conditions as well as species specificity, sensitivity, reproducibility, precision, DNA mixture, and tissue/organ consistency. The results of developmental validation showed that the 22-STR loci achieved high species specificity among 10 animal species and human beings, and the sensitivity of this panel was 0.09 ng. This 22-STR panel identified different meats in mixed samples, and the minimum detected mixture ratio in the current test was 10% (0.1 ng/1 ng). This sensitive, accurate, and specific 22-STR panel can be used for forensic species identification and the detection of meat fraud and adulteration.