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To examine the regulatory mechanisms of oligopeptides derived from soft-shelled turtle protein on tubulin activity in tumor cells, this study employed three specific oligopeptides D-7-A, E-7-W, and G-8-V obtained from soft-shelled turtle protein. The objective was to assess their impact on tubulin polymerization and depolymerization, investigate their interaction with tubulin, and elucidate the mechanisms induce apoptosis in tumor cells. The results showed that the three oligopeptides exhibited similarities to vincristine and acted as inhibitors of tubulin polymerization, their respective IC50 values was 3.72, 5.01, and 5.95 µmol/L. Furthermore, the molecular docking analysis revealed that D-7-A interacted with the active center of α-tubulin 4X1I, specifically binding to Ser178 and Thr179. The active center of E-7-W was found to be bound by Gln15, Thr225, Tyr224, Tyr210 and Arg214. Similarly, G-8-V was observed to bind to Gln11, Ser178 and Asp329 at the active center, exerting a significant impact on α-tubulin. The results of cell experiments showed that D-7-A, E-7-W and G-8-V had dose-dependent inhibitory effects on A549 cells, with IC50 values of 2003±72, 1877±102 and 1789±137 μmol/L, respectively. The G-8-V exhibited the most potent inhibitory effect on A549 cells, leading to the induction of apoptosis. Moreover, the apoptosis rate demonstrated a significant increase with prolonged drug treatment time. The underlying mechanism of action involved the inhibition of tubulin polymerization and the induction of apoptosis by perturbing the valence bond of α-tubulin.

CRISPR-Cas9 systems constitute bacterial adaptive immune systems that protect against phage infections. Bacteriophages encode anti-CRISPR proteins (Acrs) that mitigate the bacterial immune response. However, the structural basis for their inhibitory actions from a molecular perspective remains elusive. In this study, through microsecond atomistic molecular dynamics simulations, we demonstrated the remarkable flexibility of Streptococcus pyogenes Cas9 (SpyCas9) and its conformational adaptability during interactions with AcrIIA4 and AcrIIA2. Specifically, we demonstrated that the binding of AcrIIA4 and AcrIIA2 to SpyCas9 induces a conformational rearrangement that causes spatial separation between the nuclease and cleavage sites, thus making the endonuclease inactive. This separation disrupts the transmission of signals between the protospacer adjacent motif recognition and nuclease domains, thereby impeding the efficient processing of double-stranded DNA. The simulation also reveals that AcrIIA4 and AcrIIA2 cause different structural variations of SpyCas9. Our research illuminates the precise mechanisms underlying the suppression of SpyCas9 by AcrIIA4 and AcrIIA2, thus presenting new possibilities for controlling genome editing with higher accuracy.

The task of modeling and analyzing intercepted multifunction radars (MFRs) pulse trains is vital for cognitive electronic reconnaissance. Existing methodologies predominantly rely on prior information or heavily constrained models, posing challenges for non-cooperative applications. This paper introduces a novel approach to model MFRs using a Bayesian network, where the conditional probability density function is approximated by an autoregressive kernel mixture network (ARKMN). Utilizing the estimated probability density function, a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains. Simulation results affirm the proposed method's efficacy in modeling MFRs, outperforming the state-of-the-art in pulse train denoising and change point detection.

This study aimed to examine the changes in the structural characterization of cross-linked tapioca treated with sodium trimetaphosphate(STMP). The degree of substitution in the cross-linked starch was determined by the iodine absorption method. Scanning electron microscopy, particle size measurement, Fourier-transform infrared spectroscopy (FT-IR), and x-ray diffraction (XRD) were used to characterize the structure of modified tapioca starch at different substitution degrees. The results demonstrated that the degree of substitution of cross-linked starch increased with the increase in the amount of the cross-linking agent. The modified starch particles aggregated to form a mass structure, but the average particle size did not change with the cross-linker content and was about 17 μm. FT-IR and XRD experiments showed that the cross-linking esterification of starch with sodium trimetaphosphate generated new phosphate groups, increasing the content of phosphoric acid in starch, and the A-type crystalline structure of starch was not changed.

Terrestrial evapotranspiration (ET) is an important control factor for water cycling and energy transport, serving as a crucial link between ecological and hydrological processes. Accurate estimation of ET is essential for enhancing efficient utilization of water resources, improving agricultural productivity, preserving ecosystems, and advancing climate change research. Despite its significance, high spatiotemporal resolution continuous ET datasets remain scarce. In ET estimation, machine learning methods have been widely adopted, with tree-based machine learning models gaining increasing attention due to their computational efficiency and reliability accuracy. However, research comparing the performance of these models remains relatively limited. In this study, we use data from flux observation sites and various remote sensing sources to explore the performance of four tree-based machine learning models in ET estimation across the Contiguous United States (CONUS). Our findings demonstrate the proficient performance of all four models in estimating terrestrial ET across CONUS. Particularly noteworthy is the outstanding performance of the extremely randomized trees (ERT) model, showing a high correlation (R2 = 0.84), low bias (BIAS = −0.0003 mm/d), and low root mean square error (RMSE = 0.72 mm/d) with flux observation site data. Using this model, we successfully obtained a seamless terrestrial ET dataset (ERT_ET) with a spatial resolution of 1 km and multiple temporal resolutions (daily, 8-day, monthly, and seasonal) from 2008 to 2018 across the CONUS. Compared to the MOD16 ET product, the ERT_ET outperforms with a higher R2 by 0.40 and lower RMSE by 5.31 mm/8d, providing better performance in capturing detailed features. Moreover, our ERT_ET product is comparable to other widely used ET products (MOD16, PML-V2, and ETMonitor), further highlighting its reliability. These findings will contribute to studies in various fields, including global climate change, hydrological cycles, and drought monitoring.

Abstract Background This study was designed to assess the associations between emerging cardiometabolic indices—the atherogenic index of plasma (AIP), the stress hyperglycemia ratio (SHR), the triglyceride-glucose (TyG) index, and the homeostasis model assessment of insulin resistance (HOMA-IR)—and the incidence of diabetic kidney disease (DKD) in type 2 diabetes (T2D) patients. Methods We consecutively enrolled 4351 T2D patients. The AIP, SHR, TyG index, and HOMA-IR were calculated from baseline parameters. DKD was defined as a urine albumin/creatinine ratio > 30 mg/g or an eGFR

With the development of green data centers, a large number of Uninterruptible Power Supply (UPS) resources in Internet Data Center (IDC) are becoming idle assets owing to their low utilization rate. The revitalization of these idle UPS resources is an urgent problem that must be addressed. Based on the energy storage type of the UPS (EUPS) and using renewable sources, a solution for IDCs is proposed in this study. Subsequently, an EUPS cluster classification method based on the concept of shared mechanism niche (CSMN) was proposed to effectively solve the EUPS control problem. Accordingly, the classified EUPS aggregation unit was used to determine the optimal operation of the IDC. An IDC cost minimization optimization model was established, and the Quantum Particle Swarm Optimization (QPSO) algorithm was adopted. Finally, the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.

Crowd flow connects various geographic spaces in cities, revealing inter-regional associations that are crucial for urban land – use identification. Existing research mainly focuses on binary connections between pairs of regions, overlooking associations among multiple regions. Addressing this gap, we propose a network model that uses a hypergraph neural network to extract key features of higher-order connections for urban land – use classification. Additionally, a similarity enhancement module is incorporated to augment the recognition capabilities of the model. Compared with graph neural networks, incorporating higher-order connections among multiple regions improves urban land – use identification. Metrics show a decrease of 0.4 in L1 distance, 2.35 in KL divergence, and 0.14 in Chebyshev distance, while cosine similarity increased by 0.25, particularly in areas with high crowd mobility. The similarity enhancement module further refines the ability of the model to capture regional similarities, particularly effective in large contiguous areas or regions with extreme points of interest distributions. Additionally, the degree of land – use mixing and inter-regional movement influences the effectiveness of higher-order connections in recognizing land use, with noticeable negative and positive impacts, respectively. This study provides methods and insights for the utilization of higher-order associations in land-use identification and urban studies on crowd flow.

This study investigates the effect of a novel negative temperature early-strength agent (NEA) in enhancing concrete performance under varying curing temperatures, with a discussion on the underlying mechanism through analysis of hydration products and microstructure.The results indicate NEA markedly accelerates early cement hydration, reduces setting time, and enhances compressive strength. Moreover, the efficacy of NEA is more pronounced at lower temperatures. With an optimal dosage of 3 %, the incorporation of NEA leads to a remarkable 285.9 % increase in compressive strength at 28 days under −5°C curing temperature. Despite accelerating cement hydration can induce increased chemical shrinkage, NEA effectively mitigates early freezing expansion under negative temperature and significantly diminishes chloride ion permeability. Higher temperatures facilitate early cement hydration but also compromise long-term hydration continuity. Consequently, the highest hydration degree and loweast porosity are observed at 28 days under 5°C curing temperature. NEA's air-entraining component introduces numerous superfine pores into the paste, augmenting early pore volume while optimizing pore structure. As the early-strength component fosters hydration, these pores undergo further filling and refinement, ultimately reducing the total pore volume at 28 days. This study provides insights into optimizing concrete performance in cold environments.

ABSTRACTSpatial interaction imputation aims to compensate for missing stable connections in geographical space, bolstering interaction network integrity and accuracy. Graph neural networks excel in graph-structured interaction data. However, existing research often focuses on homogeneous networks, neglecting the impact of heterogeneous interaction relationships influenced by distance decay on interaction imputation. Neglecting edge heterogeneity constrains the ability to effectively model the network structure, consequently leading to suboptimal performance in interaction imputation. This study introduces an interaction imputation graph convolutional network model. It constructs a heterogeneous interaction network with multi-distance relationships, considering distance decay. The model performs graph embedding based on interaction relationships between nodes. It comprehensively incorporates multiple interaction modes, topological structures, and node attributes to enhance spatial interaction imputation accuracy. Empirically validated using Beijing taxi travel data, our model outperforms existing models, improving imputation accuracy by approximately 8.70%. Our model consistently maintains superior accuracy in interaction networks of various sizes, demonstrating the stable superiority of our model. We also demonstrated that the variation in the number of interaction relationships affects imputation accuracy. A reasonable number of relationships and a larger feature dimension of geographical units yield better interaction imputation results.

Multidrug-resistant bacterial infections are a major global health challenge, especially the emergence and rapid spread of methicillin-resistant Staphylococcus aureus (MRSA) urgently require alternative treatment options. Our study has identified that a magnolol derivative 6i as a promising agent with significant antibacterial activity against S. aureus and clinical MRSA isolates (MIC = 2–8 μg/mL), showing high membrane selectivity. Unlike traditional antibiotics, 6i demonstrated rapid bactericidal efficiency and a lower propensity for inducing bacterial resistance. Compound 6i also could inhibit biofilm formation and eradicate bacteria within biofilms. Mechanistic studies further revealed that 6i could target bacterial cell membranes, disrupting the integrity of the cell membrane and leading to increased DNA leakage, resulting in potent antibacterial effects. Meanwhile, 6i also showed good plasma stability and excellent biosafety. Notably, 6i displayed good in vivo antibacterial activity in a mouse skin abscess model of MRSA-16 infection, which was comparable to the positive control vancomycin. These findings indicated that the magnolol derivative 6i possessed the potential to be a novel anti-MRSA infection agent.

In response to the localization challenges posed by multi-modal motion mode transitions during the locomotion of tracked-wheel robots in sheltered spaces, this paper presents a combination localization method based on Inertial Navigation System/Odometry (INS/OD). Leveraging the unique locomotion structure of tracked-wheel robots, a multi-modal odometry motion model and non-holonomic constrained (NHC) velocity model are established. A closed-loop extended Kalman filter is employed to construct the combination localization model, facilitating real-time computation and correction of the robot’s heading angle using the odometry motion model, effectively mitigating system errors and preventing heading divergence. The wheeled-track robot was subjected to outdoor and underground tunnel real-world localization tests. The results demonstrate that the proposed combined localization method achieved an absolute positioning accuracy of the wheeled-track robot within 0.3578 meters in outdoor experiments. This accounts for only 0.31% of the total distance traveled in the outdoor experiments, with an average error of 0.1611 meters. In the context of relative positioning within the underground tunnel, the localization error was measured at 0.2143 meters, representing a mere 0.2306% of the total distance covered in the tunnel. These findings affirm the capability of the combined localization method to meet the practical localization requirements of the wheeled-track robot, both in outdoor and in-sheltered space tunnel scenarios.

Abstract Immune cells, such as macrophages, B cells, neutrophils and T cell subsets, have been implicated in the context of obesity. However, the specific role of Th2 cells in adipose tissue function has remained elusive. Eight-week-old male CD3ε─/─ mice were randomly divided into two groups (≥ 5 mice per group): one received intravenous injection of Th2 cells isolated from LATY136F mice, while the other receiving PBS as a control. Both of groups were subjected to a high-fat diet (HFD). The adoptive transfer of polarized Th2 cells led to a significant reduction in obesity following a HFD. This reduction was accompanied by improvements in hepatic steatosis, glucose intolerance, and insulin resistance. Mechanistically, Th2 cell treatment promoted oxidative phosphorylation of adipocytes, thereby contributing to a reduction of lipid droplet accumulation. These findings suggest that Th2 cell therapy represents a novel approach for treating diet-induced obesity and other diseases involving lipid droplet accumulation disorders.

Abstract We report an organocatalyst that combines a triazolium N-heterocyclic carbene (NHC) with a squaramide as a hydrogen-bonding donor (HBD), which can effectively catalyze the atroposelective ring-opening of biaryl lactams via a unique amide C–N bond cleavage mode. The free carbene species attacks the amide carbonyl, forming an axially chiral acyl-azolium intermediate. Various axially chiral biaryl amines can be accessed by this methodology with up to 99% ee and 99% yield. By using mercaptan as a catalyst turnover agent, the resulting thioester synthon can be transformed into several interesting atropisomers. Both control experiments and theoretical calculations reveal the crucial role of the hybrid NHC-HBD skeleton, which activates the amide via H-bonding and brings it spatially close to the carbene centre. This discovery illustrates the potential of the NHC-HBD chimera and demonstrates a complementary strategy for amide bond activation and manipulation.

Abstract Plant-sourced aromatic amino acid (AAA) derivatives are a vast group of compounds with broad applications. Here, we present the development of a yeast consortium for efficient production of (S)-norcoclaurine, the key precursor for benzylisoquinoline alkaloid biosynthesis. A xylose transporter enables the concurrent mixed-sugar utilization in Scheffersomyces stipitis, which plays a crucial role in enhancing the flux entering the highly regulated shikimate pathway located upstream of AAA biosynthesis. Two quinate permeases isolated from Aspergillus niger facilitates shikimate translocation to the co-cultured Saccharomyces cerevisiae that converts shikimate to (S)-norcoclaurine, resulting in the maximal titer (11.5 mg/L), nearly 110-fold higher than the titer reported for an S. cerevisiae monoculture. Our findings magnify the potential of microbial consortium platforms for the economical de novo synthesis of complex compounds, where pathway modularization and compartmentalization in distinct specialty strains enable effective fine-tuning of long biosynthetic pathways and diminish intermediate buildup, thereby leading to increases in production.

IntroductionCarbapenem-resistant hypervirulent Klebsiella pneumoniae (CR-HvKP) strains combining virulence and multidrug resistance (MDR) features pose a great public health concern. The aim of this study is to explore the evolutionary characteristics of virulence in CR-HvKP by investigating the genetic features of resistance and virulence hybrid plasmids.MethodsThe resistance and virulence phenotypes were determined by using antimicrobial susceptibility testing and the mouse bacteremia infection model, respectively. Plasmid profiles were investigated by S1 nuclease pulsed-field gel electrophoresis (S1-PFGE) and Southern blotting, conjugation assay, and whole genome sequencing (WGS). Bioinformatics tools were used to uncover the genetic features of the resistance and virulence hybrid plasmids.ResultsTwo ST11-KL64 CRKP clinical isolates (KP18-3-8 and KP18-2079), which exhibited enhanced virulence compared with the classic CRKP, were detected positive for blaKPC−2 and rmpA2. The virulence level of the hypermucoviscous strain KP18-3-8 was higher than that of KP18-2079. S1-PFGE, Southern hybridization and WGS analysis identified two novel hybrid virulence plasmids in KP18-3-8 (pKP1838-KPC-vir, 228,158 bp) and KP18-2079 (pKP1838-KPC-vir, 182,326 bp), respectively. The IncHI1B/repB-type plasmid pKP1838-KPC-vir co-harboring blaKPC−2 and virulence genes (rmpA2, iucABCD and iutA) but lacking type IV secretion system could transfer into non-hypervirulent ST11 K. pneumoniae with the assistance of a helper plasmid in conjugation. The IncFII/IncR-type virulence plasmid pKP18-2079-vir may have been generated as a result of recombination between a typical pLVPK-like virulence plasmid and an MDR plasmid.ConclusionOur studies further highlight co-evolution of the virulence and resistance plasmids in ST11-CRKP isolates. Close surveillance of such hybrid virulence plasmids in clinical K. pneumoniae should be performed.

BackgroundExercise training is commonly employed as a efficacious supplementary treatment for individuals suffering from heart failure, but the optimal exercise regimen is still controversial. The objective of the review was to compare the effects of high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) on the exercise capacity, cardiac function, quality of life (QoL) and heart rate among patients with heart failure with reduced ejection fraction.MethodsA systematic search was performed using the following eight databases from their inception to July 5, 2023: PubMed, Web of Science, Embase, Cochrane Library, Clinical Trials, China Knowledge Network, Wan fang Data, and the China Biology Medicine databases. The meta-analysis results were presented as mean difference (MD) and 95% confidence interval (CI). The Cochrane Risk of Bias tool was used for the included studies. The Grading of Recommendations Assessment, Development, and Evaluations was used to assess the certainty of evidence.ResultsThirteen randomized controlled trials were included in the study. The results showed that HIIT had a significant positive effect on peak oxygen uptake (MD = 1.78, 95% CI for 0.80–2.76), left ventricular ejection fraction (MD = 3.13, 95% CI for 1.25–5.02), six-minute walk test (MD = 28.13, 95% CI for 14.56–41.70), and Minnesota Living with Heart Failure Questionnaire (MD = −4.45, 95% CI for −6.25 to −2.64) compared to MICT. However, there were no statistically significant differences observed in resting heart rate and peak heart rate.ConclusionsHIIT significantly improves peak oxygen uptake, left ventricular ejection fraction, six-minute walk test, and Minnesota Living with Heart Failure Questionnaire in patients with heart failure with reduced ejection fraction. Additionally, HIIT exhibits greater effectiveness in improving peak oxygen uptake among patients with lower body mass index.Systematic Review Registrationhttps://www.doi.org/10.37766/inplasy2023.7.0100, identifier (INPLASY2023.7.0100).

High-precision mapping based on portable X-ray fluorescence (PXRF) data is currently being studied extensively; however, owing to poor correlation with soil metal concentration, the original PXRF data directly used for co-kriging interpolation (CKI) cannot accurately map contaminated sites with heterogeneous concentrations. Therefore, this study selected a landfill-contaminated site for research, explored the best correlation mode between PXRF variants and actual heavy metal concentration, analyzed the impact of improving the correlation model on the CKI of the spatial distribution of heavy metals, and explored the most appropriate CKI mode and point density. The results showed the following: (1) After nonlinear transformation, the correlation model between PXRF and the actual concentration was significantly improved, and the correlation coefficients of five heavy metals increased from 0.214–0.232 to 0.936–0.986. (2) The introduction of corrected PXRF data significantly improves the accuracy of CKI. Compared with the original PXRF co-kriging interpolation (OP-CKI), the ME of the corrected PXRF co-kriging interpolation (CP-CKI) for Zn, Pb, and Cu decreased by 78.2 %, 45.5 %, and 65.3 %, respectively. In terms of the spatial distribution of heavy metal pollutant concentrations, CP-CKI effectively improved the influence of local anomalous high-value points on the interpolation accuracy. (3) When the sample density measured by inductively coupled plasma mass spectrometry (ICP-MS) was less than 4 boreholes/hm2, CKI accuracy decreased significantly, indicating that the sample density should not be less than a certain threshold during CKI. (4) When the sample density measured by PXRF exceeded 7 boreholes/hm2, the mean error and root mean square error of CKI continued to decrease, suggesting that the introduction of enough sample density measured by PXRF can effectively improve the accuracy of CKI.

Abstract Cognitive impairment is a core feature of schizophrenia, which is aggravated by antipsychotics-induced metabolic disturbance and lacks effective pharmacologic treatments in clinical practice. Our previous study demonstrated the efficiency of metformin in alleviating metabolic disturbance following antipsychotic administration. Here we report that metformin could ameliorate cognitive impairment and improve functional connectivity (FC) in prefrontal regions. This is an open-labeled, evaluator-blinded study. Clinically stable patients with schizophrenia were randomly assigned to receive antipsychotics plus metformin (N = 48) or antipsychotics alone (N = 24) for 24 weeks. The improvement in cognition was assessed by the MATRICS Consensus Cognitive Battery (MCCB). Its association with metabolic measurements, and voxel-wise whole-brain FC with dorsolateral prefrontal cortex (DLPFC) subregions as seeds were evaluated. When compared to the antipsychotics alone group, the addition of metformin resulted in significantly greater improvements in the MCCB composite score, speed of processing, working memory, verbal learning, and visual learning. A significant time × group interaction effect of increased FC between DLPFC and the anterior cingulate cortex (ACC)/middle cingulate cortex (MCC), and between DLPFC subregions were observed after metformin treatment, which was positively correlated with MCCB cognitive performance. Furthermore, the FC between left DLPFC A9/46d to right ACC/MCC significantly mediated metformin-induced speed of processing improvement; the FC between left A46 to right ACC significantly mediated metformin-induced verbal learning improvement. Collectively, these findings demonstrate that metformin can improve cognitive impairments in schizophrenia patients and is partly related to the FC changes in the DLPFC. Trial Registration: The trial was registered with ClinicalTrials.gov (NCT03271866). The full trial protocol is provided in Supplementary Material.

Abstract The Entomobryoidea, the largest superfamily of Collembola, encompasses over 2,000 species in the world. However, the lack of high-quality genomes hinders our understanding of the evolution and ecology of this group. This study presents a chromosome-level genome of Entomobrya proxima by combining PacBio long reads, Illumina short reads, and Hi-C data. The genome has a size of 362.37 Mb, with a scaffold N50 size of 57.67 Mb, and 97.12% (351.95 Mb) of the assembly is located on six chromosomes. The BUSCO analysis of our assembly indicates a completeness of 96.1% (n = 1,013), including 946 (93.4%) single-copy BUSCOs and 27 (2.7%) duplicated BUSCOs. We identified that the genome contains 22.16% (80.06 Mb) repeat elements and 20,988 predicted protein-coding genes. Gene family evolution analysis of E. proxima identified 177 gene families that underwent significant expansions, which were primarily associated with detoxification and metabolism. Moreover, our inter-genomic synteny analysis showed strong chromosomal synteny between E. proxima and Sinella curviseta. Our study provides valuable genomic information for comprehending the evolution and ecology of Collembola.

The incidence and mortality of cancer are increasing, making it a leading cause of death worldwide. Conventional treatments such as surgery, radiotherapy, and chemotherapy face significant limitations due to therapeutic resistance. Autophagy, a cellular self-degradation mechanism, plays a crucial role in cancer development, drug resistance, and treatment. This review investigates the potential of autophagy inhibition as a therapeutic strategy for cancer. A systematic search was conducted on Embase, PubMed, and Google Scholar databases from 1967 to 2024 to identify studies on autophagy inhibitors and their mechanisms in cancer therapy. The review includes original articles utilizing in vitro and in vivo experimental methods, literature reviews, and clinical trials. Key terms used were “Autophagy”, “Inhibitors”, “Molecular mechanism”, “Cancer therapy”, and “Clinical trials”. Autophagy inhibitors such as chloroquine (CQ) and hydroxychloroquine (HCQ) have shown promise in preclinical studies by inhibiting lysosomal acidification and preventing autophagosome degradation. Other inhibitors like wortmannin and SAR405 target specific components of the autophagy pathway. Combining these inhibitors with chemotherapy has demonstrated enhanced efficacy, making cancer cells more susceptible to cytotoxic agents. Clinical trials involving CQ and HCQ have shown encouraging results, although further investigation is needed to optimize their use in cancer therapy. Autophagy exhibits a dual role in cancer, functioning as both a survival mechanism and a cell death pathway. Targeting autophagy presents a viable strategy for cancer therapy, particularly when integrated with existing treatments. However, the complexity of autophagy regulation and the potential side effects necessitate further research to develop precise and context-specific therapeutic approaches.

The persistent increase in forest pest outbreaks requires timely detection methods to monitor the disaster precisely. However, early detection is challenging due to insufficient temporal observation and subtle tree changes. This article proposed a novel framework that collaborates multi-source remote sensing data and uses a change detection algorithm to archive early detection of infestation caused by Dendrolimus tabulaeformis Tsai et Liu (D. tabulaeformis) attacks. First, all available Sentinel-2 images with less than 20% cloud cover were utilized. During periods with long intervals (>16 days) between Sentinel-2 images, Landsat-8 images with less than 20% cloud cover were downscaled to a spatial resolution of 10 m using a deep learning algorithm to meet the requirement for a high temporal frequency of clear observations. Second, the spectral index differences between healthy and infested trees were examined to address the challenge of detecting subtle changes in pest attacks. The Enhanced Vegetation Index (EVI) was selected for early defoliation detection. On this basis, the EWMACD (Exponentially Weighted Moving Average Change Detection) algorithm, which is sensitive to subtle changes, was enhanced to improve the capability of detecting early D. tabulaeformis attacks. The assessment showed that the overall accuracy of the change detection (F1 score) reached 0.86 during the early stage and 0.88 during the late stage. The temporal accuracy (Precision) was 84.1% during the early stage. The accuracy significantly improved compared to using a single remote sensing data source. This study presents a new framework capable of monitoring early forest defoliation caused by D. tabulaeformis attacks and offering opportunities for predicting future outbreaks and implementing preventive measures.

Simple and efficient sample pretreatment methods are important for analysis and detection of chemical warfare agents (CWAs) in environmental and biological samples. Despite many commercial materials or reagents that have been already applied in sample preparation, such as SPE columns, few materials with specificity have been utilized for purification or enrichment. In this study, ionic magnetic mesoporous nanomaterials such as poly(4-VB)@M-MSNs (magnetic mesoporous silicon nanoparticles modified by 4-vinyl benzene sulfonic acid) and Co2+@M-MSNs (magnetic mesoporous silicon nanoparticles modified by cobalt ions) with high absorptivity for ethanol amines (EAs, nitrogen mustard degradation products) and cyanide were successfully synthesized. The special nanomaterials were obtained by modification of magnetic mesoporous particles prepared based on co-precipitation using -SO3H and Co2+. The materials were fully characterized in terms of their composition and structure. The results indicated that poly(4-VB)@M-MSNs or Co2+@M-MSNs had an unambiguous core-shell structure with a BET of 341.7 m2·g−1 and a saturation magnetization intensity of 60.66 emu·g−1 which indicated the good thermal stability. Poly(4-VB)@M-MSNs showed selective adsorption for EAs while the Co2+@M-MSNs were for cyanide, respectively. The adsorption capacity quickly reached the adsorption equilibrium within the 90 s. The saturated adsorption amounts were MDEA = 35.83 mg·g−1, EDEA = 35.00 mg·g−1, TEA = 17.90 mg·g−1 and CN−= 31.48 mg·g−1, respectively. Meanwhile, the adsorption capacities could be maintained at 50–70% after three adsorption–desorption cycles. The adsorption isotherms were confirmed as the Langmuir equation and the Freundlich equation, respectively, and the adsorption mechanism was determined by DFT calculation. The adsorbents were applied for enrichment of targets in actual samples, which showed great potential for the verification of chemical weapons and the destruction of toxic chemicals.

Abstract Background Despite that several original researchers have investigated the association between neck circumference (NC) and the risk of diabetes mellitus (DM), their results remain controversial. This review aimed to quantitatively determine the risk of DM in relation to the NC. Methods We conducted a literature search of PubMed, Embase, and the Web of Science from these databases’ inception through September 2022 to identify observational studies that examined the association between NC and the risk of DM. A meta-analysis of the random-effects model was applied to combine the results of the enrolled studies. Results Sixteen observational studies involving 4,764 patients with DM and 26,159 participants were assessed. The pooled results revealed that NC was significantly associated with the risk of type 2 DM (T2DM) (OR = 2.17; 95% CI: 1.30–3.62) and gestational DM (GDM) (OR = 1.31; 95% CI: 1.17–1.48). Subgroup analysis revealed that after controlling for BMI, the relationship between the NC and T2DM remained statistically significant (OR = 1.94; 95% CI: 1.35–2.79). Moreover, the pooled OR of T2DM was found to be 1.16 (95% CI: 1.07–1.27) for an increment per each centimeter in the NC. Conclusions Integrated epidemiological evidence supports the hypothesis that a greater NC is associated with an increased risk of T2DM and GDM.

Abstract Background Genetic diversity, genetic structure, and gene flow in plant populations and their influencing factors are important in conservation biology. Cypripedium macranthos is one of the few wild orchids with high ornamental value in northern China. However, over the past decade, excessive collection, trading, tourism development, habitat fragmentation, deceptive pollination, and seed germination difficulties have all caused a sharp decline in the number of C. macranthos individuals and its population. In order to propose a scientific and effective conservation strategy, the genetic diversity, genetic structure and gene flow of the current CM population are urgent scientific issues to be clarified. Results Here, 99 individuals of C. macranthos from north and northeast China were analyzed to evaluate the genetic diversity, gene flow among populations, and genetic structure by genotyping-by-sequencing. More than 68.44 Gb high-quality clean reads and 41,154 SNPs were obtained. Our data based on bioinformatics methods revealed that C. macranthos has lower genetic diversity, high levels of historical gene flow, and moderate-to-high genetic differentiation between populations. The gene migration model revealed that the direction of gene flow was mainly from northeast populations to north populations in China. The results of genetic structure analysis showed that 11 C. macranthos populations can be considered as two groups, and further divided into four subgroups. Moreover, the Mantel test detected no significant “Isolation by Distance” between populations. Conclusions Our study demonstrates that the present genetic diversity and genetic structure of C. macranthos populations were mainly caused by biological characteristics, human interference, habitat fragmentation, and restricted gene flow. Finally, constructive measures, which can provide a basis for the proposal of conservation strategies, have been suggested.

Porous has been formed in samples when mullite whiskers are prepared by firing compacts of high-aluminum fly ash and ammonium aluminum sulfate hydrate with a small amount of sodium dihydrogen phosphate and SiO2 at high temperature for 4 h. The effects of the particle size of fly ash, the firing temperature, and the content of sodium dihydrogen phosphate on the porous structure and the formation of whiskers are discussed. The results show that the smaller the particle size of fly ash, the more uniform porous in sample after fired, which is more conducive to the nucleation of mullite whiskers. Additionally, the amount of whiskers formation increased significantly with the firing temperature increasing, nonetheless, the pore size of porous reduced and the aspect ratio of whiskers gradually increased by reducing the content of sodium dihydrogen phosphate; owing to the reduction of glass melt in the matrix, the Al2O3/SiO2 ratio of the formed mullite whiskers gradually decreased. Thus, in order to obtain mullite whiskers self-reinforcement porous ceramics with the relatively low cost and no pollution to the environment, through the above preparation process, the addition amount of sodium dihydrogen phosphate and calcination temperature should be properly controlled.

With a growing number of research reports on microplastics (MPs), there is increasing concern regarding MPs-induced contamination in soil ecological systems. Notwithstanding, the interaction between the plastisphere and rhizosphere microbial hotspots in soil-plant systems, as well as the diversity and composition of plastisphere microbial communities in such systems, remain largely unexplored. This study evaluated the response of rhizosphere bacterial communities to MPs at three growth stages of pepper and examined the bacterial communities present on MPs (plastisphere). The 16 S rRNA revealed that, under the stress of MPs, the Chao1 and Shannon index of the pepper soil bacterial community decreased. Meanwhile the relative abundance of Actinobacteriota was decreased, and that of Proteobacteria was increased. Furthermore, the plastisphere serves as a unique microbial habitat (niche) that recruits the colonization of specific bacterial groups, including potential plastic-degrading bacteria and potential pathogens (e.g., Massilia and Pseudomonas). Simultaneously, the plastisphere recruits specific bacteria that may impact the rhizosphere soil bacterial communities, thus indirectly affecting plant growth. Functional prediction using PICRUSt2 revealed higher activity in the plastisphere for Metabolism of terpenoids and polyketides, Human diseases, and Xenobiotics biodegradation and metabolism. Notably, the human diseases metabolic pathway exhibited increased activity, suggesting potential ecological risks associated with pathogens. These results highlighted that the plastisphere serves as a unique microbial habitat (niche) in the soil ecological systems, recruiting specific bacteria and potentially interfering with the surrounding soil microbial community, thereby influencing the functional characteristics of the soil ecological systems.

Abstract We aimed to investigate the relationship between spatiotemporal changes of amyloid deposition and Alzheimer’s disease (AD) profiles in cognitively normal (CN) and those with mild cognitive impairment (MCI). Using a data-driven method and amyloid-PET data, we identified and validated two subtypes in two independent datasets (discovery dataset: N = 548, age = 72.4 ± 6.78, 49% female; validation dataset: N = 348, age = 74.9 ± 8.16, 47% female) from the Alzheimer’s Disease Neuroimaging Initiative across a range of individuals who were CN or had MCI. The two subtypes showed distinct regional progression patterns and presented distinct genetic, clinical and biomarker characteristics. The cortex-priority subtype was more likely to show typical clinical syndromes of symptomatic AD and vice versa. Furthermore, the regional progression patterns were associated with clinical and biomarker profiles. In sum, our findings suggest that the spatiotemporal variants of amyloid depositions are in close association with disease trajectories; these findings may provide insight into the disease monitoring and enrollment of therapeutic trials in AD.

Phytophthora root rot is a devastating disease of soybean caused by Phytophthora sojae. However, the resistance mechanism is not yet clear. Our previous studies have shown that GmAP2 enhances sensitivity to P. sojae in soybean, and GmMYB78 is downregulated in the transcriptome analysis of GmAP2-overexpressing transgenic hairy roots. Here, GmMYB78 was significantly induced by P. sojae in susceptible soybean, and the overexpressing of GmMYB78 enhanced sensitivity to the pathogen, while silencing GmMYB78 enhances resistance to P. sojae, indicating that GmMYB78 is a negative regulator of P. sojae. Moreover, the jasmonic acid (JA) content and JA synthesis gene GmAOS1 was highly upregulated in GmMYB78-silencing roots and highly downregulated in overexpressing ones, suggesting that GmMYB78 could respond to P. sojae through the JA signaling pathway. Furthermore, the expression of several pathogenesis-related genes was significantly lower in GmMYB78-overexpressing roots and higher in GmMYB78-silencing ones. Additionally, we screened and identified the upstream regulator GmbHLH122 and downstream target gene GmbZIP25 of GmMYB78. GmbHLH122 was highly induced by P. sojae and could inhibit GmMYB78 expression in resistant soybean, and GmMYB78 was highly expressed to activate downstream target gene GmbZIP25 transcription in susceptible soybean. In conclusion, our data reveal that GmMYB78 triggers soybean sensitivity to P. sojae by inhibiting the JA signaling pathway and the expression of pathogenesis-related genes or through the effects of the GmbHLH122-GmMYB78-GmbZIP25 cascade pathway.

Water splitting is considered a renewable and eco−friendly technique for future clean energy requirements to realize green hydrogen production, which is, to a large extent, hindered by the oxygen evolution reaction (OER) process. In recent years, two−dimensional (2D) carbon−based electrocatalysts have drawn sustained attention owing to their good electrical conductivity, unique physicochemical properties, and excellent electrocatalytic performance. Particularly, it is easy for 2D carbon−based materials to form nanocomposites, which further provides an effective strategy for electrocatalytic applications. In this review, we discuss recent advances in synthetic methods, structure−property relationships, and a basic understanding of electrocatalytic mechanisms of 2D carbon−based electrocatalysts for water oxidation. In detail, precious, non−precious metal−doped, and non−metallic 2D carbon−based electrocatalysts, as well as 2D carbon−based confined electrocatalysts, are introduced to conduct OER. Finally, current challenges, opportunities, and perspectives for further research directions of 2D carbon−based nanomaterials are outlined. This review can provide significant comprehension of high−performance 2D carbon−based electrocatalysts for water-splitting applications.

Accurate classification of forest tree species holds great significance in the context of forest biodiversity assessment and the management of forest resources. In this study, we utilized Sentinel-2 time series data with high temporal and spatial resolution for tree species classification. To address potential classification errors stemming from spectral differences due to tree age variations, we implemented the Continuous Change Detection and Classification (CCDC) algorithm to estimate tree ages, which were integrated as additional features into our classification models. Four different combinations of classification features were created for both the random forest (RF) algorithm and extreme gradient boosting (XGB) algorithm: spectral band (Spec), spectral band combined with tree age feature (SpecAge), spectral band combined with spectral index (SpecVI), and spectral band combined with spectral index and tree age feature (SpecVIAge). The results demonstrated that the XGB-based models outperformed the RF-based ones, with the SpecVIAge model achieving the highest accuracy at 78.8%. The incorporation of tree age as a classification feature led to an improvement in accuracy by 2% to 3%. The improvement effect on classification accuracy varies across tree species, due to the varying uniformity of tree age among different tree species. These results also showed it is feasible to accurately map regional tree species based on a time-series multi-feature tree species classification model which takes into account tree age.

With the growing scarcity of spectrum resources, wideband spectrum sensing is necessary to process a large volume of data at a high sampling rate. For some applications, only second-order statistics are required for spectrum estimation. In this case, a fast power spectrum sensing solution is proposed based on the generalized coprime sampling. The solution involves the inherent structure of the sensing vector to reconstruct the autocorrelation sequence of inputs from sub-Nyquist samples, which requires only parallel Fourier transform and simple multiplication operations. Thus, it takes less time than the state-of-the-art methods while maintaining the same performance, and it achieves higher performance than the existing methods within the same execution time without the need to pre-estimate the number of inputs. Furthermore, the influence of the model mismatch has only a minor impact on the estimation performance, allowing for more efficient use of the spectrum resource in a distributed swarm scenario. Simulation results demonstrate the low complexity in sampling and computation, thus making it a more practical solution for real-time and distributed wideband spectrum sensing applications.

For the problem of difficult tracking of small leaks in buried gas pipelines, a video detection of small leaks in buried gas pipelines method is proposed for the detection robot inside buried gas pipelines. Firstly, collecting images and videos of leaks inside buried gas pipelines to establish a dataset. Secondly, build a video detection model for small leaks in buried gas pipelines, introduce a bidirectional feature pyramid network into YOLOv5s (You Only Look Once), and build a feature fusion network to enhance the model’s ability to fuse small leaks. Thirdly, building a small target detection layer and a small target detection head in the YOLOv5s classification prediction network enhances the model’s ability to fuse small leaks. Fourthly, the video detection model for small leaks in buried gas pipelines is trained using the dataset. Lastly, the model’s video detection effect of small leaks is verified through leak detection experiments in various situations. The experimental results show that the precision rate of this method is 94.1%, the recall is 94.8%, and the average precision is 94.5%, which has a good detection effect and strong generalization ability.

Understanding the distribution of wetland plant communities is critical to biodiversity conservation and wetland habitat sustainable management, especially for migratory birds. However, limited road accessibility and low spectral discriminability make the mapping of wetland plant communities inadequate for wetland health assessment, necessitating the improvement of classification methods. In this study, we proposed a random forest classifier that combined multi-source remote sensing features for wetland plant community classification and evaluated this method for the Momoge Ramsar wetland site (MRWS) in China. The major result of this work was that the phenological and time-series features based on Sentinel-2 images were the most valuable discriminators for wetland plant community classification in the MRWS. The SAR_sum extracted from Sentinel-1 images also had high importance for classification. Moreover, the spatial pattern of different wetland plant communities was revealed, and the resultant classification map had a high overall accuracy (91.3%) and Kappa coefficient (0.90). And the six important features prompted the classification accuracy to reach 84.8%. In 2020, the total coverage area of natural wetland plant communities in MRWS reached 628.5 km2 (42.1%), of which Carex meyeriana distributed the most widely, followed by Suaeda glauca, Phragmites australis, Typha orientalis, and Scirpus triquater. The findings of this study can provide scientific decision-making support for the protection and management of migratory birds and plants in the MRWS. The proposed method employed freely available Sentinel 1/2 satellite images and fully automated programs on Google Earth Engine, and has guiding significance for large-scale and long-time-series wetland classification.

Abstract Background Mycoplasma synoviae (MS) is an important pathogen causing respiratory diseases and arthritis in chickens and turkeys, thus, resulting in serious economic losses to the poultry industry. Membrane-associated proteins are thought to play important roles in cytoadherence and pathogenesis. NADH oxidase (NOX) is an oxidoreductase involved in glycolysis, which is thought to be a multifunctional protein and potential virulence factor in some pathogens. However, little is known regarding the NOX of MS (MSNOX). We previously demonstrated that MSNOX was a metabolic enzyme distributed in not only the cytoplasm but also the MS membrane. This study was aimed at exploring NOX’s potential as a diagnostic antigen and its role in MS cytoadherence. Results Western blots and ELISAs indicated that recombinant MSNOX (rMSNOX) protein reacted with sera positive for various MS isolates, but not MG isolates or other avian pathogens, thus, suggesting that rMSNOX is a potential diagnostic antigen. In addition, rabbit anti-rMSNOX serum showed substantial complement-dependent mycoplasmacidal activity toward various MS isolates and MG Rlow. MSNOX protein was found not only in the cytoplasm but also on the membrane of MS through suspension immunofluorescence and immunogold electron microscopy assays. Indirect immunofluorescence assays indicated that rMSNOX adhered to DF-1 cells, and this adherence was inhibited by rabbit anti-rMSNOX, but not anti-MG serum. Furthermore, indirect immunofluorescence and colony counting assays confirmed that the rabbit anti-rMSNOX serum inhibited the adherence of various MS isolates but not MG Rlow to DF-1 cells. Moreover, plasminogen (Plg)- and fibronectin (Fn)-binding assays demonstrated that rMSNOX bound Plg and Fn in a dose-dependent manner, thereby further confirming that MSNOX may be a putative adhesin. Conclusion MSNOX was identified to be a surface immunogenic protein that has good immunoreactivity and specificity in Western blot and ELISA, and therefore, may be used as a potential diagnostic antigen in the future. In addition, rMSNOX adhered to DF-1 cells, an effect inhibited by rabbit anti-rMSNOX, but not anti-MG serum, and anti-rMSNOX serum inhibited the adherence of various MS isolates, but not MG Rlow, to DF-1 cells, thus indicating that the inhibition of adherence by anti-MSNOX serum was MS specific. Moreover, rMSNOX adhered to extracellular matrix proteins including Plg and Fn, thus suggesting that NOX may play important roles in MS cytoadherence and pathogenesis. Besides, rabbit anti-rMSNOX serum presented complement-dependent mycoplasmacidal activity toward both MS and MG, indicating the MSNOX may be further studied as a potential protective vaccine candidate.

For automated grinding, such as robotic grinding, high stability of the manufacturing system is required during the machining process. Little attention has been paid to the effect of the uniformity of the matching margin on the machining stability as a result of visual matching. In fact, it has an important relationship with maintaining the stability of robotic grinding and keeping the uniformity of grinding depth. In this paper, important process features of the blade part are extracted to improve the matching efficiency with a point cloud, while the normal direction of the contour after finishing grinding is used as the normal direction for registration. The objective function of minimization of the allowance variance is constructed to improve the uniformity of the grinding depth. The overall uniformity of the calculation results is also considered, and the effect of no tangential distance tampering is achieved by constraining the shape centers of the two point cloud models. The method in this paper is compared with the classical matching method to verify the feasibility of the margin variance minimization algorithm based on the decentered point cloud edges in terms of algorithm speed, convergence, accuracy, spatial complexity, and the uniformity of the obtained results.

Background To explore the relationship between duration of hypertension and inflammatory cell levels and to assess whether long duration might aggravate these inflammatory cells among Chinese urban community residents. Methods A cross-sectional study of 5199 hypertensive and 2675 no-hypertensive participants who registered in community health service centers for physical examination was performed in Tianjin, China. Data of blood pressure and inflammatory cells were collected. Binary logistic regression was performed to assess the effect of hypertensive duration on the level of inflammatory cells before and after adjustment for the potential confounding factors. Results Individuals with hypertension had significantly higher level of leukocyte count, neutrophil proportion, neutrophil-to-lymphocyte ratio (NLR), and lower level of lymphocyte proportion than those without hypertension. Two-way ANOVA showed that hypertension duration, rather than blood pressure control or their interaction, had significant influence on the levels of neutrophil proportion, lymphocyte proportion, and NLR. With the prolongation of the duration of hypertension, the level of neutrophil proportion and NLR increased, and the level of lymphocyte proportion decreased. Long hypertension duration (>10 years) was significantly associated with high level of neutrophil proportion (OR = 1.48, 95% CI: 1.25, 1.75), high level of NLR (OR = 1.53, 95% CI: 1.29, 1.81), and low level of lymphocyte proportion (OR = 1.54, 95% CI: 1.30, 1.82) in comparison with short duration (

Hepatic fibrosis is caused by liver damage as a consequence of wound healing response. Recent studies have shown that hepatic fibrosis could be effectively reversed, partly through regression of activated hepatic stellate cells (HSCs). Transcription factor 21 (TCF21), a member of the basic helix-loop-helix (bHLH) transcription factor, is involved in epithelial-mesenchymal transformation in various diseases. However, the mechanism by which TCF21 regulates epithelial-mesenchymal transformation in hepatic fibrosis has not been elucidated. In this research, we found that hnRNPA1, the downstream binding protein of TCF21, accelerates hepatic fibrosis reversal by inhibiting the NF-κB signaling pathway. Furthermore, the combination of DNMT3a with TCF21 promoter results in TCF21 hypermethylation. Our results suggest that DNMT3a regulation of TCF21 is a significant event in reversing hepatic fibrosis. In conclusion, this research identifies a novel signaling axis, DNMT3a-TCF21-hnRNPA1, that regulates HSCs activation and hepatic fibrosis reversal, providing a novel treatment strategy for hepatic fibrosis. The clinical trial was registered in the Research Registry (researchregistry9079).

Heart failure with preserved ejection fraction (HFpEF) is typically preceded by cardiac hypertrophy. Lycium barbarum L. as functional food show the health benefits of cardiovascular diseases. Whether Lycium barbarum L. protein-derived angiotensin-converting enzyme inhibitory peptide (ACEIP) has effects on improving cardiac hypertrophy is unknown. Here, HFpEF mouse model was established by a high-fat diet and 0.5 g/L Nѡ-Nitro-L-arginine methyl ester hydrochloride (L-NAME) in drinking water. Interventricular septum thickness (IVS), left ventricular posterior wall thickness (LVPW), left ventricular diameter (LVD), ejection fraction (EF) and left ventricular mass (LV Mass) were evaluated by transthoracic echocardiography. Serum n-terminal pro-brain natriuretic peptide (NT-proBNP), Ang II, Ang (1–7) concentrations were measured by ELISA, and hypertrophy by WGA staining. We found that ACEIP significantly improved diastolic dysfunction as reflected by the increase in mitral inflow E and A wave ratio (E/A ratio) and decrease in isovolumic relaxation time (IVRT). Meanwhile, LV Mass, IVS, LVPW and LVD in systolic or diastolic period were significantly reduced by ACEIP. Furthermore, ACEIP reversed protein kinase C β1 (PKCβ1) and extracellular signal-regulated kinase 1/2 (ERK1/2) phosphorylation, which were related to ACE2/Ang (1–7)/MasR axis activation. Our findings indicated that ACEIP could be a potential nutritional approach for the intervention of HFpEF.

An oxygen reduction reaction (ORR) is the key half reaction of proton exchange membrane fuel cells (PEMFCs), and is highly dependent on Pt-based nanocrystals as core electrocatalysts. Despite the exceptional ORR activity from adjusting the electronic structures of surface or near-surface atoms, several serious issues, including the corrosion of carbon supports, the preferential leaching of active metal elements, the instability of surface low-coordinated atoms and the sintering/agglomeration of nanocrystals, still exist, challenging the ORR durability of developed Pt-based ORR catalysts. From the point of view of the catalyst structure design, in this review, we summarized the state-of-the-art structural regulation strategies for improving the ORR durability of Pt-based catalysts. The current limitation of Pt-based binary catalysts for ORR electrocatalysis is firstly discussed, and the detailed strategies are further classified into the optimization of supports, metal-doped alloys, core/shell structures, intermetallics and high-entropy alloys, etc. The structure–performance relationship is detailedly explained, especially emphasizing the elimination of the above restrictions. Finally, the existing challenges and future research direction are further presented, aiming at practicing the PEMFC devices of the ultradurable Pt-based catalysts.

Tea (Camellia sinensis) is one of the significant cash crops in China. As a leaf crop, nitrogen supply can not only increase the number of new shoots and leaves but also improve the tenderness of the former. However, a conundrum remains in science, which is the molecular mechanism of nitrogen use efficiency, especially long non-coding RNA (lncRNA). In this study, a total of 16,452 lncRNAs were identified through high-throughput sequencing analysis of lateral roots under nitrogen stress and control conditions, of which 9,451 were differentially expressed lncRNAs (DE-lncRNAs). To figure out the potential function of nitrogen-responsive lncRNAs, co-expression clustering was employed between lncRNAs and coding genes. KEGG enrichment analysis revealed nitrogen-responsive lncRNAs may involve in many biological processes such as plant hormone signal transduction, nitrogen metabolism and protein processing in endoplasmic reticulum. The expression abundance of 12 DE-lncRNAs were further verified by RT-PCR, and their expression trends were consistent with the results of RNA-seq. This study expands the research on lncRNAs in tea plants, provides a novel perspective for the potential regulation of lncRNAs on nitrogen stress, and valuable resources for further improving the nitrogen use efficiency of tea plants.

ABSTRACT Generation of hybrid MDR plasmids accelerated the evolution and transmission of resistance genes. In this study, we characterized a blaKPC-2- and blaIMP-4-coharboring conjugative hybrid plasmid constituted of an IncHI5 plasmid-like region, an IncFII(Yp)/IncFIA plasmid-like region, and a KPN1344 chromosome-like region from a clinical ST852-KL18 Klebsiella quasipneumoniae strain. The blaIMP-4 gene was captured by a novel integron In1965, and the blaKPC-2 gene was located on a new non-Tn4401 group I NTEKPC element. Both blaKPC-2- and blaIMP-4-containing genetic architectures were distinguished from classical structures, highlighting the constant evolution of these genetic elements. IMPORTANCE The emergence of carbapenem-resistant Enterobacterales (CRE) that coexpress serine- and metallo-carbapenemases is a severe threat to the efficacy of ceftazidime-avibactam (CZA), which has been proven to be extremely effective against KPC-producing Enterobacterales strains. Our study described the cooccurrence of KPC-2, a serine β-lactamase, and IMP-4, a metallo-β-lactamase (MBL), on a conjugative hybrid plasmid from a clinical carbapenem-resistant K. quasipneumoniae strain, and it revealed an alternative route for IncHI5 plasmid to evolve by recombining with other plasmids to form a hybrid plasmid. Moreover, this hybrid plasmid can be transferred into other Klebsiella species and stably persist during passage. The propagation of two important carbapenemase genes with a new genetic background using well-evolved plasmids in the clinical setting promotes the emergence of superbugs that require careful monitoring.

Abstract Tumor necrosis factor-α-induced protein 8 (TNFAIP8 or TIPE) is a member of the TNFAIP8 family. While TIPE was broadly considered to be pro-cancerous, its precise roles in carcinogenesis especially those of the intestinal tract are not clear. Here, we show that genetic deletion of TIPE in mice exacerbated chemical-induced colitis and colitis-associated colon cancer. Loss of TIPE exacerbated inflammatory responses and inflammation-associated dysbiosis, leading to the activation of NF-κB and STAT3, and it also accelerated dysplasia, DNA damage and proliferation of intestinal epithelial cells. We further show that colon microbiota were essential for increased tumor growth and progression in Tipe −/− mice. The tumor suppressive function of TIPE originated primarily from the non-hematopoietic compartment. Importantly, TIPE was downregulated in human colorectal cancers, and patients with low levels of Tipe mRNA were associated with reduced survival. These results indicate that TIPE serves as an important modulator of colitis and colitis-associated colon cancer.

Abstract Background Only when people feel they have received timely disclosure will they have sufficient incentive to implement community prevention and control measures. The timely and standardized information published by authorities as a response to the crisis can better inform the public and enable better preparations for the pandemic during the low transmission period of COVID-19; however, there is limited evidence of whether people consent that information is disclosed timely and influencing factors. Methods A cross-sectional survey was conducted in China from 4 to 26 February 2021. Convenient sampling strategy was adopted to recruit participators. Participants were asked to filled out the questions that assessed questionnaire on the residents’ attitudes to information disclosure timely. A binary logistic regression analysis was performed to identify the risk factors affecting the residents’ attitudes. Results A total of 2361 residents filled out the questionnaire. 1704 (72.17%) consented COVID-19 information has been disclosed timely. Furthermore, age (OR = 0.093, 95%CI = 0.043 ~ 0.201), gender (OR = 1.396, 95%CI = 1.085 ~ 1.797), place of residence (OR = 0.650, 95%CI = 0.525 ~ 0.804), employed status (OR = 2.757, 95%CI = 1.598 ~ 4.756), highest educational level (OR = 0.394, 95%CI = 0.176 ~ 0.880), region (OR = 0.561, 95%CI = 0.437 ~ 0.720) and impact on life by the COVID-19 (OR = 0.482, 95%CI = 0.270 ~ 0.861) were mainly factors associated with residents’ attitudes. Conclusions The aims of this study were to evaluate the residents attitudes to information disclosure timely during the low transmission period in China and to provide a scientific basis for effective information communication in future public health crises. Timely and effective efforts to disclose information need to been made during the low transmission period. Continued improvements to local authority reporting will contribute to more effective public communication and efficient public health research responses. The development of protocols and the standardization of epidemic message templates—as well as the use of uniform operating procedures to provide regular information updates—should be prioritized to ensure a coordinated national response.

Since ocean mobile observation equipment and numerical models have achieved remarkable results, the combination of the two has become an influential topic. A numerical model provides auxiliary information for the arrangement of observation equipment. As feedback, observations help produce more accurate initial conditions when incorporated into data assimilation. However, it is still worth investigating the ways to select the most valuable observation sites within the computational domain and the ways to design the observation scheme of the mobile platform. To improve the efficiency of observation, researchers attempt to select the best observation region in the vast ocean. The approach of deploying additional observing assets in selected regions is referred to as targeted observation. By combining the features of the targeted observation and the mobile observing platform, we propose a design approach for the observation scheme. First, based on a model, we estimate the initial perturbation that causes the greatest change in the sea surface temperature in the future. Then, according to the spatial component of the perturbation, we divide the experimental regions into sensitive regions and non-sensitive regions. Observing system simulation experiments are carried out to verify that samples in sensitive regions are more helpful to improve model prediction. Afterward, considering the variation of the perturbation with time, we propose a hybrid sampling scheme design method for an underwater unmanned vehicle combining Q-learning and particle swarm optimization algorithm. Finally, the effectiveness of the hybrid algorithm is verified by comparing the sampling schemes designed in static environment. This approach provides a dynamic basis for the path planning of mobile observing platforms.

Background: To investigate the effects of social factors including social support and social restriction on postpartum depression (PPD) during the COVID-19 pandemic. Methods: We conducted a systematic review with PubMed and Web of Science from 1 January 2020 to 1 April 2023. Articles focusing on social factors and PPD during COVID-19 pandemic were investigated. Newcastle-Ottawa Scale and the Agency for Healthcare Research and Quality Scale were used to evaluate quality of literatures. Results: We included 31 articles (22 cross-sectional studies and 9 cohort studies) with good quality. Edinburgh Postnatal Depression Scale (EPDS) was used as the preferred measure for defining PPD. Social support reduced the prevalence of developing PPD by 30%–40%. Social restriction increased the risk of PPD but there was lack of evidence or effective scales to define restriction. Conclusions: We found that social support acted as an important protective factor for PPD during the COVID-19 pandemic and that social restriction increased the risk of PPD. EPDS may be an optimal choice for researchers to define PPD. Obstetricians should underline social factors for both pregnancy women and their families. The study was registered on PROSPERO (https://www.crd.york.ac.uk/prospero/), registration number: CRD42023434485.

The gradually increasing presence of arsenic, a highly toxic heavy metal, poses a significant threat to both soil environmental safety and human health. Pteris vittata has long been recognized as an efficient hyperaccumulator plant for arsenic pollution. However, the pattern of arsenic accumulation in soil impacts its bioavailability and restricts the extraction efficiency of Pteris vittata. To address this issue, microorganisms have the potential to improve the arsenic accumulation efficiency of Pteris vittata. In this work, we employed anthropogenic enrichment methods to extract functional iron–sulfur-reducing bacteria from soil as a raw material. These bacteria were then utilized to assist Pteris vittata in the phytoremediation of arsenic-contaminated soil. Furthermore, the utilization of organic fertilizer produced from fermented crop straw significantly boosted the remediation effect. This led to an increase in the accumulation efficiency of arsenic by Pteris vittata by 87.56%, while simultaneously reducing the content of available arsenic in the soil by 98.36%. Finally, the experimental phenomena were studied through a soil-microbial batch leaching test and plant potting test. And the mechanism of the microorganism-catalyzed soil iron–sulfur geochemical cycle on arsenic release and transformation in soil as well as the extraction effect of Pteris vittata were systematically investigated using ICP, BCR sequential extraction and XPS analysis. The results demonstrated that using iron–sulfur-reducing microorganisms to enhance the phytoremediation effect is an effective strategy in the field of ecological restoration.

Microwave radiometers are passive remote sensing devices that are widely used in marine atmospheric observations. The accuracy of its inversion of temperature and humidity profiles is an important indicator of its performance. Back Propagation (BP) neural networks are widely used in the study of microwave radiometer inversion problems. However, the BP network which is carried by the radiometer inversion suffers from profile data collapse. To address this, this study introduced a residual network to improve the accuracy of water vapor vertical profiles. Aiming at the problem of large inversion temperature error due to the effect of turbulence on the light-travel phase induced by stationary fronts along the seashore in the subtropical monsoon climate region, we used historical data to establish the seasonal a priori mean profile and design a dead-zone residual adjustment model. The accuracy of the residual network and the deadband-adjusted residual network was verified using the meteorological records of the Taizhou region from 2013–2018, with the experimental data and BP hierarchical network as the comparison term. We found no data collapse in the temperature and humidity profile inversion results of the residual network. Relative to the initial BP hierarchical algorithm, where the error of water vapor in the range 6–10 km was reduced by 80%, the dead zone residual adjustment model in the inverse-temperature phenomenon reduced the sum of squares error by 21%, compared with the ordinary residual network inversion results. Our findings provide new insights into the accuracy improvement of radiometer remote sensing.

To address difficulties in the early detection of small gas pipeline leaks, a method is proposed for the image recognition of micropores inside small gas pipelines. First, we design a feature fusion network (Neck) for a small gas pipeline internal micropores identification network model, introducing BiFPN (bi-directional feature pyramid network) into the Neck, and enhancing the micropores feature fusion capability of the network model using cross-scale feature information fusion. Second, we design a classification prediction network (Head) for a small gas pipeline internal micropores identification network model, constructing a small target detection layer in the classification prediction network, and improving the microporosity detection rate of the network model by improving the detection capability for small leakages. Then, micropore datasets are fed into the network model separately to train the model. Finally, comparison and ablation experiments are used to verify the micropore recognition ability of the network model. The experimental results show that the precision of the image recognition method for micropores inside small gas pipelines is 94.7%, the detection rate is 96.6%, and the average precision is 95.5%, which can better realize the early detection of leakages in small gas pipelines.