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Human metapneumovirus (HMPV), which is distributed worldwide, is a significant viral respiratory pathogen responsible for causing acute respiratory tract infections (ARTIs) in children. The aim of the present study was to investigate the epidemiological and genetic characteristics of HMPV in pediatric patients in Hangzhou China following the peak of onset of coronavirus disease 2019 (COVID-19). A total of 1442 throat swabs were collected from the pediatric patients with a diagnosis of ARTI from November 2020 to March 2021. The following viruses were detected by real-time polymerase chain reaction analysis: HMPV, RSV, adenovirus, hPIV1-3, influenza A, and influenza B. A two-step method was used to amplify the F genes of the HMPV-positive samples. Following sequencing, phylogenetic analyses were conducted using the MEGA version 7 software package. Among the 1442 samples, 103 (7.14%) were positive for HMPV. No significant differences were observed in the gender distribution. The highest incidence of HMPV occurred in children older than 6 years and the lowest was noted in children younger than 6 months. Lower respiratory tract infections were diagnosed at a higher rate than upper respiratory tract infections in HMPV-infected children. Only 10 HMPV-infected children (5.41%) were inpatients compared with 93 outpatients (7.39%). Co-infection was observed in 31 HMPV-positive samples including 24 samples of double infection and seven samples of triple infection. A total of 61F gene fragments of HMPV, which were approximately 727 bp in length were successfully sequenced. All the HMPVs belonged to the genotype B and were clustered into subgenotypes B1 (1.6%, 1/61) and B2 (98.4%, 60/61). A total of four specific amino acid substitutions were noted as follows: aa280, aa296, aa392, and aa396. These substitutions were present between sequences derived from the subgenotypes B1 and B2 in the fusion open reading frame from position 244 to 429. In conclusion, the present study provided significant information regarding the epidemiological and genetic characteristics of HMPV in children living in Hangzhou. Following the first peak of the COVID-19 pandemic, HMPV was considered an important viral respiratory pathogen present in children with ARTI.

Aging is characterized with a progressive decline in many cognitive functions, including behavioral flexibility, an important ability to respond appropriately to changing environmental contingencies. However, the underlying mechanisms of impaired behavioral flexibility in aging are not clear. In this study, we reported that necroptosis-induced reduction of neuronal activity in the basolateral amygdala (BLA) plays an important role in behavioral inflexibility in 5-month-old mice of the senescence-accelerated mice prone-8 (SAMP8) line, a well-established model with age-related phenotypes. Application of Nec-1s, a specific inhibitor of necroptosis, reversed the impairment of behavioral flexibility in SAMP8 mice. We further observed that the loss of glycogen synthase kinase 3α (GSK-3α) was strongly correlated with necroptosis in the BLA of aged mice and the amygdala of aged cynomolgus monkeys (Macaca fascicularis). Moreover, genetic deletion or knockdown of GSK-3α led to the activation of necroptosis and impaired behavioral flexibility in wild-type mice, while the restoration of GSK-3α expression in the BLA arrested necroptosis and behavioral inflexibility in aged mice. We further observed that GSK-3α loss resulted in the activation of mTORC1 signaling to promote RIPK3-dependent necroptosis. Importantly, we discovered that social isolation, a prevalent phenomenon in aged people, facilitated necroptosis and behavioral inflexibility in 4-month-old SAMP8 mice. Overall, our study not only revealed the molecular mechanisms of the dysfunction of behavioral flexibility in aged people but also identified a critical lifestyle risk factor and a possible intervention strategy.

Background. Insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) plays a key role in tumorigenesis and tumor progression. Lung cancer is the leading cause of cancer-related death in men and women all over the world. However, the relationship between IGF2BP3 and small-cell lung cancer (SCLC) has not been reported yet. Methods. SCLC and normal samples (GSE19945 and GSE149507) were obtained in the Gene Expression Omnibus (GEO) dataset. Differential genes were screened by R software, and functional analysis and signal pathway enrichment analysis were carried out. In addition, we used the survival analysis database to analyze the relationship between prognosis and gene expression. Besides, immunohistochemistry (IHC) and quantitative real-time PCR (qPCR) were used for further research. Results. Five differentially expressed miRNAs and 9 differentially expressed mRNAs were selected by using R software. Survival analysis database results show that C7, CLIC5, PRDX1, IGF2BP3, and LDB2 were related the overall survival of patients with SCLC. Furthermore, multivariate analysis included that IGF2BP3 was independent risk factors for SCLC patients. Besides, gene function and signal pathway enrichment analysis showed that differentially expressed miRNAs were involved in the process of tumorigenesis and development. Furthermore, IHC and qPCR outcomes showed that the expression level of hsa-miR-182, hsa-miR-183, and IGF2BP3 was differentially expressed in normal lung tissues (NLTs) and SCLC tissues (SCLCTs). Conclusions. Our results concluded that hsa-miR-182, hsa-miR-183, and IGF2BP3 may take part in the development of SCLC.

The JRC Global Surface Water (GSW) dataset is a valuable resource for tracking changes in global water resources over 38 years (1984-2021), with a high-resolution of 30 meters. This dataset plays an essential role in understanding climate change, water management, and environmental monitoring. However, around one-third of the dataset is affected by gap or invalid observations, mainly due to issues with the Landsat archive. This data gap can significantly limit the dataset's usefulness in various real-world applications. To overcome this challenge, we developed a self-supervised learning method to fill in the data gap in seasonal water areas. Our deep learning model is trained to recognize the spatio-temporal patterns of water bodies and effectively fill in the data gap. We created a large training dataset using the JRC GSW dataset with simulated gap areas to teach the model how to fill in the gaps. During the training process, our model focuses on seasonal water areas, while permanent water areas can be reliably filled by applying a threshold to the water occurrence frequency. We tested our model's performance using 10,000 simulated JRC GSW labels and 639 cloud-free Sentinel-2 images on pixels that the model couldn't observe. The results showed F1 scores of 0.83 for water and 0.75 for land in seasonal water pixels with an average of 45% valid observations. In cases with only 2% valid observations, the model achieved F1 scores of 0.8 for water and 0.72 for land. These promising results demonstrate the effectiveness of our self-supervised learning approach to fill in the data gaps in the JRC GSW dataset. Dataset Structure The dataset is grouped by 10-degree longitude intervals for easier analysis of data within specific longitudinal regions. For each 10-degree longitudinal range, the corresponding 1x1 degree folders are compressed into a single zip file, named in the format "StartLonDir-EndLonDir.zip" (e.g., "1E-10E.zip" or "11W-20W.zip"). Each GeoTIFF file in the dataset represents a 1x1degree area. The TIFF file has two bands: 1. Water/Non-Water (Binary): Indicates whether the pixel is water (1) or non-water (0). 2. Good Quality/Bad Quality (Binary): Indicates whether the pixel is of good quality (1) or bad quality (0). The dataset uses a single-bit storage format to save memory. Quality Score Calculation The quality score in our dataset is a binary score assigned to each 256x256 gap-filled block. It is calculated based on the proportion of valid observations within the block. A quality score of 1 indicates a high-quality block, while a score of 0 represents a low-quality block. The quality score calculation process is as follows: 1. For each 256x256 block, count the number of valid observations (pixels with a "good quality" value of 1 in the Good Quality/Bad Quality band). Calculate the percentage of valid observations in the block by dividing the number of valid observations by the total number of pixels in the block (256x256 = 65,536). 2. Assign a binary quality score to the block: If the percentage of valid observations is equal to or greater than 2%, assign a quality score of 1 (high quality). If the percentage of valid observations is less than 2%, assign a quality score of 0 (low quality). License This dataset is freely available for research and non-commercial purposes. Please cite the appropriate references when using this dataset in your work. Dataset Availability and Expansion Due to the large computational demand required to produce this gap-filled dataset, currently, only the regions covering China have been processed and made available. However, we are actively working on expanding the dataset to cover other regions across the globe. We estimate that it will take approximately 6months to complete the processing of the entire dataset.

The differences in plaque histology between symptomatic and asymptomatic patients have been widely accepted. Whether there is a heterogeneity of cells between symptomatic and asymptomatic plaques remains largely unclear. To reveal the potential heterogeneity within different plaques, which may contribute to different stroke incidences, we obtained the scRNA-seq data from symptomatic and asymptomatic patients and identified eight cell types present in plaques. Further analysis of endothelial cells (ECs) revealed three distinct EC subpopulations appeared to be endowed with specific biological functions such as antigen processing and presentation, cell adhesion, and smooth muscle cell proliferation. Of note, the differentially expressed genes of the EC 2 subpopulation showed that the genes involved in cell adhesion were up-regulated in asymptomatic plaques compared to symptomatic plaques. Integrating the data of intraplaque haemorrhage and plaque stability, the 5th top-enriched biological process was cell adhesion in the stable or non-haemorrhaged plaques compared to unstable plaques or haemorrhaged plaques. Among these cell adhesion-related genes, the intersection gene AOC3 may play a vital role in plaque haemorrhage and plaque stability. Targeting cell adhesion and the specialized genes may provide potential new therapeutic directions to prevent asymptomatic patients from stroke.

Background We aimed to investigate associations between pre-diagnostic anti-Epstein-Barr virus (EBV) antibodies, including interactions with hepatitis B virus (HBV), and risk of primary liver cancer in southern China. Methods In a population-based nested case-control study, we measured pre-diagnostic immunoglobulin A (IgA) against EBV nuclear antigen 1 (EBNA1) and viral capsid antigen (VCA) in 125 primary liver cancer cases and 2077 matched controls. We also explored the interaction between HBV surface antigen (HBsAg) and anti-EBV antibodies. Results Participants with positive EBNA1-IgA, positive VCA-IgA or single-positive anti-EBV antibodies had two-fold odds of developing liver cancer, compared with seronegative subjects. The odds ratios (ORs) between the relative optical density of EBNA1-IgA and VCA-IgA and primary cancer, controlling for age and HBsAg, were 1.59 (95% confidence interval (CI): 1.17, 2.14) and 1.60 (95% CI: 1.07, 2.41), respectively. Subjects with both HBsAg and anti-EBV antibody seropositivity were at 50-fold increased risk compared with those negative for both biomarkers (OR: 50.67, 95% CI: 18.28, 140.46), yielding a relative excess risk due to interaction of 30.81 (95% CI: 3.42, 114.93). Conclusion Pre-diagnostic seropositivity for EBNA1-IgA and/or VCA-IgA was positively associated with primary liver cancer risk, especially in combination with HBsAg positivity. EBV may interact with HBV in the development of primary liver cancer, and anti-EBV antibodies might be potential biomarkers for primary liver cancer in this high-risk population.

This repository contains a subset of data for the paper "Reconstruction of Missing Data in the Global Surface Water Dataset with a Self-Supervised Deep Learning Model" (Under Review, Remote Sensing of Environment) for review by reviewers. The data includes reconstruction results for 50 reservoirs from the GRAND dataset, with each reservoir containing two parts: 1) reconstructions, and 2) paired images of JRC GSW, reconstructions, and differences (reconstructions minus JRC GSW) for each month for the convenience of reviewers. It is worth noting that this study does not reconstruct valid data (JRC GSW pixel values of 1 (land) or 2(water)) on the JRC GSW, and therefore, misclassifications on the JRC GSW are retained, sometimes resulting in visually incorrect images. The occurrence map (100_Occurrence.png) for each reservoir is provided with the maximum value of 100, meaning a pixel is always water across 440 months in the JRC GSW. The name of each folder (e.g. 00012) corresponds to the GRAND_ID in the GRAND dataset. Description of the color meaning in the comparison images (.png): In the JRC GSW and Reconstruction images, "red" represents missing values, "blue" represents water bodies, and "white" represents land. The "blue" in the Diff map represents newly identified water bodies, the "white" represents newly identified lands, and the "red" represents no change after reconstruction. Data Quality: The format of the file name is: YearMonth_Quality. Since the quality of the reconstruction is largely determined by the density of valid data available for reference in the current month, the quality of the reconstructed data is classified into three categories: "A": void data was less than 33%, "B": void data was between 33% and 66%, and "C": void data was greater than 66%.

Alpine land cover (ALC) is facing many challenges with climatic change, biodiversity reduction and other cascading ecosystem damage triggered by natural and anthropogenic interference. Although several global land cover products and thematic maps are already available, their mapping accuracy of alpine and montane regions remains unsatisfactory due to the data acquisition, methodology, and workflow design constraints. Therefore, in this paper, a deep convolutional neural network (DCNN) in Google Earth Engine (GEE) was developed to map the ALC types of the Yarlung Zangbo river basin (YZRB) in the Tibetan plateau using multi-source remote sensing data. The DCNN algorithm was offline trained using automatically generating samples and online deployed in the GEE for a large-scale ALC mapping. Moreover, a set of fine land cover classification system (containing 14 ALC types) was also established in accordance with the natural situation of the YZRB. The overall accuracy and kappa were 86.24% and 0.8156, which were higher than traditional classification algorithms. The spatial distribution of ALC types was analyzed in different gradient zones, and a clear altitudinal characteristic was noticed. The terrain of the YZRB from upper-stream to down-stream with an elevation dramatically decreases, and corresponding to vertical zonal changes from glacier and permanent snow/ice, barren gravel land, alpine desert steppe, alpine steppe, alpine meadow, shrubs, to tree cover. The product can provide valuable land cover information to support alpine ecosystem conservation.

The spatiotemporal reflectance fusion method is used to blend high-temporal and low-spatial resolution images with their low-temporal and high-spatial resolution counterparts that were previously acquired by various satellite sensors. Recently, a wide variety of learning-based solutions have been developed, but challenges remain. These solutions usually require two sets of data acquired before and after the prediction time, making them unsuitable for near-real-time predicting. The solutions are always trained band by band and thus do not consider the spectral correlation. High-resolution temporal changes are difficult to reconstruct accurately with the network structure used, which lowers the accuracy of the fusion result. To address these problems, this study proposes a novel spatiotemporal adaptive reflectance fusion model using a generative adversarial network (GASTFN). In GASTFN, an end-to-end network, including a generative and discriminative network, is simultaneously trained for all spectral bands. The proposed model can be applied to the one-pair case, consider the spectral correlation of each band, and improve the process of producing super-resolution imagery by adopting the discriminative network for image reflectance values rather than temporal changes in reflectance. The proposed model has been verified with two actual satellite data sets acquired in heterogeneous landscapes and areas with abrupt changes, with a comparison of the state-of-art methods. The results show that GASTFN can generate the most accurate fusion images with more detailed textures, more realistic spatial shapes, and higher accuracy, demonstrating that the GASTFN is effective for predicting near-real-time changes in image reflectance and preserves the most valuable spatial information.

An effective coupling of an In2O3/WO3 type-II heterojunction with CdS quantum dots-polydopamine nanospheres labeling achieves the high-performance photoelectrochemical sandwich immunoassay of CYFRA21-1.

Rivers are important elements of the Earth’s ecosystem, and their spatial distribution information is critical for the study of hydrological and biogeochemical processes. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery has been widely used for river mapping due to its high temporal resolution and long-term observation records, which are essential to capture the rapid fluctuation of rivers. However, when the conventional hard classification methods are used, the accuracy of the river maps produced from MODIS data (and especially for those rivers with narrow widths) is often limited because mixed pixels are common in MODIS imagery, due to the coarse spatial resolution. In this paper, a cascaded spectral-spatial information combined deep convolutional neural network (CNN) model for super-resolution river mapping (DeepRivSRM) is proposed to produce Landsat-like fine-resolution river maps from MODIS images. In DeepRivSRM, a CNN made up of a spectral unmixing module and a super-resolution mapping (SRM) module is introduced to handle the spectral and spatial information simultaneously. Moreover, for training the DeepRivSRM model, an adaptive cross-entropy loss function incorporating the fraction information of the rivers is designed to improve the performance of the DeepRivSRM model for small rivers. The proposed method was evaluated with MODIS images from three test sites and was compared with hard classification, a conventional SRM method, and a CNN-based SRM method. The results show that DeepRivSRM can generate more accurate river maps by effectively learning the subpixel-scale spatial-spectral information in MODIS imagery.

Superresolution mapping (SRM) is a commonly used method to cope with the problem of mixed pixels when predicting the spatial distribution within low-resolution pixels. Central to the popular SRM method is the spatial pattern model, which is utilized to represent the land cover spatial distribution within mixed pixels. The use of an inappropriate spatial pattern model limits such SRM analyses. Alternative approaches, such as deep-learning-based algorithms, which learn the spatial pattern from training data through a convolutional neural network, have been shown to have considerable potential. Deep learning methods, however, are limited by issues such as the way the fraction images are utilized. Here, a novel SRM model based on a generative adversarial network (GAN), GAN-SRM, is proposed that uses an end-to-end network to address the main limitations of existing SRM methods. The potential of the proposed GAN-SRM model was assessed using four land cover subsets and compared to hard classification and several popular SRM methods. The experimental results show that of the set of methods explored, the GAN-SRM model was able to generate the most accurate high-resolution land cover maps.

Microfluidic spinning technology (MST), incorporating microfluidics with chemical reactions, has fastened considerable interests in constructing anisotropic advanced microfibers, especially helical microfibers. However, subsistent efforts suffer from the limited material choices, restricting their applications. Here, a new phase inversion-based microfluidic spinning (PIMS) method is proposed for producing helical microfibers. This method undergoes a physicochemical phase inversion process, which is capable of efficiently manufacturing strong (tensile stress of more than 25 MPa), stretchable, flexible and biocompatible helical microfibers. The helical microfibers can be used to fabricate bi-oriented stretchable artificial abdominal skin, preventing incisional hernia formation and promoting the wound healing without conglutination. This research not only offers a universal approach to design helical microfibers, but also provides a new insight into artificial skins.

The Forel–Ule index (FUI) is one of the indicators used to assess the trophic state of inland waters, which can be objectively determined based on satellite-retrieved corrected hue angles (α). Howe...

Objective This study examined the relationship between circadian typology and risk-taking behavior. Methods A cross-sectional study was conducted, involving 755 primary and junior school students aged 11–16 years. The Adolescent Risk-Taking Questionnaire and Morningness-Eveningness Questionnaire were administered to assess risk-taking behavior and circadian typology, respectively. Multiple linear regression analyses were performed to analyze the factors influencing risk-taking behavior. Results Circadian typology was negatively correlated with risk-taking behavior, with age and sex controlled for (Model 1). When family and class circumstances were added in Model 2, the significance of the association persisted (β = −1.67, 95% CI: [−2.93, −0.42], P = .009). However, the association between circadian typology and risk-taking behavior showed no significance (P = 0.050) when personality characteristics were controlled for in Model 3. Conclusions These results provide evidence indicating a relationship between circadian typology and risk-taking behavior, suggesting that evening circadian type is a risk factor for risk-taking behavior in adolescents.

In this paper, a typical passive nuclear power plant small break LOCA is taken as the research object. Based on the theory of Risk Informed Safety Margin Characterization methodology, the development process of the accident is studied. The specific sequence is selected to be studied. The important systems are discretely branched (such as Automatic Depressurization System), and the important uncertainty parameters are sampled (such as ADS valve and IRWST valve resistance coefficient). The event tree is re-established, and the success criterion of the head is modified. The fault tree is reestablished. After that the uncertainty parameters are sampled for specific sequences and the accident simulation of the whole plant is carried out for each condition. Thus, the frequency of each sequence and the conditional failure probability under the specific condition are obtained, and the core damage frequency (CDF) value based on RISMC method is finally calculated. This analysis is mainly aimed at the configuration and sensitivity of ADS. The selected sequence 45,53,64 are generated from the risk significant core damage sequence S in the original model. Through the analysis and calculation of RISMC method, it is concluded that sequence 45 is ok sequence, and the assumption in the original model is too conservative. OK cases with epidemic proportions existed in sequence 53; sequence 64 is consistent with the original hypothesis, which is a CD sequence. Therefore, the calculated value of CDF is reduced to a certain extent. This paper verifies the feasibility of RISMC method in application on passive NPPs and also proves that the method can remove unnecessary conservatism. more realistic assessment of the risk helps us quantify the safety margin of nuclear power plants better.

Previous studies showed bacterial lysates were effective for pediatric asthma. However, evidence of polyvalent bacterial lysate Qipian is lacking.In this real-world retrospective cohort study, data of children with asthma, aged six months to 14 years old, attending to Jiangxi Provincial Children's Hospital from January 2021 to April 2022, prescribed routine treatment for asthma plus Qipian (Qipian group) or not (control group) were extracted. To minimize the impact of confounders on the outcomes, baseline characteristics were utilized to perform propensity score matching through a multivariable logistic regression model. After matching, asthma control, exacerbation, etc. were compared.Totally, 795 patients were included (337 in the Qipian group and 458 in the control group), with 278 pairs (556 patients) matched. Most baseline characteristics were well-balanced. The proportion of males were 68.3% and 70.1% in the two groups. The Qipian group favored better asthma control, with more "controlled" [3-month: 257 (92.4%)According to this retrospective study, Qipian may be effective for improved pediatric asthma control. Safety profile and mechanisms of action of Qipian need further investigation. Further randomized controlled trials are warranted to confirm our results.

ObjectiveThe study aimed to ascertain the comparative efficacy of these two forms on reducing anxiety scores of scales in patients with a generalized anxiety disorder (GAD) by examining the available evidence for face-to-face cognitive behavior therapy (CBT) and internet-based cognitive behavior therapy (ICBT). Moreover, this study attempted to determine whether ICBT can obtain similar benefits as CBT for GAD patients during coronavirus disease 2019 (COVID-19) due to the quarantine policy and the requirement of social distance.MethodsThis meta-analysis was registered with the International Prospective Register of Systematic Reviews (PROSPERO) according to the guidelines in the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement (registration number CRD42021241938). Therefore, a meta-analysis of randomized controlled trials (RCTs) examining CBT or ICBT was conducted in this study to treat GAD patients diagnosed with DMS-IV. The researchers searched PubMed, MEDLINE, Embase, PsycINFO, and the Cochrane Database of Systematic Reviews for relevant studies published from 2000 to July 5, 2022. Evidence from RCTs was synthesized by Review Manager 5.4 as mean difference (MD) for change in scores of scales through a random-effects meta-analysis.ResultsA total of 26 trials representing 1,687 participants were pooled. The results demonstrated that ICBT and CBT were very close in the effect size of treating GAD (MD = −2.35 vs. MD = −2.79). Moreover, they still exhibited a similar response (MD = −3.45 vs. MD = −2.91) after studies with active control were removed.ConclusionRegarding the treatment of GAD, ICBT can achieve a similar therapeutic effect as CBT and could be CBT's candidate substitute, especially in the COVID-19 pandemic era, since the internet plays a crucial role in handling social space constraints.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=241938, identifier CRD42021241938.

Early cancer detection requires high-performing biomarkers and effective tools to enable early intervention. Telomeres, located at the terminal of linear chromosomes, affect genome integrity and cell immortalization. At the very end of the telomere is a 3’ overhanging guanine-rich tail (G-tail) of which shortening is closely related to cell divisions. However, G-tail DNA cannot be detected with conventional sequencing technologies. Here we develop a high-preservation technology to quantify telomeres in circulating cell-free DNA and show that short G-tail DNA was nearly 20-fold higher in hepatocellular carcinoma patients with blood collected at diagnosis than controls and was also strongly correlated with poorer survival. We then used an independent prospective population cohort to show that the abundance of circulating short telomere G-tail increased with time approaching cancer diagnosis. These results indicate that circulating short-telomere G-tail, measured using our novel bilateral single-strand sequencing method, can effectively detect early and aggressive hepatocellular carcinoma.