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Abstract Background The postponement of parenthood is a global public health issue that has received attention of many public health experts. However, few studies have investigated the postponement in marriage age, marriage and conception interval, and pregnancy age in terms of demographic and regional heterogenicities. Methods This is a cross-sectional, registry-based study, and a total of 13 894 601 nulliparous couples who participated in the National Free Pre-Pregnancy Check-ups Project and became pregnant during 2013–2019 were included. We calculated annual percentage change and forest plots for marriage age, marriage and conception interval, and pregnancy age. Results Late marriage (marriage age ≥ 35 years), long marriage and conception interval (marriage and conception interval ≥ 2 years), and advanced pregnancy (pregnancy age ≥ 35 years) increased from 1.20%, 22.01%, and 1.88% in 2013 to 1.69%, 32.75%, and 2.79% in 2019, respectively. The corresponding annual percentage changes were 6.55%, 8.44%, and 8.17%. Participants without higher education had a higher annual percentage change, but comparable prevalence for long marriage and conception interval with participants with higher education. Participants residing in second- or new first-tier cities, and the northeast of China who had a higher prevalence of parenthood postponement also had higher corresponding annual percentage changes. Conclusions Structural postponement of parenthood with demographic and regional heterogenicities was observed among Chinese nulliparous couples with planned pregnancies during 2013–2019. Inclusive and comprehensive parenting support should be developed and implemented in mainland China to minimize the negative health effects arising from the postponement, especially for couples without higher education and living in new first/second-tier cities or the northeast China.

BackgroundOmicron (B.1.1.529), a variant of SARS-CoV-2, has emerged as a dominant strain in COVID-19 pandemic. This development has raised concerns about the effectiveness of vaccination to Omicron, particularly in the context of children and adolescents. Our study evaluated the efficacy of different COVID-19 vaccination regimens in children and adolescents during the Omicron epidemic phase.MethodsWe searched PubMed, Cochrane, Web of Science, and Embase electronic databases for studies published through March 2023 on the association between COVID-19 vaccination and vaccine effectiveness (VE) against SARS-CoV-2 infection in children and adolescents at the Omicron variant period. The effectiveness outcomes included mild COVID-19 and severe COVID-19. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and was prospectively registered in PROSPERO (CRD42023390481).ResultsA total of 33 studies involving 16,532,536 children were included in the analysis. First, in children and adolescents aged 0–19 years, the overall VE of the COVID-19 vaccine is 45% (95% confidence interval [CI]: 40 to 50%). Subgroup analysis of VE during Omicron epidemic phase for different dosage regimens demonstrated that the VE was 50% (95% CI: 44 to 55%) for the 2-dose vaccination and 61% (95% CI: 45 to 73%) for the booster vaccination. Upon further analysis of different effectiveness outcomes during the 2-dose vaccination showed that the VE was 41% (95% CI: 35 to 47%) against mild COVID-19 and 71% (95% CI: 60 to 79%) against severe COVID-19. In addition, VE exhibited a gradual decrease over time, with the significant decline in the efficacy of Omicron for infection before and after 90 days following the 2-dose vaccination, registering 54% (95% CI: 48 to 59%) and 34% (95% CI: 21 to 56%), respectively.ConclusionDuring the Omicron variant epidemic, the vaccine provided protection against SARS-CoV-2 infection in children and adolescents aged 0–19 years. Two doses of vaccination can provide effective protection severe COVID-19, with booster vaccination additionally enhancing VE.

Introduction: Esophageal stenting is a widely used treatment for esophageal diseases, which can also be used for adjuvant therapy and feeding after chemotherapy for esophageal cancer. The structural parameters of the stent have a significant impact on its mechanical properties and patient comfort.Methods: In the present work, we reconstructed the esophagus model based on the patient’s computed tomography (CT) data, and designed stents with different structural parameters. We used 3D printing technology to achieve rapid production of the designed stents by using Thermoplastic polyurethane (TPU)/Poly-ε-caprolactone (PCL) blends as the materials. The mechanical properties and effects on the esophagus of polymer stents with four different structural parameters of diameter, wall thickness, length and flaring were investigated by in vitro tests of radial compression and migration of the stents, as well as by finite element simulations of the stent implantation process in the esophagus and of the stent migration process. An artificial neural network model was established to predict the radial force of the stent and the maximum equivalent stress of the esophagus during implantation based on these four structural parameters.Results: The results show that wall thickness was the structural parameter that had the greatest impact on the radial force of the stent (statistically significant, p < 0.01), and flaring was the structural parameter that had the greatest impact on the maximum equivalent stress of the esophageal wall after stent implantation (statistically significant, p < 0.01). No. 6 stent had a maximum radial force of 18.07 N, which exceeded that of commercial esophageal stents and had good mechanical properties. And the maximum equivalent force on the esophagus caused by its implantation was only 30.39 kPa, which can improve patient comfort. The predicted values of the constructed back propagation (BP) neural network model had an error of less than 10% from the true values, and the overall prediction accuracies were both above 97%, which can provide guidance for optimizing the design of the stent and for clinical research.Discussion: 3D printing technology presents a wide range of applications for the rapid fabrication of personalized TPU/PCL blend stents that are more suitable for individual patients.

Sprouty4 (SPRY4) has been frequently reported as a tumor suppressor and is therefore downregulated in various cancers. For the first time, we report that SPRY4 is epigenetically upregulated in colorectal cancer (CRC). In this study, we explored DNA methylation and hydroxymethylation levels of SPRY4 in CRC cells and patient samples and correlated these findings with mRNA and protein expression levels. Three loci within the promoter region of SPRY4 were evaluated for 5mC levels in CRC using the combined bisulfite restriction analysis. In addition, hydroxymethylation levels within SPRY4 were measured in CRC patients. Lastly, DNA methylation and mRNA expression data were extracted from CRC patients in multiple high-throughput data repositories like Gene Expression Omnibus and The Cancer Genome Atlas. Combined in vitro and in silico analysis of promoter methylation levels of SPRY4 clearly demonstrates that the distal promoter region undergoes hypomethylation in CRC patients and is associated with increased expression. Moreover, a decrease in gene body hydroxymethylation and an increase in gene body methylation within the coding region of SPRY4 were found in CRC patients and correlated with increased expression. SPRY4 is epigenetically upregulated in CRC by promoter hypomethylation and hypermethylation within the gene body that warrants future investigation of atypical roles of this established tumor suppressor.

Abstract Background The advancement of sequencing technologies today has made a plethora of whole-genome re-sequenced (WGRS) data publicly available. However, research utilizing the WGRS data without further configuration is nearly impossible. To solve this problem, our research group has developed an interactive Allele Catalog Tool to enable researchers to explore the coding region allelic variation present in over 1,000 re-sequenced accessions each for soybean, Arabidopsis, and maize. Results The Allele Catalog Tool was designed originally with soybean genomic data and resources. The Allele Catalog datasets were generated using our variant calling pipeline (SnakyVC) and the Allele Catalog pipeline (AlleleCatalog). The variant calling pipeline is developed to parallelly process raw sequencing reads to generate the Variant Call Format (VCF) files, and the Allele Catalog pipeline takes VCF files to perform imputations, functional effect predictions, and assemble alleles for each gene to generate curated Allele Catalog datasets. Both pipelines were utilized to generate the data panels (VCF files and Allele Catalog files) in which the accessions of the WGRS datasets were collected from various sources, currently representing over 1,000 diverse accessions for soybean, Arabidopsis, and maize individually. The main features of the Allele Catalog Tool include data query, visualization of results, categorical filtering, and download functions. Queries are performed from user input, and results are a tabular format of summary results by categorical description and genotype results of the alleles for each gene. The categorical information is specific to each species; additionally, available detailed meta-information is provided in modal popups. The genotypic information contains the variant positions, reference or alternate genotypes, the functional effect classes, and the amino-acid changes of each accession. Besides that, the results can also be downloaded for other research purposes. Conclusions The Allele Catalog Tool is a web-based tool that currently supports three species: soybean, Arabidopsis, and maize. The Soybean Allele Catalog Tool is hosted on the SoyKB website ( https://soykb.org/SoybeanAlleleCatalogTool/ ), while the Allele Catalog Tool for Arabidopsis and maize is hosted on the KBCommons website ( https://kbcommons.org/system/tools/AlleleCatalogTool/Zmays and https://kbcommons.org/system/tools/AlleleCatalogTool/Athaliana ). Researchers can use this tool to connect variant alleles of genes with meta-information of species.

Abstract Certain cultivars of maize show increased tolerance to water deficit conditions by maintenance of root growth. To better understand the molecular mechanisms related to this adaptation, nodal root growth zone samples were collected from the reference inbred line B73 and inbred line FR697, which exhibits a relatively greater ability to maintain root elongation under water deficits. Plants were grown under various water stress levels in both field and controlled environment settings. FR697-specific RNA-Seq datasets were generated and used for a de novo transcriptome assembly to characterize any genotype-specific genetic features. The assembly was aided by an Iso-Seq library of transcripts generated from various FR697 plant tissue samples. The Necklace pipeline was used to combine a Trinity de novo assembly along with a reference guided assembly and the Viridiplantae proteome to generate an annotated consensus “SuperTranscriptome” assembly of 47,915 transcripts with a N50 of 3152 bp in length. The results were compared by Blastn to maize reference genes, a Benchmarking Universal Single-Copy Orthologs (BUSCO) genome completeness report and compared with three maize reference genomes. The resultant ‘SuperTranscriptome’ was demonstrated to be of high-quality and will serve as an important reference for analysis of the maize nodal root transcriptomic response to environmental perturbations.

ObjectivesIn the process of the emergency deceleration and reversing operations of marine steam power systems, back-steam control technology is an effective means of alleviating the problem of boiler drum overpressure，so it is necessary to study its influence on the system. MethodsThis paper establishes a steam power system simulation model of a supercharged boiler, main steam turbine and propeller, and carries out the simulation and comparative analysis of the characteristics of back-steam control in the process of the emergency deceleration and reversing of large ships under the three working conditions of quick closing valve with/without back-steam and slow closing valve without back-steam. ResultsThe simulation results show that the steam return control technology can effectively prevent boiler drum overpressure in the process of emergency deceleration and reversing, and the system stability time is shortened by about 3 mins and 1 min respectively. ConclusionThe results of this study can provide references for ship maneuverability and safety design.

ObjectiveThis study aimed to summarize the effectiveness of rhythmic auditory stimulation (RAS) for the treatment of gait and motor function in Parkinson's disease (PD) through a systematic review and meta-analysis.MethodsAll studies were retrieved from eight databases. The effects of RAS on PD were determined using the following indicators: gait parameters including step length, stride width, step cadence, velocity, stride length; motor function including 6 min walk test (6MWT) and timed up-and-go test (TUGT); the Unified Parkinson's Disease Rating Scale (UPDRS); and the Berg Balance Scale (BBS). The risk map of bias of the quality of the studies and the meta-analysis results of the indicators was prepared with RevMan 5.2 software.ResultsTwenty-one studies were included in the systematic review, and 14 studies were included in the meta-analysis. In the meta-analysis, the results of gait parameters, namely, velocity, step length, and stride length, were statistically significant (P < 0.05), whereas the results of cadence and stride width were not statistically significant (P ≧ 0.05). The results of 6MWT and TUGT for motor function as well as UPDRS-II, UPDRS-III, and BBS were statistically significant (P < 0.05).ConclusionsRAS could improve gait parameters, walking function, balance function, and daily living activities of individuals with PD. The application of RAS in conventional rehabilitation approaches can enhance motor performance in PD. Future studies should use a large sample size and a rigorous design to obtain strong conclusions about the advantages of RAS for the treatment of gait and motor function in PD.

Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta.

Accurate monitoring of emergent aquatic vegetation (EAV) and floating-leave aquatic vegetation (FAV), is vital because vegetation provides a critical habitat for aquatic animals and plays a critical role in protecting biodiversity. However, owing to the interference of water spectrum signals, it is difficult to extract the emerging aquatic vegetation information from remote sensing images, especially to distinguish between EAV and FAV. This study first proposed an enhanced aquatic vegetation index based on the difference in two short-wave infrared (SWIR) bands to extract the aquatic vegetation above the water surface, and then, the EAV and FAV were further distinguished coupled with the phenological characteristics, subsequently, a case study of Taihu Lake for accurately extracting seasonal and annual distributions. The results demonstrate that the proposed aquatic vegetation index is highly sensitive to EAV and FAV, and can further distinguish EAV from them coupled with phenological difference. Additionally, the dramatic changes in EAV and FAV in time and space indicated that human purse seine culture has had a great influence on the succession of aquatic vegetation, which may lead to the deterioration of the water ecology environment. Using the SWIR bands coupled with phenological difference is a promising method for recognizing aquatic vegetation above the water surface owing that it can weaken and eliminate the impact of algal bloom at the same time.

Background: The world is transitioning to an obese future, but few studies have measured the burden of increased maternal body mass index (BMI) on pathological fetal overgrowth, especially the trends in this burden and its heterogeneity in populations with different characteristics. Methods: A population-based retrospective cohort study was conducted with 7,998,620 Chinese females who had participated in the National Free Pre-Pregnancy Check-ups Project and became pregnant during 2013–2017. The proportions of macrosomic and LGA neonates attributable to high BMI (population attributable fraction, PAF) and annual percent change of yearly PAFs were estimated. Results: We found that the burden of macrosomic and LGA (large-for-gestational-age) neonates attributable to high pre-pregnancy BMI increased among Chinese females with planned pregnancies during 2013–2017. The PAF of macrosomia attributable to high BMI increased from 3.16% (95% confidence interval: 2.97–3.35%) to 7.11% (6.79–7.42%) by 23.60% (16.76–30.85%) annually, and the PAF of LGA increased from 2.35% (2.21–2.48%) to 5.00% (4.79–5.21%) by 21.98% (16.14–28.11%) annually. Our study identified that participants with disadvantaged socioeconomic status (including those without higher education, living in provinces with GDP per capita < 40,000 CNY, tier IV, and tier V cities) and residing in northern and southwestern China were at high risk of a rapidly expanding burden. Conclusions: Government authorities should control pre-pregnancy BMI through nationwide intervention programs and direct more resources to focus on the unfair burden on females with disadvantaged socioeconomic status.

In this study, a cooperative control method based on model predictive control and multiagent theory is proposed to control an interconnected air suspension system with three controllable structures of interconnection mode, damping, and vehicle height. The model predictive controller is constructed based on a discrete-time state-space model. The optimal interval for suspension force is obtained through solving cost functions while satisfying a set of constraints on controlled variables and thereby reducing the coupling complexity of a multivariable control system. Deliberative agents are involved in building cost functions of interconnection mode, vehicle height adjustment, and damping force, and the energy consumption control strategy is established to realize suspension force distribution with low energy consumption. Finally, the test results show that the proposed control method can significantly improve vehicle ride comfort and restrain rollover on the premise of ensuring energy efficiency. Compared with traditional control, the peak value of the sprung mass acceleration speed decreases by 70% and the peak value of the unsprung mass acceleration speed decreases by 75% under straight-driving condition. The roll angle decreases by 40% under the steering condition. As for the traditional control, they are skyhook, imitation skyhook, and PID-PWM control strategies.

Frequent geohazards have knock-on effects on ecological quality. Timely and dynamically monitoring the damage of geohazards to ecological quality is important to the geological hazards prevention, ecological restoration, and policy formulation. Existing studies mainly focused on the impacts of climate change, urbanization, and extreme weather on the ecological quality, largely ignoring the role of frequent geohazards in the highly susceptible area. At present, the impact mechanism of the high susceptibility of geohazards on ecological quality remains unknown. To fill this knowledge gap, we use the Remote Sensing Ecological Index (RSEI, a widely accepted ecological quality index) calculated on the Google Earth Engine (GEE) platform, geohazard density data, and the Landsat series of surface reflectance datasets to explore the mechanism that drives spatial–temporal variations of ecological quality. Taking the Danba County as the study area, our results indicate that the total number of geohazards is 944 during 1995–2019, and the number of geohazards fluctuates and rises every year (10 in 1995 and 82 in 2019). A conceptual framework was proposed to quantify the impact of the high susceptibility of geohazards on ecological quality by separately exploring its impact on the 4 ecological components of RSEI (i.e., greenness, wetness, dryness, and heat). We found that the density of geohazards is significantly negatively correlated with greenness (R = 0.48, Pearson Correlation Coefficient (PCC) = −0.528, p

Abstract Background Knowledge Base Commons (KBCommons) v1.1 is a universal and all-inclusive web-based framework providing generic functionalities for storing, sharing, analyzing, exploring, integrating and visualizing multiple organisms’ genomics and integrative omics data. KBCommons is designed and developed to integrate diverse multi-level omics data and to support biological discoveries for all species via a common platform. Methods KBCommons has four modules including data storage, data processing, data accessing, and web interface for data management and retrieval. It provides a comprehensive framework for new plant-specific, animal-specific, virus-specific, bacteria-specific or human disease-specific knowledge base (KB) creation, for adding new genome versions and additional multi-omics data to existing KBs, and for exploring existing datasets within current KBs. Results KBCommons has an array of tools for data visualization and data analytics such as multiple gene/metabolite search, gene family/Pfam/Panther function annotation search, miRNA/metabolite/trait/SNP search, differential gene expression analysis, and bulk data download capacity. It contains a highly reliable data privilege management system to make users’ data publicly available easily and to share private or pre-publication data with members in their collaborative groups safely and securely. It allows users to conduct data analysis using our in-house developed workflow functionalities that are linked to XSEDE high performance computing resources. Using KBCommons’ intuitive web interface, users can easily retrieve genomic data, multi-omics data and analysis results from workflow according to their requirements and interests. Conclusions KBCommons addresses the needs of many diverse research communities to have a comprehensive multi-level OMICS web resource for data retrieval, sharing, analysis and visualization. KBCommons can be publicly accessed through a dedicated link for all organisms at http://kbcommons.org/.

Bloom occurrence probability prediction is a critical issue for freshwater resource management and protection. As the mechanism of algal blooms is not understood, the construction of prediction model mainly depends on statistical data. Therefore, knowledge on prior bloom occurrence derived from statistical data plays a significant role in establishing a prediction model. In this study, a naïve Bayesian model incorporating prior information was constructed to predict algal bloom occurrence probabilities 1–7 days in advance under different weather conditions in Dianchi Lake. The proposed model utilised the following data from the MODIS images, the floating algae index (FAI) for the previous 7 days and five meteorological variables (mean wind speed, air pressure, relative humidity on the prediction day, accumulated sunshine hours in the previous 3 days, and accumulated air temperature in the previous 7 days). The prior probabilities of each pixel were calculated on a monthly timescale to highlight the bloom’s temporal-spatial differences, and the 1–7 day posterior probabilities were calculated by combining the prior and conditional probabilities. The predictive effect was tested by the area under the receiver-operating characteristic curve (AUC), and the results showed the number of pixels with estimation predictions classified as ‘Good+’ and ‘Excellent’ were 91.3%, 91.4%, 82.7%, 83.3%, 89.0%, 86.6%, and 90.0% for the predictions on days 1–7, respectively. Additionally, the independent validation datasets showed that the percentage of correctly classified instances (CCI%) of approximately 90% of the pixels were greater than 50%. The proposed algal bloom prediction model based on the naïve Bayesian method has pixel-level prediction abilities, is applicable to other inland water systems, and can provide a reference for water environment risk forecasting and management.

In this paper, we used electroencephalography (EEG)-eye movement (EM) synchronization acquisition network to simultaneously record both EEG and EM physiological signals of the mild depression and normal controls during free viewing. Then, we consider a multimodal feature fusion method that can best discriminate between mild depression and normal control subjects as a step toward achieving our long-term aim of developing an objective and effective multimodal system that assists doctors during diagnosis and monitoring of mild depression. Based on the multimodal denoising autoencoder, we use two feature fusion strategies (feature fusion and hidden layer fusion) for fusion of the EEG and EM signals to improve the recognition performance of classifiers for mild depression. Our experimental results indicate that the EEG-EM synchronization acquisition network ensures that the recorded EM and EEG data require that both the data streams are synchronized with millisecond precision, and both fusion methods can improve the mild depression recognition accuracy, thus demonstrating the complementary nature of the modalities. Compared with the unimodal classification approach that uses only EEG or EM, the feature fusion method slightly improved the recognition accuracy by 1.88%, while the hidden layer fusion method significantly improved the classification rate by up to 7.36%. In particular, the highest classification accuracy achieved in this paper was 83.42%. These results indicate that the multimodal deep learning approaches with input data using a combination of EEG and EM signals are promising in achieving real-time monitoring and identification of mild depression.

China is rich of oil shale as one of alternative fossil energy resources. The exploitation and utilization of oil shale are strategically important in alleviating the shortage of oil and gas resources in China. However, low utilization rate of raw material, low oil yield, and high content of heavy components in the oil are the main problems in current Fushun type oil shale refinery technology. An indirectly heated moving bed is developed to de-bottleneck Fushun type technology. The oil shale refinery process with the indirectly heated moving bed is modeled and simulated in this work. Based on the simulation, a techno-economic analysis is performed and compared with the conventional Fushun oil shale refinery process. Results show that: for a shale refinery of 3.0 Mt/y scale, 375 t/h oil shale retorting requires 149.6 MW of heat, in which 60% of the heat is produced by combustion of the oil shale pyrolysis gas, while remaining 40% from the coal gasified gas. In consideration of investment and operation, the cost of product shale oil of the new process is 2636 CNY/t, which is 12% lower than that of the conventional Fushun refinery process. This benefit comes from higher utilization of raw material and oil yield of the new process. The competitive crude oil price of the indirectly heated moving bed refinery process is about 51 $/bbl, while that of the Fushun refinery process is 58 $/bbl. During 2017 year, the crude oil price fluctuates at around 50 $/bbl, thus the new indirectly heated moving bed refinery process shows better economic competitiveness. Keywords: Indirectly heated moving bed, Shale refinery, Modeling, Analysis

The durability of in-service reinforced concrete bridges will be greatly reduced under the action of corrosion and the repeated load such as vehicles. In this paper, six reinforced concrete beams were cast and subjected to sustained load test for one year, and then, the alternating test of corrosion and fatigue load were carried out with the damaged concrete beams. The long-term deflection, fatigue lifetime, failure modes, and crack growth were investigated under different corrosion and fatigue working conditions. The fracture section of steel bars was scanned by electron microscopy at the end of fatigue test. The results indicate that the deflection of the beams will continue to increase under a long-term load. The chloride salt in the beam has little influence on the deflection performance, but will shorten the fatigue lifetime due to the corrosion of the steel bars. Moreover, the corrosion environment can accelerate the rusting of the beam bar and reduce the fatigue lifetime, the accumulation of deflection damage and crack damage, and other fatigue characteristics. The crack initiation-propagation-fracture stage of steel bars occurs in the process of fatigue.

Conventional wisdom is that Sprouty2 (SPRY2), a suppressor of Receptor Tyrosine Kinase (RTK) signaling, functions as a tumor suppressor and is downregulated in many solid tumors. We reported, for the first time, that increased expression of SPRY2 augments cancer phenotype and Epithelial-Mesenchymal-Transition (EMT) in colorectal cancer (CRC). In this report, we assessed epigenetic DNA modifications that regulate SPRY2 expression in CRC. A total of 4 loci within SPRY2 were evaluated for 5mC using Combined Bisulfite Restriction Analysis (COBRA). Previously sequenced 5hmC nano-hmC seal data within SPRY2 promoter and gene body were evaluated in CRC. Combined bioinformatics analyses of SPRY2 CRC transcripts by RNA-seq/microarray and 450K methyl-array data archived in The Cancer Genome Atlas (TCGA) and GEO database were performed. SPRY2 protein in CRC tumors and cells was measured by Western blotting. Increased SPRY2 mRNA was observed across several CRC datasets and increased protein expression was observed among CRC patient samples. For the first time, SPRY2 hypomethylation was identified in adenocarcinomas in the promoter and gene body. We also revealed, for the first time, increases of 5hmC deposition in the promoter region of SPRY2 in CRC. SPRY2 promoter hypomethylation and increased 5hmC may play an influential role in upregulating SPRY2 in CRC.

With the acceleration of urbanization, increasing water pollution means that monitoring and evaluating urban water quality are of great importance. Although highly accurate, traditional evaluation methods are time consuming, laborious, and vastly insufficient in terms of the continuity of spatiotemporal coverage. In this study, a water quality assessment method based on remote sensing reflectance optical classification and the traditional grading principle is proposed. In this method, an optical water type (OWT) library was first constructed using the measured in situ remote sensing reflectance dataset based on fuzzy clustering technology. Then, comprehensive scoring rules were established by combining OWTs and 12 water quality parameters, and water quality was graded into different urban water quality levels (UWQLs) based on the scoring results. Using the proposed method, the relative water quality of urban waterbodies was qualitatively evaluated at the macro level based on images from the multispectral imager of Sentinel-2. In addition, there was a significant positive correlation between the UWQLs and the water quality index (WQI). These results indicate the potential of this method for quantitative assessment of urban water quality, providing a new way to evaluate water quality using remote sensing algorithms in the future.

Plant roots are composed of many differentiated tissue types, with each tissue exhibiting differential quantitative and qualitative accumulation of metabolites. The large-scale nontargeted metabolite profiles of these differentiated tissues are complex, which complicates the interpretation and development of hypotheses relative to the biological roles of differentially localized metabolites. Thus, we created a data visualization tool to aid in the visualization and understanding of differential metabolite accumulations in Medicago truncatula roots. This was achieved through the development of the Medicago truncatula Metabolite Atlas based upon an adaptation of the Arabidopsis Electronic Fluorescent Pictograph (eFP) Browser. Medicago truncatula roots were dissected into border cells, root cap, elongation zone, mature root, and root secretions. Each tissue was then analyzed by UHPLC-QTOF-MS and GC-Q-MS. Data were uploaded into a MySQL database and displayed in the Medicago truncatula Metabolite Atlas. The data revealed unique differential spatial localization of many metabolites, some of which are discussed here. Ultimately, the Medicago truncatula Metabolite Atlas compiles metabolite data into a singular, useful, and publicly available web-based tool that enables the visualization and understanding of differential metabolite accumulation and spatial localization.

The authors would like to make a correction to their published paper [...]

In this paper, we study in hybrid peer-to-peer (P2P) streaming system, which includes fixed peers and mobile peers in the same network, and propose a flow rate allocation algorithm to optimize the streaming system. In the proposed algorithm, based on transcoding technique, we describe how to exchange original and transcoded data among video source server, fixed and mobile peers. The purpose of our algorithm is to reduce the bandwidth demand of video source server, while ensuring the flow rate of video data sending to any peer no less than its video coding rate. We compare the performance of P2P streaming system using our algorithm with traditional design in various situations in the simulation experiment, and test how much benefit the system can get from the new algorithm. The results show that, if the flow rate of video data is allocated appropriately, better performance of streaming system can be achieved.

The accurate remote estimation of the Secchi disk depth (ZSD) in turbid waters is essential in the monitoring the ecological environment of lakes. Using the field measured ZSD and the remote sensing reflectance (Rrs(λ)) data, a new semi-analytical algorithm (denoted as ZSDZ) for retrieving ZSD was developed from Rrs(λ), and it was applied to Geostationary Ocean Color Imager (GOCI) images in extremely turbid waters. Our results are as follows: (1) the ZSDZ performs well in estimating ZSD in turbid water bodies (0.15 m < ZSD < 2.5 m). By validating with the field measured data that were collected in four turbid inland lakes, the determination coefficient (R2) is determined to be 0.89, with a mean absolute square percentage error (MAPE) of 22.39%, and root mean square error (RMSE) of 0.24 m. (2) The ZSDZ improved the retrieval accuracy of ZSD in turbid waters and outperformed the existing semi-analytical schemes. (3) The developed algorithm and GOCI data are in order to map the hourly variation of ZSD in turbid inland waters, the GOCI-derived results reveal a significant spatiotemporal variation in our study region, which are significantly driven by wind forcing. This study can provide a new approach for estimating water transparency in turbid waters, offering important support for the management of inland waters.

In colorectal cancer (CRC), upregulation of the C-X-C motif chemokine receptor 4 (CXCR4) is correlated with metastasis and poor prognosis, highlighting the need to further elucidate CXCR4’s regulation in CRC. For the first time, DNA methylation and 5-hydroxymethylcytosine aberrations were investigated to better understand the epigenetic regulation of CXCR4 in CRC. CXCR4 expression levels were measured using qPCR and immunoblotting in normal colon tissues, primary colon cancer tissues and CRC cell lines. Publicly available RNA-seq and methylation data from The Cancer Genome Atlas (TCGA) were extracted from tumors from CRC patients. The DNA methylation status spanning CXCR4 gene was evaluated using combined bisulfite restriction analysis (COBRA). The methylation status in the CXCR4 gene body was analyzed using previously performed nano-hmC-seal data from colon cancers and adjacent normal colonic mucosa. CXCR4 expression levels were significantly increased in tumor stromal cells and in tumor colonocytes, compared to matched cell types from adjacent normal-appearing mucosa. CXCR4 promoter methylation was detected in a minority of colorectal tumors in the TCGA. The CpG island of the CXCR4 promoter showed increased methylation in three of four CRC cell lines. CXCR4 protein expression differences were also notable between microsatellite stable (MSS) and microsatellite instable (MSI) tumor cell lines. While differential methylation was not detected in CXCR4, enrichment of 5-hydroxymethylcytosine (5hmC) in CXCR4 gene bodies in CRC was observed compared to adjacent mucosa.

This study aimed to explore a metallic striped grid array planar antenna, analyze it numerically in terms of its parameters, and optimize it for best performance. It may be an appropriate candidate for long-range point-to-point connectivity in wireless sensor networks. Antenna gain and frequency impedance bandwidth are two important performance parameters. For an efficient antenna, its gain should be high while maintaining operating bandwidth wide enough to accommodate the entire frequency range for which it has been designed. Concurrently, antenna size should also be small. In this study, antenna dimensions were kept as small as possible without compromising its performance. Its dimensions were 300 mm × 210 mm × 9.9 mm, which made it compact and miniature. It had a maximum gain of 16.72 dB at 2.45 GHz and maximum frequency impedance bandwidth of 7.68% relative to 50 Ω. It operated across a frequency band ranging from 2.38 GHz to 2.57 GHz, encapsulating the entire ISM 2.4 GHz band. Its radiation efficiency remained above 93% in this band with a maximum of 98.5% at 2.45 GHz. Moreover, it also had narrow HPBWs in horizontal and vertical planes having values of 18.52° and 31.25°, respectively. [ABSTRACT FROM AUTHOR]

Finite element analysis and experimental studies are presented on in-plane tensile and compressive properties under quasi-static loading for two types of hybrid composites made by using unidirectional T620S carbon and E-glass fabrics in a common matrix, epoxy resin. Results are also generated for plain T620S carbon/epoxy and plain E-glass/epoxy composite laminates. Quantitative data for tensile and compressive properties are presented. It is observed that for hybrid composites, placing carbon and glass fiber parts alternately in every layer (intralayer configuration) gives higher tensile and compressive strengths. Tensile failure strain is higher for intralayer compared to interlayer hybrid configuration. [ABSTRACT FROM AUTHOR]

Background: Protein structure data in Protein Data Bank (PDB) are widely used in studies of protein function and evolution and in protein structure prediction. However, there are two main barriers in large-scale usage of PDB data: 1) PDB data are highly redundant in terms of sequence and structure similarity; and 2) many PDB files have issues due to inconsistency of data and standards as well as missing residues, so that automated retrieval and analysis are often difficult. Description: To address these issues, we have created MUFOLD-DB http://mufold.org/mufolddb.php, a web-based database, to collect and process the weekly PDB files thereby providing users with non-redundant, cleaned and partially-predicted structure data. For each of the non-redundant sequences, we annotate the SCOP domain classification and predict structures of missing regions by loop modelling. In addition, evolutional information, secondary structure, disorder region, and processed three-dimensional structure are computed and visualized to help users better understand the protein. Conclusions: MUFOLD-DB integrates processed PDB sequence and structure data and multiple computational results, provides a friendly interface for users to retrieve, browse and download these data, and offers several useful functionalities to facilitate users' data operation. [ABSTRACT FROM AUTHOR]

Background Predicting the functional impact of amino acid substitutions (AAS) caused by nonsynonymous single nucleotide polymorphisms (nsSNPs) is becoming increasingly important as more and more novel variants are being discovered. Bioinformatics analysis is essential to predict potentially causal or contributing AAS to human diseases for further analysis, as for each genome, thousands of rare or private AAS exist and only a very small number of which are related to an underlying disease. Existing algorithms in this field still have high false prediction rate and novel development is needed to take full advantage of vast amount of genomic data. Results Here we report a novel algorithm that features two innovative changes: 1. making better use of sequence conservation information by grouping the homologous protein sequences into six blocks according to evolutionary distances to human and evaluating sequence conservation in each block independently, and 2. including as many such homologous sequences as possible in analyses. Random forests are used to evaluate sequence conservation in each block and to predict potential impact of an AAS on protein function. Testing of this algorithm on a comprehensive dataset showed significant improvement on prediction accuracy upon currently widely-used programs. The algorithm and a web-based application tool implementing it, EFIN (Evaluation of Functional Impact of Nonsynonymous SNPs) were made freely available (http://paed.hku.hk/efin/) to the public. Conclusions Grouping homologous sequences into different blocks according to the evolutionary distance of the species to human and evaluating sequence conservation in each group independently significantly improved prediction accuracy. This approach may help us better understand the roles of genetic variants in human disease and health. [ABSTRACT FROM AUTHOR]

With the development of wireless technologies, mobile communication applies more and more extensively in the various walks of life. The social network of both fixed andmobile users can be seen as networked agent system. At present, kinds of devices and access network technology are widely used. Different users in this networked agent system may need different coding rates multimedia data due to their heterogeneous demand. This paper proposes a distributed flow rate control algorithmto optimizemultimedia data transmission of the networked agent systemwith the coexisting various coding rates. In this proposed algorithm, transmission path and upload bandwidth of different coding rate data between source node, fixed and mobile nodes are appropriately arranged and controlled. On the one hand, this algorithm can provide user nodes with differentiated coding rate data and corresponding flow rate. On the other hand, it makes the different coding rate data and user nodes networked, which realizes the sharing of upload bandwidth of user nodes which require different coding rate data. The study conducts mathematical modeling on the proposed algorithmandcompares the systemthat adopts theproposedalgorithmwith the existingsystembasedon the simulationexperiment andmathematical analysis. The results show that the system that adopts the proposed algorithmachieves higher upload bandwidth utilization of user nodes and lower upload bandwidth consumption of source node. [ABSTRACT FROM AUTHOR]

Aiming at the solution to the time-consuming and low-precision disadvantage of present methodologies,the light field depth estimation method combining context information of the scene is proposed.This method is based on an end-to-end convolutional neural network,with the advantage of obtaining depth map from a single light field image.On merit of the reduced computational cost from this method,the time consumption is consequently decreased.For improvement in calculation accuracy,multi orientation epipolar plane image volumes of the light field images are input to network,from which feature can be extracted by the multi-stream encoding module,and then aggregated by the encoding-decoding architecture with skip connection,resulting in fuse the context information of the neighborhood of the target pixel in the process of per-pixel disparity estimation.Furthermore,the model uses convolutional blocks of different depths to extract the structural features of the scene from the central viewpoint image,by introducing these structural features into the corresponding skip connection,additional references for edge features are obtained and the calculation accuracy is further improved.Experiments in the HCI 4D Light Field Benchmark show that the BadPix index and MSE index of the proposed method are respectively 31.2% and 54.6% lower than those of the comparison me-thod,and the average calculation time of depth estimation is 1.2 seconds,which is much faster than comparison method.

Circular RNAs (circRNAs) are crucial for the pathogenesis of human diseases, including osteoarthritis (OA). Here, we set to elucidate the biological action of circ-LRP1B in OA pathogenesis. Human C28/I2 chondrocytes were stimulated by lipopolysaccharide (LPS). Circ-LRP1B, nuclear factor, erythroid 2 like 1 (NRF1) and microRNA (miR)-34a-5p were quantified by quantitative real-time PCR (qRT-PCR) or immunoblotting. Cell viability, proliferation, and apoptosis abilities were gauged by MTT, 5-Ethynyl-2'-Deoxyuridine (EdU) staining, and flow cytometry assays, respectively. Direct relationship between miR-34a-5p and circ-LRP1B or NRF1 was validated by RNA pull-down and dual-luciferase reporter assays. Circ-LRP1B was found to be underexpressed in OA cartilage and LPS-stimulated C28/I2 chondrocytes. Enforced expression of circ-LRP1B promoted cell proliferation, and repressed apoptosis and oxidative stress, as well as impacted OA-specific hallmarks expression of LPS-stimulated C28/I2 cells. NRF1 was identified as a downstream effector of circ-LRP1B function. Moreover, NRF1 was identified as a miR-34a-5p target in LPS-stimulated C28/I2 cells. Circ-LRP1B acted as a competing endogenous RNA (ceRNA) for miR-34a-5p to involve the post-transcriptional regulation of NRF1 expression. Furthermore, the effects of circ-LRP1B overexpression partly depended on the reduction of available miR-34a-5p. These findings demonstrate that circ-LRP1B functions as a ceRNA to regulate the proliferation, apoptosis and oxidative stress of LPS-stimulated human C28/I2 chondrocytes by miR-34a-5p/NRF1 network., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)