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Lately, the ongoing issue of haze pollution in multiple cities across China has had a profound impact on the residents’ wellbeing and overall health. The urgent necessity to address haze pollution is undeniable. Meanwhile, the rapidly evolving digital economy has sprung up as a pivotal driver for China’s economic growth, providing innovative approach to tackle haze pollution. This research explores the impact of the digital economy on haze pollution through utilizing the big data pilot zone as a quasi-natural experiment. Panel data from 280 prefecture-level cities in China is adopted, covering the period from 2011 through 2020. This analysis incorporates various models, namely, difference-in-differences model (DID), mediation effect model, and difference-in-differences spatial Durbin model. The findings highlight the substantial contributions of digital economy in alleviating haze pollution. Furthermore, these findings hold true even after a series of rigorous robustness checks. The constraint influence of the digital economy on haze pollution is particularly prominent in metropolitan areas, cities with elevated administrative levels, primary environmental protection cities, as well as economically developed cities. Financial development and technological innovation serve as important mechanisms through which the digital economy suppresses haze pollution. After accounting for spatial factors, the digital economy exhibits consequential spillover effects, leading to substantial inhibition of haze pollution in surrounding cities as well as locally. The novel aspects of this paper are as follows: Firstly, it regards the establishment of big data experimental zones as the exogenous policy shock of digital economy and employs a multi period DID model to evaluate the impact of the digital economy on haze pollution. Secondly, it explores the transmission pathways through which the digital economy affects haze pollution from the perspectives of financial development and technological innovation. Thirdly, it investigates the heterogeneous characteristics of the impact of the digital economy on haze pollution. Fourthly, it examines the spatial spillover effects of the digital economy on haze pollution from the perspective of spatial econometrics.

This study aimed to investigate the differences in volatile composition and aroma of different fractions of Osmanthus fragrans absolute purified by molecular distillation. Gas chromatography-mass spectrometry (GC-MS) combined with orthogonal partial least-squares discrimination analysis (OPLS-DA), P-value, variable importance in the projection (VIP), and relative odor activity value (ROAV) were used for analysis. The light, heavy, and residue fractions of Osmanthus fragrans absolute were analyzed. A total of 73 volatile components belonging to 8 major categories were detected in the three fractions, including 22 alcohols, 5 aldehydes, 10 acids, 3 phenols, 5 ketones, 17 esters, 5 alkanes, and 6 olefins. Alcohols were the main contributors to the volatile components in each fraction, and the types and relative contents of volatile components significantly differed among the fractions (P1) of the three fractions, the key aroma contributing components (ROAV>1) of the 14 light fractions, 9 recombinant fractions, and 11 residue fractions were analyzed by the aroma profile analysis. Light fractions exhibited more intense floral, sweet, and pine woody aromas compared to recombinant and residue fractions. The light fractions had the highest types and relative contents of volatile components and showcased the most intense Osmanthus fragrans floral flavor. This study provided a theoretical basis for the comprehensive utilization of different fractions of Osmanthus fragrans absolute through molecular distillation using GC-MS combined with chemometrics methods.

Abstract Background The precise characterization of individual tumors and immune microenvironments using transcriptome sequencing has provided a great opportunity for successful personalized cancer treatment. However, the cancer treatment response is often characterized by in vitro assays or bulk transcriptomes that neglect the heterogeneity of malignant tumors in vivo and the immune microenvironment, motivating the need to use single-cell transcriptomes for personalized cancer treatment. Methods Here, we present comboSC, a computational proof-of-concept study to explore the feasibility of personalized cancer combination therapy optimization using single-cell transcriptomes. ComboSC provides a workable solution to stratify individual patient samples based on quantitative evaluation of their personalized immune microenvironment with single-cell RNA sequencing and maximize the translational potential of in vitro cellular response to unify the identification of synergistic drug/small molecule combinations or small molecules that can be paired with immune checkpoint inhibitors to boost immunotherapy from a large collection of small molecules and drugs, and finally prioritize them for personalized clinical use based on bipartition graph optimization. Results We apply comboSC to publicly available 119 single-cell transcriptome data from a comprehensive set of 119 tumor samples from 15 cancer types and validate the predicted drug combination with literature evidence, mining clinical trial data, perturbation of patient-derived cell line data, and finally in-vivo samples. Conclusions Overall, comboSC provides a feasible and one-stop computational prototype and a proof-of-concept study to predict potential drug combinations for further experimental validation and clinical usage using the single-cell transcriptome, which will facilitate and accelerate personalized tumor treatment by reducing screening time from a large drug combination space and saving valuable treatment time for individual patients. A user-friendly web server of comboSC for both clinical and research users is available at www.combosc.top . The source code is also available on GitHub at https://github.com/bm2-lab/comboSC .

The microbial potential of Penicillium has received critical attention. The present research aimed to elucidate the efficacy of crude enzyme secreted from Penicillium oxalicum WX-209 in degrading citrus segments and evaluate the safety of the process. Results showed that citrus segment membranes gradually dissolved after treatment with the crude enzyme solution, indicating good degradation capability. No significant differences in body weight, food ingestion rate, hematology, blood biochemistry, and weight changes of different organs were found between the enzyme intake and control groups. Serial experiments showed that the crude enzyme had high biological safety. Moreover, the whole genome of P. oxalicum WX-209 was sequenced by PacBio and Illumina platforms. Twenty-five scaffolds were assembled to generate 36 Mbp size of genome sequence comprising 11 369 predicted genes modeled with a GC content of 48.33 %. A total of 592 genes were annotated to encode enzymes related to carbohydrates, and some degradation enzyme genes were identified in strain P. oxalicum WX-209. Data availability: The whole genome sequencing data in this study can be found in GenBank database of NCBI website with the accession number PRJNA 715321. The link: http://www.ncbi.nlm.nih.gov/bioproject/715321.

Abstract Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell’s entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology.

Energy-consuming rights trading policies (ECRTPs) represent a significant institutional innovation for China aimed at achieving the dual control targets of total energy consumption and energy consumption intensity. However, the effectiveness of these policies in curbing air pollution remains uncertain. This study treats ECRTPs as a quasi-natural experiment to empirically analyze their impact on air pollution, utilizing panel data encompassing 277 prefecture-level cities in China covering the period from 2011 to 2021. Analytical methods applied include a Difference-in-Differences model, a mediation effects model, and a triple differences model to explore the effects of ECRTPs on air pollution. The findings reveal that ECRTP can significantly suppress air pollution, and this conclusion remains valid even after conducting robustness tests. Mechanism analysis indicates that ECRTPs suppress air pollution by boosting energy efficiency, advancing industrial structure upgrading, and facilitating technological innovation. Further heterogeneous studies show that ECRTPs have a more pronounced inhibitory effect on air pollution in cities that are economically and socially developed, exhibit greater energy-saving potential, are characterized as resource-based cities, and serve as key regions for the prevention and control of air pollution. The research conclusion provides empirical evidence and policy implications for evaluating the environmental effects of ECRTPs and further improving China’s energy-consuming rights trading system, as well as offering references and guidance for other developing countries to put forward ECRTPs.

Introduction: As the evaluation indices, cancer grading and subtyping have diverse clinical, pathological, and molecular characteristics with prognostic and therapeutic implications. Although researchers have begun to study cancer differentiation and subtype prediction, most of relevant methods are based on traditional machine learning and rely on single omics data. It is necessary to explore a deep learning algorithm that integrates multi-omics data to achieve classification prediction of cancer differentiation and subtypes.Methods: This paper proposes a multi-omics data fusion algorithm based on a multi-view graph neural network (MVGNN) for predicting cancer differentiation and subtype classification. The model framework consists of a graph convolutional network (GCN) module for learning features from different omics data and an attention module for integrating multi-omics data. Three different types of omics data are used. For each type of omics data, feature selection is performed using methods such as the chi-square test and minimum redundancy maximum relevance (mRMR). Weighted patient similarity networks are constructed based on the selected omics features, and GCN is trained using omics features and corresponding similarity networks. Finally, an attention module integrates different types of omics features and performs the final cancer classification prediction.Results: To validate the cancer classification predictive performance of the MVGNN model, we conducted experimental comparisons with traditional machine learning models and currently popular methods based on integrating multi-omics data using 5-fold cross-validation. Additionally, we performed comparative experiments on cancer differentiation and its subtypes based on single omics data, two omics data, and three omics data.Discussion: This paper proposed the MVGNN model and it performed well in cancer classification prediction based on multiple omics data.

This study utilized sprouted buckwheat as the main component and aimed to optimize its combination with other grains to produce reconstituted rice with enhanced taste and a reduced glycemic index (GI). The optimal blend comprised wheat flour, sprouted buckwheat flour, black rice flour, and purple potato flour in a ratio of 34.5:28.8:26.7:10.0. Based on this blend, the reconstituted rice processed through extrusion puffing exhibited a purple-black hue; meanwhile, the instant reconstituted rice, produced through further microwave puffing, displayed a reddish-brown color. both imparted a rich cereal flavor. The starch in both types of rice exhibited a V-shaped structure with lower relative crystallinity. Compared to commercial rice, the reconstituted rice and instant reconstituted rice contained higher levels of flavonoids, polyphenols, and other flavor compounds, along with 1.63-fold and 1.75-fold more proteins, respectively. The GI values of the reconstituted rice and the instant reconstituted rice were 68.86 and 69.47, respectively; thus, they are medium-GI foods that can alleviate the increase in blood glucose levels.

Abstract The cGAS‐STING pathway, as a vital innate immune signaling pathway, has attracted considerable attention in tumor immunotherapy research. However, STING agonists are generally incapable of targeting tumors, thus limiting their clinical applications. Here, a photodynamic polymer (P1) is designed to electrostatically couple with 56MESS–a cationic platinum (II) agent–to form NPPDT‐56MESS. The accumulation of NPPDT‐56MESS in the tumors increases the efficacy and decreases the systemic toxicity of the drugs. Moreover, NPPDT‐56MESS generates reactive oxygen species (ROS) under the excitation with an 808 nm laser, which then results in the disintegration of NPPDT‐56MESS. Indeed, the ROS and 56MESS act synergistically to damage DNA and mitochondria, leading to a surge of cytoplasmic double‐stranded DNA (dsDNA). This way, the cGAS‐STING pathway is activated to induce anti‐tumor immune responses and ultimately enhance anti‐cancer activity. Additionally, the administration of NPPDT‐56MESS to mice induces an immune memory effect, thus improving the survival rate of mice. Collectively, these findings indicate that NPPDT‐56MESS functions as a chemotherapeutic agent and cGAS‐STING pathway agonist, representing a combination chemotherapy and immunotherapy strategy that provides novel modalities for the treatment of uveal melanoma.

Summary: Extranodal natural killer/T cell lymphoma, nasal type (ENKTL) is an aggressive lymphoid malignancy with a poor prognosis and lacks standard treatment. Targeted therapies are urgently needed. Here we systematically investigated the druggable mechanisms through chemogenomic screening and identified that Bcl-xL-specific BH3 mimetics effectively induced ENKTL cell apoptosis. Notably, the specific accumulation of Bcl-xL, but not other Bcl-2 family members, was verified in ENKTL cell lines and patient tissues. Furthermore, Bcl-xL high expression was shown to be closely associated with worse patient survival. The critical role of Bcl-xL in ENKTL cell survival was demonstrated utilizing selective inhibitors, genetic silencing, and a specific degrader. Additionally, the IL2-JAK1/3-STAT5 signaling was implicated in Bcl-xL dysregulation. In vivo, Bcl-xL inhibition reduced tumor burden, increased apoptosis, and prolonged survival in ENKTL cell line xenograft and patient-derived xenograft models. Our study indicates Bcl-xL as a promising therapeutic target for ENKTL, warranting monitoring in ongoing clinical trials by targeting Bcl-xL.

Abstract Lithium–air batteries (LABs), owing to their ultrahigh theoretical energy density, are recognized as one of the next‐generation energy storage techniques. However, it remains a tricky problem to find highly active cathode catalyst operating within ambient air. In this contribution, a highly active Fe2Mo3O12 (FeMoO) garnet cathode catalyst for LABs is reported. The experimental and theoretical analysis demonstrate that the highly stable polyhedral framework, composed of FeO octahedrons and MO tetrahedrons, provides a highly effective air catalytic activity and long‐term stability, and meanwhile keeps good structural stability. The FeMoO electrode delivers a cycle life of over 1800 h by applying a simple half‐sealed condition in ambient air. It is found that surface‐rich Fe vacancy can act as an O2 pump to accelerate the catalytic reaction. Furthermore, the FeMoO catalyst exhibits a superior catalytic capability for the decomposition of Li2CO3. H2O in the air can be regarded as the main contribution to the anode corrosion and the deterioration of LAB cells could be attributed to the formation of LiOH·H2O at the end of cycling. The present work provides in‐depth insights to understand the catalytic mechanism in air and constitutes a conceptual breakthrough in catalyst design for efficient cell structure in practical LABs.

Abstract Here, we present a multi-modal deep generative model, the single-cell Multi-View Profiler (scMVP), which is designed for handling sequencing data that simultaneously measure gene expression and chromatin accessibility in the same cell, including SNARE-seq, sci-CAR, Paired-seq, SHARE-seq, and Multiome from 10X Genomics. scMVP generates common latent representations for dimensionality reduction, cell clustering, and developmental trajectory inference and generates separate imputations for differential analysis and cis-regulatory element identification. scMVP can help mitigate data sparsity issues with imputation and accurately identify cell groups for different joint profiling techniques with common latent embedding, and we demonstrate its advantages on several realistic datasets.

Hemodynamic prediction of carotid artery stenosis (CAS) is of great clinical significance in the diagnosis, prevention, and treatment prognosis of ischemic strokes. While computational fluid dynamics (CFD) is recognized as a useful tool, it shows a crucial issue that the high computational costs are usually required for real-time simulations of complex blood flows. Given the powerful feature-extraction capabilities, the deep learning (DL) methodology has a high potential to implement the mapping of anatomic geometries and CFD-driven flow fields, which enables accomplishing fast and accurate hemodynamic prediction for clinical applications. Based on a brain/neck CT angiography database of 280 subjects, image based three-dimensional CFD models of CAS were constructed through blood vessel extraction, computational domain meshing and setting of the pulsatile flow boundary conditions; a series of CFD simulations were undertaken. A DL strategy was proposed and accomplished in terms of point cloud datasets and a DL network with dual sampling-analysis channels. This enables multimode mapping to construct the image-based geometries of CAS while predicting CFD-based hemodynamics based on training and testing datasets. The CFD simulation was validated with the mass flow rates at two outlets reasonably agreed with the published results. Comprehensive analysis and error evaluation revealed that the DL strategy enables uncovering the association between transient blood flow characteristics and artery cavity geometric information before and after surgical treatments of CAS. Compared with other methods, our DL-based model trained with more clinical data can reduce the computational cost by 7,200 times, while still demonstrating good accuracy (error

Methotrexate is one of the cornerstones of rheumatoid arthritis (RA) therapy. Genetic factors or single nucleotide polymorphisms (SNPs) are responsible for 15%–30% of the variation in drug response. Identification of clinically effective SNP biomarkers for predicting methotrexate (MTX) sensitivity has been a challenge. The aim of this study was to explore the association between the disease related outcome of MTX treatment and 23 SNPs in 8 genes of the MTX pathway, as well as one pro-inflammatory related gene in RA patients naïve to MTX. Categorical outcomes such as Disease Activity Score (DAS)-based European Alliance of Associations for Rheumatology (EULAR) non-response at 4 months, The American College of Rheumatology and EULAR (ACR/EULAR) non-remission at 6 months, and failure to sustain MTX monotherapy from 12 to 24 months were assessed, together with continuous outcomes of disease activity, joint pain and fatigue. We found that the SNPs rs1801394 in the MTRR gene, rs408626 in DHFR gene, and rs2259571 in AIF-1 gene were significantly associated with disease activity relevant continuous outcomes. Additionally, SNP rs1801133 in the MTHFR gene was identified to be associated with improved fatigue. Moreover, associations with p values at uncorrected significance level were found in SNPs and different categorical outcomes: 1) rs1476413 in the MTHFR gene and rs3784864 in ABCC1 gene are associated with ACR/EULAR non-remission; 2) rs1801133 in the MTHFR gene is associated with EULAR response; 3) rs246240 in the ABCC1 gene, rs2259571 in the AIF-1 gene, rs2274808 in the SLC19A1 gene and rs1476413 in the MTHFR gene are associated with failure to MTX monotherapy after 12–24 months. The results suggest that SNPs in genes associated with MTX activity may be used to predict MTX relevant-clinical outcomes in patients with RA.

How bacteria respond at the systems level to both genetic and environmental perturbations imposed at the same time is one fundamental yet open question in biology. Bioengineering or synthetic biology provides an ideal system for studying such responses, as engineered strains always have genetic changes as opposed to wildtypes and are grown in conditions which often change during growth for maximal yield of desired products. So, engineered strains were used to address the outstanding question. Two Bacillus subtilis strains (MT1 and MT2) were created previously for the overproduction of N-acetylglucosamine (GlcNAc), which were grown in an environment with a carbon shift from glucose to glucose and xylose in the same culture system. We had four groups: (1) a wildtype (WT) grown with glucose at t1; (2) a WT with glucose and xylose at t2; (3) a mutant (MT1) grown with glucose at t1; and (4) MT1 with glucose and xylose at t2. By measuring transcriptomes and metabolomes, we found that GlcNAc-producing mutants, particularly MT2, had a higher yield of N-acetylglucosamine than WT but displayed a smaller maximum growth rate than the wildtype, despite MT1 reaching higher carrying capacity. Underlying the observed growth, the engineered pathways leading to N-acetylglucosamine had both higher gene expression and associated metabolite concentrations in MT1 than WT at both t1 and t2; in bioenergetics, there was higher energy supply in terms of ATP and GTP, with the energy state metric higher in MT1 than WT at both timepoints. Additionally, most top key precursor metabolites were equally abundant in MT1 and WT at either timepoints. Besides that, one prominent feature was the high consistency between transcriptomics and metabolomics in revealing the response. First, both metabolomes and transcriptomes revealed the same PCA clusters of the four groups. Second, we found that the important functions enriched both by metabolomes and transcriptomes overlapped, such as amino acid metabolism and ABC transport. Strikingly, these functions overlapped those enriched by the genes showing a high (positive or negative) correlation with metabolites. Furthermore, these functions also overlapped the enriched KEGG pathways identified using weighted gene coexpression network analysis. All these findings suggest that the responses to simultaneous genetic and environmental perturbations are well coordinated at the metabolic and transcriptional levels: they rely heavily on bioenergetics, but core metabolism does not differ much, while amino acid metabolism and ABC transport are important. This serves as a design guide for bioengineering, synthetic biology, and systems biology.

Lithium-oxygen batteries have attracted considerable attention in the past several years due to their ultra-high theoretical energy density. However, there are still many serious issues that must be addressed before considering practical applications, including the sluggish oxygen redox kinetics, the limited capacity far from the theoretical value, and the poor cycle stability. This study proposes a surface modification strategy that can enhance the catalytic activity by loading Fe3C particles on carbon fibers, and the microstructure of Fe3C particle-modified carbon fibers is studied by multiple materials characterization methods. Experiments and density functional theory (DFT) calculations show that the discharge products on the Fe3C carbon fiber (Fe3C-CF) cathode are mainly Li2−xO2. Fe3C-CF exhibits high catalytic ability based on its promotion of the formation/decomposition processes of Li2−xO2. Consequently, the well-designed electrode catalyst exhibits a large specific capacity of 17,653.1 mAh g−1 and an excellent cyclability of 263 cycles at a current of 200 mA g−1.

Objective: After coronary artery bypass grafting (CABG) surgery, the main causes of poor instant patency of left internal mammary arteries (LIMAs) are competitive flow and anastomotic stenosis, but how to determine the cause of LIMA non-patency without interfering with the native coronary artery is still a difficult problem to be solved urgently.Methods: In this study, a 0D-3D coupled multiscaled CABG model of anastomotic stenosis and competitive flow was constructed. After calculation, the flow waveform of the LIMA was extracted, and the waveform shape, common clinical parameters (average flow, PI, and DF), and graft flow FFT ratio results (F0/H1 and F0/H2) were analyzed.Results: For LIMA, these three common clinical parameters did not differ significantly between the anastomotic stenosis group and competitive flow group. However, the waveform shape and FFT ratio (especially F0/H2) of the competitive flow group were significantly different from those of the anastomotic stenosis group. When the cause was competitive flow, there was systolic backflow, and F0/H2 was too high (>14.89). When the cause was anastomotic stenosis, the waveform maintained a bimodal state and F0/H2 was in a normal state (about 1.17).Conclusion: When poor instant patency of the LIMA is found after CABG, the causes can be determined by graft flow waveform shape and F0/H2.

Dietary fiber (DF) was extracted from navel orange peel residue by enzyme (E-DF) and ultrasound-assisted deep eutectic solvent (US-DES-DF), and its physicochemical and prebiotic properties were characterized. Based on Fourier-transform infrared spectroscopy, all DF samples exhibited typical polysaccharide absorption spectra, indicating that DES could separate lignin while leaving the chemical structure of DF unchanged, yielding significantly higher extraction yields (76.69 ± 1.68%) compared to enzymatic methods (67.27 ± 0.13%). Moreover, ultrasound-assisted DES extraction improved the properties of navel orange DFs by significantly increasing the contents of soluble dietary fiber and total dietary fiber (3.29 ± 1.33% and 10.13 ± 0.78%, respectively), as well as a notable improvement in the values of water-holding capacity, oil-holding capacity, and water swelling capacity. US-DES-DF outperformed commercial citrus fiber in stimulating the proliferation of probiotic Bifidobacteria strains in vitro. Overall, ultrasound-assisted DES extraction exhibited potential as an industrial extraction method, and US-DES-DF could serve as a valuable functional food ingredient. These results provide a new perspective on the prebiotic properties of dietary fibers and the preparation process of prebiotics.

Abstract Background The purpose of this study was to explore echocardiographic views and methods of aortic arch anomalies in infants, so as to improve the screening sensitivity and diagnostic accuracy. Methods 140 children with abnormal aortic arch diagnosed by ultrasound in Children’s Hospital of Hebei Province from January 2014 to December 2019 were selected for retrospective analysis. All were confirmed by surgery or/and computerized tomography angiography. Series of views for aortic arch (the three-vessel and tracheal view, aortic arch short axis view, left aortic arch long axis view, aortic arch long axis continuous scan views) were performed in all cases on the basis of the routine views of echocardiography. The screening sensitivity and diagnostic coincidence rate of different echocardiographic views for aortic arch anomalies were analyzed. Results Among the 140 infants, right aortic arch were 21 cases (6/21 were accompanied by mirror branch and 15/21 were with aberrant left subclavian artery). Left aortic arch with aberrant right subclavian artery were 2 cases, and double aortic arch with both arches open were 20 cases. Double aortic arch with left arch atresia were 2 cases, and atresia of the proximal aorta with aortic arch dysplasia was 1 case. Coarctation of the aorta were 67 cases, and interruption of aortic arch were 27 cases. All the patients were correctly diagnosed except that 2 infants with interruption of aortic arch were incorrectly diagnosed as coarctation of the aorta, and 1 infant with coarctation of the aorta was misdiagnosed as interruption of aortic arch by echocardiography. The screening sensitivities of four views and four-view combination for abnormal aortic arch were 99.3, 73.6, 87.1, 99.3, and 100%; the diagnostic coincidence rates were 85.7, 27.1,66.4, 95.0%, and 97.9% respectively. On the basis of traditional left aortic long axis view, other three views had their own advantages. The screening sensitivity and diagnostic coincidence rate of four-view combination were significantly improved. Conclusions The three-vessel trachea view is simple and feasible, which is suitable for screening abnormal aortic arch. The combination of four views conduces to improving screening sensitivity and diagnostic accuracy of aortic arch abnormalities.

Anzai et al. propose a deep learning approach to estimate the 3D hemodynamics of complex aorta-coronary artery geometry in the context of coronary artery bypass surgery. Their method reduces the calculation time 600-fold, while allowing high resolution and similar accuracy as traditional computational fluid dynamics (CFD) method.

The study was carried out to explore the biomechanical properties of embracing and non-embracing rib plates. Forty-eight adult cadaver rib specimens were divided randomly into six groups: three fixation model groups were made using embracing plates (two pairs of equals on both sides of the broken end), and the other three groups were fixed with a pre-shaped anatomical plate (three locking screws on each side of the end were equally spaced). The biomechanical properties of these models were analyzed using non-destructive three-point bending tests, non-destructive torsion experiments, and destructive axial compression tests. In this study, the gap of fracture ends was widened in embracing plate group in the non-destructive three-point bending experiment. No change in the fracture ends was detected in the pre-shaped anatomical plate group. The bending stress of the pre-shaped anatomical plate group was significantly enhanced at the 2–12 mm displacement points (p

Abstract Background Compared with disease biomarkers in blood and urine, biomarkers in saliva have distinct advantages in clinical tests, as they can be conveniently examined through noninvasive sample collection. Therefore, identifying human saliva-secretory proteins and further detecting protein biomarkers in saliva have significant value in clinical medicine. There are only a few methods for predicting saliva-secretory proteins based on conventional machine learning algorithms, and all are highly dependent on annotated protein features. Unlike conventional machine learning algorithms, deep learning algorithms can automatically learn feature representations from input data and thus hold promise for predicting saliva-secretory proteins. Results We present a novel end-to-end deep learning model based on multilane capsule network (CapsNet) with differently sized convolution kernels to identify saliva-secretory proteins only from sequence information. The proposed model CapsNet-SSP outperforms existing methods based on conventional machine learning algorithms. Furthermore, the model performs better than other state-of-the-art deep learning architectures mostly used to analyze biological sequences. In addition, we further validate the effectiveness of CapsNet-SSP by comparison with human saliva-secretory proteins from existing studies and known salivary protein biomarkers of cancer. Conclusions The main contributions of this study are as follows: (1) an end-to-end model based on CapsNet is proposed to identify saliva-secretory proteins from the sequence information; (2) the proposed model achieves better performance and outperforms existing models; and (3) the saliva-secretory proteins predicted by our model are statistically significant compared with existing cancer biomarkers in saliva. In addition, a web server of CapsNet-SSP is developed for saliva-secretory protein identification, and it can be accessed at the following URL: http://www.csbg-jlu.info/CapsNet-SSP/ . We believe that our model and web server will be useful for biomedical researchers who are interested in finding salivary protein biomarkers, especially when they have identified candidate proteins for analyzing diseased tissues near or distal to salivary glands using transcriptome or proteomics.

Urine excretory proteins are among the most commonly used biomarkers in body fluids. Computational identification of urine excretory proteins can provide very useful information for identifying targeted disease biomarkers in urine by linking transcriptome or proteomics data. There are few methods based on conventional machine learning algorithms for predicting urine excretory proteins, and most of these methods strongly depend on the extraction of features from urine excretory proteins. An end-to-end model for urine excretory protein prediction, called DeepUEP, is presented using deep neural networks relying on only amino acid sequence information. The model achieves good performance and outperforms existing methods on training and testing sets. By comparing known urinary protein biomarkers with the results of the model, we find that the model can achieve a true-positive rate of over 80% for urinary protein biomarkers that have been detected in more than one study. We also combine our model with transcriptome and proteomics data from lung cancer patients to predict the potential urinary protein biomarkers of lung cancer. A web server is developed for the prediction of urine excretory proteins, and it can be accessed at the following URL: http://www.csbg-jlu.info/DeepUEP/. We believe that our prediction model and web server are useful for biomedical researchers who are interested in identifying urinary protein biomarkers, especially for candidate proteins in transcriptome or proteomics analyses of diseased tissues.

BackgroundIt is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.ObjectiveThis study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.MethodThe cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.ResultsWith respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.ConclusionA deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.

Asphalt concrete (AC) is a composite material consisting of binder, aggregates and air voids. The quantitative effect of aggregate-to-aggregate contact on the mechanical performance of AC is an important and complex issue, which has not been fully understood yet. To fill this gap, this study aims to characterize the aggregate contacts in AC and evaluate their effects on the viscoelastic behavior of AC through micromechanical finite element (FE) modeling. To this end, 3D microstructural models were generated through digital image processing (DIP) method and aggregate contacts were captured in the model via contact zone (CZ) elements. A CZ model was proposed and verified by a parametric study to identify the viscoelastic properties of CZ elements, while the viscoelastic properties of matrix phase were determined through laboratory tests. Steady-state dynamic (SSD) analysis was then conducted to investigate the macro-scale viscoelastic response of AC. It was found that the proposed modeling approach captures the measured response accurately. Accounting for aggregate contacts results in higher predicted AC dynamic moduli and lower phase angles, thus improving the agreement between modeling and experimental results. The numerical model developed in this study provides a promising approach for investigating the effect of aggregate contacts on the mechanical performance of AC.

Permeable pavements are often affected by pore clogging, which leads to their functional failure and reduced service life. However, the clogging mechanism and its impact on the permeability and complex pore microstructures in pervious pavement remain unclear. The aim of current study is to quantify the clogging behavior in pervious pavement materials and carry out investigations on the development of pore characteristics and permeability. Novel polyurethane-bound pervious mixture (PUPM) was adopted for comparative study in present research with conventional Porous Asphalt (PA). The Aachen Polishing Machine (APM) was selected to perfectly serve as a simulator for clogging process of pavement in the actual service condition. The permeability and pore microstructure of the pervious pavement material were then characterized by using the self-developed permeameter and X-ray Computed Tomography (CT) scanning, respectively. The development of pore characteristics in terms of clogging was experimentally illustrated. Based on the pore characteristics, the flow behavior of PUPM subjected to different clogging periods was predicted based on the developed non-Darcy flow model. The developed experiments and analysis can further strengthen the understanding of the clogging mechanism within the porous pavement material. The results can also serve for the optimization of the pervious pavement design in the engineering application.

The interventional treatment of cerebral aneurysm requires hemodynamics to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in calculating cerebral aneurysm hemodynamics before and after flow-diverting (FD) stent placement. However, the complex operation (such as the construction and placement simulation of fully resolved or porous-medium FD stent) and high computational cost of CFD hinder its application. To solve these problems, we applied aneurysm hemodynamics point cloud data sets and a deep learning network with double input and sampling channels. The flexible point cloud format can represent the geometry and flow distribution of different aneurysms before and after FD stent (represented by porous medium layer) placement with high resolution. The proposed network can directly analyze the relationship between aneurysm geometry and internal hemodynamics, to further realize the flow field prediction and avoid the complex operation of CFD. Statistical analysis shows that the prediction results of hemodynamics by our deep learning method are consistent with the CFD method (error function

Controlled atmosphere (CA) has been demonstrated to maintain the shelf-life quality of fruits, but its effect on the antioxidant activities and phenolic content of yellow peach is not comprehensive. This study analyzed the role of CA on the quality of shelf period, phenolic content and antioxidant activity of “Jinxiu” yellow peach. Yellow peach was left under specific aeration conditions (3.5–4% CO2, 2–3% O2, 92–95.5% N2, 1 ± 0.5 °C) and the control (1 ± 0.5 °C) for 21 d, to observe changes in physiological parameters of the fruit during 10 d of the shelf life (25 ± 1 °C). The result showed that CA reduced the weight loss rate (WLR), decay rate (DR), and browning index (BI) of yellow peaches. Furthermore, the CA held a high level of total flavonoid content (TFC), total phenol content (TPC) and phenolic content in the fruit. Antioxidant analysis showed that polyphenol oxidase (PPO) enzyme activity was lower and free radical scavenging capacity (DPPH, ABTS, and FRAP) and antioxidant enzyme activities (POD and PAL) were higher in the CA group. Combining the results of significance analysis, correlation analysis, principal component analysis (PCA) and hierarchical cluster analysis (HCA) clearly identified the differences between the CA group and the control group. The results showed that the CA could maintain higher phenolic content and reduce the oxidation of yellow peach fruit and enhance fruit quality by affecting the antioxidant activities of yellow peach.

Delayed-type hypersensitivity arthritis (DTHA) is a recently established experimental model of rheumatoid arthritis (RA) in mice with pharmacological values. Despite an indispensable role of CD4+ T cells in inducing DTHA, a potential role for CD4+ T cell subsets is lacking. Here we have quantified CD4+ subsets during DTHA development and found that levels of activated, pro-inflammatory Th1, Th17, and memory CD4+ T cells in draining lymph nodes were increased with differential dynamic patterns after DTHA induction. Moreover, according to B-cell depletion experiments, it has been suggested that this cell type is not involved in DTHA. We show that DTHA is associated with increased levels of B cells in draining lymph nodes accompanied by increased levels of circulating IgG. Finally, using the anti-rheumatoid agents, methotrexate (MTX) and the anti-inflammatory drug dexamethasone (DEX), we show that MTX and DEX differentially suppressed DTHA-induced paw swelling and inflammation. The effects of MTX and DEX coincided with differential regulation of levels of Th1, Th17, and memory T cells as well as B cells. Our results implicate Th1, Th17, and memory T cells, together with activated B cells, to be involved and required for DTHA-induced paw swelling and inflammation.

The use of diameter stenosis (DS), as revealed by coronary angiography, for predicting fractional flow reserve (FFR) usually results in a high error rate of detection. In this study, we investigated a method for predicting FFR in patients with coronary stenosis based on multiple independent risk factors. The aim of the study was to improve the accuracy of detection. First, we searched the existing literature to identify multiple independent risk factors and then calculated the corresponding odds ratios. The improved analytic hierarchy process (IAHP) was then used to determine the weighted value of each independent risk factor, based on the corresponding odds ratio. Next, we developed a novel method, based on the top seven independent risk factors with the highest weighted values, to predict FFR. This model was then used to predict the FFR of 253 patients with coronary stenosis, and the results were then compared with previous methods (DS alone and a simplified scoring system). In addition to DS, we identified a range of other independent risk factors, with the highest weighted values, for predicting FFR, including gender, body mass index, location of stenosis, type of coronary artery distribution, left ventricular ejection fraction, and left myocardial mass. The area under the receiver-operating characteristic curve (AUC) for the newly developed method was 84.3% (95% CI: 79.2–89.4%), which was larger than 65.3% (95% CI: 61.5–69.1%) of DS alone and 74.8% (95% CI: 68.4–81.2%) of the existing simplified scoring system. The newly developed method, based on multiple independent risk factors, effectively improves the prediction accuracy for FFR.

The comprehensive discovery of structure variations (SVs) is fundamental to many genomics studies and high-throughput sequencing has become a common approach to this task. However, due the limited length, it is still non-trivial to state-of-the-art tools to accurately align short reads and produce high-quality SV callsets. Pan-genome provides a novel and promising framework to short read-based SV calling since it enables to comprehensively integrate known variants to reduce the incompleteness and bias of single reference to breakthrough the bottlenecks of short read alignments and provide new evidences to the detection of SVs. However, it is still an open problem to develop effective computational approaches to fully take the advantage of pan-genomes. Herein, we propose Pan-genome augmented Structure Variation calling tool with read Re-alignment (PanSVR), a novel pan-genome-based SV calling approach. PanSVR uses several tailored methods to implement precise re-alignment for SV-spanning reads against well-organized pan-genome reference with plenty of known SVs. PanSVR enables to greatly improve the quality of short read alignments and produce clear and homogenous SV signatures which facilitate SV calling. Benchmark results on real sequencing data suggest that PanSVR is able to largely improve the sensitivity of SV calling than that of state-of-the-art SV callers, especially for the SVs from repeat-rich regions and/or novel insertions which are difficult to existing tools.

Aspergillus aculeatus ZC-1005 (ZC-1005 was used as the abbreviation of this strain) is a hemicellulase-producing strain isolated from rotten citrus rind buried in the soil. Our previous study has shown its biochemical properties including high xylanase activity, mannanase activity, and degradation reaction with citrus mesocarp. In this study, we focused more on the enzyme safety evaluation and the genome sequencing via PacBio and Illumina platforms. High biological safety of the crude enzymes of ZC-1005 has been proven by the acute oral toxicity test, sub-chronic toxicity test, micronucleus test, and sperm malformation test. The genome of ZC-1005 had a GC content of 52.53%, with a size of 35,458,484 bp, and encoded 10,147 genes. Strain ZC-1005 harbored 269 glycosyl hydrolase (GH) genes of 64 families. The fungus produces cellulose-acting (GH3, GH5, GH12, and GH1) and hemicellulose-acting enzymes (GH16, GH31, GH2, and GH92). In genome annotation, we paid more attention to the genes encoding xylanase, such as gene 01512, gene 05833, gene 05469, gene 07781, gene 08432, gene 09042, gene 08008, and gene 09694. The collaboration between complete genome information and the degradation test confirmed that ZC-1005 could degrade cellulose and xylan. Our results showed that the citrus enzymatic decapsulation technology was efficacious and safe for canned citrus product processing, which may also solve the industrial waste problem. Therefore, ZC-1005 and the crude enzyme secreted from the strain were very promising to be used in the citrus processing industry.

Chenpi is a kind of dried citrus peel from Citrus reticulata, and it is often used as traditional Chinese medicine to treat dyspepsia and respiratory tract inflammation. In this study, to determine which way of chenpi treatment plays a better effect on the prevention of obesity in healthy mice, we conducted 16S ribosomal RNA (rRNA) gene sequencing for intestinal microbiota and gas chromatography-mass spectrometry detector (GC/MSD) analysis for short-chain fatty acids (SCFAs) of female rats fed with either chenpi decoction or chenpi powder-based diet (n = 10 per group) for 3 weeks. Chenpi powder (CP) group significantly reduced abdominal adipose tissues, subcutaneous adipose tissue, and the serum level of total triacylglycerol (TG). At a deeper level, chenpi powder has a better tendency to increase the ratio of Bacteroidetes to Firmicutes. It alters the Muribaculaceae and Muribaculum in intestinal microbiota, though it is not significant. The concentrations of acetic acid, valeric acid, and butyric acid increased slightly but not significantly in the CP group. Chenpi decoction just reduced perirenal adipose tissues, but it shows better antioxidant activity. It has little effect on intestinal microbiota. No differences were found for SCFAs in the chenpi decoction (CD) group. The results indicated that chenpi powder has a better effect in preventing obesity in mice. It can provide a basis for the development of functional products related to chenpi powder.

Single-cell CRISPR screening combines pooled CRISPR screening with scRNA-seq analysis to expand the resolution power of genetic screening. Here, the authors develop MUSIC, a computational pipeline for analyzing single-cell CRISPR screening data.

Starches were isolated from bulbs of seven lily cultivars grown in China and their morphological, structural, and physicochemical properties were investigated. All the isolated starch samples showed triangular, elliptical, less scalloped, round, and irregular shaped granules. The starch of different lily cultivars exhibited B-type crystalline pattern. The median diameters (D50) were found to be in range from 17.79 μm to 45.06 μm and the amylose content in range from 23.63 to 30.38 g/100 g starch. The weight average molar mass (Mw) of amylopectin ranged from 4.03 × 107 g/mol to 11.00 × 107 g/mol and root-mean-square rotation radius (Rz) ranged from 203.90 nm to 264.20 nm. HPAEC-PAD chain distribution analysis revealed that the average chain length of amylopectin ranged from DP 23.19–25.29. Starch samples of different lily cultivars showed peak viscosity ranging from 1699.7 cP to 2963.0 cP, and pasting temperature varying from 63.83°C to 71.00°C. Furthermore, the transition temperatures of the starch samples isolated from different lily cultivars were found to be in range from 50.76°C to 57.59°C for onset temperature (To), 54.57°C to 64.30°C for peak temperature (Tp), and 68.07°C to 77.16°C for conclusion temperature (Tc). The gelatinization temperature range (ΔTr) was found to be from 16.66°C to 21.65°C and enthalpy (ΔH) was in range from 11.46 J/g to 16.29 J/g. These results indicated that lily starch properties were significantly different depending on lily variety, which could help in understanding the convenience of these starches for the application in different food and non-food industries.

With the development of convolutional neural networks, the effect of pedestrian detection has been greatly improved by deep learning models. However, the presence of pseudo pedestrians will lead to accuracy reduction in pedestrian detection. To solve the problem that the existing pedestrian detection algorithms cannot distinguish pseudo pedestrians from real pedestrians, a real and pseudo pedestrian detection method with CA-YOLOv5s based on stereo image fusion is proposed in this paper. Firstly, the two-view images of the pedestrian are captured by a binocular stereo camera. Then, a proposed CA-YOLOv5s pedestrian detection algorithm is used for the left-view and right-view images, respectively, to detect the respective pedestrian regions. Afterwards, the detected left-view and right-view pedestrian regions are matched to obtain the feature point set, and the 3D spatial coordinates of the feature point set are calculated with Zhengyou Zhang’s calibration method. Finally, the RANSAC plane-fitting algorithm is adopted to extract the 3D features of the feature point set, and the real and pseudo pedestrian detection is achieved by the trained SVM. The proposed real and pseudo pedestrian detection method with CA-YOLOv5s based on stereo image fusion effectively solves the pseudo pedestrian detection problem and efficiently improves the accuracy. Experimental results also show that for the dataset with real and pseudo pedestrians, the proposed method significantly outperforms other existing pedestrian detection algorithms in terms of accuracy and precision.

There is still a lack of fast and accurate classification tools to identify the taxonomies of noisy long reads, which is a bottleneck to the use of the promising long-read metagenomic sequencing technologies. Herein, we propose de Bruijn graph-based Sparse Approximate Match Block Analyzer (deSAMBA), a tailored long-read classification approach that uses a novel pseudo alignment algorithm based on sparse approximate match block (SAMB). Benchmarks on real sequencing datasets demonstrate that deSAMBA enables to achieve high yields and fast speed simultaneously, which outperforms state-of-the-art tools and has many potentials to cutting-edge metagenomics studies.

Lemon essential oil (LEO) is a kind of citrus essential oil with antioxidant, anti-inflammatory, and antimicrobial activities, but low water solubility and biological instability hinder its industrial application. In this study, LEO was nanoemulsified to solve these problems. The preparation procedure of lemon essential oil nanoemulsions (LEO-NEs) was optimized, and the physicochemical characterization and antioxidant activities were explored. Single-factor experiments (SFEs) and response surface methodology (RSM) were conducted for the effects on the mean droplet size of LEO-NEs. Five factors of SFE which may influence the droplet size were identified: HLB value, concentration of essential oil, concentration of surfactant, ultrasonic power, and ultrasonic time. On the basis of the SFE, the RSM approach was used to optimize the preparation procedure to obtain LEO-NEs with the smallest droplet size. LEO-NEs exhibited good antioxidant activity when the HLB value was 13, content of surfactant was 0.157 g/mL, ultrasonic time was 23.50 min, and ultrasonic power was 761.65 W. In conclusion, these results can provide a good theoretical basis for the industrial application of lemon essential oil.

The abnormal diameter of the coronary artery is twice or more than the normal diameter, which is a coronary artery aneurysm (CAA). According to the clinical statistics, CAA shows high occurrence on right coronary artery (RCA). The most common cause of CAA in adults is atherosclerosis, which destroys the elastic fibers in the middle layer of the blood vessel. Under the intravascular pressure, the weak wall bulges outward and form CAA. This article aims to explain the hemodynamic mechanism of coronary artery aneurysm shows high occurrence on RCA. Occurrence of CAA was simulated by the volume growth of coronary artery. Firstly, a 0–3D multi-scale model of normal coronary artery was constructed to obtain the hemodynamic environments of coronary artery. Then, fluid-structure interaction of normal and atherosclerotic blood vessel was performed to obtain volume growth rate of the coronary artery. Atherosclerosis was simulated by modifying Young’s modulus in middle layer of the blood vessel. Finally, creep simulation was performed to compare the deformation of the blood vessels under the accumulation of time. Under normal condition, the volume growth rate of the RCA is 2.28 times and 1.55 times of the LAD and the LCX. After atherosclerosis, the volume growth rate of the RCA was 2.69 times and 2.12 times of the LAD and the LCX. And the volume growth rate of the RCA was 3.85 times and 3.45 times of the LAD and the LCX after further deepening of atherosclerosis. The expansion time above the average volume growth rate of the RCA, the LAD and the LCX respectively were 0.194, 0.168 and 0.179 s. The RCA is 2.06 times the original, the LAD and LCX are 1.53 times and 1.56 times after 10 years in creep simulation. It can be concluded that the RCA is more prone to aneurysms originated from the larger expansion of the RCA under normal physiological condition, and the larger expansion is magnified under atherosclerosis condition with destroyed vessel elasticity, and further magnified during the time accumulated viscoelastic creep to develop to aneurysm eventually.

Aspergillus japonicas PJ01 (A. japonicas PJ01) is a strain isolated from the rotten branches. In previ-ous studies, it was shown that it can produce complex enzymes to degrade polysaccharide com-ponents. In this study, we evaluated the safety of its crude enzyme solution. Acute oral toxicity, subchronic toxicity, micronucleus and sperm malformation tests all validated the high biologi-cal safety for the crude enzymes. Secondly, we carried out the citrus segment degradation ex-periment of crude enzyme solution. Compared with the control group, the crude enzyme solu-tion of A. japonicas PJ01 can completely degrade the segments in 50 min, which provides the basis for enzymatic peeling during juice processing. The whole genome sequencing showed that the genome of A. japonicus PJ01 has a GC content of 51.37% with a size of 36204647 bp, and encoded 10070 genes. GO, COG, KEGG and CAZy databases were used in gene annotation analyses. Pathway enrichment showed many genes related to carbohydrate metabolism, rich in genes re-lated to pectinase, xylanase and carboxylcellulase. Therefore, the complex enzyme produced by A. japonicus PJ01 can be used in gizzard juice processing to achieve efficient enzymatic decapsu-lation.

To solve the problems of low teaching programming efficiency and poor flexibility in robot welding of complex box girder structures, a method of seam trajectory recognition based on laser scanning displacement sensing was proposed for automated guidance of a welding torch in the skip welding of a spatially intermittent welding seam. Firstly, a laser scanning displacement sensing system for measuring angles adaptively is developed to detect corner features of complex structures. Secondly, a weld trajectory recognition algorithm based on Euclidean distance discrimination is proposed. The algorithm extracts the shape features by constructing the characteristic triangle of the weld trajectory, and then processes the set of shape features by discrete Fourier analysis to solve the feature vector used to describe the shape. Finally, based on the Euclidean distance between the feature vector of the test sample and the class matching library, the class to which the sample belongs is identified to distinguish the weld trajectory. The experimental results show that the classification accuracy rate of four typical spatial discontinuous welds in complex box girder structure is 100%. The overall processing time for weld trajectory detection and classification does not exceed 65 ms. Based on this method, the field test was completed in the folding special container production line. The results show that the system proposed in this paper can accurately identify discontinuous welds during high-speed metal active gas arc welding (MAG) welding with a welding speed of 1.2 m/min, and guide the welding torch to automatically complete the skip welding, which greatly improves the welding manufacturing efficiency and quality stability in the processing of complex box girder components. This method does not require a time-consuming pre-welding teaching programming and visual inspection system calibration, and provides a new technical approach for highly efficient and flexible welding manufacturing of discontinuous welding seams of complex structures, which is expected to be applied to the welding manufacturing of core components in heavy and large industries such as port cranes, large logistics transportation equipment, and rail transit.

Expansion of penguin activity in maritime Antarctica, under ice thaw, increases the chances of penguin feces affecting soil microbiomes. The detail of such effects begins to be revealed. By comparing soil geochemistry and microbiome composition inside (one site) and outside (three sites) of the rookery, we found significant effects of penguin feces on both. First, penguin feces change soil geochemistry, causing increased moisture content (MC) of ornithogenic soils and nutrients C, N, P, and Si in the rookery compared to non-rookery sites, but not pH. Second, penguin feces directly affect microbiome composition in the rookery, not those outside. Specifically, we found 4,364 operational taxonomical units (OTUs) in 404 genera in six main phyla: Proteobacteria, Actinobacteria, Gemmatimonadetes, Acidobacteria, Chloroflexi, and Bacteroidetes. Although the diversity is similar among the four sites, the composition is different. For example, penguin rookery has a lower abundance of Acidobacteria, Chloroflexi, and Nitrospirae but a higher abundance of Bacteroidetes, Firmicutes, and Thermomicrobia. Strikingly, the family Clostridiaceae of Firmicutes of penguin-feces origin is most abundant in the rookery than non-rookery sites with two most abundant genera, Tissierella and Proteiniclasticum. Redundancy analysis showed all measured geochemical factors are significant in structuring microbiomes, with MC showing the highest correlation. We further extracted 21 subnetworks of microbes which contain 4,318 of the 4,364 OTUs using network analysis and are closely correlated with all geochemical factors except pH. Our finding f penguin feces, directly and indirectly, affects soil microbiome suggests an important role of penguins in soil geochemistry and microbiome structure of maritime Antarctica.

The effects of two different processing methods on the volatile components of candied kumquats were investigated via headspace−gas chromatography−ion mobility spectrometry (HS−GC−IMS). The characteristic volatile fingerprints of fresh kumquats (FKs), vacuum sugaring osmosis combined with hot-air drying kumquats (VS-ADKs), and atmospheric pressure sugaring osmosis combined with hot-air drying kumquats (AS-ADKs) were established using 3D topographic plots. From the fingerprints, 40 signal peaks for 22 compounds were confirmed and quantified in all types of kumquats, namely, two terpenes, four esters, seven aldehydes, three ketones, and six alcohols. 3-Pentanone was identified as the major component of FKs; followed by 1-hexanol and the Z-3-hexen-1-ol dimer. The hexanal dimer, 2-hexen-1-ol, and the ethyl acetate dimer were the major markers of VS-ADKs. Benzaldehyde and furfurol were the prominent constituent parts of AS-ADKs. Compared with that in FKs, the pentanal and dimethyl ketone contents of VS-ADKs and AS-ADKs exhibited a dramatic increase (p < 0.05). By contrast, the change in ethanol dimer tended to decrease (p < 0.05). Principal component analysis (PCA) clearly showed that the samples, which were distributed in a separate space could be well-distinguished. Furthermore, the similarity of different processed kumquats and their corresponding volatile components was demonstrated via heat map clustering analysis. The results confirmed the potential of HS−GC−IMS-based approaches to evaluate processed kumquats with various volatile profiles.

In this contribution, the authors present their preliminary investigations into modeling the rainfall⁻runoff generation relation in a large subtropical catchment (Jiulong River catchment) on the southeast coast of China. Previous studies have mostly focused on modeling the streamflow and water quality of its small rural subcatchments. However, daily runoff on the scale of the whole catchment has not been modeled before, and hourly runoff data are desirable for some oceanographic applications. Three methods are proposed for modeling streamflow using rainfall outputted by the Weather Research Forecast (WRF) model, calculated potential evaporation (PET), and land cover type: (i) a ridge regression model; (ii) NPRED-KNN: a nonparametric k-nearest neighbor model (KNN) employing a parameter selection method (NPRED) based on partial information coefficient; (iii) the Hydrological Simulation Program-Fortran (HSPF) model with an hourly time step. Results show that the NPRED-KNN approach is the most unsuitable method of those tested. The HSPF model was manually calibrated, and ridge regression performs no worse than HSPF based on daily verification, whilst HSPF can produce realist daily flow time series, of which ridge regression is incapable. The HSPF model seems less prone to systematic underprediction when replicating monthly-annual water balance, and it successfully replicates the baseflow index (the flow intensity) of the Jiulong River catchment system.

Partial discharge (PD) is not only an important symptom for monitoring the imperfections in the insulation system of a gas-insulated switchgear (GIS), but also the factor that accelerates the degradation. At present, monitoring ultra-high-frequency (UHF) signals induced by PDs is regarded as one of the most effective approaches for assessing the insulation severity and classifying the PDs. Therefore, in this paper, a deep learning-based PD classification algorithm is proposed and realized with a multi-column convolutional neural network (CNN) that incorporates UHF spectra of multiple resolutions. First, three subnetworks, as characterized by their specified designed temporal filters, frequency filters, and texture filters, are organized and then intergraded by a fully-connected neural network. Then, a long short-term memory (LSTM) network is utilized for fusing the embedded multi-sensor information. Furthermore, to alleviate the risk of overfitting, a transfer learning approach inspired by manifold learning is also present for model training. To demonstrate, 13 modes of defects considering both the defect types and their relative positions were well designed for a simulated GIS tank. A detailed analysis of the performance reveals the clear superiority of the proposed method, compared to18 typical baselines. Several advanced visualization techniques are also implemented to explore the possible qualitative interpretations of the learned features. Finally, a unified framework based on matrix projection is discussed to provide a possible explanation for the effectiveness of the architecture.

Aiming at the problem of recognition effect is not stable when 2D facial expression recognition in the complex illumination and posture changes. A facial expression recognition algorithm based on RGB-D dynamic sequence analysis is proposed. The algorithm uses LBP features which are robust to illumination, and adds depth information to study the facial expression recognition. The algorithm firstly extracts 3D texture features of preprocessed RGB-D facial expression sequence, and then uses the CNN to train the dataset. At the same time, in order to verify the performance of the algorithm, a comprehensive facial expression library including 2D image, video and 3D depth information is constructed with the help of Intel RealSense technology. The experimental results show that the proposed algorithm has some advantages over other RGB-D facial expression recognition algorithms in training time and recognition rate, and has certain reference value for future research in facial expression recognition.

The continual accumulation of power grid failure logs provides a valuable but rarely used source for data mining. Sequential analysis, aiming at exploiting the temporal evolution and exploring the future trend in power grid failures, is an increasingly promising alternative for predictive scheduling and decision-making. In this paper, a temporal Latent Dirichlet Allocation (TLDA) framework is proposed to proactively reduce the cardinality of the event categories and estimate the future failure distributions by automatically uncovering the hidden patterns. The aim was to model the failure sequence as a mixture of several failure patterns, each of which was characterized by an infinite mixture of failures with certain probabilities. This state space dependency was captured by a hierarchical Bayesian framework. The model was temporally extended by establishing the long-term dependency with new co-occurrence patterns. Evaluation of the high voltage circuit breakers (HVCBs) demonstrated that the TLDA model had higher fidelities of 51.13%, 73.86%, and 92.93% in the Top-1, Top-5, and Top-10 failure prediction tasks over the baselines, respectively. In addition to the quantitative results, we showed that the TLDA can be successfully used for extracting the time-varying failure patterns and capture the failure association with a cluster coalition method.