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Abstract Background Circular RNAs (circRNAs) are involved in regulatory processes of ubiquitination and deubiquitination in various tumors at post-transcriptional epigenetic modification level. However, the underlying mechanism and its biological functions of circRNAs in the advanced laryngeal squamous cell carcinoma (LSCC) remain obscure. Methods RNA sequencing and quantitative real-time PCR (qRT-PCR) assays were applied to screen for circRNAs differentially expressed in LSCC tissues and cell lines. The candidate RNA-binding proteins and target signalling pathway were detected by RNA pull-down and mass spectrometry, in situ hybridization (ISH), immunohistochemistry (IHC), qRT-PCR assays, and bioinformatics analysis. The functional roles of these molecules were investigated using in vitro and in vivo experiments including EdU, transwell, wound healing, western blot assays, and the xenograft mice models. The molecular mechanisms were identified using RNA pull-down assays, RNA immunoprecipitation (RIP), Co-IP, ISH, Ubiquitination assay, bioinformatics analysis, and the rescue experiments. Results Here, we unveil that microtubule cross-linking factor 1 circRNA (circMTCL1, circ0000825) exerts its critical oncogenic functions by promoting complement C1q-binding protein (C1QBP)-dependent ubiquitin degradation and subsequently activating Wnt/β-catenin signalling in laryngeal carcinoma initiation and development. Specifically, circMTCL1 was remarkably up-regulated in the paired tissues of patients with LSCC (n = 67), which predicted a worse clinical outcome. Functionally, circMTCL1 exerted oncogenic biological charactersistics by promoting cell proliferative capability and invasive and migrative abilities. Ectopic circMTCL1 augumented cell proliferation, migration, and invasion of LSCC cells, and this effect could be reversed by C1QBP knocking down in vitro and in vivo. Mechanistically, circMTCL1 directly recruited C1QBP protein by harboring the specific recognized sequence (+ 159 − + 210), thereby accelerating the translation of C1QBP expression by inhibiting its ubiquitin–proteasome-mediated degradation. Importantly, the direct interaction of C1QBP with β-catenin protein was enhanced via suppressing the β-catenin phosphorylation and accelerating its accumulation in cytoplasm and nucleus. Conclusion Our findings manifested a novel circMTCL1-C1QBP-β-catenin signaling axis involving in LSCC tumorigenesis and progression, which shed new light on circRNAs-ubiquitous acidic glycoprotein mediated ubiquitin degradation and provided strategies and targets in the therapeutic intervention of LSCC.

To investigate the damage characteristics of reinforced concrete (RC) buildings during explosive incidents, a large RC slab (4 m × 5 m × 0.15 m) was meticulously designed, fabricated, and subjected to explosion experiments, which were complemented by comprehensive numerical simulations. The dynamic response parameters of the RC slabs under 0.5–1 kg TNT explosions were tested using polyvinylidene fluoride (PVDF) pressure sensors, displacement sensors, and acceleration sensors. The damage morphologies under 5–40 kg TNT explosions were investigated using ANSYS/LS–DYNA 17.0 software. The results show that, with an increase in TNT charge, the RC slab gradually showed minor damage (5 kg), moderate damage (10–20 kg), heavy damage (25 kg), and complete destruction (30–40 kg). For the 20 kg TNT explosion condition, a 1020 mm × 760 mm explosion crater appeared on the top surface, which was in agreement with the 934 mm × 906 mm explosion crater obtained from the simulation. Based on the results, suitable P–I (pressure–impulse) curves for the 4 m × 5 m × 0.15 m RC slab were established. The results can provide a reference for damage assessments of large-sized buildings during explosion accidents.

Aiming to address the issue of low efficiency and a high false-detection rate in artificial defect detection in nitrile medical gloves, CCA-YOLO was proposed on the basis of YOLOv5 to realize the detection of tear and scratch defects. CCA-YOLO added a small-target detection layer to the YOLOv5 network backbone and further proposed an innovative channel coordinate attention mechanism. According to the different characteristics of tears and scratches, focal and efficient IoU loss and α-IoU loss functions were introduced to further improve the positioning accuracy. The data enhancement method was used to generate a dataset of nitrile gloves, which was divided into datasets for horizontal angular tear detection, vertical angular tear detection, and scratch detection. The problem of class imbalance with few defect samples was solved. Our experiments show that CCA-YOLO can effectively identify tear and scratch defects in nitrile medical gloves in the self-made datasets. Compared with YOLOv5, the mean average precision (mAP) of the three models for horizontal angular tear detection, vertical angular tear detection, and scratch detection can reach 99.3%, 99.8%, and 99.6%, showing increments of 4.2%, 5.3%, and 12.4%, respectively, thereby meeting the performance requirements of glove defect detection.

Polysaccharides are widely present in herbs with multiple activities, especially immunity regulation and metabolic benefits for metabolic disorders. However, the underlying mechanisms are not well understood. Functional metabolomics is increasingly used to investigate systemic effects on the host by identifying metabolites with particular functions. This study explores the mechanisms underlying the metabolic benefits of Astragalus polysaccharides (APS) by adopting a functional metabolomics strategy. The effects of APS were determined in eight-week high-fat diet (HFD)-fed obese mice. Then, gas chromatography–time-of-flight mass spectrometry (GC–TOFMS)-based untargeted metabolomics was performed for an analysis of serum and liver tissues, and liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based targeted metabolomics was performed. The potential functions of the metabolites were tested with in vitro and in vivo models of metabolic disorders. Our results first confirmed the metabolic benefits of APS in obese mice. Then, metabolomics analysis revealed that APS supplementation reversed the HFD-induced metabolic changes, and identified 2-hydroxybutyric acid (2-HB) as a potential functional metabolite for APS activity that was significantly decreased by a HFD and reversed by APS. Further study indicated that 2-HB inhibited oleic acid (OA)-induced triglyceride (TG) accumulation. It was also found to stimulate the expression of proteins in lipid degradation in hepatocytes and TG lipolysis in 3T3-L1 cells. Moreover, it was found to reduce serum TG and regulate the proteins involved in lipid degradation in high-fat and high-sucrose (HFHS)-fed mice. In conclusion, our study demonstrates that the metabolic benefits of APS are at least partially due to 2-HB generation, which modulated lipid metabolism both in vitro and in vivo. Our results also highlight that functional metabolomics is practical for investigating the mechanism underlying the systemic benefits of plant polysaccharides.

ObjectiveTo explore the prevalence and indoor environment risk factors of pneumonia among preschool children in Urumqi city of Xinjiang Uygur Autonomous Region for providing evidences to the prevention of the disease.MethodsUsing stratified random cluster sampling, we recruited 10 000 children aged 2 – 7 years in 60 kindergartens in 6 districts of Urumqi city. Then we conducted a self-administered survey among the parents of the selected children during August 2019 using a self-designed questionnaire based on the International Study of Asthma and Allergies in Childhood (ISAAC) and Dampness in Buildings and Health (DBH).ResultsAmong 8 153 children with valid information provided by their parents, the prevalence of doctor-diagnosed pneumonia was 29.5%. Multivariate logistic regression results revealed that the children exposed to following indoor conditions were more likely to have pneumonia: home decoration or installation of new furniture during the period from maternal pregnancy to age of one year (odds ratio [OR] = 1.18, 95% confidence interval [95% CI]: 1.04 – 1.35), emergence of cockroaches, flies or mosquitoes at home (OR = 1.69, 95% CI: 1.49 – 1.91), plantation of flowering plants in the house since children′s birth (OR = 1.19, 95% CI: 1.07 – 1.32), keeping pets at home since children′s birth (OR = 1.31, 95% CI: 1.15 – 1.49), and exposed to passive smoking at home (OR = 1.31, 95% CI: 1.19 – 1.45).ConclusionPneumonia prevalence is relatively high and influenced by several indoor environment risk factors among preschool children in Urumqi city. The results suggest that some indoor conditions need to be improved for pneumonia prevention in the children.

Color consistency optimization for multiple images is a challenging task in remote sensing and computer vision. To ensure that the visual quality of corrected images is satisfactory, not only should the color discrepancies between multiple images be invisible but also the contrast of individual images should be visually appealing. Most color correction approaches focus on eliminating drastic color discrepancies, but ignore the problem of image contrast enhancement. Some color correction approaches even tend to degrade the image contrast to ensure that the image tones are consistent especially when the contrast of input images is low. To solve this problem, we present a contrast-aware color consistency correction approach in this article. We attempt to eliminate the drastic color differences and enhance the contrast of input images simultaneously. We creatively integrate the problems of color consistency correction and image contrast enhancement into the same global energy optimization framework, and we also design a special cost function to minimize the color discrepancies and enhance the image contrast using the original color information. Thus, although the contrast of input images is low, our approach can still generate the corrected images with consistent tones and visually appealing contrast. At last, we select several challenging datasets to evaluate our approach. The experimental results visually and quantitatively demonstrate the effectiveness and superiority of the proposed contrast-aware color consistency correction approach. The results also demonstrate that our approach significantly outperforms the existing approaches, especially when the contrast of the input images is low.

Global color consistency correction for multiview images in three-dimensional (3-D) reconstruction is an important problem. The color differences between the images will affect the result of dense matching, thereby reducing the geometric accuracy of the mesh model. Moreover, it will also affect the result of texture mapping, causing color differences in the textured model. The color correction method based on global optimization is mainly used to solve this problem. And existing methods usually use sparse matching points as the color correspondences, but the correction results are not accurate enough as a result of the sparsity of the points. Besides, their efficiency of solving large-scale images globally is low. This article proposes a novel color correction method to eliminate the color differences between large-scale multiview images effectively. The core idea of our method is to group images by graph partition algorithm, and then perform intragroup correction and intergroup correction in sequence. First, for each pair of images, we calculate the reliable matching regions around the sparse points as the color correspondences according to the local homography principle. Compared with sparse matching points, our strategy can achieve more accurate color correction results. Next, for large-scale images, we partition them into many groups. For each group of images, the correction parameters are solved to eliminate the color differences of the images included in the group. Finally, we eliminate the color differences between groups by intergroup correction to achieve overall color consistency. Experimental results on typical datasets demonstrate that the proposed method is better than the current representative methods. The proposed method shows better color consistency in the extreme cases, and also exhibits higher computational efficiency on large-scale image sets.

Abstract Background Freezing injury is a devastating yet common damage that occurs to winter rapeseed during the overwintering period which directly reduces the yield and causes heavy economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding. Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipments. These methods are time-consuming, inefficient and laborious. In addition, the accuracy of these traditional methods depends heavily on the knowledge and experience of the experts. Methods To solve these problems of existing methods, we propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning and unmanned aerial vehicle (UAV) images captured by a consumer UAV. We formulate the problem of freezing-tolerant material recognition as a binary classification problem, which can be solved well using deep learning. The proposed method can automatically and efficiently recognize the freezing-tolerant rapeseed materials from a large number of crop candidates. To train the deep learning network, we first manually construct the real dataset using the UAV images of rapeseed materials captured by the DJI Phantom 4 Pro V2.0. Then, five classic deep learning networks (AlexNet, VGGNet16, ResNet18, ResNet50 and GoogLeNet) are selected to perform the freezing-tolerant rapeseed material recognition. Result and conclusion The accuracy of the five deep learning networks used in our work is all over 92%. Especially, ResNet50 provides the best accuracy (93.33 $$\%$$ % ) in this task. In addition, we also compare deep learning networks with traditional machine learning methods. The comparison results show that the deep learning-based methods significantly outperform the traditional machine learning-based methods in our task. The experimental results show that it is feasible to recognize the freezing-tolerant rapeseed using UAV images and deep learning.

Geographical traceability is crucial to the quality and safety control of gelatin. However, currently, methods for gelatin traceability have not been established anywhere in the world. This study aimed to investigate the possibility of differentiating the geographical origins of gelatin from different regions in China using stable isotope technology. To achieve this objective, 47 bovine stick bone samples from three different regions (Inner Mongolia, Shandong, and Guangxi, respectively) in China were collected, and gelatin was extracted from these bones using the enzymatic method. The fingerprint characteristics of stable isotopes of δ13C, δ15N, and δ2H of gelatin from different regions in China were studied. Moreover, isotopic changes from the bone to gelatin during the processing were examined to evaluate the effectiveness of these factors as origin indicators. The results of the one-way analysis of variance (ANOVA) showed that the δ13C, δ15N, and δ2H of gelatin from different regions display significant differences, and using the linear discriminant analysis (LDA), the correct differentiation of origin reached 97.9%. Certain differences in stable isotope ratios were observed during the processing of bone to gelatin samples. Nonetheless, the fractionation effect caused by the processing of bone to gelatin samples was not sufficient to influence the identification of gelatin from different origins, which proves that δ13C, δ15N, and δ2H are effective origin indicators of gelatin. In conclusion, the stable isotope ratio analysis combined with the chemometric analysis can be used as a reliable tool for identifying gelatin traceability.

The deterioration of the environment, shortage of resources, and frequent occurrence of food safety issues have made people increasingly concerned about themselves while maintaining their health and protecting the environment through food. Organic food, as a healthy and eco-friendly option, is gradually gaining attention. Based on the value-belief-norm theory, this study explores why individuals consume organic food and the range of factors that lead to this consumer behavior. This study adopted a cross-sectional design and collected quantitative data from conveniently selected 300 youth participants in Bangladesh using an online survey. The findings revealed that health values and motivation have a significant positive effect on healthy eating beliefs, which, together with the awareness of the consequences, affect personal norms toward organic food consumption. Personal norms also have a significant positive effect on organic food consumption behavior among Bangladeshi youth. Finally, trust on organic food positively moderates the effect of personal norms on organic food consumption. The findings of this study are expected to foster the development of a comprehensive framework to promote programs and policies focused on organic and healthy food consumption culture among youth in developing nations.

Flowering is a crucial developing stage for rapeseed (Brassica napus L.) plants. Flowers develop on the main and branch inflorescences of rapeseed plants and then grow into siliques. The seed yield of rapeseed heavily depends on the total flower numbers per area throughout the whole flowering period. The number of rapeseed inflorescences can reflect the richness of rapeseed flowers and provide useful information for yield prediction. To count rapeseed inflorescences automatically, we transferred the counting problem to a detection task. Then, we developed a low-cost approach for counting rapeseed inflorescences using YOLOv5 with the Convolutional Block Attention Module (CBAM) based on unmanned aerial vehicle (UAV) Red–Green–Blue (RGB) imagery. Moreover, we constructed a Rapeseed Inflorescence Benchmark (RIB) to verify the effectiveness of our model. The RIB dataset captured by DJI Phantom 4 Pro V2.0, including 165 plot images and 60,000 manual labels, is to be released. Experimental results showed that indicators R2 for counting and the mean Average Precision (mAP) for location were over 0.96 and 92%, respectively. Compared with Faster R-CNN, YOLOv4, CenterNet, and TasselNetV2+, the proposed method achieved state-of-the-art counting performance on RIB and had advantages in location accuracy. The counting results revealed a quantitative dynamic change in the number of rapeseed inflorescences in the time dimension. Furthermore, a significant positive correlation between the actual crop yield and the automatically obtained rapeseed inflorescence total number on a field plot level was identified. Thus, a set of UAV- assisted methods for better determination of the flower richness was developed, which can greatly support the breeding of high-yield rapeseed varieties.

The equipment in railway station is complicated and diverse, and the health status assessment and prediction of equipment is crucial to the safe and stable operation of stations. Graph Neural Networks (GNNs) effectively combine graph data with deep learning technology, which has stronger data and knowledge representation capability and can efficiently handle some non-Euclidean spatial data problems with irregular station equipment associated network structure. Based on this, this paper takes the automatic gate machine and X-ray security checker as an example and proposes a health status assessment and prediction scheme for railway passenger station equipment based on Graph Long Short-Term Memory (G-LSTM) neural network. This paper first analyzes the main factors affecting the health status of passenger station equipment, as well as the correlation between the equipment. Then, the initial graph network structure of the passenger station equipment is constructed, and the G-LSTM model is used to evaluate and predict the health status of the passenger station equipment. Finally, this paper takes the automatic gate machine and X-ray security checker of a high-speed railway station in Beijing as an example to verify the proposed method. The experimental results show that all evaluation metrics perform well, indicating that the G-LSTM model has high accuracy in assessing and predicting the health status of automatic gate machine and X-ray security checker. This paper realizes the health status assessment and prediction of railway passenger station equipment, which can provide some reference for the Prognostics and Health Management (PHM) of equipment in railway stations.

Abstract Objective This study aims to evaluate the diagnostic value of D-dimer for acute aortic dissection (AAD) by the method of meta-analysis. Methods PubMed, Cochrane Library, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), and Wanfang databases from the establishment of the databases to December 2020 were systematically searched, and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) system was used to evaluate the quality of the literature. STATA 15.0 software was applied to calculate the pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (+LR), negative likelihood ratio (−LR) to draw summary receiver operating characteristics (SROC) curve and calculate the area under the curve (AUC). Meta-regression and subgroup analyses were used to explore the source of heterogeneity. Results A total of 16 clinical studies were enrolled in this study, including 1135 patients. The results of the meta-analysis showed that the pooled sensitivity was 0.96 (95% CI 0.91–0.98), the pooled specificity was 0.70 (95% CI 0.57–0.81), and the pooled DOR was 56.57 (95% CI 25.11–127.44), the pooled +LR was 3.25 (95% CI 2.18–4.85), the pooled −LR was 0.06 (95% CI 0.03–0.12), and the AUC was 0.94 (95% CI 0.91–0.95). Meta-regression and subgroup analysis results showed that publication year, sample size and cutoff value might be sources of heterogeneity. When the concentration of D-dimer was less than or equal to 500 ng/ml, the sensitivity significantly increased. Conclusion D-dimer has an excellent diagnostic value for AAD. It is a useful tool for detecting suspected AAD because of the excellent pooled sensitivity. D-dimer ≤ 500 ng/ml increases the potential to identify the suspected patients with AAD.

Abstract Background Current guidelines support different management of cryptococcosis between severely immunodeficient and immunocompetent populations. However, few studies have focused on cryptococcosis patients with mild-to-moderate immunodeficiency. We performed this study to determine the clinical features of pulmonary (PC) and extrapulmonary cryptococcosis (EPC) and compared them among populations with different immune statuses to support appropriate clinical management of this public health threat. Methods All cases were reported by 14 tertiary teaching hospitals in Jiangsu Province, China from January 2013 to December 2018. The trends in incidence, demographic data, medical history, clinical symptoms, laboratory test indicators, imaging characteristics and diagnostic method of these patients were then stratified by immune status, namely immunocompetent (IC, patients with no recognized underlying disease or those with an underlying disease that does not influence immunity, such as hypertension), mild-to-moderate immunodeficiency (MID, patients with diabetes mellitus, end-stage liver or kidney disease, autoimmune diseases treated with low-dose glucocorticoid therapy, and cancer treated with chemotherapy) and severe immunodeficiency (SID, patients with acquired immunodeficiency syndrome, haematologic malignancies, solid organ transplantation or haematologic stem cell transplantation, idiopathic CD4 lymphocytosis, agranulocytosis, aggressive glucocorticoid or immunosuppressive therapy and other conditions or treatments that result in severe immunosuppression). Results The clinical data of 255 cryptococcosis patients were collected. In total, 66.3% of patients (169) were IC, 16.9% (43) had MID, and 16.9% (43) had SID. 10.1% of the patients (17) with IC were EPC, 18.6% of the patients (8) with MID were EPC, and 74.4% of patients (32) were EPC (IC/MID vs. SID, p

This paper uses the EBM model to measure the carbon emission efficiency of the logistics industry in 30 provinces in China from 2010 to 2019 and analyzes its spatial and temporal evolution characteristics using ARCGIS visualization. On this basis, the structural characteristics of the spatial correlation network of carbon emission efficiency of the logistics industry in China and its influencing factors are explored and analyzed by using the social network analysis method and the quadratic distribution method (QAP). The study shows that: 1) The national average logistics industry carbon emission efficiency increased from 2010 to 2019, and the spatial logistics industry carbon emission efficiency shows the characteristics of East > Central > Northeast > West, and most of the provinces in China are still in the middle and low logistics industry carbon emission efficiency zone. 2) The carbon emission efficiency of logistics industry in 30 provinces in China has formed a stable spatial correlation network, and there is an obvious spatial spillover relationship. However, the structure of the spatial association network is loose, and there are obvious gaps in the status of each province in the spatial association network. The provinces in the eastern region are at the core of the spatial correlation network, while the provinces in the northeast and west are at the periphery of the spatial correlation network. 3) The reduction of geographical distance between provinces, the greater the differences in logistics development level, logistics energy intensity and logistics environmental protection level, and the higher the similarity of logistics informatization level, the more it can promote the formation of spatial correlation network.

Shockwaves from fuel-air explosive (FAE) cloud explosions may cause significant casualties. The ground overpressure field is usually used to evaluate the damage range of explosion shockwaves. In this paper, a finite element model of multi-sources FAE explosion is established to simulate the process of multiple shockwaves propagation and interaction. The model is verified with the experimental data of a fourfold-source FAE explosion, with the total fuel mass of 340 kg. Simulation results show that the overpressure fields of multi-sources FAE explosions are different from that of the single-source. In the case of multi-sources, the overpressure fields are influenced significantly by source scattering distance and source number. Subsequently, damage ranges of overpressure under three different levels are calculated. Within a suitable source scattering distance, the damage range of multi-sources situation is greater than that of the single-source, under the same amount of total fuel mass. This research provides a basis for personnel shockwave protection from multi-sources FAE explosion.

Abstract Background To evaluate the prevalence of myopia in school students in Urumqi, China, and explore the influence of the interaction between parental myopia and poor reading and writing habits on myopia to identify the at-risk population and provide evidence to help school students avoid developing myopia. Methods A cross-sectional survey was conducted with 6,883 school students aged 7–20 years in Urumqi in December 2019. The Standard Eye Chart and mydriatic optometry were used to determine whether students had myopia. Falconer’s method was used to calculate the heritability of parental myopia. Multivariate unconditional logistic regression models were used to analyze the risk factors for myopia and the additive and multiplicative interaction of parental myopia and poor reading and writing habits. Results After standardizing the age of the 6,883 students, the overall prevalence rate of myopia was 47.50 %. The heritability of parental myopia was 66.57 % for boys, 67.82 % for girls, 65.02 % for the Han group, and 52.71 % for other ethnicities. There were additive interactions between parental myopia and poor reading and writing habits; among them, parental myopia and poor eye habits when reading and writing (the distance between the eyes and book is less than 30 cm when reading and writing, fingers block the sight of one eye while holding the pen, and leaning one’s body when reading and writing; habit 1) increased the risk of myopia by 10.99 times (odds ratio [OR] = 10.99, 95 % confidence interval [CI] = 8.33–14.68), parental myopia and poor reading posture (reading while lying down, walking, or in the car; habit 2) increased the risk of myopia by 5.92 times (OR = 5.92, 95 % CI = 4.84–7.27). There was no multiplicative interaction between parental myopia and habit 1 or habit 2 (OR = 0.69, 95 % CI = 0.44–1.08; OR = 0.89, 95 % CI = 0.66–1.21, respectively). Conclusion The prevalence of myopia among students in Urumqi, Xinjiang is relatively high. The risk of developing myopia is affected by parental myopia and poor reading and writing habits. In addition, parental myopia amplifies the harm caused by poor reading and writing habits, thereby increasing the risk of myopia. Students with parents who have myopia should be targeted during myopia prevention efforts.

Abstract Background Eczema is a chronic inflammatory disease associated with impaired quality of life. We identified indoor environmental risk factors, to provide strong evidence for the prevention and control of eczema in preschool children. Methods Using a cross-sectional study with stratified random cluster sampling, we conducted a self-administered questionnaire survey among 8153 parents of children aged 2–8 years in 60 kindergartens in six districts of Urumqi city during August 2019. Results Among 8153 preschool children, 12.0% of the children have been diagnosed with eczema. Multivariate logistic regression analysis showed that caesarean section (odds ratio [OR] = 1.18, 95% confidence interval [CI]: 1.03–1.36), being an only child (OR = 1.36, 95% CI: 1.18–1.57), presence of mould or moisture in the mother’s home before pregnancy (OR = 1.53, 95% CI: 1.17–2.00), presence of flies or mosquitoes in the dwelling currently (OR = 1.31, 95% CI: 1.10–1.55), pets kept in the child’s home currently (OR = 1.23, 95% CI: 1.01–1.51), presence of pets during child’s first year (OR = 1.45, 95% CI: 1.14–1.85), and family history of eczema (OR = 3.53, 95% CI: 2.98–4.19) are the risk factors for the development of eczema, whereas ethnicity other than the Han Chinese (OR = 0.77, 95% CI: 0.61–0.96) is a protective factor for eczema. Conclusion Preschool children in Urumqi are at a high risk of eczema, particularly those of the Han Chinese ethnicity. Parents should be attentive to the indoor living environment of children and take actions to reduce indoor humidity, pest control and elimination, and avoid raising pets to reduce the risk of development of eczema in children.

Abstract Background Amber-like compounds form in tobacco (Nicotiana tabacum) during leaf curing and impact aromatic quality. In particular, cis-abienol, a polycyclic labdane-related diterpenoid, is of research interest as a precursor of these compounds. Glandular trichome cells specifically express copalyl diphosphate synthase (NtCPS2) at high levels in tobacco, which, together with NtABS, are major regulators of cis-abienol biosynthesis in tobacco. Results To identify the genes involved in the biosynthesis of cis-abienol in tobacco, we constructed transgenic tobacco lines based on an NtCPS2 gene-knockdown model using CRISPR/Cas9 genome-editing technology to inhibit NtCPS2 function in vitro. In mutant plants, cis-abienol and labdene diol contents decreased, whereas the gibberellin and abscisic acid (ABA) contents increased compared with those in wild-type tobacco plants. RNA sequencing analysis revealed the presence of 9514 differentially expressed genes (DEGs; 4279 upregulated, 5235 downregulated) when the leaves of wild-type and NtCPS2-knockdown tobacco plants were screened. Among these DEGs, the genes encoding cis-abienol synthase, ent-kaurene oxidase, auxin/ABA-related proteins, and transcription factors were found to be involved in various biological and physiochemical processes, including diterpenoid biosynthesis, plant hormone signal transduction, and plant-pathogen interactions. Conclusions The present study provides insight into the unique transcriptome profile of NtCPS2 knockdown tobacco, allowing for a better understanding of the biosynthesis of cis-abienol in tobacco.

Summary: Long noncoding RNAs (lncRNAs) participate in host antiviral responses; however, how viruses exploit host lncRNAs for immune evasion remains largely unexplored. Functional screening of differentially expressed lncRNA profile in patients infected with influenza A virus (IAV) revealed that lncNSPL (Gene Symbol: LOC105370355) was highly expressed in monocytes. Deregulated lncNSPL expression in infected monocytes significantly increased type I interferon (IFN-I) production and inhibited IAV replication. Moreover, lncNSPL overexpression in mice increased the susceptibility to IAV infection and impaired IFN-I production. LncNSPL directly bound to retinoic acid-inducible gene I (RIG-I) and blocked the interaction between RIG-I and E3 ligase tripartite interaction motif 25 (TRIM25), reducing TRIM25-mediated lysine 63 (K63)-linked RIG-I ubiquitination and limiting the downstream production of antiviral mediators during the late stage of IAV infection. Our findings provide mechanistic insights into the means by which lncNSPL promotes IAV replication and immune escape via restricting the TRIM25-mediated RIG-I K63-linked ubiquitination. Thus, lncNSPL may represent a promising pharmaceutical target for anti-IAV therapy.

Abstract A novel esterase gene was isolated from a soil metagenomic library. The gene encoded a protein of 520 amino acids which contained a 21 aa signal peptide. Primary structure analysis of the protein sequence revealed that it contained a conserved active site motif (SxSxG) and a structural motif (CS-D-HC). Then the esterase gene was cloned and expressed in Escherichia coli BL21(DE3). SDS-PAGE analysis of the purified esterase showed that it was expressed in a highly soluble form and its molecular mass was estimated to be 55 kDa. Characterization of the esterase revealed that it exhibited high activity toward p-nitrophenyl esters with short acyl chains and especially p-nitrophenyl acetate, suggesting that it was a typical carboxylesterase rather than a lipase. With p-nitrophenyl acetate as substrate, the enzyme showed its optimal activity at pH 7.0 and 30 °C, and it was stable at a broad pH range from 4.5 to 10.0 and temperature not higher than 50 °C. Furthermore, the enzyme showed different substrate specificity from known esterase, it was not only hydrolyzing against p-nitrophenyl esters, but also hydrolyzing all hydroxybenzoic esters and hydroxycinnamic ester assayed. As it was an enzyme active on a broad range of phenolic esters, simultaneously possessing feruloyl esterase, chlorogenate esterase and tannase activities, it could serve as a valuable candidate for applications in biotechnology.

This paper investigates the impact of uncertain shade behavior on three facades (east, south and west) on energy performance. A previously developed stochastic shade behavior model and an occupancy model were used to conduct building co-simulation for determining energy-related performance uncertainty (annual energy demand and peak heating/cooling loads) at different building scales. Statistical test and Monte Carlo random sampling techniques were used to determine the required minimum simulation runs for obtaining reliable annual energy performance and peak loads. The results show that there is no significant difference in shading performance among seasons and orientations. In China, most newly-built office buildings are high-rise ones and thus, the influence of manual shades on the energy uncertainty of these office buildings can be neglected and a single simulation run is enough to obtain reliable annual heating, cooling and total energy demands. While for peak heating/cooling loads, the conclusion is different from annual energy demand and at least five simulation runs are required to obtain peak heating/cooling loads at building scales. For a smaller building, 120 simulation runs are suggested for peak load determination.

Skeleton-based action recognition has attracted extensive attention recently in the computer vision community. Previous studies, especially GCN-based methods, have presented remarkable improvements for this task. However, in existing GCN-based methods, global average pooling is applied to the extracted features before the classifier. This may hurt the recognition performance since it neglects the fact that not all features are equally important in the temporal dimension. To tackle this issue, in this article, we propose a feature selection network (FSN) with actor-critic reinforcement learning. Given the extracted feature sequence, FSN learns to adaptively select the most representative features and discard the ambiguous features for action recognition. In addition, conventional graph convolution is a local operation, it cannot fully capture the non-local joint dependencies that could be vital to recognize the action. Thus, we also propose a generalized graph generation module to capture latent dependencies and further propose a generalized graph convolution network (GGCN). The GGCN and FSN are combined in a three-stream recognition framework, in which different types of information from skeleton data are further fused to improve the recognition accuracy. Extensive experiments demonstrate that the proposed FSN is a flexible and effective module that can cooperate with any existing GCN-based framework to enhance the recognition accuracy, the proposed GGCN can extract richer skeleton features for skeleton-based action recognition, and our method achieves superior performance over several public datasets, e.g. 95.7 top-1 accuracy on NTU-RGB+D, 86.7 top-1 accuracy on NTU-RGB+D 120, etc.

The production of high-modulus and high-strength polymer nanofibers using centrifugal or rotary jet spinning (RJS) was explored. Co-polyimide nanofibers based on 3,3′,4,4′-biphenyl-tetracarboxylic dianhydride (BPDA)/p-phenylenediamine (PDA)/4,4′-oxydianiline (ODA) (BPO) were successfully spun by RJS from a polyamic acid precursor solution before conversion into highly oriented and chain extended BPO co-polyimide fibers via an imidization step. Fourier transform infrared (FTIR) characterization was used to evaluate the chemical conversion of the fibers. Nanocomposite laminates based on co-polyimide nonwoven fiber mats in epoxy were manufactured for mechanical testing. Analysis using the generalized rule of mixtures resulted in a back-calculated fiber modulus and strength of around 50 and 2 GPa, approaching that of high-performance fibers like Kevlar® 29, and equaling those of co-polyimide fibers obtained via electrospinning, making them the strongest centrifugal spun fibers ever reported.

In order to ensure the disaster control during infrastructure construction and operation, a method of tilt photogrammetry of prefabricated buildings using UAV is proposed. A comparative study of the key steps in the 3D construction process of building (structure) was carried out, and a set of 3D morphology construction method based on UAV tilt photogrammetry was proposed, and the key steps were optimized to realize automation and visualization in the process of model construction. The experimental results show that in the application case of a school district, the error of the three directions is distributed between -4 cm and 4 cm, accounting for 74%, and that between -2 cm and 2 cm, accounting for 37%; the root mean square error of the three directions was calculated to be 4.0 cm, 3.8 cm, and 2.8 cm, respectively. In an engineering application case, the error size of the three directions distributed between -4 cm and 4 cm accounted for 74%, and that between -2 cm and 2 cm accounted for 50%; the root mean square error of the three directions was calculated as 2.3 cm, 2.5 cm, and 3.3 cm, respectively. Conclusion. This method can not only obtain high-precision 3D models of urban complex and single buildings, but also meet the requirements of digital city construction for 3D model construction and obtain clear and high-precision digital land surface model.

Objective. The present study was designed to study the effect of genistein on spinal cord injury (SCI) in mice and to explore its underlying mechanisms. Methods. We established SCI mouse model, and genistein was administered for treatment. We used the Basso, Beattie, and Bresnahan (BBB) exercise rating scale to evaluate exercise recovery, and the detection of spinal cord edema was done using the wet/dry weight method. Apoptosis was determined by TUNEL staining, and inflammation was evaluated by measuring inflammatory factors by an ELISA kit. The expression of M1 and M2 macrophage markers was determined using flow cytometry, and the expression of proteins was detected using immunoblotting. Results. Genistein treatment not only improved the BBB score but also reduced spinal cord edema in SCI mice. Genistein treatment reduced apoptosis by increasing Bcl2 protein expression and decreasing Bax and caspase 3 protein expression. It also reduced the expression of inflammatory cytokines (TNF-α, IL-1β, IL-6, and IL-8) in the SCI area of SCI mice. Flow cytometry analysis indicated that genistein treatment significantly decreased the ratio of M1 macrophages (CD45+/Gr-1-/CD11b+/iNOS+) and increased the ratio of M2 macrophages (CD45+/Gr-1-/CD11b+/Arginase 1+) in the SCI area of SCI mice on the 28th day after being treated with genistein. We also found that genistein treatment significantly decreased the expression of TLR4, MyD88, and TRAF6 protein in the SCI area of SCI mice on 28th day after being treated with genistein. Conclusion. Our findings suggested that genistein exerted neuroprotective action by inhibiting neuroinflammation by promoting the activation of M2 macrophages, and its underlying mechanisms might be related to the inhibition of the TLR4-mediated MyD88-dependent signaling pathway.

Acrolein (ACR) is a metabolic byproduct in vivo and a ubiquitous environmental toxicant. It is implicated in the initiation and development of many diseases through multiple mechanisms, including the induction of oxidative stress. Currently, our understanding of the body defense mechanism against ACR toxicity is still limited. Given that hydrogen sulfide (H2S) has strong antioxidative actions and it shares several properties of ACR scavenger glutathione (GSH), we, therefore, tested whether H2S could be involved in ACR detoxification. Taking advantage of two cell lines that produced different levels of endogenous H2S, we found that the severity of ACR toxicity was reversely correlated with H2S-producing ability. In further support of the role of H2S, supplementing cells with exogenous H2S increased cell resistance to ACR, whereas inhibition of endogenous H2S sensitized cells to ACR. In vivo experiments showed that inhibition of endogenous H2S with CSE inhibitor markedly increased mouse susceptibility to the toxicity of cyclophosphamide and ACR, as evidenced by the increased mortality and worsened organ injury. Further analysis revealed that H2S directly reacted with ACR. It promoted ACR clearance and prevented ACR-initiated protein carbonylation. Collectively, this study characterized H2S as a presently unrecognized endogenous scavenger of ACR and suggested that H2S can be exploited to prevent and treat ACR-associated diseases.

In this paper, the window-opening behavior in a three-star green building with an operation mark in Ningbo was investigated. Four single offices facing south were selected, and the measurement lasted 20–40 days in the cooling season. An analysis of the relationship between window use and environmental factors was assessed by monitoring the occupancy state, the window action and window state, the time, and the temperature and humidity. A statistical analysis method was conducted to reveal the similarities and differences in window use among different occupants. The main findings were as follows: The window adjustment behaviors of different people vary significantly. In terms of adjustment time, there are two modes of behaviors. In addition, the occupancy state had a great impact on window action. Moreover, some people prefer to open the windows in the morning and close the windows at departure time, However, some of them prefer to open the windows at departure time and close the windows at the arrival time. Some irrational window opening behaviors were found in this research. Through correlation analysis, it was shown that the longer time the occupants were in the office, the higher window-opening duration and window-opening frequency would be. If the window was closed at arrival time, the total opening duration of window was significantly reduced. Also, window opening duration and closing duration are proportional to each other. With the help of principal component analysis (PCA) of the 12 features that affected window adjustment, the correlation between the window opening behaviors and the features could be clearly illustrated. A stochastic model of window-opening behavior based on logistics regression and uncertainty analysis was used in this paper. The cooling energy consumption of the stochastic model is 99.9% higher than the energy consumption generated by a fixed number of air changes, and the average energy consumption exceeds the fixed value by more than 21%. The findings of this study have certain guiding significance for promoting energy conservation by occupants’ behaviors in green buildings in this region.

The popular ornamental plant Petunia is also a valuable model plant in tissue culture. Cellular conversions during differentiation and regeneration have been investigated using various combinations of phytohormones; however, studies on genes for reprogramming toward desired tissue identities have been limited. In this study, we isolated Petunia protoplasts and cultured them in the callus, rooting, or shooting stages, which were used to establish the optimal protoplast culture conditions and to identify genes that epigenetically function as tissue identifiers. The optimal conditions for plasmolysis and enzyme digestion to obtain healthy protoplasts were compared, in which combinations of Viscozyme, Celluclast, and Pectinex (VCP) enzymes were more efficient in isolating protoplasts when followed by 21 to 25% sucrose purification and washing processes. The filtered and washed protoplasts started to divide at 1 day and developed into colonies after 3 weeks of culture, which showed higher efficiency in the Murashige and Skoog (MS) salt culture media compared to that in the Kao and Michayluk (KM) salt media. The pluripotent colonies formed calli on the solid medium supplemented with 3% sucrose after 4 weeks, and were destined to the same cell mass, rooting, or shooting on the regeneration medium. Three epigenetic controllers, ATXR2, ATX4A, and ATX4B, were highly expressed in calli, shoots, and organs of shoots and roots, respectively, confirming that dedifferentiation and regeneration of tissue identity is plastic.

To explore the contributions of chemical components in the particulate matter in Lhasa, the size distribution characteristics of the chemical components, such as carbonaceous species (organic carbon, OC; elemental carbon, EC), water-soluble ions (NH4+, Cl−, SO42−, and NO3−) and trace elements (Na, Ca, Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, K, Mg, and Pb), were investigated from August 2018 to May 2019. Principal component analysis (PCA) was also used to simulate the source of the particulate matter. The chemical components showed bimodal size distributions, except for Cu. The highest mass concentration of OC appeared at < 0.49 μm, and the second highest one existed at 1.5–3.0 μm in winter or at 3.0–7.2 μm in other seasons. The maximum concentrations of NH4+, Cl−, SO42−, and NO3− were at < 0.49 μm and peaked at 0.95–1.5 μm or > 3.0 μm. For seasonality, the concentrations of NO3− and SO42− were considerably higher in summer and autumn; trace elements (except for Cu, Zn, V, and Ni), OC, and EC presented higher values during late autumn and winter. The ratio between OC and EC (4.15–33.80) indicated the existence of secondary pollution in Lhasa. The [NO3−]–[SO42−] ratios during summer and autumn exceeding 1 suggested that the currently predominant vehicle exhaust made a greater contribution to the aerosols. According to the results of a PCA, the main pollution sources of particulate matter in Lhasa were suspended dust, biomass burning, fossil fuel combustion, secondary pollution, and vehicular emissions.

Replant disease caused by continuous cropping commonly occurs in yam with consecutive monoculture. However, little is known about how the continuous cropping of yam affects the rhizospheric soil bacterial community structure. In this study, the effects of continuous cropping on rhizospheric soil characteristics, bacterial diversity, and community structure were investigated in the Yongfeng yam fields under monoculture for 1, 5, 10, 15, and 20 years. Long-term monoculture caused soil acidification and increased the concentration of available potassium (AK) and available phosphorus (AP), and soil bacterial richness, but decreased the soil bacterial diversity. An exception was for the field under monoculture for 20 years as it showed the highest bacterial diversity. The relative abundance of beneficial bacteria, such as Proteobacteria, Actinobacteria, and Chloroflexi decreased while the relative abundance of harmful bacteria, including Gemmatimonadetes and Acidobacteria, increased with an extended continuous cultivation time. The networks varied among yams with different cultivation years and became complex with the increase in cultivation years. However, after time in monoculture, the bacterial network decreased gradually and existed stably. These changes in bacterial community composition and co-occurrence of networks may increase the potential risk of soil-borne disease and reduce the yield and quality of Yongfeng yam.

In order to realize low-carbon and high-quality development, this study took the carbon emissions of each district and county in the Chengdu–Chongqing region from 2005 to 2017 as the research object and used the spatial autocorrelation model to analyze the spatial and temporal evolution characteristics of carbon emissions in the counties of the Chengdu–Chongqing region, so as to fill in the research blank of carbon emissions in the counties of the Chengdu–Chongqing region. Then, the geographical detector model is used to explore the interaction among influencing factors of carbon emissions and reveal the time changes and regional differences of influencing factors, so as to improve the lack of spatial and temporal heterogeneity of influencing factors of carbon emissions by geographical detector. The results show the following: (i) The overall carbon emissions of counties show a year-on-year growth trend with the main urban areas of Chengdu and Chongqing as the core, but the growth rate slows down after 2010. (ii) The carbon emissions showed a significant positive spatial autocorrelation, and the neighboring counties showed a spatial clustering characteristic of “high-high” or “low-low”, and the clustering status tended to be enhanced. (iii) The carbon emissions are strongly influenced by industrial structure, economic development, investment level, financial situation, urbanization rate and social consumption, and their interactions are all enhanced, but the influence mostly tends to rise first and then fall. (iv) County carbon emissions can be divided into four types of geographical types, such as population size influencing type, urbanization rate influencing type, economic development influencing type and industrial structure influencing type. Therefore, a variety of factors need to be considered comprehensively, a multi-pronged approach, and a comprehensive policy to realize low-carbon transformation in the Chengdu–Chongqing region.

Thiol antioxidants play important roles in cell and body defense against oxidative stress. In body fluid, albumin is the richest source of thiol antioxidants. One recent study showed that the reductive modification of thiol residues in albumin potentiated its antioxidative activity. Given that whey protein (WP) contains albumin and other thiol-active proteins, this property of WP could be exploited to develop novel thiol antioxidants. The aim of this study was to address this possibility. WP was reductively modified with dithiothreitol (DTT). The modified protein exhibited significantly elevated free sulfhydryl groups (-SH) and thiol antioxidative activity. It detoxified H2O2 and prevented H2O2-initiated protein oxidation and cell death in a -SH group-dependent way in vitro. In addition, it reacted with GSH/GSSG and altered the GSH/GSSG ratio via thiol–disulfide exchange. In vivo, oral administration of the reductively modified WP prevented oxidative stress and renal damage in a mouse model of renal injury caused by ischemia reperfusion. It significantly improved renal function, oxidation, inflammation, and cell injury. These protective effects were not observed in the WP control and were lost after blocking the -SH groups with maleimide. Furthermore, albumin, one of the ingredients of WP, also exhibited similar protective effects when reductively modified. In conclusion, the reductive modification of thiol residues in WP transformed it into a potent thiol antioxidant that protected kidneys from ischemia reperfusion injury. Given that oxidative stress underlies many life-threatening diseases, the reductively modified dietary protein could be used for the prevention and treatment of many oxidative-stress-related conditions, such as cardiovascular diseases, cancer, and aging.

Vitamin C (ascorbic acid; AA) and copper (Cu2+) are well used supplements with many health-promoting actions. However, when they are used in combination, the Fenton reaction occurs, leading to the formation of highly reactive hydroxyl radicals. Given that kidney is vulnerable to many toxicants including free radicals, we speculated that the in vivo administration of AA plus Cu2+ may cause oxidative kidney injury. The purpose of this study was to address this possibility. Mice were administered with AA and Cu2+, alone or in combination, via oral gavage once a day for various periods. Changes in the systemic oxidative status, as well renal structure and functions, were examined. The administration of AA plus Cu2+ elevated protein oxidation in serum, intestine, bladder, and kidney, as evidenced by the increased sulfenic acid formation and decreased level of free sulfhydryl groups (-SH). The systemic oxidative stress induced by AA plus Cu2+ was associated with a significant loss of renal function and structure, as indicated by the increased blood urea nitrogen (BUN), creatinine and urinary proteins, as well as glomerular and tubular cell injury. These effects of AA and Cu2+ were only observed when used in combination, and could be entirely prevented by thiol antioxidant NAC. Further analysis using cultured renal tubular epithelial cells revealed that AA plus Cu2+ caused cellular protein oxidation and cell death, which could be abolished by NAC and catalase. Moreover, coincubation of AA and Cu2+ led to H2O2 production. Collectively, our study revealed that a combined administration of AA and Cu2+ resulted in systemic oxidative stress and renal cell injury. As health-promoting supplements, AA and Cu2+ should not be used together.

In this paper, the coupling effect of building design variables on building energy performance was studied by taking office in hot summer and cold winter zone as an example. The Latin hypercube method was introduced to sample the architectural design features under different combinations of 10 variables, and simultaneously, the simulations on energy consumption were conducted through Energyplus. The potential interaction effect between design variables were analysed by using treed Gaussian process. Meanwhile, the coupling effect of these variables was further analysed quantitatively by using interaction in linear regression. The study showed that the coupling effect of the design variables and their impact on energy consumption trends could not be revealed by ordinary linear regression. Besides, the influence of interactions between design variables on energy consumption needs to be taken into account. The coupling relationships were found amongst the windows' heat transfer coefficient, the window-to-wall ratio, and the air infiltration. Therefore, these parameters should be selected properly based on the features of energy consumption and coupling variables. Furthermore, it was recommended to consider summer heat insulation/dissipation when the window-to-wall ratio is large in this district, instead of strengthening heat preservation.

This paper aims to answer whether and to what extent energy standard based shade assumptions over predict energy and economic performance for the hot summer and cold winter zone of China using modeling approaches. Occupant behaviors on shade action, occupancy and related lighting, office equipment and heating/cooling operation were considered and the energy and economic performances of manual shades were compared with standard assumption. Results show that annual energy uncertainty is significant, indicating a high uncertainty of energy performance. Standard assumption about occupant behavior adopted by building energy codes leads to an over prediction of energy saving by about 3 times compared with behavioral models. An interesting finding is that using manual shades may result in an energy increase (the probability is 7.26%) rather than energy saving. Meanwhile, net present value analysis shows that there is a significant overestimation of economic performance by shade behavior model alone as compared to occupancy plus shade models, and may lead to a biased economic prediction or even an inappropriate design decision on manual shades. A proposed shade behavior improvement strategy shows that it not only has a more robust shading performance but also a lower energy demand.

In natural communities, microbes exchange a variety of metabolites (public goods) with each other, which drives the evolution of auxotroph and shapes interdependent patterns at community-level. However, factors that determine the strategy of public goods synthesis for a given community member still remains to be elucidated. In anaerobic methanogenic communities, energy availability of different community members is largely varied. We hypothesized that this uneven energy availability contributed to the heterogeneity of public goods synthesis ability among the members in these communities. We tested this hypothesis by analyzing the synthetic strategy of amino acids of the bacterial and archaeal members involved in four previously enriched anaerobic methanogenic communities residing in thermophilic chemostats. Our analyses indicate that most of the members in the communities did not possess ability to synthesize all the essential amino acids, suggesting they exchanged these essential public goods to establish interdependent patterns for survival. Importantly, we found that the amino acid synthesis ability of a functional group was largely determined by how much energy it could obtain from its metabolism in the given environmental condition. Moreover, members within a functional group also possessed different amino acid synthesis abilities, which are related to their features of energy metabolism. Our study reveals that energy availability is a key driver of microbial evolution in presence of metabolic specialization at community level and suggests the feasibility of managing anaerobic methanogenic communities for better performance through controlling the metabolic interactions involved.

We propose a new model to identify epilepsy EEG signals. Some existing intelligent recognition technologies require that the training set and test set have the same distribution when recognizing EEG signals, some only consider reducing the marginal distribution distance of the data while ignoring the intra-class information of data, and some lack of interpretability. To address these deficiencies, we construct a TSK transfer learning fuzzy system (TSK-TL) based on the easy-to-interpret TSK fuzzy system the transfer learning method. The proposed model is interpretable. By using the information contained in the source domain and target domains more effectively, the requirements for data distribution are further relaxed. It realizes the identification of epilepsy EEG signals in data drift scene. The experimental results show that compared with the existing algorithms, TSK-TL has better performance in EEG recognition of epilepsy.

The frequent safety accidents involving lithium-ion batteries (LIBs) have aroused widespread concern around the world. The safety standards of LIBs are of great significance in promoting usage safety, but they need to be constantly upgraded with the advancements in battery technology and the extension of the application scenarios. This study comprehensively reviews the global safety standards and regulations of LIBs, including the status, characteristics, and application scope of each standard. A standardized test for thermal runaway triggering is also introduced. The recent fire accidents in electric vehicles and energy storage power stations are discussed in relation to the upgrading of the rational test standards. Finally, the following four suggestions for improving battery safety are proposed to optimize the safety standards: (1) early warning and cloud alarms for the battery’s thermal runaway; (2) an innovative structural design for a no-fire battery pack; (3) the design of a fire water injection interface for the battery pack; (4) the design of an immersive energy storage power station. This study provides insights for promoting the effectiveness of relevant safety standards for LIBs, thereby reducing the failure hazards.

Thermal runaway (TR) and the thermal runaway propagation (TRP) of Li-ion batteries can lead to safety incidents and cause explosion or fire accidents. Therefore, TR is a critical issue for the thermal safety of Li-ion batteries. In this study, the TR and TRP behavior of Li-ion batteries using different abuse methods (nail penetration, side heating, and overcharge) was investigated experimentally. First, the Extended Volume Accelerating Rate Calorimetry (EV-ARC) test was performed using the cell with an internal implantation thermocouple for a comparative study. Three abuse methods were used to induce TR and TRP for the cells and modules. At the cell level, the maximum temperature inside the cell under the EV-ARC test, nail penetration, and side-heating abuse was 994.8 °C, 964.3 °C, and 1020 °C, respectively. The thermocouple inside the cell under the overcharge abuse test was broken, and the experimental phenomenon indicated that the cell was most severely damaged under the overcharging abuse test. At the module level, the TRP behavior using the three abuse methods was different than in the first two TR cells, while the behavior of the other cells was similar. It was evidenced that TRP triggered by the overcharge abuse was the most hazardous, followed by the side-heating abuse, and lastly, the nail-penetration abuse was the least.

Carbon dots (CDs) have excellent optical properties, low toxicity and easy preparation, which have led to them being widely used in biomedicine, sensing and optical devices. However, although great progress has been made in the preparation of CDs, the detailed exploration of their photoluminescence (PL) mechanism is still under debate due to their complex structures and surface functionalities. Here, we proposed a single change in the pH of the synthesis condition, which had no effect on the CDs intrinsic core states and avoided the mutual influence of multiple PL origins. The m-phenylenediamine (m–PD) served as a carbon source, whose protonation degree determined the surface state of the resulting CDs and the accompanying fluorescence characteristics. The as-obtained CDs materials can be applied in the chemical sensor and anti-counterfeiting fields in a targeted manner. Therefore, our work not only contributes to the explanation of the CDs PL mechanism, but also obtains a series of CDs materials with controllable PL properties.

Large-scale measurements of soil moisture play a critical role in many fields, such as agriculture, hydrology, and engineering. The distributed temperature sensing (DTS) technology, based on a dual-probe heat pulse (DPHP), is a novel approach to realizing large-scale soil moisture estimation. However, the application of the method is limited by the complex optical cable layout, calculation algorithm, and lack of standardized heating strategy. In this paper, an improved DPHP-DTS method considering the soil bulk density was proposed. The measurement accuracy of the DPHP-DTS method under different heating strategies was studied in laboratory experiments, and its long-term stability in regard to the monitoring of soil moisture during natural evaporation in different soils was tested. The results show that the improved DPHP-DTS method can accurately measure the soil moisture, and the fitting algorithm can reduce the error caused by the accuracy of the DTS temperature measurement under the low-power heating strategy. Its measurement accuracy increases with the increase in the heating strength and duration. In addition, the improved DPHP-DTS method can describe soil evaporation in both sand and loess with good reliability and stability.

BackgroundChronic liver fibrosis is an inevitable stage for the development of patients with chronic hepatitis B (CHB). However, anti-fibrotic therapies have been unsuccessful so far. The biological functions and molecular mechanisms of long non-coding RNAs (lncRNAs) in the host immune system during chronic hepatitis B virus (HBV) infection, especially in fibrosis, are still largely unknown.MethodThe total RNA of peripheral blood mononuclear cells (PBMCs) from asymptomatic carriers (ASCs) or CHB receiving at least 8 years of anti-viral treatments was analyzed using Arraystar microarray and validated via quantitative real-time PCR (qRT-PCR). Correlation analysis was conducted based on correlation coefficients, Clusterprofile, and RNA Interactome Database (RAID). The functions of lncRNA in monocytes were determined via loss-of-function RNAi or gain-of-function lentivirus assays. The expression levels of mRNAs or proteins were evaluated using qRT-PCR, western blotting assay, or enzyme linked immunosorbent assays (ELISA).ResultsA total of 1,042 mRNA transcripts (630 up-regulated and 412 down-regulated) were identified being differentially expressed between ASC and CHB patients. Through enrichment analysis we focused on the transforming growth factor beta (TGF-β) signaling pathway and validated their expression in a larger cohort. Moreover, we found that lncRNA ENST00000519726 (lncRNA-HEIM) was highly expressed in monocytes and further up-regulated upon HBV infection. LncRNA-HEIM played an important role in CHB patients with long-term antiviral treatments, and its elevated expression was remarkably correlated with the TGF-β signaling pathway, especially with the two members namely TGF-β and SMAD4. Furthermore, altering the endogenous lncRNA-HEIM level in monocytes significantly affected the production of TGF-β, as well as the fibrosis of hepatic stellate cells by affecting the expression of collagen I and α-smooth muscle actin (α-SMA).ConclusionThese findings not only added knowledge to the understanding of the roles of which lncRNA-HEIM played in the activation of HSCs in CHB patients with long-term medication, but also provided a promising therapeutic target in the future treatment for liver fibrosis.

Manual solar shades rely on occupants’ control and thus its energy performance is highly related to occupants’ behavior. This paper conducted a co-simulation study on the impact of shade behavior on building energy performance and its uncertainty at different window-to-war ratios (WWRs). A previously developed stochastic behavior model for manual solar shades is used in this paper. Results show that energy uncertainty increases from 1.8% to 3.5% as WWR increases from 25% to 90% and a linear regression was given for predicting energy uncertainty with a determination coefficient of 0.9595. Whether energy uncertainty due to stochastic shade behavior has a significant impact on total energy performance depends on WWR design. To have a high and robust building performance, a small WWR that meets minimum daylighting performance is suggested for office buildings in this climate region. For a more accurate simulation of manual shades, a better consideration of manual shades than existing simplified assumptions by building energy regulations/standards is required.

Abstract Background There is growing literature suggesting a link between vitamin D and asthma lung function, but the results from systematic reviews are conflicting. We conducted this meta-analysis to investigate the relation between serum vitamin D and lung function in asthma patients. Methods Major databases, including OVID, MEDLINE, Web of Science and PUBMED, were searched until 10th October 2018. All published observational studies related to vitamin D and asthma were extracted. All meta-analyses were performed using Review Manager 5.3.5. Results This quantitative synthesis found that asthma patients with low vitamin D levels had lower forced expiratory volume In 1 s (FEV1) (mean difference (MD) = − 0.1, 95% CI = − 0.11 to − 0.08,p

In order to monitor human blood flow rate and flow velocity distribution, a blood flow meter based on multi-electrode electromagnetic measurement has been proposed to measure the flow velocity of the arteriovenous blood by obtaining the induced voltage on the skin surface. The COMSOL 3-D model has been built to simulate the physical structure of the human limb, including the size and relative position of skin, fat, bones, muscles, arteries and veins. Based on the Helmholtz coil and C-type coil, the electromagnetic induction intensity distribution of excitation structures and methods is simulated which is imposed through the body measurement section. The optimal design scheme of the excitation coil is proposed. Different excitation systems of different structures and different incentive methods are compared. The result shows that the two pairs of Helmholtz coils of orthogonal layout with syntroph excitation current has better uniformity, field center symmetry and stronger magnetic field strength, and effectively enhances the value of induction electromotive force, showing it is more suitable for multi-electrode electromagnetic measurement of non-axisymmetric flow. The research result proves that the optimization scheme of excitation system is feasible, and its uniformity plays a positive role in improving the accuracy of velocity reconstruction.

In order to measure the velocity profile of arterial and venous blood flow of human body and predict major cardiovascular diseases, the multi-electrode electromagnetic flow meter based on region weight function theory is proposed in this paper. The region weight function theory has been derived from traditional Shercliff theory and COMSOL model has been established to simulate the human limb according to the layout and relative position of skin, fat, bone, muscle, artery and vein. The cross section has been divided in to several regions and the induced voltages at different positions along the skin surface have been obtained by the electrode array. The local mean axial velocity profile of the vessel cross section is calculated, and the velocity information of the reverse flow in the arteries and veins is reconstructed. The results of three-dimensional finite element model simulation demonstrate that multi-electrode electromagnetic flow meter based on region weight function is feasible to characterize the velocity profile of arteries and veins with high accuracy. Region weight function based multi-electrode electromagnetic measurement method has some reference value for vessel blood flow velocity measurement and hemorheology anomaly monitoring of human body.

Recent advances in single image rectification and intrinsic calibration has been addressed by employing line information on the distorted image. The core issues of this technique are the separation of rectification and calibration procedures, and the suffering of geometric nonconformity. In this work, we propose a novel Geometric Consensus Rectification and Calibration algorithm, which we refer to as GCRC framework. We show how the geometric consensus rectification and calibration can be performed in a unified framework and solve the above issues. The proposed GCRC not only guarantees the geometrical consensus on the rectified images, but allows us to perform the robust intrinsic parameters estimation with the grouped circular arcs. Through “grouping by voting” in a unified framework, the geometric consensus rectification and calibration are robustly conducted on single distorted Manhattan images. Experiments on a number of distorted images, including the simulated YorkUrbanDB dataset, Panoramic Fisheye dataset, checkerboard image, and Internet images, demonstrate that the GCRC significantly improve the performance of geometrically consensus rectification and intrinsic parameters estimation. In particular, the GCRC shows relatively small variations with a different number of lines, which outperforms various previous approaches.

Skeleton-based action recognition has advanced significantly in the past decade. Among deep learning-based action recognition methods, one of the most commonly used structures is a two-stream network. This type of network extracts high-level spatial and temporal features from skeleton coordinates and optical flows, respectively. However, other features, such as the structure of the skeleton or the relations of specific joint pairs, are sometimes ignored, even though using these features can also improve action recognition performance. To robustly learn more low-level skeleton features, this paper introduces an efficient fully convolutional network to process multiple input features. The network has multiple streams, each of which has the same encoder-decoder structure. A temporal convolutional network and a co-occurrence convolutional network encode the local and global features, and a convolutional classifier decodes high-level features to classify the action. Moreover, a novel fusion strategy is proposed to combine independent feature learning and dependent feature relating. Detailed ablation studies are performed to confirm the network’s robustness to all feature inputs. If more features are combined and the number of streams increases, performance can be further improved. The proposed network is evaluated on three skeleton datasets: NTU-RGB + D, Kinetics, and UTKinect. The experimental results show its effectiveness and performance superiority over state-of-the-art methods.

Orthoimage mosaicking with obvious parallax caused by geometric misalignment is a challenging problem in the field of remote sensing. Because the obvious objects are not included in the digital terrain model (DTM), large parallax exists in these objects. A common strategy is to search an optimal seamline between orthoimages, avoiding the majority of obvious objects. However, stitching artifacts may remain because (1) the seamline may still cross several obvious objects and (2) the orthoimages may not be precisely aligned in geometry when the accuracy of the DTM is low. While applying general image warping methods to orthoimages can improve the local geometric consistency of adjacent images, these methods usually significantly modify the geometric properties of orthophoto maps. To the best of our knowledge, no approach has been proposed in the field of remote sensing to solve the problem of local geometric misalignments after orthoimage mosaicking with obvious parallax. In this paper, we creatively propose a method to optimize local alignment along the seamline after seamline detection. It consists of the following main processes. First, we locate regions with geometric misalignments along the seamline based on the similarity measure. Second, for any one region, we find one-dimensional (1D) feature matches along the seamline using a semi-global matching approach. The deformation vectors are calculated for these matches. Third, these deformation vectors are robustly and smoothly propagated into the buffer region centered on the seamline by minimizing the associated energy function. Finally, we directly warp the orthoimages to eliminate the local parallax under the guidance of dense deformation vectors. The experimental results on several groups of orthoimages show that our proposed approach is capable of eliminating the local parallax existing in the seamline while preserving most geometric properties of digital orthophoto maps, and that it outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.