Search

Emerging evidence has unveiled the secondary infection as one of the mortal causes of post-SARS-CoV-2 infection, but the factors related to secondary bacterial or fungi infection remains largely unexplored. We here systematically investigated the factors that might contribute to secondary infection. By clinical examination index analysis of patients, combined with the integrative analysis with RNA-seq analysis in the peripheral blood mononuclear cell isolated shortly from initial infection, this study showed that the antibiotic catabolic process and myeloid cell homeostasis were activated while the T-cell response were relatively repressed in those with the risk of secondary infection. Further monitoring analysis of immune cell and liver injury analysis showed that the risk of secondary infection was accompanied by severe lymphocytopenia at the intermediate and late stages and liver injury at the early stages of SARS-CoV-2. Moreover, the metagenomics analysis of bronchoalveolar lavage fluid and the microbial culture analysis, to some extent, showed that the severe pneumonia-related bacteria have already existed in the initial infection. [ABSTRACT FROM AUTHOR]

L-theanine, a unique non-protein amino acid predominantly found in tea plants (Camellia sinensis), plays a pivotal role in plant responses to abiotic stress and significantly influences tea quality. In this review, the metabolism and transport mechanisms of L-theanine are comprehensively discussed, highlighting its spatial distribution in tea plants, where it is most abundant in young leaves and less so in roots, stems, and older leaves. The biosynthesis of L-theanine occurs through the enzymatic conversion of glutamate and ethylamine, catalyzed by theanine synthase, primarily in the roots, from where it is transported to aerial parts of the plant for further catabolism. Environmental factors such as temperature, light, drought, elevated CO2, nutrient unavailability, and heavy metals significantly affect theanine biosynthesis and hydrolysis, with plant hormones and transcription factors playing crucial regulatory roles. Furthermore, it has been demonstrated that applying L-theanine exogenously improves other crops' resistance to a range of abiotic stresses, suggesting its potential utility in improving crop resilience amid climate change. This review aims to elucidate the physiological mechanisms and biological functions of L-theanine metabolism under stress conditions, providing a theoretical foundation for enhancing tea quality and stress resistance in tea cultivation. [ABSTRACT FROM AUTHOR]

β-Lactoglobulin (β-Lg) is a prevalent allergenic protein found in most dairy products, which poses great food safety risks for individuals with allergies, especially for infants. Sensitive and effective detection methods for such allergens are essential to reduce the risk of allergies in daily life. Herein, a fluorescent aptamer bioassay based on a dual and cyclic bidirectional strand displacement means is developed for the sensitive detection of β-Lg in infant rice porridge and milk. The aptamer in the duplex DNA probe binds β-Lg to release the assistance strand to further hybridize with two hairpins, which triggers the initiation of two cyclic amplification cycles through the polymerization, displacement, and nicking of the strands under the action of DNA polymerase and endonuclease restriction enzymes. The amplification cycles lead to the unfolding of many fluorescently quenched signal probes to exhibit substantially enhanced fluorescence recovery for detecting β-Lg. The assay can realize detection of β-Lg in concentrations as low as 4.41 pM within the range of 0.01 to 10 nM. Furthermore, our sensing method has the capability to discriminate β-Lg from other proteins with high selectivity, resulting in low levels of β-Lg detection in rice porridge and milk samples, demonstrating promising potentials of the developed sensing method for monitoring various food allergens. [ABSTRACT FROM AUTHOR]

This paper constructs a digital technology multilayer convergence network model to explore the mechanisms of digital technology convergence. The analysis is based on patent data from China's A-share listed companies from 2012 to 2021. The results show a continuous increase in network scale and structural complexity, with intensified cross-domain interactions. The company collaboration subnetwork evolved from decentralization to a more centralized structure, while the technology convergence subnetwork expanded and became increasingly complex. Core technologies maintained dominant positions, and the co-evolution between companies and technologies showed sustained development. This study reveals intricate interdependencies between technological convergence and company collaboration, providing theoretical insights and practical implications for digital technology innovation and strategic decision-making. [ABSTRACT FROM AUTHOR]

Work is becoming more and more flexible nowadays. It can take place in the office, at home, or even on the go; tasks may encompass activities in the form of documents, multimedia, or 3D models in virtual space. Under such circumstances, personal computers (PC), the most widely used productivity devices for work today, cannot well address the diverse needs such as screen size, privacy, and flexibility to display diverse content formats (e.g., 2D to 3D), due to their fixed hardware specs. We believe the solution lies in Extended Reality (XR). In this article, we explored how XR glasses can be used for PC's virtual extended displays, and conducted user interviews and usability tests to propose a systematic user experience design and evaluation framework. We discovered that the design space encompasses four dimensions general placement, display specs, operating system integration, and interaction behaviors) and summarized users' corresponding preferences. We proposed a quality-of-experience (QoE) evaluation framework for XR virtual displays consisting of visual quality, visual fatigue and discomfort, as well as immersiveness, and identified clarity as the most significant factor that affects user satisfaction. Our design and evaluation frameworks could serve as a resource for both practitioners and scholars with an interest in the design and evaluation of virtual displays. [ABSTRACT FROM AUTHOR]

Background HIV-1 drug resistance is a huge challenge in the era of ART. Objectives To investigate the prevalence and characteristics of acquired HIV-1 drug resistance (ADR) in Shanghai, China. Methods An epidemiological study was performed among people living with human immunodeficiency virus (PLWH) receiving ART in Shanghai from January 2017 to December 2021. A total of 8669 PLWH were tested for drug resistance by genotypic resistance testing. Drug resistance mutations (DRMs) were identified using the Stanford University HIV Drug Resistance Database program. Results Ten HIV-1 subtypes/circulating recombinant forms (CRFs) were identified, mainly including CRF01_AE (46.8%), CRF07_BC (35.7%), B (6.4%), CRF55_01B (2.8%) and CRF08_BC (2.4%). The prevalence of ADR was 48% (389/811). Three NRTI-associated mutations (M184V/I/L, S68G/N/R and K65R/N) and four NNRTI-associated mutations (V179D/E/T/L, K103N/R/S/T, V106M/I/A and G190A/S/T/C/D/E/Q) were the most common DRMs. These DRMs caused high-level resistance to lamivudine, emtricitabine, efavirenz and nevirapine. The DRM profiles appeared to be significantly different among different subtypes. Conclusions We revealed HIV-1 subtype characteristics and the DRM profile in Shanghai, which provide crucial guidance for clinical treatment and management of PLWH. [ABSTRACT FROM AUTHOR]

We adopt a multilayer networks approach to assess how network structural embeddedness affects corporate technological innovation. Our findings indicate an annual increase in both single-layer and multilayer networks, although adoption of the latter by Chinese listed companies is comparatively low. We found that structural embeddedness of multilayer networks positively impacts corporate technological innovation. By reducing uncertainty within the internal environment, these networks bolster technological innovation. Moreover, such embeddedness notably spurs innovation in non-state-owned companies and those with greater internal transparency and robust external oversight. Our analysis reveals an intermediate effect where structural embeddedness in multilayer networks influences innovation. Our work provides new insights into enhancing innovation capacity via network embeddedness and supplies empirical data on utilizing network resources for innovation. We also offer actionable guidance and policy advice for managers, investors, and policymakers, especially relevant amidst economic transformation and pursuit of technological self-reliance of China. [ABSTRACT FROM AUTHOR]

Epigallocatechin-3-gallate (EGCG), a secondary metabolite (flavonoid) with strong antioxidant capacity, can alleviate oxidative stress. The present study examined the role of EGCG in plant response to glyphosate and carbendazim treatments in melon (Cucumis melo L.). The results showed that exogenous EGCG application alleviated pesticide phytotoxicity as reflected by significantly increased net photosynthetic rate (Pn), attenuated oxidative stress, and decreased pesticide residues in melon leaves. Under glyphosate treatment, EGCG increased glutathione content, glutathione reductase (GR) activity, glutathione-S-transferase (GST) activity, and the expression of GR and GST1. Under carbendazim treatment, EGCG induced the activities of ascorbate peroxidase (APX), catalase (CAT) and peroxidase (POD) and upregulated the expression levels of APX3, CAT1 and POD2. These results revealed that exogenous EGCG application potentially accelerated glyphosate degradation by regulating the glutathione-dependent pesticide metabolism pathway, while EGCG alleviated carbendazim phytotoxicity through the glutathione-independent pathway by enhancing the activity of antioxidant enzymes and associated transcripts in melon leaves. The findings suggest that EGCG promotes the degradation of carbendazim and glyphosate possibly through two different pathways of xenobiotic detoxification. The study provides a theoretical basis for the application of EGCG in vegetable crops to attenuate pesticide phytotoxicity and improve food safety. [ABSTRACT FROM AUTHOR]

Background: Functional gastrointestinal disorders (FGIDs) are closely related to disorders of brain-gut interaction. FGIDs are the dominant disease of acupuncture treatment, which can improve the symptoms and emotional state. Aim: To evaluate the results and quality of the available clinical evidence and to summarize the central mechanism and effect of acupuncture on FGIDs. Methods: PubMed, EMBASE, Web of science, Cochrane Library, China National Knowledge Infrastructure (CNKI) were searched by computer to collect the randomized controlled trials (RCTs), which contained central mechanisms via fMRI research of acupuncture in the treatment of FGIDs patients. The search time limit was from the establishment of the database to June 22, 2022. Two researchers independently screened the literature, extracted data, and evaluated the quality. Results: Ten RCTs involving fMRI data were included in this study, including 4 Functional dyspepsia (FD) studies, 3 irritable bowel syndrome (IBS) studies, and 3 functional constipation (FC) studies. The score of improvements in both gastrointestinal symptoms and psychological symptoms showed that acupuncture could significantly improve the clinical symptoms of FGIDs patients, including abdominal pain, abdominal distension, frequency of defecation, and stool characteristics, and could relieve anxiety and depression symptoms of patients. Acupuncture could regulate brain functional connections and functional activity in FGIDs patients, mainly including insula, anterior cingulate cortex, prefrontal cortex, thalamus, hippocampus, amygdala and other brain regions. Conclusion: Acupuncture can improve gastrointestinal symptoms and psychological status in FGIDs patients, and regulate functional connectivity and activity of brain regions such as insula, ACC, PFC, thalamus, HIPP, amygdala, etc. These changes in brain activity may related to visceral sensation, pain regulation, emotion, but further studies of high quality are still necessary. [ABSTRACT FROM AUTHOR]

Shading is an effective agronomic technique to protect tea plants from intense sunlight. However, there are currently very few studies on more effective shading methods to improve the quality of summer tea. In this study, 'Longjing43' plants were grown under four different shading treatments for 14 days, with no shading as the control. Among the four shading treatments, double-layer-net shadings had the most positive impact on the tea quality, resulting in higher levels of amino acids but lower levels of tea polyphenols. Additionally, double-layer-net shadings provided more suitable microenvironments for tea plants. The tea leaves in T4 (double nets 50 cm above the plant canopy) contained 16.13 mg∙g−1 of umami and sweet amino acids, which was significantly higher than in other treatments. T4 had the lowest air temperature and the most suitable and stable soil water content. Interestingly, the ratio of red light to far-red light in T4 was only 1.65, much lower than other treatments, which warrants further study. In conclusion, the microenvironment induced by shading can greatly affect the tea quality, and double-layer-net shading is better for improving the quality of summer tea. [ABSTRACT FROM AUTHOR]

With the advancement of China's satellite technology, the HuanJingJianZai-2 A/B (HJ-2 A/B) satellites, equipped with whisk-broom infrared sensors, represent a significant leap forward in environmental monitoring and Earth observation capabilities. This technological leap, however, introduces new challenges in calibration. The unique structure of the HJ-2 A/B infrared spectroradiometer (IRS) necessitates innovative calibration techniques, as traditional methods primarily focused on exterior orientation parameters (EOPs) and often overlooked the importance of interior orientation accuracy, which is essential for accurate multispectral band registration and color rendering. Addressing this gap, we introduce an innovative multi-focal-plane-array joint calibration method specifically designed for whisk-broom cameras. Our method involves selecting a master band from each focal plane array for accurate focal length calibration and deriving ground control points from image matching and altitude interpolation for comprehensive bundle adjustment. This adjustment refines EOPs and interior orientation parameters (IOPs), ensuring globally optimal EOPs and enhanced IOPs calibration stability. The application of our method to the HJ-2 A/B IRS yielded substantial improvements in georeferencing and band registration accuracies, surpassing traditional methods. This paper details the multi-focal-plane-array joint calibration method, describes the IRS and experimental setup, presents the experimental results, and concludes with the implications and potential applications of our findings. [ABSTRACT FROM AUTHOR]

This correction notice in the journal "Frontiers in Immunology" addresses an error in the Funding statement of an article on risk factors for secondary infection post-SARS-COV-2 infection in severe and critical COVID-19 patients. The correct grant number for the funding received from the National Natural Science Foundation of China is provided. The authors acknowledge the mistake but clarify that it does not impact the scientific conclusions of the article. [Extracted from the article]

In this paper, we proposed a deep fusion model for telephone comments recognition, named CNN-BiGRU. Traditionally, the most used algorithms in text classification are Convolutional Neural Network (CNN), Long and Short Term Memory (LSTM) and Bi-Gated Recurrent Neural Network (BiGRU). For CNN, it can extract the feature form the neighbors, and a softmax layer is followed for classification. The global feature is not included in the CNN model. LSTM introduces the gate, which can capture the information before the node. BiGRU is developed from LSTM, and it can find the features in the context. So compared to LSTM, BiGRU not only includes the information before, but also can capture the following features. Thus, LSTM and BiGRU can extract the global features, but cannot capture the local features. In order to deal with this weakness, we proposed a fusion model for comments classification, which combines the CNN and BiGRU in our model. Different from other methods, CNN and BiGRU are parallelly connected. CNN model can extract the local feature, and BiGRU can find the global feature. Then we concatenate the two kinds of features and feed to recognition layer for classification. Then we use our model to classify the telephone comments; compared with the traditional machine SVM and tow deep neural models — CNN and BiGRU — our model performed better. [ABSTRACT FROM AUTHOR]

Diagram Question Answering (DQA) aims to correctly answer questions about given diagrams, which demands an interplay of good diagram understanding and effective reasoning. However, the same appearance of objects in diagrams can express different semantics. This kind of visual semantic ambiguity problem makes it challenging to represent diagrams sufficiently for better understanding. Moreover, since there are questions about diagrams from different perspectives, it is also crucial to perform flexible and adaptive reasoning on content-rich diagrams. In this paper, we propose a Disentangled Adaptive Visual Reasoning Network for DQA, named DisAVR, to jointly optimize the dual-process of representation and reasoning. DisAVR mainly comprises three modules: improved region feature learning, question parsing, and disentangled adaptive reasoning. Specifically, the improved region feature learning module is designed to first learn robust diagram representation by integrating detail-aware patch features and semantically-explicit text features with region features. Subsequently, the question parsing module decomposes the question into three types of question guidance including region, spatial relation and semantic relation guidance to dynamically guide subsequent reasoning. Next, the disentangled adaptive reasoning module decomposes the whole reasoning process by employing three visual reasoning cells to construct a soft fully-connected multi-layer stacked routing space. These three cells in each layer reason over object regions, semantic and spatial relations in the diagram under the corresponding question guidance. Moreover, an adaptive routing mechanism is designed to flexibly explore more optimal reasoning paths for specific diagram-question pairs. Extensive experiments on three DQA datasets demonstrate the superiority of our DisAVR. [ABSTRACT FROM AUTHOR]

China's renewable energy innovation is essential for realizing its carbon neutrality targets and the low-carbon transition, but few studies have spatially examined its characteristics and spillover effects. To fill the research gap, this study investigates its distribution and trends from a spatiotemporal dimension and focuses on the spatial effects of the influencing factors to identify those that have a significant impact on renewable energy innovation by using China's provincial panel data from 2006 to 2019. The results show the following findings. (1) Renewable energy innovation shows distinct spatial differences across China's provinces such that it is high in the east and south and low in the west and north, which exhibits spatial locking and path-dependence. (2) There is a positive spatial correlation with renewable energy innovation. (3) R&D investment and GDP per capita significantly promote renewable energy innovation, but the former effect is mainly observed in the local area, whereas the latter shows spatial effects. More market-oriented policies should be taken for the improvement of renewable energy innovation and the establishment of regional coordination mechanisms are proposed. [ABSTRACT FROM AUTHOR]

The convenient and sensitive assay of antibiotic residuals is important for monitoring food safety and human health. Here, with a new self-constrained metal ion-dependent DNAzyme and hybridization chain reaction (HCR) signal amplifications, a simple label-free and highly sensitive aptamer-based fluorescent sensing assay for sarafloxacin is established. The target sarafloxacin molecules bind the aptamers in the duplex DNA probes to release the complementary strands, which cyclically activate the catalytic activity of the self-constrained DNAzymes to cut the substrate sequences to liberate many ssDNA strands. Subsequent HCR of two hairpins is initiated by these ssDNA strands for yielding long dsDNAs with lots of G-quadruplexes, which intercalate thioflavin T dye to generate drastically magnified fluorescence for non-label and sensitive sarafloxacin detection at the detection limit of 29 pM. Also, a highly selective assay of low levels of sarafloxacin in diluted milk samples has been demonstrated, showing the great potential of our approach for the development of versatile sensitive and convenient aptasensors for monitoring different antibiotics. [ABSTRACT FROM AUTHOR]

Session-based recommendation (SBR) is to predict the next item, given an anonymous interaction sequence. Recently, many advanced SBR models have shown great recommending performance, but few studies note that they suffer from popularity bias seriously: the model tends to recommend popular items and fails to recommend long-tail items. The only few debias works relieve popularity bias indeed. However, they ignore individual's conformity toward popular items and thus decrease recommending performance on popular items. Besides, conformity is always entangled with individual's real interest, which hinders extracting one's comprehensive preference. To tackle the problem, we propose an SBR framework with Disentangling InteRest and Conformity for eliminating popularity bias in SBR. In this framework, two groups of item encoders and session modeling modules are devised to extract interest and conformity, respectively, and a fusion module is designed to combine these two types of preference. Also, a discrepancy loss is utilized to disentangle the representation of interest and conformity. Besides, our devised framework can integrate with several SBR models seamlessly. We conduct extensive experiments on three real-world datasets with four advanced SBR models. The results show that our framework outperforms other state-of-the-art debias methods consistently. [ABSTRACT FROM AUTHOR]

In the past few years, the continuous pandemic of COVID-19 caused by SARS-CoV-2 has placed a huge burden on public health. In order to effectively deal with the emergence of new SARS-CoV-2 variants, it becomes meaningful to further enhance the immune responses of individuals who have completed the first-generation vaccination. To understand whether sequential administration using different variant sequence-based inactivated vaccines could induce better immunity against the forthcoming variants, we tried five inactivated vaccine combinations in a mouse model and compared their immune responses. Our results showed that the sequential strategies have a significant advantage over homologous immunization by inducing robust antigen-specific T cell immune responses in the early stages of immunization. Furthermore, the three-dose vaccination strategies in our research elicited better neutralizing antibody responses against the BA.2 Omicron strain. These data provide scientific clues for finding the optimal strategy within the existing vaccine platform in generating cross-immunity against multiple variants including previously unexposed strains. [ABSTRACT FROM AUTHOR]

Performance prediction is an important research facet of educational data mining. Most models extract student behavior features from campus card data for prediction. However, most of these methods have coarse time granularity, difficulty in extracting useful high-order behavior combination features, dependence on 6 historical achievements, etc. To solve these problems, this paper utilizes prediction of grade point average (GPA prediction) and whether a specific student has failing subjects (failing prediction) in a term as the goal of performance prediction and proposes a comprehensive performance prediction model of college students based on behavior features. First, a method for representing campus card data based on behavior flow is introduced to retain higher time accuracy. Second, a method for extracting student behavior features based on multi-head self-attention mechanism is proposed to automatically select more important high-order behavior combination features. Finally, a performance prediction model based on student behavior feature mode difference is proposed to improve the model's prediction accuracy and increases the model's robustness for students with significant changes in performance. The performance of the model is verified on actual data collected by the teaching monitoring big data platform of Xi'an Jiaotong University. The results show that the model's prediction performance is better than the comparison algorithms on both the failing prediction and GPA prediction. [ABSTRACT FROM AUTHOR]

Background: Aldosterone relieves transcriptional repression of epithelial sodium channel (ENaC) by inhibiting Dot1a and Af9 expression and their interaction with ENaC promoter in various tissues. Expressions of ENaC and Af9 in inner ear have been identified. However, it is not known how Dot1l is regulated by aldosterone in inner ear. Methods: Twenty-eight adult guinea pigs were randomly divided into the control group and treatment group. Aldosterone 1 mg/kg/d was injected intraperitoneally in the treatment group and saline in the control group for 7 days. Animals were killed 1 month later following auditory brainstem response examination. Histomorphology of cochlea was detected with hematoxylin–eosin staining, and Dot1l expression was examined with immunohistochemistry and Western blot. Results: There was no significant difference in ABR thresholds before and after injection of aldosterone or saline in either group. Endolymphatic hydrops was found in 75% of animals in the treatment group. Dot1l was found in both groups in the stria vascularis, Reissner's membrane, spiral limbus, organ of Corti and spiral ligament. Dot1l expression in the treatment group was decreased by aldosterone. Conclusions: Dot1l in guinea pig cochlea is inhibited by aldosterone with induction of endolymphatic hydrops. Dot1l may be closely related to endolymph regulation by aldosterone and to pathogenesis of Meniere's disease. [ABSTRACT FROM AUTHOR]

Induced circular dichroism (ICD) is widely used in miniature polarizers, molecular detection, and negative refractive index media. However, enhancing and the dynamic regulation of ICD signals of achiral nanostructures in the visible and near-infrared range remain the current challenges. Here, monolayer borophene (MB) with anisotropic conductance was incorporated into achiral nanostructures, which consisted of achiral dielectric elliptical hole arrays (DENAs) placed on a silver substrate. Two narrowband ICD signals for DENAs/MB were achieved in the near-infrared range under different circularly polarized lights. The distributions of the magnetic field of DENAs/MB could explain the two narrowband ICD signals originating from the coupling of surface plasmon polariton resonances along the x- and y directions. Not only could the ICD signals be tuned by the structural parameters of DENAs/MB, but they could also be actively tuned by the incident angles of the excitation light and the carrier concentration of MB. In addition, the sensitivity and the figure of merit of DENAs/MB could reach 302/RIU and 61.0. These results provide a concise method for the design of dynamically adjustable chiral devices based on borophene and promote its application in molecular recognition and chiral catalysis. [ABSTRACT FROM AUTHOR]

Recently, graph convolutional networks (GCNs) for text classification have received considerable attention in natural language processing. However, most current methods just use original documents and words in the corpus to construct the topology of graph which may lose some effective information. In this paper, we propose a Multi-Stream Graph Convolutional Network (MS-GCN) for text classification via Representative-Word Document (RWD) mining, which is implemented in PyTorch. In the proposed method, we first introduce temporary labels and mine the RWDs which are treated as additional documents in the corpus. Then, we build a heterogeneous graph based on relations among a Group of RWDs (GRWDs), words and original documents. Furthermore, we construct the MS-GCN based on multiple heterogeneous graphs according to different GRWDs. Finally, we optimize our MS-GCN model through updated mechanism of GRWDs. We evaluate the proposed approach on six text classification datasets, 20NG, R8, R52, Ohsumed, MR and Pheme. Extensive experiments on these datasets show that our proposed approach outperforms state-of-the-art methods for text classification. [ABSTRACT FROM AUTHOR]

Approaching landing is the most difficult and complicated operation in the entire flight process of the aircraft, and it is also the stage where frequent flight accidents occur. According to the statistics released by International Civil Aviation Organization (ICAO), 49% of flight accidents occurred in the stage of approaching landing. The Instrument Landing System (ILS), commonly known as the blind landing system, is the main navigation equipment for guiding the aircraft's precision approach and landing in the terminal area of the airport. Therefore, the uninterrupted normal operation of the ILS is the key to ensuring the safety of aircraft landing. [ABSTRACT FROM AUTHOR]

Background: Natural language processing could assist multiple tasks in systematic reviews to reduce workflow, including the extraction of PICO elements such as study populations, interventions, comparators and outcomes. The PICO framework provides a basis for the retrieval and selection for inclusion of evidence relevant to a specific systematic review question, and automatic approaches to PICO extraction have been developed particularly for reviews of clinical trial findings. Considering the difference between preclinical animal studies and clinical trials, developing separate approaches is necessary. Facilitating preclinical systematic reviews will inform the translation from preclinical to clinical research. Methods: We randomly selected 400 abstracts from the PubMed Central Open Access database which described in vivo animal research and manually annotated these with PICO phrases for Species, Strain, methods of Induction of disease model, Intervention, Comparator and Outcome. We developed a two-stage workflow for preclinical PICO extraction. Firstly we fine-tuned BERT with different pre-trained modules for PICO sentence classification. Then, after removing the text irrelevant to PICO features, we explored LSTM-, CRF- and BERT-based models for PICO entity recognition. We also explored a self-training approach because of the small training corpus. Results: For PICO sentence classification, BERT models using all pre-trained modules achieved an F1 score of over 80%, and models pre-trained on PubMed abstracts achieved the highest F1 of 85%. For PICO entity recognition, fine-tuning BERT pre-trained on PubMed abstracts achieved an overall F1 of 71% and satisfactory F1 for Species (98%), Strain (70%), Intervention (70%) and Outcome (67%). The score of Induction and Comparator is less satisfactory, but F1 of Comparator can be improved to 50% by applying self-training. Conclusions: Our study indicates that of the approaches tested, BERT pre-trained on PubMed abstracts is the best for both PICO sentence classification and PICO entity recognition in the preclinical abstracts. Self-training yields better performance for identifying comparators and strains. [ABSTRACT FROM AUTHOR]

We sought to apply natural language processing to the task of automatic risk of bias assessment in preclinical literature, which could speed the process of systematic review, provide information to guide research improvement activity, and support translation from preclinical to clinical research. We use 7840 full‐text publications describing animal experiments with yes/no annotations for five risk of bias items. We implement a series of models including baselines (support vector machine, logistic regression, random forest), neural models (convolutional neural network, recurrent neural network with attention, hierarchical neural network) and models using BERT with two strategies (document chunk pooling and sentence extraction). We tune hyperparameters to obtain the highest F1 scores for each risk of bias item on the validation set and compare evaluation results on the test set to our previous regular expression approach. The F1 scores of best models on test set are 82.0% for random allocation, 81.6% for blinded assessment of outcome, 82.6% for conflict of interests, 91.4% for compliance with animal welfare regulations and 46.6% for reporting animals excluded from analysis. Our models significantly outperform regular expressions for four risk of bias items. For random allocation, blinded assessment of outcome, conflict of interests and animal exclusions, neural models achieve good performance; for animal welfare regulations, BERT model with a sentence extraction strategy works better. Convolutional neural networks are the overall best models. The tool is publicly available which may contribute to the future monitoring of risk of bias reporting for research improvement activities. [ABSTRACT FROM AUTHOR]

Energy efficiency is one of the important indicators for the evaluation of green buildings, and it is also related to the sustainable development of the building industry and energy conservation. Using agricultural waste in concrete to produce biomass recycled aggregates can effectively utilize agricultural solid waste to develop new wall materials with economic and energy-efficient properties. In this study, industrial wastes such as ground, granulated blast-furnace slag (GGBS) and fly ash (FA) were used to replace cement as cementitious material and coconut shell (CC) as lightweight coarse aggregate (LWA) in lightweight concrete. The lightweight coconut shell aggregate concrete with a density of less than 1950 kg/m3 was used as structural concrete. The thermal conductivity of synthesized biomass recycled aggregate concrete (SBRAC) was about 0.47 W/mK, which is 217% and 19% lower than that of natural aggregate concrete (NAC) and crushed coconut shell aggregate concrete (CCSAC), respectively. With the same volume, the costs of SBRAC and CCSAC are 25.1% and 4.9% lower than that of NAC, respectively. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman scattering (SERS) is recognized as one of the most favored techniques for enhancing Raman signals. The morphology of the SERS substrate profoundly affects molecular Raman spectra. This study aimed to construct a ring-mounted nanostructured substrate via liquidâ€"liquid two-phase self-assembly incorporated with anodic aluminum oxide (AAO) membrane transfer techniques. High-density nanoparticles (NPs) assembled on AAO membranes were ascribed to reduce the diameters of the nanopores, with Auâ€"Ag alloy NPs to regulate the dielectric constant so as to reveal the local surface plasmon resonance tunability. SERS engineered in this way allowed for the fabrication of a ring-mounted nanostructured substrate where the distribution density of NPs and dielectric constant could be independently fine-tuned. High SERS activity of the substrate was revealed by detecting the enhanced factor of crystal violet and rhodamine 6G molecules, which was up to 1.56 × 106. Moreover, SERS of thiram target molecules confirmed the supersensitivity and repeatability of the substrate as a practical application. The results of this study manifested a low-cost but high-efficiency ring-mounted nanostructured SERS substrate that might be suitable in many fields, including biosensing, medical research, environmental monitoring, and optoelectronics. [ABSTRACT FROM AUTHOR]

Emerging evidence has unveiled the secondary infection as one of the mortal causes of post-SARS-CoV-2 infection, but the factors related to secondary bacterial or fungi infection remains largely unexplored. We here systematically investigated the factors that might contribute to secondary infection. By clinical examination index analysis of patients, combined with the integrative analysis with RNA-seq analysis in the peripheral blood mononuclear cell isolated shortly from initial infection, this study showed that the antibiotic catabolic process and myeloid cell homeostasis were activated while the T-cell response were relatively repressed in those with the risk of secondary infection. Further monitoring analysis of immune cell and liver injury analysis showed that the risk of secondary infection was accompanied by severe lymphocytopenia at the intermediate and late stages and liver injury at the early stages of SARS-CoV-2. Moreover, the metagenomics analysis of bronchoalveolar lavage fluid and the microbial culture analysis, to some extent, showed that the severe pneumonia-related bacteria have already existed in the initial infection. [ABSTRACT FROM AUTHOR]

Chiral plasmonic nanostructures have become a promising platform for polarization converters and molecular analysis. However, the circular dichroism (CD) of planar chiral plasmonic nanostructures is always weak and difficult for dynamic adjustment. In this work, graphene sheets (GSs) are introduced in planar metal chiral split rings (MCSRs) to enhance and dynamically adjust their CD effect. The chiral split rings consist of rotated big and small split rings. Simulation results show that the plasmonic coupling between MCSRs and GSs can enhance the absorption and CD spectra of MCSRs at two resonant wavelengths. The surface current distributions reveal that the CD signals are due to the localized surface plasmon resonances on the big and small split rings, respectively. The loss distributions illustrate that the increased loss mainly locates in GSs. In addition, the CD spectra of MCSRs/GSs can be dynamically adjusted and influenced by the Fermi energy of GSs, the geometric parameters of MCSRs and, the mediums in the environment. It can be used to detect the environmental temperature and concentration. The results help to design dynamically adjustable chiral nanostructures and promote their applications in environment detection. [ABSTRACT FROM AUTHOR]

In this study, the effect of boric acid in the cooling water system of nuclear power plants on the durability of reinforced concrete was experimentally studied. The mechanical properties of reinforced concrete under boric acid solution environments were studied by accelerated test methods. In addition, the effect of boric acid on the electrochemical behavior of steel bar and microstructure of concrete was studied. The results showed that boric acid corrosion does not affect reinforced concrete to a large extent, as corrosion only occurs on the surface of reinforced concrete, and thus, the internal reinforced concrete still maintains a high alkaline environment. At a boric acid concentration of 3%, corrosion products are crystallized on the surface of the specimen, which inhibits further corrosion. [ABSTRACT FROM AUTHOR]

Metric learning is a critical problem in classification. Most classifiers are based on a metric, the simplest one is the KNN classifier, whose outcome is directly decided by the given metric. This paper will discuss semi-supervised metric learning. Most traditional semi-supervised metric learning algorithms preserve the local structure of all the samples (including labeled and unlabeled) in the input space, when making the same labeled samples together and separating different labeled samples. In most existing methods, the local structure is calculated by the Euclidean distance which uses all the features. As we all know, high dimensional data lies on a low dimension manifold, and not all the features are discriminative. Thus, in this paper, we try to explore the latent structure of the samples and use the more discriminative features to calculate the local structure. The latent structure is learned by clustering random forest and cast into similarity between samples. Based on the hierarchical structure of the trees and the split function, the similarity is obtained from discriminant features. Experimental results on public data sets show our algorithm outperforms the traditional similar related algorithms. [ABSTRACT FROM AUTHOR]

In this study, the effect of 8% UEA the reason why the UEA content is 8% is as follows: the expansion agent content in the actual mix proportion of the project is 8%, which is selected in this test to fit the reality better. expansion agent on the compressive strength, chloride ion penetration resistance, and carbonation resistance of civil air defense concrete were studied by simulating the rapid carbonation and chloride solution immersion of concrete structure in coastal civil air defense engineering environment. The results of this study show that the early compressive strength of concrete decreased by adding the UEA expansion agent and was also affected by the curing time. Moreover, the addition of UEA expansion agent decreased the content of free chloride ions and calcium carbonate in concrete and reduced the early compressive strength of concrete. [ABSTRACT FROM AUTHOR]

Background: Histone lysine methylation plays an important role in plant development and stress responses by activating or repressing gene expression. Histone lysine methylation is catalyzed by a class of SET-domain group proteins (SDGs). Although an increasing number of studies have shown that SDGs play important regulatory roles in development and stress responses, the functions of SDGs in apple remain unclear. Results: A total of 67 SDG members were identified in the Malus×domestica genome. Syntenic analysis revealed that most of the MdSDG duplicated gene pairs were associated with a recent genome-wide duplication event of the apple genome. These 67 MdSDG members were grouped into six classes based on sequence similarity and the findings of previous studies. The domain organization of each MdSDG class was characterized by specific patterns, which was consistent with the classification results. The tissue-specific expression patterns of MdSDGs among the 72 apple tissues in the different apple developmental stages were characterized to provide insight into their potential functions in development. The expression profiles of MdSDGs were also investigated in fruit development, the breaking of bud dormancy, and responses to abiotic and biotic stress; the results indicated that MdSDGs might play a regulatory role in development and stress responses. The subcellular localization and putative interaction network of MdSDG proteins were also analyzed. Conclusions: This work presents a fundamental comprehensive analysis of SDG histone methyltransferases in apple and provides a basis for future studies of MdSDGs involved in apple development and stress responses. [ABSTRACT FROM AUTHOR]

Regulatory T cell can protect against severe forms of coronaviral infections attributable to host inflammatory responses. But its role in the pathogenesis of COVID‐19 is still unclear. In this study, frequencies of total and multiple subsets of lymphocytes in peripheral blood of COVID‐19 patients and discharged individuals were analyzed using a multicolor flow cytometry assay. Plasma concentration of IL‐10 was measured using a microsphere‐based immunoassay kit. Comparing to healthy controls, the frequencies of total lymphocytes and T cells decreased significantly in both acutely infected COVID‐19 patients and discharged individuals. The frequencies of total lymphocytes correlated negatively with the frequencies of CD3−CD56+ NK cells. The frequencies of regulatory CD8+CD25+ T cells correlated with CD4+/CD8+ T cell ratios positively, while the frequencies of regulatory CD4+CD25+CD127− T cells correlated negatively with CD4+/CD8+ T cell ratios. Ratios of CD4+/CD8+ T cells increased significantly in patients beyond age of 45 years. And accordingly, the frequencies of regulatory CD8+CD25+ T cells were also found significantly increased in these patients. Collectively, the results suggest that regulatory CD4+ and CD8+ T cells may play distinct roles in the pathogenesis of COVID‐19. Moreover, the data indicate that NK cells might contribute to the COVID‐19 associated lymphopenia. [ABSTRACT FROM AUTHOR]

Song, M.; Wang, Q.; Wang, S., and Zhou, L., 2021. Specialization and diversification of the marine industry and marine economic growth: An example from Chinese coastal areas. Journal of Coastal Research, 37(1), 203–215. Coconut Creek (Florida), ISSN 0749-0208. With an increasing shortage of land resources and increasing pressure regarding population and environment, many countries and regions have turned their attention toward oceans. Based on the externality theory, this study innovatively examines how specialization and diversification affect marine economic growth. Based on the panel data of 11 Chinese coastal provinces from 2006 to 2014, the article use location entropy and diversification indexes to measure the specialization and diversification of marine industry. The study analyzes both linear and nonlinear relationships between specialization and diversification and marine economic growth. The empirical results show that both specialization and diversification have a positive influence on marine economic growth; however, diversification was found to play a more important role. This study specifically studied the differences in effects on regions with different industrial scales. The findings show that there are different U-shaped nonlinear relationships between specialization and diversification and marine economic growth under different industrial scales. Finally, this article offers implications of the study and corresponding policy recommendations for restructuring the marine industry in each area. [ABSTRACT FROM AUTHOR]

An important factor for successful translational stroke research is study quality. Low-quality studies are at risk of biased results and effect overestimation, as has been intensely discussed for small animal stroke research. However, little is known about the methodological rigor and quality in large animal stroke models, which are becoming more frequently used in the field. Based on research in two databases, this systematic review surveys and analyses the methodological quality in large animal stroke research. Quality analysis was based on the Stroke Therapy Academic Industry Roundtable and the Animals in Research: Reporting In Vivo Experiments guidelines. Our analysis revealed that large animal models are utilized with similar shortcomings as small animal models. Moreover, translational benefits of large animal models may be limited due to lacking implementation of important quality criteria such as randomization, allocation concealment, and blinded assessment of outcome. On the other hand, an increase of study quality over time and a positive correlation between study quality and journal impact factor were identified. Based on the obtained findings, we derive recommendations for optimal study planning, conducting, and data analysis/reporting when using large animal stroke models to fully benefit from the translational advantages offered by these models. [ABSTRACT FROM AUTHOR]

Totiviridae, a viral family of double-stranded RNA (dsRNA) viruses, contain a single dsRNA genome 4.6–7.0 kb in length. Totiviridae were initially only known to infect fungi and other eukaryotes as well as plants, but an increase in totiviruses has been detected in insects, mosquitoes, and bats. Here, we describe the isolation and characterization of a strain belonging to the family Totiviridae isolated from Culex tritaeniorhynchus in Kenli, China, in 2016. We isolated a totivirus from field-collected mosquitoes in China by cell culture in Aedes albopictus C6/36 cells, identified the virus by morphological observation and complete genome sequencing, and characterized it by phylogenetic analysis. Transmission electron microscopy identified icosahedral, non-enveloped virus particles with a mean diameter of 35–40 nm. The genome was 7612 bp in length, including two open reading frames (ORFs). ORF1 (5058 nt) encodes the capsid protein, while ORF2 (2216 nt) encodes the viral RNA-dependent RNA polymerase (RdRp). Nucleotide and amino acid homology analysis of isolate showed higher levels of sequence identity with isolate CTV_NJ2 (China, 2010) with 94.87% nucleic acid identity and 97.32% amino acid identity. The isolate was designated C. tritaeniorhynchus totivirus KL (CTV-KL). This is the first identification of a totivirus in a C. tritaeniorhynchus in northern China. Analysis of the virus's morphology, characteristic and genome organization will further enrich our understanding of the molecular and biological characteristics of dsRNA Totiviridae viruses. [ABSTRACT FROM AUTHOR]

Endocrine therapy represents a mainstay adjuvant treatment of estrogen receptor-positive (ER+) breast cancer in clinical practice with an overall survival (OS) benefit. However, the emergence of resistance is inevitable over time and is present in one-third of the ER+ breast tumors. Several mechanisms of endocrine resistance in ER+/HER2− advanced breast cancers, through ERα itself, receptor tyrosine signaling, or cell cycle pathway, have been identified to be pivotal in endocrine therapy. The epigenetic alterations also contribute to ensuring tumor cells' escape from endocrine therapies. The strategy of combined hormone therapy with targeted pharmaceutical compounds has shown an improvement of progression-free survival or OS in clinical practice, including three different classes of drugs: CDK4/6 inhibitors, selective inhibitor of PI3Kα and mTOR inhibitors. Many therapeutic targets of cell cycle pathway and cell signaling and their combination strategies have recently entered clinical trials. This review focuses on Cyclin D–CDK4/6–RB axis, PI3K pathway and HDACs. Additionally, genomic evolution is complex in tumors exposed to hormonal therapy. We highlight the genomic alterations present in ESR1 and PIK3CA genes to elucidate adaptive mechanisms of endocrine resistance, and discuss how these mutations may inform novel combinations to improve clinical outcomes in the future. [ABSTRACT FROM AUTHOR]

Upregulation of fetal hemoglobin expression can alleviate the severity of β-thalassaemia. This study aimed to investigate the effects of Oridonin (ORI, a diterpenoid compound) on γ-globin expression in human erythroid precursor cells and the potential underlying mechanisms. Erythroid precursor cells were enriched from 12 patients with β-thalassaemia by two-phase culture. The cells were then treated with different doses of ORI and the survival of erythroid precursor cells was determined. In addition, the expression levels of γ-globin and potential mechanisms were analyzed by reverse transcription-quantitative PCR, western blotting and chromatin immunoprecipitation. Treatment with 0.5 µM ORI preferably enhanced γ-globin expression and exhibited little cytotoxicity. Similar to sodium butyrate (NaB, a histone deacetylase inhibitor), ORI significantly increased p38 mitogen-activated protein kinase (MAPK) activation, γ-globin expression, histone H3 and H4 acetylation at the Gγ- and Aγ-globin promoters, and cAMP-response element binding protein 1 (CREB1) phosphorylation. These effects were significantly mitigated by treatment with SB23580, a p38 MAPK inhibitor, in erythroid precursor cells. Therefore, ORI may effectively enhance γ-globin expression by activating p38 MAPK and CREB1, leading to histone modification in γ-globin gene promoters during the maturation of erythroid precursor cells. These findings suggested that ORI may be a novel and potential therapeutic agent for the treatment of β-thalassaemia. [ABSTRACT FROM AUTHOR]

Similarity measurement is crucial for unsupervised learning and semi-supervised learning. Unsupervised methods need a similarity to do clustering. Semi-supervised algorithms need a similarity to take advantage of unlabeled data. In this paper, we develop a boosted similarity learning algorithm. The ensemble similarity is the weighted sum of a few component similarities. Each component similarity is learned form a graph G(V, E), where V = { x 1 , x 2 , ... , x n } represent the data and the edges E represent the distance (or similarity) between them. For a given graph, we propose "within graph-cluster scatter S w " and "between graph-cluster scatter S b " to analyze the discrimination of the graph. So the contributions of this paper are: (i) we develop a boosting similarity learning strategy based on a few graphs, so the proposed strategy can take advantage of a few graphs rather than only one; (ii) we propose "within graph-cluster scatter S w " and "between graph-cluster scatter S b " to measure the discrimination of a graph. Experimental results on both synthetic and public available data sets show that the proposed method outperforms the sate-of-the-arts. [ABSTRACT FROM AUTHOR]

We report a systematic review and meta-analysis of research using animal models of chemotherapy-induced peripheral neuropathy (CIPN). We systematically searched 5 online databases in September 2012 and updated the search in November 2015 using machine learning and text mining to reduce the screening for inclusion workload and improve accuracy. For each comparison, we calculated a standardised mean difference (SMD) effect size, and then combined effects in a random-effects meta-analysis. We assessed the impact of study design factors and reporting of measures to reduce risks of bias. We present power analyses for the most frequently reported behavioural tests; 337 publications were included. Most studies (84%) used male animals only. The most frequently reported outcome measure was evoked limb withdrawal in response to mechanical monofilaments. There was modest reporting of measures to reduce risks of bias. The number of animals required to obtain 80% power with a significance level of 0.05 varied substantially across behavioural tests. In this comprehensive summary of the use of animal models of CIPN, we have identified areas in which the value of preclinical CIPN studies might be increased. Using both sexes of animals in the modelling of CIPN, ensuring that outcome measures align with those most relevant in the clinic, and the animal’s pain contextualised ethology will likely improve external validity. Measures to reduce risk of bias should be employed to increase the internal validity of studies. Different outcome measures have different statistical power, and this can refine our approaches in the modelling of CIPN. [ABSTRACT FROM AUTHOR]

Key message: In Magnolia sinostellata plants grown under shading and waterlogging conditions, solar noon PFD level of 1050 ± 30 µmol·m− 2·s− 1 and running water combined treatment (L1W2) triggers changes in chlorophyll metabolism and photosynthesis, which benefits growth of this endangered species.Abstract: Magnolias are widely used as ornamental plants in cities, but they are facing extinction in the wild. It has been hypothesized that shading from trees in the upper canopy and stress from waterlogging conditions reduce their survival rates. To test this hypothesis, we grew 2-year-old Magnolia sinostellata seedlings either under natural light level (solar noon PFD of 1400 ± 28 µmol·m− 2·s− 1) and normal watering conditions (watered every 3 days) (control, CK) or reduced light levels (L1 1050 ± 30 µmol·m− 2·s− 1; L2 700 ± 27 µmol·m− 2·s− 1 and L3 490 ± 25 µmol·m− 2·s− 1) in combination with two types of waterlogging scenarios: still water treatment (W1, water level kept at 2 cm above soil surface) or running water treatment (W2, constantly irrigated via drip-irrigation). Measurements of chlorophyll content, gas exchange, and relative expression of genes involved in chlorophyll synthesis revealed that chlorophyll content, net photosynthetic rate, stomatal conductance, and transpiration rate were significantly higher in L1W2 treatment than the control or any of the other treatment examined, whereas the intercellular CO2 concentration was lower under the L1W2 treatment. The expression levels of genes involved in chlorophyll biosynthesis were higher under L1W2 than CK conditions, but lower under L1W1, L2W1, L2W2, L3W1, and L3W2 treatment. These results suggest that L1W2 condition is most suitable for chlorophyll synthesis and photosynthesis of M. sinostellata. This study provides new insights into the physiology and development of endangered plants and useful guidance for conservation efforts aimed at protecting wild magnolia species. [ABSTRACT FROM AUTHOR]

Japanese encephalitis virus (JEV) is a representative virus of the JEV serogroup in genus Flavivirus, family Flaviviridae. JEV is a mosquito-borne virus that causes Japanese encephalitis (JE), one of the most severe viral encephalitis diseases in the world. JEV is divided into five genotypes (G1–G5), and each genotype has its own distribution pattern. However, the distribution of different JEV genotypes has changed markedly in recent years. JEV G1 has replaced G3 as the dominant genotype in the traditional epidemic areas in Asia, while G3 has spread from Asia to Europe and Africa and caused domestic JE cases in Africa. G2 and G5, which were endemic in Malaysia, exhibited great geographical changes as well. G2 migrated southward and led to prevalence of JE in Australia, while G5 emerged in China and South Korea after decades of silence. Along with these changes, JE occurred in some non-traditional epidemic regions as an emerging infectious disease. The regional changes in JEV pose a great threat to human health, leading to huge disease burdens. Therefore, it is of great importance to strengthen the monitoring of JEV as well as virus genotypes, especially in non-traditional epidemic areas. [ABSTRACT FROM AUTHOR]

With the diversification of customer’s demand and the shortage of social resources, meeting diverse requirements of customers and reducing logistics costs have attracted great attention in logistics area. In this paper, we address an integrated optimization problem that combines fashion clothing assortment packing with collaborative shipping simultaneously. We formulate this problem as a mixed integer nonlinear programming model (MINLP) and then convert the proposed model into a simplified model. We use LINGO 11.0 to solve the transformed model. Numerical experiments have been conducted to verify the effectiveness and efficiency of the proposed model, and the numerical results show that the proposed model is beneficial to the fashion clothing assortment packing and collaborative shipping planning. [ABSTRACT FROM AUTHOR]

Semi-supervised boosting strategy aims at improving the performance of a given classifier with a multitude of unlabeled data. In semi-supervised boosting strategy, a similarity is needed to select unlabeled samples and then a pseudo label will be assigned to the unlabeled sample. A good similarity is helpful to assign a more proper pseudo label to unlabeled samples. Those selected samples with their pseudo labels will serve as labeled samples to train the new component classifier. So, similarity is important in semi-supervised boosting. Gaussian kernel similarity Sij=exp(−∥xi−xj∥22σ2) is used in semi-supervised boosting strategy. There are two drawbacks, first, the Euclidean distance ∥xi−xj∥2 cannot characterize the complicated relationship between the data samples; second, the parameter σ needs to set carefully. So, this paper proposes a novel adaptive similarity based on sparse representation for semi-supervised boosting. Our sparse representation is learned from a “clean” dictionary, which is a low rank matrix obtained from the sample matrix. We evaluate the proposed method on COIL20 databases. Experimental results show that: the semi-supervised boosting algorithm with sparse representation similarity outperforms the algorithm with Gaussian kernel similarity. [ABSTRACT FROM AUTHOR]

Copyright of Acta Oto-Laryngologica is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)