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Abstract Background Coronavirus disease 2019 (COVID-19) is frequntly accompanied by venous thromboembolism (VTE), and its mechanism may be related to the abnormal inflammation and immune status of COVID-19 patients. It has been proved that interleukin-6 (IL-6), ferritin and lactate dehydrogenase (LDH) may play an important role in the occurrence of VTE in COVID-19 infection. But whether they can server as predictors for VTE in COVID-19 is still unclear. In this study, we performed a systematic review and meta-analysis to compare IL-6, ferritin and LDH in VTE and non-VTE COVID-19 patients in order to shed light on the prevention and treatment of VTE. Methods Related literatures were searched in PubMed, Embase, Web of Science, Google Scholar, China National Knowledge Infrastructure (CNKI), WANGFANG. COVID-19 patients were divided into VTE group and non-VTE group. Meta-analysis was then conducted to compare levels of IL-6, ferritin and LDH between the two groups. Results We finally included and analyzed 17 literatures from January 2019 to October 2022. There was a total of 7,035 COVID-19 patients, with a weighted mean age of 60.01 years. Males accounted for 62.64% and 61.34% patients were in intensive care unit (ICU). Weighted mean difference (WMD) of IL-6, ferritin and LDH was 31.15 (95% CI: 9.82, 52.49), 257.02 (95% CI: 51.70, 462.33) and 41.79 (95% CI: -19.38, 102.96), respectively. The above results indicated that than compared with non-VTE group, VTE group had significantly higher levels of IL-6 and ferritin but similar LDH. Conclusion This systematic review and meta-analysis pointed out that elevated levels of IL-6 and ferritin were significantly possitive associated with VTE, thus could be used as biological predictive indicators of VTE among COVID-19 patients. However, no association was found between level of LDH and VTE. Therefore, close monitoring of changes in IL-6 and ferritin concentrations is of great value in assisting clinicans to rapidly identify thrombotic complications among COVID-19 patients, hence facilitating the timely effective managment. Further studies are required in terms of the clinical role of cytokines in the occurrence of VTE among COVID-19 infection, with more reliable systematic controls and interventional trials.

In light of continuous advancements in science and technology, the global economy is experiencing rapid growth. However, this growth has been accompanied by significant depletion of natural resources and environmental degradation. Consequently, there is a burgeoning global emphasis on energy conservation, emissions reduction, and sustainable development. In this study, based on the Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) databases from 1990 to 2022, a statistical analysis of energy conservation and emission reduction in alignment with Sustainable Development Goals (SDGs)-related publications was undertaken using biblimometric methods. The findings reveal that (1) In recent years, there has been a discernible increase in global research on this subject, especially since 2009, with a sustained trend of exceeding 100 publications per annum. China prominently contributing to this domain, the proportion reached 34.2%, reflecting a growing emphasis on eco-friendly development trends. (2) Due to the burgeoning significance of energy conservation and emission reduction, there has been a notable escalation in research efforts pertaining to “Energy and Fuels,” “Environmental Science” and “Green and Sustainable Science and Technology” and other related subjects. (3) Regarding the keyword analysis, “renewable energy” as the most frequently encountered term, often paired with “CO2 emissions.” This association underscores the pivotal role of renewable energy technologies in advancing green development initiatives and mitigating emissions. (4) China, United States and United Kingdom occupy central positions in terms of both paper publication volume and collaborative networks, collectively accounting for about 54.7%, and these countries are pivotal contributors to the scholarly discourse on sustainable development and environmental conservation. (5) From 1990 to 2022, the top 20 cited articles predominantly address diverse sub-goals of Sustainable Development Goal 7, with a common emphasis on enhancing energy efficiency, sustainability and renewable energy. These findings furnish valuable analytical insights for subsequent researchers investigating energy conservation and emission reduction as well as sustainable development endeavors.

Abstract In recent years, increased species extinction and habitat loss have significantly reduced biodiversity, posing a serious threat to both nature and human survival. Environmental factors strongly influence bird distribution and diversity. The potential distribution patterns and species richness offer a conservation modeling framework for policymakers to assess the effectiveness of natural protected areas (PAs) and optimize their existing ones. Very few such studies have been published that cover a large and complete taxonomic group with fine resolution at regional scale. Here, using birds as a study group, the maximum entropy model (MaxEnt) was used to analyze the pattern of bird species richness in Jiangsu Province. Using an unparalleled amount of occurrence data, we created species distribution models (SDMs) for 312 bird species to explore emerging diversity patterns at a resolution of 1 km2. The gradient of species richness is steep, decreasing sharply away from water bodies, particularly in the northern part of Jiangsu Province. The migratory status and feeding habits of birds also significantly influence the spatial distribution of avian species richness. This study reveals that the regions with high potential bird species richness are primarily distributed in three areas: the eastern coastal region, the surrounding area of the lower reaches of the Yangtze River, and the surrounding area of Taihu Lake. Compared with species richness hotspots and existing PAs, we found that the majority of hotspots are well‐protected. However, only a small portion of the regions, such as coastal areas of Sheyang County in Yancheng City, as well as some regions along the Yangtze River in Nanjing and Zhenjiang, currently have relatively weak protection. Using stacked SDMs, our study reveals effective insights into diversity patterns, directly informing conservation policies and contributing to macroecological research advancements.

ObjectiveAnticoagulation is crucial for patients hospitalized with coronavirus disease 2019 (COVID-19) due to the high risk of venous thromboembolism (VTE). However, the optimal anticoagulation regimen needs further exploration. Therefore, we evaluated the efficacy and safety of diverse anticoagulation dosage dosages for COVID-19.MethodsAn updated meta-analysis was performed to assess the effect of thromboprophylaxis (standard, intermediate, and therapeutic dose) on the incidence of VTE, mortality and major bleeding among COVID-19 patients. Literature was searched via PubMed, EMBASE, Web of Science, and China National Knowledge Infrastructure (CNKI) database. The odds ratio (OR) and 95% confidence interval (CI) were calculated for effect estimates.ResultsNineteen studies involving 25,289 participants without VTE history were included. The mean age of patients was 59.3 years old. About 50.96% were admitted to the intensive care unit. In the pooled analysis, both therapeutic-dose and intermediate-dose anticoagulation did not have a significant advantage in reducing VTE risk over standard dosage (OR = 1.09, 95% CI: 0.58–2.02, and OR = 0.89, 95% CI: 0.70–1.12, respectively). Similarly, all-cause mortality was not further decreased in either therapeutic-dose group (OR = 1.12, 95% CI: 0.75–1.67) or intermediate-dose group (OR = 1.34, 95% CI: 0.83–2.17). While the major bleeding risk was significantly elevated in the therapeutic-dose group (OR = 2.59, 95%CI: 1.87–3.57) as compared with the standard-dose regimen. Compared with intermediate dosage, therapeutic anticoagulation did not reduce consequent VTE risk (OR = 0.85, 95% CI: 0.52–1.38) and all-cause mortality (OR = 0.84, 95% CI: 0.60–1.17), but significantly increased major bleeding rate (OR = 2.42, 95% CI: 1.58–3.70). In subgroup analysis of patients older than 65 years, therapeutic anticoagulation significantly lowered the incidence of VTE in comparation comparison with standard thromboprophylaxis, however, at the cost of elevated risk of major bleeding.ConclusionOur results indicated that for most hospitalized patients with COVID-19, standard-dose prophylactic anticoagulation might be the optimal choice. For elderly patients at low risk of bleeding, therapeutic-dose anticoagulation could further reduce VTE risk and should be considered especially when there were other strong risk factors of VTE during hospital stay.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier, CRD42023388429.

Abstract Dilated cardiomyopathy (DCM) is the leading cause of heart transplantation. By microRNA (miRNA) array, a Kaposi’s sarcoma-associated herpes virus (KSHV)-encoded miRNA, kshv-miR-K12-1-5p, was detected in patients with DCM. The KSHV DNA load and kshv-miR-K12-1-5p level in plasma from 696 patients with DCM were measured and these patients were followed-up. Increased KSHV seropositivity and quantitative titers were found in the patients with DCM compared with the non-DCM group (22.0% versus 9.1%, p

Contaminants are the critical targets of food safety supervision and risk assessment. In existing research, food safety knowledge graphs are used to improve the efficiency of supervision since they supply the relationship between contaminants and foods. Entity relationship extraction is one of the crucial technologies of knowledge graph construction. However, this technology still faces the issue of single entity overlap. This means that a head entity in a text description may have multiple corresponding tail entities with different relationships. To address this issue, this work proposes a pipeline model with neural networks for multiple relations enhanced entity pairs extraction. The proposed model can predict the correct entity pairs in terms of specific relations by introducing the semantic interaction between relation identification and entity extraction. We conducted various experiments on our own dataset FC and on the open public available data set DuIE2.0. The results of experiments show our model reaches the state-of-the-art, and the case study indicates our model can correctly extract entity-relationship triplets to release the problem of single entity overlap.

BackgroundStaphylococcus aureus (S. aureus) is the most common causative agent of burn wound infection, that often leads to high morbidity and mortality. However, there is not enough knowledge about the molecular epidemiology and antimicrobial susceptibility of S. aureus isolates from burn wound infections in Fujian, China.MethodsBetween 2016 and 2021, 90 S. aureus isolates were collected from burn wound infections in Fujian, China, including 59 methicillin-resistant (MRSA) strains and 31 methicillin-susceptible (MSSA) strains. These were investigated for molecular characteristics, virulence genes, biofilms, and antimicrobial susceptibility. All the isolates were genotyped by multilocus sequence typing (MLST), spa typing, agr typing, and SCCmec typing. Conventional PCR was performed for the detection of virulence genes. Biofilm formation capacity was assessed by tissue culture plate assay (TCP). The antimicrobial susceptibility of the isolates was evaluated using the dilution method.ResultsIn total, 37 sequence types (ST) and 34 Staphylococcal protein A (spa) types (including a new type named spa-t20720) were identified based on multilocus sequence typing (MLST) and spa typing, respectively. CC8-ST239-t030-agrI-SCCmecIII (57.6%,34/59) and CC7-ST7-t091-agrI (16.1%, 5/31) represented the main clone of MRSA and MSSA isolates, respectively. Antibiotic susceptibility testing identified a significant difference in resistance rates between ST239 and non-ST239 isolates (p

Proline/P5C cycle between mitochondria and cytosol, play important roles in energy supply and ROS (Reactive oxygen species) generation in mammalian cell. Recently, in plant, proline-dependent ROS via proline/P5C cycle was proposed to be involved in hypersensitive reaction during plant response to avirulent pathogens. However, much remains to be elucidated about the regulation of proline/P5C cycle upon pathogen infection. Here, we reported the isolation and characterization of an Arabidopsis mutant proline resistance 2 (pre2), with a single dominant mutation in a single gene. Our results showed that the proline resistance phenotype of pre2 is not due to decreased intracellular proline content, when treated with exogenous proline. Upon proline treatment, pre2 showed reduced induction of PDH transcript level and enhanced induction of that of P5CDH, accompanied by lower level of mitochondrial ROS, suggesting an attenuated proline/P5C cycle activity. Proline-induced SA (Salicylic acid) signaling was also less activated in pre2, as evidenced by reduced free SA content and PR1 transcript level, compared to the WT. On the other hand, SA activation on the proline/P5C activity is to a lesser extent in pre2 than in WT. Significantly, pre2 demonstrated increased susceptibility to infection by avirulent pathogen Pst. DC3000 (avrRps4), accompanied also by lesser induced proline/P5C cycle activity by the pathogen. Our results indicated that there is a correlation between proline/P5C cycle and plant response to avirulent pathogen.

In the realm of smart agriculture technology’s rapid advancement, the integration of various sensors and Internet of Things (IoT) devices has become prevalent in the agricultural sector. Within this context, the precise identification of pests and diseases using unmanned robotic systems assumes a crucial role in ensuring food security, advancing agricultural production, and maintaining food reserves. Nevertheless, existing recognition models encounter inherent limitations such as suboptimal accuracy and excessive computational efforts when dealing with similar pests and diseases in real agricultural scenarios. Consequently, this research introduces the lightweight cross-layer aggregation neural network (LCA-Net). To address the intricate challenge of fine-grained pest identification in agricultural environments, our approach initially enhances the high-performance large-scale network through lightweight adaptation, concurrently incorporating a channel space attention mechanism. This enhancement culminates in the development of a cross-layer feature aggregation (CFA) module, meticulously engineered for seamless mobile deployment while upholding performance integrity. Furthermore, we devised the Cut-Max module, which optimizes the accuracy of crop pest and disease recognition via maximum response region pruning. Thorough experimentation on comprehensive pests and disease datasets substantiated the exceptional fine-grained performance of LCA-Net, achieving an impressive accuracy rate of 83.8%. Additional ablation experiments validated the proposed approach, showcasing a harmonious balance between performance and model parameters, rendering it suitable for practical applications in smart agricultural supervision.

BackgroundConditional survival (CS) is defined as the possibility of further survival after patients have survived for several years since diagnosis. This may be highly valuable for real-time prognostic monitoring, especially when considering individualized factors. Such prediction tools were lacking for non-metastatic triple-negative breast cancer (TNBC). Therefore, this study estimated CS and developed a novel CS-nomogram for real-time prediction of 10-year survival.MethodsWe recruited 32,836 non-metastatic TNBC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2019), who were divided into training and validation groups according to a 7:3 ratio. The Kaplan-Meier method estimated overall survival (OS), and the CS was calculated using the formula CS(y|x) =OS(y+x)/OS(x), where OS(x) and OS(y+x) were the survival of x- and (x+y)-years, respectively. The least absolute shrinkage and selection operator (LASSO) regression identified predictors to develop the CS-nomogram.ResultsCS analysis reported gradual improvement in real-time survival over time since diagnosis, with 10-year OS updated annually from an initial 69.9% to 72.8%, 78.1%, 83.0%, 87.0%, 90.3%, 93.0%, 95.0%, 97.0%, and 98.9% (after 1-9 years of survival, respectively). The LASSO regression identified age, marriage, race, T status, N status, chemotherapy, surgery, and radiotherapy as predictors of CS-nomogram development. This model had a satisfactory predictive performance with a stable 10-year time-dependent area under the curves (AUCs) between 0.75 and 0.86.ConclusionsSurvival of non-metastatic TNBC survivors improved dynamically and non-linearly with survival time. The study developed a CS-nomogram that provided more accurate prognostic data than traditional nomograms, aiding clinical decision-making and reducing patient anxiety.

The video-based commonsense captioning task aims to add multiple commonsense descriptions to video captions to understand video content better. This paper aims to consider the importance of cross-modal mapping. We propose a combined framework called Class-dependent and Cross-modal Memory Network considering SENtimental features (CCMN-SEN) for Video-based Captioning to enhance commonsense caption generation. Firstly, we develop class-dependent memory for recording the alignment between video features and text. It only allows cross-modal interactions and generation on cross-modal matrices that share the same labels. Then, to understand the sentiments conveyed in the videos and generate accurate captions, we add sentiment features to facilitate commonsense caption generation. Experiment results demonstrate that our proposed CCMN-SEN significantly outperforms the state-of-the-art methods. These results have practical significance for understanding video content better.

Abstract Introduction Hypertrophic scar (HS), a fibroproliferative disorder of the skin with some tumor‐like properties, is closely related to dysregulated inflammation. PD‐1/PD‐L1 inhibitor is a promising medication for cancer therapy as its potent functions on adaptive immune response; whether it could be a candidate for HS therapy has aroused our interest. This study aimed to explore the effect and the mechanism of BMS‐202, a PD‐1/PD‐L1 inhibitor, in HS. Methods Ten HS and adjacent normal skin tissues collected from HS patients were used to detect α‐SMA, collagen I, and PD‐L1 expression by Quantitative reverse transcription‐polymerase chain reaction and western blot (WB) analysis. Fibroblasts derived from HS tissues (HFBs) were exposed to diverse concentrations of BMS‐202, of which proliferation, migration, apoptosis, and collagen synthesis were evaluated by Cell Counting Kit‐8, wound healing, terminal deoxynucleotidyl transferase (TdT) dUTP Nick‐End labeling, and [3H]‑proline incorporation assays, respectively. The effect of BMS‐202 on α‐SMA and collagen I expression, and transforming growth factor beta 1 (TGFβ1)/Smad signaling in HFBs was also determined by WB and enzyme‐linked immunosorbent assay. Results The expression level of PD‐L1 was significantly elevated in both HS tissues and HFBs, which was positively correlated with α‐SMA and collagen I expressions. BMS‐202 exerted a significant suppression effect on the cell proliferation, migration, collagen synthesis, and α‐SMA and collagen I expression of HFBs in a concentration‐dependent way; but did not affect apoptosis. Finally, BMS‐202 could reduce the phosphorylation of ERK1/2, Smad2, and Smad3, and the TGFβ1 expression once its concentration reached 2.5 nM. Conclusion BMS‐202 effectively suppressed proliferation, migration, and extracellular matrix deposition of HFBs, potentially through the regulation of the ERK and TGFβ1/Smad signaling pathways.

Dialog systems have attracted attention as they are promising in many intelligent applications. Generating fluent and informative responses is of critical importance for dialog systems. Some recent studies introduce documents as extra knowledge to improve the performance of dialog generation. However, it is hard to understand the unstructured document and extract crucial information related to dialog history and current utterance. This leads to uninformative and inflexible responses in existing studies. To address this issue, we propose a generative model of a neural network with an attention mechanism for document-grounded multi-turn dialog. This model encodes the context of utterances that contains the given document, dialog history, and the last utterance into distributed representations via a triple-channel. Then, it introduces a hierarchical attention interaction between dialog contexts and previously generated utterances into the decoder for generating an appropriate response. We compare our model with various baselines on dataset CMU_DoG in terms of the evaluation criteria. The experimental results demonstrate the state-of-the-art performance of our model as compared to previous studies. Furthermore, the results of ablation experiments show the effectiveness of the hierarchical attention interaction and the triple channel for encoding. We also conduct human judgment to evaluate the informativeness of responses and the consistency of responses with dialog history.

The existing methods for document-level event extraction mainly face two challenges. The first challenge is effectively capturing event information that spans across sentences. The second challenge is using predefined orders to extract event arguments while disregarding the dynamic adjusting of the order according to the importance of argument roles. To address these issues, we propose a model based on graph neural networks which realizes the semantic interaction among documents, sentences, and entities. Additionally, our model adopts a dynamic argument detection strategy, extracting arguments depending on their number in correspondence with each role. The experimental results confirm the outperformance of our model, which surpasses previous methods by 7% and 1.9% in terms of an F1 score.

BackgroundEsophageal squamous cell carcinoma is the predominant subtype of esophageal cancer in China and so differs from presentations in Western countries. Common metastatic locations of esophageal cancer include the liver, lung, bone, and brain. In contrast, metastases in subcutaneous soft tissue are exceedingly rare.Case presentationWe present the experience of a 57-year-old man with a complaint of hand and leg dysfunction on the right side. He had a past medical history of esophageal squamous cell carcinoma. Further imaging workup revealed a solitary brain metastasis, thickening of the esophageal wall, swollen lymph nodes in the mediastinum, and right adrenal gland metastasis. Gamma knife radiosurgery of the brain metastasis and intensity-modulated radiotherapy of the esophagus and lymph nodes were administered. After 1.5 months, he was admitted to our hospital again, and nodules were identified in the anterior abdominal wall and left posterior chest wall. Ultrasound, CT, and radical excision of the abdominal wall mass were undertaken and revealed metastatic squamous cell carcinoma with neuroendocrine differentiation. We administered immunotherapy followed by targeted therapy. A PET/CT scan was performed to identify other organ metastases; the scan revealed multiple areas of fluorodeoxyglucose uptake and foci in the esophagus, lung, liver, bone, and right adrenal gland; and in various lymph nodes. In addition, an intensely hypermetabolic lesion was localized in the left posterior thorax.ConclusionThis case highlights the diagnosis and treatment of uncommon metastases of esophageal squamous cell carcinoma. We hope that our clinical experience provides insights into these uncommon metastases.

Multimodal Named Entity Recognition (MNER) and multimodal Relationship Extraction (MRE) play an important role in processing multimodal data and understanding entity relationships across textual and visual domains. However, irrelevant image information may introduce noise that misleads the recognition of information. Additionally, visual and semantic features originate from different modalities, and modal disparity hinders semantic alignment. Therefore, this paper proposes the Visual Description Augmentation Integration Network (VDAIN), which introduces an image description generation technique that allows semantic features generated from image descriptions to be presented in the same modality as the semantic features of textual information. This not only reduces the modal gap but also captures more accurately the high-level semantic information and underlying visual structure in the images. To filter out the modal noise, we use VDAIN to adaptively fuse visual features, semantic features of image descriptions, and textual information, thus eliminating irrelevant modal noise. The F1 score of the proposed model in this paper reaches 75.8% and 87.78% for the MNER task and 82.54% for the MRE task on the three public data sets, respectively, which are significantly better than the baseline model. The experimental results demonstrate the effectiveness of the proposed method in solving the modal noise and modal gap problems.

Background The aim of the study was to identify biomarkers that can facilitate early diagnosis and treatment of fulminant myocarditis (FM) in order to reduce mortality. Methods and Results First, the expression profiles of circulating cytokines were determined in the plasma samples from 4 patients with FM and 4 controls using human cytokine arrays. The results showed that 39 cytokines from patients with FM were changed at admission. Among them, 8 cytokines returned to normal levels at discharge, including soluble ST2 (sST2), which showed the most marked dynamic changes from disease onset to resolution. Then, in a cohort of 76 patients with FM, 57 patients with acute hemodynamic dysfunction attributable to other causes, and 56 patients with non‐FM, receiver operating characteristic curve analyses suggested that plasma sST2 level was able to differentiate FM from non‐FM or other FM‐unrelated acute heart failure more robustly N‐terminal pro‐B‐type natriuretic peptide or cardiac troponin I. Moreover, longitudinal analysis of plasma sST2 was performed in 10 patients with FM during hospitalization and 16 patients with FM during follow‐up. Finally, the diagnostic value was validated in an additional 26 patients with acute onset of unstable hemodynamics. The cutoff value of plasma sST2 for optimal diagnosis of FM was established at 58.39 ng/mL, where a sensitivity of 85.7% and specificity of 94.7% were achieved. Conclusions Elevated sST2 level was associated with mechanical stress or inflammation. Especially, sST2 might be used as a potential biomarker for the rapid diagnosis of FM, which was characterized by strong mechanical stretch stimulation and severe inflammatory response. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03268642.

Abstract Background The traditional treatment for diabetes usually requires frequent insulin injections to maintain normoglycemia, which is painful and difficult to achieve blood glucose control. Results To solve these problems, a non-invasive and painless oral delivery nanoparticle system with bioadhesive ability was developed by amphipathic 2-nitroimidazole–l-cysteine–alginate (NI–CYS–ALG) conjugates. Moreover, in order to enhance blood glucose regulation, an intelligent glucose-responsive switch in this nanoparticle system was achieved by loading with insulin and glucose oxidase (GOx) which could supply a stimulus-sensitive turnover strategy. In vitro tests illustrated that the insulin release behavior was switched “ON” in response to hyperglycemic state by GOx catalysis and “OFF” by normal glucose levels. Moreover, in vivo tests on type I diabetic rats, this system displayed a significant hypoglycemic effect, avoiding hyperglycemia and maintaining a normal range for up to 14 h after oral administration. Conclusion The stimulus-sensitive turnover strategy with bioadhesive oral delivery mode indicates a potential for the development of synthetic GR-NPs for diabetes therapy, which may provide a rational design of proteins, low molecular drugs, as well as nucleic acids, for intelligent releasing via the oral route.

General secret sharing schemes comprise a secret dealer and a number of participants. The dealer can randomly generate a secret and then distribute shares of the secret to the participants. Verifiable multi-secret sharing enables a dealer to share multiple secrets among a group of participants such that the deceptive behaviors of the dealer and the participants can be detected. However, in the absence of secure channels, few verifiable secret sharing schemes can simultaneously share multiple secrets at one time. In this paper, we present an information privacy protection and verifiable multi-secret sharing scheme with a simple structure and high security. Each participant can verify correctness of the share distribution process based on public information published by the dealer and guarantee validity of the received share to avoid offering fake information in the process of restoring the original secret. Our performance and security analysis indicate that the newly proposed scheme is more efficient and practical while maintaining the same level of security compared with similar protocols available.

A composite adsorbent was prepared by montmorillonite and biochar from peanut shell. The adsorption experiment of the Cu2+ ions from aqueous solution by the montmorillonite-biochar composite was carried out in detail. The effects of initial concentration of Cu2+ ions in aqueous solution and contact time on adsorption efficiency were studied. FTIR spectroscopy, SEM and TEM analyses, standard N2 adsorption-desorption techniques, EDS and XPS were used to evaluate the physico-chemical, textural and crystalline properties of the montmorillonite-biochar composite. Results showed that the montmorillonite-biochar composite was mesoporous material. The surface of montmorillonite-biochar composite was rough with irregular layer structure. According to the experimental data, pseudo first order kinetics model and pseudo second order kinetics model were applied. The adsorption process fits well with the pseudo second order kinetics model. The predominant process is chemisorption, which involves a sharing of electrons between the adsorbate and the surface of the adsorbent. Adsorption data were correlated well by Freundlich isotherm models and adsorption process was chemical. It is concluded that the montmorillonite-biochar composite can be used as an effective adsorbent for the removal of Cu2+ ions from aqueous solutions.

AimsLong non-coding RNAs (lncRNAs) are critical regulators of viral infection and inflammatory responses. However, the roles of lncRNAs in acute myocarditis (AM), especially fulminant myocarditis (FM), remain unclear.MethodsFM and non-fulminant myocarditis (NFM) were induced by coxsackie B3 virus (CVB3) in different mouse strains. Then, the expression profiles of the lncRNAs in the heart tissues were detected by sequencing. Finally, the patterns were analyzed by Pearson/Spearman rank correlation, Kyoto Encyclopedia of Genes and Genomes, and Cytoscape 3.7.ResultsFirst, 1,216, 983, 1,606, and 2,459 differentially expressed lncRNAs were identified in CVB3-treated A/J, C57BL/6, BALB/c, and C3H mice with myocarditis, respectively. Among them, 88 lncRNAs were commonly dysregulated in all four models. Quantitative real-time polymerase chain reaction analyses further confirmed that four out of the top six commonly dysregulated lncRNAs were upregulated in all four models. Moreover, the levels of ENSMUST00000188819, ENSMUST00000199139, and ENSMUST00000222401 were significantly elevated in the heart and spleen and correlated with the severity of cardiac inflammatory infiltration. Meanwhile, 923 FM-specific dysregulated lncRNAs were detected, among which the levels of MSTRG.26098.49, MSTRG.31307.11, MSTRG.31357.2, and MSTRG.32881.28 were highly correlated with LVEF.ConclusionExpression of lncRNAs is significantly dysregulated in acute myocarditis, which may play different roles in the progression of AM.

Abstract As an information carrier with rich semantics, images contain more sentiment than texts and audios. So, images are increasingly used by people to express their opinions and sentiments in social network. The sentiments of the images are overall and should come from different regions. So, the recognition of the sentiment regions will help to concentrate on important factors the affect the sentiments. Meanwhile, deep learning method for image sentiment classification needs simple and efficient approach for simultaneously carrying out pruning and feature selection whilst optimizing the weights. Motivated by these observations, we design a region-based convolutional neural network using group sparse regularization for image sentiment classification: R-CNNGSR. The method obtains the initial sentiment prediction model through CNN using group sparse regularization to get compact neural network, and then automatically detect the sentiment regions by combining the underlying features and sentimental features. Finally, the whole image and the sentiment region are fused to predict the overall sentiment of the images. Experiment results demonstrate that our proposed R-CNNGSR significantly outperforms the state-of-the-art methods in image sentiment classification.

Abstract Background Bile salt hydrolase plays an important role in bile acid-mediated signaling pathways, which regulate lipid absorption, glucose metabolism, and energy homeostasis. Several reports suggest that changes in the composition of bile acids are found in many diseases caused by dysbacteriosis. Results Here, we present the taxonomic identification of bile salt hydrolase (BSH) in human microbiota and elucidate the abundance and activity differences of various bacterial BSH among 11 different populations from six continents. For the first time, we revealed that bile salt hydrolase protein sequences (BSHs) are distributed in 591 intestinal bacterial strains within 117 genera in human microbiota, and 27.52% of these bacterial strains containing BSH paralogs. Significant variations are observed in BSH distribution patterns among different populations. Based on phylogenetic analysis, we reclassified these BSHs into eight phylotypes and investigated the abundance patterns of these phylotypes among different populations. From the inspection of enzyme activity among different BSH phylotypes, BSH-T3 showed the highest enzyme activity and is only found in Lactobaclillus. The phylotypes of BSH-T5 and BSH-T6 mainly from Bacteroides with high percentage of paralogs exhibit different enzyme activity and deconjugation activity. Furthermore, we found that there were significant differences between healthy individuals and patients with atherosclerosis and diabetes in some phylotypes of BSHs though the correlations were pleiotropic. Conclusion This study revealed the taxonomic and abundance profiling of BSH in human gut microbiome and provided a phylogenetic-based system to assess BSHs activity by classifying the target sequence into specific phylotype. Furthermore, the present work disclosed the variation patterns of BSHs among different populations of geographical regions and health/disease cohorts, which is essential to understand the role of BSH in the development and progression of related diseases.

A novel all-pass slot microring resonator (SMRR), intended for label-free optical biosensing based on silicon-on-insulator platforms, is proposed. The sensor consists of a bent asymmetric directional coupler and an asymmetric-slot microring waveguide. The appropriate slot width of 140 nm is identified by the three-dimensional finite-difference time-domain (3D-FDTD) method for better light–matter interaction in applications. According to numerical calculations, the SMRR sensor with a footprint of 10 µm × 10 µm has a concentration sensitivity of 725.71 pm/% for sodium chloride (NaCl) solutions. The corresponding refractive index sensitivity is 403 nm/RIU (refractive index unit), which is approximately six times greater than that of traditional microring resonator sensors. A low detection limit of 0.129% is also achieved. This SMRR is an excellent candidate for label-free optical biosensors due to its compact structure and excellent sensing capability.

The rapid development of Internet of Medical Things (IoMT) is remarkable. However, IoMT faces many problems including privacy disclosure, long delay of service orders, low retrieval efficiency of medical data, and high energy cost of fog computing. For these, this paper proposes a data privacy protection and efficient retrieval scheme for IoMT based on low-cost fog computing. First, a fog computing system is located between a cloud server and medical workers, for processing data retrieval requests of medical workers and orders for controlling medical devices. Simultaneously, it preprocesses physiological data of patients uploaded by IoMT, collates them into various data sets, and transmits them to medical institutions in this way. It makes the entire execution process of low latency and efficient. Second, multidimensional physiological data are of great value, and we use ciphertext retrieval to protect privacy of patient data in this paper. In addition, this paper uses range tree to build an index for storing physiological data vectors, and meanwhile a range retrieval method is also proposed to improve data search efficiency. Finally, bat algorithm (BA) is designed to allocate cost on a fog server group for significant energy cost reduction. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.

Based on the conjugated complex PVK: Ir(ppy)3, a green-emitting organic quasicrystal microcavity laser is demonstrated driven by optical pumping. The organic laser adopts a sandwich structure of DBR/organic gain membrane/output Ag-layer for the vertical oscillation and an octagonal quasi-crystal for in-plane light localization. The experimental results show that the single-mode lasing action is observed at 521 nm with an FWHM of 0.8 nm. The threshold of lasing is lowered to 0.181 μJ/cm2.

As an information carrier with rich semantics, image plays an increasingly important role in real-time monitoring of logistics management. Abnormal objects are typically closely related to the specific region. Detecting abnormal objects in the specific region is conducive to improving the accuracy of detection and analysis, thereby improving the level of logistics management. Motivated by these observations, we design the method called abnormal object detection in a specific region based on Mask R-convolutional neural network: A bnormal O bject D etection in S pecific R egion. In this method, the initial instance segmentation model is obtained by the traditional Mask R-convolutional neural network method, then the region overlap of the specific region is calculated and the overlapping ratio of each instance is determined, and these two parts of information are fused to predict the exceptional object. Finally, the abnormal object is restored and detected in the original image. Experimental results demonstrate that our proposed A bnormal O bject D etection in S pecific R egion can effectively identify abnormal objects in a specific region and significantly outperforms the state-of-the-art methods.

Along with the development of social media on the internet, dialogue systems are becoming more and more intelligent to meet users’ needs for communication, emotion, and social intercourse. Previous studies usually use sequence-to-sequence learning with recurrent neural networks for response generation. However, recurrent-based learning models heavily suffer from the problem of long-distance dependencies in sequences. Moreover, some models neglect crucial information in the dialogue contexts, which leads to uninformative and inflexible responses. To address these issues, we present a bichannel transformer with context encoding (BCTCE) for document-driven conversation. This conversational generator consists of a context encoder, an utterance encoder, and a decoder with attention mechanism. The encoders aim to learn the distributed representation of input texts. The multihop attention mechanism is used in BCTCE to capture the interaction between documents and dialogues. We evaluate the proposed BCTCE by both automatic evaluation and human judgment. The experimental results on the dataset CMU_DoG indicate that the proposed model yields significant improvements over the state-of-the-art baselines on most of the evaluation metrics, and the generated responses of BCTCE are more informative and more relevant to dialogues than baselines.

Aluminum (Al) toxicity is a major factor limiting crop productivity on acid soils. Soybean (Glycine max) is an important oil crop and there is great variation in Al tolerance in soybean germplasms. However, only a few Al-tolerance genes have been reported in soybean. Therefore, the purpose of this study was to identify candidate Al tolerance genes by comparative transcriptome analysis of two contrasting soybean varieties in response to Al stress. Two soybean varieties, M90-24 (M) and Pella (P), which showed significant difference in Al tolerance, were used for RNA-seq analysis. We identified a total of 354 Al-tolerance related genes, which showed up-regulated expression by Al in the Al-tolerant soybean variety M and higher transcript levels in M than P under Al stress. These genes were enriched in the Gene Ontology (GO) terms of cellular glucan metabolic process and regulation of transcription. Five out of 11 genes in the enriched GO term of cellular glucan metabolic process encode cellulose synthases, and one cellulose synthase gene (Glyma.02G205800) was identified as the key hub gene by co-expression network analysis. Furthermore, treatment of soybean roots with a cellulose biosynthesis inhibitor decreased the Al tolerance, indicating an important role of cellulose production in soybean tolerance to Al toxicity. This study provides a list of candidate genes for further investigation on Al tolerance mechanisms in soybean.

Electronic medical records (EMRs) are extremely important for patients’ treatment, doctors’ diagnoses, and medical technology development. In recent years, the distributed healthcare blockchain system has been researched for solving the information isolated island problem in centralized healthcare service systems. However, there still exists a series of important problems such as the patients’ sensitive information security, cross-institutional data sharing, medical quality, and efficiency. In this paper, we establish a lightweight privacy-preserving mechanism for a healthcare blockchain system. First, we apply an interleaving encoder to encrypt the original EMRs. This can hide the sensitive information of EMRs to protect the patient’s privacy security. Second, a ( t , n )-threshold lightweight message sharing scheme is presented. The EMRs are mapped to n different short shares, and it can be reconstructed by at least t shares. The EMR shares rather than the original EMRs are stored in the blockchain nodes. This can guarantee high security for EMR sharing and improve the data reconstruction efficiency. Third, the indexes of the stored EMR shares are employed to generate blocks that are chained together and finally form a blockchain. The authorized data users or institutions can recover an EMR by requesting at least t shares of the EMR from the blockchain nodes. In this way, the healthcare blockchain system can not only facilitate the cross-institution sharing process, but also provide proper protections for the EMRs. The security proof and analysis indicate that the proposed scheme can protect the privacy and security of patients’ medical information. The simulation results show that our proposed scheme is more efficient than similar literature in terms of energy consumption and storage space, and the healthcare blockchain system is more stable with the proposed message sharing scheme.

Stress has been demonstrated to play an important role in hair follicle function and the pathogenesis of some hair disorders. The central hypothalamic-pituitary-adrenal (HPA) axis is activated by stress stimuli, synthesizes and releases various components and eventually induces the pathogenesis and recurrence of peripheral diseases. Our aim is to compare the different responses under exposure of stress in hair follicle function among different mouse strains, and to detect the involvement of the central HPA axis after stress in hair follicle growth and melanogenesis. In this study, we exposed different mouse strains (C57BL/6, CBA/J, C3H/HeN, BALB/c and ICR) to a 21-day chronic restraint stress protocol and selected C57BL/6, CBA/J and BALB/c mice for further study because of their significant behavioral alterations. Then, we evaluated and compared the different responses and sensitivity to chronic restraint stress in hair follicle function and central HPA axis among the selected strains. The results showed that expression of POMC, CRF and GR mRNA and protein and serum levels of corticosterone were inhibited in response to stress. These findings suggested that chronic restraint stress may inhibit hair follicle growth and melanogenesis via regulating the key elements of the central HPA axis. In addition, the results revealed different mouse strains exhibit different responses in the central HPA axis and hair follicle after stress exposure. C57BL/6 might be the most sensitive strain among the three strains tested as well as an appropriate strain to study possible pathophysiological mechanisms by which the nervous system influences skin function and screen dermatological drugs suitable for psychotherapy. We believe the current study will provide some useful information for researchers who are interested in the bidirectional communication between the nervous and skin systems and the management of stress-induced cutaneous diseases.

As metabolomics is widely used in the study of disease mechanisms, an increasing number of studies have found that metabolites play an important role in the occurrence of diseases. The aim of this study is to investigate the effects and mechanisms of quercetin in high-fat-sucrose diet (HFD)-induced nonalcoholic fatty liver disease (NAFLD) development using nontargeted metabolomics. A rat model of NAFLD was established by feeding with an HFD for 30 and 50 days. The results indicated quercetin exhibited hepatoprotective activity in 30-day HFD-induced NAFLD rats by regulating fatty acid related metabolites (adrenic acid, etc.), inflammation-related metabolites (arachidonic acid, etc.), oxidative stress-related metabolites (2-hydroxybutyric acid) and other differential metabolites (citric acid, etc.). However, quercetin did not improve NAFLD in the 50-day HFD; perhaps quercetin was unable to reverse the inflammation induced by a long-term high-fat diet. These data indicate that dietary quercetin may be beneficial to NAFLD in early stages. Furthermore, combining metabolomics and experimental approaches opens avenues to study the effects and mechanisms of drugs for complex diseases.

BACKGROUND: Although the systemic administration of deferoxamine (DFO) is protective in experimental models of normal ischemic flap and diabetic wound, its effect on diabetic flap ischemia using a local injection remains unknown. OBJECTIVE: To explore the feasibility of local injection of DFO to improve the survival of ischemic random skin flaps in streptozotocin (STZ)-induced diabetic mice. METHODS: Ischemic random skin flaps were made in 125 mice. Animals were divided into the DFO-treated (n = 20), PBS-treated (n = 16) and untreated (n = 16) groups. Surviving area, vessel density, and expression of vascular endothelial growth factor (VEGF) and hypoxia-inducible factor-1α (HIF-1α) were evaluated on the seventh day after local injection. RESULTS: The viability of DFO-treated flap was significantly enhanced, with increased regional blood perfusion and capillary density compared with those in the two control groups. Fluorescence-activated cell sorting (FACS) analysis demonstrated a marked increase in systemic Flk-1+/CD11b- endothelial progenitor cells (EPCs) in DFO-treated mice. Furthermore, the expression of VEGF and HIF-1α was increased not only in diabetic flap tissue, but also in dermal fibroblasts cultured under hyperglycemic and hypoxic conditions. CONCLUSIONS: Local injection of DFO could exert preventive effects against skin flap necrosis in STZ-induced diabetic mice by elevating the expression of HIF-1α and VEGF, increased EPC mobilization, which all contributed to promote ischemic diabetic flap survival.