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BackgroundObservational researches have suggested a connection between iron deficiency anemia (IDA) and an increased likelihood of ischemic stroke (IS), yet establishing causality is challenging owing to the inherent limitations of such studies, including their vulnerability to confounding factors and the potential for reverse causation. This study employs a bidirectional two-sample Mendelian randomization (MR) approach to assess the causal linkage between IDA and IS and its subtypes.MethodsIdentifiable single nucleotide polymorphisms (SNPs) with significant links to either IDA or IS and its subtypes were employed as instrumental variables (IVs). The relationship between IDA and any IS, small vessel stroke (SVS), cardioembolic stroke (CES), and large artery stroke (LAS), was quantified using the inverse variance weighted (IVW) method. Complementary analyses utilizing MR-Egger and weighted median methods further supplemented the IVW findings. Moreover, the leave-one-out analysis, MR-Egger intercept test, MR-PRESSO global test, and Cochrane’s Q test were conducted for sensitivity analyses.ResultsThis study revealed no correlation between IDA and any IS (IVW method: OR [95% CI] = 0.977 [0.863–1.106]; p = 0.716), LAS (OR [95% CI] = 1.158 [0.771–1.740]; p = 0.479), CES (OR [95% CI] = 1.065 [0.882–1.285]; p = 0.512), or SVS (OR [95% CI] = 1.138 [0.865–1.498]; p = 0.357). Conducting a reverse MR analysis, it was determined that there is no causal connection between any IS, LAS, CES, SVS, and IDA (all p > 0.05). Sensitivity analysis indicated that heterogeneity was not significant and no evidence of horizontal pleiotropy was detected.ConclusionThis MR study suggested no causal effect of IDA on IS, LAS, CES, and SVS. Through reverse MR analyses, it was determined that IS and its subtypes did not exert a causal impact on IDA.

Background: Takotsubo syndrome, which is often induced by physical or psychological stress, is typically a cardiac syndrome with transient left ventricular dysfunction in the absence of obstructive coronary artery disease. Subarachnoid hemorrhage with typical symptoms and signs is frequently reported, whereas the incidence of subarachnoid hemorrhage with Takotsubo syndrome as the prominent manifestation without a typical headache is rarely reported. Case description: We present a rare case of a 63-year-old male patient with cough and fever as the first manifestations, accompanied by mild dizziness, headache, and mental discomfort; however, the patient was eventually diagnosed with atypical subarachnoid hemorrhage with Takotsubo syndrome. The patient underwent general anesthesia downwards stent-assisted spring coil embolization and was discharged from the hospital after postoperative treatment consisting of anti-cerebrovascular spasm, anti-platelet aggregation, and cerebrospinal fluid replacement. Conclusion: This case demonstrates the association between Takotsubo syndrome and subarachnoid hemorrhage. When patients present with unexplained pulmonary edema with mild neurologic symptoms, clinicians should be alerted to subarachnoid hemorrhage and Takotsubo syndrome.

Abstract Chemotherapy represents a major type of clinical treatment against colorectal cancer (CRC). Aberrant drug efflux mediated by transporters acts as a key approach for tumor cells to acquire chemotherapy resistance. Increasing evidence implies that tumor-associated macrophages (TAMs) play a pivotal role in both tumorigenesis and drug resistance. Nevertheless, the specific mechanism through which TAMs regulate drug efflux remains elusive. Here, we discovered that TAMs endow CRC cells with resistance to 5-fluorouracil (5-FU) treatment via a cell-cell interaction-mediated MRP1-dependent drug efflux process. Mechanistically, TAM-secreted C-C motif chemokine ligand 17 (CCL17) and CCL22, via membrane receptor CCR4, activated the PI3K/AKT pathway in CRC tumor cells. Specifically, phosphorylation of AKT inactivated IP3R and induced calcium aggregation in the ER, resulting in the activation of ATF6 and upregulation of GRP78. Accordingly, excessive GRP78 can interact with MRP1 and promote its translocation to the cell membrane, causing TAM-induced 5-FU efflux. Taken together, our results demonstrated that TAMs promote CRC chemotherapy resistance via elevating the expression of GRP78 to promote the membrane translocation of MRP1 and drug efflux, providing direct proof for TAM-induced drug resistance.

Extracting knowledge from cyber threat intelligence is essential for understanding cyber threats and implementing proactive defense measures. However, there is a lack of open datasets in the Chinese cybersecurity field that support both entity and relation extraction tasks. This paper addresses this gap by analyzing vulnerability description texts, which are standardized and knowledge-dense, to create a vulnerability knowledge ontology comprising 13 entities and 15 relations. We annotated 27,311 unique vulnerability description sentences from the China National Vulnerability Database, resulting in a dataset named BVTED for cybersecurity knowledge triple extraction tasks. BVTED contains 97,391 entities and 69,614 relations, with entities expressed in a mix of Chinese and English. To evaluate the dataset’s value, we trained five deep learning-based named entity recognition models, two relation extraction models, and two joint entity–relation extraction models on BVTED. Experimental results demonstrate that models trained on this dataset achieve excellent performance in vulnerability knowledge extraction tasks. This work enhances the extraction of cybersecurity knowledge triples from mixed Chinese and English threat intelligence corpora by providing a comprehensive ontology and a new dataset, significantly aiding in the mining, analysis and utilization of the knowledge embedded in cyber threat intelligence.

BackgroundThis overview of systematic reviews (SRs) and meta-analysis (MAs) aimed to systematically collate, appraise and synthesize evidence of probiotics for functional constipation (FC) in children.MethodsSRs/MAs of probiotics for FC in children were systematic identified by searching Cochrane Library, PubMed, Embase, and Web of science. Assessment of Multiple Systematic Reviews 2 (AMSTAR-2), Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), and Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) were unitized by two reviewers independently to assess the methodological quality, reporting quality, and quality of evidence, respectively.ResultsSeven SRs/MAs met the eligibility criteria and were included in this study. According to AMSTAR-2, a very low methodological quality assessment was given to the included SRs/MAs due to the limitations of items 2, 4 and 7. For the PRISMA statement, the overall quality of reporting was unsatisfactory due to the lack of reporting on protocol, risk of bias across studies, synthesis of results, and additional analysis. According to GRADE, the quality of evidence for outcomes was rated as very low to moderate.ConclusionsProbiotics may be beneficial in improving FC in children. Because of limitations and inconsistent conclusions, further rigorous, normative and comprehensive SRs/MAs are needed to provide robust evidence for definitive conclusions.

Cigarette smoking is an independent risk factor for lung cancer. Nicotine, as an addictive substance in tobacco and e-cigarettes, is known to promote tumor progression and metastasis despite being a non-carcinogen. As a tumor suppressor gene, JWA is widely involved in the inhibition of tumor growth and metastasis and the maintenance of cellular homeostasis, including in non-small cell lung cancer (NSCLC). However, the role of JWA in nicotine-induced tumor progression remains unclear. Here, we reported for the first time that JWA was significantly downregulated in smoking-related lung cancer and associated with overall survival. Nicotine exposure reduced JWA expression in a dose-dependent manner. Gene Set Enrichment Analysis (GSEA) analysis showed the tumor stemness pathway was enriched in smoking-related lung cancer, and JWA was negatively associated with stemness molecules CD44, SOX2, and CD133. JWA also inhibited nicotine-enhanced colony formation, spheroid formation, and EDU incorporation in lung cancer cells. Mechanically, nicotine downregulated JWA expression via the CHRNA5-mediated AKT pathway. Lower JWA expression enhanced CD44 expression through inhibition of ubiquitination-mediated degradation of Specificity Protein 1 (SP1). The in vivo data indicated that JAC4 through the JWA/SP1/CD44 axis inhibited nicotine-triggered lung cancer progression and stemness. In conclusion, JWA via down-regulating CD44 inhibited nicotine-triggered lung cancer cell stemness and progression. Our study may provide new insights to develop JAC4 for the therapy of nicotine-related cancers.

Tumor vascular normalization prevents tumor cells from breaking through the basement membrane and entering the vasculature, thereby inhibiting metastasis initiation. In this study, we report that the antitumor peptide JP1 regulated mitochondrial metabolic reprogramming through AMPK/FOXO3a/UQCRC2 signaling, which improved the tumor microenvironment hypoxia. The oxygen-rich tumor microenvironment inhibited the secretion of IL-8 by tumor cells, thereby promoting tumor vascular normalization. The normalized vasculature resulted in mature and regular blood vessels, which made the tumor microenvironment form a benign feedback loop consisting of vascular normalization, sufficient perfusion, and an oxygen-rich microenvironment, prevented tumor cells from entering the vasculature, and inhibited metastasis initiation. Moreover, the combined therapy of JP1 and paclitaxel maintained a certain vascular density in the tumor and promoted tumor vascular normalization, increasing the delivery of oxygen and drugs and enhancing the antitumor effect. Collectively, our work highlights the antitumor peptide JP1 as an inhibitor of metastasis initiation and its mechanism of action.

Ningnanmycin is a widely used antibiotic in agricultural production that effectively controls fungal and viral diseases in tea trees and chrysanthemums. The polarity characteristic of ningnanmycin has posed limitations on the development of robust detection methods, thereby hindering effective monitoring and control measures. By combining cation exchange solid phase extraction (SPE) with hydrophilic interaction chromatography tandem mass spectrometry (HILIC-MS/MS), we have effectively tackled the issue pertaining to the separation and retention of ningnanmycin. The average recoveries of ningnanmycin in green tea, black tea, and chrysanthemum were 77.3–82.0%, 80.1–81.5%, and 74.0–80.0%, respectively. The intraday and interday relative standard deviations (RSDs) were below and equal to 7.7%. Good linearity was observed in the concentration range of 1–1000 μg/L (R2 > 0.998). The limits of detection (LODs) ranged from 1.1 μg/kg to 7.1 μg/kg, and the limits of quantification (LOQs) ranged from 3.6 μg/kg to 23.7 μg/kg for ningnanmycin. These results indicate the good accuracy, repeatability, reproducibility, and sensitivity of the method. It is suitable for detecting ningnanmycin in tea and chrysanthemum.

Abstract Background Pseudohypoparathyroidism (PHP) is a series of diseases related to pathological changes and neurocognitive and endocrine abnormalities, mainly due to the GNAS mutation on chromosome 20q13.2, which weakens receptor‐mediated hormone signal transduction. Considering its complex genetic and epigenetic characteristics, GNAS may produce complex clinical phenotypes in families or sporadic cases. This study presented a case of familial PHP caused by a deletion mutation in the 20q13.2 region. Methods and Results The proband and her second daughter had PHP, and the proband's mother had pseudo‐PHP. Whole‐genome sequencing revealed that the proband had an 849.81 kb deletion spanning GNAS near the maternal 20q13.2 chromosome. Multiplex ligation‐dependent probe amplification methylation analysis indicated that the proband as well as her mother and second daughter had seemingly abnormal GNAS methylation. This is different from the phenotype (feeding difficulty, slow growth, and special facial features) of previously reported cases with the deletion of fragments near the 20q13.2 chromosome. Conclusions This report demonstrated the variability of 20q13.2 deletion phenotypes and the clinical importance of using multiple molecular genetic detection methods.

Abstract Triple negative breast cancer (TNBC) is a type of breast cancer with poor prognosis, and has no ideal therapeutic target and ideal medicine. Downregulation of JWA is closely related to the poor overall survival in many cancers including TNBC. In this study, we reported at the first time that JWA gene activating compound 1 (JAC1) inhibited the proliferation of TNBC in vitro and in vivo experimental models. JAC1 specifically bound to YY1 and eliminated its transcriptional inhibition of JWA gene. The rescued JWA induced G1 phase arrest and apoptosis in TNBC cells through the p38 MAPK signaling pathway. JAC1 also promoted ubiquitination and degradation of YY1. In addition, JAC1 disrupted the interaction between YY1 and HSF1, and suppressed the oncogenic role of HSF1 in TNBC through p-Akt signaling pathway. In conclusion, JAC1 suppressed the proliferation of TNBC through the JWA/P38 MAPK signaling and YY1/HSF1/p-Akt signaling. JAC1 maybe a potential therapeutic agent for TNBC.

While realizing global coverage, random access, on-demand service, security and credibility, the space-integrated-ground information network(SGIN) is confronted with more complex and variable security threats.Compared with the internet, satellite network has a lot of diff erences, such as highly dynamic changes of topology, transfer protocol and data format.In terms of these questions, it was necessary to integrated the rule of satellite network attacks into the rules of APT attacks, developed the cybersecurity knowledge graph and the attack rules library to analyzed the attacks of the SGIN.The pattern of attack rules was expressed based on time-space data model, which was used to completed the attack chain when there were non-continuous missed data or false positives data.The attack analysis framework proposed in this paper could accurately perceived the security status of the SGIN in real time , and provided decision support for the SGIN.

Abstract The overexpression of HER2 is associated with a malignant proliferation of breast cancer. In this study, we developed a non-cytotoxic JWA gene activating compound 1 (JAC1) to inhibit the proliferation of HER2-positive breast cancer cells in vitro and in vivo experimental models. JAC1 increased the ubiquitination of HER2 at the K716 site through the E3 ubiquitin ligase SMURF1 which was due to the decreased expression of NEDD4, the E3 ubiquitin ligase of SMURF1. In conclusion, JAC1 suppresses the proliferation of HER2-positive breast cancer cells through the JWA triggered HER2 ubiquitination signaling. JAC1 may serve as a potential therapeutic agent for HER2-positive breast cancer.

In order to solve the problem that the existing graph attention mechanism tends to cause attention distribution to certain relations with high frequency when performing link prediction related tasks, a new link prediction model based on hierarchical attention mechanism was proposed.In the link prediction task, a hierarchical attention mechanism was designed to give different attention to the relationships of different relationship types connected to a given entity in the knowledge graph according to the relationship in the prediction task.While the characteristics of multi-hop neighbor entities were pay attention to, the relationship characteristics was pay more attention to find the relationship type that matches the target relationship.Through comparison experiments with the mainstream models on multiple benchmark data sets, the results show that the performance of the model is better than the mainstream models and has good robustness.

Lung adenocarcinoma (LUAD) is the most common lung cancer, with high mortality. As a tumor-suppressor gene, JWA plays an important role in blocking pan-tumor progression. JAC4, a small molecular-compound agonist, transcriptionally activates JWA expression both in vivo and in vitro. However, the direct target and the anticancer mechanism of JAC4 in LUAD have not been elucidated. Public transcriptome and proteome data sets were used to analyze the relationship between JWA expression and patient survival in LUAD. The anticancer activities of JAC4 were determined through in vitro and in vivo assays. The molecular mechanism of JAC4 was assessed by Western blot, quantitative real-time PCR (qRT-PCR), immunofluorescence (IF), ubiquitination assay, co-immunoprecipitation, and mass spectrometry (MS). Cellular thermal shift and molecule-docking assays were used for confirmation of the interactions between JAC4/CTBP1 and AMPK/NEDD4L. JWA was downregulated in LUAD tissues. Higher expression of JWA was associated with a better prognosis of LUAD. JAC4 inhibited LUAD cell proliferation and migration in both in-vitro and in-vivo models. Mechanistically, JAC4 increased the stability of NEDD4L through AMPK-mediated phosphorylation at Thr367. The WW domain of NEDD4L, an E3 ubiquitin ligase, interacted with EGFR, thus promoting ubiquitination at K716 and the subsequent degradation of EGFR. Importantly, the combination of JAC4 and AZD9191 synergistically inhibited the growth and metastasis of EGFR-mutant lung cancer in both subcutaneous and orthotopic NSCLC xenografts. Furthermore, direct binding of JAC4 to CTBP1 blocked nuclear translocation of CTBP1 and then removed its transcriptional suppression on the JWA gene. The small-molecule JWA agonist JAC4 plays a therapeutic role in EGFR-driven LUAD growth and metastasis through the CTBP1-mediated JWA/AMPK/NEDD4L/EGFR axis.

Parkinson’s disease (PD) is the fastest-growing neurodegeneration disease, characterized typically by a progressive loss of dopaminergic neurons in the substantia nigra, and there are no effective therapeutic agents to cure PD. Rotenone (Rot) is a common and widely used pesticide which can directly inhibit mitochondrial complex I, leading to a loss of dopaminergic neurons. Our previous studies proved that the JWA gene (arl6ip5) may play a prominent role in resisting aging, oxidative stress and inflammation, and JWA knockout in astrocytes increases the susceptibility of mice to 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD. JWA-activating compound 4 (JAC4) is a small-molecule activator of the JWA gene, but its role in and mechanism against PD have not yet been clarified. In the present study, we showed that the JWA expression level is strongly related to tyrosine hydroxylase (TH) in different growth periods of mice. Additionally, we constructed models with Rot in vivo and in vitro to observe the neuroprotective effects of JAC4. Our results demonstrated that JAC4 prophylactic intervention improved motor dysfunction and dopaminergic neuron loss in mice. Mechanistically, JAC4 reduced oxidative stress damage by reversing mitochondrial complex I damage, reducing nuclear factor kappa-B (NF-κB) translocation and repressing nucleotide-binding domain, leucine-rich-containing family and pyrin domain-containing-3 (NLRP3) inflammasome activation. Overall, our results provide proof that JAC4 could serve as a novel effective agent for PD prevention.

The females and males of dioecious plants have evolved sex-specific characteristics in terms of their morphological and physiological properties. However, the differentiation of phyllosphere microorganism of dioecious plants between parents and hybrid offspring remain largely unexplored. Here, the phyllosphere bacterial and fungal community diversity and composition of female (Populus nigra ‘DH5’ (PNDH5)), male (P. simonii ‘DH4’ (PSDH4)), and the hybrid offspring (P. simonii × P. nigra ‘DH1’ (PSPNDH1), P. simonii × P. nigra ‘DH2’ (PSPNDH2), P. simonii × P. nigra ‘DH3’ (PSPNDH3)) were investigated using 16S rDNA/ITS rDNA gene-based Illumina NovaSeq 6000 sequencing. There was considerable variation of plant height, diameter at breast height, leaf area, length of petioles, leaf moisture content, and starch among different samples, and PSDH2 owned the highest plant height, diameter at breast height, and length of petioles. No distinct differences of phyllosphere bacterial community diversity were observed among PSDH4, PNDH5, PSPNDH1, PSPNDH2, and PSPNDH3; while, PSPNDH2 owned the highest fungal Pielou_e index, Shannon index, and Simpson index. Firmicutes and Ascomycota were the predominant phyllosphere bacterial and fungal community at the phylum level, respectively. Bacilli and Gammaproteobacteria were the two most dominant bacterial classes regardless of parent and the hybrid offspring. The predominant phyllosphere fungal community was Dothideomycetes at the class level. The NMDS demonstrated that phyllosphere microbial community obviously differed between parents and offspring, while the phyllosphere microbial community presented some similarities under different hybrid progeny. Also, leaf characteristics contributed to the differentiation of phyllosphere bacterial and fungal communities between parents and hybrid offspring. These results highlighted the discrimination of phyllosphere microorganisms on parent and hybrid offspring, which provided clues to potential host-related species in the phyllosphere environment.

Existing de-anonymization technologies are mainly based on the network structure.To learn and express network structure is the key step of de-anonymization.The purpose of the user identity linkage is to detect the same user from different social networking platforms.DeepLink is a cross-social network user alignment technology.It learns the structural of the social networks and align anchor nodes through deep neural networks.DeepLink was used to identify de-anonymization social networks,and the results outperforms the traditional methods.

Breast cancer is the most frequently diagnosed malignant tumor in women. Foxtail millet is an important cereal food and exhibits a wide range of nutrition. We previously extracted total polyphenols composed of 12 compounds from foxtail millet bran (named BPIS). In this study, we find that BPIS inhibits the proliferation of breast cancer cells by restraining the cellular membrane synthesis via suppressing the conversion of saturated fatty acids (SFA) to monounsaturated fatty acids (MUFA). Mechanically, BPIS decreases the glucose regulated protein 78 (GRP78) level, which further inhibits the expression of SREBP-1 and downstream-target SCD1. Combining molecular docking, MTT screening and in vivo model, we ultimately identify that vitexin and syringic acid are the main active ingredients. Our data showed that the combination of vitexin and syringic acid suppress the proliferation of breast cancer cells, which support further study of BPIS as a new dietary assistant strategy to improve breast cancer.

Entity attribute extraction, which converts chaotic text data to structured knowledge, plays an important role in natural language processing (NLP). Many previous studies proved that the representation of text has a significant impact on the results of attribute extraction. In this paper, we propose a novel model to obtain the discriminative representation of sentences by applying the Siamese architecture. Specifically, we simultaneously input two variable-length sentences in the training stage of our model, namely, the main sentence and its similar or dissimilar partner. This scheme of pair is beneficial for entity attribute extraction. First, the entity attribute extraction community suffers the insufficient but expensive labeled data, the two input sets produce much more samples for the representation learning and can be treated as a useful data augmentation method. More importantly, the co-learning by the Siamese architecture achieves more interesting embedding than the separate way, since the informative relation between the focused sentence and its partner help the representation learning to explore more essential semantics and keep stable to the variation of wording and syntax of a sentence. The experiments on the Wikipedia data show that our model takes advantage of the Siamese architecture for sentence embedding and achieves significant improvements on attribute extraction as compared with baselines. To the best of our knowledge, we are the first to introduce the Siamese network into the person entity attribute extraction, which we proved to achieve the state of the art.

Entity linking refers to the task of aligning mentions of entities in the text to their corresponding entries in a specific knowledge base, which is of great significance for many natural language process applications such as semantic text understanding and knowledge fusion. The pivotal of this problem is how to make effective use of contextual information to disambiguate mentions. Moreover, it has been observed that, in most cases, mention has similar or even identical strings to the entity it refers to. To prevent the model from linking mentions to entities with similar strings rather than the semantically similar ones, in this paper, we introduce the advanced language representation model called BERT (Bidirectional Encoder Representations from Transformers) and design a hard negative samples mining strategy to fine-tune it accordingly. Based on the learned features, we obtain the valid entity through computing the similarity between the textual clues of mentions and the entity candidates in the knowledge base. The proposed hard negative samples mining strategy benefits entity linking from the larger, more expressive pre-trained representations of BERT with limited training time and computing sources. To the best of our knowledge, we are the first to equip entity linking task with the powerful pre-trained general language model by deliberately tackling its potential shortcoming of learning literally, and the experiments on the standard benchmark datasets show that the proposed model yields state-of-the-art results.

WaDang recognition can provide valuable information for digital protection of cultural relics. However, WaDang recognition is challenging due to a great number of variations, such as different characters, different background pattern, and different scale and rotation. In this paper, we try to solve these problems by using hand-crafted and deep features and integrate them into multiple instance learning, in which the features of instances are divided into two parts and their similarity is computed by multiple kernel learning. The experiments on our collected WaDang images, MUSK, and COREL datasets show that the proposed algorithm is effective and its performance is compared with other state-of-art algorithms.

In this paper, we refer to a real case study of an industrial partner recently committed to its project on the design of a multi-unit and multi-product manufacturing system. Although the considered problem refers to an actual complex manufacturing system, it can be theoretically classified as a union of two key problems that need to be solved during the transfer line design stage: the transfer line balancing problem (TLBP) and the buffer allocation problem (BAP). As two closely related problems, TLBP and BAP usually have similar optimizing directions and share the same purpose: finding a balance between the performance of the transfer line system as well as minimizing investment costs. These problems are usually solved sequentially, but this leads to solutions close to a local optimum in the solution space and not to the global optimum of the overall problem. This paper presents a multi-objective optimization for concurrently solving transfer line balancing and buffer allocation problems. The new approach is based on a combination of evolutionary and heuristic-based algorithms and takes into account the uncertainty of market demand. To validate the proposed approach, an industrial case study in a multi-unit manufacturing system producing multiple products (four engine blocks) is discussed.

With the rapid development of artificial intelligence, Cybernetics, and other High-tech subject technology, robots have been made and used in increasing fields. And studies on robots have attracted growing research interests from different communities. The knowledge graph can act as the brain of a robot and provide intelligence, to support the interaction between the robot and the human beings. Although the large-scale knowledge graphs contain a large amount of information, they are still incomplete compared with real-world knowledge. Most existing methods for knowledge graph completion focus on entity representation learning. However, the importance of relation representation learning is ignored, as well as the cross-interaction between entities and relations. In this paper, we propose an encoder-decoder model which embeds the interaction between entities and relations, and adds a gate mechanism to control the attention mechanism. Experimental results show that our method achieves better link prediction performance than state-of-the-art embedding models on two benchmark datasets, WN18RR and FB15k-237.

With the rapid popularity of agent technology, a public opinion early warning agent has attracted wide attention. Furthermore, a deep learning model can make the agent more automatic and efficient. Therefore, for the agency of a public opinion early warning task, the deep learning model is very suitable for completing tasks such as popularity prediction or emergency outbreak. In this context, improving the ability to automatically analyze and predict the virality of information cascades is one of the tasks that deep learning model approaches address. However, most of the existing studies sought to address this task by analyzing cascade underlying network structure. Recent studies proposed cascade virality prediction for agnostic-networks (without network structure), but did not consider the fusion of more effective features. In this paper, we propose an innovative cascade virus prediction model named CasWarn. It can be quickly deployed in intelligent agents to effectively predict the virality of public opinion information for different industries. Inspired by the agnostic-network model, this model extracts the key features (independent of the underlying network structure) of an information cascade, including dissemination scale, emotional polarity ratio, and semantic evolution. We use two improved neural network frameworks to embed these features, and then apply the classification task to predict the cascade virality. We conduct comprehensive experiments on two large social network datasets. Furthermore, the experimental results prove that CasWarn can make timely and effective cascade virality predictions and verify that each feature model of CasWarn is beneficial to improve performance.

Radiation-induced intestinal injury is one of the major side effects in patients receiving radiation therapy. There is no specific treatment for radiation-induced enteritis in the clinic. We synthesized a compound, named JAC4, which is an agonist and can increase JWA protein expression. JWA has been shown to reduce oxidative stress, DNA damage, anti-apoptosis, and anti-inflammatory; in addition, the small intestine epithelium showed dysplasia in JWA knockout mice. We hypothesized that JAC4 might exert a protective effect against radiation-induced intestinal damage. Herein, X-ray radiation models were built both in mice and in intestinal crypt epithelial cells (IEC-6). C57BL/6J mice were treated with JAC4 by gavage before abdominal irradiation (ABI); the data showed that JAC4 significantly reduced radiation-induced intestinal mucosal damage and increased the survival rate. In addition, radiation-induced oxidative stress damage and systemic inflammatory response were also mitigated by JAC4 treatment. Moreover, JAC4 treatment alleviated DNA damage, decreased cell apoptosis, and maintained intestinal epithelial cell proliferation in mice. In vitro data showed that JAC4 treatment significantly inhibited ROS formation and cell apoptosis. Importantly, all the above protective effects of JAC4 on X-ray radiation-triggered intestinal injury were no longer determined in the intestinal epithelium of JWA knockout mice. Therefore, our results provide the first evidence that JAC4 protects the intestine from radiation-induced enteritis through JWA-mediated anti-oxidation/inflammation signaling.

Abstract Background Microbial fermentation has been widely applied in traditional Chinese medicine (TCM) for thousands of years in China. Various beneficial effects of fermentation for applications in TCM or herbals have been reported, such as enhanced anti-ovarian cancer, antioxidative activity, and neuroprotective effects. Ge-Gen-Qin-Lian decoction (GQD), a classic TCM formula, has been used to treat type 2 diabetes mellitus in China. In this study, GQD was fermented with Saccharomyces cerevisiae, and the antidiabetic activities and overall chemical profiles of raw and fermented GQD (FGQD) were systematically compared. Methods First, the antidiabetic effects of GQD and FGQD on high-fat diet and streptozotocin (STZ)-induced diabetic rats were compared. Then, high-performance liquid chromatography Q Exactive MS was applied for rapid characterization of the chemical components of GQD. Additionally, we proposed an integrated chromatographic technique based untargeted metabolomics identifying differential chemical markers between GQD and FGQD and targeted analysis determining the fermenting-induced quantitative variation tendencies of chemical marker strategy for overall chemical profiling of raw and fermented GQD. Results Both GQD and FGQD displayed effects against HFD and STZ-induced diabetes, and FGQD showed a better recovery trend associated with profound changes in the serum lipoprotein profile and body weight gain. In addition, 133 compounds were characterized from GQD. It was demonstrated that the integrated strategy holistically illuminated 30 chemical markers contributed to the separation of GQD and FGQD, and further elucidated the fermenting-induced chemical transformation mechanisms and inherent chemical connections of secondary metabolites. Although there were no new secondary metabolites in FGQD compared with GQD, the amounts of secondary metabolites, which were mostly deglycosylated, were redistributed in FGQD. Conclusion The anti-diabetic activities of GQD could be improved by applying fermentation technology. Moreover, the proposed strategy could serve as a powerful tool for systematically exploring the chemical profiles of raw and fermented formulas.

Cyberattack forms are complex and varied, and the detection and prediction of dynamic types of attack are always challenging tasks. Research on knowledge graphs is becoming increasingly mature in many fields. At present, it is very significant that certain scholars have combined the concept of the knowledge graph with cybersecurity in order to construct a cybersecurity knowledge base. This paper presents a cybersecurity knowledge base and deduction rules based on a quintuple model. Using machine learning, we extract entities and build ontology to obtain a cybersecurity knowledge base. New rules are then deduced by calculating formulas and using the path-ranking algorithm. The Stanford named entity recognizer (NER) is also used to train an extractor to extract useful information. Experimental results show that the Stanford NER provides many features and the useGazettes parameter may be used to train a recognizer in the cybersecurity domain in preparation for future work. Keywords: Cybersecurity, Knowledge graph, Knowledge deduction

Tumor invasion/metastasis is still the major cause of death in cancer patients. Membrane type-1 matrix metalloproteinase (MT1-MMP) is directly related to tumor invasion/metastasis. To accurately and quickly distinguish the risk of invasion/metastasis of primary tumor cells, it is urgent to develop a simple and precise quantitative method to distinguish the expression level of MT1-MMP. In this work, we have constructed red fluorescent Au clusters with peroxidase-like properties that could specifically bind to MT1-MMP on human cervical cancer cells. After MT1-MMP was labelled with Au clusters, we could visually see red fluorescence of MT1-MMP on cervical cancer cells via fluorescence microscopy and catalytic color imaging using an ordinary optical microscope. The constructed Au clusters contained 26 Au atoms; thus, the amount of MT1-MMP on cervical cancer cells could be accurately quantified using inductively coupled plasma mass spectrometry (ICP-MS). More importantly, the invasion/metastasis capabilities of the cervical cancer Siha, Caski and Hela cells with different MT1-MMP amounts could be accurately distinguished by fluorescence/catalysis qualitative imaging and ICP-MS quantitative analysis. This method of qualitative/quantitative analysis of tumor-associated proteins on cancer cells has great potential for accurately diagnosing aggressive tumor cells and assessment of their invasion/metastasis risk.

Predicting the popularity of online content is an important task for content recommendation, social influence prediction and so on. Recent deep learning models generally utilize graph neural networks to model the complex relationship between information cascade graph and future popularity, and have shown better prediction results compared with traditional methods. However, existing models adopt simple graph pooling strategies, e.g., summation or average, which prone to generate inefficient cascade graph representation and lead to unsatisfactory prediction results. Meanwhile, they often overlook the temporal information in the diffusion process which has been proved to be a salient predictor for popularity prediction. To focus attention on the important users and exclude noises caused by other less relevant users when generating cascade graph representation, we learn the importance coefficient of users and adopt sample mechanism in graph pooling process. In order to capture the temporal features in the diffusion process, we incorporate the inter-infection duration time information into our model by using LSTM neural network. The results show that temporal information rather than cascade graph information is a better predictor for popularity. The experimental results on real datasets show that our model significantly improves the prediction accuracy compared with other state-of-the-art methods.

Objective: We aimed to investigate Th22 cells and their association with Th17 and Treg cells in the etiology of severe preeclampsia (sPE). Methods: Thirty sPE patients and 30 healthy pregnant women were recruited in this study. The percentages of Th17, Th22, and regulatory T cells (Tregs) in the peripheral blood were measured by flow cytometry. ELISA was used to measure the plasma concentrations of interleukin (IL)-17, IL-22, and IL-10. Results: The percentages of Th17 and Th22 cells and the plasma concentrations of IL-17 and IL-22 were significantly increased in sPE patients along with a decreased percentage of Treg cells and a decreased plasma IL-10 concentration. There was a positive correlation between the levels of Th22 cells and Th17 cells in sPE patients. Moreover, a positive correlation was found between plasma IL-22 concentration and the percentage of Th22 cells in sPE patients. Conclusions: Increased circulating Th22 cells, which were correlated with Th17 cells, were observed in patients with sPE. The immune imbalance between T helper (Th) cells may contribute to the pathogenesis of sPE.

Recently, Li et al. proposed a novel smart card and dynamic ID-based remote user authentication scheme for multi-server environments. They claimed that their scheme can resist several types of attacks. However, through careful analysis, we find that Li et al.'s scheme is vulnerable to stolen smart card and off-line dictionary attacks, replay attacks, impersonation attacks and server spoofing attacks. By analyzing other similar schemes, we find that a certain type of dynamic ID-based multi-server authentication scheme in which only hash functions are used and whereby no registration center participates in the authentication and session key agreement phase faces difficulties in providing perfectly efficient and secure authentication. To compensate for these shortcomings, we propose a novel dynamic ID-based remote user authentication scheme for multi-server environments based on pairing and self-certified public keys. Security and performance analyses show that the proposed scheme is secure against various attacks and has many excellent features.

As a subject area of symmetry, multiple instance learning (MIL) is a special form of a weakly supervised learning problem where the label is related to the bag, not the instances contained in it. The difficulty of MIL lies in the incomplete label information of instances. To resolve this problem, in this paper, we propose a novel diverse density (DD) and multiple part similarity combination method for multiple instance learning, named MILDMS. First, we model the target concepts optimization with a DD function constraint on positive and negative instance space, which can greatly improve the robustness to label noise problem. Next, we combine the positive and negative instances in the bag (generated by hand-crafted and convolutional neural network features) with multiple part similarities to construct an MIL kernel. We evaluate the proposed approach on the MUSK dataset, whose results MUSK1 (91.9%) and MUSK2 (92.2%) show our method is comparable to other MIL algorithms. To further demonstrate generality, we also present experimental results on the PASCAL VOC 2007 and 2012 (46.5% and 42.2%) and COREL (78.6%) that significantly outperforms the state-of-the-art algorithms including deep MIL and other non-deep MIL algorithms.

Exercise has been proposed for the treatment of traumatic brain injury (TBI). However, the proper intensity of exercise in the early phase following a severe TBI is largely unknown. To compare two different treadmill exercise intensities on the cognitive function following a severe TBI in its early phase, rats experienced a controlled cortical impact (CCI) and were forced to treadmill exercise for 14 days. The results revealed that the rats in the low intensity exercise group had a shorter latency to locate a platform and a significantly better improvement in spatial memory in the Morris water maze (MWM) compared to the control group (p < 0.05). The high intensity exercise group showed a longer latency and a mild improvement in spatial memory compared to the control group rats in the MWM; however, this difference was not statistically significant (p > 0.05). The brain-derived neurotrophic factor (BDNF) and p-CREB protein levels in the contralateral hippocampus were increased significantly in the low intensity exercise group. Our results suggest that 2 weeks of low intensity of treadmill exercise is beneficial for improving cognitive function and increasing hippocampal BDNF expression after a severe TBI in its early phase.

Renal interstitial fibrosis is a common pathological process in the progression of kidney disease. A nuclear magnetic resonance (NMR) based metabolomic approach was used to analyze the kidney tissues of rats with renal interstitial fibrosis (RIF), induced by unilateral ureteral obstruction (UUO). The combination of a variety of statistical methods were used to screen out 14 significantly changed potential metabolites, which are related with multiple biochemical processes including amino acid metabolism, adenine metabolism, energy metabolism, osmolyte change and induced oxidative stress. The exploration of the contralateral kidneys enhanced the understanding of the disease, which was also supported by serum biochemistry and kidney histopathology results. In addition, the pathological parameters (clinical chemistry, histological and immunohistochemistry results) were correlated with the significantly changed differential metabolites related with RIF. This study showed that targeted tissue metabolomic analysis can be used as a useful tool to understand the mechanism of the disease and provide a novel insight in the pathogenesis of RIF.

The big data security problems during the process of data production,storage,use,transmission,sharing and destruction have been explored.Firstly,the development strategies of big data security worldwide were summarized.Second,from the perspective of the whole life cycle management,the current technical research and the progress of big data security protection were introduced by focusing on its collection,storage,transport,use and open sharing,destruction and management strategy.Finally,some new problems that need to be solved in the future research of big data security were discussed.

In network security, how to use efficient response methods against cascading failures of complex networks is very important. In this paper, concerned with the highest-load attack (HL) and random attack (RA) on one edge, we define five kinds of weighting strategies to assign the external resources for recovering the edges from cascading failures in heterogeneous scale-free (SF) networks. The influence of external resources, the tolerance parameter, and the different weighting strategies on SF networks against cascading failures is investigated carefully. We find that, under HL attack, the fourth kind of weighting method can more effectively improve the integral robustness of SF networks, simultaneously control the spreading velocity, and control the outburst of cascading failures in SF networks than other methods. Moreover, the third method is optimal if we only knew the local structure of SF networks and the uniform assignment is the worst. The simulations of the real-world autonomous system in, Internet have also supported our findings. The results are useful for using efficient response strategy against the emergent accidents and controlling the cascading failures in the real-world networks.

Privacy disclosure issue becomes more and more serious due to big data analysis.Privacy-preserving techniques should be conductive to the big data applications while preserving data privacy.Since big data has the characteristics of huge scale,numerous sources and dynamic update,most traditional privacy preserving technologies are not suitable any more.Therefore,the concept of privacy and life cycle protection model of big data era were introduced firstly.Technical state of big data privacy preservation was elaborated from the points of view of four stages in big data life cycle,i.e.data publishing,storage,analysis and use.The relative merits and scope of application of each technology were investigated as well.Finally,some important direction and tendency of privacy preservation technologies for big data were suggested.

Our previous studies indicated that JWA plays an important role in DNA damage repair, cell migration, and regulation of MAPKs. In this study, we investigated the role of JWA in chemical carcinogenesis using conditional JWA knockout (JWA(Δ2/Δ2)) mice and two-stage model of skin carcinogenesis. Our results indicated that JWA(Δ2/Δ2) mice were resistant to the development of skin papillomas initiated by 7, 12-dimethylbenz(a)anthracene (DMBA) followed by promotion with 12-O-tetradecanoylphorbol-13-acetate (TPA). In JWA(Δ2/Δ2) mice, the induction of papilloma was delayed, and the tumor number and size were reduced. In primary keratinocytes from JWA(Δ2/Δ2) mice, DMBA exposure induced more intensive DNA damage, while TPA-promoted cell proliferation was reduced. The further mechanistic studies showed that JWA deficiency blocked TPA-induced activation of MAPKs and its downstream transcription factor Elk1 both in vitro and in vivo. JWA(Δ2/Δ2) mice are resistance to tumorigenesis induced by DMBA/TPA probably through inhibition of transcription factor Elk1 via MAPKs. These results highlight the importance of JWA in skin homeostasis and in the process of skin tumor development.

Credit Card Fraud Detection (CCFD) is an essential technology for banking institutions to control fraud risks and safeguard their reputation. Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD, which significantly impact classification models' performance. To address these issues, this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks (MFGAN). The MFGAN model consists of two modules: a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space, and a balance module based on the generative adversarial network to decrease the class imbalance ratio. The effectiveness of theMFGAN model is validated on two actual credit card datasets. The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed, and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments. Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall, F1, and Area Under the Curve (AUC) metrics, which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses. [ABSTRACT FROM AUTHOR]

The new era background and new social background put forward higher goals for the cultivation of innovative talents. The accurate prediction of the demand for innovation and entrepreneurship vocational ability of higher vocational students provides an important reference for higher vocational educators to formulate refined innovation and entrepreneurship education management strategies, more balanced allocation of limited innovation and entrepreneurship education resources, and make more scientific innovation and entrepreneurship education decisions. This paper studies the prediction of innovation and entrepreneurship vocational ability demand of higher vocational students under the background of digital informatization and theoretically discusses the demand for innovation and entrepreneurship vocational ability of higher vocational students and its connotation. It constructs the demand prediction framework of innovation and entrepreneurship vocational ability of higher vocational students for data identified on the Knowledge Retrieval Engine or High-quality Knowledge Q&A and Sharing Community, and develops the collection and use methods of students' innovation and entrepreneurship resource retrieval data. It makes the feature selection of the keyword sequence and the corresponding time lag sequence selected by the higher vocational students' innovation and entrepreneurship resource retrieval and builds the prediction model of the vocational ability demand for innovation and entrepreneurship of higher vocational students. Experimental results verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Different teaching and interaction methods applied by teachers in the virtual teaching environment will lead to certain differences in the characteristics of their teaching behaviors. An in-depth understanding of the teaching behavior characteristics of teachers in virtual digital graphic design teaching and the influencing factors to the teacher-student interaction effect can help different types of teachers cater to students' extensive needs in learning digital graphic design skills. This paper studies the optimization of teachers' teaching behaviors in the virtual teaching environment of digital graphic design. Firstly, the problems existing in the application of virtual reality technology in digital graphic design teaching were summarized, a structural model for the virtual teaching environment of digital graphic design was constructed, and the optimization of teachers' teaching behaviors under the virtual teaching environment of digital graphic design was quantified based on the LDA model. An effect model for teachers' teaching behaviors was constructed, and through analysis of the evaluation results of students on the virtual digital graphic design teaching effect of teachers, the effectiveness indicator of teachers' teaching behavior decisions was obtained. The experimental results verified the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Deep learning (DL)-based methods have been successfully employed as asynchronous classification algorithms in the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. However, these methods often suffer from the limited amount of electroencephalography (EEG) data, leading to overfitting. This study proposes an effective data augmentation approach called EEG mask encoding (EEG-ME) to mitigate overfitting. EEG-ME forces models to learn more robust features by masking partial EEG data, leading to enhanced generalization capabilities of models. Three different network architectures, including an architecture integrating convolutional neural networks (CNN) with Transformer (CNN-Former), time domain-based CNN (tCNN), and a lightweight architecture (EEGNet) are utilized to validate the effectiveness of EEG-ME on publicly available benchmark and BETA datasets. The results demonstrate that EEG-ME significantly enhances the average classification accuracy of various DL-based methods with different data lengths of time windows on two public datasets. Specifically, CNN-Former, tCNN, and EEGNet achieve respective improvements of 3.18%, 1.42%, and 3.06% on the benchmark dataset as well as 11.09%, 3.12%, and 2.81% on the BETA dataset, with the 1-second time window as an example. The enhanced performance of SSVEP classification with EEG-ME promotes the implementation of the asynchronous SSVEP-BCI system, leading to improved robustness and flexibility in human-machine interaction.

In the age of the Internet, the teaching of dynamic visual communication design (DVCD) is an irresistible trend for the innovation of visual art teaching. The DVCD teaching quality can be improved to a new level by providing feedbacks to applications with the data on students' needs for practical perception (PPN). However, the current practical perception and service applications rarely tackle the feature extraction and application of highlevel needs for practical perception in the context of digital media. To solve the problem, this paper explores the practical perception and quality evaluation for DVCD teaching in the context of digital media. Specifically, the students' PPN feature extraction problem was described in the field of DVCD teaching, and fuzzy clustering was introduced to extract students' PPN features. Then, the authors detailed the dynamic evolution of students' PPN in the context of digital media, and selected multiple layers of evaluation indices for DVCD teaching quality. Finally, the evolution drivers and patterns of students' PPN were demonstrated, and the distribution of teaching quality scores was obtained through experiments. [ABSTRACT FROM AUTHOR]

This study presents two cases of type II mixed cryoglobulinemia. One case is essential, while the other is presumably associated with hepatitis B virus (HBV) infection. Both patients tested positive for monoclonal IgMκ, but negative for MyD88 mutation. They showed resistance to rituximab combined with a glucosteroid regimen, but responded positively to BTK inhibitors. These cases highlight the remarkable effectiveness of BTK inhibitors in treating refractory type II cryoglobulinemia without MyD88 mutation. The first patient achieved rapid complete remission of nephrotic syndrome within one month of starting ibrutinib, along with a significant reduction in cryoglobulin levels and abnormal clonal cells. The second patient had a rapid disappearance of rash within three days and accelerated wound healing within one week of initiating orelabrutinib, accompanied by a reduction in C-reactive protein. However, there was no reduction in cryoglobulin levels during the 12-month follow-up. These findings suggest varied mechanisms of action of BTK inhibitors in type II cryoglobulinemia through different mechanisms.