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Environmental DNA technology develops rapidly in the field of biodiversity detection. Selecting appropriate primers may be one of the key questions. However, there is currently a lack of systematic studies on differences in primer detection efficiencies. This research investigates the efficiency of six universal primers (12S, 16S, 18S, MiFish, Cytb, and COI) in detecting fish species across diverse aquatic ecosystems using an environmental DNA approach. The research spans five study areas, representing marine, river, wetland, lake, and reservoir ecosystems. Illumina MiSeq sequencing and bioinformatics tools were employed for primer performance evaluation. Results indicate that MiFish consistently outperforms other primers, detecting the highest number of fish species across all ecosystems and exhibiting superior taxonomic coverage. Furthermore, marine ecosystems consistently show higher detection numbers across all primers. The absence of commonly identified species detected by all primers emphasizes the necessity of using multiple primers for a comprehensive assessment. This study provides valuable insights into the strengths and limitations of universal primers, highlighting the importance of primer selection for accurate eDNA-based fish monitoring. The findings contribute to the scientific basis for the comprehensive management of aquatic ecosystems, assisting researchers and ecosystem managers in screening suitable fish universal primers for eDNA methods. The study also calls for further research into factors influencing primer performance and encourages the refinement of primers to enhance biodiversity monitoring precision in various ecosystems.

Network-perspective based marine ecology research has gained popularity. Many research papers across various domains can find visualized network figures. However, many ecological network studies remain predominantly “descriptive”, reflecting variations in understanding of network concepts. In this paper, we systematically introduce the origins, fundamental theories, and concepts of ecological networks, emphasizing their application in marine ecosystem conservation and management. Network analysis, rooted in the development of network science, provides a comprehensive understanding of species interactions in ecosystems. Exploring the marine ecological networks is crucial for enhancing our comprehension of future threats. It informs the development of sustainable strategies for marine conservation and resource exploitation, such as designing marine protected areas, assessing ecosystem services, and managing fisheries. The ecological network concept has undergone significant development from theory to application. Looking ahead, with the rise of artificial intelligence and complexity science, our understanding of complex marine ecological networks is poised to advance further.

Introduction and aim: Critical thinking is a style of thinking that requires new and student-oriented education. Nursing mentors should try to train students interested in continuous learning according to the current conditions of clinical settings. This study was conducted with the aim of determining the effect of network-based learning on the critical thinking skills of nursing students studying at the Islamic Azad University of Golestan province in 2022. Method: This is an experimental pre/post-test study with two intervention and control groups, which was conducted on undergraduate nursing students studying at Islamic Azad University of Golestan in 2022. Convenience sampling method was used in this study to collect the sample. A total of 50 students participated in this study, who were divided into two intervention and control groups by simple random allocation method found on the website. Data collection tools in this study included the demographic information form and the critical thinking disposition inventory (CTDI). Findings: Before the intervention, the mean score of critical thinking was 343.76 ± 12.73 in the intervention group and 343.76 ± 12.73 in the control group, which showed no statistically significant difference (P=0.2). After the intervention, the mean score of critical thinking was 373.28 ± 18.55 in the intervention group and 340.2 ± 10.38 in the control group, which showed no statistically significant difference between the two groups (P<0.001). Conclusion: The findings of this study showed that problem solving training through virtual means can be effective in improving the critical thinking of nursing students. Perhaps the use of this method can have a significant effect on education of students and consequently, improve the quality of nursing care. [ABSTRACT FROM AUTHOR]

Abstract Background Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis. Methods We performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). To this end, we used two different approaches, first we compared the molecular omics profiles between ARDS groups, and second, we correlated clinical manifestations within each group with the omics profiles. Results The comparison of the two ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis. Conclusion In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.

Background: Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis. Methods: We performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). To this end, we used two different approaches, first we compared the molecular omics profiles between ARDS groups, and second, we correlated clinical manifestations within each group with the omics profiles. Results: The comparison of the two ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis. Conclusion: In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes. [ABSTRACT FROM AUTHOR]

A community is a group of people that shares something in common. The definition of the community can be generalized as things that have common properties. By using this definition, community detection can be used to solve different problems in various areas. In this study, we propose a new network-based community detection algorithm that can work on different types of datasets. The proposed algorithm works on unweighted graphs and determines the weight by using cosine similarity. We apply a bottom-up approach and find the disjoint communities. First, we accept each node as an independent community. Then, the merging process is applied by using the modularity value as a stopping criterion. We use real datasets and evaluate the algorithm with modularity, normalized mutual information, and performance metrics. In addition, we test our algorithm by using central nodes. We also take into consideration the number of communities in the case they are known. The proposed algorithm has high modularity and accuracy in different datasets. [ABSTRACT FROM AUTHOR]

• We analyzed 21 leading research-based pharmaceutical companies by global sales 2019 regarding their inbound R&D activities between 2015 and 2019. • Over the last decade open innovation (OI) has found its way into and is a widely used R&D model of the research-based pharmaceutical industry. • The depth and breadth of implementation differs greatly across major industry players: While four pharmaceutical companies (Novartis, Otsuka, Gilead, and Allergan) rely more on traditional R&D concepts, the vast majority (15 out of 21 companies) also uses network-based OI models to supplement R&D. And two companies (Bayer and AstraZeneca) have opened their R&D into ecosystem-enabled. Open innovation (OI) holds promise to accelerate, diversify, and innovate research and development (R&D) in the pharmaceutical industry. It remains to be assessed in which way and to what extent OI is leveraged in practice by current pharmaceutical R&D organizations. Therefore, here we comprehensively analyzed 21 research-based pharmaceutical companies and benchmarked their implementation of OI. Our data showed that OI is an integral part of R&D of all assessed pharmaceutical companies; models typically used are research collaborations, innovation incubators, academic centers of excellence, public–private partnerships (PPPs), mergers and acquisitions (M&A), licensing, or corporate venture capital (VC) funds. In addition, we conclude that the implementation of OI differs greatly across corporations and, consequently, that R&D organizations of research-based pharmaceutical companies can be classified based on their level of OI implementation into three distinct types: predominantly traditional R&D; network-based R&D; and R&D ecosystems. [ABSTRACT FROM AUTHOR]

Wantong Han,1– 3 Lingzhong Xu,1– 3 Aimin Niu,4 Yurong Jing,1– 3 Wenzhe Qin,1– 3 Jiao Zhang,1– 3 Xiang Jing,5 Yali Wang6 1Centre for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, People’s Republic of China; 2NHC Key Laboratory of Health Economics and Policy Research, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, People’s Republic of China; 3Center for Health Economics Experiment and Public Policy Research, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China; 4Department of Public Health, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, People’s Republic of China; 5Yuncheng Central Hospital, Yuncheng, Shanxi, People’s Republic of China; 6Henan Provincial Center for Disease Control and Prevention, Zhengzhou, Henan, People’s Republic of ChinaCorrespondence: Lingzhong XuSchool of Public Health, Cheeloo College of Medicine, Shandong University, Mailbox No. 110, 44 Wenhuaxi Road, Jinan, 250012, Shandong, People’s Republic of ChinaTel +86-0531-8838-2648Fax +86-0531-88382533Email lzxu@sdu.edu.cnAimin NiuDepartment of Public Health, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324 Jingwu Road, Huaiyin District, Jinan, 250021, People’s Republic of ChinaTel +86-1866-370-2722Fax +86-0531-87921022Email nam1106@163.comPurpose: Studies have suggested that public health emergencies can have many psychological effects on college students, therefore, the aim of this study is to investigate current situation of college students’ anxiety and its determinants in the time of an unexpected pandemic.Patients and Methods: We conducted convenience sampling to collect the data through network-based online questionnaires in February 2020, a total of 17,876 college students were included in the analysis. Chi-square test and multivariate logistic were used to identify the associations between the outbreak experiences and anxiety detection.Results: This study found that detection rate of anxiety among college students was 18.2%. The differences in male students, students whose self-perceived risk of infection were high, who were greatly affected by the outbreak, eager to go back to school, reluctant to leave home and stay at home enough were of statistical significance among different anxiety level (OR> 1, P< 0.05). And the severe anxiety rate of students who living in cities was significantly higher (2.337[1.468, 3.721]).Conclusion: Although our results show that anxiety among college students was at a low level, various universities should focus on the online activities and develop appropriate epidemic management plans to prevent their feelings of worry, tension and panic.Keywords: anxiety, college students, network-based, COVID-19

With deep learning creeping up into the ranks of big data, new models based on deep learning and massive data have made great leaps forward rapidly in the field of drug repositioning. However, there is no relevant review to summarize the transformations and development process of models and their data in the field of drug repositioning. Among all the computational methods, network‐based methods play an extraordinary role. In view of these circumstances, understanding and comparing existing network‐based computational methods applied in drug repositioning will help us recognize the cutting‐edge technologies and offer valuable information for relevant researchers. Therefore, in this review, we present an interpretation of the series of important network‐based methods applied in drug repositioning, together with their comparisons and development process. [ABSTRACT FROM AUTHOR]

The need for systematic drug repurposing has seen a steady increase over the past decade and may be particularly valuable to quickly remedy unexpected pandemics. The abundance of functional interaction data has allowed mapping of substantial parts of the human interactome modeled using functional association networks, favoring network-based drug repurposing. Network crosstalk-based approaches have never been tested for drug repurposing despite their success in the related and more mature field of pathway enrichment analysis. We have, therefore, evaluated the top performing crosstalk-based approaches for drug repurposing. Additionally, the volume of new interaction data as well as more sophisticated network integration approaches compelled us to construct a new benchmark for performance assessment of network-based drug repurposing tools, which we used to compare network crosstalk-based methods with a state-of-the-art technique. We find that network crosstalk-based drug repurposing is able to rival the state-of-the-art method and in some cases outperform it. [ABSTRACT FROM AUTHOR]

The need for systematic drug repurposing has seen a steady increase over the past decade and may be particularly valuable to quickly remedy unexpected pandemics. The abundance of functional interaction data has allowed mapping of substantial parts of the human interactome modeled using functional association networks, favoring network-based drug repurposing. Network crosstalk-based approaches have never been tested for drug repurposing despite their success in the related and more mature field of pathway enrichment analysis. We have, therefore, evaluated the top performing crosstalk-based approaches for drug repurposing. Additionally, the volume of new interaction data as well as more sophisticated network integration approaches compelled us to construct a new benchmark for performance assessment of network-based drug repurposing tools, which we used to compare network crosstalk-based methods with a state-of-the-art technique. We find that network crosstalk-based drug repurposing is able to rival the state-of-the-art method and in some cases outperform it.

Background: Targeted therapy using anti-TNF (tumor necrosis factor) is the first option for patients with rheumatoid arthritis (RA). Anti-TNF therapy, however, does not lead to meaningful clinical improvement in many RA patients. To predict which patients will not benefit from anti-TNF therapy, clinical tests should be performed prior to treatment beginning.Objective: Although various efforts have been made to identify biomarkers and pathways that may be helpful to predict the response to anti-TNF treatment, gaps remain in clinical use due to the low predictive power of the selected biomarkers.Methods: In this paper, we used a network-based computational method to identify the select the predictive biomarkers to guide the treatment of RA patients.Results: We select 69 genes from peripheral blood expression data from 46 subjects using a sparse network-based method. The result shows that the selected 69 genes might influence biological processes and molecular functions related to the treatment.Conclusions: Our approach advances the predictive power of anti-TNF therapy response and provides new genetic markers and pathways that may influence the treatment. [ABSTRACT FROM AUTHOR]

Background: Chronic obstructive pulmonary disease (COPD) causes chronic obstructive conditions, chronic bronchitis, and emphysema, and is a major cause of death worldwide. Although several efforts for identifying biomarkers and pathways have been made, specific causal COPD mechanism remains unknown.Objective: This study combined biological interaction data with gene expression data for a better understanding of the biological process and network module for COPD.Methods: Using a sparse network-based method, we selected 49 genes from peripheral blood mononuclear cell expression data of 136 subjects, including 42 ex-smoking controls and 94 subjects with COPD.Results: These 49 genes might influence biological processes and molecular functions related to COPD. For example, our result suggests that FoxO signaling may contribute to the atrophy of COPD peripheral muscle tissues via oxidative stress.Conclusions: Our approach enhances the existing understanding of COPD disease pathogenesis and predicts new genetic markers and pathways that may influence COPD pathogenesis. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is one of the leading causes of death worldwide. Identification of potential therapeutic and diagnostic biomarkers can be helpful to screen cancer progress. This study was implemented to discover potential biomarkers for HCC within a network-based approach integrated with microarray data. Methods: Through downloading a gene expression profile GSE62232 differentially expressed genes (DEGs) were identified. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for DEGs were performed utilizing Enrichr server. Following reconstruction of protein-protein interaction network of DEGs with STRING, network visualization, analyses, and clustering into structural modules were carried out using Cytoscape. Considering degree centrality, 15 hub genes were selected as early biomarker candidates for final validation. To validate hub genes, the GEPIA server was used to perform overall survival (OS) and disease-free survival (DFS). Results: In our approach, 1996 DEGs were identified including 995 up-regulated genes and 1001 down-regulated genes. KEGG pathway enrichment analysis shows that DEGs are associated with chemical carcinogenesis and cell cycle. GO term enrichment analysis indicated the relation of DEGs with epoxygenase P450 pathway, arachidonic acid monooxygenase activity, and secretory granule lumen. Following analysis of the protein-protein interaction network of DEGs, the top three structural modules and 15 early hub genes were selected. Validation of hub genes was performed using GEPIA. Consequently, CDK1, CCNB1, CCNA2, CDC20, AURKA, MAD2L1, TOP2A, KIF11, BUB1B, TYMS, EZH2, and BUB1 were considered as our final proposed biomarkers. Conclusion: using an integrated network-based approach with microarray data our results revealed 12 final candidates with the potential to be considered as biomarkers in hepatocellular carcinoma.

The ongoing COVID-19 pandemic still requires fast and effective efforts from all fronts, including epidemiology, clinical practice, molecular medicine, and pharmacology. A comprehensive molecular framework of the disease is needed to better understand its pathological mechanisms, and to design successful treatments able to slow down and stop the impressive pace of the outbreak and harsh clinical symptomatology, possibly via the use of readily available, off-the-shelf drugs. This work engages in providing a wider picture of the human molecular landscape of the SARS-CoV-2 infection via a network medicine approach as the ground for a drug repurposing strategy. Grounding on prior knowledge such as experimentally validated host proteins known to be viral interactors, tissue-specific gene expression data, and using network analysis techniques such as network propagation and connectivity significance, the host molecular reaction network to the viral invasion is explored and exploited to infer and prioritize candidate target genes, and finally to propose drugs to be repurposed for the treatment of COVID-19. Ranks of potential target genes have been obtained for coherent groups of tissues/organs, potential and distinct sites of interaction between the virus and the organism. The normalization and the aggregation of the different scores allowed to define a preliminary, restricted list of genes candidates as pharmacological targets for drug repurposing, with the aim of contrasting different phases of the virus infection and viral replication cycle.

The underlying causes of Parkinson's disease (PD) remain unclear, making it difficult to determine how particular aspects of a patient's health may affect PD development and their treatment response. One alternative approach of investigating interactions between PD and co-morbidities (or other health factors) is to compare the dysregulated cellular or gene pathways they share with affected tissues. To achieve this, we developed a quantitative framework to reveal significant interactions between PD and other significant health factors that may be impacted. In this study, we analyzed gene expression microarray data from tissues affected by PD, type II diabetes (T2D), aging (AG), sedentary lifestyle (SL), high dietary fat (HFD), hypercholesterolemia (HC), high body fat (HBF), high red meat diet (RMD), high alcohol consumption (AC), smoking (SM) and control datasets. We have developed genetic associations of these factors with PD, based on the neighborhood-based benchmarking and multilayer network topology. We identified 1323 differentially expressed genes (DEGs) in the PD patients compared to healthy controls, 779 genes with down-regulated expression and 544 genes up-regulated. 69 dysregulated genes were common to PD and AC datasets; PD datasets also shared, respectively, 51, 45, 43 and 42 DEGs with the T2D, AG, HFD and HBF datasets. Ontological and pathway analyses identified significant gene ontology and molecular pathways with the potential to enhance our understanding of the fundamental molecular factors associated with PD progression. Moreover, we employed a validation gene expression dataset along with Mendelian Inheritance in Man (OMIM) and dbGaP as gold benchmark databases to validate the identified PD associated genes and molecular pathways. Our formulated methodologies used a network-based approach and identified a number of significant genes and pathways that may particularly affect PD, signifying how PD incidence and development may be influenced by the risk factors that may indicate avenues to new therapeutic approaches.

Alzheimer's disease (AD) is a progressive neurodegenerative disease, the causes of which are poorly understood, although a number of strong risk factors for AD are known. Understanding how these risk factors affect cell pathways that are altered in AD could identify important causal pathways that could be targeted by therapeutics. We thus pro- posed network-based quantitative frameworks to investigate these risk factor-AD relation- ships. We analyzed gene expression microarray datasets from tissues affected by AD as well as by ageing, high alcohol consumption, type II diabetes (T2D), high dietary fat, obesity, high dietary red meat, sedentary lifestyle, and smoking. These datasets derived from studies that compared tissues affected by these factors with control tissues (not exposed to these factors) to identify differentially expressed genes (DEGs) specific to the risk factors. We employed these to develop gene association and diseasome networks based on neighborhood-based benchmarking and multilayer network topology. We identified 484 DEGs from AD brain tissue, of which 27 were also seen in the smoking DEGs gene set. AD data also showed 21 DEGs in common with T2D, and 12 with sedentary lifestyle datasets. AD shared less than ten DEGs with the other factors, but 3 (HLA-DRB4, IGH and IGHA2) were commonly up-regulated among the AD, T2D and high alcohol consumption datasets. IGHD and IGHG1 were up-regulated among AD, T2D, alcohol and sedentary lifestyle datasets. Protein-protein interaction networks identified 10 hub genes: CREBBP, PRKCB, ITGB1, GAD1, GNB5, PPP3CA, CABP1, SMARCA4, SNAP25 and GRIA1. Ontological and pathway analyses identified significant gene ontology and molecular pathways that could enhance our understanding of the mechanisms of AD progression by suggesting new therapeutic approaches to affect the development of AD. We verified genes from ontological and pathway analyses with gold benchmark gene-disease associations databases including Online Mendelian Inheritance in Man (OMIM) and dbGaP. This supports our identification of disease associations for the putative AD target genes. These outcomes provide further evidence that network-based approaches can generate new insights into AD progression.

This article discusses the outsourcing of materials handling activities and investigates different options for its implementation. The article uses descriptive case studies found in literature from the Western European automotive industry to map out differences in current practice and to evaluate frameworks found in the literature. These frameworks appear to be limited to decision making at a strategic level. In addition, they only allow decisions relating to the outsourcing of the logistics function in general, not of materials handling in particular. Based on this study and other descriptions of materials handling practice in the literature, a new conceptual framework for outsourcing of materials handling is proposed, which facilitates decision making at a tactical/operational level. The functionality of the framework is tested in a series of cases at a major production and logistics facility of Scania. [ABSTRACT FROM AUTHOR]

This study examines a distributed direct load control (DLC) problem for maximising customer welfare in a power system for the network communication of energy management controllers (EMCs). A model is first built to describe the dynamics and communication intervals of the EMCs with a distributed and uniform controller. The controller conditions are then derived to stabilise the system and to converge the power imbalance to zero at an assigned rate. The control condition that maximises customer welfare is then found. Furthermore, an optimal controller that maximises customer welfare over a given network communication is proposed, and the performance degradation caused by distributed management is evaluated. This study reveals that even though moving from a centralised to a distributed DLC can degrade customer welfare, this degradation can be reduced by considering consumer properties and network topologies of the EMCs. Numerical examples with real consumption data are also presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network-enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder with no effective cure. Targeting endoplasmic reticulum (ER) stress pathway may offer a novel approach to ameliorate cognitive deficits in AD. Bushen-Yizhi formula (BSYZ), a traditional Chinese medicine (TCM) prescription, has shown potential benefits for AD. To facilitate the development of new therapeutic agents for AD, it is important to identify the active components and the underlying mechanisms of BSYZ against AD. The aim of this study was to systematically screen the active components of BSYZ that could improve learning and memory impairment in AD by modulating ER stress pathway. A drug-target (D-T) network was constructed to analyze the herbal components of BSYZ. Network proximity method was used to identify the potential anti-AD components that targeted ER stress and evaluate their synergistic effects. The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and the literature evidence were considered to select promising candidates for further validation. The selected components were tested in vitro using an AD cell model (APPswe-SH-SY5Y). In vivo anti-AD effects of the components were assessed in APP/PS1 double-transgenic mice. 58 potential anti-AD components targeting ER stress were detected by network proximity analysis, and 13 out of them were selected based on ADMET properties and literature evidence. In vitro experiments confirmed that 5 components, namely gomisin B, β-Carotene, imperatorin, chrysophanol, and osthole (OST), exhibited anti-AD effects on the APPswe-SH-SY5Y model. Moreover, network proximity analysis suggested that OST and Gomisin B might have synergistic effects on modulating ER stress. In vivo experiments demonstrated that OST, Gomisin B, OST+Gomisin B, and BSYZ all improved learning and memory function in APP/PS1 mice. Gomisin B and OST also restored cellular morphology and tissue structure in APP/PS1 mice. Thioflavine-S (Th-S) staining revealed that they reduced amyloid plaque deposition in the brain tissue of AD model mice. The qPCR results indicated that BSYZ, OST, and Gomisin B differentially regulated IRE1α, PERK, EIF2α, DDIT3, and Caspase 12 expression levels, while the OST and Gomisin B co-administration group showed better efficacy. This trend was further confirmed by immunofluorescence experiments. This study identified the active components of BSYZ that could ameliorate learning and memory impairment in AD by targeting ER stress pathway. OST and Gomisin B exhibited synergistic effects on modulating ER stress and reducing amyloid plaque deposition in vivo. Overall, our study elucidated the molecular mechanisms of BSYZ and its active components in attenuating AD symptoms which suggested the therapeutic potential of TCM for AD. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract Background In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based methods have advantageous performance than regression-based methods, and to what extent do they outperform. Methods Simulations under different scenarios (the input variables are independent or in network relationship) as well as an application were conducted to assess the prediction performance of four typical methods including Bayesian network, neural network, logistic regression and regression splines. Results The simulation results reveal that Bayesian network showed a better performance when the variables were in a network relationship or in a chain structure. For the special wheel network structure, logistic regression had a considerable performance compared to others. Further application on GWAS of leprosy show Bayesian network still outperforms other methods. Conclusion Although regression-based methods are still popular and widely used, network-based approaches should be paid more attention, since they capture the complex relationship between variables.

Highlights • Classifying DDoS attacks as network/transport layer and application layer. • Classifying defense mechanisms against DDoS as network/transport and application layer. • Reviewing class properties and introducing defensive mechanisms of each class. • Comparing defense mechanisms categorization and discussing each category's properties. • Surveying challenges present in defense mechanisms and directing future works. Abstract Distributed Denial of Service (DDoS) attacks are a group of collaborative attacks performed by attackers threatening internet security and violating services. In this attack, the attacker makes use of compromised systems to prevent legitimate users from having access to the server resources and use them to provide extensive attacks against the victim. In this paper, we surveyed defense mechanisms against DDoS attacks which are useful in internet. We categorized the mechanisms into two layer-based main groups of network/transport layer and application layer. Then, the network/transport layer is classified into four classes of source-based, network-based, destination-based and hybrid mechanisms, and the application layer mechanisms are categorized into two classes of destination-based and hybrid mechanisms. We surveyed important developments in each of the aforementioned classes and outlined new challenges. This survey paper provides a discussion of the difference between the aforementioned mechanisms categorizations based on characteristics of the way of detection, defense, and response as well as orientations for future researches. [ABSTRACT FROM AUTHOR]

Stochastic simulation has been widely used to model the dynamics of biochemical reaction networks. Several algorithms have been proposed that are exact solutions of the chemical master equation, following the work of Gillespie. These stochastic simulation approaches can be broadly classified into two categories: network-based and -free simulation. The network-based approach requires that the full network of reactions be established at the start, while the network-free approach is based on reaction rules that encode classes of reactions, and by applying rule transformations, it generates reaction events as they are needed without ever having to derive the entire network. In this study, we compare the efficiency and limitations of several available implementations of these two approaches. The results allow for an informed selection of the implementation and methodology for specific biochemical modeling applications. [ABSTRACT FROM AUTHOR]

Aging that refers the accumulation of genetic and physiology changes in cells and tissues over a lifetime has been shown a high risk of developing various complex diseases, such as neurodegenerative disease, cardiovascular disease and cancer. Over the past several decades, natural products have been demonstrated as anti-aging interveners via extending lifespan and preventing aging-associated disorders. In this study, we developed an integrated systems pharmacology infrastructure to uncover new indications for aging-associated disorders by natural products. Specifically, we incorporated 411 high-quality aging-associated human genes or human-orthologous genes from mus musculus (MM), saccharomyces cerevisiae (SC), caenorhabditis elegans (CE), and drosophila melanogaster (DM). We constructed a global drug-target network of natural products by integrating both experimental and computationally predicted drug-target interactions (DTI). We further built the statistical network models for identification of new anti-aging indications of natural products through integration of the curated aging-associated genes and drug-target network of natural products. High accuracy was achieved on the network models. We showcased several network-predicted anti-aging indications of four typical natural products (caffeic acid, metformin, myricetin, and resveratrol) with new mechanism-of-actions. In summary, this study offers a powerful systems pharmacology infrastructure to identify natural products for treatment of aging-associated disorders.

The enormous growth of social media provides a platform for displaying harmful, offensive online behaviour, which keeps increasing with time. The popularity of smartphones and the anonymity of the internet have made online offensive behaviour very common. Therefore, research on social media offensive behaviour has increased in recent years. In this paper, we have endeavoured to depict the variety of abusive behaviour one can encounter online and the significance of detecting them by classifying them into four categories: Content-Based, Sentiment and Emotion Based, User or Profile Based, and Network or Graph-Based approach. We review the state-of-the-art methods to detect bullies and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the future research directions that serve as a reference to overcome offensive content in social media. • Cyberbullying is a severe offense, and it may lead a victim to self-destruction. • The variety of abusive behaviour one can encounter online. • Factors that drive offenders to indulge in offensive activity and preventive measures. • The significance of detecting cyberbullying. • The state-of-the-art methods to detect bullies and abusive content on social media. [ABSTRACT FROM AUTHOR]

Background: Pathway analysis combining multiple types of high-throughput data, such as genomics and proteomics, has become the first choice to gain insights into the pathogenesis of complex diseases. Currently, several pathway analysis methods have been developed to study complex diseases. However, these methods did not take into account the interaction between internal and external genes of the pathway and between pathways. Hence, these approaches still face some challenges. Here, we propose a network-based pathway-expanding approach that takes the topological structures of biological networks into account. Results: First, two weighted gene-gene interaction networks (tumor and normal) are constructed integrating protein-protein interaction(PPI) information, gene expression data and pathway databases. Then, they are used to identify significant pathways through testing the difference of topological structures of expanded pathways in the two weighted networks. The proposed method is employed to analyze two breast cancer data. As a result, the top 15 pathways identified using the proposed method are supported by biological knowledge from the published literatures and other methods. In addition, the proposed method is also compared with other methods, such as GSEA and SPIA, and estimated using the classification performance of the top 15 expanded pathways. Conclusions: A novel network-based pathway-expanding approach is proposed to avoid the limitations of existing pathway analysis approaches. Experimental results indicate that the proposed method can accurately and reliably identify significant pathways which are related to the corresponding disease. [ABSTRACT FROM AUTHOR]

Stochastic simulation has been widely used to model the dynamics of biochemical reaction networks. Several algorithms have been proposed that are exact solutions of the chemical master equation, following the work of Gillespie. These stochastic simulation approaches can be broadly classified into two categories: network-based and -free simulation. The network-based approach requires that the full network of reactions be established at the start, while the network-free approach is based on reaction rules that encode classes of reactions, and by applying rule transformations, it generates reaction events as they are needed without ever having to derive the entire network. In this study, we compare the efficiency and limitations of several available implementations of these two approaches. The results allow for an informed selection of the implementation and methodology for specific biochemical modeling applications.