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Journal articles on the topic 'Network meta-analyze'
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Gupta, A. K., Bianca Maria Piraccini, Vincent Piguet, and Antonella Tosti. "15952 Network meta-analysis to analyze alopecia areata treatment." Journal of the American Academy of Dermatology 83, no. 6 (December 2020): AB164. http://dx.doi.org/10.1016/j.jaad.2020.06.746.
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Albareda, Laura, and Sandra Waddock. "Networked CSR Governance: A Whole Network Approach to Meta-Governance." Business & Society 57, no. 4 (January 13, 2016): 636–75. http://dx.doi.org/10.1177/0007650315624205.
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Meta-governance is Earth system governance for dealing with the global commons. This article develops a whole network approach to meta-governance to explore the potential for collective action for sustainable development by a loosely coupled network of networks. Networked corporate social responsibility (CSR) governance has emerged around corporate sustainability and responsibility in the first years of the 21st century. Growing agreements and interactions among CSR initiatives suggest the development, structure, and governance of networked CSR governance as a network that can analytically be viewed as a whole and as a platform for learning about systemic change. Using the evolution of CSR initiatives from about 1990 to 2014, the authors differentiate four developmental stages: independent and fragmented multistakeholder networks as CSR governance, collaborative CSR governance, networked CSR governance, and integrated networked CSR governance. The authors then present a framework to analyze networked CSR governance as a whole network experimenting with meta-governance.
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Sun, Yuhong, Qinghai Liu, and Hua Wang. "SIR Meta Distribution in the Heterogeneous and Hybrid Networks." Wireless Communications and Mobile Computing 2020 (December 1, 2020): 1–13. http://dx.doi.org/10.1155/2020/8856459.
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With the development of the technology, the wireless systems are becoming more heterogeneous with the introduction of various power nodes including femtocells, relays, or distributed antennas. Among the research of wireless network performance, the meta distribution of the signal-to-interference ratio (SIR) has attracted significant attention. Compared to the standard success (coverage) probability, the meta distribution provides much more fine-grained information about the network performance. In this paper, we analyze the meta distribution of the SIR in the multi-tier heterogeneous and hybrid networks, where each tier is based on a homogeneous independent Poisson point process model. For the open tiers (the users can associate with any tier) and the closed tiers (the users can only associate with a certain tier), we study the b th moment of the conditional success probability for the typical user and give the beta approximation of the meta distribution from analysis and simulations. Furthermore, we analyze the per-link rate control for open tiers and closed tiers, which answers the question: “how to set the SIR threshold to meet a target reliability?”. We give the approximate value of the SIR threshold to meet a target reliability and show how the value is related to the path loss exponent and densities.
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Ardila, Alfredo, Byron Bernal, and Monica Rosselli. "Language and Visual Perception Associations: Meta-Analytic Connectivity Modeling of Brodmann Area 37." Behavioural Neurology 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/565871.
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Background.Understanding the functions of different brain areas has represented a major endeavor of neurosciences. Historically, brain functions have been associated with specific cortical brain areas; however, modern neuroimaging developments suggest cognitive functions are associated to networks rather than to areas.Objectives.The purpose of this paper was to analyze the connectivity of Brodmann area (BA) 37 (posterior, inferior, and temporal/fusiform gyrus) in relation to (1) language and (2) visual processing.Methods.Two meta-analyses were initially conducted (first level analysis). The first one was intended to assess the language network in which BA37 is involved. The second one was intended to assess the visual perception network. A third meta-analysis (second level analysis) was then performed to assess contrasts and convergence between the two cognitive domains (language and visual perception). The DataBase of Brainmap was used.Results.Our results support the role of BA37 in language but by means of a distinct network from the network that supports its second most important function: visual perception.Conclusion.It was concluded that left BA37 is a common node of two distinct networks—visual recognition (perception) and semantic language functions.
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Sembroiz, David, Behnam Ojaghi, Davide Careglio, and Sergio Ricciardi. "A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks." Applied Sciences 9, no. 21 (October 27, 2019): 4564. http://dx.doi.org/10.3390/app9214564.
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Wireless Sensor Networks (WSN) have lately been gaining momentum thanks to the hardware improvements and standardization software efforts. Moreover, the appearance of Internet of Things (IoT) and its reliance on sensors are helping to widely extend the usage of WSNs. However, such networks present drawbacks, mainly because of limited sensor batteries and their vulnerability against physical attacks due to the lack of protection and security. Additionally, not all the sensors inside the network have the same responsibility in terms of traffic handling. In this paper, we firstly analyze the fact that some nodes are more critical than others, considering the most critical node the one that, once incapacitated, causes the most deterioration on the network performance. Such performance is analyzed using two metrics, namely network latency and lifetime. We present a result comparison between a Mixed Integer Programming (MIP) model and a Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic for small networks. For bigger networks, GRASP meta-heuristic results are presented to understand how the network degrades as the number of both critical and network nodes increase, by distributing them into two different areas: fixed and incremental to maintain node density.
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Yang, Zheming, and Wen Ji. "Meta measurement of intelligence with crowd network." International Journal of Crowd Science 4, no. 3 (June 13, 2020): 295–307. http://dx.doi.org/10.1108/ijcs-03-2020-0008.
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Purpose The multiple factors of intelligence measurement are critical in intelligent science. The intelligence measurement is typically built as a model based on multiple factors. The different agent is generally difficult to measure because of the uncertainty between multiple factors. The purpose of this paper is to solve the problem of uncertainty between multiple factors and propose an effective method for universal intelligence measurement for the different agents. Design/methodology/approach In this paper, the authors propose a universal intelligence measurement method based on meta-analysis for crowd network. First, the authors get study data through keywords in the database and delete the low-quality data. Second, they compute the effect value by odds ratio, relative risk and risk difference. Then, they test the homogeneity by Q-test and analyze the bias by funnel plots. Third, they select the fixed effect and random effect as a statistical model. Finally, through the meta-analysis of time, complexity and reward, the weight of each factor in the intelligence measurement is obtained and then the meta measurement model is constructed. Findings This paper studies the relationship among time, complexity and reward through meta-analysis and effectively combines the measurement of heterogeneous agents such as human, machine, enterprise, government and institution. Originality/value This paper provides a universal intelligence measurement model for crowd network. And it can provide a theoretical basis for the research of crowd science.
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Fazlollahtabar, Hamed, Iraj Mahdavi, and Nezam Mahdavi-Amiri. "Meta modelling of job satisfaction effective factors for improvement policy making in organizations." Benchmarking: An International Journal 23, no. 2 (March 7, 2016): 388–405. http://dx.doi.org/10.1108/bij-11-2013-0107.
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Purpose – The purpose of this paper is to propose a Meta modeling based on regression, neural network, and clustering to analyze the job satisfaction factors and improvement policy making. Design/methodology/approach – Since any job satisfaction evaluation supposes to improve the status by prescribing specific strategies to be performed in the organization, proposing applicable strategies is decisively important. Task demand, social structure and leader-member exchange (LMX) are general applications easily conceptualized while proposing job satisfaction improvement strategies. Findings – On the basis of these empirical findings, the authors first aim to identify relationships between LMX, task demand, social structure and individual factors, organizational factors, job properties, which are easier to be employed in strategy formulation for job satisfaction, and then determine the sub-factors and subsequently cluster them. The effectiveness of the proposed model is verified by a case study. Originality/value – Here, a Meta modeling based on regression, neural network, and clustering is proposed to analyze the job satisfaction factors and improvement policy making.
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Wang, Shudong, Jixiao Wang, Xinzeng Wang, Yuanyuan Zhang, and Tao Yi. "Identification of Critical Core Genes of Sarcoma Based on Centrality Analysis of Networks Nodes." Journal of Medical Imaging and Health Informatics 10, no. 7 (July 1, 2020): 1776–84. http://dx.doi.org/10.1166/jmihi.2020.3080.
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Genome-wide association studies (GWAS) are powerful tools for identifying pathogenic genes of complex diseases and revealing genetic structure of diseases. However, due to gene-to-gene interactions, only a part of the hereditary factors can be revealed. The meta-analysis based on GWAS can integrate gene expression data at multiple levels and reveal the complex relationship between genes. Therefore, we used meta-analysis to integrate GWAS data of sarcoma to establish complex networks and discuss their significant genes. Firstly, we established gene interaction networks based on the data of different subtypes of sarcoma to analyze the node centralities of genes. Secondly, we calculated the significant score of each gene according to the Staged Significant Gene Network Algorithm (SSGNA). Then, we obtained the critical gene set HYC of sarcoma by ranking the scores, and then combined Gene Ontology enrichment analysis and protein network analysis to further screen it. Finally, the critical core gene set Hcore containing 47 genes was obtained and validated by GEPIA analysis. Our method has certain generalization performance to the study of complex diseases with prior knowledge and it is a useful supplement to genome-wide association studies.
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Takahashi, Marissa, Marta Indulska, and John Steen. "Collaborative Research Project Networks." Project Management Journal 49, no. 4 (July 17, 2018): 36–52. http://dx.doi.org/10.1177/8756972818781630.
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Many businesses seeking enhanced innovation have corporate research teams that engage in collaborative research projects (CRPs), with external entities such as universities, public organizations, or customers. Anecdotal evidence, however, suggests mixed outcomes of CRPs in terms of corporate research impact, which implies successful transfer of novel knowledge generated within CRPs to company-internal business networks to develop radically innovative products. We use the multiple regression quadratic assignment procedure (MRQAP) and meta-analysis to analyze six CRP networks. Our findings indicate that the network’s relational characteristics (tie strength) and structural characteristics (network range) are important determinants of knowledge transfer at the fuzzy front end of innovation.
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Markowski, Marcin. "Heuristic algorithms for joint optimization of unicast and anycast traffic in elastic optical network–based large–scale computing systems." International Journal of Applied Mathematics and Computer Science 27, no. 3 (September 1, 2017): 605–22. http://dx.doi.org/10.1515/amcs-2017-0043.
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AbstractIn recent years elastic optical networks have been perceived as a prospective choice for future optical networks due to better adjustment and utilization of optical resources than is the case with traditional wavelength division multiplexing networks. In the paper we investigate the elastic architecture as the communication network for distributed data centers. We address the problems of optimization of routing and spectrum assignment for large-scale computing systems based on an elastic optical architecture; particularly, we concentrate on anycast user to data center traffic optimization. We assume that computational resources of data centers are limited. For this offline problems we formulate the integer linear programming model and propose a few heuristics, including a meta-heuristic algorithm based on a tabu search method. We report computational results, presenting the quality of approximate solutions and efficiency of the proposed heuristics, and we also analyze and compare some data center allocation scenarios.
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Ibarra-Cerdeña, Carlos N., Leopoldo Valiente-Banuet, Víctor Sánchez-Cordero, Christopher R. Stephens, and Janine M. Ramsey. "Trypanosoma cruzi reservoir—triatomine vector co-occurrence networks reveal meta-community effects by synanthropic mammals on geographic dispersal." PeerJ 5 (April 12, 2017): e3152. http://dx.doi.org/10.7717/peerj.3152.
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Contemporary patterns of land use and global climate change are modifying regional pools of parasite host species. The impact of host community changes on human disease risk, however, is difficult to assess due to a lack of information about zoonotic parasite host assemblages. We have used a recently developed method to infer parasite-host interactions for Chagas Disease (CD) from vector-host co-occurrence networks. Vector-host networks were constructed to analyze topological characteristics of the network and ecological traits of species’ nodes, which could provide information regarding parasite regional dispersal in Mexico. Twenty-eight triatomine species (vectors) and 396 mammal species (potential hosts) were included using a data-mining approach to develop models to infer most-likely interactions. The final network contained 1,576 links which were analyzed to calculate centrality, connectivity, and modularity. The model predicted links of independently registeredTrypanosoma cruzihosts, which correlated with the degree of parasite-vector co-occurrence. Wiring patterns differed according to node location, while edge density was greater in Neotropical as compared to Nearctic regions. Vectors with greatest public health importance (i.e., Triatoma dimidiata,T. barberi,T. pallidipennis,T. longipennis, etc), did not have stronger links with particular host species, although they had a greater frequency of significant links. In contrast, hosts classified as important based on network properties were synanthropic mammals. The latter were the most common parasite hosts and are likely bridge species between these communities, thereby integrating meta-community scenarios beneficial for long-range parasite dispersal. This was particularly true for rodents, >50% of species are synanthropic and more than 20% have been identified asT. cruzihosts. In addition to predicting potential host species using the co-occurrence networks, they reveal regions with greater expected parasite mobility. The Neotropical region, which includes the Mexican south and southeast, and the Transvolcanic belt, had greatest potential activeT. cruzidispersal, as well as greatest edge density. This information could be directly applied for stratification of transmission risk and to design and analyze human-infected vector contact intervention efficacy.
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Prasitlumkum, Narut, Wisit Cheungpasitporn, Nithi Tokavanich, Kimberly R. Ding, Jakrin Kewcharoen, Charat Thongprayoon, Wisit Kaewput, Tarun Bathini, Saraschandra Vallabhajosyula, and Ronpichai Chokesuwattanaskul. "Antidepressants and Risk of Sudden Cardiac Death: A Network Meta-Analysis and Systematic Review." Medical Sciences 9, no. 2 (April 23, 2021): 26. http://dx.doi.org/10.3390/medsci9020026.
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Background: Antidepressants are one of the most prescribed medications, particularly for patients with mental disorders. Nevertheless, there are still limited data regarding the risk of ventricular arrhythmia (VA) and sudden cardiac death (SCD) associated with these medications. Thus, we performed systemic review and meta-analysis to characterize the risks of VA and SCD among patients who used common antidepressants. Methods: A literature search for studies that reported risk of ventricular arrhythmias and sudden cardiac death in antidepressant use from MEDLINE, EMBASE, and Cochrane Database from inception through September 2020. A random-effects model network meta-analysis model was used to analyze the relation between antidepressants and VA/SCD. Surface Under Cumulative Ranking Curve (SUCRA) was used to rank the treatment for each outcome. Results: The mean study sample size was 355,158 subjects. Tricyclic antidepressant (TCA) patients were the least likely to develop ventricular arrhythmia events/sudden cardiac deaths at OR 0.24, 0.028–1.2, OR 0.32 (95% CI 0.038–1.6) for serotonin and norepinephrine reuptake inhibitors (SNRI), and OR 0.36 (95% CI 0.043, 1.8) for selective serotonin reuptake inhibitors (SSRI), respectively. According to SUCRA analysis, TCA was on a higher rank compared to SNRI and SSRI considering the risk of VA/SCD. Conclusion: Our network meta-analysis demonstrated the low risk of VA/SCD among patients using antidepressants for SNRI, SSRI and especially, TCA. Despite the relatively lowest VA/SCD in TCA, drug efficacy and other adverse effects should be taken into account in patients with mental disorders.
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Chierrito de Oliveira, Danielly, Patricia Guerrero de Sousa, Camila Borges dos Reis, Fernanda Stumpf Tonin, Laiza Maria Steimbach, Suzane Virtuoso, Fernando Fernandez-Llimos, Roberto Pontarolo, and Andréia Cristina Conegero Sanches. "Safety of Treatments for ADHD in Adults: Pairwise and Network Meta-Analyses." Journal of Attention Disorders 23, no. 2 (April 3, 2017): 111–20. http://dx.doi.org/10.1177/1087054717696773.
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Objective: The aim of the study was to analyze evidence comparing the profile of drugs used to treat ADHD in adult patients. Method: Systematic searches were conducted in electronic databases. Randomized, double-blind, parallel controlled trials that evaluated the safety of drugs in ADHD were included. The statistical analyses were conducted by pairwise meta-analyses and mixed treatment comparison (MTC). Results: Ten ( n = 3006) trials were included in the analyses. We observed statistical differences for the following outcomes: decreased appetite between atomoxetine and placebo (odds ratio [OR] = 0.15, 95% credibility interval [CrI] = [0.05, 0.38]) and extended-release mixed amphetamine salts and placebo (OR = 0.06, 95% CrI = [0.00, 0.51]); insomnia between atomoxetine and placebo (OR = 0.48, 95% CrI = [0.27, 0.88]) and extended-release mixed amphetamine salts and placebo (OR = 0.23, 95% CrI = [0.06, 0.76]); sleepiness between atomoxetine and methylphenidate OROS (OR = 0.24, 95% CrI = [0.06, 0.97]); and decreased libido between atomoxetine and placebo (OR = 0.28, 95% CrI = [0.08, 0.90]). Conclusion: It was possible to generate evidence about the safety profile of different ADHD drugs.
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Wei, Fei-Long, Ya Liu, Cheng-Pei Zhou, Si-Guo Sun, Kai-Long Zhu, Ming-Rui Du, Hao-Ran Gao, et al. "Management for lumbar spinal stenosis: Protocol for a network meta-analysis and systematic review." Journal of Orthopaedic Surgery 28, no. 3 (May 1, 2020): 230949902097521. http://dx.doi.org/10.1177/2309499020975212.
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Introduction: Lumbar spinal stenosis (LSS) is caused by structural changes of the spine, which lead to several severe symptoms, including back pain, leg pain, numbness and tingling in the legs, as well as reduced physical function. However, there is little evidence suggesting whether a patient with LSS should be treated with surgery. If surgery is recommended, which type of surgery benefits the patient most? To answer these questions, we will conduct a network meta-analysis and a systematic review to compare surgical and nonsurgical interventions in terms of efficacy as well as safety in adult patients with LSS. Methods and analysis: We will search the PubMed, Cochrane library, and EMBASE databases for articles published prior to October 10, 2019. We will search for randomized controlled trials assessing surgical and nonsurgical interventions for adult patients with degenerative LSS without any language restrictions. The primary outcome measures will be pain and disability. The secondary outcomes will include adverse events (number of events or number of people with each type of adverse event), reoperations, complications, blood loss and operation time. We will obtain the full texts of the potentially relevant studies and independently assess them. The quality of evidence will be evaluated according to the Grading of Recommendations Assessment, Development and Evaluation framework. A random-effects network meta-analysis will be performed to analyze all the evidence under the frequentist framework, and the ranking results will be presented. We will generate plots depicting the network geometry using Stata. The network meta-analysis will be performed according to the Bayesian framework. Ethics and dissemination Ethics approval is not required. The research will be published in a peer-reviewed journal.
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Mehrsai, Afshin, Hamid-Reza Karimi, Klaus-Dieter Thoben, and Bernd Scholz-Reiter. "Using Metaheuristic and Fuzzy System for the Optimization of Material Pull in a Push-Pull Flow Logistics Network." Mathematical Problems in Engineering 2013 (2013): 1–19. http://dx.doi.org/10.1155/2013/359074.
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Alternative material flow strategies in logistics networks have crucial influences on the overall performance of the networks. Material flows can follow push, pull, or hybrid systems. To get the advantages of both push and pull flows in networks, the decoupling-point strategy is used as coordination mean. At this point, material pull has to get optimized concerning customer orders against pushed replenishment-rates. To compensate the ambiguity and uncertainty of both dynamic flows, fuzzy set theory can practically be applied. This paper has conceptual and mathematical parts to explain the performance of the push-pull flow strategy in a supply network and to give a novel solution for optimizing the pull side employing Conwip system. Alternative numbers of pallets and their lot-sizes circulating in the assembly system are getting optimized in accordance with a multi-objective problem; employing a hybrid approach out of meta-heuristics (genetic algorithm and simulated annealing) and fuzzy system. Two main fuzzy sets as triangular and trapezoidal are applied in this technique for estimating ill-defined waiting times. The configured technique leads to smoother flows between push and pull sides in complex networks. A discrete-event simulation model is developed to analyze this thesis in an exemplary logistics network with dynamics.
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Meghdouri, Fares, Tanja Zseby, and Félix Iglesias. "Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic." Applied Sciences 8, no. 11 (November 9, 2018): 2196. http://dx.doi.org/10.3390/app8112196.
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The consolidation of encryption and big data in network communications have made deep packet inspection no longer feasible in large networks. Early attack detection requires feature vectors which are easy to extract, process, and analyze, allowing their generation also from encrypted traffic. So far, experts have selected features based on their intuition, previous research, or acritically assuming standards, but there is no general agreement about the features to use for attack detection in a broad scope. We compared five lightweight feature sets that have been proposed in the scientific literature for the last few years, and evaluated them with supervised machine learning. For our experiments, we use the UNSW-NB15 dataset, recently published as a new benchmark for network security. Results showed three remarkable findings: (1) Analysis based on source behavior instead of classic flow profiles is more effective for attack detection; (2) meta-studies on past research can be used to establish satisfactory benchmarks; and (3) features based on packet length are clearly determinant for capturing malicious activity. Our research showed that vectors currently used for attack detection are oversized, their accuracy and speed can be improved, and are to be adapted for dealing with encrypted traffic.
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Lee, Jun Mo, Ye Jin Cho, Eun Jin Ahn, Geun Joo Choi, and Hyun Kang. "Pharmacological strategies to prevent postoperative delirium: a systematic review and network meta-analysis." Anesthesia and Pain Medicine 16, no. 1 (January 31, 2021): 28–48. http://dx.doi.org/10.17085/apm.20079.
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Background: Postoperative delirium (POD) is a condition of cerebral dysfunction and a common complication after surgery. This study aimed to compare and determine the relative efficacy of pharmacological interventions for preventing POD using a network meta-analysis (NMA).Methods: We performed a systematic and comprehensive search to identify and analyze all randomized controlled trials until June 29, 2020, comparing two or more pharmacological interventions, including placebo, to prevent or reduce POD. The primary outcome was the incidence of POD. We performed a network meta-analysis and used the surface under the cumulative ranking curve (SUCRA) values and rankograms to present the hierarchy of the pharmacological interventions evaluated.Results: According to the SUCRA value, the incidence of POD decreased in the following order: the combination of propofol and acetaminophen (86.1%), combination of ketamine and dexmedetomidine (86.0%), combination of diazepam, flunitrazepam, and pethidine (84.8%), and olanzapine (75.6%) after all types of anesthesia; combination of propofol and acetaminophen (85.9%), combination of ketamine and dexmedetomidine (83.2%), gabapentin (82.2%), and combination of diazepam, flunitrazepam, and pethidine (79.7%) after general anesthesia; and ketamine (87.1%), combination of propofol and acetaminophen (86.0%), and combination of dexmedetomidine and acetaminophen (66.3%) after cardiac surgery. However, only the dexmedetomidine group showed a lower incidence of POD than the control group after all types of anesthesia and after general anesthesia.Conclusions: Dexmedetomidine reduced POD compared with the control group. The combination of propofol and acetaminophen and the combination of ketamine and dexmedetomidine seemed to be effective in preventing POD. However, further studies are needed to determine the optimal pharmacological intervention to prevent POD.
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Xu, Shan, Ali Sak, and Yasin Bahadir Erol. "Network Meta-analysis of First-Line Systemic Treatment for Patients With Metastatic Colorectal Cancer." Cancer Control 28 (January 2021): 107327482110334. http://dx.doi.org/10.1177/10732748211033497.
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Purpose To assess the relative efficacy and safety of first-line systemic therapies in patients with metastatic colorectal cancer. Experimental Design A comprehensive literature review was conducted including MEDLINE, Embase, and the Cochrane Central Registry of Controlled Trials for phase II or III randomized controlled trials (RCTs) published up to and including July 15, 2019. We included RCTs in which at least 1 intervention was either chemotherapeutic agents (such as fluorouracil, irinotecan, or oxaliplatin) or antibodies targeting angiogenesis (such as bevacizumab) or agents that act on the epidermal growth factor receptor pathway (such as cetuximab and panitumumab) or studies reported at least one of the following outcomes: overall survival (OS), progression-free survival (PFS), and/or Grade 3 + adverse events (AEs). Using a random effect model, we performed a Bayesian network meta-analysis to analyze the probability of optimal therapeutic regime obtained from direct comparisons with indirect evidences. We estimated hazard ratios for OS and PFS. Results A total of 30 RCTs comprising 12,146 mCRC patients with 25 different treatment strategies were included. The triple combination FOLFOXIRI [fluorouracil, leucovorin, oxaliplatin, and irinotecan] plus bevacizumab provided significant survival benefits with improved OS over all other treatments. The network meta-analysis also indicated a significant advantage of using FOLFOXIRI plus bevacizumab in comparison to other treatment strategies for PFS. Besides, FOLFOXIRI plus bevacizumab was associated with the well-tolerated adverse events. Conclusions Our study supported the use of FOLFOXIRI plus bevacizumab as the best first-line regimen and potentially effective and safe strategy for the management of patients with mCRC.
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Wang, Xiao, Yiding Zhang, and Chuan Shi. "Hyperbolic Heterogeneous Information Network Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5337–44. http://dx.doi.org/10.1609/aaai.v33i01.33015337.
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Heterogeneous information network (HIN) embedding, aiming to project HIN into a low-dimensional space, has attracted considerable research attention. Most of the exiting HIN embedding methods focus on preserving the inherent network structure and semantic correlations in Euclidean spaces. However, one fundamental problem is that whether the Euclidean spaces are the appropriate or intrinsic isometric spaces of HIN? Recent researches argue that the complex network may have the hyperbolic geometry underneath, because the underlying hyperbolic geometry can naturally reflect some properties of complex network, e.g., hierarchical and power-law structure. In this paper, we make the first effort toward HIN embedding in hyperbolic spaces. We analyze the structures of two real-world HINs and discover some properties, e.g., the power-law distribution, also exist in HIN. Therefore, we propose a novel hyperbolic heterogeneous information network embedding model. Specifically, to capture the structure and semantic relations between nodes, we employ the meta-path guided random walk to sample the sequences for each node. Then we exploit the distance in hyperbolic spaces as the proximity measurement. The hyperbolic distance is able to meet the triangle inequality and well preserve the transitivity in HIN. Our model enables the nodes and their neighborhoods have small hyperbolic distances. We further derive the effective optimization strategy to update the hyperbolic embeddings iteratively. The experimental results, in comparison with the state-of-the-art, demonstrate that our proposed model not only has superior performance on network reconstruction and link prediction tasks but also shows its ability of capture hierarchy structure in HIN via visualization.
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Zhang, Pei, Ying Li, Dong Wang, and Jiyue Wang. "RS-SSKD: Self-Supervision Equipped with Knowledge Distillation for Few-Shot Remote Sensing Scene Classification." Sensors 21, no. 5 (February 24, 2021): 1566. http://dx.doi.org/10.3390/s21051566.
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While growing instruments generate more and more airborne or satellite images, the bottleneck in remote sensing (RS) scene classification has shifted from data limits toward a lack of ground truth samples. There are still many challenges when we are facing unknown environments, especially those with insufficient training data. Few-shot classification offers a different picture under the umbrella of meta-learning: digging rich knowledge from a few data are possible. In this work, we propose a method named RS-SSKD for few-shot RS scene classification from a perspective of generating powerful representation for the downstream meta-learner. Firstly, we propose a novel two-branch network that takes three pairs of original-transformed images as inputs and incorporates Class Activation Maps (CAMs) to drive the network mining, the most relevant category-specific region. This strategy ensures that the network generates discriminative embeddings. Secondly, we set a round of self-knowledge distillation to prevent overfitting and boost the performance. Our experiments show that the proposed method surpasses current state-of-the-art approaches on two challenging RS scene datasets: NWPU-RESISC45 and RSD46-WHU. Finally, we conduct various ablation experiments to investigate the effect of each component of the proposed method and analyze the training time of state-of-the-art methods and ours.
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Li, Hao, Ming-Hui Chen, Joseph G. Ibrahim, Sungduk Kim, Arvind K. Shah, Jianxin Lin, and Andrew M. Tershakovec. "Bayesian inference for network meta-regression using multivariate random effects with applications to cholesterol lowering drugs." Biostatistics 20, no. 3 (April 18, 2018): 499–516. http://dx.doi.org/10.1093/biostatistics/kxy014.
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Summary Low-density lipoprotein cholesterol (LDL-C) has been identified as a causative factor for atherosclerosis and related coronary heart disease, and as the main target for cholesterol- and lipid-lowering therapy. Statin drugs inhibit cholesterol synthesis in the liver and are typically the first line of therapy to lower elevated levels of LDL-C. On the other hand, a different drug, Ezetimibe, inhibits the absorption of cholesterol by the small intestine and provides a different mechanism of action. Many clinical trials have been carried out on safety and efficacy evaluation of cholesterol lowering drugs. To synthesize the results from different clinical trials, we examine treatment level (aggregate) network meta-data from 29 double-blind, randomized, active, or placebo-controlled statins +/$-$ Ezetimibe clinical trials on adult treatment-naïve patients with primary hypercholesterolemia. In this article, we propose a new approach to carry out Bayesian inference for arm-based network meta-regression. Specifically, we develop a new strategy of grouping the variances of random effects, in which we first formulate possible sets of the groups of the treatments based on their clinical mechanisms of action and then use Bayesian model comparison criteria to select the best set of groups. The proposed approach is especially useful when some treatment arms are involved in only a single trial. In addition, a Markov chain Monte Carlo sampling algorithm is developed to carry out the posterior computations. In particular, the correlation matrix is generated from its full conditional distribution via partial correlations. The proposed methodology is further applied to analyze the network meta-data from 29 trials with 11 treatment arms.
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Zhang, Yiding, Xiao Wang, Nian Liu, and Chuan Shi. "Embedding Heterogeneous Information Network in Hyperbolic Spaces." ACM Transactions on Knowledge Discovery from Data 16, no. 2 (April 30, 2022): 1–23. http://dx.doi.org/10.1145/3468674.
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Heterogeneous information network (HIN) embedding, aiming to project HIN into a low-dimensional space, has attracted considerable research attention. Most of the existing HIN embedding methods focus on preserving the inherent network structure and semantic correlations in Euclidean spaces. However, one fundamental problem is whether the Euclidean spaces are the intrinsic spaces of HIN? Recent researches find the complex network with hyperbolic geometry can naturally reflect some properties, e.g., hierarchical and power-law structure. In this article, we make an effort toward embedding HIN in hyperbolic spaces. We analyze the structures of three HINs and discover some properties, e.g., the power-law distribution, also exist in HINs. Therefore, we propose a novel HIN embedding model HHNE. Specifically, to capture the structure and semantic relations between nodes, HHNE employs the meta-path guided random walk to sample the sequences for each node. Then HHNE exploits the hyperbolic distance as the proximity measurement. We also derive an effective optimization strategy to update the hyperbolic embeddings iteratively. Since HHNE optimizes different relations in a single space, we further propose the extended model HHNE++. HHNE++ models different relations in different spaces, which enables it to learn complex interactions in HINs. The optimization strategy of HHNE++ is also derived to update the parameters of HHNE++ in a principle manner. The experimental results demonstrate the effectiveness of our proposed models.
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Manjunath, T. G., and Ashok Kusagur. "Analysis of Different Meta Heuristics Method in Intelligent Fault Detection of Multilevel Inverter with Photovoltaic Power Generation Source." International Journal of Power Electronics and Drive Systems (IJPEDS) 9, no. 3 (September 1, 2018): 1214. http://dx.doi.org/10.11591/ijpeds.v9.i3.pp1214-1222.
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<p>Meta Heuristic methods have made a deep impact in the area of optimization in different streams of engineering. The performance of these algorithms is of importance because the hardware implementation of these algorithms is to be carried out for different engineering applications. As an important application in High Voltage DC (HVDC) transmission and Industrial Drives the multilevel inverter fault diagnosis is carried out using the different meta-heuristic methods with Neural Network as the decision making algorithm. The optimization of the weight and the bias values in the neural network diagnosis system is carried out in order to analyze the performance by means of comparing the Mean Square Error (MSE) while the Neural Network is getting trained for different fault conditions in the multilevel inverter. Matlab based implementation is carried out and the results are tabulated and inferred for a Multilevel Inverter fed from the Photovoltaic power generation system. In order to increase the robustness of the fault detection, with renewable energy based power generation system as the source for the Multilevel Inverter, the feature extracted from the multilevel inverter are positive, negative and zero sequence voltage along with the THD of the output voltage. The optimization algorithm used is Particle Swarm Optimization (PSO), Cuckoo Search Algorithm(CSA), Genetic Algorithm(GA) and Tabu Search Algorithm (TSA).</p>
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Kalantari, Esmaeel, Gholamali Montazer, and Sepehr Ghazinoory. "Mapping of a science and technology policy network based on social network analysis." Journal of Entrepreneurship, Management and Innovation 17, no. 3 (2021): 115–47. http://dx.doi.org/10.7341/20211734.
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PURPOSE: The main purpose of this paper is to define a science and technology policy network in the form of a social network, from the perspective of policy documents, and then analyze it using the social networks analysis (SNA) method. METHODOLOGY: As a case study, the science and technology policymaking network in Iran is analyzed using the suggested framework in this research. The data used in this study were collected through the content analysis of 25 policy documents and an interview with 20 Iranian science and technology policy elites, before being interpreted using the social network analysis method and software such as NetDraw and UCINet. FINDINGS: The most pivotal science and technology policymaking institutions in Iran and the interactions between them were determined from the network viewpoint. This was achieved by performing a two-dimensional core-periphery analysis, identifying the cut points and blocks, and measuring the structural power of each institution using the degree centrality, closeness centrality, and betweenness centrality methods. IMPLICATIONS FOR THEORY AND PRACTICE: The most important practical implications of this research are: the integration of a number of policymaking institutions, the division of clear and precise work between policy institutions, the design of vertical and horizontal coordination mechanisms between institutions, the elimination of interferences of some institutions in the tasks of the others, the design of complementary mechanisms to control the role of cutting points, and paying attention to the important activities in the margins of the network. ORIGINALITY AND VALUE: The most important contribution of this research is to develop a framework for studying science and technology policy and then to develop a method for studying science and technology policy based on SNA. Therefore, the framework for studying science and technology policy in a cycle consists of three stages: 1- Agenda setting and prioritization (at two levels of mega policies and meta policies); 2- Design and implementation or executive policies (in three parts: demand-side policies, supply-side policies, and networking and interconnection infrastructure policies); 3- evaluation and policy learning.
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Piciocchi, Alfonso, Katia Bontempi, Paola Fazi, Marco Vignetti, and Franco Mandelli. "Forest Plot to Compare Therapies in a Network Meta-Analysis in First Line Chronic Lymphocytic Leukemia." Blood 120, no. 21 (November 16, 2012): 4596. http://dx.doi.org/10.1182/blood.v120.21.4596.4596.
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Abstract Abstract 4596 Several therapies, composed by single agent or combinations, are available for untreated patients in chronic lymphocytic leukemia and it is difficult to rank treatments according to their effect size when not all treatments have been compared directly. Direct randomized comparison is the most reliable way of comparing treatments but when the number of available treatments increases the number of possible pairwise comparisons increases quadratically, so it is common for only a small fraction of the possible comparisons to be performed. Network meta-analysis permits to add indirect estimates for assessing the relative effectiveness of two treatments when they have not been compared directly in a randomized trial but have each been compared to other treatments. Network meta-analysis of randomized controlled trials was used to combine direct and indirect estimates of the effect of four drugs and their combinations from five randomized controlled trials (Fig. IA) in CLL patients based on a systematic review conducted by M.M. Cheng et al. (Cancer Treatment Review 2012) on 2625 patients where Chorambucil, Fludarabine, Rituximab, Alemtuzumab, Bendamustine, Cladribine and Pentostatin have been considered as primary first-line agents for CLL. Patients were younger (59–65 years), had good performance status (ECOG: 0–1) and early stage disease (Rai 0-II, Binet A or B). We carried out an analysis based on the endpoint of PFS, described as the duration of the time from randomization until disease progression or death from any cause and to consider the heterogeneity among the studies a random effects model was performed. The differences in PFS of all therapies considered in the analysis were compared with combination of Fludarabine and Chorambucil (FC). The results were summarized by forest plot (Fig IB) in terms of reduction or increase of Hazard Ratio. As the forest plot shows, FCR has relatively higher potential of preventing disease progression in younger patients affected by chronic lymphocytic leukemia. Respect to a traditional meta-analysis, which usually compares only two treatments, network meta-analysis is a useful method to combine direct and indirect comparisons of treatments from RCTs and to analyze the hierarchy in treatment effects and tests for consistency of the relations of the network. Disclosures: No relevant conflicts of interest to declare.
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Shourian, M., S. Jamshid Mousavi, M. B. Menhaj, and E. Jabbari. "Neural-network-based simulation-optimization model for water allocation planning at basin scale." Journal of Hydroinformatics 10, no. 4 (October 1, 2008): 331–43. http://dx.doi.org/10.2166/hydro.2008.057.
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Heuristic search techniques are highly flexible, though they represent computationally intensive optimization methods that may require thousands of evaluations of expensive objective functions. This paper integrates MODSIM, a generalized river basin network flow model, a particle swarm optimization (PSO) algorithm and artificial neural networks into a modeling framework for optimum water allocations at basin scale. MODSIM is called in the PSO model to simulate a river basin system operation and to evaluate the fitness of each set of selected design and operational variables with respect to the model's objective function, which is the minimization of the system's design and operational cost. Since the direct incorporation of MODSIM into a PSO algorithm is computationally prohibitive, an ANN model as a meta-model is trained to approximate the MODSIM modeling tool. The resulting model is used in the problem of optimal design and operation of the upstream Sirvan river basin in Iran as a case study. The computational efficiency of the model makes it possible to analyze the model performance through changing its parameters so that better solutions are obtained compared to those of the original PSO–MODSIM model.
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Ayrapetyan, David, and Frans Hermans. "Introducing a Multiscalar Framework for Biocluster Research: A Meta-Analysis." Sustainability 12, no. 9 (May 9, 2020): 3890. http://dx.doi.org/10.3390/su12093890.
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Bioclusters have grown in popularity in the last decade in response to the global environmental and climate challenges. These clusters envisage sustainable and local production value chains in different sectors of the bioeconomy. However, the sustainability of these clusters is often questioned because of the negative social and environmental effects they can have both inside and outside of their region. At present, a framework is missing to analyze these effects that span multiple levels and multiple scales. The aim of this paper is to develop such a multiscalar framework. For this aim, we conducted a meta-analysis of biocluster case studies. As a result, we constructed a framework that combines the aspects of sustainability, knowledge and resource flows, cluster network properties, and the political and institutional structures. We tested this framework on the question of how the different scales of biocluster performance interact and depend on each other.
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Zhang, Jinhuan, Yongfeng Liu, Xingxian Huang, Yirong Chen, Liyu Hu, Kai Lan, and Haibo Yu. "Efficacy Comparison of Different Acupuncture Treatments for Functional Dyspepsia: A Systematic Review with Network Meta-Analysis." Evidence-Based Complementary and Alternative Medicine 2020 (March 19, 2020): 1–18. http://dx.doi.org/10.1155/2020/3872919.
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Background. Acupuncture has been found to be an effective treatment for functional dyspepsia (FD). Currently, several types of acupuncture have been developed but it is not clear which type is suitable for FD. Currently, doctors often rely on experience to decide which form of acupuncture to apply. Herein, we employed network meta-analysis (NMA) to compare the effectiveness of various methods of acupuncture in the treatment of functional dyspepsia. Methods. We searched for randomized controlled trials (RCTs) of acupuncture treatments for functional dyspepsia in seven databases; PubMed, the Cochrane Library, Embase, Wanfang database, China National Knowledge Infrastructure (CNKI) database, Chinese Science and Technique Journals (CQVIP), and Chinese Biomedical Database (CBM) from the date of database inception to October 10, 2019. Cochrane risk of bias tool was used to analyze the risk of bias of the included RCTs. Pairwise meta-analyses were performed with RevMan 5.3 and the network meta-analysis of the included RCTs was performed using the frequentist framework. Results. A total of 35 studies involving 3301 patients and 10 interventions were eligible for this study. NMA results showed that five types of acupuncture (manual acupuncture, acupoint application, moxibustion, acupoint catgut embedding, and warm acupuncture alone) all were superior to prokinetics (itopride, mosapride, and domperidone) and sham acupuncture in terms of improving the symptoms of functional dyspepsia. Specifically, manual acupuncture and electroacupuncture were more effective in improving the MOS 36 Item Short-Form Health Survey (SF-36) compared to itopride and sham acupuncture, and electroacupuncture was the best among the three acupuncture therapies (acupuncture, electroacupuncture, and acupoint catgut embedding). Moxibustion and manual acupuncture were more effective in improving Nepean Dyspepsia Life Quality Index (NDLQI) compared to itopride, domperidone, and sham acupuncture; moxibustion ranks first among the three acupuncture therapies (acupuncture, electroacupuncture, moxibustion). Conclusions. These results showed that manual acupuncture alone was the most effective therapy for FD. It should, therefore, be considered as an alternative treatment for FD patients who are unresponsive to prokinetics or intolerant to the adverse effects of prokinetics. We recommend further multiple centers and high-quality RCT studies to confirm the present findings.
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Yuan, Ting, Feng Zhang, Qingmin Yao, Yanxia Liu, Xiaojuan Zhu, and Peng Chen. "Maintenance therapy for untreated diffuse large B-cell lymphoma: a systematic review and network meta-analysis." Therapeutic Advances in Hematology 12 (January 2021): 204062072110188. http://dx.doi.org/10.1177/20406207211018894.
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Background: Several clinical trials have been conducted to evaluate the role of maintenance therapy in untreated diffuse large B-cell lymphoma (DLBCL) patients with complete response or partial response following standard immunochemotherapy; however, the effect of maintenance therapy remains uncertain, and a suitable maintenance strategy has not been determined because of the lack of direct/indirect comparisons. Methods: We performed a systematic review and Bayesian network meta-analysis (NMA) to analyze and compare the effectiveness of different maintenance regimens in newly diagnosed DLBCL patients. We searched the PubMed Medline, Embase, Web of Science, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials for relevant papers from inception to 18 March 2021. Our study was prospectively registered in the International Prospective Register of Systematic Reviews (CRD42020168864). Data on overall survival (OS) were extracted and the treatments were ranked using the surface under the cumulative ranking (SUCRA) curve. Results: Eight trials and seven treatments involving 3525 patients were analyzed. OS analysis indicated that none of the drugs showed any benefit compared with non-maintenance therapy. Maintenance therapy with lenalidomide (SUCRA 69.3%) was ranked first in terms of OS. Conclusion: Based on the OS results observed in this NMA, we do not recommend maintenance therapy in patients with newly diagnosed DLBLC after first-line therapy.
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Fuentes, Harry E., Robert McBane, Waldemar Wysokinski, Alfonso Javier Tafur, Charles L. Loprinzi, M. Hassan Murad, and Irbaz Bin Riaz. "Direct oral factor Xa inhibitors for the treatment of acute cancer-associated venous thromboembolism: A systematic review and network meta-analysis." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e23156-e23156. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e23156.
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e23156 Background: A direct meta-analysis was performed to explore the efficacy and safety of direct oral factor Xa inhibitors with dalteparin in patients with cancer associated acute venous thromboembolism (VTE). Also, the comparative efficacy and safety of apixaban, rivaroxaban, and edoxaban was assessed with a network meta-analysis. Methods: MEDLINE, CENTRAL, and EMBASE were searched for trials comparing direct oral anticoagulants (DOACs) to dalteparin for the management of cancer associated acute VTE. A network meta-analysis using both frequentist and Bayesian methods was performed to analyze VTE recurrence, major and clinically relevant non-major bleeding (CRNMB). Results: Three randomized control trials, at low risk of bias, enrolled 1,739 patients with cancer associated VTE. Direct comparison showed a lower rate of VTE recurrence in DOAC compared to dalteparin groups (odds Ratio [OR]:0.48, 95% Confidence interval [CI]:0.24-0.96; I2:46%). Indirect comparison suggested that apixaban had greater reduction in VTE recurrence compared to dalteparin (OR: 0.10; 95% CI: 0.01–0.82), but not rivaroxaban or edoxaban. Apixaban also had the highest probability of being ranked most effective. By direct comparisons, there was an increased likelihood of major bleeding in the DOAC group compared to dalteparin (OR: 1.70; 95% CI: 1.04–2.78). CRNMB did not differ. Indirect estimates were imprecise. Subgroup analyses in gastrointestinal cancers suggested that dalteparin may have the lowest risk of bleeding whereas estimates in urothelial cancer were imprecise. Conclusions: DOACs appear to lower the risk of VTE recurrence compared to daltaparin while increasing major bleeding. Apixaban may be associated with the lowest risk of VTE recurrence compared to the other DOACs.
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Kang, Wanmo, Jinhyung Chon, and GoWoon Kim. "Urban Ecosystem Services: A Review of the Knowledge Components and Evolution in the 2010s." Sustainability 12, no. 23 (November 25, 2020): 9839. http://dx.doi.org/10.3390/su12239839.
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In an effort to reconnect urban populations to the biosphere, which is an urgent task to ensure human sustainability, the concept of urban ecosystem services (UES) has recently garnered scholarly and political attention. With an aim to examine the emerging research trends and gaps in UES, we present an up-to-date, computer-based meta-analysis of UES from 2010 to 2019 by implementing a keyword co-occurrence network (KCN) approach. A total of 10,247 author keywords were selected and used to analyze undirected and weighted networks of these keywords. Specifically, power-law distribution fitting was performed to identify overall UES keyword trends, and clusters of keywords were examined to understand micro-level knowledge trends. The knowledge components and structures of UES literature exhibited scale-free network characteristics, which implies that the KCN of the UES throughout the 2010s was dominated by a small number of keywords such as “urbanization”, “land use and land cover”, “urban green space” and “green infrastructure”. Finally, our findings indicate that knowledge of stakeholder involvement and qualitative aspects of UES are not as refined as spatial UES approaches. The implications of these knowledge components and trends are discussed in the context of urban sustainability and policy planning.
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Ivaniushina, Valeria, and Vera Titkova. "Peer influence in adolescent drinking behavior: A meta-analysis of stochastic actor-based modeling studies." PLOS ONE 16, no. 4 (April 16, 2021): e0250169. http://dx.doi.org/10.1371/journal.pone.0250169.
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Objectives To measure the effects of peer influence and peer selection on drinking behavior in adolescence through a rigorous statistical approach designed to unravel these interrelated processes. Methods We conducted systematic searches of electronic databases, thesis collections and conference proceedings to identify studies that used longitudinal network design and stochastic actor-oriented modeling to analyze drinking behavior in adolescents. Parameter estimates collected from individual studies were analyzed using multilevel random-effects models. Results We identified 26 articles eligible for meta-analysis. Meta-analyses for different specifications of the peer influence effect were conducted separately. The peer influence effect was positive for every specification: for average similarity (avSim) mean log odds ratio was 1.27 with 95% confidence interval [0.04; 2.49]; for total similarity (totSim) 0.46 (95% CI = [0.44; 0.48]), and for average alter (avAlt) 0.70 (95% CI = [-0.01; 1.41]). The peer selection effect (simX) was also positive: 0.46 (95% CI = [0.28; 0.63]). Conversion log odds ratio values to Cohen’s d gives estimates from 0.25 to 0.70, which is considered as medium to large effect. Conclusions Advances in methodology for social network analysis have made it possible to accurately estimate peer influence effects free from peer selection effects. More research is necessary to clarify the roles of age, gender, and individual susceptibility on the changing behavior of adolescents under the influence of their peers. Understanding the effects of peer influence should inform practitioners and policy makers to design and deliver more effective prevention programs.
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Wang, Maorong, and Yerong Yu. "THERAPEUTIC EFFECTS OF COMBINATION REGIMENS INCLUDING METHIMAZOLE ON GRAVES HYPERTHYROIDISM: A NETWORK META-ANALYSIS OF RANDOMIZED CONTROLLED TRIALS." Endocrine Practice 26, no. 6 (June 2020): 675–87. http://dx.doi.org/10.4158/ep-2019-0555.
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Objective: To analyze the effects of methimazole (MMI)-containing combination regimens on the thyroid status and relapse rates in patients with Graves hyperthyroidism (GH) using a network meta-analysis to provide guidance for clinical application. Methods: We conducted a literature review, which identified 21 trials for inclusion. The major outcomes were serum free triiodothyronine (FT3) and free thyroxine (FT4) concentrations. The secondary outcome was relapse rate. A network meta-analysis was used to compare multiple regimens to identify the most advantageous regimen. Results: The types of combined drugs included anti-oxidant complexes, selenium, vitamin D3, cholestyramine, risedronate, iodine, potassium bromide, immunosuppressants, and β-adrenergic antagonists. Regarding the FT3 results, the rank probability of the best result showed that potassium bromide (0.897) and vitamin D3 (0.833) had relative advantages in reducing FT3 at the 1-month time point. According to the time trend analysis, compared with the control treatment, cholestyramine and iodine showed advantages in reducing FT3 during the early stage (0 to 3 months). The immunosuppressants showed advantages in reducing FT3 during the late stage (>9 months) but not the early stage. Regarding the FT4 results, potassium bromide had the highest P-score (.965) at the 1-month time point. Iodine and cholestyramine had advantages in reducing FT4 during the early stage. The immunosuppressants had advantages during both the early and late stages. Conclusion: MMI combined with cholestyramine or iodine was shown to regulate serum FT3 and FT4 during the early stage of GH. MMI combined with immunosuppressants had a long-term advantage in FT3/FT4 regulation and reduced the relapse rate. Abbreviations: ATD = antithyroid drug; CI = confidence interval; FT3 = free triiodothyronine; FT4 = free thyroxine; GH = Graves hyperthyroidism; MMI = methimazole; OR = odds ratio; RCT = randomized controlled trial; SMD = standard mean difference; TCM = traditional Chinese medicine
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Ma, Wenting. "Study on the Influencing Factors on Network-based Autonomous Learning in Mechanical Engineering English." International Journal of Emerging Technologies in Learning (iJET) 11, no. 03 (March 30, 2016): 57. http://dx.doi.org/10.3991/ijet.v11i03.5536.
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Exploring the influencing factors of network-based autonomous learning is very important for colleges and universities in the world who have adopted this teaching mode in their education system. Many studies have discussed the teaching mode of autonomous learning, but failed to pay enough attention to the factors influencing students learning results. The purpose of this study is to analyze the relationship between the influencing factors and students’ learning results among different levels, and find better teaching method according to students’ learning condition. Mechanical Engineering students in Luoyang Institute of Science and Technology are employed as the data source. A number of data are collected and compared, and an analysis of various factors that influence the achievement of students’ network-based autonomous learning is performed. Data calculation shows that: students from different grades and levels differ in their learning motivations and beliefs; in learning strategies, they have no difference in cognitive stratagem, but in Meta-cognitive strategy and resource management strategies they are different. There are significant positive correlations between students’ intrinsic motivation and belief of resource management, students’ responsibility strategy use; extrinsic motivation and strategy use are a negative correlation, but not significant.
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Warner, Jeremy L., Peter C. Yang, and Gil Alterovitz. "Overcoming the Straw Man Effect in Oncology: Visualization and Ranking of Chemotherapy Regimens Using an Information Theoretic Approach." JCO Clinical Cancer Informatics, no. 1 (November 2017): 1–9. http://dx.doi.org/10.1200/cci.17.00079.
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Purpose Despite the plethora of randomized controlled trial (RCT) data, most cancer treatment recommendations are formulated by experts. Alternatively, network meta-analysis (NMA) is one method of analyzing multiple indirect treatment comparisons. However, NMA does not account for mixed end points or temporality. Previously, we described a prototype information theoretical approach for the construction of ranked chemotherapy treatment regimen networks. Here, we propose modifications to overcome an apparent straw man effect, where the most studied regimens were the most negatively valued. Methods RCTs from two scenarios—upfront treatment of chronic myelogenous leukemia and relapsed/refractory multiple myeloma—were assembled into ranked networks using an automated algorithm based on effect sizes, statistical significance, surrogacy of end points, and time since RCT publication. Vertex and edge color, transparency, and size were used to visually analyze the network. This analysis led to the additional incorporation of value propagation. Results A total of 18 regimens with 42 connections (chronic myelogenous leukemia) and 28 regimens with 25 connections (relapsed/refractory multiple myeloma) were analyzed. An initial negative correlation between vertex value and size was ameliorated after value propagation, although not eliminated. Updated rankings were in close agreement with published guidelines and NMAs. Conclusion Straw man effects can distort the comparative efficacy of newer regimens at the expense of older regimens, which are often cheaper or less toxic. Using an automated method, we ameliorated this effect and produced rankings consistent with common practice and published guidelines in two distinct cancer settings. These findings are likely to be generalizable and suggest a new means of ranking efficacy in cancer trials.
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Ashraf, Adnan, Sobia Pervaiz, Waqas Haider Bangyal, Kashif Nisar, Ag Asri Ag. Ibrahim, Joel j. P. C. Rodrigues, and Danda B. Rawat. "Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences." Applied Sciences 11, no. 17 (September 3, 2021): 8190. http://dx.doi.org/10.3390/app11178190.
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To solve different kinds of optimization challenges, meta-heuristic algorithms have been extensively used. Population initialization plays a prominent role in meta-heuristic algorithms for the problem of optimization. These algorithms can affect convergence to identify a robust optimum solution. To investigate the effectiveness of diversity, many scholars have a focus on the reliability and quality of meta-heuristic algorithms for enhancement. To initialize the population in the search space, this dissertation proposes three new low discrepancy sequences for population initialization instead of uniform distribution called the WELL sequence, Knuth sequence, and Torus sequence. This paper also introduces a detailed survey of the different initialization methods of PSO and DE based on quasi-random sequence families such as the Sobol sequence, Halton sequence, and uniform random distribution. For well-known benchmark test problems and learning of artificial neural network, the proposed methods for PSO (TO-PSO, KN-PSO, and WE-PSO), BA (BA-TO, BA-WE, and BA-KN), and DE (DE-TO, DE-WE, and DE-KN) have been evaluated. The synthesis of our strategies demonstrates promising success over uniform random numbers using low discrepancy sequences. The experimental findings indicate that the initialization based on low discrepancy sequences is exceptionally stronger than the uniform random number. Furthermore, our work outlines the profound effects on convergence and heterogeneity of the proposed methodology. It is expected that a comparative simulation survey of the low discrepancy sequence would be beneficial for the investigator to analyze the meta-heuristic algorithms in detail.
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Pagnini, Cristiano, Spyros I. Siakavellas, and Giorgos Bamias. "Systematic Review with Network Meta-Analysis: Efficacy of Induction Therapy with a Second Biological Agent in Anti-TNF-Experienced Crohn’s Disease Patients." Gastroenterology Research and Practice 2018 (July 17, 2018): 1–9. http://dx.doi.org/10.1155/2018/6317057.
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Background and Aim. Crohn’s disease (CD) is a chronic inflammatory condition of the gastrointestinal tract with the potential to progress to a severe debilitating state. Treatment with biological agents is highly efficient, improving both short-term outcomes and long-term prognosis. Nonetheless, up to 60% of patients receiving biological therapy will exhibit nonresponse at some point. The optimal management of such patients is not clearly defined. Besides traditional anti-TNF agents (infliximab, adalimumab, and certolizumab), alternative biological therapies (natalizumab, vedolizumab, and ustekinumab) are currently available for the treatment of CD. Our aim was to analyze all available evidence regarding efficacy of a second biological in “biological-treatment-experienced” patients. Methods. A systematic review of the literature was conducted using specific criteria for selecting relevant randomized clinical trials evaluating response to administration of secondary biological therapy in “anti-TNF-experienced” CD patients. Data from these studies was used to perform (a) traditional meta-analysis to ascertain the effect of secondary treatment versus placebo and (b) network meta-analysis to compare indirectly the efficacy of available biological agents. Results. Out of initially 977 studies, only eight were included for analysis, providing a total of 1281 treated and 733 placebo-receiving CD patients. Treatment with a second biological was found to be superior to placebo for both induction of remission (OR 2.2, 95% CI 1.7 to 3) and response (OR 1.9, 95% CI 1.5 to 2.5), with global rates of 24% and 42%, respectively (placebo rate: 11% and 27%, p<0.0001 for both). Indirect comparisons performed with network meta-analysis suggest no specific agent is clearly superior to others, with relatively better results observed for adalimumab in inducing disease remission. Conclusion. In anti-TNF-experienced CD patients, secondary biological administration may be efficient, while no specific agent seems to outperform the others. Further research is needed to identify optimal management strategies for this challenging subset of patients.
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Rivano, Melania, Luca Cancanelli, Lorenzo Di Spazio, Marco Chiumente, Daniele Mengato, and Andrea Messori. "Restricted mean survival time as outcome measure in advanced urothelial bladder cancer: analysis of 4 clinical studies." Immunotherapy 13, no. 2 (February 2021): 95–101. http://dx.doi.org/10.2217/imt-2020-0160.
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Background: The purpose of this study was to assess the effectiveness of immune checkpoint inhibitors (ICIs) in advanced urothelial carcinoma. Materials & methods: We selected seven cohorts of patients published in four clinical trials. The restricted mean survival time (RMST) was used to analyze survival curves, perform the comparisons and rank the treatments based on their effectiveness. The performance of RMST was compared with that of a network meta-analysis. Results: Three ICIs, vinflunine and best standard care, given as second line, were examined. ICIs significantly improved overall survival compared with best standard care. However, the survival gain was quite limited (up to 2.27 months). Post hoc pairwise comparisons were calculated. Conclusion: Our results summarized the efficacy of these treatments and confirmed the good methodological performance of RMST.
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Kaveh, Firoozeh, Reza Tavakkoli-Moghaddam, Chefi Triki, Yaser Rahimi, and Amin Jamili. "A new bi-objective model of the urban public transportation hub network design under uncertainty." Annals of Operations Research 296, no. 1-2 (November 8, 2019): 131–62. http://dx.doi.org/10.1007/s10479-019-03430-9.
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AbstractThis paper presents a new bi-objective multi-modal hub location problem with multiple assignment and capacity considerations for the design of an urban public transportation network under uncertainty. Because of the high construction costs of hub links in an urban public transportation network, it is not economic to create a complete hub network. Moreover, the demand is assumed to be dependent on the utility proposed by each hub. Thus, the elasticity of the demand is considered in this paper. The presented model also has the ability to compute the number of each type of transportation vehicles between every two hubs. The objectives of this model are to maximize the benefits of transportation by establishing hub facilities and to minimize the total transportation time. Since exact values of some parameters are not known in advance, a fuzzy multi-objective programming based approach is proposed to optimally solve small-sized problems. For medium and large-sized problems, a meta-heuristic algorithm, namely multi-objective particle swarm optimization is applied and its performance is compared with results from the non-dominated sorting genetic algorithm. Our experimental results demonstrated the validity of our developed model and approaches. Moreover, an intensive sensitivity analyze study is carried out on a real-case application related to the monorail project of the holy city of Qom.
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Zhou, Dongrui, Liandi Xie, Yao Wang, Shuang Wu, Fengzhi Liu, Shuangshuang Zhang, Ruijia Liu, and Lingqun Zhu. "Clinical Efficacy of Tonic Traditional Chinese Medicine Injection on Acute Cerebral Infarction: A Bayesian Network Meta-Analysis." Evidence-Based Complementary and Alternative Medicine 2020 (November 23, 2020): 1–12. http://dx.doi.org/10.1155/2020/8318792.
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Western medicine (WM) has certain limitations in terms of treating acute cerebral infarction (ACI), while tonic traditional Chinese medicine injections (TCMIs) have been shown to have obvious clinical effects as an adjunct to WM for ACI. However, most randomized controlled trials (RCTs) to date have not performed direct comparisons of efficacy among tonic TCMIs. This study designed a Bayesian network meta-analysis (NMA) to explore the therapeutic effect of tonic TCMIs on ACI. A comprehensive search of RCTs of TCMIs combined with WM for ACI was conducted using electronic databases for studies dated from the start date of each database until February 2020. Stata 13.0 and ADDIS 1.16.7 software were used to plot and analyze the data. Sixty-six RCTs with a total of 5,989 patients involving 7 kinds of tonic TCMIs were included. Among TCMIs, Shenfu injection (SFI) + WM ranked first in terms of improving clinical efficacy and the activities of daily living (ADLs) rating and reducing interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) levels. While Ciwujia injection (CI) + WM was the best choice for reducing neurological impairment and the high-cut viscosity of whole blood (HCV). Shenmai injection (SI) + WM had the greatest effects in terms of decreasing the levels of low-cut viscosity of whole blood (LCV), fibrinogen (FIB), and plasma viscosity (PV). Based on the cluster analysis of the clinical efficacy and the neurological impairment, CI + WM and Shenqifuzheng (SQI) + WM were the best options for treating ACI. With respect to adverse drug reactions (ADRs), 35 RCTs did not monitor ADRs during treatment. In conclusion, tonic TCMIs could assist WM in benefiting patients with ACI. However, due to the limitations of the current study, strict monitoring of ADRs and data from high-quality RCTs will be required in future to verify the advantage of TCMIs.
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Wu, Yun-bo, Yun-kai Dai, Ling Zhang, Huai-geng Pan, Wei-jing Chen, Ru-liu Li, and Ling Hu. "Pharmacological treatments of Chinese herbal medicine for irritable bowel syndrome in adults: A network meta-analysis of randomized controlled trials." PLOS ONE 16, no. 8 (August 6, 2021): e0255665. http://dx.doi.org/10.1371/journal.pone.0255665.
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Introduction Plenty of clinical studies have suggested the value of Chinese herbal medicine (CHM) for patients with irritable bowel syndrome (IBS), but their efficacy and safety have not been systematically concluded yet. This article aimed to compare and rank the therapeutic effect and safety of CHM with routine pharmacotherapies and placebo in the treatment of IBS. Methods Randomized controlled trials regarding CHM to treat IBS were searched in six databases from inception to Jan 31, 2020. A network meta-analysis was conducted to analyze the data of included publications. The quality assessment was assessed by Cochrane Handbook and GRADEpro software. The risk ratio was calculated for dichotomous outcomes while the standardized mean difference was used for continuous variables with 95% credible intervals. A Funnel plot was performed to evaluate publication bias. The surface under the cumulative ranking curve was conducted to rank the included interventions. Data were analyzed with STATA 15.0 and Review Manager 5.3. Result 3194 records were searched, and 28 eligible trials involving 3323 patients ere identified. Compared with conventional therapies and placebo, Jianpi-Chushi therapy showed significant improvement in adequate relief and IBS symptom severity scale; Shugan-Jianpi therapy showed the best efficacy in relieving the abdominal pain and abdominal distension; Wenshen-Jianpi therapy had a better effect on avoiding adverse effects and improving stool character. Conclusion This study confirmed that CHM could be beneficial for patients with IBS in relieving their clinical symptoms and should be recommended as alternative therapies. The quality of evidence in this study based on the GRADE system was “low”.
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Faridi, A., A. Golian, A. Heravi Mousavi, and J. France. "Bootstrapped neural network models for analyzing the responses of broiler chicks to dietary protein and branched chain amino acids." Canadian Journal of Animal Science 94, no. 1 (March 2014): 79–85. http://dx.doi.org/10.4141/cjas2013-078.
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Faridi, A., Golian, A., Heravi Mousavi, A. and France, J. 2014. Bootstrapped neural network models for analyzing the responses of broiler chicks to dietary protein and branched chain amino acids. Can. J. Anim. Sci. 94: 79–85. Reliable prediction of avian responses to dietary nutrients is essential for planning, management, and optimization activities in poultry nutrition. In this study, two bootstrapped neural network (BNN) models, each containing 100 separated neural networks (SNN), were developed for predicting average daily gain (ADG) and feed efficiency (FE) of broiler chicks in response to intake of protein and branched chain amino acids (BCAA) in the starter period. Using a re-sampling method, 100 different batches of data were generated for both the ADG and FE sets. Starting with 270 data lines extracted from eight studies in the literature, SNN models were trained, tested, and validated with 136, 67, and 67 data lines, respectively. All 200 SNN models developed, along with their respective BNN ones, were subjected to optimization (to find the optimum dietary protein and BCAA levels that maximize ADG and FE). Statistical analysis indicated that based on R 2, the BNN models were more accurate in 76 and 56 cases (out of 100) compared with the SNN models developed for ADG and FE, respectively. Optimization of the BNN models showed protein, isoleucine, leucine, and valine requirements for maximum ADG were 231.80, 9.05, 14.03 and 10.90 g kg−1 of diet, respectively. Also, maximum FE was obtained when the diet contained 232.30, 9.07, 14.50, and 11.04 g kg−1 of protein, isoleucine, leucine, and valine, respectively. The results of this study suggest that in meta-analytic modelling, bootstrap re-sampling algorithms should be used to better analyze available data and thereby take full advantage of them. This issue is of importance in the animal sciences as producing reliable data is both expensive and time-consuming.
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He, Tailai, Fei Han, Jiahao Wang, Yihe Hu, and Jianxi Zhu. "Efficacy and safety of anticoagulants for postoperative thrombophylaxis in total hip and knee arthroplasty: A PRISMA-compliant Bayesian network meta-analysis." PLOS ONE 16, no. 6 (June 17, 2021): e0250096. http://dx.doi.org/10.1371/journal.pone.0250096.
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Objective To search, review, and analyze the efficacy and safety of various anticoagulants from randomized clinical trials (RCTs) of anticoagulants for THA and TKA. Design PRISMA-compliant Bayesian Network Meta-analysis. Data sources and study selection The databases of The Medline, Embase, ClinicalTrial, and Cochrane Library databases were searched until March 2017 for RCTs of patients undergoing a THA or TKA. Main outcomes and measures The primary efficacy measurement was the venous thromboembolism Odds ratio (OR). The safety measurement was the odds ratio of major or clinically relevant bleeding. OR with 95% credibility intervals (95%CrIs) were calculated. Findings were interpreted as associations when the 95%CrIs excluded the null value. Results Thirty-five RCTs (53787 patients; mean age range, mostly 55–70 years; mean weight range, mostly 55–90 kg; and a higher mean proportion of women than men, around 60%) included the following Anticoagulants categories: fondaparinux, edoxaban, rivaroxaban, apixaban, dabigatran, low-molecular-weight heparin, ximelagatran, aspirin, warfarin. Anticoagulants were ranked for effectiveness as follows: fondaparinux (88.89% ± 10.90%), edoxaban (85.87% ± 13.34%), rivaroxaban (86.08% ± 10.23%), apixaban (68.26% ± 10.82%), dabigatran (41.63% ± 12.26%), low-molecular-weight heparin (41.03% ± 9.60%), ximelagatran (37.81% ± 15.87%), aspirin (35.62% ± 20.60%), warfarin (9.89% ± 9.07%), and placebo (4.56% ± 6.37%). Ranking based on clinically relevant bleeding events was as follows: fondaparinux (14.53% ± 15.25%), ximelagatran (18.93% ± 17.49%), rivaroxaban (23.86% ± 15.14%), dabigatran (28.30% ± 14.18%), edoxaban (38.76% ± 24.25%), low-molecular-weight heparin (53.28% ± 8.40%), apixaban (71.81% ± 10.92%), placebo (76.26% ± 14.61%), aspirin (86.32% ± 25.74%), and warfarin (87.95% ± 11.27%). No statistically significant heterogeneity was observed between trials. Conclusions and relevance According to our results, all anticoagulant drugs showed some effectiveness for VTE prophylaxis. Our ranking indicated that fondaparinux and rivaroxaban were safer and more effective than other anticoagulant drugs for patients undergoing THA or TKA.
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Maity, Debotyam, and Fred Aminzadeh. "Novel fracture zone identifier attribute using geophysical and well log data for unconventional reservoirs." Interpretation 3, no. 3 (August 1, 2015): T155—T167. http://dx.doi.org/10.1190/int-2015-0003.1.
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We have characterized a promising geothermal prospect using an integrated approach involving microseismic monitoring data, well logs, and 3D surface seismic data. We have used seismic as well as microseismic data along with well logs to better predict the reservoir properties to try and analyze the reservoir for improved mapping of natural and induced fractures. We used microseismic-derived velocity models for geomechanical modeling and combined these geomechanical attributes with seismic and log-derived attributes for improved fracture characterization of an unconventional reservoir. We have developed a workflow to integrate these data to generate rock property estimates and identification of fracture zones within the reservoir. Specifically, we introduce a new “meta-attribute” that we call the hybrid-fracture zone-identifier attribute (FZI). The FZI makes use of elastic properties derived from microseismic as well as log-derived properties within an artificial neural network framework. Temporal analysis of microseismic data can help us understand the changes in the elastic properties with reservoir development. We demonstrate the value of using passive seismic data as a fracture zone identification tool despite issues with data quality.
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Mazrouee, Sepideh, Susan J. Little, and Joel O. Wertheim. "Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment." PLOS Computational Biology 17, no. 9 (September 22, 2021): e1009336. http://dx.doi.org/10.1371/journal.pcbi.1009336.
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HIV molecular epidemiology estimates the transmission patterns from clustering genetically similar viruses. The process involves connecting genetically similar genotyped viral sequences in the network implying epidemiological transmissions. This technique relies on genotype data which is collected only from HIV diagnosed and in-care populations and leaves many persons with HIV (PWH) who have no access to consistent care out of the tracking process. We use machine learning algorithms to learn the non-linear correlation patterns between patient metadata and transmissions between HIV-positive cases. This enables us to expand the transmission network reconstruction beyond the molecular network. We employed multiple commonly used supervised classification algorithms to analyze the San Diego Primary Infection Resource Consortium (PIRC) cohort dataset, consisting of genotypes and nearly 80 additional non-genetic features. First, we trained classification models to determine genetically unrelated individuals from related ones. Our results show that random forest and decision tree achieved over 80% in accuracy, precision, recall, and F1-score by only using a subset of meta-features including age, birth sex, sexual orientation, race, transmission category, estimated date of infection, and first viral load date besides genetic data. Additionally, both algorithms achieved approximately 80% sensitivity and specificity. The Area Under Curve (AUC) is reported 97% and 94% for random forest and decision tree classifiers respectively. Next, we extended the models to identify clusters of similar viral sequences. Support vector machine demonstrated one order of magnitude improvement in accuracy of assigning the sequences to the correct cluster compared to dummy uniform random classifier. These results confirm that metadata carries important information about the dynamics of HIV transmission as embedded in transmission clusters. Hence, novel computational approaches are needed to apply the non-trivial knowledge collected from inter-individual genetic information to metadata from PWH in order to expand the estimated transmissions. We note that feature extraction alone will not be effective in identifying patterns of transmission and will result in random clustering of the data, but its utilization in conjunction with genetic data and the right algorithm can contribute to the expansion of the reconstructed network beyond individuals with genetic data.
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Tur, Carmen, Tomas Kalincik, Jiwon Oh, Maria P. Sormani, Mar Tintoré, Helmut Butzkueven, and Xavier Montalban. "Head-to-head drug comparisons in multiple sclerosis." Neurology 93, no. 18 (October 7, 2019): 793–809. http://dx.doi.org/10.1212/wnl.0000000000008319.
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Disease-modifying drugs are changing the natural history of multiple sclerosis (MS). However, currently available clinical trial data are insufficient to develop accurate personalized treatment algorithms to assign the best possible treatment to each person with MS according to disease features, treatment history, and comorbidities. Such accurate algorithms would require the presence of numerous head-to-head trials of long duration, which is virtually impossible, given the economic costs, required time, and difficulties with attrition. Thus, efforts are being made to compare relative treatment efficacy through observational designs, using large multicenter prospective cohorts or “big MS data,” and network meta-analyses. Although such studies can yield useful information, they are liable to biases and their results should be confirmed in other study populations, including smaller, single-center cohorts, where some of these biases can be minimized. In this View article, we analyze the potential benefits and biases of all these strategies alternative to head-to-head trials in MS. Finally, we propose the combination of all these types of studies to obtain reliable head-to-head drug comparisons in the absence of randomized designs.
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Zhang, Zhang, Shen, and Sun. "Novel MicroRNA Biomarkers for Colorectal Cancer Early Diagnosis and 5-Fluorouracil Chemotherapy Resistance but Not Prognosis: A Study from Databases to AI-Assisted Verifications." Cancers 12, no. 2 (February 3, 2020): 341. http://dx.doi.org/10.3390/cancers12020341.
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Colorectal cancer (CRC) is one of the major causes of cancer death worldwide. In general, early diagnosis for CRC and individual therapy have led to better survival for the cancer patients. Accumulating studies concerning biomarkers have provided positive evidence to improve cancer early diagnosis and better therapy. It is, however, still necessary to further investigate the precise biomarkers for cancer early diagnosis and precision therapy and predicting prognosis. In this study, AI-assisted systems with bioinformatics algorithm integrated with microarray and RNA sequencing (RNA-seq) gene expression (GE) data has been approached to predict microRNA (miRNA) biomarkers for early diagnosis of CRC based on the miRNA-messenger RNA (mRNA) interaction network. The relationships between the predicted miRNA biomarkers and other biological components were further analyzed on biological networks. Bayesian meta-analysis of diagnostic test was utilized to verify the diagnostic value of the miRNA candidate biomarkers and the combined multiple biomarkers. Biological function analysis was performed to detect the relationship of candidate miRNA biomarkers and identified biomarkers in pathways. Text mining was used to analyze the relationships of predicted miRNAs and their target genes with 5-fluorouracil (5-FU). Survival analyses were conducted to evaluate the prognostic values of these miRNAs in CRC. According to the number of miRNAs single regulated mRNAs (NSR) and the number of their regulated transcription factor gene percentage (TFP) on the miRNA-mRNA network, there were 12 promising miRNA biomarkers were selected. There were five potential candidate miRNAs (miRNA-186-5p, miRNA-10b-5, miRNA-30e-5p, miRNA-21 and miRNA-30e) were confirmed as CRC diagnostic biomarkers, and two of them (miRNA-21 and miRNA-30e) were previously reported. Furthermore, the combinations of the five candidate miRNAs biomarkers showed better prediction accuracy for CRC early diagnosis than the single miRNA biomarkers. miRNA-10b-5p and miRNA-30e-5p were associated with the 5-FU therapy resistance by targeting the related genes. These miRNAs biomarkers were not statistically associated with CRC prognosis.
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Wang, Mengqi, Wenwen Gu, Fuguang Kui, Fan Gao, Yuji Niu, Wenwen Li, Yaru Zhang, Lijuan Guo, Shengnan Geng, and Gangjun Du. "The Clinical Efficiency and the Mechanism of Sanzi Yangqin Decoction for Chronic Obstructive Pulmonary Disease." Evidence-Based Complementary and Alternative Medicine 2021 (June 10, 2021): 1–21. http://dx.doi.org/10.1155/2021/5565562.
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This work is carried out to evaluate the clinical efficacy of Sanzi Yangqin decoction (SZYQD) treating chronic obstructive pulmonary disease (COPD) and to analyze its mechanism. The clinical efficacy of SZYQD treating COPD was evaluated by meta-analysis, and its mechanism was analyzed by network pharmacology. Molecular docking validation of the main active compounds and the core targets was performed by AutoDock vina software. A cigarette smoke (CS) and LPS-induced COPD model in ICR mice was constructed to confirm the effects of luteolin on COPD. Results showed that SZYQD has a greater benefit on the total effect (OR = 3.85, 95% CI [3.07, 4.83], P = 1 ) in the trial group compared with the control group. The percentage of forced expiratory volume in one second (FEV1%) (MD = 0.5, 95% CI [0.41, 0.59], P < 0.00001 ) and first seconds breathing volume percentage of forced vital capacity (FEV1%/FVC) were improved (MD = 5.97, 95% CI [3.23, 8.71], P < 0.00001 ). There are 27 compounds in SZYQD targeting 104 disease targets related to COPD. PPI network analysis indicated that EGFR, MMP9, PTGS2, MMP2, APP, and ERBB2 may be the core targets for the treatment of COPD. Molecular docking demonstrated that luteolin in SZYQD showed the strongest binding activity to core targets. Experimental results revealed that the expression of COPD-related targets in lung tissue was significantly increased in the COPD group and was improved in the luteolin group. Our data indicated that SZYQD has a curative effect on COPD and luteolin is a candidate compound for COPD treatment by regulating EGFR, MMP9, PTGS2, MMP2, APP, and ERBB2.
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García-Sánchez, Pablo, Antonio Velez-Estevez, Juan Julián Merelo, and Manuel Jesús Cobo. "The Simpsons did it: Exploring the film trope space and its large scale structure." PLOS ONE 16, no. 3 (March 31, 2021): e0248881. http://dx.doi.org/10.1371/journal.pone.0248881.
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Creating a story is a challenging task due to the the complex relations between the parts that make it up, which is why many new stories are built on those cohesive elements or patterns, called tropes that have been shown to work in the past. A trope is a recurring storytelling device or pattern, or sometimes a meta-element, used by the authors to express ideas that the audience can recognize or relate to, such as the Hero’s Journey. Discovering tropes and how they cluster in popular works and doing it at scale to generate new plots may benefit writers; in this paper, we analyze them and use a principled procedure to identify trope combinations, or communities, that could possible be successful. The degree of development of these different communities can help us identify areas that are under-developed and, thus, susceptible to such a type of development. To detect these communities, with their associated degree of development and interest, we propose a methodology based on scientometric and complex network analysis techniques. As a secondary objective, we will obtain a general perspective in the trope and films network: the tropesphere. We have used a dataset of 10,766 movies and 25,776 tropes associated with them, together with rating, genres and popularity. Our analysis has shown that not only there are different trope communities associated with specific genres, and that there are significant differences between the rating and popularity of these communities but also there are differences on the level of development between them: emerging/declining, specific, transversal or motor.
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Lavalle, Carlo, Luca Di Lullo, Antonio Bellasi, Claudio Ronco, Stefano Radicchia, Vincenzo Barbera, Gioacchino Galardo, et al. "Adverse Drug Reactions during Real-Life Use of Direct Oral Anticoagulants in Italy: An Update Based on Data from the Italian National Pharmacovigilance Network." Cardiorenal Medicine 10, no. 4 (2020): 266–76. http://dx.doi.org/10.1159/000507046.
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Background: The availability of direct oral anticoagulants (DOAC) in clinical practice has transformed the health care provided to patients for the prevention and treatment of thromboembolism. Safety and efficacy data guide clinicians in the choice of the drug used. To date, no evidence is available from head-to-head trials comparing different DOAC with regard to safety and efficacy; information is mainly derived from several meta-analyses and real-life studies. Conclusions from these studies are inconsistent and unsatisfactory. The evaluation of self-reported adverse drug reactions (ADR) available from databases of drug-regulatory agencies such as the Italian Medicines Agency (AIFA) pharmacovigilance database represents a novel aid to guide decision-making. Objective: To analyze potential suspected ADR of DOAC using a previously described risk index (RI) in daily clinical practice in Italy. Methods: The National Pharmacovigilance Network database (from the AIFA website) was searched in order to retrieve information on all ADR related to oral anticoagulants occurring from 2013 to 2018. The ADR RI for each drug was calculated, where an RI = 1 indicates a balance between the percentage of ADR share and the percentage of market share for each DOAC; and an RI <1 indicates a rate of ADR lower than the rate of market share (safer DOAC). The following DOAC molecules were considered: dabigatran, rivaroxaban, apixaban, and edoxaban. Results: The results showed that rivaroxaban is the DOAC with the lowest RI among the 4 molecules available today in Italy. Conclusions: Based on the RI, we identified rivaroxaban as the DOAC having the best safety profile.