A systematic study of models of abstract data types

AbstractThe term-generated models of an abstract data type can be represented by congruence relations on the term algebra. Total and partial heterogeneous algebras are considered as models of hierarchical abstract data types.Particular classes of models are studied and it is investigated under which conditions they form a complete lattice. This theory allows also to describe programming languages (and their semantic models) by abstract types. As example we present a simple deterministic stream processing language

Can large language models replace humans in the systematic review process? Evaluating GPT-4's efficacy in screening and extracting data from peer-reviewed and grey literature in multiple languages

Systematic reviews are vital for guiding practice, research, and policy, yet they are often slow and labour-intensive. Large language models (LLMs) could offer a way to speed up and automate systematic reviews, but their performance in such tasks has not been comprehensively evaluated against humans, and no study has tested GPT-4, the biggest LLM so far. This pre-registered study evaluates GPT-4's capability in title/abstract screening, full-text review, and data extraction across various literature types and languages using a 'human-out-of-the-loop' approach. Although GPT-4 had accuracy on par with human performance in most tasks, results were skewed by chance agreement and dataset imbalance. After adjusting for these, there was a moderate level of performance for data extraction, and - barring studies that used highly reliable prompts - screening performance levelled at none to moderate for different stages and languages. When screening full-text literature using highly reliable prompts, GPT-4's performance was 'almost perfect.' Penalising GPT-4 for missing key studies using highly reliable prompts improved its performance even more. Our findings indicate that, currently, substantial caution should be used if LLMs are being used to conduct systematic reviews, but suggest that, for certain systematic review tasks delivered under reliable prompts, LLMs can rival human performance.Comment: 9 pages, 2 figures, 1 tabl

In silico method for systematic analysis of feature importance in microRNA-mRNA interactions

<p>Abstract</p> <p>Background</p> <p>MicroRNA (miRNA), which is short non-coding RNA, plays a pivotal role in the regulation of many biological processes and affects the stability and/or translation of mRNA. Recently, machine learning algorithms were developed to predict potential miRNA targets. Most of these methods are robust but are not sensitive to redundant or irrelevant features. Despite their good performance, the relative importance of each feature is still unclear. With increasing experimental data becoming available, research interest has shifted from higher prediction performance to uncovering the mechanism of microRNA-mRNA interactions.</p> <p>Results</p> <p>Systematic analysis of sequence, structural and positional features was carried out for two different data sets. The dominant functional features were distinguished from uninformative features in single and hybrid feature sets. Models were developed using only statistically significant sequence, structural and positional features, resulting in area under the receiver operating curves (AUC) values of 0.919, 0.927 and 0.969 for one data set and of 0.926, 0.874 and 0.954 for another data set, respectively. Hybrid models were developed by combining various features and achieved AUC of 0.978 and 0.970 for two different data sets. Functional miRNA information is well reflected in these features, which are expected to be valuable in understanding the mechanism of microRNA-mRNA interactions and in designing experiments.</p> <p>Conclusions</p> <p>Differing from previous approaches, this study focused on systematic analysis of all types of features. Statistically significant features were identified and used to construct models that yield similar accuracy to previous studies in a shorter computation time.</p

Longitudinal analysis of crash frequency data

This study comprises mainly of three papers. First, a systematic evaluation of the effects of Missouri\u27s Strategic Highway Safety Plan between 2004 and 2007 is presented. Negative binomial regression models were developed for the before-through-change conditions for the various collision types and crash severities. The models were used to predict the expected number of crashes assuming with and without the implementation of MSHSP. This procedure estimated significant reductions of 10% in crashes frequency and a 30% reduction for fatal crashes. Reductions in the number of different collision types were estimated to be 18-37%. The results suggest that the MSHSP was successful in decreasing fatalities. Second, ten years (2002 - 2011) of Missouri Interstate highway crash data was utilized to develop a longitudinal negative binomial model using generalized estimating equation (GEE) procedure. This model incorporated the temporal correlations in crash frequency data was compared to the more traditional NB model and was found to be superior. The GEE model does not underestimate the variance in the coefficient estimates, and provides more accurate and less biased estimates. Furthermore, the autoregressive correlation structure used for the temporal correlation of the data was found to be an appropriate structure for longitudinal type of data used in this study. Third, this study developed another longitudinal negative binomial model that takes into account the seasonal effects of crash causality factors using Missouri crash data. A GEE with autoregressive correlation structure was used again for model estimation. The results improves the understanding of seasonality and whether the magnitude and/or type of various effects are different according to climatic changes. It was found that the traffic volume has a higher effect in increasing the crash occurrence in spring and lower effect in winter, compared to fall season. The crash reducing effect of better pavements was found to be highest in spring season followed by summer and winter, compared to the fall season. The results suggest that winter season has the highest effect in increasing crash occurrences followed by summer and spring --Abstract, page v

MPHASYS: a mouse phenotype analysis system

<p>Abstract</p> <p>Background</p> <p>Systematic, high-throughput studies of mouse phenotypes have been hampered by the inability to analyze individual animal data from a multitude of sources in an integrated manner. Studies generally make comparisons at the level of genotype or treatment thereby excluding associations that may be subtle or involve compound phenotypes. Additionally, the lack of integrated, standardized ontologies and methodologies for data exchange has inhibited scientific collaboration and discovery.</p> <p>Results</p> <p>Here we introduce a Mouse Phenotype Analysis System (MPHASYS), a platform for integrating data generated by studies of mouse models of human biology and disease such as aging and cancer. This computational platform is designed to provide a standardized methodology for working with animal data; a framework for data entry, analysis and sharing; and ontologies and methodologies for ensuring accurate data capture. We describe the tools that currently comprise MPHASYS, primarily ones related to mouse pathology, and outline its use in a study of individual animal-specific patterns of multiple pathology in mice harboring a specific germline mutation in the DNA repair and transcription-specific gene Xpd.</p> <p>Conclusion</p> <p>MPHASYS is a system for analyzing multiple data types from individual animals. It provides a framework for developing data analysis applications, and tools for collecting and distributing high-quality data. The software is platform independent and freely available under an open-source license <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>.</p

Epidemiologic natural history and clinical management of Human Papillomavirus (HPV) Disease: a critical and systematic review of the literature in the development of an HPV dynamic transmission model

<p>Abstract</p> <p>Background</p> <p>Natural history models of human papillomavirus (HPV) infection and disease have been used in a number of policy evaluations of technologies to prevent and screen for HPV disease (e.g., cervical cancer, anogenital warts), sometimes with wide variation in values for epidemiologic and clinical inputs. The objectives of this study are to: (1) Provide an updated critical and systematic review of the evidence base to support epidemiologic and clinical modeling of key HPV disease-related parameters in the context of an HPV multi-type disease transmission model which we have applied within a U.S. population context; (2) Identify areas where additional studies are particularly needed.</p> <p>Methods</p> <p>Consistent with our and other prior HPV natural history models, the literature review was confined to cervical disease and genital warts. Between October 2005 and January 2006, data were gathered from the published English language medical literature through a search of the PubMed database and references were examined from prior HPV natural history models and review papers. Study design and data quality from individual studies were compared and analyses meeting pre-defined criteria were selected.</p> <p>Results</p> <p>Published data meeting review eligibility criteria were most plentiful for natural history parameters relating to the progression and regression of cervical intraepithelial neoplasia (CIN) without HPV typing, and data concerning the natural history of HPV disease due to specific HPV types were often lacking. Epidemiologic evidence to support age-dependency in the risk of progression and regression of HPV disease was found to be weak, and an alternative hypothesis concerning the time-dependence of transition rates is explored. No data were found on the duration of immunity following HPV infection. In the area of clinical management, data were observed to be lacking on the proportion of clinically manifest anogenital warts that are treated and the proportion of cervical cancer cases that become symptomatic by stage.</p> <p>Conclusion</p> <p>Knowledge of the natural history of HPV disease has been considerably enhanced over the past two decades, through the publication of an increasing number of relevant studies. However, considerable opportunity remains for advancing our understanding of HPV natural history and the quality of associated models, particularly with respect to examining HPV age- and type-specific outcomes, and acquired immunity following infection.</p

Internet of Things security with machine learning techniques:a systematic literature review

Abstract. The Internet of Things (IoT) technologies are beneficial for both private and businesses. The growth of the technology and its rapid introduction to target fast-growing markets faces security challenges. Machine learning techniques have been recently used in research studies as a solution in securing IoT devices. These machine learning techniques have been implemented successfully in other fields. The objective of this thesis is to identify and analyze existing scientific literature published recently regarding the use of machine learning techniques in securing IoT devices. In this thesis, a systematic literature review was conducted to explore the previous research on the use of machine learning in IoT security. The review was conducted by following a procedure developed in the review protocol. The data for the study was collected from three databases i.e. IEEE Xplore, Scopus and Web of Science. From a total of 855 identified papers, 20 relevant primary studies were selected to answer the research question. The study identified 7 machine learning techniques used in IoT security, additionally, several attack models were identified and classified into 5 categories. The results show that the use of machine learning techniques in IoT security is a promising solution to the challenges facing security. Supervised machine learning techniques have better performance in comparison to unsupervised and reinforced learning. The findings also identified that data types and the learning method affects the performance of machine learning techniques. Furthermore, the results show that machine learning approach is mostly used in securing the network

A Systematic Review of Tracing Solutions in Software Product Lines

Software Product Lines are large-scale, multi-unit systems that enable massive, customized production. They consist of a base of reusable artifacts and points of variation that provide the system with flexibility, allowing generating customized products. However, maintaining a system with such complexity and flexibility could be error prone and time consuming. Indeed, any modification (addition, deletion or update) at the level of a product or an artifact would impact other elements. It would therefore be interesting to adopt an efficient and organized traceability solution to maintain the Software Product Line. Still, traceability is not systematically implemented. It is usually set up for specific constraints (e.g. certification requirements), but abandoned in other situations. In order to draw a picture of the actual conditions of traceability solutions in Software Product Lines context, we decided to address a literature review. This review as well as its findings is detailed in the present article.Comment: 22 pages, 9 figures, 7 table

THERACOM: a systematic review of the evidence base for interventions to improve Therapeutic Communications between black and minority ethnic populations and staff in specialist mental health services.

PMCID: PMC3599664This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.BACKGROUND: Black and Minority Ethnic (BME) groups in receipt of specialist mental health care have reported higher rates of detention under the mental health act, less use of psychological therapies, and more dissatisfaction. Although many explanations have been put forward to explain this, a failure of therapeutic communications may explain poorer satisfaction, disengagement from services and ethnic variations in access to less coercive care. Interventions that improve therapeutic communications may offer new approaches to tackle ethnic inequalities in experiences and outcomes. METHODS: The THERACOM project is an HTA-funded evidence synthesis review of interventions to improve therapeutic communications between black and minority ethnic patients in contact with specialist mental health services and staff providing those services. This article sets out the protocol methods for a necessarily broad review topic, including appropriate search strategies, dilemmas for classifying different types of therapeutic communications and expectations of the types of interventions to improve them. The review methods will accommodate unexpected types of study and interventions. The findings will be reported in 2013, including a synthesis of the quantitative and grey literature. DISCUSSION: A particular methodological challenge is to identify and rate the quality of many different study types, for example, randomised controlled trials, observational quantitative studies, qualitative studies and case studies, which comprise the full range of hierarchies of evidence. We discuss the preliminary methodological challenges and some solutions. (PROSPERO registration number: CRD42011001661)

A systematic review of health economic models of opioid agonist therapies in maintenance treatment of non-prescription opioid dependence

Background: Opioid dependence is a chronic condition with substantial health, economic and social costs. The study objective was to conduct a systematic review of published health-economic models of opioid agonist therapy for non-prescription opioid dependence, to review the different modelling approaches identified, and to inform future modelling studies. Methods: Literature searches were conducted in March 2015 in eight electronic databases, supplemented by hand-searching reference lists and searches on six National Health Technology Assessment Agency websites. Studies were included if they: investigated populations that were dependent on non-prescription opioids and were receiving opioid agonist or maintenance therapy; compared any pharmacological maintenance intervention with any other maintenance regimen (including placebo or no treatment); and were health-economic models of any type. Results: A total of 18 unique models were included. These used a range of modelling approaches, including Markov models (n = 4), decision tree with Monte Carlo simulations (n = 3), decision analysis (n = 3), dynamic transmission models (n = 3), decision tree (n = 1), cohort simulation (n = 1), Bayesian (n = 1), and Monte Carlo simulations (n = 2). Time horizons ranged from 6 months to lifetime. The most common evaluation was cost-utility analysis reporting cost per quality-adjusted life-year (n = 11), followed by cost-effectiveness analysis (n = 4), budget-impact analysis/cost comparison (n = 2) and cost-benefit analysis (n = 1). Most studies took the healthcare provider’s perspective. Only a few models included some wider societal costs, such as productivity loss or costs of drug-related crime, disorder and antisocial behaviour. Costs to individuals and impacts on family and social networks were not included in any model. Conclusion: A relatively small number of studies of varying quality were found. Strengths and weaknesses relating to model structure, inputs and approach were identified across all the studies. There was no indication of a single standard emerging as a preferred approach. Most studies omitted societal costs, an important issue since the implications of drug abuse extend widely beyond healthcare services. Nevertheless, elements from previous models could together form a framework for future economic evaluations in opioid agonist therapy including all relevant costs and outcomes. This could more adequately support decision-making and policy development for treatment of non-prescription opioid dependence