Evaluation of person-level heterogeneity of treatment effects in published multiperson N-of-1 studies: systematic review and reanalysis.

OBJECTIVE:Individual patients with the same condition may respond differently to similar treatments. Our aim is to summarise the reporting of person-level heterogeneity of treatment effects (HTE) in multiperson N-of-1 studies and to examine the evidence for person-level HTE through reanalysis. STUDY DESIGN:Systematic review and reanalysis of multiperson N-of-1 studies. DATA SOURCES:Medline, Cochrane Controlled Trials, EMBASE, Web of Science and review of references through August 2017 for N-of-1 studies published in English. STUDY SELECTION:N-of-1 studies of pharmacological interventions with at least two subjects. DATA SYNTHESIS:Citation screening and data extractions were performed in duplicate. We performed statistical reanalysis testing for person-level HTE on all studies presenting person-level data. RESULTS:We identified 62 multiperson N-of-1 studies with at least two subjects. Statistical tests examining HTE were described in only 13 (21%), of which only two (3%) tested person-level HTE. Only 25 studies (40%) provided person-level data sufficient to reanalyse person-level HTE. Reanalysis using a fixed effect linear model identified statistically significant person-level HTE in 8 of the 13 studies (62%) reporting person-level treatment effects and in 8 of the 14 studies (57%) reporting person-level outcomes. CONCLUSIONS:Our analysis suggests that person-level HTE is common and often substantial. Reviewed studies had incomplete information on person-level treatment effects and their variation. Improved assessment and reporting of person-level treatment effects in multiperson N-of-1 studies are needed

Tracking internet interest in anabolic-androgenic steroids using Google Trends

Background: There is a perception that the prevalence of anabolic-androgenic steroid (AAS) use is increasing in the UK, with consequent individual and public health risks. Nevertheless, there is a lack of real-time surveillance data to support the development of effective policy. This paper explores the potential of Google Trends to complement existing surveillance methods by analysing user generated search term data. Methods: The Google Trends web tool was used to identify patterns of UK online interest in 15 AAS from January 2011 to December 2015, with 10 ultimately suitable for further analysis. Time series analysis was applied to the data. Results: 10 steroids were ranked from most to least popular. All compounds had peaks in interest between April to July, potentially indicating a consumer driven desire to attain a desired physique in time for summer. Oral steroids were among the most searched for drugs which may have relevance for current service provision to steroid users. Conclusion: Alternative data sources such Google Trends may provide useful additional information to supplement existing surveillance data. The limitations of this method however makes cautious interpretation and triangulation with other data sources essential

Visual analytics of Hebrew manuscripts codicological metadata

This paper presents the CodicoDaViz research project, developed with the goal of applying data visualisation techniques to the field of codicology. Adding to the multidisciplinary nature of digital humanities (DH), this project brings together a group of experts of DH, business intelligence and computer science. Using Hebrew manuscript data as a starting point, CodicoDaViz proposes an environment for exploratory analysis to be used by Humanities experts to deepen their understanding of codicological data, and to formulate new research hypotheses. In this paper we demonstrate how data visualisation was instrumental in understanding and structuring the dataset. Examples of the dashboards that have been designed (in Tableau) to enable an interactive and ad-hoc exploration of data are also discussed.info:eu-repo/semantics/acceptedVersio

Proteomics

Despite years of preclinical development, biological interventions designed to treat complex diseases such as asthma often fail in phase III clinical trials. These failures suggest that current methods to analyze biomedical data might be missing critical aspects of biological complexity such as the assumption that cases and controls come from homogeneous distributions. Here we discuss why and how methods from the rapidly evolving field of visual analytics can help translational teams (consisting of biologists, clinicians, and bioinformaticians) to address the challenge of modeling and inferring heterogeneity in the proteomic and phenotypic profiles of patients with complex diseases. Because a primary goal of visual analytics is to amplify the cognitive capacities of humans for detecting patterns in complex data, we begin with an overview of the cognitive foundations for the field of visual analytics. Next, we organize the primary ways in which a specific form of visual analytics called networks has been used to model and infer biological mechanisms, which help to identify the properties of networks that are particularly useful for the discovery and analysis of proteomic heterogeneity in complex diseases. We describe one such approach called subject-protein networks, and demonstrate its application on two proteomic datasets. This demonstration provides insights to help translational teams overcome theoretical, practical, and pedagogical hurdles for the widespread use of subject-protein networks for analyzing molecular heterogeneities, with the translational goal of designing biomarker-based clinical trials, and accelerating the development of personalized approaches to medicine.1UL1TR000071/TR/NCATS NIH HHS/United StatesHHSN268201000037C/HV/NHLBI NIH HHS/United StatesHHSN268201000037C-0-0-1/PHS HHS/United StatesKL2 TR000072/TR/NCATS NIH HHS/United StatesR21 OH009441/OH/NIOSH CDC HHS/United StatesR21OH009441-01A2/OH/NIOSH CDC HHS/United StatesUL1 TR000071/TR/NCATS NIH HHS/United States2015-06-18T00:00:00Z25684269PMC447133

Visualization and analytics of codicological data of Hebrew books

The goal is to provide a proper data model, using a common vocabulary, to decrease the heterogenous nature of these datasets as well as its inherent uncertainty caused by the descriptive nature of the field of Codicology. This research project was developed with the goal of applying data visualization and data mining techniques to the field of Codicology and Digital Humanities. Using Hebrew manuscript data as a starting point, this dissertation proposes an environment for exploratory analysis to be used by Humanities experts to deepen their understanding of codicological data, to formulate new, or verify existing, research hypotheses, and to communicate their findings in a richer way. To improve the scope of visualizations and knowledge discovery we will try to use data mining methods such as Association Rule Mining and Formal Concept Analysis. The present dissertation aims to retrieve information and structure from Hebrew manuscripts collected by codicologists. These manuscripts reflect the production of books of a specific region, namely "Sefarad" region, within the period between 10th and 16th.A presente dissertação tem como objetivo obter conhecimento estruturado de manuscritos hebraicos coletados por codicologistas. Estes manuscritos refletem a produção de livros de uma região específica, nomeadamente a região "Sefarad", no período entre os séculos X e XVI. O objetivo é fornecer um modelo de dados apropriado, usando um vocabulário comum, para diminuir a natureza heterogénea desses conjuntos de dados, bem como sua incerteza inerente causada pela natureza descritiva no campo da Codicologia. Este projeto de investigação foi desenvolvido com o objetivo de aplicar técnicas de visualização de dados e "data mining" no campo da Codicologia e Humanidades Digitais. Usando os dados de manuscritos hebraicos como ponto de partida, esta dissertação propõe um ambiente para análise exploratória a ser utilizado por especialistas em Humanidades Digitais e Codicologia para aprofundar a compreensão dos dados codicológicos, formular novas hipóteses de pesquisa, ou verificar existentes, e comunicar as suas descobertas de uma forma mais rica. Para melhorar as visualizações e descoberta de conhecimento, tentaremos usar métodos de data mining, como a "Association Rule Mining" e "Formal Concept Analysis"

Dashboard Framework. A Tool for Threat Monitoring on the Example of Covid-19

The aim of the study is to create a dashboard framework to monitor the spread of the Covid-19 pandemic based on quantitative and qualitative data processing. The theoretical part propounds the basic assumptions underlying the concept of the dashboard framework. The paper presents the most important functions of the dashboard framework and examples of its adoption. The limitations related to the dashboard framework development are also indicated. As part of empirical research, an original model of the Dash-Cov framework was designed, enabling the acquisition and processing of quantitative and qualitative data on the spread of the SARS-CoV-2 virus. The developed model was pre-validated. Over 25,000 records and around 100,000 tweets were analyzed. The adopted research methods included statistical analysis and text analysis methods, in particular the sentiment analysis and the topic modeling

Sentiment Analysis on Twitters Big Data Against the Covid- 19 Pandemic Using Machine Learning Algorithms

This paper analyzes users reactions on Twitter to the COVID-19 pandemic, using machine learning and data mining algorithms to classify tweets according to economic and health fears. A large dataset of tweets is explored, extracted, transformed, loaded, cleansed, and analyzed. The proposed framework improves prediction quality with a proposed dictionary that is used to classify tweets. The study compares four supervised machine learning algorithms and finds that people discuss the pandemics dangers from economic and health perspectives with equal frequency. The Naive Bayes algorithm achieves the highest percentage of correct predictions

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE