On predictability of rare events leveraging social media: a machine learning perspective

Information extracted from social media streams has been leveraged to forecast the outcome of a large number of real-world events, from political elections to stock market fluctuations. An increasing amount of studies demonstrates how the analysis of social media conversations provides cheap access to the wisdom of the crowd. However, extents and contexts in which such forecasting power can be effectively leveraged are still unverified at least in a systematic way. It is also unclear how social-media-based predictions compare to those based on alternative information sources. To address these issues, here we develop a machine learning framework that leverages social media streams to automatically identify and predict the outcomes of soccer matches. We focus in particular on matches in which at least one of the possible outcomes is deemed as highly unlikely by professional bookmakers. We argue that sport events offer a systematic approach for testing the predictive power of social media, and allow to compare such power against the rigorous baselines set by external sources. Despite such strict baselines, our framework yields above 8% marginal profit when used to inform simple betting strategies. The system is based on real-time sentiment analysis and exploits data collected immediately before the games, allowing for informed bets. We discuss the rationale behind our approach, describe the learning framework, its prediction performance and the return it provides as compared to a set of betting strategies. To test our framework we use both historical Twitter data from the 2014 FIFA World Cup games, and real-time Twitter data collected by monitoring the conversations about all soccer matches of four major European tournaments (FA Premier League, Serie A, La Liga, and Bundesliga), and the 2014 UEFA Champions League, during the period between Oct. 25th 2014 and Nov. 26th 2014.Comment: 10 pages, 10 tables, 8 figure

Implementation of routine thromboplastin-plasma cell block technique in the evaluation of non-gynecologic specimens: A methodologic comparison with conventional cytology

AbstractThe cell block is an ancillary technique used in cytology to increase the diagnostic accuracy in the analysis of effusions and aspirations. In our laboratory, we implemented the routine use of the Thromboplastin-Plasma Cell-Block (TP-CB) technique because it is simple, reproducible and has low cost. The aim of this prospective study was to proof the utility of performing routine cell blocks in non-gynecologic cytology by comparing the diagnostic concordance, cellularity, and contribution to diagnosis from paired TP-CB and Conventional Cytological (CC) preparations. For this, all non-gynecologic specimens including effusions, body fluids and aspirations, were collected for an 8-month period. A total of 179 TP-CBs were prepared from the remaining fluid following CC preparations. Absolute concordance was found in 81.6% cases between both techniques (κ=0.56). The cell block aided the diagnosis in 28% of cases and ICC studies were done in 12%. The use of routine TP-CB complements and enhances the diagnostic accuracy of CC, allows the performance of ancillary studies and improves the diagnostic approach and treatment

Simulation and validation of the gas flow in close-coupled gas atomisation process: Influence of the inlet gas pressure and the throat width of the supersonic gas nozzle.

The effectiveness of a close-coupled gas atomisation process largely depends on the operational and the geometric variables. In this study, Computational Fluid Dynamics (CFD) techniques are used to model and simulate the gas flow in the melt nozzle area for a convergent-divergent, close-coupled gas atomiser in the absence of the melt stream. Firstly, a reference case, in which the atomisation gas is nitrogen at 50 bar and a supersonic gas nozzle with a throat width of L0 has been modelled, is presented. Then, the influence of both the inlet gas pressure and this design parameter are investigated, comparing the numerical results provided by simulations varying the inlet pressure from 5 to 80 bar and modelling different convergent-divergent gas nozzles with throat widths of 0.29 center dot Lo, 0.5 center dot Lo, 0.77 center dot Lo and 2 center dot Lo respectively. The simulation results show how similarly these two parameters modify gas mass flow rates, gas velocity fields, aspiration pressures in the melt delivery tube or the size of the recirculation zones below the melt nozzle. Therefore, it can be stated that this geometric variable of the gas nozzle may be as relevant as the inlet pressure in the atomisation process. The most important novelty of this study is related to experimental validation of the numerical results using the Particle Image Velocimetry (PIV) technique and through direct measurements of gas mass flow rates, with a clear correlation between simulated and measured data. Moreover, some results obtained with experimental atomisations using copper and nitrogen are also presented. The experimental results show that finer powders are produced by increasing th

Prevalence of Depressive Disorder in the Adult population of Latin America: a systematic review and meta-analysis

Background: Depressive disorder is one of the leading causes of disability worldwide; however its prevalence and association with inequality and crime is poorly characterised in Latin America. This study aimed to: i. systematically review population-based studies of prevalence of ICD/DSM depressive disorder in Latin America, ii. report pooled regional, country, and sex-specific prevalence estimates, and iii. test its association with four country-level development indicators: human development (HDI), income (Gini) and gender inequality (GII), and intentional homicide rate (IHR). Methods: We conducted a systematic review and meta-analysis of population-based studies reporting primary data on the prevalence of ICD/DSM depressive disorder in Latin America from 1990 to 2023, irrespective of language. We searched PubMed, PsycINFO, Cochrane Library, SciELO (regional database), LILAC (regional database), and available grey literature. Study quality was assessed using JBI’s critical appraisal tools. We generated pooled estimates using random-effects meta-analysis; heterogeneity was assessed using the I2 statistic. Meta-regression analyses were used to test associations of depression prevalence with indicators of inequality and human development. The study was registered with PROSPERO (CRD42019143054). Findings: Using data from 40 studies in Latin America, lifetime, 12-month, and current prevalence of ICD/DSM depressive disorder were calculated at 12.58% (95% CI 11.00%–14.16%); 5.30% (4.55–6.06%), and 3.12% (2.22–4.03), respectively. Heterogeneity was high across lifetime, 12-month, and current prevalence, sex, and countries. 12-month and current prevalence was associated with higher Gini and GII, 12-month prevalence with lower HDI, and current prevalence with higher IHR. Interpretation We found a high prevalence of ICD/DSM depressive disorders in Latin America, and a statistically significant association with inequality and development indicators. The high heterogeneity found across prevalence periods and the major gaps in country representation underscore the need to escalate efforts to improve mental health access and research capabilities in Latin America. Systematic, comparable prevalence estimates would inform more effective decision-making in the region

Beating the news using social media: the case study of American Idol

We present a contribution to the debate on the predictability of social events using big data analytics. We focus on the elimination of contestants in the American Idol TV shows as an example of a well defined electoral phenomenon that each week draws millions of votes in the USA. This event can be considered as basic test in a simplified environment to assess the predictive power of Twitter signals. We provide evidence that Twitter activity during the time span defined by the TV show airing and the voting period following it correlates with the contestants ranking and allows the anticipation of the voting outcome. Twitter data from the show and the voting period of the season finale have been analyzed to attempt the winner prediction ahead of the airing of the official result. We also show that the fraction of tweets that contain geolocation information allows us to map the fanbase of each contestant, both within the US and abroad, showing that strong regional polarizations occur. The geolocalized data are crucial for the correct prediction of the final outcome of the show, pointing out the importance of considering information beyond the aggregated Twitter signal. Although American Idol voting is just a minimal and simplified version of complex societal phenomena such as political elections, this work shows that the volume of information available in online systems permits the real time gathering of quantitative indicators that may be able to anticipate the future unfolding of opinion formation events

Nivel de citación en Google Académico de las investigaciones pedagógicas publicadas en la revista Medisur, período 2008 a octubre 2013

Fundamento: la revista Medisur tiene un amplio perfil de publicación, considera aquellos trabajos que estén relacionados con: Salud Pública, Administración Sanitaria, Ciencias Básicas, Ciencias Clínicas, Enfermería, Pedagogía, entre otras áreas que estén vinculadas con la salud y los Servicios de Salud.Objetivo: analizar el nivel de citación que han alcanzado las investigaciones pedagógicas, en Google Académico, que han sido publicadas en la revista Medisur en el período 2008-octubre 2013.Métodos: para estimar los indicadores de citación en Google Académico se utilizó el sofware Publish or Perish versión 4.4.6, mediante la opción Journal Impact Analysis, teniendo en cuenta el rango temporal entre 2008 y octubre de 2013. Se realizó la búsqueda utilizando el título exacto de la revista “Medisur”.Resultados: se encontraron 153 artículos con un total de 860 citas en el período, dentro de ellas, 95 fueron a los 26 artículos de Pedagogía, lo que significa un 11 %. Se destacó el año 2010, con 13 artículos que recibieron 43 citas. El 16, 9 % de los artículos que recibieron al menos una cita pertenece al área de Pedagogía.Conclusiones: teniendo en cuenta que la revista Medisur está orientada fundamentalmente a las investigaciones médicas, es importante destacar que más del 15 % de los artículos que reciben al menos una cita pertenezca al área de Pedagogía, lo cual indica la calidad de estos trabajos, fundamentado en que los que más citas reciben se relacionan con calidad y utilidad para la comunidad científica de esa área de la ciencia.</p

Production of extracts with anaesthetic activity from the culture of Heterosigma akashiwo in pilot-scale photobioreactors

The shear-sensitive microalga Heterosigma akashiwo is known to produce brevetoxin-like compounds that are active in voltage-dependent sodium channels. In this work, we present a study on the production of anaesthetic extracts from H. akashiwo biomass obtained in low-shear bioreactors at different growth phases. The photobioreactors (PBRs) used had specific configurations and were operated in such a way as to avoid cellular damage by hydrodynamic stress. Cultures were developed in small static-control flasks and PBRs with volumes ranging from 1.5 L to 200 L. The bioactivity of the produced extracts was assessed in vitro (Neuro-2a cell-based assay) and in vivo (Zebra fish model). Bioactivity depended slightly on the growth phase and culture system, with greater toxicity with the Neuro-2a model when stationary-phase extracts were used. Interestingly, extracts were not cytotoxic against other human cell lines. Steady production of anaesthetic bioactives was observed. In vivo anaesthetic efficacy, assessed with the Zebra fish model, was similar to that of commercial products, and without any observed mortality at 24-h post exposure

Trump vs. Hillary: What went Viral during the 2016 US Presidential Election

In this paper, we present quantitative and qualitative analysis of the top retweeted tweets (viral tweets) pertaining to the US presidential elections from September 1, 2016 to Election Day on November 8, 2016. For everyday, we tagged the top 50 most retweeted tweets as supporting or attacking either candidate or as neutral/irrelevant. Then we analyzed the tweets in each class for: general trends and statistics; the most frequently used hashtags, terms, and locations; the most retweeted accounts and tweets; and the most shared news and links. In all we analyzed the 3,450 most viral tweets that grabbed the most attention during the US election and were retweeted in total 26.3 million times accounting over 40% of the total tweet volume pertaining to the US election in the aforementioned period. Our analysis of the tweets highlights some of the differences between the social media strategies of both candidates, the penetration of their messages, and the potential effect of attacks on bothComment: Paper to appear in Springer SocInfo 201