Attitudes and emotions through written text: The case of textual deformation in Internet chat rooms

Spanish Internet chat rooms are visited by a lot of young people who use language in a very creative way (e.g. repetition of letters and punctuation marks). In this paper, several hypotheses concerning the uses of textual deformation assess their communicative usefulness. The goal of these hypotheses is to check whether these deformations favour a more accurate identification and evaluation of the senders’ underlying attitudes (propositional or affective) and emotions. The answers to a questionnaire indicate that despite the supplementary level of information that textual deformation provides, readers tend not to agree on the exact quality of the sender’s underlying attitudes and emotions, nor do they tend to establish degrees of intensity related to the quantity of text typed. However, and despite this evidence, textual deformation seems to play a part in the eventual quality of chat users’ interpretations of the messages sent to chat rooms.Los chats españoles de Internet son visitados por muchos jóvenes que usan el lenguaje de una forma muy creativa (ej. repetición de letras y signos de puntuación). En este artículo se evalúan varias hipótesis sobre el uso de la deformación textual respecto a su eficacia comunicativa. Se trata de comprobar si estas deformaciones favorecen una identificación y evaluación más adecuada de las actitudes (proposicionales o afectivas) y emociones de sus autores. Las respuestas a un cuestionario revelan que a pesar de la información adicional que la deformación textual aporta, los lectores no suelen coincidir en la cualidad exacta de estas actitudes y emociones, ni establecen grados de intensidad relacionados con la cantidad de texto tecleada. Sin embargo, y a pesar de estos resultados, la deformación textual parece jugar un papel en la interpretación que finalmente se elige de estos mensajes enviados a los chats.The research for this paper has been supported by IULMA (Instituto Interuniversitario de Lenguas Modernas Aplicadas)

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

Molecular Mechanisms Used by Salmonella to Evade the Immune System

Human and animal pathogens are able to circumvent, at least temporarily, the sophisticated immune defenses of their hosts. Several serovars of the Gram-negative bacterium Salmonella enterica have been used as models for the study of pathogen-host interactions. In this review we discuss the strategies used by Salmonella to evade or manipulate three levels of host immune defenses: physical barriers, innate immunity and adaptive immunity. During its passage through the digestive system, Salmonella has to face the acidic pH of the stomach, bile and antimicrobial peptides in the intestine, as well as the competition with resident microbiota. After host cell invasion, Salmonella manipulates inflammatory pathways and the autophagy process. Finally, Salmonella evades the adaptive immune system by interacting with dendritic cells, and T and B lymphocytes. Mechanisms allowing the establishment of persistent infections are also discussed.European Regional Development Fund SAF2013-46229-R, SAF2016-75365-