Andreasenschack1643

We present Shabd, a psycholinguistic database in Hindi. It is based on a corpus of 1.4 billion words from electronic newspapers and news websites. Word frequencies and part of speech information have been derived and are made available in a cleaned list of 34 thousand hand-selected words, and a list of 96 thousand words observed with a frequency of more than 100 times in the corpus. Next to the Shabd database, we also make a list with all 2.3 million word types available and a list with the 2.5 million most frequent word pairs (word bigrams). The quality of the word frequency measure was tested in two lexical decision tasks. We observed that the Shabd word frequencies outperform existing frequencies based on smaller corpora of newspapers but not the Worldlex word frequencies based on an analysis of blogs. We also observed that word frequency accounts for as much variance as contextual diversity (operationalized as the number of documents in which the words were observed). The Shabd database is freely available for research.Recently, the possibilities of detecting psychosocial stress from speech have been discussed. Yet, there are mixed effects and a current lack of clarity in relations and directions for parameters derived from stressed speech. The aim of the current study is - in a controlled psychosocial stress induction experiment - to apply network modeling to (1) look into the unique associations between specific speech parameters, comparing speech networks containing fundamental frequency (F0), jitter, mean voiced segment length, and Harmonics-to-Noise Ratio (HNR) pre- and post-stress induction, and (2) examine how changes pre- versus post-stress induction (i.e., change network) in each of the parameters are related to changes in self-reported negative affect. Results show that the network of speech parameters is similar after versus before the stress induction, with a central role of HNR, which shows that the complex interplay and unique associations between each of the used speech parameters is not impacted by psychosocial stress (aim 1). Moreover, we found a change network (consisting of pre-post stress difference values) with changes in jitter being positively related to changes in self-reported negative affect (aim 2). These findings illustrate - for the first time in a well-controlled but ecologically valid setting - the complex relations between different speech parameters in the context of psychosocial stress. Longitudinal and experimental studies are required to further investigate these relationships and to test whether the identified paths in the networks are indicative of causal relationships.Large-scale surveys are common in social and behavioral science research. Missing data often occur at item levels due to nonresponses or planned missing data designs. In practice, the item scores are typically aggregated into scale scores (i.e., sum or mean scores) for further analyses. Although several strategies to handle item-level missing data have been proposed, most of them are not easy to implement, especially for applied researchers. Using Monte Carlo simulations, we examined a practical hybrid approach to deal with item-level missing data in Likert scale items with a varying number of categories (i.e., four, five, and seven) and missing data mechanisms. Specifically, the examined approach first uses proration to calculate the scale scores for a participant if a certain proportion of item scores is available (a cutoff criterion of proration) and then use full information maximum likelihood to deal with missing data at the scale level when scale scores cannot be computed due to the selected proration cutoff criterion. Our simulation results showed that the hybrid approach was generally acceptable when the missing data were randomly spread over the items, even when they had different thresholds/means and loadings, with caution to be taken when the missingness is determined by one of the scale items. Based on the results, we recommend using the cutoff of 30% or 40% for proration when the sample size is small and the cutoff of 40% or 50% when the sample size is moderate or large.Crowdsourced psychological and other biobehavioral research using platforms like Amazon's Mechanical Turk (MTurk) is increasingly common - but has proliferated more rapidly than studies to establish data quality best practices. Thus, this study investigated whether outcome scores for three common screening tools would be significantly different among MTurk workers who were subject to different sets of quality control checks. We conducted a single-stage, randomized controlled trial with equal allocation to each of four study arms Arm 1 (Control Arm), Arm 2 (Bot/VPN Check), Arm 3 (Truthfulness/Attention Check), and Arm 4 (Stringent Arm - All Checks). Data collection was completed in Qualtrics, to which participants were referred from MTurk. Subjects (n = 1100) were recruited on November 20-21, 2020. Eligible workers were required to claim U.S. residency, have a successful task completion rate > 95%, have completed a minimum of 100 tasks, and have completed a maximum of 10,000 tasks. MEK inhibitor cancer Participants completed the US-Alcohol Use Disorders Identification Test (USAUDIT), the Patient Health Questionnaire (PHQ-9), and a screener for Generalized Anxiety Disorder (GAD-7). We found that differing quality control approaches significantly, meaningfully, and directionally affected outcome scores on each of the screening tools. Most notably, workers in Arm 1 (Control) reported higher scores than those in Arms 3 and 4 for all tools, and a higher score than workers in Arm 2 for the PHQ-9. These data suggest that the use, or lack thereof, of quality control questions in crowdsourced research may substantively affect findings, as might the types of quality control items.We empirically investigate the role of small, almost imperceptible balance and breathing movements of the head on the level and colour of noise in data from five commercial video-based P-CR eye trackers. By comparing noise from recordings with completely static artificial eyes to noise from recordings where the artificial eyes are worn by humans, we show that very small head movements increase levels and colouring of the noise in data recorded from all five eye trackers in this study. This increase of noise levels is seen not only in the gaze signal, but also in the P and CR signals of the eye trackers that provide these camera image features. The P and CR signals of the SMI eye trackers correlate strongly during small head movements, but less so or not at all when the head is completely still, indicating that head movements are registered by the P and CR images in the eye camera. By recording with artificial eyes, we can also show that the pupil size artefact has no major role in increasing and colouring noise. Our findings add to and replicate the observation by Niehorster et al., (2021) that lowpass filters in video-based P-CR eye trackers colour the data. Irrespective of source, filters or head movements, coloured noise can be confused for oculomotor drift. We also find that usage of the default head restriction in the EyeLink 1000+, the EyeLink II and the HiSpeed240 result in noisier data compared to less head restriction. Researchers investigating data quality in eye trackers should consider not using the Gen 2 artificial eye from SR Research / EyeLink. Data recorded with this artificial eye are much noisier than data recorded with other artificial eyes, on average 2.2-14.5 times worse for the five eye trackers.Experimental design is a key ingredient of reproducible empirical research. Yet, given the increasing complexity of experimental designs, researchers often struggle to implement ones that allow them to measure their variables of interest without confounds. SweetPea ( https//sweetpea-org.github.io/ ) is an open-source declarative language in Python, in which researchers can describe their desired experiment as a set of factors and constraints. The language leverages advances in areas of computer science to sample experiment sequences in an unbiased way. In this article, we provide an overview of SweetPea's capabilities, and demonstrate its application to the design of psychological experiments. Finally, we discuss current limitations of SweetPea, as well as potential applications to other domains of empirical research, such as neuroscience and machine learning.Psychologists and linguists collect various data on word and concept properties. In psychology, scholars have accumulated norms and ratings for a large number of words in languages with many speakers. In linguistics, scholars have accumulated cross-linguistic information about the relations between words and concepts. Until now, however, there have been no efforts to combine information from the two fields, which would allow comparison of psychological and linguistic properties across different languages. The Database of Cross-Linguistic Norms, Ratings, and Relations for Words and Concepts (NoRaRe) is the first attempt to close this gap. Building on a reference catalog that offers standardization of concepts used in historical and typological language comparison, it integrates data from psychology and linguistics, collected from 98 data sets, covering 65 unique properties for 40 languages. The database is curated with the help of manual, automated, semi-automated workflows and uses a software API to control and access the data. The database is accessible via a web application, the software API, or using scripting languages. In this study, we present how the database is structured, how it can be extended, and how we control the quality of the data curation process. To illustrate its application, we present three case studies that test the validity of our approach, the accuracy of our workflows, and the integrative potential of the database. Due to regular version updates, the NoRaRe database has the potential to advance research in psychology and linguistics by offering researchers an integrated perspective on both fields.In this manuscript, COVID-19, Ebola virus disease, Nipah virus infection, SARS, and MERS are suggested to be considered for a novel immunological reclassification as acute onset immune dysrhythmia syndrome (n-AIDS) due to altered monocytic, Th1/Th2, as well as cytokines and chemokines balances. n-AIDs is postulated to be the cause of the acute respiratory distress and multi-inflammatory syndromes which are described with fatal COVID-19, and immunomodulators are suggested to effectively manage the mentioned diseases as well as for other disorders caused by Th1/Th2 imbalance. Meanwhile, para COVID syndrome is suggested to describe various immune-related complications, whether before or after recovery, and to embrace a potential of a latent infection, that might be discovered later, as occurred with Ebola virus disease. Finally, our hypothesis has evolved out of our real-life practice that uses immunomodulatory drugs to manage COVID-19 safely and effectively.

To estimate the association of emotional distress with both consumption of energy-dense micronutrient-poor foods (EDF) and body mass index (BMI) and the association between EDF consumption and change in BMI, during COVID-19 pandemic in patients with prior bariatric surgery.

This cross-sectional study applied an online structured questionnaire to 75postoperative bariatric patients during the first Portuguese lockdown. Emotional distress was assessed trough the Hospital Anxiety and Depression Scale (HADS) and dietary intake was evaluated by Food Frequency Questionnaire (FFQ). Self-reported BMI prior to and at the end of confinement was used to compute BMI change. Pre-surgery BMI was computed from measured height and weight from clinical records.

After adjustment for education, sex, time since surgery, pre-surgery BMI, and exercise practice, moderate/severe scores in HADS were significantly positively associated with consumption of EDF (ẞ = 0.799; 95% CI 0.051, 1.546), but not with BMI. Daily EDF consumption significantly increased the odds of maintaining/increasing BMI (OR = 3.