pyphysio: A physiological signal processing library for data science approaches in physiology

The lack of open-source tools for physiological signal processing hinders the development of standardized pipelines in physiology. Researchers usually must rely on commercial software that, by implementing black-box algorithms, undermines the control on the analysis and prevents the comparison of the results, ultimately affecting the scientific reproducibility. We introduce pyphysio as a step towards a data science approach oriented to compute physiological indicators, in particular of the Autonomic Nervous System activity. pyphysio serves as a basis for machine learning modules and it implements a suite of combinable algorithms for processing of signals from either by wearable or medical-grade quality devices. Keywords: Physiological signal processing, Psychophysiology, Autonomic indicators, Data science, Pytho

Determination of Fe2+ and Fe3+ species by FIA-CRC-ICP-MS in Antarctic ice samples

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I'm alone but not lonely. U-shaped pattern of self-perceived loneliness during the COVID-19 pandemic in the UK and Greece.

OBJECTIVES: In the past months, many countries have adopted varying degrees of lockdown restrictions to control the spread of the COVID-19 virus. According to the existing literature, some consequences of lockdown restrictions on people's lives are beginning to emerge yet the evolution of such consequences in relation to the time spent in lockdown is understudied. To inform policies involving lockdown restrictions, this study adopted a data-driven Machine Learning approach to uncover the short-term time-related effects of lockdown on people's physical and mental health. STUDY DESIGN: An online questionnaire was launched on 17 April 2020, distributed through convenience sampling and was self-completed by 2,276 people from 66 different countries. METHODS: Focusing on the UK sample (N = 325), 12 aggregated variables representing the participant's living environment, physical and mental health were used to train a RandomForest model to estimate the week of survey completion. RESULTS: Using an index of importance, Self-Perceived Loneliness was identified as the most influential variable for estimating the time spent in lockdown. A significant U-shaped curve emerged for loneliness levels, with lower scores reported by participants who took part in the study during the 6th lockdown week (p = 0.009). The same pattern was replicated in the Greek sample (N = 137) for week 4 (p = 0.012) and 6 (p = 0.009) of lockdown. CONCLUSIONS: From the trained Machine Learning model and the subsequent statistical analysis, Self-Perceived Loneliness varied across time in lockdown in the UK and Greek populations, with lower symptoms reported during the 4th and 6th lockdown weeks. This supports the dissociation between social support and loneliness, and suggests that social support strategies could be effective even in times of social isolation

Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study

The global Covid-19 pandemic has forced countries to impose strict lockdown restrictions and mandatory stay-at-home orders with varying impacts on individual's health. Combining a data-driven machine learning paradigm and a statistical approach, our previous paper documented a U-shaped pattern in levels of self-perceived loneliness in both the UK and Greek populations during the first lockdown (17 April to 17 July 2020). The current paper aimed to test the robustness of these results by focusing on data from the first and second lockdown waves in the UK. We tested a) the impact of the chosen model on the identification of the most time-sensitive variable in the period spent in lockdown. Two new machine learning models - namely, support vector regressor (SVR) and multiple linear regressor (MLR) were adopted to identify the most time-sensitive variable in the UK dataset from Wave 1 (n = 435). In the second part of the study, we tested b) whether the pattern of self-perceived loneliness found in the first UK national lockdown was generalisable to the second wave of the UK lockdown (17 October 2020 to 31 January 2021). To do so, data from Wave 2 of the UK lockdown (n = 263) was used to conduct a graphical inspection of the week-by-week distribution of self-perceived loneliness scores. In both SVR and MLR models, depressive symptoms resulted to be the most time-sensitive variable during the lockdown period. Statistical analysis of depressive symptoms by week of lockdown resulted in a U-shaped pattern between weeks 3 and 7 of Wave 1 of the UK national lockdown. Furthermore, although the sample size by week in Wave 2 was too small to have a meaningful statistical insight, a graphical U-shaped distribution between weeks 3 and 9 of lockdown was observed. Consistent with past studies, these preliminary results suggest that self-perceived loneliness and depressive symptoms may be two of the most relevant symptoms to address when imposing lockdown restrictions

The Interaction between Serotonin Transporter Allelic Variation and Maternal Care Modulates Instagram Sociability in a Sample of Singaporean Users

Human social interactions ensure recognition and approval from others, both in offline and online environments. This study applies a model from behavioral genetics on Instagram sociability to explore the impact of individual development on behavior on social networks. We hypothesize that sociable attitudes on Instagram resulted from an interaction between serotonin transporter gene alleles and the individual’s social relationship with caregivers. We assess the environmental and genetic components of 57 Instagram users. The self-report questionnaire Parental Bonding Instrument is adopted to determine the quality of parental bonding. The number of posts, followed users (“followings”), and followers are collected from Instagram as measures of online social activity. Additionally, the ratio between the number of followers and followings (“Social Desirability Index”) was calculated to estimate the asymmetry of each user’s social network. Finally, buccal mucosa cell samples were acquired, and the polymorphism rs25531 (T/T homozygotes vs. C-carriers) within the serotonin transporter gene was examined. In the preliminary analysis, we identified a gender effect on the number of followings. In addition, we specifically found a gene–environment interaction on the standardized Instagram “Social Desirability Index” in line with our predictions. Users with the genotype more sensitive to environmental influences (T/T homozygotes) showed a higher Instagram “Social Desirability Index” than nonsensitive ones (C-carriers) when they experienced positive maternal care. This result may contribute to understanding online social behavior from a gene*environment perspective

Alterations in Cortisol Profiles among Mothers of Children with ASD Related to Poor Child Sleep Quality

Caregivers of children with autism spectrum disorder (ASD) experience poorer sleep, but studies have not yet used objective measures to investigate how child and caregiver sleep affect each other. In this study, 29 mothers and their child with ASD aged between 6 and 16 years were recruited. Questionnaires measuring child autism, maternal depression, and maternal and child sleep quality were administered. Cortisol salivary samples were also obtained from the mothers over the course of a day. Results revealed that maternal depression is significantly correlated with their subjective sleep quality, sleep latency and daytime dysfunction. Child sleep quality was also found to be significantly correlated with ASD severity. In terms of maternal cortisol profiles, a significant number of mothers showed a flattened diurnal cortisol expression, and children of mothers with a flattened cortisol profile had significantly more sleep problems. Overall, results suggest that maternal and child sleep are affected by the child’s disability but also are mutually related. Future studies may consider employing measures such as actigraphy or somnography to quantify sleep quality and establish causal pathways between sleep, cortisol expression and caregiver and child outcomes. The present study has clinical implications in examining family sleep when considering treatment for ASD

Short Report: Lack of Diurnal Variation in Salivary Cortisol Is Linked to Sleep Disturbances and Heightened Anxiety in Adolescents with Williams Syndrome

Objective: The aim of the current study was to examine the potential relationship between sleep patterns, cortisol levels, and anxiety profiles in adolescents with Williams Syndrome (WS) compared to typically developing adolescents. Method: Thirteen adolescents with WS and thirteen TD adolescents (age range 12–18 years) were recruited. Participants were provided with a “testing kit”, containing instructions for collecting data through a sleep diary, MotionWare actigraphy, the Childhood Sleep Habits Questionnaire (CSHQ), and the Spence Children’s Anxiety Scale, and a salivary cortisol collection kit. Results: Adolescents in the WS group did not show diurnal variation in salivary cortisol. Significantly higher scores were reported for two CSHQ subsections, night wakings and parasomnias, in the WS group. Regarding the actigraphy, only significantly longer sleep latency was observed in the WS group. In comparison to the TD group, the WS group had significantly higher anxiety. As expected, the TD group showed typical diurnal variation in cortisol, whereas the WS group showed a flattened cortisol profile throughout the day. Conclusions: From the developmental perspective, this study provides new data supporting the conclusion that sleep problems are not transient but continue to persist into adolescence in WS. Future studies ought to consider examining the role of cortisol and its interplay with anxiety levels and sleep problems across the lifespan in individuals with WS

Predictors of Contemporary under-5 Child Mortality in Low- and Middle-Income Countries: A Machine Learning Approach

Child Mortality (CM) is a worldwide concern, annually affecting as many as 6.81% children in low- and middle-income countries (LMIC). We used data of the Multiple Indicators Cluster Survey (MICS) (N = 275,160) from 27 LMIC and a machine-learning approach to rank 37 distal causes of CM and identify the top 10 causes in terms of predictive potency. Based on the top 10 causes, we identified households with improved conditions. We retrospectively validated the results by investigating the association between variations of CM and variations of the percentage of households with improved conditions at country-level, between the 2005–2007 and the 2013–2017 administrations of the MICS. A unique contribution of our approach is to identify lesser-known distal causes which likely account for better-known proximal causes: notably, the identified distal causes and preventable and treatable through social, educational, and physical interventions. We demonstrate how machine learning can be used to obtain operational information from big dataset to guide interventions and policy makers

Culture and the assumptions about appearance and reality: a scientometric look at a century of research

IntroductionPeople represent the world in terms of two constructs: how something appears on the surface (appearance) and what it is underneath that surface (reality). Both constructs are central to various bodies of literature. What has not been done, however, is a systematic look at this collection of literature for overarching themes. Motivated by this research gap, the present scientometric review aimed to identify the common themes that penetrate through a century of scholarly work on appearance and reality. In doing so, this review also sketched a scientometric outline of the international network, pinpointing where the work was carried out.MethodsWith CiteSpace software, we computed an optimized document co-citation analysis with a sample of 4,771 documents (1929–2022), resulting in a network of 1,785 nodes.Results and discussionWe identified impactful publications, summarized major intellectual movements, and identified five thematic clusters (“Perception of Counseling Services”, “Appearance and Reality in Sociocultural Evolution,” “Cultural Heritage and Identity,” “Media and Culture,” and “Cultural Identity”), all with theoretical and pragmatic implications which we discuss. A deeper look at these clusters reveals new empirical questions and promising directions for future research

Habilitation of sleep problems among mothers and their children with autism spectrum disorder: Insights from multi-level exploratory dyadic analyses

Few habilitation strategies for children with autism spectrum disorder (ASD) consider their sleep-related problems. Together with the fact that caregivers of children with ASD also face issues with sleep, there may be yet-to-be uncovered relationships between caregiver-child sleep patterns and sleep quality, offering a key opportunity for clinicians to consider the needs of both child and caregiver in terms of sleep. 29 dyads of mothers and their children with ASD were recruited for this cohort study and both subjective (self-report questionnaires and sleep diaries) and objective (cortisol samples and actigraphy) measures of sleep were collected to investigate significant predictors of sleep quality. Comparative, correlational, and hierarchical analyses were conducted. Findings indicated that both mother and child experience sleep deprivation in terms of shorter sleep duration and poor sleep quality in terms of longer sleep onset latencies and a higher frequency of wake bouts. Exploratory hierarchical analyses also found that child-related sleep difficulties such as sleep disordered breathing and night waking significantly predict mothers’ sleep quality, which may point to the bi-directional influence of mother-child sleep. Based on these findings, it is recommended that clinicians adopt a family systems perspective and consider the sleep environment of the household, particularly that of the caregiver and child, when designing interventions for sleep-related problems in ASD. Finally, there is a need for additional support to promote good quality sleep among caregivers of children with ASD to bolster out-of-clinic care