Optimal Spindle Detection Parameters for Predicting Cognitive Performance

Study Objectives Alterations in sleep spindles have been linked to cognitive impairment. This finding has contributed to a growing interest in identifying sleep-based biomarkers of cognition and neurodegeneration, including sleep spindles. However, flexibility surrounding spindle definitions and algorithm parameter settings present a methodological challenge. The aim of this study was to characterize how spindle detection parameter settings influence the association between spindle features and cognition and to identify parameters with the strongest association with cognition. Methods Adult patients (n=167, 49 ± 18 years) completed the NIH Toolbox Cognition Battery after undergoing overnight diagnostic polysomnography recordings for suspected sleep disorders. We explored 1000 combinations across seven parameters in Luna, an open-source spindle detector, and used four features of detected spindles (amplitude, density, duration, and peak frequency) to fit linear multiple regression models to predict cognitive scores. Results Spindle features (amplitude, density, duration, and mean frequency) were associated with the ability to predict raw fluid cognition scores (r=0.503) and age-adjusted fluid cognition scores (r=0.315) with the best spindle parameters. Fast spindle features generally showed better performance relative to slow spindle features. Spindle features weakly predicted total cognition and poorly predicted crystallized cognition regardless of parameter settings. Conclusion Our exploration of spindle detection parameters identified optimal parameters for studies of fluid cognition and revealed the role of parameter interactions for both slow and fast spindles. Our findings support sleep spindles as a sleep-based biomarker of fluid cognition.

Readings in Philippine History: Course Review, Best Practices, and Challenges among Higher Education Institutions

History is essential in the curriculum. The Readings in Philippine History (RPH) syllabus and instruction should be advanced. However, curricular changes and the pandemic affected the instruction. Thus, this study reviewed the course specification and syllabus of RPH among higher educational institutions in Kabankalan City, Philippines. Anchored on the CHED recommended syllabus in RPH, the study reviewed the course and determined the areas for improvement. Likewise, it identified the best practices and challenges. Using a descriptive design and employing purposive and stratified sampling, 269 external reviewers, teachers, and students reviewed the study. The mean, frequency count, rank, and percentage distribution were employed in data analysis. Generally, the course adheres to the standard. However, the main issue is students' learning readiness and the misalignment of teachers' specialization. Thus, a strong foundation on Philippine History among the students is necessary to ensure quality. Also, the retention of qualified teachers is essential in advancing the instruction.

Real-time alerting system for COVID-19 and other stress events using wearable data

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.

The Study on Compound Drought and Heatwave Events in China Using Complex Networks

Compound extreme events can severely impact water security, food security, and social and economic development. Compared with single-hazard events, compound extreme events cause greater losses. Therefore, understanding the spatial and temporal variations in compound extreme events is important to prevent the risks they cause. Only a few studies have analyzed the spatial and temporal relations of compound extreme events from the perspective of a complex network. In this study, we define compound drought and heatwave events (CDHEs) using the monthly scale standard precipitation index (SPI), and the definition of a heatwave is based on daily maximum temperature. We evaluate the spatial and temporal variations in CDHEs in China from 1961 to 2018 and discuss the impact of maximum temperature and precipitation changes on the annual frequency and annual magnitude trends of CDHEs. Furthermore, a synchronization strength network is established using the event synchronization method, and the proposed synchronization strength index (SSI) is used to divide the network into eight communities to identify the propagation extent of CDHEs, where each community represents a region with high synchronization strength. Finally, we explore the impact of summer Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO) on CDHEs in different communities. The results show that, at a national scale, the mean frequency of CDHEs takes on a non-significant decreasing trend, and the mean magnitude of CDHEs takes on a non-significant increasing trend. The significant trends in the annual frequency and annual magnitude of CDHEs are attributed to maximum temperature and precipitation changes. AMO positively modulates the mean frequency and mean magnitude of CDHEs within community 1 and 2, and negatively modulates the mean magnitude of CDHEs within community 3. PDO negatively modulates the mean frequency and mean magnitude of CDHEs within community 4. AMO and PDO jointly modulate the mean magnitude of CDHEs within community 6 and 8. Overall, this study provides a new understanding of CDHEs to mitigate their severe effects.

The Bouba-Kiki effect reflects generic shape-sound associations

Humans robustly associate spiky shapes to words like “Kiki” and round shapes to words like “Bouba”. A popular explanation is that the mouth forms an angular shape while saying “Kiki” and a rounded shape while saying “Bouba”, leading to this association. Alternatively, there could be generic associations between the shapes of objects and the sounds they produce. These possibilities can be distinguished using unpronounceable sounds: the mouth-shape hypothesis predicts no effect, whereas the generic shape hypothesis predicts a systematic effect. Here, we show that the Bouba-Kiki effect is present for a variety of unpronounceable sounds ranging from reversed versions of Bouba-like and Kiki-like words and natural real object sounds to even pure tones. The effect was strongly correlated with the mean frequency of a sound independent of its pronounceability. Thus, the Bouba-Kiki effect reflects generic associations between sounds and object shape rather than mouth shape.

Human spindle variability

In humans, sleep spindles are 10-16 Hz oscillations lasting approximately 0.5-2 s. Spindles, along with cortical slow oscillations, facilitate memory consolidation by enabling synaptic plasticity. Early recordings of spindles at the scalp found anterior channels had overall slower frequency than central-posterior channels. This robust, topographical finding led to dichotomizing spindles as slow versus fast, modeled as two distinct spindle generators in frontal versus posterior cortex. Using a large dataset of intracranial sEEG recordings (n=20, 365 bipolar recordings), we show that the difference in spindle frequency between frontal and parietal channels is comparable to the variability in spindle frequency within the course of individual spindles, across different spindles recorded by a given site, and across sites within a given region. Thus, fast and slow spindles only capture average differences that obscure a much larger underlying overlap in frequency. Furthermore, differences in mean frequency are only one of several ways that spindles differ. For example, compared to parietal, frontal spindles are smaller, tend to occur after parietal when both are engaged, and show a larger decrease in frequency within-spindles. However, frontal and parietal spindles are similar in being longer, less variable, and more widespread than occipital, temporal, and Rolandic spindles. These characteristics are accentuated in spindles which are highly phase-locked to posterior hippocampal spindles. We propose that rather than a strict parietal-fast/frontal-slow dichotomy, spindles differ continuously and quasi-independently in multiple dimensions, with variability due about equally to within-spindle, within-region and between-region factors.

Effects of the COVID-19 pandemic on mental health (anxiety and depression symptoms) in the United States of America

Introduction: The COVID-19 pandemic has had a serious mental health impact in the United States of America, as well as all over the world. Objective: To assess some of the effects of the COVID-19 pandemic on the mental health of US adult population, as well as the relationship between the average number of new COVID-19 cases and the average frequency of symptoms of anxiety, of depression, and of anxiety or depression between April 23 and October 26 2020. Materials and methods: Retrospective study. Psychosocial and demographic data were obtained from the online community-based Household Pulse Survey website. Data about the number of new COVID-19 cases detected in USA during the study period were taken from the Our World in Data website. The Spearman’s rank correlation coefficient was used to evaluate the strength of the relationships between the average new cases of COVID-19 during the study period and the average frequency of symptoms of anxiety, of depression, and of anxiety or depression. These correlations were also assessed in a subgroup analysis (gender, age group, education level, and ethnicity). Results: A total of 1,351,911 US adults completed the survey. The average rates of symptoms of depression, of anxiety, and of anxiety or depression were 25.7±1.6%, 31.9±2.0%, and 36.8±2.0%, respectively. The average number of new COVID-19 cases was positively correlated with the mean frequency of symptoms of anxiety, of depression, and of anxiety or depression (r=0.858, r=0.710, and r=0.887; p<0.001). Likewise, positive correlations between the average number of new cases and the mean frequency of anxiety or depressive symptoms were found in the subgroups (r=0.484-0.917). Conclusions: According to our results, the number of new COVID-19 cases detected during the study period in USA was positively correlated with the frequency of anxiety or depression symptoms in the participants, that is, as the number of new cases increased, so did the frequency of the symptoms of these mental disorders.

Detailed Emotional Profile of Secondary Education Students Toward Learning Physics and Chemistry

The present research arises from the need to identify the emotions that K-7 to K-10 students experience toward the learning of Physics and Chemistry, since it is a fact that there is a decrease in the number of students choosing itineraries related to Science. Different blocks of contents have been considered in each subject in order to identify emotions toward each one of them. The considered sample consisted of 149 K-8 students, 152 K-9 students and 130 K-10 students from several middle and high schools in Badajoz (Spain) during the 2014–2015 school year. Students experienced more positive emotions toward the content of Chemistry than toward those of Physics. A decrease was detected in the mean frequency of positive emotions such as joy, fun, and tranquility from K-8 to K-10, as well as an increase in negative emotions such as boredom, anxiety, disgust, fear, nervousness, worry, and sadness. It has also been found that positive emotions toward Chemistry contents are mainly related to teachers’ methods and attitudes, while negative emotions toward Physics contents are related to the exclusive use of the textbook, solving Physics problems, or giving oral presentations of the topics in class.