1,721,008 research outputs found
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Sex Differences in Variability of Physical Activity Measurements Across Multiple Timescales Recorded by a Wearable Device
Full text linkSex is an important consideration in biomedical research. Efforts to expand sex inclusion have had some success, but females are still underrepresented in both animal and human biomedical research despite increasing evidence in support of sex-inclusive study design. Hesitancy to include female subjects is partially due to the hypothesis that ovarian rhythms increase female variability and weaken statistical power. We recently used continuous skin temperature data from wearable devices to test this hypothesis and found that the data did not support the hypothesis that females, cycling or not, reduce statistical power. However, ovarian rhythms are not the only timescale of change that might shape variability in human data. Additionally, whereas temperature is linked to endocrine and physiological systems, physical activity captured by wearables may be more related to behavioral patterns that exist independently of endogenous physiological rhythms, and so is worthy of investigation separate from temperature.Here we used minute-level metabolic equivalent task (MET) data spanning 206 days each from 596 individuals to explore physical activity (PA), focusing on comparing the scale of sex differences in variability of PA to the scale of differences in variability arising at the timescales of days, weeks, menstrual cycles, and decades of life. We report that females have lower intra-individual variability than males as a whole and the presence of menstrual cycles did not increase variability. PA patterns reflective of behavioral patterns were found on weekly time scales and across decades of life. The exclusion of either sex was not supported by our analysis
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Classification Pipeline to Identify Cyclic Structure in Wearable Device Temperature Measurements
No full textThe menstrual cycle is a fundamental biological rhythm affecting physiological signals, yet remains under-characterized in wearable device data and digital health models. This thesis presents a signal processing pipeline to identify cyclicity in wearable temperature data without relying on self-reported menstrual annotations. We processed nightly peak temperature data from 6,460 females and 6,460 males using a Butterworth band-pass filter, autocorrelation function (ACF), and Lomb-Scargle (LS) periodogram to extract features reflecting cyclic structure consistent with menstrual cycling. Gaussian Mixture Models (GMMs) defined population-level heuristics to classify individuals into cyclic, acyclic, and unknown groups based on LS power, frequency, and ACF amplitude. Our method demonstrates robust detection of cyclic patterns in temperature signals and reveals age-related trends of decreasing cycle length within the cyclic group, aligning with biological expectations. We further examined the intersection of age and ethnicity in cycle length using generalized additive models (GAMs), finding that while age was the primary driver of cycle length shortening, Black individuals showed a trend toward shorter cycle lengths, consistent with prior literature. These findings highlight the value of physiologically-informed feature extraction for understanding biological variability in wearable datasets and underscore the importance of fitting models to specific populations to ensure model interpretation and appropriate data collection
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Towards the Use of Commercial Wearable Devices for Acute Infectious Disease Mitigation
Full text linkThe landscape of health technologies is rapidly evolving and commercial wearabledevices equipped with health sensors offer a promising avenue for continuous health monitoring.
The ubiquity of these devices among consumers presents an unprecedented opportunity to
leverage the wealth of data that wearables collect for health applications. Despite their widespread
use, there have only recently been developments towards utilizing these data for enhancing health
monitoring. However, research stemming from recent efforts to gather large-scale longitudinal
wearable datasets suggests these devices might hold potential in detecting the presence of and
characterizing aspects of acute physiological changes. The application of wearable device data
might be particularly useful in the context of acute physiological changes given wearables’
ability to monitor a number of physiological vital signs both passively and longitudinally.
Passive monitoring uniquely enables longitudinal comparisons of individuals to themselves
over time and real-time identification of significant deviations indicative of changes in health
status. This thesis explores the application of data from wearable devices for detecting and
characterizing physiological changes following significant health events, specifically vaccination
for COVID-19 and the onset of fever. I also present the first comprehensive analysis of multiple
large-scale, longitudinal wearable device datasets, therein assessing the generalizability of
algorithms for monitoring acute illnesses and characterizing the biases in these datasets, some
of which are correlated with demographic variables. Through this research, I demonstrate the
potential of commercial wearable devices in enhancing our understanding and monitoring of
acute physiological changes, and present a framework through which industry and research
standards might emerge to speed the evolution of this field
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Advancing inverse folding models: exploring diverse optimization techniques
No full textInverse folding is an important task in protein engineering. This problem was once a significant challenge, but recent developments in the field of deep learning have led to the emergence of many effective models, exemplified by ProteinMPNN. However, some deficiencies remain in the field of inverse folding. On one hand, while the relationship between sequence and structure is many-to-many, most existing models focus solely on predicting the reference sequence corresponding to the original structure as the optimization objective during model training. On the other hand, the sequence recovery rate has long been the primary and often the only evaluation metric used to evaluate inverse folding models. Based on this situation, this work primarily explores two aspects: firstly, we designed and compared some strategies to improve the performance of the inverse folding model by using a modular approach with information beyond the reference sequence itself during the training process. Secondly, we introduced a very comprehensive series of assessments of model performance, which allows for a detailed comparison of the strengths and weaknesses of the models, and focuses on the practical applications of the models
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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