Abstract
Introduction
Isolated rapid-eye-movement (REM) sleep behavior disorder (iRBD) affects over 1% of middle-aged and older adults and is in most cases a prodromal stage of alpha-synucleinopathy. ...However, a small fraction of them is currently diagnosed due to poor access to the gold-standard diagnostic procedure polysomnography (PSG). We aimed to test an ambulatory diagnostic procedure for iRBD based on wrist actigraphy alone and combined with a short questionnaire on nonmotor symptoms.
Methods
A total of 35 PSG-confirmed iRBD and 28 age-matched clinic and community control participants with and without a sleep disorder (1:1 ratio) wore high-frequency (25 Hz) wrist actigraphy for at least 7 nights and completed sleep diaries. Raw accelerometer data recorded during sleep was analyzed by deriving an activity count and extracting movement-related features for each night. Additionally, participants completed the Innsbruck RBD inventory (RBD-I) and a 3-item questionnaire on hyposmia, constipation, and orthostasis. We fitted machine learning models, specifically, boosted decision trees, in a leave-one-out cross-validation framework to classify iRBD patients from controls based on either actigraphy or questionnaire data. For each participant, model predictions from actigraphy were averaged across all available nights.
Results
The boosted decision trees classified iRBD with an area under the receiver-operator-characteristics (ROC) curve (AUC) of 0.972, a sensitivity of 97.1%, and a specificity of 89.3%. Analyses revealed that performance plateaued after one week of actigraphy. Best single feature “short immobile bursts” achieved an AUC of 0.958, a sensitivity of 94.3%, and a specificity of 78.6%. In this population, RBD-I item 3 best discriminated between groups with an AUC of 0.892, a sensitivity of 91.4%, and a specificity of 85.7%. The combination of a positive RBD-I item 3 and a positive actigraphy-based classification achieved a sensitivity of 88.6% and a specificity of 96.4%.
Conclusion
High-frequency actigraphy using machine learning detects iRBD with high accuracy. Addition of a single RBD question to this procedure increased specificity. These results need to be validated in a larger sample and lay the groundwork for an ambulatory screening paradigm in the general population.
Support (If Any)
The Klarman Family Foundation and the Feldman Foundation Ca.
The formation of the molluscan shell nacre is regulated to a large extent by a matrix of extracellular macromolecules that are secreted by the shell-forming tissue, the mantle. This so-called ...‘calcifying matrix’ is a complex mixture of proteins, glycoproteins and polysaccharides that is assembled and occluded within the mineral phase during the calcification process. Better molecular-level characterization of the substances that regulate nacre formation is still required. Notable advances in expressed tag sequencing of freshwater mussels, such as Elliptio complanata and Villosa lienosa, provide a pre-requisite to further characterize bivalve nacre proteins by a proteomic approach. In this study, we have identified a total of 48 different proteins from the insoluble matrices of the nacre, 31 of which are common to both E. complanata and V. lienosa. A few of these proteins, such as PIF, MSI60, CA, shematrin-like, Kunitz-like, LamG, chitin-binding-containing proteins, together with A-, D-, G-, M- and Q-rich proteins, appear to be analogues, if not true homologues, of proteins previously described from the pearl oyster or the edible mussel nacre matrices, thus forming a remarkable list of deeply conserved nacre proteins. This work constitutes a comprehensive nacre proteomic study of non-pteriomorphid bivalves that has enabled us to describe the molecular basis of a deeply conserved biomineralization toolkit among nacreous shell-bearing bivalves, with regard to proteins associated with other shell microstructures, with those of other mollusc classes (gastropods, cephalopods) and, finally, with other lophotrochozoans (brachiopods).
We propose an extension of a recent work with convolutional neural networks and drones such as Learning to fly by driving (DroNet)1 that can possibly safely drive a drone autonomously. In other ...words, we propose a model that will extend this work in order to safely track any object with a drone. The combination of (i) the DroNet architecture and weights to apply to CNNs to avoid the crashes; (ii) combining it with DLIB tracker, a correlation implemented tracker based on Danelljan et al.’s paper 2; (iii) the extraction of descriptors using Speeded Up Robust Features3; and (iv) Fast Library for Approximate Nearest Neighbors4 for the feature matching – leads a drone to track any object and avoid crashes autonomously without any prior information about the object.
En este artículo, se propone un nuevo método de inicialización de poblaciones para algoritmos metaheurísticos. En este enfoque, el conjunto inicial de soluciones iniciales se obtiene a través del ...muestreo de la función objetivo aplicando la técnica de Metropolis-Hastings (MH). Bajo este método, el conjunto inicial de soluciones adopta un valor cercano a los valores prominentes de la función objetivo a optimizar. A diferencia de la mayoría de los métodos de inicialización que únicamente consideran una distribución espacial, en el método, los puntos iniciales representan regiones promisorias del espacio de búsqueda, las cuales merecen ser explotadas para identificar la solución óptima global de una manera más rápida. brindando al algoritmo una convergencia más rápida y mejorando la calidad de las soluciones obtenidas. Con el objetivo de demostrar el rendimiento del método de inicialización a algoritmos metaheurísticos, éste ha sido embebido en el algoritmo de Differential Evolution (DE) clásico, y el sistema completo ha sido puesto a prueba en un conjunto representativo de funciones de benchmark extraído de diferentes conjuntos de datos. Los resultados experimentales demuestran una mejora en la rapidez de convergencia y un incremento en la calidad de las soluciones por parte del enfoque propuesto, a comparación de otros métodos similares.
This study was conducted to determine if the use of the technology known as Classroom Performance System (CPS), specifically referred to as "Clickers", improves the learning gains of students ...enrolled in a biology course for science majors. CPS is one of a group of developing technologies adapted for providing feedback in the classroom using a learner-centered approach. It supports and facilitates discussion among students and between them and teachers, and provides for participation by passive students. Advocates, influenced by constructivist theories, claim increased academic achievement. In science teaching, the results have been mixed, but there is some evidence of improvements in conceptual understanding. The study employed a pretest-posttest, non-equivalent groups experimental design. The sample consisted of 226 participants in six sections of a college biology course at a large community college in South Florida with two instructors trained in the use of clickers. Each instructor randomly selected their sections into CPS (treatment) and non-CPS (control) groups. All participants filled out a survey that included demographic data at the beginning of the semester. The treatment group used clicker questions throughout, with discussions as necessary, whereas the control groups answered the same questions as quizzes, similarly engaging in discussion where necessary. The learning gains were assessed on a pre/post-test basis. The average learning gains, defined as the actual gain divided by the possible gain, were slightly better in the treatment group than in the control group, but the difference was statistically non-significant. An Analysis of Covariance (ANCOVA) statistic with pretest scores as the covariate was conducted to test for significant differences between the treatment and control groups on the posttest. A second ANCOVA was used to determine the significance of differences between the treatment and control groups on the posttest scores, after controlling for sex, GPA, academic status, experience with clickers, and instructional style. The results indicated a small increase in learning gains but these were not statistically significant. The data did not support an increase in learning based on the use of the CPS technology. This study adds to the body of research that questions whether CPS technology merits classroom adaptation.
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