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Copyright of Ciência Florestal (01039954) is the property of Ciencia Florestal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Reliable functional electrical stimulation parameters assisted the drinking task; • Functional electrical stimulation parameters presented high to very high repeatability; • Upper limb kinematics presented moderate to very high repeatability. No specific guidelines for the management of functional electrical stimulation (FES) parameters in post stroke patients have been defined yet, despite its frequent use. The purpose of this study is to characterize the optimal FES parameters that assist the reaching phase of drinking task ("drinking task - reaching phase") on post stroke subjects and to analyze the related upper limb (UL) movement quality indicators repeatability. An observational study with a test and re-test design involving ten post stroke subjects with UL dysfunction was performed. End-point and joint kinematics of contralesional UL were assessed during the "drinking task - reaching phase" with FES through a test and retest design. FES parameters were adjusted to improve UL function according to a consensus between physiotherapists and patients' perspective. It was possible to establish reliable FES parameters that assisted the "drinking task - reaching phase". All FES parameters presented high to very high repeatability and led to moderate to very high repeatability in almost UL movement quality indicators during the "drinking task - reaching phase". These findings show that the main characteristics of FES parameters that improves patient perception of change are quite stable, which facilitate its implementation in clinical practice by allowing consistence between intervention sessions. [ABSTRACT FROM AUTHOR]

To characterize the optimal functional electrical stimulation (FES) parameters that assist the turn on the light task (TOTL) on poststroke participants and to analyze the related upper limb (UL) kinematics repeatability. Cross-sectional study. Human movement research center. Poststroke individuals (N=11) with history of a single unilateral stroke that resulted in a motor control dysfunction of the contralesional UL. FES based on surface multifield technology applied to the contralesional wrist and finger extensors during the TOTL. FES outcome metrics (virtual electrodes, stimulation duration, intensity) and kinematic metrics (end-point kinematics [absolute and relative duration, mean and peak velocities, relative instant of peak velocity, index of curvature, number of movement units] and joint kinematics [shoulder, elbow, wrist end position and range of movement]). Outcome measures were assessed 2 times with a 72-hour maximum time interval. It was possible to establish reliable FES parameters that assisted the TOTL on poststroke participants. These stimulation parameters led to high to very high repeatability in terms of UL kinematics for most of the cases. • Reliable functional electrical stimulation parameters assisted the light task. • Functional electrical stimulation parameters presented high to very high repeatability. • Upper limb kinematics presented high to very high repeatability. [ABSTRACT FROM AUTHOR]

Copyright of Nova Economia is the property of Nova Economia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Portuguesa de Ciências do Desporto is the property of Universidade do Porto, Faculdade de Desporto and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Enteroendocrine cells (EECs) survey the gut luminal environment and coordinate hormonal, immune and neuronal responses to it. They exhibit well-characterised physiological roles ranging from the control of local gut function to whole body metabolism, but little is known regarding the regulatory networks controlling their differentiation, especially in the human gut. The small molecule isoxazole-9 (ISX-9) has been shown to stimulate neuronal and pancreatic beta-cell differentiation, both closely related to EEC differentiation. Our aim was to use ISX-9 as a tool to explore EEC differentiation. We investigated the effects of ISX-9 on EEC differentiation in mouse and human intestinal organoids, using real-time quantitative polymerase chain reaction (RT-qPCR), fluorescent-activated cell sorting, immunostaining and single-cell RNA sequencing. ISX-9 increased the number of neurogenin3-RFP (Ngn3)-positive endocrine progenitor cells and upregulated NeuroD1 and Pax4, transcription factors that play roles in mouse EEC specification. Single-cell analysis showed induction of Pax4 expression in a developmentally late Ngn3+ population of cells and potentiation of genes associated with progenitors biased toward serotonin-producing enterochromaffin (EC) cells. Further, we observed enrichment of organoids with functional EC cells that was partly dependent on stimulation of calcium signalling in a population of cells residing outside the crypt base. Inducible Pax4 overexpression, in ileal organoids, uncovered its importance as a component of early human endocrine specification and highlighted the potential existence of two major endocrine lineages, the early appearing enterochromaffin lineage and the later developing peptidergic lineage which contains classical gut hormone cell types. Our data provide proof-of-concept for the controlled manipulation of specific endocrine lineages with small molecules, whilst also shedding new light on human EEC differentiation and its similarity to the mouse. Given their diverse roles, understanding endocrine lineage plasticity and its control could have multiple therapeutic implications. Image 1 • ISX-9 promotes flux through the Ngn3 lineage and enriches it with enterochromaffin cells. • ISX-9 engages an enterochromaffin biased transcriptional programme in endocrine fated cells. • Enterochromaffin bias is partly dependent on calcium signalling in progenitor cells. • ISX-9 reveals conserved gut endocrine specification between mouse and human. • Pax4 overexpression in human ileum organoids mimics the effects of ISX-9 on EC bias. [ABSTRACT FROM AUTHOR]

Background and purpose: To review the methods used to analyze the kinematics of upper limbs (ULs) of healthy and poststroke adults, namely the motion capture systems and kinematic metrics. Summary of review: A database of articles published in the last decade was compiled using the following search terms combinations: ("upper extremity" OR "upper limb" OR arm) AND (kinematic OR motion OR movement) AND (analysis OR assessment OR measurement). The articles included in this review: (1) had the purpose to analyze objectively three-dimension kinematics of ULs, (2) studied functional movements or activities of daily living involving ULs, and (3) studied healthy and/or poststroke adults. Fourteen articles were included (four studied a healthy sample, three analyzed poststroke patients, and seven examined both poststroke and healthy participants). Conclusion: Most articles used optoelectronic systems with markers; however, the presentation of laboratory and task-specific errors is missing. Markerless systems, used in some studies, seem to be promising alternatives for implementation of kinematic analysis in hospitals and clinics, but the literature proving their validity is scarce. Most articles analyzed "joint kinematics" and "end-point kinematics," mainly related with reaching. The different stroke locations of the samples were not considered in their analysis and only three articles described their psychometric properties. Implication of key findings: Future research should validate portable motion capture systems, document their specific error at the acquisition place and for the studied task, include grasping and manipulation analysis, and describe psychometric properties. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Recurrence Quantification Analysis (RQA) applied to cardio-respiratory data. • Newly developed features for sleep/wake classification derived from 313 healthy subjects for sleep/wake classification. • High sleep/wake stage discrimination complementary to well-known literature features. • Receiver Operating Characteristics and Precision Recall curves, Area Under the Curve and Cohen's kappa metrics for evaluation. • Age and sex dependency analysis. Abstract In this work the method of Recurrence Quantification Analysis (RQA), often used for the analysis of complex dynamic systems, is employed to extract novel features for sleep/wake classification only using cardio-respiratory signals like electrocardiogram (ECG), heart rate (HR) and respiratory effort (RE). A polysomnography data set consisting of 313 full-night recordings is used to evaluate the features. The sleep/wake classification is performed with a classifier based on Linear Discriminant Analysis (LDA) and it is validated in a Leave One Subject Out cross-validation (LOSOCV) scheme. More than 1300 features are extracted from five different cardio-respiratory modalities (ECG, RE, HR, and combinations of ECG + RE and RE + HR). Each modality is processed to obtain 13 basic RQA features, five post-processed versions and, furthermore, three normalizations are applied, leading to a total count of 975 RQA features. From literature, 126 known cardio-respiratory and actigraphy features, normalized with the same procedures thus resulting in 378 distinct features, are used for performance comparison. A feature selection method based on the Mahalanobis distance and the inter-feature correlation is used to determine the most relevant features of both sets, resulting in a set of 158 from the existing features (set A) and 232 from the RQA based features (set B). The pooled Cohen's kappa coefficient for set A and set B is 0.586 and 0.522, respectively. The combination of both feature sets (set C) improves the kappa value to 0.625. In addition, ROC and PR curves with their corresponding Area Under Curve (AUC) values are computed. It is also shown that derived sleep statistics (sleep efficiency, sleep onset, total sleep time, etc.) deviate less with respect to the ground truth annotations using the additional RQA features compared to the use of literature-based features only. Besides the overall performance, the data set is split into six different but almost equally sized age and sex groups to allow unbiased comparisons among each other. It is shown, that the classification performance decreases with increasing age but is almost independent from sex. Hence, for future work it is suggested to implement the RQA features and also to focus on the improvement of features especially for elderly persons. [ABSTRACT FROM AUTHOR]

• Novel audio-based features that exploit snoring characteristics including the variability of snoring sounds and trend in snore energy were proposed. • An extreme gradient boosting model was trained for apnea-hypopnea index estimation with a medium real-world clinical OSA population. • Audio-based estimates of the apnea-hypopnea index showed a significant correlation with the polysomnographic gold standard, outperforming the model with the baseline feature set from a previous study. Snoring is a prima symptom of obstructive sleep apnea (OSA). Here, we add audio-based snoring features to improve the non-obtrusive assessment of sleep apnea, by estimating the apnea-hypopnea index (AHI) and classifying OSA severity. We propose novel features to quantify temporal changes between snores (snore rate variability) and to describe trends in snore energy, based on the assessment of snore sounds from audio signals over the full night. We then combined those features with age, body mass index (BMI) and features described in literature. An extreme gradient boosting algorithm was trained with all these features on AHI estimation. The estimated AHI was then used to classify OSA severity. Audio-based estimated AHI showed a significant Spearman's correlation with the AHI based on gold-standard polysomnography (R = 0.786, P < 0.0001). Our results outperformed a model trained with solely previously described features in our dataset (R = 0.676, P < 0.0001) and a model trained with the combination of previously described features, age, and BMI (R = 0.731, P < 0.0001). The mean absolute error of AHI estimation was 7.26 events/h. Area under the receiver operating characteristic curve outcomes were 0.90, 0.87 and 0.93 for classifying patients with varying severity separated by the canonical thresholds of 5, 15 and 30 events/h respectively. The accuracy of classifying subjects to four classes (no, mild, moderate, and severe OSA) was 59.3 %. Additional audio-based snore features can improve the performance of non-obtrusive AHI estimation and OSA severity classification methods. [ABSTRACT FROM AUTHOR]

Copyright of Investigación Económica is the property of Universidad Nacional Autonoma de Mexico, Facultad de Economia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Crítica de Ciências Sociais is the property of Centro de Estudos Sociais and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Display omitted] • A chest-worn accelerometer was used by 146 participants during an overnight sleep recording. • A neural network was automatically generated and trained to extract respiratory activity. • Reference and predicted respiratory waveforms were automatically aligned. • The estimation error was halved compared to Principal Component Analysis. • Differences in the respiratory signal between sleep stages could be reproduced. The objective is to develop a new deep learning method for the estimation of respiratory effort from a chest-worn accelerometer during sleep. We evaluate performance, compare it against a state-of-the art method, and assess whether it can differentiate between sleep stages. In 146 participants undergoing overnight polysomnography data were collected from an accelerometer worn on the chest. The study data were partitioned into train, validation, and holdout (test) sets. We used the train and validation sets to generate and train a convolutional neural network and performed model selection respectively, while we used the holdout set (72 participants) to evaluate performance. A convolutional neural network with 9 layers and 207,855 parameters was automatically generated and trained. The neural network significantly outperformed the best performing conventional method, based on Principal Component Analysis; it reduced the Mean Squared Error from 0.26 to 0.11 and it also performed better in the detection of breaths (Sensitivity 98.4 %, PPV 98.2 %). In addition, the neural network exposed significant differences in characteristics of respiratory effort between sleep stages (p < 0.001). The deep learning method predicts respiratory effort with low error and is sensitive and precise in the detection of breaths. In addition, it reproduces differences between sleep stages, which may enable automatic sleep staging, using just a chest-worn accelerometer. [ABSTRACT FROM AUTHOR]

Copyright of Revista Portuguesa de Ciências do Desporto is the property of Universidade do Porto, Faculdade de Desporto and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tempo (1413-7704) is the property of Universidade Federal Fluminense, Departamento de Historia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Seed Science is the property of Associacao Brasileira de Tecnologia de Sementes and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: In multiple sclerosis (MS), even in the absence of a clinical episode of optic neuritis (ON), the optic nerve and retinal nerve fiber layer (RNFL) may be damaged leading to dyschromatopsia. Subclinical dyschromatopsia has been described in MS associated with lower motor and cognitive performances. Objectives: To set the prevalence of dyschromatopsia in eyes of MS patients without a history of ON, to compare its prevalence in patients with and without ON history, and to explore the association between dyschromatopsia and disease duration, average peripapillary RNFL thickness, macular volume, and cognitive and motor performances. Methods: An observational cross-sectional study was conducted at multiple medical centers. Data were collected after single neurological and ophthalmological evaluations. Dyschromatopsia was defined by the presence of at least 1 error using Hardy-Rand-Rittler plates. Results: In our population of 125 patients, 79 patients (63.2%) never had ON and 35 (28.8%) had unilateral ON. The prevalence of dyschromatopsia in eyes of patients without ON was 25.7%. Patients with dyschromatopsia had a statistically significant lower RNFL thickness (P = 0.004 and P = 0.040, right and left eyes, respectively) and worse performance in symbol digit modalities test (P = 0.012). No differences were found in macular volume or motor function tasks. Conclusions: Dyschromatopsia occurs frequently in MS patients. It may be associated with a worse disease status and possibly serve as a marker for the detection of subclinical disease progression since it was detected even in the absence of ON. It correlated with thinner peripapillary RNFL thickness and inferior cognitive performance. [ABSTRACT FROM AUTHOR]

In this work we employ a nonlinear data analysis method called recurrence quantification analysis (RQA) to analyze differences between sleep stages and wake using cardio-respiratory signals, only. The data were recorded during full-night polysomnographies of 313 healthy subjects in nine different sleep laboratories. The raw signals are first normalized to common time bases and ranges. Thirteen different RQA and cross-RQA features derived from ECG, respiratory effort, heart rate and their combinations are additionally reconditioned with windowed standard deviation filters and ZSCORE normalization procedures leading to a total feature count of 195. The discriminative power between Wake , NREM and REM of each feature is evaluated using the Cohen's kappa coefficient. Besides kappa performance, sensitivity, specificity, accuracy and inter-correlations of the best 20 features with high discriminative power is also analyzed. The best kappa values for each class versus the other classes are 0.24, 0.12 and 0.31 for NREM , REM and Wake , respectively. Significance is tested with ANOVA F -test (mostly p < 0.001). The results are compared to known cardio-respiratory features for sleep analysis. We conclude that many RQA features are suited to discriminate between Wake and Sleep , whereas the differentiation between REM and the other classes remains in the midrange. [ABSTRACT FROM AUTHOR]

Respiratory effort has been widely used for objective analysis of human sleep during bedtime. Several features extracted from respiratory effort signal have succeeded in automated sleep stage classification throughout the night such as variability of respiratory frequency, spectral powers in different frequency bands, respiratory regularity and self-similarity. In regard to the respiratory amplitude, it has been found that the respiratory depth is more irregular and the tidal volume is smaller during rapid-eye-movement (REM) sleep than during non-REM (NREM) sleep. However, these physiological properties have not been explicitly elaborated for sleep stage classification. By analyzing the respiratory effort amplitude, we propose a set of 12 novel features that should reflect respiratory depth and volume, respectively. They are expected to help classify sleep stages. Experiments were conducted with a data set of 48 sleepers using a linear discriminant (LD) classifier and classification performance was evaluated by overall accuracy and Cohen's Kappa coefficient of agreement. Cross validations (10-fold) show that adding the new features into the existing feature set achieved significantly improved results in classifying wake, REM sleep, light sleep and deep sleep (Kappa of 0.38 and accuracy of 63.8%) and in classifying wake, REM sleep and NREM sleep (Kappa of 0.45 and accuracy of 76.2%). In particular, the incorporation of these new features can help improve deep sleep detection to more extent (with a Kappa coefficient increasing from 0.33 to 0.43). We also revealed that calibrating the respiratory effort signals by means of body movements and performing subject-specific feature normalization can ultimately yield enhanced classification performance. [ABSTRACT FROM AUTHOR]

Sleep is characterized by an intricate variation of brain activity over time. Measuring these temporal sleep dynamics is relevant for elucidating healthy and pathological sleep mechanisms. The rapidly increasing possibilities for obtaining and processing sleep registrations have led to an abundance of data, which can be challenging to analyze and interpret. This review provides a structured overview of approaches to represent temporal sleep dynamics, categorized based on the way the source data is compressed. For each category of representations, we describe advantages and disadvantages. Standard human-defined 30-s sleep stages have the advantages of standardization and interpretability. Alternative human-defined representations are less standardized but offer a higher temporal resolution (in case of microstructural events such as sleep spindles), or reflect non-categorical information (for example spectral power analysis). Machine-learned representations offer additional possibilities: automated sleep stages are useful for handling large quantities of data, while alternative sleep stages obtained from clustering data-driven features could aid finding new patterns and new possible clinical interpretations. While newly developed sleep representations may offer relevant insights, they can be difficult to interpret in for example a clinical context. Therefore, there should always be a balance between developing these sophisticated sleep analysis techniques and maintaining clinical explainability. [ABSTRACT FROM AUTHOR]

The article discusses the analysis of several variables which are connected to the economic performance of Brazil as of 2013.

Copyright of Sociologia (08723419) is the property of Universidade do Porto, Faculdade de Letras and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ensaios FEE is the property of Fundacao de Economia e Estatistica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)