1,721,083 research outputs found
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
Altered Microcirculation in Alzheimer’s Disease Assessed by Machine Learning Applied to Functional Thermal Imaging Data
Full text linkAlzheimer’s disease (AD) is characterized by progressive memory failures accompanied by microcirculation alterations. Particularly, impaired endothelial microvascular responsiveness and altered flow motion patterns have been observed in AD patients. Of note, the endothelium influences the vascular tone and also the small superficial blood vessels, which can be evaluated through infrared thermography (IRT). The advantage of IRT with respect to other techniques relies on its contactless features and its capability to preserve spatial information of the peripheral microcirculation. The aim of the study is to investigate peripheral microcirculation impairments in AD patients with respect to age-matched healthy controls (HCs) at resting state, through IRT and machine learning (ML) approaches. Particularly, several classifiers were tested, employing as regressors the power of the nose tip temperature time course in different physiological frequency bands. Among the ML classifiers tested, the Decision Tree Classifier (DTC) delivered the best cross-validated accuracy (accuracy = 82%) when discriminating between AD and HCs. The results further demonstrate the alteration of microvascular patterns in AD in the early stages of the pathology, and the capability of IRT to assess vascular impairments. These findings could be exploited in clinical practice, fostering the employment of IRT as a support for the early diagnosis of AD
Advancing Voice Authentication: Insights from Cepstral Coefficients and Recursive Feature Elimination in Speech Signal
No full textVoice manipulation and synthesis pose a growing threat to digital security, raising the need for effective systems to detect artificial speech. This study investigates the feasibility of distinguishing between real and synthetic voices through machine learning techniques applied to the Fake or Real (FoR) Dataset from York University. The dataset contains over 70,000 text-to-speech (TTS) recordings, balanced in gender, class, sample rate, volume, and number of channels. The approach utilizes Gammatone Frequency Cepstral Coefficients (GTCC) and Delta Gammatone Frequency Cepstral Coefficients (ΔGTCC) as key features for voice characterization. A logistic regression model, enhanced with Recursive Feature Elimination (RFE), was employed to identify the most discriminative coefficients for this task. RFE iteratively removed less significant features, enhancing both the performance and interpretability of the model. The final model achieved a 70% accuracy in testing, using only five ΔGTCC features. This comparative analysis of GTCC and ΔGTCC revealed their respective strengths in voice classification tasks, offering insights for future developments in voice authentication systems. The study not only advances voice authentication technologies but also highlights the crucial role of feature selection in improving the robustness of models designed to safeguard against synthetic voice threats
Autonomic correlates of osteopathic manipulative treatment on facial functional mapping: an innovative approach based on thermal imaging
Full text linkOsteopathic manipulative treatment (OMT) has shown efficacy in various clinical conditions and age groups. Understanding its neurobiological, particularly autonomic, mechanisms of action remain limited. Preliminary studies suggested a parasympathetic effect of OMT, evidenced by heart-rate-variability analysis. A cross-over RCT on healthy adults was conducted to compare OMT with sham therapy. Thirty-seven participants underwent two sessions (OMT and sham), comprising baseline, tactile treatment, and post-touch. Novel thermal imaging data analyses in combination with seed correlation analyses (SCA) were employed to explore the OMT effects on autonomic parameters. Particularly, the sham group exhibited an elevated warming effect on the cheeks, nose, and chin. Inversely, for the OMT group a conspicuous cooling trend in the nose, but not in the cheeks and chin was observed. Considering SCA maps, the intensity of the correlation for nose tip, glabella and GSR seeds showed higher values in the OMT compared to the sham group. The comparative analysis of thermal maps and SCA results represents a significant advancement in our understanding of the physiological mechanisms underlying OMT’s effects on autonomic functions. By elucidating specific patterns of temperature change, correlation intensity and specific clusters, this research provides valuable insights for optimizing clinical practice and refining theoretical models of manual therapy
Advanced Machine Learning Approaches for Classifying Parkinson’s Disease Using fNIRS Data from Gait Analysis
No full textThis study investigates the use of machine learning to differentiate between Parkinson's patients and healthy individuals using functional near infrared spectroscopy (fNIRS) signals collected during walking. fNIRS data, which monitor brain activity, were acquired from both groups to identify distinctive patterns. Using machine learning techniques, the model was trained to discriminate the two classes, with the goal of improving early and non-invasive diagnosis of Parkinson's disease and the method delivered an accuracy of 72.00%. The results demonstrate the effectiveness of the method in accurately distinguishing patients from healthy individuals from cortical activity during walking
Imaging facial signs of neurophysiological responses
No full textIn the present paper, we introduce an integrated framework for detecting peripheral sympathetic responses through purely imaging means. The measurements are performed on three facial areas of sympathetic importance, that is, periorbital, supraorbital, and maxillary. To the best of our knowledge, this is the first time that the sympathetic importance of the maxillary area is analyzed. Because the imaging measurements are thermal in nature and are composed of multiple components of variable frequency (i.e., blood flow, sweat gland activation, and breathing), we chose wavelets as the image analysis framework. The measurements also carry substantial noise due to imperfections in tissue tracking and segmentation. The image analysis is grounded on galvanic skin response (GSR) signals, which are still considered the golden standard in peripheral neurophysiological and psychophysiological studies. The experimental results show that monitoring of the facial channels yields similar detecting power to GSR's. However, detailed quantification of the responses, although feasible in GSR through appropriate modeling, is quite difficult in the facial channels for the moment. Further improvements in facial tissue tracking and segmentation are bound to overcome this limitation. This paper opens a new research area that leads to unobtrusive screening technologies in neurophysiology and psychophysiology
Facial functional networks during resting state revealed by thermal infrared imaging
Full text linkIn recent decades, an increasing number of studies on psychophysiology and, in general, on clinical medicine has employed the technique of facial thermal infrared imaging (IRI), which allows to obtain information about the emotional and physical states of the subjects in a completely non-invasive and contactless fashion. Several regions of interest (ROIs) have been reported in literature as salient areas for the psychophysiological characterization of a subject (i.e. nose tip and glabella ROIs). There is however a lack of studies focusing on the functional correlation among these ROIs and about the physiological basis of the relation existing between thermal IRI and vital signals, such as the electrodermal activity, i.e. the galvanic skin response (GSR). The present study offers a new methodology able to assess the functional connection between salient seed ROIs of thermal IRI and all the pixel of the face. The same approach was also applied considering as seed signal the GSR and its phasic and tonic components. Seed correlation analysis on 63 healthy volunteers demonstrated the presence of a common pathway regulating the facial thermal functionality and the electrodermal activity. The procedure was also tested on a pathological case study, finding a completely different pattern compared to the healthy cases. The method represents a promising tool in neurology, physiology and applied neurosciences
Editorial: Effect of neurophysiological conditions and mental workload on physical and cognitive performances: a multidimensional perspective
No full textVariations 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
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