1,720,968 research outputs found
Effective telemonitoring for the optimization of new deep brain stimulation therapies for parkinson’s disease patients
Full text linkIntroduction: Deep brain stimulation (DBS) outcomes could benefit from monitoring the fluctuation of Parkinson’s disease symptoms in order to set the best stimulation parameters. A correct and constant monitoring of this time-frame is essential to regulate their therapies. The scope of this work is to develop and validate a system architecture to continuously monitor the patient through personally collected data. In addition, it could support the development and implementation of new DBS approaches aimed to real-time adapt DBS parameters according to the evaluation of the patient’s clinical state (i.e., adaptive DBS, aDBS).
Methods: The implementation followed a three-step approach. First, a dedicated Android App was developed to provide a clinical e-diary to be filled in by the patients at predefined times. The App was paired with a smartwatch that acquires tri-axial accelerometric data from the wrist. The data acquired were used to verify whether this combination of two simple technologies was enough to collect data relevant for tracking patient’s activity and symptoms. Second, accelerometric data and diary data were integrated to neurophysiological data, in order to obtain a comprehensive view on the patient’s state. Subthalamic nucleus local field potentials (STN LFPs) were recorded from the implanted DBS electrodes and integrated with the self-collected data. A web-based platform was developed to support data collection and analysis. The platform expanded the architecture of an already established technology in order to introduce a standards-based architecture aimed to implement a bidirectional exchange between patient-generated data and the clinical data repository. The validation study enrolled 13 Parkinson’s disease patients undergoing DBS electrode implant surgery. During an 8 hours session, the patients were asked to fill in the e-diary and to wear the smartwatch. A clinician assessed their condition compiling a Unified Parkinson’s Disease Rating Scale part III (UPDRSIII). After the 8 hour trial, a clinician asked the patients if the smartwatch was uncomfortable during the day.
Results: In total, of the 13 patients, 2 were dropped due to technical issues. Two algorithms, the Bradykinesia Accelerometric Score (BAS) and Bradykinesia Index (BradIndex) were developed using the data collected through the smartwatches to estimate bradykinesia. Both provided significative inverse correlation with UPDRSIII evaluation (BAS: Pearson’s correlation coefficient, 0.541, p<0.004; BradIndex: Pearson’s correlation coefficient -0.500, p < 0.0005). The patient reported e-diary status provided significative correlation with the UPDRSIII assessment (Pearson’s correlation coefficient -0. 7416, p < 0.0005) and also with the BradIndex accelerometric index (Pearson’s correlation coefficient 0.6042, p < 0.05). LFP recordings were modulated during walking, with respect to talking and relaxing (beta power change from baseline during walking: -14%±4.212, talking:-11.2 %±2.724, and relaxing: -8.811%±2.418, one-way ANOVA p<0.0001). USE-CASE tests were performed to validate the overall architecture, denoting a good patient e-diary compliance but a considerable accelerometer data loss caused by the consumer-grade smartwatch was observed.
Conclusions: This work provided good results supporting the use of consumer-grade devices to allow DBS patient’s telemonitoring. Personally-recorded data were successfully integrated with neurophysiological data, providing essential insights for the implementation of new aDBS therapy. The platform was able to support the study with meaningful results, the smartwatches were well tolerated, and the mobile app was used by the majority of patients to fill in the diary. The system could be therefore used in the future in the home environment to monitor PD patients with DBS implant, and to collect additional data for building up a holistic view on the patient’s state
Synaesthetic interactions between sounds and colour afterimages: revisiting Werner and Zietz’s approach
Full text linkWe ran a pilot experiment to explore, using a new psychophysical method, the hypothesis proposed by Zietz and Werner in the ’30s, that a sound presented simultaneously with
an afterimage can change its phenomenal appearance in non-synaesthetes. The method
we adopted is able to directly collect and visualise the apparent changes in intensity of
the afterimages, by recording observers’ interactions with a physical feedback mechanism
(the paths that the observers generated by moving a cursor), without referring to verbal
descriptions. These first findings support some of the most meaningful observations re-
ported by Werner (1934) and Zietz (1931), according to which the colours of the after-
images ‘disintegrate’ at the hearing of a low sound and ‘concentrate’ for a high sound. This
relationship is particularly evident with the Yellow stimulus, where the perceived colour
intensity of its afterimage seems to have a faster negative change with a low-pitched tone
sound, and an increase in intensity and duration when perceived simultaneously with a
soprano sound. These data are also coherent with the crossmodal correspondences be-
tween both pitch and loudness in audition and lightness and brightness in vision reported
in the literature
Optimization of SEM Instrumental Parameters for Enhanced Imaging Applying Machine Learning
No full textThe scanning electron microscope (SEM) is one of the most versatile instruments available for
characterizing the micro- and nanostructural properties of solid samples. A major reason is the high
resolution that can be achieved when examining bulk objects which makes SEM indispensable in various
fields ranging from materials science to biology.
Although scanning electron microscopy (SEM) is a well-established technology, there is still room for
improvement in both imaging quality and automation. The goal is to achieve an optimal balance between
ease of use and performance while minimizing hardware complexity and instrumentation costs, which
remain a key advantage of SEM. In this context, the implementation of advanced algorithmic methods
for automating acquisition settings is essential to enhance efficiency and reproducibility in analyses.
Given the versatility of SEM and its application to a wide range of materials and samples, we have
developed a generalized approach that is not limited to a specific sample type, maximizing adaptability
and effectiveness across various experimental settings. Instrumental SEM parameters should be
carefully optimized for high-quality SEM imaging. The key parameters that influence image quality can
be recognized as: Electron beam parameters including acceleration voltage, beam current, and spot size;
Geometrical and imaging parameters, which encompass working distance, aperture size, magnification,
focus, and stigmation. Detection and image acquisition settings cover detector type, scan speed (dwell
time), and drift correction. Sample preparation and environmental conditions include sample coating
and chamber pressure.
Optimization of the acquisition parameters is crucial for obtaining high-quality images and accurate
data. Operator-based adjusting of these parameters presents several challenges, including the
interdependence of settings, the risk of sample damage, time-consuming operations, and operator skill
dependencies. Moreover, the sample dependency of the acquisition parameters becomes crucial due to
the wide range of possible applications of SEM characterization. These difficulties underscore the
importance of automation in optimizing operations of SEM settings, making the process more efficient,
consistent, and less prone to error. We developed an algorithm-based approach that can identify the
optimum conditions for accelerating voltage, working distance, and aperture size for each type of
sample.
SEM experiments on target sample are conducted based on a full-factorial design in which parameters
are varied simultaneously, to explore also the cross-correlation of the considered parameters. To assess
image quality, we needed a suitable metric. Since the reference image—obtained under optimum
conditions—served as the goal of our investigation, we chose a no-reference image quality metric. The
acquired images were analyzed using this metric. A polynomial regression model—a type of supervised
learning—was employed as a baseline to determine the optimal operating conditions within the selected
SEM parameter space. By predicting the no‐reference image quality metric for any given setting, the
model guides the identification of parameter adjustments that minimize NIQE (thus maximizing image
quality). Given the small training set, at each new image acquisition, the predicted NIQE is compared
with the computed value, and the model is updated with every new data point, continuously refining its
predictions and enabling fully autonomous optimization while significantly reducing operator
intervention. This approach not only streamlines the SEM imaging process but also significantly reduces
the time required for each imaging session by automating the optimization of key parameters. The
proposed algorithm promises to enhance the efficiency of SEM operations, minimize operatorintervention, and ensure more consistent, accurate, and faster sample characterization across a wide
range of applications
Personally Collected Health Data for Precision Medicine and Longitudinal Research
Full text linkHealth data autonomously collected by users are presently considered as largely beneficial for wellness, prevention, disease management, as well as clinical research, especially when longitudinal, chronic, home-based monitoring is needed. However, data quality and reliability are the main barriers to overcome, in order to exploit such potential. To this end, we designed, implemented, and tested a system to integrate patient-generated personally collected health data into the clinical research data workflow, using a standards-based architecture that ensures the fulfillment of the major requirements for digital data in clinical studies. The system was tested in a clinical investigation for the optimization of deep brain stimulation (DBS) therapy in patients with Parkinson's disease that required both the collection of patient-generated data and of clinical and neurophysiological data. The validation showed that the implemented system was able to provide a reliable solution for including the patient as direct digital data source, ensuring reliability, integrity, security, attributability, and auditability of data. These results suggest that personally collected health data can be used as a reliable data source in longitudinal clinical research, thus improving holistic patient's personal assessment and monitoring
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
Dominant and Nondominant Leg Kinematics During Kicking in Young Soccer Players: A Cross-Sectional Study
No full textThe goal of the study is to analyze the kinematics and provide an EMG analysis of the support limb during an instep kick in adolescent players. We set a video camera, two torque transducers on the knee, and EMG sensors. A sample of 16 adolescent soccer players between 10 and 12 years old performed kicks. The kinematics shows a p = .039 on frontal plane (dominant 15.4 ± 1.8, nondominant 18.8 ± 1.7); the EMG analysis shows a p = .04 on muscular activation timing for the vastus medialis. A difference between the legs on the frontal plane emerges. Moreover, a huge difference on sagittal plane between the adolescent pattern and adult pattern exists (15° in adolescent population, 40° in adult population). The result shows a greater activation of the vastus medialis in the nondominant leg; probably, in this immature pattern, the adolescents use this muscle more than necessary
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
Artificial intelligence to support early diagnosis of temporomandibular disorders: A preliminary case study
Full text linkBackground Temporomandibular disorders (TMDs) are disabling conditions with a negative impact on the quality of life. Their diagnosis is a complex and multi-factorial process that should be conducted by experienced professionals, and most TMDs remain often undetected. Increasing the awareness of un-experienced dentists and supporting the early TMD recognition may help reduce this gap. Artificial intelligence (AI) allowing both to process natural language and to manage large knowledge bases could support the diagnostic process. Objective In this work, we present the experience of an AI-based system for supporting non-expert dentists in early TMD recognition. Methods The system was based on commercially available AI services. The prototype development involved a preliminary domain analysis and relevant literature identification, the implementation of the core cognitive computing services, the web interface and preliminary testing. Performance evaluation included a retrospective review of seven available clinical cases, together with the involvement of expert professionals for usability testing. Results The system comprises one module providing possible diagnoses according to a list of symptoms, and a second one represented by a question and answer tool, based on natural language. We found that, even when using commercial services, the training guided by experts is a key factor and that, despite the generally positive feedback, the application's best target is untrained professionals. Conclusion We provided a preliminary proof of concept of the feasibility of implementing an AI-based system aimed to support non-specialists in the early identification of TMDs, possibly allowing a faster and more frequent referral to second-level medical centres. Our results showed that AI is a useful tool to improve TMD detection by facilitating a primary diagnosis
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