Wearable Technology to Detect Motor Fluctuations in Parkinson's Disease Patients: Current State and Challenges.

Monitoring of motor symptom fluctuations in Parkinson's disease (PD) patients is currently performed through the subjective self-assessment of patients. Clinicians require reliable information about a fluctuation's occurrence to enable a precise treatment rescheduling and dosing adjustment. In this review, we analyzed the utilization of sensors for identifying motor fluctuations in PD patients and the application of machine learning techniques to detect fluctuations. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Ten studies were included between January 2010 and March 2021, and their main characteristics and results were assessed and documented. Five studies utilized daily activities to collect the data, four used concrete scenarios executing specific activities to gather the data, and only one utilized a combination of both situations. The accuracy for classification was 83.56-96.77%. In the studies evaluated, it was not possible to find a standard cleaning protocol for the signal captured, and there is significant heterogeneity in the models utilized and in the different features introduced in the models (using spatiotemporal characteristics, frequential characteristics, or both). The two most influential factors in the good performance of the classification problem are the type of features utilized and the type of model.

Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification.

Bicycle Sharing Systems (BSSs) are exponentially increasing in the urban mobility sector. They are traditionally conceived as a last-mile complement to the public transport system. In this paper, we demonstrate that BSSs can be seen as a public transport system in their own right. To do so, we build a mathematical framework for the classification of BSS trips. Using trajectory information, we create the trip index, which characterizes the intrinsic purpose of the use of BSS as transport or leisure. The construction of the trip index required a specific analysis of the BSS shortest path, which cannot be directly calculated from the topology of the network given that cyclists can find shortcuts through traffic lights, pedestrian crossings, etc. to reduce the overall traveled distance. Adding a layer of complication to the problem, these shortcuts have a non-trivial existence in terms of being intermittent, or short lived. We applied the proposed methodology to empirical data from BiciMAD, the public BSS in Madrid (Spain). The obtained results show that the trip index correctly determines transport and leisure categories, which exhibit distinct statistical and operational features. Finally, we inferred the underlying BSS public transport network and show the fundamental trajectories traveled by users. Based on this analysis, we conclude that 90.60% of BiciMAD's use fall in the category of transport, which demonstrates our first statement.

Correction: Graphemic-phonetic diachronic linguistic invariance of the frequency and of the Index of Coincidence as cryptanalytic tools.

[This corrects the article DOI: 10.1371/journal.pone.0213710.].

Graphemic-phonetic diachronic linguistic invariance of the frequency and of the Index of Coincidence as cryptanalytic tools.

Languages have inherent characteristics that make them their own and differentiated entities within their phyla and families. Even messages written in any language and later encrypted by cryptographic systems do not lose all of their characteristics, there remain aspects that help the cryptanalyst to recover them without knowing the decryption keys. For the characterization of the languages we will consider the frequencies of their graphemic and phonetic units and the Index of Coincidence, tools of fundamental utility in the field of Cryptography. Their diachronic invariance or survival over time in one language and their ability to discriminate against other languages will be analized. In order to do so, we will examine a total of 101 languages of which 261 texts have been taken. All of them are very diverse in style and time, taking us through a wide linguistic and temporal spectrum that will cover the period from the 6th century BC to the present day.

Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling.

Large scale phase-contrast images taken at high resolution through the life of a cultured neuronal network are analyzed by a graph-based unsupervised segmentation algorithm with a very low computational cost, scaling linearly with the image size. The processing automatically retrieves the whole network structure, an object whose mathematical representation is a matrix in which nodes are identified neurons or neurons' clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocytochemistry techniques, our non invasive measures entitle us to perform a longitudinal analysis during the maturation of a single culture. Such an analysis furnishes the way of individuating the main physical processes underlying the self-organization of the neurons' ensemble into a complex network, and drives the formulation of a phenomenological model yet able to describe qualitatively the overall scenario observed during the culture growth.

Unconstrained and contactless hand geometry biometrics.

This paper presents a hand biometric system for contact-less, platform-free scenarios, proposing innovative methods in feature extraction, template creation and template matching. The evaluation of the proposed method considers both the use of three contact-less publicly available hand databases, and the comparison of the performance to two competitive pattern recognition techniques existing in literature: namely support vector machines (SVM) and k-nearest neighbour (k-NN). Results highlight the fact that the proposed method outcomes existing approaches in literature in terms of computational cost, accuracy in human identification, number of extracted features and number of samples for template creation. The proposed method is a suitable solution for human identification in contact-less scenarios based on hand biometrics, providing a feasible solution to devices with limited hardware requirements like mobile devices.

Cáncer epitelial de ovario: experiencia de 10 años en el Servicio de Oncología del Hospital Carlos Andrade Marín

Se presentaron 146 casos de tumores malignos de ovario, representando el 11 por ciento de todos los tumores ginecológicos, siendo la tercera neoplasia más frecuente con el 91 por ciento, seguidos por los tumores del estroma y cordones sexuales con el 5 por ciento, los tumores de células lípidas 3 por ciento y sarcomas con el 1 por ciento. De los tumores epiteliales de ovario la variedad histológica más común correspondió al adenocarcinoma seroso con el 74 por ciento, el mucinoso 14 por ciento, tumor de células claras 4.5 por ciento, endomteroide 4 por ciento de Brenner, 2 por ciento y el indiferenciado con el 1.5 por ciento. La edad más frecuente de presentación fue de 50 a 60 años con una incidencia anual de 13.1 casos por año...