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The causes of ventricular fibrillation (VF) are not yet elucidated, and it has been proposed that different mechanisms might exist. Moreover, conventional analysis methods do not seem to provide time or frequency domain features that allow for recognition of different VF patterns in electrode-recorded biopotentials. The present work aims to determine whether low-dimensional latent spaces could exhibit discriminative features for different mechanisms or conditions during VF episodes. For this purpose, manifold learning using autoencoder neural networks was analyzed based on surface ECG recordings. The recordings covered the onset of the VF episode as well as the next 6 min, and comprised an experimental database based on an animal model with five situations, including control, drug intervention (amiodarone, diltiazem, and flecainide), and autonomic nervous system blockade. The results show that latent spaces from unsupervised and supervised learning schemes yielded moderate though quite noticeable separability among the different types of VF according to their type or intervention. In particular, unsupervised schemes reached a multi-class classification accuracy of 66%, while supervised schemes improved the separability of the generated latent spaces, providing a classification accuracy of up to 74%. Thus, we conclude that manifold learning schemes can provide a valuable tool for studying different types of VF while working in low-dimensional latent spaces, as the machine-learning generated features exhibit separability among different VF types. This study confirms that latent variables are better VF descriptors than conventional time or domain features, making this technique useful in current VF research on elucidation of the underlying VF mechanisms.

Introduction and objectives: The choice of renal replacement therapy (RRT) is an important decision that determines the quality of life and survival. Most patients change from one RRT modality to another to adapt RRT to clinical and psychosocial needs. This has been called «integrated model of RRT» that implies new questions about the best sequence of techniques. Material and methods: The study describes the impact of transitions between RRT modalities on survival using the Madrid Registry of Renal Patients (2008–2018). This study used the proportional hazards models and competitive risk models to perform an intention-to-treat (ITT), according to their 1st RRT modality and as-treated (AT) analysis, that consider also their 1st transition. Results: A total of 8971 patients started RRT during this period in Madrid (6.6 Million population): 7207 (80.3%) on hemodialysis (HD), 1401 (15.6%) on peritoneal dialysis (PD) and 363 (4.2%) received a pre-emptive kidney transplantation (KT). Incident HD-patients were older (HD group 65.3 years (SD 15.3) vs PD group 58.1 years (SD 14.8) vs KTX group 52 years (SD 17.2); p

Resumen: Introducción y objetivos: La elección del tratamiento sustitutivo renal (TSR) es una decisión importante que determina la calidad de vida y la supervivencia. La mayoría de los pacientes cambiará de una modalidad de TSR a otra para adaptarla a sus necesidades dentro de lo que se conoce como modelo de TSR integrado. En estas circunstancias surgen nuevas preguntas sobre la mejor secuencia de técnicas o las consecuencias de las transiciones. Material y métodos: Describimos las transiciones entre técnicas de TSR y su impacto en la supervivencia a partir del Registro Madrileño de Enfermos Renales (REMER), durante un periodo de 11 años. Se utilizaron los modelos de riesgos proporcionales y de riesgos competitivos para realizar un análisis por intención de tratar (ITT) según su 1.er tratamiento y como tratado (AT) considerando la 1.ª transición. Resultados: Un total de 8.971 pacientes iniciaron su primer TSR durante este periodo en Madrid (6,6 millones habitantes): 7.207 (80,3%) en hemodiálisis (HD), 1.401 (15,6%) en diálisis peritoneal (DP) y 363 (4,1%) recibieron un trasplante renal anticipado (TXR). En el análisis ITT, los pacientes incidentes en HD eran mayores (HD 65,3 años (DE 15,3) vs. DP 58,1 años [DE 14,8] vs. TXR 52,0 años (DE 17,2); p

This article provides a review of the Citizen Broadband Radio Service (CBRS) and its characteristics. It also describes the Spectrum Access System, which allows the bands not used by primary users to be intelligently occupied by secondary users to optimize this resource. As a rule, CBRS operates between 3550 MHz and 3700 MHz to allow users’ connectivity from different operators in high-traffic locations. Using Neutral Hosts minimizes the installation and operation costs of the Advanced Mobile Service’s operators. CBRS also functions as a private LTE network with several advantages over other wireless network technologies. This innovative proposal marks a milestone in shifting from fixed allocation of the radio spectrum to dynamic controlled access, enabling connectivity to a wide range of wireless devices.

Electrocardiographic imaging (ECGI) is a technique to reconstruct non-invasively the electrical activity on the heart surface from body-surface potential recordings and geometric information of the torso and the heart. ECGI has shown scientific and clinical value when used to characterize and treat both atrial and ventricular arrhythmias. Regarding atrial fibrillation (AF), the characterization of the electrical propagation and the underlying substrate favoring AF is inherently more challenging than for ventricular arrhythmias, due to the progressive and heterogeneous nature of the disease and its manifestation, the small volume and wall thickness of the atria, and the relatively large role of microstructural abnormalities in AF. At the same time, ECGI has the advantage over other mapping technologies of allowing a global characterization of atrial electrical activity at every atrial beat and non-invasively. However, since ECGI is time-consuming and costly and the use of electrical mapping to guide AF ablation is still not fully established, the clinical value of ECGI for AF is still under assessment. Nonetheless, AF is known to be the manifestation of a complex interaction between electrical and structural abnormalities and therefore, true electro-anatomical-structural imaging may elucidate important key factors of AF development, progression, and treatment. Therefore, it is paramount to identify which clinical questions could be successfully addressed by ECGI when it comes to AF characterization and treatment, and which questions may be beyond its technical limitations. In this manuscript we review the questions that researchers have tried to address on the use of ECGI for AF characterization and treatment guidance (for example, localization of AF triggers and sustaining mechanisms), and we discuss the technological requirements and validation. We address experimental and clinical results, limitations, and future challenges for fruitful application of ECGI for AF understanding and management. We pay attention to existing techniques and clinical application, to computer models and (animal or human) experiments, to challenges of methodological and clinical validation. The overall objective of the study is to provide a consensus on valuable directions that ECGI research may take to provide future improvements in AF characterization and treatment guidance.

Fast and accurate taxonomic identification of invasive trans-located ladybird beetle species is essential to prevent significant impacts on biological communities, ecosystem functions, and agricultural business economics. Therefore, in this work we propose a two-step automatic detector for ladybird beetles in random environment images as the first stage towards an automated classification system. First, an image processing module composed of a saliency map representation, simple linear iterative clustering superpixels segmentation, and active contour methods allowed us to generate bounding boxes with possible ladybird beetles locations within an image. Subsequently, a deep convolutional neural network-based classifier selects only the bounding boxes with ladybird beetles as the final output. This method was validated on a 2, 300 ladybird beetle image data set from Ecuador and Colombia obtained from the iNaturalist project. The proposed approach achieved an accuracy score of 92% and an area under the receiver operating characteristic curve of 0.977 for the bounding box generation and classification tasks. These successful results enable the proposed detector as a valuable tool for helping specialists in the ladybird beetle detection problem.

Artificial intelligence (AI) has recently intensified in the global economy due to the great competence that it has demonstrated for analysis and modeling in many disciplines. This situation is accelerating the shift towards a more automated society, where these new techniques can be consolidated as a valid tool to face the difficult challenge of credit fraud detection (CFD). However, tight regulations do not make it easy for financial entities to comply with them while using modern techniques. From a methodological perspective, autoencoders have demonstrated their effectiveness in discovering nonlinear features across several problem domains. However, autoencoders are opaque and often seen as black boxes. In this work, we propose an interpretable and agnostic methodology for CFD. This type of approach allows a double advantage: on the one hand, it can be applied together with any machine learning (ML) technique, and on the other hand, it offers the necessary traceability between inputs and outputs, hence escaping from the black-box model. We first applied the state-of-the-art feature selection technique defined in the companion paper. Second, we proposed a novel technique, based on autoencoders, capable of evaluating the relationship among input and output of a sophisticated ML model for each and every one of the samples that are submitted to the analysis, through a single transaction-level explanation (STE) approach. This technique allows each instance to be analyzed individually by applying small fluctuations of the input space and evaluating how it is triggered in the output, thereby shedding light on the underlying dynamics of the model. Based on this, an individualized transaction ranking (ITR) can be formulated, leveraging on the contributions of each feature through STE. These rankings represent a close estimate of the most important features playing a role in the decision process. The results obtained in this work were consistent with previous published papers, and showed that certain features, such as living beyond means, lack or absence of transaction trail, and car loans, have strong influence on the model outcome. Additionally, this proposal using the latent space outperformed, in terms of accuracy, our previous results, which already improved prior published papers, by 5.5% and 1.5% for the datasets under study, from a baseline of 76% and 93%. The contribution of this paper is twofold, as far as a new outperforming CFD classification model is presented, and at the same time, we developed a novel methodology, applicable across classification techniques, that allows to breach black-box models, erasingthe dependencies and, eventually, undesirable biases. We conclude that it is possible to develop an effective, individualized, unbiased, and traceable ML technique, not only to comply with regulations, but also to be able to cope with transaction-level inquiries from clients and authorities.

Fast and accurate identification of biting midges is crucial in the study of Culicoides-borne diseases. In this work, we propose a two-stage method for automatically analyzing Culicoides (Diptera: Ceratopogonidae) species. First, an image preprocessing task composed of median and Wiener filters followed by equalization and morphological operations is used to improve the quality of the wing image in order to allow an adequate segmentation of particles of interest. Then, the segmentation of the zones of interest inside the biting midge wing is made using the watershed transform. The proposed method is able to produce optimal feature vectors that help to identify Culicoides species. A database containing wing images of C. obsoletus, C. pusillus, C. foxi, and C. insignis species was used to test its performance. Feature relevance analysis indicated that the mean of hydraulic radius and eccentricity were relevant for the decision boundary between C. obsoletus and C. pusillus species. In contrast, the number of particles and the mean of the hydraulic radius was relevant for deciding between C. foxi and C. insignis species. Meanwhile, for distinguishing among the four species, the number of particles and zones, and the mean of circularity were the most relevant features. The linear discriminant analysis classifier was the best model for the three experimental classification scenarios previously described, achieving averaged areas under the receiver operating characteristic curve of 0.98, 0.90, and 0.96, respectively.