New perspectives and methods for stream learning in the presence of concept drift.

153 p.Applications that generate data in the form of fast streams from non-stationary environments, that is,those where the underlying phenomena change over time, are becoming increasingly prevalent. In thiskind of environments the probability density function of the data-generating process may change overtime, producing a drift. This causes that predictive models trained over these stream data become obsoleteand do not adapt suitably to the new distribution. Specially in online learning scenarios, there is apressing need for new algorithms that adapt to this change as fast as possible, while maintaining goodperformance scores. Examples of these applications include making inferences or predictions based onfinancial data, energy demand and climate data analysis, web usage or sensor network monitoring, andmalware/spam detection, among many others.Online learning and concept drift are two of the most hot topics in the recent literature due to theirrelevance for the so-called Big Data paradigm, where nowadays we can find an increasing number ofapplications based on training data continuously available, named as data streams. Thus, learning in nonstationaryenvironments requires adaptive or evolving approaches that can monitor and track theunderlying changes, and adapt a model to accommodate those changes accordingly. In this effort, Iprovide in this thesis a comprehensive state-of-the-art approaches as well as I identify the most relevantopen challenges in the literature, while focusing on addressing three of them by providing innovativeperspectives and methods.This thesis provides with a complete overview of several related fields, and tackles several openchallenges that have been identified in the very recent state of the art. Concretely, it presents aninnovative way to generate artificial diversity in ensembles, a set of necessary adaptations andimprovements for spiking neural networks in order to be used in online learning scenarios, and finally, adrift detector based on this former algorithm. All of these approaches together constitute an innovativework aimed at presenting new perspectives and methods for the field

New perspectives and methods for stream learning in the presence of concept drift.

153 p.Applications that generate data in the form of fast streams from non-stationary environments, that is,those where the underlying phenomena change over time, are becoming increasingly prevalent. In thiskind of environments the probability density function of the data-generating process may change overtime, producing a drift. This causes that predictive models trained over these stream data become obsoleteand do not adapt suitably to the new distribution. Specially in online learning scenarios, there is apressing need for new algorithms that adapt to this change as fast as possible, while maintaining goodperformance scores. Examples of these applications include making inferences or predictions based onfinancial data, energy demand and climate data analysis, web usage or sensor network monitoring, andmalware/spam detection, among many others.Online learning and concept drift are two of the most hot topics in the recent literature due to theirrelevance for the so-called Big Data paradigm, where nowadays we can find an increasing number ofapplications based on training data continuously available, named as data streams. Thus, learning in nonstationaryenvironments requires adaptive or evolving approaches that can monitor and track theunderlying changes, and adapt a model to accommodate those changes accordingly. In this effort, Iprovide in this thesis a comprehensive state-of-the-art approaches as well as I identify the most relevantopen challenges in the literature, while focusing on addressing three of them by providing innovativeperspectives and methods.This thesis provides with a complete overview of several related fields, and tackles several openchallenges that have been identified in the very recent state of the art. Concretely, it presents aninnovative way to generate artificial diversity in ensembles, a set of necessary adaptations andimprovements for spiking neural networks in order to be used in online learning scenarios, and finally, adrift detector based on this former algorithm. All of these approaches together constitute an innovativework aimed at presenting new perspectives and methods for the field

Ascidiacea (Chordata, Tunicata) de Uruguay (Atlántico SO): Checklist y consideraciones zoogeográficas

The diversity of ascidians from the Southwestern Atlantic between 30°S and 40°S (southern Brazil, Uruguay and northern Argentina) remains as one of the poorest known of the West Atlantic. The objective of this work is to compile, analyze and discuss all published records of ascidians from Uruguay. They show the historical relevance of the studies performed by Herdman, Monniot F. and Monniot C. on ascidians collected at deep-sea stations by the HMS Challenger and the RV Atlantis II in the Argentine Basin. Total literature records include 38 ascidian species which are enumerated here for the first time. On the basis of the current knowledge, the ascidian fauna of Uruguayan waters encompasses: a) shallow-water species with temperate distribution (3 spp.); b) shelf and deep-sea species with Antarctic and Sub-Antarctic distribution (13 spp.); c) deep-sea species until now only collected off Río de La Plata (11 spp.); d) deep-sea species displaying a wide distribution (11 spp.). Only nine species have been recorded for the continental shelf; the remaining species were collected either from the slope (21) or the abyssal plain (5) or both deep-sea zones (3). Future research should be directed to record coastline and shelf species, assess the presence of exotic elements, and re-describe enigmatic species first described by Herdman (1882, 1886).Fil: Fabrizio Scarabino. Centro Universitario Regional del Este - CURE, Universidad de la República y Museo Nacional de Historia Natural, Montevideo, ; UruguayFil: Maggioni, Tamara. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Diversidad y Ecología Animal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Diversidad y Ecología Animal; ArgentinaFil: Taverna, Anabela Jesús. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Diversidad y Ecología Animal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Diversidad y Ecología Animal; ArgentinaFil: Lagger, Cristian Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; ArgentinaFil: Schwindt, Evangelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; ArgentinaFil: Guzmán López. Dirección Nacional de Recursos Acuáticos, Montevideo; UruguayFil: Leonardo Ortega. Dirección Nacional de Recursos Acuáticos, Montevideo; UruguayFil: Felipe García-Rodríguez. Centro Universitario Regional del Este -CURE, Universidad de la República, Montevideo ; UruguayFil: Tatian, Marcos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Diversidad y Ecología Animal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Diversidad y Ecología Animal; Argentin

Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum

The current IT market is more and more dominated by the “cloud continuum”. In the “traditional” cloud, computing resources are typically homogeneous in order to facilitate economies of scale. In contrast, in edge computing, computational resources are widely diverse, commonly with scarce capacities and must be managed very efficiently due to battery constraints or other limitations. A combination of resources and services at the edge (edge computing), in the core (cloud computing), and along the data path (fog computing) is needed through a trusted cloud continuum. This requires novel solutions for the creation, optimization, management, and automatic operation of such infrastructure through new approaches such as infrastructure as code (IaC). In this paper, we analyze how artificial intelligence (AI)-based techniques and tools can enhance the operation of complex applications to support the broad and multi-stage heterogeneity of the infrastructural layer in the “computing continuum” through the enhancement of IaC optimization, IaC self-learning, and IaC self-healing. To this extent, the presented work proposes a set of tools, methods, and techniques for applications’ operators to seamlessly select, combine, configure, and adapt computation resources all along the data path and support the complete service lifecycle covering: (1) optimized distributed application deployment over heterogeneous computing resources; (2) monitoring of execution platforms in real time including continuous control and trust of the infrastructural services; (3) application deployment and adaptation while optimizing the execution; and (4) application self-recovery to avoid compromising situations that may lead to an unexpected failure.This research was funded by the European project PIACERE (Horizon 2020 research and innovation Program, under grant agreement no 101000162)

Quality of Life Impact Related to Foot Health in a Sample of Older People with Hallux Valgus

[Abstract] Hallux Valgus (HV) is a highly prevalent forefoot deformity in older people associated with progressive subluxation and osteoarthritis of the first metatarsophalangeal (MTP) joint and it is believed to be associated with varying degrees of HV effect on the quality of life related to foot health. The aim of this study is to compare the impact of varying degrees of HV on foot health in a sample of older people. The sample consisted of 115 participants, mean age 76.7 ± 9.1, who attended an outpatient center where self-report data were recorded. The degree of HV deformity was determined in both feet using the Manchester Scale (MS) from stage 1 (mild) to 4 (very severe). Scores obtained on the Foot Health Status Questionnaire (FHSQ) were compared. This has 13 questions that assess 4 health domains of the feet, namely pain, function, general health and footwear. The stage 4 of HV shown lower scores for the footwear domain (11.23 ± 15.6); general foot health (27.62 ± 19.1); foot pain (44.65 ± 24.5); foot function (53.04 ± 27.2); vigour (42.19 ± 16.8); social capacity (44.46 ± 28.1); and general health (41.15 ± 25.5) compared with stage 1 of HV (P<0.05) and there were no differences of physical activity (62.81 ± 24.6). Often, quality of life decreases in the elderly population based in large part on their foot health. There is a progressive reduction in health in general and foot health with increasing severity of hallux valgus deformity which appears to be associated with the presence of greater degree of HV, regardless of gender

Detección de alérgenos de cacahuete mediante un sensor de ADN

In the present work an electrochemical genosensor for detecting a DNA segment encoding part of the allergenic protein peanut Ara h 2 is proposed. Genosensors is based on a sandwich assay format, the analyte hybridized with two base sequences, one immobilized onto a screen printed gold electrode, forming a self-assembled monolayer. The optimization of the device was performed using Response Surface Methodology. The maximum response was found to be 1 µM of capture probe concentration and 2,5 mM of blocking agent concentration.En el presente trabajo se propone un genosensor electroquímico para la detección de un segmento de ADN que codifica parte de la proteína alergénica Ara h 2 del cacahuete. El genosensor se basa en un ensayo tipo sándwich, el analito hibrida con dos secuencias de bases, una de ellas inmovilizada sobre un electrodo de oro serigrafiado, formando una monocapa autoensamblada. La optimización del dispositivo se realizó utilizando la metodología de Superficies de Respuesta. La máxima respuesta se encontró para concentraciones de sonda de captura y agente bloqueante, 1 μM y 2,5 mM respectivamente

Adapted assistance and resistance training with a knee exoskeleton after stroke

Studies on robotic interventions for gait rehabilitation after stroke require: (i) rigorous performance evidence; (ii) systematic procedures to tune the control parameters; and (iii) combination of control modes. In this study, we investigated how stroke individuals responded to training for two weeks with a knee exoskeleton (ABLE-KS) using both Assistance and Resistance training modes together with auditory feedback to train peak knee flexion angle. During the training, the torque provided by the ABLE-KS and the biofeedback were systematically adapted based on the subject’s performance and perceived exertion level. We carried out a comprehensive experimental analysis that evaluated a wide range of biomechanical metrics, together with usability and users’ perception metrics. We found significant improvements in peak knee flexion ( p=0.0016 ), minimum knee angle during stance ( p=0.0053 ), paretic single support time ( p=0.0087 ) and gait endurance ( p=0.022 ) when walking without the exoskeleton after the two weeks of training. Participants significantly ( p<0.00025 ) improved the knee angle during the stance and swing phases when walking with the exoskeleton powered in the high Assistance mode in comparison to the No Exo and the Unpowered conditions. No clinically relevant differences were found between Assistance and Resistance training sessions. Participants improved their performance with the exoskeleton (24-55 %) for the peak knee flexion angle throughout the training sessions. Moreover, participants showed a high level of acceptability of the ABLE-KS (QUEST 2.0 score: 4.5 ± 0.3 out of 5). Our preliminary findings suggest that the proposed training approach can produce similar or larger improvements in post-stroke individuals than other studies with knee exoskeletons that used higher training intensities.This work was supported in part by the Agency for Management of University and Research Grants (AGAUR) along with the Secretariat of Universities and Research of the Catalan Ministry of Research and Universities and the European Social Fund (ESF) under Grant 2020 FI_B 00331, in part by the Spanish Ministry of Science and Innovation (MCI)—Agencia Estatal de Investigación (AEI) under Grant PTQ2018-010227, in part by “La Caixa” Foundation under Grant LCF/TR/CC20/52480002, and in part by the Eurostars-3 Joint Program with co-financing from CDTI and the European Union’s Horizon Europe Research and Innovation Framework Program under Eureka Application Number 1789 under Grant CIIP-20221022Peer ReviewedPostprint (published version

Effectiveness of Fosfomycin for the Treatment of Multidrug-Resistant Escherichia coli Bacteremic Urinary Tract Infections A Randomized Clinical Trial

Importance The consumption of broad-spectrum drugs has increased as a consequence of the spread of multidrug-resistant (MDR) Escherichia coli. Finding alternatives for these infections is critical, for which some neglected drugs may be an option. Objective To determine whether fosfomycin is noninferior to ceftriaxone or meropenem in the targeted treatment of bacteremic urinary tract infections (bUTIs) due to MDR E coli. Design, Setting, and Participants This multicenter, randomized, pragmatic, open clinical trial was conducted at 22 Spanish hospitals from June 2014 to December 2018. Eligible participants were adult patients with bacteremic urinary tract infections due to MDR E coli; 161 of 1578 screened patients were randomized and followed up for 60 days. Data were analyzed in May 2021. Interventions Patients were randomized 1 to 1 to receive intravenous fosfomycin disodium at 4 g every 6 hours (70 participants) or a comparator (ceftriaxone or meropenem if resistant; 73 participants) with the option to switch to oral fosfomycin trometamol for the fosfomycin group or an active oral drug or parenteral ertapenem for the comparator group after 4 days. Main Outcomes and Measures The primary outcome was clinical and microbiological cure (CMC) 5 to 7 days after finalization of treatment; a noninferiority margin of 7% was considered. Results Among 143 patients in the modified intention-to-treat population (median [IQR] age, 72 [62-81] years; 73 [51.0%] women), 48 of 70 patients (68.6%) treated with fosfomycin and 57 of 73 patients (78.1%) treated with comparators reached CMC (risk difference, −9.4 percentage points; 1-sided 95% CI, −21.5 to ∞ percentage points; P = .10). While clinical or microbiological failure occurred among 10 patients (14.3%) treated with fosfomycin and 14 patients (19.7%) treated with comparators (risk difference, −5.4 percentage points; 1-sided 95% CI, −∞ to 4.9; percentage points; P = .19), an increased rate of adverse event–related discontinuations occurred with fosfomycin vs comparators (6 discontinuations [8.5%] vs 0 discontinuations; P = .006). In an exploratory analysis among a subset of 38 patients who underwent rectal colonization studies, patients treated with fosfomycin acquired a new ceftriaxone-resistant or meropenem-resistant gram-negative bacteria at a decreased rate compared with patients treated with comparators (0 of 21 patients vs 4 of 17 patients [23.5%]; 1-sided P = .01). Conclusions and Relevance This study found that fosfomycin did not demonstrate noninferiority to comparators as targeted treatment of bUTI from MDR E coli; this was due to an increased rate of adverse event–related discontinuations. This finding suggests that fosfomycin may be considered for selected patients with these infections