Luxación posterior bilateral de la articulación escapulohumeral

Se presenta un caso de un hombre de 51 años, que tras sufrir una crisis convulsiva presentó dolor e impotencia funcional en ambos hombros. Tras realizar estudio radiográ- fico y TAC, se comprobó la existencia de una luxación escapulohumeral bilateral. El paciente fue tratado con reducción cerrada e inmovilización del miembro en abducción y rotación externa con un yeso toracobraquial.A case of bilateral posterior dislocation of the scapulo-humeral joint ocurring in a 51-year-old men after a convulsive attack is reported. Inmediately after convulsions, the patient refered pain and funtional disability in both shoulders and bilateral scapulo-humeral dislocation was confirmed by conventional radiograhips and CT-scan. Treatment consisted of closed reduction and inmobilization of the upper extremity in abduction and external rotation with a thoraco-braquial plaster cast

Water leak detection using self-supervised time series classification

Leaks in water distribution networks cause a loss of water that needs to be com- pensated to ensure a continuous supply for all customers. This compensation is achieved by increasing the flow of the network, which entails an undesirable economical expense as well as negative consequences for the environment. For these reasons, detecting and fixing leaks is a relevant task for water distribution companies. This paper proposes a water leak detection method based on a self- supervised classification of flow time series. The aim is to detect the leaks in the network, providing a low false positive rate. The proposed method is applied to two water distribution networks and compared to two other methods in the literature, obtaining the best balance between the number of false positives and detected leaks.IT1244-19 PID2019-104966GB-I0

A Review on Outlier/Anomaly Detection in Time Series Data

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.KK/2019-00095 IT1244-19 TIN2016-78365-R PID2019-104966GB-I0

Delineation of site‐specific management zones using estimation of distribution algorithms

In this paper, we present a novel methodology to solve the problem of delineating homogeneous site-specific management zones (SSMZ) in agricultural fields. This problem consists of dividing the field into small regions for which a specific rate of inputs is required. The objec- tive is to minimize the number of management zones, which must be homogeneous according to a specific soil property: physical or chem- ical. Furthermore, as opposed to oval zones, SSMZ with rectangular shapes are preferable since they are more practical for agricultural technologies. The methodology we propose is based on evolutionary computation, specifically on a class of the estimation of distribution algorithms (EDAs). One of the strongest contributions of this study is the representation used to model the management zones, which gener- ates zones with orthogonal shapes, e.g., L or T shapes, and minimizes the number of zones required to delineate the field. The experimental results show that our method is efficient to solve real-field and ran- domly generated instances. The average improvement of our method consists in reducing the number of management zones in the agricul- tural fields concerning other operations research methods presented in the literature. The improvement depends on the size of the field and the level of homogeneity established for the resulting management zones.IT1244-19 TIN2016-78365-R PID2019-104966GB-I0

Learning a Battery of COVID-19 Mortality Prediction Models by Multi-objective Optimization

The COVID-19 pandemic is continuously evolving with drastically changing epidemiological situations which are approached with different decisions: from the reduction of fatalities to even the selection of patients with the highest probability of survival in critical clinical situations. Motivated by this, a battery of mortality prediction models with different performances has been developed to assist physicians and hospital managers. Logistic regression, one of the most popular classifiers within the clinical field, has been chosen as the basis for the generation of our models. Whilst a standard logistic regression only learns a single model focusing on improving accuracy, we propose to extend the possibilities of logistic regression by focusing on sensitivity and specificity. Hence, the log-likelihood function, used to calculate the coefficients in the logistic model, is split into two objective functions: one representing the survivors and the other for the deceased class. A multi-objective optimization process is undertaken on both functions in order to find the Pareto set, composed of models not improved by another model in both objective functions simultaneously. The individual optimization of either sensitivity (deceased patients) or specificity (survivors) criteria may be conflicting objectives because the improvement of one can imply the worsening of the other. Nonetheless, this conflict guarantees the output of a battery of diverse prediction models. Furthermore, a specific methodology for the evaluation of the Pareto models is proposed. As a result, a battery of COVID-19 mortality prediction models is obtained to assist physicians in decision-making for specific epidemiological situations.This research is supported by the Basque Government (IT1504- 22, Elkartek) through the BERC 2022–2025 program and BMTF project, and by the Ministry of Science, Innovation and Universities: BCAM Severo Ochoa accreditation SEV-2017-0718 and PID2019-104966GB-I00. Furthermore, the work is also supported by the AXA Research Fund project “Early prognosis of COVID-19 infections via machine learning”

Impact of a COVID-19 Outbreak in an Elderly Care Home after Primary Vaccination

[EN] Elderly care home residents are particularly vulnerable to COVID-19 due to immunesenescence, pre-existing medical conditions, and the risk of transmission from staff and visitors. This study aimed to describe the outcomes of a COVID-19 outbreak in a long-term care facility for elderly persons following the initial vaccination. A single-center, retrospective, observational design was used to analyze the variables associated with hospitalization and death rate by logistic regression. Adjusted odds ratios (aOR) and their 95% confidence intervals (CI) were calculated. Sixty-eight residents received the first dose of the COVID-19 vaccine. Despite being negative six days after vaccination, the performance of a second test 4 days later revealed 51 positives (75.0%) among residents and 18 among workers (56.3%). A total of 65 of the 68 residents (95.58%) had positive results with symptoms, whereas 34.9% required hospitalization, and 25.8% died. The best-fitting model to explain the distribution of cases reflects three points at the time of infection.. The time from vaccination to symptom onset explains the hospitalization and mortality rates since a day elapsed halves the risk of hospitalization (aOR = 0.57; CI = 0.38−0.75) and the risk of death by a quarter (aOR = 0.74; CI = 0.63−0.88). Nursing homes present an elevated risk of transmission and severity of SARS-CoV-2 infection. Although vaccination reduces the risk of hospitalization and death, extreme prevention and control measures are essential in these institutions despite the high vaccination coverage.S

Localized leishmaniasis of the oral mucosa. A report of three cases

El término leishmaniasis comprende un grupo de enfermedades causadas por diferentes especies de un protozoo llamado Leishmania. La leishmaniasis se produce en todo el mundo, considerándose endémica en 88 países. Existen tres formas clínicas principales de leishmaniasis: leishmaniasis visceral, leishmaniasis cutánea y leishmaniasis mucocutánea. La afectación de la mucosa, de manera exclusiva, por la Leishmania es muy rara. Presentamos una serie de tres casos de leishmaniasis mucosa localizados en la cavidad oral. El hecho de que todos los casos se produjeran en España, área endémica de L infantum, nos hace presuponer que éste fue el agente causal. La única manifestación de enfermedad de leishmaniasis en los casos descritos, fue la aparición de una lesión oral. Se administró tratamiento con antimoniato de meglumina en dos de ellos, respondiendo favorablemente. Uno de los pacientes abandonó el hospital tras el diagnóstico sin recibir tratamiento y se desconoce la evolución. Realizamos también una revisión de la literatura

Gravitational Duality in MacDowell-Mansouri Gauge Theory

Strong-weak duality invariance can only be defined for particular sectors of supersymmetric Yang-Mills theories. Nevertheless, for full non-Abelian non-supersymmetric theories, dual theories with inverted couplings, have been found. We show that an analogous procedure allows to find the dual action to the gauge theory of gravity constructed by the MacDowell-Mansouri model plus the superposition of a $\Theta$ term.Comment: 9 pages, LaTeX, no figure