Grado de madurez organizacional en la gestión de proyectos de la empresa Instalaciones Hidráulicas y Sanitarias WC SAS

Trabajo de InvestigaciónSe presenta el grado de madurez organizacional en la gestión de proyectos para la empresa instalaciones hidráulicas y sanitarias WC SAS , para la gestión de proyectos es importante la estandarización y el óptimo desarrolló de proyectos, se hizo la aplicación de un cuestionario basado sobre la base de OPM3® del PMI®, el estándar para la dirección de proyectos de PMI y COBIT 4, con la recolección y tabulación de la información se logra dar un diagnóstico.INTRODUCCIÓN 1. GENERALIDADES 2. MARCOS DE REFERENCIAS 3. METODOLOGÍA 4. RESULTADOS 5. PROPUESTA PARA MEJORAR EL GRADO DE MADUREZ EN GESTIÓN DE PROYECTOS EN LA EMPRESA WC SAS 6. CONCLUSIONES BIBLIOGRAFÍA ANEXOSEspecializaciónEspecialista en Gerencia de Obras Civile

An investigation of structural stability in protein-ligand complexes reveals the balance between order and disorder

The predominant view in structure-based drug design is that small-molecule ligands, once bound to their target structures, display a well-defined binding mode. However, structural stability (robustness) is not necessary for thermodynamic stability (binding affinity). In fact, it entails an entropic penalty that counters complex formation. Surprisingly, little is known about the causes, consequences and real degree of robustness of protein-ligand complexes. Since hydrogen bonds have been described as essential for structural stability, here we investigate 469 such interactions across two diverse structure sets, comprising of 79 drug-like and 27 fragment ligands, respectively. Completely constricted protein-ligand complexes are rare and may fulfill a functional role. Most complexes balance order and disorder by combining a single anchoring point with looser regions. 25% do not contain any robust hydrogen bond and may form loose structures. Structural stability analysis reveals a hidden layer of complexity in protein-ligand complexes that should be considered in ligand design

Dues noves espècies del gènere Islamia (Gastropoda: Hydrobiidae) per al nord d’Espanya

Two new species of the genus Islamia are described from the autonomous communities of Cantabria and Asturias (north of Spain). This genus is currently represented in the Iberian Peninsula and the Balearic Islands by eight species and subspecies. The new species are compared with other congeneric species from which they differ in conchological characteristics.Es descriuen dues noves espècies del gènere Islamia procedents de les comunitats autònomes de Cantàbria i Astúries (nord d’Espanya). Aquest gènere actualment està representat a la península Ibèrica i Balears per vuit espècies i subespècies. Les noves espècies es comparen amb altres espècies congenèriques, de les quals es diferencien per diversos caràcters conquiliològics

Tarracospeum raveni, un nou gènere i espècie de la família Moitessieriidae (Mollusca: Gastropoda) per a Espanya

A new genus and a new species of the family Moitessieriidae (Mollusca: Gastropoda) are described for Spain. The main morphological characters are described, which allow for distinguishing the new genus from other known genera.Es descriu un nou gènere i espècie de la família Moitessieriidae (Mollusca: Gastropoda) per a Espanya. Es descriuen i representen els seus caràcters morfològics, que permeten distingir-lo dels altres gèneres coneguts

Development of an Automatic Pipeline for Participation in the CELPP Challenge

The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions. Quantifying the docking performance in real scenarios is essential to understanding their limitations, managing expectations and guiding future developments. Here, we present a fully automated pipeline for pose prediction validated by participating in the Continuous Evaluation of Ligand Pose Prediction (CELPP) Challenge. Acknowledging the intrinsic limitations of the docking method, we devised a strategy to automatically mine and exploit pre-existing data, defining-whenever possible-empirical restraints to guide the docking process. We prove that the pipeline is able to generate predictions for most of the proposed targets as well as obtain poses with low RMSD values when compared to the crystal structure. All things considered, our pipeline highlights some major challenges in the automatic prediction of protein-ligand complexes, which will be addressed in future versions of the pipeline. Keywords: D3R; automated pipeline; binding mode prediction; docking; pocket detection

Nova espècie del gènere Islamia Radoman, 1973 per a Espanya

A new species of the genus Islamia Radoman, 1973 is described for the Valencian Community in Spain. This is the first species of this genus discovered in this territory, and it can be distinguished from other known species by the morphology of the shell. The spring where it was found has another known endemic mollusc, thus being a place of high interest for the stygobiont malacofauna.Es descriu una espècie nova del gènere Islamia Radoman, 1973 per a Espanya, concretament a la Comunitat Valenciana. Es tracta de la primera espècie del gènere coneguda al territori, la qual es pot diferenciar de les altres espècies conegudes per la morfologia de la conquilla. La font de la troballa presenta un altre mol·lusc endèmic conegut; es tracta per tant d’un espai de gran interès per la malacofauna estigobiont

ARTE: Automated Generation of Realistic Test Inputs for Web APIs

Automated test case generation for web APIs is a thriving research topic, where test cases are frequently derived from the API specification. However, this process is only partially automated since testers are usually obliged to manually set meaningful valid test inputs for each input parameter. In this article, we present ARTE, an approach for the automated extraction of realistic test data for web APIs from knowledge bases like DBpedia. Specifically, ARTE leverages the specification of the API parameters to automatically search for realistic test inputs using natural language processing, search-based, and knowledge extraction techniques. ARTE has been integrated into RESTest, an open-source testing framework for RESTful APIs, fully automating the test case generation process. Evaluation results on 140 operations from 48 real-world web APIs show that ARTE can efficiently generate realistic test inputs for 64.9% of the target parameters, outperforming the state-of-the-art approach SAIGEN (31.8%). More importantly, ARTE supported the generation of over twice as many valid API calls (57.3%) as random generation (20%) and SAIGEN (26%), leading to a higher failure detection capability and uncovering several real-world bugs. These results show the potential of ARTE for enhancing existing web API testing tools, achieving an unprecedented level of automationJunta de Andalucía APOLO (US-1264651)Junta de Andalucía EKIPMENT-PLUS (P18-FR-2895)Ministerio de Ciencia, Innovación y Universidades RTI2018-101204-B-C21 (HORATIO)Ministerio de Ciencia, Innovación y Universidades RED2018-102472-

Thinning of the Monte Perdido Glacier in the Spanish Pyrenees since 1981

Producción CientíficaThis paper analyzes the evolution of the Monte Perdido Glacier, the third largest glacier in the Pyrenees, from 1981 to the present. We assessed the evolution of the glacier's surface area by analysis of aerial photographs from 1981, 1999, and 2006, and changes in ice volume by geodetic methods with digital elevation models (DEMs) generated from topographic maps (1981 and 1999), airborne lidar (2010) and terrestrial laser scanning (TLS, 2011, 2012, 2013, and 2014) data. We interpreted the changes in the glacier based on climate data from nearby meteorological stations. The results indicate that the degradation of this glacier accelerated after 1999. The rate of ice surface loss was almost three times greater during 1999–2006 than during earlier periods. Moreover, the rate of glacier thinning was 1.85 times faster during 1999–2010 (rate of surface elevation change  = −8.98 ± 1.80 m, glacier-wide mass balance  = −0.73 ± 0.14 m w.e. yr−1) than during 1981–1999 (rate of surface elevation change  = −8.35 ± 2.12 m, glacier-wide mass balance  = −0.42 ± 0.10 m w.e. yr−1). From 2011 to 2014, ice thinning continued at a slower rate (rate of surface elevation change  = −1.93 ± 0.4 m yr−1, glacier-wide mass balance  = −0.58 ± 0.36 m w.e. yr−1). This deceleration in ice thinning compared to the previous 17 years can be attributed, at least in part, to two consecutive anomalously wet winters and cool summers (2012–2013 and 2013–2014), counteracted to some degree by the intense thinning that occurred during the dry and warm 2011–2012 period. However, local climatic changes observed during the study period do not seem sufficient to explain the acceleration of ice thinning of this glacier, because precipitation and air temperature did not exhibit statistically significant trends during the study period. Rather, the accelerated degradation of this glacier in recent years can be explained by a strong disequilibrium between the glacier and the current climate, and likely by other factors affecting the energy balance (e.g., increased albedo in spring) and feedback mechanisms (e.g., heat emitted from recently exposed bedrock and debris covered areas).Ministerio de Economía, Industria y Competitividad - IBERNIEVE (project CGL2014-52599-P)Ministerio de Agricultura y Pesca, Alimentación y Medio Ambiente (project 844/2013

Interpretable clinical time-series modeling with intelligent feature selection for early prediction of antimicrobial multidrug resistance

Electronic health records provide rich, heterogeneous data about the evolution of the patients’ health status. However, such data need to be processed carefully, with the aim of extracting meaningful information for clinical decision support. In this paper, we leverage interpretable (deep) learning and signal processing tools to deal with multivariate time-series data collected from the Intensive Care Unit (ICU) of the University Hospital of Fuenlabrada (Madrid, Spain). The presence of antimicrobial multidrug-resistant (AMR) bacteria is one of the greatest threats to the health system in general and to the ICUs in particular due to the critical health status of the patients therein. Thus, early identification of bacteria at the ICU and early prediction of their antibiotic resistance are key for the patients’ prognosis. While intelligent data-based processing and learning schemes can contribute to this early prediction, their acceptance and deployment in the ICUs require the automatic schemes to be not only accurate but also understandable by clinicians. Accordingly, we have designed trustworthy intelligent models for the early prediction of AMR based on the combination of meaningful feature selection with interpretable recurrent neural networks. These models were created using irregularly sampled clinical measurements, both considering the health status of the patient and the global ICU environment. We explored several strategies to cope with strongly imbalance data, since only a few ICU patients are infected by AMR bacteria. It is worth noting that our approach exhibits a good balance between performance and interpretability, especially when considering the difficulty of the classification task at hand. A multitude of factors are involved in the emergence of AMR (several of them not fully understood), and the records only contain a subset of them. In addition, the limited number of patients, the imbalance between classes, and the irregularity of the data render the problem harder to solve. Our models are also enriched with SHAP post-hoc interpretability and validated by clinicians who considered model understandability and trustworthiness of paramount concern for pragmatic purposes. Moreover, we use linguistic fuzzy systems to provide clinicians with explanations in natural language. Such explanations are automatically generated from a pool of interpretable rules that describe the interaction among the most relevant features identified by SHAP. Notice that clinicians were especially satisfied with new insights provided by our models. Such insights helped them to trust the automatic schemes and use them to make (better) decisions to mitigate AMR spreading in the ICU. All in all, this work paves the way towards more comprehensible time-series analysis in the context of early AMR prediction in ICUs and reduces the time of detection of infectious diseases, opening the door to better hospital care.This work is supported by the Spanish NSF grants PID2019-106623RB-C41 (BigTheory), PID2019-105032GB-I00 (SPGraph), PID2019-107768RA-I00 (AAVis-BMR), RTI2018-099646-B-I00 (ADHERE-U); the Galician Ministry of Education, University and Professional Training grants ED431F 2018/02 (eXplica-IA) and ED431G2019/04; the Instituto de Salud Carlos III, Spain grant DTS17/00158; as well as the Community of Madrid in the framework of the Multiannual Agreement with Rey Juan Carlos University in line of action 1, “Encouragement of Young Phd students investigation” Project Ref. F661 (Mapping-UCI). Sergio M. Aguero is a recipient of the Predoctoral Contracts for Trainees URJC Grant (PREDOC21-036). Jose M. Alonso-Moral is a Ramon Cajal Researcher (RYC-2016-19802).S

Patients and healthcare professionals’ voice on preventable readmissions

Introduction Currently, about 10% of patients required unplanned readmissions within 30 days after discharge.1 2 This proportion has not changed substantially over the past several years despite intense efforts to improve the discharge process. Although several studies3 4 have been performed, including patients’ and physicians’ opinion on the preventability of readmissions and factors that would predict preventability, only a few studies have included nurses’ opinions and the consensus with all stakeholders.5 We aimed to determine the patient’s opinion on preventable readmission, associated factors and the extent to which patients, nurses and physicians agree on readmission preventability. Methods To achieve the proposed objectives, a descriptive transversal correlational multicentre study was developed. This study was approved by the Clinical Research Ethics Committee (reference number: PR114/17). From 2 April 2017 to 18 January 2019, all patients readmitted within 30 days to 2 medical and 2 surgical departments (internal medicine, pneumology, trauma and digestive surgery) at 4 university hospitals were identified. Patients who provided written informed consent were interviewed within 72 hours of readmission. Four research nurses were trained to deliver the interviews. The patient’s interview involved 23 questions6 about functional status at discharge, discharge process and follow-up care, including readmission preventability (online supplemental material). Two independent physicians and nurses of the research team concurrently reviewed electronic health records to identify factors contributing to potentially preventable readmissions.7 Clinical and demographic patients’ characteristics were also collected. We estimated that a total sample size of 276 patients was needed for a proportion of 11% of preventable readmission,7 95% confidence level and 0.04 precision and assuming 15% potentially missed cases. A logistic regression model has been used to assess the association between the patient profile and his answer to the main question of his readmission preventability. The conditions of application of the models have been validated and CIs at 95% of the estimator have been calculated whenever possible. Cohen’s kappa statistic has been calculated to assess the concordance between physicians’, nurses’ and patients’ answer to this preventability readmission question. All the analysis has been done with the statistic package R V.3.5.3 (11 March 2019) for Windows. Patients were not involved in the design, conduct, reporting or dissemination plans of this study. Results We assessed 805 consecutive patients for eligibility, of whom 529 were excluded refused or unavailable (314 presented haemodynamic instability, 107 were discharged early, 104 refused to participate and four had language barrier). Among 276 patients included, 44.2% were admitted to internal medicine, 13.8% pneumology, 8% trauma and 34.1% digestive surgery department, respectively. The mean age was 68 years and 65.9% were men. The median (IQR) time between discharge and readmission was 11 days (5–17 days) and the median (IQR) Charlson comorbidity index was 5 (3–6). Ninety-six (34.8%) patients reported that their readmission was preventable, 69 (25.0%) were undecided and 111 (40.2%) reported that their readmission was not preventable. Comparing patients who reported non-preventable readmissions to those who reported preventable readmissions or were undecided, the latter had less time between discharge and readmission, did not have a follow-up appointment scheduled with primary care or specialist at discharge, no medication reviewed and felt concerns were not addressed before discharge. Also, patients who were less satisfied with the hospital’s discharge team, who felt were discharged before being ready and felt concern during follow-up care were more likely to report preventable readmission or undecidednes