The Figure of Adam in Rom 5:12-21 and 1 Cor 15:21-22, 45-49: The New Creation and Its Ethical and Social Reconfigurations

The Adam and Christ antithesis in Paul resembles other Jewish interpreters of the story of the creation of man and of the fall that draw ethical implications. Yet Paul uniquelly interprets the story of the origins in an eschatological perspective that calls believers to transform their lives after the image of the new Adam Christ in order to attain the eschatological resurrection in Him

Competing risk modelling for in-hospital length of stay

In this study, we propose a framework for analysing in-hospital patient data from electronic health records. We transform longitudinal sparse vital signs measurements into cross-sectional data via descriptive statistics, imputing missing values, and evaluating variables strongly associated with time to mutually exclusive events (favourable medical discharge or deterioration). We employ competing risk and random survival forest techniques to predict patients’ length of stay and evaluate models’ performance via Brier score

Competing risk models in early warning systems for in-hospital deterioration: the role of missing data imputation

Early Warning Systems (EWS) are useful and very important tools for evaluating the health deteriorating of hospitalised patients, using vital signs (such as heart rate, temperature, etc.) as the main input, based on electronic health records (EHR) which most of the time result in sparse data sets with high rates of missing data. In this work, we aim to study the effect of different imputation techniques on time-to-event (survival) models. For each case we have patient's sex and age, as well as longitudinal data along the hospitalisation for 7 vital signs (temperature, systolic and diastolic pressure, heart and respiratory rates, oxygen saturation and neurological state). We summarise these longitudinal data with the following central tendency, order and dispersion statistics: maximum, minimum, first observation, last observation, mean, standard deviation, average variance percentage and average derivative, transforming the original variables into a cross-sectional higher dimensional space, that still having missing data problems. Each hospitalisation has two possible final states: clinical deterioration or favourable discharge. Here, we model the time-to-event with competitive risk models taking into account the covariates. In the Galdakao-Usansolo University Hospital (Basque Country, Spain), a total of 19.602 hospitalisations (lengths of stay at least 24 hours) were collected during the year 2019, of which 852 (4.35\%) resulted in deterioration. These data correspond to 55.8\% of males and 44.2\% of females. We are using a set of imputation methods, such as central tendency statistics (mean and mode), Multiple Imputation by Chained Equations (MICE), Non-Linear Principal Components Analysis (NLPCA) and Random Forest. We evaluate the performances of the imputation methods described before, via root mean square error and conclude the pros and cons of using each one in medical practice. Then, we use Fine and Gray's competitive risk models and the cause-specific Cox proportional hazard regression to model the time-to-event as a function of imputed summarised data. Finally, we evaluate these models employing the traditional and time-dependent area under the ROC curve, for horizon times of 24, 48, 72, 96 and 120 hospitalisation hours

The impact of a large object with Jupiter in July 2009

On 2009 July 19, we observed a single, large impact on Jupiter at a planetocentric latitude of 55^{\circ}S. This and the Shoemaker-Levy 9 (SL9) impacts on Jupiter in 1994 are the only planetary-scale impacts ever observed. The 2009 impact had an entry trajectory opposite and with a lower incidence angle than that of SL9. Comparison of the initial aerosol cloud debris properties, spanning 4,800 km east-west and 2,500 km north-south, with those produced by the SL9 fragments, and dynamical calculations of pre-impact orbit, indicate that the impactor was most probably an icy body with a size of 0.5-1 km. The collision rate of events of this magnitude may be five to ten times more frequent than previously thought. The search for unpredicted impacts, such as the current one, could be best performed in 890-nm and K (2.03-2.36 {\mu}m) filters in strong gaseous absorption, where the high-altitude aerosols are more reflective than Jupiter's primary cloud.Comment: 15 pages, 5 figure

A complex storm system in Saturn’s north polar atmosphere in 2018

Producción CientíficaSaturn’s convective storms usually fall in two categories. One consists of mid-sized storms ∼2,000 km wide, appearing as irregular bright cloud systems that evolve rapidly, on scales of a few days. The other includes the Great White Spots, planetary-scale giant storms ten times larger than the mid-sized ones, which disturb a full latitude band, enduring several months, and have been observed only seven times since 1876. Here we report a new intermediate type, observed in 2018 in the north polar region. Four large storms with east–west lengths ∼4,000–8,000 km (the first one lasting longer than 200 days) formed sequentially in close latitudes, experiencing mutual encounters and leading to zonal disturbances affecting a full latitude band ∼8,000 km wide, during at least eight months. Dynamical simulations indicate that each storm required energies around ten times larger than mid-sized storms but ∼100 times smaller than those necessary for a Great White Spot. This event occurred at about the same latitude and season as the Great White Spot in 1960, in close correspondence with the cycle of approximately 60 years hypothesized for equatorial Great White Spots.Ministerio de Economía, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (project AYA2015-65041-P)Gobierno Vasco (project IT-366-19

Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks

Background:A reliable anticipation of a difficult airway may notably enhance safety during anaesthesia. In current practice, clinicians use bedside screenings by manual measurements of patients’ morphology. Objective:To develop and evaluate algorithms for the automated extraction of orofacial landmarks, which characterize airway morphology. Methods:We defined 27 frontal + 13 lateral landmarks. We collected n=317 pairs of pre-surgery photos from patients undergoing general anaesthesia (140 females, 177 males). As ground truth reference for supervised learning, landmarks were independently annotated by two anaesthesiologists. We trained two ad-hoc deep convolutional neural network architectures based on InceptionResNetV2 (IRNet) and MobileNetV2 (MNet), to predict simultaneously: (a) whether each landmark is visible or not (occluded, out of frame), (b) its 2D-coordinates (x, y). We implemented successive stages of transfer learning, combined with data augmentation. We added custom top layers on top of these networks, whose weights were fully tuned for our application. Performance in landmark extraction was evaluated by 10-fold cross-validation (CV) and compared against 5 state-of-the-art deformable models. Results:With annotators’ consensus as the ‘gold standard’, our IRNet-based network performed comparably to humans in the frontal view: median CV loss L=1.277·10-3, inter-quartile range (IQR) [1.001, 1.660]; versus median 1.360, IQR [1.172, 1.651], and median 1.352, IQR [1.172, 1.619], for each annotator against consensus, respectively. MNet yielded slightly worse results: median 1.471, IQR [1.139, 1.982]. In the lateral view, both networks attained performances statistically poorer than humans: median CV loss L=2.141·10-3, IQR [1.676, 2.915], and median 2.611, IQR [1.898, 3.535], respectively; versus median 1.507, IQR [1.188, 1.988], and median 1.442, IQR [1.147, 2.010] for both annotators. However, standardized effect sizes in CV loss were small: 0.0322 and 0.0235 (non-significant) for IRNet, 0.1431 and 0.1518 (p<0.05) for MNet; therefore quantitatively similar to humans. The best performing state-of-the-art model (a deformable regularized Supervised Descent Method, SDM) behaved comparably to our DCNNs in the frontal scenario, but notoriously worse in the lateral view. Conclusions:We successfully trained two DCNN models for the recognition of 27 + 13 orofacial landmarks pertaining to the airway. Using transfer learning and data augmentation, they were able to generalize without overfitting, reaching expert-like performances in CV. Our IRNet-based methodology achieved a satisfactory identification and location of landmarks: particularly in the frontal view, at the level of anaesthesiologists. In the lateral view, its performance decayed, although with a non-significant effect size. Independent authors had also reported lower lateral performances; as certain landmarks may not be clear salient points, even for a trained human eye.BERC.2022-2025 BCAM Severo Ochoa accreditation CEX2021-001142-S / MICIN / AEI / 10.13039/50110001103

Relevance of comorbidities for main outcomes during different periods of the COVID-19 pandemic

Background: Throughout the evolution of the COVID-19 pandemic, the severity of the disease has varied. The aim of this study was to determine how patients' comorbidities affected and were related to, different outcomes during this time. Methods: Retrospective cohort study of all patients testing positive for SARS-CoV-2 infection between March 1, 2020, and January 9, 2022. We extracted sociodemographic, basal comorbidities, prescribed treatments, COVID-19 vaccination data, and outcomes such as death and admission to hospital and intensive care unit (ICU) during the different periods of the pandemic. We used logistic regression to quantify the effect of each covariate in each outcome variable and a random forest algorithm to select the most relevant comorbidities. Results: Predictors of death included having dementia, heart failure, kidney disease, or cancer, while arterial hypertension, diabetes, ischemic heart, cerebrovascular, peripheral vascular diseases, and leukemia were also relevant. Heart failure, dementia, kidney disease, diabetes, and cancer were predictors of adverse evolution (death or ICU admission) with arterial hypertension, ischemic heart, cerebrovascular, peripheral vascular diseases, and leukemia also relevant. Arterial hypertension, heart failure, diabetes, kidney, ischemic heart diseases, and cancer were predictors of hospitalization, while dyslipidemia and respiratory, cerebrovascular, and peripheral vascular diseases were also relevant. Conclusions: Preexisting comorbidities such as dementia, cardiovascular and renal diseases, and cancers were those most related to adverse outcomes. Of particular note were the discrepancies between predictors of adverse outcomes and predictors of hospitalization and the fact that patients with dementia had a lower probability of being admitted in the first wave

Dynamics of AC susceptibility and coercivity behavior in nanocrystalline TbAl1.5 Fe0.5 alloys

The static and dynamic magnetic macroscopic properties of bulk and nanocrystalline TbAl1.5Fe0.5 alloys have been investigated. In bulk state, this alloy is understood as a reentrant ferromagnet. This is characterized by a ferromagnetic Curie transition at 114 K, as deduced from magnetization including Arrott plots, higher than that of TbAl2. The reentrance is found at lower temperatures, below 66 K, with a cluster glass behavior setting in, deduced from the magnetization irreversibility. This is accompanied by an abrupt increase in the coercivity from 0.08 kOe to 15 kOe at 5 K, with respect to the TbAl2 alloy. Room temperature Mössbauer spectroscopy confirms the paramagnetic state of such a bulk alloy. The spin dynamics within the disordered magnetic state is described by the AC-susceptibility which shows a Vogel–Fulcher law for the slowing down process. This is caused by a random anisotropy affecting the existing clusters. The production of milled TbAl1.5Fe0.5 alloys enhances the presence of magnetic disorder and results in the particle downsizing toward the nanocrystalline state (close to 10 nm). In this case, two frequency-dependent contributions exist, with different activation energies, one of them cannot be described by ideal spin glass nor blocking/unblocking (nanoparticle) processes. In addition, the coercivity reduces to 1 kOe with the decrease in the size as a consequence of the existence of single domain particles. The results are explained by the intricate interplay between exchange interactions and magnetocrystalline anisotropy with disorder and size effects. © 2012 Elsevier B.V.This work has been supported by the MAT2008-06542-C04 and MAT2011-27573-C04 projects.Peer Reviewe

A Computer Application to Predict Adverse Events in the Short-Term Evolution of Patients With Exacerbation of Chronic Obstructive Pulmonary Disease

Background: Chronic obstructive pulmonary disease (COPD) is a common chronic disease. Exacerbations of COPD (eCOPD) contribute to the worsening of the disease and the patient’s evolution. There are some clinical prediction rules that may help to stratify patients with eCOPD by their risk of poor evolution or adverse events. The translation of these clinical prediction rules into computer applications would allow their implementation in clinical practice. Objective: The goal of this study was to create a computer application to predict various outcomes related to adverse events of short-term evolution in eCOPD patients attending an emergency department (ED) based on valid and reliable clinical prediction rules. Methods: A computer application, Prediction of Evolution of patients with eCOPD (PrEveCOPD), was created to predict 2 outcomes related to adverse events: (1) mortality during hospital admission or within a week after an ED visit and (2) admission to an intensive care unit (ICU) or an intermediate respiratory care unit (IRCU) during the eCOPD episode. The algorithms included in the computer tool were based on clinical prediction rules previously developed and validated within the Investigación en Resultados y Servicios de Salud COPD study. The app was developed for Windows and Android systems, using Visual Studio 2008 and Eclipse, respectively. Results: The PrEveCOPD computer application implements the prediction models previously developed and validated for 2 relevant adverse events in the short-term evolution of patients with eCOPD. The application runs under Windows and Android systems and it can be used locally or remotely as a Web application. Full description of the clinical prediction rules as well as the original references is included on the screen. Input of the predictive variables is controlled for out-of-range and missing values. Language can be switched between English and Spanish. The application is available for downloading and installing on a computer, as a mobile app, or to be used remotely via internet. Conclusions: The PrEveCOPD app shows how clinical prediction rules can be summarized into simple and easy to use tools, which allow for the estimation of the risk of short-term mortality and ICU or IRCU admission for patients with eCOPD. The app can be used on any computer device, including mobile phones or tablets, and it can guide the clinicians to a valid stratification of patients attending the ED with eCOPD.Fondo de Investigación Sanitaria (PI 06\1010, PI06\1017, PI06\714, PI06\0326, PI06\0664) Departamento de Salud del Gobierno Vasco (2012111008) Departamento de Educación, Política Lingüística y Cultura del Gobierno Vasco (IT620-13) Ministerio de Economía y Competitividad del Gobierno Español and FEDER (MTM2013-40941-P and MTM2016-74931-P) the Research Committee of the Hospital Galdakao the thematic networks -REDISSEC (Red de Investigación en Servicios de Salud en Enfermedades Crónicas) - of the Instituto de Salud Carlos III