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Evidence supports a sex disparity in clinical outcomes of COVID-19 patients, with men exhibiting higher mortality rates compared to women. We aimed to test the correlation between serum levels of sex hormones [total testosterone, estradiol (E2), estradiol to testosterone (E2/T) ratio, progesterone), prolactin and 25-hydroxyvitamin D [25(OH)D] and markers of inflammation, coagulation and sepsis at admission in hospitalized men with COVID-19.We conducted an exploratory retrospective study including symptomatic men with confirmed SARS-CoV-2 infection who were consecutively admitted to our Institution between April 1 and May 31, 2020.Patients were divided into survivors (n=20) and non-survivors (n=39). As compared to survivors, non-survivors showed significantly higher median neutrophil-to-lymphocyte ratio (NLR) values, D-dimer and procalcitonin (PCT) levels, along with significantly lower median 25(OH)D levels and total testosterone levels. Non-survivors exhibited significantly higher median values of E2/T ratio (a marker of aromatase activity). Spearman's correlation analysis revealed that total testosterone levels were significantly and inversely correlated with NLR, high-sensitivity C-reactive protein (hsCRP), interleukin-6, D-dimer and PCT. Conversely, E2/T ratio values were significantly and positively correlated with the aforementioned markers and with white blood cell (WBC) count. In a multivariate analysis performed by a logistic regression model after adjusting for major confounders (age, body mass index, hypertension and cardiovascular disease, diabetes mellitus and malignancy), total testosterone levels were significantly and inversely associated with risk of COVID-19-related in-hospital mortality.Low total testosterone levels and elevated E2/T ratio values at admission are associated with hyperinflammatory state in hospitalized men with COVID-19. Low total testosterone levels at admission represent an independent risk factor for in-hospital mortality in such patients. Therefore, total testosterone and E2/T ratio may serve as prognostic markers of disease severity in this population.

In this paper, the authors present a modified Rives' technique for the treatment of incisional hernias. This technique requires the use of a nonabsorbable prosthesis (Composix Mesh) made of a double-knit layer of monofilament polypropylene bonded with a single layer of low-porosity e-PTFE inserted behind the rectus muscles and fixed by metallic clips. The outer side (polypropylene) encourages complete host tissue incorporation to reduce recurrences, the inner side (e-PTFE) minimizes tissue attachment and, therefore, visceral complications.

Synthetic aperture radar (SAR) remote sensing is very attractive for the systematic observation of dynamic processes on the Earth’s surface since it allows high resolution imaging independent of weather conditions and sunlight illumination. Waveform-encoded SAR is a novel SAR concept based on pulse-to-pulse variation of the transmitted waveform that allows focusing the nadir echo and the range ambiguities and suppressing them through a multi-focus post-processing. However, the assessment of the ambiguity suppression performance for such a system is not trivial, as the processing involves a non-linear thresholding and blanking approach. This work proposes a novel methodology, which exploits real TerraSAR-X data to accurately simulate the effect of the range ambiguity on the useful signal and allows for a quantitative assessment of the image quality of a waveform-encoded SAR. The analysis considers different waveform variation schemes (namely up- and down-chirps and cyclically shifted chirps) and a contrast-minimization technique for threshold selection, whose performance is compared to the best achievable one (i.e., the optimum threshold). The results of this work further highlight the potentialities of the waveform-encoded SAR concept and allow accounting for its ambiguity suppression capability in the design of a SAR system.

Lesions of the colon are generally considered to be sequelae of pancreatitis. They include: localized paralytic ileus (colon cutoff sign), necrosis, fistulae, stenosis and varices. On the basis of an extensive review of the literature (332 cases), it is suggested that the real incidence of these lesions is significant. The anatomic relationship of the large bowel to the pancreas is an important factor in the genesis and localization of the lesions. Enzymatic-inflammatory and ischemic processes are involved in the most highly supported theories. Each complication shows different diagnostic and clinical patterns. In this paper, six cases of such lesions are presented, including 2 cases of necrosis, 2 of stenosis, 1 of fistula and 1 case of localized paralytic ileus.

We describe the reference software architecture of parIDL, a PSE for distributed memory parallel computers that integrates the parallel software library ScaLAPACK, needed to develop and test scientific applications related to adaptive optics simulations. It incorporates a run time support for the execution of the commercial problem solving environment IDL on parallel machines, whose development has begun in a project with astronomy researchers for adaptive optic simulation of terrestrial large binocular telescopes.

The Authors report the incidence of local relapsing in a group of 362 patients who underwent Patey's mastectomy from 1974 to 1985. The overall incidence was 9.6%. The predictive factors of major value were: presence of lymph node metastases, primary tumor size and grading.

Although breast metastases are not frequent, the most common ones are from cutaneous melanoma. In this paper two cases of breast metastases from malignant melanoma are reported. The literature is reviewed and the diagnostic and therapeutic problems related to metastatic mammary neoplasms are analyzed.

Convocato nell’ottobre del 1962 da papa Giovanni XXIII, il Vaticano II portava a compimento un rinnovamento teologico che modificava anche la liturgia volta a favorire la partecipazione attiva dei fedeli alla celebrazione dei Sacramenti. Superando l’antica tradizione che vedeva il sacerdote officiare di spalle all’aula, recitando a voce bassa e in latino, il Sacrosanctum Concilium del 1963 prevedeva la riconfigurazione degli spazi liturgici dove l’altare, la custodia eucaristica, l’ambone, il coro, la sede del celebrante, la cappella feriale, la penitenzieria e tutti gli arredi e le suppellettili sacre dovevano essere in diretta comunicazione con l’adunanza dei credenti. Alle gerarchie ecclesiastiche non sfuggì la difficoltà delle trasformazioni degli assetti preesistenti, ancora strutturati, a meno delle chiese di recente costruzione, sulle indicazioni del concilio di Trento (1545-1563). E se la sistemazione degli altari, dei cori, degli amboni e di tutti gli altri elementi centrali nella celebrazione eucaristica poteva apparire di più agile realizzazione per gli edifici sacri costruiti ex novo, ben altra questione era la riscrittura della figurazione architettonica e simbolica degli spazi celebrativi delle chiese storiche, in larga parte preziosi e complessi palinsesti di arte, architettura e spiritualità: un’azione complicata, chiamata a contemperare le innovazioni introdotte dal Vaticano II con il rispetto dei beni culturali. Si trattava quindi di definire criteri, elaborare pratiche operative e formulare ipotesi, costruendo un modus operandi e immaginando nuove connessioni tra architettura e liturgia.

We develop innovative algorithms for solving the strong-constraint formulation of four-dimensional variational data assimilation in large-scale applications. We present a space-time decomposition approach that employs domain decomposition along both the spatial and temporal directions in the overlapping case and involves partitioning of both the solution and the operators. Starting from the global functional defined on the entire domain, we obtain a type of regularized local functionals on the set of subdomains providing the order reduction of both the predictive and the data assimilation models. We analyze the algorithm convergence and its performance in terms of reduction of time complexity and algorithmic scalability. The numerical experiments are carried out on the shallow water equation on the sphere according to the setup available at the Ocean Synthesis/Reanalysis Directory provided by Hamburg University., Comment: Received: 10 March 2020 / Revised: 29 November 2021 / Accepted: 7 March 2022

In order to solve a problem in parallel we need to undertake the fundamental step of splitting the computational tasks into parts, i.e. decomposing the problem solving. A whatever decomposition does not necessarily lead to a parallel algorithm with the highest performance. This topic is even more important when complex parallel algorithms must be developed for hybrid or heterogeneous architectures. We present an innovative approach which starts from a problem decomposition into parts (sub-problems). These parts will be regarded as elements of an algebraic structure and will be related to each other according to a suitably defined dependency relationship. The main outcome of such framework is to define a set of block matrices (dependency, decomposition, memory accesses and execution) which simply highlight fundamental characteristics of the corresponding algorithm, such as inherent parallelism and sources of overheads. We provide a mathematical formulation of this approach, and we perform a feasibility analysis for the performance of a parallel algorithm in terms of its time complexity and scalability. We compare our results with standard expressions of speed up, efficiency, overhead, and so on. Finally, we show how the multilevel structure of this framework eases the choice of the abstraction level (both for the problem decomposition and for the algorithm description) in order to determine the granularity of the tasks within the performance analysis. This feature is helpful to better understand the mapping of parallel algorithms on novel hybrid and heterogeneous architectures.

The standard formulation of Kalman Filter (KF) becomes computationally intractable for solving large scale state space estimation problems as in ocean/weather forecasting due to matrix storage and inversion requirements. We introduce an innovative mathematical/numerical formulation of KF using Domain Decomposition (DD) approach. The proposed DD approach partitions ab-initio the whole KF computational method giving rise to local KF methods that can be solved independently. We present its feasibility analysis using the constrained least square model underlying variational Data Dssimilation problems. Results confirm that the accuracy of solutions of local KF methods are not impaired by DD approach.

In most algorithms of global illumination, light–surface interaction terminates declaring that result at some point is close enough to some reference ground truth data. The underlying principle of such criterion is to minimize the processing time without compromising the (subjective) visual perception of the resulting image. We introduce an objective-driven condition for stopping the simulation of light transport. It is inspired by the physical meaning of light propagation. Besides, it takes into account that computations are performed in finite precision. Its main feature is the definition of the threshold establishing the maximum number of pixels that are completed in finite precision. Its value is computed at run time depending on the brightness of the image. As a proof of concept of the validity of this approach, we employ the stopping condition in a light tracing algorithm, propagating light that is generated by the light source. We assess the quality of the computed image by measuring the Peak Signal-to-Noise Ratio and the Structured Similarity Index error metrics on the standard scene of the Cornell Box. Numerical validation is performed by comparing results with the output of the NVIDIA $$^{\circledR }$$ Iray render whose stopping condition is based on Russian roulette and on the elapsed time.

Driven by the emerging requirements of High Performance Computing (HPC) architectures, the main focus of this work is the interplay of computational and energetic aspects of a Four Dimensional Variational (4DVAR) Data Assimilation algorithm, based on Domain Decomposition (named DD-4DVAR). We report first results on the energy consumption of the DD-4DVAR algorithm on embedded processor and a mathematical analysis of the energy behavior of the algorithm by assuming the architectures characteristics as variable of the model. The main objective is to capture the essential operations of the algorithm exhibiting a direct relationship with the measured energy. The experimental evaluation is carried out on a set of mini-clusters made available by the Barcelona Supercomputing Center. The research has received funding from European Commission under H2020-MSCA-RISE NASDAC project (grant agreement no. 691184) FP7 Mont-Blanc and Mont-Blanc 2 (grant agreements no. 288777 and 610402), H2020-FET Mont-Blanc 3 (grant agreement 671697).

We consider the Incremental Strong constraint 4D VARiational (IS4DVAR) algorithm for data assimilation implemented in ROMS with the aim to study its performance in terms of strong scaling scalability on computing architectures such as a cluster of CPUs. We consider realistic test cases with data collected in enclosed and semi enclosed seas, namely, Caspian sea, West Africa/Angola, as well as data collected into the California bay. The computing architecture we use is currently available at Imperial College London. The analysis allows us to highlight that the ROMS-IS4DVAR performance on emerging architectures depends on a deep relation among the problems size, the domain decomposition approach and the computing architecture characteristics.

The scientific and application-oriented interest in the Laplace transform and its inversion is testified by more than 1000 publications in the last century. Most of the inversion algorithms available in the literature assume that the Laplace transform function is available everywhere. Unfortunately, such an assumption is not fulfilled in the applications of the Laplace transform. Very often, one only has a finite set of data and one wants to recover an estimate of the inverse Laplace function from that. We propose a fitting model of data. More precisely, given a finite set of measurements on the real axis, arising from an unknown Laplace transform function, we construct a dth degree generalized polynomial smoothing spline, where d = 2m 1, such that internally to the data interval it is a dth degree polynomial complete smoothing spline minimizing a regularization functional, and outside the data interval, it mimics the Laplace transform asymptotic behavior, i.e. it is a rational or an exponential function (the end behavior model), and at the boundaries of the data set it joins with regularity up to order m 1, with the end behavior model. We analyze in detail the generalized polynomial smoothing spline of degree d = 3. This choice was motivated by the (ill)conditioning of the numerical computation which strongly depends on the degree of the complete spline. We prove existence and uniqueness of this spline. We derive the approximation error and give a priori and computable bounds of it on the whole real axis. In such a way, the generalized polynomial smoothing spline may be used in any real inversion algorithm to compute an approximation of the inverse Laplace function. Experimental results concerning Laplace transform approximation, numerical inversion of the generalized polynomial smoothing spline and comparisons with the exponential smoothing spline conclude the work. © 2012 IOP Publishing Ltd.

Many applications are tackled using the Laplace Transform (LT) known on a countable number of real values [J. Electroanal. Chem. 608, 37–46 (2007), Int. J. solid Struct. 41, 3653–3674 (2004), Imaging 26, 1183–1196 (2008), J. Magn. Reson. 156, 213–221 (2002)]. The usual approach to solve the LT inverse problem relies on a regularization technique combined with information a priori both on the LT function and on its inverse (see for instance [http://s-provencher.com/pages/ contin.shtml]). We propose a fitting model enjoying LT properties: we define a generalized spline that interpolates the LT function values and mimics the asymptotic behavior of LT functions. Then, we prove existence and uniqueness of this model and, through a suitable error analysis, we give a priori approximation error bounds to confirm the reliability of this approach. Numerical results are presented.

Blending two different immiscible polymers is one of the most efficient strategies to prepare materials with improved performances. Recently, this multi-phase systems have been used in tissue engineering aiming at the preparation of porous scaffolds for cell culture. In this study, thermoplastic biodegradable and biocompatible multi-phase blends with co-continuous micro-structures have been prepared by melt mixing poly(s-caprolactone) (PCL) with thermoplastic gelatin (TG), a thermoplastic material prepared by mixing gelatin with glycerol. The blend have been prepared by selecting the composition into the 60/40 to 40/60 wt-% PCL-TG range. Dynamic mechanical analysis was performed in order to asses the achievement of an heterogeneous micro-structure while, gravimetric measurements and scanning electron micrograph analysis, performed after the selective extraction of the different phases, were used to evaluate the co-continuity of the different systems prepared. Finally, the PCL/TG blends have been further processed by the gas foaming technique, in order to create porous materials with well controlled open-pore micro-architectures, suitable as 3D porous scaffolds for tissue engineering applications.

We address design of a numerical algorithm for solving the linear system arising in numerical inversion of Laplace transforms in real case [L. D'Amore, A. Murli, Regularization of a Fourier series method for the Laplace transform inversion with real data, Inverse Problems 18 (2002) 1185-1205]. The matrix has a condition number that grows almost exponentially and the singular values decay gradually towards zero. In such a case, because of this intrinsic strong instability, the main difficulty of any numerical computation is the ability of discovering at run time, only using data, what is the maximum attainable accuracy on the solution. In this paper, we use GMRES with the aim of relating the current residuals to the maximum attainable accuracy of the approximate solution by using a suitable stopping rule. We prove that GMRES stops after, at most, as many iterations as the number of the largest eigenvalues (compared to the machine epsilon). We use a split preconditioner that symmetrically precondition the initial system. By this way, the largest eigenvalue dynamically provides the estimate of the condition number of the matrix. © 2006 Elsevier B.V. All rights reserved.