Ab-initio self-energy corrections in systems with metallic screening

The calculation of self-energy corrections to the electron bands of a metal requires the evaluation of the intraband contribution to the polarizability in the small-q limit. When neglected, as in standard GW codes for semiconductors and insulators, a spurious gap opens at the Fermi energy. Systematic methods to include intraband contributions to the polarizability exist, but require a computationally intensive Fermi-surface integration. We propose a numerically cheap and stable method, based on a fit of the power expansion of the polarizability in the small-q region. We test it on the homogeneous electron gas and on real metals such as sodium and aluminum.Comment: revtex, 14 pages including 5 eps figures v2: few fixe

Burning magnesium, a sparkle in acute inflammation: gleams from experimental models

Magnesium contributes to the regulation of inflammatory responses. Here, we focus on the role of magnesium in acute inflammation. Although present knowledge is incomplete to delineate an accurate scenario and a schedule of the events occurring under magnesium deficiency, it emerges that low magnesium status favors the induction of acute inflammation by sensitizing sentinel cells to the noxious agent, and then by participating to the orchestration of the vascular and cellular events that characterize the process

Mitophagy contributes to endothelial adaptation to simulated microgravity

Exposure to real or simulated microgravity is sensed as a stress by mammalian cells, which activate a complex adaptive response. In human primary endothelial cells, we have recently shown the sequential intervention of various stress proteins which are crucial to prevent apoptosis and maintain cell function. We here demonstrate that mitophagy contributes to endothelial adaptation to gravitational unloading. After 4 and 10 d of exposure to simulated microgravity in the rotating wall vessel, the amount of BCL2 interacting protein 3, a marker of mitophagy, is increased and, in parallel, mitochondrial content, oxygen consumption, and maximal respiratory capacity are reduced, suggesting the acquisition of a thrifty phenotype to meet the novel metabolic challenges generated by gravitational unloading. Moreover, we suggest that microgravity induced-disorganization of the actin cytoskeleton triggers mitophagy, thus creating a connection between cytoskeletal dynamics and mitochondrial content upon gravitational unloading

Shaping and Dilating the Fitness Landscape for Parameter Estimation in Stochastic Biochemical Models

The parameter estimation (PE) of biochemical reactions is one of the most challenging tasks in systems biology given the pivotal role of these kinetic constants in driving the behavior of biochemical systems. PE is a non-convex, multi-modal, and non-separable optimization problem with an unknown fitness landscape; moreover, the quantities of the biochemical species appearing in the system can be low, making biological noise a non-negligible phenomenon and mandating the use of stochastic simulation. Finally, the values of the kinetic parameters typically follow a log-uniform distribution; thus, the optimal solutions are situated in the lowest orders of magnitude of the search space. In this work, we further elaborate on a novel approach to address the PE problem based on a combination of adaptive swarm intelligence and dilation functions (DFs). DFs require prior knowledge of the characteristics of the fitness landscape; therefore, we leverage an alternative solution to evolve optimal DFs. On top of this approach, we introduce surrogate Fourier modeling to simplify the PE, by producing a smoother version of the fitness landscape that excludes the high frequency components of the fitness function. Our results show that the PE exploiting evolved DFs has a performance comparable with that of the PE run with a custom DF. Moreover, surrogate Fourier modeling allows for improving the convergence speed. Finally, we discuss some open problems related to the scalability of our methodology

Patterns among Patients with Chronic Pruritus: A Retrospective Analysis of 170 Patients.

Chronic pruritus profoundly affects patients' quality of life. The objective of this retrospective cross-sectional study was to characterize patients with chronic pruritus and identify patterns, in order to delineate a better diagnostic approach. Both semantic connectivity map and classical analysis were applied, linking demographic, clinical, laboratory and histopathological data with clinical and aetiological categories of 170 patients with chronic pruritus (median age 72 years, 58.2% women). The semantic map showed clinical categories separated in different hubs associated with distinct patterns concerning sex, aetiology, laboratory findings, and pharmacological treatment. Diabetes, diagnosis of cancer and psychiatric comorbidities were linked with certain clinical categories. Skin eosinophilia was a common finding of chronic pruritus, on both diseased and non-diseased skin. High frequencies of patients with chronic pruritus taking anti-arrhythmics, beta-blockers and AT-II receptor antagonists were noticed among those with underlying systemic, neurological and psychiatric diseases. This study provides a complex analysis of chronic pruritus and thus basic principles for a clinical work-up

Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design

© 2019 Elsevier B.V. Computational Intelligence methods, which include Evolutionary Computation and Swarm Intelligence, can efficiently and effectively identify optimal solutions to complex optimization problems by exploiting the cooperative and competitive interplay among their individuals. The exploration and exploitation capabilities of these meta-heuristics are typically assessed by considering well-known suites of benchmark functions, specifically designed for numerical global optimization purposes. However, their performances could drastically change in the case of real-world optimization problems. In this paper, we investigate this issue by considering the Parameter Estimation (PE) of biochemical systems, a common computational problem in the field of Systems Biology. In order to evaluate the effectiveness of various meta-heuristics in solving the PE problem, we compare their performance by considering a set of benchmark functions and a set of synthetic biochemical models characterized by a search space with an increasing number of dimensions. Our results show that some state-of-the-art optimization methods – able to largely outperform the other meta-heuristics on benchmark functions – are characterized by considerably poor performances when applied to the PE problem. We also show that a limiting factor of these optimization methods concerns the representation of the solutions: indeed, by means of a simple semantic transformation, it is possible to turn these algorithms into competitive alternatives. We corroborate this finding by performing the PE of a model of metabolic pathways in red blood cells. Overall, in this work we state that classic benchmark functions cannot be fully representative of all the features that make real-world optimization problems hard to solve. This is the case, in particular, of the PE of biochemical systems. We also show that optimization problems must be carefully analyzed to select an appropriate representation, in order to actually obtain the performance promised by benchmark results

High carotenoid mutants of Chlorella vulgaris show enhanced biomass yield under high irradiance

Microalgae represent a carbon-neutral source of bulk biomass, for extraction of high-value compounds and production of renewable fuels. Due to their high metabolic activity and reproduction rates, species of the genus Chlorella are highly productive when cultivated in photo-bioreactors. However, wild-type strains show biological limitations making algal bioproducts ex-pensive compared to those extracted from other feedstocks. Such constraints include inhomoge-neous light distribution due to high optical density of the culture, and photoinhibition of the sur-face-exposed cells. Thus, the domestication of algal strains for industry makes it increasingly important to select traits aimed at enhancing light-use efficiency while withstanding excess light stress. Carotenoids have a crucial role in protecting against photooxidative damage and, thus, represent a promising target for algal domestication. We applied chemical mutagenesis to Chlorella vulgaris and selected for enhanced tolerance to the carotenoid biosynthesis inhibitor norflurazon. The NFR (norflurazon-resistant) strains showed an increased carotenoid pool size and enhanced tolerance towards photooxidative stress. Growth under excess light revealed an improved carbon assimilation rate of NFR strains with respect to WT. We conclude that domestication of Chlorella vulgaris, by optimizing both carotenoid/chlorophyll ratio and resistance to photooxidative stress, boosted light-to-biomass conversion efficiency under high light conditions typical of photobiore-actors. Comparison with strains previously reported for enhanced tolerance to singlet oxygen, reveals that ROS resistance in Chlorella is promoted by at least two independent mechanisms, only one of which is carotenoid-dependent

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram equalization, bi-histogram equalization, encoding and decoding Gamma transformations, and sigmoid transformations. We show that MedGA considerably outperforms the other approaches in terms of signal and perceived image quality, while preserving the input mean brightness. MedGA may have a significant impact in real healthcare environments, representing an intelligent solution for Clinical Decision Support Systems in radiology practice for image enhancement, to visually assist physicians during their interactive decision-making tasks, as well as for the improvement of downstream automated processing pipelines in clinically useful measurements

Computational Intelligence for Life Sciences

Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences

Dermatological emergencies and determinants of hospitalization in Switzerland: A retrospective study.

BACKGROUND Dermatologic conditions are estimated to account worldwide for approximately 8% of all visits at emergency departments (EDs). Although rarely life-threatening, several dermatologic emergencies may have a high morbidity. Little is known about ED consultations of patients with dermatological emergencies and their subsequent hospital disposal. OBJECTIVE We explore determinants and clinical variables affecting patients' disposal and hospitalization of people attending the ED at a Swiss University Hospital, over a 56-month observational period, for a dermatological problem. METHODS De-identified patients' information was extracted from the hospital electronic medical record system. Generalized estimating equations were used to explore determinants of patient's disposition. RESULTS Out of 5096 consecutive patients with a dermatological main problem evaluated at the ED, 79% of patients were hospitalized after initial assessment. In multivariable analyses, factors which were significantly associated with an increased admission rate included length of ED stay, age ≥ 45 years, male sex, distinct vital signs, high body mass index, low oxygen saturation, admission time in the ED and number and type of dermatological diagnoses. Only 2.2% of the hospitalized patients were admitted to a dermatology ward, despite the fact that they had dermatological diagnoses critically determining the diagnostic related group (DRG) payment. The number of patients managed by dermatologists during in-patient treatment significantly decreased over the study period. CONCLUSIONS Our study identifies a number of independent predictors affecting the risk of hospital admission for patients with dermatological conditions, which may be useful to improve patients' disposal in EDs. The results indicate that the dermatological specialty is becoming increasingly marginalized in the management of patients in the Swiss hospital setting. This trend may have significant implications for the delivery of adequate medical care, outcomes and cost-effectiveness. Dermatologists should be more engaged to better position their specialty and to effectively collaborate with nondermatologists to enhance patient care