Exploring the Higgs Portal with 10/fb at the LHC

We consider the impact of new exotic colored and/or charged matter interacting through the Higgs portal on Standard Model Higgs boson searches at the LHC. Such Higgs portal couplings can induce shifts in the effective Higgs-gluon-gluon and Higgs-photon-photon couplings, thus modifying the Higgs production and decay patterns. We consider two possible interpretations of the current LHC Higgs searches based on ~ 5/fb of data at each detector: 1) a Higgs boson in the mass range (124-126) GeV and 2) a `hidden' heavy Higgs boson which is underproduced due to the suppression of its gluon fusion production cross section. We first perform a model independent analysis of the allowed sizes of such shifts in light of the current LHC data. As a class of possible candidates for new physics which gives rise to such shifts, we investigate the effects of new scalar multiplets charged under the Standard Model gauge symmetries. We determine the scalar parameter space that is allowed by current LHC Higgs searches, and compare with complementary LHC searches that are sensitive to the direct production of colored scalar states.Comment: 27 pages, 11 figures; v2: references added, correction to scalar form factor, numerical results updated with Moriond 2012 data, conclusions unchange

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

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

MSR32 COVID-19 Beds’ Occupancy and Hospital Complaints: A Predictive Model

Objectives COVID-19 pandemic limited the number of patients that could be promptly and adequately taken in charge. The proposed research aims at predicting the number of patients requiring any type of hospitalizations, considering not only patients affected by COVID-19, but also other severe viral diseases, including untreated chronic and frail patients, and also oncological ones, to estimate potential hospital lawsuits and complaints. Methods An unsupervised learning approach of artificial neural network’s called Self-Organizing Maps (SOM), grounding on the prediction of the existence of specific clusters and useful to predict hospital behavioral changes, has been designed to forecast the hospital beds’ occupancy, using pre and post COVID-19 time-series, and supporting the prompt prediction of litigations and potential lawsuits, so that hospital managers and public institutions could perform an impacts’ analysis to decide whether to invest resources to increase or allocate differentially hospital beds and humans capacity. Data came from the UK National Health Service (NHS) statistic and digital portals, concerning a 4-year time horizon, related to 2 pre and 2 post COVID-19 years. Results Clusters revealed two principal behaviors in selecting the resources allocation. In case of increase of non-COVID hospitalized patients, a reduction in the number of complaints (-55%) emerged. A higher number of complaints was registered (+17%) against a considerable reduction in the number of beds occupied (-26%). Based on the above, the management of hospital beds is a crucial factor which can influence the complaints trend. Conclusions The model could significantly support in the management of hospital capacity, helping decision-makers in taking rational decisions under conditions of uncertainty. In addition, this model is highly replicable also in the estimation of current hospital beds, healthcare professionals, equipment, and other resources, extremely scarce during emergency or pandemic crises, being able to be adapted for different local and national settings

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

Developmental and evolutionary assumptions in a study about the impact of premature birth and low income on mother–infant interaction

In order to study the impact of premature birth and low income on mother–infant interaction, four Portuguese samples were gathered: full-term, middle-class (n=99); premature, middle-class (n=63); full-term, low income (n=22); and premature, low income (n=21). Infants were filmed in a free play situation with their mothers, and the results were scored using the CARE Index. By means of multinomial regression analysis, social economic status (SES) was found to be the best predictor of maternal sensitivity and infant cooperative behavior within a set of medical and social factors. Contrary to the expectations of the cumulative risk perspective, two factors of risk (premature birth together with low SES) were as negative for mother–infant interaction as low SES solely. In this study, as previous studies have shown, maternal sensitivity and infant cooperative behavior were highly correlated, as was maternal control with infant compliance. Our results further indicate that, when maternal lack of responsiveness is high, the infant displays passive behavior, whereas when the maternal lack of responsiveness is medium, the infant displays difficult behavior. Indeed, our findings suggest that, in these cases, the link between types of maternal and infant interactive behavior is more dependent on the degree of maternal lack of responsiveness than it is on birth status or SES. The results will be discussed under a developmental and evolutionary reasonin

Perspectives on utilization of edible coatings and nano-laminate coatings for extension of postharvest storage of fruits and vegetables

It is known that in developing countries, a large quantity of fruit and vegetable losses results at postharvest and processing stages due to poor or scarce storage technology and mishandling during harvest. The use of new and innovative technologies for reducing postharvest losses is a requirement that has not been fully covered. The use of edible coatings (mainly based on biopolymers) as a postharvest technique for agricultural commodities has offered biodegradable alternatives in order to solve problems (e.g., microbiological growth) during produce storage. However, biopolymer-based coatings can present some disadvantages such as: poor mechanical properties (e.g., lipids) or poor water vapor barrier properties (e.g., polysaccharides), thus requiring the development of new alternatives to solve these drawbacks. Recently, nanotechnology has emerged as a promising tool in the food processing industry, providing new insights about postharvest technologies on produce storage. Nanotechnological approaches can contribute through the design of functional packing materials with lower amounts of bioactive ingredients, better gas and mechanical properties and with reduced impact on the sensorial qualities of the fruits and vegetables. This work reviews some of the main factors involved in postharvest losses and new technologies for extension of postharvest storage of fruits and vegetables, focused on perspective uses of edible coatings and nano-laminate coatings.María L. Flores-López thanks Mexican Science and Technology Council (CONACYT, Mexico) for PhD fellowship support (CONACYT Grant Number: 215499/310847). Miguel A. Cerqueira (SFRH/BPD/72753/2010) is recipient of a fellowship from the Fundação para a Ciência e Tecnologia (FCT, POPH-QREN and FSE Portugal). The authors also thank the FCT Strategic Project of UID/ BIO/04469/2013 unit, the project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and the project ‘‘BioInd Biotechnology and Bioengineering for improved Industrial and AgroFood processes,’’ REF. NORTE-07-0124-FEDER-000028 Co-funded by the Programa Operacional Regional do Norte (ON.2 – O Novo Norte), QREN, FEDER. Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico – FUNCAP, CE Brazil (CI10080-00055.01.00/13)

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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Piecewise polynomial approximation of probability density functions with application to uncertainty quantification for stochastic PDEs

The probability density function (PDF) associated with a given set of samples is approximated by a piecewise-linear polynomial constructed with respect to a binning of the sample space. The kernel functions are a compactly supported basis for the space of such polynomials, i.e. finite element hat functions, that are centered at the bin nodes rather than at the samples, as is the case for the standard kernel density estimation approach. This feature naturally provides an approximation that is scalable with respect to the sample size. On the other hand, unlike other strategies that use a finite element approach, the proposed approximation does not require the solution of a linear system. In addition, a simple rule that relates the bin size to the sample size eliminates the need for bandwidth selection procedures. The proposed density estimator has unitary integral, does not require a constraint to enforce positivity, and is consistent. The proposed approach is validated through numerical examples in which samples are drawn from known PDFs. The approach is also used to determine approximations of (unknown) PDFs associated with outputs of interest that depend on the solution of a stochastic partial differential equation