Convergence of Minima of Integral Functionals, with Applications to Optimal Control and Stochastic Optimization

Epi-convergence of integral functionals is derived under new conditions that can be used in the infinite dimensional case. Applications include: the convergence of the solutions of approximating optimal control problems and of stochastic optimization problems

Every animal matters! Evaluating the selectivity of a Mediterranean bottom trawl fishery from a species community perspective

Bottom trawl fisheries often catch several species simultaneously. However, most studies addressing the catch performance and selectivity of a specific trawl focus on a few commercially important or most vulnerable species requiring management measures. By contrast, the present study considers the multispecies nature of Mediterranean bottom trawl fisheries through a holistic approach that accounts for the full species community in the catches. Specifically, we evaluated and compared the catch performance of the two codends allowed for this fishery, made of 40 mm square (SM40) and 50 mm diamond (DM50) meshes. Results showed that 50 and 80% of the catch in weight and count numbers, respectively, consisted of species without commercial value, demonstrating that large proportions of the catch are not considered when using the existing approach to evaluate the ecological impact of the fishing activity. Significant differences in catch profiles between the two codends were observed, especially for two commercial flatfish species, Arnoglossus laterna and Citharus linguatula, with larger contributions in the SM40. Further, the SM40 codend had a significantly higher retention, compared to DM50 codend, for specific sizes of Merluccius merluccius and Mullus barbatus. The outcomes of the study can be useful for the Mediterranean bottom trawl fisheries management

A multidisciplinary approach to study the reproductive biology of wild prawns

This work aims to provide deeper knowledge on reproductive biology of P. kerathurus in a multidisciplinary way. Upon 789 examined females, 285 were found inseminated. The logistic equation enabled to estimate the size at first maturity at 30.7 mm CL for female. The Gono-Somatic Index (GSI) showed a pronounced seasonality, ranged from 0.80 ± 0.34 to 11.24 ± 5.72. Histological analysis highlighted five stages of ovarian development. Gonadal fatty acids analysis performed with gas chromatograph evidenced a pronounced seasonal variation; total lipids varied from 1.7% dry weight (dw) in Winter, to 7.2% dw in Summer. For the first time, a chemometric approach (Principal Component Analysis) was applied to relate GSI with total lipid content and fatty acid composition of gonads. The first two components (PC1 and PC2) showed that seasonality explained about 84% of the variability of all data set. In particular, in the period February-May, lipids were characterized by high PUFAs content, that were probably utilized during embryogenesis as energy source and as constituent of the cell membranes. During the summer season, gonads accumulated saturated FAs, that will be used during embryogenesis and early larval stages, while in the cold season total lipids decreased drastically and the gonad reached a quiescent state

Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study

BACKGROUND: Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA).METHODS: Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting "medical" morbidity leading to LOS &gt; 4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014-2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performances were analyzed using precision, area under receiver operating (AUROC) and precision recall curves (AUPRC), as well as the Mathews Correlation Coefficient. Variable importance was analyzed using Shapley Additive Explanations values.RESULTS: Using a threshold of 20% "risk-patients" (n:782), precision, AUROC and AUPRC were 13.6%, 76.3% and 15.5% vs. 12.4%, 74.7% and 15.6% for the machine-learning and logistic regression model, respectively. The parsimonious machine-learning model performed better than the full logistic regression model. Of the top ten variables, eight were shared between the machine-learning and logistic regression models, but with a considerable age-related variation in importance of specific types of medication.CONCLUSION: A machine-learning model using preoperative characteristics and prescriptions slightly improved identification of patients in high-risk of "medical" complications after fast-track THA and TKA compared to a logistic regression model. Such algorithms could help find a manageable population of patients who may benefit most from intensified perioperative care.</p

Solving ill-posed bilevel programs

This paper deals with ill-posed bilevel programs, i.e., problems admitting multiple lower-level solutions for some upper-level parameters. Many publications have been devoted to the standard optimistic case of this problem, where the difficulty is essentially moved from the objective function to the feasible set. This new problem is simpler but there is no guaranty to obtain local optimal solutions for the original optimistic problem by this process. Considering the intrinsic non-convexity of bilevel programs, computing local optimal solutions is the best one can hope to get in most cases. To achieve this goal, we start by establishing an equivalence between the original optimistic problem an a certain set-valued optimization problem. Next, we develop optimality conditions for the latter problem and show that they generalize all the results currently known in the literature on optimistic bilevel optimization. Our approach is then extended to multiobjective bilevel optimization, and completely new results are derived for problems with vector-valued upper- and lower-level objective functions. Numerical implementations of the results of this paper are provided on some examples, in order to demonstrate how the original optimistic problem can be solved in practice, by means of a special set-valued optimization problem

The polymyxin B-induced transcriptomic response of a clinical, multidrug-resistant Klebsiella pneumoniae involves multiple regulatory elements and intracellular targets

Background: The emergence of multidrug-resistant Klebsiella pneumoniae is a major public health concern. Many K. pneumoniae infections can only be treated when resorting to last-line drugs such as polymyxin B (PB). However, resistance to this antibiotic is also observed, although insufficient information is described on its mode of action as well as the mechanisms used by resistant bacteria to evade its effects. We aimed to study PB resistance and the influence of abiotic stresses in a clinical K. pneumoniae strain using whole transcriptome profiling. Results: We sequenced 12 cDNA libraries of K. pneumoniae Kp13 bacteria, from two biological replicates of the original strain Kp13 (Kp13) and five derivative strains: induced high-level PB resistance in acidic pH (Kp13(pH)), magnesium deprivation (Kp13(Mg)), high concentrations of calcium (Kp13(Ca)) and iron (Kp13(Fe)), and a control condition with PB (Kp13(PolB)). Our results show the involvement of multiple regulatory loci that differentially respond to each condition as well as a shared gene expression response elicited by PB treatment, and indicate the participation of two-regulatory components such as ArcA-ArcB, which could be involved in re-routing the K. pneumoniae metabolism following PB treatment. Modules of co-expressed genes could be determined, which correlated to growth in acid stress and PB exposure. We hypothesize that polymyxin B induces metabolic shifts in K. pneumoniae that could relate to surviving against the action of this antibiotic. Conclusions: We obtained whole transcriptome data for K. pneumoniae under different environmental conditions and PB treatment. Our results supports the notion that the K. pneumoniae response to PB exposure goes beyond damaged membrane reconstruction and involves recruitment of multiple gene modules and intracellular targets.Fundacao de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ)Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)Lab Nacl Comp Cient, Petropolis, RJ, BrazilFiocruz MS, Ctr Pesquisas Goncalo Moniz, Salvador, BA, BrazilUniv Fed Sao Paulo, Escola Paulista Med, Dept Internal Med, Lab Alerta,Div Infect Dis, Sao Paulo, SP, BrazilUniv Catolica Cordoba, Fac Ingn, CONICET, Cordoba, ArgentinaUniv Fed Sao Paulo, Escola Paulista Med, Dept Internal Med, Lab Alerta,Div Infect Dis, Sao Paulo, SP, BrazilFAPERJ: E-26/110.315/2014FAPESP: 2010/12891-9CAPES: 23038.010041/2013-13Web of Scienc