Efficient Minimum Flow Decomposition via Integer Linear Programming

Extended version of RECOMB 2022 paperMinimum flow decomposition (MFD) is an NP-hard problem asking to decompose a network flow into a minimum set of paths (together with associated weights). Variants of it are powerful models in multiassembly problems in Bioinformatics, such as RNA assembly. Owing to its hardness, practical multiassembly tools either use heuristics or solve simpler, polynomial time-solvable versions of the problem, which may yield solutions that are not minimal or do not perfectly decompose the flow. Here, we provide the first fast and exact solver for MFD on acyclic flow networks, based on Integer Linear Programming (ILP). Key to our approach is an encoding of all the exponentially many solution paths using only a quadratic number of variables. We also extend our ILP formulation to many practical variants, such as incorporating longer or paired-end reads, or minimizing flow errors. On both simulated and real-flow splicing graphs, our approach solves any instance inPeer reviewe

Reducing Dimensionality to Improve Search in Semantic Genetic Programming

Genetic programming approaches are moving from analysing the syntax of individual solutions to look into their semantics. One of the common definitions of the semantic space in the context of symbolic regression is a n-dimensional space, where n corresponds to the number of training examples. In problems where this number is high, the search process can became harder as the number of dimensions increase. Geometric semantic genetic programming (GSGP) explores the semantic space by performing geometric semantic operations—the fitness landscape seen by GSGP is guaranteed to be conic by construction. Intuitively, a lower number of dimensions can make search more feasible in this scenario, decreasing the chances of data overfitting and reducing the number of evaluations required to find a suitable solution. This paper proposes two approaches for dimensionality reduction in GSGP: (i) to apply current instance selection methods as a pre-process step before training points are given to GSGP; (ii) to incorporate instance selection to the evolution of GSGP. Experiments in 15 datasets show that GSGP performance is improved by using instance reduction during the evolution

Contribution to the knowledge of the genus proceratium roger (Hymenoptera: Formicidae: Proceratiinae) in the new world

The genus Proceratium Roger comprises rare ants that are irregularly distributed in tropical and temperate regions of the world. Despite this global distribution, these ants are rarely collected, likely due to their cryptobiotic lifestyle. In the New World, the genus comprises 22 known species distributed from Southern Canada to the South of Brazil, and in some Caribbean islands. The taxonomy of the genus Proceratium is here updated for South America. We describe P. amazonicum sp. nov, from Rondônia state and provide distribution data for P. brasiliense, P. convexipes, and P. silaceum. We also present, for the first time, high-resolution images of the P. colombicum type and P. ecuadoriense, and provide a new record of P. micrommatum from Peru, and comment about its morphological variation and distribution. A key for the workers of the P. micrommatum clade is also provided. The species we describe belongs to P. micrommatum clade and represents the second species recorded from Brazil after 60 years, since only P. brasiliense was known previously in the country. © 2019 Universidade Estadual de Feira de Santana. All rights reserved

Generalized Central Limit Theorem and Renormalization Group

We introduce a simple instance of the renormalization group transformation in the Banach space of probability densities. By changing the scaling of the renormalized variables we obtain, as fixed points of the transformation, the L\'evy strictly stable laws. We also investigate the behavior of the transformation around these fixed points and the domain of attraction for different values of the scaling parameter. The physical interest of a renormalization group approach to the generalized central limit theorem is discussed.Comment: 16 pages, to appear in J. Stat. Phy

Gathering patients and rheumatologists' perceptions to improve outcomes in idiopathic inflammatory myopathies

Objective: Therapeutic targets in Idiopathic Inflammatory Myopathies (IIM) are based on the opinions of physicians/specialists, which may not reflect the main concerns of patients. The authors, therefore, assessed the outcome concerns of patients with IIM and compared them with the concerns of rheumatologists in order to develop an IIM outcome standard set. Methods: Ninety-three IIM patients, 51 rheumatologists, and one physiotherapist were invited to participate. An open questionnaire was initially applied. The top 10 answers were selected and applied in a multiple-choice questionnaire, inquiring about the top 3 major concerns. Answers were compared, and the agreement rate was calculated. Concerns were gathered in an IIM outcome standard set with validated measures. Results: The top three outcome concerns raised by patients were medication side effects/muscle weakness/prevention functionality loss. The top three concerns among rheumatologists were to prevent loss of functionality/to ensure the quality of life/to achieve disease remission. Other's outcomes concerns only pointed out by patients were muscle pain/diffuse pain/skin lesions/fatigue. The agreement rate between both groups was 41%. Assessment of these parameters guided the development of an IIM standard set which included Myositis Disease Activity Assessment Visual Analogue Scale/Manual Muscle Testing/fatigue and pain Global Visual Analogue Scale/Health Assessment Questionnaire/level of physical activity. Conclusion: The authors propose a novel standard set to be pursued in IIM routine follow-up, which includes not only the main patients/rheumatologist outcome concerns but also additional important outcomes only indicated by patients. Future studies are necessary to confirm if this comprehensive approach will result in improved adherence and ultimately in better assistance

A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification

Feature selection is a widespread preprocessing step in the data mining field. One of its purposes is to reduce the number of original dataset features to improve a predictive model’s performance. Despite the benefits of feature selection for the classification task, to the best of our knowledge, few studies in the literature address feature selection for the hierarchical classification context. This paper proposes a novel feature selection method based on the general variable neighborhood search metaheuristic, combining a filter and a wrapper step, wherein a global model hierarchical classifier evaluates feature subsets. We used twelve datasets from the proteins and images domains to perform computational experiments to validate the effect of the proposed algorithm on classification performance when using two global hierarchical classifiers proposed in the literature. Statistical tests showed that using our method for feature selection led to predictive performances that were consistently better than or equivalent to that obtained by using all features with the benefit of reducing the number of features needed, which justifies its efficiency for the hierarchical classification scenario

Presupernova Structure of Massive Stars

Issues concerning the structure and evolution of core collapse progenitor stars are discussed with an emphasis on interior evolution. We describe a program designed to investigate the transport and mixing processes associated with stellar turbulence, arguably the greatest source of uncertainty in progenitor structure, besides mass loss, at the time of core collapse. An effort to use precision observations of stellar parameters to constrain theoretical modeling is also described.Comment: Proceedings for invited talk at High Energy Density Laboratory Astrophysics conference, Caltech, March 2010. Special issue of Astrophysics and Space Science, submitted for peer review: 7 pages, 3 figure

Functional rarity and evenness are key facets of biodiversity to boost multifunctionality

The functional traits of organisms within multispecies assemblages regulate biodiversity effects on ecosystem functioning. Yet how traits should assemble to boost multiple ecosystem functions simultaneously (multifunctionality) remains poorly explored. In a multibiome litter experiment covering most of the global variation in leaf trait spectra, we showed that three dimensions of functional diversity (dispersion, rarity, and evenness) explained up to 66% of variations in multifunctionality, although the dominant species and their traits remained an important predictor. While high dispersion impeded multifunctionality, increasing the evenness among functionally dissimilar species was a key dimension to promote higher multifunctionality and to reduce the abundance of plant pathogens. Because too-dissimilar species could have negative effects on ecosystems, our results highlight the need for not only diverse but also functionally even assemblages to promote multifunctionality. The effect of functionally rare species strongly shifted from positive to negative depending on their trait differences with the dominant species. Simultaneously managing the dispersion, evenness, and rarity in multispecies assemblages could be used to design assemblages aimed at maximizing multifunctionality independently of the biome, the identity of dominant species, or the range of trait values considered. Functional evenness and rarity offer promise to improve the management of terrestrial ecosystems and to limit plant disease risks.This work was funded by the British Ecological Society (SR17\1297 grant, PI: P.G.-P.) and by the European Research Council (ERC Grant Agreement #647038, BIODESERT, PI: F.T.M.). Y.L.B.-P. was supported by a Marie Sklodowska-Curie Actions Individual Fellowship within the European Program Horizon 2020 (DRYFUN Project #656035). H.S. was supported by a Juan de la Cierva-Formación grant from the Spanish Ministry of Economy and Competitiveness (FJCI-2015-26782). F.T.M. and S.A. were supported from the Generalitat Valenciana (CIDEGENT/2018/041). M.D. was supported by a Formación del Profesorado Universitario (FPU) fellowship from the Spanish Ministry of Education, Culture and Sports (FPU-15/00392). S.A. was supported by the Spanish MINECO for financial support via the DIGGING_DEEPER project through the 2015 to 2016 BiodivERsA3/FACCE‐JPI joint call for research proposals. B.K.S. research on biodiversity-ecosystem functions was supported by the Australian Research Council (DP170104634 and DP190103714). P.G.-P. was supported by a Ramón y Cajal grant from the Spanish Ministry of Science and Innovation (RYC2018-024766-I). R.M. was supported by MINECO (Grants CGL2014-56567-R and CGL2017-83855-R)