Simplified tabu search with random-based searches for bound constrained global optimization

This paper proposes a simplified version of the tabu search algorithm that solely uses randomly generated direction vectors in the exploration and intensification search procedures, in order to define a set of trial points while searching in the neighborhood of a given point. In the diversification procedure, points that are inside any already visited region with a relative small visited frequency may be accepted, apart from those that are outside the visited regions. The produced numerical results show the robustness of the proposed method. Its efficiency when compared to other known metaheuristics available in the literature is encouraging.FCT - Fundação para a Ciência e a Tecnologia(UIDB/00013/2020); FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, UIDB/00013/2020 and UIDP/00013/2020 of CMAT-UM

Legitimacy in REDD+ governance in Indonesia

This paper addresses the question of legitimacy in REDD+ governance in Indonesia. It develops a legitimacy framework that builds on elements of Scharpf (J Eur Pub Policy 4(1):18–36, 1997) input and output legitimacy concept and the political economy lens described by Brockhaus and Angelsen (Analysing REDD+: Challenges and choices, CIFOR, Bogor, 2012). Using data collected through key informant interviews and focus groups, we identify and explore stakeholder perceptions of legitimacy. The analysis reveals a complex interplay between input and output legitimacy, finding that state, non-state and hybrid actors perceive output legitimacy (i.e. project outcomes) as highly dependent on the level of input legitimacy achieved during the governance process. Non-state actors perceive proxies for input legitimacy, such as participation and inclusion of local people, as goals in themselves. In the main, they perceive inclusion to be integral to the empowerment of local people. They perceive output legitimacy as less important because of the intangibility of REDD+ outcomes at this stage in the process. The findings also highlight the challenges associated with measuring the legitimacy of REDD+ governance in Indonesia

Combining machine learning and metaheuristics algorithms for classification method PROAFTN

© Crown 2019. The supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive learning techniques can improve performances of the PROAFTN classifier. The improved PROAFTN classifier is described and compared to well known classifiers, in terms of their learning methodology and classification accuracy. Through this chapter we have shown the ability of the metaheuristics when embedded to PROAFTN method to solve efficiency the classification problems

Delayed Recovery of Skeletal Muscle Mass following Hindlimb Immobilization in mTOR Heterozygous Mice

The present study addressed the hypothesis that reducing mTOR, as seen in mTOR heterozygous (+/−) mice, would exaggerate the changes in protein synthesis and degradation observed during hindlimb immobilization as well as impair normal muscle regrowth during the recovery period. Atrophy was produced by unilateral hindlimb immobilization and data compared to the contralateral gastrocnemius. In wild-type (WT) mice, the gradual loss of muscle mass plateaued by day 7. This response was associated with a reduction in basal protein synthesis and development of leucine resistance. Proteasome activity was consistently elevated, but atrogin-1 and MuRF1 mRNAs were only transiently increased returning to basal values by day 7. When assessed 7 days after immobilization, the decreased muscle mass and protein synthesis and increased proteasome activity did not differ between WT and mTOR+/− mice. Moreover, the muscle inflammatory cytokine response did not differ between groups. After 10 days of recovery, WT mice showed no decrement in muscle mass, and this accretion resulted from a sustained increase in protein synthesis and a normalization of proteasome activity. In contrast, mTOR+/− mice failed to fully replete muscle mass at this time, a defect caused by the lack of a compensatory increase in protein synthesis. The delayed muscle regrowth of the previously immobilized muscle in the mTOR+/− mice was associated with a decreased raptor•4EBP1 and increased raptor•Deptor binding. Slowed regrowth was also associated with a sustained inflammatory response (e.g., increased TNFα and CD45 mRNA) during the recovery period and a failure of IGF-I to increase as in WT mice. These data suggest mTOR is relatively more important in regulating the accretion of muscle mass during recovery than the loss of muscle during the atrophy phase, and that protein synthesis is more sensitive than degradation to the reduction in mTOR during muscle regrowth

A Network Analysis of the Human T-Cell Activation Gene Network Identifies Jagged1 as a Therapeutic Target for Autoimmune Diseases

Understanding complex diseases will benefit the recognition of the properties of the gene networks that control biological functions. Here, we set out to model the gene network that controls T-cell activation in humans, which is critical for the development of autoimmune diseases such as Multiple Sclerosis (MS). The network was established on the basis of the quantitative expression from 104 individuals of 20 genes of the immune system, as well as on biological information from the Ingenuity database and Bayesian inference. Of the 31 links (gene interactions) identified in the network, 18 were identified in the Ingenuity database and 13 were new and we validated 7 of 8 interactions experimentally. In the MS patients network, we found an increase in the weight of gene interactions related to Th1 function and a decrease in those related to Treg and Th2 function. Indeed, we found that IFN-ß therapy induces changes in gene interactions related to T cell proliferation and adhesion, although these gene interactions were not restored to levels similar to controls. Finally, we identify JAG1 as a new therapeutic target whose differential behaviour in the MS network was not modified by immunomodulatory therapy. In vitro treatment with a Jagged1 agonist peptide modulated the T-cell activation network in PBMCs from patients with MS. Moreover, treatment of mice with experimental autoimmune encephalomyelitis with the Jagged1 agonist ameliorated the disease course, and modulated Th2, Th1 and Treg function. This study illustrates how network analysis can predict therapeutic targets for immune intervention and identified the immunomodulatory properties of Jagged1 making it a new therapeutic target for MS and other autoimmune diseases