Finding an Effective Metric Used for Bijective S-Box Generation by Genetic Algorithms

In cryptography, S-box is a basic component of symmetric key algorithms which performs nonlinear substitution. S-boxes need to be highly nonlinear, so that the cipher can resist linear cryptanalysis. The main criteria for cryptographically strong (n × n) S-box are: • High non linearity; • High algebraic degree; • Balanced structure; • Good auto correlation properties. Our task was to give some suggestions for finding an effective metric used for generation bijective optimal S-Box. Because of the given problem’s complexity, our group considered different approaches and we gave a few suggestions for problem solving

Survival of Yersinia pseudotuberculosis in soil

The dynamics of the pseudotuberculous microbes population number in the soil was monitored with the use of bacteriological method. The number of this microbe increased during the first week to 106 -5x106 CFU/ml, after which it stabilized until the third week at level 106, after which there is a continuous decline in the number of Yersinia pseudotuberculosis until the end of the second month, when their growth stops

Synchronizing inventory and transport within supply chain management

The problem considers synchronized optimization of inventory and transport, and focuses on producer-distributor relations. Particular attention is paid to developing a mathematical model and an optimization problem that can be used to minimize the overall distribution cost by an appropriate placement of warehouses and cross-docking points. Solutions to this problem are explored using genetic algorithms and ideas from graph/network theory. Note: there are three separate reports contained within the uploaded .pdf file

Psychotropic drug concentrations and clinical outcomes in children and adolescents: a systematic review

Introduction: The use of psychotropic drugs in children and adolescents is widespread but associated with suboptimal treatment effects. Therapeutic drug monitoring (TDM) can improve safety of psychotropic drugs in children and adolescents but is not routinely performed. A major reason is that the relationship between drug concentrations and effects is not well known. Areas covered: This systematic review evaluated studies assessing the relationship between psychotropic drug concentrations and clinical outcomes in children and adolescents, including antipsychotics, psychostimulants, alpha-agonists, antidepressants, and mood-stabilizers. PRISMA guidelines were used and a quality assessment of the retrieved studies was performed. Sixty-seven eligible studies involving 24 psychotropic drugs were identified from 9,298 records. The findings were generally heterogeneous and the majority of all retrieved studies were not of sufficient quality. For 11 psychotropic drugs, a relationship between drug concentrations and side-effects and/or effectiveness was evidenced in reasonably reported and executed studies, but these findings were barely replicated. Expert opinion: In order to better support routine TDM in child- and adolescent psychiatry, future work must improve in aspects of study design, execution and reporting to demonstrate drug concentration-effect relationships. The quality criteria proposed in this work can guide future TDM research. Systematic review protocol and registration PROSPERO CRD42018084159

Within-individual phenotypic plasticity in flowers fosters pollination niche shift

Authors thank Raquel Sánchez, Angel Caravante, Isabel Sánchez Almazo, Tatiana López Pérez, Samuel Cantarero, María José Jorquera and Germán Fernández for helping us during several phases of the study and Iván Rodríguez Arós for drawing the insect silhouettes. This research is supported by grants from the Spanish Ministry of Science, Innovation and Universities (CGL2015-71634-P, CGL2015-63827-P, CGL2017-86626-C2-1-P, CGL2017- 86626-C2-2-P, UNGR15-CE-3315, including EU FEDER funds), Junta de Andalucía (P18- FR-3641), Xunta de Galicia (CITACA), BBVA Foundation (PR17_ECO_0021), and a contract grant to C.A. from the former Spanish Ministry of Economy and Competitiveness (RYC-2012-12277). This is a contribution to the Research Unit Modeling Nature, funded by the Consejería de Economía, Conocimiento, Empresas y Universidad, and European Regional Development Fund (ERDF), reference SOMM17/6109/UGR.Phenotypic plasticity, the ability of a genotype of producing different phenotypes when exposed to different environments, may impact ecological interactions. We study here how within-individual plasticity in Moricandia arvensis flowers modifies its pollination niche. During spring, this plant produces large, cross-shaped, UV-reflecting lilac flowers attracting mostly long-tongued large bees. However, unlike most co-occurring species, M. arvensis keeps flowering during the hot, dry summer due to its plasticity in key vegetative traits. Changes in temperature and photoperiod in summer trigger changes in gene expression and the production of small, rounded, UV-absorbing white flowers that attract a different assemblage of generalist pollinators. This shift in pollination niche potentially allows successful reproduction in harsh conditions, facilitating M. arvensis to face anthropogenic perturbations and climate change. Floral phenotypes impact interactions between plants and pollinators. Here, the authors show that Moricandia arvensis displays discrete seasonal plasticity in floral phenotype, with large, lilac flowers attracting long-tongued bees in spring and small, rounded, white flowers attracting generalist pollinators in summer.Spanish Ministry of Science, Innovation and Universities (EU FEDER funds) CGL2015-71634-P CGL2015-63827-P CGL2017-86626-C2-1-P CGL2017-86626-C2-2-P UNGR15-CE-3315Junta de Andalucia P18-FR-3641Xunta de GaliciaBBVA Foundation PR17_ECO_0021Spanish Ministry of Economy and Competitiveness RYC-2012-12277Consejeria de Economia, Conocimiento, Empresas y Universidad SOMM17/6109/UGREuropean Union (EU) SOMM17/6109/UG

Control of Stochastic Gene Expression by Host Factors at the HIV Promoter

The HIV promoter within the viral long terminal repeat (LTR) orchestrates many aspects of the viral life cycle, from the dynamics of viral gene expression and replication to the establishment of a latent state. In particular, after viral integration into the host genome, stochastic fluctuations in viral gene expression amplified by the Tat positive feedback loop can contribute to the formation of either a productive, transactivated state or an inactive state. In a significant fraction of cells harboring an integrated copy of the HIV-1 model provirus (LTR-GFP-IRES-Tat), this bimodal gene expression profile is dynamic, as cells spontaneously and continuously flip between active (Bright) and inactive (Off) expression modes. Furthermore, these switching dynamics may contribute to the establishment and maintenance of proviral latency, because after viral integration long delays in gene expression can occur before viral transactivation. The HIV-1 promoter contains cis-acting Sp1 and NF-κB elements that regulate gene expression via the recruitment of both activating and repressing complexes. We hypothesized that interplay in the recruitment of such positive and negative factors could modulate the stability of the Bright and Off modes and thereby alter the sensitivity of viral gene expression to stochastic fluctuations in the Tat feedback loop. Using model lentivirus variants with mutations introduced in the Sp1 and NF-κB elements, we employed flow cytometry, mRNA quantification, pharmacological perturbations, and chromatin immunoprecipitation to reveal significant functional differences in contributions of each site to viral gene regulation. Specifically, the Sp1 sites apparently stabilize both the Bright and the Off states, such that their mutation promotes noisy gene expression and reduction in the regulation of histone acetylation and deacetylation. Furthermore, the NF-κB sites exhibit distinct properties, with κB site I serving a stronger activating role than κB site II. Moreover, Sp1 site III plays a particularly important role in the recruitment of both p300 and RelA to the promoter. Finally, analysis of 362 clonal cell populations infected with the viral variants revealed that mutations in any of the Sp1 sites yield a 6-fold higher frequency of clonal bifurcation compared to that of the wild-type promoter. Thus, each Sp1 and NF-κB site differentially contributes to the regulation of viral gene expression, and Sp1 sites functionally “dampen” transcriptional noise and thereby modulate the frequency and maintenance of this model of viral latency. These results may have biomedical implications for the treatment of HIV latency

Molecular control of HIV-1 postintegration latency: implications for the development of new therapeutic strategies

The persistence of HIV-1 latent reservoirs represents a major barrier to virus eradication in infected patients under HAART since interruption of the treatment inevitably leads to a rebound of plasma viremia. Latency establishes early after infection notably (but not only) in resting memory CD4+ T cells and involves numerous host and viral trans-acting proteins, as well as processes such as transcriptional interference, RNA silencing, epigenetic modifications and chromatin organization. In order to eliminate latent reservoirs, new strategies are envisaged and consist of reactivating HIV-1 transcription in latently-infected cells, while maintaining HAART in order to prevent de novo infection. The difficulty lies in the fact that a single residual latently-infected cell can in theory rekindle the infection. Here, we review our current understanding of the molecular mechanisms involved in the establishment and maintenance of HIV-1 latency and in the transcriptional reactivation from latency. We highlight the potential of new therapeutic strategies based on this understanding of latency. Combinations of various compounds used simultaneously allow for the targeting of transcriptional repression at multiple levels and can facilitate the escape from latency and the clearance of viral reservoirs. We describe the current advantages and limitations of immune T-cell activators, inducers of the NF-κB signaling pathway, and inhibitors of deacetylases and histone- and DNA- methyltransferases, used alone or in combinations. While a solution will not be achieved by tomorrow, the battle against HIV-1 latent reservoirs is well- underway

Using PPI Networks in Hierarchical Multi-label Classification Trees for Gene Function Prediction

Motivation: Catalogs, such as Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically (general functions include more specific functions). This has recently motivated the development of several machine learning algorithms under the assumption that instances may belong to multiple hierarchy organized classes. Besides relationships among classes, it is also possible to identify relationships among examples. Although such relationships have been identified and extensively studied in the in the area of protein-to-protein interaction (PPI) networks, they have not received much attention in hierarchical protein function prediction. The use of such relationships between genes introduces autocorrelation and violates the assumption that instances are independently and identically distributed, which underlines most machine learning algorithms. While this consideration introduces additional complexity to the learning process, we expect it would also carry substantial benefits. Results: This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). The empirical evaluation of the proposed algorithm, called NHMC, on 24 yeast datasets using MIPSFUN and GO annotations and exploiting three different PPI networks, clearly shows that taking autocorrelation into account improves performance. Conclusions: Our results suggest that explicitly taking network autocorrelation into account increases the predictive capability of the models, especially when the underlying PPI network is dense. Furthermore, NHMC can be used as a tool to assess network data and the information it provides with respect to the gene function

Learning Hierarchical Multi-label Classification Trees from Network Data

We present an algorithm for hierarchical multi-label classifi- cation (HMC) in a network context. It is able to classify instances that may belong to multiple classes at the same time and consider the hierar- chical organization of the classes. It assumes that the instances are placed in a network and uses information on the network connections during the learning of the predictive model. Many real world prediction problems have classes that are organized hierarchically and instances that can have pairwise connections. One example is web document classification, where topics (classes) are typically organized into a hierarchy and documents are connected by hyperlinks. Another example, which is considered in this paper, is gene/protein function prediction, where genes/proteins are connected and form protein-to-protein interaction (PPI) networks. Net- work datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. Combining the hierarchical multi-label classification task with network prediction is thus not trivial and re- quires the introduction of the new concept of network autocorrelation for HMC. The proposed algorithm is able to profitably exploit network autocorrelation when learning a tree-based prediction model for HMC. The learned model is in the form of a Predictive Clustering Tree (PCT) and predicts multiple (hierarchically organized) labels at the leaves. Ex- periments show the effectiveness of the proposed approach for different problems of gene function prediction, considering different PPI networks. The results show that different networks introduce different benefits in different problems of gene function prediction

Global and Local Spatial Autocorrelation in Predictive Clustering Trees

Spatial autocorrelation is the correlation among data values, strictly due to the relative location proximity of the objects that the data refer to. This statistical property clearly indicates a violation of the assumption of observation independence - a pre-condition assumed by most of the data mining and statistical models. Inappropriate treatment of data with spatial dependencies could obfuscate important insights when spatial autocorrelation is ignored. In this paper, we propose a data mining method that explicitly considers autocorrelation when building the models. The method is based on the concept of predictive clustering trees (PCTs). The proposed approach combines the possibility of capturing both global and local effects and dealing with positive spatial autocorrelation. The discovered models adapt to local properties of the data, providing at the same time spatially smoothed predictions. Results show the effectiveness of the proposed solution