Multi-Objective Optimization Based on Desirability Estimation of Several Interrelated Responses (MOOp-DESIRe): A Computer-Aided Methodology for Multi-Criteria Drug Discovery

Doutoramento em Ciências FarmacêuticasPhD in Pharmaceutical Science

Predicting Proteome-Early Drug Induced Cardiac Toxicity Relationships (Pro-EDICToRs) with Node Overlapping Parameters (NOPs) of a new class of Blood Mass-Spectra graphs

The 11th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryBlood Serum Proteome-Mass Spectra (SP-MS) may allow detecting Proteome-Early Drug Induced Cardiac Toxicity Relationships (called here Pro-EDICToRs). However, due to the thousands of proteins in the SP identifying general Pro-EDICToRs patterns instead of a single protein marker may represents a more realistic alternative. In this sense, first we introduced a novel Cartesian 2D spectrum graph for SP-MS. Next, we introduced the graph node-overlapping parameters (nopk) to numerically characterize SP-MS using them as inputs to seek a Quantitative Proteome-Toxicity Relationship (QPTR) classifier for Pro-EDICToRs with accuracy higher than 80%. Principal Component Analysis (PCA) on the nopk values present in the QPTR model explains with one factor (F1) the 82.7% of variance. Next, these nopk values were used to construct by the first time a Pro-EDICToRs Complex Network having nodes (samples) linked by edges (similarity between two samples). We compared the topology of two sub-networks (cardiac toxicity and control samples); finding extreme relative differences for the re-linking (P) and Zagreb (M2) indices (9.5 and 54.2 % respectively) out of 11 parameters. We also compared subnetworks with well known ideal random networks including Barabasi-Albert, Kleinberg Small World, Erdos-Renyi, and Epsstein Power Law models. Finally, we proposed Partial Order (PO) schemes of the 115 samples based on LDA-probabilities, F1-scores and/or network node degrees. PCA-CN and LDA-PCA based POs with Tanimoto’s coefficients equal or higher than 0.75 are promising for the study of Pro-EDICToRs. These results shows that simple QPTRs models based on MS graph numerical parameters are an interesting tool for proteome researchThe authors thank projects funded by the Xunta de Galicia (PXIB20304PR and BTF20302PR) and the Ministerio de Sanidad y Consumo (PI061457). González-Díaz H. acknowledges tenure track research position funded by the Program Isidro Parga Pondal, Xunta de Galici

La acción pública de la central campesina independiente (CCI), de la masculinización a la feminización

El trabajo que presentamos a la consideración académica contiene un punto de vista distinto acerca de las organizaciones campesinas, debido a que algunos investigadores de las organizaciones campesinas vinculadas con el Estado, han tratado de entender la acción de éstas a través del concepto de corporativismo.1 Si bien, esto ayuda a entender la simbiosis Estadosociedad civil rural, no permite captar de qué manera las organizaciones campesinas realizan cotidianamente esa relación simbiótica. Es por esa razón que hemos preferido cambiar el enfoque para observar a la organización campesina como un sujeto de acción social. En tal sentido, nuestro propósito es estudiar de qué manera tiene lugar la acción pública en una organización campesina particular como es el caso de la Central Campesina Independiente (CCI), y a la cual hemos conocido poco después de realizar nuestras prácticas de servicio social. A tal fin hemos retomado uno de los conceptos fundamentales de la sociología: el de acción social y lo hemos transformado en el de acción pública para enfocar mejor el quehacer de la CCI. De esta manera, en este trabajo se observa a la CCI como un actor colectivo que se mueve en la esfera pública siguiendo un plan de acción conforme a estatutos y suministrando suficientes incentivos para motivar a sus miembros a colaborar y participar en ella. La CCI también es un actor público que se propone objetivos de poder político. Como se verá, lo característico de su acción pública es la función de intermediación instancia de gobierno-militancia campesina

Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems

Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relationships (links). Experimental confirmation of all possible links is difficult and generates a huge volume of information. Consequently, computational prediction becomes an important goal. Artificial Neural Networks (ANNs) are Machine Learning (ML) algorithms that may be used to predict BENs, using as input Shannon entropy information measures (Sh(k)) of known ecosystems to train them. However, it is difficult to select a priori which ANN topology will have a higher accuracy. Interestingly, Auto Machine Learning (AutoML) methods focus on the automatic selection of the more efficient ML algorithms for specific problems. In this work, a preliminary study of a new approach to AutoML selection of ANNs is proposed for the prediction of BENs. We call it the Net-Net AutoML approach, because it uses for the first time Shk values of both networks involving BENs (networks to be predicted) and ANN topologies (networks to be tested). Twelve types of classifiers have been tested for the Net-Net model including linear, Bayesian, trees-based methods, multilayer perceptrons and deep neuronal networks. The best Net-Net AutoML model for 338,050 outputs of 10 ANN topologies for links of 69 BENs was obtained with a deep fully connected neuronal network, characterized by a test accuracy of 0.866 and a test AUROC of 0.935. This work paves the way for the application of Net-Net AutoML to other systems or ML algorithms.The authors acknowledge Basque Government (Eusko Jaurlaritza) grant (IT1045-16) - 2016-2021 for consolidated research groups. This work was supported by the "Collaborative Project in Genomic Data Integration (CICLOGEN)" PI17/01826 funded by the Carlos III Health Institute, as part of the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER). This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and "Drug Discovery Galician Network" Ref. ED431G/01 and the "Galician Network for Colorectal Cancer Research" (Ref. ED431D 2017/23), and finally by the Spanish Ministry of Economy and Competitiveness for its support through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union. CR Munteanu acknowledges the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research

Efficient plot-based floristic assessment of tropical forests

The tropical flora remains chronically understudied and the lack of floristic understanding hampers ecological research and its application for large-scale conservation planning. Given scarce resources and the scale of the challenge there is a need to maximize the efficiency of both sampling strategies and sampling units, yet there is little information on the relative efficiency of different approaches to floristic assessment in tropical forests. This paper is the first attempt to address this gap. We repeatedly sampled forests in two regions of Amazonia using the two most widely used plot-based protocols of floristic sampling, and compared their performance in terms of the quantity of floristic knowledge and ecological insight gained scaled to the field effort required. Specifically, the methods are assessed first in terms of the number of person-days required to complete each sample (‘effort’), secondly by the total gain in the quantity of floristic information that each unit of effort provides (‘crude inventory efficiency’), and thirdly in terms of the floristic information gained as a proportion of the target species pool (‘proportional inventory efficiency’). Finally, we compare the methods in terms of their efficiency in identifying different ecological patterns within the data (‘ecological efficiency’) while controlling for effort. There are large and consistent differences in the performance of the two methods. The disparity is maintained even after accounting for regional and site-level variation in forest species richness, tree density and the number of field assistants. We interpret our results in the context of selecting the appropriate method for particular research purposes

Evolutionary Computation and QSAR Research

[Abstract] The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.Instituto de Salud Carlos III, PIO52048Instituto de Salud Carlos III, RD07/0067/0005Ministerio de Industria, Comercio y Turismo; TSI-020110-2009-53)Galicia. Consellería de Economía e Industria; 10SIN105004P

Protección al derecho fundamental a la vivienda de los no propietarios: análisis desde el enfoque jurisprudencial de la Sala Constitucional Salvadoreña

RESUMEN: El presente trabajo muestra un enfoque especial en la figura de los no propietarios, y por ellos es que en este proyecto se harán connotaciones de tipo históricas, doctrinales, jurídicas y teóricas relacionadas con el derecho a la vivienda; esta investigación se integra en tres partes, la primera parte lleva por nombre: Proyección de investigación, en la cual se desarrollan las directrices generales del problema de investigación, en la segunda parte del proyecto habla sobre cómo identificar el derecho a la vivienda como un derecho social tratándose aspectos de vital importancia como lo son el origen, desarrollo y acceso a la vivienda a través del tiempo en la sociedad, finalmente llegamos a la tercera y última parte, en donde se realizan consideraciones en cuanto al contenido formal, material y jurisprudencial del derecho a la vivienda, por lo que en esta parte se plante todo lo relacionado con los alcances jurídicos. OBJETIVOS: Analizar el contenido teórico, práctico y jurisprudencial del derecho fundamental a la vivienda digna de los no propietarios, explicando también el contenido material de la vivienda digna como un requisito fundamental para el desarrollo del ser humano; por otra parte pero contando con la misma relevancia, analizar el papel de las instituciones estatales encargadas de ofrecer soluciones a la población más desfavorecida. CONCLUSIÓN: el término vivienda no debe de interpretarse como un lugar donde una persona habita y que las políticas de vivienda junto a las instituciones como FONAVIPO son las encargadas de brindar las facilidades para que las personas que no poseen viviendas puedan ser dueñas de una. ABSTRACT: This work shows a special focus on the figure of non-owners, and it is for them that this project will make connotations of a historical, doctrinal, legal and theoretical nature related to the housing right; This research is integrated into three parts, the first part is called: Research projection, in which the general guidelines of the research problem are developed, in the second part of the project it talks about how to identify the right to housing as a right dealing with aspects of vital importance such as the origin, development and access to housing over time in society, finally we come to the third and last part, where considerations are made regarding the formal, material and jurisprudential content of the right to housing, so in this part everything related to the legal scope is raised. OBJECTIVES: Analyze the theoretical, practical and jurisprudential content of the fundamental right to decent housing of non-owners, also explaining the material content of decent housing as a fundamental requirement for the development of the human being; on the other hand, but with the same relevance, analyze the role of state institutions in charge of offering solutions to the most disadvantaged population. CONCLUSION: the term housing should not be interpreted as a place where a person lives and that housing policies together with institutions such as FONAVIPO are in charge of providing facilities so that people who do not own homes can own one

Unify Markov model for Rational Design and Synthesis of More Safe Drugs. Predicting Multiple Drugs Side Effects

The 9th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryMost of present mathematical models for rational design and synthesis of new drugs consider just the molecular structure. In the present article we pretend extending the use of Markov Chain models to define novel molecular descriptors, which consider in addition other parameters like target site or biological effect. Specifically, this model takes into consideration not only the molecular structure but the specific biological system the drug affects too. Herein, it is developed a general Markov model that describes 19 different drugs side effects grouped in 8 affected biological systems for 178 drugs, being 270 cases finally. The data was processed by Linear Discriminant Analysis (LDA) classifying drugs according to their specific side effects, forward stepwise was fixed as strategy for variables selection. The average percentage of good classification and number of compounds used in the training/predicting sets were 100/95.8% for endocrine manifestations(18 out of 18)/(13 out of 14); 90.5/92.3% for gastrointestinal manifestations (38 out of 42)/(30 out of 32); 88.5/86.5% for systemic phenomena (23 out of 26)/(17 out of 20); 81.8/77.3% for neurological manifestations (27 out of 33)/(19 out of 25); 81.6/86.2% for dermal manifestations (31 out of 38)/(25 out of 29); 78.4/85.1% for cardiovascular manifestation (29 out of 37)/(24 out of 28); 77.1/75.7% for breathing manifestations (27 out of 35)/(20 out of 26) and 75.6/75% for psychiatric manifestations (31 out of 41)/(23 out of 31). Additionally a Back-Projection Analysis (BPA) was carried out for two ulcerogenic drugs to prove in structural terms the physic interpretation of the models obtained. This article develops a model that encompasses a large number of drugs side effects grouped in specifics biological systems using stochastic absolute probabilities of interaction (Apk (j)) by the first time

QSAR for Anti-RNA-Virus Activity, Synthesis, and Assay of Anti-RSV Carbonucleosides Given an Unify Representation of Spectraö Moments, Quadratic, and Topologic Indices

The 9th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryThe unify representation of spectral moments, classic topologic indices, quadratic indices, and stochastic molecular descriptors shown that all these molecular descriptors lie within the same family. Consequently, the same priori probability for a success quantitative-structure-activity-relationship (QSAR) may be expected no matter which indices are selected. Herein, we used stochastic spectral moments as molecular descriptors to seek a QSAR using a database of 221 bioactive compounds previously tested against diverse RNA-viruses and 402 non-active ones. The QSAR model thus obtained correctly classifies 90.9 % of compounds in training. The model also correctly classifies a total of 87.9 % of 207 compounds on additional external predicting series, 73 of them having anti-RNA-virus activity and 134 non-active ones. In addition, all compounds were regrouped into five different subsets for leave-group-out studies: 1) antiinfluenza, 2) anti-picornavirus, 3) anti-paramyxovirus, 4) anti-RSV/anti-influenza, and 5) broad range anti-RNA-virus activity. The model has retained overall accuracies about 90 % on these studies validating model robustness. Finally, we exemplify the practical use of the model with the discovery of compounds 124 and 128. These compounds presented MIC50 values = 3.2 and 8 µg/mL against respiratory syncytial virus (RSV) respectively. Both compounds have also low cytotoxicity expressed by their Minimal Cytotoxic Concetrations > 400 µg/mL for HeLa cells. The present approach represent and effort toward a formalization and application of molecular indices in bioinformatics, bioorganic and medicinal chemistryAuthors would like to express their gratitude by partial financial support to the Department of Organic Chemistry, University of Santiago de Compostel

Gender bias in ecosystem restoration: from science to practice

The declaration of the United Nations Decade on Ecosystem Restoration 2020–2030 has established the need to focus on human rights in restoration initiatives, including gender equality. Although this goal raises a need to monitor gender biases on ecosystem restoration, we still lack basic gender information and evaluations on the current situation. The main purpose of this study is to analyze gender bias in ecosystem restoration covering three dimensions: research, outreach, and practice. We used scientific publications from the Restoration Ecology journal, mentions of these articles in Altmetric Explorer and Twitter, and projects from the Society for Ecological Restoration's database. First, we study gender bias among people leading ecosystem restoration initiatives in the three dimensions. Second, we assessed factors that could influence gender bias, including year, target ecosystem, and socioeconomic country development. Third, we analyzed whether the impact of scientific knowledge in society depends on the gender of the scientific team. Our results indicate that men were primary leaders in research, outreach, and practice initiatives in ecosystem restoration. There seems to be a trend over time toward equality in research, but gender inequality is still present in most types of ecosystems, with women leading more projects in more developed countries. The impact of scientific knowledge is independent of the author's gender, but research of male senior authors seems to reach society more easily. This broad perspective of inequality in the three dimensions can evolve toward gender equality, by applying gender approaches in restoration policies and initiatives. © 2022 Society for Ecological Restoration.Raw data is publicly available thanks to Web of Science, Altmetrics, Twitter and SER. Data and scripts used for the analysis are available via Figshare (Cruz‐Alonso et al. 2022 ). Funding: V.C.‐A.—Real Colegio Complutense postdoc fellowship; A.R.‐U.—Spanish State Research Agency through María de Maeztu Excellence Unit accreditation 2018–2022 (MDM‐2017‐0714); L.M.—Swiss National Science Foundation (PCEFP2_181115) and a Margarita Salas Postdoctoral Fellowship from Universidad de Alcalá; L.M.‐B.—Ministerio de Ciencia e Innovación (PID2019‐106806GB‐I00) and a Margarita Salas Postdoctoral Fellowship from Universidad de Alcalá; N.M.—predoctoral grant from Universidad de Alcalá; E.V.‐A.—European Commission (project SHOWCASE, H2020: 862480). We appreciate the support of the FIRE Foundation and the comments of M. Almaraz, M. Pajares, A. S. Moya, and D. Rohrer to improve the manuscript.Raw data is publicly available thanks to Web of Science, Altmetrics, Twitter and SER. Data and scripts used for the analysis are available via Figshare (Cruz‐Alonso et al. 2022 ). Funding: V.C.‐A.—Real Colegio Complutense postdoc fellowship; A.R.‐U.—Spanish State Research Agency through María de Maeztu Excellence Unit accreditation 2018–2022 (MDM‐2017‐0714); L.M.—Swiss National Science Foundation (PCEFP2_181115) and a Margarita Salas Postdoctoral Fellowship from Universidad de Alcalá; L.M.‐B.—Ministerio de Ciencia e Innovación (PID2019‐106806GB‐I00) and a Margarita Salas Postdoctoral Fellowship from Universidad de Alcalá; N.M.—predoctoral grant from Universidad de Alcalá; E.V.‐A.—European Commission (project SHOWCASE, H2020: 862480). We appreciate the support of the FIRE Foundation and the comments of M. Almaraz, M. Pajares, A. S. Moya, and D. Rohrer to improve the manuscript