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

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

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

Global Antifungal Profile Optimization of Chlorophenyl Derivatives against Botrytis cinerea and Colletotrichum gloeosporioides

Twenty-two aromatic derivatives bearing a chlorine atom and a different chain in the para or meta position were prepared and evaluated for their in vitro antifungal activity against the phytopathogenic fungi Botrytis cinerea and Colletotrichum gloeosporioides. The results showed that maximum inhibition of the growth of these fungi was exhibited for enantiomers S and R of 1-(40-chlorophenyl)- 2-phenylethanol (3 and 4). Furthermore, their antifungal activity showed a clear structure-activity relationship (SAR) trend confirming the importance of the benzyl hydroxyl group in the inhibitory mechanism of the compounds studied. Additionally, a multiobjective optimization study of the global antifungal profile of chlorophenyl derivatives was conducted in order to establish a rational strategy for the filtering of new fungicide candidates from combinatorial libraries. The MOOPDESIRE methodology was used for this purpose providing reliable ranking models that can be used later

Multiclasificadores basados en aprendizaje automático como herramienta para la evaluación del perfil neurotóxico de líquidos iónicos

Los líquidos iónicos poseen un perfil fisicoquímico único, el cual los provee de un amplio rango de aplicaciones. Su variabilidad estructural casi ilimitada permite su diseño para tareas específicas. Sin embargo, su sustentabilidad, específicamente su seguridad desde el punto de vista toxicológico, ha sido frecuentemente cuestionada. Este último aspecto limita significativamente el cumplimiento de las regulaciones establecidas por la Unión Europea para el registro, evaluación, autorización y restricción de compuestosquímicos (REACH), así como su aplicación final. Debido a que la mayoría de los líquidos iónicos no han sido sintetizados, se hace evidente la importancia del desarrollo de herramientas quimioinformáticas que, de forma eficiente, permitan evaluar el potencial toxicológico de estos compuestos. En este sentido, el uso combinado de múltiples clasificadores ha demostrado superar las limitaciones de desempeño asociadas al uso de clasificadores individuales. En el presente trabajo fueron evaluadas varias estrategias alternativas de multiclasificadores basados en técnicas de aprendizaje automático supervisado, como herramientas para la evaluación del perfil neurotóxico de líquidos iónicos basado en la inhibición de la enzima acetilcolinesterasa, como indicador de neurotoxicidad. Se obtuvieron dos multiclasificadores con una alta capacidad predictiva sobre un conjunto de validación externa (no utilizado en el proceso de aprendizaje de los modelos). De acuerdo a los resultados obtenidos el 96% de un conjunto de nuevos líquidos iónicos podrá ser correctamente clasificado con la utilizaciónde estos multiclasificadores, los cuales constituyen herramientas de toma de decisión útiles en el campo del diseño y desarrollo de nuevos líquidos iónicos sustentables

Looking the void in the eyes - the kSZ effect in LTB models

As an alternative explanation of the dimming of distant supernovae it has recently been advocated that we live in a special place in the Universe near the centre of a large void described by a Lemaitre-Tolman-Bondi (LTB) metric. The Universe is no longer homogeneous and isotropic and the apparent late time acceleration is actually a consequence of spatial gradients in the metric. If we did not live close to the centre of the void, we would have observed a Cosmic Microwave Background (CMB) dipole much larger than that allowed by observations. Hence, until now it has been argued, for the model to be consistent with observations, that by coincidence we happen to live very close to the centre of the void or we are moving towards it. However, even if we are at the centre of the void, we can observe distant galaxy clusters, which are off-centre. In their frame of reference there should be a large CMB dipole, which manifests itself observationally for us as a kinematic Sunyaev-Zeldovich (kSZ) effect. kSZ observations give far stronger constraints on the LTB model compared to other observational probes such as Type Ia Supernovae, the CMB, and baryon acoustic oscillations. We show that current observations of only 9 clusters with large error bars already rule out LTB models with void sizes greater than approximately 1.5 Gpc and a significant underdensity, and that near future kSZ surveys like the Atacama Cosmology Telescope, South Pole Telescope, APEX telescope, or the Planck satellite will be able to strongly rule out or confirm LTB models with giga parsec sized voids. On the other hand, if the LTB model is confirmed by observations, a kSZ survey gives a unique possibility of directly reconstructing the expansion rate and underdensity profile of the void.Comment: 20 pages, 9 figures, submitted to JCA

Statistics of the excursion sets in models with local primordial non-Gaussianity

We use the statistics of regions above or below a temperature threshold (excursion sets) to study the cosmic microwave background (CMB) anisotropy in models with primordial non-Gaussianity of the local type. By computing the full-sky spatial distribution and clustering of pixels above/below threshold from a large set of simulated maps with different levels of non-Gaussianity, we find that a positive value of the dimensionless non-linearity parameter f_NL enhances the number density of the cold CMB excursion sets along with their clustering strength, and reduces that of the hot ones. We quantify the robustness of this effect, which may be important to discriminate between the simpler Gaussian hypothesis and non-Gaussian scenarios, arising either from non-standard inflation or alternative early-universe models. The clustering of hot and cold pixels exhibits distinct non-Gaussian signatures, particularly at angular scales of about 75 arcmin (i.e. around the Doppler peak), which increase linearly with f_NL. Moreover, the clustering changes strongly as a function of the smoothing angle. We propose several statistical tests to maximize the detection of a local primordial non-Gaussian signal, and provide some theoretical insights within this framework, including an optimal selection of the threshold level. We also describe a procedure which aims at minimizing the cosmic variance effect, the main limit within this statistical framework.Comment: 19 pages, 12 figures, accepted for publication in MNRAS. A new figure and three new appendices added, to address the referee's comment

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