Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

Fuzzy complex formation between the intrinsically disordered prothymosin α and the Kelch domain of Keap1 involved in the oxidative stress response.

Kelch-like ECH-associated protein 1 (Keap1) is an inhibitor of nuclear factor erythroid 2-related factor 2 (Nrf2), a key transcription factor for cytoprotective gene activation in the oxidative stress response. Under unstressed conditions, Keap1 interacts with Nrf2 in the cytoplasm via its Kelch domain and suppresses the transcriptional activity of Nrf2. During oxidative stress, Nrf2 is released from Keap1 and is translocated into the nucleus, where it interacts with the small Maf protein to initiate gene transcription. Prothymosin α (ProTα), an intrinsically disordered protein, also interacts with the Kelch domain of Keap1 and mediates the import of Keap1 into the nucleus to inhibit Nrf2 activity. To gain a molecular basis understanding of the oxidative stress response mechanism, we have characterized the interaction between ProTα and the Kelch domain of Keap1 by using nuclear magnetic resonance spectroscopy, isothermal titration calorimetry, peptide array analysis, site-directed mutagenesis, and molecular dynamic simulations. The results of nuclear magnetic resonance chemical shift mapping, amide hydrogen exchange, and spin relaxation measurements revealed that ProTα retains a high level of flexibility, even in the bound state with Kelch. This finding is in agreement with the observations from the molecular dynamic simulations of the ProTα-Kelch complex. Mutational analysis of ProTα, guided by peptide array data and isothermal titration calorimetry, further pinpointed that the region (38)NANEENGE(45) of ProTα is crucial for the interaction with the Kelch domain, while the flanking residues play relatively minor roles in the affinity of binding

Efficient transfer entropy analysis of non-stationary neural time series

Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience. Estimating transfer entropy from two processes requires the observation of multiple realizations of these processes to estimate associated probability density functions. To obtain these observations, available estimators assume stationarity of processes to allow pooling of observations over time. This assumption however, is a major obstacle to the application of these estimators in neuroscience as observed processes are often non-stationary. As a solution, Gomez-Herrero and colleagues theoretically showed that the stationarity assumption may be avoided by estimating transfer entropy from an ensemble of realizations. Such an ensemble is often readily available in neuroscience experiments in the form of experimental trials. Thus, in this work we combine the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series. We present an efficient implementation of the approach that deals with the increased computational demand of the ensemble method's practical application. In particular, we use a massively parallel implementation for a graphics processing unit to handle the computationally most heavy aspects of the ensemble method. We test the performance and robustness of our implementation on data from simulated stochastic processes and demonstrate the method's applicability to magnetoencephalographic data. While we mainly evaluate the proposed method for neuroscientific data, we expect it to be applicable in a variety of fields that are concerned with the analysis of information transfer in complex biological, social, and artificial systems.Comment: 27 pages, 7 figures, submitted to PLOS ON

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

Aesthetic Sensitivity

[eng] Aesthetic sensitivity is a central idea in the field of empirical aesthetics. The present research contributes a historical-critical review of its origin and development through the history of the discipline, a new theoretical approach aligned with current knowledge, novel methodological tools to investigate this and other relevant psychological constructs, and empirical evidence based on this conception that advances scientific understanding of sensory valuation.[spa] La sensibilidad estética es una idea central en el campo de la estética empírica. La presente investigación aporta una revisión histórico-crítica de su origen y desarrollo a través de la historia de la disciplina, un nuevo enfoque teórico de acuerdo con los conocimientos actuales, novedosas herramientas metodológicas para investigar éste y otros constructos psicológicos relevantes, y evidencia empírica basada en esta concepción que avanza la comprensión científica de la valoración sensorial.[cat] La sensibilitat estètica és una idea central en el camp de l'estètica empírica. La present investigació aporta una revisió històric-crítica del seu origen i desenvolupament a través de la història de la disciplina, un nou enfocament teòric alineat amb els coneixements actuals, noves eines metodològiques per investigar aquest i altres constructes psicològics rellevants, i evidència empírica basada en aquesta concepció que avança la comprensió científica de la valoració sensorial

Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network

The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein–ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method’s promise for lead hopping within or outside a protein target, directly based on binding site information