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Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforward means of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed the most variation across different modes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders.

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CONTEXT: The electron localization is a concept that allows scientists to better understand the physical and chemical properties of electronic systems. It is associated with the propensity of electron pairs with opposite spins to accumulate as well as with their response to external perturbations. This paper contains a detailed description of the design and implementation of the program KLD, which was primarily developed in our research group to elucidate electron localization in molecular systems by evaluating the information content of electron-pair density functions. KLD employs two information-based functions as a real space measure of the Fermi and Coulomb holes for same-spin electrons and shows a better resolution as compared to other methods (i.e., ELF). Information about the acceleration of the code is also included in the present work, being noticeable the reduction of wall-time calculation and the error calculation between versions. METHODS: KLD was designed to be easy to use, extend, and maintain; thus, many principles of modern software development, extensive testing, and package management were adopted. The latest version of the KLD program was created utilizing the Compute Unified Device Architecture (CUDA) version, which allows it to use the computational capacity of NVIDIA Graphics Processing Units (GPUs) for processing purposes. The electron-pair conditional density was calculated from the canonical molecular orbitals obtained at the HF/6-31G(2df,p) level, or alternatively the natural orbitals in the case of explicit correlated wavefunctions computed at the MP2/6-31G(2df,p)//HF/6-31G(2df,p) level.

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Chili pepper fruits of the genus Capsicum represent excellent experimental models to study the growth, development, and ripening processes in a non-climacteric species at the physiological, biochemical, and molecular levels. Fruit growth, development, and ripening involve a complex, harmonious, and finely controlled regulation of gene expression. The purpose of this study was to estimate the changes in transcriptome diversity and specialization, as well as gene specificities during fruit development in this crop, and to illustrate the advantages of estimating these parameters. To achieve these aims, we programmed and made publicly available an R package. In this study, we applied these methods to a set of 179 RNA-Seq libraries from a factorial experiment that includes 12 different genotypes at various stages of fruit development. We found that the diversity of the transcriptome decreases linearly from the flower to the mature fruit, while its specialization follows a complex and non-linear behavior during this process. Additionally, by defining sets of genes with different degrees of specialization and applying Gene Ontology enrichment analysis, we identified processes, functions, and components that play a central role in particular fruit development stages. In conclusion, the estimation of diversity, specialization, and specificity summarizes the global properties of the transcriptomes, providing insights that are difficult to achieve by other means.

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Even though molecules are fundamentally quantum entities, the concept of a molecule retains certain classical attributes concerning its constituents. This includes the empirical separability of a molecule into its three-dimensional, rigid structure in Euclidean space, a framework often obtained through experimental methods like X-Ray crystallography. In this work, we delve into the mathematical implications of partitioning a molecule into its constituent parts using the widely recognized Atoms-In-Molecules (AIM) schemes, aiming to establish their validity within the framework of Information Theory concepts. We have uncovered information-theoretical justifications for employing some of the most prevalent AIM schemes in the field of Chemistry, including Hirshfeld (stockholder partitioning), Bader's (topological dissection), and the quantum approach (Hilbert's space definition). In the first approach we have applied the generalized principle of minimum relative entropy derived from the Sharma-Mittal two-parameter functional, avoiding the need for an arbitrary selection of reference promolecular atoms. Within the ambit of topological-information partitioning, we have demonstrated that the Fisher information of Bader's atoms conform to a comprehensive theory based on the Principle of Extreme Physical Information avoiding the need of employing the Schwinger's principle, which has been proven to be problematic. For the quantum approach we have presented information-theoretic justifications for conducting Löwdin symmetric transformations on the density matrix to form atomic Hilbert spaces generating orthonormal atomic orbitals with maximum occupancy for a given wavefunction.

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Missing person cases typically require a genetic kinship test to determine the relationship between an unidentified individual and the relatives of the missing person. When not enough genetic evidence has been collected the lack of statistical power of these tests might lead to unreliable results. This is particularly true when just a few distant relatives are available for genotyping. In this contribution, we considered a Bayesian network approach for kinship testing and proposed several information theoretic metrics in order to quantitatively evaluate the information content of pedigrees. We show how these statistics are related to the widely used likelihood ratio values and could be employed to efficiently prioritize family members in order to optimize the statistical power in missing person problems. Our methodology seamlessly integrates with Bayesian modeling approaches, like the GENis platform that we have recently developed for high-throughput missing person identification tasks. Furthermore, our approach can also be easily incorporated into Elston-Stewart forensic frameworks. To facilitate the application of our methodology, we have developed the forensIT package, freely available on CRAN repository, which implements all the methodologies described in our manuscript.

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Bradycardia, frequently observed in preterm infants, presents significant risks due to the immaturity of their autonomic nervous system (ANS) and respiratory systems. These infants may face cardiorespiratory events, leading to severe complications like hypoxemia and neurodevelopmental disorders. Although neonatal care has advanced, the influence of bradycardia on cardiorespiratory coupling (CRC) remains elusive. This exploratory study delves into CRC in preterm infants, emphasizing disparities between events with and without bradycardia. Using the Preterm Infant Cardio-Respiratory Signals (PICS) database, we analyzed interbeat (R-R) and inter-breath intervals (IBI) from 10 preterm infants. The time series were segmented into bradycardic (B) and non-bradycardic (NB) segments. Employing information theory measures, we quantified the irregularity of cardiac and respiratory time series. Notably, B segments had significantly lower entropy values for R-R and IBI than NB segments, while mutual information was higher in NB segments. This could imply a reduction in the complexity of respiratory and cardiac dynamics during bradycardic events, potentially indicating weaker CRC. Building on these insights, this research highlights the distinctive physiological characteristics of preterm infants and underscores the potential of emerging non-invasive diagnostic tools.

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While subcellular components of cognition and affectivity that involve the interaction between experience, environment, and physiology -such as learning, trauma, or emotion- are being identified, the physical mechanisms of phenomenal consciousness remain more elusive. We are interested in exploring whether ancient, simpler organisms such as nematodes have minimal consciousness. Is there something that feels like to be a worm? Or are worms blind machines? 'Simpler' models allow us to simultaneously extract data from multiple levels such as slow and fast neural dynamics, structural connectivity, molecular dynamics, behavior, decision making, etc., and thus, to test predictions of the current frameworks in dispute. In the present critical review, we summarize the current models of consciousness in order to reassess in light of the new evidence whether Caenorhabditis elegans, a nematode with a nervous system composed of 302 neurons, has minimal consciousness. We also suggest empirical paths to further advance consciousness research using C. elegans.

Mientras que los componentes subcelulares de fenómenos cognitivos y afectivos que involucran la interacción entre experiencia, ambiente y fisiología -tales como aprendizaje, trauma, o emociones- son identificados con cada vez mayor detalle, los mecanismos biofísicos de la consciencia fenoménica permanecen elusivos. Nos interesa explorar si organismos sencillos como los nemátodos presentan consciencia mínima. ¿Hay algo que se sienta como ser un gusano? ¿O acaso los gusanos son máquinas carentes de toda experiencia? Los modelos "sencillos" nos permiten extraer datos de múltiples niveles en simultáneo: dinámica neuronal rápida y lenta, conectividad estructural, dinámica molecular, conducta, toma de decisiones, etc., y así testear predicciones de las propuestas teóricas actuales en disputa. En esta revisión compendiamos los modelos actuales de consciencia para evaluar, considerando la evidencia reciente, si Caenorhabditis elegans, un nemátodo con un sistema nervioso de 302 neuronas, tiene consciencia mínima. Sugerimos además vías empíricas para desarrollar investigaciones en consciencia utilizando C. elegans.

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In this text, I propose the concept of "metapresentiality" as a fundamental element for a Critical Theory of Digital Health. First, I present the concepts of technique, technology, and technical object, central to the theories of Álvaro Vieira Pinto and Milton Santos. Secondly, based on Luciano Floridi's philosophy of information, I question the relevance of the dichotomy real-material-concrete vs. digital-virtual-informational as an ontological foundation for concepts of reality, place, and presence, highlighting the notions of virtual reality and extended reality. Thirdly, I introduce an etymological-historical critique of the series presence-telepresence-metapresence, focusing on the emerging notion of meta-presentiality in the form of proto-concept and its eventual formalization as a conceptual foundation for a socio-technical appropriation and technosocial integration of digital technologies. Finally, I discuss Digital Health as a field of knowledge, techniques and practices and evaluate epistemological and pragmatic advantages of metapresentiality as a concept in the fields of computing, education and health.

En este texto, propongo el concepto de "metapresencialidad" como elemento fundante para una teoría crítica de la salud digital. En primer lugar, presento los conceptos de técnica, tecnología y objeto técnico, centrales en las teorías de Álvaro Vieira Pinto y Milton Santos. En segundo lugar, a partir de la filosofía de la información de Luciano Floridi, cuestiono la pertinencia de la dicotomía real-material-concreto versus digital-virtual-informacional como fundamento ontológico de los conceptos de realidad, lugar y presencia, destacando las nociones de realidad virtual y realidad extendida. En tercer lugar, introduzco una crítica etimológica e histórica de la serie presencia-telepresencia-metapresencia, enfocando la noción emergente de metapresencialidad en forma de protoconcepto y su eventual formalización como fundamento conceptual para una apropiación sociotécnica y una integración tecnosocial de las tecnologías digitales. Finalmente, discuto la salud digital como campo de saberes, técnicas y prácticas y evalúo las ventajas epistemológicas y pragmáticas de la metapresencialidad como concepto en los campos de la informática, la educación y la salud.

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Seismic data have improved in quality and quantity over the past few decades, enabling better statistical analysis. Statistical physics has proposed new ways to deal with these data to focus the attention on specific matters. The present paper combines these two progressions to find indicators that can help in the definition of areas where seismic risk is developing. Our data comes from the IPOC catalog for 2007 to 2014. It covers the intense seismic activity near Iquique in Northern Chile during March/April 2014. Centered in these hypocenters we concentrate on the rectangle Lat-22-18 and Lon-68-72 and deepness between 5 and 70 km, where the major earthquakes originate. The analysis was performed using two complementary techniques: Tsallis entropy and mutability (dynamical entropy). Two possible forecasting indicators emerge: (1) Tsallis entropy (mutability) increases (decreases) broadly about two years before the main MW8.1 earthquake. (2) Tsallis entropy (mutability) sharply decreases (increases) a few weeks before the MW8.1 earthquake. The first one is about energy accumulation, and the second one is because of energy relaxation in the parallelepiped of interest. We discuss the implications of these behaviors and project them for possible future studies.

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Integrated Information Theory (IIT) is currently one of the most influential scientific theories of consciousness. Here, we focus specifically on a metaphysical aspect of the theory's most recent version (IIT 4.0), what we may call its idealistic ontology, and its tension with a kind of realism about the external world that IIT also endorses. IIT 4.0 openly rejects the mainstream view that consciousness is generated by the brain, positing instead that consciousness is ontologically primary while the physical domain is just "operational". However, this philosophical position is presently underdeveloped and is not rigorously formulated in IIT, potentially leading to many misinterpretations and undermining its overall explanatory power. In the present paper we aim to address this issue. We argue that IIT's idealistic ontology should be understood as a specific combination of phenomenal primitivism, reductionism regarding Φ-structures and complexes, and eliminativism about non-conscious physical entities. Having clarified this, we then focus on the problematic tension between IIT's idealistic ontology and its simultaneous endorsement of realism, according to which there is some kind of external reality independent of our minds. After refuting three potential solutions to this theoretical tension, we propose the most plausible alternative: understanding IIT's realism as an assertion of the existence of other experiences beyond one's own, what we call a non-solipsistic idealist realism. We end with concluding remarks and future research avenues.

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Sleep deprivation, a widespread phenomenon that affects one-third of normal American adults, induces adverse changes in physical and cognitive performance, which in turn increases the occurrence of accidents. Sleep deprivation is known to increase resting blood pressure and decrease muscle sympathetic nerve activity. Monitoring changes in the interplay between the central and autonomic sympathetic nervous system can be a potential indicator of human's readiness to perform tasks that involve a certain level of cognitive load (e.g., driving). The electroencephalogram (EEG) is the standard to assess the brain's activity. The electrodermal activity (EDA) is a reflection of the general state of arousal regulated by the activation of the sympathetic nervous system through sweat gland stimulation. In this work, we calculated the mutual information between EDA and EEG recordings in order to consider linear and non-linear interactions and provide an insight of the relationship between brain activity and peripheral autonomic sympathetic activity. We analyzed EEG and EDA data from ten participants performing four cognitive tasks every two hours during 24 h (12 trials). We decomposed EEG data into delta, theta, alpha, beta, and gamma spectral components, and EDA into tonic and phasic components. The results demonstrate high values of mutual information between the EDA and delta component of EEG, mainly in working memory tasks. Additionally, we found an increase in the theta component of EEG in the presence of fatigue caused by sleep deprivation, the alpha component in tasks demanding inhibition and attention, and the delta component in working memory tasks. In terms of the location of brain activity, most of the tasks report high mutual information in frontal regions in the initial trials, with a trend to decrease and become uniform for all the nine analyzed EEG channels as a consequence of the sleep deprivation effect. Our results evidence the interplay between central and sympathetic nervous activity and can be used to mitigate the consequences of sleep deprivation.

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Abstract The present review attempts to discuss how some of the central concepts from the Lurian corpus of theories are relevant to the modern neuropsychology of epilepsy and epilepsy surgery. Through the lenses of the main Lurian concepts (such as the qualitative syndrome analysis), we discuss the barriers to clinical reasoning imposed by quadrant-based views of the brain, or even atheoretical, statistically-based and data-driven approaches. We further advice towards a systemic view inspired by Luria's clinical work and theorizing, given their importance towards our clinical practice, by contrasting it to the modular views when appropriate. Luria provided theory-guided methods of assessment and rehabilitation of higher cortical functions. Although his work did not specifically address epilepsy, his theory and clinical approaches actually apply to the whole neuropathology spectrum and accounting for the whole panorama of neurocognition. This holistic and systemic approach to the brain is consistent with the network approach of the neuroimaging era. As to epilepsy, the logic of cognitive functions organized into complex functional systems, contrary to modular views of the brain, heralds current knowledge of epilepsy as a network disease, as well as the concept of the functional deficit zone.

Resumo A presente revisão tenta discutir como alguns dos conceitos centrais do corpus de teorias lurianas são relevantes para a moderna neuropsicologia da epilepsia e cirurgia da epilepsia. Através das lentes dos principais conceitos lurianos (como a análise qualitativa de síndromes), discutimos as barreiras ao raciocínio clínico impostas por visões do cérebro baseadas em quadrantes, ou mesmo abordagens ateóricas, baseadas em estatísticas e orientadas por dados. Aconselhamos ainda uma visão sistêmica inspirada na clínica e na teorização de Luria, dada sua importância para nossa prática clínica, contrastando-a com as visões modulares quando apropriado. Luria forneceu métodos teóricos de avaliação e reabilitação de funções corticais superiores. Embora seu trabalho não abordasse especificamente a epilepsia, sua teoria e abordagens clínicas na verdade se aplicam a todo o espectro da neuropatologia e respondem por todo o panorama da neurocognição. Essa abordagem holística e sistêmica do cérebro é consistente com a abordagem de rede da era da neuroimagem. Quanto à epilepsia, a lógica das funções cognitivas organizadas em sistemas funcionais complexos, ao contrário das visões modulares do cérebro, anuncia o conhecimento atual da epilepsia como uma doença em rede, bem como o conceito de zona de déficit funcional.

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The ability to build more robust clustering from many clustering models with different solutions is relevant in scenarios with privacy-preserving constraints, where data features have a different nature or where these features are not available in a single computation unit. Additionally, with the booming number of multi-view data, but also of clustering algorithms capable of producing a wide variety of representations for the same objects, merging clustering partitions to achieve a single clustering result has become a complex problem with numerous applications. To tackle this problem, we propose a clustering fusion algorithm that takes existing clustering partitions acquired from multiple vector space models, sources, or views, and merges them into a single partition. Our merging method relies on an information theory model based on Kolmogorov complexity that was originally proposed for unsupervised multi-view learning. Our proposed algorithm features a stable merging process and shows competitive results over several real and artificial datasets in comparison with other state-of-the-art methods that have similar goals.

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Profile hidden Markov models (HMMs) are a powerful way of modeling biological sequence diversity and constitute a very sensitive approach to detecting divergent sequences. Here, we report the development of protocols for the rational design of profile HMMs. These methods were implemented on TABAJARA, a program that can be used to either detect all biological sequences of a group or discriminate specific groups of sequences. By calculating position-specific information scores along a multiple sequence alignment, TABAJARA automatically identifies the most informative sequence motifs and uses them to construct profile HMMs. As a proof-of-principle, we applied TABAJARA to generate profile HMMs for the detection and classification of two viral groups presenting different evolutionary rates: bacteriophages of the Microviridae family and viruses of the Flavivirus genus. We obtained conserved models for the generic detection of any Microviridae or Flavivirus sequence, and profile HMMs that can specifically discriminate Microviridae subfamilies or Flavivirus species. In another application, we constructed Cas1 endonuclease-derived profile HMMs that can discriminate CRISPRs and casposons, two evolutionarily related transposable elements. We believe that the protocols described here, and implemented on TABAJARA, constitute a generic toolbox for generating profile HMMs for the highly sensitive and specific detection of sequence classes.

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Cerebral hemodynamics describes an important physiological system affected by components such as blood pressure, CO2 levels, and endothelial factors. Recently, novel techniques have emerged to analyse cerebral hemodynamics based on the calculation of entropies, which quantifies or describes changes in the complexity of this system when it is affected by a pathological or physiological influence. One recently described measure is transfer entropy, which allows for the determination of causality between the various components of a system in terms of their flow of information, and has shown positive results in the multivariate analysis of physiological signals. This study aims to determine whether conditional transfer entropy reflects the causality in terms of entropy generated by hypocapnia on cerebral hemodynamics. To achieve this, non-invasive signals from 28 healthy individuals who undertook a hyperventilation maneuver were analyzed using conditional transfer entropy to assess the variation in the relevance of CO2 levels on cerebral blood velocity. By employing a specific method to discretize the signals, it was possible to differentiate the influence of CO2 levels during the hyperventilation phase (22.0% and 20.3% increase for the left and right hemispheres, respectively) compared to normal breathing, which remained higher during the recovery phase (15.3% and 15.2% increase, respectively).

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RESUMEN En este texto, propongo el concepto de "metapresencialidad" como elemento fundante para una teoría crítica de la salud digital. En primer lugar, presento los conceptos de técnica, tecnología y objeto técnico, centrales en las teorías de Álvaro Vieira Pinto y Milton Santos. En segundo lugar, a partir de la filosofía de la información de Luciano Floridi, cuestiono la pertinencia de la dicotomía real-material-concreto versus digital-virtual-informacional como fundamento ontológico de los conceptos de realidad, lugar y presencia, destacando las nociones de realidad virtual y realidad extendida. En tercer lugar, introduzco una crítica etimológica e histórica de la serie presencia-telepresencia-metapresencia, enfocando la noción emergente de metapresencialidad en forma de protoconcepto y su eventual formalización como fundamento conceptual para una apropiación sociotécnica y una integración tecnosocial de las tecnologías digitales. Finalmente, discuto la salud digital como campo de saberes, técnicas y prácticas y evalúo las ventajas epistemológicas y pragmáticas de la metapresencialidad como concepto en los campos de la informática, la educación y la salud.

ABSTRACT In this text, I propose the concept of "metapresentiality" as a fundamental element for a Critical Theory of Digital Health. First, I present the concepts of technique, technology, and technical object, central to the theories of Álvaro Vieira Pinto and Milton Santos. Secondly, based on Luciano Floridi's philosophy of information, I question the relevance of the dichotomy real-material-concrete vs. digital-virtual-informational as an ontological foundation for concepts of reality, place, and presence, highlighting the notions of virtual reality and extended reality. Thirdly, I introduce an etymological-historical critique of the series presence-telepresence-metapresence, focusing on the emerging notion of meta-presentiality in the form of proto-concept and its eventual formalization as a conceptual foundation for a socio-technical appropriation and technosocial integration of digital technologies. Finally, I discuss Digital Health as a field of knowledge, techniques and practices and evaluate epistemological and pragmatic advantages of metapresentiality as a concept in the fields of computing, education and health.

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We examine the price disorder and informational efficiency of five cryptocurrencies (Bitcoin, BNB, Cardano, Ethereum, and XRP) before and during the COVID-19 pandemic. In this sense, we estimate the permutation entropy and Fisher information measure (FIM). We use these complexity measures to construct the Shannon-Fisher causality plane (SFCP) to map these cryptocurrencies and their respective locations in a two-dimensional plane and then apply the sliding time window approach to study the temporal evolution of informational efficiency. All cryptocurrencies exhibit high but slightly varying informational efficiency during both periods. Cardano was the most efficient cryptocurrency. These results might point to the increasing maturity and lower potential for price predictability, which matter to cryptocurrencies' usage for liquidity risk diversification strategy.

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Several harmonization techniques have recently been proposed for connectomics/networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) acquired at multiple sites. These techniques have the objective of mitigating site-specific biases that complicate its subsequent analysis and, therefore, compromise the quality of the results when these images are analyzed together. Thus, harmonization is indispensable when large cohorts are required in which the data obtained must be independent of the particular condition of each resonator, its make and model, its calibration, and other features or artifacts that may affect the significance of the acquisition. To date, no assessment of the actual efficacy of these harmonization techniques has been proposed. In this work, we apply recently introduced Information Theory tools to analyze the effectiveness of these techniques, developing a methodology that allows us to compare different harmonization models. We demonstrate the usefulness of this methodology by applying it to some of the most widespread harmonization frameworks and datasets. As a result, we are able to show that some of these techniques are indeed ineffective since the acquisition site can still be determined from the fMRI data after the processing.

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Sequence logos are a simple way to display a set of aligned sequences, and they are useful to identify conserved patterns. Since their introduction, several tools have been developed for generating these representations at the single residue level (amino acids or nucleotides). We have developed a tool to build sequence logos of protein-coding sequences at the codon level, allowing more accurate analysis of coding-sequences as they represent synonymous and non-synonymous changes instead of showing only changes that imply on amino acid substitutions. We built CoCoView on top of the Logomaker Python API. It creates codon sequence logos from a multiple sequence alignment of protein-coding sequences. Some properties of the data and the generated logos can be controlled by the end-users, such as data redundancy, plot type and alphabet color. • Split aligned sequences into codon positions; • For each position compute codon frequency and information content; • Use the computed information to plot the graphic.