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The Dyson index, ß, plays an essential role in random matrix theory, as it labels the so-called "three-fold way" that refers to the symmetries satisfied by ensembles under unitary transformations. As is known, its 1, 2, and 4 values denote the orthogonal, unitary, and symplectic classes, whose matrix elements are real, complex, and quaternion numbers, respectively. It functions, therefore, as a measure of the number of independent non-diagonal variables. On the other hand, in the case of ß ensembles, which represent the tridiagonal form of the theory, it can assume any real positive value, thus losing that function. Our purpose, however, is to show that, when the Hermitian condition of the real matrices generated with a given value of ß is removed, and, as a consequence, the number of non-diagonal independent variables doubles, non-Hermitian matrices exist that asymptotically behave as if they had been generated with a value 2ß. Therefore, it is as if the ß index were, in this way, again operative. It is shown that this effect happens for the three tridiagonal ensembles, namely, the ß-Hermite, the ß-Laguerre, and the ß-Jacobi ensembles.

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Gap junctions between neurons of the brain are thought to be present in only certain cell types, and they mostly connect dendrites, somata, and axons. Synapses with gap junctions serve bidirectional metabolic and electrical coupling between connected neuronal compartments. Although plasticity of electrical synapses has been described, recent evidence of the presence of silent, but activatable, gap junctions suggests that electrical nodes in a neuronal circuit can be added or suppressed by changes in the synaptic microenvironment. This opens the possibility of reconfiguration of neuronal ensembles in response to activity. Moreover, the coexistence of gap junctions in a glutamatergic synapse may add electric and metabolic coupling to a neuronal aggregate and may serve to constitute primed ensembles within a higher-order neural network. The interaction of chemical with electrical synapses should be further explored to find, especially, emerging properties of neuronal ensembles. It will be worth to reexamine in a new light the "functional" implications of the "anatomic" concepts: "continuity" and "contiguity," which were championed by Golgi and Ramón y Cajal, respectively. In any case, exploring the versatility of the gap junctions will likely enrich the heuristic aspects of the neural and network postulates.

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We describe in this paper an approach for predicting the COVID-19 time series in the world using a hybrid ensemble modular neural network, which combines nonlinear autoregressive neural networks. At the level of the modular neural network, which is formed with several modules (ensembles in this case), the modules are designed to be efficient predictors for each country. In this case, an integrator is used to combine the outputs of the modules, in this way achieving the goal of predicting a set of countries. At the level of the ensembles, forming a part of the modular network, these are constituted by a set of modules, which are nonlinear autoregressive neural networks that are designed to be efficient predictors under particular conditions for each country. In each ensemble, the results of the modules are combined with an aggregator to achieve a better and improved result for the ensemble. Publicly available datasets of coronavirus cases around the globe from the last months have been used in the analysis. Interesting conclusions have been obtained that could be helpful in deciding the best strategies in dealing with this virus for countries in their fight against the coronavirus pandemic. In addition, the proposed approach could be helpful in proposing strategies for similar countries.

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In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19). The descriptors were investigated through different associations. The most relevant combinations, defined through a ranking algorithm, were analyzed via a heterogeneous ensemble of classifiers with the support vector machine, naive Bayes, random forest and K-nearest neighbors algorithms. The proposed scheme was applied to histological samples representative of breast cancer, colorectal cancer, oral dysplasia and liver tissue. The best results were accuracy rates of 94.83% to 100%, with the identification of pattern ensembles for classifying multiple histological images. The computational scheme indicated solutions exploring a reduced number of features (a maximum of 25 descriptors) and with better performance values than those observed in the literature. The presented information in this study is useful to complement and improve the development of computer-aided diagnosis focused on histological images.

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Multi-recording techniques show evidence that neurons coordinate their firing forming ensembles and that brain networks are made by connections between ensembles. While "canonical" microcircuits are composed of interconnected principal neurons and interneurons, it is not clear how they participate in recorded neuronal ensembles: "groups of neurons that show spatiotemporal co-activation". Understanding synapses and their plasticity has become complex, making hard to consider all details to fill the gap between cellular-synaptic and circuit levels. Therefore, two assumptions became necessary: First, whatever the nature of the synapses these may be simplified by "functional connections". Second, whatever the mechanisms to achieve synaptic potentiation or depression, the resultant synaptic weights are relatively stable. Both assumptions have experimental basis cited in this review, and tools to analyze neuronal populations are being developed based on them. Microcircuitry processing followed with multi-recording techniques show temporal sequences of neuronal ensembles resembling computational routines. These sequences can be aligned with the steps of behavioral tasks and behavior can be modified upon their manipulation, supporting the hypothesis that they are memory traces. In vitro, recordings show that these temporal sequences can be contained in isolated tissue of histological scale. Sequences found in control conditions differ from those recorded in pathological tissue obtained from animal disease models and those recorded after the actions of clinically useful drugs to treat disease states, setting the basis for new bioassays to test drugs with potential clinical use. These findings make the neuronal ensembles theoretical framework a dynamic neuroscience paradigm.

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Significance: The identification and manipulation of spatially identified neuronal ensembles with optical methods have been recently used to prove the causal link between neuronal ensemble activity and learned behaviors. However, the standardization of a conceptual framework to identify and manipulate neuronal ensembles from calcium imaging recordings is still lacking. Aim: We propose a conceptual framework for the identification and manipulation of neuronal ensembles using simultaneous calcium imaging and two-photon optogenetics in behaving mice. Approach: We review the computational approaches that have been used to identify and manipulate neuronal ensembles with single cell resolution during behavior in different brain regions using all-optical methods. Results: We proposed three steps as a conceptual framework that could be applied to calcium imaging recordings to identify and manipulate neuronal ensembles in behaving mice: (1) transformation of calcium transients into binary arrays; (2) identification of neuronal ensembles as similar population vectors; and (3) targeting of neuronal ensemble members that significantly impact behavioral performance. Conclusions: The use of simultaneous two-photon calcium imaging and two-photon optogenetics allowed for the experimental demonstration of the causal relation of population activity and learned behaviors. The standardization of analytical tools to identify and manipulate neuronal ensembles could accelerate interventional experiments aiming to reprogram the brain in normal and pathological conditions.

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A pipeline is proposed here to describe different features to study brain microcircuits on a histological scale using multi-scale analyses, including the uniform manifold approximation and projection (UMAP) dimensional reduction technique and modularity algorithm to identify neuronal ensembles, Runs tests to show significant ensembles activation, graph theory to show trajectories between ensembles, and recurrence analyses to describe how regular or chaotic ensembles dynamics are. The data set includes ex-vivo NMDA-activated striatal tissue in control conditions as well as experimental models of disease states: decorticated, dopamine depleted, and L-DOPA-induced dyskinetic rodent samples. The goal was to separate neuronal ensembles that have correlated activity patterns. The pipeline allows for the demonstration of differences between disease states in a brain slice. First, the ensembles were projected in distinctive locations in the UMAP space. Second, graphs revealed functional connectivity between neurons comprising neuronal ensembles. Third, the Runs test detected significant peaks of coactivity within neuronal ensembles. Fourth, significant peaks of coactivity were used to show activity transitions between ensembles, revealing recurrent temporal sequences between them. Fifth, recurrence analysis shows how deterministic, chaotic, or recurrent these circuits are. We found that all revealed circuits had recurrent activity except for the decorticated circuits, which tended to be divergent and chaotic. The Parkinsonian circuits exhibit fewer transitions, becoming rigid and deterministic, exhibiting a predominant temporal sequence that disrupts transitions found in the controls, thus resembling the clinical signs of rigidity and paucity of movements. Dyskinetic circuits display a higher recurrence rate between neuronal ensembles transitions, paralleling clinical findings: enhancement in involuntary movements. These findings confirm that looking at neuronal circuits at the histological scale, recording dozens of neurons simultaneously, can show clear differences between control and diseased striatal states: "fingerprints" of the disease states. Therefore, the present analysis is coherent with previous ones of striatal disease states, showing that data obtained from the tissue are robust. At the same time, it adds heuristic ways to interpret circuitry activity in different states.

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The employment of smart meters for energy consumption monitoring is essential for planning and management of power generation systems. In this context, forecasting energy consumption is a valuable asset for decision making, since it can improve the predictability of forthcoming demand to energy providers. In this work, we propose a data-driven ensemble that combines five single well-known models in the forecasting literature: a statistical linear autoregressive model and four artificial neural networks: (radial basis function, multilayer perceptron, extreme learning machines, and echo state networks). The proposed ensemble employs extreme learning machines as the combination model due to its simplicity, learning speed, and greater ability of generalization in comparison to other artificial neural networks. The experiments were conducted on real consumption data collected from a smart meter in a one-step-ahead forecasting scenario. The results using five different performance metrics demonstrate that our solution outperforms other statistical, machine learning, and ensembles models proposed in the literature.

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Lung cancer causes more deaths globally than any other type of cancer. To determine the best treatment, detecting EGFR and KRAS mutations is of interest. However, non-invasive ways to obtain this information are not available. Furthermore, many times there is a lack of big enough relevant public datasets, so the performance of single classifiers is not outstanding. In this paper, an ensemble approach is applied to increase the performance of EGFR and KRAS mutation prediction using a small dataset. A new voting scheme, Selective Class Average Voting (SCAV), is proposed and its performance is assessed both for machine learning models and CNNs. For the EGFR mutation, in the machine learning approach, there was an increase in the sensitivity from 0.66 to 0.75, and an increase in AUC from 0.68 to 0.70. With the deep learning approach, an AUC of 0.846 was obtained, and with SCAV, the accuracy of the model was increased from 0.80 to 0.857. For the KRAS mutation, both in the machine learning models (0.65 to 0.71 AUC) and the deep learning models (0.739 to 0.778 AUC), a significant increase in performance was found. The results obtained in this work show how to effectively learn from small image datasets to predict EGFR and KRAS mutations, and that using ensembles with SCAV increases the performance of machine learning classifiers and CNNs. The results provide confidence that as large datasets become available, tools to augment clinical capabilities can be fielded.

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The striatum is the largest entrance to the basal ganglia. Diverse neuron classes make up striatal microcircuit activity, consisting in the sequential activation of neuronal ensembles. How different neuron classes participate in generating ensemble sequences is unknown. In control mus musculus brain slices in vitro, providing excitatory drive generates ensemble sequences. In Parkinsonian microcircuits captured by a highly recurrent ensemble, a cortical stimulus causes a transitory reconfiguration of neuronal groups alleviating Parkinsonism. Alternation between neuronal ensembles needs interconnectivity, in part due to interneurons, preferentially innervated by incoming afferents. One main class of interneuron expresses parvalbumin (PV+ neurons) and mediates feed-forward inhibition. However, its more global actions within the microcircuit are unknown. Using calcium imaging in ex vivo brain slices simultaneously recording dozens of neurons, we aimed to observe the actions of PV+ neurons within the striatal microcircuit. PV+ neurons in active microcircuits are 5%-11% of the active neurons even if, anatomically, they are <1% of the total neuronal population. In resting microcircuits, optogenetic activation of PV+ neurons turns on circuit activity by activating or disinhibiting, more neurons than those actually inhibited, showing that feed-forward inhibition is not their only function. Optostimulation of PV+ neurons in active microcircuits inhibits and activates different neuron sets, resulting in the reconfiguration of neuronal ensembles by changing their functional connections and ensemble membership, showing that neurons may belong to different ensembles at different situations. Our results show that PV+ neurons participate in the mechanisms that generate alternation of neuronal ensembles, therefore provoking ensemble sequences.

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Intrinsically disordered proteins (IDPs) lack stable tertiary structure under physiological conditions. The unique composition and complex dynamical behaviour of IDPs make them a challenge for structural biology and molecular evolution studies. Using NMR ensembles, we found that IDPs evolve under a strong site-specific evolutionary rate heterogeneity, mainly originated by different constraints derived from their inter-residue contacts. Evolutionary rate profiles correlate with the experimentally observed conformational diversity of the protein, allowing the description of different conformational patterns possibly related to their structure-function relationships. The correlation between evolutionary rates and contact information improves when structural information is taken not from any individual conformer or the whole ensemble, but from combining a limited number of conformers. Our results suggest that residue contacts in disordered regions constrain evolutionary rates to conserve the dynamic behaviour of the ensemble and that evolutionary rates can be used as a proxy for the conformational diversity of IDPs.

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The mouse motor cortex exhibits spontaneous activity in the form of temporal sequences of neuronal ensembles in vitro without the need of tissue stimulation. These neuronal ensembles are defined as groups of neurons with a strong correlation between its firing patterns, generating what appears to be a predetermined neural conduction mode that needs study. Each ensemble is commonly accompanied by one or more parvalbumin expressing neurons (PV+) or fast spiking interneurons. Many of these interneurons have functional connections between them, helping to form a circuit configuration similar to a small-world network. However, rich club metrics show that most connected neurons are neurons not expressing parvalbumin, mainly pyramidal neurons (PV-) suggesting feed-forward propagation through pyramidal cells. Ensembles with PV+ neurons are connected to these hubs. When ligand-gated fast GABAergic transmission is blocked, temporal sequences of ensembles collapse into a unique synchronous and recurrent ensemble, showing the need of inhibition for coding cortical spontaneous activity. This new ensemble has a duration and electrophysiological characteristics of brief recurrent interictal epileptiform discharges (IEDs) composed by the coactivity of both PV- and PV+ neurons, demonstrating that GABA transmission impedes its occurrence. Synchronous ensembles are clearly divided into two clusters one of them lasting longer and mainly composed by PV+ neurons. Because an ictal-like event was not recorded after several minutes of IEDs recording, it is inferred that an external stimulus and/or fast GABA transmission are necessary for its appearance, making this preparation ideal to study both the neuronal machinery to encode cortical spontaneous activity and its transformation into brief non-ictal epileptiform discharges.

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In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.

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The combination of organic and inorganic materials at the nanoscale to form functional hybrid structures is a powerful strategy to develop novel electronic devices. The knowledge on semiconductor thin-film polarization brings direct benefits to the hybrid organic/inorganic electronics, becoming primordial for the development of devices such as electromechanical logic gates, solar cells, miniaturized valves, organic diodes, and molecular supercapacitors, among others. Here, we report on the dielectric polarization of ultrathin organic semiconducting films-ca. 5 nm thick metal phthalocyanine ensembles (viz., CuPc, CoPc, F16CuPc)-employed to build up hybrid metal/oxide/molecule heterojunctions. Such hybrid heterostructures are fully integrated into self-rolled nanomembrane-based capacitors and further investigated by impedance spectroscopy measurements as a function of temperature (from 6 to 300 K). The dielectric polarization of the metal phthalocyanines is found to be thermally activated above a specific threshold temperature, which depends on the molecular structure. Below this threshold, the current leakage across the system is suppressed, thus evidencing intrinsic-like polarization mechanisms. The temperature-independent permittivities of the ultrathin molecular films are found to be strongly dependent on the organic/inorganic hybrid interfaces, while the calculated relaxation times are more likely related to each single-molecule polarization. Beyond the advances in determining the temperature dependence of the permittivity for ultrathin phthalocyanine films integrated within solid-state electronics, our results also support the deterministic design of novel functional devices based on nanoscale hybrid organic/inorganic heterojunctions.

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Nanomembranes (NMs) are freestanding structures with few-nanometer thickness and lateral dimensions up to the microscale. In nanoelectronics, NMs have been used to promote reliable electrical contacts with distinct nanomaterials, such as molecules, quantum dots, and nanowires, as well as to support the comprehension of the condensed matter down to the nanoscale. Here, we propose a tunable device architecture that is capable of deterministically changing both the contact geometry and the current injection in nanoscale electronic junctions. The device is based on a hybrid arrangement that joins metallic NMs and molecular ensembles, resulting in a versatile, mechanically compliant element. Such a feature allows the devices to accommodate a mechanical stimulus applied over the top electrodes, enlarging the junctions' active area without compromising the molecules. A model derived from the Hertzian mechanics is employed to correlate the contact dynamics with the electronic transport in these novel devices denominated as variable-area transport junctions (VATJs). As a proof of concept, we propose a direct application of the VATJs as compression gauges envisioning the development of hypersensitive pressure pixels. Regarding sensitivity (∼480 kPa-1), the VATJ-based transducers constitute a breakthrough in nanoelectronics, with the prospect of carrying its sister-field of molecular electronics out of the laboratory via integrative, hybrid organic/inorganic nanotechnology.

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Cytochrome P450cam (CYP101A1) catalyzes the stereospecific 5-exo hydroxylation of d-camphor by molecular oxygen. Previously, residual dipolar couplings measured for backbone amide 1H-15N correlations in both substrate-free and bound forms of CYP101A1 were used as restraints in soft annealing molecular dynamic simulations in order to identify average conformations of the enzyme with and without substrate bound. Multiple substrate-dependent conformational changes remote from the enzyme active site were identified, and site-directed mutagenesis and activity assays confirmed the importance of these changes in substrate recognition. The current work makes use of perturbation response scanning (PRS) and umbrella sampling molecular dynamic of the residual dipolar coupling-derived CYP101A1 structures to probe the roles of remote structural features in enforcing the regio- and stereospecific nature of the hydroxylation reaction catalyzed by CYP101A1. An improper dihedral angle Ψ was defined and used to maintain substrate orientation in the CYP101A1 active site, and it was observed that different values of Ψ result in different PRS response maps. Umbrella sampling methods show that the free energy of the system is sensitive to Ψ, and bound substrate forms an important mechanical link in the transmission of mechanical coupling through the enzyme structure. Finally, a qualitative approach to interpreting PRS maps in terms of the roles of secondary structural features is proposed.

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The availability of fruits and consumption of fruits by bats in semievergreenforest remnants in central Veracruz, Mexico, were studied. To do this goal, five fragments were sampled bimonthly from March 2009 to May 2010. Was it found a relationship between availability and consumption of fruits with the abundance of bats. The results allowed verifying the marked determinism of these ensembles by the resources used. Finally, the contribution is the one to discuss these issues with this group in semi-evergreen forests.

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Under the assumption that wing shape predicts bat species flight performance, I analyzed wing shape data of phylostomid bats from Rio Doce State Park (PERD), located in the Brazilian Atlantic Forest. Correlations between size-related variables in phyllostomid species were positive as expected, with the exception of those between mass and aspect ratio. Wing loadings varied from low to very high, and increased with body mass less than expected. Aspect ratios were homogeneously low, and wingtips were more variable in length than in area. Most phyllostomids had tip shape indexes close to or larger than 1, with the exceptions of Chiroderma and Tonatia. The foraging guilds matrix incorporating three fruit bats ensembles amplified the number of potential, suitable niche categories for bats from PERD: (1) understorey frugivores that consume Piperaceae and have broad wing surfaces, developed interfemoral membranes, low wing loadings (WL) aspect ratios (AR), and high tip shape indexes (TSI); (2) canopy frugivores that consume Cecropiaceae and Moraceae, have high WL, variable AR tending to low, and pointed wingtips with large areas; and (3) frugivores that consume plants distributed both in open and forest habitats, with broad dietary spectrum, high WL, intermediate AR, and TSI close to 1. I hypothesize that the ecology of the bat species from PERD is constrained by flight modes correlated with species-specific flightrelated characters, which consequently constraints habitat and microhabitat selection by those bats, and has implications for food choice.

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The availability of fruits and consumption of fruits by bats in semievergreenforest remnants in central Veracruz, Mexico, were studied. To do this goal, five fragments were sampled bimonthly from March 2009 to May 2010. Was it found a relationship between availability and consumption of fruits with the abundance of bats. The results allowed verifying the marked determinism of these ensembles by the resources used. Finally, the contribution is the one to discuss these issues with this group in semi-evergreen forests.(AU)

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Under the assumption that wing shape predicts bat species flight performance, I analyzed wing shape data of phylostomid bats from Rio Doce State Park (PERD), located in the Brazilian Atlantic Forest. Correlations between size-related variables in phyllostomid species were positive as expected, with the exception of those between mass and aspect ratio. Wing loadings varied from low to very high, and increased with body mass less than expected. Aspect ratios were homogeneously low, and wingtips were more variable in length than in area. Most phyllostomids had tip shape indexes close to or larger than 1, with the exceptions of Chiroderma and Tonatia. The foraging guilds matrix incorporating three fruit bats ensembles amplified the number of potential, suitable niche categories for bats from PERD: (1) understorey frugivores that consume Piperaceae and have broad wing surfaces, developed interfemoral membranes, low wing loadings (WL) aspect ratios (AR), and high tip shape indexes (TSI); (2) canopy frugivores that consume Cecropiaceae and Moraceae, have high WL, variable AR tending to low, and pointed wingtips with large areas; and (3) frugivores that consume plants distributed both in open and forest habitats, with broad dietary spectrum, high WL, intermediate AR, and TSI close to 1. I hypothesize that the ecology of the bat species from PERD is constrained by flight modes correlated with species-specific flightrelated characters, which consequently constraints habitat and microhabitat selection by those bats, and has implications for food choice.(AU)

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