Voltage-gated Ca(V)2.1 (P/Q-type) Ca2+ channels play a crucial role in regulating neurotransmitter release, thus contributing to synaptic plasticity and to processes such as learning and memory. Despite their recognized importance in neural function, there is limited information on their potential involvement in neurodegenerative conditions such as Alzheimer's disease (AD). Here, we aimed to explore the impact of AD pathology on the density and nanoscale compartmentalization of Ca(V)2.1 channels in the hippocampus in association with GABA(B) receptors. Histoblotting experiments showed that the density of Ca(V)2.1 channel was significantly reduced in the hippocampus of APP/PS1 mice in a laminar-dependent manner. Ca(V)2.1 channel was enriched in the active zone of the axon terminals and was present at a very low density over the surface of dendritic tree of the CA1 pyramidal cells, as shown by quantitative SDS-digested freeze-fracture replica labelling (SDS-FRL). In APP/PS1 mice, the density of Ca(V)2.1 channel in the active zone was significantly reduced in the strata radiatum and lacunosum-moleculare, while it remained unaltered in the stratum oriens. The decline in Cav2.1 channel density was found to be associated with a corresponding impairment in the GABAergic synaptic function, as evidenced by electrophysiological experiments carried out in the hippocampus of APP/PS1 mice. Remarkably, double SDS-FRL showed a co-clustering of Ca(V)2.1 channel and GABA(B1) receptor in nanodomains (similar to 40-50 nm) in wild type mice, while in APP/PS1 mice this nanoarchitecture was absent. Together, these findings suggest that the AD pathology-induced reduction in Ca(V)2.1 channel density and Ca(V)2.1-GABA(B1) de-clustering may play a role in the synaptic transmission alterations shown in the AD hippocampus. Therefore, uncovering these layer-dependent changes in P/Q calcium currents associated with AD pathology can benefit the development of future strategies for AD management.

In this study, we perform land use classification (LUC) using sequences of multispectral Sentinel-2 images taken over multiple years. It evaluates the convolutional neural network (CNN) applied in successive years after the training one because in many cases the availability of field data for updating the model involves a notable delay and the identification of irrigated crops must be near real-time to be properly managed. The strategy consists of interpolating data to the same dates every year with a format of 2D-fingerprints to make their patterns explicit and aligned to use a CNN to capture them. It reaches a 76%-80% accuracy in classifying pixels among 11 classes across different years being trained only using one year. The main interest is focused on identifying irrigated fields, reaching over 90% F1-Score in most classes. Consequently, this model is useful for monitoring crops and aids experts in the generation of data for decision-making.

Introduction Artificial intelligence (AI) is a milestone for human technology. In medicine, AI is set to play an important role as we progress into a new era. In plastic surgery, AI can participate in breast symmetry assessment, which until now has been mainly subjective, allowing for inconsistencies. This study aims to improve this evaluation process by integrating a novel trained neural network with the breast symmetry calculator, BAS-Calc. Materials and Methods We combined the BAS-Calc tool with a custom-made neural network trained to automatically detect key features of the breast. This integrated system was tested on 81 images of patients who had undergone breast reconstruction post-breast cancer treatment. Its performance was evaluated against two human observers using statistical analysis. Results Our model successfully detected 399/405 (98.51%) of landmarks. Spearman and Pearson correlation indicated a strong positive relationship while Cohen's kappa demonstrated moderate to strong agreement between human observers and AI model. Notably, the average calculation time for the AI was 0.92 seconds, 16 times faster than the 14.09 seconds for humans. Conclusions Our AI model successfully calculated breast symmetry from images of patients who had undergone reconstructive oncological breast surgery, demonstrating high correlation with human assessments and a markedly reduced processing time. As AI continues to evolve, it is poised to become a pivotal tool in Medicine. Therefore, it is crucial for medical professionals to proactively engage in implementing AI technologies safely and effectively. Further studies are required to broaden our understanding and maximize the potential benefits in this area. Takeaway bullet points Artificial intelligence (AI) is an upcoming force to be reckoned with. AI should find its way into practical applications in plastic surgery. AI can be applied to improve patient care and evaluate aesthetic results. In this work, we present a novel AI model that automatically evaluates breast symmetry. Level of Evidence IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Background: In plastic surgery, evaluation of breast symmetry is an important aspect of clinical practice. Computer programs have been developed for this purpose, but most of them require operator input. Artificial intelligence has been introduced into many aspects of medicine. In plastic surgery, automated neural networks for breast evaluation could improve quality of care. In this work, the authors evaluate the identification of breast features with an ad hoc trained neural network. Methods: An ad hoc convolutional neural network was developed on the YOLOV3 platform to detect key features of the breast that are commonly used in plastic surgery for symmetry evaluation. The program was trained with 200 frontal photographs of patients who underwent breast surgery and was tested on 47 frontal images of patients who underwent breast reconstruction after breast cancer surgery. Results: The program was able to detect key features in 97.74% of cases (boundaries of the breast in 94 of 94 cases, the nipple-areola complex in 94 of 94 cases, and the suprasternal notch in 41 of 47 cases). Mean time of detection was 0.52 seconds. Conclusions: The ad hoc neural network was successful in localizing key breast features, with a total detection rate of 97.74%. Neural networks and machine learning have the potential to improve the evaluation of breast symmetry in plastic surgery by automated and quick detection of features used by surgeons in practice. More studies and development are needed to further knowledge in this area.

One important point in the development of a brain-machine Interface (BMI) commanding an exoskeleton is the assessment of the cognitive engagement of the subject during the motor imagery tasks conducted. However, there are not many databases that provide electroencephalography (EEG) data during the use of a lower-limb exoskeleton. The current paper presents a database designed with an experimental protocol aiming to assess not only motor imagery during the control of the device, but also the attention to gait on flat and inclined surfaces. The research was conducted as an EUROBENCH subproject in the facilities sited in Hospital Los Madroños, Brunete (Madrid). The data validation reaches accuracies over 70% in the assessment of motor imagery and attention to gait, which marks the present database as a valuable resource for researches interested on developing and testing new EEG-based BMIs.

Neurorehabilitation has gradually become one of the most hopeful tools in some kind of injuries and diseases during the last decade. Several studies have shown that conscious movement effected by patients with mobility difficulties, assisted by a clinical device such as an exoskeleton, contributes positively to their mobility recovery, shortening the rehabilitation times and improving its results. Besides, other studies have hypothesized that the motor cortex is particularly active during specific phases of gait cycle. In this study, a multilinear regression model has been applied to eight users in order to decode lower limb kinematics from EEG signals, reaching an average similitude between real and decoded trajectories (Pearson Correlation Coefficient) of 0.35 and up to 0.42 after optimizing the model parameters. These results encourage us to undertake a deeper analysis of the multilinear regression model as well as consider other processing approaches to perform the decoding in the future.

Alzheimer’s disease (AD) is characterized by a reorganization of brain activity determining network hyperexcitability and loss of synaptic plasticity. Precisely, a dysfunction in metabotropic GABA B receptor signalling through G protein-gated inwardly rectifying K + (GIRK or Kir3) channels on the hippocampus has been postulated. Thus, we determined the impact of amyloid-β (Aβ) pathology in GIRK channel density, subcellular distribution, and its association with GABA B receptors in hippocampal CA1 pyramidal neurons from the APP/PS1 mouse model using quantitative SDS-digested freeze-fracture replica labelling (SDS-FRL) and proximity ligation in situ assay (P-LISA). In wild type mice, single SDS-FRL detection revealed a similar dendritic gradient for GIRK1 and GIRK2 in CA1 pyramidal cells, with higher densities in spines, and GIRK3 showed a lower and uniform distribution. Double SDS-FRL showed a co-clustering of GIRK2 and GIRK1 in post- and presynaptic compartments, but not for GIRK2 and GIRK3. Likewise, double GABA B1 and GIRK2 SDS-FRL detection displayed a high degree of co-clustering in nanodomains (40–50 nm) mostly in spines and axon terminals. In APP/PS1 mice, the density of GIRK2 and GIRK1, but not for GIRK3, was significantly reduced along the neuronal surface of CA1 pyramidal cells and in axon terminals contacting them. Importantly, GABA B1 and GIRK2 co-clustering was not present in APP/PS1 mice. Similarly, P-LISA experiments revealed a significant reduction in GABA B1 and GIRK2 interaction on the hippocampus of this animal model. Overall, our results provide compelling evidence showing a significant reduction on the cell surface density of pre- and postsynaptic GIRK1 and GIRK2, but not GIRK3, and a decline in GABA B receptors and GIRK2 channels co-clustering in hippocampal pyramidal neurons from APP/PS1 mice, thus suggesting that a disruption in the GABA B receptor–GIRK channel membrane assembly causes dysregulation in the GABA B signalling via GIRK channels in this AD animal model.

Land use classification (LUC) is the process of providing information on land cover and the types of human activity involved in land use. In this study, we perform agricultural LUC using sequences of multispectral reflectance Sentinel-2 images taken in 2018. LUC can be carried out using machine or deep learning techniques. Some existing models process data at the pixel level, performing LUC successfully with a reduced number of images. Part of the pixel information corresponds to multispectral temporal patterns that, despite not being especially complex, might remain undetected by models such as random forests or multilayer perceptrons. Thus, we propose to arrange pixel information as 2D yearly fingerprints so as to render such patterns explicit and make use of a CNN to model and capture them. The results show that our proposal reaches a 91% weighted accuracy in classifying pixels among 19 classes, outperforming random forest by 8%, or a specifically tuned multilayer perceptron by 4%. Furthermore, models were also used to perform a ternary classification in order to detect irrigated fields, reaching a 97% global accuracy. We can conclude that this is a promising operational tool for monitoring crops and water use over large areas.

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El uso de interfaces cerebro máquina (BCIs) supone un importante avance en el control de dispositivos para la rehabilitación de pacientes. Además, la detección de la atención durante la marcha puede ser fundamental a la hora de garantizar la seguridad en el control de estos dispositivos mediante electroencefalogramas (EEG) y evitar activaciones involuntarias. En esta investigación, realizamos un análisis de diferentes configuraciones de los electrodos que componen una BCI, tratando de discernir qué configuraciones detectan la atención de manera más precisa

Metabotropic γ-aminobutyric acid (GABAB) receptors contribute to the control of network activity and information processing in hippocampal circuits by regulating neuronal excitability and synaptic transmission. The dysfunction in the dentate gyrus (DG) has been implicated in Alzheimer´s disease (AD). Given the involvement of GABAB receptors in AD, to determine their subcellular localisation and possible alteration in granule cells of the DG in a mouse model of AD at 12 months of age, we used high-resolution immunoelectron microscopic analysis. Immunohistochemistry at the light microscopic level showed that the regional and cellular expression pattern of GABAB1 was similar in an AD model mouse expressing mutated human amyloid precursor protein and presenilin1 (APP/PS1) and in age-matched wild type mice. High-resolution immunoelectron microscopy revealed a distance-dependent gradient of immunolabelling for GABAB receptors, increasing from proximal to distal dendrites in both wild type and APP/PS1 mice. However, the overall density of GABAB receptors at the neuronal surface of these postsynaptic compartments of granule cells was significantly reduced in APP/PS1 mice. Parallel to this reduction in surface receptors, we found a significant increase in GABAB1 at cytoplasmic sites. GABAB receptors were also detected at presynaptic sites in the molecular layer of the DG. We also found a decrease in plasma membrane GABAB receptors in axon terminals contacting dendritic spines of granule cells, which was more pronounced in the outer than in the inner molecular layer. Altogether, our data showing post- and presynaptic reduction in surface GABAB receptors in the DG suggest the alteration of the GABAB-mediated modulation of excitability and synaptic transmission in granule cells, which may contribute to the cognitive dysfunctions in the APP/PS1 model of AD.

Synapse loss occurs early in Alzheimer's disease (AD) patients and animal models. Alterations at synaptic level are a major morphological correlate of the memory deficits and related symptoms of AD. Given the predominant roles of synaptic AMPA receptors (AMPARs) in excitatory synaptic transmission in the brain, changes in their dynamic regulation are also implicated in the pathophysiology of AD. Here, we used immunolocalization techniques to analyze the expression and subcellular distribution of AMPARs in the hippocampal region of APP/PS1 mouse model of AD. Immunoblots and histoblots revealed that the total amount of AMPARs and their regional expression pattern in the hippocampus was similar in APP/PS1 mice and in age-matched wild type mice. At the ultrastructural level, two synapse populations were examined using SDS-digested freeze-fracture replica labeling in the stratum radiatum in mice: (i) on spines of CA1 pyramidal cells; and (ii) on randomly found dendritic shafts of CA1 interneurons. While 1- and 6-months-old APP/PS1 mice exhibited no change, we observed a significant reduction at 12 months in AMPAR density at synapses in both pyramidal cells and interneurons, compared to wild-type. This reduction of AMPARs in dendritic spines was accompanied by a significant increase in AMPAR subunit proteins identified in intracellular compartments. Our data demonstrate an age-dependent reduction of synaptic AMPARs in APP/PS1 mice, which may contribute to impaired learning and memory at later stages of AD.

In the last few years, the platforms for online learning, such as MOOCs, are becoming more and more popular. Particularly, in fields like computer science, students very often choose this way, instead of official programs, to complete their formation. In this context, universities must adapt to changes in order to offer the kind of formation that is demanded nowadays. In this article we present CiDAEN, and online course in data science and cloud applications that is offered from the University of Castilla-La Mancha (Spain). We describe how it has been implemented in a public university through a non-official program. We also share our (successful) experience, describe the methodology, the tools, and the importance of communication channels in maintain the interest of students.

The hippocampus plays key roles in learning and memory and is a main target of Alzheimer's disease (AD), which causes progressive memory impairments. Despite numerous investigations about the processes required for the normal hippocampal functions, the neurotransmitter receptors involved in the synaptic deficits by which AD disables the hippocampus are not yet characterized. By combining histoblots, western blots, immunohistochemistry and high‐resolution immunoelectron microscopic methods for GABAB receptors, this study provides a quantitative description of the expression and the subcellular localization of GABAB1 in the hippocampus in a mouse model of AD at 1, 6 and 12 months of age. Western blots and histoblots showed that the total amount of protein and the laminar expression pattern of GABAB1 were similar in APP/PS1 mice and in age‐matched wild‐type mice. In contrast, immunoelectron microscopic techniques showed that the subcellular localization of GABAB1 subunit did not change significantly in APP/PS1 mice at 1 month of age, was significantly reduced in the stratum lacunosum‐moleculare of CA1 pyramidal cells at 6 months of age and significantly reduced at the membrane surface of CA1 pyramidal cells at 12 months of age. This reduction of plasma membrane GABAB1 was paralleled by a significant increase of the subunit at the intracellular sites. We further observed a decrease of membrane‐targeted GABAB receptors in axon terminals contacting CA1 pyramidal cells. Our data demonstrate compartment‐ and age‐dependent reduction of plasma membrane‐targeted GABAB receptors in the CA1 region of the hippocampus, suggesting that this decrease might be enough to alter the GABAB‐mediated synaptic transmission taking place in AD.

Metabotropic GABA(B) receptors mediate slow inhibitory effects presynaptically and postsynaptically through the modulation of different effector signalling pathways. Here, we analysed the distribution of GABA(B) receptors using highly sensitive SDS-digested freeze-fracture replica labelling in mouse cerebellar Purkinje cells. Immunoreactivity for GABA(B1) was observed on presynaptic and, more abundantly, on postsynaptic compartments, showing both scattered and clustered distribution patterns. Quantitative analysis of immunoparticles revealed a somato-dendritic gradient, with the density of immunoparticles increasing 26-fold from somata to dendritic spines. To understand the spatial relationship of GABA(B) receptors with two key effector ion channels, the G protein-gated inwardly rectifying K+ (GIRK/Kir3) channel and the voltage-dependent Ca2+ channel, biochemical and immunohistochemical approaches were performed. Co-immunoprecipitation analysis demonstrated that GABA(B) receptors co-assembled with GIRK and Ca(V)2.1 channels in the cerebellum. Using double-labelling immunoelectron microscopic techniques, co-clustering between GABA(B1) and GIRK2 was detected in dendritic spines, whereas they were mainly segregated in the dendritic shafts. In contrast, co-clustering of GABA(B1) and Ca(V)2.1 was detected in dendritic shafts but not spines. Presynaptically, although no significant co-clustering of GABA(B1) and GIRK2 or Ca(V)2.1 channels was detected, inter-cluster distance for GABA(B1) and GIRK2 was significantly smaller in the active zone than in the dendritic shafts, and that for GABA(B1) and Ca(V)2.1 was significantly smaller in the active zone than in the dendritic shafts and spines. Thus, GABA(B) receptors are associated with GIRK and Ca(V)2.1 channels in different subcellular compartments. These data provide a better framework for understanding the different roles played by GABA(B) receptors and their effector ion channels in the cerebellar network.

Regression trees (RTs) are simple, but powerful models, which have been widely used in the last decades in different scopes. Fuzzy RTs (FRTs) add fuzziness to RTs with the aim of dealing with uncertain environments. Most of the FRT learning approaches proposed in the literature aim to improve the accuracy, measured in terms of mean squared error, and often neglect to consider the computation time and/or the memory requirements. In today's application domains, which require the management of huge amounts of data, this carelessness can strongly limit their use. In this paper, we propose a distributed FRT (DFRT) learning scheme for generating binary RTs from big datasets, that is based on the MapReduce paradigm. We have designed and implemented the scheme on the Apache Spark framework. We have used eight real-world and four synthetic datasets for evaluating its performance, in terms of mean squared error, computation time and scalability. As a baseline, we have compared the results with the distributed RT (DRT) and the Distributed Random Forest (DRF) available in the Spark MLlib library. Results show that our DFRT scales similarly to DRT and better than DRF. Regarding the performance, DFRT generalizes much better than DRT and similarly to DRF.

The small-conductance, Ca2+-activated K+ (SK) channel subtype SK2 regulates the spike rate and firing frequency, as well as Ca2+ transients in Purkinje cells (PCs). To understand the molecular basis by which SK2 channels mediate these functions, we analyzed the exact location and densities of SK2 channels along the neuronal surface of the mouse cerebellar PCs using SDS-digested freeze-fracture replica labeling (SDS-FRL) of high sensitivity combined with quantitative analyses. Immunogold particles for SK2 were observed on post- and pre-synaptic compartments showing both scattered and clustered distribution patterns. We found an axo-somato-dendritic gradient of the SK2 particle density increasing 12-fold from soma to dendritic spines. Using two different immunogold approaches, we also found that SK2 immunoparticles were frequently adjacent to, but never overlap with, the postsynaptic density of excitatory synapses in PC spines. Co-immunoprecipitation analysis demonstrated that SK2 channels form macromolecular complexes with two types of proteins that mobilize Ca2+: CaV2.1 channels and mGlu1α receptors in the cerebellum. Freeze-fracture replica double-labeling showed significant co-clustering of particles for SK2 with those for CaV2.1 channels and mGlu1α receptors. SK2 channels were also detected at presynaptic sites, mostly at the presynaptic active zone (AZ), where they are close to CaV2.1 channels, though they are not significantly co-clustered. These data demonstrate that SK2 channels located in different neuronal compartments can associate with distinct proteins mobilizing Ca2+, and suggest that the ultrastructural association of SK2 with CaV2.1 and mGlu1α provides the mechanism that ensures voltage (excitability) regulation by distinct intracellular Ca2+ transients in PCs.

Bayesian networks learning is computationally expensive even in the case of sacrificing the optimality of the result. Many methods aim at obtaining quality solutions in affordable times. Most of them are based on local search algorithms, as they allow evaluating candidate networks in a very efficient way, and can be further improved by using local search-based metaheuristics to avoid getting stuck in local optima. This approach has been successfully applied in searching for network structures in the space of directed acyclic graphs. Other algorithms search for the networks in the space of equivalence classes. The most important of these is GES (greedy equivalence search). It guarantees obtaining the optimal network under certain conditions. However, it can also get stuck in local optima when learning from datasets with limited size. This article proposes the use of local search-based metaheuristics as a way to improve the behaviour of GES in such circumstances. These methods also guarantee asymptotical optimality, and the experiments show that they improve upon the score of the networks obtained with GES. (C) 2017 Elsevier B.V. All rights reserved.

Structural learning of Bayesian networks is a very expensive task even when sacrifying the optimality of the result. Because of that, there are some proposals aimed at obtaining relative-quality solutions in short times. One of them, namely Chain-ACO, searches an ordering among all variables with Ant Colony Optimization and a chain-structured surrogate model, and then uses this ordering to build a Bayesian network by means of the well-known K2 algorithm. This work is based on Chain-ACO. We evaluate the impact of using a tree-structured surrogate model instead of a chain to evaluate orderings during the search. Moreover, we propose a variation of the way K2 builds the network, which consists of allowing some changes in the positions of the variables whenever they such changes not produce a cycle. This modification of the process may improve the score of the final network, without (almost) any additional cost.