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ed platforms were standardized for reference samples, their use is recommended for the measurement of CSF AD biomarkers compared with unstandardized manual methods, such as Innotest ELISA, that have demonstrated a high inter and intra-laboratory variability.Background Behavioral and psychological symptoms of dementia (BPSD) are a distressful condition. We aimed to investigate the BPSD distribution in subjects with cognitive impairment, and the potential correlations between BPSD and neurodegeneration in terms of cerebrospinal fluid (CSF) tau and brain atrophy. Methods One-hundred patients with mild cognitive impairment (MCI) or dementia (Alzheimer's disease, AD; Lewy-body disease, LBD; frontotemporal dementia, FTD; vascular dementia, VD) underwent a complete diagnostic workup, including 3T-MRI and/or CT and CSF. Cortical atrophy was assessed with medial temporal atrophy (MTA), posterior atrophy (PA), and global cortical atrophy-frontal lobe (GCA-F) scales. BPSD were rated using the Neuropsychiatric Inventory (NPI), and BPSD clusters were defined according to the European Alzheimer Disease Consortium. Results Delusions, hallucinations, and psychosis cluster were differently distributed among the diagnostic groups (p less then 0.05, p less then 0.001, and p lssociated with the occurrence and severity of BPSD in clinical practice. Longitudinal studies are however required to ascertain their actual predictive value.This review summarizes our current knowledge of human disease-relevant genetic variants within the family of voltage gated Ca2+ channels. Ca2+ channelopathies cover a wide spectrum of diseases including epilepsies, autism spectrum disorders, intellectual disabilities, developmental delay, cerebellar ataxias and degeneration, severe cardiac arrhythmias, sudden cardiac death, eye disease and endocrine disorders such as congential hyperinsulinism and hyperaldosteronism. A special focus will be on the rapidly increasing number of de novo missense mutations identified in the pore-forming α1-subunits with next generation sequencing studies of well-defined patient cohorts. In contrast to likely gene disrupting mutations these can not only cause a channel loss-of-function but can also induce typical functional changes permitting enhanced channel activity and Ca2+ signaling. Such gain-of-function mutations could represent therapeutic targets for mutation-specific therapy of Ca2+-channelopathies with existing or novel Ca2+-channel inhibitors. Moreover, many pathogenic mutations affect positive charges in the voltage sensors with the potential to form gating-pore currents through voltage sensors. If confirmed in functional studies, specific blockers of gating-pore currents could also be of therapeutic interest.Occlusions, restricted field of view and limited resolution all constrain a robot's ability to sense its environment from a single observation. In these cases, the robot first needs to actively query multiple observations and accumulate information before it can complete a task. In this paper, we cast this problem of active vision as active inference, which states that an intelligent agent maintains a generative model of its environment and acts in order to minimize its surprise, or expected free energy according to this model. We apply this to an object-reaching task for a 7-DOF robotic manipulator with an in-hand camera to scan the workspace. A novel generative model using deep neural networks is proposed that is able to fuse multiple views into an abstract representation and is trained from data by minimizing variational free energy. We validate our approach experimentally for a reaching task in simulation in which a robotic agent starts without any knowledge about its workspace. Each step, the next view pose is chosen by evaluating the expected free energy. We find that by minimizing the expected free energy, exploratory behavior emerges when the target object to reach is not in view, and the end effector is moved to the correct reach position once the target is located. Similar to an owl scavenging for prey, the robot naturally prefers higher ground for exploring, approaching its target once located.Gaze and language are major pillars in multimodal communication. Gaze is a non-verbal mechanism that conveys crucial social signals in face-to-face conversation. However, compared to language, gaze has been less studied as a communication modality. The purpose of the present study is 2-fold (i) to investigate gaze direction (i.e., aversion and face gaze) and its relation to speech in a face-to-face interaction; and (ii) to propose a computational model for multimodal communication, which predicts gaze direction using high-level speech features. Twenty-eight pairs of participants participated in data collection. The experimental setting was a mock job interview. The eye movements were recorded for both participants. The speech data were annotated by ISO 24617-2 Standard for Dialogue Act Annotation, as well as manual tags based on previous social gaze studies. A comparative analysis was conducted by Convolutional Neural Network (CNN) models that employed specific architectures, namely, VGGNet and ResNet. The results showed that the frequency and the duration of gaze differ significantly depending on the role of participant. Moreover, the ResNet models achieve higher than 70% accuracy in predicting gaze direction.Reinforcement learning is a paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse rewards. To partition an environment into discrete states, implementations in spiking neuronal networks typically rely on input architectures involving place cells or receptive fields specified ad hoc by the researcher. Paeoniflorin in vitro This is problematic as a model for how an organism can learn appropriate behavioral sequences in unknown environments, as it fails to account for the unsupervised and self-organized nature of the required representations. Additionally, this approach presupposes knowledge on the part of the researcher on how the environment should be partitioned and represented and scales poorly with the size or complexity of the environment. To address these issues and gain insights into how the brain generates its own task-relevant mappings, we propose a learning architecture that combines unsupervised learning on the input projections with biologically motivated clustered connectivity within the representation layer.
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