Dendrite and axon arbor wiring patterns determine the connectivity and computational characteristics of a neuron. The identities of these dendrite and axon arbors are created by differential polarization of their microtubule arrays, and their complexity and pattern are generated by the extension and organization of these arrays. We describe how several molecularly distinct microtubule organizing center (MTOC) mechanisms function during neuron differentiation to generate and arrange dendrite and axon microtubules. The temporal and spatial organization of these MTOCs generates, patterns, and diversifies arbor wiring.

CUX2 gene encodes a transcription factor that controls neuronal proliferation, dendrite branching and synapse formation, locating at the epilepsy-associated chromosomal region 12q24 that we previously identified by a genome-wide association study (GWAS) in Japanese population. A CUX2 recurrent de novo variant p.E590K has been described in patients with rare epileptic encephalopathies and the gene is a candidate for the locus, however the mutation may not be enough to generate the genome-wide significance in the GWAS and whether CUX2 variants appear in other types of epilepsies and physiopathological mechanisms are remained to be investigated. Here in this study, we conducted targeted sequencings of CUX2, a paralog CUX1 and its short isoform CASP harboring a unique C-terminus on 271 Japanese patients with a variety of epilepsies, and found that multiple CUX2 missense variants, other than the p.E590K, and some CASP variants including a deletion, predominantly appeared in patients with temporal lobe epilepsy (TLE). The CUX2 variants showed abnormal localization in human cell culture analysis. While wild-type CUX2 enhances dendritic arborization in fly neurons, the effect was compromised by some of the variants. Cux2- and Casp-specific knockout mice both showed high susceptibility to kainate, increased excitatory cell number in the entorhinal cortex, and significant enhancement in glutamatergic synaptic transmission to the hippocampus. CASP and CUX2 proteins physiologically bound to each other and co-expressed in excitatory neurons in brain regions including the entorhinal cortex. These results suggest that CUX2 and CASP variants contribute to the TLE pathology through a facilitation of excitatory synaptic transmission from entorhinal cortex to hippocampus.

The Internet of Things (IoT) is a key and growing technology for many critical real-life applications, where it can be used to improve decision making. The existence of several sources of uncertainty in the IoT infrastructure, however, can lead decision makers into taking inappropriate actions. The present work focuses on proposing a risk-based IoT decision-making framework in order to effectively manage uncertainties in addition to integrating domain knowledge in the decision-making process. A structured literature review of the risks and sources of uncertainty in IoT decision-making systems is the basis for the development of the framework and Human Activity Recognition (HAR) case studies. More specifically, as one of the main targeted challenges, the potential sources of uncertainties in an IoT framework, at different levels of abstraction, are firstly reviewed and then summarized. The modules included in the framework are detailed, with the main focus given to a novel risk-based analytics module, where an ensemble-based data analytic approach, called Calibrated Random Forest (CRF), is proposed to extract useful information while quantifying and managing the uncertainty associated with predictions, by using confidence scores. Its output is subsequently integrated with domain knowledge-based action rules to perform decision making in a cost-sensitive and rational manner. The proposed CRF method is firstly evaluated and demonstrated on a HAR scenario in a Smart Home environment in case study I and is further evaluated and illustrated with a remote health monitoring scenario for a diabetes use case in case study II. The experimental results indicate that using the framework’s raw sensor data can be converted into meaningful actions despite several sources of uncertainty. The comparison of the proposed framework to existing approaches highlights the key metrics that make decision making more rational and transparent.