Ataxia in children: early recognition and clinical evaluation

Background: Ataxia is a sign of different disorders involving any level of the nervous system and consisting of impaired coordination of movement and balance. It is mainly caused by dysfunction of the complex circuitry connecting the basal ganglia, cerebellum and cerebral cortex. A careful history, physical examination and some characteristic maneuvers are useful for the diagnosis of ataxia. Some of the causes of ataxia point toward a benign course, but some cases of ataxia can be severe and particularly frightening. Methods: Here, we describe the primary clinical ways of detecting ataxia, a sign not easily recognizable in children. We also report on the main disorders that cause ataxia in children. Results: The causal events are distinguished and reported according to the course of the disorder: acute, intermittent, chronic-non-progressive and chronic-progressive. Conclusions: Molecular research in the field of ataxia in children is rapidly expanding; on the contrary no similar results have been attained in the field of the treatment since most of the congenital forms remain fully untreatable. Rapid recognition and clinical evaluation of ataxia in children remains of great relevance for therapeutic results and prognostic counseling

Revealing radiationless sources with multi-harmonic mantle cloaking

A general formula for the scattering suppression of simultaneous cylindrical harmonic waves is reported for a bare dielectric cylinder. A proper surface impedance condition is imposed at the boundary between dielectric and background regions, revealing a parity-time condition for the internal electromagnetic field in order to be nonradiating. Depending on the maximum number M_max of cylindrical harmonic waves to suppress along the azimuthal variable phi, such surface impedance function profile reveals several higher order nonradiating modes. The determination of M_max is consistent with the degrees of freedom of the scattered fields. Moreover, the multi-harmonic mantle cloaking technique automatically generates radiationless sources suitable for the implementation with metal-dielectric metasurfaces or all-dielectric metamolecules

Cervical neurenteric cyst and Klippel-Feil syndrome: An abrupt onset of myelopathic signs in a young patient

Neurenteric cysts (NECs), also called enterogenous cysts or enterogenic cysts, are congenital malformative anomalies of endodermal origin that manifest with a variety of disorders, including spine anomalies. Neurenteric cysts are uncommon developmental disorders reported in 0.7%–1.3% of all spinal tumors. Klippel-Feil syndrome (KFS) defines a malformative spine disorder presenting with congenital fusion of cervical vertebrae and/or other parts of the spine. In patients with KFS, NECs are rarely reported; they may be silent for long periods of time, showing a slow progressive course or manifesting with an acute, severe neurological presentation or with fluctuating myelopathic symptoms. We report a young patient affected by KFS associated with a NEC which, in a short period of time, progressively caused myelopathic symptomatology. Surgical intervention resulted in resolution of the neurological signs. Keywords: Neurenteric cyst, Klippel-Feil syndrome, Intramedullary cys

Deep learning for Gaussian process tomography model selection using the ASDEX Upgrade SXR system

Gaussian process tomography (GPT) is a method used for obtaining real-time tomographic reconstructions of the plasma emissivity profile in a tokamak, given some model for the underlying physical processes involved. GPT can also be used, thanks to Bayesian formalism, to perform model selection -- i.e., comparing different models and choosing the one with maximum evidence. However, the computations involved in this particular step may become slow for data with high dimensionality, especially when comparing the evidence for many different models. Using measurements collected by the ASDEX Upgrade Soft X-ray (SXR) diagnostic, we train a convolutional neural network (CNN) to map SXR tomographic projections to the corresponding GPT model whose evidence is highest. We then compare the network's results, and the time required to calculate them, with those obtained through analytical Bayesian formalism. In addition, we use the network's classifications to produce tomographic reconstructions of the plasma emissivity profile, whose quality we evaluate by comparing their projection into measurement space with the existing measurements themselves

Primary headaches in children: clinical findings on the association with other conditions.

The aim of the present study is to report on the frequency of some comorbidities in primary headaches in childhood. Two hundred and eighty children (175 males and 105 females; ratio 1.7:1), aged 4 to 14 years, affected by primary headaches were consecutively enrolled in this study. In direct interviews, parents and children gave information about the association of their headaches with different conditions including asthma and allergic disorders, convulsive episodes, sleep disorders and increased body weight, affections some time associated in the literature to headache as comorbidities. In addition, anxiety and depression, attention deficit/hyperactivity disorder, tics, learning disabilities and obsessive-compulsive disorders, using psycho-diagnostic scales were evaluated. Two hundred and eighty children matched for age, sex, race and socio-economic status, were used as controls. No significant association of primary headaches was found with asthma and allergic disorders, convulsive episodes, sleep disorders and increased body weight. Overall behavioral disorders were more common in children who experienced headache than in controls. A significant association of primary headache was found with anxiety and depression (p value <0.001), but not with the other psychiatric disorders. Primary headaches in children are not associated with most of the psychiatric and systemic conditions herein investigated. On the contrary, there was a significant association with anxiety and depression, as frequently reported in adults

On the Co-Design of AV-Enabled Mobility Systems

The design of autonomous vehicles (AVs) and the design of AV-enabled mobility systems are closely coupled. Indeed, knowledge about the intended service of AVs would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management decisions. This calls for tools to study such a coupling and co-design AVs and AV-enabled mobility systems in terms of different objectives. In this paper, we instantiate a framework to address such co-design problems. In particular, we leverage the recently developed theory of co-design to frame and solve the problem of designing and deploying an intermodal Autonomous Mobility-on-Demand system, whereby AVs service travel demands jointly with public transit, in terms of fleet sizing, vehicle autonomy, and public transit service frequency. Our framework is modular and compositional, allowing one to describe the design problem as the interconnection of its individual components and to tackle it from a system-level perspective. To showcase our methodology, we present a real-world case study for Washington D.C., USA. Our work suggests that it is possible to create user-friendly optimization tools to systematically assess costs and benefits of interventions, and that such analytical techniques might gain a momentous role in policy-making in the future.Comment: 8 pages, 4 figures. Published in the Proceeding of the 23rd IEEE Intelligent Transportation Systems Conference, ITSC 2020. arXiv admin note: substantial text overlap with arXiv:1910.07714, arXiv:2008.0897

Optical mapping of neuronal activity during seizures in zebrafish

Mapping neuronal activity during the onset and propagation of epileptic seizures can provide a better understanding of the mechanisms underlying this pathology and improve our approaches to the development of new drugs. Recently, zebrafish has become an important model for studying epilepsy both in basic research and in drug discovery. Here, we employed a transgenic line with pan-neuronal expression of the genetically-encoded calcium indicator GCaMP6s to measure neuronal activity in zebrafish larvae during seizures induced by pentylenetretrazole (PTZ). With this approach, we mapped neuronal activity in different areas of the larval brain, demonstrating the high sensitivity of this method to different levels of alteration, as induced by increasing PTZ concentrations, and the rescuing effect of an anti-epileptic drug. We also present simultaneous measurements of brain and locomotor activity, as well as a high-throughput assay, demonstrating that GCaMP measurements can complement behavioural assays for the detection of subclinical epileptic seizures, thus enabling future investigations on human hypomorphic mutations and more effective drug screening methods. Notably, the methodology described here can be easily applied to the study of many human neuropathologies modelled in zebrafish, allowing a simple and yet detailed investigation of brain activity alterations associated with the pathological phenotype

Image convolution: a linear programming approach for filters design

AbstractImage analysis is a branch of signal analysis that focuses on the extraction of meaningful information from images through digital image processing techniques. Convolution is a technique used to enhance specific characteristics of an image, while deconvolution is its inverse process. In this work, we focus on the deconvolution process, defining a new approach to retrieve filters applied in the convolution phase. Given an image I and a filtered image $$I' = f(I)$$ I ′ = f ( I ) , we propose three mathematical formulations that, starting from I and $$I'$$ I ′ , are able to identify the filter $$f'$$ f ′ that minimizes the mean absolute error between $$I'$$ I ′ and $$f'(I)$$ f ′ ( I ) . Several tests were performed to investigate the applicability of our approaches in different scenarios. The results highlight that the proposed algorithms are able to identify the filter used in the convolution phase in several cases. Alternatively, the developed approaches can be used to verify whether a specific input image I can be transformed into a sample image $$I'$$ I ′ through a convolution filter while returning the desired filter as output