Motor recovery following capsular stroke

The functional anatomy of motor recovery was studied by assessing motor function quantitatively in 23 patients following capsular or striatocapsular stroke. While selective basal ganglia lesions (caudate and/or putamen exclusively) did not affect voluntary movements of the extremities, lesions of the anterior (plus caudate/putamen) or posterior limb of the internal capsule led to an initially severe motor impairment followed by excellent recovery, hand function included. In contrast, lesions of the posterior limb of the internal capsule in combination with damage to lateral thalamus compromised motor outcome. In experimental tracing of the topography of the internal capsule in macaque monkeys, we found axons of primary motor cortex passing through the middle third of the posterior limb of the internal capsule. Axons of premotor cortex (dorsolateral and post-arcuate area 6) passed through the capsular genu, and those of supplementary motor area (mesial area 6) through the anterior limb. Small capsular lesion can therefore disrupt the output of functionally and anatomically distinct motor areas selectively. The clinically similar motor deficits with a similar course of functional restitution following disruption of these different descending motor pathways indicate a parallel operation of cortical motor areas. They may have the further capability of substituting each other functionally in the process of recovery from hemiparesis

Simplified Frame and Symbol Synchronization for 4–CPFSK with h=0.25

This paper examines the problem of rapid frame and symbol synchronization techniques intended particularly for constant envelope modulation formats M–CPFSK with modulation index h=1/M which are used in strictly bandwidth limited narrowband industrial applications. The data aided and non data aided versions of the algorithm based on digital frequency discrimination are discussed and compared against the synchronization techniques found in literature. Sample wise pattern correlation technique for joint frame and symbol synchronization is also studied. With the focus on a practical digital implementation the advantages and disadvantages of the described approaches are discussed

Implementation of Industrial Narrow Band Communication System into SDR Concept

The rapid expansion of the digital signal processing has penetrated recently into a sphere of high performance industrial narrow band communication systems which had been for long years dominated by the traditional analog circuit design. Although it brings new potential to even increase the efficiency of the radio channel usage it also forces new challenges and compromises radio designers have to face. In this article we describe the design of the IF sampling industrial narrowband radio receiver, optimize a digital receiver structure implemented in a single FPGA circuit and study the performance of such radio receiver architecture. As an evaluation criterion the communication efficiency in form of maximum usable receiver sensitivity, co-channel rejection, adjacent channel selectivity and radio blocking measurement have been selected

Anodic dissolution of metals in oxide-free cryolite melts

The anodic behavior of metals in molten cryolite-alumina melts has been investigated mostly for use as inert anodes for the Hall-Héroult process. In the present work, gold, platinum, palladium, copper, tungsten, nickel, cobalt and iron metal electrodes were anodically polarized in an oxide-free cryolite melt (11%wt. excess AlF3 ; 5%wt. CaF2) at 1273 K. The aim of the experiments was to characterize the oxidation reactions of the metals occurring without the effect of oxygen-containing dissolved species. The anodic dissolution of each metal was demonstrated, and electrochemical reactions were assigned using reversible potential calculation. The relative stability of metals as well as the possibility of generating pure fluorine is discussed

Molecular structure retrieval directly from laboratory-frame photoelectron spectra in laser-induced electron diffraction

Ubiquitous to most molecular scattering methods is the challenge to retrieve bond distance and angle from the scattering signals since this requires convergence of pattern matching algorithms or fitting methods. This problem is typically exacerbated when imaging larger molecules or for dynamic systems with little a priori knowledge. Here, we employ laser-induced electron diffraction (LIED) which is a powerful means to determine the precise atomic configuration of an isolated gas-phase molecule with picometre spatial and attosecond temporal precision. We introduce a simple molecular retrieval method, which is based only on the identification of critical points in the oscillating molecular interference scattering signal that is extracted directly from the laboratory-frame photoelectron spectrum. The method is compared with a Fourier-based retrieval method, and we show that both methods correctly retrieve the asymmetrically stretched and bent field-dressed configuration of the asymmetric top molecule carbonyl sulfide (OCS), which is confirmed by our quantum-classical calculations

Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

IntroductionDementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).MethodsAtlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer’s disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).ResultsThe binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.DiscussionResults suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer’s disease

Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes

Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context. Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging. Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes. Interventions: N.A. Main outcomes and measures: Cohen's kappa, accuracy, and F1-score to assess model performance. Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy. Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best