Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks

Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques

A case report of isolated distal upper extremity weakness due to cerebral metastasis involving the hand knob area

Unilateral weakness of an upper extremity is most frequently caused by traumatic nerve injury or compression neuropathy. In rare cases, lesion of the central nervous system may result in syndromes suggesting peripheral nerve damage by the initial examination. Pseudoperipheral hand palsy is the best known of these, most frequently caused by a small lesion in the contralateral motor cortex of the brain. The 'hand knob' area refers to a circumscribed region in the precentral gyrus of the posterior frontal lobe, the lesion of which leads to isolated weakness of the upper extremity mimicking peripheral nerve damage. The etiology of this rare syndrome is almost exclusively related to an embolic infarction.We present the case of a 70-year-old male patient with isolated left sided upper extremity weakness and clumsiness without sensory disturbance suggesting a lesion of the radial nerve. Nerve conduction studies had normal results excluding peripheral nerve damage. Neuroimaging (cranial CT and MRI) detected 3 space occupying lesions, one of them in the right precentral gyrus. An irregularly shaped tumor was found by CT in the left lung with multiple associated lymph node conglomerates. The metastasis from this mucinous tubular adenocarcinoma with solid anaplastic parts to the 'hand knob' area was responsible for the first clinical sign related to the pulmonary malignancy.Pseudoperipheral palsy of the upper extremity is not necessarily the consequence of an embolic stroke. If nerve conduction studies have normal results, neuroimaging - preferably MRI - should be performed, as lesion in the hand-knob area of the precentral gyrus can also be caused by a malignancy

Striatal Proteomic Analysis Suggests that First L-Dopa Dose Equates to Chronic Exposure

L-3,4-dihydroxypheylalanine (L-dopa)-induced dyskinesia represent a debilitating complication of therapy for Parkinson's disease (PD) that result from a progressive sensitization through repeated L-dopa exposures. The MPTP macaque model was used to study the proteome in dopamine-depleted striatum with and without subsequent acute and chronic L-dopa treatment using two-dimensional difference in-gel electrophoresis (2D-DIGE) and mass spectrometry. The present data suggest that the dopamine-depleted striatum is so sensitive to de novo L-dopa treatment that the first ever administration alone would be able (i) to induce rapid post-translational modification-based proteomic changes that are specific to this first exposure and (ii), possibly, lead to irreversible protein level changes that would be not further modified by chronic L-dopa treatment. The apparent equivalence between first and chronic L-dopa administration suggests that priming would be the direct consequence of dopamine loss, the first L-dopa administrations only exacerbating the sensitization process but not inducing it

Thoracic epidural analgesia: a new approach for the treatment of acute pancreatitis?

This review article analyzes, through a nonsystematic approach, the pathophysiology of acute pancreatitis (AP) with a focus on the effects of thoracic epidural analgesia (TEA) on the disease. The benefit-risk balance is also discussed. AP has an overall mortality of 1 %, increasing to 30 % in its severe form. The systemic inflammation induces a strong activation of the sympathetic system, with a decrease in the blood flow supply to the gastrointestinal system that can lead to the development of pancreatic necrosis. The current treatment for severe AP is symptomatic and tries to correct the systemic inflammatory response syndrome or the multiorgan dysfunction. Besides the removal of gallstones in biliary pancreatitis, no satisfactory causal treatment exists. TEA is widely used, mainly for its analgesic effect. TEA also induces a targeted sympathectomy in the anesthetized region, which results in splanchnic vasodilatation and an improvement in local microcirculation. Increasing evidence shows benefits of TEA in animal AP: improved splanchnic and pancreatic perfusion, improved pancreatic microcirculation, reduced liver damage, and significantly reduced mortality. Until now, only few clinical studies have been performed on the use of TEA during AP with few available data regarding the effect of TEA on the splanchnic perfusion. Increasing evidence suggests that TEA is a safe procedure and could appear as a new treatment approach for human AP, based on the significant benefits observed in animal studies and safety of use for human. Further clinical studies are required to confirm the clinical benefits observed in animal studies

A multimodal retina-iris biometric system using the levenshtein distance for spatial feature comparison

The recent developments of information technologies, and the consequent need for access to distributed services and resources, require robust and reliable authentication systems. Biometric systems can guarantee high levels of security and multimodal techniques, which combine two or more biometric traits, warranting constraints that are more stringent during the access phases. This work proposes a novel multimodal biometric system based on iris and retina combination in the spatial domain. The proposed solution follows the alignment and recognition approach commonly adopted in computational linguistics and bioinformatics; in particular, features are extracted separately for iris and retina, and the fusion is obtained relying upon the comparison score via the Levenshtein distance. We evaluated our approach by testing several combinations of publicly available biometric databases, namely one for retina images and three for iris images. To provide comprehensive results, detection error trade-off-based metrics, as well as statistical analyses for assessing the authentication performance, were considered. The best achieved False Acceptation Rate and False Rejection Rate indices were and 3.33%, respectively, for the multimodal retina-iris biometric approach that overall outperformed the unimodal systems. These results draw the potential of the proposed approach as a multimodal authentication framework using multiple static biometric traits

Embedded knowledge-based speech detectors for real-time recognition tasks

Speech recognition has become common in many application domains, from dictation systems for professional practices to vocal user interfaces for people with disabilities or hands-free system control. However, so far the performance of Automatic Speech Recognition (ASR) systems are comparable to Human Speech Recognition (HSR) only under very strict working conditions, and in general much lower. Incorporating acoustic-phonetic knowledge into ASR design has been proven a viable approach to raise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as detectors for manner of articulation attributes starting from representations of speech signal frames. In this paper the full system implementation is described. The system has a first stage for MFCC extraction followed by a second stage implementing a sinusoidal based multi-layer perceptron for speech event classification. Implementation details over a Celoxica RC203 board are given. © 2006 IEEE