Traumatic neuroma after torticollis surgery: a rare occurrence

We report a 15 years old girl who admitted to our hospital with signs of recurrent torticollis after two failed operations and consistent pain at the side of surgery. The past operations were performed at 1 and 6 years of age and she has been suffering pain from previous incisions with neck movements. At physical examination, the sternocleidomastoid (SCM) muscle behaved like a fibrous band, restricting the neck movements and resulting in pain. The operation was indicated for the fibrotic SCM. At operation two separate incisions were performed on each end of the SCM to remove all of the fibrotic muscle. The histopathological examination demonstrated a traumatic neuroma which respectively correlates with the pain symptoms. The patient discharged on the second postoperative day and physiotherapy was started. The patient is symptom free one year after the surgery. This case demonstrates a rare occurrence of traumatic neuroma after torticollis surgery, which can manifest with pain.Keywords: neuroma, torticollis, traum

Operation of Faddeev-Kernel in Configuration Space

We present a practical method to solve Faddeev three-body equations at energies above three-body breakup threshold as integral equations in coordinate space. This is an extension of previously used method for bound states and scattering states below three-body breakup threshold energy. We show that breakup components in three-body reactions produce long-range effects on Faddeev integral kernels in coordinate space, and propose numerical procedures to treat these effects. Using these techniques, we solve Faddeev equations for neutron-deuteron scattering to compare with benchmark solutions.Comment: 20 pages, 8 figures, to be published in Few-Body System

Separation of Dependent Sources in Astrophysical Radiation Maps using Second Order Statistics

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Network lifetime maximising distributed forwarding strategies in Ad Hoc wireless sensor networks

International audienceThe authors propose three variants of distributed and stateless forwarding strategies for wireless sensor networks, namely greedy minimum energy consumption forwarding protocol (GMFP), lifetime maximising GMFP (LM-GMFP) and variance minimising GMFP (VAR-GMFP), which aim at maximising the network lifetime while achieving a high forwarding success rate. GMFP selects a forwarding node that minimises per-packet energy consumption while maximising the forwarding progress. LM-GMFP extends the GMFP algorithm by also taking into account the remaining energy at the prospective one-hop forwarding nodes. In VAR-GMFP, on the other hand, the packet is forwarded to the next node that ensures a locally high mean and low variance of nodal remaining energy. Through simple probabilistic analysis the authors prove the intuition behind the optimum forwarding node selection for network lifetime maximisation. They then model the lifetime maximisation of a sensor network as an optimisation problem and compare the practical protocol-dependent network lifetime with the theoretical upper bound. Through extensive simulations the author demonstrate that the proposed protocols outperform the existing energy-aware protocols in terms of network lifetime and end-to-end delay

Measures of Learning, Memory and Processing Speed Accurately Predict Smoking Status in Short-term Abstinent Treatment-seeking Alcohol-dependent Individuals

Aim: Chronic cigarette smoking appears to adversely affect several domains of neurocognition in those with alcohol use disorders (AUDs). The primary goal of this study was to identify which measures commonly used to assess neurocognition in AUDs accurately predict smoking status of individuals seeking treatment of alcohol dependence. Methods: Treatment-seeking alcohol-dependent participants (ALC; n = 92) completed a comprehensive neuropsychological battery after 33 ± 9 days of abstinence. Measures significantly different between smoking and non-smoking ALC were entered as predictors in binary logistic regression and discriminant analysis models, with smoking status as the dependent variable. Results: Smoking ALC performed significantly worse than non-smoking ALC on measures assessing processing speed, auditory–verbal and visuospatial learning and memory. Using these measures as predictors, a logistic regression model accurately classified 91% of smokers and non-smokers into their respective groups overall and accounted for 68% of the variance in smoking status. The discriminant analysis confirmed the findings from the logistic regression. In smoking ALC, smoking chronicity was inversely related to performance on multiple measures after controlling for lifetime alcohol consumption. Conclusions: Measures of processing speed, learning and memory robustly predicted the smoking status of ALC with high sensitivity and specificity during early abstinence. The results identified specific measures within a comprehensive neurocognitive battery that discriminated smoking and non-smoking alcohol-dependent individuals with a high sensitivity and specificity. The association of greater smoking chronicity and poorer performance on multiple measures after control for alcohol consumption suggests that chronic smoking adds an additional burden to neurocognitive function in those with alcohol dependence

SI-Lab Annual Research Report 2020

The Signal & Images Laboratory (http://si.isti.cnr.it/) is an interdisciplinary research group in computer vision, signal analysis, smart vision systems and multimedia data understanding. It is part of the Institute for Information Science and Technologies of the National Research Council of Italy. This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2020

A multi-sensor network for the protection of cultural heritage

The paper presents a novel automatic early warning system to remotely monitor areas of archaeological and cultural interest from the risk of fire. Since these areas have been treasured and tended for very long periods of time, they are usually surrounded by old and valuable vegetation or situated close to forest regions, which exposes them to an increased risk of fire. The proposed system takes advantage of recent advances in multi-sensor surveillance technologies, using optical and infrared cameras, wireless sensor networks capable of monitoring different modalities (e.g. temperature and humidity) as well as local weather stations on the deployment site. The signals collected from these sensors are transmitted to a monitoring centre, which employs intelligent computer vision and pattern recognition algorithms as well as data fusion techniques to automatically analyze sensor information. The system is capable of generating automatic warning signals for local authorities whenever a dangerous situation arises, as well as estimating the propagation of the fire based on the fuel model of the area and other important parameters such as wind speed, slope, and aspect of the ground surface. © 2011 EURASIP

Thinking about Eating Food Activates Visual Cortex with Reduced Bilateral Cerebellar Activation in Females with Anorexia Nervosa: An fMRI Study

Background: Women with anorexia nervosa (AN) have aberrant cognitions about food and altered activity in prefrontal cortical and somatosensory regions to food images. However, differential effects on the brain when thinking about eating food between healthy women and those with AN is unknown. Methods: Functional magnetic resonance imaging (fMRI) examined neural activation when 42 women thought about eating the food shown in images: 18 with AN (11 RAN, 7 BPAN) and 24 age-matched controls (HC). Results: Group contrasts between HC and AN revealed reduced activation in AN in the bilateral cerebellar vermis, and increased activation in the right visual cortex. Preliminary comparisons between AN subtypes and healthy controls suggest differences in cortical and limbic regions. Conclusions: These preliminary data suggest that thinking about eating food shown in images increases visual and prefrontal cortical neural responses in females with AN, which may underlie cognitive biases towards food stimuli and ruminations about controlling food intake. Future studies are needed to explicitly test how thinking about eating activates restraint cognitions, specifically in those with restricting vs. binge-purging AN subtypes