Creeping through the cranium: A rare and interesting presentation of a meningioma.

A meningioma is a rare primary CNS tumor that tends to present more so in females in a slowly progressive fashion. The cavernous sinus and cerebellopontine angle are uncommon locations for meningiomas. We present a case of a meningioma in a 60-year-old female presenting to the emergency department for a sudden onset headache, vertigo, facial paresthesias, and chest pain.

Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer.

Background: One of the most critical topics in sports safety today is the reduction in injury risks through controlled fatigue using non-invasive athlete monitoring. Due to the risk of injuries, it is prohibited to use accelerometer-based smart trackers, activity measurement bracelets, and smart watches for recording health parameters during performance sports activities. This study analyzes the synergy feasibility of medical radar sensors and tri-axial acceleration sensor data to predict physical activity key performance indexes in performance sports by using machine learning (ML).

Focal Diastematomyelia in an Adult: A Case Report.

Diastematomyelia (DSM) is a rare congenital malformation that splits the spinal cord longitudinally into two by either cartilage, bone, or fibrous septum. There are multiple case reports of DSM in the pediatric population, but only a few cases of DSM in adult patients have been reported in the literature. This case report describes a middle-aged female patient who presented to the hospital with progressive worsening bilateral proximal lower extremity weakness.

Detecting Selection from Linked Sites Using an -Model.

Allele frequencies vary across populations and loci, even in the presence of migration. While most differences may be due to genetic drift, divergent selection will further increase differentiation at some loci. Identifying those is key in studying local adaptation, but remains statistically challenging.

Impact of inflammation-mediated response on pan-coronary plaque vulnerability, myocardial viability and ventricular remodeling in the postinfarction period - the VIABILITY study: Protocol for a non-randomized prospective clinical study.

Introduction: While the role of inflammation in acute coronary events is well established, the impact of inflammatory-mediated vulnerability of coronary plaques from the entire coronary tree, on the extension of ventricular remodeling and scaring, has not been clarified yet.

Materials And Methods: The present manuscript describes the procedures of the VIABILITY trial, a descriptive prospective single-center cohort study. The main purpose of this trial is to assess the link between systemic inflammation, pan-coronary plaque vulnerability (referring to the plaque vulnerability within the entire coronary tree), myocardial viability and ventricular remodeling in patients who had suffered a recent ST-segment elevation acute myocardial infarction (STEMI).

Breast involvement in mixed connective tissue disease.

To date, mammographic involvement in mixed connective tissue disease has not been described in the literature to the authors' knowledge. In addition, only 2 case reports have described panniculitis in mixed connective tissue disease. We present a patient with mixed connective tissue disease with coarse sheet-like calcifications on mammography bilaterally, which increased over time corresponding to rise in levels of anti-dsDNA.

Inference of Evolutionary Jumps in Large Phylogenies using Lévy Processes.

Although it is now widely accepted that the rate of phenotypic evolution may not necessarily be constant across large phylogenies, the frequency and phylogenetic position of periods of rapid evolution remain unclear. In his highly influential view of evolution, G. G.

A modified two-stage Markov clustering algorithm for large and sparse networks.

Background: Graph-based hierarchical clustering algorithms become prohibitively costly in both execution time and storage space, as the number of nodes approaches the order of millions.

Objective: A fast and highly memory efficient Markov clustering algorithm is proposed to perform the classification of huge sparse networks using an ordinary personal computer.

Methods: Improvements compared to previous versions are achieved through adequately chosen data structures that facilitate the efficient handling of symmetric sparse matrices.

A fast hierarchical clustering algorithm for large-scale protein sequence data sets.

TRIBE-MCL is a Markov clustering algorithm that operates on a graph built from pairwise similarity information of the input data. Edge weights stored in the stochastic similarity matrix are alternately fed to the two main operations, inflation and expansion, and are normalized in each main loop to maintain the probabilistic constraint. In this paper we propose an efficient implementation of the TRIBE-MCL clustering algorithm, suitable for fast and accurate grouping of protein sequences.

Efficient Markov clustering algorithm for protein sequence grouping.

In this paper we propose an efficient reformulation of a Markov clustering algorithm, suitable for fast and accurate grouping of protein sequences, based on pairwise similarity information. The proposed modification consists of optimal reordering of rows and columns in the similarity matrix after every iteration, transforming it into a matrix with several compact blocks along the diagonal, and zero similarities outside the blocks. These blocks are treated separately in later iterations, thus reducing the computational burden of the algorithm.

Efficient inhomogeneity compensation using fuzzy c-means clustering models.

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into classification or clustering algorithms, they generally have difficulties when INU reaches high amplitudes and usually suffer from high computational load. This study reformulates the design of c-means clustering based INU compensation techniques by identifying and separating those globally working computationally costly operations that can be applied to gray intensity levels instead of individual pixels.

A patient specific electro-mechanical model of the heart.

This paper presents a patient specific deformable heart model that involves the known electrical and mechanical properties of the cardiac cells and tissue. The whole heart model comprises ten Tusscher's ventricular and Nygren's atrial cell models, the anatomical and electrophysiological model descriptions of the atria (introduced by Harrild et al.) and ventricle (given by Winslow et al.

Inhomogeneity compensation for MR brain image segmentation using a multi-stage FCM-based approach.

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for MR image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms. This paper proposes a multiple stage fuzzy c-means (FCM) based algorithm for the estimation and compensation of the slowly varying additive or multiplicative noise, supported by a pre-filtering technique for Gaussian and impulse noise elimination.

A novel ECG telemetry and monitoring system based on Z-Wave communication.

This paper presents a novel ECG telemetry system based on Z-Wave communication protocol. The proposed system consists of small portable devices that acquire, compress and transmit the ECG to a RF-USB interface connected to a central monitoring computer. The received signals are filtered, QRS complexes and P and T waves are localized, and different waveforms are classified in order to be able to provide diagnosis tools like heart rate variability and turbulence analysis.

Sensibility analysis of the Arruda localization method.

This paper presents an analysis of the Arruda accessory pathway localization method (for patients suffering from Wolff-Parkinson-White syndrome) with suggestions to increase the overall performance. The Arruda method was tested on a total of 121 patients, and a 90% localization performance was reached. This was considered almost as performing result as the highest published (90%) by L.

Quick ECG analysis for on-line holter monitoring systems.

Computer-aided bedside patient monitoring requires real-time analysis of vital functions. On-line Holter monitors need reliable and quick algorithms to perform all the necessary signal processing tasks. This paper presents the methods that were conceptualized and implemented at the development of such a monitoring system at Medical Clinic No.

3D Heart Simulation And Recognition Of Various Events.

This paper presents a new way to solve the inverse problem of electrocardiography in terms of heart model parameters. The developed event estimation and recognition method is based on an optimization system of heart model parameters. An ANN-based preliminary ECG analyzer system has been created to reduce the searching space of the optimization algorithm.

Quick QRS Complex Detection for On-Line ECG and Holter Systems.

This paper presents a new QRS complex detection algorithm that can be applied in various on-line ECG processing systems. The algorithm is performed in two steps: first a wavelet transform filtering is applied to the signal, then QRS complex localization is performed using a maximum detection and peak classification algorithm. The algorithm has been tested in two phases.

Medication status affects the relationship of symptoms to prepulse inhibition of acoustic startle in schizophrenia.

Inhibition of the acoustic startle response by a smaller preliminary nonstartling stimulus is termed prepulse inhibition (PPI). Schizophrenia patients have impairments in PPI that may not fully normalize even when they are clinically stable on medication, particularly typical antipsychotics. There is evidence that more severe symptoms are associated with more severe PPI abnormalities, but the effect of antipsychotics on this relationship is not clear.

Effects of D-cycloserine on negative symptoms in schizophrenia.

Introduction: The negative and cognitive symptoms of schizophrenia are poorly responsive to neuroleptic treatment. Glutamatergic dysfunction may mediate some of these symptoms. Low dose D-cycloserine (DCS) is a partial agonist at the glycine site of the NMDA-associated receptor complex, noncompetitively enhancing NMDA neurotransmission.

Menstrual cycle phase effects on prepulse inhibition of acoustic startle.

Prepulse inhibition (PPI) represents an attenuation of the startle reflex following the presentation of a weak prepulse at brief intervals prior to the startle eliciting pulse. It has been shown that increases in striatal dopamine levels decrease PPI; because dopamine release is sensitive to estrogen, it is likely that PPI varies across the menstrual cycle. Cross-sectional studies looking at estrogen effects suggest that this may be true.

Prepulse inhibition of acoustic startle in subjects with schizophrenia treated with olanzapine or haloperidol.

Studies of the acoustic startle response and of its inhibition by the presentation of a non-startling preliminary stimulus (prepulse inhibition, PPI) have revealed deficits in PPI in schizophrenic subjects compared to healthy controls. Animal studies indicate that atypical antipsychotics improve PPI deficits induced by NMDA antagonists more consistently than typical antipsychotics. The effect of medication status on PPI in schizophrenia is unresolved in the literature.

Effect of treatment status on prepulse inhibition of acoustic startle in schizophrenia.

Rationale: The acoustic startle response is inhibited when the startling stimulus is preceded by a weaker non-startling acoustic stimulus. This phenomenon, termed prepulse inhibition of acoustic startle (PPI), is impaired in schizophrenics compared to normal controls. To date, there is conflicting evidence regarding whether PPI impairments improve with antipsychotic treatment.

Inferior frontal white matter anisotropy and negative symptoms of schizophrenia: a diffusion tensor imaging study.

Objective: The purpose of this study was test the hypothesis that abnormalities of inferior frontal white matter are related to the negative symptoms of schizophrenia.

Method: Fractional anisotropy of white matter tracts in the prefrontal area of 10 schizophrenic patients was determined by diffusion tensor imaging. Patients were also assessed for severity of negative symptoms by using the Schedule for the Assessment of Negative Symptoms (SANS).