Magnetic resonance arthrography with positional manoeuvre for the diagnosis of synovial fold of posterior shoulder joint capsule.

Objectives: The objective of this study is to prospectively assess the effectiveness of shoulder magnetic resonance (MR) arthrograms with positional manoeuvres in detecting posterior synovial folds.

Methods: Two radiologists independently assessed all axial MR arthrograms in internal rotation, neutral position, and external rotation for the presence of a posterior synovial fold. The diagnostic performances of the MR arthrograms were then compared, with results validated through arthroscopy.

Anomalous origin of the temporopolar artery from the internal carotid artery and aneurysms at the temporopolar and internal carotid arteries: a very rare case report.

The temporopolar artery (TPA) originates directly from the sphenoidal segment of the middle cerebral artery (MCA). Its originating from the M1 segment of the MCA as a branch of the anterior temporal artery is not uncommon. However, internal carotid artery origination is a very rare variation of the TPA.

Hilbert Vibration Decomposition-based epileptic seizure prediction with neural network.

Epilepsy is one of the most prominent brain disorders in the world, and epileptic patients suffer from sudden seizures that have a substantial negative impact on their lives. A seizure prediction system, therefore, is essential in overcoming the difficulties that epileptic individuals experience. This study designs and demonstrates a non-patient specific seizure prediction system that uses the Hilbert Vibration Decomposition (HVD) method on surface EEG recordings of 10 patients from the CHB-MIT database.

Sonoarthrographic examination of posterior labrocapsular structures of the shoulder joint.

Objective: To describe the posterior labral lesions and labrocapsular abnormalities of the shoulder on sonoarthrography and to compare these findings with MR arthrography results.

Methods: 82 shoulders were initially evaluated with ultrasonography and MRI and then were examined with sonoarthrography and MR arthrography following intraarticular injection of diluted gadolinium solution. The ultrasonography images were prospectively evaluated for the presence of posterior labral tear, sublabral cleft, and posterior capsular abnormalities by two radiologists.

Single-Image-Referenced Colorimetric Water Quality Detection Using a Smartphone.

In this paper, we present a smartphone platform for colorimetric water quality detection based on the use of built-in camera for capturing a single-use reference image. A custom-developed app processes this image for training and creates a reference model to be used later in real experimental conditions to calculate the concentration of the unknown solution. This platform has been tested on four different water quality colorimetric assays with various concentration levels, and results show that the presented platform provides approximately 100% accuracy for colorimetric assays with noticeable color difference.

Smartphone-based colorimetric detection via machine learning.

We report the application of machine learning to smartphone-based colorimetric detection of pH values. The strip images were used as the training set for Least Squares-Support Vector Machine (LS-SVM) classifier algorithms that were able to successfully classify the distinct pH values. The difference in the obtained image formats was found not to significantly affect the performance of the proposed machine learning approach.

Postoperative upper extremity deep vein thrombosis in a gynecologic oncology patient: A case report.

Introduction: Upper extremity deep vein thrombosis (UEDVT) represents approximately 10% of all thromboembolic events. It is a rare condition after a gynecologic surgery and highly related with pulmonary embolism.

Presentation Of Case: Herein, we present a very rare case of a unilateral left upper extremity deep vein thrombosis in a morbidly obese patient with synchronous primary cancers of endometrium and ovary.

A signal-processing-based approach to time-varying graph analysis for dynamic brain network identification.

In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity.

Inferring effective connectivity in the brain from EEG time series using dynamic Bayesian networks.

Effective connectivity, defined as the influence of a neuronal population on another, is known to have great significance for understanding the organization of the brain. Disruptions in the effective connectivity patterns occur in the case of neurological and psychopathological diseases. Therefore, it is important to develop models of effective brain connectivity from non-invasive neuroimaging data.