Transcranial Direct Current Stimulation and Working Memory: Comparison of effect on Learning Shapes and English Letters

We present the results of a study investigating whether there is an effect of Anodal-Transcranial Direct Current Stimulation (A-tDCS) on working memory (WM) performance. The relative effectiveness of A-tDCS on WM is investigated using a 2-back test protocol using two commonly used memory visual stimuli (shapes and letters). In a double-blinded, randomised, crossover, sham-controlled experiment, real A-tDCS and sham A-tDCS were applied separately to the left dorsolateral prefrontal cortex (L-DLPFC) of twenty healthy subjects. There was a minimal interval of one week between sham and real A-tDCS sessions. For the letters based stimulus experiment, 2-back test recall accuracy was measured for a set of English letters (A-L) which were presented individually in a randomised order where each was separated by a blank interval. A similar 2-back protocol was used for the shapes based stimuli experiment where instead of letters, a set of 12 geometric shapes were used. The working memory accuracy scores measured appeared to be significantly affected by memory stimulus type used and by the application of A-tDCS (repeated measures ANOVA p<0.05). A large effect size (d = 0.98) and statistical significance between sham and real A-tDCS WM scores (p = 0.01) was found when shapes were used as a visual testing stimulus, while low (d = 0.38) effect size and insignificant difference (p = 0.15) was found when letters were used. This results are important as they show that recollection different stimuli used in working memory can be affected differently by A-tDCS application. This highlights the importance of considering using multiple methods of WM testing when assessing the effectiveness of A-tDCS

Effect of Anodal-tDCS on Event-Related Potentials:A Controlled Study

We aim to measure the postintervention effects of A-tDCS (anodal-tDCS) on brain potentials commonly used in BCI applications, namely, Event-Related Desynchronization (ERD), Event-Related Synchronization (ERS), and P300. Ten subjects were given sham and 1.5 mA A-tDCS for 15 minutes on two separate experiments in a double-blind, randomized order. Postintervention EEG was recorded while subjects were asked to perform a spelling task based on the “oddball paradigm” while P300 power was measured. Additionally, ERD and ERS were measured while subjects performed mental motor imagery tasks. ANOVA results showed that the absolute P300 power exhibited a statistically significant difference between sham and A-tDCS when measured over channel Pz (p=0.0002). However, the difference in ERD and ERS power was found to be statistically insignificant, in controversion of the the mainstay of the litrature on the subject. The outcomes confirm the possible postintervention effect of tDCS on the P300 response. Heightening P300 response using A-tDCS may help improve the accuracy of P300 spellers for neurologically impaired subjects. Additionally, it may help the development of neurorehabilitation methods targeting the parietal lobe

Using Mixed Data Sampling (MIDAS) To Study The Impact Exchange Rate Volatility On Consumer Prices In Syria During The Period 2011-2019

This research aims to study the method of using MIDAS regression models in the econometric,  By stating the problem addressed by these models, These models are characterized by the possibility of interpreting a variable measured at a frequency (annual - quarterly) as a function of the current and previous values ​​of a variable measured at a higher frequency (monthly - weekly), To obtain more accurate results in the study of impact, nowcasting and forecasting by taking advantage of the full information content of high frequency data. And to understand the mechanism of using MIDAS regression models, The effect was studied Exchange rate volatility Which is measured quarterly monthly on the consumer price index, which is measured frequency monthly And the use of the estimated model for forecasting consumer prices based on data from the Central Bank of Syria and the Central Bureau of Statistics in Syria during the period 2011-2019. يهدف هذا البحث إلى بيان آلية وأهمية استخدام نماذج الانحدار ذات الترددات الزمنية المختلفة MIDAS، وذلك من خلال بيان المشكلة التي عالجتها هذه النماذج عن سابقتها، حيث تمتاز هذه النماذج بإمكانية تفسير متغير يتم قياسه عند تردد ما (سنوي – فصلي) كدالة للقيم الحالية والسابقة لمتغير يتم قياسه بتردد أعلى (شهري – اسبوعي)، وذلك للحصول على نتائج أكثر دقة في دراسة التأثير والتنبؤ الآني والتوقع من خلال الاستفادة من كامل المحتوى المعلوماتي للبيانات ذات التردد المرتفع. ولتوضيح آلية استخدام نماذج الانحدار MIDAS، تمّت دراسة تأثير تقلبات متغير سعر الصرف الذي يتم قياس تردده بشكل ربع شهري على متغير أسعار المستهلك والذي يتم قياس تردده بشكل شهري، واستخدام النموذج المقدر للتنبؤ بأسعار المستهلك وذلك بالاعتماد على بيانات مصرف سورية المركزي والمكتب المركزي للإحصاء في سورية خلال الفترة الممتدة 2011 – 2019

A Multi-Institutional Meningioma MRI Dataset for Automated Multi-Sequence Image Segmentation

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients

Magnetic Induction Spectroscopy for Biomass Measurement: A Feasibility Study

Background: Biomass measurement and monitoring is a challenge in a number of biotechnology processes where fast, inexpensive, and non-contact measurement techniques would be of great benefit. Magnetic induction spectroscopy (MIS) is a novel non-destructive and contactless impedance measurement technique with many potential industrial and biomedical applications. The aim of this paper is to use computer modeling and experimental measurements to prove the suitability of the MIS system developed at the University of South Wales for controlled biomass measurements. Methods: The paper reports experimental measurements conducted on saline solutions and yeast suspensions at different concentrations to test the detection performance of the MIS system. The commercial electromagnetic simulation software CST was used to simulate the measurement outcomes with saline solutions and compare them with those of the actual measurements. We adopted two different ways for yeast suspension preparation to assess the system’s sensitivity and accuracy. Results: For saline solutions, the simulation results agree well with the measurement results, and the MIS system was able to distinguish saline solutions at different concentrations even in the small range of 0–1.6 g/L. For yeast suspensions, regardless of the preparation method, the MIS system can reliably distinguish yeast suspensions with lower concentrations 0–20 g/L. The conductivity spectrum of yeast suspensions present excellent separability between different concentrations and dielectric dispersion property at concentrations higher than 100 g/L. Conclusions: The South Wales MIS system can achieve controlled yeast measurements with high sensitivity and stability, and it shows promising potential applications, with further development, for cell biology research where contactless monitoring of cellular density is of relevance

Prostate cancer classification using multispectral imagery and evolutionary heuristics

A compilation of the latest trends in the field, Computational Intelligence in Medical Imaging: Techniques and Applications explores how intelligent computing can bring enormous benefit to existing technology in medical image processing as well as improve medical imaging research. The contributors also cover state-of-the-art research toward integrating medical image processing with artificial intelligence and machine learning approaches. The book presents numerous techniques, algorithms, and models. It describes neural networks, evolutionary optimization techniques, rough sets, support vector machines, tabu search, fuzzy logic, a Bayesian probabilistic framework, a statistical parts-based appearance model, a reinforcement learning-based multistage image segmentation algorithm, a machine learning approach, Monte Carlo simulations, and intelligent, deformable models. The contributors discuss how these techniques are used to classify wound images, extract the boundaries of skin lesions, analyze prostate cancer, handle the inherent uncertainties in mammographic images, and encapsulate the natural intersubject anatomical variance in medical images. They also examine prostate segmentation in transrectal ultrasound images, automatic segmentation and diagnosis of bone scintigraphy, 3-D medical image segmentation, and the reconstruction of SPECT and PET tomographic images