Arabic cursive text recognition from natural scene images

© 2019 by the authors. This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years' publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers

Soluble TWEAK and Cardiovascular Morbidity and Mortality in Chronic Kidney Disease Patients

Introduction: Cardiovascular disease (CVD) is a major cause of morbidity and mortality in chronic kidney patients (CKD). The aim of this study was to demonstrate the role of soluble tumor necrosis factor (TNF) weak inducer of apoptosis (sTWEAK) as a marker of cardiovascular morbidity and mortality in CKD patients.Methods: The study included 75 CKD patients classified according to eGFR into three groups; group-1 included 15 patients with stage-1 CKD, group-2 included 30 patients with stage-2 and stage-3 CKD, and group-3 included 30 patients with stage-4 and stage-5 CKD. The three groups were compared to 20 matched controls. Interleukin-6 (IL-6) and sTWEAK were measured using ELISA and chemiluminescent techniques respectively. Carotid intima-media thickness (IMT) was also measured.Results: We found that IL-6 showed significant difference between patient groups and controls, being highest in stage 4 and 5 CKD patients and lowest in controls. Soluble TWEAK showed significant difference between patient groups and controls, being lowest in stage 4 and 5 CKD patients and highest in controls. Soluble TWEAK level showed significant negative correlation with IL-6 (r = -0.68; P<0.01) and carotid IMT (r = -0.95; P<0.01). After two years follow up, nine out of 75 CKD patients developed ischemic heart disease (IHD). Two patients developed cerebrovascular stroke and another patient developed peripheral arterial disease. These patients had significantly lower levels of sTWEAK at baseline compared to other patients (160.5± 60.2 versus 274.8±90 pg/mL; P < 0.05).Conclusion: Soluble TWEAK can be a novel biomarker of atherosclerosis and endothelial dysfunction as well as cardiovascular outcome in CKD patients

Evaluation of handwritten Urdu text by integration of MNIST dataset learning experience

© 2019 IEEE. The similar nature of patterns may enhance the learning if the experience they attained during training is utilized to achieve maximum accuracy. This paper presents a novel way to exploit the transfer learning experience of similar patterns on handwritten Urdu text analysis. The MNIST pre-trained network is employed by transferring it's learning experience on Urdu Nastaliq Handwritten Dataset (UNHD) samples. The convolutional neural network is used for feature extraction. The experiments were performed using deep multidimensional long short term (MDLSTM) memory networks. The obtained result shows immaculate performance on number of experiments distinguished on the basis of handwritten complexity. The result of demonstrated experiments show that pre-trained network outperforms on subsequent target networks which enable them to focus on a particular feature learning. The conducted experiments presented astonishingly good accuracy on UNHD dataset

A comparison of smartphone and paper data collection tools in the Burden of Obstructive Lung Disease (BOLD) study in Gezira state, Sudan

Introduction: Data collection using paper-based questionnaires can be time consuming and return errors affect data accuracy, completeness, and information quality in health surveys. We compared smartphone and paper-based data collection systems in the Burden of Obstructive Lung Disease (BOLD) study in rural Sudan. Methods: This exploratory pilot study was designed to run in parallel with the cross-sectional household survey. The Open Data Kit was used to programme questionnaires in Arabic into smartphones. We included 100 study participants (83% women; median age = 41.5 ± 16.4 years) from the BOLD study from 3 rural villages in East-Gezira and Kamleen localities of Gezira state, Sudan. Questionnaire data were collected using smartphone and paper-based technologies simultaneously. We used Kappa statistics and inter-rater class coefficient to test agreement between the two methods. Results: Symptoms reported included cough (24%), phlegm (15%), wheezing (17%), and shortness of breath (18%). One in five were or had been cigarette smokers. The two data collection methods varied between perfect to slight agreement across the 204 variables evaluated (Kappa varied between 1.00 and 0.02 and inter-rater coefficient between 1.00 and -0.12). Errors were most commonly seen with paper questionnaires (83% of errors seen) vs smartphones (17% of errors seen) administered questionnaires with questions with complex skip-patterns being a major source of errors in paper questionnaires. Automated checks and validations in smartphone-administered questionnaires avoided skip-pattern related errors. Incomplete and inconsistent records were more likely seen on paper questionnaires. Conclusion: Compared to paper-based data collection, smartphone technology worked well for data collection in the study, which was conducted in a challenging rural environment in Sudan. This approach provided timely, quality data with fewer errors and inconsistencies compared to paper-based data collection. We recommend this method for future BOLD studies and other population-based studies in similar settings

UPC++: A high-performance communication framework for asynchronous computation

UPC++ is a C++ library that supports high-performance computation via an asynchronous communication framework. This paper describes a new incarnation that differs substantially from its predecessor, and we discuss the reasons for our design decisions. We present new design features, including future-based asynchrony management, distributed objects, and generalized Remote Procedure Call (RPC). We show microbenchmark performance results demonstrating that one-sided Remote Memory Access (RMA) in UPC++ is competitive with MPI-3 RMA; on a Cray XC40 UPC++ delivers up to a 25% improvement in the latency of blocking RMA put, and up to a 33% bandwidth improvement in an RMA throughput test. We showcase the benefits of UPC++ with irregular applications through a pair of application motifs, a distributed hash table and a sparse solver component. Our distributed hash table in UPC++ delivers near-linear weak scaling up to 34816 cores of a Cray XC40. Our UPC++ implementation of the sparse solver component shows robust strong scaling up to 2048 cores, where it outperforms variants communicating using MPI by up to 3.1x. UPC++ encourages the use of aggressive asynchrony in low-overhead RMA and RPC, improving programmer productivity and delivering high performance in irregular applications

Application of Magnetic Resonance to Assess Lyophilized Drug Product Reconstitution

Purpose Dynamic in-situ proton (1H) magnetic resonance imaging (MRI) and 1H T2-relaxometry experiments are described in an attempt to: (i) understand the physical processes, that occur during the reconstitution of lyophilized bovine serum albumen (BSA) and monoclonal antibody (mAb) proteins; and (ii) objectify the reconstitution time. Methods Rapid two-dimensional 1H MRI and diffusion weighted MRI were used to study the temporal changes in solids dissolution and characterise water mass transport characteristics. One-shot T2 relaxation time measurements were also acquired in an attempt to quantify the reconstitution time. Both MRI data and T2-relaxation data were compared to standard visual observations currently adopted by industry. The 1H images were further referenced to MRI calibration data to give quantitative values of protein concentration and, percentage of remaining undissolved solids. Results An algorithmic analysis the 1H T2-relaxation data shows it is possible to classify the reconstitution event into three regimes (undissolved, transitional and dissolved). Moreover, a combined analysis of the 2D 1H MRI and 1H T2-relaxation data gives a unique time point that characterises the onset of a reconstituted protein solution within well-defined error bars. These values compared favourably with those from visual observations. Diffusion weighted MRI showed that low concentration BSA and mAb samples showed distinct liquid-liquid phase separation attributed to two liquid layers with significant density gradients. Conclusions T2 relaxation time distributions (whose interpretation is validated from the 2D 1H MR images) provides a quick and effective framework to build objective, quantitative descriptors of the reconstitution process that facilitate the interpretation of subjective visual observations currently adopted as the standard practice industry.Medimmune PL

The VITAH Trial-Vitamin D Supplementation and Cardiac Autonomic Tone in Patients with End-Stage Kidney Disease on Hemodialysis: A Blinded, Randomized Controlled Trial

End-stage kidney disease (ESKD) patients are at increased cardiovascular risk. Vitamin D deficiency is associated with depressed heart rate variability (HRV), a risk factor depicting poor cardiac autonomic tone and risk of cardiovascular death. Vitamin D deficiency and depressed HRV are highly prevalent in the ESKD population. We aimed to determine the effects of oral vitamin D supplementation on HRV ((low frequency (LF) to high frequency (HF) spectral ratio (LF:HF)) in ESKD patients on hemodialysis. Fifty-six subjects with ESKD requiring hemodialysis were recruited from January 2013-March 2015 and randomized 1:1 to either conventional (0.25 mcg alfacalcidol plus placebo 3×/week) or intensive (0.25 mcg alfacalcidol 3×/week plus 50,000 international units (IU) ergocalciferol 1×/week) vitamin D for six weeks. The primary outcome was the change in LF:HF. There was no difference in LF:HF from baseline to six weeks for either vitamin D treatment (conventional: p = 0.9 vs. baseline; intensive: p = 0.07 vs. baseline). However, participants who remained vitamin D-deficient (25-hydroxyvitamin D < 20 ng/mL) after treatment demonstrated an increase in LF:HF (conventional: n = 13, ∆LF:HF: 0.20 ± 0.06, p < 0.001 vs. insufficient and sufficient vitamin D groups; intensive: n = 8: ∆LF:HF: 0.15 ± 0.06, p < 0.001 vs. sufficient vitamin D group). Overall, six weeks of conventional or intensive vitamin D only augmented LF:HF in ESKD subjects who remained vitamin D-deficient after treatment. Our findings potentially suggest that while activated vitamin D, with or without additional nutritional vitamin D, does not appear to improve cardiac autonomic tone in hemodialysis patients with insufficient or sufficient baseline vitamin D levels, supplementation in patients with severe vitamin D deficiency may improve cardiac autonomic tone in this higher risk sub-population of ESKD. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01774812

Competing biosecurity and risk rationalities in the Chittagong poultry commodity chain, Bangladesh

This paper anthropologically explores how key actors in the Chittagong live bird trading network perceive biosecurity and risk in relation to avian influenza between production sites, market maker scenes and outlets. They pay attention to the past and the present, rather than the future, downplaying the need for strict risk management, as outbreaks have not been reported frequently for a number of years. This is analysed as ‘temporalities of risk perception regarding biosecurity’, through Black Swan theory, the idea that unexpected events with major effects are often inappropriately rationalized (Taleb in The Black Swan. The impact of the highly improbable, Random House, New York, 2007). This incorporates a sociocultural perspective on risk, emphasizing the contexts in which risk is understood, lived, embodied and experienced. Their risk calculation is explained in terms of social consent, practical intelligibility and convergence of constraints and motivation. The pragmatic and practical orientation towards risk stands in contrast to how risk is calculated in the avian influenza preparedness paradigm. It is argued that disease risk on the ground has become a normalized part of everyday business, as implied in Black Swan theory. Risk which is calculated retrospectively is unlikely to encourage investment in biosecurity and, thereby, points to the danger of unpredictable outlier events