Reverse Engineering Aspects to Derive Application Class Models

Aspects provide a nice way to modify behavior and implement cross-cutting concerns in object-oriented systems. As such, aspects do not have an existence of their own the application classes that the aspects refer to must be present in order to instantiate the aspects. In this research, we present a reverse engineering approach to generate a minimal class model that has all the structural elements necessary in order to complete exercise a set of aspect

The Reflection of Effective Interactive Graphic Design

Graphic design has a rich history as a creative practice, which connects different design elements to build effective message. It combines typography and visual elements like Illustrations or photos to create a visual message, graphic design is considered as an important branch in the modern communication technology. Graphic design nowadays has becoming more advanced and moves from the static phase to the dynamic phase which includes unlimited technologies like virtual reality, 3D motion and interactivity, which becomes one of the important factors that affects the successes of the visual communication or the message that has to be sent via the design, where interactive design is the management and meaningful transferring of information through different media as its the intersection point between graphic design, different media and technologies. This interaction in design will affects positively the perception of the design by the target audience especially if it was scientifically studied based on the target audience’s criteria

Reverse Engineering Aspects to Derive Application Class Models

Aspects provide a nice way to modify behavior and implement cross-cutting concerns in object-oriented systems. As such, aspects do not have an existence of their own the application classes that the aspects refer to must be present in order to instantiate the aspects. In this research, we present a reverse engineering approach to generate a minimal class model that has all the structural elements necessary in order to complete exercise a set of aspect

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

How to build a bone: PHOSPHO1, biomineralization and beyond

Since its characterization two decades ago, the phosphatase PHOSPHO1 has been the subject of an increasing focus of research. This work has elucidated PHOSPHO1’s central role in the biomineralization of bone and other hard tissues, but has also implicated the enzyme in other biological processes in health and disease. During mineralization PHOSPHO1 liberates inorganic phosphate (Pi) to be incorporated into the mineral phase through hydrolysis of its substrates phosphocholine (PCho) and phosphoethanolamine (PEA). Localization of PHOSPHO1 within matrix vesicles allows accumulation of Pi within a protected environment where mineral crystals may nucleate and subsequently invade the organic collagenous scaffold. Here, we examine the evidence for this process, first discussing the discovery and characterization of PHOSPHO1, before considering experimental evidence for its canonical role in matrix vesicle-mediated biomineralization. We also contemplate roles for PHOSPHO1 in disorders of dysregulated mineralization such as vascular calcification, along with emerging evidence of its activity in other systems including choline synthesis and homeostasis, and energy metabolism

Development of functional metal organic frameworks for wastewater treatment

Novel amine-functionalized zirconium-based metal organic frameworks (MOFs) were solvothermally synthesized for the efficient and selective removal of Pb (II) in concentrated multi-component heavy metal ions systems. Pristine UiO-66 and UiO-67 MOFs were grafted with thiourea and amidinothiourea via a facile one-step post synthetic modification. Successful grafting was confirmed by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). The microporous amidinothiourea modified MOFs demonstrated excellent performance in their high maximum adsorption capacities of 246 and 367 mg.g-1, respectively, as well as their remarkable selectivity for Pb (II) ions in highly concentrated multi-ion solutions. This is, in addition, to their effective removal efficiencies which reached up to more than 95% at a high range of Pb (II) concentrations (25 - 250 ppm). Furthermore, the structural stability of the MOF crystals was maintained after adsorption and the MOF was completely regenerated for up to four cycles. Additionally, an as-synthesized Zr-based MOF was solvothermally synthesized for the efficient removal of Diclofenac sodium (DCF) from an aqueous medium. The as-synthesized microporous DUT-67 MOF was characterized by XRD and BET. Equilibrium studies revealed that the sorption process followed the Langmuir isotherm, which accordingly demonstrated a significant adsorption capacity of 484 mg.g-1, attaining maximum removal efficiencies of about 90% at 75 and 150 ppm DCF initial concentrations. Kinetically, the adsorption process followed the pseudo-second order and the intraparticle diffusion models. The Dubinin-Radushkevich model along with thermodynamic analysis suggested a physisorption interaction that is exothermic and spontaneous

MRI Findings in Desmoplakin-related Arrhythmogenic Left Ventricular Cardiomyopathy in a Pediatric Patient: A Case Report.

Arrhythmogenic cardiomyopathy (ACM) is a heart muscle disorder that cannot be explained by ischemic, hypertensive, or valvular heart disease and often results in sudden cardiac death. Arrhythmogenic right ventricular cardiomyopathy (ARVC) is the best-characterized ACM and can be diagnosed using the revised task force criteria. In contrast, there are no accepted clinical diagnostic criteria for arrhythmogenic left ventricular cardiomyopathy (ALVC), another subtype of ACM. Cardiac MRI aids in ARVC diagnosis by delineating biventricular structural and functional abnormalities and can be instrumental in diagnosing ALVC. This report presents a pediatric case of desmoplakin cardiomyopathy, a distinct subtype of ALVC, with findings overlapping myocarditis and LV noncompaction

Applying Artificial Intelligence to Pediatric Chest Imaging: Will Leveraging Adult-Based Artificial Intelligence Models Prove Reliable?

OBJECTIVE: The scarcity of artificial intelligence (AI) applications designed for use in pediatric patients can cause a significant health disparity in this vulnerable population. We investigated the performance of an adult-trained algorithm in detecting pneumonia in a pediatric population to explore the viability of leveraging adult-trained algorithms to accelerate pediatric AI research. METHODS: We analyzed a publicly available pediatric chest x-ray dataset using an AI algorithm from TorchXRayVision. A 60% threshold was used to make binary predictions for pneumonia presence. Predictions were compared with dataset labels. Performance measures including true-positive rate, false-positive rate, true-negative rate, false-negative rate, sensitivity, specificity, positive predictive value (PPV), negative predictive value, accuracy, and F1-score were calculated for the complete dataset and bacterial and viral pneumonia subsets. RESULTS: Overall (n = 5,856), the algorithm identified 3,923 cases with pneumonia (67.00%) and 1,933 (33.00%) normal cases. In comparison with the actual image labels, there were 3,411 (58.25%) true-positive cases, 512 (8.74%) false-positive cases, 1,071 (18.29%) true-negative cases, and 862 (14.72%) false-negative cases resulting in 79.83% sensitivity, 67.66% specificity, 86.95% PPV, 55.41% negative predictive value, and 76.54% accuracy and an F1-score of 0.83. Although the performance remained consistent in the bacterial pneumonia group, there was a significant decrease in PPV (69.9%) and F1-score (0.74) in the viral pneumonia group. CONCLUSION: An adult-trained model adequately detected pneumonia in pediatric patients aged 1 to 5 years. Though models trained exclusively on pediatric images performed better, leveraging adult-based algorithms and datasets can expedite pediatric AI research

An Information Theoretic Framework for Field Monitoring Using Autonomously Mobile Sensors

We consider a mobile sensor network monitoring a spatio-temporal field. Given limited cache sizes at the sensor nodes, the goal is to develop a distributed cache management algorithm to efficiently answer queries with a known probability distribution over the spatial dimension. First, we propose a novel distributed information theoretic approach in which the nodes locally update their caches based on full knowledge of the space-time distribution of the monitored phenomenon. At each time instant, local decisions are made at the mobile nodes concerning which samples to keep and whether or not a new sample should be acquired at the current location. These decisions account for minimizing an entropic utility function that captures the average amount of uncertainty in queries given the probability distribution of query locations. Second, we propose a different correlation-based technique, which only requires knowledge of the second-order statistics, thus relaxing the stringent constraint of having a priori knowledge of the query distribution, while significantly reducing the computational overhead. It is shown that the proposed approaches considerably improve the average field estimation error by maintaining efficient cache content. It is further shown that the correlation-based technique is robust to model mismatch in case of imperfect knowledge of the underlying generative correlation structure