Epidemiological Insights of Foot and Mouth Disease Virus Infection among Cattle and Buffaloes in Sharkia Governorate, Egypt

Foot-and-mouth disease (FMD) is endemic in Egypt and in most parts of Africa causing huge economic losses. Control of FMD using vaccination requires information on the occurrence of various FMDV serotypes. This study aimed to determine the prevalence of FMDV serotypes in Sharkia Governorate, Egypt. A total number of 643 different samples, within ten different localities, were collected from both cattle and buffaloes (n = 283) of different, age, sex, immune status against FMD, and health status. Field samples (n = 360) have been screened for FMDV by RT-PCR using universal primers and were further subtyped using serotype-specific primers. Additionally, serum samples (n = 283) have been analyzed by applying FMDV serotype-specific antibody ELISA. The RT-PCR screening revealed that a total number of 39/283 (13.8%), 61/283 (21.6%) and 17/38 (44.7%) animals were positive for FMDV serotype O, A and SAT2, respectively. While, by ELISA, neutralizing antibodies directed against FMDV serotype O, A, and SAT2, were found in 177/283 (62.5%), 171/283 (60.4%) and 27/38 (71.1%) serum samples, respectively. These results indicated the endemic status of the FMDV serotypes O, A and SAT2 in Sharkia Governorate despite routine FMD vaccination programs. Although many variations of disease prevalence were recorded between animals of different, age, sex and immune and health status but it was obvious that FMD was more prominent and prevalent in buffaloes (47.1%) than in cattle (34.1%). Therefore, control efforts should focus on reducing the circulation of FMDV among susceptible livestock with special attention towards water buffaloes. Continuous surveillance, at molecular and immunological levels, of FMDV serotypes is needed for the effectiveness of any adopted control strategy targeting FMD including vaccination

Synthesis and Analysis of Entangled Photonic Qubits in Spatial-Parity Space

We present the novel embodiment of a photonic qubit that makes use of one continuous spatial degree of freedom of a single photon and relies on the the parity of the photon's transverse spatial distribution. Using optical spontaneous parametric downconversion to produce photon pairs, we demonstrate the controlled generation of entangled-photon states in this new space. Specifically, two Bell states, and a continuum of their superpositions, are generated by simple manipulation of a classical parameter, the optical-pump spatial parity, and not by manipulation of the entangled photons themselves. An interferometric device, isomorphic in action to a polarizing beam splitter, projects the spatial-parity states onto an even--odd basis. This new physical realization of photonic qubits could be used as a foundation for future experiments in quantum information processing.Comment: 6 pages, 5 figures, submitted to PR

Role of entanglement in two-photon imaging

The use of entangled photons in an imaging system can exhibit effects that cannot be mimicked by any other two-photon source, whatever the strength of the correlations between the two photons. We consider a two-photon imaging system in which one photon is used to probe a remote (transmissive or scattering) object, while the other serves as a reference. We discuss the role of entanglement versus correlation in such a setting, and demonstrate that entanglement is a prerequisite for achieving distributed quantum imaging.Comment: 15 pages, 2 figure

TPPSO: A Novel Two-Phase Particle Swarm Optimization

Particle swarm optimization (PSO) is a stout and rapid searching algorithm that has been used in various applications. Nevertheless, its major drawback is the stagnation problem that arises in the later phases of the search process. To solve this problem, a proper balance between investigation and manipulation throughout the search process should be maintained. This article proposes a new PSO variant named two-phases PSO (TPPSO). The concept of TPPSO is to split the search process into two phases. The first phase performs the original PSO operations with linearly decreasing inertia weight, and its objective is to focus on exploration. The second phase focuses on exploitation by generating two random positions in each iteration that are close to the global best position. The two generated positions are compared with the global best position sequentially. If a generated position performs better than the global best position, then it replaces the global best position. To prove the effectiveness of the proposed algorithm, sixteen popular unimodal, multimodal, shifted, and rotated benchmarking functions have been used to compare its performance with other existing well-known PSO variants and non-PSO algorithms. Simulation results show that TPPSO outperforms the other modified and hybrid PSO variants regarding solution quality, convergence speed, and robustness. The convergence speed of TPPSO is extremely fast, making it a suitable optimizer for real-world optimization problems

The Association of Persistent Symptoms of Depression and Anxiety with Recurrent Acute Coronary Syndrome Events: A Prospective Observational Study

The purpose of this study was to examine the role of persistent symptoms of depression and anxiety in a second acute coronary syndrome (ACS) event. Data presented in this study were from an RCT study. A follow-up for 24 months after baseline to detect a second ACS event among 1162 patients from five hospitals. Hierarchal Cox regression analyses were used. The results showed that persistent depression only (HR 2.27; 95% CI: 1.35–3.81; p = 0.002), and comorbid persistent depression and anxiety (HR 2.03; 95% CI: 1.03–3.98; p = 0.040) were the significant predictors of a second ACS event. Secondary education level compared to primary educational level (HR 0.63; 95% CI: 0.43–0.93; p = 0.020) and college or more education level compared to primary educational level (HR 0.47; 95% CI: 0.27–0.84; p = 0.011) were the only demographic variables that were significant predictors of a second event. The study reveals that attention must be paid by healthcare providers to assess and manage persistent depression; particularly when it is co-morbid with anxiety

Diagnostic Imaging and Radiation Therapy in the Arab World: A New Model of Advanced Practice

This study aimed at suggesting a new model for advanced practice in the diagnostic imaging and radiation therapy in the Arab World by presenting a comparative study between the different medical imaging techniques, the concepts, benefits, risks and medical applications of these techniques has been presented with details. Attempting For building a new model of advanced practice for the diagnostic role of  imaging and radiation therapy in the Arab World; by analyzing the current status of the imaging and radiation therapy in the Arab World, and then surveying the different medical imaging techniques. Then  to suggest a model of best practices upon the outcomes of the study

The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey.

Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications

One-Way Entangled-Photon Autocompensating Quantum Cryptography

A new quantum cryptography implementation is presented that combines one-way operation with an autocompensating feature that has hitherto only been available in implementations that require the signal to make a round trip between the users. Using the concept of advanced waves, it is shown that this new implementation is related to the round-trip implementations in the same way that Ekert's two-particle scheme is related to the original one-particle scheme of Bennett and Brassard. The practical advantages and disadvantages of the proposed implementation are discussed in the context of existing schemes.Comment: 5 pages, 1 figure; Minor edits--conclusions unchanged; accepted for publication in Physical Review

Computer Aided Autism Diagnosis Using Diffusion Tensor Imaging

© 2013 IEEE. Autism Spectrum Disorder (ASD), commonly known as autism, is a lifelong developmental disorder associated with a broad range of symptoms including difficulties in social interaction, communication skills, and restricted and repetitive behaviors. In autism spectrum disorder, numerous studies suggest abnormal development of neural networks that manifest itself as abnormalities of brain shape, functionality, and/ or connectivity. The aim of this work is to present our automated computer aided diagnostic (CAD) system for accurate identification of autism spectrum disorder based on the connectivity of the white matter (WM) tracts. To achieve this goal, two levels of analysis are provided for local and global scores using diffusion tensor imaging (DTI) data. A local analysis using the Johns Hopkins WM atlas is exploited for DTI atlas-based segmentation. Furthermore, WM integrity is examined by extracting the most notable features representing WM connectivity from DTI. Interactions of WM features between different areas in the brain, demonstrating correlations between WM areas were used, and feature selection among those associations were made. Finally, a leave-one-subject-out classifier is employed to yield a final per-subject decision. The proposed system was tested on a large dataset of 263 subjects from the National Database of Autism Research (NDAR) with their Autism Diagnostic Observation Schedule (ADOS) scores and diagnosis (139 typically developed: 66 males, and 73 females, and 124 autistics: 66 males, and 58 females), with ages ranging from 96 to 215 months, achieving an overall accuracy of 73%. In addition to this achieved global accuracy, diagnostically-important brain areas were identified, allowing for a better understanding of ASD-related brain abnormalities, which is considered as an essential step towards developing early personalized treatment plans for children with autism spectrum disorder