Diagnostic Accuracy of Sixty Four Multi-Slice CT Angiography in Assessment of Arterial Cut-Off and Run-Off in Comparison with Surgical Findings

Background/Objective: The accurate anatomic mapping and determination of the severity of arterial disease, an important health problem of the elderly, is of great significance. We aimed to determine the diagnostic value of 64-multislice CT angiography (MSCTA) in run-off and cut-off sites of arterial disease. Patients and Methods: Throughout the study, MSCTA followed by an operative intervention was carried out on a total of 38 patients with clinical signs and symptoms suggestive of arterial disease (AD) all of whom had the indication for vascular surgery. The mean age of patients was 34±15.86 (range, 23 to 93) years. MSCTA was executed using a 64-slice CT scanner, during the arterial phase of injecting the nonionic, contrast medium with a power injector at the rate of 5 ml/sec into the antecubital vein and exploration and revascularization of peripheral arterial disease was performed intraoperatively. Results: Atherosclerosis and arterial disease, the most common causes of vascular occlusion, were more common in the lower extremities. According to MSCTA findings, the most frequent site of stenosis was the superficial femoral artery. Spearman’s correlation coefficient showed a high degree of agreement amongst the raters. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and the accuracy of MSCTA compared to surgery were 83.8%, 96%, 96.8%, 81.3% and 89%, respectively. MSCTA findings were compared with surgery as a standard of reference, which showed concordance in the majority of cases (81.6%). Cut-off sites were correctly identified by MSCTA in 97.3% of the patients and the most common sites of discordance were the run-off sites (18.2%). Conclusion: MSCTA angiography as a novel diagnostic modality may be a suitable alternative and a viable choice for routine clinical diagnosis

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&

Deep Multi-modality Soft-decoding of Very Low Bit-rate Face Videos

We propose a novel deep multi-modality neural network for restoring very low bit rate videos of talking heads. Such video contents are very common in social media, teleconferencing, distance education, tele-medicine, etc., and often need to be transmitted with limited bandwidth. The proposed CNN method exploits the correlations among three modalities, video, audio and emotion state of the speaker, to remove the video compression artifacts caused by spatial down sampling and quantization. The deep learning approach turns out to be ideally suited for the video restoration task, as the complex non-linear cross-modality correlations are very difficult to model analytically and explicitly. The new method is a video post processor that can significantly boost the perceptual quality of aggressively compressed talking head videos, while being fully compatible with all existing video compression standards.Comment: Accepted by Proceedings of the 28th ACM International Conference on Multimedia(ACM MM),202

Transgenic Microalgae With Increased Production Of At Least One Omega-3 Long Chain Polyunsaturated Fatty Acid (Patent US 2018/0312888 A1)

The invention relates to genetically modified organisms with enhanced production of omega-3 long chain polyunsaturated fatty acids

Recombinant Organisms (Patent US 2015/0275243 A1)

The invention relates to genetically modified organisms with enhanced production of omega-3 long chain polyunsaturated fatty acids

Recombinant Organisms (Patent WO 2014/053821 A1)

The invention relates to genetically modified organisms with enhanced production of omega-3 long chain polyunsaturated fatty acids

PARTICLES SIZE DISTRIBUTION EFFECT ON 3D PACKING OF NANOPARTICLES INTO A BOUNDED REGION

Abstract In this paper, the effects of two different Particle Size Distributions (PSD) on packing behavior of ideal rigid spherical nanoparticles using a novel packing model based on parallel algorithms have been reported. A mersenne twister algorithm was used to generate pseudorandom numbers for the particles initial coordinates. Also, for this purpose a nanosized tetragonal confined container with a square floor (300 * 300 nm) were used in this work. The Andreasen and the Lognormal PSDs were chosen to investigate the packing behavior in a 3D bounded region. The effects of particle numbers on packing behavior of these two PSDs have been investigated. Also the reproducibility and the distribution of packing factor of these PSDs were compared. Keyword

GGE biplot and AMMI analysis of barley yield performance in Iran

Successful production and development of stable and adaptable cultivars only depend on the positive results achieved from the interaction between genotype and environment that consequently has significant effect on breeding strategies. The objectives of this study were to evaluate genotype by environment interactions for grain yield in barley advanced lines and to determine their stability and general adaptability. For these purposes, 18 advanced lines along with two local cultivars were evaluated at five locations (Gachsaran, Lorestan, Ilam, Moghan and Gonbad) during three consecutive years (2012–2015). The results of the AMMI analysis indicated that main effects due to genotype (G), environment (E) and GE interaction as well as four interaction principal component axes were significant, representing differential responses of the lines to the environments and the need for stability analysis. According to AMMI stability parameters, lines G5 and G7 were the most stable lines across environments. Biplot analysis determined two barley mega-environments in Iran. The first mega-environment contained of Ilam and Gonbad locations, where the recommended G13, G19 and G1 produced the highest yields. The second mega-environment comprised of Lorestan, Gachsarn and Moghan locations, where G2, G9, G5 and G7 were the best adapted lines. Our results revealed that lines G5, G7, G9 and G17 are suggested for further inclusion in the breeding program due to its high grain yield, and among them G5 recommended as the most stable lines for variable semi-warm and warm environments. In addition, our results indicated the efficiency of AMMI and GGE biplot techniques for selecting genotypes that are stable, high yielding, and responsive