Association between autism spectrum disorder and diabetes: systematic review and meta-analysis

There is mixed evidence on the link between autism spectrum disorder (ASD) and diabetes. We conducted the first systematic review/meta-analysis on their association. Based on a pre-registered protocol (PROSPERO: CRD42021261114), we searched Pubmed, Ovid, and Web of Science databases up to 6 December 2021, with no language/type of document restrictions. We assessed study quality using the Newcastle-Ottawa Scale (NOS). We included 24 studies (total: 3427,773 individuals; 237,529 with ASD and 92,832 with diabetes) in the systematic review and 20 in the meta-analysis (mean stars number on the NOS: 5.89/10). There was a significant association, albeit characterized by significant heterogeneity, when pooling unadjusted OR (1.535, 95% CI = 1.109-2.126), which remained significant when restricting the analysis to children and type 2 diabetes, but became non-significant when considering adjusted ORs (OR: 1.528, 95% CI = 0.954-2.448). No significant prospective association was found (n = 2) on diabetes predicting ASD (HR: 1.232, 0.826-11.837). Therefore, the association between ASD and diabetes is likely confounded by demographic and clinical factors that should be systematically investigated in future studies

National innovation systems, developing countries, and the role of intermediaries: a critical review of the literature

Developed over the past three decades, the national innovation system concept (NIS) has been widely used by both scholars and policy makers to explain how interactions between a set of distinct, nationally bounded institutions supports and facilitates technological change and the emergence and diffusion of new innovations. This concept provides a framework by which developing countries can adopt for purposes of catching up. Initially conceived on structures and interactions identified in economically advanced countries, the application of the NIS concept to developing countries has been gradual and has coincided – in the NIS literature – with a move away from overly macro-interpretations to an emphasis on micro-level interactions and processes, with much of this work questioning the nation state as the most appropriate level of analysis, as well as the emergence of certain intermediary actors thought to facilitate knowledge exchange between actors and institutions. This paper reviews the NIS literature chronologically, showing how this shift in emphasis has diminished somewhat the importance of both institutions, particularly governments, and the process of institutional capacity building. In doing so, the paper suggests that more recent literature on intermediaries such as industry associations may offer valuable insights to how institutional capacity building occurs and how it might be directed, particularly in the context of developing countries where governance capacities are often lacking, contributing to less effective innovation systems, stagnant economies, and unequal development

Association between autism spectrum disorder and diabetes: systematic review and meta-analysis

There is mixed evidence on the link between autism spectrum disorder (ASD) and diabetes. We conducted the first systematic review/meta-analysis on their association. Based on a pre-registered protocol (PROSPERO: CRD42021261114), we searched Pubmed, Ovid, and Web of Science databases up to 6 December 2021, with no language/type of document restrictions. We assessed study quality using the Newcastle-Ottawa Scale (NOS). We included 24 studies (total: 3427,773 individuals; 237,529 with ASD and 92,832 with diabetes) in the systematic review and 20 in the meta-analysis (mean stars number on the NOS: 5.89/10). There was a significant association, albeit characterized by significant heterogeneity, when pooling unadjusted OR (1.535, 95% CI = 1.109-2.126), which remained significant when restricting the analysis to children and type 2 diabetes, but became non-significant when considering adjusted ORs (OR: 1.528, 95% CI = 0.954-2.448). No significant prospective association was found (n = 2) on diabetes predicting ASD (HR: 1.232, 0.826-11.837). Therefore, the association between ASD and diabetes is likely confounded by demographic and clinical factors that should be systematically investigated in future studies.ope

Influence of Digital Signage Usage on Product Sale among Leading Supermarkets in Kenya

In the changing business environment, retailers are always searching new strategies to attract and hold customers. Digital signage is a very attractive medium for advertising and general communication in open spaces. It has been adopted by many business sectors that have benefited from the advantages it offers. This study sought to establish the effect digital signage has on the sale of products among leading supermarkets in Kenya. The objective of this study was to find out the influence of digital signage on product sales among leading supermarkets in Kenya. This study adopted a descriptive research design and respondents were drawn from Tuskys, Uchumi and Nakumatt supermarkets. They included managers, assistant managers, supervisors and merchandisers.  A questionnaire was used for data collection.  Data collected was analyzed using Statistical Package for Social Scientists (SPSS). The data was presented in Tables and Figures using frequencies and percentages. The findings show that digital signage does influence the sales of products. It was also found out that digital signage was perceived to be helpful by informing the respondents of the products and influencing their purchase decisions. The location of digital signage was found to be critical to the success of the advertisement. However, positioning of the screens and advertisement content for relevance and completeness were the recommended remedies

K-Nearest Neighbours Based Classifiers for Moving Object Trajectories Reconstruction

This article presents an exemplary prototype implementation of an Application Programming Interface (API) for incremental reconstruction of the trajectories of moving objects captured by Closed-Circuit Television (CCTV) and High-Definition Television (HDTV) cameras based on KNearest Neighbor (KNN) classifiers. This paper proposes a model-driven approach for trajectory reconstruction based on machine learning algorithms which is more efficient than other approaches for dynamic tracking, such as RGB-D (Red, Green and Red Color model with Depth) images or scale or rotation approaches. The existing approaches typically need a low-level information from the input video stream but the environment factors (indoor light, outdoor light) affect the results. The use of a predefined model allows to avoid this since the data is naturally filtered. Experiments on different input video streams demonstrate that the proposed approach is efficient for solving the tracking of moving objects in input streams in real time because it needs less granular information from the input stream. The research reported here is part of a research program of the Cyber Security Research Centre of London Metropolitan University for real-time video analytics with applicability to surveillance in security, disaster recovery and safety management, and customer insight

Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity

Retinopathy of Prematurity (ROP) is a potentially blinding eye disorder because of damage to the eye's retina which can affect babies born prematurely. Screening of ROP is essential for early detection and treatment. This is a laborious and manual process which requires trained physician performing dilated ophthalmological examination which can be subjective resulting in lower diagnosis success for clinically significant disease. Automated diagnostic methods can assist ophthalmologists increase diagnosis accuracy using deep learning. Several research groups have highlighted various approaches. This paper proposes the use of new novel fundus preprocessing methods using pretrained transfer learning frameworks to create hybrid models to give higher diagnosis accuracy. The evaluations show that these novel methods in comparison to traditional imaging processing contribute to higher accuracy in classifying Plus disease, Stages of ROP and Zones. We achieve accuracy of 97.65% for Plus disease, 89.44% for Stage, 90.24% for Zones with limited training dataset.Comment: 10 pages, 4 figures, 7 tables. arXiv admin note: text overlap with arXiv:1904.08796 by other author

Content-adaptive color transform for image compression

Cataloged from PDF version of article.In this paper, an adaptive color transform for image compression is introduced. In each block of the image, coefficients of the color transform are determined from the previously compressed neighboring blocks using weighted sums of the RGB pixel values, making the transform block-specific. There is no need to transmit or store the transform coeffi- cients because they are estimated from previous blocks. The compression efficiency of the transform is demonstrated using the JPEG image coding scheme. In general, the suggested transformation results in better peak signal-to-noise ratio (PSNR) values for a given compression level. ( C) 2011 Society of Photo-Optical Instrumentation Engineer

A 58.6 mW 30 Frames/s Real-Time Programmable Multiobject Detection Accelerator With Deformable Parts Models on Full HD 1920×1080 Videos

This paper presents a programmable, energy-efficient, and real-time object detection hardware accelerator for low power and high throughput applications using deformable parts models, with 2x higher detection accuracy than traditional rigid body models. Three methods are used to address the high computational complexity of eight deformable parts detection: classification pruning for 33x fewer part classification, vector quantization for 15x memory size reduction, and feature basis projection for 2x reduction in the cost of each classification. The chip was fabricated in a 65 nm CMOS technology, and can process full high definition 1920 × 1080 videos at 60 frames/s without any OFF-chip storage. The chip has two programmable classification engines (CEs) for multiobject detection. At 30 frames/s, the chip consumes only 58.6 mW (0.94 nJ/pixel, 1168 GOPS/W). At a higher throughput of 60 frames/s, the CEs can be time multiplexed to detect even more than two object classes. This proposed accelerator enables object detection to be as energy-efficient as video compression, which is found in most cameras today.United States. Defense Advanced Research Projects AgencyTexas Instruments Incorporate