The visual exploration of retinal blood vessels assists ophthalmologists in the diagnoses of different abnormalities of the eyes such as diabetic retinopathy, glaucoma, cardiovascular ailment, high blood pressure, arteriosclerosis, and age-related macular degeneration. The manual inspection of retinal vasculature is an extremely challenging and tedious task for medical experts due to the complex structure of an eye, tiny blood vessels, and variation in vessels width. Several automatic retinal vessels extraction techniques have been proposed in contemporary literature, which assist ophthalmologists in the timely identification of an eye disorders. However, due to the fast evolution of such techniques, a comprehensive survey is needed. This survey presents a comprehensive review of such techniques, strategies, and algorithms presented to date. The techniques are classified into logical groups based on the underlying methodology employed for retinal vessel extraction. The performance of existing techniques is reported on the publicly accessible datasets in term of various performance measures, providing a valuable comparison among the techniques. Thus, this survey presents a valuable resource for researchers working toward automatic extraction of retinal vessels.

No abstract

Coronaviruses (CoVs) infect a wide range of domestic and wild mammals. These viruses have a potential and tendency to cross-species barriers and infect humans. Novel human coronavirus 2019-nCoV (hCoV-19) emerged from Wuhan, China, and has caused a global pandemic. Genomic features of SARS-CoV-2 may attribute inter-species transmission and adaptation to a novel host, and therefore is imperative to explicate the evolutionary dynamics of the viral genome and its propensity for differential host selection. We conducted an in silico analysis of all the coding gene sequences of SARS-CoV-2 strains (n = 39) originating from a range of non-human mammalian species, including pangolin, bat, dog, cat, tiger, mink, mouse, and the environmental samples such as wastewater, air and surface samples from the door handle and seafood market. Compared to the reference SARS-CoV-2 strain (MN908947; Wuhan-Hu-1), phylogenetic and comparative residue analysis revealed the circulation of three variants, including hCoV-19 virus from humans and two hCoV-19-related precursors from bats and pangolins. A lack of obvious differences as well as a maximum genetic homology among dog-, cat-, tiger-, mink-, mouse-, bat-and pangolin-derived SARS-CoV-2 sequences suggested a likely evolution of these strains from a common ancestor. Several residue substitutions were observed in the receptor-binding domain (RBD) of the spike protein, concluding a promiscuous nature of the virus for host species where genomic alternations may be required for the adaptation to novel host/s. However, such speculation needs in vitro investigations to unleash the influence of substitutions towards species-jump and disease pathogenesis.

A smart space is an increasingly important application of Internet of Things (IoT), which is being developed throughout the world for different purposes ranging from home automation to smart grids. However, a common approach for requirement capture and specification at an abstract level is needed in smart space development that is independent of the continuously changing technology. To provide a systematic process, the standard software engineering practices can be used in the smart space development, and hence take advantages of. In this context, this paper contributes in the field of requirement engineering for smart spaces by introducing the requirement specification technique based on software engineering Use Case concept. The conventional Use Case structure and the way it is described are modified to suit the requirements of smart spaces. The case of a hypothetical smart space development is considered, where the proposed requirement specification technique is applied and a Use Case repository is developed that can be used as a canonical resource for other researchers to draw upon. The implementation provides profound descriptions on how to use the proposed requirement specification technique during the development of smart spaces and in other application areas of IoT. This work can be used as a first step towards defining the smart space development framework, of which the proposed technique would be a key element.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections has affected more than 15 million people and, as of 22 July 2019, caused deaths of more than 0.6 million individuals globally. With the excretion of SARS-CoV-2 in the stool of symptomatic and asymptomatic COVID-19 patients, its genome detection in the sewage water can be used as a powerful epidemiological tool to predict the number of positive cases in a population. This study was conducted to detect SARS-CoV-2 genome in sewage water during the lockdown. Sewage samples, from 28 pre-selected sites, were collected on alternate days from 13-25 July, 2020 from two selected areas [Johar Town (n = 05) and Township (n = 23)], where smart lockdown were implemented by the government authorities on 9th July, 2020. Genomic RNA was extracted and the SARS-CoV-2 was detected and quantified using commercially available kit through Real-Time PCR. Out of 28, sixteen samples were positive on day one while 19, 17, 23, 17, 05 and 09 samples were positive on day 3, 5, 7, 9, 11, and 13, respectively. These results indicate the decreasing viral copy load with the passage of time however few sites did not followed a clear pattern indicating the complexities in sewage water based surveillance i.e time of sampling. Hourly sampling from two sites for 24 hours also revealed the impact of time sampling time on detection of SARS-CoV-2 genome in sewage pipelines and lift/disposal stations. Results of current study indicate a possible role of sewage-based COVID-19 surveillance in monitoring and execution of smart lockdowns.