The ability of a virus to spread between individuals, its replication capacity and the clinical course of the infection are macroscopic consequences of a multifaceted molecular interaction of viral components with the host cell. The heavy impact of COVID-19 on the world population, economics and sanitary systems calls for therapeutic and prophylactic solutions that require a deep characterization of the interactions occurring between virus and host cells. Unveiling how SARS-CoV-2 engages with host factors throughout its life cycle is therefore fundamental to understand the pathogenic mechanisms underlying the viral infection and to design antiviral therapies and prophylactic strategies. Two years into the SARS-CoV-2 pandemic, this review provides an overview of the interplay between SARS-CoV-2 and the host cell, with focus on the machinery and compartments pivotal for virus replication and the antiviral cellular response. Starting with the interaction with the cell surface, following the virus replicative cycle through the characterization of the entry pathways, the survival and replication in the cytoplasm, to the mechanisms of egress from the infected cell, this review unravels the complex network of interactions between SARS-CoV-2 and the host cell, highlighting the knowledge that has the potential to set the basis for the development of innovative antiviral strategies.

Endophytic fungi reside in the internal plant tissues and are potential source of the natural products that may be used in the discovery of novel antibacterial, antifungal and antitumor agents. The recent work was conducted to isolate and identify endophytic fungi from the three medicinal plants Papaver somniferum (Opium poppy), Cassia fistula (Amaltaas) and Catharanthus roseus (Madagascar periwinkle) and to conduct antimicrobial activities of the crude extract obtained from these endophytic fungal strains. A total of four endophytic fungal strains were isolated of which two were isolated from the roots of P. somniferum, one from leaves of C. fistula and one from the mid rib of C. roseus using Sabouraud Dextrose Agar and labelled as PS3, PS4, CF1 and CR1 respectively. Three of the bioactive fungal strains were identified as Aspergillus spp. and one of genus Curvularia based on the cultural and morphological characteristics using bright field microscopy. After identification, the endophytic fungal strains were grown in Czapek yeast extract broth medium for the production of crude metabolites. Antibacterial activity of the crude isolates were tested against 4 human infectious bacterial species including two gram negative bacteria i.e. Salmonella typhi and Pseudomonas aeruginosa, and two gram positive bacteria Bacillus subtilis and Staphylococcus aureus. Different concentration of crude extract were tested i.e. 24 mg/ml, 12 mg/ml and 6 mg/ml, among them the concentration 24 mg/ml showed best antibacterial activity. The crude extract was also tested against two pathogenic fungal strains i.e. Aspergillus niger and Alternaria solani. Our results suggest that the crude extract from these endophytic fungal strains may be potential source in the discovery of novel antibacterial and antifungal drugs.

the coronavirus 2019 (COVID-19) outbreak, initiated in Wuhan, China, from where it subsequently spread globally, thereby posing serious threats to public health around the world. [1,2] The International Committee on Taxonomy of Viruses (ICTV), which classifies the Coronaviridae family, investigated the novelty of this virus and named it 2019-nCoV. This novel strain of virus is a sister of SARS-CoVs and on 11 th February 2020 the WHO formally named the disease SARS-CoV-2 COVID-19. The COVID-19 outbreak continues to spread around the world and, as an international health issue, has attained pandemic status, necessitating global solidarity to combat the spread of the virus. [3] The concatenation of events that immediately followed the COVID-19 outbreak was momentous; on 1st January the Huanan seafood market was closed down; on 2nd January Chaolin Huang and his colleagues reported the clinical features of 42 patients; on 7 th January isolation of novel coronavirus was achieved; on 11 th January COVID-19 genome was deposited on www.virological.org and GenBank. Moreover, on 20th January, human to human transmission of infection was confirmed in health care workers treating COVID-19 patients, on 21 st January Real -time polymerase chain reaction (RT-PCR) was used for the detection of COVID-19, on 30 th January WHO confirmed COVID-19 as a public health emergency of international concern (PHEIV). On 12 th of February WHO announced that saliva from 11 patients in Hong Kong tested for COVID-19 using RT-PCR. On 11 th March COVID-19 was classified as a pandemic by WHO and on 16th March the first clinical trial was commenced by National Health Institute (NHI) in USA. [4][5][6][7] A study carried out in 2001 reported that 500 patients exhibited flu symptoms and about 3.6% tested positive for a strain of corona virus. It was previously thought to be a simple and non-fatal virus until the year 2002 when, in the Chinese province of Guangdong, the spread of coronavirus sparked an outbreak in several other countries including Singapore, Hong Kong, Taiwan, Vietnam, Thailand and United States of America. The spread resulted in severe acute respiratory syndrome (SARS), which was mostly responsible for virus-related fatalities. Furthermore, it was discovered by looking at the disease pathogenesis that it was a different strain of coronavirus that infected the

Plants disease identification plays a major role in agriculture yield prevention. Traditionally, manual plant surface examination is conducted which is time-consuming and relatively less efficient. Therefore, this study incorporates machine vision-based techniques, for plant disease identification (i.e. healthy leaf, Alternaria Alternate, and bacterial blight). The developed method employs a dataset comprised of more than 10,000 data points. Initially, Image processing is performed followed by image pre-processing techniques for noise removal. Subsequently, image segmentation is performed for the region of interest (ROI). A multiclass classifier is introduced for plant disease analysis, which results in a percentage of disease spreading in healthy leaf. The results were compared with other methods and it is evident that the developed method has shown 95% accuracy in plant disease identification.

Determination of nitrogen levels in plants is essential for variable rate fertilizer application in precision agriculture. In the past, several techniques have been developed for nitrogen concentration estimation in plants and crops employing vision system, however, they are computationally expensive and hence requires a considerable amount of time to produce accurate results. The technique developed in this work the determination of nitrogen levels in plants could be achieved effectively in real-time time by advance image processing techniques, machine visions and support vector machine (SVM) with MATLAB. The developed technique processes leaf's colored image via examining it Red, Green and Blue (RGB) values and compares them with standard intensity levels. The experimental results show effectiveness of the developed technique and accurately detect low or high concentration levels in corn. In addition, this method depends on two techniques for a final result, i.e. color intensity and SVM. If the answer is not similar between the two techniques the process will be repeated until the detection is similar. This study could be applied to a variety of crops, since this technique does not require large collection of data for training and special expertise for its on-field application.