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"Raj, V"
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Development of a handheld GPU-assisted DSC-TransNet model for the real-time classification of plant leaf disease using deep learning approach
In agriculture, promptly and accurately identifying leaf diseases is crucial for sustainable crop production. To address this requirement, this research introduces a hybrid deep learning model that combines the visual geometric group version 19 (VGG19) architecture features with the transformer encoder blocks. This fusion enables the accurate and précised real-time classification of leaf diseases affecting grape, bell pepper, and tomato plants. Incorporating transformer encoder blocks offers enhanced capability in capturing intricate spatial dependencies within leaf images, promising agricultural sustainability and food security. By providing farmers and farming stakeholders with a reliable tool for rapid disease detection, our model facilitates timely intervention and management practices, ultimately leading to improved crop yields and mitigated economic losses. Through extensive comparative analyses on various datasets and filed tests, the proposed depth wise separable convolutional-TransNet (DSC-TransNet) architecture has demonstrated higher performance in terms of accuracy (99.97%), precision (99.94%), recall (99.94), sensitivity (99.94%), F1-score (99.94%), AUC (0.98) for Grpae leaves across different datasets including bell pepper and tomato. Furthermore, including DSC layers enhances the computational efficiency of the model while maintaining expressive power, making it well-suited for real-time agricultural applications. The developed DSC-TransNet model is deployed in NVIDIA Jetson Nano single board computer. This research contributes to advancing the field of automated plant disease classification, addressing critical challenges in modern agriculture and promoting more efficient and sustainable farming practices.
Journal Article
Barriers to entrepreneurship: opportunity recognition vs. opportunity pursuit
Entrepreneurship is a phenomenon associated with wealth generation and economic development in a region or country. However, despite significant advancement in our understanding of this complex concept our understanding of its heterogeneity in countries across the world is limited. In this paper, we utilize barriers to entrepreneurship to explain that heterogeneity. We define barriers to entrepreneurship as conditions that prevent opportunity recognition and/or opportunity pursuit. We propose that cognitive-psychological factors may lead to discovery- lifestyle and growth–well-being barriers; social-institutional factors may create access and legitimacy barriers; and economic-operational factors may erect location and magnitude barriers. Using an abduction method, we enrich this theory by analyzing the stories of entrepreneurs and the coping strategies they use to overcome barriers to entrepreneurship.
Journal Article
Identification of sialic acid-binding function for the Middle East respiratory syndrome coronavirus spike glycoprotein
Middle East respiratory syndrome coronavirus (MERS-CoV) targets the epithelial cells of the respiratory tract both in humans and in its natural host, the dromedary camel. Virion attachment to host cells is mediated by 20-nm-long homotrimers of spike envelope protein S. The N-terminal subunit of each S protomer, called S1, folds into four distinct domains designated S1A through S1D. Binding of MERS-CoV to the cell surface entry receptor dipeptidyl peptidase 4 (DPP4) occurs via S1B. We now demonstrate that in addition to DPP4, MERS-CoV binds to sialic acid (Sia). Initially demonstrated by hemagglutination assay with human erythrocytes and intact virus, MERS-CoV Sia-binding activity was assigned to S subdomain S1A. When multivalently displayed on nanoparticles, S1 or S1A bound to human erythrocytes and to human mucin in a strictly Sia-dependent fashion. Glycan array analysis revealed a preference for α2,3-linked Sias over α2,6-linked Sias, which correlates with the differential distribution of α2,3-linked Sias and the predominant sites of MERS-CoV replication in the upper and lower respiratory tracts of camels and humans, respectively. Binding is hampered by Sia modifications such as 5-N-glycolylation and (7,)9-O-acetylation. Depletion of cell surface Sia by neuraminidase treatment inhibited MERS-CoV entry of Calu-3 human airway cells, thus providing direct evidence that virus–Sia interactions may aid in virion attachment. The combined observations lead us to propose that high-specificity, low-affinity attachment of MERS-CoV to sialoglycans during the preattachment or early attachment phase may form another determinant governing the host range and tissue tropism of this zoonotic pathogen.
Journal Article
An orthopoxvirus-based vaccine reduces virus excretion after MERS-CoV infection in dromedary camels
Middle East respiratory syndrome coronavirus (MERS-CoV) infections have led to an ongoing outbreak in humans, which was fueled by multiple zoonotic MERS-CoV introductions from dromedary camels. In addition to the implementation of hygiene measures to limit further camel-to-human and human-to-human transmissions, vaccine-mediated reduction of MERS-CoV spread from the animal reservoir may be envisaged. Here we show that a modified vaccinia virus Ankara (MVA) vaccine expressing the MERS-CoV spike protein confers mucosal immunity in dromedary camels. Compared with results for control animals, we observed a significant reduction of excreted infectious virus and viral RNA transcripts in vaccinated animals upon MERS-CoV challenge. Protection correlated with the presence of serum neutralizing antibodies to MERS-CoV. Induction of MVA-specific antibodies that cross-neutralize camelpox virus would also provide protection against camelpox.
Journal Article
Social Indicators Research: A Retrospective Using Bibliometric Analysis
Social Indicators Research (SIR) publishes novel and groundbreaking research focusing on social indicators related to quality of life and sustainability. Using bibliometrics, this study aims to offer a retrospective of the major trends (e.g., publication, citation, and top contributing authors, institutions, and countries) and intellectual structure of SIR. The retrospective indicates that SIR, which has grown substantially in productivity and impact, attracts contributions worldwide, notably from the USA, with 11 major themes revealed between 1974 and 2019. Using a zero-inflated negative binomial regression, this study also reveals the factors that influence the citation count of SIR publications, namely article age, number of author keywords, title novelty, title length, USA affiliation, and number of authors. Noteworthily, this study, which represents the inaugural review of SIR, should be useful for readers to gain rich insights into the state of research on social indicators related to quality of life and sustainability.
Journal Article
Dipeptidyl peptidase 4 is a functional receptor for the emerging human coronavirus-EMC
Human coronavirus-EMC (hCoV-EMC) is a new coronavirus that has killed around half of the few humans infected so far; this study now identifies DPP4 as the receptor that this virus uses to infect cells.
Human receptor for emerging coronavirus
The emerging pathogenic coronavirus hCoV-EMC, first identified in September 2012, has been fatal in about half of the few humans infected so far. Bart Haagmans and colleagues have now identified the receptor that this virus uses to infect cells. In contrast to the related virus SARS-CoV, which uses angiotensin converting enzyme 2, the functional receptor for hCoV-EMC is dipeptidyl peptidase 4 (DPP4, also known as CD26), an exopeptidase found on non-ciliated cells in the lower respiratory tract. This enzyme is highly conserved across different species, and hCoV-EMC can also use bat DPP4 as a functional receptor — a possible clue as to the host range and epidemiological history of this new virus. The findings may also be important for the development of intervention strategies.
Most human coronaviruses cause mild upper respiratory tract disease but may be associated with more severe pulmonary disease in immunocompromised individuals
1
. However, SARS coronavirus caused severe lower respiratory disease with nearly 10% mortality and evidence of systemic spread
2
. Recently, another coronavirus (human coronavirus-Erasmus Medical Center (hCoV-EMC)) was identified in patients with severe and sometimes lethal lower respiratory tract infection
3
,
4
. Viral genome analysis revealed close relatedness to coronaviruses found in bats
5
. Here we identify dipeptidyl peptidase 4 (DPP4; also known as CD26) as a functional receptor for hCoV-EMC. DPP4 specifically co-purified with the receptor-binding S1 domain of the hCoV-EMC spike protein from lysates of susceptible Huh-7 cells. Antibodies directed against DPP4 inhibited hCoV-EMC infection of primary human bronchial epithelial cells and Huh-7 cells. Expression of human and bat (
Pipistrellus pipistrellus
) DPP4 in non-susceptible COS-7 cells enabled infection by hCoV-EMC. The use of the evolutionarily conserved DPP4 protein from different species as a functional receptor provides clues about the host range potential of hCoV-EMC. In addition, it will contribute critically to our understanding of the pathogenesis and epidemiology of this emerging human coronavirus, and may facilitate the development of intervention strategies.
Journal Article
Middle East respiratory syndrome coronavirus neutralising serum antibodies in dromedary camels: a comparative serological study
A new betacoronavirus—Middle East respiratory syndrome coronavirus (MERS-CoV)—has been identified in patients with severe acute respiratory infection. Although related viruses infect bats, molecular clock analyses have been unable to identify direct ancestors of MERS-CoV. Anecdotal exposure histories suggest that patients had been in contact with dromedary camels or goats. We investigated possible animal reservoirs of MERS-CoV by assessing specific serum antibodies in livestock.
We took sera from animals in the Middle East (Oman) and from elsewhere (Spain, Netherlands, Chile). Cattle (n=80), sheep (n=40), goats (n=40), dromedary camels (n=155), and various other camelid species (n=34) were tested for specific serum IgG by protein microarray using the receptor-binding S1 subunits of spike proteins of MERS-CoV, severe acute respiratory syndrome coronavirus, and human coronavirus OC43. Results were confirmed by virus neutralisation tests for MERS-CoV and bovine coronavirus.
50 of 50 (100%) sera from Omani camels and 15 of 105 (14%) from Spanish camels had protein-specific antibodies against MERS-CoV spike. Sera from European sheep, goats, cattle, and other camelids had no such antibodies. MERS-CoV neutralising antibody titres varied between 1/320 and 1/2560 for the Omani camel sera and between 1/20 and 1/320 for the Spanish camel sera. There was no evidence for cross-neutralisation by bovine coronavirus antibodies.
MERS-CoV or a related virus has infected camel populations. Both titres and seroprevalences in sera from different locations in Oman suggest widespread infection.
European Union, European Centre For Disease Prevention and Control, Deutsche Forschungsgemeinschaft.
Journal Article
Interpretive Structural Modeling (ISM) and its application in analyzing factors inhibiting implementation of Total Productive Maintenance (TPM)
Purpose
– The purpose of this paper is to highlight the application of Interpretive Structural Modeling (ISM) to analyze the barriers in implementation of Total Productive Maintenance (TPM). TPM is explained in brief with emphasis on maintenance programs to improve quality of products, reliability of processes and reduction in cost. Barriers in implementation of TPM are also discussed. Concept of ISM and steps in developing ISM are described in detail. The authors then illustrate the research methodology which involves applying ISM to analyze barriers in TPM.
Design/methodology/approach
– The paper starts off by describing the concepts of TPM and ISM. Barriers in implementation of TPM are discussed. It explains ISM as a methodology to understand the underlying interrelationship among the inhibiting factors. The authors draw up an action plan to carry out research on the usage of ISM to study the TPM inhibitors, to develop an integrated model to establish the relationship among the different TPM inhibiting factors and to suggest action plan to mitigate these factors.
Findings
– Interpretive Structural Modeling (ISM) can be used to analyze the driving and dependence power of the variables inhibiting implementation of TPM. The barriers to implement TPM are described with detailed explanation. The complexity of the problem and the degree of interconnection among the variables can be found out. This will help Managers take action on mitigating the barriers.
Practical implications
– By analyzing the interrelationships among the barriers and their strengths, management can chalk out the strategy to implement TPM in an organization. Management will become aware of the barriers which have the maximum influence and then can act accordingly to mitigate these barriers. This will help in implementing TPM faster and in an organized manner.
Originality/value
– Many authors have used ISM to study various issues. A couple of authors have used ISM to determine barriers in implementation of TPM. The authors feel that most of the papers describe ISM in brief making it slightly difficult for readers to understand. This paper aims to explain elaborately step-by-step on how to develop an ISM making it easier for researchers to understand the ISM concept. Even though there are papers on TPM and difficulties in implementation of TPM, this paper explains the barriers in implementing TPM based on the experience of the corresponding author having worked in the refinery industry.
Journal Article
The Association between Biofilm Formation and Antimicrobial Resistance with Possible Ingenious Bio-Remedial Approaches
Biofilm has garnered a lot of interest due to concerns in various sectors such as public health, medicine, and the pharmaceutical industry. Biofilm-producing bacteria show a remarkable drug resistance capability, leading to an increase in morbidity and mortality. This results in enormous economic pressure on the healthcare sector. The development of biofilms is a complex phenomenon governed by multiple factors. Several attempts have been made to unravel the events of biofilm formation; and, such efforts have provided insights into the mechanisms to target for the therapy. Owing to the fact that the biofilm-state makes the bacterial pathogens significantly resistant to antibiotics, targeting pathogens within biofilm is indeed a lucrative prospect. The available drugs can be repurposed to eradicate the pathogen, and as a result, ease the antimicrobial treatment burden. Biofilm formers and their infections have also been found in plants, livestock, and humans. The advent of novel strategies such as bioinformatics tools in treating, as well as preventing, biofilm formation has gained a great deal of attention. Development of newfangled anti-biofilm agents, such as silver nanoparticles, may be accomplished through omics approaches such as transcriptomics, metabolomics, and proteomics. Nanoparticles’ anti-biofilm properties could help to reduce antimicrobial resistance (AMR). This approach may also be integrated for a better understanding of biofilm biology, guided by mechanistic understanding, virtual screening, and machine learning in silico techniques for discovering small molecules in order to inhibit key biofilm regulators. This stimulated research is a rapidly growing field for applicable control measures to prevent biofilm formation. Therefore, the current article discusses the current understanding of biofilm formation, antibiotic resistance mechanisms in bacterial biofilm, and the novel therapeutic strategies to combat biofilm-mediated infections.
Journal Article