Chikungunya as a cause of acute febrile illness in southern Sri Lanka

10.1371/journal.pone.0082259PLoS ONE812-POLN

Digital Twin for Agricultural Machinery: From Concept to Application

SignificanceAgricultural machinery serves as the fundamental support for implementing advanced agricultural production concepts. The key challenge for the future development of smart agriculture lies in how to enhance the design, manufacturing, operation, and maintenance of these machines to fully leverage their capabilities. To address this, the concept of the digital twin has emerged as an innovative approach that integrates various information technologies and facilitates the integration of virtual and real-world interactions. By providing a deeper understanding of agricultural machinery and its operational processes, the digital twin offers solutions to the complexity encountered throughout the entire lifecycle, from design to recycling. Consequently, it contributes to an all-encompassing enhancement of the quality of agricultural machinery operations, enabling them to better meet the demands of agricultural production. Nevertheless, despite its significant potential, the adoption of the digital twin for agricultural machinery is still at an early stage, lacking the necessary theoretical guidance and methodological frameworks to inform its practical implementation.ProgressDrawing upon the successful experiences of the author's team in the digital twin for agricultural machinery, this paper presents an overview of the research progress made in digital twin. It covers three main areas: The digital twin in a general sense, the digital twin in agriculture, and the digital twin for agricultural machinery. The digital twin is conceptualized as an abstract notion that combines model-based system engineering and cyber-physical systems, facilitating the integration of virtual and real-world environments. This paper elucidates the relevant concepts and implications of digital twin in the context of agricultural machinery. It points out that the digital twin for agricultural machinery aims to leverage advanced information technology to create virtual models that accurately describe agricultural machinery and its operational processes. These virtual models act as a carrier, driven by data, to facilitate interaction and integration between physical agricultural machinery and their digital counterparts, consequently yielding enhanced value. Additionally, it proposes a comprehensive framework comprising five key components: Physical entities, virtual models, data and connectivity, system services, and business applications. Each component's functions operational mechanism, and organizational structure are elucidated. The development of the digital twin for agricultural machinery is still in its conceptual phase, and it will require substantial time and effort to gradually enhance its capabilities. In order to advance further research and application of the digital twin in this domain, this paper integrates relevant theories and practical experiences to propose an implementation plan for the digital twin for agricultural machinery. The macroscopic development process encompasses three stages: Theoretical exploration, practical application, and summarization. The specific implementation process entails four key steps: Intelligent upgrading of agricultural machinery, establishment of information exchange channels, construction of virtual models, and development of digital twin business applications. The implementation of digital twin for agricultural machinery comprises four stages: Pre-research, planning, implementation, and evaluation. The digital twin serves as a crucial link and bridge between agricultural machinery and the smart agriculture. It not only facilitates the design and manufacturing of agricultural machinery, aligning them with the realities of agricultural production and supporting the advancement of advanced manufacturing capabilities, but also enhances the operation, maintenance, and management of agricultural production to better meet practical requirements. This, in turn, expedites the practical implementation of smart agriculture. To fully showcase the value of the digital twin for agricultural machinery, this paper addresses the existing challenges in the design, manufacturing, operation, and management of agricultural machinery. It expounds the methods by which the digital twin can address these challenges and provides a technical roadmap for empowering the design, manufacturing, operation, and management of agricultural machinery through the use of the digital twin. In tackling the critical issue of leveraging the digital twin to enhance the operational quality of agricultural machinery, this paper presents two research cases focusing on high-powered tractors and large combine harvesters. These cases validate the feasibility of the digital twin in improving the quality of plowing operations for high-powered tractors and the quality of grain harvesting for large combine harvesters.Conclusions and ProspectsThis paper serves as a reference for the development of research on digital twin for agricultural machinery, laying a theoretical foundation for empowering smart agriculture and intelligent equipment with the digital twin. The digital twin provides a new approach for the transformation and upgrade of agricultural machinery, offering a new path for enhancing the level of agricultural mechanization and presenting new ideas for realizing smart agriculture. However, existing digital twin for agricultural machinery is still in its early stages, and there are a series of issues that need to be explored. It is necessary to involve more professionals from relevant fields to advance the research in this area

Exploring the origin and potential for spread of the 2013 dengue outbreak in Luanda, Angola

INTRODUCTION: Dengue in Africa is underreported. Simultaneous reports of travellers with dengue returning from Luanda, Angola, to six countries on four continents suggest that a major dengue outbreak is currently occurring in Angola, South West Africa. METHODS: To identify the origin of the imported dengue virus, we sequenced the virus from Angola and investigated the interconnectivity via air travel between dengue-endemic countries and Angola. RESULTS AND CONCLUSION: Our analyses show that the Angola outbreak was most likely caused by an endemic virus strain that had been circulating in West Africa for many years. We also show that Portugal and South Africa are most likely at the highest risk of importation of dengue from Angola due to the large number of air passengers between Angola and these countries

Presence of anti-chikungunya IgG by age (years) in febrile patients, southern Sri Lanka, 2007.

<p>Presence of anti-chikungunya IgG by age (years) in febrile patients, southern Sri Lanka, 2007.</p

Phylogenetic relationship of Sri Lankan chikungunya virus (CHIKV) isolates.

<p>Sequences corresponding to the CHIKV structural genes from the six Sri Lankan isolates were compared to published CHIKV sequences [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082259#B6" target="_blank">6</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082259#B8" target="_blank">8</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082259#B10" target="_blank">10</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082259#B11" target="_blank">11</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082259#B16" target="_blank">16</a>]. Region of isolation is indicated by text color: West Africa (Red), Asia (Blue), East/South/Central Africa (Green), Reunion Island (Pink) and India/Sri Lanka (Orange). Isolates from the current study are indicated in bold-italics.</p

Age distribution of patients with and without acute chikungunya infection, southern Sri Lanka, 2007.

<p>Age distribution of patients with and without acute chikungunya infection, southern Sri Lanka, 2007.</p

Distribution of febrile patients with and without acute chikungunya infection by month, southern Sri Lanka, 2007.

<p>Distribution of febrile patients with and without acute chikungunya infection by month, southern Sri Lanka, 2007.</p

Alignment of the amino acid sequence surrounding the chikungunya virus E1-226 region.

<p>Amino acid sequence corresponding to the E1 gene was translated from the nucleotide sequences of the three Sri Lankan isolates and compared the amino acid sequences of the isolates described in Schuffennecker et al 2006, PLoS Med and Kumar et al 2008, JGV [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082259#B10" target="_blank">10</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082259#B11" target="_blank">11</a>]. Position E1-226 is highlighted (E1-226 corresponds to position 1035 in the polyprotein). </p

Efficacy and safety of celgosivir in patients with dengue fever (CELADEN): a phase 1b, randomised, double-blind, placebo-controlled, proof-of-concept trial.

BACKGROUND: Dengue infection is the most common mosquito-borne viral disease worldwide, but no suitable antiviral drugs are available. We tested the α-glucosidase inhibitor celgosivir as a treatment for acute dengue fever. METHODS: To establish eligibility for inclusion in a phase 1b, randomised, double-blind, placebo-controlled, proof-of-concept trial, individuals aged 21-65 years who had had a fever (≥38°C) for less than 48 h, met at least two criteria indicating probable dengue infection, and had a positive result on a dengue point-of-care test kit or PCR assay were referred for screening at a centre in Singapore between July 30, 2012, and March 4, 2013. Using a web-based system, we randomly assigned patients who met full inclusion criteria after screening (1:1; random permuted block length four) to celgosivir (initial 400 mg loading dose within 6 h of randomisation, followed by 200 mg every 12 h for a total of nine doses) or matched placebo. Patients and the entire study team were masked to group assignment. The primary endpoints were mean virological log reduction (VLR) from baseline for days 2, 3, and 4, and area under the fever curve (AUC) for a temperature above 37°C from 0 h to 96 h. Efficacy analyses were by intention to treat. This study is registered with ClinicalTrials.gov, number NCT01619969. FINDINGS: We screened 69 patients and randomly assigned 50 (24 to celgosivir, 26 to placebo). Mean VLR was greater in the celgosivir group (-1·86, SD 1·07) than in the placebo group (-1·64, 0·75), but the difference was non-significant (-0·22, 90% CI -0·65 to 0·22; one-sided p=0·203). The mean AUC was also higher in the celgosivir group (54·92, SD 31·04) than in the placebo group (40·72, 18·69), but again the difference was non-significant (14·20, 90% CI 2·16-26·25; one-sided p=0·973). We noted similar incidences of adverse events between groups. INTERPRETATION: Although generally safe and well tolerated, celgosivir does not seem to reduce viral load or fever burden in patients with dengue. FUNDING: STOP Dengue Translational Clinical Research