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Abstract
The coronavirus (COVID-19) pandemic has witnessed a significant loss for farming in
India due to restrictions on movement, limited social interactions and labor shortage.
In this scenario, Artificial Intelligence (AI) could act as a catalyst for helping
the farmers to continue with their farming. This study undertakes an analysis of the
applications and benefits of AI in agri-food supply chain, while highlights the challenges
facing the adoption of AI. Data were obtained from 543 farmers in Odisha (India) through
a survey, and then interpreted using “Interpretive Structural Modelling (ISM)”; MICMAC;
and “Step-Wise-Assessment and Ratio-Analysis (SWARA)”. Response time and accuracy
level; lack of standardization; availability of support for big data; big data support;
implementation costs; flexibility; lack of contextual awareness; job-losses; affordability
issues; shortage of infrastructure; unwillingness of farmers; and AI safety-related
issues are some challenges facing the AI adoption in agri-food supply chain. Implications
were drawn for farmers and policy makers.
Dear Editor, The rapid emergence of COVID-19 in Wuhan city, Hubei Province, China, has resulted in thousands of deaths [1]. Many infected patients, however, presented mild flu-like symptoms and quickly recover [2]. To effectively prioritize resources for patients with the highest risk, we identified clinical predictors of mild and severe patient outcomes. Using the database of Jin Yin-tan Hospital and Tongji Hospital, we conducted a retrospective multicenter study of 68 death cases (68/150, 45%) and 82 discharged cases (82/150, 55%) with laboratory-confirmed infection of SARS-CoV-2. Patients met the discharge criteria if they had no fever for at least 3 days, significantly improved respiratory function, and had negative SARS-CoV-2 laboratory test results twice in succession. Case data included demographics, clinical characteristics, laboratory results, treatment options and outcomes. For statistical analysis, we represented continuous measurements as means (SDs) or as medians (IQRs) which compared with Student’s t test or the Mann–Whitney–Wilcoxon test. Categorical variables were expressed as numbers (%) and compared by the χ 2 test or Fisher’s exact test. The distribution of the enrolled patients’ age is shown in Fig. 1a. There was a significant difference in age between the death group and the discharge group (p < 0.001) but no difference in the sex ratio (p = 0.43). A total of 63% (43/68) of patients in the death group and 41% (34/82) in the discharge group had underlying diseases (p = 0.0069). It should be noted that patients with cardiovascular diseases have a significantly increased risk of death when they are infected with SARS-CoV-2 (p < 0.001). A total of 16% (11/68) of the patients in the death group had secondary infections, and 1% (1/82) of the patients in the discharge group had secondary infections (p = 0.0018). Laboratory results showed that there were significant differences in white blood cell counts, absolute values of lymphocytes, platelets, albumin, total bilirubin, blood urea nitrogen, blood creatinine, myoglobin, cardiac troponin, C-reactive protein (CRP) and interleukin-6 (IL-6) between the two groups (Fig. 1b and Supplementary Table 1). Fig. 1 a Age distribution of patients with confirmed COVID-19; b key laboratory parameters for the outcomes of patients with confirmed COVID-19; c interval from onset of symptom to death of patients with confirmed COVID-19; d summary of the cause of death of 68 died patients with confirmed COVID-19 The survival times of the enrolled patients in the death group were analyzed. The distribution of survival time from disease onset to death showed two peaks, with the first one at approximately 14 days (22 cases) and the second one at approximately 22 days (17 cases) (Fig. 1c). An analysis of the cause of death was performed. Among the 68 fatal cases, 36 patients (53%) died of respiratory failure, five patients (7%) with myocardial damage died of circulatory failure, 22 patients (33%) died of both, and five remaining died of an unknown cause (Fig. 1d). Based on the analysis of the clinical data, we confirmed that some patients died of fulminant myocarditis. In this study, we first reported that the infection of SARS-CoV-2 may cause fulminant myocarditis. Given that fulminant myocarditis is characterized by a rapid progress and a severe state of illness [3], our results should alert physicians to pay attention not only to the symptoms of respiratory dysfunction but also the symptoms of cardiac injury. Further, large-scale studies and the studies on autopsy are needed to confirm our analysis. In conclusion, predictors of a fatal outcome in COVID-19 cases included age, the presence of underlying diseases, the presence of secondary infection and elevated inflammatory indicators in the blood. The results obtained from this study also suggest that COVID-19 mortality might be due to virus-activated “cytokine storm syndrome” or fulminant myocarditis. Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary material 1 (DOCX 38 kb)
Increasing population and consumption are placing unprecedented demands on agriculture and natural resources. Today, approximately a billion people are chronically malnourished while our agricultural systems are concurrently degrading land, water, biodiversity and climate on a global scale. To meet the world's future food security and sustainability needs, food production must grow substantially while, at the same time, agriculture's environmental footprint must shrink dramatically. Here we analyse solutions to this dilemma, showing that tremendous progress could be made by halting agricultural expansion, closing 'yield gaps' on underperforming lands, increasing cropping efficiency, shifting diets and reducing waste. Together, these strategies could double food production while greatly reducing the environmental impacts of agriculture.
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