To Act Towards Alarming Situation: A Huge Gap Between Food Supply & Demand

The United Nations Sustainable Development Goals also talk about the ZERO HUNGER by 2030, which itself shows that we must think about this alarming situation, the situation is becoming worse due to the Covid -19 pandemic, which has increased the level of stress. As the recent trend, the number of people affected by hunger will surpass 840 million by 2030, or 9.8 percent of the global population [16]. With soaring food prices pushing millions of people into food insecurity, the United Nations International Fund for Agricultural Development (IFAD) is calling on governments and the private sector to urgently step up their investments in small-scale agriculture focused on locally produced, nutrition-rich food [14].This move is very essential and will be effective too, as if local food production will increase, the food will be made easily available to the people & at a much lower rate, this in turn will reduce the insecurity of the food at a much higher level. After realizing the United Nations Goal 2 -ZERO HUNGER and looking at the announcement of the International Fund for Agriculture Development ahead of Tokyo Nutrition for Growth (N4G) Summit on 7-8 of December, 2021, we may conclude that to overcome the fear of hunger in the near future, we must rebuild our agriculture system to produce the maximum output with the available resources & for this we must use the latest technological tools and ICT also.

A multi-modal data harmonisation approach for discovery of COVID-19 drug targets.

Despite the volume of experiments performed and data available, the complex biology of coronavirus SARS-COV-2 is not yet fully understood. Existing molecular profiling studies have focused on analysing functional omics data of a single type, which captures changes in a small subset of the molecular perturbations caused by the virus. As the logical next step, results from multiple such omics analysis may be aggregated to comprehensively interpret the molecular mechanisms of SARS-CoV-2. An alternative approach is to integrate data simultaneously in a parallel fashion to highlight the inter-relationships of disease-driving biomolecules, in contrast to comparing processed information from each omics level separately. We demonstrate that valuable information may be masked by using the former fragmented views in analysis, and biomarkers resulting from such an approach cannot provide a systematic understanding of the disease aetiology. Hence, we present a generic, reproducible and flexible open-access data harmonisation framework that can be scaled out to future multi-omics analysis to study a phenotype in a holistic manner. The pipeline source code, detailed documentation and automated version as a R package are accessible. To demonstrate the effectiveness of our pipeline, we applied it to a drug screening task. We integrated multi-omics data to find the lowest level of statistical associations between data features in two case studies. Strongly correlated features within each of these two datasets were used for drug-target analysis, resulting in a list of 84 drug-target candidates. Further computational docking and toxicity analyses revealed seven high-confidence targets, amsacrine, bosutinib, ceritinib, crizotinib, nintedanib and sunitinib as potential starting points for drug therapy and development.