Growing grass for a green biorefinery - an option for Ireland?

Growing grass for a green biorefinery – an option for Ireland? Mind the gap: deciphering the gap between good intentions and healthy eating behaviour Halting biodiversity loss by 2020 – implications for agriculture A milk processing sector model for Irelan

Genetic improvement in sunflower breeding—integrated omics approach

Foresight in climate change and the challenges ahead requires a systematic approach to sunflower breeding that will encompass all available technologies. There is a great scarcity of desirable genetic variation, which is in fact undiscovered because it has not been sufficiently researched as detection and designing favorable genetic variation largely depends on thorough genome sequencing through broad and deep resequencing. Basic exploration of genomes is insufficient to find insight about important physiological and molecular mechanisms unique to crops. That is why integrating information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics enables a comprehensive understanding of the molecular mechanisms in the background of architecture of many important quantitative traits. Omics technologies offer novel possibilities for deciphering the complex pathways and molecular profiling through the level of systems biology and can provide important answers that can be utilized for more efficient breeding of sunflower. In this review, we present omics profiling approaches in order to address their possibilities and usefulness as a potential breeding tools in sunflower genetic improvement

Big Data Coordination Platform: Full Proposal 2017-2022

This proposal for a Big Data and ICT Platform therefore focuses on enhancing CGIAR and partner capacity to deliver big data management, analytics and ICT-focused solutions to CGIAR target geographies and communities. The ultimate goal of the platform is to harness the capabilities of Big Data to accelerate and enhance the impact of international agricultural research. It will support CGIAR’s mission by creating an enabling environment where data are expertly managed and used effectively to strengthen delivery on CGIAR SRF’s System Level Outcome (SLO) targets. Critical gaps were identified during the extensive scoping consultations with CGIAR researchers and partners (provided in Annex 8). The Platform will achieve this through ambitious partnerships with initiatives and organizations outside CGIAR, both upstream and downstream, public and private. It will focus on promoting CGIAR-wide collaboration across CRPs and Centers, in addition to developing new partnership models with big data leaders at the global level. As a result, CGIAR and partner capacity will be enhanced, external partnerships will be leveraged, and an institutional culture of collaborative data management and analytics will be established. Important international public goods such as new global and regional datasets will be developed, alongside new methods that support CGIAR to use the data revolution as an additional means of delivering on SLOs

International Nuclear Medicine Consensus on the Clinical Use of Amyloid Positron Emission Tomography in Alzheimer's Disease.

Alzheimer's disease (AD) is the main cause of dementia, with its diagnosis and management remaining challenging. Amyloid positron emission tomography (PET) has become increasingly important in medical practice for patients with AD. To integrate and update previous guidelines in the field, a task group of experts of several disciplines from multiple countries was assembled, and they revised and approved the content related to the application of amyloid PET in the medical settings of cognitively impaired individuals, focusing on clinical scenarios, patient preparation, administered activities, as well as image acquisition, processing, interpretation and reporting. In addition, expert opinions, practices, and protocols of prominent research institutions performing research on amyloid PET of dementia are integrated. With the increasing availability of amyloid PET imaging, a complete and standard pipeline for the entire examination process is essential for clinical practice. This international consensus and practice guideline will help to promote proper clinical use of amyloid PET imaging in patients with AD

Crop development with data-driven approach towards sustainable agriculture: Lifting the achievements and opportunities of collaborative research between CIAT and Japan

International Center for Tropical Agriculture (CIAT) has a long history of collaboration with Japanese institutions to develop improved crop varieties and adopt agricultural technologies with pioneering approaches for farmers’ benefit. Since 1972, Japan has enjoyed a long history of successful cooperation with the CGIAR (formerly the Consultative Group on International Agricultural Research) after becoming its 22nd Member to meet global challenges such as climate change affecting agriculture and food systems in diplomacy through Japan’s advanced science and technology. This long-standing collaboration adopts agricultural technologies developed in Japan and promotes agricultural innovation through cutting-edge technologies such as remote sensing for Internet of Things (IoT) and Artificial Intelligence (AI) that will provide novel solutions to the agriculture and food sectors in the tropics. Nowadays, it is presumed that this approach to data-driven agriculture realization will help establish a sustainable agroecosystem with increased agricultural productivity and sustainability by adapting or mitigating the effects of climate change and efficient use of natural resources and establishing a sustainable food value chain. This review highlights the research collaborations and new opportunities on advanced breeding technologies and digital innovations towards data-driven agriculture. In conjunction, we present a potential collaborative research between CIAT and Japan, prioritizing the current and future challenges in agriculture and food systems

The technology, opportunities, and challenges of Synthetic Biological Intelligence

Integrating neural cultures developed through synthetic biology methods with digital computing has enabled the early development of Synthetic Biological Intelligence (SBI). Recently, key studies have emphasized the advantages of biological neural systems in some information processing tasks. However, neither the technology behind this early development, nor the potential ethical opportunities or challenges, have been explored in detail yet. Here, we review the key aspects that facilitate the development of SBI and explore potential applications. Considering these foreseeable use cases, various ethical implications are proposed. Ultimately, this work aims to provide a robust framework to structure ethical considerations to ensure that SBI technology can be both researched and applied responsibly