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In the last 20 years Welsh Hill farming has faced many challenges; long-supported specialisation in beef and sheep production systems has been called into question as the policy focus shifted and markets have offered poor returns. Drivers of evolution changed and some near-extinct enterprises have reappeared in the Welsh hills (e.g. dairying, poultry), linked to both market and policy changes. Two in-depth agrarian diagnoses in hill and upland areas of North Wales (Bala and the Vale of Clwyd) show a differentiation of production systems. The apparently homogenous pastoral landscape now sports different levels of management and land use intensity (in fodder output, stocking rates and livestock types). Farms have become increasingly polarised depending on their business opportunities, and these farmed landscapes in Wales are changing rapidly. With the Brexit process and in the context of Covid, changes have intensified as the farming sector seeks to adapt and plan ahead. We assess the likely impact of possible Welsh and UK governments’ future policies in the emerging market context, to understand their likely impact, using a typology of production systems in the two landscapes. On this basis, we expect the trend of polarisation in the Welsh hills to continue, raising some challenges for future policy aspirations.

Numerous clinical trials of drug candidates for Alzheimer's disease (AD) have failed, and computational drug repositioning approaches using omics data have been proposed as effective alternative approaches to the discovery of drug candidates. However, little multi-omics data is available for AD, due to limited availability of brain tissues. Even if omics data exist, systematic drug repurposing study for AD has suffered from lack of big data, insufficient clinical information, and difficulty in data integration on account of sample heterogeneity derived from poor diagnosis or shortage of qualified post-mortem tissue. In this study, we developed a proteotranscriptomic-based computational drug repositioning method named Drug Repositioning Perturbation Score/Class (DRPS/C) based on inverse associations between disease- and drug-induced gene and protein perturbation patterns, incorporating pharmacogenomic knowledge. We constructed a Drug-induced Gene Perturbation Signature Database (DGPSD) comprised of 61,019 gene signatures perturbed by 1,520 drugs from the Connectivity Map (CMap) and the L1000 CMap. Drugs were classified into three DRPCs (High, Intermediate, and Low) according to DRPSs that were calculated using drug- and disease-induced gene perturbation signatures from DGPSD and The Cancer Genome Atlas (TCGA), respectively. The DRPS/C method was evaluated using the area under the ROC curve, with a prescribed drug list from TCGA as the gold standard. Glioblastoma had the highest AUC. To predict anti-AD drugs, DRPS were calculated using DGPSD and AD-induced gene/protein perturbation signatures generated from RNA-seq, microarray and proteomic datasets in the Synapse database, and the drugs were classified into DRPCs. We predicted 31 potential anti-AD drug candidates commonly belonged to high DRPCs of transcriptomic and proteomic signatures. Of these, four drugs classified into the nervous system group of Anatomical Therapeutic Chemical (ATC) system are voltage-gated sodium channel blockers (bupivacaine, topiramate) and monamine oxidase inhibitors (selegiline, iproniazid), and their mechanism of action was inferred from a potential anti-AD drug perspective. Our approach suggests a shortcut to discover new efficacy of drugs for AD.

With increasing global trade and growing emphasis on security, enhanced information sharing between actors in global supply chains is required. Currently, the data about cargo available in the supply chain does not provide a timely and accurate description of the goods. To solve this data quality issue, data should be captured upstream at the point where goods are packed for transport to the buyer. Without ICT, it was not possible to get timely access to the original trade data. The data pipeline concept is an IT innovation to enable capturing data at the source. The data pipeline accesses existing information systems used by the parties in international supply chains. This paper explores the data pipeline concept and the benefits that businesses and governments could obtain from such an innovation. This study also identifies the need for a public-private governance model that has to accompany the technical innovation.

With increasing global trade and growing emphasis on security, enhanced information sharing between actors in global supply chains is required. Currently, the data about cargo available in the supply chain does not provide a timely and accurate description of the goods. To solve this data quality issue, data should be captured upstream at the point where goods are packed for transport to the buyer. Without ICT, it was not possible to get timely access to the original trade data. The data pipeline concept is an IT innovation to enable capturing data at the source. The data pipeline accesses existing information systems used by the parties in international supply chains. This paper explores the data pipeline concept and the benefits that businesses and governments could obtain from such an innovation. This study also identifies the need for a public-private governance model that has to accompany the technical innovation.