Collaborative partnerships between organic farmers

A survey of Danish dairy farmers show that - around 70% of all organic dairt farmers collaborate around manure - the main factors for success in collaboration are trust, reliabilty and timely communication -organic exporting farmers are less concerned with distance because the organic network is more dispersed. Development of well functioning collaborative partnerships may increase farm robustness to changing conditions

Fostering Agricultural and Rural Policy Dialogue

Agricultural and rural policies can benefit from potential synergies when designed correctly. Broadly speaking, agricultural policies target farms and food production, while rural policies focus on ensuring the development of a territory and the well-being of the rural population. Despite these differences, both policies are often applied within the same territory and share a growing interest in improving environmental sustainability and adapting to climate change, as well as improving inclusiveness, food security and nutrition, and increasing productivity and innovation. This paper calls for a constructive dialogue on policies and processes to enhance the synergies and coherence in policy advice, and helping to resolve possible trade-offs between agricultural and rural policies. There are many opportunities to build on potential synergies, including on the role of agriculture in structural change in rural areas, on diversifying farm and rural economies, and on ensuring environmental sustainability

Convolutional neural network can recognize drug resistance of single cancer cells

It is known that single or isolated tumor cells enter cancer patients' circulatory systems. These circulating tumor cells (CTCs) are thought to be an effective tool for diagnosing cancer malignancy. However, handling CTC samples and evaluating CTC sequence analysis results are challenging. Recently, the convolutional neural network (CNN) model, a type of deep learning model, has been increasingly adopted for medical image analyses. However, it is controversial whether cell characteristics can be identified at the single-cell level by using machine learning methods. This study intends to verify whether an AI system could classify the sensitivity of anticancer drugs, based on cell morphology during culture. We constructed a CNN based on the VGG16 model that could predict the efficiency of antitumor drugs at the single-cell level. The machine learning revealed that our model could identify the effects of antitumor drugs with ~0.80 accuracies. Our results show that, in the future, realizing precision medicine to identify effective antitumor drugs for individual patients may be possible by extracting CTCs from blood and performing classification by using an AI system