Fisheries Stakeholders and Their Livelihoods in Tamil Nadu and Puducherry

Fisheries Management for Sustainable Livelihoods (FIMSUL), is a project implemented by the Food and Agriculture Organization of the United Nations (FAO) with the Government of Tamil Nadu and Puducherry in India under the World Bank Trust Fund. The project aims at establishing frameworks, processes and building capacities of various stakeholders especially the Government, to facilitate the planning, design and implementation of appropriate fisheries development and management policies. The project includes a series of stakeholder consultations and consensus building apart from detailed review and analysis in the areas of stakeholders, livelihoods, policy, legal and institutional frame work and fisheries management. Based on this, the project comes up with various options. Stakeholder and livelihoods analysis is an essential part of the project. Hence, the team developed a detailed methodology for stakeholder consultations which includes district level stake holder consultation, focus group discussions, household interviews and validation meetings. The stakeholder and livelihoods analysis following the above steps were done through six NGO partners working along the coast of Tamil Nadu and Puducherry who were initially trained on the methodology. The NGO partners : PLANT, GUIDE, FERAL, SIFFS, DHAN Foundation and TMSSS, especially a team of dedicated staff engaged by them had done an excellent work in completing comprehensive field exercises and bringing out 12 district/regional reports. These are published separately. This report is a compilation, and complete analysis of the stakeholders and livelihoods based on all the field level consultations.This report is expected to be an important reference to primary stakeholders' perspective of the important stakeholders in the sector, the livelihoods and livelihoods changes, the adaptive and coping mechanism, the relationships between the stakeholders and their hopes and aspirations. For any development intervention for any sector or stakeholder group, region-wise in marine fisheries in Tamil Nadu and Puducherry, the information from this report could be an important starting point

A rapid and gentle method for the salt extraction of chromatin core histones H2A, H2B, H3 and H4 from rat liver nuclei

A complex of histones H2A, H2B, H3 and H4 has been isolated from purified rat liver nuclei by a method which is both gentle and rapid. Nuclei were homogenised in 0.25 I sucrose and the residual nuclear material obtained after centrifligation was adsorbed on calcium phosphate gel. After removing histone H1 from the adsorbed material by washing with 1M NaCl in 25 mM sodium phosphate buffer, pH 6.0, histones H2A, H2B, H3 and H4 were eluted together, with 2 I NaCl in 25 mM sodium phosphate buffer, pH 7.0. The core histones so obtained migrated as a single sharp band on polyacrylamide gel electrophoresis under non-denaturing conditions. Fractionation of the freshly prepared core histones on a Sephadex G-100 column yielded two major protein peaks. The peak having the larger elution volume contained histones H2A and H2B in equal amounts while the peak with the smaller elution volume contained all the four histones. Histones H3 and H4 were present in larger proportions in the second peak

Dynamic Graph Message Passing Networks for Visual Recognition

Modelling long-range dependencies is critical for scene understanding tasks in computer vision. Although convolution neural networks (CNNs) have excelled in many vision tasks, they are still limited in capturing long-range structured relationships as they typically consist of layers of local kernels. A fully-connected graph, such as the self-attention operation in Transformers, is beneficial for such modelling, however, its computational overhead is prohibitive. In this paper, we propose a dynamic graph message passing network, that significantly reduces the computational complexity compared to related works modelling a fully-connected graph. This is achieved by adaptively sampling nodes in the graph, conditioned on the input, for message passing. Based on the sampled nodes, we dynamically predict node-dependent filter weights and the affinity matrix for propagating information between them. This formulation allows us to design a self-attention module, and more importantly a new Transformer-based backbone network, that we use for both image classification pretraining, and for addressing various downstream tasks (object detection, instance and semantic segmentation). Using this model, we show significant improvements with respect to strong, state-of-the-art baselines on four different tasks. Our approach also outperforms fully-connected graphs while using substantially fewer floating-point operations and parameters. Code and models will be made publicly available at https://github.com/fudan-zvg/DGMN2Comment: PAMI extension of CVPR 2020 oral work arXiv:1908.0695