Dynamic Decentralized Functional Encryption with Strong Security

Decentralized Multi-Client Functional Encryption (DMCFE) extends the basic functional encryption to multiple clients that do not trust each other. They can independently encrypt the multiple inputs to be given for evaluation to the function embedded in the functional decryption key. And they keep control on these functions as they all have to contribute to the generation of the functional decryption keys. Dynamic Decentralized Functional Encryption (DDFE) is the ultimate extension where one can dynamically join the system and the keys and ciphertexts can be built by dynamic subsets of clients. As any encryption scheme, all the FE schemes provide privacy of the plaintexts. But the functions associated to the functional decryption keys might be sensitive too (e.g. a model in machine learning). The function-hiding property has thus been introduced to additionally protect the function evaluated during the decryption process. In this paper, we first provide a generic conversion from DMCFE to DDFE, that preserves the security properties, in both the standard and the function-hiding setting. Then, new proof techniques allow us to analyze a new concrete construction of function-hiding DMCFE for inner products, that can thereafter be converted into a DDFE, with strong security guarantees: the adversary can adaptively query multiple challenge ciphertexts and multiple challenge keys. Previous constructions were proven secure in the selective setting only

Decoding Corticotropin-Releasing Factor Receptor Type 1 Crystal Structures.

The structural analysis of class B G protein-coupled receptors (GPCR), cell surface proteins responding to peptide hormones, has until recently been restricted to the extracellular domain (ECD). Corticotropin-releasing factor receptor type 1 (CRF1R) is a class B receptor mediating stress response and also considered a drug target for depression and anxiety. Here we report the crystal structure of the transmembrane domain of human CRF1R in complex with the small-molecule antagonist CP-376395 in a hexagonal setting with translational non-crystallographic symmetry. Molecular dynamics and metadynamics simulations on this novel structure and the existing TMD structure for CRF1R provides insight as to how the small molecule ligand gains access to the induced-fit allosteric binding site with implications for the observed selectivity against CRF2R. Furthermore, molecular dynamics simulations performed using a full-length receptor model point to key interactions between the ECD and extracellular loop 3 of the TMD providing insight into the full inactive state of multidomain class B GPCRs.This is the accepted manuscript. It is currently embargoed pending publication

Guide methodologique: Méthode communautaire participative d’inventaire et de priorisation des technologies / pratiques d’agriculture élevage-agroforesterie climato-intelligentes

Ce guide traite des questions d’identification des interventions prioritaires pour les communautés dans le contexte du changement climatique. Il s’agit d’une approche participative d’inventaire et de priorisation des technologies / pratiques d’agriculture-élevage-agroforesterie et sociales climato-intelligentes. Le guide fournit aux agents de terrain un accompagnement et́ape par et́ape pour travailler avec les acteurs clédans les sites cibles, pour identifier les pratiques prometteuses qui aideraient ces derniers às’adapter aux variabilités climatiques dans leurs activités de production. Le guide a étéproduit dans le cadre d’un projet “Building resilient agro-sylvopastoral systems in West Africa through participatory action research” (BRASPAR)” qui est l’un des projets de la Composante 2 financée par le programme de recherche du CGIAR sur les Changements Climatiques, l’Agriculture etla Seć urité Alimentaire (CCAFS). La composante 2 du CCAFS, quitraite des pratiques ettechnologies climato-intelligentes, s’attaque aux défis de comment passer àune agriculture climato-intelligente (ACI) àplus grande échelle pour permettre aux system̀ es agricoles d’et̂re transformeś et reó rienteś pour soutenir la seć uritéalimentaire dans le contexte actuel de changement climatique. Pilotépar l’ICRAFWCA/Sahel, le projet BRAS- PAR est mis en œuvre au Burkina Faso, Ghana, Niger et Seń eǵ al par un consortium d’institutions nationales de recherche (INERA, SARI, INRAN et ISRA), IUCN et ILRI

Methodological guide: Community participatory inventory and prioritization of climate-smart crop-livestock agroforestry technologies / practices

This guide addresses the issue of identifying priority interventions for communities in the face of climate change. The manual is about participatory approach of inventorizing and prioritizing climate-smart crop-livestock-agroforestry and social technologies / practices. The guide provides a step by step guidance on how project/extension workers can work with communities and other development stakeholders in the target sites to identify practices that can help local communities to better adapt to climate variability in production. The guide was developed within the framework of a project “Building resilient agro-sylvo-pastoral systems in West Africa through participatory action research” (BRAS-PAR)” which is one ofthe flagship 2 projects funded by the CGIAR Research Program on Climate Change Agriculture and Food Security (CCAFS). The flagship 2 of CCAFS, which is about climate-smart technologies and practices, addresses the challenge of how to transition to a climate-smart agriculture (CSA) at a large scale for enabling agricultural systems to be transformed and reoriented to support food security under the new realities of climate change. Led by ICRAF-WCA/Sahel, the BRAS-PAR project is being implemented by a consortium of National research institutes in Burkina Faso, Ghana, Niger and Senegal, IUCN, and ILRI

Mise en place d’un Village Intelligent face au Climat pour la réduction des risques climatiques et de l’insécurité alimentaire à Daga-Birame, Sénégal. Guide de visite de terrain pour la Réunion du Comité Scientifique Indépendant du CCAFS

À Daga Birame, au Sénégal, CCAFS et ses partenaires ont mis en place un village climato-intelligent (CSV) dans lequel plusieurs activités sont menées. Sur la base de la vision du village et de son avenir, un ensemble d'actions ont été identifiées par la communauté afin d'atteindre les changements souhaités dans la productivité agricole et la sécurité alimentaire tels que les activités génératrices de revenus, l'amélioration de la résilience et la gestion durable des ressources naturelles du village. Ces actions ont été structurées autour de quatre composantes: Les services d'information climatologique (SIC); le développement des technologies / pratiques agricoles adaptées au climat; le renforcement de capacités des villageois et celle des connaissances et des institutions locales

Unsupervised Bayesian linear unmixing of gene expression microarrays

Background: This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Results: Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. Conclusions: The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor

Capacitating stakeholders to using Climate Information in West Africa: Achievements and lessons learned from the WAAPP-funded CaSCIERA-TA project

This Info note summarizes the achievements and lessons learned from the implementation of a 2-year project on ‘’Capacitating Stakeholders in Using Climate Information for Enhanced Resilience in the Agricultural Sector in West Africa (CaSCIERA-TA)’’, funded by Conseil Ouest et Centre Africain pour la Recherche et le Développement Agricole (CORAF) under the West Africa Agricultural Productivity Program (WAAPP)”. This project was implemented by a consortium of partners led by ICRAF Sahel Office and included CCAFS West Africa Program, AGRHYMET, INRAB-Benin, IRAG-Guinea, INRAN-Niger and ITRA-Togo. The project aimed at strengthening the capacity of the stakeholders of four WAAPP implementing countries to mainstream and implement Climate Smart Agriculture (CSA) into their activities

Further evidence for association of hepatitis C infection with parenteral schistosomiasis treatment in Egypt

BACKGROUND: Hepatitis C virus (HCV) infection and schistosomiasis are major public health problems in the Nile Delta of Egypt. To control schistosomiasis, mass treatment campaigns using tartar emetic injections were conducted in the 1960s through 1980s. Evidence suggests that inadequately sterilized needles used in these campaigns contributed to the transmission of HCV in the region. To corroborate this evidence, this study evaluates whether HCV infections clustered within houses in which household members had received parenteral treatment for schistosomiasis. METHODS: A serosurvey was conducted in a village in the Nile Delta and residents were questioned about prior treatment for schistosomiasis. Sera were evaluated for the presence of antibodies to HCV. The GEE2 approach was used to test for clustering of HCV infections, where correlation of HCV infections within household members who had been treated for schistosomiasis was the parameter of interest. RESULTS: A history of parenteral treatment for schistosomiasis was observed to cluster within households, OR for clustering: 2.44 (95% CI: 1.47–4.06). Overall, HCV seropositivity was 40% (321/796) and was observed to cluster within households that had members who had received parenteral treatment for schistosomiasis, OR for clustering: 1.76 (95% CI: 1.05–2.95). No such evidence for clustering was found in the remaining households. CONCLUSION: Clustering of HCV infections and receipt of parenteral treatment for schistosomiasis within the same households provides further evidence of an association between the schistosomiasis treatment campaigns and the high HCV seroprevalence rates currently observed in the Nile delta of Egypt

Evaluating Active U: an Internet-mediated physical activity program.

Background: Engaging in regular physical activity can be challenging, particularly during the winter months. To promote physical activity at the University of Michigan during the winter months, an eight-week Internet-mediated program (Active U) was developed providing participants with an online physical activity log, goal setting, motivational emails, and optional team participation and competition. Methods: This study is a program evaluation of Active U. Approximately 47,000 faculty, staff, and graduate students were invited to participate in the online Active U intervention in the winter of 2007. Participants were assigned a physical activity goal and were asked to record each physical activity episode into the activity log for eight weeks. Statistics for program reach, effectiveness, adoption, and implementation were calculated using the Re-Aim framework. Multilevel regression analyses were used to assess the decline in rates of data entry and goal attainment during the program, to assess the likelihood of joining a team by demographic characteristics, to test the association between various predictors and the number of weeks an individual met his or her goal, and to analyze server load. Results: Overall, 7,483 individuals registered with the Active U website (≈16% of eligible), and 79% participated in the program by logging valid data at least once. Staff members, older participants, and those with a BMI < 25 were more likely to meet their weekly physical activity goals, and average rate of meeting goals was higher among participants who joined a competitive team compared to those who participated individually (IRR = 1.28, P < .001). Conclusion: Internet-mediated physical activity interventions that focus on physical activity logging and goal setting while incorporating team competition may help a significant percentage of the target population maintain their physical activity during the winter months