Informal pathbreakers: civil society networks in china and vietnam

This thesis re-conceptualises civil society as a process of cross-sectoral networking and alliance building among individual activists and organisations. Civil society networks are built on personal connections and develop into flexible, often informal structures that engage in path-breaking advocacy with authorities and elites. In the challenging political contexts of China and Vietnam, civil society networks have brought about significant social change. The findings of extensive fieldwork in both countries demonstrate a wider range of advocacy techniques and strategies than previously documented in one-party authoritarian political systems. Four in-depth qualitative case studies are presented to illustrate a range of network structures, histories and advocacy strategies: the Bright Future Group of people with disabilities (Vietnam), Women’s Network against AIDS (China), the Reunification Park public space network (Vietnam), and the China Rivers Network. Research questions concern how civil society networks form, how they operate, and what strategies they select to influence and interact with state actors and other stakeholders, as well as how network members evaluate the effectiveness of their actions. The thesis concludes with comparative evaluations of the case studies and recommendations for donors and international partners to support networks that form organically

A Regional Approach: Mine and UXO Risk Reduction in Vietnam, Laos and Cambodia

Since Vietnam, Laos and Cambodia have similar mine and unexploded ordnance risk problems, a regional approach may contribute to finding solutions for these three. Understanding common features and challenges is a first step toward reducing the number of casualties in the region

From "land to the tiller" to the "new landlords"? The debate over Vietnam's latest land reforms

Between Vietnam’s independence and its reunification in 1975, the country’s socialist land tenure system was underpinned by the principle of “land to the tiller”. During this period, government redistributed land to farmers that was previously owned by landlords. The government’s “egalitarian” approach to land access was central to the mass support that it needed during the Indochinese war. Even when the 1993 Land Law transitioned agricultural land from collectivized to household holdings with 20-year land use certificates, the “land to the tiller” principle remained largely sacrosanct in state policy. Planned amendments to the current Land Law (issued in 2013), however, propose a fundamental shift from “land to the tiller” to the concentration of land by larger farming concerns, including private sector investors. This is explained as being necessary for the modernization of agricultural production. The government’s policy narrative concerning this change emphasizes the need to overcome the low productivity that arises from land fragmentation, the prevalence of unskilled labor and resource shortages among smallholders. This is contrasted with the readily available resources and capacity of the private sector, together with opportunities for improved market access and high-tech production systems, if holdings were consolidated by companies. This major proposed transition in land governance has catalyzed heated debate over the potential risks and benefits. Many perceive it as a shift from a “pro-poor” to “pro-rich” policy, or from “land to the tiller” to the establishment of a “new landlord”—with all the historical connotations that this badge invokes. Indeed, the growing level of public concern over land concentration raises potential implications for state legitimacy. This paper examines key narratives on the government-supported land concentration policy, to understand how the risks, benefits and legitimacy of the policy change are understood by different stakeholders. The paper considers how the transition could change land access and governance in Vietnam, based on early experience with the approach.This research was partially supported the Australian Research Council’s Discovery Projects funding scheme (project DP180101495)

Localized Fetomaternal Hyperglycemia: Spatial and Kinetic Definition by Positron Emission Tomography

to isolated hyperglycemia in the pregnant rat. mg/dL) localized to the left uterine artery was sustained for at least 48 hours while maternal euglycemia was maintained. fetal effects of isolated hyperglycemia. Broadly, this approach can be extended to study a variety of maternal-sided perturbations suspected to directly affect fetal health

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts.</p

Infectious causes of microcephaly: epidemiology, pathogenesis, diagnosis, and management.

Microcephaly is an important sign of neurological malformation and a predictor of future disability. The 2015-16 outbreak of Zika virus and congenital Zika infection brought the world's attention to links between Zika infection and microcephaly. However, Zika virus is only one of the infectious causes of microcephaly and, although the contexts in which they occur vary greatly, all are of concern. In this Review, we summarise important aspects of major congenital infections that can cause microcephaly, and describe the epidemiology, transmission, clinical features, pathogenesis, management, and long-term consequences of these infections. We include infections that cause substantial impairment: cytomegalovirus, herpes simplex virus, rubella virus, Toxoplasma gondii, and Zika virus. We highlight potential issues with classification of microcephaly and show how some infants affected by congenital infection might be missed or incorrectly diagnosed. Although Zika virus has brought the attention of the world to the problem of microcephaly, prevention of all infectious causes of microcephaly and appropriately managing its consequences remain important global public health priorities

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives