Sharing perspectives on public-private sector interaction Proceedings of a workshop, 10 April 2001 ICRISAT, Patancheru, India

This book presents several papers presented at a workshop to gather perspectives on the patterns of interaction between private and public agricultural research institutions. The importance of research partnerships, the types of interaction that are possible, and some new ways of exploring this from a policy perspective, are explained

Post-harvest innovations in innovation: reflections on partnership and learning

In the post-harvest area and in agriculture research in general, both in India and internationally, policy attention is returning to the question of how innovation can be encouraged and promoted and thus how impact on the poor can be achieved. This publication assembles several cases from the post-harvest sector. These provide examples of successful innovation that emerged in quite different ways. Its purpose is to illustrate and analyze the diversity and often highly context-specific nature of the processes that lead to and promote innovation. The presented cases suggest a number of generic principles needed to develop the capacity of innovation systems: the need to pay more attention to revealing and managing the historical and institutional context of partnerships and relationship; the need to build on local contexts and circumstance rather than introducing external blueprints; and the need to strengthen the learning process and to link this to the broader agenda of institutional change, particularly concerning the governance of public science endeavors

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques

Space use patterns of a large mammalian herbivore distinguished by activity state: fear versus food?

Space use patterns have generally been interpreted using home‐range concepts without distinguishing the particular activities performed in different regions. The relative influences of food resources, security from predation and shelter from thermal extremes on space occupation are likely to vary with time of day and changing conditions over the seasonal cycle. We used hourly movement rates obtained from GPS telemetry to infer the predominant activity states of blue wildebeest in the Kruger National Park, South Africa, at different times of day. Food procurement was assumed to be the primary consideration during the morning and late afternoon, shade seeking to become important over midday, and security from predation to be the overriding factor at night when stalking predators are most active. Travelling excursions were expected to occur mostly during daylight when lurking predators are most readily detected. Movements beyond the preferred range should occur more frequently in the late dry season when food has been depleted and surface water sources become restricted. As anticipated, we observed shifts in space occupation by the collared wildebeest herds with time of day and activity state. During the night, wildebeest herds remained within the ranges they occupied during prime foraging times in the early morning and late afternoon. However, they contracted their space occupation away from habitat edges where concealment for stalking lions increased, both while resting and while foraging. Herds inconsistently expanded their space use into surrounding areas with more shade but taller grass over midday. Risky excursions beyond the prime foraging ranges became more frequent late in the dry season. Security from predation seemed to be the overriding influence and restricted access to food resources. By taking into account temporal variation in prevailing activity states and other influences, space occupation patterns can be related to particular vital needs and their interactions

New Agendas for Agricultural Research in Developing Countries: Policy Analysis and Institutional Implications

This article argues that the goals of agricultural research in poor countries have changed substantially over the last four decades. In particular they have broadened from the early (and narrow) emphasis on food production to a much wider agenda that includes poverty alleviation, environmental degradation, and social inclusion. Conversely, agricultural research systems have proved remarkably resistant to the concomitant need for changes in research focus. As a result many, at both the national and international level, are under great strain. In terms of public policy the article goes on to suggest that shortcomings of existing conceptual approaches to technology development could be supplemented by adopting analytical principles that view innovation in systemic terms. An approach where flows of knowledge between institutional nodes is a key to innovative performance (the “National Systems of Innovation” approach) is suggested as one such conceptual framework that might help supplement conventional policy analysis. An earlier version of this paper was presented at a workshop “New Policy Agendas for Agricultural Research: Implications for Institutional Arrangements” held on 28 March 2000 at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India. The workshop was supported by the UK Department of International Development (DFID) Crop Post-Harvest Programme as an output of the project “Optimising Institutional Arrangements.

Wolves in Panna National Park

Volume: 95Start Page: 327End Page: 32

Simulating microarrays using a parameterized model

Simulating a microarray includes defining a number of parameters. A microarray is generated according to the parameters using an imaging procedure. The microarray is compared to a known value, and the imaging procedure is evaluated in response to the comparison. A simulated microarray image can be generated based on parameters. The simulated microarray can be associated with known values. An imaging procedure is applied to the simulated microarray image to generate observed values. The known values (e.g., intensities) can be compared to the observed values to evaluate the imaging procedure.U