(Un)intended effects of participation in sustainability science: A criteria-guided comparative case study

The impact of collaborative research approaches on science and society has been subject to much debate and speculation. However, empirically grounded analyses of the process-impact link remain the exception. That includes comparing participation planning, intended processes, expectations and implementation. This paper delivers a theoretically informed comparison between different approaches to participation that are practised. It does so by performing a criteria-guided analysis of 31 participatory sustainability studies covering different areas of study and spatial levels. This provides an understanding of how participation is translated from theory into practice, what challenges occur that contradict initial aims, and how these potentially influence expected effects. The results show stark divergences between planning and implementation: persistent normative ideals in the planning phase, echoing deliberative and emancipatory claims, contrast with an emphasis on effectiveness during implementation. This leads to a systematic over-representation of experts and an under-representation of diverse societal actors in the studies. The focus is on producing directly measurable results rather than promoting possible (long-term) societal effects. These findings facilitate a deeper discussion of which conditions and procedures could aid the design and delivery of high-impact collaboration in the future

Data on the scope of the literature on participatory sustainability science 2000–2018: Bibliography and meta-analysis of selected studies

The impact of collaborative research approaches on science and society has been subject to much debate and speculation. However, empirically grounded analyses of the process-impact link remain the exception. That includes comparing participation planning, intended processes, expectations and implementation. This paper delivers a theoretically informed comparison between different approaches to participation that are practised. It does so by performing a criteria-guided analysis of 31 participatory sustainability studies covering different areas of study and spatial levels. This provides an understanding of how participation is translated from theory into practice, what challenges occur that contradict initial aims, and how these potentially influence expected effects. The results show stark divergences between planning and implementation: persistent normative ideals in the planning phase, echoing deliberative and emancipatory claims, contrast with an emphasis on effectiveness during implementation. This leads to a systematic over-representation of experts and an under-representation of diverse societal actors in the studies. The focus is on producing directly measurable results rather than promoting possible (long-term) societal effects. These findings facilitate a deeper discussion of which conditions and procedures could aid the design and delivery of high-impact collaboration in the future

The Governance of the Creative Industries in European Metropolitain Regions

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Diversity of coryneforms found in infections following prosthetic joint insertion and open fractures

Summary: In a 5-year period, 73 coryneform isolates from prosthetic joint and open fracture infections in 60 patients treated in a hospital specialized in orthopedic surgery were speciated. The most frequent species wereCorynebacterium amycolatum, Corynebacterium striatum, Corynebacterium diphtheriae biotypemitis, andCorynebacterium jeikeium. At least 14 isolates were deemed clinically significant as sole agents of infectio

Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)

With increasing data and model complexities, the time required to train neural networks has become prohibitively large. To address the exponential rise in training time, users are turning to data parallel neural networks (DPNN) and large-scale distributed resources on computer clusters. Current DPNN approaches implement the network parameter updates by synchronizing and averaging gradients across all processes with blocking communication operations after each forward-backward pass. This synchronization is the central algorithmic bottleneck. We introduce the Distributed Asynchronous and Selective Optimization (DASO) method, which leverages multi-GPU compute node architectures to accelerate network training while maintaining accuracy. DASO uses a hierarchical and asynchronous communication scheme comprised of node-local and global networks while adjusting the global synchronization rate during the learning process. We show that DASO yields a reduction in training time of up to 34% on classical and state-of-the-art networks, as compared to current optimized data parallel training methods

Microglia in the normally aged hippocampus

The hippocampus plays important roles in the regulation and combination of short and long term memory and spatial navigation with other brain centers. Aging is accompanied by a functional decline of the hippocampus and degenerative disease. Microglia are major immune cells in the central nervous system and response to degenerative changes in the aged brain. In this respect, functional and morphological changes of the hippocampus have been closely related to microglial changes during normal aging with or without disease. Therefore, in this review, we discuss morphological and functional changes of the hippocampus and microglia in the aging brain

The ribosome stabilizes partially folded intermediates of a nascent multi-domain protein

Co-translational folding is crucial to ensure the production of biologically active proteins. The ribosome can alter the folding pathways of nascent polypeptide chains, yet a structural understanding remains largely inaccessible experimentally. We have developed site-specific labelling of nascent chains to detect and measure, using 19F nuclear magnetic resonance (NMR) spectroscopy, multiple states accessed by an immunoglobulin-like domain within a tandem repeat protein during biosynthesis. By examining ribosomes arrested at different stages during translation of this common structural motif, we observe highly broadened NMR resonances attributable to two previously unidentified intermediates, which are stably populated across a wide folding transition. Using molecular dynamics simulations and corroborated by cryo-electron microscopy, we obtain models of these partially folded states, enabling experimental verification of a ribosome-binding site that contributes to their high stabilities. We thus demonstrate a mechanism by which the ribosome could thermodynamically regulate folding and other co-translational processes

Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)

With increasing data and model complexities, the time required to train neural networks has become prohibitively large. To address the exponential rise in training time, users are turning to data parallel neural networks (DPNN) to utilize large-scale distributed resources on computer clusters. Current DPNN approaches implement the network parameter updates by synchronizing and averaging gradients across all processes with blocking communication operations. This synchronization is the central algorithmic bottleneck. To combat this, we introduce the Distributed Asynchronous and Selective Optimization (DASO) method which leverages multi-GPU compute node architectures to accelerate network training. DASO uses a hierarchical and asynchronous communication scheme comprised of node-local and global networks while adjusting the global synchronization rate during the learning process. We show that DASO yields a reduction in training time of up to 34% on classical and state-of-the-art networks, as compared to other existing data parallel training methods