Public Procurement of Innovation Diffusion: Exploring the Role of Institutions and Institutional Coordination

The role of the public agency as a pacer of private sector innovation has been emphasised over the recent years, especially in the context of the EU. The general ambition has been to encourage public agencies to actively stimulate private sector innovation by requesting innovation instead of procuring currently existing products. This has also triggered an increased interest among researchers and practitioners to identify examples of best practice where public agencies have successfully procured innovation. Rather than addressing this demand-oriented perspective this paper focuses on the public agency as an adopter of private-sector innovation, and how this mechanism can contribute to innovation in general. The theoretical point of departure is diffusion theory, with an emphasis on the role of institutions as identified in systemic approaches to innovation studies. A particular concern of this paper is those institutions that hinder or enable adoption of an innovation in an organisational context. The paper draws on an explorative case study looking at the introduction of a new catheter into the English National Health Service supply chain and its diffusion among NHS trusts in England. Different institutional factors are identified which have had an affect on the adoption and diffusion.public procurement; innovation diffusion; institutions; England

Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes

Pool-based active learning (AL) is a promising technology for increasing data-efficiency of machine learning models. However, surveys show that performance of recent AL methods is very sensitive to the choice of dataset and training setting, making them unsuitable for general application. In order to tackle this problem, the field Learning Active Learning (LAL) suggests to learn the active learning strategy itself, allowing it to adapt to the given setting. In this work, we propose a novel LAL method for classification that exploits symmetry and independence properties of the active learning problem with an Attentive Conditional Neural Process model. Our approach is based on learning from a myopic oracle, which gives our model the ability to adapt to non-standard objectives, such as those that do not equally weight the error on all data points. We experimentally verify that our Neural Process model outperforms a variety of baselines in these settings. Finally, our experiments show that our model exhibits a tendency towards improved stability to changing datasets. However, performance is sensitive to choice of classifier and more work is necessary to reduce the performance the gap with the myopic oracle and to improve scalability. We present our work as a proof-of-concept for LAL on nonstandard objectives and hope our analysis and modelling considerations inspire future LAL work.Comment: Accepted at ECML 202

Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics

In this paper, we address the problem of real-time motion planning for multiple robotic manipulators that operate in close proximity. We build upon the concept of dynamic fabrics and extend them to multi-robot systems, referred to as Multi-Robot Dynamic Fabrics (MRDF). This geometric method enables a very high planning frequency for high-dimensional systems at the expense of being reactive and prone to deadlocks. To detect and resolve deadlocks, we propose Rollout Fabrics where MRDF are forward simulated in a decentralized manner. We validate the methods in simulated close-proximity pick-and-place scenarios with multiple manipulators, showing high success rates and real-time performance.Comment: 6 pages + 1 page references, 2 tables, 4 figures, preprint version to accepted paper to IEEE International Symposium on Multi-Robot & Multi-Agent Systems, Boston, 202

The missing link: Predicting connectomes from noisy and partially observed tract tracing data

Our understanding of the wiring map of the brain, known as the connectome, has increased greatly in the last decade, mostly due to technological advancements in neuroimaging techniques and improvements in computational tools to interpret the vast amount of available data. Despite this, with the exception of the C. elegans roundworm, no definitive connectome has been established for any species. In order to obtain this, tracer studies are particularly appealing, as these have proven highly reliable. The downside of tract tracing is that it is costly to perform, and can only be applied ex vivo. In this paper, we suggest that instead of probing all possible connections, hitherto unknown connections may be predicted from the data that is already available. Our approach uses a 'latent space model' that embeds the connectivity in an abstract physical space. Regions that are close in the latent space have a high chance of being connected, while regions far apart are most likely disconnected in the connectome. After learning the latent embedding from the connections that we did observe, the latent space allows us to predict connections that have not been probed previously. We apply the methodology to two connectivity data sets of the macaque, where we demonstrate that the latent space model is successful in predicting unobserved connectivity, outperforming two baselines and an alternative model in nearly all cases. Furthermore, we show how the latent spatial embedding may be used to integrate multimodal observations (i.e. anterograde and retrograde tracers) for the mouse neocortex. Finally, our probabilistic approach enables us to make explicit which connections are easy to predict and which prove difficult, allowing for informed follow-up studies

Computer-aided detection of fasciculations and other movements in muscle with ultrasound:Development and clinical application

Objective: To develop an automated algorithm for detecting fasciculations and other movements in muscle ultrasound videos. Fasciculation detection in muscle ultrasound is routinely performed online by observing the live videos. However, human observation limits the objective information gained. Automated detection of movement is expected to improved sensitivity and specificity and increase reliability.Methods: We used 42 ultrasound videos from 11 neuromuscular patients for an iterative learning process between human observers and automated computer analysis, to identify muscle ultrasound movements. Two different datasets were selected from this, one to develop the algorithm and one to validate it. The outcome was compared to manual movement identification by clinicians. The algorithm also quantifies specific parameters of different movement types, to enable automated differentiation of events.Results: The algorithm reliably detected fasciculations. With algorithm guidance, observers found more fasciculations compared to visual analysis alone, and prescreening the videos with the algorithm saved clinicians significant time compared to reviewing full video sequences. All videos also contained other movements, especially contraction pseudotremor, which confused human interpretation in some.Conclusions: Automated movement detection is a feasible and attractive method to screen for fasciculations in muscle ultrasound videos.Significance: Our findings affirm the potential clinical usefulness of automated movement analysis in muscle ultrasound

SGLT2 inhibition versus sulfonylurea treatment effects on electrolyte and acid-base balance:secondary analysis of a clinical trial reaching glycemic equipoise: Tubular effects of SGLT2 inhibition in Type 2 diabetes

Sodium-glucose transporter (SGLT)2 inhibitors increase plasma magnesium and plasma phosphate and may cause ketoacidosis, but the contribution of improved glycemic control to these observations as well as effects on other electrolytes and acid-base parameters remain unknown. Therefore, our objective was to compare the effects of SGLT2 inhibitors dapagliflozin and sulfonylurea gliclazide on plasma electrolytes, urinary electrolyte excretion, and acid-base balance in people with Type 2 diabetes (T2D). We assessed the effects of dapagliflozin and gliclazide treatment on plasma electrolytes and bicarbonate, 24-hour urinary pH and excretions of electrolytes, ammonium, citrate, and sulfate in 44 metformin-treated people with T2D and preserved kidney function. Compared with gliclazide, dapagliflozin increased plasma chloride by 1.4 mmol/l (95% CI 0.4-2.4), plasma magnesium by 0.03 mmol/l (95% CI 0.01-0.06), and plasma sulfate by 0.02 mmol/l (95% CI 0.01-0.04). Compared with baseline, dapagliflozin also significantly increased plasma phosphate, but the same trend was observed with gliclazide. From baseline to week 12, dapagliflozin increased the urinary excretion of citrate by 0.93 ± 1.72 mmol/day, acetoacetate by 48 μmol/day (IQR 17-138), and β-hydroxybutyrate by 59 μmol/day (IQR 0-336), without disturbing acid-base balance. In conclusion, dapagliflozin increases plasma magnesium, chloride, and sulfate compared with gliclazide, while reaching similar glucose-lowering in people with T2D. Dapagliflozin also increases urinary ketone excretion without changing acid-base balance. Therefore, the increase in urinary citrate excretion by dapagliflozin may reflect an effect on cellular metabolism including the tricarboxylic acid cycle. This potentially contributes to kidney protection

Nile Red Quantifier:A novel and quantitative tool to study lipid accumulation in patient-derived circulating monocytes using confocal microscopy

The inflammatory profile of circulating monocytes is an important biomarker for atherosclerotic plaque vulnerability. Recent research revealed that peripheral lipid uptake by monocytes alters their phenotype toward an inflammatory state and this coincides with an increased lipid droplet (LD) content. Determination of lipid content of circulating monocytes is, however, not very well established. Based on Nile Red (NR) neutral LD imaging, using confocal microscopy and computational analysis, we developed NR Quantifier (NRQ), a novel quantification method to assess LD content in monocytes. Circulating monocytes were isolated from blood and used for the NR staining procedure. In monocytes stained with NR, we clearly distinguished, based on 3D imaging, phospholipids and exclusively intracellular neutral lipids. Next, we developed and validated NRQ, a semi-automated quantification program that detects alterations in lipid accumulation. NRQ was able to detect LD alterations after ex vivo exposure of isolated monocytes to freshly isolated LDL in a time-and dose-dependent fashion. Finally, we validated NRQ in patients with familial hypercholesterolemia and obese subjects in pre- and postprandial state. In conclusion, NRQ is a suitable tool to detect even small differences in neutral LD content in circulating monocytes using NR staining

Simulating the 128-ka Antarctic climate response to Northern Hemisphere ice sheet melting using the isotope-enabled HadCM3

Warmer than present Antarctic and Southern Ocean temperatures during the last interglacial, approximately 128,000 years ago, have been attributed to changes in north‐south ocean heat transport, causing opposing hemispheric temperature anomalies. We investigate the magnitude of Antarctic warming and Antarctic ice core isotopic enrichment in response to Northern Hemisphere meltwater input during the early last interglacial. A 1,600‐year HadCM3 simulation driven by 0.25 Sv of meltwater input reproduces 50–60% of the peak Southern Ocean summer sea surface temperature anomaly, sea ice retreat, and ice core isotope enrichment. We also find a robust increase in the proportion of cold season precipitation during the last interglacial, leading to lower isotopic values at the Antarctic ice core sites. These results suggest that a HadCM3 simulation including 0.25 Sv for 3,000–4,000 years would reconcile the last interglacial observations, providing a potential solution for the last interglacial missing heat problem