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The paper analyzes the prerequisites for a regulatory-driven transition toward radically lower air and water pollution in industry. This is achieved in the empirical context of the Swedish mining and metals industry, and by investigating the environmental licensing processes during two regulatory systems. The paper derives an analytical framework that explores under what circumstances such licensing processes can result in radical emissions reductions without seriously jeopardizing the competitiveness of the industry. Archived material covering six environmental licensing processes, three during each system, is used to illustrate the various design and implementation issues. The results suggest that regulatory-driven green transitions benefit from trust-based bargaining procedures in which companies are involved in repeated interactions with regulatory authorities, and which extended probation periods permit tests of novel abatement technologies (including innovation). The findings also illustrate the importance of abstaining from simplified normative notions about policy instrument choice (e.g. taxes versus standards).

Characterizing statistical properties of solutions of inverse problems is essential for decision making. Bayesian inversion offers a tractable framework for this purpose, but current approaches are computationally unfeasible for most realistic imaging applications in the clinic. We introduce two novel deep learning based methods for solving large-scale inverse problems using Bayesian inversion: a sampling based method using a WGAN with a novel mini-discriminator and a direct approach that trains a neural network using a novel loss function. The performance of both methods is demonstrated on image reconstruction in ultra low dose 3D helical CT. We compute the posterior mean and standard deviation of the 3D images followed by a hypothesis test to assess whether a "dark spot" in the liver of a cancer stricken patient is present. Both methods are computationally efficient and our evaluation shows very promising performance that clearly supports the claim that Bayesian inversion is usable for 3D imaging in time critical applications. QC 20191021

The relationships between pathophysiological processes in Alzheimer's disease remain largely unclear. In a longitudinal, multitracer PET study, Rodriguez-Vieitez et al. reveal that progression of autosomal dominant Alzheimer's disease is accompanied by prominent early and then declining astrocytosis, increasing amyloid plaque deposition and decreasing glucose metabolism. Astrocyte activation may initiate Alzheimer pathology.See Schott and Fox (doi: 10.1093/brain/awv405) for a scientific commentary on this article. The relationships between pathophysiological processes in Alzheimer's disease remain largely unclear. In a longitudinal, multitracer PET study, Rodriguez-Vieitez et al. reveal that progression of autosomal dominant Alzheimer's disease is accompanied by prominent early and then declining astrocytosis, increasing amyloid plaque deposition and decreasing glucose metabolism. Astrocyte activation may initiate Alzheimer pathology.Alzheimer's disease is a multifactorial dementia disorder characterized by early amyloid-beta, tau deposition, glial activation and neurodegeneration, where the interrelationships between the different pathophysiological events are not yet well characterized. In this study, longitudinal multitracer positron emission tomography imaging of individuals with autosomal dominant or sporadic Alzheimer's disease was used to quantify the changes in regional distribution of brain astrocytosis (tracer C-11-deuterium-L-deprenyl), fibrillar amyloid-beta plaque deposition (C-11-Pittsburgh compound B), and glucose metabolism (F-18-fluorodeoxyglucose) from early presymptomatic stages over an extended period to clinical symptoms. The 52 baseline participants comprised autosomal dominant Alzheimer's disease mutation carriers (n = 11; 49.6 +/- 10.3 years old) and non-carriers (n = 16; 51.1 +/- 14.2 years old; 10 male), and patients with sporadic mild cognitive impairment (n = 17; 61.9 +/- 6.4 years old; nine male) and sporadic Alzheimer's disease (n = 8; 63.0 +/- 6.5 years old; five male); for confidentiality reasons, the gender of mutation carriers is not revealed. The autosomal dominant Alzheimer's disease participants belonged to families with known mutations in either presenilin 1 (PSEN1) or amyloid precursor protein (APPswe or APParc) genes. Sporadic mild cognitive impairment patients were further divided into C-11-Pittsburgh compound B-positive (n = 13; 62.0 +/- 6.4; seven male) and C-11-Pittsburgh compound B-negative (n = 4; 61.8 +/- 7.5 years old; two male) groups using a neocortical standardized uptake value ratio cut-off value of 1.41, which was calculated with respect to the cerebellar grey matter. All baseline participants underwent multitracer positron emission tomography scans, cerebrospinal fluid biomarker analysis and neuropsychological assessment. Twenty-six of the participants underwent clinical and imaging follow-up examinations after 2.8 +/- 0.6 years. By using linear mixed-effects models, fibrillar amyloid-beta plaque deposition was first observed in the striatum of presymptomatic autosomal dominant Alzheimer's disease carriers from 17 years before expected symptom onset; at about the same time, astrocytosis was significantly elevated and then steadily declined. Diverging from the astrocytosis pattern, amyloid-beta plaque deposition increased with disease progression. Glucose metabolism steadily declined from 10 years after initial amyloid-beta plaque deposition. Patients with sporadic mild cognitive impairment who were C-11-Pittsburgh compound B-positive at baseline showed increasing amyloid-beta plaque deposition and decreasing glucose metabolism but, in contrast to autosomal dominant Alzheimer's disease carriers, there was no significant longitudinal decline in astrocytosis over time. The prominent initially high and then declining astrocytosis in autosomal dominant Alzheimer's disease carriers, contrasting with the increasing amyloid-beta plaque load during disease progression, suggests astrocyte activation is implicated in the early stages of Alzheimer's disease pathology.

Deep learning approaches suffer from the so called interpretability problem and can therefore be very hard to visualise. Embedded Prototype Subspace Classifiers is one attempt in the field of explainable AI, which is both fast and efficient since it does not require repeated learning epochs and has no hidden layers. In this paper we investigate how ensembles and cascades of ensembles perform on some popular datasets. The focus is on handwritten data such as digits, letters and signs. It is shown how cascading can be efficiently implemented in order to both increase accuracy as well as speed up the classification. q2b

Recent studies have demonstrated that people living with dementia, contrary to common believes, are capable of novel learning without structured interventions. Opportunities for learning throughout an individuals lifespan have been acknowledged as important factors in facilitating social participation and promoting wellbeing. However, little is still known about the situated practices used in the learning process for people living with dementia. This study aims to explore how people living with dementia in Swedish residential care facilities position, perceive, and assert, themselves as learners in a novel activity. The study is based on video recordings of eight people living with dementia, who for the first time use tablet computers as a social activity on a one-to-one basis with their formal caregivers. Through interaction analysis, we show how the participants living with dementia use the engagement displays of requests, accounts, formulations and metacomments to make their active undertaking in the ongoing activity public to their communication partner. Our findings suggest that people living with dementia might still perceive themselves as individuals capable of novel learning and that they are active and engaged agents in this process.

Spatial overlap between predator and prey is a prerequisite for predation, but the degree of overlap is not necessarily proportional to prey consumption. This is because many of the behavioural processes that precede ingestion are non‐linear and depend on local prey densities. In aquatic environments, predators and prey distribute not only across a surface, but also vertically in the water column, adding another dimension to the interaction. Integrating and simplifying behavioural processes across space and time can lead to systematic biases in our inference about interaction strength. To recognise situations when this may occur, we must first understand processes underlying variation in prey consumption by individuals. Here we analysed the diet of a major predator in the Barents Sea, the Atlantic cod Gadus morhua, aiming to understand drivers of variation in cod's feeding on its main prey capelin Mallotus villosus. Cod and capelin only partly share habitats, as cod mainly reside near the seafloor and capelin inhabit the free water masses. We used data on stomach contents from ~2000 cod individuals and their surrounding environment collected over 12 years, testing hypotheses on biological and physical drivers of variation in cod's consumption of capelin, using generalized additive models. Specifically, effects of capelin abundance, capelin depth distribution, bottom depth and cod abundance on capelin consumption were evaluated at a resolution scale of 2 km. We found no indication of food competition as cod abundance had no effect on capelin consumption. Capelin abundance had small effects on consumption, while capelin depth distribution was important. Cod fed more intensively on capelin when capelin came close to the seafloor, especially at shallow banks and bank edges. Spatial overlap as an indicator for interaction strength needs to be evaluated in three dimensions instead of the conventional two when species are partly separated in the water column.

Social-ecological (SE) traps refer to persistent mismatches between the responses of people, or organisms, and their social and ecological conditions that are undesirable from a sustainability perspective. Until now, the occurrence of SE traps is primarily explained from a lack of adaptive capacity; not much attention is paid to other causal factors. In our article, we address this concern by theorizing the variety of human responses to SE traps and the effect of these responses on trap dynamics. Besides (adaptive) capacities, we theorize desires, abilities and opportunities as important additional drivers to explain the diversity of human responses to traps. Using these theoretical concepts, we construct a typology of human responses to SE traps, and illustrate its empirical relevance with three cases of SE traps: Swedish Baltic Sea fishery; amaXhosa rural livelihoods; and Pamir smallholder farming. We conclude with a discussion of how attention to the diversity in human response to SE traps may inform future academic research and planned interventions to prevent or dissolve SE traps.