Dystopian Realities : Investigating the Perception of and Interaction with Surveillance Practices

This article seeks to sketch out how the field of surveillance studies has conceptualized surveillance practices, and how cultural and technological shifts have prompted scholars to re-imagine these theoretical frameworks. The article investigates the interplay of (dystopian) popular cultural representations of surveillance cultures and the perception of and attitude towards contemporary surveillance practices, as well as how individuals react to and interact with them. The article also outlines a study regarding the aforementioned issues that was conducted among a sample of 150 university students, which focused especially on each participant’s subjective ability to distinguish between fictional scenarios and real-life surveillance practices

How Lives Come to Matter – Rethinking Mediation and Materiality in Autobiography

Stories of the Self eröffnet den Horizont traditioneller Autobiografiestudien, indem es Praktiken der Selbstverschriftlichung in Medien jenseits des Buchs fokussiert. In den Analysekapiteln, die sich je unterschiedlichen Phänomenen widmen, argumentiert Poletti, dass die mediale und materielle Dimension der jeweiligen Autobiografien ihre Produktion und Rezeption beeinflusst, und, dass ihnen erst qua dieser Bedeutsamkeit beigemessen wird. Hierbei werden innovativ Theorien aus benachbarten Disziplinen verbunden, um passgenau individuelle Zugänge zu entwickeln.Stories of the Self opens the purview of autobiography studies to come to terms with the proliferation of ‘self-life-inscription’ in media and matter beyond the book. In analyses of a variety of phenomena, Poletti argues that the material dimension of autobiographical practices fundamentally determines not only how they are produced and received but also what significance we attach to them. Innovatively combining theories from neighboring disciplines in this way, Poletti develops a number of specific ways of reading mediated lives and selves in their inescapable relationality

The genomes of two key bumblebee species with primitive eusocial organization

Background: The shift from solitary to social behavior is one of the major evolutionary transitions. Primitively eusocial bumblebees are uniquely placed to illuminate the evolution of highly eusocial insect societies. Bumblebees are also invaluable natural and agricultural pollinators, and there is widespread concern over recent population declines in some species. High-quality genomic data will inform key aspects of bumblebee biology, including susceptibility to implicated population viability threats. Results: We report the high quality draft genome sequences of Bombus terrestris and Bombus impatiens, two ecologically dominant bumblebees and widely utilized study species. Comparing these new genomes to those of the highly eusocial honeybee Apis mellifera and other Hymenoptera, we identify deeply conserved similarities, as well as novelties key to the biology of these organisms. Some honeybee genome features thought to underpin advanced eusociality are also present in bumblebees, indicating an earlier evolution in the bee lineage. Xenobiotic detoxification and immune genes are similarly depauperate in bumblebees and honeybees, and multiple categories of genes linked to social organization, including development and behavior, show high conservation. Key differences identified include a bias in bumblebee chemoreception towards gustation from olfaction, and striking differences in microRNAs, potentially responsible for gene regulation underlying social and other traits. Conclusions: These two bumblebee genomes provide a foundation for post-genomic research on these key pollinators and insect societies. Overall, gene repertoires suggest that the route to advanced eusociality in bees was mediated by many small changes in many genes and processes, and not by notable expansion or depauperation

Efficacy and safety of statin therapy in older people: a meta-analysis of individual participant data from 28 randomised controlled trials

Background: Statin therapy has been shown to reduce major vascular events and vascular mortality in a wide range of individuals, but there is uncertainty about its efficacy and safety among older people. We undertook a meta-analysis of data from all large statin trials to compare the effects of statin therapy at different ages. Methods: In this meta-analysis, randomised trials of statin therapy were eligible if they aimed to recruit at least 1000 participants with a scheduled treatment duration of at least 2 years. We analysed individual participant data from 22 trials (n=134 537) and detailed summary data from one trial (n=12 705) of statin therapy versus control, plus individual participant data from five trials of more intensive versus less intensive statin therapy (n=39 612). We subdivided participants into six age groups (55 years or younger, 56–60 years, 61–65 years, 66–70 years, 71–75 years, and older than 75 years). We estimated effects on major vascular events (ie, major coronary events, strokes, and coronary revascularisations), cause-specific mortality, and cancer incidence as the rate ratio (RR) per 1·0 mmol/L reduction in LDL cholesterol. We compared proportional risk reductions in different age subgroups by use of standard χ2 tests for heterogeneity when there were two groups, or trend when there were more than two groups. Findings: 14 483 (8%) of 186 854 participants in the 28 trials were older than 75 years at randomisation, and the median follow-up duration was 4·9 years. Overall, statin therapy or a more intensive statin regimen produced a 21% (RR 0·79, 95% CI 0·77–0·81) proportional reduction in major vascular events per 1·0 mmol/L reduction in LDL cholesterol. We observed a significant reduction in major vascular events in all age groups. Although proportional reductions in major vascular events diminished slightly with age, this trend was not statistically significant (ptrend=0·06). Overall, statin or more intensive therapy yielded a 24% (RR 0·76, 95% CI 0·73–0·79) proportional reduction in major coronary events per 1·0 mmol/L reduction in LDL cholesterol, and with increasing age, we observed a trend towards smaller proportional risk reductions in major coronary events (ptrend=0·009). We observed a 25% (RR 0·75, 95% CI 0·73–0·78) proportional reduction in the risk of coronary revascularisation procedures with statin therapy or a more intensive statin regimen per 1·0 mmol/L lower LDL cholesterol, which did not differ significantly across age groups (ptrend=0·6). Similarly, the proportional reductions in stroke of any type (RR 0·84, 95% CI 0·80–0·89) did not differ significantly across age groups (ptrend=0·7). After exclusion of four trials which enrolled only patients with heart failure or undergoing renal dialysis (among whom statin therapy has not been shown to be effective), the trend to smaller proportional risk reductions with increasing age persisted for major coronary events (ptrend=0·01), and remained non-significant for major vascular events (ptrend=0·3). The proportional reduction in major vascular events was similar, irrespective of age, among patients with pre-existing vascular disease (ptrend=0·2), but appeared smaller among older than among younger individuals not known to have vascular disease (ptrend=0·05). We found a 12% (RR 0·88, 95% CI 0·85–0·91) proportional reduction in vascular mortality per 1·0 mmol/L reduction in LDL cholesterol, with a trend towards smaller proportional reductions with older age (ptrend=0·004), but this trend did not persist after exclusion of the heart failure or dialysis trials (ptrend=0·2). Statin therapy had no effect at any age on non-vascular mortality, cancer death, or cancer incidence. Interpretation: Statin therapy produces significant reductions in major vascular events irrespective of age, but there is less direct evidence of benefit among patients older than 75 years who do not already have evidence of occlusive vascular disease. This limitation is now being addressed by further trials. Funding: Australian National Health and Medical Research Council, National Institute for Health Research Oxford Biomedical Research Centre, UK Medical Research Council, and British Heart Foundation

Social acceptance of geothermal technology on a global view: a systematic review

Abstract Background The role of geothermal technology in the context of global efforts toward carbon-free and clean energy production is becoming increasingly important. Social acceptance is a decisive factor in the successful implementation of geothermal projects. Main text This systematic review summarizes the major aspects and evaluates the crucial outcomes of recent research on community acceptance as a dimension of social acceptance of geothermal technology since 2011, on a global scale. From the literature, we identified and grouped researched acceptance factors into five main acceptance categories, namely ‘project organization and process’, ‘environment’, ‘municipality’, ‘technology’, and ‘governance’. Each category comprises a number of specific acceptance factors addressed by different survey methods (e.g., interviews, questionnaires, content analyses) in the relevant publications. The acceptance factor categories ‘technology’ and ‘governance’ are remarkably underrepresented, whereas the acceptance factors combined in the categories ‘project organization’ and ‘municipality’ are frequently mentioned in the literature. Acceptance factors combined within the category ‘environment’, ‘trust in key actors’, and ‘information about the project’ are expectedly the most dominant ones in the papers studied. Interestingly, acceptance categories and number of mentions of acceptance factors are comparable in all survey methods applied in the various studies. Besides the acceptance factors combined in the categories ‘environment’ and ‘project organization and process’, ‘knowledge about geothermal technology’ (an acceptance factor from the category ‘municipality’) represents the predominant acceptance factor of geothermal technology. Conclusions Deeper knowledge, in particular about the technical aspects of geothermal energy generation, might enable a more comprehensive and holistic view on geothermal technology. Furthermore, the integration of all relevant groups of stakeholders in the process of implementation of geothermal projects strongly influences their social acceptance. Following the results of our systematic literature review, we propose these aspects should be addressed in more detail in future research on the community acceptance of geothermal technology and energy production

Applying machine learning to optical coherence tomography images for automated tissue classification in brain metastases

Purpose!#!A precise resection of the entire tumor tissue during surgery for brain metastases is essential to reduce local recurrence. Conventional intraoperative imaging techniques all have limitations in detecting tumor remnants. Therefore, there is a need for innovative new imaging methods such as optical coherence tomography (OCT). The purpose of this study is to discriminate brain metastases from healthy brain tissue in an ex vivo setting by applying texture analysis and machine learning algorithms for tissue classification to OCT images.!##!Methods!#!Tumor and healthy tissue samples were collected during resection of brain metastases. Samples were imaged using OCT. Texture features were extracted from B-scans. Then, a machine learning algorithm using principal component analysis (PCA) and support vector machines (SVM) was applied to the OCT scans for classification. As a gold standard, an experienced pathologist examined the tissue samples histologically and determined the percentage of vital tumor, necrosis and healthy tissue of each sample. A total of 14.336 B-scans from 14 tissue samples were included in the classification analysis.!##!Results!#!We were able to discriminate vital tumor from healthy brain tissue with an accuracy of 95.75%. By comparing necrotic tissue and healthy tissue, a classification accuracy of 99.10% was obtained. A generalized classification between brain metastases (vital tumor and necrosis) and healthy tissue was achieved with an accuracy of 96.83%.!##!Conclusions!#!An automated classification of brain metastases and healthy brain tissue is feasible using OCT imaging, extracted texture features and machine learning with PCA and SVM. The established approach can prospectively provide the surgeon with additional information about the tissue, thus optimizing the extent of tumor resection and minimizing the risk of local recurrences

Applying machine learning to optical coherence tomography images for automated tissue classification in brain metastases

$\bf Purpose$ A precise resection of the entire tumor tissue during surgery for brain metastases is essential to reduce local recurrence. Conventional intraoperative imaging techniques all have limitations in detecting tumor remnants. Therefore, there is a need for innovative new imaging methods such as optical coherence tomography (OCT). The purpose of this study is to discriminate brain metastases from healthy brain tissue in an ex vivo setting by applying texture analysis and machine learning algorithms for tissue classification to OCT images. $\bf Methods$ Tumor and healthy tissue samples were collected during resection of brain metastases. Samples were imaged using OCT. Texture features were extracted from B-scans. Then, a machine learning algorithm using principal component analysis (PCA) and support vector machines (SVM) was applied to the OCT scans for classification. As a gold standard, an experienced pathologist examined the tissue samples histologically and determined the percentage of vital tumor, necrosis and healthy tissue of each sample. A total of 14.336 B-scans from 14 tissue samples were included in the classification analysis. $\bf Results$ We were able to discriminate vital tumor from healthy brain tissue with an accuracy of 95.75%. By comparing necrotic tissue and healthy tissue, a classification accuracy of 99.10% was obtained. A generalized classification between brain metastases (vital tumor and necrosis) and healthy tissue was achieved with an accuracy of 96.83%. $\bf Conclusions$ An automated classification of brain metastases and healthy brain tissue is feasible using OCT imaging, extracted texture features and machine learning with PCA and SVM. The established approach can prospectively provide the surgeon with additional information about the tissue, thus optimizing the extent of tumor resection and minimizing the risk of local recurrences

Deletion or Inhibition of the Oxygen Sensor PHD1 Protects against Ischemic Stroke via Reprogramming of Neuronal Metabolism

The oxygen-sensing prolyl hydroxylase domain proteins (PHDs) regulate cellular metabolism, but their role in neuronal metabolism during stroke is unknown. Here we report that PHD1 deficiency provides neuroprotection in a murine model of permanent brain ischemia. This was not due to an increased collateral vessel network. Instead, PHD1(-/-) neurons were protected against oxygen-nutrient deprivation by reprogramming glucose metabolism. Indeed, PHD1(-/-) neurons enhanced glucose flux through the oxidative pentose phosphate pathway by diverting glucose away from glycolysis. As a result, PHD1(-/-) neurons increased their redox buffering capacity to scavenge oxygen radicals in ischemia. Intracerebroventricular injection of PHD1-antisense oligonucleotides reduced the cerebral infarct size and neurological deficits following stroke. These data identify PHD1 as a regulator of neuronal metabolism and a potential therapeutic target in ischemic stroke

Deletion or Inhibition of the Oxygen Sensor PHD1 Protects against Ischemic Stroke via Reprogramming of Neuronal Metabolism

The oxygen-sensing prolyl hydroxylase domain proteins (PHDs) regulate cellular metabolism, but their role in neuronal metabolism during stroke is unknown. Here we report that PHD1 deficiency provides neuroprotection in a murine model of permanent brain ischemia. This was not due to an increased collateral vessel network. Instead, PHD1(-/-) neurons were protected against oxygen-nutrient deprivation by reprogramming glucose metabolism. Indeed, PHD1(-/-) neurons enhanced glucose flux through the oxidative pentose phosphate pathway by diverting glucose away from glycolysis. As a result, PHD1(-/-) neurons increased their redox buffering capacity to scavenge oxygen radicals in ischemia. Intracerebroventricular injection of PHD1-antisense oligonucleotides reduced the cerebral infarct size and neurological deficits following stroke. These data identify PHD1 as a regulator of neuronal metabolism and a potential therapeutic target in ischemic stroke.publisher: Elsevier articletitle: Deletion or Inhibition of the Oxygen Sensor PHD1 Protects against Ischemic Stroke via Reprogramming of Neuronal Metabolism journaltitle: Cell Metabolism articlelink: http://dx.doi.org/10.1016/j.cmet.2015.12.007 content_type: article copyright: Copyright © 2016 Elsevier Inc. All rights reserved.status: publishe