Showing papers by "University of Stirling published in 2018"

Applications of Deep Learning and Reinforcement Learning to Biological Data

Abstract: Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics , bioimaging , medical imaging , and (brain/body)–machine interfaces . These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

Plant functional trait change across a warming tundra biome

Abstract: The tundra is warming more rapidly than any other biome on Earth, and the potential ramifications are far-reaching because of global feedback effects between vegetation and climate. A better understanding of how environmental factors shape plant structure and function is crucial for predicting the consequences of environmental change for ecosystem functioning. Here we explore the biome-wide relationships between temperature, moisture and seven key plant functional traits both across space and over three decades of warming at 117 tundra locations. Spatial temperature-trait relationships were generally strong but soil moisture had a marked influence on the strength and direction of these relationships, highlighting the potentially important influence of changes in water availability on future trait shifts in tundra plant communities. Community height increased with warming across all sites over the past three decades, but other traits lagged far behind predicted rates of change. Our findings highlight the challenge of using space-for-time substitution to predict the functional consequences of future warming and suggest that functions that are tied closely to plant height will experience the most rapid change. They also reveal the strength with which environmental factors shape biotic communities at the coldest extremes of the planet and will help to improve projections of functional changes in tundra ecosystems with climate warming.

Evidence review of e-cigarettes and heated tobacco products 2018: a report commissioned by Public Health England.

Abstract: The report covers e-cigarette use among young people and adults, public attitudes, the impact on quitting smoking, an update on risks to health and the role of nicotine It also reviews heated tobacco products The main findings of PHE’s evidence review are that: • vaping poses only a small fraction of the risks of smoking and switching completely from smoking to vaping conveys substantial health benefits • e-cigarettes could be contributing to at least 20,000 successful new quits per year and possibly many more • e-cigarette use is associated with improved quit success rates over the last year and an accelerated drop in smoking rates across the country • many thousands of smokers incorrectly believe that vaping is as harmful as smoking; around 40% of smokers have not even tried an e-cigarette • there is much public misunderstanding about nicotine (less than 10% of adults understand that most of the harms to health from smoking are not caused by nicotine) • the use of e-cigarettes in the UK has plateaued over the last few years at just under 3 million • the evidence does not support the concern that e-cigarettes are a route into smoking among young people (youth smoking rates in the UK continue to decline, regular use is rare and is almost entirely confined to those who have smoked)

The GALAH Survey: Second Data Release

Abstract: The Galactic Archaeology with HERMES (GALAH) survey is a large-scale stellar spectroscopic survey of theMilkyWay, designed to deliver complementary chemical information to a large number of stars covered by the Gaia mission. We present the GALAH second public data release (GALAH DR2) containing 342 682 stars. For these stars, the GALAH collaboration provides stellar parameters and abundances for up to 23 elements to the community. Here we present the target selection, observation, data reduction, and detailed explanation of how the spectra were analysed to estimate stellar parameters and element abundances. For the stellar analysis, we have used a multistep approach. We use the physics-driven spectrum synthesis of Spectroscopy Made Easy (SME) to derive stellar labels (T eff , logg, [Fe/H], [X/Fe], v mic , vsin i, AK S ) for a representative training set of stars. This information is then propagated to the whole sample with the data-driven method of The Cannon. Special care has been exercised in the spectral synthesis to only consider spectral lines that have reliable atomic input data and are little affected by blending lines. Departures from local thermodynamic equilibrium (LTE) are considered for several key elements, including Li, O, Na, Mg, Al, Si, and Fe, using 1D MARCS stellar atmosphere models. Validation tests including repeat observations, Gaia benchmark stars, open and globular clusters, and K2 asteroseismic targets lend confidence to our methods and results. Combining the GALAH DR2 catalogue with the kinematic information from Gaia will enable a wide range of Galactic Archaeology studies, with unprecedented detail, dimensionality, and scope.

Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis

Abstract: Sentiment analysis has emerged as one of the most popular natural language processing (NLP) tasks in recent years. A classic setting of the task mainly involves classifying the overall sentiment polarity of the inputs. However, it is based on the assumption that the sentiment expressed in a sentence is unified and consistent, which does not hold in the reality. As a fine-grained alternative of the task, analyzing the sentiment towards a specific target and aspect has drawn much attention from the community for its more practical assumption that sentiment is dependent on a particular set of aspects and entities. Recently, deep neural models have achieved great successes on sentiment analysis. As a functional simulation of the behavior of human brains and one of the most successful deep neural models for sequential data, long short-term memory (LSTM) networks are excellent in learning implicit knowledge from data. However, it is impossible for LSTM to acquire explicit knowledge such as commonsense facts from the training data for accomplishing their specific tasks. On the other hand, emerging knowledge bases have brought a variety of knowledge resources to our attention, and it has been acknowledged that incorporating the background knowledge is an important add-on for many NLP tasks. In this paper, we propose a knowledge-rich solution to targeted aspect-based sentiment analysis with a specific focus on leveraging commonsense knowledge in the deep neural sequential model. To explicitly model the inference of the dependent sentiment, we augment the LSTM with a stacked attention mechanism consisting of attention models for the target level and sentence level, respectively. In order to explicitly integrate the explicit knowledge with implicit knowledge, we propose an extension of LSTM, termed Sentic LSTM. The extended LSTM cell includes a separate output gate that interpolates the token-level memory and the concept-level input. In addition, we propose an extension of Sentic LSTM by creating a hybrid of the LSTM and a recurrent additive network that simulates sentic patterns. In this paper, we are mainly concerned with a joint task combining the target-dependent aspect detection and targeted aspect-based polarity classification. The performance of proposed methods on this joint task is evaluated on two benchmark datasets. The experiment shows that the combination of proposed attention architecture and knowledge-embedded LSTM could outperform state-of-the-art methods in two targeted aspect sentiment tasks. We present a knowledge-rich solution for the task of targeted aspect-based sentiment analysis. Our model can effectively incorporate the commonsense knowledge into the deep neural network and be trained in an end-to-end manner. We show that the two-step attentive neural architecture as well as the proposed Sentic LSTM and H-Sentic-LSTM can achieve an improved performance on resolving the aspect categories and sentiment polarity for a targeted entity in its context over state-of-the-art systems.

Genes for de novo biosynthesis of omega-3 polyunsaturated fatty acids are widespread in animals.

Abstract: This work received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) funded by the Scottish Funding Council (grant reference HR09011), and their support is gratefully acknowledged. Access to the Institute of Aquaculture laboratories was funded by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 262336 (AQUAEXCEL), Transnational Access Project Number 0095/06/03/13.

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Abstract: Background: In the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life. Objective: This systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends” in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling. Results: All the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data. Conclusions: The monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise.

Nutritional labelling for healthier food or non‐alcoholic drink purchasing and consumption

Abstract: Background Nutritional labelling is advocated as a means to promote healthier food purchasing and consumption, including lower energy intake. Internationally, many different nutritional labelling schemes have been introduced. There is no consensus on whether such labelling is effective in promoting healthier behaviour. Objectives To assess the impact of nutritional labelling for food and non-alcoholic drinks on purchasing and consumption of healthier items. Our secondary objective was to explore possible effect moderators of nutritional labelling on purchasing and consumption. Search methods We searched 13 electronic databases including CENTRAL, MEDLINE and Embase to 26 April 2017. We also handsearched references and citations and sought unpublished studies through websites and trials registries. Selection criteria Eligible studies: were randomised or quasi-randomised controlled trials (RCTs/Q-RCTs), controlled before-and-after studies, or interrupted time series (ITS) studies; compared a labelled product (with information on nutrients or energy) with the same product without a nutritional label; assessed objectively measured purchasing or consumption of foods or non-alcoholic drinks in real-world or laboratory settings. Data collection and analysis Two authors independently selected studies for inclusion and extracted study data. We applied the Cochrane 'Risk of bias' tool and GRADE to assess the quality of evidence. We pooled studies that evaluated similar interventions and outcomes using a random-effects meta-analysis, and we synthesised data from other studies in a narrative summary. Main results We included 28 studies, comprising 17 RCTs, 5 Q-RCTs and 6 ITS studies. Most (21/28) took place in the USA, and 19 took place in university settings, 14 of which mainly involved university students or staff. Most (20/28) studies assessed the impact of labelling on menus or menu boards, or nutritional labelling placed on, or adjacent to, a range of foods or drinks from which participants could choose. Eight studies provided participants with only one labelled food or drink option (in which labelling was present on a container or packaging, adjacent to the food or on a display board) and measured the amount consumed. The most frequently assessed labelling type was energy (i.e. calorie) information (12/28). Eleven studies assessed the impact of nutritional labelling on purchasing food or drink options in real-world settings, including purchases from vending machines (one cluster-RCT), grocery stores (one ITS), or restaurants, cafeterias or coffee shops (three RCTs, one Q-RCT and five ITS). Findings on vending machines and grocery stores were not interpretable, and were rated as very low quality. A meta-analysis of the three RCTs, all of which assessed energy labelling on menus in restaurants, demonstrated a statistically significant reduction of 47 kcal in energy purchased (MD −46.72 kcal, 95% CI −78.35, −15.10, N = 1877). Assuming an average meal of 600 kcal, energy labelling on menus would reduce energy purchased per meal by 7.8% (95% CI 2.5% to 13.1%). The quality of the evidence for these three studies was rated as low, so our confidence in the effect estimate is limited and may change with further studies. Of the remaining six studies, only two (both ITS studies involving energy labels on menus or menus boards in a coffee shop or cafeteria) were at low risk of bias, and their results support the meta-analysis. The results of the other four studies which were conducted in a restaurant, cafeterias (2 studies) or a coffee shop, were not clearly reported and were at high risk of bias. Seventeen studies assessed the impact of nutritional labels on consumption in artificial settings or scenarios (henceforth referred to as laboratory studies or settings). Of these, eight (all RCTs) assessed the effect of labels on menus or placed on a range of food options. A meta-analysis of these studies did not conclusively demonstrate a reduction in energy consumed during a meal (MD −50 kcal, 95% CI −104.41, 3.88, N = 1705). We rated the quality of the evidence as low, so our confidence in the effect estimate is limited and may change with further studies. Six laboratory studies (four RCTs and two Q-RCTs) assessed the impact of labelling a single food or drink option (such as chocolate, pasta or soft drinks) on energy consumed during a snack or meal. A meta-analysis of these studies did not demonstrate a statistically significant difference in energy (kcal) consumed (SMD 0.05, 95% CI −0.17 to 0.27, N = 732). However, the confidence intervals were wide, suggesting uncertainty in the true effect size. We rated the quality of the evidence as low, so our confidence in the effect estimate is limited and may change with further studies. There was no evidence that nutritional labelling had the unintended harm of increasing energy purchased or consumed. Indirect evidence came from five laboratory studies that involved mislabelling single nutrient content (i.e. placing low energy or low fat labels on high-energy foods) during a snack or meal. A meta-analysis of these studies did not demonstrate a statistically significant increase in energy (kcal) consumed (SMD 0.19, 95% CI −0.14to 0.51, N = 718). The effect was small and the confidence intervals wide, suggesting uncertainty in the true effect size. We rated the quality of the evidence from these studies as very low, providing very little confidence in the effect estimate. Authors' conclusions Findings from a small body of low-quality evidence suggest that nutritional labelling comprising energy information on menus may reduce energy purchased in restaurants. The evidence assessing the impact on consumption of energy information on menus or on a range of food options in laboratory settings suggests a similar effect to that observed for purchasing, although the evidence is less definite and also of low quality. Accordingly, and in the absence of observed harms, we tentatively suggest that nutritional labelling on menus in restaurants could be used as part of a wider set of measures to tackle obesity. Additional high-quality research in real-world settings is needed to enable more certain conclusions. Further high-quality research is also needed to address the dearth of evidence from grocery stores and vending machines and to assess potential moderators of the intervention effect, including socioeconomic status.

Genetic Improvement of Software: A Comprehensive Survey

Abstract: Genetic improvement (GI) uses automated search to find improved versions of existing software. We present a comprehensive survey of this nascent field of research with a focus on the core papers in the area published between 1995 and 2015. We identified core publications including empirical studies, 96% of which use evolutionary algorithms (genetic programming in particular). Although we can trace the foundations of GI back to the origins of computer science itself, our analysis reveals a significant upsurge in activity since 2012. GI has resulted in dramatic performance improvements for a diverse set of properties such as execution time, energy and memory consumption, as well as results for fixing and extending existing system functionality. Moreover, we present examples of research work that lies on the boundary between GI and other areas, such as program transformation, approximate computing, and software repair, with the intention of encouraging further exchange of ideas between researchers in these fields.

Large-scale replication study reveals a limit on probabilistic prediction in language comprehension

Abstract: Do people routinely pre-activate the meaning and even the phonological form of upcoming words? The most acclaimed evidence for phonological prediction comes from a 2005 Nature Neuroscience publication by DeLong, Urbach and Kutas, who observed a graded modulation of electrical brain potentials (N400) to nouns and preceding articles by the probability that people use a word to continue the sentence fragment (‘cloze’). In our direct replication study spanning 9 laboratories (N=334), pre-registered replication-analyses and exploratory Bayes factor analyses successfully replicated the noun-results but, crucially, not the article-results. Pre-registered single-trial analyses also yielded a statistically significant effect for the nouns but not the articles. Exploratory Bayesian single-trial analyses showed that the article-effect may be non-zero but is likely far smaller than originally reported and too small to observe without very large sample sizes. Our results do not support the view that readers routinely pre-activate the phonological form of predictable words.

Smart contracts vulnerabilities: a call for blockchain software engineering?

Abstract: Smart Contracts have gained tremendous popularity in the past few years, to the point that billions of US Dollars are currently exchanged every day through such technology. However, since the release of the Frontier network of Ethereum in 2015, there have been many cases in which the execution of Smart Contracts managing Ether coins has led to problems or conflicts. Compared to traditional Software Engineering, a discipline of Smart Contract and Blockchain programming, with standardized best practices that can help solve the mentioned problems and conflicts, is not yet sufficiently developed. Furthermore, Smart Contracts rely on a non-standard software life-cycle, according to which, for instance, delivered applications can hardly be updated or bugs resolved by releasing a new version of the software. In this paper we advocate the need for a discipline of Blockchain Software Engineering, addressing the issues posed by smart contract programming and other applications running on blockchains.We analyse a case of study where a bug discovered in a Smart Contract library, and perhaps "unsafe" programming, allowed an attack on Parity, a wallet application, causing the freezing of about 500K Ethers (about 150M USD, in November 2017). In this study we analyze the source code of Parity and the library, and discuss how recognised best practices could mitigate, if adopted and adapted, such detrimental software misbehavior. We also reflect on the specificity of Smart Contract software development, which makes some of the existing approaches insufficient, and call for the definition of a specific Blockchain Software Engineering.

How can interventions increase motivation for physical activity? A systematic review and meta-analysis

Abstract: Motivation is a proximal determinant of behaviour, and increasing motivation is central to most health behaviour change interventions. This systematic review and meta-analysis sought to identify features of physical activity interventions associated with favourable changes in three prominent motivational constructs: intention, stage of change and autonomous motivation. A systematic literature search identified 89 intervention studies (k = 200; N = 19,212) which assessed changes in these motivational constructs for physical activity. Intervention descriptions were coded for potential moderators, including behaviour change techniques (BCTs), modes of delivery and theory use. Random effects comparative subgroup analyses identified 18 BCTs and 10 modes of delivery independently associated with changes in at least one motivational outcome (effect sizes ranged from d = 0.12 to d = 0.74). Interventions delivered face-to-face or in gym settings, or which included the BCTs ‘behavioural goal setting’, ‘self...

Optical types of inland and coastal waters

Abstract: Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in-water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n = 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions.

No Compelling Evidence that Preferences for Facial Masculinity Track Changes in Women’s Hormonal Status:

Abstract: Although widely cited as strong evidence that sexual selection has shaped human facial-attractiveness judgments, findings suggesting that women’s preferences for masculine characteristics in men’s faces are related to women’s hormonal status are equivocal and controversial Consequently, we conducted the largest-ever longitudinal study of the hormonal correlates of women’s preferences for facial masculinity (N = 584) Analyses showed no compelling evidence that preferences for facial masculinity were related to changes in women’s salivary steroid hormone levels Furthermore, both within-subjects and between-subjects comparisons showed no evidence that oral contraceptive use decreased masculinity preferences However, women generally preferred masculinized over feminized versions of men’s faces, particularly when assessing men’s attractiveness for short-term, rather than long-term, relationships Our results do not support the hypothesized link between women’s preferences for facial masculinity and their h

Temperature Effects Explain Continental Scale Distribution of Cyanobacterial Toxins

Abstract: Insight into how environmental change determines the production and distribution of cyanobacterial toxins is necessary for risk assessment. Management guidelines currently focus on hepatotoxins (microcystins). Increasing attention is given to other classes, such as neurotoxins (e.g., anatoxin-a) and cytotoxins (e.g., cylindrospermopsin) due to their potency. Most studies examine the relationship between individual toxin variants and environmental factors, such as nutrients, temperature and light. In summer 2015, we collected samples across Europe to investigate the effect of nutrient and temperature gradients on the variability of toxin production at a continental scale. Direct and indirect effects of temperature were the main drivers of the spatial distribution in the toxins produced by the cyanobacterial community, the toxin concentrations and toxin quota. Generalized linear models showed that a Toxin Diversity Index (TDI) increased with latitude, while it decreased with water stability. Increases in TDI were explained through a significant increase in toxin variants such as MC-YR, anatoxin and cylindrospermopsin, accompanied by a decreasing presence of MC-LR. While global warming continues, the direct and indirect effects of increased lake temperatures will drive changes in the distribution of cyanobacterial toxins in Europe, potentially promoting selection of a few highly toxic species or strains.

A systematic review of the relationship between weight status perceptions and weight loss attempts, strategies, behaviours and outcomes.

Abstract: It is commonly assumed that a person identifying that they are ‘overweight’ is an important prerequisite to successful weight management. However, there has been no systematic evaluation of evidence supporting this proposition. The aim of the present research was to systematically review evidence on the relationship between perceived overweight and (i) weight loss attempts, (ii) weight control strategies (healthy and unhealthy), (iii) weight‐related behaviours (physical activity and eating habits), (iv) disordered eating and (v) weight change. We synthesized evidence from 78 eligible studies and evaluated evidence linking perceived overweight with outcome variables separately according to the gender, age and objective weight status of study participants. Results indicated that perceived overweight was associated with an increased likelihood of attempting weight loss and with healthy and unhealthy weight control strategies in some participant groups. However, perceived overweight was not reliably associated with physical activity or healthy eating and was associated with greater disordered eating in some groups. Rather than being associated with improved weight management, there was consistent evidence that perceived overweight was predictive of increased weight gain over time. Individuals who perceive their weight status as overweight are more likely to report attempting weight loss but over time gain more weight.

Assessing the impact of Bacillus strains mixture probiotic on water quality, growth performance, blood profile and intestinal morphology of Nile tilapia, Oreochromis niloticus

Abstract: Mabrouk Elsabagh, Radi Mohamed, Eman M. Moustafa, Ahmad Hamza, Foad Farrag, 5 Olivier Decamp, Mahmoud A.O. Dawood, Mahmoud Eltholth 9 6 7 Department of Nutrition and Clinical Nutrition, Faculty of Veterinary Medicine, Kafrelsheikh 8 University, Kafrelsheikh, Egypt 9 Department of Aquaculture, Faculty of Aquatic and Fisheries Sciences, Kafrelsheikh 10 University, Kafrelsheikh, Egypt 11 3 Department of Fish Diseases and Management, Faculty of Veterinary Medicine, Kafrelsheikh 12 University, Kafrelsheikh, Egypt 13 AQUAVET for fish nutrition and health solutions, Alfarouk towers, Zohdy Square, 14 Kafrelsheikh, Egypt 15 5 Department of Anatomy and Embryology, Faculty of Veterinary Medicine, Kafrelsheikh 16 University, Kafrelsheikh, Egypt 17 6 INVE Asia Services 471 Bond St., Tambon Bangpood, Amphur Pakkred, Nonthaburi 11120, 18 Thailand 19 Department of Animal Production, Faculty of Agriculture, Kafrelsheikh University, 20 Kafrelsheikh, Egypt 21 Department of Hygiene and Preventive Medicine, Faculty of Veterinary Medicine, 22 Kafrelsheikh University, Kafrelsheikh, Egypt 23 9 Institute of Aquaculture, University of Stirling, Stirling, United Kingdom, FK9 4LA 24 25

BRITICE Glacial Map, version 2: a map and GIS database of glacial landforms of the last British-Irish Ice Sheet

Abstract: During the last glaciation, most of the British Isles and the surrounding continental shelf were covered by the British–Irish Ice Sheet (BIIS). An earlier compilation from the existing literature (BRITICE version 1) assembled the relevant glacial geomorphological evidence into a freely available GIS geodatabase and map (Clark et al. 2004: Boreas 33, 359). New high-resolution digital elevation models, of the land and seabed, have become available casting the glacial landform record of the British Isles in a new light and highlighting the shortcomings of the V.1 BRITICE compilation. Here we present a wholesale revision of the evidence, onshore and offshore, to produce BRITICE version 2, which now also includes Ireland. All published geomorphological evidence pertinent to the behaviour of the ice sheet is included, up to the census date of December 2015. The revised GIS database contains over 170 000 geospatially referenced and attributed elements – an eightfold increase in information from the previous version. The compiled data include: drumlins, ribbed moraine, crag-and-tails, mega-scale glacial lineations, glacially streamlined bedrock (grooves, roches moutonnees, whalebacks), glacial erratics, eskers, meltwater channels (subglacial, lateral, proglacial and tunnel valleys), moraines, trimlines, cirques, trough-mouth fans and evidence defining ice-dammed lakes. The increased volume of features necessitates different map/database products with varying levels of data generalization, namely: (i) an unfiltered GIS database containing all mapping; (ii) a filtered GIS database, resolving data conflicts and with edits to improve geo-locational accuracy (available as GIS data and PDF maps); and (iii) a cartographically generalized map to provide an overview of the distribution and types of features at the ice-sheet scale that can be printed at A0 paper size at a 1:1 250 000 scale. All GIS data, the maps (as PDFs) and a bibliography of all published sources are available for download from: https://www.sheffield.ac.uk/geography/staff/clark_chris/britice.

The hidden architecture of higher education: Building a big data infrastructure for the ‘smarter university’

Abstract: Universities are increasingly organized and managed through digital data. The collection, processing and dissemination of Higher Education data is enabled by complex new data infrastructures that include both human and nonhuman actors, all framed by political, economic and social contingencies. HE data infrastructures need to be seen not just as technical programs but as practical relays of political objectives to reform the sector. This article focuses on a major active data infrastructure project in Higher Education in the United Kingdom. It examines the sociotechnical networks of organizations, software programs, standards, dashboards and visual analytics technologies that constitute the infrastructure, and how these technologies are fused to governmental imperatives of market reform. The analysis foregrounds how HE is being reimagined through the utopian ideal of the ‘smarter university’ while simultaneously being reformed through the political project of marketization.

Standards for UNiversal reporting of patient Decision Aid Evaluation studies: the development of SUNDAE Checklist

Abstract: Background Patient decision aids (PDAs) are evidence-based tools designed to help patients make specific and deliberated choices among healthcare options. The International Patient Decision Aid Standards (IPDAS) Collaboration review papers and Cochrane systematic review of PDAs have found significant gaps in the reporting of evaluations of PDAs, including poor or limited reporting of PDA content, development methods and delivery. This study sought to develop and reach consensus on reporting guidelines to improve the quality of publications evaluating PDAs. Methods An international workgroup, consisting of members from IPDAS Collaboration, followed established methods to develop reporting guidelines for PDA evaluation studies. This paper describes the results from three completed phases: (1) planning, (2) drafting and (3) consensus, which included a modified, two-stage, online international Delphi process. The work was conducted over 2 years with bimonthly conference calls and three in-person meetings. The workgroup used input from these phases to produce a final set of recommended items in the form of a checklist. Results The SUNDAE Checklist (Standards for UNiversal reporting of patient Decision Aid Evaluations) includes 26 items recommended for studies reporting evaluations of PDAs. In the two-stage Delphi process, 117/143 (82%) experts from 14 countries completed round 1 and 96/117 (82%) completed round 2. Respondents reached a high level of consensus on the importance of the items and indicated strong willingness to use the items when reporting PDA studies. Conclusion The SUNDAE Checklist will help ensure that reports of PDA evaluation studies are understandable, transparent and of high quality. A separate Explanation and Elaboration publication provides additional details to support use of the checklist.

Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: validation for coastal and inland waters

Abstract: Image correction for atmospheric effects (iCOR) is an atmospheric correction tool that can process satellite data collected over coastal, inland or transitional waters and land. The tool is...

Primates in peril: the significance of Brazil, Madagascar, Indonesia and the Democratic Republic of the Congo for global primate conservation

Abstract: Primates occur in 90 countries, but four-Brazil, Madagascar, Indonesia, and the Democratic Republic of the Congo (DRC)-harbor 65% of the world's primate species (439) and 60% of these primates are Threatened, Endangered, or Critically Endangered (IUCN Red List of Threatened Species 2017-3). Considering their importance for global primate conservation, we examine the anthropogenic pressures each country is facing that place their primate populations at risk. Habitat loss and fragmentation are main threats to primates in Brazil, Madagascar, and Indonesia. However, in DRC hunting for the commercial bushmeat trade is the primary threat. Encroachment on primate habitats driven by local and global market demands for food and non-food commodities hunting, illegal trade, the proliferation of invasive species, and human and domestic-animal borne infectious diseases cause habitat loss, population declines, and extirpation. Modeling agricultural expansion in the 21st century for the four countries under a worst-case-scenario, showed a primate range contraction of 78% for Brazil, 72% for Indonesia, 62% for Madagascar, and 32% for DRC. These pressures unfold in the context of expanding human populations with low levels of development. Weak governance across these four countries may limit effective primate conservation planning. We examine landscape and local approaches to effective primate conservation policies and assess the distribution of protected areas and primates in each country. Primates in Brazil and Madagascar have 38% of their range inside protected areas, 17% in Indonesia and 14% in DRC, suggesting that the great majority of primate populations remain vulnerable. We list the key challenges faced by the four countries to avert primate extinctions now and in the future. In the short term, effective law enforcement to stop illegal hunting and illegal forest destruction is absolutely key. Long-term success can only be achieved by focusing local and global public awareness, and actively engaging with international organizations, multinational businesses and consumer nations to reduce unsustainable demands on the environment. Finally, the four primate range countries need to ensure that integrated, sustainable land-use planning for economic development includes the maintenance of biodiversity and intact, functional natural ecosystems.

Growth performance, feed utilization and body composition of advanced nursing Nile tilapia (Oreochromis niloticus) fed diets containing Black Soldier Fly (Hermetia illucens) larvae meal

Abstract: A 32-day experiment was conducted to evaluate the effects on the performance, feed utilization efficiency and body composition of a strategic inclusion of Black Soldier Fly larvae meal (MM) in a commercially formulated diet for advance nursing Nile tilapia (Oreochromis niloticus) Four isonitrogenous and isoenergetic diets were commercially formulated and manufactured as a control and three test diets with strategic inclusions of MM inclusions (0, 30, 50 and 80 g/kg) and poultry by-product meal substituting gradually three conventional expensive feedstuffs: fish meal, fish oil and soybean meal Fish (57 ± 05 g/fish) were nursed in a cage-in-lake system (Volta Lake, Ghana), under conditions similar to commercial farming practices Control and experimental diets were fed to triplicate cages by hand to visual satiety, six times per day Growth performance (final weight; weight gain and SGR), feed utilization efficiency indices (FCR and PER) and feed intake were not significantly different (p ≥ 05) between treatments Survival was significantly different (p < 05) but more likely explained by the stress related to frequent handling on the smaller fish Fish whole body composition (dry matter, crude protein, lipid, ash and fibre) was unaffected by the treatment (p ≥ 05), except for the fatty acid compositions which mirrored that of the diets

Assessing the Role of Muscle Protein Breakdown in Response to Nutrition and Exercise in Humans

Abstract: Muscle protein breakdown (MPB) is an important metabolic component of muscle remodeling, adaptation to training, and increasing muscle mass. Degradation of muscle proteins occurs via the integration of three main systems—autophagy and the calpain and ubiquitin-proteasome systems. These systems do not operate independently, and the regulation is complex. Complete degradation of a protein requires some combination of the systems. Determination of MPB in humans is technically challenging, leading to a relative dearth of information. Available information on the dynamic response of MPB primarily comes from stable isotopic methods with expression and activity measures providing complementary information. It seems clear that resistance exercise increases MPB, but not as much as the increase in muscle protein synthesis. Both hyperaminoacidemia and hyperinsulinemia inhibit the post-exercise response of MPB. Available data do not allow a comprehensive examination of the mechanisms behind these responses. Practical nutrition recommendations for interventions to suppress MPB following exercise are often made. However, it is likely that some degree of increased MPB following exercise is an important component for optimal remodeling. At this time, it is not possible to determine the impact of nutrition on any individual muscle protein. Thus, until we can develop and employ better methods to elucidate the role of MPB following exercise and the response to nutrition, recommendations to optimize post exercise nutrition should focus on the response of muscle protein synthesis. The aim of this review is to provide a comprehensive examination of the state of knowledge, including methodological considerations, of the response of MPB to exercise and nutrition in humans.

A Brain-Inspired Trust Management Model to Assure Security in a Cloud based IoT Framework for Neuroscience Applications

Abstract: Rapid popularity of Internet of Things (IoT) and cloud computing permits neuroscientists to collect multilevel and multichannel brain data to better understand brain functions, diagnose diseases, and devise treatments. To ensure secure and reliable data communication between end-to-end (E2E) devices supported by current IoT and cloud infrastructure, trust management is needed at the IoT and user ends. This paper introduces a Neuro-Fuzzy based Brain-inspired trust management model (TMM) to secure IoT devices and relay nodes, and to ensure data reliability. The proposed TMM utilizes node behavioral trust and data trust estimated using Adaptive Neuro-Fuzzy Inference System and weighted-additive methods respectively to assess the nodes trustworthiness. In contrast to the existing fuzzy based TMMs, the NS2 simulation results confirm the robustness and accuracy of the proposed TMM in identifying malicious nodes in the communication network. With the growing usage of cloud based IoT frameworks in Neuroscience research, integrating the proposed TMM into the existing infrastructure will assure secure and reliable data communication among the E2E devices.

The social life of Learning Analytics: cluster analysis and the 'performance' of algorithmic education

Abstract: This paper argues that methods used for the classification and measurement of online education are not neutral and objective, but involved in the creation of the educational realities they claim to measure. In particular, the paper draws on material semiotics to examine cluster analysis as a ‘performative device’ that, to a significant extent, creates the educational entities it claims to objectively represent through the emerging body of knowledge of Learning Analytics (LA). It also offers a more critical and political reading of the algorithmic assemblages of LA, of which cluster analysis is a part. Our argument is that if we want to understand how algorithmic processes and techniques like cluster analysis function as performative devices, then we need methodological sensibilities that consider critically both their political dimensions and their technical-mathematical mechanisms. The implications for critical research in educational technology are discussed.