bluuebunny/crossref_metadata_2025 huggingface dataset:
Trading quality for relevance: non-health decision-makers’ use of evidence on the social determinants of health
Authors: McGill Elizabeth , Egan Matt , Petticrew Mark , Mountford Lesley , Milton Sarah , Whitehead Margaret , Lock Karen
Published: April 2015

Objectives

Local government services and policies affect health determinants across many sectors such as planning, transportation, housing and leisure. Researchers and policymakers have argued that decisions affecting wider determinants of health, well-being and inequalities should be informed by evidence. This study explores how information and evidence are defined, assessed and utilised by local professionals situated beyond the health sector, but whose decisions potentially affect health: in this case, practitioners working in design, planning and maintenance of the built environment.

Design

A qualitative study using three focus groups. A thematic analysis was undertaken.

Setting

The focus groups were held in UK localities and involved local practitioners working in two UK regions, as well as in Brazil, USA and Canada.

Participants

UK and international practitioners working in the design and management of the built environment at a local government level.

Results

Participants described a range of data and information that constitutes evidence, of which academic research is only one part. Built environment decision-makers value empirical evidence, but also emphasise the legitimacy and relevance of less empirical ways of thinking through narratives that associate their work to art and philosophy. Participants prioritised evidence on the acceptability, deliverability and sustainability of interventions over evidence of longer term outcomes (including many health outcomes). Participants generally privileged local information, including personal experiences and local data, but were less willing to accept evidence from contexts perceived to be different from their own.

Conclusions

Local-level built environment practitioners utilise evidence to make decisions, but their view of ‘best evidence’ appears to prioritise local relevance over academic rigour. Academics can facilitate evidence-informed local decisions affecting social determinants of health by working with relevant practitioners to improve the quality of local data and evaluations, and by advancing approaches to improve the external validity of academic research.
EXPERTS 1—experiences of long-term life-limiting conditions among patients and carers: protocol for a qualitative meta-synthesis and conceptual modelling study
Authors: May Carl R , Masters Jayne , Welch Lindsay , Hunt Katherine , Pope Catherine , Myall Michelle , Griffiths Peter , Roderick Paul , Glanville Julie , Richardson Alison
Published: April 2015

Introduction

Increasing numbers of the population are living with long-term life-limiting conditions with a significant proportion characterised by multimorbidity. Patients with these conditions often experience high volumes of clinical interaction involving them, their caregivers and healthcare providers in complex patterns of organising, coordinating, negotiating and managing care. A better understanding of the sources of experienced complexity and multimorbidity, from the patient perspective is paramount to improve capacity and manage workload to promote improved experience of illness, more effective healthcare utilisation and improved healthcare outcomes. To better understand the sources of complexity we will undertake an evidence synthesis of qualitative studies of patient and informal carer experiences of three common long-term life-limiting conditions. We will investigate what is known about these diseases at different stages in disease progression, treatment regimens and places of care.

Method and analysis

We will include qualitative studies of patients’ and carers’ (aged >18) accounts of their experiences of healthcare provision in a range of settings and healthcare systems. We will conduct an extensive electronic database search of publications in English between 2000 and 2014. Results and discussions sections of the papers will be regarded as formal data using the constant comparison method of qualitative analysis. From the meta-synthesis results, we will build a conceptual model of mechanisms and processes that shape patients’ journeys towards end of life to suggest where in the patient journey new interventions to improve patient and carer experience can be developed and delivered. The study is being conducted between 1 December 2014 and 31 December 2015.

Ethics and dissemination

No human subjects or personal data are involved and no ethical issues are anticipated. An important element of dissemination is informing user communities about the practical implications of the work through workshops, meetings and social media. Scientific results will be published in peer reviewed journals and disseminated through conferences.

Trial registration number

PROSPERO CRD42014014547.
Efficacy and safety of the glucagon-like peptide-1 receptor agonist liraglutide added to insulin therapy in poorly regulated patients with type 1 diabetes—a protocol for a randomised, double-blind, placebo-controlled study: The Lira-1 study
Authors: Dejgaard Thomas Fremming , Knop Filip Krag , Tarnow Lise , Frandsen Christian Seerup , Hansen Tanja Stenbæk , Almdal Thomas , Holst Jens Juul , Madsbad Sten , Andersen Henrik Ullits
Published: April 2015

Introduction

Intensive insulin therapy is recommended for the treatment of type 1 diabetes (T1D). Hypoglycaemia and weight gain are the common side effects of insulin treatment and may reduce compliance. In patients with insulin-treated type 2 diabetes, the addition of glucagon-like peptide-1 receptor agonist (GLP-1RA) therapy has proven effective in reducing weight gain and insulin dose. The present publication describes a protocol for a study evaluating the efficacy and safety of adding a GLP-1RA to insulin treatment in overweight patients with T1D in a randomised, double-blinded, controlled design.

Methods and analysis

In total, 100 patients with type 1 diabetes, poor glycaemic control (glycated haemoglobin (HbA1c) >8%) and overweight (body mass index >25 kg/m2) will be randomised to either liraglutide 1.8 mg once daily or placebo as an add-on to intensive insulin therapy in this investigator initiated, double-blinded, placebo-controlled parallel study. The primary end point is glycaemic control as measured by changes in HbA1c. Secondary end points include changes in the insulin dose, hypoglyacemic events, body weight, lean body mass, fat mass, food preferences and adverse events. Glycaemic excursions, postprandial glucagon levels and gastric emptying rate during a standardised liquid meal test will also be studied.

Ethics and dissemination

The study is approved by the Danish Medicines Authority, the Regional Scientific-Ethical Committee of the Capital Region of Denmark and the Data Protection Agency. The study will be carried out under the surveillance and guidance of the good clinical practice (GCP) unit at Copenhagen University Hospital Bispebjerg in accordance with the ICH-GCP guidelines and the Helsinki Declaration.

Trial registration number

NCT01612468.
Young-age prostate cancer
Authors: Hussein S , Satturwar S , Van der Kwast T
Published: April 2015

Although prostate cancer is a disease of the elderly, its diagnosis is not uncommonly made in men younger than 55 years. Both ethnic, familial and genetic factors play a role in the early onset of prostate cancer, but the biology, particularly of low-grade prostate cancers detected at young age is not well understood. Autopsy studies have shown a high prevalence of Gleason score 6 prostate cancers in men under 55 years, but mortality of prostate cancer at this young age is almost negligible. Recently, a number of susceptibility genes such asBRCA2andHOXB13were reported, each with their own specific biological and histopathology features. In this review, we provide an update on the most recent findings in young-age prostate cancer.
Structural and stratigraphic evolution of the Iberia–Newfoundland hyper-extended rifted margin: a quantitative modelling approach
Authors: Mohn Geoffroy , Karner Garry D. , Manatschal Gianreto , Johnson Christopher A.
Published: April 2015

We investigate the evolution of the Iberia–Newfoundland margin from Permian post-orogenic extension to Early Cretaceous break-up. We used a Quantitative Basin Analysis approach to integrate seismic stratigraphic interpretations and drill-hole data of two representative sections across the Iberia–Newfoundland margin with kinematic models for lithospheric thinning and subsequent flexural readjustment. We model the distribution of extension and thinning, palaeobathymetry, crustal structure, and subsidence and uplift history as functions of space and time. We start our modelling following post-orogenic extension, magmatic underplating and thermal re-equilibration of the Permian lithosphere. During the Late Triassic–Early Jurassic, broadly distributed, depth-independent lithospheric extension evolved into Late Jurassic–Early Cretaceous depth-dependent thinning as crustal extension progressed from distributed to focused deformation. During this time, palaeobathymetries rapidly deepened across the margin. Modelling of the southern and northern profiles highlighted the rapid development of crustal deformation from south to north over a 5–10 myr period, which accounts for the rapid change in Tithonian–Valanginian, deep- to shallow-water sedimentary facies between the Abyssal Plain and the adjacent Galicia Bank, respectively. Late-stage deformation of both margins was characterized by brittle deformation of the remaining continental crust, which led to exhumation of subcontinental mantle and, eventually, continental break-up and seafloor spreading.
Accurate and Robust Moving-Object Segmentation for Telepresence Systems
Authors: Huang Meiyu , Chen Yiqiang , Ji Wen , Miao Chunyan
Published: April 2015

Moving-object segmentation is the key issue of Telepresence systems. With monocular camera--based segmentation methods, desirable segmentation results are hard to obtain in challenging scenes with ambiguous color, illumination changes, and shadows. Approaches based on depth sensors often cause holes inside the object and missegmentations on the object boundary due to inaccurate and unstable estimation of depth data. This work proposes an adaptive multi-cue decision fusion method based on Kinect (which integrates a depth sensor with an RGB camera). First, the algorithm obtains an initial foreground mask based on the depth cue. Second, the algorithm introduces a postprocessing framework to refine the segmentation results, which consists of two main steps: (1) automatically adjusting the weight of two weak decisions to identify foreground holes based on the color and contrast cue separately; and (2) refining the object boundary by integrating the motion probability weighted temporal prior, color likelihood, and smoothness constraint. The extensive experiments we conducted demonstrate that our method can segment moving objects accurately and robustly in various situations in real time.
Modeling Location-Based User Rating Profiles for Personalized Recommendation
Authors: Yin Hongzhi , Cui Bin , Chen Ling , Hu Zhiting , Zhang Chengqi
Published: April 2015

This article proposes LA-LDA, a location-aware probabilistic generative model that exploits location-based ratings to model user profiles and produce recommendations. Most of the existing recommendation models do not consider the spatial information of users or items; however, LA-LDA supports three classes of location-based ratings, namely spatial user ratings for nonspatial items, nonspatial user ratings for spatial items, and spatial user ratings for spatial items. LA-LDA consists of two components, ULA-LDA and ILA-LDA, which are designed to take into account user and item location information, respectively. The component ULA-LDA explicitly incorporates and quantifies the influence from local public preferences to produce recommendations by considering user home locations, whereas the component ILA-LDA recommends items that are closer in both taste and travel distance to the querying users by capturing item co-occurrence patterns, as well as item location co-occurrence patterns. The two components of LA-LDA can be applied either separately or collectively, depending on the available types of location-based ratings. To demonstrate the applicability and flexibility of the LA-LDA model, we deploy it to both top-
k
recommendation and cold start recommendation scenarios. Experimental evidence on large-scale real-world data, including the data from Gowalla (a location-based social network), DoubanEvent (an event-based social network), and MovieLens (a movie recommendation system), reveal that LA-LDA models user profiles more accurately by outperforming existing recommendation models for top-
k
recommendation and the cold start problem.
Mobility Increases Localizability
Authors: Yang Zheng , Wu Chenshu , Zhou Zimu , Zhang Xinglin , Wang Xu , Liu Yunhao
Published: April 2015

Wireless indoor positioning has been extensively studied for the past 2 decades and continuously attracted growing research efforts in mobile computing context. As the integration of multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to nowadays smartphones in recent years, human-centric mobility sensing is emerging and coming into vogue. Mobility information, as a new dimension in addition to wireless signals, can benefit localization in a number of ways, since location and mobility are by nature related in the physical world. In this article, we survey this new trend of mobility enhancing smartphone-based indoor localization. Specifically, we first study how to measure human mobility: what types of sensors we can use and what types of mobility information we can acquire. Next, we discuss how mobility assists localization with respect to enhancing location accuracy, decreasing deployment cost, and enriching location context. Moreover, considering the quality and cost of smartphone built-in sensors, handling measurement errors is essential and accordingly investigated. Combining existing work and our own working experiences, we emphasize the principles and conduct comparative study of the mainstream technologies. Finally, we conclude this survey by addressing future research directions and opportunities in this new and largely open area.
Anticipatory Mobile Computing
Authors: Pejovic Veljko , Musolesi Mirco
Published: April 2015

Today’s mobile phones are far from the mere communication devices they were 10 years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users’ location, activity, social setting, and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.
Graph-Based Label Propagation in Digital Media
Authors: Zoidi Olga , Fotiadou Eftychia , Nikolaidis Nikos , Pitas Ioannis
Published: April 2015

The expansion of the Internet over the last decade and the proliferation of online social communities, such as Facebook, Google+, and Twitter, as well as multimedia sharing sites, such as YouTube, Flickr, and Picasa, has led to a vast increase of available information to the user. In the case of multimedia data, such as images and videos, fast querying and processing of the available information requires the annotation of the multimedia data with semantic descriptors, that is, labels. However, only a small proportion of the available data are labeled. The rest should undergo an annotation-labeling process. The necessity for the creation of automatic annotation algorithms gave birth to label propagation and semi-supervised learning. In this study, basic concepts in graph-based label propagation methods are discussed. Methods for proper graph construction based on the structure of the available data and label inference methods for spreading label information from a few labeled data to a larger set of unlabeled data are reviewed. Applications of label propagation algorithms in digital media, as well as evaluation metrics for measuring their performance, are presented.
Use of Local Group Information to Identify Communities in Networks
Authors: Soundarajan Sucheta , Hopcroft John E.
Published: April 2015

The recent interest in networks has inspired a broad range of work on algorithms and techniques to characterize, identify, and extract communities from networks. Such efforts are complicated by a lack of consensus on what a “community” truly is, and these disagreements have led to a wide variety of mathematical formulations for describing communities. Often, these mathematical formulations, such as modularity and conductance, have been founded in the general principle that communities, like a
G
(
n
,
p
) graph, are “round,” with connections throughout the entire community, and so algorithms were developed to optimize such mathematical measures. More recently, a variety of algorithms have been developed that, rather than expecting connectivity through the entire community, seek out very small groups of well-connected nodes and then connect these groups into larger communities. In this article, we examine seven real networks, each containing external annotation that allows us to identify “annotated communities.” A study of these annotated communities gives insight into why the second category of community detection algorithms may be more successful than the first category. We then present a flexible algorithm template that is based on the idea of joining together small sets of nodes. In this template, we first identify very small, tightly connected “subcommunities” of nodes, each corresponding to a single node’s “perception” of the network around it. We then create a new network in which each node represents such a subcommunity, and then identify communities in this new network. Because each node can appear in multiple subcommunities, this method allows us to detect overlapping communities. When evaluated on real data, we show that our template outperforms many other state-of-the-art algorithms.
A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion
Authors: Huang Shanshan , Ma Jun , Cheng Peizhe , Wang Shuaiqiang
Published: April 2015

Collaborative Filtering (CF) is one of the most successful algorithms in recommender systems. However, it suffers from data sparsity and scalability problems. Although many clustering techniques have been incorporated to alleviate these two problems, most of them fail to achieve further significant improvement in recommendation accuracy. First of all, most of them assume each user or item belongs to a single cluster. Since usually users can hold multiple interests and items may belong to multiple categories, it is more reasonable to assume that users and items can join multiple clusters (groups), where each cluster is a subset of like-minded users and items they prefer. Furthermore, most of the clustering-based CF models only utilize historical rating information in the clustering procedure but ignore other data resources in recommender systems such as the social connections of users and the correlations between items. In this article, we propose HMCoC, a Hybrid Multigroup CoClustering recommendation framework, which can cluster users and items into multiple groups simultaneously with different information resources. In our framework, we first integrate information of user--item rating records, user social networks, and item features extracted from the DBpedia knowledge base. We then use an optimization method to mine meaningful user--item groups with all the information. Finally, we apply the conventional CF method in each cluster to make predictions. By merging the predictions from each cluster, we generate the top-n recommendations to the target users for return. Extensive experimental results demonstrate the superior performance of our approach in top-n recommendation in terms of MAP, NDCG, and F1 compared with other clustering-based CF models.
Kinect Depth Recovery Using a Color-Guided, Region-Adaptive, and Depth-Selective Framework
Authors: Chen Chongyu , Cai Jianfei , Zheng Jianmin , Cham Tat Jen , Shi Guangming
Published: April 2015

Considering that the existing depth recovery approaches have different limitations when applied to Kinect depth data, in this article, we propose to integrate their effective features including adaptive support region selection, reliable depth selection, and color guidance together under an optimization framework for Kinect depth recovery. In particular, we formulate our depth recovery as an energy minimization problem, which solves the depth hole filling and denoising simultaneously. The energy function consists of a fidelity term and a regularization term, which are designed according to the Kinect characteristics. Our framework inherits and improves the idea of guided filtering by incorporating structure information and prior knowledge of the Kinect noise model. Through analyzing the solution to the optimization framework, we also derive a local filtering version that provides an efficient and effective way of improving the existing filtering techniques. Quantitative evaluations on our developed synthesized dataset and experiments on real Kinect data show that the proposed method achieves superior performance in terms of recovery accuracy and visual quality.
Higher Homotopies in a Hierarchy of Univalent Universes
Authors: Kraus Nicolai , Sattler Christian
Published: April 2015

For Martin-Löf type theory with a hierarchy U
0
:U
1
:U
2
:… of univalent universes, we show that U

n

is not an
n
-type. Our construction also solves the problem of finding a type that
strictly
has some high truncation level without using higher inductive types. In particular, U
n
is such a type if we restrict it to
n
-types.

We have fully formalized and verified our results within the dependently typed language and proof assistant Agda.
Impact of Size and Productivity on Testing and Rework Efforts for Web-based Development Projects
Authors: Bhardwaj Mridul , Rana Ajay
Published: April 2015

In the planning of a software development project, a major challenge faced by project managers is to predict the rework effort. (Rework effort is the effort required to fix the software defects identified during system testing). The project manager's objective is to deliver the software within the time, cost and quality requirements given by the client. To ensure the quality of the software, many testing cycles will be conducted before it is finally delivered to the client for acceptance. Each testing cycle is a costly affair as it involves running all possible test scenarios in all possible environments, followed by defect fixing and re-verification of defect fixes. On average, two to three testing cycles are conducted but this depends on the number of defects identified during testing. The number of defects will depend on the team expertise and whether they earlier worked on similar projects and technologies. Therefore, it becomes critical to predict the number of defects that will be identified during testing and it is a very challenging task as it requires a good model to predict the rework effort. In this paper, we describe the relationships among software size, number of software defects, productivity and efforts for web-based development projects. These relationships are established by using the multiple linear regression technique on the benchmarking data published by International Software Benchmarking Standard Group. Results suggest that in web-based projects the number of defects identified is directly proportional to the productivity, i.e. higher productivity will led to more defects found and lower productivity will lead to fewer defects found, therefore, less testing and rework effort will be required if project is planned with lower productivity because we can spend more time on development (i.e. time spend till construction phase) to reduce the number of defects found during testing and it will directly contribute in reducing the rework efforts. We infer from the relationship that software size has a significant impact on the total number of defect identified. We also infer that while planning a software project we should use appropriate tools to reduce the margin of error in size estimation and we should also re-estimate the size after every phase of the development life cycle to re-calibrate overall efforts and to minimize the impact on the project plan.
Performance Measurement and Analysis of High-Availability Clusters
Authors: Kaur Arvinder , Verma Shraddha
Published: April 2015

High-availability clusters are groups of servers that provide a reliable framework for applications to achieve a minimum downtime and quick recovery time without any human intervention and yet are completely opaque to the users. Almost all industries are continuously pursuing the goal to minimize their critical application downtimes by using various techniques such as fault tolerance, redundancy, mirroring and clustering. During downtime, applications become unavailable to end users, which can lead to financial, reputation and regulatory business impacts. High-availability clusters provide a mechanism to migrate complete applications or services from one server to another seamlessly without any human intervention at the time of failure of any critical component or the complete server. Hence, an application would start on a healthy server, without end users realizing this failover.

In this work, key features and aspects of two cluster products, 'Symantec Veritas Cluster Suite' and 'Red Hat Cluster' were compared against each other based on various parameters. A simulated environment was created to perform a comprehensive analysis of performances of both products. In this work, measurement of average failover time was taken and compared as the key reliability and serviceability attribute. Thus, based on this experimental work, it is concluded that in a controlled test environment running a simple web-server application, Red Hat cluster gives a better failover performance as compared to Veritas Cluster Suite. However, in a large-scale environment, if we consider factors like operating system compatibility, supported applications, compatibility with volume managers, hardware compatibility, reliability, fault tolerance, predictive failure, self-healing etc., then the Veritas Cluster Suite comes out as the winner.
Neuro-Fuzzy Model to Estimate & Optimize Quality and Performance of Component Based Software Engineering
Authors: Kumar Gaurav , Bhatia Pradeep Kumar
Published: April 2015

Component Based Software Engineering (CBSE) constructs a quality software system by reusing existing components. For the construction of high-quality software system, reusability plays an important role. Software component should be designed and implemented in such a way that many different programs can reuse them. Reuse of software can increase the productivity and quality of software by reducing effort, time and cost which was elapsed in designing and developing reusable software component. In this paper, a Neuro-fuzzy model has been proposed that uses software component design patterns for analysis and Chidamber and Kemerer (CK) metric for evaluation, optimization and categorization of reusability for component based software. The work is divided into 2 phases. In the first phase, analysis and optimization of reusability are empirically evaluated with high precision value using CK metric and unsupervised Self Organizing Map (SOM) Neural Network. In the second phase, reusability is categorized as very low, low, medium, high and very high using a supervised Back propagation Neural Network (BPNN) and fuzzy inference rules applied on CK metric values. The proposed model may help a software designer to evaluate and optimize the reusability of components while designing software to make quality software system.
Application Multi-Tenancy for Software as a Service
Authors: Pal Sanjukta , Mandal Amit Kr , Sarkar Anirban
Published: April 2015

Software as a service has evolved as a new software deployment paradigm in the cloud, which offers information technology services dynamically, "on-demand". Application Multi-tenancy in SaaS leads to improved resource utilization and reduces overall application costs by sharing the same applications, resources and data services through multiple tenants. In this paper a graph-based approach called SaaS Level Application Multi Tenancy graph is proposed to represent the multi-tenant aspects of the SaaS model of cloud environment formally. The proposed approach will facilitate the analysis of service interaction paths for accessing shared sets of services in SaaS by the multiple tenants. The proposed approach is capable enough to model several kinds of tenant like isolated tenant, shared tenants with single or multiple data services and multiple tenants in SaaS. Further, several metrics are defined for the proposed approach to describe the essential features of multi-tenant SaaS applications. The expressiveness of the proposed approach is illustrated using several examples and a detailed case study.
Analysis of Conflicting User Requirements in Web Applications Using Graph Transformation
Authors: Phalnikar Rashmi , Jinwala Devesh
Published: April 2015

User satisfaction is recognized as an important contributor to the success of software applications. It is subjective and influenced by several factors that are linked to the non-functional requirements. Although non-functional requirements provide good criteria for selection of web service in Service-Oriented Architecture, specifying them during the discovery process is a difficult task. Dealing with the non-functional requirements that have an inherent ability to conflict is a complex task and modeling them can facilitate analyzing them. The aim of our research work is to propose a framework to improve design stage during web service selection so as to produce minimal conflicts and dependencies between requirements. We argue that the design structure that best supports and embodies the user constraints is the one that best meets a user's needs. Our framework is proposed after a detailed study of the issues and challenges identified by published literature by researchers working on non-functional requirement To exemplify our work we consider a Remote Patient Monitoring System scenario and propose a complete framework that will take the user requirements as input and analyze how they interact using graph transformation rules. The output matrix of dependencies can help the designer to select the most desirable design solution. We make use of simple natural language processing techniques, graph transformation and the aspect oriented paradigm. By integrating the framework into the design phase, it is possible to limit the impact of changes and also to understand in advance the likely interdependencies. conflicts.
ACM SIGSOFT FSE 2014 Conference Summary
Authors: Cheung Shing-Chi , Orso Alessandro , Storey Margaret-Anne
Published: April 2015

The 22nd ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2014) was held in Hong Kong from November 16 to 21, 2014. The conference brought together over 400 researchers, practitioners, and educators to present and discuss the most recent innovations, trends, experiences, and challenges in software engineering. This year's program continues the great tradition of previous FSE meetings by being rich and exciting, but the location of the conference was particularly noteworthy---for the first time, this major international software engineering conference was held outside North America. Hong Kong is renowned as a lively city with a beautiful harbor and landscape, and is famous for its finance, shopping, and gourmet cuisine, blending Eastern and Western cultures.
Robust Multiview Feature Learning for RGB-D Image Understanding
Authors: Zha Zheng-Jun , Yang Yang , Tang Jinhui , Wang Meng , Chua Tat-Seng
Published: April 2015

The availability of massive RGB-depth (RGB-D) images poses a compelling need for effective RGB-D content understanding techniques. RGB-D images provide synchronized information from multiple views (e.g., color and depth) of real-world objects and scenes. This work proposes learning compact and discriminative features from the multiple views of RGB-D content toward effective feature representation for RGB-D image understanding. In particular, a robust multiview feature learning approach is developed, which exploits the intrinsic relations among multiple views. The feature learning in multiple views is jointly optimized in an integrated formulation. The joint optimization essentially exploits the intrinsic relations among the views, leading to effective features and making the learning process robust to noises. The feature learning function is formulated as a robust nonnegative graph embedding function over multiple graphs in various views. The graphs characterize the local geometric and discriminating structure of the multiview data. The joint sparsity in ℓ
1
-norm graph embedding and ℓ
21
-norm data factorization further enhances the robustness of feature learning. We derive an efficient computational solution for the proposed approach and provide rigorous theoretical proof with regard to its convergence. We apply the proposed approach to two RGB-D image understanding tasks: RGB-D object classification and RGB-D scene categorization. We conduct extensive experiments on two real-world RGB-D image datasets. The experimental results have demonstrated the effectiveness of the proposed approach.
A Real-Time Hand Posture Recognition System Using Deep Neural Networks
Authors: Tang Ao , Lu Ke , Wang Yufei , Huang Jie , Li Houqiang
Published: April 2015

Hand posture recognition (HPR) is quite a challenging task, due to both the difficulty in detecting and tracking hands with normal cameras and the limitations of traditional manually selected features. In this article, we propose a two-stage HPR system for Sign Language Recognition using a Kinect sensor. In the first stage, we propose an effective algorithm to implement hand detection and tracking. The algorithm incorporates both color and depth information, without specific requirements on uniform-colored or stable background. It can handle the situations in which hands are very close to other parts of the body or hands are not the nearest objects to the camera and allows for occlusion of hands caused by faces or other hands. In the second stage, we apply deep neural networks (DNNs) to automatically learn features from hand posture images that are insensitive to movement, scaling, and rotation. Experiments verify that the proposed system works quickly and accurately and achieves a recognition accuracy as high as 98.12%.
Atmospheric Escape from Solar System Terrestrial Planets and Exoplanets
Authors: Tian Feng
Published: April 2015

It has been known for decades that atmospheric escape is important for the evolution of terrestrial planets in the Solar System, although exactly how atmospheric escape changes the atmospheres of these bodies is still hotly debated. Rapidly increasing numbers of exoplanet observations provide new targets against which atmospheric escape models are tested. In this review we summarize recent studies related to atmospheric escape from exoplanets. The most important conclusions are that (a) escape can significantly influence the volatile contents of low-mass exoplanets (with mass lower than those of Uranus and Neptune) and the atmosphere and climate evolution histories of Solar System terrestrial planets; (b) models including detailed physics and chemistry in planetary upper atmospheres will be important for the interpretation of existing and future observations of exoplanets; and (c) fluid models considering 2D or 3D planetary upper atmospheres and particle models for planetary exospheres will be important not only for comparisons with observations but also for order of magnitude estimates of atmospheric escape rates. Our understanding of how escape shapes planetary atmospheres and influences the climate of low-mass planets can be expected to advance substantially in the coming decade.
Policy Implications of Dynamic Public Finance
Authors: Golosov Mikhail , Tsyvinski Aleh
Published: April 2015

The dynamic public finance literature underwent significant changes over the past decade. This research agenda has now reached a stage at which it is able to analyze the design of social insurance programs and optimal taxation in rich environments that can be closely matched to microeconomic data. We provide an overview of the recent advances in this literature, discuss the key trade-offs, and explain how the prescriptions for the optimal policy depend on the specific parameters that can be estimated in the data. We also describe the relationship between the dynamic mechanism design approach to optimal taxation and the approach that considers sophisticated tax functions chosen within parametrically restricted classes.
Vertical 2D Heterostructures
Authors: Lotsch Bettina V.
Published: April 2015

Graphene's legacy has become an integral part of today's condensed matter science and has equipped a whole generation of scientists with an armory of concepts and techniques that open up new perspectives for the postgraphene area. In particular, the judicious combination of 2D building blocks into vertical heterostructures has recently been identified as a promising route to rationally engineer complex multilayer systems and artificial solids with intriguing properties. The present review highlights recent developments in the rapidly emerging field of 2D nanoarchitectonics from a materials chemistry perspective, with a focus on the types of heterostructures available, their assembly strategies, and their emerging properties. This overview is intended to bridge the gap between two major—yet largely disjunct—developments in 2D heterostructures, which are firmly rooted in solid-state chemistry or physics. Although the underlying types of heterostructures differ with respect to their dimensions, layer alignment, and interfacial quality, there is common ground, and future synergies between the various assembly strategies are to be expected.
Symmetry-Based Computational Tools for Magnetic Crystallography
Authors: Perez-Mato J.M. , Gallego S.V. , Tasci E.S. , Elcoro L. , de la Flor G. , Aroyo M.I.
Published: April 2015

In recent years, two important advances have opened new doors for the characterization and determination of magnetic structures. Firstly, researchers have produced computer-readable listings of the magnetic or Shubnikov space groups. Secondly, they have extended and applied the superspace formalism, which is presently the standard approach for the description of nonmagnetic incommensurate structures and their symmetry, to magnetic structures. These breakthroughs have been the basis for the subsequent development of a series of computer tools that allow a more efficient and comprehensive application of magnetic symmetry, both commensurate and incommensurate. Here we briefly review the capabilities of these computation instruments and present the fundamental concepts on which they are based, providing various examples. We show how these tools facilitate the use of symmetry arguments expressed as either a magnetic space group or a magnetic superspace group and allow the exploration of the possible magnetic orderings associated with one or more propagation vectors in a form that complements and goes beyond the traditional representation method. Special focus is placed on the programs available online at the Bilbao Crystallographic Server ( http://www.cryst.ehu.es ).
Nrf2 regulates PU.1 expression and activity in the alveolar macrophage
Authors: Staitieh Bashar S. , Fan Xian , Neveu Wendy , Guidot David M.
Published: April 2015

Alveolar macrophage (AM) immune function depends on the activation of the transcription factor PU.1 by granulocyte macrophage colony-stimulating factor. We have determined that chronic alcohol ingestion dampens PU.1 signaling via an unknown zinc-dependent mechanism; specifically, although PU.1 is not known to be a zinc-dependent transcription factor, zinc treatment reversed alcohol-mediated dampening of PU.1 signaling. Nuclear factor (erythroid-derived 2)-like 2 (Nrf2), a zinc-dependent basic leucine zipper protein essential for antioxidant defenses, is also impaired by chronic alcohol ingestion and enhanced by zinc treatment. We hypothesized that the response of PU.1 to zinc treatment may result from the action of Nrf2 on PU.1. We first performed Nrf2/PU.1 protein coimmunoprecipitation on a rat AM cell line (NR8383) and found no evidence of protein-protein interactions. We then found evidence of increased Nrf2 binding to the PU.1 promoter region by chromatin immunoprecipitation. We next activated Nrf2 using either sulforaphane or an overexpression vector and inhibited Nrf2 with silencing RNA to determine whether Nrf2 could actively regulate PU.1. Nrf2 activation increased protein expression of both factors as well as gene expression of their respective downstream effectors, NAD(P)H dehydrogenase[quinone] 1 (NQO1) and cluster of differentiation antigen-14 (CD14). In contrast, Nrf2 silencing decreased the expression of both proteins, as well as gene expression of their effectors. Activating and inhibiting Nrf2 in primary rat AMs resulted in similar effects. Taken together, these findings suggest that Nrf2 regulates the expression and activity of PU.1 and that antioxidant response and immune activation are coordinately regulated within the AM.
Plasma arginine metabolites reflect airway dysfunction in a murine model of allergic airway inflammation
Authors: Scott Jeremy A. , North Michelle L. , Rafii Mahrouk , Huang Hailu , Pencharz Paul , Grasemann Hartmut
Published: April 2015

l-Arginine metabolism is important in the maintenance of airway tone. Shift of metabolism from the nitric oxide synthase to arginase pathways contributes to the increased airway responsiveness in asthma. We tested the hypothesis that systemic levels of l-arginine metabolites are biomarkers reflective of airway dysfunction. We used a mouse model of acute allergic airway inflammation to OVA that manifests with significant airway hyperresponsiveness to methacholine. To determine tissue arginase activity in vivo, the isotopic enrichment of an infused l-arginine stable isotope and its product amino acid l-ornithine were measured in lung and airway homogenates using liquid chromatography-tandem mass spectrometry. Tissue and plasma concentrations of other l-arginine metabolites, including l-citrulline and symmetric and asymmetric dimethylarginine, were measured and correlated with lung arginase activity and methacholine responsiveness of the airways. The effectiveness of intratracheal instillation of an arginase inhibitor (boronoethylcysteine) on pulmonary arginase activity and circulating concentrations of l-arginine metabolites was also studied. We demonstrate that 1) plasma indexes of l-arginine bioavailability and impairment of nitric oxide synthase function correlate with airway responsiveness to methacholine; 2) plasma levels of l-ornithine predict in vivo pulmonary arginase activity and airway function; and 3) acute arginase inhibition reduces in vivo pulmonary arginase activity to control levels and normalizes plasma l-ornithine, but not l-arginine, bioavailability in this model. We conclude that plasma l-ornithine may be useful as a systemic biomarker to predict responses to therapeutic interventions targeting airway arginase in asthma.
Accuracy of Molecular Data Generated with FFPE Biospecimens: Lessons from the Literature
Authors: Greytak Sarah R. , Engel Kelly B. , Bass B. Paige , Moore Helen M.
Published: April 2015

Formalin-fixed and paraffin-embedded (FFPE) tissue biospecimens are a valuable resource for molecular cancer research. Although much can be gained from their use, it remains unclear whether the genomic and expression profiles obtained from FFPE biospecimens accurately reflect the physiologic condition of the patient from which they were procured, or if such profiles are confounded by biologic effects from formalin fixation and processing. To assess the physiologic accuracy of genomic and expression data generated with FFPE specimens, we surveyed the literature for articles investigating genomic and expression endpoints in case-matched FFPE and fresh or frozen human biospecimens using the National Cancer Institute's Biospecimen Research Database (http://biospecimens.cancer.gov/brd). Results of the survey revealed that the level of concordance between differentially preserved biospecimens varied among analytical parameters and platforms but also among reports, genes/transcripts of interest, and tumor status. The identified analytical techniques and parameters that resulted in strong correlations between FFPE and frozen biospecimens may provide guidance when optimizing molecular protocols for FFPE use; however, discrepancies reported for similar assays also illustrate the importance of validating protocols optimized for use with FFPE specimens with a case-matched fresh or frozen cohort for each platform, gene or transcript, and FFPE processing regime. On the basis of evidence published to date, validation of analytical parameters with a properly handled frozen cohort is necessary to ensure a high degree of concordance and confidence in the results obtained with FFPE biospecimens. Cancer Res; 75(8); 1541–7. ©2015 AACR.
Ormeloxifene Suppresses Desmoplasia and Enhances Sensitivity of Gemcitabine in Pancreatic Cancer
Authors: Khan Sheema , Ebeling Mara C. , Chauhan Neeraj , Thompson Paul A. , Gara Rishi K. , Ganju Aditya , Yallapu Murali M. , Behrman Stephen W. , Zhao Haotian , Zafar Nadeem , Singh Man Mohan , Jaggi Meena , Chauhan Subhash C.
Published: April 2015

The management of pancreatic ductal adenocarcinoma (PDAC) is extremely poor due to lack of an efficient therapy and development of chemoresistance to the current standard therapy, gemcitabine. Recent studies implicate the intimate reciprocal interactions between epithelia and underlying stroma due to paracrine Sonic hedgehog (SHH) signaling in producing desmoplasia and chemoresistance in PDAC. Herein, we report for the first time that a nonsteroidal drug, ormeloxifene, has potent anticancer properties and depletes tumor-associated stromal tissue by inhibiting the SHH signaling pathway in PDAC. We found that ormeloxifene inhibited cell proliferation and induced death in PDAC cells, which provoked us to investigate the combinatorial effects of ormeloxifene with gemcitabine at the molecular level. Ormeloxifene caused potent inhibition of the SHH signaling pathway via downregulation of SHH and its related important downstream targets such as Gli-1, SMO, PTCH1/2, NF-κB, p-AKT, and cyclin D1. Ormeloxifene potentiated the antitumorigenic effect of gemcitabine by 75% in PDAC xenograft mice. Furthermore, ormeloxifene depleted tumor-associated stroma in xenograft tumor tissues by inhibiting the SHH cellular signaling pathway and mouse/human collagen I expression. Xenograft tumors treated with ormeloxifene in combination with gemcitabine restored the tumor-suppressor miR-132 and inhibited stromal cell infiltration into the tumor tissues. In addition, invasiveness of tumor cells cocultivated with TGFβ-stimulated human pancreatic stromal cells was effectively inhibited by ormeloxifene treatment alone or in combination with gemcitabine. We propose that ormeloxifene has high therapeutic index and in a combination therapy with gemcitabine, it possesses great promise as a treatment of choice for PDAC/pancreatic cancer. Cancer Res; 75(11); 2292–304. ©2015 AACR.