bluuebunny/crossref_metadata_2025 huggingface dataset:
A Role for Circular Non-Coding RNAs in the Pathogenesis of Sporadic Parathyroid Adenomas and the Impact of Gender-Specific Epigenetic Regulation
Authors: Yavropoulou Maria P. , Poulios Christos , Michalopoulos Nickos , Gatzou Ariadni , Chrisafi Sofia , Mantalovas Stylianos , Papavramidis Theodosis , Daskalaki Emily , Sofou Electra , Kotsa Kalliopi , Kesisoglou Isaak , Zebekakis Pantelis , Yovos John G.
Published: December 2018

Epigenetic changes, including altered small non-coding RNAs, appear to be implicated in the pathogenesis of sporadic parathyroid adenomas (PAs). In this study, we investigated the circular RNAs (circRNAs) expression profile in sporadic PAs. Sixteen tissue samples of sporadic PAs, and four samples of normal parathyroid tissue (NPT) were investigated. Sample preparation and microarray hybridization were performed based on the Arraystar’s standard protocols, and circRNAs sequences were predicted by bioinformatics tools. We identified 35 circRNAs that were differentially expressed in sporadic PAs compared to NPT; 22 were upregulated, and 13 were downregulated, according to the pre-defined thresholds of fold-change > 2.0 and p < 0.05. In the subgroup analysis of PAs from male patients (n = 7) compared to PAs from female patients (n = 9), we also find a different expression profile. In particular, 19 circRNAs were significantly upregulated, and four circRNAs were significantly downregulated in male patients, compared to female counterparts. We show here for the first time a differential circRNA expression pattern in sporadic PAs compared to NPT, and a different expression profile in PA samples from male compared to female patients, suggesting an epigenetic role in the PA pathogenesis, and also an effect of gender in the epigenetic regulation of PAs.
Substrate-Induced Proximity Effect in Superconducting Niobium Nanofilms
Authors: Rezvani S.J. , Perali Andrea , Fretto Matteo , De Leo Natascia , Flammia Luca , Milošević Milorad , Nannarone Stefano , Pinto Nicola
Published: December 2018

Structural and superconducting properties of high-quality niobium nanofilms with different thicknesses are investigated on silicon oxide (SiO2) and sapphire substrates. The role played by the different substrates and the superconducting properties of the Nb films are discussed based on the defectivity of the films and on the presence of an interfacial oxide layer between the Nb film and the substrate. The X-ray absorption spectroscopy is employed to uncover the structure of the interfacial layer. We show that this interfacial layer leads to a strong proximity effect, especially in films deposited on a SiO2 substrate, altering the superconducting properties of the Nb films. Our results establish that the critical temperature is determined by an interplay between quantum-size effects, due to the reduction of the Nb film thicknesses, and proximity effects. The detailed investigation here provides reference characterizations and has direct and important implications for the fabrication of superconducting devices based on Nb nanofilms.
Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response
Authors: Jordan Sophie E. , Hovet Sierra E. , Fung Isaac Chun-Hai , Liang Hai , Fu King-Wa , Tse Zion Tsz Ho
Published: December 2018

Twitter is a social media platform where over 500 million people worldwide publish their ideas and discuss diverse topics, including their health conditions and public health events. Twitter has proved to be an important source of health-related information on the Internet, given the amount of information that is shared by both citizens and official sources. Twitter provides researchers with a real-time source of public health information on a global scale, and can be very important in public health research. Classifying Twitter data into topics or categories is helpful to better understand how users react and communicate. A literature review is presented on the use of mining Twitter data or similar short-text datasets for public health applications. Each method is analyzed for ways to use Twitter data in public health surveillance. Papers in which Twitter content was classified according to users or tweets for better surveillance of public health were selected for review. Only papers published between 2010–2017 were considered. The reviewed publications are distinguished by the methods that were used to categorize the Twitter content in different ways. While comparing studies is difficult due to the number of different methods that have been used for applying Twitter and interpreting data, this state-of-the-art review demonstrates the vast potential of utilizing Twitter for public health surveillance purposes.
Neural Networks in Big Data and Web Search
Authors: Serrano Will
Published: December 2018

As digitalization is gradually transforming reality into Big Data, Web search engines and recommender systems are fundamental user experience interfaces to make the generated Big Data within the Web as visible or invisible information to Web users. In addition to the challenge of crawling and indexing information within the enormous size and scale of the Internet, e-commerce customers and general Web users should not stay confident that the products suggested or results displayed are either complete or relevant to their search aspirations due to the commercial background of the search service. The economic priority of Web-related businesses requires a higher rank on Web snippets or product suggestions in order to receive additional customers. On the other hand, web search engine and recommender system revenue is obtained from advertisements and pay-per-click. The essential user experience is the self-assurance that the results provided are relevant and exhaustive. This survey paper presents a review of neural networks in Big Data and web search that covers web search engines, ranking algorithms, citation analysis and recommender systems. The use of artificial intelligence (AI) based on neural networks and deep learning in learning relevance and ranking is also analyzed, including its utilization in Big Data analysis and semantic applications. Finally, the random neural network is presented with its practical applications to reasoning approaches for knowledge extraction.
Infected with Scabies Again? Focus in Management in Long-Term Care Facilities
Authors: Ong Chong Yau , Vasanwala Farhad Fakhrudin
Published: December 2018

Scabies is a significant public health condition in long-term care facilities, plaguing even developed countries. Although treatments are available, eradication and control of scabies cases still remain a challenge due to delays in diagnosis and difficulties in maintaining preventive and surveillance measures. Prompt treatment of patients and their contacts that are affected, along with concomitant education of health staff and family members, are paramount. Environmental disinfestation is also a concern.
Computing the Partial Correlation of ICA Models for Non-Gaussian Graph Signal Processing
Authors: Belda Jordi , Vergara Luis , Safont Gonzalo , Salazar Addisson
Published: December 2018

Conventional partial correlation coefficients (PCC) were extended to the non-Gaussian case, in particular to independent component analysis (ICA) models of the observed multivariate samples. Thus, the usual methods that define the pairwise connections of a graph from the precision matrix were correspondingly extended. The basic concept involved replacing the implicit linear estimation of conventional PCC with a nonlinear estimation (conditional mean) assuming ICA. Thus, it is better eliminated the correlation between a given pair of nodes induced by the rest of nodes, and hence the specific connectivity weights can be better estimated. Some synthetic and real data examples illustrate the approach in a graph signal processing context.
Detection Games under Fully Active Adversaries
Authors: Tondi Benedetta , Merhav Neri , Barni Mauro
Published: December 2018

We study a binary hypothesis testing problem in which a defender must decide whether a test sequence has been drawn from a given memoryless source P 0 , while an attacker strives to impede the correct detection. With respect to previous works, the adversarial setup addressed in this paper considers an attacker who is active under both hypotheses, namely, a fully active attacker, as opposed to a partially active attacker who is active under one hypothesis only. In the fully active setup, the attacker distorts sequences drawn both from P 0 and from an alternative memoryless source P 1 , up to a certain distortion level, which is possibly different under the two hypotheses, to maximize the confusion in distinguishing between the two sources, i.e., to induce both false positive and false negative errors at the detector, also referred to as the defender. We model the defender–attacker interaction as a game and study two versions of this game, the Neyman–Pearson game and the Bayesian game. Our main result is in the characterization of an attack strategy that is asymptotically both dominant (i.e., optimal no matter what the defender’s strategy is) and universal, i.e., independent of P 0 and P 1 . From the analysis of the equilibrium payoff, we also derive the best achievable performance of the defender, by relaxing the requirement on the exponential decay rate of the false positive error probability in the Neyman–Pearson setup and the tradeoff between the error exponents in the Bayesian setup. Such analysis permits characterizing the conditions for the distinguishability of the two sources given the distortion levels.
Wall-Normal Variation of Spanwise Streak Spacing in Turbulent Boundary Layer With Low-to-Moderate Reynolds Number
Authors: Wang Wenkang , Pan Chong , Wang Jinjun
Published: December 2018

Low-speed streaks in wall-bounded turbulence are the dominant structures in the near-wall turbulent self-sustaining cycle. Existing studies have well characterized their spanwise spacing in the buffer layer and below. Recent studies suggested the existence of these small-scale structures in the higher layer where large-scale structures usually receive more attention. The present study is thus devoted to extending the understanding of the streak spacing to the log layer. An analysis is taken on two-dimensional (2D) wall-parallel velocity fields in a smooth-wall turbulent boundary layer with R e τ = 440∼2400, obtained via either 2D Particle Image Velocimetry (PIV) measurement taken here or public Direct Numerical Simulation (DNS). Morphological-based streak identification analysis yields a R e -independent log-normal distribution of the streak spacing till the upper bound of the log layer, based on which an empirical model is proposed to account for its wall-normal growth. The small-scale part of the spanwise spectra of the streamwise fluctuating velocity below y + = 100 is reasonably restored by a synthetic simulation that distributes elementary streak units based on the proposed empirical streak spacing model, which highlights the physical significance of streaks in shaping the small-scale part of the velocity spectra beyond the buffer layer.
Applying Authentic Learning through Cultivation of the Entrepreneurial Mindset in the Engineering Classroom
Authors: Bosman Lisa , Fernhaber Stephanie
Published: December 2018

Higher education provides plenty of opportunity for theory and in many cases, even the ability to apply theory in a laboratory setting. Yet, there remains limited opportunity for students to learn by doing through participation in authentic learning experiences. The purpose of this paper is to provide one potential solution for integrating authentic learning into the engineering classroom by cultivating the entrepreneurial mindset. As entrepreneurship serves as an integral part of the economy, developing an entrepreneurial mindset through authentic learning experiences is essential for engineers. While online discussion prompts represent a useful way for educators to facilitate student learning, less is known about how to facilitate online discussions in a way that truly cultivates an entrepreneurial mindset for engineering students. In this paper, the key intentions of entrepreneurially-minded learning are reviewed, and a guide for creating online discussions applicable to any engineering course is presented. Example online discussion prompts incorporating the entrepreneurial mindset are then shared from an Introduction to Engineering course. Results from a mixed methods survey instrument suggests positive impacts towards student perceptions of online discussions and development of the entrepreneurial mindset. Recommendations for effectively and efficiently facilitating online discussions for entrepreneurially-minded learning in the engineering classroom are offered.
Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production
Authors: Huertas Tato Javier , Centeno Brito Miguel
Published: December 2018

Solar energy forecasting is an active research problem and a key issue to increase the competitiveness of solar power plants in the energy market. However, using meteorological, production, or irradiance data from the past is not enough to produce accurate forecasts. This article aims to integrate a prediction algorithm (Smart Persistence), irradiance, and past production data, using a state-of-the-art machine learning technique (Random Forests). Three years of data from six solar PV modules at Faro (Portugal) are analyzed. A set of features that combines past data, predictions, averages, and variances is proposed for training and validation. The experimental results show that using Smart Persistence as a Machine Learning input greatly improves the accuracy of short-term forecasts, achieving an NRMSE of 0.25 on the best panels at short horizons and 0.33 on a 6 h horizon.
Research on Key Parameters Operation Range of Central Air Conditioning Based on Binary K-Means and Apriori Algorithm
Authors: Yan Liangwen , Qian Fengfeng , Li Wei
Published: December 2018

As the energy-saving control of central air conditioning has been widely applied in modern architecture, research of real-time optimal control based on historical data and identification of its optimal control strategies are of great importance for reducing energy wasting of buildings. However, due to the property of easily falling into local optimum, conventional k-means approach cannot achieve the goal of real-time optimal control, we therefore propose an innovative binary k-means clustering algorithm which is used to adjust the target value of temperature difference (TD) in the control system of chilled water and cooling water of central air conditioning system (CACS). Thanks to the clustering control, among the 304 test data, the coefficient of performance (COP) of 211 sets of data, which accounted for 69.41%, are higher than those of the traditional control method. In the simulation system, the COP of 191 sets of data, which accounted for 62.83%, are higher than those of traditional control methods, achieving better energy efficiency. To achieve the goal of identify potential energy-saving control strategies, the Apriori algorithm is proposed to correlate the key parameters and energy consumption efficiency of the CACS. The results show when the chilled water temperature difference (CWTD) > 2.0 °C and the cooling water temperature difference (COWTD) > 2.4 °C, some rules are discovered as follows: 1. The probability of a larger system COP will increase if the CWTD is set lower than the third quartile value or the COWTD is set lower than the first quartile value. 2. The probability of a larger system COP will also increase if the CTWD is set lower than the first quartile and the COWTD is set between the first and the third quartile. These underlying regularity is useful for technicians to adjust the control parameters of the equipment, to improve energy efficiency and to reduce energy consumption.
An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level
Authors: Guo Tao , He Wei , Jiang Zhonglian , Chu Xiumin , Malekian Reza , Li Zhixiong
Published: December 2018

Daily water level forecasting is of significant importance for the comprehensive utilization of water resources. An improved least squares support vector machine (LSSVM) model was introduced by including an extra bias error control term in the objective function. The tuning parameters were determined by the cross-validation scheme. Both conventional and improved LSSVM models were applied in the short term forecasting of the water level in the middle reaches of the Yangtze River, China. Evaluations were made with both models through metrics such as RMSE (Root Mean Squared Error), MAPE (Mean Absolute Percent Error) and index of agreement (d). More accurate forecasts were obtained although the improvement is regarded as moderate. Results indicate the capability and flexibility of LSSVM-type models in resolving time sequence problems. The improved LSSVM model is expected to provide useful water level information for the managements of hydroelectric resources in Rivers.
Automated Statistical Methods for Fault Detection in District Heating Customer Installations
Authors: Månsson Sara , Davidsson Kristin , Lauenburg Patrick , Thern Marcus
Published: December 2018

In order to develop more sustainable district heating systems, the district heating sector is currently trying to increase the energy efficiency of these systems. One way of doing so is to identify customer installations in the systems that have poor cooling performance. This study aimed to develop an algorithm that was able to detect the poorly performing installations automatically using meter readings from the installations. The algorithm was developed using statistical methods and was tested on a data set consisting of data from 3000 installations located in a district heating system in Sweden. As many as 1273 installations were identified by the algorithm as having poor cooling performance. This clearly shows that it is of major interest to the district heating companies to identify the installations with poor cooling performance rapidly and automatically, in order to rectify them as soon as possible.
Multi-Leader Comprehensive Learning Particle Swarm Optimization with Adaptive Mutation for Economic Load Dispatch Problems
Authors: Lin Anping , Sun Wei
Published: December 2018

Particle swarm optimization (PSO) is one of the most popular, nature inspired optimization algorithms. The canonical PSO is easy to implement and converges fast, however, it suffers from premature convergence. The comprehensive learning particle swarm optimization (CLPSO) can achieve high exploration while it converges relatively slowly on unimodal problems. To enhance the exploitation of CLPSO without significantly impairing its exploration, a multi-leader (ML) strategy is combined with CLPSO. In ML strategy, a group of top ranked particles act as the leaders to guide the motion of the whole swarm. Each particle is randomly assigned with an individual leader and the leader is refreshed dynamically during the optimization process. To activate the stagnated particles, an adaptive mutation (AM) strategy is introduced. Combining the ML and the AM strategies with CLPSO simultaneously, the resultant algorithm is referred to as multi-leader comprehensive learning particle swarm optimization with adaptive mutation (ML-CLPSO-AM). To evaluate the performance of ML-CLPSO-AM, the CEC2017 test suite was employed. The test results indicate that ML-CLPSO-AM performs better than ten popular PSO variants and six other types of representative evolutionary algorithms and meta-heuristics. To validate the effectiveness of ML-CLPSO-AM in real-life applications, ML-CLPSO-AM was applied to economic load dispatch (ELD) problems.
Stress-Dependent Permeability of Fractures in Tight Reservoirs
Authors: Cao Nai , Lei Gang , Dong Pingchuan , Li Hong , Wu Zisen , Li Yudan
Published: December 2018

Permeability is one of the key factors involved in the optimization of oil and gas production in fractured porous media. Understanding the loss in permeability influenced by the fracture system due to the increasing effective stress aids to improve recovery in tight reservoirs. Specifically, the impacts on permeability loss caused by different fracture parameters are not yet clearly understood. The principal aim of this paper is to develop a reasonable and meaningful quantitative model that manifests the controls on the permeability of fracture systems with different extents of fracture penetration. The stress-dependent permeability of a fracture system was studied through physical tests and numerical simulation with the finite element method (FEM). In addition, to extend capability beyond the existing model, a theoretical stress-dependent permeability model is proposed with fracture penetration extent as an influencing factor. The results presented include (1) a friendly agreement between the predicted permeability reduction under different stress conditions and the practical experimental data; (2) rock permeability of cores with fractures first reduces dramatically due to the closure of the fractures, then the permeability decreases gradually with the increase in effective stress; and (3) fracture penetration extent is one of the main factors in permeability stress sensitivity. The sensitivity is more influenced by fracture systems with a larger fracture penetration extent, whereas matrix compaction is the leading influencing factor in permeability stress sensitivity for fracture systems with smaller fracture penetration extents.
Traveling Wave Fault Location Using Layer Peeling
Authors: Robson Stephen , Haddad Abderrahmane , Griffiths Huw
Published: December 2018

Many fault-location algorithms rely on a simulation model incorporating network parameters which closely represent the real network. Estimations of the line parameters are usually based on limited geometrical information which do not reflect the complexity of a real network. In practice, obtaining an accurate model of the network is difficult without comprehensive field measurements of each constituent part of the network in question. Layer-peeling algorithms offer a solution to this problem by providing a fast “mapping” of the network based only on the response of a probing impulse. Starting with the classical “Schur” layer-peeling algorithm, this paper develops a new approach to map the reflection coefficients of an electrical network, then use this information post-fault to determine accurately and robustly the location of either permanent or incipient faults on overhead networks. The robustness of the method is derived from the similarity between the post-fault energy reaching the observation point and the predicted energy, which is based on real network observations rather than a simulation model. The method is shown to perform well for different noise levels and fault inception angles on the IEEE 13-bus network, indicating that the method is well suited to radial distribution networks.
Remote Sensing and Historical Morphodynamics of Alluvial Plains. The 1909 Indus Flood and the City of Dera Ghazi Khan (Province of Punjab, Pakistan)
Authors: Garcia Arnau , Orengo Hector , Conesa Francesc , Green Adam , Petrie Cameron
Published: December 2018

This paper explores the historical inundation of the city of Dera Ghazi Kkan (Punjab, Pakistan) in 1909. The rich documentation about this episode available—including historic news reports, books and maps—is used to reconstruct the historical dynamics between an urban settlement and the river morphodynamics in the Indus alluvial plain. Map and document-based historical regressive analysis is complemented with the examination of images obtained through different Remote Sensing techniques, including the use of new algorithms specifically developed for the study of topography and seasonal water availability which make possible to assess long-term changes in the Indus River basin. This case of study provides an opportunity to examine: (1) how historical hydrological dynamics are reflected in RS produced images; (2) the implications of river morphodynamics in the interpretation of settlement patterning; and (3) the documented socio-political responses to such geomorphological change. The results of this analysis are used to consider the long-term dynamics that have influenced the archaeo/cultural landscapes of the Indus River basin. This assessment provides critical insights for: (1) understanding aspects of the formation, preservation of representation of the archaeological record; (2) identifying traces of morphodynamics and their possible impact over the cultural heritage; and (3) offering insights into the role that recent historical documents can have in the interpretation of RS materials. This paper should be read in conjunction with the paper by Cameron Petrie et al. in the same issue of Geosciences, which explores the Survey of India 1” to 1-mile map series and outlines methods for using these historical maps for research on historical landscapes and settlement distribution.
The Relationships between Park Quality, Park Usage, and Levels of Physical Activity in Low-Income, African American Neighborhoods
Authors: Knapp Megan , Gustat Jeanette , Darensbourg Revonda , Myers Leann , Johnson Carolyn
Published: December 2018

Parks can be an important, low-cost neighborhood resource to increase physical activity and reduce overweight and obesity. The quality of parks, however, may impact use. This study used observational data to examine the relationships between park quality, park usage and levels of physical activity among users in 31 parks within low-income, African American neighborhoods. Relationships between park use and park characteristics (signs of disorder, attractiveness, and number of activity settings) varied by gender and user activity level. No variables of interest were significant for overall number of male users; whereas, disorder and attractiveness were significant for overall number of female users. Parks with signs of disorder were associated with 49% fewer female users (IRR = 0.51, 95% CI = (0.34–0.77)) and attractive parks with 146% more female users (IRR = 2.46, 95% CI = (1.39–4.33)). Similar significant relationships were found among active but not sedentary female users. Communities may consider increasing park maintenance and addressing attractiveness in existing parks as a relatively low-cost environmental strategy to encourage park use, increase physical activity, and reduce the burden of obesity, especially among women in low-income, African-American communities.
Negative Emotion under Haze: An Investigation Based on the Microblog and Weather Records of Tianjin, China
Authors: Sun Xuan , Yang Wenting , Sun Tao , Wang Ya Ping
Published: December 2018

Nowadays, many big cities are suffering from heavy air pollution and continuous haze weather. Compared with the threat on physical health, the influence of haze on people’s mental health is much less discussed in the current literature. Emotion is one of the most important indicators of mental health. To understand the negative impact of haze weather on the emotion of the people, we conducted an investigation based on historical weather records and microblog data in Tianjin, China. Specifically, an emotional thesaurus was generated with a microblog corpus collected from sample data. Based on the thesaurus, the public emotion under haze was statistically described. Then, through correlation analysis and comparative study, the relation and seasonal variation of haze and negative emotion of the public were well discussed. According to the study results, there was indeed a correlation between haze and negative emotion of the public, but the strength of this relationship varied under different conditions. The level of air pollution and weather context were both important factors that influence the mental effects of haze, and diverse patterns of negative emotion expression were demonstrated in different seasons of a year. Finally, for the benefit of people’s mental health under haze, recommendations were given for haze control from the side of government.
Direct Hospitalization Cost of Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease in Vietnam
Authors: Ngo Chau Quy , Thi Bui Thuy , Vu Giap Van , Chu Hanh Thi , Phan Phuong Thu , Ngoc Pham Ha , Vu Giang Thu , Nguyen Long Hoang , Ha Giang Hai , Tran Bach Xuan , Latkin Carl A. , Ho Cyrus S. H. , Ho Roger C. M.
Published: December 2018

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) have been found to contribute, predominantly, to increasing costs of COPD—a major public health issue. This study aimed to fill the gap in literature concerning costs of AECOPD in Vietnam, by examining the direct cost of AECOPD hospitalization and determining potentially associated factors. A cross-sectional study was conducted at the Respiratory Center of Bach Mai Hospital, Hanoi. A total of 57 participants were selected. Information regarding sociodemographic features, clinical characteristics, and hospitalization costs were collected. A multivariate generalized linear regression model was utilized to determine the factors associated with hospitalization costs. The mean total and daily hospitalization cost were 18.3 million VND (SD = 12.9) and 2.5 million VND (SD = 3.2), respectively. Medication cost accounted for 53.9% of hospitalization cost (from 44.0% in the Global Initiative for Chronic Obstructive Lung Disease Classification A (GOLD A) to 55.3% in GOLD C). Patients having GOLD D COPD (Coef. = 5.78; 95% CI = 0.73–10.83), higher age (Coef. = 0.37; 95% CI = 0.13–0.61), and higher duration of hospitalization (Coef. = 1.91; 95% CI = 1.28–2.53) had higher hospitalization costs (p < 0.05). This study suggested that interventions to screen COPD patients as well as provide timely treatment should be conducted widely in the community in order to avoid any unnecessary hospitalization cost, consequently reducing the economic burden of COPD.
Urban Metabolic Analysis of a Food-Water-Energy System for Sustainable Resources Management
Authors: Hu Ming-Che , Fan Chihhao , Huang Tailin , Wang Chi-Fang , Chen Yu-Hui
Published: December 2018

Urban metabolism analyzes the supply and consumption of nutrition, material, energy, and other resources within cities. Food, water, and energy are critical resources for the human society and have complicated cooperative/competitive influences on each other. The management of interactive resources is essential for supply chain analysis. This research analyzes the food-water-energy system of urban metabolism for sustainable resources management. A system dynamics model is established to investigate the urban metabolism of food, water, and energy resources. This study conducts a case study of Shihmen Reservoir system, hydropower generation, paddy rice irrigation of Taoyuan and Shihmen Irrigation Associations, and water consumption in Taoyuan, New Taipei, and Hsinchu cities. The interactive influence of the food-water-energy nexus is quantified in this study; the uncertainty analysis of food, water, and energy nexus is presented.
Waste Separation in Cafeterias: A Study among University Students in the Netherlands
Authors: Árnadóttir Ágústa D. , Kok Gerjo , Van Gils Suzanne , Ten Hoor Gill A.
Published: December 2018

Recycling waste is important to reduce the production of greenhouse gasses. The aim of this project was to understand determinants of cafeteria waste separation behavior among university students. First, the determinants of waste separation behavior among university students (n = 121) were explored using an online questionnaire. In study 2 (pre-/post-test design), the effect of a small intervention (based on study 1) on actual waste sorting behavior was observed. Finally, a semi-qualitative study in 59 students was conducted as process evaluation of the intervention. The following results were revealed: (1) Students have limited knowledge about waste separation, have a high intention to separate waste, are positive about waste separation in general, and believe that they can separate waste correctly. (2) Just over half of the waste is correctly recycled. An intervention with extra information had no significant effect on improving recycling behavior. (3) Students evaluated the intervention positively. Some students suggested that more information should be available where the actual decision making takes place. Ultimately, this paper concludes that although students have a positive attitude and are willing to behave pro-environmentally, there is a gap between intention and actual behavior. These results may also apply to other organizations and members of those organizations. New interventions are needed to trigger students to make correct waste separation decisions where the actual decision making takes place.
Environmental Tobacco Smoke and Cardiovascular Disease
Authors: DiGiacomo Sydne I. , Jazayeri Mohammad-Ali , Barua Rajat S. , Ambrose John A.
Published: December 2018

Environmental tobacco smoke (ETS) and its sequelae are among the largest economic and healthcare burdens in the United States and worldwide. The relationship between active smoking and atherosclerosis is well-described in the literature. However, the specific mechanisms by which ETS influences atherosclerosis are incompletely understood. In this paper, we highlight the definition and chemical constituents of ETS, review the existing literature outlining the effects of ETS on atherogenesis and thrombosis in both animal and human models, and briefly outline the public health implications of ETS based on these data.
Distribution and Health Risk Assessment of Trace Metals in Soils in the Golden Triangle of Southern Fujian Province, China
Authors: Huang Sha , Shao Guofan , Wang Luyan , Wang Lin , Tang Lina
Published: December 2018

In recent years, intensified industrialization and rapid urbanization have accelerated the accumulation of trace metals in topsoils of the Golden Triangle of Southern Fujian Province in China. Trace metals can cause adverse impacts on ecosystems and human health. In order to assess the ecological and human health risks of trace metals in the Golden Triangle region and to determine the distribution and degree of pollution of trace metals, 456 soil samples were collected from 28 districts. The concentrations of six metals (As, Cr, Cu, Ni, Pb, and Zn) were analyzed to assess ecological risk using the geoaccumulation index (Igeo) and the potential ecological risk index (RI). The United States Environmental Protection Agency (USEPA) model was applied to calculate health risk. The average soil concentrations of the six elements are ranked as follows: As < Ni < Cu < Cr < Pb < Zn. Inverse distance weighting (IDW) interpolation maps showed that Cr, Cu, Ni, and Zn are enriched in the soils of developed areas, while As and Pb are enriched in the soils of undeveloped areas. The Igeo showed that the levels of metals in most soil samples are below polluting levels. Similarly, RI values indicated that trace metals pose low potential ecological risk in the region’s soils. The Hazard Quotient (HQ) ranked the mean total noncarcinogenic risk of the six metals, for both children and adults, as follows: As > Pb > Cr > Ni > Cu >Zn. The mean carcinogenic risk (CR) of the metals in the region’s soils are ranked as follows: Cr > As > Ni. The Hazard Index (HI) values indicated that 3.7% of soils contained unsafe levels of toxic metals for children and total carcinogenic risk (TCR) values indicated that 23.3% of soils contained unsafe levels, indicating that children are facing both noncarcinogenic and carcinogenic risks from trace metals. Principal component analysis (PCA) and matrix cluster analysis were used to identify pollution sources and classified trace metals and soil samples into two and five groups, respectively. The five groups represented the effects of different land use types, including agricultural area, residential and public area, industrial area, forest, and industrial area and roadside, based on the contents of trace metals in soils. Industrial, agricultural and traffic activities attribute to the enrichment of Cr, Cu, Ni, Pb, and Zn in the region’s soils. Moreover, the accumulation of As and Pb are also attributed to atmospheric deposition. These results can contribute to a better understanding of pollution, ecological risks, and human health risks from trace metals on large regional scales like the Golden Triangle of Southern Fujian Province.
A New Grey Relational Analysis Model Based on the Characteristic of Inscribed Core (IC-GRA) and Its Application on Seven-Pilot Carbon Trading Markets of China
Authors: Wang Lihong , Yin Kedong , Cao Yun , Li Xuemei
Published: December 2018

In recent years, the study of the factors affecting the carbon trading price plays an important role in promoting the carbon trading markets and the sustainable development of green economy. However, due to the short establishment time of China’s carbon trading market, the carbon trading price data of the pilot markets were not complete and have the typical characteristics of poor information. The traditional grey correlation model cannot effectively identify the volatility and the grey correlation coefficient of trading data. In this paper, an inscribed cored grey relational analysis model (IC-GRA) is constructed by extracting the values of the triangle inscribed center of the time series sample. Through numerical examples and empirical analysis, it is verified that IC-GRA not only satisfies the four axioms of traditional grey correlation but also avoids the influence of outliers of time series fluctuation and improves the discriminability of the grey correlation coefficient. The empirical results of the IC-GRA model in China’s seven pilot carbon trading markets show that: 1. among international carbon trade factor, the biggest influence factor carbon trade price is different in pilot markets. The price of natural gas has a greater correlation with the carbon price of carbon trading markets in Shenzhen, Guangzhou, and Chongqing. The futures price of Certified Emission Reduction (CER) has a strong correlation with the carbon price of Shanghai and Beijing carbon trading markets; the price of Hubei carbon trading market is the largest related to crude oil future price in the New York Mercantile Exchange ( NYMEX). 2. Air Quality Index (AQI) is most relevant to the market carbon price of carbon trading, followed by the trading turnover and trading volume of the carbon trading market. Therefore, studying the carbon trading price of the carbon trading market plays a positive role in improving the sustainable development in those areas.
Prediction of Hourly Effect of Land Use on Crime
Authors: Matijosaitiene Irina , Zhao Peng , Jaume Sylvain , Gilkey Jr Joseph
Published: December 2018

Predicting the exact urban places where crime is most likely to occur is one of the greatest interests for Police Departments. Therefore, the goal of the research presented in this paper is to identify specific urban areas where a crime could happen in Manhattan, NY for every hour of a day. The outputs from this research are the following: (i) predicted land uses that generates the top three most committed crimes in Manhattan, by using machine learning (random forest and logistic regression), (ii) identifying the exact hours when most of the assaults are committed, together with hot spots during these hours, by applying time series and hot spot analysis, (iii) built hourly prediction models for assaults based on the land use, by deploying logistic regression. Assault, as a physical attack on someone, according to criminal law, is identified as the third most committed crime in Manhattan. Land use (residential, commercial, recreational, mixed use etc.) is assigned to every area or lot in Manhattan, determining the actual use or activities within each particular lot. While plotting assaults on the map for every hour, this investigation has identified that the hot spots where assaults occur were ‘moving’ and not confined to specific lots within Manhattan. This raises a number of questions: Why are hot spots of assaults not static in an urban environment? What makes them ‘move’—is it a particular urban pattern? Is the ‘movement’ of hot spots related to human activities during the day and night? Answering these questions helps to build the initial frame for assault prediction within every hour of a day. Knowing a specific land use vulnerability to assault during each exact hour can assist the police departments to allocate forces during those hours in risky areas. For the analysis, the study is using two datasets: a crime dataset with geographical locations of crime, date and time, and a geographic dataset about land uses with land use codes for every lot, each obtained from open databases. The study joins two datasets based on the spatial location and classifies data into 24 classes, based on the time range when the assault occurred. Machine learning methods reveal the effect of land uses on larceny, harassment and assault, the three most committed crimes in Manhattan. Finally, logistic regression provides hourly prediction models and unveils the type of land use where assaults could occur during each hour for both day and night.
Discovery of High Affinity Receptors for Dityrosine through Inverse Virtual Screening and Docking and Molecular Dynamics
Authors: Wang Fangfang , Yang Wei , Hu Xiaojun
Published: December 2018

Dityrosine is the product of oxidation that has been linked to a number of serious pathological conditions. Evidence indicates that high amounts of dityrosine exist in oxidized milk powders and some milk related foodstuffs, further reducing the nutritional value of oxidized proteins. Therefore, we hypothesize that some receptors related to special diseases would be targets for dityrosine. However, the mechanisms of the interaction of dityrosine with probable targets are still unknown. In the present work, an inverse virtual screening approach was performed to screen possible novel targets for dityrosine. Molecular docking studies were performed on a panel of targets extracted from the potential drug target database (PDTD) to optimize and validate the screening results. Firstly, two different conformations cis- and trans- were found for dityrosine during minimization. Moreover, Tubulin (αT) (−11.0 kcal/mol) was identified as a target for cis-dityrosine (CDT), targets including αT (−11.2 kcal/mol) and thyroid hormone receptor beta-1 (−10.7 kcal/mol) presented high binding affinities for trans-dityrosine (TDT). Furthermore, in order to provide binding complexes with higher precision, the three docked systems were further refined by performing thermo dynamic simulations. A series of techniques for searching for the most stable binding pose and the calculation of binding free energy are elaborately provided in this work. The major interactions between these targets and dityrosine were hydrophobic, electrostatic and hydrogen bonding. The application of inverse virtual screening method may facilitate the prediction of unknown targets for known ligands, and direct future experimental assays.
Lung Macrophages: Multifunctional Regulator Cells for Metastatic Cells
Authors: Mukaida Naofumi , Nosaka Takuto , Nakamoto Yasunari , Baba Tomohisa
Published: December 2018

Metastasis is responsible for most of the cancer-associated deaths and proceeds through multiple steps. Several lines of evidence have established an indispensable involvement of macrophages present at the primary tumor sites in various steps of metastasis, from primary tumor growth to its intravasation into circulation. The lungs encompass a large, dense vascular area and, therefore, are vulnerable to metastasis, particularly, hematogenous ones arising from various types of neoplasms. Lung tissues constitutively contain several types of tissue-resident macrophages and circulating monocytes to counteract potentially harmful exogenous materials, which directly reach through the airway. Recent advances have provided an insight into the ontogenetic, phenotypic, and functional heterogeneity of these lung macrophage and monocyte populations, under resting and inflammatory conditions. In this review, we discuss the ontogeny, trafficking dynamics, and functions of these pulmonary macrophages and monocytes and their potential roles in lung metastasis and measures to combat lung metastasis by targeting these populations.
Beyond a Measure of Liver Function—Bilirubin Acts as a Potential Cardiovascular Protector in Chronic Kidney Disease Patients
Authors: Tsai Ming-Tsun , Tarng Der-Cherng
Published: December 2018

Bilirubin is a well-known neurotoxin in newborn infants; however, current evidence has shown that a higher serum bilirubin concentration in physiological ranges is associated with a lower risk for the development and progression of both chronic kidney disease (CKD) and cardiovascular disease (CVD) in adults. The protective mechanisms of bilirubin in CVD, CKD, and associated mortality may be ascribed to its antioxidant and anti-inflammatory properties. Bilirubin further improves insulin sensitivity, reduces low-density lipoprotein cholesterol levels and inhibits platelet activation in at-risk individuals. These effects are expected to maintain normal vascular homeostasis and thus reduce the incidence of CKD and the risks of cardiovascular complications and death. In this review, we highlight the recent advances in the biological actions of bilirubin in the pathogenesis of CVD and CKD progression, and further propose that targeting bilirubin metabolism could be a potential approach to ameliorate morbidity and mortality in CKD patients.
NAP Family CG5017 Chaperone Pleiotropically Regulates Human AHR Target Genes Expression in Drosophila Testis
Authors: Akishina Angelina , Vorontsova Julia , Cherezov Roman , Slezinger Mikhail , Simonova Olga , Kuzin Boris
Published: December 2018

To study the regulatory mechanism of the Aryl hydrocarbon receptor (AHR), target genes of transcription are necessary for understanding the normal developmental and pathological processes. Here, we examined the effects of human AHR ligands on male fecundity. To induce ectopic human AhR gene expression, we used Drosophila melanogaster transformed with human AhR under the control of a yeast UAS promoter element capable of activation in the two-component UAS-GAL4 system. We found that exogenous AHR ligands decrease the number of Drosophila gonadal Tj-positive cells. We also found both an increase and decrease of AHR target gene expression, including in genes that control homeostasis and testis development. This suggests that gonadal AHR activation may affect the expression of gene networks that control sperm production and could be critical for fertility not just in Drosophila but also in humans. Finally, we found that the activation of the expression for some AHR target genes depends on the expression of testis-specific chaperone CG5017 in gonadal cells. Since CG5017 belongs to the nucleosome assembly protein (NAP) family and may participate in epigenetic regulation, we propose that this nucleotropic chaperone is essential to provide the human AHR with access to only the defined set of its target genes during spermatogenesis.