bluuebunny/crossref_metadata_2025 huggingface dataset:
Sensors Based on Bio and Biomimetic Receptors in Medical Diagnostic, Environment, and Food Analysis
Authors: Kozitsina Alisa , Svalova Tatiana , Malysheva Natalia , Okhokhonin Andrei , Vidrevich Marina , Brainina Khiena
Published: April 2018

Analytical chemistry is now developing mainly in two areas: automation and the creation of complexes that allow, on the one hand, for simultaneously analyzing a large number of samples without the participation of an operator, and on the other, the development of portable miniature devices for personalized medicine and the monitoring of a human habitat. The sensor devices, the great majority of which are biosensors and chemical sensors, perform the role of the latter. That last line is considered in the proposed review. Attention is paid to transducers, receptors, techniques of immobilization of the receptor layer on the transducer surface, processes of signal generation and detection, and methods for increasing sensitivity and accuracy. The features of sensors based on synthetic receptors and additional components (aptamers, molecular imprinted polymers, biomimetics) are discussed. Examples of bio- and chemical sensors’ application are given. Miniaturization paths, new power supply means, and wearable and printed sensors are described. Progress in this area opens a revolutionary era in the development of methods of on-site and in-situ monitoring, that is, paving the way from the “test-tube to the smartphone”.
The Use of a Polyphenoloxidase Biosensor Obtained from the Fruit of Jurubeba (Solanum paniculatum L.) in the Determination of Paracetamol and Other Phenolic Drugs
Authors: Antunes Rafael , Garcia Luane , Somerset Vernon , Gil Eric , Lopes Flavio
Published: April 2018

The vegetable kingdom is a wide source of a diverse variety of enzymes with broad biotechnological applications. Among the main classes of plant enzymes, the polyphenol oxidases, which convert phenolic compounds to the related quinones, have been successfully used for biosensor development. The oxidation products from such enzymes can be electrochemically reduced, and the sensing is easily achieved by amperometric transducers. In this work, the polyphenoloxidases were extracted from jurubeba (Solanum paniculatum L.) fruits, and the extract was used to construct a carbon paste-based biosensor for pharmaceutical analysis and applications. The assay optimization was performed using a 0.1 mM catechol probe, taking into account the amount of enzymatic extract (50 or 200 μL) and the optimum pH (3.0 to 9.0) as well as some electrochemical differential pulse voltammetric (DPV) parameters (e.g., pulse amplitude, pulse range, pulse width, scan rate). Under optimized conditions, the biosensor was evaluated for the quantitative determination of acetaminophen, acetylsalicylic acid, methyldopa, and ascorbic acid. The best performance was obtained for acetaminophen, which responded linearly in the range between 5 and 245 μM (R = 0.9994), presenting a limit of detection of 3 μM and suitable repeatability ranging between 1.52% and 1.74% relative standard deviation (RSD).
Bladder Cancer: New Insights into Its Molecular Pathology
Authors: Inamura Kentaro
Published: April 2018

Bladder cancer is one of the most prevalent cancers worldwide. Unfortunately, there have been few advances in its clinical management due to a poor understanding of the correlations between its molecular and clinical features. Mounting evidence suggests that bladder cancer comprises a group of molecularly heterogeneous diseases that undergo a variety of clinical courses and possess diverse therapeutic responses. Owing to the close association between its molecular subtypes and clinicopathological features, specific therapeutic strategies have recently been suggested. This review summarizes the current understanding of the molecular pathology of bladder cancer, including its molecular biomarkers/pathways and molecular subtypes that have been newly identified using high-throughput technologies. It also discusses advances in our understanding of personalized treatments for specific molecular subtypes.
EBV+ and MSI Gastric Cancers Harbor High PD-L1/PD-1 Expression and High CD8+ Intratumoral Lymphocytes
Authors: De Rosa Simona , Sahnane Nora , Tibiletti Maria , Magnoli Francesca , Vanoli Alessandro , Sessa Fausto , Chiaravalli Anna
Published: April 2018

Both EBV+ and MSI gastric cancers (GCs) have high lymphoid infiltration which is rare in MSS/EBV− cancers. PD-L1/PD-1 interaction leads to a down-regulated immune response and it is one of the most promising targets for gastric cancer immunotherapy. PD-L1/PD-1 and CD8 expression were immunohistochemically investigated in a series of 169 FFPE GCs, including 33 EBV+, 59 MSI and 77 MSS/EBV− cases. PD-L1 membrane immunoreactivity in more than 5% of tumor cells was present in 31/169 GCs and was associated with high levels of CD8 intraepithelial lymphocytes (TILs; p < 0.001). PD-L1+ cases were mainly poorly differentiated (71%), intestinal type (85%) and high lymphoid response (HLR; 90%) tumors. PD-L1 expression was only present in EBV⁺ (46%), MSI (24%) and rare MSS/EBV− (3%) GCs with high CD8+ TILs (p < 0.001). Despite being associated with a better prognosis both in the whole series (p < 0.05) and in the MSI subset, PD-L1 is not an independent prognostic factor. PD-L1 gene amplification was detected in 3/17 cases, including 2/7 EBV+ and 1/8 MSI GC. PD-1⁺ TILs were significantly higher in EBV⁺ than MSI and MSS/EBV− cases. PD-L1/PD-1 pathway is selectively activated in HLR GCs and could be considered an emerging therapeutic target, particularly for EBV and MSI GCs.
Assessing Coping Strategies in Response to Drought: A Micro Level Study in the North-West Region of Bangladesh
Authors: Mardy Trisna , Uddin Mohammed , Sarker Md. , Roy Debashis , Dunn Emily
Published: April 2018

Drought is an extreme and frequent event in the north-west region of Bangladesh and it adversely affects the livelihood of the farming community. Identifying the coping strategies that farmers use in the face of drought is crucial in order to understand how farmers minimize the effects of drought on their production, especially in the face of climatic changes that may impact the occurrence of extreme weather events. The purpose of this study was to assess farmers’ coping strategies for droughts by identifying which strategies are used and the influencing factors. A mixed methods approach using qualitative and quantitative data was employed. Preliminary data were collected using structured interviews and focus group discussions in which the findings were triangulated in order to design a questionnaire. The study respondents were 100 farmers operating in north-west Bangladesh. The findings were analyzed using descriptive statistics, coefficient of correlation, multiple linear and step-wise regressions. The results reveal that the respondents have limited drought coping strategies, even though the region is prone to frequent droughts. Among the fourteen identified drought coping strategies, the use of deep tube wells for irrigation water was the most widely reported and the farmers perceived it as the most important coping strategy. Shallow tube wells closely followed as the second most commonly used coping strategy reported by the respondents. Among the identified coping strategies, the least practiced was the use of treadle pumps. The findings from the study showed that age, education, farm size, annual family income, extension media contact, and organization participation were significantly associated with the choice of coping strategy that the farmers employed. Additionally, farm size, age, and education were identified as influential factors that affected the farmers’ choice of which drought coping strategies to use. The study identified important issues for policy makers engaged with governmental programs that aim to enhance the farmers’ drought coping mechanisms. The methods employed and the results of this study could be usefully applied in other districts of Bangladesh, or other areas of the world suffering from the negative effects of drought on agricultural production.
Rice Water: A Traditional Ingredient with Anti-Aging Efficacy
Authors: Marto Joana , Neves Ângela , Gonçalves Lídia , Pinto Pedro , Almeida Cristina , Simões Sandra
Published: April 2018

The skin healing benefits of rice have been known for centuries. Rice (Oryza sativa) water is a food processing waste that can potentially be incorporated into cosmetic formulations. However, no scientific evidence supports their role in skincare products. The aim of this project is to design and develop a topical gel formulation containing rice water and to evaluate its biological properties, namely, the anti-aging and antioxidant rice water properties. Rice water was evaluated in terms of physico-chemical composition and in terms of in vitro biological antioxidant activity and elastase inhibitory effect. Rice water was incorporated into a hydrogel and the developed formulation was subjected to pharmacotechnical tests such as pH and viscosity. Biological and sensory effects were evaluated on a panel of 12 volunteers for 28 days. The safety evaluation study was performed on rice water gel, using the Human Repeat Insult Patch test protocol. Rice water presented in vitro biological antioxidant activity and elastase inhibitory effect. The gel formulation containing 96% rice water was biocompatible with the human skin and presented suitable cosmetic properties. Rice water should be thus considered as an anti-aging ingredient to be used as raw material for skincare applications.
SIMADL: Simulated Activities of Daily Living Dataset
Authors: Alshammari Talal , Alshammari Nasser , Sedky Mohamed , Howard Chris
Published: April 2018

With the realisation of the Internet of Things (IoT) paradigm, the analysis of the Activities of Daily Living (ADLs), in a smart home environment, is becoming an active research domain. The existence of representative datasets is a key requirement to advance the research in smart home design. Such datasets are an integral part of the visualisation of new smart home concepts as well as the validation and evaluation of emerging machine learning models. Machine learning techniques that can learn ADLs from sensor readings are used to classify, predict and detect anomalous patterns. Such techniques require data that represent relevant smart home scenarios, for training, testing and validation. However, the development of such machine learning techniques is limited by the lack of real smart home datasets, due to the excessive cost of building real smart homes. This paper provides two datasets for classification and anomaly detection. The datasets are generated using OpenSHS, (Open Smart Home Simulator), which is a simulation software for dataset generation. OpenSHS records the daily activities of a participant within a virtual environment. Seven participants simulated their ADLs for different contexts, e.g., weekdays, weekends, mornings and evenings. Eighty-four files in total were generated, representing approximately 63 days worth of activities. Forty-two files of classification of ADLs were simulated in the classification dataset and the other forty-two files are for anomaly detection problems in which anomalous patterns were simulated and injected into the anomaly detection dataset.
Compression of a Deep Competitive Network Based on Mutual Information for Underwater Acoustic Targets Recognition
Authors: Shen Sheng , Yang Honghui , Sheng Meiping
Published: April 2018

The accuracy of underwater acoustic targets recognition via limited ship radiated noise can be improved by a deep neural network trained with a large number of unlabeled samples. However, redundant features learned by deep neural network have negative effects on recognition accuracy and efficiency. A compressed deep competitive network is proposed to learn and extract features from ship radiated noise. The core idea of the algorithm includes: (1) Competitive learning: By integrating competitive learning into the restricted Boltzmann machine learning algorithm, the hidden units could share the weights in each predefined group; (2) Network pruning: The pruning based on mutual information is deployed to remove the redundant parameters and further compress the network. Experiments based on real ship radiated noise show that the network can increase recognition accuracy with fewer informative features. The compressed deep competitive network can achieve a classification accuracy of 89.1 % , which is 5.3 % higher than deep competitive network and 13.1 % higher than the state-of-the-art signal processing feature extraction methods.
Multi-Graph Multi-Label Learning Based on Entropy
Authors: Zhu Zixuan , Zhao Yuhai
Published: April 2018

Recently, Multi-Graph Learning was proposed as the extension of Multi-Instance Learning and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on Multi-Graph Multi-Label Learning, where each object is represented as a bag containing a number of graphs and each bag is marked with multiple class labels. It is an interesting problem existing in many applications, such as image classification, medicinal analysis and so on. In this paper, we propose an innovate algorithm to address the problem. Firstly, it uses more precise structures, multiple Graphs, instead of Instances to represent an image so that the classification accuracy could be improved. Then, it uses multiple labels as the output to eliminate the semantic ambiguity of the image. Furthermore, it calculates the entropy to mine the informative subgraphs instead of just mining the frequent subgraphs, which enables selecting the more accurate features for the classification. Lastly, since the current algorithms cannot directly deal with graph-structures, we degenerate the Multi-Graph Multi-Label Learning into the Multi-Instance Multi-Label Learning in order to solve it by MIML-ELM (Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine). The performance study shows that our algorithm outperforms the competitors in terms of both effectiveness and efficiency.
Decomposing the Bonferroni Inequality Index by Subgroups: Shapley Value and Balance of Inequality
Authors: Giorgi Giovanni M. , Guandalini Alessio
Published: April 2018

Additive decomposability is an interesting feature of inequality indices which, however, is not always fulfilled; solutions to overcome such an issue have been given by Deutsch and Silber (2007) and by Di Maio and Landoni (2017). In this paper, we apply these methods, based on the “Shapley value” and the “balance of inequality” respectively, to the Bonferroni inequality index. We also discuss a comparison with the Gini concentration index and highlight interesting properties of the Bonferroni index.
The Relationship between Export and Growth: Panel Data Evidence from Turkish Sectors
Authors: Aslan Alper , Topcu Ebru
Published: April 2018

The aim of this study is to examine the impact of sectoral exports on economic growth in Turkey over the period 2000–2015. To this end, empirical models are estimated using panel data techniques in which quarterly data are gathered for eight sectors. Findings in the case of the pooled panel indicate the validity of the export-led growth hypothesis. Disaggregated evidences, on the other hand, reveal the validity of export-led growth hypothesis in the case of (i) agriculture and forestry; (ii) fishing; (iii) mining and quarrying; (iv) manufacturing; (v) electricity, gas and water supply; and (vi) wholesale and retail trade while it is found to be invalid in the case of (i) real estate, renting and business activities; and (ii) other community, social and personal service activities. The sectors that have the highest growth contributions are listed as follows: (i) agriculture and forestry; (ii) mining and quarrying; and (iii) manufacturing. Causality results also provide a strong support in favor of an export-led growth hypothesis for four sectors in addition to the feedback hypothesis which is valid for three sectors.
A Trial and Perceptions Assessment of APP-Based Flipped Classroom Teaching Model for Medical Students in Learning Immunology in China
Authors: Ma Xingming , Luo Yanping , Zhang Lifeng , Wang Jingqiu , Liang Yaling , Yu Hongjuan , Wu Yufeng , Tan Jiying , Cao Mingqiang
Published: April 2018

The application-based flipped classroom (APP-FC) is an innovative teaching-learning model that has not been applied and assessed in basic medical curricula teaching in China. The aim of this investigation is to assess students’ perceptions to the APP-based flipped classroom (APP-FC) teaching model in an immunology course. The data of this study were collected from second-year medical students (n = 92) at Lanzhou University. One class (n = 50), as a control group, was offered lecture-based learning (LBL), while the other class (n = 42), as the APP-FC group, was given lecture-based instruction and the APP-FC teaching model during September–November 2017. Afterward, the perceptions of students on APP-FC teaching model were evaluated using questionnaires. Students responded that APP-FC improves their motivation (83%) and interest in learning immunology (81%), as well as their self-directed learning skills (81%). Compared to the traditional lecture-based instruction, the APP-FC noticeably improved students’ motivation in learning (P = 0.011), self-directed learn skills (P = 0.001), memory abilities (P = 0.009), and problem-solving abilities (P = 0.010). Most medical students’ scores (60%) in the final examination were more than 80 points after implementing an APP-FC model as compared to the control group (40%). The majority of students (70%) preferred the APP-FC teaching approach over traditional lecture-based pedagogy. The implementation of the APP-FC teaching model could improve students’ learning motivation, self-directed learn skills, and problem-solving abilities, which is a preferable teaching model for medical immunology courses in China.
Optimization of Pre-Treatment Process Parameters to Generate Biodiesel from Microalga
Authors: Onumaegbu Chukwuma , Alaswad Abed , Rodriguez Cristina , Olabi Abdul
Published: April 2018

Cell disruption is an integral part of microalga production process, which improves the release of intracellular products that are essential for biofuel production. In this work, pre-treatment parameters that will enhance the efficiency of lipid production using high-pressure homogenizer on microalgae biomass will be investigated. The high-pressure homogenizer that is considered is a GYB40-10S/GY60-6S; with a pre-treatment pressure of 1000 psi, 2000 psi, and 3000 psi, the number of passes; 1, 2, and 3, a reaction time of 3, 3.5, and 4 h. Pressure and cavitation increase the efficiency of the pre-treatment process of the homogenizer. In addition, homogenization shear force and pressure are the basic significant factors that enhance the efficiency of microalgae cell rupture. Also, the use of modelling to simulate pre-treatment processes (Response Surface Methodology (RSM), Box-Behnken Designs (BBD), and design of experiment (DOE) for process optimization will be adopted in this study. The results clearly demonstrate that high-pressure homogenization pre-treatment can effectively disrupt microalga cell walls to enhance lipid recovery efficiency, with a relatively short extraction time, both that are essential for maintaining a good quality of lipids for biofuel production. A maximum of 18% lipid yields were obtained after 3 h of HPH pre-treatment at 3000 psi.
Carbon Debt Payback Time for a Biomass Fired CHP Plant—A Case Study from Northern Europe
Authors: Madsen Kristian , Bentsen Niclas
Published: April 2018

The European Union (EU) has experienced a large increase in the use of biomass for energy in the last decades. In 2015, biomass used to generate electricity, heat, and to a limited extent, liquid fuels accounted for 51% of the EU’s renewable energy production. Bioenergy use is expected to grow substantially to meet energy and climate targets for 2020 and beyond. This development has resulted in analyses suggesting the increased use of biomass for energy might initially lead to increased greenhouse gas (GHG) emissions to the atmosphere, a so-called carbon debt. Here, we analyze carbon debt and payback time of substituting coal with forest residues for combined heat and power generation (CHP). The analysis is, in contrast to most other studies, based on empirical data from a retrofit of a CHP plant in northern Europe. The results corroborate findings of a carbon debt, here 4.4 kg CO2eq GJ−1. The carbon debt has a payback time of one year after conversion, and furthermore, the results show that GHG emissions are reduced to 50% relative to continued coal combustion after about 12 years. The findings support the use of residue biomass for energy as an effective means for climate change mitigation.
Utilization of Energy Crops and Sewage Sludge in the Process of Co-Gasification for Sustainable Hydrogen Production
Authors: Smoliński Adam , Howaniec Natalia , Bąk Andrzej
Published: April 2018

The increasing world energy demand driven by economic growth and technical development contributes to the severe depletion of conventional energy resources and various environmental issues. The need for the employment of low-emission, highly efficient technologies of thermochemical conversion, flexible in terms of both raw resources and product applications is declared, when the utilization of solid, alternative fuels is considered. Gasification is the proven technology of lower unit emission of contaminants and higher efficiency than combustion systems, as well as versatile applicability of the synthesis gas, as its main product. While the conversion of fossil fuels in gasification systems is technically mature, the co-utilization of biomass and waste still requires research and optimization in various technical and economic aspects. In this paper, the results of experimental work on co-gasification of energy crops biomass and sewage sludge with steam to produce hydrogen-rich gas are presented. The process is performed at 700, 800 and 900 °C under atmospheric pressure. The experimental results are analyzed with the application of the Hierarchical Clustering Analysis. The optimal results in terms of hydrogen production in co-gasification of selected biomass and sewage sludge are observed for Helianthus tuberosus L. blends of 10% w/w of sewage sludge content at 900 °C.
Mini-Grids for the Base of the Pyramid Market: A Critical Review
Authors: Bhattacharyya Subhes
Published: April 2018

The lack of access to electricity of more than 1.1 billion people around the world remains a major developmental challenge and Goal 7 of the Sustainable Development Goals (SDG) as well as Sustainable Energy for All (SE4ALL) have set a target of universal electrification by 2030. Various studies have identified mini-grid-based electrification as a possible solution. There is a growing body of literature available now that has explored the feasibility, practical application and policy interventions required to support mini-grids. Through a review of available literature, this paper explores whether mini-grids can be a solution for the base of the pyramid (BoP) market and the challenges faced in deploying mini-grids in such markets. Interventions to support the mini-grid deployment are also discussed. The paper finds that the mini-grids are targeting the BoP market but the business is not attractive in profitability terms and requires financial support. Lack of regulatory clarity and non-coordinated policies affect the financial viability of projects, which requires careful support. Mini-grid electrification has hardly been embedded in rural development agenda and hence they have not contributed significantly to livelihood generation. Careful realignment of policies, regulatory frameworks and support systems can better support mini-grid deployment in developing countries.
Improving the Penetration of Wind Power with Dynamic Thermal Rating System, Static VAR Compensator and Multi-Objective Genetic Algorithm
Authors: Teh Jiashen , Lai Ching-Ming , Cheng Yu-Huei
Published: April 2018

The integration of renewable energy sources, especially wind energy, has been on the rise throughout power systems worldwide. Due to this relatively new introduction, the integration of wind energy is often not optimized. Moreover, owing to the technical constraints and transmission congestions of the power network, most of the wind energy has to be curtailed. Due to various factors that influence the connectivity of wind energy, this paper proposes a well-organized posterior multi-objective (MO) optimization algorithm for maximizing the connections of wind energy. In this regard, the dynamic thermal rating (DTR) system and the static VAR compensator (SVC) have been identified as effective tools for improving the loadability of the network. The propose MO algorithm in this paper aims to minimize: (1) wind energy curtailment, (2) operation cost of the network considering all investments and operations, also known as the total social cost, and (3) SVC operation cost. The proposed MO problem was solved using the non-dominated sorting genetic algorithm (NSGA) II and it was tested on the modified IEEE reliability test system (IEEE-RTS). The results demonstrate the applicability of the proposed algorithm in aiding power system enhancement planning for integrating wind energy.
Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables
Authors: Chapagain Kamal , Kittipiyakul Somsak
Published: April 2018

The quality of short-term electricity demand forecasting is essential for the energy market players for operation and trading activities. Electricity demand is significantly affected by non-linear factors, such as climatic conditions, calendar components and seasonal behavior, which have been widely reported in the literature. This paper considers parsimonious forecasting models to explain the importance of atmospheric variables for hourly electricity demand forecasting. Many researchers include temperature as a major weather component. If temperature is included in a model, other weather components, such as relative humidity and wind speed, are considered as less effective. However, several papers mention that there is a significant impact of atmospheric variables on electricity demand. Therefore, the main purpose of this study is to investigate the impact of the following atmospheric variables: rainfall, relative humidity, wind speed, solar radiation, and cloud cover to improve the forecasting accuracy. We construct three different multiple linear models (Model A, Model B, and Model C) including the auto-regressive moving average with exogenous variables (ARMAX) with the mentioned exogenous weather variables to compare the performances for Hokkaido Prefecture, Japan. The Bayesian approach is applied to estimate the weight of each variable with Gibbs sampling to approximate the estimation of the coefficients. The overall mean absolute percentage error (MAPE) performances of Model A, Model B, and Model C are estimated as 2.43%, 1.98% and 1.72%, respectively. This means that the accuracy is improved by 13.4% by including rainfall, snowfall, solar radiation, wind speed, relative humidity, and cloud cover data. The results of the statistical test indicate that these atmospheric variables and the improvement in accuracy are statistically significant in most of the hours. More specifically, they are significant during highly fluctuating and peak hours.
Novel Approach for Lithium-Ion Battery On-Line Remaining Useful Life Prediction Based on Permutation Entropy
Authors: Chen Luping , Xu Liangjun , Zhou Yilin
Published: April 2018

The degradation of lithium-ion battery often leads to electrical system failure. Battery remaining useful life (RUL) prediction can effectively prevent this failure. Battery capacity is usually utilized as health indicator (HI) for RUL prediction. However, battery capacity is often estimated on-line and it is difficult to be obtained by monitoring on-line parameters. Therefore, there is a great need to find a simple and on-line prediction method to solve this issue. In this paper, as a novel HI, permutation entropy (PE) is extracted from the discharge voltage curve for analyzing battery degradation. Then the similarity between PE and battery capacity are judged by Pearson and Spearman correlation analyses. Experiment results illustrate the effectiveness and excellent similar performance of the novel HI for battery fading indication. Furthermore, we propose a hybrid approach combining Variational mode decomposition (VMD) denoising technique, autoregressive integrated moving average (ARIMA), and GM(1,1) models for RUL prediction. Experiment results illustrate the accuracy of the proposed approach for lithium-ion battery on-line RUL prediction.
A 2-Deoxyglucose-Resistant Mutant of Saccharomyces cerevisiae Shows Enhanced Maltose Fermentative Ability by the Activation of MAL Genes
Authors: Orikasa Yoshitake , Mikumo Dai , Ohwada Takuji
Published: April 2018

Saccharomyces cerevisiae MCD4 is a 2-deoxyglucose (2-DOG)-resistant mutant derived from the wild-type strain, AK46, wherein the 2-DOG resistance improves the maltose fermentative ability. In the MAL gene cluster, mutations were detected in MAL11 and MAL31, which encode maltose permeases, and in MAL13 and MAL33, which encode transcriptional activators. In maltose medium, the expression of MAL11 and MAL31 in MCD4 was 2.1 and 4.2 times significantly higher than that in AK46, respectively. Besides, the expression of MAL13 and MAL33 also tended to be higher than that of AK46. Although no mutations were found in MAL12 and MAL32 (which encode α-glucosidases), their expression was significantly higher (4.9 and 4.4 times, respectively) than that in AK46. Since the expression of major catabolite repression-related genes did not show significant differences between MCD4 and AK46, these results showed that the higher maltose fermentative ability of MCD4 is due to the activation of MAL genes encoding two maltose permeases and two α-glucosidases.
Associations between Parents’ Health Literacy and Sleeping Hours in Children: A Cross-Sectional Study
Authors: Ogi Hiroto , Nakamura Daisuke , Ogawa Masato , Nakamura Teruhiko , Izawa Kazuhiro
Published: April 2018

Background: Sleep in preschool children is an important factor for their health and active lives. The lack of adequate sleep in preschool children is a serious public problem in Japan. The relationship between health literacy (HL) and health status is well recognized. The purpose of this study was to investigate the association between the sleep duration of preschool children and the HL of their parents. Methods: In the present study, participants were preschool children (3–6 years) and their parents. We assessed the HL of the parents with the 14-item Health Literacy Scale (HLS-14) questionnaire. Sleep duration of the children was reported by their parents. We divided parents into two groups according to HLS-14 score and analyzed children’s sleeping time separately. Results: Data from 279 parents and their children were ultimately analyzed. The high HL group comprised 210 families (75.3%) and the low HL group comprised 69 families (24.7%). Average children’s sleep duration was significantly longer in the high HL group (9.5 ± 0.9 h) than in the low HL group (9.1 ± 1.1 h) (p = 0.013). A positive correlation was found in the low HL group between parents’ HL and their children’s sleeping times (p < 0.01, r = 0.32) but the difference was not significant in the high HL group (p = 0.98, r = −0.0009). Conclusion: The HL of parents appears to affect their children’s sleep duration, suggesting that parental HL may be an appropriate target for interventions aiming to lengthen children’s sleeping time.
Extremely Low Frequency-Magnetic Field (ELF-MF) Exposure Characteristics among Semiconductor Workers
Authors: Choi Sangjun , Cha Wonseok , Park Jihoon , Kim Seungwon , Kim Won , Yoon Chungsik , Park Ju-Hyun , Ha Kwonchul , Park Donguk
Published: April 2018

We assessed the exposure of semiconductor workers to extremely low frequency-magnetic fields (ELF-MF) and identified job characteristics affecting ELF-MF exposure. These were demonstrated by assessing the exposure of 117 workers involved in wafer fabrication (fab) and chip packaging wearing personal dosimeters for a full shift. A portable device was used to monitor ELF-MF in high temporal resolution. All measurements were categorized by operation, job and working activity during working time. ELF-MF exposure of workers were classified based on the quartiles of ELF-MF distribution. The average levels of ELF-MF exposure were 0.56 µT for fab workers, 0.59 µT for chip packaging workers and 0.89 µT for electrical engineers, respectively. Exposure to ELF-MF differed among types of factory, operation, job and activity. Workers engaged in the diffusion and chip testing activities showed the highest ELF-MF exposure. The ELF-MF exposures of process operators were found to be higher than those of maintenance engineers, although peak exposure and/or patterns varied. The groups with the highest quartile ELF-MF exposure level are operators in diffusion, ion implantation, module and testing operations, and maintenance engineers in diffusion, module and testing operations. In conclusion, ELF-MF exposure among workers can be substantially affected by the type of operation and job, and the activity or location.
Preventing Opioid Use Disorders among Fishing Industry Workers
Authors: Walter Angela , Morocho Cesar , King Lauren , Bartlett John , Kelsey Debra , DeSousa Monica , Biesecker Gretchen , Punnett Laura
Published: April 2018

Fishing industry workers are at high risk for work-related musculoskeletal disorders (MSDs) and injuries. Prescription opioids used to treat pain injuries may put these workers at increased risk for developing substance disorders. Using a Community-Based Participatory Research approach, formative research was conducted to inform the eventual development of relevant interventions to prevent and reduce opioid use disorders among fishing industry workers. Qualitative interviews (n = 21) were conducted to assess: knowledge and attitudes about opioid use disorders; features of fishing work that might affect use and/or access to treatment; and community and organizational capacity for prevention and treatment. Participants reported numerous pathways connecting commercial fishing with opioid use. The combination of high stress and physically tasking job duties requires comprehensive workplace interventions to prevent chronic pain and MSDs, in addition to tailored and culturally responsive treatment options to address opioid use disorders in this population. Public health programs must integrate workplace health and safety protection along with evidence-based primary, secondary, and tertiary interventions in order to address opioid use disorders, particularly among workers in strenuous jobs.
Is a Water Content of 60% Maximum Water Holding Capacity Suitable for Folsomia candida Reproduction Tests? A Study with Silver Nanoparticles and AgNO3
Authors: McKee Moira , Megía Guerrero Amelia , Filser Juliane
Published: April 2018

Silver nanoparticles (AgNP) are increasingly emitted to the environment due to a rise in application in various products; therefore, assessment of their potential risks for biota is important. In this study the effects of AgNP at environmentally relevant concentrations (0.6–375 µg kg−1 soil) on the soil invertebrate Folsomia candida in OECD (Organisation for Economic Co-operation and Development) soil was examined at different soil water contents. Animals were retrieved by heat extraction, which had an efficiency of about 90% compared with the floatation method. The tested water content range is set by OECD Guideline 232 (40–60% of the maximum water holding capacity, WHC), and we detected significant differences in toxicity due to these. With AgNO3, used as an ionic control, the number of juveniles significantly decreased only at 40% WHC, which might be due to dilution of the toxicant at higher soil water content. In turn, at 60% WHC, the reproduction of F. candida significantly increased in the presence of AgNP compared with in the control. However, at this water content, the required number of juveniles in the control treatment was not reached in three independent tests. The fact that the OECD validity criterion is not met indicates that the soil conditions are not suitable for reproduction at 60% WHC.
Electrode Modified by Reduced Graphene Oxide for Monitoring of Total Thallium in Grain Products
Authors: Karbowska Bozena , Rębiś Tomasz , Milczarek Grzegorz
Published: April 2018

Grain products and the associated industry have a notable economic and social impact all over the world. The toxicological safety of grain products is a nutritional prerogative. This study focused on the determination of thallium content in grain product samples collected from a commercial brand commonly available in Poland. The samples were analyzed with the use of differential pulse anodic stripping voltammetry (DPASV) with graphene oxide based on glassy carbon. The stripping anodic peak current of thallium was linear over its concentration range from 9.78 × 10−9 to 97.8 × 10−9 M. The limit of detection (LOD) was calculated according to the formula LOD = (κ × SDa)/b, where κ is 3.3, SDa is the standard deviation of the intercept, and b is the slope. The determined value of LOD was 1.229 µg L−1 (6.01 × 10−9 M). The proposed method was successfully applied for the determination of thallium ions in samples of actual grain products. The obtained results confirmed that thallium was present in the studied cereal samples (average content at 0.0268 ± 0.0798 mg/kg). Thallium has a half-life of 60 days; therefore, the consumption of foods with thallium content of approximately 0.08 mg/kg has the potential for harmful bioaccumulation in the body. Thallium contamination in cereal products should be a critical parameter for health environmental regulations.
The Effect of Leisure Activity Diversity and Exercise Time on the Prevention of Depression in the Middle-Aged and Elderly Residents of Taiwan
Authors: Lee Hsiao-Yun , Yu Chia-Pin , Wu Chih-Da , Pan Wen-Chi
Published: April 2018

Previous studies have confirmed that activity participation is beneficial to mental health, but few studies focus on older adults’ depression. Based on the theory of social integration, this study examined the effects of leisure activity diversity and exercise time on depression in late adulthood. Subjects in the 2011 Survey of Health and Living Status of the Middle-Aged and Elderly in Taiwan were extracted. A series of logistic regressions were conducted to discern factors related to the odds of having depression. Among study subjects (N = 3727; age ≥ 58), 20.9% indicated an inclination of having depression (CESD-10 score ≥ 8). This study found that participating in diverse leisure activities and longer exercise time decreases older adults’ risk of having depression. Additionally, the results confirmed that depression is positively correlated with chronic diseases. Consequently, efforts should be continually spent on encouraging older adults’ participation in activities to reduce the prevalence of depression.
Full Atrioventricular Block Secondary to Acute Poisoning Mercury: A Case Report
Authors: Peregrina-Chávez Amelia , Ramírez-Galindo María , Chávez-Martínez Rolando , Delahanty-Delgado Cesar , Vazquez-Alaniz Fernando
Published: April 2018

Background: The biological behaviour and clinical significance of mercury toxicity vary according to its chemical structure. Mercury differs in its degree of toxicity and in its effects on the nervous, digestive and immune systems as well as on organs such as the lungs, kidneys, skin, eyes and heart. Human exposure occurs mainly through inhalation of elemental mercury vapours during industrial and artisanal processes such as artisanal and small-scale gold mining. Case presentation: A 52-years-old female, housewife, with a body mass index of 25.3 kg/cm2, without smoking or alcohol habits or any important clinical or chronic cardiovascular history, was admitted to the emergency room due to probable accidental poisoning by butane gas. Clinical manifestations with a headache, dizziness, cough, and dyspnoea of medium to small efforts. An initial physical exploration with Glasgow scored at 15, with arrhythmic heart sounds, pulmonary fields with bilateral subcrepitant rales and right basal predominance. Electrocardiographic findings were as follows: a cardiac frequency of 50 beats per minute and atrioventricular dissociation. Laboratory parameters were: white blood cells at 15.8 × 109/L; aspartate aminotransferase at 38 U/L; lactate dehydrogenase at 1288 U/L; creatine-kinase at 115 U/L; CK-MB fraction at 28 U/L; and other biochemical parameters were within the reference values. A radiographic evaluation showed flow cephalization, diffuse bilateral infiltrates with right basal predominance. In addition, the patient presented data of low secondary expenditure to third-degree atrioventricular (AV) block for which the placement of a transvenous pacemaker was decided, substantially improving the haemodynamic parameters. Subsequently, after a family interrogation, the diagnosis of mercury inhalation poisoning was established. An initial detection of mercury concentration (Hg(0)) was carried out, reporting 243.5 µg/L. In view of this new evidence, mercury chelation therapy with intravenous calcium disodium ethylenediamine tetraacetic acid (CaNa2·EDTA) was initiated. After 8-days of hospital stay, she presented a favourable evolution with both clinical and radiological improvements, so that the mechanical ventilation progressed to extubating. Subsequently, she was referred for cardiology because of her persistent 3rd-degree atrioventricular block, deciding to place a definitive bicameral pacemaker. The patient was discharged from the hospital 14 days after admission due to clinical improvements with mercury plasma levels at 5 µmol/L and a heart rhythm from the pacemaker. Conclusions: We show evidence that acute exposure to elemental mercury can affect the heart rhythm, including a complete atrioventricular blockage.
Associations between Job Strain and Arterial Stiffness: A Large Survey among Enterprise Employees from Thailand
Authors: Kaewboonchoo Orawan , Sembajwe Grace , Li Jian
Published: April 2018

As an intermediate endpoint to cardiovascular disease, arterial stiffness has received much attention recently. So far, the research on work stress and arterial stiffness is still sparse and inconsistent, and no investigations on work stress and cardiovascular health among the Thai working population have been reported. Therefore, we conducted an epidemiological study among 2141 Thai enterprise employees (858 men and 1283 women) who were free from any diagnosed cardiovascular disease. Work stress was measured using Karasek’s Job Demand–Control model for job strain (a combination of high demand and low control). Arterial stiffness was evaluated by a non-invasive approach using pulse-wave analysis based on a finger photoplethysmogram. Multivariable linear regression was applied to examine associations between job strain and arterial stiffness. In men, job strain was significantly associated with arterial stiffness (β  =  0.078, 95% confidence interval  =  0.026 to 0.130), after accounting for sociodemographic, behavioral, dietary and biomedical factors. However, the association in women was not significant. As the first study in Thailand on work stress and cardiovascular risk, we found that job strain might be an important risk factor for cardiovascular disease among Thai working men. Further studies with longitudinal design are warranted.
Improving Chronic Disease Self-Management by Older Home Health Patients through Community Health Coaching
Authors: Dye Cheryl , Willoughby Deborah , Aybar-Damali Begum , Grady Carmelita , Oran Rebecca , Knudson Alana
Published: April 2018

The purpose of the study was to pilot test a model to reduce hospital readmissions and emergency department use of rural, older adults with chronic diseases discharged from home health services (HHS) through the use of volunteers. The study’s priority population consistently experiences poorer health outcomes than their urban counterparts due in part to lower socioeconomic status, reduced access to health services, and incidence of chronic diseases. When they are hospitalized for complications due to poorly managed chronic diseases, they are frequently readmitted for the same conditions. This pilot study examines the use of volunteer community members who were trained as Health Coaches to mentor discharged HHS patients in following the self-care plan developed by their HHS RN; improving chronic disease self-management behaviors; reducing risk of falls, pneumonia, and flu; and accessing community resources. Program participants increased their ability to monitor and track their chronic health conditions, make positive lifestyle changes, and reduce incidents of falls, pneumonia and flu. Although differences in the ED and hospital admission rates after discharge from HHS between the treatment and comparison group (matched for gender, age, and chronic condition) were not statistically significant, the treatment group’s rate was less than the comparison group thus suggesting a promising impact of the HC program (90 day: 263 comparison vs. 129 treatment; p = 0.65; 180 day 666.67 vs. 290.32; p = 0.19). The community health coach model offers a potential approach for improving the ability of discharged older home health patients to manage chronic conditions and ultimately reduce emergent care.
Molecular Structural Changes in Alfalfa Detected by ATR-FTIR Spectroscopy in Response to Silencing of TT8 and HB12 Genes
Authors: Lei Yaogeng , Hannoufa Abdelali , Christensen David , Shi Haitao , Prates Luciana , Yu Peiqiang
Published: April 2018

This study investigated the spectral changes in alfalfa molecular structures induced by silencing of Transparent Testa 8 (TT8) and Homeobox 12 (HB12) genes with univariate and multivariate analyses. TT8-silenced (TT8i), HB12-silenced (HB12i) and wild type (WT) alfalfa were grown in a greenhouse under normal conditions and were harvested at early-to-mid vegetative stage. Samples were free-dried and grounded through 0.02 mm sieve for spectra collections with attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy. Afterwards, both univariate and multivariate analyses were conducted on amide, carbohydrate and lipid regions. Univariate results showed that silencing of TT8 and HB12 genes affected peak heights of most total carbohydrate (TC) and structural carbohydrate (STC), and structural carbohydrate area (STCA) in carbohydrate regions; and β-sheet height, amide areas, and ratios of amide I/II and α-helix/β-sheet in amide region; and symmetric CH2 (SyCH2), asymmetric CH2 (AsCH2) and (a)symmetric CH2 and CH3 area (ASCCA) in the lipid region. Multivariate analysis showed that both hierarchy cluster analysis (HCA) and principal component analysis (PCA) clearly separated WT from transgenic plants in all carbohydrate regions and (a)symmetric CH2 and CH3 (ASCC) lipid region. In the amide region, PCA separated WT, TT8i and HB12i into different groups, while HCA clustered WT into a separate group. In conclusion, silencing of TT8 and HB12 affected intrinsic molecular structures of both amide and carbohydrate profiles in alfalfa, and multivariate analyses successfully distinguished gene-silenced alfalfa from its parental WT control.