bluuebunny/crossref_metadata_2025 huggingface dataset:
Single-Step Self-Assembly and Physical Crosslinking of PEGylated Chitosan Nanoparticles by Tannic Acid
Authors: Smith Raven A. , Walker Rebecca C. , Levit Shani L. , Tang Christina
Published: April 2019

Chitosan-based nanoparticles are promising materials for potential biomedical applications. We used Flash NanoPrecipitation as a rapid, scalable, single-step method to achieve self-assembly of crosslinked chitosan nanoparticles. Self-assembly was driven by electrostatic interactions, hydrogen bonding, and hydrophobic interactions; tannic acid served to precipitate chitosan to seed nanoparticle formation and crosslink the chitosan to stabilize the resulting particles. The size of the nanoparticles can be tuned by varying formulation parameters including the total solids concentration and block copolymer to core mass ratio. We demonstrated that hydrophobic moieties can be incorporated into the nanoparticle using a lipophilic fluorescent dye as a model system.
Modification of Montmorillonite with Polyethylene Oxide and Its Use as Support for Pd0 Nanoparticle Catalysts
Authors: Shu Guiqing , Zhao Jing , Zheng Xiu , Xu Mengdie , Liu Qi , Zeng Minfeng
Published: April 2019

In this study, montmorillonite (MMT) was modified by intercalating polyethylene oxide (PEO) macromolecules between the interlayer spaces in an MMT-water suspension system. X-ray diffraction results revealed that the galleries of MMT were expanded significantly after intercalation of different loading of PEO. MMT/PEO 80/20 composite was chosen as the support platform for immobilization of Pd species in preparing novel heterogeneous catalysts. After immobilization of Pd species, the interlayer spacing of MMT/PEO (80/20) (1.52 nm) was further increased to 1.72 nm (Pd2+@MMT/PEO) and 1.73 nm (Pd0@MMT/PEO), confirming the well-immobilization of the Pd species in the interlayer spaces of PEO-modified MMT. High-resolution transmission electron microscopy (HR-TEM) observation results confirmed that Pd nanoparticles were confined inside the interlayer space of MMT and/or dispersed well on the outer surface of MMT. The conversion of Pd2+ to Pd0 species was evidenced by binding energy characterization with X-ray photo electron spectroscopy (XPS). The microstructure variation caused by the Pd immobilization was sensitively detected by positron annihilation lifetime spectroscopy (PALS) studies. The prepared Pd0@MMT/PEO (0.2/80/20) catalytic composite exhibits good thermal stability up to around 200 °C, and it showed high activities for Heck reactions between aryl iodides and butyl acrylates and could be recycled for five times. The correlations between the microstructure and properties of the Pd@MMT/PEO catalytic composites were discussed.
Correction: Sofan, P.; Bruce, D.; Jones, E.; Marsden, J. Detection and Validation of Tropical Peatland Flaming and Smouldering Using Landsat-8 SWIR and TIRS Bands. Remote Sens. 2019, 11, 465
Authors: Sofan Parwati , Bruce David , Jones Eriita , Marsden Jackie
Published: April 2019

The authors wish to make the following corrections to this paper [...]
Road Extraction by Using Atrous Spatial Pyramid Pooling Integrated Encoder-Decoder Network and Structural Similarity Loss
Authors: He Hao , Yang Dongfang , Wang Shicheng , Wang Shuyang , Li Yongfei
Published: April 2019

The technology used for road extraction from remote sensing images plays an important role in urban planning, traffic management, navigation, and other geographic applications. Although deep learning methods have greatly enhanced the development of road extractions in recent years, this technology is still in its infancy. Because the characteristics of road targets are complex, the accuracy of road extractions is still limited. In addition, the ambiguous prediction of semantic segmentation methods also makes the road extraction result blurry. In this study, we improved the performance of the road extraction network by integrating atrous spatial pyramid pooling (ASPP) with an Encoder-Decoder network. The proposed approach takes advantage of ASPP’s ability to extract multiscale features and the Encoder-Decoder network’s ability to extract detailed features. Therefore, it can achieve accurate and detailed road extraction results. For the first time, we utilized the structural similarity (SSIM) as a loss function for road extraction. Therefore, the ambiguous predictions in the extraction results can be removed, and the image quality of the extracted roads can be improved. The experimental results using the Massachusetts Road dataset show that our method achieves an F1-score of 83.5% and an SSIM of 0.893. Compared with the normal U-net, our method improves the F1-score by 2.6% and the SSIM by 0.18. Therefore, it is demonstrated that the proposed approach can extract roads from remote sensing images more effectively and clearly than the other compared methods.
Topology-Aware Road Network Extraction via Multi-Supervised Generative Adversarial Networks
Authors: Zhang Yang , Xiong Zhangyue , Zang Yu , Wang Cheng , Li Jonathan , Li Xiang
Published: April 2019

Road network extraction from remote sensing images has played an important role in various areas. However, due to complex imaging conditions and terrain factors, such as occlusion and shades, it is very challenging to extract road networks with complete topology structures. In this paper, we propose a learning-based road network extraction framework via a Multi-supervised Generative Adversarial Network (MsGAN), which is jointly trained by the spectral and topology features of the road network. Such a design makes the network capable of learning how to “guess” the aberrant road cases, which is caused by occlusion and shadow, based on the relationship between the road region and centerline; thus, it is able to provide a road network with integrated topology. Additionally, we also present a sample quality measurement to efficiently generate a large number of training samples with a little human interaction. Through the experiments on images from various satellites and the comprehensive comparisons to state-of-the-art approaches on the public datasets, it is demonstrated that the proposed method is able to provide high-quality results, especially for the completeness of the road network.
IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices
Authors: Mavrogiorgou Argyro , Kiourtis Athanasios , Perakis Konstantinos , Pitsios Stamatios , Kyriazis Dimosthenis
Published: April 2019

It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices’ datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy.
Capillary Sensor with Disposable Optrode for Diesel Fuel Quality Testing
Authors: Borecki Michal , Prus Przemyslaw , Korwin-Pawlowski Michael L.
Published: April 2019

Diesel fuel quality can be considered from many different points of view. Fuel producers, fuel consumers, and ecologists have their own ideas. In this paper, a sensor of diesel fuel quality type, and fuel condition that is oriented to the fuel’s consumers, is presented. The fuel quality types include premium, standard, and full bio-diesel classes. The fuel conditions include fuel fit for use and fuel degraded classes. The classes of fuel are connected with characteristics of engine operation. The presented sensor uses signal processing of an optoelectronic device monitoring fuel samples that are locally heated to the first step of boiling. Compared to previous works which consider diesel fuel quality sensing with disposable optrodes which use a more complex construction, the sensor now consists only of a capillary probe and advanced signal processing. The signal processing addresses automatic conversion of the data series to form a data pattern, estimates the measurement uncertainty, eliminates outlier data, and determines the fuel quality with an intelligent artificial neural network classifier. The sensor allows the quality classification of different unknown diesel fuel samples in less than a few minutes with the measurement costs of a single disposable capillary probe and two plugs.
Wirelessly Powered Light and Temperature Sensors Facilitated by Electrically Small Omnidirectional and Huygens Dipole Antennas
Authors: Lin Wei , Ziolkowski Richard
Published: April 2019

Wirelessly powered, very compact sensors are highly attractive for many emerging Internet-of-things (IoT) applications; they eliminate the need for on-board short-life and bulky batteries. In this study, two electrically small rectenna-based wirelessly powered light and temperature sensors were developed that operate at 915 MHz in the 902–928-MHz industrial, scientific, and medical (ISM) bands. First, a metamaterial-inspired near-field resonant parasitic (NFRP) Egyptian axe dipole (EAD) antenna was seamlessly integrated with a highly efficient sensor-augmented rectifier without any matching network. It was electrically small and very thin, and its omnidirectional property was ideal for capturing incident AC wireless power from any azimuthal direction and converting it into DC power. Both a photocell as the light sensor and a thermistor as the temperature sensor were demonstrated. The resistive properties of the photocell and thermistor changed the rectifier’s output voltage level; an acoustic alarm was activated once a threshold value was attained. Second, an electrically small, low-profile NFRP Huygens antenna was similarly integrated with the same light- and temperature-sensor-augmented rectifiers. Their unidirectional nature was very suitable for surface-mounted wireless power transfer (WPT) applications (i.e., on-body and on-wall sensors). Measurements of the prototypes of both the light- and temperature-sensor-augmented omni- and unidirectional rectenna systems confirmed their predicted performance characteristics.
Bearing Fault Diagnosis Based on the Switchable Normalization SSGAN with 1-D Representation of Vibration Signals as Input
Authors: Zhao Dongdong , Liu Feng , Meng He
Published: April 2019

The bearing is a component of the support shaft that guides the rotational movement of the shaft, widely used in the mechanical industry and also called a mechanical joint. In bearing fault diagnosis, the accuracy much depends on the feature extraction, which always needs a lot of training samples and classification in the commonly used methods. Neural networks are good at latent feature extraction and fault classification, however, they have problems with instability and over-fitting, and more labeled samples must be trained. Switchable normalization and semi-supervised learning are introduced to solve the above obstacles in this paper, which proposes a novel bearing fault diagnosis method based on switchable normalization semi-supervised generative adversarial networks (SN-SSGAN) with 1-dimensional representation of vibration signals as input. Experimental results showed that the proposed method has a desirable 99.93% classification accuracy in the case of less labeled data from the public data set of West Reserve University, which is better than the state-of-the-art methods.
A Deep Learning-Based Satellite Target Recognition Method Using Radar Data
Authors: Lu Wang , Zhang Yasheng , Xu Can , Lin Caiyong , Huo Yurong
Published: April 2019

A novel satellite target recognition method based on radar data partition and deep learning techniques is proposed in this paper. For the radar satellite recognition task, orbital altitude is introduced as a distinct and accessible feature to divide radar data. On this basis, we design a new distance metric for HRRPs called normalized angular distance divided by correlation coefficient (NADDCC), and a hierarchical clustering method based on this distance metric is applied to segment the radar observation angular domain. Using the above technology, the radar data partition is completed and multiple HRRP data clusters are obtained. To further mine the essential features in HRRPs, a GRU-SVM model is designed and firstly applied for radar HRRP target recognition. It consists of a multi-layer GRU neural network as a deep feature extractor and linear SVM as a classifier. By training, GRU neural network successfully extracts effective and highly distinguishable features of HRRPs, and feature visualization technology shows its advantages. Furthermore, the performance testing and comparison experiments also demonstrate that GRU neural network possesses better comprehensive performance for HRRP target recognition than LSTM neural network and conventional RNN, and the recognition performance of our method is almost better than that of other several common feature extraction methods or no data partition.
ECNet: Efficient Convolutional Networks for Side Scan Sonar Image Segmentation
Authors: Wu Meihan , Wang Qi , Rigall Eric , Li Kaige , Zhu Wenbo , He Bo , Yan Tianhong
Published: April 2019

This paper presents a novel and practical convolutional neural network architecture to implement semantic segmentation for side scan sonar (SSS) image. As a widely used sensor for marine survey, SSS provides higher-resolution images of the seafloor and underwater target. However, for a large number of background pixels in SSS image, the imbalance classification remains an issue. What is more, the SSS images contain undesirable speckle noise and intensity inhomogeneity. We define and detail a network and training strategy that tackle these three important issues for SSS images segmentation. Our proposed method performs image-to-image prediction by leveraging fully convolutional neural networks and deeply-supervised nets. The architecture consists of an encoder network to capture context, a corresponding decoder network to restore full input-size resolution feature maps from low-resolution ones for pixel-wise classification and a single stream deep neural network with multiple side-outputs to optimize edge segmentation. We performed prediction time of our network on our dataset, implemented on a NVIDIA Jetson AGX Xavier, and compared it to other similar semantic segmentation networks. The experimental results show that the presented method for SSS image segmentation brings obvious advantages, and is applicable for real-time processing tasks.
Lamb-Wave-Based Multistage Damage Detection Method Using an Active PZT Sensor Network for Large Structures
Authors: Hameed M. Saqib , Li Zheng , Chen Jianlin , Qi Jiahong
Published: April 2019

A multistage damage detection method is introduced in this work that uses piezoelectric lead zirconate titanate (PZT) transducers to excite/sense the Lamb wave signals. A continuous wavelet transformation (CWT), based on the Gabor wavelet, is applied to accurately process the complicated wave signals caused by the damage. For a network of transducers, the damage can be detected in one detection cell based on the signals scattered by the damage, and then it can be quantitatively estimated by three detection stages using the outer tangent circle and least-squares methods. First, a single-stage damage detection method is carried out by exciting a transducer at the center of the detection cell to locate the damaged subcell. Then, the corner transducers are excited in the second and third stages of detection to improve the damage detection, especially the size estimation. The method does not require any baseline signal, and it only utilizes the same arrangement of transducers and the same data processing technique in all stages. The results from previous detection stages contribute to the improvement of damage detection in the subsequent stages. Both numerical simulation and experimental evaluation were used to verify that the method can accurately quantify the damage location and size. It was also found that the size of the detection cell plays a vital role in the accuracy of the results in this Lamb-wave-based multistage damage detection method.
Virtual Sensors for Optimal Integration of Human Activity Data
Authors: Aguileta Antonio A. , Brena Ramon F. , Mayora Oscar , Molino-Minero-Re Erik , Trejo Luis A.
Published: April 2019

Sensors are becoming more and more ubiquitous as their price and availability continue to improve, and as they are the source of information for many important tasks. However, the use of sensors has to deal with noise and failures. The lack of reliability in the sensors has led to many forms of redundancy, but simple solutions are not always the best, and the precise way in which several sensors are combined has a big impact on the overall result. In this paper, we discuss how to deal with the combination of information coming from different sensors, acting thus as “virtual sensors”, in the context of human activity recognition, in a systematic way, aiming for optimality. To achieve this goal, we construct meta-datasets containing the “signatures” of individual datasets, and apply machine-learning methods in order to distinguish when each possible combination method could be actually the best. We present specific results based on experimentation, supporting our claims of optimality.
Signal Status Recognition Based on 1DCNN and Its Feature Extraction Mechanism Analysis
Authors: Huang Shuzhan , Tang Jian , Dai Juying , Wang Yangyang
Published: April 2019

In this paper, we construct a one-dimensional convolutional neural network (1DCNN), which directly takes as the input the vibration signal in the mechanical operation process. It can realize intelligent mechanical fault diagnosis and ensure the authenticity of signal samples. Moreover, due to the excellent interpretability of the 1DCNN, we can explain the feature extraction mechanism of convolution and the synergistic work ability of the convolution kernel by analyzing convolution kernels and their output results in the time-domain, frequency-domain. What’s more, we propose a novel network parameter-optimization method by matching the features of the convolution kernel with those of the original signal. A large number of experiments proved that, this optimization method improve the diagnostic accuracy and the operational efficiency greatly.
Use of Bio-Based Plastics in the Fruit Supply Chain: An Integrated Approach to Assess Environmental, Economic, and Social Sustainability
Authors: Blanc Simone , Massaglia Stefano , Brun Filippo , Peano Cristiana , Mosso Angela , Giuggioli Nicole Roberta
Published: April 2019

The challenge of developing a sustainable production system includes the reduction of emissions, the efficient use of resources, and the transition to renewable energy. The bioeconomy proposes a development model aimed at reducing impacts and risks associated with the use of non-renewable resources considering the life cycle of products. The European Union is promoting products from renewable sources focused on biochemicals and bio-based plastics, which are high added value products when compared to biofuels. The aim of this paper is to consider sustainability in terms of the environmental, economic, and social aspects of use of bio-based plastics in the fruit chain, considering the case study of raspberry supply chains in northwestern Italy. Different analyses (life-cycle assessment (LCA), life-cycle costing (LCC), and externality assessment (ExA)) were used to assess the impacts along the whole chain by means of an integrated approach. The results show that the bio-based plastic scenario has lower environmental and social impacts than the conventional one, whereas the latter is the best choice according to a classic economic approach. The introduction of bio-based plastics as a replacement for traditional plastics in agri-food chains is the first step toward the use of renewable resources with a low impact on society.
Effectiveness and Sustainability of Grain Price Support Policies in China
Authors: Lyu Jie , Li Xiaolei
Published: April 2019

We evaluated the effectiveness and sustainability of the grain price support policies in China using the structural break regime switching model. Based on the rice, wheat, and corn monthly price data from 1987 to 2017, we provide strong evidence that the Chinese grain price support policies have been effective in stabilizing the domestic grain price. A structural change occurred in grain price patterns in 2004 when the price support policies were established. Since then, Chinese grain prices have followed a regime with significantly lower volatility. We documented several problems challenging the sustainability of the Chinese grain price support policies in the future, including high economic costs that can trigger high support prices, high public stock level, and high grain import pressure. Our findings shed new light on the functioning of the grain pricing policies and provide useful implications for the market-oriented reforms in the Chinese grain market.
Mechanical and Microstructure Properties of Biochar-Based Mortar: An Internal Curing Agent for PCC
Authors: Mrad Rayane , Chehab Ghassan
Published: April 2019

In pursuing sustainability targets, the construction industry has witnessed significant efforts exerted on exploring new alternatives for raw materials. Such initiatives aim to alleviate concerns of overexploitation of natural resources leading to their depletion. On a different note, the disposal of municipal solid waste (MSW) has also become a major concern in some countries, such as the case of Lebanon, where illegal dumping continues to take its toll on the environment. Pyrolysis has been introduced as a biomass decomposition process of MSW and is considered an environmentally friendly process that can mitigate open dumping. However, pyrolysis produces significant amounts of biochar as a byproduct that in turn needs to be disposed of or treated. This research aims to investigate the viability of using biochar as a sustainable alternative material to sand in cementitious mortar composites. A thorough microscale physicochemical characterization of the biochar is conducted prior to its inclusion in mortar. Then, its incorporation as a partial replacement of sand in mortar is assessed at the mesoscale level, based on performance indices that included compressive strength and microstructure properties. Analysis of the experimental results are used to provide guidelines and recommendations as to the effective incorporation of biochar fraction in cementitious mortar.
Selecting Socially Responsible Portfolios: A Fuzzy Multicriteria Approach
Authors: García Fernando , González-Bueno Jairo , Oliver Javier , Riley Nicola
Published: April 2019

We propose a multi-objective approach for portfolio selection, which allows investors to consider not only return and downside risk criteria but also to include environmental, social and governance (ESG) scores in the investment decision-making process. Owing to the uncertain environment of portfolio selection, the return and ESG score of each asset are considered as independent L-R power fuzzy variables. To make the model more realistic, we take budget, floor ceiling and cardinality constraints into account. In order to select the optimal portfolio along the efficient frontier, we apply the Sortino ratio in a credibilistic environment. The subsequent empirical application uses a data set from Bloomberg’s ESG Data in combination with US Dow Jones Industrial Average data. The experimental results show that the proposed model offers promising results for socially responsible investors seeking ethical and sustainability goals beyond the return-risk trade-off and its ability to beat the benchmark.
Digitizing Vanishing Architectural Heritage; The Design and Development of Qatar Historic Buildings Information Modeling [Q-HBIM] Platform
Authors: Fadli Fodil , AlSaeed Mahmoud
Published: April 2019

The documentation of vanishing architectural heritage includes intensive information and data gathering, filtering, classification and digitization. Enabling such an endeavor necessitates the act of documentation, which requires the cooperation of several professionals from different fields, extensive resources, precise organization, and robust structuring. Therefore, the design and development of an innovative digital platform to facilitate the interaction between users from different specialties is highly desirable to enable dynamic real-time preservation and protection of such invaluable heritage. The aim of this study is to investigate the existing Qatari built heritage, urban conservation methods and the principles of modern digitizing and archiving techniques to create a sustainable and interactive archiving platform—Qatar Historic Buildings Information Modeling (Q-HBIM) platform. This unique digital platform aims to respond to the needs of the society as well as the experts in the fields of urban heritage conservation and buildings restoration. A literature review was conducted on topics relating to Qatar urban heritage and the principles of three-dimensional digitization technology including an exploration of the aspects of sustainable urban preservation. Accordingly, site analysis (physical, photogrammetric and 3D scanning techniques were used), and in-depth interviews were adopted as the main data collection techniques. The findings of this study elucidate the principles of innovative architectural heritage digitization tools involving Building Information Modeling (BIM) technology and processes. It is anticipated that the present work will formulate an advanced and unique interactive archiving tool in the context of Qatar and GCC/MENA regions in the shape of the: Qatar Historic Building Information Modeling (Q-HBIM) platform.
Is South Korea’s Emission Trading Scheme Effective? An Analysis Based on the Marginal Abatement Cost of Coal-Fueled Power Plants
Authors: Choi Yongrok , Qi Chao
Published: April 2019

This paper examines not only the effectiveness of the South Korean emissions-trading scheme (ETS), but also the determinants of its performance. We apply a directional distance function and stochastic frontier analysis method to estimate environmental efficiency and the marginal CO2 abatement costs for 50 coal-fueled power plants from 2008 to 2016. The result indicates that the efficiency differs between the Lee Myung-bak and Park Geun-hye administration periods, with performance in the former period being relatively more effective than in the latter. Meanwhile, there were price gaps of 57.98% and 72.76% in 2015 and 2016, respectively, between the realized carbon market price and our result, indicating significant suppressed pressure on the market. We conclude that the Korean government should strengthen its regulations to more effectively implement a green economy. Policymakers could impose a carbon tax; substantially decrease the carbon emissions-free quota; and provide more incentives, especially to the energy-intensive, resource-saving coal-fueled power plants. This would ultimately keep market prices within a reasonable range and achieve a balance between the supply and demand for carbon emission allowances in the market.
Toxicity and Toxin Composition of the Greater Blue-Ringed Octopus Hapalochlaena lunulata from Ishigaki Island, Okinawa Prefecture, Japan
Authors: Asakawa Manabu , Matsumoto Takuya , Umezaki Kohei , Kaneko Kyoichiro , Yu Ximiao , Gomez-Delan Gloria , Tomano Satoshi , Noguchi Tamao , Ohtsuka Susumu
Published: April 2019

The toxicity of the greater blue-ringed octopus Hapalochlaena lunulata, whose bite is fatal to humans, was examined to better understand and prevent deaths from accidental bites. Living specimens were collected from tide pools on Ishigaki Island, Okinawa Prefecture, Japan, in November and December of 2015, 2016, and 2017. The specimens were examined for the anatomical distribution of the toxicity, which was expressed in terms of mouse units (MU), by the standard bioassay method for tetrodotoxin (TTX) in Japan. Paralytic toxicity to mice was detected in all of the soft parts. The posterior salivary glands exhibited the highest toxicity score with a maximum level of 9276 MU/g, which was classified as “strongly toxic” (more than 1000 MU/g tissue) according to the classification of toxicity established by the Ministry of Health, Labor and Welfare of Japan, followed by the hepatopancreas (21.1 to 734.3 MU/g), gonads (not detectable to 167.6 MU/g), arms (5.3 to 130.2 MU/g), and other body areas (17.3 to 107.4 MU/g). Next, the toxin from the salivary glands was partially purified by a Sep-Pak C18 cartridge and an Amicon Ultra Centrifugal Filter with a 3000-Da cut-off, and analyzed by liquid chromatography-mass spectrometry (LC-MS) equipped with a φ2.0 × 150-mm (5 μm) TSKgel Amide-80 column (Tosoh, Tokyo, Japan) with a mixture of 16 mM ammonium formate buffer (pH 5.5) and acetonitrile (ratio 3:7, v/v) as a mobile phase. This study aimed to clarify the toxicity and the composition of TTX and its derivatives in this toxic octopus. The main toxin in this toxic octopus was identified as TTX, along with 4-epi TTX, 4, 9-anhydroTTX and 6-epi TTX. Further, the toxicity of this species is also significant from a food hygiene point of view.
Adeno-Associated Virus VP1u Exhibits Protease Activity
Authors: Kurian Justin J. , Lakshmanan Renuk , Chmely William M. , Hull Joshua A. , Yu Jennifer C. , Bennett Antonette , McKenna Robert , Agbandje-McKenna Mavis
Published: April 2019

Adeno-associated viruses (AAVs) are being developed for gene delivery applications, with more than 100 ongoing clinical trials aimed at the treatment of monogenic diseases. In this study, the unique N-terminus of AAV capsid viral protein 1 (VP1u), containing a canonical group XIII PLA2 enzyme domain, was observed to also exhibit proteolytic activity. This protease activity can target casein and gelatin, two standard substrates used for testing protease function but does not self-cleave in the context of the capsid or target globular proteins, for example, bovine serum albumin (BSA). However, heated BSA is susceptible to VP1u-mediated cleavage, suggesting that disordered proteins are substrates for this protease function. The protease activity is partially inhibited by divalent cation chelators ethylenediaminetetraacetic acid (EDTA) and ethylene-bis(oxyethylenenitrilo)tetraacetic acid (EGTA), and human alpha-2-macroglobulin (A2M), a non-specific protease inhibitor. Interestingly, both the bovine pancreatic (group VIIA) and bee venom (group III) PLA2 enzymes also exhibit protease function against casein. This indicates that PLA2 groups, including VP1u, have a protease function. Amino acid substitution of the PLA2 catalytic motif (76HD/AN) in the AAV2 VP1u resulted in attenuation of protease activity, suggesting that the protease and PLA2 active sites are related. However, the amino acid substitution of histidine H38, which is not involved in PLA2 function, to alanine, also affects protease activity, suggesting that the active site/mechanism of the PLA2 and protease function are not identical.
Conserved Secondary Structures in Viral mRNAs
Authors: Kiening Michael , Ochsenreiter Roman , Hellinger Hans-Jörg , Rattei Thomas , Hofacker Ivo , Frishman Dmitrij
Published: April 2019

RNA secondary structure in untranslated and protein coding regions has been shown to play an important role in regulatory processes and the viral replication cycle. While structures in non-coding regions have been investigated extensively, a thorough overview of the structural repertoire of protein coding mRNAs, especially for viruses, is lacking. Secondary structure prediction of large molecules, such as long mRNAs remains a challenging task, as the contingent of structures a sequence can theoretically fold into grows exponentially with sequence length. We applied a structure prediction pipeline to Viral Orthologous Groups that first identifies the local boundaries of potentially structured regions and subsequently predicts their functional importance. Using this procedure, the orthologous groups were split into structurally homogenous subgroups, which we call subVOGs. This is the first compilation of potentially functional conserved RNA structures in viral coding regions, covering the complete RefSeq viral database. We were able to recover structural elements from previous studies and discovered a variety of novel structured regions. The subVOGs are available through our web resource RNASIV (RNA structure in viruses).
Evaluating the Impact of Wastewater Effluent on Microbial Communities in the Panke, an Urban River
Authors: Nega Marcella , Braun Burga , Künzel Sven , Szewzyk Ulrich
Published: April 2019

Pharmaceuticals are consumed in high amounts and can enter as emerging organic compounds in surface waters as they are only partially retained in wastewater treatment plants (WWTPs). Receiving pharmaceuticals may burden the aquatic environment, as they are designed to be bioactive even at low concentrations. Sediment biofilm populations were analyzed in river sediments due to the exposure of an inflow of WWTP effluents. Illumina MiSeq 16S rRNA gene amplicon sequencing was performed of 108 sediment samples, which were taken from multiple cores within three sampling locations in the Panke River, with one sampling site located downstream of the inflow. Sequencing data were processed to infer microbial community structure in samples concerning the environmental variables, such as micropollutants and physicochemical parameters measured for each core. More than 25 different micropollutants were measured in pore water samples, in which bezafibrate, clofibric acid, carbamazepine, and diclofenac were detected at high concentrations. Bacterial 16S rRNA gene amplicons revealed Nitrospirae, Proteobacteria, Chloroflexi, Actinobacteria, Acidobacteria, Bacteroidetes, and Ignavibacteriae as the most abundant groups in the samples. Differences in microbial community composition were observed with respect to micropollutants. However, our findings revealed that the composition of the microbial community was not only governed by the effluent. The significant changes in the alpha- and beta-diversity were explained by phenobarbital and SO42−, which did not originate from the WWTP indicating that more unobserved factors are also likely to play a role in affecting the biofilm community’s composition.
Quantification of Temporal Variations in Base Flow Index Using Sporadic River Data: Application to the Bua Catchment, Malawi
Authors: Kelly Laura , Kalin Robert M. , Bertram Douglas , Kanjaye Modesta , Nkhata Macpherson , Sibande Hyde
Published: April 2019

This study investigated how sporadic river datasets could be used to quantify temporal variations in the base flow index (BFI). The BFI represents the baseflow component of river flow which is often used as a proxy indicator for groundwater discharge to a river. The Bua catchment in Malawi was used as a case study, whereby the smoothed minima method was applied to river flow data from six gauges (ranging from 1953 to 2009) and the Mann-Kendall (MK) statistical test was used to identify trends in BFI. The results showed that baseflow plays an important role within the catchment. Average annual BFIs > 0.74 were found for gauges in the lower reaches of the catchment, in contrast to lower BFIs < 0.54 which were found for gauges in the higher reaches. Minimal difference between annual and wet season BFI was observed, however dry season BFI was >0.94 across all gauges indicating the importance of baseflow in maintaining any dry season flows. Long term trends were identified in the annual and wet season BFI, but no evidence of a trend was found in the dry season BFI. Sustainable management of the investigated catchment should, therefore, account for the temporal variations in baseflow, with special regard to water resources allocation within the region and consideration in future scheme appraisals aimed at developing water resources. Further, this demonstration of how to work with sporadic river data to investigate baseflow serves as an important example for other catchments faced with similar challenges.
Analysis of Greenhouse Gas Emissions in Centralized and Decentralized Water Reclamation with Resource Recovery Strategies in Leh Town, Ladakh, India, and Potential for Their Reduction in Context of the Water–Energy–Food Nexus
Authors: Lahmouri Mounia , Drewes Jörg E. , Gondhalekar Daphne
Published: April 2019

With the constant increase of population and urbanization worldwide, stress on water, energy, and food resources is growing. Climate change constitutes a source of vulnerability, raising the importance of implementing actions to mitigate it. Within this, the water and wastewater sector represents an important source of greenhouse gas (GHG) emissions, during both the construction and operation phase. The scope of this study is to analyze the GHG emissions from the current and future water supply scheme, as well as to draw a comparison between possible water reclamation with resource recovery scenarios in the town Leh in India: a centralized scheme, a partly centralized combined with a decentralized scheme, and a household level approach. Precise values of emission factors, based on the IPCC Guidelines for National Greenhouse Gas Inventories, previous studies, and Ecoinvent database, have been adopted to quantify the different emissions. Potential sources of reduction of GHG emissions through sludge and biogas utilization have been identified and quantified to seize their ability to mitigate the carbon footprint of the water and wastewater sector. The results show that the future water supply scheme will lead to a significant increase of the GHG emissions during its operation. Further, it is shown that decentralizing wastewater management in Leh town has the least carbon footprint during both construction and operation phases. These results have implications for cities worldwide.
Modifications du faciès, observations de certains défauts dans des calcites synthétiques
Authors: Lefaucheux Françoise
Published: April 2019

On étudie les modification de faciès que présentent des monocristaux de calcite synthétisés par précipitation chimique. Après avoir décrit le dispositif expérimental utilisé en synthèse hydrothermale, on passe en revue un certain nombre de défauts décelés dans des cristaux poussés par cette méthode.
Water precipitation and diffusion in wet quartz and wet berlinite AIPO4
Authors: Cordier Patrick , Boulogne Bruno , Doukhan Jean-Claude
Published: April 2019

Annealing wet crystals of quartz and berlinite AlPO4 induces water precipitation. The evolution of precipitation has been studied by transmission electron microscopy on samples annealed in various conditions of temperature, pressure and duration. At the beginning one observes unresolved small contrast features, then tiny bubbles connected to sessile dislocation loops become visible. These dislocations are nucleated to relax the fluid pressure in the bubbles. To minimize the corresponding nucleation energy a partial dislocation loop is first nucleated, then a second partial with a complementary Burgers vector rapidly grows in the same plane and joins the first one. By measuring the mean distance X between precipitates for various annealing conditions one deduces the diffusion coefficient D of the water point defects with the help of the relation X/2 = (2D.t)1/2 where t is the duration of the annealing. One finds in the temperature range (350-1000 °C) for quartz

D(m2s-1) = 10-12 exp (—95 kJ mole-1/RT).

There is no visible effect of the confining pressure, at least between atmospheric pressure and 700 MPa. The α-β transition slightly affects the D values between 500 and 600 °C but except for this temperature range all the other experimental data fit well the above equation.

Two theoretical models of water precipitation are developed. They are based on two contrasted hypotheses about the initial mode of water incorporation. In the first case all the water content is assumed to be initially dissolved as a highly supersaturated concentration of substitutional (4H)Si point defects. Water precipitation would thus occur by homogeneous nucleation of critical embryos, this first stage being followed by a stage of growth while new critical embryos would be continuously nucleated. In contrast, in the second case one assumes that the concentration of point defects is initially the equilibrium concentration at the growth conditions which is very low. Almost all the water would thus be incorporated as tiny clusters of water molecules too small to be detected and precipitation would occur by intercluster diffusion, the bigger clusters growing at the expense of the smaller ones which would progressively redissolve. In fact none of these models fully renders account of the experimental results. These results can only be interpreted by an intermediate situation. Water is incorporated during growth as tiny clusters and as a supersaturated concentration of point defects as well. In the quartz studied extensively in this article this concentration of point defects becomes approximately equal to the equilibrium one only at P ⋍ 700 MPa, T ⋍ 550 °C.
Neutron diffraction investigation of strontianite, SrCO3
Authors: Jarosch Dietmar , Heger Gernot
Published: April 2019

The crystal structure of strontianite (a = 5.1039(3), b = 8.4022(4), c = 6.0220(4) Å ; Z = 4 ; space group : Pmcn) was refined with 520 independent neutron diffraction data to RW = 0.011 . The aplanarity of the CO3 group, d = 0.0218(5) Å, is smaller by 0.006 Å than that in aragonite ; the average C-O bond length turns out to be greater by 0.003 Å than in aragonite.
Electron paramagnetic resonance of a new Fe3+ centre in cassiterite
Authors: Ruck Régine , Dusausoy Yves , Gaite Jean-Marie
Published: April 2019

The EPR study of cassiterites of different origin reveals a new Fe3+ paramagnetic centre, present only in cassiterites rich in iron and tantalum. The X-band measurement of a single crystal has allowed to determine the second order constants of the fine structure of the spin-Hamiltonian : B2 = 0.186 ± 0.001 cm-1, B5 = 0. 116 ± 0.001 cm-1. The centre has the D2h Laue symmetry of the tin site. So, Fe3+ substitutes for Sn4+. Charge compensation is achieved locally by the mechanism :

2 Sn4+ → Fe3+ + Ta5+

This new centre named Sd3 has nearly the same characteristics as the Sd2 centre due to Fe3+ associated with Nb5+.
Zeolitization of a basaltic flow in a continental environment : an example of mass transfer under thermal control
Authors: Robert Christian , Goffé Bruno , Saliot Pierre
Published: April 2019

The zeolitization of basanite lava flows in the Plateau des Coirons, Ardèche, France, is controlled by the initial location of the flows in paleorivers and by the thermal evolution of the flows during cooling. It predates the development of a well-defined columnar jointing. The zeolites (phillipsite and chabazite) are associated with ferro-magnesian or aluminous smectites (and sometimes carbonates) and crystallize in basalt vesicles and veinlets, showing chemical variations between the top and the deeply altered base of the flow. Their distribution is accounted for by hydrothermal mass transfer driven by the temperature variations within the flow during cooling :

— The centre of the flow is at first hotter than the base. Ferro-magnesian smectites crystallize in the median part and aluminous smectites crystallize at the base of the flow.

— Then, the base of the flow becomes the hotter zone. Phillipsite crystallizes in the median part and ferro-magnesian smectites in the lower part of the flow.

— The end of cooling leads to a more general development of phillipsite and chabazite crystallization at the base.
Géochronologie U-Pb et leucogranites varisques : cas des massifs de Grandrieu (Lozère) et de la Porcherie (Limousin), Massif Central français
Authors: Lafon Jean-Michel , Respaut Jean-Patrick
Published: April 2019

U-Pb systematics is applied to the study of two leucogranites from the French Massif Central : the "La Porcherie" leucogranite which cross cuts major Variscan thrusts in the Saint-Germain-les-Belles area (Limousin), and the "Grandrieu" leucogranite which is associated with the Margeride calc-alkaline granite (Lozère). It is shown that :

1) The U-Pb system of zircon is unsuitable for the dating of the emplacement. For "La Porcherie" leucogranite, the zircons contain xenocryst core with euhedral overgrowths ; this old inherited component seems to be characteristic for leucogranites associated with major shear zones (Galicia, South Brittany). Such a feature is absent in the "Grandrieu" leucogranite.

2) Monazites appear to be a good tool for the geochemical and geochronological studies of leucogranites. U-Pb data for these monazites yield sub-concordant ages interpreted as emplacement ages : 317 ± 3 Ma for "La Porcherie" leucogranite, 305 ± 4 Ma for "Grandrieu" leucogranite.
Експериментальна перевірка ефективності підготовки майбутніх учителів основ здоров’я до застосування методики функціональної асиметрії мозку людини у професійній діяльності
Authors: Goncharenko M.S. , Stroilova D.V.
Published: April 2019

Мета роботи – Розробити механізм оцінки рівня реабілітаційного потенціалу шляхом математичного моделювання. Матеріал і методи. 120 чоловіків з розладами психіки і поведінки внаслідок вживання алкоголю віком 25-40 років розділені на 3 підгрупи (по 40 людей в кожній): І група – з високим рівнем реабілітаційного потенціалу, ІІ група – з середнім рівнем та ІІІ група – з низьким рівнем реабілітаційного потенціалу. Проведено анкетування та клінічне обстеження для визначення факторів, які представляють важливість для багатофакторного регресійного аналізу. Результати. Встановлено, що реабілітаційний потенціал людей з розладами психіки і поведінки внаслідок вживання алкоголю є комплексним показником, який поєднує різні фактори. Серед них виявлено: клініко-функціональні, експертні, психо-біологічні та соціально-реабілітаційні фактори реабілітаціного потенціалу. В кожній групі пацієнтів вони мають свою ступінь вираженості. Виявлений кореляційний взаємозв’язок між цими показниками дозволив розробити рівняння багатофакторної регресії. Висновок. Для створення індивідуальних програм фізичної терапії та ерготерапії доцільно застосовувати багатофакторну статистично обгрунтовану математичну модель реабілітаційного потенціалу. Показано, що об’єктивна та рання оцінка реабілітаційного потенціалу сприятиме плануванню адекватних цілісних програм фізичної терапії та ерготерапії для людей з розладами психіки і поведінки внаслідок вживання алкоголю. Це дозволить підвищувати рівень реабілітаційного потенціалу протягом всього періоду впровадження у практику програм фізичної терапії та ерготерапії для таких пацієнтів. Це в свою чергу може використовуватись не тільки для відновлення звичайних видів життєдіяльності, працездатності та соціалізації, але й в якості прогностичного фактора для встановлення успішності заходів фізичної терапії та ерготерапії.