bluuebunny/crossref_metadata_2025 huggingface dataset:
Detecting Fingerprints of Waterborne Bacteria on a Sensor
Authors: Saylan Yeşeren , Erdem Özgecan , Cihangir Nilüfer , Denizli Adil
Published: July 2019

Human fecal contamination is a crucial threat that results in difficulties in access to clean water. Enterococcus faecalis is a bacteria which is utilized as an indicator in polluted water. Nevertheless, existing strategies face several challenges, including low affinity and the need for labelling, which limit their access to large scale applications. Herein, a label-free fingerprint of the surface proteins of waterborne bacteria on a sensor was demonstrated for real-time bacteria detection from aqueous and water samples. The kinetic performance of the sensor was evaluated and shown to have a range of detection that spanned five orders of magnitude, having a low detection limit (3.4 × 104 cfu/mL) and a high correlation coefficient (R2 = 0.9957). The sensor also designated a high selectivity while other competitor bacteria were employed. The capability for multiple usage and long shelf-life are superior to other modalities. This is an impressive surface modification method that uses the target itself as a recognition element, ensuring a broad range of variability to replicate others with different structure, size and physical and chemical properties.
Coherent Magnetization Rotation of a Layered System Observed by Polarized Neutron Scattering under Grazing Incidence Geometry
Authors: Maruyama Ryuji , Bigault Thierry , Saerbeck Thomas , Honecker Dirk , Soyama Kazuhiko , Courtois Pierre
Published: July 2019

The in-plane magnetic structure of a layered system composed of polycrystalline grains smaller than the ferromagnetic exchange length was studied to elucidate the mechanism controlling the magnetic properties considerably different from the bulk using polarized neutron scattering under grazing incidence geometry. The measured result, together with quantitative analysis based on the distorted wave Born approximation, showed that the in-plane length of the area with a uniform orientation of moments ranging from 0.5–1.1 μ m was not significantly varied during the process of demagnetization followed by remagnetization. The obtained behavior of moments is in good agreement with the two-dimensional random anisotropy model where coherent magnetization rotation is dominant.
A Comprehensive Study of Orchid Seed Production Relative to Pollination Traits, Plant Density and Climate in an Urban Reserve in Western Australia
Authors: Brundrett Mark C.
Published: July 2019

The pollination of 20 common terrestrial orchids was studied in a 60-ha urban banksia and eucalypt dominated woodland in Western Australia. Five years of data (24,000 flowers, 6800 plants) measured fruit set relative to floral areas, capsule volumes, climate, phenology, pollination mechanisms, disturbance tolerance and demography. Pollination varied from 0–95% of flowers, floral displays from 90–3300 mm2 and capsules from 15–1300 mm3 per spike. Pollination traits strongly influenced outcomes, with self-pollination highest (59—95%), followed by sexually deceptive autumn or winter-flowering (18–39%), visual deception (0–48%) and sexually deceptive spring-flowering (13–16%). Pollination was limited by drought in autumn or spring and cool winter temperatures. Some orchids were resilient to drought and one formed seed after the leaves withered. Plant density had the greatest impact on fruit set for orchids forming large groups, especially for sexually deceptive pollination. Consequently, small group average (SGA) pollination was up to 4× greater than overall averages and peak seed production occurred in the best locations for genetic exchange and dispersal. SGA rates and seedpod volumes were strongly linked to clonality, but not to demographic trends. Resource competition limited flowering at higher plant densities and competition within spikes resulted in smaller, later-forming seedpods. Pollination data from co-occurring common orchids identified five evolutionary trade-offs linked to pollination, provided baseline data for rare species and revealed impacts of changing climate.
Symmetry Properties of Mixed and Heat Photo-Assisted Noise in the Quantum Hall Regime
Authors: Ronetti Flavio , Acciai Matteo , Ferraro Dario , Rech Jérôme , Jonckheere Thibaut , Martin Thierry , Sassetti Maura
Published: July 2019

We investigate the photo-assisted charge-heat mixed noise and the heat noise generated by periodic drives in Quantum Hall states belonging to the Laughlin sequence. Fluctuations of the charge and heat currents are due to weak backscattering induced in a quantum point contact geometry and are evaluated at the lowest order in the tunneling amplitude. Focusing on the cases of a cosine and Lorentzian periodic drive, we show that the different symmetries of the photo-assisted tunneling amplitudes strongly affect the overall profile of these quantities as a function of the AC and DC voltage contributions, which can be tuned independently in experiments.
An Extended Constructive Alignment Model in Teaching Electromagnetism to Engineering Undergraduates
Authors: Maxworth Ashanthi
Published: July 2019

Introducing abstract concepts to students from applied fields can be challenging. Electromagnetics is one of those courses where abstract concepts are introduced. This work presents a conceptual model which defines learning objectives in three levels for Engineering Electromagnetics. Each level is aligned with its own assessment and evaluation methods. The advantage is that the three-level learning objectives can be extended as student self-assess and instructor assessment rubrics, and a detailed implementation is presented here. This model gives students more accessibility to the learning objectives and increases the transparency of the learning and grading processes. The main goal of this conceptual model is to make students learn with the end in mind.
Self-Regulation as a Basic Element of the Professional Culture of Engineers
Authors: Odinokaya Maria , Krepkaia Tatyana , Karpovich Irina , Ivanova Tatiana
Published: July 2019

This paper addresses the problem of the formation of the self-regulation of educational activities of students studying in a technical university. The purpose of this paper is to discuss the problem of the self-regulation of educational activities of students, presenting data from an experimental study of students’ self-regulation. Special attention is paid to the distinction between the terms self-control and self-regulation. This paper presents data on the state of self-regulation among students studying technical specialties. The research methodology is based on the theoretical analysis of the results of scientific works, which present the main provisions on the formation of students’ self-regulation. The research methods include questioning and a methodical experiment. The stages of the formation of students’ self-regulation are highlighted, and the main directions of teachers’ work on the formation of students’ self-regulation are determined. The structure of self-regulation is described and the condition for its formation is justified using the technology of self-regulation formation. Based on the study, students’ self-regulation is linked to the awareness of their professional and personal goals, as well as to the level of regulatory and reflective qualities. Personal and professional self-improvement in the process of vocational education depends on the level of the formation of regulatory and reflexive qualities.
Homomorphic Encryption and Network Coding in IoT Architectures: Advantages and Future Challenges
Authors: Peralta Goiuri , Cid-Fuentes Raul G. , Bilbao Josu , Crespo Pedro M.
Published: July 2019

The introduction of the Internet of Things (IoT) is creating manifold new services and opportunities. This new technological trend enables the connection of a massive number of devices among them and with the Internet. The integration of IoT with cloud platforms also provides large storage and computing capabilities, enabling Big Data analytics and bidirectional communication between devices and users. Novel research directions are showing that Network Coding (NC) can increase the robustness and throughput of wireless networks, as well as that Homomorphic Encryption (HE) can be used to perform computations in the cloud while maintaining data privacy. In this paper, we overview the benefits of NC and HE along the entire vertical of cloud-based IoT architectures. By merging both technologies, the architecture may offer manifold advantages: First, it provides end-to-end data privacy, from end-devices to end-users. Second, sensitive data can be stored in public cloud platforms without concern about their privacy. In addition, clouds can perform advanced operations in a confidential manner, without the need to access actual data. Finally, latency can be reduced and the reliability of the system is increased. We show state-of-the-art works that demonstrate the role of both technologies in this type of architectures on a review basis. Furthermore, we describe the main characteristics of NC and HE and also discuss their benefits and limitations, as well as the emerging open challenges.
Blockchain-Based Secure Storage Management with Edge Computing for IoT
Authors: Nyamtiga Baraka William , Sicato Jose Costa Sapalo , Rathore Shailendra , Sung Yunsick , Park Jong Hyuk
Published: July 2019

As a core technology to manage decentralized systems, blockchain is gaining much popularity to deploy such applications as smart grid and healthcare systems. However, its utilization in resource-constrained mobile devices is limited due to high demands of resources and poor scalability with frequent-intensive transactions. Edge computing can be integrated to facilitate mobile devices in offloading their mining tasks to cloud resources. This integration ensures reliable access, distributed computation and untampered storage for scalable and secure transactions. It is imperative therefore that crucial issues of security, scalability and resources management be addressed to achieve successful integration. Studies have been conducted to explore suitable architectural requirements, and some researchers have applied the integration to deploy some specific applications. Despite these efforts, however, issues of anonymity, adaptability and integrity still need to be investigated further to attain a practical, secure decentralized data storage. We based our study on peer-to-peer and blockchain to achieve an Internet of Things (IoT) design supported by edge computing to acquire security and scalability levels needed for the integration. We investigated existing blockchain and associated technologies to discover solutions that address anonymity, integrity and adaptability issues for successful integration of blockchain in IoT systems. The discovered solutions were then incorporated in our conceptual design of the decentralized application prototype presented for secure storage of IoT data and transactions.
Fine Frequency Acquisition Scheme in Weak Signal Environment for a Communication and Navigation Fusion System
Authors: Deng Zhongliang , Jia Buyun , Tang Shihao , Fu Xiao , Mo Jun
Published: July 2019

A novel communication and navigation fusion system (CNFS) was developed to realized high accuracy positioning in constrained conditions. Communication and navigation fusion signal transmitted by base stations are in the same time and frequency band but are allocated different power levels. The positioning receiver of CNFS requires signal coverage of at least four base stations to realize positioning. The improvement of receiver sensitivity is an important way to expand signal coverage of base station. As an essential stage of signal processing in CNFS positioning receiver, signal acquisition requires a trade-off between improvement of acquisition frequency accuracy and reduction in computational load. A new acquisition algorithm called PMF-FC-BA-FFT method is proposed to acquire the carrier frequency accurately with lower computational load in a weak signal environment. The received signal is firstly filtered by partially matched filters (PMF) with local replica pseudorandom noise (PRN) sequences being coefficients to strip off the PRN code in the signal. Frequency compensation (FC) was performed to avoid the large attenuation in block accumulation (BA) and generate a series of signals with a small frequency offset step. Block accumulation was then executed. Finally, the acquisition detection was performed based on a series of fast Fourier transformation (FFT) outputs to obtain acquisition results with fine frequency estimation. Simulations and experimental tests results show that the proposed method can realize high accuracy frequency acquisition in a weak signal environment with fewer computational resources compared with existing acquisition methods.
Polarization-Independent Tunable Ultra-Wideband Meta-Absorber in Terahertz Regime
Authors: Liu Shuxiang , Deng Li , Qu Meijun , Li Shufang
Published: July 2019

In this paper, we demonstrate an ultra-broadband terahertz bilayer graphene-based absorption structure. It has two stacking graphene layers sandwiched by an Au cylinders array, backed by a metallic ground plane. Au cylinders are used to adjust the input impedance to be closely matched to the free space, enabling an ultra-broadband absorption. The absorption spectrum of the bilayer graphene-based absorption structure with Au cylinder arrays shows a bandwidth of 7.1 THz, with the absorption exceeding 80%. The achieved ultra-wideband THz meta-absorber has high absorption, independence of polarization property, simultaneously, illustrating to be a promising candidate for teraherz broadband absorption application.
Machine Learning Interpretability: A Survey on Methods and Metrics
Authors: Carvalho Diogo V. , Pereira Eduardo M. , Cardoso Jaime S.
Published: July 2019

Machine learning systems are becoming increasingly ubiquitous. These systems’s adoption has been expanding, accelerating the shift towards a more algorithmic society, meaning that algorithmically informed decisions have greater potential for significant social impact. However, most of these accurate decision support systems remain complex black boxes, meaning their internal logic and inner workings are hidden to the user and even experts cannot fully understand the rationale behind their predictions. Moreover, new regulations and highly regulated domains have made the audit and verifiability of decisions mandatory, increasing the demand for the ability to question, understand, and trust machine learning systems, for which interpretability is indispensable. The research community has recognized this interpretability problem and focused on developing both interpretable models and explanation methods over the past few years. However, the emergence of these methods shows there is no consensus on how to assess the explanation quality. Which are the most suitable metrics to assess the quality of an explanation? The aim of this article is to provide a review of the current state of the research field on machine learning interpretability while focusing on the societal impact and on the developed methods and metrics. Furthermore, a complete literature review is presented in order to identify future directions of work on this field.
High-Accuracy Parameter Identification Method for Equivalent-Circuit Models of Lithium-Ion Batteries Based on the Stochastic Theory Response Reconstruction
Authors: Wen Fazheng , Duan Bin , Zhang Chenghui , Zhu Rui , Shang Yunlong , Zhang Junming
Published: July 2019

The precision of battery modeling is usually determined by the identification of model parameters, which is dependent on the measured outside characteristic data of batteries. However, there is a lot of noise because of the environment noise and measurement error, leading to poor estimation accuracy of model parameters. This paper proposes a stochastic theory response reconstruction (STRR) method to reconstruct the measured battery voltage data, which can eliminate the noise interference and ensure high-precision model parameter identification. The relationship between the battery voltage and current is established based on the the second-order equivalent circuit model (ECM) by the convolution theorem, and the impulse function is calculated by the correlation function between the measured voltage and current. Then, the battery voltage is reconstructed and used to identify model parameters with the recursive least squares (RLS) algorithm. All data for model parameter identification is produced through the pseudo random binarysequence (PRBS) excitation signal. Finally, the Urban Dynamometer Driving Schedule (UDDS) and Federal Urban Driving Schedule (FUDS) tests are conducted to validate the performance of the proposed method. Experimental results show that when compared with the traditional solution using low-pass filter, the proposed method can eliminate the noise interference more effectively and has higher identification accuracy.
Visual Closed-Loop Dynamic Model Identification of Parallel Robots Based on Optical CMM Sensor
Authors: Li Pengcheng , Ghasemi Ahmad , Xie Wenfang , Tian Wei
Published: July 2019

Parallel robots present outstanding advantages compared with their serial counterparts; they have both a higher force-to-weight ratio and better stiffness. However, the existence of closed-chain mechanism yields difficulties in designing control system for practical applications, due to its highly coupled dynamics. This paper focuses on the dynamic model identification of the 6-DOF parallel robots for advanced model-based visual servoing control design purposes. A visual closed-loop output-error identification method based on an optical coordinate-measuring-machine (CMM) sensor for parallel robots is proposed. The main advantage, compared with the conventional identification method, is that the joint torque measurement and the exact knowledge of the built-in robot controllers are not needed. The time-consuming forward kinematics calculation, which is employed in the conventional identification method of the parallel robot, can be avoided due to the adoption of optical CMM sensor for real time pose estimation. A case study on a 6-DOF RSS parallel robot is carried out in this paper. The dynamic model of the parallel robot is derived based on the virtual work principle, and the built dynamic model is verified through Matlab/SimMechanics. By using an outer loop visual servoing controller to stabilize both the parallel robot and the simulated model, a visual closed-loop output-error identification method is proposed and the model parameters are identified by using a nonlinear optimization technique. The effectiveness of the proposed identification algorithm is validated by experimental tests.
Low-Cost Multi-Objective Optimization of Multiparameter Antenna Structures Based on the l1 Optimization BPNN Surrogate Model
Authors: Dong Jian , Qin Wenwen , Mo Jinjun
Published: July 2019

The development of modern wireless communication systems not only requires the antenna to be lightweight, low cost, easy to manufacture and easy to integrate but also imposes requirements on the miniaturization, wideband, and multiband design of the antenna. Therefore, designing an antenna that quickly and effectively meets multiple performance requirements is of great significance. To solve the problem of the large computational cost of traditional multi-objective antenna design methods, this paper proposes a backpropagation neural network surrogate model based on l1 optimization (l1-BPNN). The l1 optimization method tends to punish larger weight values and select smaller weight values so as to preserve a small amount of important weights and reset relatively unimportant weights to zero. By using l1 optimization method, the network mapping structure can be automatically adjusted to achieve the most suitable and compact structure of the surrogate model. Furthermore, for multi-parameter antenna design problems, a fast multi-objective optimization framework is constructed using the proposed l1-BPNN as a surrogate model. The framework is illustrated using a miniaturized multiband antenna design case, and a comparison with previously published methods, as well as numerical validation, is also provided.
Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing
Authors: Wang Zhanle , Paranjape Raman , Chen Zhikun , Zeng Kai
Published: July 2019

Demand response (DR) programs encourage consumers to adapt the time of using electricity based on certain factors, such as cost of electricity, renewable energy availability, and ancillary request. It is one of the most economical methods to improve power system stability and energy efficiency. Residential electricity consumption occupies approximately one-third of global electricity usage and has great potential in DR applications. In this study, we propose a multi-agent optimization approach to incorporate residential DR flexibility into the power system and electricity market. The agents collectively optimize their own interests; meanwhile, the global optimal solution is achieved. The agent perceives its environment, predicts electricity consumption, and forecasts electricity price, based on which it takes intelligent actions to minimize electrical energy cost and time delay of using household appliances. The decision-making action is formulated into a convex program (CP) model. A distributed heuristic algorithm is developed to solve the proposed multi-agent optimization model. Case studies and numerical analysis show promising results with low variation of the aggregated load profile and reduction of electrical energy cost. The proposed approaches can be utilized to investigate various emerging technologies and DR strategies.
Model Predictive Control of Uninterruptible Power Supply with Robust Disturbance Observer
Authors: Danayiyen Yahya , Lee Kyungsuk , Choi Minho , Lee Young Il
Published: July 2019

This paper presents a robust continuous control set model predictive control (CCS-MPC) method to control the output voltage of a three-phase inverter in uninterruptible power supplies (UPS). A robust disturbance observer (DOB) is proposed to estimate the load current of the three-phase UPS without a steady-state error, taking the effect of model uncertainties into account. A CCS-MPC is designed using the DOB for reference voltage tracking purpose, and input constraints are considered in the controller design to calculate the optimal control input. Model uncertainties are defined using polytopic uncertainty class, and a linear matrix inequality (LMI) optimization method is used to compute the optimal observer gain matrix. Another robust controller (RC) is designed based on the DOB and compared with CCS-MPC. The effectiveness of the proposed method (the DOB based CCS-MPC) is evaluated for resistive, inductive, and nonlinear loads then compared with other control methods using a three-phase 5-KVA laboratory experiment UPS system.
Reversible Efficiency Variation of Tandem Amorphous/Microcrystalline Si Photovoltaic Modules in Outdoor Operation
Authors: Ricco Galluzzo Fabio , Gerardi Cosimo , Canino Andrea , Lombardo Salvatore
Published: July 2019

The Staebler-Wronski effect in amorphous silicon based photovoltaic devices is responsible for degradation of their power conversion efficiency, within approximately the first one thousand hours of light soaking. Several experimental studies led to highlight the performance instability phenomena for the mentioned devices, underling that recovery and improvement of such performance are observable, by subjecting such devices (both of single-junction and tandem types) to DC reverse bias stresses under illumination, or to operation in the Maximum Power Point (MPP) under variable conditions of temperature and illumination. In this work, we present and discuss the results of novel recent outdoor tests on stabilized specimens (i.e., exposed to 1000 h extended light soaking, before our tests) of tandem amorphous/microcrystalline Si (a-Si/µc-Si) photovoltaic (PV) minimodules operating in their MPP, by analyzing the causes of the performance instability effects, systematically observed on a daily scale. During the mentioned tests, we have monitored the solar cell operating temperature and the incident solar spectrum at various times in different days to verify the effect of cell temperature and solar spectrum changes on the cell performances. The experimental results show a clear correlation between performance improvements of the photovoltaic modules and their thermal history during the outdoor tests, proving the interplay between defect build-up at a lower temperature and defect annealing at a higher temperature, taking place in the solar cells operated in MPP during conventional outdoor operation.
Assessment of Offshore Wind Characteristics and Wind Energy Potential in Bohai Bay, China
Authors: Yu Jianxing , Fu Yiqin , Yu Yang , Wu Shibo , Wu Yuanda , You Minjie , Guo Shuai , Li Mu
Published: July 2019

Wind energy, one of the most sustainable renewable energy sources, has been extensively developed worldwide. However, owing to the strong regional and seasonal differences, it is necessary to first evaluate wind energy resources in detail. In this study, the offshore wind characteristics and wind energy potential of Bohai Bay (38.7° N, 118.7° E), China, were statistically analyzed using two-year offshore wind data with a time interval of one second. Furthermore, Nakagami and Rician distributions were used for wind energy resource assessment. The results show that the main wind direction in Bohai Bay is from the east (−15°–45°), with a speed below 12 m/s, mainly ranging from 4 to 8 m/s. The main wind speed ranges in April and October are higher than those in August and December. The night wind speed is generally higher than that in the daytime. The Nakagami and Rician distributions performed reasonably in calculating the wind speed distributions and potential assessments. However, Nakagami distribution provided better wind resource assessment in this region. The wind potential assessment results suggest that Bohai Bay could be considered as a wind class I region, with east as the dominant wind direction.
Exploring Low-Carbon Futures: A Web Service Approach to Linking Diverse Climate-Energy-Economy Models
Authors: Belete Getachew F. , Voinov Alexey , Arto Iñaki , Dhavala Kishore , Bulavskaya Tatyana , Niamir Leila , Moghayer Saeed , Filatova Tatiana
Published: July 2019

The use of simulation models is essential when exploring transitions to low-carbon futures and climate change mitigation and adaptation policies. There are many models developed to understand socio-environmental processes and interactions, and analyze alternative scenarios, but hardly one single model can serve all the needs. There is much expectation in climate-energy research that constructing new purposeful models out of existing models used as building blocks can meet particular needs of research and policy analysis. Integration of existing models, however, implies sophisticated coordination of inputs and outputs across different scales, definitions, data and software. This paper presents an online integration platform which links various independent models to enhance their scope and functionality. We illustrate the functionality of this web platform using several simulation models developed as standalone tools for analyzing energy, climate and economy dynamics. The models differ in levels of complexity, assumptions, modeling paradigms and programming languages, and operate at different temporal and spatial scales, from individual to global. To illustrate the integration process and the internal details of our integration framework we link an Integrated Assessment Model (GCAM), a Computable General Equilibrium model (EXIOMOD), and an Agent Based Model (BENCH). This toolkit is generic for similar integrated modeling studies. It still requires extensive pre-integration assessment to identify the ‘appropriate’ models and links between them. After that, using the web service approach we can streamline module coupling, enabling interoperability between different systems and providing open access to information for a wider community of users.
Non-Intrusive Load Monitoring System Based on Convolution Neural Network and Adaptive Linear Programming Boosting
Authors: Min Chao , Wen Guoquan , Yang Zhaozhong , Li Xiaogang , Li Binrui
Published: July 2019

Non–intrusive load monitoring based on power measurements is a promising topic of appliance identification in the research of smart grid; where the key is to avoid the power sub-item measurement in load monitoring. In this paper; a three–step non–intrusive load monitoring system (TNILM) is proposed. Firstly; a one dimension convolution neural network (CNN) is constructed based on the structure of GoogLeNet with 2D convolution; which can zoom in on the differences in features between the different appliances; and then effectively extract various transient features of appliances. Secondly; comparing with various classifiers; the Linear Programming boosting with adaptive weights and thresholds (ALPBoost) is proposed and applied to recognize single–appliance and multiple–appliance. Thirdly; an update process is adopted to adjust and balance the parameters between the one dimension CNN and ALPBoost on–line. The TNILM is tested on a real–world power consumption dataset; which comprises single or multiple appliances potentially operated simultaneously. The experiment result shows the effectiveness of the proposed method in both identification rates.
Carbon Dioxide Capture from Flue Gas Using Tri-Sodium Phosphate as an Effective Sorbent
Authors: Sakpal Tushar , Kumar Asheesh , Aman Zachary M. , Kumar Rajnish
Published: July 2019

Fossil fuels are dominant as an energy source, typically producing carbon dioxide (CO2) and enhancing global climate change. The present work reports the application of low-cost tri-sodium phosphate (TSP) to capture CO2 from model flue gas (CO2 + N2) mixture, in a batch mode and fixed-bed setup. It is observed that TSP has a high CO2 capture capacity as well as high CO2 selectivity. At ambient temperature, TSP shows a maximum CO2 capture capacity of 198 mg CO2/g of TSP. Furthermore, the CO2 capture efficiency of TSP over a flue gas mixture was found to be more than 90%. Fresh and spent materials were characterized using powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), and Fourier transformed infrared spectroscopy (FTIR). Preliminary experiments were also conducted to evaluate the performance of regenerated TSP. The spent TSP was regenerated using sodium hydroxide (NaOH) and its recyclability was tested for three consecutive cycles. A conceptual prototype for post-combustion CO2 capture based on TSP material has also been discussed.
Seasonal Variation in Phenolic Compounds and Antioxidant Activity in Leaves of Cyclocarya paliurus (Batal.) Iljinskaja
Authors: Cao Yanni , Fang Shengzuo , Fu Xiangxiang , Shang Xulan , Yang Wanxia
Published: July 2019

Cyclocarya paliurus (Batal.) Iljinskaja is a plant with nutraceutical importance since its leaves have been used historically as folk medicines for hundreds of years. The content of 10 phenolic compounds was determined throughout the growing season by high-performance liquid chromatography (HPLC) with UV detector, while the antioxidant activities of C. paliurus leaf extracts were evaluated by 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt radical cation (ABTS), and ferric reducing antioxidant power (FRAP) methods. Seasonal variations in phenolic concentration and antioxidant activity as well as linkage between the phenolic composition and antioxidant activity were assessed. A significant seasonal variation of phenolic compounds was observed in the leaves and the highest content appeared in May, July, and November. Seventy percent ethanol extract of C. paliurus leaves possessed a good radical scavenging potency. Meanwhile, a significant correlation between antioxidant activities and contents of phenolics was detected. Results of the relationship between molecular structures and their antioxidant activities showed that both the number and configuration of H-donating hydroxyl groups are the main structural features influencing the antioxidant capacity of phenolics, while glycosylation may reduce the antioxidant capacity. The information provided by this study not only revealed the accumulative dynamics of phenolic compounds, but also established a basis for determining the optimal time for harvesting to improve the content of beneficial compounds in the leaves of C. paliurus in the future.
The Impact of Near Natural Forest Management on the Carbon Stock and Sequestration Potential of Pinus massoniana (Lamb.) and Cunninghamia lanceolata (Lamb.) Hook. Plantations
Authors: Ming Angang , Yang Yujing , Liu Shirong , Nong You , Li Hua , Tao Yi , Sun Dongjing , Lei Liqun , Zeng Ji , An Ning
Published: July 2019

Quantifying the impact of forest management on carbon (C) stock is important for evaluating and enhancing the ability of plantations to mitigate climate change. Near natural forest management (NNFM) through species enrichment planting in single species plantations, structural adjustment, and understory protection is widely used in plantation management. However, its long-term effect on forest ecosystem C stock remains unclear. We therefore selected two typical coniferous plantations in southwest China, Pinus massoniana (Lamb.) and Cunninghamia lanceolate (Lamb.) Hook., to explore the effects of long-term NNFM on ecosystem C storage. The C content and stock of different components in the pure plantations of P. massoniana (PCK) and C. lanceolata (CCK), and their corresponding near natural managed forests (PCN and CCN, respectively), were investigated during eight years of NNFM beginning in 2008. In 2016, there was no change in the vegetation C content, while soil C content in the 0–20 cm and 20–40 cm layers significantly increased, compared to the pure forests. In the P. massoniana and C. lanceolata plantations, NNFM increased the ecosystem C stock by 31.8% and 24.3%, respectively. Overall, the total C stock of soil and arborous layer accounted for 98.2%–99.4% of the whole ecosystem C stock. The increase in the biomass of the retained and underplanted trees led to a greater increase in the arborous C stock in the near natural forests than in the controls. The NNFM exhibited an increasingly positive correlation with the ecosystem C stock over time. Long-term NNFM enhances ecosystem C sequestration by increasing tree growth rate at individual and stand scales, as well as by likely changing the litter decomposition rate resulting from shifts in species composition and stand density. These results indicated that NNFM plays a positive role in achieving multi-objective silviculture and climate change mitigation.
A New Method for Measuring Fabric Drape with a Novel Parameter for Classifying Fabrics
Authors: Pan Ning , Lin Chengwei , Xu Jun
Published: July 2019

With huge varieties of fabrics, the first challenge for any performance evaluation is to categorize the vast types of the products into fewer, more homogeneous and akin groups. Classification or sorting is arguably the first step of any scientific investigation, and comparison of product quality is meaningful only when conducted within a group of comparable products. A new criterion termed fabric linear density λ is first proposed in this paper so that fabrics can in general be divided into four groups. The derivation and validation of this parameter are provided. The importance of fabric drape is almost self-evident, but there are still no effective ways to easily measure this fabric attribute. A few existing instruments, notably the Cusick Drapemeter, suffer from low repeatability and low sensitivity and are hence not widely or frequently used. It is demonstrated in this study that, by using the PhabrOmeter, along with the fabric linear density λ, a much more efficient alternative for fabric drape test can be achieved. By actually testing 40 various fabrics, the principle, procedure and results of this method is presented in this paper.
Patagonian Berries: Healthy Potential and the Path to Becoming Functional Foods
Authors: Fuentes Lida , Figueroa Carlos R. , Valdenegro Monika , Vinet Raúl
Published: July 2019

In recent years, there has been an increasing interest in studying food and its derived ingredients that can provide beneficial effects for human health. These studies are helping to understand the bases of the ancestral use of several natural products, including native fruits as functional foods. As a result, the polyphenol profile and the antioxidant capacity of the extracts obtained from different Patagonian native berries have been described. This review aims to provide valuable information regarding fruit quality, its particular compound profile, and the feasibility of producing functional foods for human consumption to prevent disorders such as metabolic syndrome and cardiovascular diseases. We also discuss attempts concerning the domestication of these species and generating knowledge that strengthens their potential as traditional fruits in the food market and as a natural heritage for future generations. Finally, additional efforts are still necessary to fully understand the potential beneficial effects of the consumption of these berries on human health, the application of suitable technology for postharvest improvement, and the generation of successfully processed foods derived from Patagonian berries.
Fermented Sea Tangle (Laminaria japonica Aresch) Suppresses RANKL-Induced Osteoclastogenesis by Scavenging ROS in RAW 264.7 Cells
Authors: Jeong Jin-Woo , Ji Seon Yeong , Lee Hyesook , Hong Su Hyun , Kim Gi-Young , Park Cheol , Lee Bae-Jin , Park Eui Kyun , Hyun Jin Won , Jeon You-Jin , Choi Yung Hyun
Published: July 2019

Sea tangle (Laminaria japonica Aresch), a brown alga, has been used for many years as a functional food ingredient in the Asia-Pacific region. In the present study, we investigated the effects of fermented sea tangle extract (FST) on receptor activator of nuclear factor-κB (NF-κB) ligand (RANKL)-stimulated osteoclast differentiation, using RAW 264.7 mouse macrophage cells. FST was found to inhibit the RANKL-stimulated activation of tartrate-resistance acid phosphatase (TRAP) and F-actin ring structure formation. FST also down-regulated the expression of osteoclast marker genes like TRAP, matrix metalloproteinase-9, cathepsin K and osteoclast-associated receptor by blocking RANKL-induced activation of NF-κB and expression of nuclear factor of activated T cells c1 (NFATc1), a master transcription factor. In addition, FST significantly abolished RANKL-induced generation of reactive oxygen species (ROS) by activation of nuclear factor-erythroid 2-related factor 2 (Nrf2) and its transcriptional targets. Hence, it seems likely that FST may have anti-osteoclastogenic potential as a result of its ability to inactivate the NF-κB-mediated NFATc1 signaling pathway and by reducing ROS production through activation of the Nrf2 pathway. Although further studies are needed to inquire its efficacy in vivo, FST appears to have potential use as an adjunctive or as a prophylactic treatment for osteoclastic bone disease.
Analysis of Sensory Properties in Foods: A Special Issue
Authors: Chambers Edgar
Published: July 2019

The sensory properties of foods are the most important reason people eat the foods they eat. What those properties are and how we best measure those properties are critical to understanding food and eating behavior. Appearance, flavor, texture, and even the sounds of food can impart a desire to eat or cause us to dismiss the food as unappetizing, stale, or even inappropriate from a cultural standpoint. This special issue focuses on how sensory properties, including consumer perceptions, are measured, the specific sensory properties of various foods, which properties might be most important in certain situations, and how consumers use sensory attributes and consumer information to make decisions about what they believe about food and what they will eat.
SiNPle: Fast and Sensitive Variant Calling for Deep Sequencing Data
Authors: Ferretti Luca , Tennakoon Chandana , Silesian Adrian , Freimanis Graham , Ribeca Paolo
Published: July 2019

Current high-throughput sequencing technologies can generate sequence data and provide information on the genetic composition of samples at very high coverage. Deep sequencing approaches enable the detection of rare variants in heterogeneous samples, such as viral quasi-species, but also have the undesired effect of amplifying sequencing errors and artefacts. Distinguishing real variants from such noise is not straightforward. Variant callers that can handle pooled samples can be in trouble at extremely high read depths, while at lower depths sensitivity is often sacrificed to specificity. In this paper, we propose SiNPle (Simplified Inference of Novel Polymorphisms from Large coveragE), a fast and effective software for variant calling. SiNPle is based on a simplified Bayesian approach to compute the posterior probability that a variant is not generated by sequencing errors or PCR artefacts. The Bayesian model takes into consideration individual base qualities as well as their distribution, the baseline error rates during both the sequencing and the PCR stage, the prior distribution of variant frequencies and their strandedness. Our approach leads to an approximate but extremely fast computation of posterior probabilities even for very high coverage data, since the expression for the posterior distribution is a simple analytical formula in terms of summary statistics for the variants appearing at each site in the genome. These statistics can be used to filter out putative SNPs and indels according to the required level of sensitivity. We tested SiNPle on several simulated and real-life viral datasets to show that it is faster and more sensitive than existing methods. The source code for SiNPle is freely available to download and compile, or as a Conda/Bioconda package.
Comparative Genomic Analysis of Rice with Contrasting Photosynthesis and Grain Production under Salt Stress
Authors: Lekklar Chakkree , Suriya-arunroj Duangjai , Pongpanich Monnat , Comai Luca , Kositsup Boonthida , Chadchawan Supachitra , Buaboocha Teerapong
Published: July 2019

Unfavourable environmental conditions, including soil salinity, lead to decreased rice (Oryza sativa L.) productivity, especially at the reproductive stage. In this study, we examined 30 rice varieties, which revealed significant differences in the photosynthetic performance responses under salt stress conditions during the reproductive stage, which ultimately affected yield components after recovery. In rice with a correlation between net photosynthetic rate (PN) and intercellular CO2 concentration (Ci) under salt stress, PN was found to be negatively correlated with filled grain number after recovery. Applying stringent criteria, we identified 130,317 SNPs and 15,396 InDels between two “high-yield rice” varieties and two “low-yield rice” varieties with contrasting photosynthesis and grain yield characteristics. A total of 2089 genes containing high- and moderate-impact SNPs or InDels were evaluated by gene ontology (GO) enrichment analysis, resulting in over-represented terms in the apoptotic process and kinase activity. Among these genes, 262 were highly expressed in reproductive tissues, and most were annotated as receptor-like protein kinases. These findings highlight the importance of variations in signaling components in the genome and these loci can serve as potential genes in rice breeding to produce a variety with salt avoidance that leads to increased yield in saline soil.
Characterization and Phylogenetic Implications of the Complete Mitochondrial Genome of Syrphidae
Authors: Li Hu
Published: July 2019

In this study, the complete mitochondrial genomes (mitogenomes) of two hoverfly species of Korinchia angustiabdomena (Huo, Ren, and Zheng) and Volucella nigricans Coquillett (Diptera: Syrphidae) were determined and analyzed. The circular mitogenomes were 16,473 bp in K. angustiabdomena (GenBank No. MK870078) and 15,724 bp in V. nigricans (GenBank No. MK870079). Two newly sequenced mitogenomes both contained 37 genes, and the gene order was similar with other syrphine species. All the protein-coding genes (PCGs) were started with the standard ATN codons; and most of PCGs were terminated with a TAA stop codon, while ND1 in K. angustiabdomena ended with a TAG codon, and ND5 terminated with truncated T stop codons in both species. The phylogenetic relationship between K. angustiabdomena and V. nigricans with related lineages was reconstructed using Bayesian inference and Maximum-likelihood analyses. The monophyly of each family considered within Muscomorpha was confirmed by the clades in the phylogenetic tree, and superfamily of the Oestroidea (Calliphoridae, Sarcophagidae, and Oestridae) was unexpectedly found to be a paraphyletic group based on our selected data. This mitogenome information for K. angustiabdomena and V. nigricans could facilitate future studies of evolutionarily related insects.
Rodent Models Assessing Mammary Tumor Prevention by Soy or Soy Isoflavones
Authors: Moorehead Roger A.
Published: July 2019

While epidemiological studies performed in Asian countries generally show that high levels of dietary soy are associated with reduced breast cancer risk, studies in Western countries have typically failed to show this correlation. In an attempt to model the preventative actions of soy on mammary tumor development, rodent models have been employed. Thirty-four studies were identified that evaluated the impact of soy products or purified soy isoflavones on mammary tumor initiation (studies evaluating established mammary tumors or mammary tumor cell lines were not included) and these studies were separated into mammary tumors induced by chemical carcinogens or transgenic expression of oncogenes based on the timing of soy administration. Regardless of when soy-based diets or purified isoflavones were administered, no consistent protective effects were observed in either carcinogen-induced or oncogene-induced mammary tumors. While some studies demonstrated that soy or purified isoflavones could reduce mammary tumor incidence, other studies showed either no effect or tumor promoting effects of soy products or isoflavones. Most importantly, only five studies found a decrease in mammary tumor incidence and six studies observed a decrease in tumor multiplicity, two relevant measures of the tumor preventative effects of soy or isoflavones. The variable outcomes of the studies examined were not completely surprising given that few studies employed the same experimental design. Future studies should be carefully designed to more accurately emulate soy consumption observed in Asian cultures including lifetime exposure to less refined soy products and potentially the incorporation of multigenerational feeding studies.