bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection of Multiple Intracellular Bacterial Pathogens in Haemaphysalis flava Ticks Collected from Hedgehogs in Central China
Authors: Fang Li-Zhu , Lei Si-Cong , Yan Zhi-Jian , Xiao Xiao , Liu Jian-Wei , Gong Xiao-Qing , Yu Hao , Yu Xue-Jie
Published: January 2021

Tickborne intracellular bacterial pathogens including Anaplasma, Coxiella burnetti, Ehrlichia, and Rickettsia cause emerging infectious diseases worldwide. PCR was used to amplify the genes of these pathogens in Haemaphysalis flava ticks collected from hedgehogs in Central China. Among 125 samples including 20 egg batches, 24 engorged females, and 81 molted male and female adult ticks, the DNA sequences and phylogenetic analysis showed that the minimum infection rate of the ticks was 4% (5/125) for A. bovis, 3.2% (4/125) for C. burnetti, 9.6%, (12/125) for E. ewingii, and 5.6% for Rickettsia including R.japonica (3.2%, 4/125) and R. raoultii (2.4%, 3/125), respectively. The prevalence of these pathogens was significantly higher in dead engorged females (83.3%, 20/24) than in eggs (5%, 1/20) and molted ticks (8.6%, 7/81). Our study indicated that H. flava ticks could be infected with multiple species of tickborne pathogens including Anaplasma, C. burnetti, Ehrlichia, and Rickettsia in Central China, and the prevalence of these pathogens was reduced during transovarial and transstadial transmission in ticks, suggesting that ticks may not be real reservoirs but only vectors for these tickborne pathogens.
The Roles of Inflammasomes in Host Defense against Mycobacterium tuberculosis
Authors: Ma Jialu , Zhao Shasha , Gao Xiao , Wang Rui , Liu Juan , Zhou Xiangmei , Zhou Yang
Published: January 2021

Mycobacterium tuberculosis (MTB) infection is characterized by granulomatous lung lesions and systemic inflammatory responses during active disease. Inflammasome activation is involved in regulation of inflammation. Inflammasomes are multiprotein complexes serving a platform for activation of caspase-1, which cleaves the proinflammatory cytokines such as interleukin-1β (IL-1β) and IL-18 into their active forms. These cytokines play an essential role in MTB control. MTB infection triggers activation of the nucleotide-binding domain, leucine-rich-repeat containing family, pyrin domain-containing 3 (NLRP3) and absent in melanoma 2 (AIM2) inflammasomes in vitro, but only AIM2 and apoptosis-associated speck-like protein containing a caspase-activation recruitment domain (ASC), rather than NLRP3 or caspase-1, favor host survival and restriction of mycobacterial replication in vivo. Interferons (IFNs) inhibits MTB-induced inflammasome activation and IL-1 signaling. In this review, we focus on activation and regulation of the NLRP3 and AIM2 inflammasomes after exposure to MTB, as well as the effect of inflammasome activation on host defense against the infection.
Progress in Mesoporous Silica Nanoparticles as Drug Delivery Agents for Cancer Treatment
Authors: Mohamed Isa Eleen Dayana , Ahmad Haslina , Abdul Rahman Mohd Basyaruddin , Gill Martin R.
Published: January 2021

Cancer treatment and therapy have made significant leaps and bounds in these past decades. However, there are still cases where surgical removal is impossible, metastases are challenging, and chemotherapy and radiotherapy pose severe side effects. Therefore, a need to find more effective and specific treatments still exists. One way is through the utilization of drug delivery agents (DDA) based on nanomaterials. In 2001, mesoporous silica nanoparticles (MSNs) were first used as DDA and have gained considerable attention in this field. The popularity of MSNs is due to their unique properties such as tunable particle and pore size, high surface area and pore volume, easy functionalization and surface modification, high stability and their capability to efficiently entrap cargo molecules. This review describes the latest advancement of MSNs as DDA for cancer treatment. We focus on the fabrication of MSNs, the challenges in DDA development and how MSNs address the problems through the development of smart DDA using MSNs. Besides that, MSNs have also been applied as a multifunctional DDA where they can serve in both the diagnostic and treatment of cancer. Overall, we argue MSNs provide a bright future for both the diagnosis and treatment of cancer.
Effect of Three Nanoparticles (Se, Si and Cu) on the Bioactive Compounds of Bell Pepper Fruits under Saline Stress
Authors: González-García Yolanda , Cárdenas-Álvarez Claribel , Cadenas-Pliego Gregorio , Benavides-Mendoza Adalberto , Cabrera-de-la-Fuente Marcelino , Sandoval-Rangel Alberto , Valdés-Reyna Jesús , Juárez-Maldonado Antonio
Published: January 2021

The bell pepper is a vegetable with high antioxidant content, and its consumption is important because it can reduce the risk of certain diseases in humans. Plants can be affected by different types of stress, whether biotic or abiotic. Among the abiotic factors, there is saline stress that affects the metabolism and physiology of plants, which causes damage, decreasing productivity and quality of fruits. The objective of this work was to evaluate the application of selenium, silicon and copper nanoparticles and saline stress on the bioactive compounds of bell pepper fruits. The bell pepper plants were exposed to saline stress (25 mM NaCl and 50 mM) in the nutrient solution throughout the crop cycle. The nanoparticles were applied drenching solution of these to substrate (Se NPs 10 and 50 mg L−1, Si NPs 200 and 1000 mg L−1, Cu NPs 100 and 500 mg L−1). The results show that saline stress reduces chlorophylls, lycopene, and β-carotene in leaves; but increased the activity of some enzymes (e.g., glutathione peroxidase and phenylalanine ammonia lyase, and glutathione). In fruits, saline stress decreased flavonoids and glutathione. The nanoparticles increased chlorophylls, lycopene and glutathione peroxidase activity in the leaves; and ascorbate peroxidase, glutathione peroxidase, catalase and phenylalanine ammonia lyase activity, and also phenols, flavonoids, glutathione, β-carotene, yellow carotenoids in fruits. The application of nanoparticles to bell pepper plants under saline stress is efficient to increase the content of bioactive compounds in fruits.
Regulation of Sixth Seminal Root Formation by Jasmonate in Triticum aestivum L.
Authors: Pigolev Alexey , Miroshnichenko Dmitry , Dolgov Sergey , Savchenko Tatyana
Published: January 2021

A well-developed root system is an important characteristic of crop plants, which largely determines their productivity, especially under conditions of water and nutrients deficiency. Being Poaceous, wheat has more than one seminal root. The number of grown seminal roots varies in different wheat accessions and is regulated by environmental factors. Currently, the molecular mechanisms determining the number of germinated seminal roots remain poorly understood. The analysis of the root system development in germinating seeds of genetically modified hexaploid wheat plants with altered activity of jasmonate biosynthesis pathway and seeds exogenously treated with methyl jasmonate revealed the role of jasmonates in the regulation of sixth seminal root development. This regulatory effect strongly depends on the jasmonate concentration and the duration of the exposure to this hormone. The maximum stimulatory effect of exogenously applied methyl jasmonate on the formation of the sixth seminal root was achieved at 200 μM concentration after 48 h of treatment. Further increase in concentration and exposure time does not increase the stimulating effect. While 95% of non-transgenic plants under non-stress conditions possess five or fewer seminal roots, the number of plants with developed sixth seminal root reaches up to 100% when selected transgenic lines are treated with methyl jasmonate.
Root Tropisms: New Insights Leading the Growth Direction of the Hidden Half
Authors: Izzo Luigi Gennaro , Aronne Giovanna
Published: January 2021

Tropisms are essential responses of plants, orienting growth according to a wide range of stimuli. Recently, considerable attention has been paid to root tropisms, not only to improve cultivation systems, such as those developed for plant-based life support systems for future space programs, but also to increase the efficiency of root apparatus in water and nutrient uptake in crops on Earth. To date, the Cholodny–Went theory of differential auxin distribution remains the principal tropistic mechanism, but recent findings suggest that it is not generally applicable to all root tropisms, and new molecular pathways are under discussion. Therefore, an in-depth understanding of the mechanisms and functions underlying root tropisms is needed. Contributions to this special issue aimed to embrace reviews and research articles that deepen molecular, physiological, and anatomical processes orchestrating root tropisms from perception of the stimulus to bending. The new insights will help in elucidating plant–environment interactions, providing potential applications to improve plant growth on Earth and in space where microgravity diminishes or nullifies the gravitropism dominance.
Evolution of Plant Na+-P-Type ATPases: From Saline Environments to Land Colonization
Authors: Dabravolski Siarhei A. , Isayenkov Stanislav V.
Published: January 2021

Soil salinity is one of the major factors obstructing the growth and development of agricultural crops. Eukaryotes have two main transport systems involved in active Na+ removal: cation/H+ antiporters and Na+-P-type ATPases. Key transport proteins, Na+/K+-P-ATPases, are widely distributed among the different taxa families of pumps which are responsible for keeping cytosolic Na+ concentrations below toxic levels. Na+/K+-P-ATPases are considered to be absent in flowering plants. The data presented here are a complete inventory of P-type Na+/K+-P-ATPases in the major branches of the plant kingdom. We also attempt to elucidate the evolution of these important membrane pumps in plants in comparison with other organisms. We were able to observe the gradual replacement of the Na+-binding site to the Ca2+-binding site, starting with cyanobacteria and moving to modern land plants. Our results show that the α-subunit likely evolved from one common ancestor to bacteria, fungi, plants, and mammals, whereas the β-subunit did not evolve in green algae. In conclusion, our results strongly suggest the significant differences in the domain architecture and subunit composition of plant Na+/K+-P-ATPases depending on plant taxa and the salinity of the environment. The obtained data clarified and broadened the current views on the evolution of Na+/K+-P-ATPases. The results of this work would be helpful for further research on P-type ATPase functionality and physiological roles.
Volatile Profiles of Five Variants of Abeliophyllum distichum Flowers Using Headspace Solid-Phase Microextraction Gas Chromatography–Mass Spectrometry (HS-SPME-GC-MS) Analysis
Authors: Lee Yeong-Geun , Choi Won-Sil , Yang Seung-Ok , Hwang-Bo Jeon , Kim Hyoun-Geun , Fang Minzhe , Yi Tae-Hoo , Kang Se Chan , Lee Youn-Hyung , Baek Nam-In
Published: January 2021

Abeliophyllum distichum (Oleaceae), which is the only species in the monotypic genus and is grown only on the Korean peninsula, has a high scarcity value. Its five variants (white, pink, round, blue, and ivory) have different morphological characteristics in terms of the color of petals and sepals or shape of the fruits. Despite its high value, there has been no study on variant classification except in terms of their morphological characteristics. Thus, we performed a volatile component analysis of A. distichum flowers and multivariate data analyses to reveal the relationship between fragments emitted from five variants of A. distichum flowers with their morphological characteristics. As a result, 66 volatile components of this plant were identified by headspace solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME-GC-MS), showing unique patterns for each set of morphological characteristics, especially the color of the petals. These results suggest that morphological characteristics of each variant are related to the volatile composition.
Summary of Natural Products Ameliorate Concanavalin A-Induced Liver Injury: Structures, Sources, Pharmacological Effects, and Mechanisms of Action
Authors: Ibrahim Sabrin R. M. , Sirwi Alaa , Eid Basma G. , Mohamed Shaimaa G. A. , Mohamed Gamal A.
Published: January 2021

Liver diseases represent a threat to human health and are a significant cause of mortality and morbidity worldwide. Autoimmune hepatitis (AIH) is a progressive and chronic hepatic inflammatory disease, which may lead to severe complications. Concanavalin A (Con A)-induced hepatic injury is regarded as an appropriate experimental model for investigating the pathology and mechanisms involved in liver injury mediated by immune cells as well as T cell-related liver disease. Despite the advances in modern medicine, the only available strategies to treat AIH, include the use of steroids either solely or with immunosuppressant drugs. Unfortunately, this currently available treatment is associated with significant side-effects. Therefore, there is an urgent need for safe and effective drugs to replace and/or supplement those in current use. Natural products have been utilized for treating liver disorders and have become a promising therapy for various liver disorders. In this review, the natural compounds and herbal formulations as well as extracts and/or fractions with protection against liver injury caused by Con A and the underlying possible mechanism(s) of action are reviewed. A total of 53 compounds from different structural classes are discussed and over 97 references are cited. The goal of this review is to attract the interest of pharmacologists, natural product researchers, and synthetic chemists for discovering novel drug candidates for treating immune-mediated liver injury.
Ethnomedicinal Plants Traditionally Used for the Treatment of Jaundice (Icterus) in Himachal Pradesh in Western Himalaya—A Review
Authors: Raghuvanshi Disha , Dhalaria Rajni , Sharma Anjali , Kumar Dinesh , Kumar Harsh , Valis Martin , Kuča Kamil , Verma Rachna , Puri Sunil
Published: January 2021

Ethnomedicinal plants have a significant role in the lives of people of rural and tribal areas. Thousands of medicinal plant species are used to treat various diseases, including jaundice, and are considered an important therapeutic resource to minimize these diseases. Jaundice (icterus) is a chronic disease that occurs when the amount of bilirubin in the blood increases. This review describes different ethnomedicinal plants used for curing jaundice by tribal and rural people of Himachal Pradesh. The study reveals 87 ethnomedicinal plant species belonging to 51 different families, which are used for treating jaundice in Himachal Pradesh. These plants are arranged in a systematic way, which includes a description of their common name, botanical name, along with its family, plant parts used, region, and mode of use in tabulated form. Some of the plant extracts have already been explored for their phytochemical and pharmacological significance and proved their potential in the preparation of new medicines or drugs against the treatment of jaundice. This review is an attempt to highlight the indigenous knowledge of medicinal plants, which are specifically used for the treatment of jaundice. The data mentioned in the present review is compiled from various sources like existing literature, books, Google Scholar, and Scopus publications. Among all the observed plant species, most used medicinal plants for the treatment of jaundice include Justicia adhatoda, Emblica officinalis, Ricinus communis, Saccharum officinarum, Terminalia chebula, Berberis aristata, Cuscuta reflexa, and Tinospora cordifolia. Plants that are mostly utilized for the treatment of jaundice need to be scientifically validated by pharmacological analysis and should be subsequently used for the preparation of new drugs, which may prove far more beneficial than the existing one.
Iron (II) Metallo-Supramolecular Polymers Based on Thieno[3,2-b]thiophene for Electrochromic Applications
Authors: Chernyshev Andrei , Acharya Udit , Pfleger Jiří , Trhlíková Olga , Zedník Jiří , Vohlídal Jiří
Published: January 2021

Four new bis(tpy) unimers with different linkers between the thieno[3,2-b]thiophene-2,5-diyl central unit and terpyridine-4′-yl (tpy) end-groups: no linker (Tt), ethynediyl (TtE), 1,4-phenylene (TtPh) and 2,2′-bithophene-5,5′-diyl (TtB) are prepared, characterized, and assembled with Fe2+ ions to metallo-supramolecular polymers (Fe-MSPs). The Fe-MSP films prepared by spin-casting on Indium Tin Oxide (ITO) glass are characterized by atomic force microscope (AFM) microscopy, cyclic voltammetry, and UV/vis spectroscopy and studied for their electrochromism and effect of the unimer structure on their electrochromic performance. Of the studied MSPs, Fe-Tt shows the highest optical contrast as well as coloration efficiency (CE = 641 cm2 C−1) and the fastest optical response. This makes it an excellent candidate for possible use in electrochromic devices.
3D Bioprinting of In Vitro Models Using Hydrogel-Based Bioinks
Authors: Choi Yeong-Jin , Park Honghyun , Ha Dong-Heon , Yun Hui-Suk , Yi Hee-Gyeong , Lee Hyungseok
Published: January 2021

Coronavirus disease 2019 (COVID-19), which has recently emerged as a global pandemic, has caused a serious economic crisis due to the social disconnection and physical distancing in human society. To rapidly respond to the emergence of new diseases, a reliable in vitro model needs to be established expeditiously for the identification of appropriate therapeutic agents. Such models can be of great help in validating the pathological behavior of pathogens and therapeutic agents. Recently, in vitro models representing human organs and tissues and biological functions have been developed based on high-precision 3D bioprinting. In this paper, we delineate an in-depth assessment of the recently developed 3D bioprinting technology and bioinks. In particular, we discuss the latest achievements and future aspects of the use of 3D bioprinting for in vitro modeling.
Enhanced Electromechanical Property of Silicone Elastomer Composites Containing TiO2@SiO2 Core-Shell Nano-Architectures
Authors: Gao Shuyan , Zhao Hang , Zhang Na , Bai Jinbo
Published: January 2021

Dielectric elastomer (DE) is one type of promising field-activated electroactive polymer. However, its significant electromechanical actuated properties are always obtained under a giant electric voltage, which greatly restricts the potential applications of DE. In the present work, the well-constructed core-shell TiO2@SiO2 nanoparticles were fabricated by using the classical Stöber method. A series of TiO2@SiO2 nano-architectures-filled polydimethylsiloxane (PDMS) composites were prepared via solution blending and compression-molding procedures. Benefiting from the additional SiO2 shell, both the interfacial compatibility between fillers and matrix and core-shell interfacial interaction can be improved. The TiO2@SiO2/PDMS nanocomposites exhibit a significantly enhanced in-plane actuated strain of 6.08% under a low electric field of 30 V·μm−1 at 16 vol.% TiO2@SiO2 addition, which is 180% higher than that of neat PDMS. The experimental results reveal that the well-designed core-shell structure can play an important role in both improving the electromechanical actuated property and maintaining a good flexibility of DE composites. This research provides a promising approach for the design of the novel composites with advanced low-field actuated electromechanical property in next generation DE systems.
Ability of Constitutive Models to Characterize the Temperature Dependence of Rubber Hyperelasticity and to Predict the Stress-Strain Behavior of Filled Rubber under Different Defor Mation States
Authors: Fu Xintao , Wang Zepeng , Ma Lianxiang
Published: January 2021

In this paper, some representative hyperelastic constitutive models of rubber materials were reviewed from the perspectives of molecular chain network statistical mechanics and continuum mechanics. Based on the advantages of existing models, an improved constitutive model was developed, and the stress–strain relationship was derived. Uniaxial tensile tests were performed on two types of filled tire compounds at different temperatures. The physical phenomena related to rubber deformation were analyzed, and the temperature dependence of the mechanical behavior of filled rubber in a larger deformation range (150% strain) was revealed from multiple angles. Based on the experimental data, the ability of several models to describe the stress–strain mechanical response of carbon black filled compound was studied, and the application limitations of some constitutive models were revealed. Combined with the experimental data, the ability of Yeoh model, Ogden model (n = 3), and improved eight-chain model to characterize the temperature dependence was studied, and the laws of temperature dependence of their parameters were revealed. By fitting the uniaxial tensile test data and comparing it with the Yeoh model, the improved eight-chain model was proved to have a better ability to predict the hyperelastic behavior of rubber materials under different deformation states. Finally, the improved eight-chain model was successfully applied to finite element analysis (FEA) and compared with the experimental data. It was found that the improved eight-chain model can accurately describe the stress–strain characteristics of filled rubber.
Biofunctional Peptide-Modified Extracellular Vesicles Enable Effective Intracellular Delivery via the Induction of Macropinocytosis
Authors: Nakase Ikuhiko
Published: January 2021

We previously reported that macropinocytosis (accompanied by actin reorganization, ruffling of the plasma membrane, and engulfment of large volumes of extracellular fluid) is an important process for the cellular uptake of extracellular vesicles, exosomes. Accordingly, we developed techniques to induce macropinocytosis by the modification of biofunctional peptides on exosomal membranes, thereby enhancing their cellular uptake. Arginine-rich cell-penetrating peptides have been shown to induce macropinocytosis via proteoglycans; accordingly, we developed peptide-modified exosomes that could actively induce macropinocytotic uptake by cells. In addition, the activation of EGFR induces macropinocytosis; based on this knowledge, we developed artificial leucine-zipper peptide (K4)-modified exosomes. These exosomes can recognize E3 sequence-fused EGFR (E3-EGFR), leading to the clustering and activation of E3-EGFR by coiled-coil formation (E3/K4), which induces cellular exosome uptake by macropinocytosis. In addition, modification of pH-sensitive fusogenic peptides (e.g., GALA) also enhances the cytosolic release of exosomal contents. These experimental techniques and findings using biofunctional peptides have contributed to the development of exosome-based intracellular delivery systems.
Experiences from City-Scale Simulation of Thermal Grids
Authors: Simonsson Johan , Atta Khalid Tourkey , Schweiger Gerald , Birk Wolfgang
Published: January 2021

Dynamic simulation of district heating and cooling networks has an increased importance in the transition towards renewable energy sources and lower temperature district heating grids, as both temporal and spatial behavior need to be considered. Even though much research and development has been performed in the field, there are several pitfalls and challenges towards dynamic district heating and cooling simulation for everyday use. This article presents the experiences from developing and working with a city-scale simulator of a district heating grid located in Luleå, Sweden. The grid model in the case study is a physics based white-box model, while consumer models are either data-driven black-box or gray-box models. The control system and operator models replicate the manual and automatic operation of the combined heat and power plant. Using the functional mock-up interface standard, a co-simulation environment integrates all the models. Further, the validation of the simulator is discussed. Lessons learned from the project are presented along with future research directions, corresponding to identified gaps and challenges.
Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review
Authors: Liu Rongrong , Nageotte Florent , Zanne Philippe , de Mathelin Michel , Dresp-Langley Birgitta
Published: January 2021

Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has become a source of inspiration in the era of artificial intelligence. Another subfield of machine learning named reinforcement learning, tries to find an optimal behavior strategy through interactions with the environment. Combining deep learning and reinforcement learning permits resolving critical issues relative to the dimensionality and scalability of data in tasks with sparse reward signals, such as robotic manipulation and control tasks, that neither method permits resolving when applied on its own. In this paper, we present recent significant progress of deep reinforcement learning algorithms, which try to tackle the problems for the application in the domain of robotic manipulation control, such as sample efficiency and generalization. Despite these continuous improvements, currently, the challenges of learning robust and versatile manipulation skills for robots with deep reinforcement learning are still far from being resolved for real-world applications.
Estimation of Field-Level NOx Emissions from Crop Residue Burning Using Remote Sensing Data: A Case Study in Hubei, China
Authors: Shen Yonglin , Jiang Changmin , Chan Ka Lok , Hu Chuli , Yao Ling
Published: January 2021

Crop residue burning is the major biomass burning activity in China, strongly influencing the regional air quality and climate. As the cultivation pattern in China is rather scattered and intricate, it is a challenge to derive an accurate emission inventory for crop residue burning. In this study, we proposed a remote sensing-based method to estimate nitrogen oxide (NOx) emissions related to crop residue burning at the field level over Hubei, China. The new method considers differences in emission factors and the spatial distribution for different crop types. Fire radiative power (FRP) derived from moderate-resolution imaging spectroradiometer (MODIS) was used to quantify NOx emissions related to agricultural biomass combustion. The spatial distribution of different crops classified by multisource remote sensing data was used as an a priori constraint. We derived a new NOx emission database for Hubei from 2014 to 2016 with spatial resolution of 1 × 1 km. Significant seasonal patterns were observed from the NOx emission database. Peak NOx emission occurring in October was related to the residue burning in late autumn harvesting. Another peak was observed between January and April, which was due to the frequent burning of stubble before spring sowing. Our results were validated by comparing our emission inventory with geostationary satellite observations, previous studies, global fire emission database (GFED), NO2 vertical column densities (VCDs) from ozone monitoring instrument (OMI) satellite observations, and measurements from environmental monitoring stations. The comparisons showed NOx emission from GFED database was 47% lower than ours, while the evaluations from most of the statistical studies were significantly higher than our results. The discrepancies were likely related to the differences of methodology and data sources. The spatiotemporal variations of NOx emission in this study showed strong correlations with NO2 VCDs, which agreed well with geostationary satellite observations. A reasonable correlation between in situ NO2 observations and our results in agricultural regions demonstrated that our method is reliable. We believe that the new NOx emission database for crop residue burning derived in this study can potentially improve the understanding of pollution sources and can provide additional information for the design of pollution control measures.
Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations
Authors: Nickmilder Charles , Tedde Anthony , Dufrasne Isabelle , Lessire Françoise , Tychon Bernard , Curnel Yannick , Bindelle Jérome , Soyeurt Hélène
Published: January 2021

Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass expressed as compressed sward height (CSH) from readily accessible meteorological, optical (Sentinel-2) and radar satellite data (Sentinel-1). This study assumed that combining heterogeneous data sources, data transformations and machine learning methods would improve the robustness and the accuracy of the developed models. A total of 72,795 records of CSH with a spatial positioning, collected in 2018 and 2019, were used and aggregated according to a pixel-like pattern. The resulting dataset was split into a training one with 11,625 pixellated records and an independent validation one with 4952 pixellated records. The models were trained with a 19-fold cross-validation. A wide range of performances was observed (with mean root mean square error (RMSE) of cross-validation ranging from 22.84 mm of CSH to infinite-like values), and the four best-performing models were a cubist, a glmnet, a neural network and a random forest. These models had an RMSE of independent validation lower than 20 mm of CSH at the pixel-level. To simulate the behavior of the model in a decision support system, performances at the paddock level were also studied. These were computed according to two scenarios: either the predictions were made at a sub-parcel level and then aggregated, or the data were aggregated at the parcel level and the predictions were made for these aggregated data. The results obtained in this study were more accurate than those found in the literature concerning pasture budgeting and grassland biomass evaluation. The training of the 124 models resulting from the described framework was part of the realization of a decision support system to help farmers in their daily decision making.
Detection-Based Object Tracking Applied to Remote Ship Inspection
Authors: Xie Jing , Stensrud Erik , Skramstad Torbjørn
Published: January 2021

We propose a detection-based tracking system for automatically processing maritime ship inspection videos and predicting suspicious areas where cracks may exist. This system consists of two stages. Stage one uses a state-of-the-art object detection model, i.e., RetinaNet, which is customized with certain modifications and the optimal anchor setting for detecting cracks in the ship inspection images/videos. Stage two is an enhanced tracking system including two key components. The first component is a state-of-the-art tracker, namely, Channel and Spatial Reliability Tracker (CSRT), with improvements to handle model drift in a simple manner. The second component is a tailored data association algorithm which creates tracking trajectories for the cracks being tracked. This algorithm is based on not only the intersection over union (IoU) of the detections and tracking updates but also their respective areas when associating detections to the existing trackers. Consequently, the tracking results compensate for the detection jitters which could lead to both tracking jitter and creation of redundant trackers. Our study shows that the proposed detection-based tracking system has achieved a reasonable performance on automatically analyzing ship inspection videos. It has proven the feasibility of applying deep neural network based computer vision technologies to automating remote ship inspection. The proposed system is being matured and will be integrated into a digital infrastructure which will facilitate the whole ship inspection process.
A General Framework for Making Context-Recognition Systems More Energy Efficient
Authors: Janko Vito , Luštrek Mitja
Published: January 2021

Context recognition using wearable devices is a mature research area, but one of the biggest issues it faces is the high energy consumption of the device that is sensing and processing the data. In this work we propose three different methods for optimizing its energy use. We also show how to combine all three methods to further increase the energy savings. The methods work by adapting system settings (sensors used, sampling frequency, duty cycling, etc.) to both the detected context and directly to the sensor data. This is done by mathematically modeling the influence of different system settings and using multiobjective optimization to find the best ones. The proposed methodology is tested on four different context-recognition tasks where we show that it can generate accurate energy-efficient solutions—in one case reducing energy consumption by 95% in exchange for only four percentage points of accuracy. We also show that the method is general, requires next to no expert knowledge about the domain being optimized, and that it outperforms two approaches from the related work.
Dual Band and Dual Diversity Four-Element MIMO Dipole for 5G Handsets
Authors: Jamshed Muhammad Ali , Ur-Rehman Masood , Frnda Jaroslav , Althuwayb Ayman A. , Nauman Ali , Cengiz Korhan
Published: January 2021

The increasing popularity of using wireless devices to handle routine tasks has increased the demand for incorporating multiple-input-multiple-output (MIMO) technology to utilize limited bandwidth efficiently. The presence of comparatively large space at the base station (BS) makes it straightforward to exploit the MIMO technology’s useful properties. From a mobile handset point of view, and limited space at the mobile handset, complex procedures are required to increase the number of active antenna elements. In this paper, to address such type of issues, a four-element MIMO dual band, dual diversity, dipole antenna has been proposed for 5G-enabled handsets. The proposed antenna design relies on space diversity as well as pattern diversity to provide an acceptable MIMO performance. The proposed dipole antenna simultaneously operates at 3.6 and 4.7 sub-6 GHz bands. The usefulness of the proposed 4×4 MIMO dipole antenna has been verified by comparing the simulated and measured results using a fabricated version of the proposed antenna. A specific absorption rate (SAR) analysis has been carried out using CST Voxel (a heterogeneous biological human head) model, which shows maximum SAR value for 10 g of head tissue is well below the permitted value of 2.0 W/kg. The total efficiency of each antenna element in this structure is −2.88, −3.12, −1.92 and −2.45 dB at 3.6 GHz, while at 4.7 GHz are −1.61, −2.19, −1.72 and −1.18 dB respectively. The isolation, envelope correlation coefficient (ECC) between the adjacent ports and the loss in capacity is below the standard margin, making the structure appropriate for MIMO applications. The effect of handgrip and the housing box on the total antenna efficiency is analyzed, and only 5% variation is observed, which results from careful placement of antenna elements.
Multi-Layer Blockchain-Based Security Architecture for Internet of Things
Authors: Honar Pajooh Houshyar , Rashid Mohammad , Alam Fakhrul , Demidenko Serge
Published: January 2021

The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also providing a network authentication mechanism. The open-source Hyperledger Fabric Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The simulation results demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported approaches. The proposed lightweight blockchain model is also shown to be better suited to balance network latency and throughput as compared to a traditional global blockchain.
Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia
Authors: Herzog Nitsa J. , Magoulas George D.
Published: January 2021

Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.
Energy-Efficient Wireless Sensor Network with an Unequal Clustering Protocol Based on a Balanced Energy Method (EEUCB)
Authors: Jasim Ahmed A. , Idris Mohd Yamani Idna , Razalli Bin Azzuhri Saaidal , Issa Noor Riyadh , Rahman Muhammad Towfiqur , Khyasudeen Muhammad Farris b
Published: January 2021

A hot spot problem is a problem where cluster nodes near to the base station (BS) tend to drain their energy much faster than other nodes due to the need to perform more communication. Unequal clustering methods such as unequal clustering routing (UDCH) and energy-efficient fuzzy logic for unequal clustering (EEFUC) have been proposed to address this problem. However, these methods only concentrate on utilizing residual energy and the distance of sensor nodes to the base station, while limited attention is given to enhancing the data transmission process. Therefore, this paper proposes an energy-efficient unequal clustering scheme based on a balanced energy method (EEUCB) that utilizes minimum and maximum distance to reduce energy wastage. Apart from that, the proposed EEUCB also utilizes the maximum capacity of node energy and double cluster head technique with a sleep-awake mechanism. Furthermore, EEUCB has devised a clustering rotation strategy based on two sub-phases, namely intra- and inter-clustering techniques, that considers the average energy threshold, average distance threshold, and BS layering node. The performance of the proposed EEUCB protocol is then compared with various prior techniques. From the result, it can be observed that the proposed EEUCB protocol shows lifetime improvements of 57.75%, 19.63%, 14.7%, and 13.06% against low-energy adaptive clustering hierarchy (LEACH), factor-based LEACH FLEACH, EEFUC, and UDCH, respectively.
Deciphering the Disaggregation Mechanism of Amyloid Beta Aggregate by 4-(2-Hydroxyethyl)-1-Piperazinepropanesulfonic Acid Using Electrochemical Impedance Spectroscopy
Authors: Le Hien T. Ngoc , Cho Sungbo
Published: January 2021

Aggregation of amyloid-β (aβ) peptides into toxic oligomers, fibrils, and plaques is central in the molecular pathogenesis of Alzheimer’s disease (AD) and is the primary focus of AD diagnostics. Disaggregation or elimination of toxic aβ aggregates in patients is important for delaying the progression of neurodegenerative disorders in AD. Recently, 4-(2-hydroxyethyl)-1-piperazinepropanesulfonic acid (EPPS) was introduced as a chemical agent that binds with toxic aβ aggregates and transforms them into monomers to reduce the negative effects of aβ aggregates in the brain. However, the mechanism of aβ disaggregation by EPPS has not yet been completely clarified. In this study, an electrochemical impedimetric immunosensor for aβ diagnostics was developed by immobilizing a specific anti-amyloid-β (aβ) antibody onto a self-assembled monolayer functionalized with a new interdigitated chain-shaped electrode (anti-aβ/SAM/ICE). To investigate the ability of EPPS in recognizing AD by extricating aβ aggregation, commercially available aβ aggregates (aβagg) were used. Electrochemical impedance spectroscopy was used to probe the changes in charge transfer resistance (Rct) of the immunosensor after the specific binding of biosensor with aβagg. The subsequent incubation of the aβagg complex with a specific concentration of EPPS at different time intervals divulged AD progression. The decline in the Rct of the immunosensor started at 10 min of EPPS incubation and continued to decrease gradually from 20 min, indicating that the accumulation of aβagg on the surface of the anti-aβ/SAM/ICE sensor has been extricated. Here, the kinetic disaggregation rate k value of aβagg was found to be 0.038. This innovative study using electrochemical measurement to investigate the mechanism of aβagg disaggregation by EPPS could provide a new perspective in monitoring the disaggregation periods of aβagg from oligomeric to monomeric form, and then support for the prediction and handling AD symptoms at different stages after treatment by a drug, EPPS.
MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network
Authors: Ajmi Nader , Helali Abdelhamid , Lorenz Pascal , Mghaieth Ridha
Published: January 2021

Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput.
Job Attributes and Mental Health: A Comparative Study of Sex Work and Hairstyling
Authors: McCarthy Bill , Jansson Mikael , Benoit Cecilia
Published: January 2021

A growing literature advocates for using a labor perspective to study sex work. According to this approach, sex work involves many of the costs, benefits, and possibilities for exploitation that are common to many jobs. We add to the field with an examination of job attributes and mental health. Our analysis is comparative and uses data from a panel study of people in sex work and hairstyling. We examined job attributes that may differ across these occupations, such as stigma and customer hostility, as well as those that may be more comparable, such as job insecurity, income, and self-employment. Our analysis used mixed-effects regression and included an array of time-varying and time-invariant variables. Our results showed negative associations between mental health and job insecurity and stigma, for both hairstyling and sex work. We also found two occupation-specific relationships: for sex work, limited discretion to make decisions while at work was negatively related to mental health, whereas for hairstyling, mental health was positively associated with self-employment. Our results highlight the usefulness of an inter-occupational labor perspective for understanding the mental health consequences of being in sex work compared to hairstyling.
Big Data: Some Ethical Concerns for the Social Sciences
Authors: Weinhardt Michael
Published: January 2021

While big data (BD) has been around for a while now, the social sciences have been comparatively cautious in its adoption for research purposes. This article briefly discusses the scope and variety of BD, and its research potential and ethical implications for the social sciences and sociology, which derive from these characteristics. For example, BD allows for the analysis of actual (online) behavior and the analysis of networks on a grand scale. The sheer volume and variety of data allow for the detection of rare patterns and behaviors that would otherwise go unnoticed. However, there are also a range of ethical issues of BD that need consideration. These entail, amongst others, the imperative for documentation and dissemination of methods, data, and results, the problems of anonymization and re-identification, and the questions surrounding the ability of stakeholders in big data research and institutionalized bodies to handle ethical issues. There are also grave risks involved in the (mis)use of BD, as it holds great value for companies, criminals, and state actors alike. The article concludes that BD holds great potential for the social sciences, but that there are still a range of practical and ethical issues that need addressing.