bluuebunny/crossref_metadata_2025 huggingface dataset:
Manipulation of Promyelocytic Leukemia Protein Nuclear Bodies by Marek’s Disease Virus Encoded US3 Protein Kinase
Authors: Liao Yifei , Lupiani Blanca , Reddy Sanjay M.
Published: March 2021

Promyelocytic leukemia protein nuclear bodies (PML-NBs) are dynamic nuclear structures, shown to be important for herpesvirus replication; however, their role in regulating Marek’s disease virus (MDV) infection has not been studied. MDV is an oncogenic alphaherpesvirus that causes lymphoproliferative disease in chickens. MDV encodes a US3 serine/threonine protein kinase that is important for MDV replication and gene expression. In this study, we studied the role of MDV US3 in regulating PML-NBs. Using an immunofluorescence assay, we found that MDV US3 disrupts PML and SP100 in a kinase dependent manner. In addition, treatment with MG-132 (a proteasome inhibitor) could partially restore the levels of PML and SP100, suggesting that a cellular proteasome dependent degradation pathway is involved in MDV US3 induced disruption of PML and SP100. These findings provide the first evidence for the interplay between MDV proteins and PML-NBs.
Rear-Seat Productivity in Virtual Reality: Investigating VR Interaction in the Confined Space of a Car
Authors: Li Jingyi , George Ceenu , Ngao Andrea , Holländer Kai , Mayer Stefan , Butz Andreas
Published: March 2021

Ubiquitous technology lets us work in flexible and decentralised ways. Passengers can already use travel time to be productive, and we envision even better performance and experience in vehicles with emerging technologies, such as virtual reality (VR) headsets. However, the confined physical space constrains interactions while the virtual space may be conceptually borderless. We therefore conducted a VR study (N = 33) to examine the influence of physical restraints and virtual working environments on performance, presence, and the feeling of safety. Our findings show that virtual borders make passengers touch the car interior less, while performance and presence are comparable across conditions. Although passengers prefer a secluded and unlimited virtual environment (nature), they are more productive in a shared and limited one (office). We further discuss choices for virtual borders and environments, social experience, and safety responsiveness. Our work highlights opportunities and challenges for future research and design of rear-seat VR interaction.
Consensus and Controversy in the Debate over the Biphasic Impact of Alcohol Consumption on the Cardiovascular System
Authors: Stătescu Cristian , Clement Alexandra , Șerban Ionela-Lăcrămioara , Sascău Radu
Published: March 2021

In the past few decades, research has focused on the importance of addressing modifiable risk factors as a means of lowering the risk of cardiovascular disease (CVD), which represents the worldwide leading cause of death. For quite a long time, it has been considered that ethanol intake has a biphasic impact on the cardiovascular system, mainly depending on the drinking pattern, amount of consumption, and type of alcoholic beverage. Multiple case-control studies and meta-analyses reported the existence of a “U-type” or “J-shaped” relationship between alcohol and CVD, as well as mortality, indicating that low to moderate alcohol consumption decreases the number of adverse cardiovascular events and deaths compared to abstinence, while excessive alcohol use has unquestionably deleterious effects on the circulatory system. However, beginning in the early 2000s, the cardioprotective effects of low doses of alcohol were abnegated by the results of large epidemiological studies. Therefore, this narrative review aims to reiterate the association of alcohol use with cardiac arrhythmias, dilated cardiomyopathy, arterial hypertension, atherosclerotic vascular disease, and type 2 diabetes mellitus, highlighting literature disagreements over the risk and benefits of low to moderate drinking on the cardiovascular system.
Determination of an Acceptable Portion Size of Daal for a Bangladeshi Community-Based Iron Intervention in Adolescent Girls: A Feasibility Study
Authors: Yunus Fakir Md. , Jalal Chowdhury , Zello Gordon A. , Afsana Kaosar , Vandenberg Albert , DellaValle Diane M.
Published: March 2021

Widely consumed daal (lentils) in Bangladesh are an ideal vehicle for iron (Fe) fortification; however, an acceptable portion size in meals needs to be determined to carry out a community feeding study in at-risk adolescent girls. A non-randomized crossover trial was conducted with n = 100 Bangladeshi girls (12.9 ± 2.0 years of age). Two recipes (thin and thick) and three portion sizes (25 g, 37.5 g, 50 g of raw lentil) of daal were served with 250 g of cooked white rice in a counter-balanced manner over 12 weeks. Each meal was fed to participants 5 days/week for two weeks. Ratings of hunger, satiety, and palatability were measured before and after each meal using Visual Analog Scales (VAS). The thick preparation in the 37.5 g portion (~200 g cooked) elicited higher VAS ratings of hunger, satiety, and palatability compared to all other meals. The 50 g portion of the thin preparation had VAS ratings similar to those of the 37.5 g thick preparation. Consuming the 37.5 g portion of fortified daal would provide 6.9 mg Fe/day to girls in a community-based effectiveness study. This would meet ~86% and ~46% of the Recommended Dietary Allowance (RDA) for Fe for girls aged 9–13 and 14–18 years, respectively.
Seven Weeks of High-Dose Vitamin D Treatment Reduces the Need for Infliximab Dose-Escalation and Decreases Inflammatory Markers in Crohn’s Disease during One-Year Follow-Up
Authors: Bendix Mia , Dige Anders , Jørgensen Søren Peter , Dahlerup Jens Frederik , Bibby Bo Martin , Deleuran Bent , Agnholt Jørgen
Published: March 2021

Background: Seven weeks of high-dose vitamin D treatment decreases intestinal IL17A and IFN-γ mRNA expression in active Crohn’s disease (CD). In this follow-up study, we investigated whether seven-week vitamin D treatment affected the infliximab response in the following 45 weeks compared to placebo. Methods: CD patients (n = 40) were initially randomised into four groups: infliximab + vitamin-D; infliximab + placebo-vitamin-D; placebo-infliximab + vitamin-D; and placebo-infliximab + placebo-vitamin-D. Infliximab (5 mg/kg) or placebo-infliximab was administered at weeks 0, 2 and 6. Vitamin D (5 mg bolus followed by 0.5 mg/day for 7 weeks) or placebo-vitamin D was handed out. After the 7-week vitamin D period, all patients received infliximab during follow-up. Results are reported for Group D+ (infliximab + vitamin-D and placebo-infliximab + vitamin-D) and Group D- (infliximab + placebo-vitamin-D and placebo-infliximab + placebo-vitamin-D). Results: Group D- patients had greater needs for infliximab dose escalation during follow-up compared to group D+ (p = 0.05). Group D+ had lower median calprotectin levels week 15 (p = 0.02) and week 23 (p = 0.04) compared to group D-. Throughout follow-up, group D+ had 2.2 times (95% CI: 1.1–4.3) (p = 0.02) lower median CRP levels compared with group D-. Conclusions: Seven weeks high-dose vitamin D treatment reduces the need for later infliximab dose-escalation and reduces inflammatory markers. EudraCT no. 2013-000971-34.
Less Is Better: How Nutrition and Low-Carbon Labels Jointly Backfire on the Evaluation of Food Products
Authors: Huang Yuanhao , Yang Xiaoke , Li Xianguo , Chen Qian
Published: March 2021

(1) Background: Labeling is one of the significant strategies to guide sustainable consumption behaviors. Nowadays, multi labels being displayed on the front-of-pack of food products is a common phenomenon. However, labels seldom operate solo, and competition or complement effects may be exerted on different labels. Therefore, the research objective is to explore the interaction effect when nutrition and low-carbon labels appear simultaneously; (2) Methods: Across four scenario-based experiments, including ice cream, yogurt, steak, and toast, this study manipulated the separate and joint occurrences of low-carbon and nutrition labels, the interaction effect of joint labels was tested, and the serial mediation model, which includes resource allocation and anticipated enjoyment of food consumption, was verified; (3) Results: Results show that people have a positive preference for the nutrition label and the carbon label, respectively, while these two labels working simultaneously attenuate the positive effect of the single label. When facing nutrition and carbon labels simultaneously, people would infer partial resources are allocated to healthy and environmental aspects so they have a lower anticipated enjoyment from food consumption. Thus, these two labels working simultaneously attenuate the positive effect of the single label, and consumers have a lower evaluation of food products. In addition, the joint backfire on the effect is only exerted on people with a higher level of zero-sum bias and only when joint labels have a high consistency of labels; (4) Conclusions: This study solved the contradictory problem of the joint effect of positive labels. The findings in this research contribute to promote sustainable food consumption. We suggest that similar labels should be avoided in the same front-of-pack of food, and manufacturers need to use ads to bring down consumers’ zero-sum bias.
Research of Scattering Properties in Solid-Core Polarization-Maintaining Photonic Crystal Fibers
Authors: Hu Xueyan , Zheng Shengheng , Song Xingyuan , Huang Xiaoxia , Guo Huaiwen , Zhao Bowang , Zhou Wei
Published: March 2021

The scattering from air–glass interfaces within solid-core polarization-maintaining photonic crystal fiber (PM–PCF) will increase the fiber attenuation coefficient, which may lead to high transmission loss. Therefore, it is necessary to describe scattering properties to guide research into reducing fiber loss. In this paper, the loss resulting from roughness scattering at multi-hole interfaces within PM–PCF was theoretically and experimentally analyzed. A PM–PCF scattering model was established to explore the scattering distribution. On the other hand, a fully automatic testing device was built to enable the measurement of a three-dimensional scattering sphere. Simulations were in good agreement with experimental measurements. Moreover, this new proposed measurement method could apply to other PCFs and it will be a useful tool for further scattering research.
Image of the Electron Suggested by Nonlinear Electrodynamics Coupled to Gravity
Authors: Dymnikova Irina
Published: March 2021

We present a systematic review of the basic features that were adopted for different electron models and show, in a brief overview, that, for electromagnetic spinning solitons in nonlinear electrodynamics minimally coupled to gravity (NED-GR), all of these features follow directly from NED-GR dynamical equations as model-independent generic features. Regular spherically symmetric solutions of NED-GR equations that describe electrically charged objects have obligatory de Sitter center due to the algebraic structure of stress–energy tensors for electromagnetic fields. By the Gürses-Gürsey formalism, which includes the Newman–Janis algorithm, they are transformed to axially symmetric solutions that describe regular spinning objects asymptotically Kerr–Newman for a distant observer, with the gyromagnetic ratio g=2. Their masses are determined by the electromagnetic density, related to the interior de Sitter vacuum and to the breaking of spacetime symmetry from the de Sitter group. De Sitter center transforms to the de Sitter vacuum disk, which has properties of a perfect conductor and ideal diamagnetic. The ring singularity of the Kerr–Newman geometry is replaced with the superconducting current, which serves as the non-dissipative source for exterior fields and source of the intrinsic magnetic momentum for any electrically charged spinning NED-GR object. Electromagnetic spinning soliton with the electron parameters can shed some light on appearance of a minimal length scale in the annihilation reaction e+e−→γγ(γ).
Fermentation Strategies for Production of Pharmaceutical Terpenoids in Engineered Yeast
Authors: Carsanba Erdem , Pintado Manuela , Oliveira Carla
Published: March 2021

Terpenoids, also known as isoprenoids, are a broad and diverse class of plant natural products with significant industrial and pharmaceutical importance. Many of these natural products have antitumor, anti-inflammatory, antibacterial, antiviral, and antimalarial effects, support transdermal absorption, prevent and treat cardiovascular diseases, and have hypoglycemic activities. Production of these compounds are generally carried out through extraction from their natural sources or chemical synthesis. However, these processes are generally unsustainable, produce low yield, and result in wasting of substantial resources, most of them limited. Microbial production of terpenoids provides a sustainable and environment-friendly alternative. In recent years, the yeast Saccharomyces cerevisiae has become a suitable cell factory for industrial terpenoid biosynthesis due to developments in omics studies (genomics, transcriptomics, metabolomics, proteomics), and mathematical modeling. Besides that, fermentation development has a significant importance on achieving high titer, yield, and productivity (TYP) of these compounds. Up to now, there have been many studies and reviews reporting metabolic strategies for terpene biosynthesis. However, fermentation strategies have not been yet comprehensively discussed in the literature. This review summarizes recent studies of recombinant production of pharmaceutically important terpenoids by engineered yeast, S. cerevisiae, with special focus on fermentation strategies to increase TYP in order to meet industrial demands to feed the pharmaceutical market. Factors affecting recombinant terpenoids production are reviewed (strain design and fermentation parameters) and types of fermentation process (batch, fed-batch, and continuous) are discussed.
Strategies to Inspire Students’ Engagement in Pharmacology Courses
Authors: Rubaiy Hussein N.
Published: March 2021

Pharmacology is a distinct discipline and offers core knowledge to broaden student programs in the provision of health care (medicine, nursing, pharmacy, and others) as well as research-oriented programs (biosciences and biomedical). Therefore, knowledge and information on topics such as prescribing medication, drug interactions, dosage adjustments, and the correct drug dose calculation for medicine are essential for preventing and reducing medication errors, which is a key element in patient safety. Recently, many institutions have been trying to enhance their quality of teaching, as students demand support and success in their studies. Pharmacology is a highly challenging interdisciplinary topic, and requires a huge effort from both educators and students to achieve the best outcomes. Therefore, creating an effective environment to support students’ learning in pharmacology courses is essential to improving students’ engagement, success and learning outcomes. In recent decades, the landscape of education has changed, and distance learning has skyrocketed. This manuscript intends to discuss and highlight the importance of student engagement in higher education in pharmacology courses. Additionally, this paper spotlights and presents a review of recent studies focusing on student engagement in pharmacology courses and possible methods for enhancing and inspiring student engagement in pharmacology courses.
Plasmonic Enhanced InP Nanowire Array Solar Cell through Optoelectronic Modeling
Authors: Adibzadeh Farzaneh , Olyaee Saeed
Published: March 2021

Vertical nanowire (NW) arrays are a promising candidate for the next generation of the optoelectronics industry because of their significant features. Here, we investigated the InP NW array solar cells and obtained the optoelectronic properties of the structure. To improve the performance of the NW array solar cells, we placed a metal layer of Au at the bottom of the NWs and considered their top part to be a conical-shaped parabola. Using optical and electrical simulations, it has been shown that the proposed structure improves the absorption of light in normal incidence, especially at wavelengths near the bandgap of InP, where photons are usually not absorbed. Under inclined radiation, light absorption is also improved in the middle part of the solar spectrum. Increased light absorption in the cell led to the generation of more electron–hole pairs, resulting in an increase in short circuit current density from 24.1 mA/cm2 to 27.64 mA/cm2, which is equivalent to 14.69% improvement.
Numerical Simulation of Thermo-Optic Effects in an Nd: Glass Slab with Low Thermally Induced Wavefront Distortion
Authors: Wang Xiaoqin , Wang Jiangfeng , Guo Jiangtao , Lu Xinghua , Wang Yamin , Xiao Qi , Fan Wei , Li Xuechun
Published: March 2021

A gain slab configuration with a low thermally induced wavefront distortion, which is based on heating the edge by the cladding layer, is proposed. The gain slab will be applied to a helium-cooled Nd: glass multislab laser amplifier with an output of 100 J at a repetition rate of 10 Hz. Additionally, a 3D numerical simulation model is developed to analyze the thermo-optic effects in the gain slab. Some parameters, including the absorption coefficient (α) of the cladding layer, the shape of the pump beam, and the gap between the pump area and absorbing cladding layer, are optimized to eliminate the thermo-optic effects. The results indicate that the peak-to-valley (P-V) of the thermally induced wavefront distortion of the specific gain slab can be reduced by 61% if other parameters remain constant.
The Tolerance of Eucalyptus globulus to Soil Contamination with Arsenic
Authors: Reboredo Fernando Henrique , Pelica João , Lidon Fernando C. , Pessoa Maria F. , Silva Maria Manuela , Guerra Mauro , Leitão Roberta , Ramalho José C.
Published: March 2021

The contamination of abandoned mining areas is a problem worldwide that needs urgent attention. Phytoremediation emerges as a successful method to extract different contaminants from the soil. In this context, Eucalyptus globulus plants growing in soils artificial contaminated with arsenic (As) were used to access its phytoremediation capabilities. The effects of As on photosynthetic performance were monitored through different physiological parameters, whereas the uptake and translocation of As and the putative effects on calcium, iron, potassium, and zinc levels on plants were evaluated by X-ray fluorescence analysis. Root system is the major accumulator organ, while the translocation to the above-ground organs is poor. In the end of the experiment, the root biomass of plants treated with 200 μg As mL−1 is 27% and 49.7% lower than equivalent biomass from plants treated with 100 μg As mL−1 and control plants, respectively. Each plant can accumulate 8.19 and 8.91 mg As after a 6-month period, when submitted to 100 As and 200 As, respectively. It seems to exist an antagonistic effect of As on Zn root uptake by E. globulus. In general, the tested concentrations do not influence negatively plant metabolism, indicating that this species is suitable for plantation in contaminated areas.
Micropropagation of Ginger (Zingiber officinale Roscoe) ‘Bentong’ and Evaluation of Its Secondary Metabolites and Antioxidant Activities Compared with the Conventionally Propagated Plant
Authors: Zahid Nisar Ahmad , Jaafar Hawa Z. E. , Hakiman Mansor
Published: March 2021

‘Bentong’ ginger is the most popular variety of Zingiber officinale in Malaysia. It is vegetatively propagated and requires a high proportion of rhizomes as starting planting materials. Besides, ginger vegetative propagation using its rhizomes is accompanied by several types of soil-borne diseases. Plant tissue culture techniques have been applied in many plant species to produce their disease-free planting materials. As ‘Bentong’ ginger is less known for its micropropagation, this study was conducted to investigate the effects of Clorox (5.25% sodium hypochlorite (NaOCl)) on explant surface sterilization, effects of plant growth regulators, and basal media on shoots’ multiplication and rooting. The secondary metabolites and antioxidant activities of the micropropagated plants were evaluated in comparison with conventionally propagated plants. Rhizome sprouted buds were effectively sterilized in 70% Clorox for 30 min by obtaining 75% contamination-free explants. Murashige and Skoog (MS) supplemented with 10 µM of zeatin was the suitable medium for shoot multiplication, which resulted in the highest number of shoots per explant (4.28). MS medium supplemented with 7.5 µM 1-naphthaleneacetic acid (NAA) resulted in the highest number of roots per plantlet. The in vitro-rooted plantlets were successfully acclimatized with a 95% survival rate in the ex vitro conditions. The phytochemical analysis showed that total phenolic acid and total flavonoid content and antioxidant activities of the micropropagated plants were not significantly different from the conventionally propagated plants of ‘Bentong’ ginger. In conclusion, the present study’s outcome can be adopted for large-scale propagation of disease-free planting materials of ‘Bentong’ ginger.
Transcriptomic Analyses Shed Light on Critical Genes Associated with Bibenzyl Biosynthesis in Dendrobium officinale
Authors: Adejobi Oluwaniyi Isaiah , Guan Ju , Yang Liu , Hu Jiang-Miao , Yu Anmin , Muraguri Sammy , Liu Aizhong
Published: March 2021

The Dendrobium plants (members of the Orchidaceae family) are used as traditional Chinese medicinal herbs. Bibenzyl, one of the active compounds in Dendrobium officinale, occurs in low amounts among different tissues. However, market demands require a higher content of thes compounds to meet the threshold for drug production. There is, therefore, an immediate need to dissect the physiological and molecular mechanisms underlying how bibenzyl compounds are biosynthesized in D. officinale tissues. In this study, the accumulation of erianin and gigantol in tissues were studied as representative compounds of bibenzyl. Exogenous application of Methyl-Jasmonate (MeJA) promotes the biosynthesis of bibenzyl compounds; therefore, transcriptomic analyses were conducted between D. officinale-treated root tissues and a control. Our results show that the root tissues contained the highest content of bibenzyl (erianin and gigantol). We identified 1342 differentially expressed genes (DEGs) with 912 up-regulated and 430 down-regulated genes in our transcriptome dataset. Most of the identified DEGs are functionally involved in the JA signaling pathway and the biosynthesis of secondary metabolites. We also identified two candidate cytochrome P450 genes and nine other enzymatic genes functionally involved in bibenzyl biosynthesis. Our study provides insights on the identification of critical genes associated with bibenzyl biosynthesis and accumulation in Dendrobium plants, paving the way for future research on dissecting the physiological and molecular mechanisms of bibenzyl synthesis in plants as well as guide genetic engineering for the improvement of Dendrobium varieties through increasing bibenzyl content for drug production and industrialization.
Static and Dynamic Behavior of Polymer/Graphite Oxide Nanocomposites before and after Thermal Reduction
Authors: Chrissopoulou Kiriaki , Androulaki Krystalenia , Labardi Massimiliano , Anastasiadis Spiros H.
Published: March 2021

Nanocomposites of hyperbranched polymers with graphitic materials are investigated with respect to their structure and thermal properties as well as the dynamics of the polymer probing the effect of the different intercalated or exfoliated structure. Three generations of hyperbranched polyester polyols are mixed with graphite oxide (GO) and the favorable interactions between the polymers and the solid surfaces lead to intercalated structure. The thermal transitions of the confined chains are suppressed, whereas their dynamics show similarities and differences with the dynamics of the neat polymers. The three relaxation processes observed for the neat polymers are observed in the nanohybrids as well, but with different temperature dependencies. Thermal reduction of the graphite oxide in the presence of the polymer to produce reduced graphite oxide (rGO) reveals an increase in the reduction temperature, which is accompanied by decreased thermal stability of the polymer. The de-oxygenation of the graphite oxide leads to the destruction of the intercalated structure and to the dispersion of the rGO layers within the polymeric matrix because of the modification of the interactions between the polymer chains and the surfaces. A significant increase in the conductivity of the resulting nanocomposites, in comparison to both the polymers and the intercalated nanohybrids, indicates the formation of a percolated rGO network.
Fully Polymeric Distillation Unit Based on Polypropylene Hollow Fibers
Authors: Kůdelová Tereza , Bartuli Erik , Strunga Alan , Hvožďa Jiří , Dohnal Miroslav
Published: March 2021

Access to pure water is a very topical issue today. Desalination represents a promising way of obtaining drinking water in areas of shortage. Currently, efforts are being made to replace the metal components of existing desalination units due to the high corrosivity of sea water. Another requirement is easy transportation and assembly. The presented solution combines two types of polymeric hollow fibers that are used to create the distillation unit. Porous polypropylene hollow fiber membranes have been used as an active surface for mass transfer in the distillation unit, while non-porous thermal polypropylene hollow fibers have been employed in the condenser. The large active area to volume ratio of the hollow fiber module improves the efficiency of both units. Hot water is pumped inside the membranes in the distillation unit. Evaporation is first observed at a temperature gradient of 10 °C. The water vapor flows through the tunnel to the condenser where cold water runs inside the fibers. The temperature gradient causes condensation of the vapor, and the condensate is collected. The article presents data for hot water at temperatures of 55, 60, and 65 °C. Optimization of the membrane module is evaluated and presented.
Recent Trends in Sustainable Remediation of Pb-Contaminated Shooting Range Soils: Rethinking Waste Management within a Circular Economy
Authors: Gómez-Sagasti María T. , Anza Mikel , Hidalgo June , Artetxe Unai , Garbisu Carlos , Becerril José M.
Published: March 2021

Soil metal contamination in recreational shooting ranges represents a widespread environmental problem. Lead (Pb) is the primary component of traditional ammunition, followed by metalloids such as antimony (Sb) and arsenic (As). Lead-based bullets and pellets deposited on the soil surface are subject to steady weathering; hence, metal(loid)s are released and accumulated in the underlying soil, with potential adverse consequences for ecosystem function and human health. Amongst the currently available environmentally-safe technologies for the remediation of metal-contaminated soils, chemical immobilization is recognized as the most practical and cost-effective one. This technology often uses inorganic and organic amendments to reduce metal mobility, bioavailability and toxicity (environmental benefits). Likewise, amendments may also promote and speed up the re-establishment of vegetation on metal-affected soils, thus facilitating the conversion of abandoned shooting ranges into public green spaces (social benefit). In line with this, the circular economy paradigm calls for a more sustainable waste management, for instance, by recycling and reusing by-products and wastes in an attempt to reduce the demand for raw materials (economic benefit). The objective of this manuscript is to present a state-of-the-art review of the different industrial and agro-food by-products and wastes used for the remediation of metal-contaminated shooting range soils.
Fully Automated Cultivation of Adipose-Derived Stem Cells in the StemCellDiscovery—A Robotic Laboratory for Small-Scale, High-Throughput Cell Production Including Deep Learning-Based Confluence Estimation
Authors: Ochs Jelena , Biermann Ferdinand , Piotrowski Tobias , Erkens Frederik , Nießing Bastian , Herbst Laura , König Niels , Schmitt Robert H.
Published: March 2021

Laboratory automation is a key driver in biotechnology and an enabler for powerful new technologies and applications. In particular, in the field of personalized therapies, automation in research and production is a prerequisite for achieving cost efficiency and broad availability of tailored treatments. For this reason, we present the StemCellDiscovery, a fully automated robotic laboratory for the cultivation of human mesenchymal stem cells (hMSCs) in small scale and in parallel. While the system can handle different kinds of adherent cells, here, we focus on the cultivation of adipose-derived hMSCs. The StemCellDiscovery provides an in-line visual quality control for automated confluence estimation, which is realized by combining high-speed microscopy with deep learning-based image processing. We demonstrate the feasibility of the algorithm to detect hMSCs in culture at different densities and calculate confluences based on the resulting image. Furthermore, we show that the StemCellDiscovery is capable of expanding adipose-derived hMSCs in a fully automated manner using the confluence estimation algorithm. In order to estimate the system capacity under high-throughput conditions, we modeled the production environment in a simulation software. The simulations of the production process indicate that the robotic laboratory is capable of handling more than 95 cell culture plates per day.
Establishment of an Effective Refining Process for Moringa oleifera Kernel Oil
Authors: Abd Hadi Husna Madihah , Tan Chin Ping , Mohamad Shah Nur Khalishah , Tan Tai Boon , Niranjan Keshavan , Mat Yusoff Masni
Published: March 2021

This study systematically established the most effective refining process for Moringa oleifera (MO) kernel oil. Acid degumming (20.33 ± 1.37 ppm) removed significantly greater phosphorus than water degumming (31.18 ± 0.90 ppm). Neutralization was more effective than deodorization in decreasing the acid (0.06 mg KOH/g) and p-Anisidine (p-AV, 0.36 ± 0.03) values of the oil. Besides improving its color properties, acid-activated bleaching earth Type B was better than Types A and C in decreasing the oil’s p-AV (0.43 ± 0.02), acid value (3.96 ± 0.02 mg KOH/g), and moisture content (0.01 ± 0.00% w/w). The selected refining stages successfully produced MO kernel oil with acceptable peroxide value (PV, 1.66–3.33 meq/kg), p-AV (1.05–1.49), total oxidation value (TOTOX, 4.38–8.15), acid value (0.03 mg KOH/g), moisture content (0.01% w/w), phosphorus content (1.28–1.94 ppm), iodine value (80.79–81.03), oleic acid (79.52–79.65%), and tocopherol content (65.26–87.00 mg/kg).
Biosynthesis of Silver Nanoparticles from Bacillus licheniformis TT01 Isolated from Quail Manure Collected in Vietnam
Authors: Tan Lam Van , Tran Thanh , Thi Van Doan
Published: March 2021

Silver nanoparticles (AgNPs) find a wide range of use in many fields, and the biosynthesis of AgNPs via biological routines has recently gained currency. In this study, Bacillus licheniformis TT01 strain was isolated from quail feces collected in Vietnam and evaluated for its ability to synthesize AgNPs. Through visual confirmation and ultraviolet and visible (UV–Vis) spectrum analysis, we found that the biosynthesis of AgNPs was realized in the process in which biomass of B. licheniformis TT01 was incubated with AgNO3 solution. Obtained AgNPs were then assayed for antibacterial activity against three species of bacteria, namely Escherichia coli, Bacillus cereus and Ralstoniasolanacearum, showing better inhibitory action than the AgNO3 solution and the bacterial extracellular fluid. The minimum inhibitory concentration (MIC) of AgNP solution was 206 ppm against E. coli and R. solanacearum and 343.3 against B. cereus. X-ray diffraction (XRD) and transmission electron microscopy (TEM) revealed that the obtained AgNPs had a spherical shape and sizes ranging from 2 to 22 nm, in which particles from 2 to 10 nm appeared with the highest frequency.
Aeromonas sobria Induced Sepsis Complicated with Necrotizing Fasciitis in a Child with Acute Lymphoblastic Leukemia
Authors: Colosimo Manuela , Galati Maria Concetta , Riccelli Umberto , Tiburzi Simona Paola , Galea Eulalia , Alcaro Teresa , Filippelli Orazio Stefano Giovanni , Abbonante Francesco , Minchella Pasquale , Gallelli Luca
Published: March 2021

Aeromonas species are gram negative and able to induce systemic diseases (i.e., gastrointestinal, respiratory, and cardiovascular, diseases, in addition to infection of brain and soft tissues). In this study, we describe the development of necrotizing fasciitis in a young immunocompromised girl, with a low response to drug treatment and who died after some months.
Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation
Authors: Liu Guichi , Gao Lei , Qi Lin
Published: March 2021

In recent years, representation-based methods have attracted more attention in the hyperspectral image (HSI) classification. Among them, sparse representation-based classifier (SRC) and collaborative representation-based classifier (CRC) are the two representative methods. However, SRC only focuses on sparsity but ignores the data correlation information. While CRC encourages grouping correlated variables together but lacks the ability of variable selection. As a result, SRC and CRC are incapable of producing satisfied performance. To address these issues, in this work, a correlation adaptive representation (CAR) is proposed, enabling a CAR-based classifier (CARC). Specifically, the proposed CARC is able to explore sparsity and data correlation information jointly, generating a novel representation model that is adaptive to the structure of the dictionary. To further exploit the correlation between the test samples and the training samples effectively, a distance-weighted Tikhonov regularization is integrated into the proposed CARC. Furthermore, to handle the small training sample problem in the HSI classification, a multi-feature correlation adaptive representation-based classifier (MFCARC) and MFCARC with Tikhonov regularization (MFCART) are presented to improve the classification performance by exploring the complementary information across multiple features. The experimental results show the superiority of the proposed methods over state-of-the-art algorithms.
SNR Enhancement of Back Scattering Signals for Bistatic Radar Based on BeiDou GEO Satellites
Authors: Li Yan , Yan Songhua , Gong Jianya , Zeng Fanku
Published: March 2021

Using scattering signals of the global navigation satellite system (GNSS) for target detection has become one of the research hotspots. This technology faces the difficulty of low signal-to-noise ratio (SNR) target echoes. Since BeiDou geostationary orbit (GEO) satellites provide the opportunity to form a bistatic radar with some advantages, such as fixed coverage area and quasi monostatic configuration to avoid the interference from the direct signals, the target echoes may have regular phases that are beneficial to SNR enhancement. This study uses BeiDou GEO satellites and ground fixed receivers to form a bistatic radar and analyzes the phase changes in the reflected signal caused by the target, then gives two methods for SNR enhancement corresponding to two applications: deformation monitoring and ship detection. This paper first introduces the basic signal processing including the intermediate frequency (IF) signal collector and the range compression in the software receiver, then describes the basic SNR enhancement method, i.e., increasing coherence integration time (CIT), and shows its limitation by two target cases: static metal reflector on the land and ships in the water. After that, this study provides an improved SNR enhancement method based on Doppler and range compensation in the moving ship detection case. The experiment shows that by the SNR enhancement, the SNRs of target echo signals in range/Doppler domain increase more than 4 dB on average. This study also demonstrates the bistatic radar’s potential for monitoring surface deformation.
Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform
Authors: Anand R , Veni S , Aravinth J
Published: March 2021

Hyperspectral image classification is an emerging and interesting research area that has attracted several researchers to contribute to this field. Hyperspectral images have multiple narrow bands for a single image that enable the development of algorithms to extract diverse features. Three-dimensional discrete wavelet transform (3D-DWT) has the advantage of extracting the spatial and spectral information simultaneously. Decomposing an image into a set of spatial–spectral components is an important characteristic of 3D-DWT. It has motivated us to perform the proposed research work. The novelty of this work is to bring out the features of 3D-DWT applicable to hyperspectral images classification using Haar, Fejér-Korovkin and Coiflet filters. Three-dimensional-DWT is implemented with the help of three stages of 1D-DWT. The first two stages of 3D-DWT are extracting spatial resolution, and the third stage is extracting the spectral content. In this work, the 3D-DWT features are extracted and fed to the following classifiers (i) random forest (ii) K-nearest neighbor (KNN) and (iii) support vector machine (SVM). Exploiting both spectral and spatial features help the classifiers to provide a better classification accuracy. A comparison of results was performed with the same classifiers without DWT features. The experiments were performed using Salinas Scene and Indian Pines hyperspectral datasets. From the experiments, it has been observed that the SVM with 3D-DWT features performs better in terms of the performance metrics such as overall accuracy, average accuracy and kappa coefficient. It has shown significant improvement compared to the state of art techniques. The overall accuracy of 3D-DWT+SVM is 88.3%, which is 14.5% larger than that of traditional SVM (77.1%) for the Indian Pines dataset. The classification map of 3D-DWT + SVM is more closely related to the ground truth map.
Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada
Authors: Deutsch Eliza S. , Cardille Jeffrey A. , Koll-Egyed Talia , Fortin Marie-Josée
Published: March 2021

Water clarity has been extensively assessed in Landsat-based remote sensing studies of inland waters, regularly relying on locally calibrated empirical algorithms, and close temporal matching between field data and satellite overpass. As more satellite data and faster data processing systems become readily accessible, new opportunities are emerging to revisit traditional assumptions concerning empirical calibration methodologies. Using Landsat 8 images with large water clarity datasets from southern Canada, we assess: (1) whether clear regional differences in water clarity algorithm coefficients exist and (2) whether model fit can be improved by expanding temporal matching windows. We found that a single global algorithm effectively represents the empirical relationship between in situ Secchi disk depth (SDD) and the Landsat 8 Blue/Red band ratio across diverse lake types in Canada. We also found that the model fit improved significantly when applying a median filter on data from ever-wider time windows between the date of in situ SDD sample and the date of satellite overpass. The median filter effectively removed the outliers that were likely caused by atmospheric artifacts in the available imagery. Our findings open new discussions on the ability of large datasets and temporal averaging methods to better elucidate the true relationships between in situ water clarity and satellite reflectance data.
A New CBAM-P-Net Model for Few-Shot Forest Species Classification Using Airborne Hyperspectral Images
Authors: Chen Long , Tian Xiaomin , Chai Guoqi , Zhang Xiaoli , Chen Erxue
Published: March 2021

High-precision automatic identification and mapping of forest tree species composition is an important content of forest resource survey and monitoring. The airborne hyperspectral image contains rich spectral and spatial information, which provides the possibility of high-precision classification and mapping of forest tree species. Few-shot learning, as an application of deep learning, has become an effective method of image classification. Prototypical networks (P-Net) is a simple and practical deep learning network, which has significant advantages in solving few-shot classification problems. Considering the high band correlation and large data volume associated with airborne hyperspectral images, how to fully extract effective features, filter or reduce redundant features is the key to improving the classification accuracy of P-Net, in order to extract effective features in hyperspectral images and obtain a high-precision forest tree species classification model with limited samples. In this research, we embedded the convolutional block attention module (CBAM) between the convolution blocks of P-Net, the CBAM-P-Net was constructed, and a method to improve the feature extraction efficiency of the P-Net was proposed, although this method makes the network more complex and increases the computational cost to a certain extent. The results show that the combination strategy using Channel First for CBAM greatly improves the feature extraction efficiency of the model. In different sample windows, CBAM-P-Net has an average increase of 1.17% and 0.0129 in testing overall accuracy (OA) and kappa coefficient (Kappa). The optimal classification window is 17 × 17, the OA reaches 97.28%, and Kappa reaches 0.97, which is an increase of 1.95% and 0.0214 along with just 49 s of training time expended, respectively, compared with P-Net. Therefore, using a suitable sample window and applying the proposed CBAM-P-Net to classify airborne hyperspectral images can achieve high-precision classification and mapping of forest tree species.
Evaluation and Comparison of Satellite-Derived Estimates of Rainfall in the Diverse Climate and Terrain of Central and Northeastern Ethiopia
Authors: Adane Girma Berhe , Hirpa Birtukan Abebe , Lim Chul-Hee , Lee Woo-Kyun
Published: March 2021

Understanding rainfall processes as the main driver of the hydrological cycle is important for formulating future water management strategies; however, rainfall data availability is challenging for countries such as Ethiopia. This study aims to evaluate and compare the satellite rainfall estimates (SREs) derived from tropical rainfall measuring mission (TRMM 3B43v7), rainfall estimation from remotely sensed information using artificial neural networks—climate data record (PERSIANN-CDR), merged satellite-gauge rainfall estimate (IMERG), and the Global Satellite Mapping of Precipitation (GSMaP) with ground-observed data over the varied terrain of hydrologically diverse central and northeastern parts of Ethiopia—Awash River Basin (ARB). Areal comparisons were made between SREs and observed rainfall using various categorical indices and statistical evaluation criteria, and a non-parametric Mann–Kendall (MK) trend test was analyzed. The monthly weighted observed rainfall exhibited relatively comparable results with SREs, except for the annual peak rainfall shifts noted in all SREs. The PERSIANN-CDR products showed a decreasing trend in rainfall at elevations greater than 2250 m above sea level in a river basin. This demonstrates that elevation and rainfall regimes may affect satellite rainfall data. On the basis of modified Kling–Gupta Efficiency, the SREs from IMERG v06, TRMM 3B43v7, and PERSIANN-CDR performed well in descending order over the ARB. However, GSMaP showed poor performance except in the upland sub-basin. A high frequency of bias, which led to an overestimation of SREs, was exhibited in TRMM 3B43v7 and PERSIANN-CDR products in the eastern and lower basins. Furthermore, the MK test results of SREs showed that none of the sub-basins exhibited a monotonic trend at 5% significance level except the GSMap rainfall in the upland sub-basin. In ARB, except for the GSMaP, all SREs can be used as alternative options for rainfall frequency-, flood-, and drought-monitoring studies. However, some may require bias corrections to improve the data quality.
Coarse-to-Fine Image Matching-Based Footprint Camera Calibration of the GF-7 Satellite
Authors: Liu Lirong , Xie Junfeng , Tang Xinming , Ren Chaofeng , Chen Jiyi , Liu Ren
Published: March 2021

The GF-7 satellite is China’s first high-resolution stereo mapping satellite that reaches sub-meter resolution, equipped with new-type payloads, such as an area array footprint camera that can achieve synchronization acquisition of laser spots. When the satellite is in space, the variation of camera parameters may occur due to launch vibration and environmental changes, and on-orbit geometric calibration thereby must be made. Coupled with the data from the GF-7 satellite, this paper constructs a geometric imaging model of the area array footprint camera based on the two-dimensional direction angle, and proposes a coarse-to-fine “LPM-SIFT + Phase correlation” matching strategy for the automatic extraction of calibration control points. The single-image calibration experiment shows that the on-orbit geometric calibration model of the footprint camera constructed in this paper is correct and effective. The matching method proposed is used to register the footprint images with the DOM (Digital Orthophoto Map) reference data to obtain dense control points. Compared with the calibration result using a small number of manually collected control points, the root mean square error (RMSE) of the residual of the control points is improved from half a pixel to 1/3, and the RMSE of the same orbit checkpoints in the image space is improved from 1 pixel to 0.7. It can be concluded that using the coarse-to-fine image matching method proposed in this paper to extract control points can significantly improve the on-orbit calibration accuracy of the footprint camera on the GF-7 satellite.
Data-Driven Object Vehicle Estimation by Radar Accuracy Modeling with Weighted Interpolation
Authors: Choi Woo Young , Yang Jin Ho , Chung Chung Choo
Published: March 2021

For accurate object vehicle estimation using radar, there are two fundamental problems: measurement uncertainties in calculating an object’s position with a virtual polygon box and latency due to commercial radar tracking algorithms. We present a data-driven object vehicle estimation scheme to solve measurement uncertainty and latency problems in radar systems. A radar accuracy model and latency coordination are proposed to reduce the tracking error. We first design data-driven radar accuracy models to improve the accuracy of estimation determined by the object vehicle’s position. The proposed model solves the measurement uncertainty problem within a feasible set for error covariance. The latency coordination is developed by analyzing the position error according to the relative velocity. The position error by latency is stored in a feasible set for relative velocity, and the solution is calculated from the given relative velocity. Removing the measurement uncertainty and latency of the radar system allows for a weighted interpolation to be applied to estimate the position of the object vehicle. Our method is tested by a scenario-based estimation experiment to validate the usefulness of the proposed data-driven object vehicle estimation scheme. We confirm that the proposed estimation method produces improved performance over the conventional radar estimation and previous methods.