bluuebunny/crossref_metadata_2025 huggingface dataset:
Advances on Bone Substitutes through 3D Bioprinting
Authors: Genova Tullio , Roato Ilaria , Carossa Massimo , Motta Chiara , Cavagnetto Davide , Mussano Federico
Published: September 2020

Reconstruction of bony defects is challenging when conventional grafting methods are used because of their intrinsic limitations (biological cost and/or biological properties). Bone regeneration techniques are rapidly evolving since the introduction of three-dimensional (3D) bioprinting. Bone tissue engineering is a branch of regenerative medicine that aims to find new solutions to treat bone defects, which can be repaired by 3D printed living tissues. Its aim is to overcome the limitations of conventional treatment options by improving osteoinduction and osteoconduction. Several techniques of bone bioprinting have been developed: inkjet, extrusion, and light-based 3D printers are nowadays available. Bioinks, i.e., the printing materials, also presented an evolution over the years. It seems that these new technologies might be extremely promising for bone regeneration. The purpose of the present review is to give a comprehensive summary of the past, the present, and future developments of bone bioprinting and bioinks, focusing the attention on crucial aspects of bone bioprinting such as selecting cell sources and attaining a viable vascularization within the newly printed bone. The main bioprinters currently available on the market and their characteristics have been taken into consideration, as well.
Ethionamide Preconditioning Enhances the Proliferation and Migration of Human Wharton’s Jelly-Derived Mesenchymal Stem Cells
Authors: Lee Na-Hee , Myeong Su Hyeon , Son Hyo Jin , Hwang Jung Won , Lee Na Kyung , Chang Jong Wook , Na Duk L.
Published: September 2020

Mesenchymal stem cells (MSCs) are a useful source for cell-based therapy of a variety of immune-mediated diseases, including neurodegenerative disorders. However, poor migration ability and survival rate of MSCs after brain transplantation hinder the therapeutic effects in the disease microenvironment. Therefore, we attempted to use a preconditioning strategy with pharmacological agents to improve the cell proliferation and migration of MSCs. In this study, we identified ethionamide via the screening of a drug library, which enhanced the proliferation of MSCs. Preconditioning with ethionamide promoted the proliferation of Wharton’s jelly-derived MSCs (WJ-MSCs) by activating phosphatidylinositol 3-kinase (PI3K)/Akt and mitogen-activated protein kinase/extracellular signal-regulated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK)1/2 signaling. Preconditioning with ethionamide also enhanced the migration ability of MSCs by upregulating expression of genes associated with migration, such as C-X-C motif chemokine receptor 4 (CXCR4) and C-X-C motif chemokine ligand 12 (CXCL12). Furthermore, preconditioning with ethionamide stimulated the secretion of paracrine factors, including neurotrophic and growth factors in MSCs. Compared to naïve MSCs, ethionamide-preconditioned MSCs (ETH-MSCs) were found to survive longer in the brain after transplantation. These results suggested that enhancing the biological process of MSCs induced by ethionamide preconditioning presents itself as a promising strategy for enhancing the effectiveness of MSCs-based therapies.
Deep Learning for Whole-Slide Tissue Histopathology Classification: A Comparative Study in the Identification of Dysplastic and Non-Dysplastic Barrett’s Esophagus
Authors: Sali Rasoul , Moradinasab Nazanin , Guleria Shan , Ehsan Lubaina , Fernandes Philip , Shah Tilak U. , Syed Sana , Brown Donald E.
Published: September 2020

The gold standard of histopathology for the diagnosis of Barrett’s esophagus (BE) is hindered by inter-observer variability among gastrointestinal pathologists. Deep learning-based approaches have shown promising results in the analysis of whole-slide tissue histopathology images (WSIs). We performed a comparative study to elucidate the characteristics and behaviors of different deep learning-based feature representation approaches for the WSI-based diagnosis of diseased esophageal architectures, namely, dysplastic and non-dysplastic BE. The results showed that if appropriate settings are chosen, the unsupervised feature representation approach is capable of extracting more relevant image features from WSIs to classify and locate the precursors of esophageal cancer compared to weakly supervised and fully supervised approaches.
Global and Regional Implications of Biome Evolution on the Hydrologic Cycle and Climate in the NCAR Dynamic Vegetation Model
Authors: Levey Jessica , Lee Jung-Eun
Published: September 2020

Vegetation influences climate by altering water and energy budgets. With intensifying threats from anthropogenic activities, both terrestrial biomes and climate are expected to change, and the need to understand land–atmosphere interactions will become increasingly crucial. We ran a climate model coupled with a Dynamic Global Vegetation Model (DGVM) to investigate the establishment of terrestrial biomes starting from a bareground scenario and how these biomes influence the climate throughout their evolution. Vegetation reaches quasi-equilibrium after ~350 years, and the vegetation establishment results in global increases in temperature (~2.5 °C), precipitation (~5.5%) and evapotranspiration as well as declines in albedo and sea ice volumes. In high latitude regions, vegetation establishment decreases albedo, causing an increase in global temperatures as well as a northward shift of the Intertropical Convergence Zone (ITCZ). Low latitude tropical afforestation results in greater evapotranspiration and precipitation, and an initial decrease in temperatures due to evaporative cooling.
Modeling and Optimization of Pollutants Removal during Simultaneous Adsorption onto Activated Carbon with Advanced Oxidation in Aqueous Environment
Authors: Dąbek Lidia , Picheta-Oleś Anna , Szeląg Bartosz , Szulżyk-Cieplak Joanna , Łagód Grzegorz
Published: September 2020

The paper presents the results of studies on the modeling and optimization of organic pollutant removal from an aqueous solution in the course of simultaneous adsorption onto activated carbons with varied physical characteristics and oxidation using H2O2. The methodology for determining the models used for predicting the sorption and catalytic parameters in the process was presented. The analysis of the influence of the sorption and catalytic parameters of activated carbons as well as the oxidizer dose on the removal dynamics of organic dyes-phenol red and crystal violet-was carried out based on the designated empirical models. The obtained results confirm the influence of specific surface area (S) of the activated carbon and oxidizer dose on the values of the reaction rate constants related to the removal of pollutants from the solution in a simultaneous process. It was observed that the lower the specific surface area of carbon (S), the greater the influence of the oxidizer on the removal of pollutants from the solution. The proposed model, used for optimization of parameters in a simultaneous process, enables to analyze the effect of selected sorbents as well as the type and dose of the applied oxidizer on the pollutant removal efficiency. The practical application of models will enable to optimize the selection of a sorbent and oxidizer used simultaneously for a given group of pollutants and thus reduce the process costs.
GeTe-TiC-C Composite Anodes for Li-Ion Storage
Authors: Kim Woo Seob , Vo Thuan Ngoc , Kim Il Tae
Published: September 2020

Germanium boasts a high charge capacity, but it has detrimental effects on battery cycling life, owing to the significant volume expansion that it incurs after repeated recharging. Therefore, the fabrication of Ge composites including other elements is essential to overcome this hurdle. Herein, highly conductive Te is employed to prepare an alloy of germanium telluride (GeTe) with the addition of a highly conductive matrix comprising titanium carbide (TiC) and carbon (C) via high-energy ball milling (HEBM). The final alloy composite, GeTe-TiC-C, is used as a potential anode for lithium-ion cells. The GeTe-TiC-C composites having different combinations of TiC are characterized by electron microscopies and X-ray powder diffraction for structural and morphological analyses, which indicate that GeTe and TiC are evenly spread out in the carbon matrix. The GeTe electrode exhibits an unstable cycling life; however, the addition of higher amounts of TiC in GeTe offers much better electrochemical performance. Specifically, the GeTe-TiC (20%)-C and GeTe-TiC (30%)-C electrodes exhibited excellent reversible cyclability equivalent to 847 and 614 mAh g−1 after 400 cycles, respectively. Moreover, at 10 A g−1, stable capacity retentions of 78% for GeTe-TiC (20%)-C and 82% for GeTe-TiC (30%)-C were demonstrated. This proves that the developed GeTe-TiC-C anodes are promising for potential applications as anode candidates for high-performance lithium-ion batteries.
Optimizing the Microstructure and Mechanical Properties of Vacuum Counter-Pressure Casting ZL114A Aluminum Alloy via Ultrasonic Treatment
Authors: Lu Gang , Huang Pengpeng , Yan Qingsong , Xu Pian , Pan Fei , Zhan Hongxing , Chen Yisi
Published: September 2020

The effect of ultrasonic temperature on density, microstructure and mechanical properties of vacuum counter-pressure casting ZL114A alloy during solidification was investigated by optical microscopy (OM), scanning electron microscope (SEM) and a tensile test. The results show that compared with the traditional vacuum counter-pressure casting aluminum alloy, the primary phase and eutectic silicon of the alloy with ultrasonic treatment has been greatly refined due to the dendrites broken by ultrasonic vibration. However, the refining effect of ultrasonic treatment on vacuum counter-pressure casting aluminum alloy will be significantly affected by ultrasonic temperature. When the ultrasonic temperature increases from 680 °C to 720 °C, the primary phase is gradually refined, and the morphology of eutectic silicon also changes from coarse needle-like flakes to fine short rods. With a further increase in the ultrasonic temperature, the microstructure will coarse again. The tensile strength and elongation of vacuum counter-pressure casting ZL114A alloy increases first and then decreases with the increase of ultrasonic temperature. The optimal mechanical properties were achieved with tensile strength of 327 MPa and the elongation of 5.57% at ultrasonic temperature of 720 °C, which is 6.3% and 8.2%, respectively, higher than that of alloy without ultrasonic treatment.
Fabrication and Mechanical Characterization of Dry Three-Dimensional Warp Interlock Para-Aramid Woven Fabrics: Experimental Methods toward Applications in Composite Reinforcement and Soft Body Armor
Authors: Abtew Mulat Alubel , Boussu Francois , Bruniaux Pascal , Liu Han
Published: September 2020

Recently, three-dimensional (3D) warp interlock fabric has been involved in composite reinforcement and soft ballistic material due to its great moldability, improved impact energy-absorbing capacity, and good intra-ply resistance to delamination behaviors. However, understanding the effects of different parameters of the fabric on its mechanical behavior is necessary before the final application. The fabric architecture and its internal yarn composition are among the common influencing parameters. The current research aims to explore the effects of the warp yarn interchange ratio in the 3D warp interlock para-aramid architecture on its mechanical behavior. Thus, four 3D warp interlock variants with different warp (binding and stuffer) yarn ratios but similar architecture and structural characteristics were engineered and manufactured. Tensile and flexural rigidity mechanical tests were carried out at macro- and meso-scale according to standard EN ISO 13 934-1 and nonwoven bending length (WSP 90.5(05)), respectively. Based on the results, the warp yarn interchange ratio in the structure revealed strong influences on the tensile properties of the fabric at both the yarn and final fabric stages. Moreover, the bending stiffness of the different structures showed significant variation in both the warp and weft directions. Thus, the interchange rations of stuffer and binding warp yarn inside the 3D warp interlock fabric were found to be very key in optimizing the mechanical performance of the fabric for final applications.
Modeling of Cutting Parameters and Tool Geometry for Multi-Criteria Optimization of Surface Roughness and Vibration via Response Surface Methodology in Turning of AISI 5140 Steel
Authors: Kuntoğlu Mustafa , Aslan Abdullah , Pimenov Danil Yurievich , Giasin Khaled , Mikolajczyk Tadeusz , Sharma Shubham
Published: September 2020

AISI 5140 is a steel alloy used for manufacturing parts of medium speed and medium load such as gears and shafts mainly used in automotive applications. Parts made from AISI 5140 steel require machining processes such as turning and milling to achieve the final part shape. Limited research has been reported on the machining vibration and surface roughness during turning of AISI 5140 in the open literature. Therefore, the main aim of this paper is to conduct a systematic study to determine the optimum cutting conditions, analysis of vibration and surface roughness under different cutting speeds, feed rates and cutting edge angles using response surface methodology (RSM). Prediction models were developed and optimum turning parameters were obtained for averaged surface roughness (Ra) and three components of vibration (axial, radial and tangential) using RSM. The results demonstrated that the feed rate was the most affecting parameter in increasing the surface roughness (69.4%) and axial vibration (65.8%) while cutting edge angle and cutting speed were dominant on radial vibration (75.5%) and tangential vibration (64.7%), respectively. In order to obtain minimum vibration for all components and surface roughness, the optimum parameters were determined as Vc = 190 m/min, f = 0.06 mm/rev, κ = 60° with high reliability (composite desirability = 90.5%). A good agreement between predicted and measured values was obtained with the developed model to predict surface roughness and vibration during turning of AISI 5140 within a 10% error range.
Exploitation of Filamentous and Picoplanktonic Cyanobacteria for Cosmetic Applications: Potential to Improve Skin Structure and Preserve Dermal Matrix Components
Authors: Morone Janaína , Lopes Graciliana , Preto Marco , Vasconcelos Vítor , Martins Rosário
Published: September 2020

The use of natural products in skin care formulations gained interest as a concern for modern societies. The undesirable side effects of synthetic compounds, as well as the associated environmental hazards, have driven investigation on photosynthetic organisms as sustainable sources of effective and environmentally friendly ingredients. The use of natural extracts in cosmetics has been highlighted and, along with plants and algae, cyanobacteria have come into focus. Due to their low culture demands, high grow rates and ability to produce a wide variability of bioactive metabolites, cyanobacteria emerged as an economic and sustainable base for the cosmetic industry. In this study, we evaluated the potential of ethanol extracts of picocyanobacteria strains of the genera Cyanobium and Synechocystis and filamentous strains of the genera Nodosilinea, Phormidium and Tychonema for skin applications, with focus in the field of anti-aging. The extracts were analyzed for their pigment profile, phenolic content, antioxidant potential, cytotoxicity against keratinocytes (HaCat), fibroblasts (3T3L1), endothelial cells (hCMEC/D3) and capacity to inhibit hyaluronidase (HAase). The total carotenoid content ranged from 118.69 to 383.89 μg g−1 of dry biomass, and the total phenolic content from 1.07 to 2.45 mg GAE g−1. Identified carotenoids consisted of zeaxanthin, lutein, canthaxanthin, echinenone and β-carotene, with zeaxanthin and lutein being the most representative (49.82 and 79.08 μg g−1, respectively). The highest antioxidant potential was found for Phormidium sp. LEGE 05292 and Tychonema sp. LEGE 07196 for superoxide anion radical (O2•−) scavenging (IC50 of 822.70 and 924 μg mL−1, respectively). Low or no cytotoxicity were registered. Regarding HAase inhibition, Tychonema sp. LEGE 07196 and Cyanobium sp. LEGE 07175 showed the best IC50 (182.74 and 208.36 μg mL−1, respectively). In addition, an increase in fibroblast proliferation was registered with these same strains. From this work, the ethanol extracts of the species Tychonema sp. and Cyanobium sp. are particularly interesting for their potential application in anti-aging formulations, once they stimulated fibroblast proliferation and inhibit hyaluronic acid digestion.
Differentiation of Endometriomas from Ovarian Hemorrhagic Cysts at Magnetic Resonance: The Role of Texture Analysis
Authors: Lupean Roxana-Adelina , Ștefan Paul-Andrei , Csutak Csaba , Lebovici Andrei , Măluțan Andrei Mihai , Buiga Rareş , Melincovici Carmen Stanca , Mihu Carmen Mihaela
Published: September 2020

Background and Objectives: To assess ovarian cysts with texture analysis (TA) in magnetic resonance (MRI) images for establishing a differentiation criterion for endometriomas and functional hemorrhagic cysts (HCs) that could potentially outperform their classic MRI diagnostic features. Materials and Methods: Forty-three patients with known ovarian cysts who underwent MRI were retrospectively included (endometriomas, n = 29; HCs, n = 14). TA was performed using dedicated software based on T2-weighted images, by incorporating the whole lesions in a three-dimensional region of interest. The most discriminative texture features were highlighted by three selection methods (Fisher, probability of classification error and average correlation coefficients, and mutual information). The absolute values of these parameters were compared through univariate, multivariate, and receiver operating characteristic analyses. The ability of the two classic diagnostic signs (“T2 shading” and “T2 dark spots”) to diagnose endometriomas was assessed by quantifying their sensitivity (Se) and specificity (Sp), following their conventional assessment on T1-and T2-weighted images by two radiologists. Results: The diagnostic power of the one texture parameter that was an independent predictor of endometriomas (entropy, 75% Se and 100% Sp) and of the predictive model composed of all parameters that showed statistically significant results at the univariate analysis (100% Se, 100% Sp) outperformed the ones shown by the classic MRI endometrioma features (“T2 shading”, 75.86% Se and 35.71% Sp; “T2 dark spots”, 55.17% Se and 64.29% Sp). Conclusion: Whole-lesion MRI TA has the potential to offer a superior discrimination criterion between endometriomas and HCs compared to the classic evaluation of the two lesions’ MRI signal behaviors.
Understanding the Role of the Constituting Elements of the AlCoCrFeNi High Entropy Alloy through the Investigation of Quaternary Alloys
Authors: Hillel Guy , Natovitz Lior , Salhov Shai , Haroush Shlomo , Pinkas Malki , Meshi Louisa
Published: September 2020

Quinary AlCoCrFeNi high entropy alloy (HEA) is one of the most studied alloys in the recent decade due to its outstanding properties. However, it is still far from becoming an applicable industrial alloy. To our understanding, in order to promote this, the role of elements, constituting the quinary alloy, needs to be defined. Knowing the role of each element, modification of the quinary alloy toward minimization of its disadvantages will be possible. In the current research, we shed some light on this subject, presenting a thorough investigation of the microstructure (carried out using scanning and transmission electron microscopy) and mechanical properties, performed by microhardness and fractography post small punch test (SPT), of five equiatomic quaternary alloys, constituting the quinary system, namely: CoCrFeNi, AlCoFeNi, AlCoCrNi, AlCoCrFe, and AlCrFeNi. CoCrFeNi (i.e., w/o Al) was found to be Face Centered Cubic (FCC) solid solution, exhibiting relatively low micro-hardness and ductile fracture post SPT measurement. AlCoFeNi (i.e., w/o Cr) was essentially single phase B2. Other alloys had a mixed BCC + B2 dual phase content with variable microstructures and sizes of particles. The fine microstructure of the alloy without Ni implies eutectic solidification or spinodal decomposition. This fine microstructure imposed remarkable high hardness though the alloy was too brittle and unmachinable. Among the BCC/B2 mixture alloys, Fe and Co-less ones resembled the most quinary AlCoCrFeNi in terms of microstructure and mechanical properties.
Electrochemical Corrosion of SAC Alloys: A Review
Authors: Gharaibeh Ali , Felhősi Ilona , Keresztes Zsófia , Harsányi Gábor , Illés Balázs , Medgyes Bálint
Published: September 2020

Tin–silver–copper (SAC) solder alloys are the most promising candidates to replace Sn–Pb solder alloys. However, their application is still facing several challenges; one example is the electrochemical corrosion behaviour, which imposes a risk to electronics reliability. Numerous investigations have been carried out to evaluate the corrosion performance of SAC lead-free alloys, regarding the effect of the corrosive environment, the different manufacturing technologies, the effect of fluxes, the metallic contents within the SAC alloys themselves, and the different alloying elements. In these studies, widely used electrochemical techniques are applied as accelerated corrosion tests, such as linear sweep voltammetry and electrochemical impedance spectroscopy. However, there is lack of studies that try to summarise the various corrosion results in terms of lead-free solder alloys including low-Ag and composite solders. This study aims to review these studies by showing the most important highlights regarding the corrosion processes and the possible future developments.
Three New Reports of Trichoderma in Algeria: T. atrobrunneum, (South) T. longibrachiatum (South), and T. afroharzianum (Northwest)
Authors: Haouhach Sadika , Karkachi Noureddine , Oguiba Bouchra , Sidaoui Abouamama , Chamorro Isabel , Kihal Mebrouk , Monte Enrique
Published: September 2020

The genus Trichoderma (Hypocreaceae, Ascomycota) consists of globally distributed fungi. In Algeria, few studies have explored the diversity of this genus, and in the majority of works identification is based on phenotypic characters. Here, nine Trichoderma strains were collected from Algeria in different locations, namely: seven in the south and two in the northwest. Also, we used 17 reference strains that were taken from the NCBI database for the phylogeny analysis. Our study is based on an integrated approach using micro and macro phenotypic characters and multiple DNA analysis (internal transcribed spacer (ITS): ITS1–4 region; translation elongation factor 1: tef1 gene). Our study reports, for the first time, three species of Trichoderma in Algeria, namely: T. atrobrunneum (south), T. longibrachiatum (south), and T. afroharzianum (northwest). It is noteworthy that T. atrobrunneum is a species previously described in European Mediterranean countries, and its presence in the soil of southern Algeria indicates that the diversity of the geographic environments and different climates of Algeria offers the possibility for the survival of diverse Trichoderma species. Knowledge on the diversity of these fungi may contribute to their future exploitation in biotechnological applications and to the biological control of plant diseases.
ODO: Design of Multimodal Chatbot for an Experiential Media System
Authors: Bhushan Ravi , Kulkarni Karthik , Pandey Vishal Kumar , Rawls Connor , Mechtley Brandon , Jayasuriya Suren , Ziegler Christian
Published: September 2020

This paper presents the design of a multimodal chatbot for use in an interactive theater performance. This chatbot has an architecture consisting of vision and natural language processing capabilities, as well as embodiment in a non-anthropomorphic movable LED array set in a stage. Designed for interaction with up to five users at a time, the system can perform tasks including face detection and emotion classification, tracking of crowd movement through mobile phones, and real-time conversation to guide users through a nonlinear story and interactive games. The final prototype, named ODO, is a tangible embodiment of a distributed multimedia system that solves several technical challenges to provide users with a unique experience through novel interaction.
Phenolic Profile, Antioxidant Activity, and Ameliorating Efficacy of Chenopodium quinoa Sprouts against CCl4-Induced Oxidative Stress in Rats
Authors: Al-Qabba Maryam M. , El-Mowafy Maha A. , Althwab Sami A. , Alfheeaid Hani A. , Aljutaily Thamer , Barakat Hassan
Published: September 2020

Quinoa (Chenopodium quinoa) is classified as one of the pseudo-cereal grains rich in both macronutrients and micronutrients. This study tracks changes in the polyphenol composition of red quinoa (RQ) and yellow quinoa (YQ) seeds during germination. The antioxidant bioactivity of raw and germinated seed was also determined in vitro. Phenolic acids and their derivatives and flavonoids were identified by using HPLC-DAD and quantified after 0, 3, and 6 days of germination. Subsequently, the extracts of 6-day-old quinoa sprouts were prepared to biologically evaluate their functional properties against CCl4-induced oxidative stress in rats. The results indicated that antioxidant activity (AOA) of total phenolic compounds (TPC), and flavonoids significantly increased in RQ and YQ sprouts during germination up to 9 days. RQ sprouts exhibited stronger bioactive compound diversity than YQ sprouts as observed in HPLC analysis. Among the 11 and 8 quantified polyphenols, ferulic acid and quercetin were predominant phenolic acid and flavonoid in RQ and YQ sprouts, respectively. After 6 days of germination, 16 and 8 polyphenols were detected and quantified in RQ and YQ sprouts, respectively. Interestingly, the treatment of rats at a dose of 30 mg of Gallic acid Equivalent (GAE) kg−1 significantly reduced fasting blood glucose (FBG), alanine aminotransferase (ALT), aspartate aminotransferase AST, and total bilirubin (TIBIL) and improved liver inflammation. Furthermore, RQ and YQ sprouts improved the blood profile by significantly decreasing low-density lipoproteins (LDL) and very low-density lipoproteins (VLDL) and increasing high-density lipoproteins (HDL). Moreover, RQ and YQ sprout extracts significantly reduced malonaldehyde (MDA) and efficiently enhanced the reduced glutathione (GSH) and superoxide dismutase (SOD) activities in oxidative stress-induced rats as compared to CCl4-rats. In conclusion, red quinoa sprouts (RQS) and yellow quinoa sprouts (YQS) provide naturally synthesized polyphenols, possessing superior antioxidant activity, and their ethanolic extracts have promising effects and potential health benefits to counter induced oxidative stress. Incorporating quinoa sprouts as functional food ingredients should be considered and scaling-up its production is beneficial.
Comparing Calculated Nutrient Intakes Using Different Food Composition Databases: Results from the European Prospective Investigation into Cancer and Nutrition (EPIC) Cohort
Authors: Van Puyvelde Heleen , Perez-Cornago Aurora , Casagrande Corinne , Nicolas Geneviève , Versele Vickà , Skeie Guri , B. Schulze Matthias , Johansson Ingegerd , María Huerta José , Oliverio Andreina , Ricceri Fulvio , Halkjær Jytte , Amiano Etxezarreta Pilar , Van Herck Koen , Weiderpass Elisabete , J. Gunter Marc , Huybrechts Inge
Published: September 2020

This study aimed to compare calculated nutrient intakes from two different food composition databases using data from the European prospective investigation into cancer and nutrition (EPIC) cohort. Dietary intake data of the EPIC cohort was recently matched to 150 food components from the U.S. nutrient database (USNDB). Twenty-eight of these nutrients were already included in the EPIC nutrient database (ENDB—based upon country specific food composition tables), and used for comparison. Paired sample t-tests, Pearson’s correlations (r), weighted kappa’s (κ) and Bland-Altman plots were used to compare the dietary intake of 28 nutrients estimated by the USNDB and the ENDB for 476,768 participants. Small but significant differences were shown between the USNDB and the ENDB for energy and macronutrient intakes. Moderate to very strong correlations (r = 0.60–1.00) were found for all macro- and micronutrients. A strong agreement (κ > 0.80) was found for energy, water, total fat, carbohydrates, sugar, alcohol, potassium and vitamin C, whereas a weak agreement (κ < 0.60) was found for starch, vitamin D and vitamin E. Dietary intakes estimated via the USNDB compare adequately with those obtained via the ENDB for most macro- and micronutrients, although the agreement was weak for starch, vitamin D and vitamin E. The USNDB will allow exposure assessments for 150 nutrients to investigate associations with disease outcomes within the EPIC cohort.
Anti-Inflammatory Potential of Cow, Donkey and Goat Milk Extracellular Vesicles as Revealed by Metabolomic Profile
Authors: Mecocci Samanta , Gevi Federica , Pietrucci Daniele , Cavinato Luca , Luly Francesco R. , Pascucci Luisa , Petrini Stefano , Ascenzioni Fiorentina , Zolla Lello , Chillemi Giovanni , Cappelli Katia
Published: September 2020

In recent years, extracellular vesicles (EVs), cell-derived micro and nano-sized structures enclosed in a double-layer membrane, have been in the spotlight for their high potential in diagnostic and therapeutic applications. Indeed, they act as signal mediators between cells and/or tissues through different mechanisms involving their complex cargo and exert a number of biological effects depending upon EVs subtype and cell source. Being produced by almost all cell types, they are found in every biological fluid including milk. Milk EVs (MEVs) can enter the intestinal cells by endocytosis and protect their labile cargos against harsh conditions in the intestinal tract. In this study, we performed a metabolomic analysis of MEVs, from three different species (i.e., bovine, goat and donkey) by mass spectroscopy (MS) coupled with Ultrahigh-performance liquid chromatography (UHPLC). Metabolites, both common or specific of a species, were identified and enriched metabolic pathways were investigated, with the final aim to evaluate their anti-inflammatory and immunomodulatory properties in view of prospective applications as a nutraceutical in inflammatory conditions. In particular, metabolites transported by MEVs are involved in common pathways among the three species. These metabolites, such as arginine, asparagine, glutathione and lysine, show immunomodulating effects. Moreover, MEVs in goat milk showed a greater number of enriched metabolic pathways as compared to the other kinds of milk.
Epidemiology of Noble Pen Shell (Pinna nobilis L. 1758) Mass Mortality Events in Adriatic Sea Is Characterised with Rapid Spreading and Acute Disease Progression
Authors: Šarić Tomislav , Župan Ivan , Aceto Serena , Villari Grazia , Palić Dušan , De Vico Gionata , Carella Francesca
Published: September 2020

From May to October 2019, multiple mass mortality events (MMEs) of Pinna nobilis were observed along Croatian coastline starting from the south-east and rapidly progressing in north-western direction. Time dynamics of the MMEs closely followed general speed and direction patterns of surface sea-currents, advancing approximately 350 km in less than 3 months. Surveillance, clinical evaluation, and sample collection were performed on multiple sites with various degrees of mortality rates. Moribund P. nobilis individuals were collected and subjected to pathological, molecular, and microscopical investigation. Affected animals were positive for Mycobacterium in 70% of the individuals, and Haplosporidium pinnae was present in 58% of the cases. Observed pathological lesions were most severe where concurrent presence of both pathogens was confirmed (in 45.8% of moribund individuals). Moderate to strong lesions were observed in animals positive for Mycobacterium only (25% of cases), and lesions were absent or minor to moderate when only H. pinnae was confirmed (16% of cases). Considering the rapid and severe spread of the MMEs, the areas less exposed to major sea currents appeared to be at lower risk of pathogen transmission. Surveillance activities along the Croatian coastline identified several P. nobilis populations in such “lower risk” areas without apparent mortality or clinical symptoms. Such areas are of particular interest as source of potentially healthy individuals to support active recovery actions.
Current Pharmacological Treatment Options for Central Serous Chorioretinopathy: A Review
Authors: Nicolò Massimo , Ferro Desideri Lorenzo , Vagge Aldo , Traverso Carlo Enrico
Published: September 2020

Central serous chorioretinopathy (CSC) is a common cause of visual impairment in patients generally aged 20 to 60 and it is characterized by acute or chronic neurosensory detachments of the retina. Although CSC resolves spontaneously in most cases, in some patients it may cause permanent visual impairment in the working population; for this reason, several approaches, including photodynamic therapy (PDT), subthreshold micropulse laser treatment and oral mineralocorticoid receptor antagonists, have been studied as first-line treatment options for CSC. To date, half-dose PDT has provided the most encouraging results in this regard, supported by large, multicenter, randomized clinical trials such as the “Prospective Randomized Controlled Treatment Trial for Chronic Central Serous Chorioretinopathy” (PLACE) trial; however, the role of novel possible non-invasive treatment options is attracting interest. This review article aims to discuss the current pharmacological treatment options investigated for the management of CSC, including aspirin, ketoconazole, beta blockers, rifampicin and many others. In particular, further evidence about oral mineralocorticoid receptor antagonists, firstly seen as promising non-invasive alternatives for treating CSC, will be provided and discussed in light of the recent “Eplerenone for chronic central serous chorioretinopathy in patients with active, previously untreated disease for more than 4 months” (VICI) trial results, which have largely resized their role as possible first-line oral treatment options for treating CSC.
Cycle Network Model of Prostaglandin H Synthase-1
Authors: Goltsov Alexey , Swat Maciej , Peskov Kirill , Kosinsky Yuri
Published: September 2020

The kinetic model of Prostaglandin H Synthase-1 (PGHS-1) was developed to investigate its complex network kinetics and non-steroidal anti-inflammatory drugs (NSAIDs) efficacy in different in vitro and in vivo conditions. To correctly describe the complex mechanism of PGHS-1 catalysis, we developed a microscopic approach to modelling of intricate network dynamics of 35 intraenzyme reactions among 24 intermediate states of the enzyme. The developed model quantitatively describes interconnection between cyclooxygenase and peroxidase enzyme activities; substrate (arachidonic acid, AA) and reducing cosubstrate competitive consumption; enzyme self-inactivation; autocatalytic role of AA; enzyme activation threshold; and synthesis of intermediate prostaglandin G2 (PGG2) and final prostaglandin H2 (PGH2) products under wide experimental conditions. In the paper, we provide a detailed description of the enzyme catalytic cycle, model calibration based on a series of in vitro kinetic data, and model validation using experimental data on the regulatory properties of PGHS-1. The validated model of PGHS-1 with a unified set of kinetic parameters is applicable for in silico screening and prediction of the inhibition effects of NSAIDs and their combination on the balance of pro-thrombotic (thromboxane) and anti-thrombotic (prostacyclin) prostaglandin biosynthesis in platelets and endothelial cells expressing PGHS-1.
Comparative Analysis on the Deployment of Machine Learning Algorithms in the Distributed Brillouin Optical Time Domain Analysis (BOTDA) Fiber Sensor
Authors: Nordin Nur Dalilla , Zan Mohd Saiful Dzulkefly , Abdullah Fairuz
Published: September 2020

This paper demonstrates a comparative analysis of five machine learning (ML) algorithms for improving the signal processing time and temperature prediction accuracy in Brillouin optical time domain analysis (BOTDA) fiber sensor. The algorithms analyzed were generalized linear model (GLM), deep learning (DL), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM). In this proof-of-concept experiment, the performance of each algorithm was investigated by pairing Brillouin gain spectrum (BGS) with its corresponding temperature reading in the training dataset. It was found that all of the ML algorithms have significantly reduced the signal processing time to be between 3.5 and 655 times faster than the conventional Lorentzian curve fitting (LCF) method. Furthermore, the temperature prediction accuracy and temperature measurement precision made by some algorithms were comparable, and some were even better than the conventional LCF method. The results obtained from the experiments would provide some general idea in deploying ML algorithm for characterizing the Brillouin-based fiber sensor signals.
Photonic Spin Hall Effect: Contribution of Polarization Mixing Caused by Anisotropy
Authors: Mazanov Maxim , Yermakov Oleh , Deriy Ilya , Takayama Osamu , Bogdanov Andrey , Lavrinenko Andrei V.
Published: September 2020

Spin-orbital interaction of light attracts much attention in nanophotonics opening new horizons for modern optical systems and devices. The photonic spin Hall effect or Imbert-Fedorov shift takes a special place among the variety of spin-orbital interaction phenomena. It exhibits as a polarization-dependent transverse light shift usually observed in specular scattering of light at interfaces with anisotropic materials. Nevertheless, the effect of the polarization mixing caused by anisotropy on the Imbert-Fedorov shift is commonly underestimated. In this work, we demonstrate that polarization mixing contribution cannot be ignored for a broad range of optical systems. In particular, we show the dominant influence of the mixing term over the standard one for the polarized optical beam incident at a quarter-wave plate within the paraxial approximation. Moreover, our study reveals a novel contribution with extraordinary polarization dependence not observable within the simplified approach. We believe that these results advance the understanding of photonic spin Hall effect and open new opportunities for spin-dependent optical phenomena.
On Improving the Training of Models for the Semantic Segmentation of Benthic Communities from Orthographic Imagery
Authors: Pavoni Gaia , Corsini Massimiliano , Callieri Marco , Fiameni Giuseppe , Edwards Clinton , Cignoni Paolo
Published: September 2020

The semantic segmentation of underwater imagery is an important step in the ecological analysis of coral habitats. To date, scientists produce fine-scale area annotations manually, an exceptionally time-consuming task that could be efficiently automatized by modern CNNs. This paper extends our previous work presented at the 3DUW’19 conference, outlining the workflow for the automated annotation of imagery from the first step of dataset preparation, to the last step of prediction reassembly. In particular, we propose an ecologically inspired strategy for an efficient dataset partition, an over-sampling methodology targeted on ortho-imagery, and a score fusion strategy. We also investigate the use of different loss functions in the optimization of a Deeplab V3+ model, to mitigate the class-imbalance problem and improve prediction accuracy on coral instance boundaries. The experimental results demonstrate the effectiveness of the ecologically inspired split in improving model performance, and quantify the advantages and limitations of the proposed over-sampling strategy. The extensive comparison of the loss functions gives numerous insights on the segmentation task; the Focal Tversky, typically used in the context of medical imaging (but not in remote sensing), results in the most convenient choice. By improving the accuracy of automated ortho image processing, the results presented here promise to meet the fundamental challenge of increasing the spatial and temporal scale of coral reef research, allowing researchers greater predictive ability to better manage coral reef resilience in the context of a changing environment.
Evaluation of the Significant Wave Height Data Quality for the Sentinel-3 Synthetic Aperture Radar Altimeter
Authors: Wan Yong , Zhang Rongjuan , Pan Xiaodong , Fan Chenqing , Dai Yongshou
Published: September 2020

Synthetic aperture radar (SAR) altimeters represent a new method of microwave remote sensing for ocean wave observations. The adoption of SAR technology in the azimuthal direction has the advantage of a high resolution. The Sentinel-3 altimeter is the first radar altimeter to acquire global observations in SAR mode; hence, the data quality needs to be assessed before extensively applying these data. The European Space Agency (ESA) evaluates the Sentinel-3 accuracy on a global scale but has yet to perform a detailed analysis in terms of different offshore distances and different water depths. In this paper, Sentinel-3 and Jason-2 significant wave height (SWH) data are matched in both time and space with buoy data from the United States East and West Coasts and the Central Pacific Ocean. The Sentinel-3 SWH data quality is evaluated according to different offshore distances and water depths in comparison with Jason-2 SWH data. In areas more than 50 km offshore, the Sentinel-3 SWH accuracy is generally high and less affected by the water depth and sea conditions (root-mean-square error of 0.28 m and correlation coefficient of 0.98); in areas less than 50 km offshore, the SWH data accuracy is slightly affected by water depth and sea conditions (especially the former). Compared with Jason-2, the observation ability of the Sentinel-3 altimeter in nearshore areas with water depths of 0 m-500 m is greatly improved, but in some deep water areas with stable sea conditions, the Jason-2 SWH data accuracy is higher than that of Sentinel-3. This work provides a reference for the refined application of Sentinel-3 SWH data in offshore deep water areas and nearshore shallow water areas.
Monitoring the Structural Health of Glass Fibre-Reinforced Hybrid Laminates Using Novel Piezoceramic Film
Authors: Schmidt René , Graf Alexander , Decker Ricardo , Heinrich Michael , Kräusel Verena , Kroll Lothar , Hardt Wolfram
Published: September 2020

This work investigates a new generation structural health monitoring (SHM) system for fibre metal laminates (FML) based on an embedded thermoplastic film with compounded piezoceramics, termed piezo-active fibre metal laminate (PFML). The PFML is manufactured using near-series processes and its potential as a passive SHM system is being investigated. A commercial Polyvinylidene fluoride (PVDF) sensor film is used for comparative evaluation of the sensor signals. Furthermore, thermoset and thermoplastic-based FML are equipped with the sensor films and evaluated. For this purpose, static and dynamic three-point bending tests are carried out and the data are recorded. The data obtained from the sensors and the testing machine are compared with the type and time of damage by means of intelligent signal processing. By using a smart sensor system, further investigations are planned which the differentiation between various failure modes, e.g., delamination or fibre breakage.
Sensing Systems for Respiration Monitoring: A Technical Systematic Review
Authors: Vanegas Erik , Igual Raul , Plaza Inmaculada
Published: September 2020

Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system.
Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living
Authors: Pandya Sharnil , Ghayvat Hemant , Sur Anirban , Awais Muhammad , Kotecha Ketan , Saxena Santosh , Jassal Nandita , Pingale Gayatri
Published: September 2020

Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.