bluuebunny/crossref_metadata_2025 huggingface dataset:
A Ratiometric and Colorimetric Hemicyanine Fluorescent Probe for Detection of SO2 Derivatives and Its Applications in Bioimaging
Authors: Qin Yan-Hong , Jiang Xiao-Yi , Que Yuan-Fang , Gu Jing-Yi , Wu Tong , Aihemaiti Ayinazhaer , Shi Ke-Xin , Kang Wen-Yu , Hu Bi-Ying , Lan Jin-Shuai , Ding Yue , Zhang Tong
Published: November 2019

Based upon the intramolecular charge transfer (ICT) mechanism, a novel ratiometric fluorescent probe EB was developed to detect SO32−/HSO3−. The probe displayed both colorimetric and ratiometric responses toward SO32−/HSO3−. It displayed a quick response (within 60 s), good selectivity and high sensitivity (a detection limit of 28 nM) towards SO32−/HSO3−. The SO32−/HSO3− sensing mechanism was confirmed as the Michael addition reaction by ESI-MS. Moreover, the probe could be applied to measure the level of sulfite in real samples, like sugar and chrysanthemum, and it could also be used to detect SO32−/HSO3− in HepG2 cells through confocal fluorescence microscopy, which proved its practical application in clinical diagnosis.
Deep Eutectic Solvents for Pretreatment, Extraction, and Catalysis of Biomass and Food Waste
Authors: Kalhor Payam , Ghandi Khashayar
Published: November 2019

Valorization of lignocellulosic biomass and food residues to obtain valuable chemicals is essential to the establishment of a sustainable and biobased economy in the modern world. The latest and greenest generation of ionic liquids (ILs) are deep eutectic solvents (DESs) and natural deep eutectic solvents (NADESs); these have shown great promise for various applications and have attracted considerable attention from researchers who seek versatile solvents with pretreatment, extraction, and catalysis capabilities in biomass- and biowaste-to-bioenergy conversion processes. The present work aimed to review the use of DESs and NADESs in the valorization of biomass and biowaste as pretreatment or extraction solvents or catalysis agents.
The Synthesis and Properties of Liquid Crystalline Polyurethanes, Chemically Modified by Polyhedral Oligomericsilsesquioxanes
Authors: Bukowczan Artur , Hebda Edyta , Czajkowski Maciej , Pielichowski Krzysztof
Published: November 2019

In this work, we report for the first time on the influence of polyhedral oligomericsilsesquioxanes (POSS) on the structure and properties of liquid crystalline polyurethane (LCPU). LCPU/POSS hybrids were synthesized via a two-step method. In the first step, 4,4′-methylenephenyl diisocyanate (MDI) and polytetramethylene ether glycol (PTMG) reacted with functionalized trisilanolphenyl POSS (TSP-POSS) bearing three hydroxyl groups. In the second step, the growing chain was extended with 4,4′-bis(hydroxyhexoxy)biphenyl (BHHBP). FTIR measurements confirmed the chemical bonding between the POSS and LCPU matrix and showed the influence of the silsesquioxane modification on the intensity of hydrogen bonds. The DSC and POM techniques confirmed the formation of liquid crystalline phases. The incorporation of silsesquixanes into the LC matrix leads to higher melting and isotropization temperatures along with the broadening phase transition effect. Scanning electron microscopy showed a good distribution of POSS moieties, both in the bulk and on the surface of the liquid crystalline PU matrix, whereby wide-angle X-ray diffraction (WAXD) patterns revealed halos from both the liquid crystalline and unmodified polyurethane matrix. The stress at the breaking points for LCPU/POSS hybrids containing 50% and 60% of elastic segments is greater than the stress at the breaking point of the reference material (LCPU), what is due to good dispersion of POSS in less elastic matrix. Thermal properties of the LCPU/POSS materials obtained, determined by TGA, revealed that the char residue increased with the amount of POSS for 40% of elastic segments materials.
Towards the Development of an In vivo Chemical Probe for Cyclin G Associated Kinase (GAK)
Authors: Asquith Christopher R. M. , Bennett James M. , Su Lianyong , Laitinen Tuomo , Elkins Jonathan M. , Pickett Julie E. , Wells Carrow I. , Li Zengbiao , Willson Timothy M. , Zuercher William J.
Published: November 2019

SGC-GAK-1 (1) is a potent, selective, cell-active chemical probe for cyclin G-associated kinase (GAK). However, 1 was rapidly metabolized in mouse liver microsomes by cytochrome P450-mediated oxidation, displaying rapid clearance in liver microsomes and in mice, which limited its utility in in vivo studies. Chemical modifications of 1 that improved metabolic stability, generally resulted in decreased GAK potency. The best analog in terms of GAK activity in cells was 6-bromo-N-(1H-indazol-6-yl)quinolin-4-amine (35) (IC50 = 1.4 μM), showing improved stability in liver microsomes while still maintaining a narrow spectrum activity across the kinome. As an alternative to scaffold modifications we also explored the use of the broad-spectrum cytochrome P450 inhibitor 1-aminobenzotriazole (ABT) to decrease intrinsic clearance of aminoquinoline GAK inhibitors. Taken together, these approaches point towards the development of an in vivo chemical probe for the dark kinase GAK.
Correction: Tienaho, J., et al. Metabolic Profiling of Water-Soluble Compounds from the Extracts of Dark Septate Endophytic Fungi (DSE) Isolated from Scots Pine (Pinus sylvestris L.) Seedlings Using UPLC–Orbitrap–MS. Molecules 2019, 24, 2330
Authors: Tienaho Jenni , Karonen Maarit , Muilu–Mäkelä Riina , Wähälä Kristiina , Leon Denegri Eduardo , Franzén Robert , Karp Matti , Santala Ville , Sarjala Tytti
Published: November 2019

The authors wish to make the following corrections to this paper published in Molecules [...]
The Synthesis and Utility of Metal-Nitrosophenolato Compounds—Highlighting the Baudisch Reaction
Authors: Nicholls Alexander J. , Barber Thomas , Baxendale Ian R.
Published: November 2019

The syntheses of the title compounds demonstrate a privileged introduction of a nitroso (and a hydroxyl via the Baudisch reaction) group to an aromatic ring. These complexes first appeared in the literature as early as 1939, and a range of applications has subsequently been published. However, optimisations of the preparative sequences were not considered, and as such, the reactions have seldom been utilised in recent years; indeed, there remains confusion in the literature as to how such complexes form. In this review, we aim to demystify the misunderstanding surrounding these remarkable complexes and consider their renewed application in the 21st century.
Brief Story on Prostaglandins, Inhibitors of their Synthesis, Hematopoiesis, and Acute Radiation Syndrome
Authors: Hofer Michal , Hoferová Zuzana , Falk Martin
Published: November 2019

Prostaglandins and inhibitors of their synthesis (cyclooxygenase (COX) inhibitors, non-steroidal anti-inflammatory drugs) were shown to play a significant role in the regulation of hematopoiesis. Partly due to their hematopoiesis-modulating effects, both prostaglandins and COX inhibitors were reported to act positively in radiation-exposed mammalian organisms at various pre- and post-irradiation therapeutical settings. Experimental efforts were targeted at finding pharmacological procedures leading to optimization of therapeutical outcomes by minimizing undesirable side effects of the treatments. Progress in these efforts was obtained after discovery of selective inhibitors of inducible selective cyclooxygenase-2 (COX-2) inhibitors. Recent studies have been able to suggest the possibility to find combined therapeutical approaches utilizing joint administration of prostaglandins and inhibitors of their synthesis at optimized timing and dosing of the drugs which could be incorporated into the therapy of patients with acute radiation syndrome.
Structure–Activity Relationship Study of Acyclic Terpenes in Blood Glucose Levels: Potential α-Glucosidase and Sodium Glucose Cotransporter (SGLT-1) Inhibitors
Authors: Valdes Miguel , Calzada Fernando , Mendieta-Wejebe Jessica
Published: November 2019

Twelve terpenoids were evaluated in the treatment of type 2 diabetes mellitus: seven monoterpenes (geranyl acetate (1), geranic acid (2), citral (3), geraniol (4), methyl geranate (5), nerol (6), and citronellic acid (7)), three sesquiterpenes (farnesal (8), farnesol (9), and farnesyl acetate (10)), one diterpene (geranylgeraniol (11)), and one triterpene (squalene (12)) were selected to carry out a study on normoglycemic and streptozotocin-induced diabetic mice. Among these, 2, 3, 7, 8, 9, and 10 showed antihyperglycemic activity in streptozotocin-induced diabetic mice. They were then selected for evaluation in oral sucrose and lactose tolerance tests (OSTT and OLTT) as well as in an oral glucose tolerance test (OGTT). In the OSTT and OLTT, compounds 3, 7, 8, 9, and 10 showed a reduction in postprandial glucose peaks 2 h after a sucrose or lactose load (comparable to acarbose). In the case of the OGTT, 2, 7, 8, 9, and 10 showed a reduction in postprandial glucose peaks 2 h after a glucose load (comparable to canagliflozin). Our results suggest that the control of postprandial hyperglycemia may be mediated by the inhibition of disaccharide digestion, such as sucrose and lactose, and the regulation of the absorption of glucose. The first case could be associated with an ∝ -glucosidase inhibitory effect and the second with an inhibition of the sodium–glucose type 1 (SGLT-1) cotransporter. Finally, five acyclic terpenes may be candidates for the development and search for new α-glucosidase and SGLT-1 cotransporter inhibitors.
Chemical Composition and Antimicrobial Activity of Artemisia herba-alba and Origanum majorana Essential Oils from Morocco
Authors: Amor Ghita , Caputo Lucia , La Storia Antonietta , De Feo Vincenzo , Mauriello Gianluigi , Fechtali Taoufiq
Published: November 2019

Essential oils (EOs) are one of the most important groups of plant metabolites responsible for their biological activities. This study was carried out to study the chemical composition and the antimicrobial effects of Artemisia herba-alba and Origanum majorana essential oils against some Gram-positive and Gram-negative bacteria, and a fungal strain isolated from spoiled butter. The plants were collected in the region Azzemour of South West Morocco and the EOs, extracted by hydrodistillation, were analyzed by GC-MS. The antimicrobial activity was determined using the agar paper disc method. The main components of A. herba-alba EO were cis-thujone, trans-thujone and vanillyl alcohol; in O. majorana EO terpinen-4-ol, isopulegol and β-phellandrene predominated. Both essential oils exhibited growth inhibiting activities in a concentration-dependent manner on several microorganism species. Our results demonstrated that O. majorana and A. herba-alba EOs could be effective natural antibacterial agents in foods.
Mercury Determination in Natural Zeolites by Thermal Decomposition Atomic Absorption Spectrometry: Method Validation in Compliance with Requirements for Use as Dietary Supplements
Authors: Senila Marin , Cadar Oana , Senila Lacrimioara , Hoaghia Alexandra , Miu Ion
Published: November 2019

Natural zeolites are hydrated aluminosilicate minerals that, due to their remarkable physical-chemical properties of being molecular sieves and cation exchangers, have applications in different areas such as environmental protection, catalysis, animal feed, and dietary supplements. Since natural zeolites may contain traces of undesirable compounds such as toxic metals, the accurate quantification of these elements is necessary. In this study, a direct method for Hg determination in zeolite samples based on the thermal desorption atomic absorption spectrometry (TD-AAS) technique is fully validated, taking into account the legislative requirements in the field. The chosen quantification limit was 0.9 µg kg−1, which is satisfactory for intended use. Trueness was evaluated by recovery rate using certified reference materials containing mercury, with satisfactory results. Other figures of merit, such as repeatability and measurement uncertainty, also fulfill the legislative requirements related to the analysis of dietary supplements. This paper presents, for the first time, a fully validated method for mercury determination in zeolite samples, and the obtained results reveal that the method can be applied successfully for the intended purpose.
Design, Synthesis and Biological Evaluation of Diosgenin-Amino Acid Derivatives with Dual Functions of Neuroprotection and Angiogenesis
Authors: Cai Desheng , Qi Jinchai , Yang Yuqin , Zhang Wenxi , Zhou Fei , Jia Xiaohui , Guo Wenbo , Huang Xuemei , Gao Feng , Chen Hongshan , Li Tong , Li Guoping , Wang Penglong , Zhang Yuzhong , Lei Haimin
Published: November 2019

Diosgenin, a natural product with steroidal structure, has a wide range of clinical applications in China. It also shows great potential in the treatment of blood clots and nerve damage. To enhance the bioavailability as well as efficacy of diosgenin, eighteen diosgenin-amino acid derivatives were designed and synthesized. The neuroprotective effects of these compounds were evaluated by SH-SY5Y cell line and the biosafety was evaluated by H9c2 cell line. The results displayed that part of the derivatives’ activities (EC50 < 20 μM) were higher than positive control edaravone (EC50 = 21.60 ± 3.04 μM), among which, DG-15 (EC50 = 6.86 ± 0.69 μM) exhibited the best neuroprotection. Meanwhile, biosafety evaluation showed that DG-15 had no cytotoxicity on H9c2 cell lines. Interestingly, combined neuroprotective and cytotoxic results, part of the derivatives without their protecting group were superior to compounds with protecting group. Subsequently, Giemsa staining and DAPI (4′,6-diamidino-2-phenylindole) staining indicated that DG-15 had a protective effect on damaged SH-SY5Y cells by reducing apoptosis. Moreover, DG-15 showed a higher role in promoting angiogenesis at high concentrations (4 mg/mL) on the chorioallantoic membrane model. This finding displayed that DG-15 had dual functions of neuroprotection and angiogenesis, which provided further insight into designing agent for the application in treatment of ischemic stroke.
Hydrolyzed Collagen—Sources and Applications
Authors: León-López Arely , Morales-Peñaloza Alejandro , Martínez-Juárez Víctor Manuel , Vargas-Torres Apolonio , Zeugolis Dimitrios I. , Aguirre-Álvarez Gabriel
Published: November 2019

Hydrolyzed collagen (HC) is a group of peptides with low molecular weight (3–6 KDa) that can be obtained by enzymatic action in acid or alkaline media at a specific incubation temperature. HC can be extracted from different sources such as bovine or porcine. These sources have presented health limitations in the last years. Recently research has shown good properties of the HC found in skin, scale, and bones from marine sources. Type and source of extraction are the main factors that affect HC properties, such as molecular weight of the peptide chain, solubility, and functional activity. HC is widely used in several industries including food, pharmaceutical, cosmetic, biomedical, and leather industries. The present review presents the different types of HC, sources of extraction, and their applications as a biomaterial.
Alkaloids with Nitric Oxide Inhibitory Activities from the Roots of Isatis tinctoria
Authors: Zhang Dongdong , Shi Yanhong , Li Jingyi , Ruan Deqing , Jia Qi , Zhu Weiliang , Chen Kaixian , Li Yiming , Wang Rui
Published: November 2019

As our ongoing research project on Ban Lan Gen (Isatis tinctoria roots), a total of 23 alkaloids were obtained. Compounds 1 and 2 contain an unusual C–C bond between the 2(1H)-quinolinone moiety and the phenol moiety and between the 2(1H)-quinolinone moiety and the 1H-indole moiety, respectively. Compound 3 possesses an unusual carbon skeleton and its putative biosynthetic pathway was discussed, and Compound 23 was deduced as a new indole alkaloid glycoside. Compounds 4–7 were identified as four new natural products by extensive spectroscopic experiments. Additionally, the anti-inflammatory activity was assessed based on nitric oxide (NO) production using Lipopolysaccharide-stimulated RAW264.7 macrophages. Compounds 9, 15, and 17 showed inhibitory effects with IC50 values of 1.2, 5.0, and 74.4 μM.
Identifying Cancer-Specific circRNA–RBP Binding Sites Based on Deep Learning
Authors: Wang Zhengfeng , Lei Xiujuan , Wu Fang-Xiang
Published: November 2019

Circular RNAs (circRNAs) are extensively expressed in cells and tissues, and play crucial roles in human diseases and biological processes. Recent studies have reported that circRNAs could function as RNA binding protein (RBP) sponges, meanwhile RBPs can also be involved in back-splicing. The interaction with RBPs is also considered an important factor for investigating the function of circRNAs. Hence, it is necessary to understand the interaction mechanisms of circRNAs and RBPs, especially in human cancers. Here, we present a novel method based on deep learning to identify cancer-specific circRNA–RBP binding sites (CSCRSites), only using the nucleotide sequences as the input. In CSCRSites, an architecture with multiple convolution layers is utilized to detect the features of the raw circRNA sequence fragments, and further identify the binding sites through a fully connected layer with the softmax output. The experimental results show that CSCRSites outperform the conventional machine learning classifiers and some representative deep learning methods on the benchmark data. In addition, the features learnt by CSCRSites are converted to sequence motifs, some of which can match to human known RNA motifs involved in human diseases, especially cancer. Therefore, as a deep learning-based tool, CSCRSites could significantly contribute to the function analysis of cancer-associated circRNAs.
South African Abietane Diterpenoids and Their Analogs as Potential Antimalarials: Novel Insights from Hybrid Computational Approaches
Authors: Musyoka Thommas , Bishop Özlem Tastan
Published: November 2019

The hemoglobin degradation process in Plasmodium parasites is vital for nutrient acquisition required for their growth and proliferation. In P. falciparum, falcipains (FP-2 and FP-3) are the major hemoglobinases, and remain attractive antimalarial drug targets. Other Plasmodium species also possess highly homologous proteins to FP-2 and FP-3. Although several inhibitors have been designed against these proteins, none has been commercialized due to associated toxicity on human cathepsins (Cat-K, Cat-L and Cat-S). Despite the two enzyme groups sharing a common structural fold and catalytic mechanism, distinct active site variations have been identified, and can be exploited for drug development. Here, we utilize in silico approaches to screen 628 compounds from the South African natural sources to identify potential hits that can selectively inhibit the plasmodial proteases. Using docking studies, seven abietane diterpenoids, binding strongly to the plasmodial proteases, and three additional analogs from PubChem were identified. Important residues involved in ligand stabilization were identified for all potential hits through binding pose analysis and their energetic contribution determined by binding free energy calculations. The identified compounds present important scaffolds that could be further developed as plasmodial protease inhibitors. Previous laboratory assays showed the effect of the seven diterpenoids as antimalarials. Here, for the first time, we demonstrate that their possible mechanism of action could be by interacting with falcipains and their plasmodial homologs. Dynamic residue network (DRN) analysis on the plasmodial proteases identified functionally important residues, including a region with high betweenness centrality, which had previously been proposed as a potential allosteric site in FP-2.
The Contribution of Carotenoids, Phenolic Compounds, and Flavonoids to the Antioxidative Properties of Marine Microalgae Isolated from Mediterranean Morocco
Authors: Haoujar Imane , Cacciola Francesco , Abrini Jamal , Mangraviti Domenica , Giuffrida Daniele , Oulad El Majdoub Yassine , Kounnoun Ayoub , Miceli Natalizia , Fernanda Taviano Maria , Mondello Luigi , Rigano Francesca , Skali Senhaji Nadia
Published: November 2019

This study aimed to investigate the potential of four sea water microalgae, isolated and cultivated at M′diq Bay in Morocco, as a new source of natural antioxidants. These microalgae belong to different classes, including Phaedactylium tricornitum (Bacillariophyceae), Nannochloropsis gaditana (Eustigmatophyceae), Nannochloris sp (Trebouxiophyceae), and Tetraselmis suecica (Chlorodendrophycea). The antioxidant properties were screened by the use of in vitro assays, namely 2,2-difenil-1-picrylhydrazyl, Ferric reducing antioxidant power, and Ferrous ions chelating activity, and compoundidentification was carried out in methanol and acetone extracts of both dried and fresh microalgae biomass by HPLC–PDA–MS analysis. Among the investigated microalgae, Phaedactylium tricornutum was the richest one regarding its carotenoid (especially all-E-fucoxanthin) and phenolic (especially protocatechuic acid) contents, as well as antioxidant activity (65.5%), followed by Nannochloris sp, Tetraselmis suicica, and Nannochloropsis gaditana, with antioxidant activity of 56.8%, 54.9%, and 51.1%, respectively.
Chemical Constituents from Albiziae Cortex and Their Ability to Ameliorate Steatosis and Promote Proliferation and Anti-Oxidation In Vitro
Authors: Shi Xuelin , Li Zhongjie , Cai Weiwei , Liu Yixiao , Li Shuangshuang , Ai Min , Sun Jiangnan , Hou Bao , Ni Lulu , Qiu Liying
Published: November 2019

This study describes the chemical constituents of Albiziae Cortex and their ability to ameliorate steatosis and promote proliferation and anti-oxidation in vitro. Together, five known lignan glycosides, (7S,8R)-erythro-syringylglycerol-β–O-4′-sinapyl ether 9-O-β-D-glucopyranoside (1), (+)-lyoniresinol-9′-O-gluco-side (2), (−)-lyoniresinol-9′-O-glucoside (3), picraquassioside C (4), and icariside E5 (5), were isolated from the Albiziae Cortex. Their structures were elucidated by extensive NMR and high-resolution mass spectrometry analysis and compared with reported data. Oil Red O staining results revealed that compounds 1, 2, and 3 attenuated lipid accumulation and lipid metabolic disorders in FFAs (oleate/palmitate, 2:1 ratio, 0.3 mM)-exposed HepG2 cells. The Cell Counting Kit 8 (CCK-8) assay results revealed that compounds 1 and 5 can significantly promote human umbilical vein endothelial cell (HUVEC) proliferation; meanwhile, these compounds did not exhibit significant cytotoxicity against HUVECs. In addition, 2′,7′-dichlorofluorescein diacetate (DCFH-DA) staining results revealed that high glucose (HG)-induced reactive oxygen species (ROS) production was abolished by compounds 1, 2, and 3. This is the first report of the isolation of lignan skeletons from the genus Albizzia julibrissin with the ability to ameliorate steatosis and promote proliferation and anti-oxidation activities.
Synergistic/Antagonistic Potential of Natural Preparations Based on Essential Oils Against Streptococcus mutans from the Oral Cavity
Authors: Alexa Vlad Tiberiu , Galuscan Atena , Popescu Iuliana , Tirziu Emil , Obistioiu Diana , Floare Alin Daniel , Perdiou Antonis , Jumanca Daniela
Published: November 2019

The present paper addresses a thematic of interest in preventive dental medicine, namely the possibility of using essential oils (EOs) for the inhibition of the development of Streptococcus mutans (S. mutans) in the oral cavity, as a viable alternative to chemical products with protective role in oral health. For this purpose, four EOs (cinnamon, clove, bergamote, and orange) were chemically characterized by gas chromatography coupled with mass spectrometry (GC-MS) and in vitro tested against S. mutans (ATCC 25175). The results obtained revealed the antibacterial effect on S. mutans exercised by the essential oils of clove (CLEO), bergamote (BEO), and orange (OEO), which were included in the production of natural emulsion-type preparations with application in dental medicine. In order to highlight the synersistic/antagonistic effects generated by the chemical constituent of essential oils, binary and tertiary emulsions were prepared and used in saliva-enhanced medium against S. mutans. The saliva tests proved the synergistic effect exercised by the active components of EOs tested from tertiary emulsions, which cause an inhibition of the development of S. mutans in oral cavities.
Pupil Function in Pseudophakia: Proximal Miosis Behavior and Optical Influence
Authors: Fonseca Elsa , Fiadeiro Paulo , Gomes Renato , Sanchez Trancon Angel , Baptista António , Serra Pedro
Published: November 2019

The pseudophakic eye lacks the ability to produce a refractive change in response to object proximity. Thus, individual anatomical features such as the pupil size play an important role in achieving functional vision levels. In this work, the range of pupil sizes at varying object distance was measured in pseudophakic participants. Furthermore, the impact of the measured values on eye optical quality was investigated using a computer simulation model. A binocular eye-tracker was used to measure the participants’ pupil sizes at six object distances, ranging from 0.33 m (i.e., vergence of 3.00 D) to 3.00 m (i.e., vergence of 0.33 D), while observing a Maltese cross with a constant angular size of 1 ∘ . In total, 58 pseudophakic participants were enrolled in this study (age mean ± standard deviation: 70.5 ± 11.3 years). The effects of object distance and age on pupil size variation were investigated using linear mixed effects regression models. Age was found to have a small contribution to individual variability. The mean infinite distance pupil size (intercept) was 4.45 ( 95 % CI: 2.74, 6.17) mm and the mean proximal miosis (slope) was − 0.23 ( 95 % CI: −0.53, 0.08) mm/D. The visual acuity (VA) estimation for a distant object ranged from − 0.1 logMAR (smallest pupil) to 0.04 logMAR (largest pupil) and the near VA ( 0.33 m) when mean proximal miosis was considered ranged from 0.28 logMAR (smallest pupil) to 0.42 logMAR (largest pupil). When mean distance pupil was considered, proximal miosis individual variability produced a variation of 0.04 logMAR for the near object and negligible variation for the distant object. These results support the importance of distance pupil size measurement for the prediction of visual performance in pseudophakia, while suggesting that proximal miosis has a negligible impact in VA variability.
The Rotating Components Performance Diagnosis of Gas Turbine Based on the Hybrid Filter
Authors: Zeng Li , Dong Shaojiang , Long Wei
Published: November 2019

Gas turbine converts chemical energy into mechanical energy and provide energy for aircraft, ships, etc. The performance diagnosis of rotating components of gas turbine are essential in terms of the high failure rate of these parts. A problem that the sudden changing of operation state of turbines may lead to the misdiagnosis due to the defect of gas turbine’s model. This paper constructs the strong tracking filter based on the unscented Kalman filter to achieve accurate estimation of gas turbine’s measured parameters when the state changes suddenly. In the strong tracking filter, a parameter optimization method based on the residual similarity of measured parameters is proposed. Next, adopt the measured parameters filtered by the strong tracking filter to construct the health parameters estimation algorithm based on the particle filter. The particle weight is optimized by the mean adjustment method. Performance diagnosis is realized by checking the changes of health parameters output by particle filter. The results show that the proposed method improves the accuracy of performance diagnosis obviously.
Performance Comparison of Machine Learning Algorithms for Estimating the Soil Salinity of Salt-Affected Soil Using Field Spectral Data
Authors: Wang Sijia , Chen Yunhao , Wang Mingguo , Li Jing
Published: November 2019

Salt-affected soil is a prominent ecological and environmental problem in dry farming areas throughout the world. China has nearly 9.9 million km 2 of salt-affected land. The identification, monitoring, and utilization of soil salinization have become important research topics for promoting sustainable progress. In this paper, using field-measured spectral data and soil salinity parameter data, through analysis and transformation of spectral data, five machine learning models, namely, random forest regression (RFR), support vector regression (SVR), gradient-boosted regression tree (GBRT), multilayer perceptron regression (MLPR), and least angle regression (Lars) are compared. The following performance measures of each model were evaluated: the collinear problems, handling data noise, stability, and the accuracy. In terms of these four aspects, the performance of each model on estimating soil salinity is evaluated. The results demonstrate that among the five models, RFR has the best performance in dealing with collinearity, RFR and MLPR have the best performance in dealing with data noise, and the SVR model is the most stable. The Lars model has the highest accuracy, with a determination coefficient ( R 2 ) of 0.87, ratio of performance to deviation (RPD) of 2.67, root mean square error (RMSE) of 0.18, and mean absolute percentage error (MAPE) of 0.11. Then, the comprehensive comparison and analysis of the five models are carried out, and it is found that the comprehensive performance of RFR model is the best; hence, this method is most suitable for estimating soil salinity using hyperspectral data. This study can provide a reference for the selection of regression methods in subsequent studies on estimating soil salinity using hyperspectral data.
Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation
Authors: Liu Maolin , Ke Yinghai , Yin Qi , Chen Xiuwan , Im Jungho
Published: November 2019

In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications.
Parking Line Based SLAM Approach Using AVM/LiDAR Sensor Fusion for Rapid and Accurate Loop Closing and Parking Space Detection
Authors: Im Gyubeom , Kim Minsung , Park Jaeheung
Published: November 2019

Parking is a challenging task for autonomous vehicles and requires a centimeter level precision of distance measurement for safe parking at a destination to avoid collisions with nearby vehicles. In order to avoid collisions with parked vehicles while parking, real-time localization performance should be maintained even when loop closing occurs. This study proposes a simultaneous localization and mapping (SLAM) method, using around view monitor (AVM)/light detection and ranging (LiDAR) sensor fusion, that provides rapid loop closing performance. We extract the parking line features by utilizing the sensor fusion data for sparse feature-based pose graph optimization that boosts the loop closing speed. Hence, the proposed method can perform the loop closing within a few milliseconds to compensate for the accumulative errors even in a large-scale outdoor environment, which is much faster than other LiDAR-based SLAM algorithms. Therefore, it easily satisfies real-time localization performance. Furthermore, thanks to the parking line features, the proposed method can detect a parking space by utilizing the accumulated parking lines in the map. The experiment was performed in three outdoor parking lots to validate the localization performance and parking space detection performance. All of the proposed methods can be operated in real-time in a single-CPU environment.
Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone
Authors: Foysal Kamrul H. , Seo Sung Eun , Kim Min Ju , Kwon Oh Seok , Chong Jo Woon
Published: November 2019

Lateral flow assay (LFA) technology has recently received interest in the biochemical field since it is simple, low-cost, and rapid, while conventional laboratory test procedures are complicated, expensive, and time-consuming. In this paper, we propose a robust smartphone-based analyte detection method that estimates the amount of analyte on an LFA strip using a smartphone camera. The proposed method can maintain high estimation accuracy under various illumination conditions without additional devices, unlike conventional methods. The robustness and simplicity of the proposed method are enabled by novel image processing and machine learning techniques. For the performance analysis, we applied the proposed method to LFA strips where the target analyte is albumin protein of human serum. We use two sets of training LFA strips and one set of testing LFA strips. Here, each set consists of five strips having different quantities of albumin—10 femtograms, 100 femtograms, 1 picogram, 10 picograms, and 100 picograms. A linear regression analysis approximates the analyte quantity, and then machine learning classifier, support vector machine (SVM), which is trained by the regression results, classifies the analyte quantity on the LFA strip in an optimal way. Experimental results show that the proposed smartphone application can detect the quantity of albumin protein on a test LFA set with 98% accuracy, on average, in real time.
Differential Temperature Sensors: Review of Applications in the Test and Characterization of Circuits, Usage and Design Methodology
Authors: Barajas Enrique , Aragones Xavier , Mateo Diego , Altet Josep
Published: November 2019

Differential temperature sensors can be placed in integrated circuits to extract a signature of the power dissipated by the adjacent circuit blocks built in the same silicon die. This review paper first discusses the singularity that differential temperature sensors provide with respect to other sensor topologies, with circuit monitoring being their main application. The paper focuses on the monitoring of radio-frequency analog circuits. The strategies to extract the power signature of the monitored circuit are reviewed, and a list of application examples in the domain of test and characterization is provided. As a practical example, we elaborate the design methodology to conceive, step by step, a differential temperature sensor to monitor the aging degradation in a class-A linear power amplifier working in the 2.4 GHz Industrial Scientific Medical—ISM—band. It is discussed how, for this particular application, a sensor with a temperature resolution of 0.02 K and a high dynamic range is required. A circuit solution for this objective is proposed, as well as recommendations for the dimensions and location of the devices that form the temperature sensor. The paper concludes with a description of a simple procedure to monitor time variability.
ACK-MSCKF: Tightly-Coupled Ackermann Multi-State Constraint Kalman Filter for Autonomous Vehicle Localization
Authors: Ma Fangwu , Shi Jinzhu , Yang Yu , Li Jinhang , Dai Kai
Published: November 2019

Visual-Inertial Odometry (VIO) is subjected to additional unobservable directions under the special motions of ground vehicles, resulting in larger pose estimation errors. To address this problem, a tightly-coupled Ackermann visual-inertial odometry (ACK-MSCKF) is proposed to fuse Ackermann error state measurements and the Stereo Multi-State Constraint Kalman Filter (S-MSCKF) with a tightly-coupled filter-based mechanism. In contrast with S-MSCKF, in which the inertial measurement unit (IMU) propagates the vehicle motion and then the propagation is corrected by stereo visual measurements, we successively update the propagation with Ackermann error state measurements and visual measurements after the process model and state augmentation. This way, additional constraints from the Ackermann measurements are exploited to improve the pose estimation accuracy. Both qualitative and quantitative experimental results evaluated under real-world datasets from an Ackermann steering vehicle lead to the following demonstration: ACK-MSCKF can significantly improve the pose estimation accuracy of S-MSCKF under the special motions of autonomous vehicles, and keep accurate and robust pose estimation available under different vehicle driving cycles and environmental conditions. This paper accompanies the source code for the robotics community.
Geographic Object-Based Analysis of Airborne Multispectral Images for Health Assessment of Capsicum annuum L. Crops
Authors: Sosa-Herrera Jesús A. , Vallejo-Pérez Moisés R. , Álvarez-Jarquín Nohemí , Cid-García Néstor M. , López-Araujo Daniela J.
Published: November 2019

Vegetation health assessment by using airborne multispectral images throughout crop production cycles, among other precision agriculture technologies, is an important tool for modern agriculture practices. However, to really take advantage of crop fields imagery, specialized analysis techniques are needed. In this paper we present a geographic object-based image analysis (GEOBIA) approach to examine a set of very high resolution (VHR) multispectral images obtained by the use of small unmanned aerial vehicles (UAVs), to evaluate plant health states and to generate cropland maps for Capsicum annuum L. The scheme described here integrates machine learning methods with semi-automated training and validation, which allowed us to develop an algorithmic sequence for the evaluation of plant health conditions at individual sowing point clusters over an entire parcel. The features selected at the classification stages are based on phenotypic traits of plants with different health levels. Determination of areas without data dependencies for the algorithms employed allowed us to execute some of the calculations as parallel processes. Comparison with the standard normalized difference vegetation index (NDVI) and biological analyses were also performed. The classification obtained showed a precision level of about 95 % in discerning between vegetation and non-vegetation objects, and clustering efficiency ranging from 79 % to 89 % for the evaluation of different vegetation health categories, which makes our approach suitable for being incorporated at C. annuum crop’s production systems, as well as to other similar crops. This methodology can be reproduced and adjusted as an on-the-go solution to get a georeferenced plant health estimation.
A Novel Data Reduction Approach for Structural Health Monitoring Systems
Authors: Bolandi Hamed , Lajnef Nizar , Jiao Pengcheng , Barri Kaveh , Hasni Hassene , Alavi Amir H.
Published: November 2019

The massive amount of data generated by structural health monitoring (SHM) systems usually affects the system’s capacity for data transmission and analysis. This paper proposes a novel concept based on the probability theory for data reduction in SHM systems. The beauty salient feature of the proposed method is that it alleviates the burden of collecting and analysis of the entire strain data via a relative damage approach. In this methodology, the rate of variation of strain distributions is related to the rate of damage. In order to verify the accuracy of the approach, experimental and numerical studies were conducted on a thin steel plate subjected to cyclic in-plane tension loading. Circular holes with various sizes were made on the plate to define damage states. Rather than measuring the entire strain response, the cumulative durations of strain events at different predefined strain levels were obtained for each damage scenario. Then, the distribution of the calculated cumulative times was used to detect the damage progression. The results show that the presented technique can efficiently detect the damage progression. The damage detection accuracy can be improved by increasing the predefined strain levels. The proposed concept can lead to over 2500% reduction in data storage requirement, which can be particularly important for data generation and data handling in on-line SHM systems.
Validation of Wireless Sensors for Psychophysiological Studies
Authors: Silva Moreira Pedro , Chaves Pedro , Dias Ruben , Dias Nuno , Almeida Pedro R
Published: November 2019

James One (MindProber Labs) is a wireless psychophysiological device comprising two sensors: one measuring electrodermal activity (EDA), the other photoplethysmography (PPG). This paper reports the validation of James One’s EDA sensor by comparing its signal against a research grade polygraph. Twenty participants were instructed to perform breathing exercises to elicit the modulation of EDA and heart rate, while the physiological signal was captured simultaneously on James One and a Biopac MP36. The resulting EDA and PPG records collected from both systems were comprehensively compared. Results suggest that James One captures EDA signal with a quality comparable to a research grade equipment, this constituting a reliable means of capturing data while minimizing setup time and intrusiveness.
Resonance-Based Microwave Technique for Body Implant Sensing
Authors: González-López Giselle , Jofre Roca Lluis , Amorós García de Valdecasas Susana , Rodríguez-Leor Oriol , Gálvez-Montón Carolina , Bayés-Genís Antoni , O’Callaghan Joan
Published: November 2019

There is an increasing need for safe and simple techniques for sensing devices and prostheses implanted inside the human body. Microwave wireless inspection may be an appropriate technique for it. The implanted device may have specific characteristics that allow to distinguish it from its environment. A new sensing technique based on the principle of differential resonance is proposed and its basic parameters are discussed. This technique allows to use the implant as a signal scattering device and to detect changes produced in the implant based on the corresponding change in its scattering signature. The technique is first tested with a canonic human phantom and then applied to a real in vivo clinical experiment to detect coronary stents implanted in swine animals.
A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data
Authors: Vanarse Anup , Osseiran Adam , Rassau Alexander , van der Made Peter
Published: November 2019

In several application domains, electronic nose systems employing conventional data processing approaches incur substantial power and computational costs and limitations, such as significant latency and poor accuracy for classification. Recent developments in spike-based bio-inspired approaches have delivered solutions for the highly accurate classification of multivariate sensor data with minimized computational and power requirements. Although these methods have addressed issues related to efficient data processing and classification accuracy, other areas, such as reducing the processing latency to support real-time application and deploying spike-based solutions on supported hardware, have yet to be studied in detail. Through this investigation, we proposed a spiking neural network (SNN)-based classifier, implemented in a chip-emulation-based development environment, that can be seamlessly deployed on a neuromorphic system-on-a-chip (NSoC). Under three different scenarios of increasing complexity, the SNN was determined to be able to classify real-valued sensor data with greater than 90% accuracy and with a maximum latency of 3 s on the software-based platform. Highlights of this work included the design and implementation of a novel encoder for artificial olfactory systems, implementation of unsupervised spike-timing-dependent plasticity (STDP) for learning, and a foundational study on early classification capability using the SNN-based classifier.
Directional Modulation-Enhanced Multiple Antenna Arrays for Secure and Precise Wireless Transmission
Authors: Zhang Wei , Le Mingnan , Li Bin , Wang Jun , Peng Jinye
Published: November 2019

Directional modulation (DM) technique has the ability to enhance the physical layer security (PLS) of wireless communications. Conventional DM schemes are usually based on a single antenna array with the basic assumption that eavesdroppers (Eves) and legitimate users (LUs) are in different directions. However, it is possible that Eves are in the same direction as LUs in practical applications. As a result, signals received by Eves will be approximately the same or even in better quality than those received by LUs. To address the neighbor security issue, we introduce a multiple antenna arrays model at the transmitter side with the help of the artificial noise (AN)-aided DM technique to achieve secure and precise DM transmission in this paper. Meanwhile, to recover the mixed useful signals, two novel DM schemes based on single- and multi-carrier multiple antenna arrays model are proposed, respectively. In addition, the symbol error rate (SER), secrecy rate, and robustness performance of the proposed DM schemes were analyzed and simulated. Simulations validate the effectiveness of the proposed DM schemes and demonstrate that multiple antenna arrays model based DM methods outperform single antenna array model aided DM methods in security.
Jammer Classification in GNSS Bands Via Machine Learning Algorithms
Authors: Morales Ferre Ruben , de la Fuente Alberto , Lohan Elena Simona
Published: November 2019

This paper proposes to treat the jammer classification problem in the Global Navigation Satellite System bands as a black-and-white image classification problem, based on a time-frequency analysis and image mapping of a jammed signal. The paper also proposes to apply machine learning approaches in order to sort the received signal into six classes, namely five classes when the jammer is present with different jammer types and one class where the jammer is absent. The algorithms based on support vector machines show up to 94.90 % accuracy in classification, and the algorithms based on convolutional neural networks show up to 91.36 % accuracy in classification. The training and test databases generated for these tests are also provided in open access.