bluuebunny/crossref_metadata_2025 huggingface dataset:
Dietary Intakes and Cardiovascular Health of Healthy Adults in Short-, Medium-, and Long-Term Whole-Food Plant-Based Lifestyle Program
Authors: Jakše Boštjan , Jakše Barbara , Pinter Stanislav , Jug Borut , Godnov Uroš , Pajek Jernej , Fidler Mis Nataša
Published: December 2019

An effective lifestyle strategy to reduce cardiovascular diseases risk (CVD) factors is needed. We examined the effects of a whole-food plant-based (WFPB) lifestyle program on dietary intake and cardiovascular (CV) risk factors in 151 adults (mean 39.6 (SD 12.5) years). Adherence was categorised into short-, medium- and long-term (years: (0.5–<2), (2–<5) and (5–10)), for both genders separately. Dietary intakes were assessed, fasting blood lipids and blood pressure (BP) were measured, and % participants reaching guideline recommended targets for LDL-cholesterol, triglycerides and BP in the primary CVD prevention was assessed. There were no statistically significant differences in intakes of energy and most nutrients among participants (both genders), that were short-, medium- and long term in our program. Diet was mainly composed of unprocessed vegetables/fruits, whole grains, legumes, potatoes, and nuts/seeds. LDL-cholesterol, triglycerides, systolic and diastolic BP were within targets for: 93%, 97%, 88% and 95% participants, respectively. In females (vs. males), total- and HDL-cholesterol were higher (mean): 3.8 (SD 0.7) vs. 3.4 (SD 0.9), p = 0.002 and 1.5 (SD 0.3) vs. 1.1 (SD 0.2) mmol/L, p < 0.001), systolic BP was lower (113 (SD 11) vs. 120 (SD 10) mmHg, p = 0.001), while there was no difference in diastolic BP (71 (SD 9) vs. 72 (SD 8) mmHg, p = 0.143). More females vs. males reached target triglycerides (99% vs. 91%, p = 0.021) and systolic BP (92% vs. 79%, p = 0.046), while similar females and males reached target LDL-cholesterol (94% vs. 91%, p = 0.500) and diastolic BP (93% vs. 100%, p = 0.107). Participation in our WFPB lifestyle program is associated with favourable dietary intakes, safety markers, and CV risk factor profiles.
Association between Urinary Cadmium-to-Zinc Intake Ratio and Adult Mortality in a Follow-Up Study of NHANES 1988–1994 and 1999–2004
Authors: Kim Kijoon , Melough Melissa M. , Sakaki Junichi R. , Ha Kyungho , Marmash Dalia , Noh Hwayoung , Chun Ock K.
Published: December 2019

Cadmium (Cd) is a toxic heavy metal associated with increased mortality, but the effect of zinc (Zn) intake on the association between Cd and mortality is unknown. The objective of this study was to examine the association of urinary Cd to Zn intake ratio (Cd/Zn ratio) and mortality risk. In total, 15642 US adults in NHANES 1988–1994 and 1999–2004 were followed until 2011 (15-year mean follow-up). Of the 5367 total deaths, 1194 were attributed to cancer and 1677 were attributed to CVD. After adjustment for potential confounders, positive associations were observed between urinary Cd and all-cause mortality (HR for highest vs. lowest quartile: 1.38; 95% CI: 1.14–1.68) and cancer mortality (HR: 1.54; CI: 1.05–2.27). Urinary Cd was positively associated with cancer mortality among the lowest Zn consumers, and the association diminished among the highest Zn consumers. Positive relationships were observed between the Cd/Zn ratio and all-cause mortality (HR: 1.54; CI: 1.23–1.93), cancer mortality (HR: 1.65; CI: 1.11–2.47) and CVD mortality (HR: 1.49; CI: 1.18–1.88). In conclusion, these findings indicate that Zn intake may modify the association between Cd and mortality. Furthermore, the Cd/Zn ratio, which was positively associated with mortality from all causes, cancer, and CVD, may be an important predictor of mortality.
Chloroplasts and Plant Immunity: Where Are the Fungal Effectors?
Authors: Kretschmer Matthias , Damoo Djihane , Djamei Armin , Kronstad James
Published: December 2019

Chloroplasts play a central role in plant immunity through the synthesis of secondary metabolites and defense compounds, as well as phytohormones, such as jasmonic acid and salicylic acid. Additionally, chloroplast metabolism results in the production of reactive oxygen species and nitric oxide as defense molecules. The impact of viral and bacterial infections on plastids and chloroplasts has been well documented. In particular, bacterial pathogens are known to introduce effectors specifically into chloroplasts, and many viral proteins interact with chloroplast proteins to influence viral replication and movement, and plant defense. By contrast, clear examples are just now emerging for chloroplast-targeted effectors from fungal and oomycete pathogens. In this review, we first present a brief overview of chloroplast contributions to plant defense and then discuss examples of connections between fungal interactions with plants and chloroplast function. We then briefly consider well-characterized bacterial effectors that target chloroplasts as a prelude to discussing the evidence for fungal effectors that impact chloroplast activities.
ZeOncoTest: Refining and Automating the Zebrafish Xenograft Model for Drug Discovery in Cancer
Authors: Cornet Carles , Dyballa Sylvia , Terriente Javier , Di Giacomo Valeria
Published: December 2019

The xenograft of human cancer cells in model animals is a powerful tool for understanding tumor progression and metastatic potential. Mice represent a validated host, but their use is limited by the elevated experimental costs and low throughput. To overcome these restrictions, zebrafish larvae might represent a valuable alternative. Their small size and transparency allow the tracking of transplanted cells. Therefore, tumor growth and early steps of metastasis, which are difficult to evaluate in mice, can be addressed. In spite of its advantages, the use of this model has been hindered by lack of experimental homogeneity and validation. Considering these facts, the aim of our work was to standardize, automate, and validate a zebrafish larvae xenograft assay with increased translatability and higher drug screening throughput. The ZeOncoTest reliability is based on the optimization of different experimental parameters, such as cell labeling, injection site, automated individual sample image acquisition, and analysis. This workflow implementation finally allows a higher precision and experimental throughput increase, when compared to previous reports. The approach was validated with the breast cancer cell line MDA-MB-231, the colorectal cancer cells HCT116, and the prostate cancer cells PC3; and known drugs, respectively RKI-1447, Docetaxel, and Mitoxantrone. The results recapitulate growth and invasion for all tested tumor cells, along with expected efficacy of the compounds. Finally, the methodology has proven useful for understanding specific drugs mode of action. The insights gained bring a step further for zebrafish larvae xenografts to enter the regulated preclinical drug discovery path.
ABC Transporters at the Blood–Brain Interfaces, Their Study Models, and Drug Delivery Implications in Gliomas
Authors: Gomez-Zepeda David , Taghi Méryam , Scherrmann Jean-Michel , Decleves Xavier , Menet Marie-Claude
Published: December 2019

Drug delivery into the brain is regulated by the blood–brain interfaces. The blood–brain barrier (BBB), the blood–cerebrospinal fluid barrier (BCSFB), and the blood–arachnoid barrier (BAB) regulate the exchange of substances between the blood and brain parenchyma. These selective barriers present a high impermeability to most substances, with the selective transport of nutrients and transporters preventing the entry and accumulation of possibly toxic molecules, comprising many therapeutic drugs. Transporters of the ATP-binding cassette (ABC) superfamily have an important role in drug delivery, because they extrude a broad molecular diversity of xenobiotics, including several anticancer drugs, preventing their entry into the brain. Gliomas are the most common primary tumors diagnosed in adults, which are often characterized by a poor prognosis, notably in the case of high-grade gliomas. Therapeutic treatments frequently fail due to the difficulty of delivering drugs through the brain barriers, adding to diverse mechanisms developed by the cancer, including the overexpression or expression de novo of ABC transporters in tumoral cells and/or in the endothelial cells forming the blood–brain tumor barrier (BBTB). Many models have been developed to study the phenotype, molecular characteristics, and function of the blood–brain interfaces as well as to evaluate drug permeability into the brain. These include in vitro, in vivo, and in silico models, which together can help us to better understand their implication in drug resistance and to develop new therapeutics or delivery strategies to improve the treatment of pathologies of the central nervous system (CNS). In this review, we present the principal characteristics of the blood–brain interfaces; then, we focus on the ABC transporters present on them and their implication in drug delivery; next, we present some of the most important models used for the study of drug transport; finally, we summarize the implication of ABC transporters in glioma and the BBTB in drug resistance and the strategies to improve the delivery of CNS anticancer drugs.
Erythrocyte Membrane-Coated Arsenic Trioxide-Loaded Sodium Alginate Nanoparticles for Tumor Therapy
Authors: Lian Yumei , Wang Xuerui , Guo Pengcheng , Li Yichen , Raza Faisal , Su Jing , Qiu Mingfeng
Published: December 2019

Arsenic trioxide (ATO) has a significant effect on the treatment of acute promyelocytic leukemia (APL) and advanced primary liver cancer, but it still faces severe side effects. Considering these problems, red blood cell membrane-camouflaged ATO-loaded sodium alginate nanoparticles (RBCM-SA-ATO-NPs, RSANs) were developed to relieve the toxicity of ATO while maintaining its efficacy. ATO-loaded sodium alginate nanoparticles (SA-ATO-NPs, SANs) were prepared by the ion crosslinking method, and then RBCM was extruded onto the surface to obtain RSANs. The average particle size of RSANs was found to be 163.2 nm with a complete shell-core bilayer structure, and the average encapsulation efficiency was 14.31%. Compared with SANs, RAW 264.7 macrophages reduced the phagocytosis of RSANs by 51%, and the in vitro cumulative release rate of RSANs was 95% at 84 h, which revealed a prominent sustained release. Furthermore, it demonstrated that RSANs had lower cytotoxicity as compared to normal 293 cells and exhibited anti-tumor effects on both NB4 cells and 7721 cells. In vivo studies further showed that ATO could cause mild lesions of main organs while RSANs could reduce the toxicity and improve the anti-tumor effects. In brief, the developed RSANs system provides a promising alternative for ATO treatment safely and effectively.
High Light Acclimation Induces Chloroplast Precursor Phosphorylation and Reduces Import Efficiency
Authors: Eisa Ahmed , Malenica Katarina , Schwenkert Serena , Bölter Bettina
Published: December 2019

Acclimation is an essential process in plants on many levels, but especially in chloroplasts under changing light conditions. It is partially known how the photosynthetic machinery reacts upon exposure to high light intensities, including rearrangement of numerous protein complexes. Since the majority of proteins residing within chloroplasts needs to be posttranslationally imported into the organelles, we endeavored to study how this important process is regulated upon subjecting plants from pea and Arabidopsis to high light. Our results reveal that acclimation takes place on the one hand in the cytosol by differential phosphorylation of preproteins and resulting from the altered expression of the responsible kinases, and on the other hand at the level of the translocation machineries in the outer (TOC) and inner (TIC) envelope membranes. Intriguingly, while phosphorylation is more pronounced under high light, import itself shows a lower efficiency, along with a reduced accumulation of the Toc receptor proteins Toc34 and Toc159.
Antitumor Potential and Phytochemical Profile of Plants from Sardinia (Italy), a Hotspot for Biodiversity in the Mediterranean Basin
Authors: Cappadone Concettina , Mandrone Manuela , Chiocchio Ilaria , Sanna Cinzia , Malucelli Emil , Bassi Vincenza , Picone Giovanna , Poli Ferruccio
Published: December 2019

Sardinia (Italy), with its wide range of habitats and high degree of endemism, is an important area for plant-based drug discovery studies. In this work, the antitumor activity of 35 samples from Sardinian plants was evaluated on human osteosarcoma cells U2OS. The results showed that five plants were strongly antiproliferative: Arbutus unedo (AuL), Cynara cardunculus (CyaA), Centaurea calcitrapa (CcA), Smilax aspera (SaA), and Tanacetum audibertii (TaA), the latter endemic to Sardinia and Corsica. Thus, their ability to induce cell cycle arrest and apoptosis was tested. All extracts determined cell cycle block in G2/M phase. Nevertheless, the p53 expression levels were increased only by TaA. The effector caspases were activated mainly by CycA, TaA, and CcA, while AuL and SaA did not induce apoptosis. The antiproliferative effects were also tested on human umbilical vein endothelial cells (HUVEC). Except for AuL, all the extracts were able to reduce significantly cell population, suggesting a potential antiangiogenic activity. The phytochemical composition was first explored by 1H NMR profiling, followed by further purifications to confirm the structure of the most abundant metabolites, such as phenolic compounds and sesquiterpene lactones, which might play a role in the measured bioactivity.
Myricetin Abrogates Cisplatin-Induced Oxidative Stress, Inflammatory Response, and Goblet Cell Disintegration in Colon of Wistar Rats
Authors: Rehman Muneeb U. , Rather Irfan A.
Published: December 2019

Cisplatin [cis-diamminedichloroplatinum II] is an extensively prescribed drug in cancer chemotherapy; it is also useful for the treatment of diverse types of malignancies. Conversely, cisplatin is associated with a range of side effects such as nephrotoxicity, hepatotoxicity, gastrointestinal toxicity, and so on. Myricetin (3,5,7-trihydroxy-2-(3,4,5-trihydroxyphenyl)-4chromenone) is a very common natural flavonoid found in fruits, tea, and plants. It has been found to have high-value pharmacological properties and strong health benefits. To examine the role of myricetin in colon toxicity induced by cisplatin, we conducted a concurrent prophylactic study in experimental animals that were treated orally with myricetin for 14 days at two doses—25 and 50 mg/kg of body weight. On the 14th day, a single intraperitoneal injection of cisplatin (7.5 mg/kg body weight) was administered in all groups except control. The effects of myricetin in cisplatin-induced toxicity in the colon were assessed in terms of antioxidant status, phase-II detoxification enzymes, the level of inflammatory markers, and goblet cell disintegration. Myricetin was found to restore the level of all the antioxidant enzymes analyzed in the study. In addition, the compound ameliorated cisplatin-induced lipid peroxidation, increase in xanthine oxidase activity, and phase-II detoxifying enzyme activity. Myricetin also attenuated deteriorative effects induced by cisplatin by regulating the level of molecular markers of inflammation (NF-κB, Nrf-2, IL-6, and TNF-α), restoring Nrf-2 levels, and controlling goblet cell disintegration. The current study reinforces the conclusion that myricetin exerts protection in colon toxicity via up-regulation of inflammatory markers, improving anti-oxidant status, and protecting tissue damage.
An End-to-End Hyperspectral Scene Simulator with Alternate Adjacency Effect Models and Its Comparison with CameoSim
Authors: Zahidi Usman A. , Yuen Peter W. T. , Piper Jonathan , Godfree Peter S.
Published: December 2019

In this research, we developed a new rendering-based end to end Hyperspectral scene simulator CHIMES (Cranfield Hyperspectral Image Modelling and Evaluation System), which generates nadir images of passively illuminated 3-D outdoor scenes in Visible, Near Infrared (NIR) and Short-Wave Infrared (SWIR) regions, ranging from 360 nm to 2520 nm. MODTRAN TM (MODerate resolution TRANsmission), is used to generate the sky-dome environment map which includes sun and sky radiance along with the polarisation effect of the sky due to Rayleigh scattering. Moreover, we perform path tracing and implement ray interaction with medium and volumetric backscattering at rendering time to model the adjacency effect. We propose two variants of adjacency models, the first one incorporates a single spectral albedo as the averaged background of the scene, this model is called the Background One-Spectra Adjacency Effect Model (BOAEM), which is a CameoSim like model created for performance comparison. The second model calculates background albedo from a pixel’s neighbourhood, whose size depends on the air volume between sensor and target, and differential air density up to sensor altitude. Average background reflectance of all neighbourhood pixel is computed at rendering time for estimating the total upwelled scattered radiance, by volumetric scattering. This model is termed the Texture-Spectra Incorporated Adjacency Effect Model (TIAEM). Moreover, for estimating the underlying atmospheric condition MODTRAN is run with varying aerosol optical thickness and its total ground reflected radiance (TGRR) is compared with TGRR of known in-scene material. The Goodness of fit is evaluated in each iteration, and MODTRAN’s output with the best fit is selected. We perform a tri-modal validation of simulators on a real hyperspectral scene by varying atmospheric condition, terrain surface models and proposed variants of adjacency models. We compared results of our model with Lockheed Martin’s well-established scene simulator CameoSim and acquired Ground Truth (GT) by Hyspex cameras. In clear-sky conditions, both models of CHIMES and CameoSim are in close agreement, however, in searched overcast conditions CHIMES BOAEM is shown to perform better than CameoSim in terms of ℓ 1 -norm error of the whole scene with respect to GT. TIAEM produces better radiance shape and covariance of background statistics with respect to Ground Truth (GT), which is key to good target detection performance. We also report that the results of CameoSim have a many-fold higher error for the same scene when the flat surface terrain is replaced with a Digital Elevation Model (DEM) based rugged one.
Fractional Fourier Transform-Based Radio Frequency Interference Suppression for High-Frequency Surface Wave Radar
Authors: Zhou Qing , Zheng Hong , Wu Xiongbin , Yue Xianchang , Chen Zhangyou , Wang Qinxiong
Published: December 2019

High-frequency surface wave radar (HF SWR) plays an important role in marine stereoscopic monitoring system. Nevertheless, the congestion of external radio frequency interference (RFI) in HF band degrades its performance seriously. In this article, two novel fractional Fourier transform (FRFT)-based RFI suppression approaches are proposed. One is based on the orthogonal projection of sequences from fractional Fourier domain, and the other is based on singular value decomposition (SVD) of Hankel matrix of sequences from fractional inverse-Fourier domain. Simulation and experimental data collected by HF SWR from Wuhan University were used to test the effectiveness as well as the application condition of the proposed RFI suppression algorithms. The FRFT-based orthogonal projection algorithm is practicable for suppressing stationary RFI with unvaried carrier frequency, while the FRFT-based SVD algorithm is applicable equally for mitigating nonstationary RFI with time-varying carrier frequency or occasional duration time. The processing results may provide useful guidelines for interference suppression of HF SWR, and inspiring the further application of the FRFT-based methods for signal processing.
Retrieval of Particulate Backscattering Using Field and Satellite Radiometry: Assessment of the QAA Algorithm
Authors: Pitarch Jaime , Bellacicco Marco , Organelli Emanuele , Volpe Gianluca , Colella Simone , Vellucci Vincenzo , Marullo Salvatore
Published: December 2019

Particulate optical backscattering (bbp) is a crucial parameter for the study of ocean biology and oceanic carbon estimations. In this work, bbp retrieval, by the quasi-analytical algorithm (QAA), is assessed using a large in situ database of matched bbp and remote-sensing reflectance (Rrs). The QAA is also applied to satellite Rrs (ESA OC-CCI project) as well, after their validation against in situ Rrs. Additionally, the effect of Raman Scattering on QAA retrievals is studied. Results show negligible biases above random noise when QAA-derived bbp is compared to in situ bbp. In addition, Rrs from the CCI archive shows good agreement with in situ data. The QAA’s functional form of spectral backscattering slope, as derived from in situ radiometry, is validated. Finally, we show the importance of correcting for Raman Scattering over clear waters prior to semi-analytical retrieval. Overall, this work demonstrates the high efficiency of QAA in the bbp detection in case of both in situ and ocean color data, but it also highlights the necessity to increase the number of observations that are severely under-sampled in respect to others environmental parameters.
Fine Land Cover Classification in an Open Pit Mining Area Using Optimized Support Vector Machine and WorldView-3 Imagery
Authors: Chen Weitao , Li Xianju , Wang Lizhe
Published: December 2019

Fine land cover classification in an open pit mining area (LCCOM) is essential in analyzing the terrestrial environment. However, researchers have been focusing on obtaining coarse LCCOM while using high spatial resolution remote sensing data and machine learning algorithms. Although support vector machines (SVM) have been successfully used in the remote sensing community, achieving a high classification accuracy of fine LCCOM using SVM remains difficult because of two factors. One is the lack of significant features for efficiently describing unique terrestrial characteristics of open pit mining areas and another is the lack of an optimized strategy to obtain suitable SVM parameters. This study attempted to address these two issues. Firstly, a novel carbonate index that was based on WorldView-3 was proposed and introduced into the used feature set. Additionally, three optimization methods—genetic algorithm (GA), k-fold cross validation (CV), and particle swarm optimization (PSO)—were used for obtaining the optimization parameters of SVM. The results show that the carbonate index was effective for distinguishing the dumping ground from other open pit mining lands. Furthermore, the three optimization methods could significantly increase the overall classification accuracy (OA) of the fine LCCOM by 8.40%. CV significantly outperformed GA and PSO, and GA performed slightly better than PSO. CV was more suitable for most of the fine land cover types of crop land, and PSO for road and open pit mining lands. The results of an independent test set revealed that the optimized SVM models achieved significant improvements, with an average of 8.29%. Overall, the proposed strategy was effective for fine LCCOM.
An Embedded, Multi-Modal Sensor System for Scalable Robotic and Prosthetic Hand Fingers
Authors: Weiner Pascal , Neef Caterina , Shibata Yoshihisa , Nakamura Yoshihiko , Asfour Tamim
Published: December 2019

Grasping and manipulation with anthropomorphic robotic and prosthetic hands presents a scientific challenge regarding mechanical design, sensor system, and control. Apart from the mechanical design of such hands, embedding sensors needed for closed-loop control of grasping tasks remains a hard problem due to limited space and required high level of integration of different components. In this paper we present a scalable design model of artificial fingers, which combines mechanical design and embedded electronics with a sophisticated multi-modal sensor system consisting of sensors for sensing normal and shear force, distance, acceleration, temperature, and joint angles. The design is fully parametric, allowing automated scaling of the fingers to arbitrary dimensions in the human hand spectrum. To this end, the electronic parts are composed of interchangeable modules that facilitate the mechanical scaling of the fingers and are fully enclosed by the mechanical parts of the finger. The resulting design model allows deriving freely scalable and multimodally sensorised fingers for robotic and prosthetic hands. Four physical demonstrators are assembled and tested to evaluate the approach.
Altering the Course of Technologies to Monitor Loosening States of Endoprosthetic Implants
Authors: Cachão João Henrique , Soares dos Santos Marco P. , Bernardo Rodrigo , Ramos António , Bader Rainer , Ferreira Jorge A. F. , Torres Marques António , Simões José A. O.
Published: December 2019

Musculoskeletal disorders are becoming an ever-growing societal burden and, as a result, millions of bone replacements surgeries are performed per year worldwide. Despite total joint replacements being recognized among the most successful surgeries of the last century, implant failure rates exceeding 10% are still reported. These numbers highlight the necessity of technologies to provide an accurate monitoring of the bone–implant interface state. This study provides a detailed review of the most relevant methodologies and technologies already proposed to monitor the loosening states of endoprosthetic implants, as well as their performance and experimental validation. A total of forty-two papers describing both intracorporeal and extracorporeal technologies for cemented or cementless fixation were thoroughly analyzed. Thirty-eight technologies were identified, which are categorized into five methodologies: vibrometric, acoustic, bioelectric impedance, magnetic induction, and strain. Research efforts were mainly focused on vibrometric and acoustic technologies. Differently, approaches based on bioelectric impedance, magnetic induction and strain have been less explored. Although most technologies are noninvasive and are able to monitor different loosening stages of endoprosthetic implants, they are not able to provide effective monitoring during daily living of patients.
Coupled Dynamic Analysis and Decoupling Optimization Method of the Laser Gyro Inertial Measurement Unit
Authors: Fang Fang , Zeng Wenhui , Li Zilong
Published: December 2019

The mechanical dithered ring laser gyro (RLG) effectively overcomes the lock-in effect and ensures the sensitive accuracy of the low angular rate for the gyro. However, in the inertial measurement unit (IMU) system, the dither excitation of three RLGs causes the coupled vibration of the IMU structure, which could seriously limit the measuring accuracy of RLGs. In this paper, the vibration frequency response characteristic of laser gyro IMU is taken as the focus point, and the method of multi-rigid body dynamics is used to establish the dynamic model of IMU suitable for vibration frequency response analysis. On the basis of the model, the multi-degree-of-freedom coupling vibration of IMU with the gyro dither excitation is clearly described. A new IMU dynamic decoupling optimization method is proposed to minimize the coupled vibration frequency response, and compared with the previous optimal design method. The prototype experimental test results show that the coupled vibration of IMU is restrained more effectively by the proposed new method than by the previous optimal design method. Finally, on the basis of this new method, the measuring accuracy of the RLGs in the IMU system is improved, which is quite useful for practical engineering application.
Adaptive Estimation of Spatial Clutter Measurement Density Using Clutter Measurement Probability for Enhanced Multi-Target Tracking
Authors: Park Seung Hyo , Chong Sa Yong , Kim Hyung June , Song Taek Lyul
Published: December 2019

The point detections obtained from radars or sonars in surveillance environments include clutter measurements, as well as target measurements. Target tracking with these data requires data association, which distinguishes the detections from targets and clutter. Various algorithms have been proposed for clutter measurement density estimation to achieve accurate and robust target tracking with the point detections. Among them, the spatial clutter measurement density estimator (SCMDE) computes the sparsity of clutter measurement, which is the reciprocal of the clutter measurement density. The SCMDE considers all adjacent measurements only as clutter, so the estimated clutter measurement density is biased for multi-target tracking applications, which may result in degraded target tracking performance. Through the study in this paper, a major source of tracking performance degradation with the existing SCMDE for multi-target tracking is analyzed, and the use of the clutter measurement probability is proposed as a remedy. It is also found that the expansion of the volume of the hyper-sphere for each sparsity order reduces the bias of clutter measurement density estimates. Based on the analysis, we propose a new adaptive clutter measurement density estimation method called SCMDE for multi-target tracking (MTT-SCMDE). The proposed method is applied to multi-target tracking, and the improvement of multi-target tracking performance is shown by a series of Monte Carlo simulation runs and a real radar data test. The clutter measurement density estimation performance and target tracking performance are also analyzed for various sparsity orders.
A Time-of-Flight Range Sensor Using Four-Tap Lock-In Pixels with High near Infrared Sensitivity for LiDAR Applications
Authors: Lee Sanggwon , Yasutomi Keita , Morita Masato , Kawanishi Hodaka , Kawahito Shoji
Published: December 2019

In this paper, a back-illuminated (BSI) time-of-flight (TOF) sensor using 0.2 µm silicon-on-insulator (SOI) complementary metal oxide semiconductor (CMOS) technology is developed for long-range laser imaging detection and ranging (LiDAR) application. A 200 µm-thick bulk silicon in the SOI substrate is fully depleted by applying high negative voltage at the backside for higher quantum efficiency (QE) in a near-infrared (NIR) region. The proposed SOI-based four-tap charge modulator achieves a high-speed charge modulation and high modulation contrast of 71% in a NIR region. In addition, in-pixel drain function is used for short-pulse TOF measurements. A distance measurement up to 27 m is carried out with +1.8~−3.0% linearity error and range resolution of 4.5 cm in outdoor conditions. The measured QE of 55% is attained at 940 nm which is suitable for outdoor use due to the reduced spectral components of solar radiation.
Gas Sensor Array and Classifiers as a Means of Varroosis Detection
Authors: Szczurek Andrzej , Maciejewska Monika , Bąk Beata , Wilk Jakub , Wilde Jerzy , Siuda Maciej
Published: December 2019

The study focused on a method of detection for bee colony infestation with the Varroa destructor mite, based on the measurements of the chemical properties of beehive air. The efficient detection of varroosis was demonstrated. This method of detection is based on a semiconductor gas sensor array and classification module. The efficiency of detection was characterized by the true positive rate (TPR) and true negative rate (TNR). Several factors influencing the performance of the method were determined. They were: (1) the number and kind of sensors, (2) the classifier, (3) the group of bee colonies, and (4) the balance of the classification data set. Gas sensor array outperformed single sensors. It should include at least four sensors. Better results of detection were attained with a support vector machine (SVM) as compared with the k-nearest neighbors (k-NN) algorithm. The selection of bee colonies was important. TPR and TNR differed by several percent for the two examined groups of colonies. The balance of the classification data was crucial. The average classification results were, for the balanced data set: TPR = 0.93 and TNR = 0.95, and for the imbalanced data set: TP = 0.95 and FP = 0.53. The selection of bee colonies and the balance of classification data set have to be controlled in order to attain high performance of the proposed detection method.
A MEMS-Enabled Deployable Trace Chemical Sensor Based on Fast Gas-Chromatography and Quartz Enhanced Photoacousic Spectoscopy
Authors: Zampolli Stefano , Mengali Sandro , Liberatore Nicola , Elmi Ivan , Masini Luca , Sanmartin Michele , Viola Roberto
Published: December 2019

This paper reports on a portable selective chemical sensor for hazardous vapors at trace levels, which combines a two-stage purge and trap vapor pre-concentration system, a Micro-Electro-Mechanical-System (MEMS) based fast gas-chromatographic (FAST-GC) separation column and a miniaturized quartz-enhanced photoacoustic spectroscopy (QEPAS) detector. The integrated sensing system provides two-dimensional selectivity combining GC retention time and QEPAS spectral information, and was specifically designed to be rugged and suitable to be deployed on unmanned robotic ground vehicles. This is the first demonstration of a miniaturized QEPAS device used as spectroscopic detector downstream of a FAST-GC separation column, enabling real-world analyses in dirty environments with response time of a few minutes. The main modules of the GC/QEPAS sensor device will be described in detail together with the system integration, and successful test results will be reported and discussed.
Using New Camera-Based Technologies for Gait Analysis in Older Adults in Comparison to the Established GAITRite System
Authors: Steinert Anika , Sattler Igor , Otte Karen , Röhling Hanna , Mansow-Model Sebastian , Müller-Werdan Ursula
Published: December 2019

Various gait parameters can be used to assess the risk of falling in older adults. However, the state-of-the-art systems used to quantify gait parameters often come with high costs as well as training and space requirements. Gait analysis systems, which use mobile and commercially available cameras, can be an easily available, marker-free alternative. In a study with 44 participants (age ≥ 65 years), gait patterns were analyzed with three different systems: a pressure sensitive walkway system (GAITRite-System, GS) as gold standard, Motognosis Labs Software using a Microsoft Kinect Sensor (MKS), and a smartphone camera-based application (SCA). Intertrial repeatability showed moderate to excellent results for MKS (ICC(1,1) 0.574 to 0.962) for almost all measured gait parameters and moderate reliability in SCA measures for gait speed (ICC(1,1) 0.526 to 0.535). All gait parameters of MKS showed a high level of agreement with GS (ICC(2,k) 0.811 to 0.981). Gait parameters extracted with SCA showed poor reliability. The tested gait analysis systems based on different camera systems are currently only partially able to capture valid gait parameters. If the underlying algorithms are adapted and camera technology is advancing, it is conceivable that these comparatively simple methods could be used for gait analysis.
Quantification of Neuropeptide Y with Picomolar Sensitivity Enabled by Guided-Mode Resonance Biosensors
Authors: Abdallah Mohammad G. , Buchanan-Vega Joseph A. , Lee Kyu J. , Wenner Brett R. , Allen Jeffery W. , Allen Monica S. , Gimlin Susanne , Wawro Weidanz Debra , Magnusson Robert
Published: December 2019

Assessing levels of neuropeptide Y (NPY) in the human body has many medical uses. Accordingly, we report the quantitative detection of NPY biomarkers applying guided-mode resonance (GMR) biosensor methodology. The label-free sensor operates in the near-infrared spectral region exhibiting distinctive resonance signatures. The interaction of NPY with bioselective molecules on the sensor surface causes spectral shifts that directly identify the binding event without additional processing. In the experiments described here, NPY antibodies are attached to the sensor surface to impart specificity during operation. For the low concentrations of NPY of interest, we apply a sandwich NPY assay in which the sensor-linked anti-NPY molecule binds with NPY that subsequently binds with anti-NPY to close the sandwich. The sandwich assay achieves a detection limit of ~0.1 pM NPY. The photonic sensor methodology applied here enables expeditious high-throughput data acquisition with high sensitivity and specificity. The entire bioreaction is recorded as a function of time, in contrast to label-based methods with single-point detection. The convenient methodology and results reported are significant, as the NPY detection range of 0.1–10 pM demonstrated is useful in important medical circumstances.
Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning
Authors: Lim Hyerim , Kim Bumjoon , Park Sukyung
Published: December 2019

Recent studies have reported the application of artificial neural network (ANN) techniques on data of inertial measurement units (IMUs) to predict ground reaction forces (GRFs), which could serve as quantitative indicators of sports performance or rehabilitation. The number of IMUs and their measurement locations are often determined heuristically, and the rationale underlying the selection of these parameter values is not discussed. Using the dynamic relationship between the center of mass (CoM), the GRFs and joint kinetics, we propose the CoM as a single measurement location with which to predict the dynamic data of the lower limbs, using an ANN. Data from seven subjects walking on a treadmill at various speeds were collected from a single IMU worn near the sacrum. The data was segmented by step and numerically processed for integration. Six segment angles of the stance and swing leg, three joint torques, and two GRFs were estimated from the kinematics of the CoM measured from a single IMU sensor, with fair accuracy. These results indicate the importance of the CoM as a dynamic determinant of multi-segment kinetics during walking. The tradeoff between data quantity and wearable convenience can be solved by utilizing a machine learning algorithm based on the dynamic characteristics of human walking.
Sensitivity Enhancement of a Surface Plasmon Resonance Sensor with Platinum Diselenide
Authors: Jia Yue , Li Zhongfu , Wang Haiqi , Saeed Muhammad , Cai Houzhi
Published: December 2019

The extraordinary optoelectronic properties of platinum diselenide (PtSe2), whose structure is similar to graphene and phosphorene, has attracted great attention in new rapidly developed two-dimensional (2D) materials beyond the other 2D material family members. We have investigated the surface plasmon resonance (SPR) sensors through PtSe2 with the transfer matrix method. The simulation results show that the anticipated PtSe2 biochemical sensors have the ability to detect analytic. It is evident that only the sensitivities of Ag or Au film biochemical sensors were observed at 118°/RIU (refractive index unit) and 130°/RIU, whereas the sensitivities of the PtSe2-based biochemical sensors reached as high as 162°/RIU (Ag film) and 165°/RIU (Au film). The diverse biosensor sensitivities with PtSe2 suggest that this kind of 2D material can adapt SPR sensor properties.
Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms
Authors: Nguyen Hoang , Choi Yosoon , Bui Xuan-Nam , Nguyen-Thoi Trung
Published: December 2019

In this study, vibration sensors were used to measure blast-induced ground vibration (PPV). Different evolutionary algorithms were assessed for predicting PPV, including the particle swarm optimization (PSO) algorithm, genetic algorithm (GA), imperialist competitive algorithm (ICA), and artificial bee colony (ABC). These evolutionary algorithms were used to optimize the support vector regression (SVR) model. They were abbreviated as the PSO-SVR, GA-SVR, ICA-SVR, and ABC-SVR models. For each evolutionary algorithm, three forms of kernel function, linear (L), radial basis function (RBF), and polynomial (P), were investigated and developed. In total, 12 new hybrid models were developed for predicting PPV in this study, named ABC-SVR-P, ABC-SVR-L, ABC-SVR-RBF, PSO-SVR-P, PSO-SVR-L, PSO-SVR-RBF, ICA-SVR-P, ICA-SVR-L, ICA-SVR-RBF, GA-SVR-P, GA-SVR-L and GA-SVR-RBF. There were 125 blasting results gathered and analyzed at a limestone quarry in Vietnam. Statistical criteria like R2, RMSE, and MAE were used to compare and evaluate the developed models. Ranking and color intensity methods were also applied to enable a more complete evaluation. The results revealed that GA was the most dominant evolutionary algorithm for the current problem when combined with the SVR model. The RBF was confirmed as the best kernel function for the GA-SVR model. The GA-SVR-RBF model was proposed as the best technique for PPV estimation.
Application of Adaptive Wave Cancellation Underwater to a Piezoelectric-Material-Based Multilayer Sensor
Authors: Lee Hyodong , Park Hwijin , Park Kwan Kyu , Yi Hak
Published: December 2019

This paper concerns the use of adaptive wave cancellation in a new multilayer smart skin sensor to attenuate the primary low-frequency noise underwater. The proposed multilayered system is designed with a piezoelectric actuator (Pb(In1/2Nb1/2)O3–Pb(Mg1/3Nb2/3)O3–PbTiO3 composite) and two layers of polyvinylidene fluoride to accelerate wave absorption. Furthermore, the use of a combination of an adaptive control scheme and a time-delay signal separation method has the potential to provide the proposed absorber system with a wave cancellation capability and thereby enable the absorber system to respond to environmental changes underwater. The use of smart piezoelectric materials and an adaptive control approach enables the absorber system to achieve the high attenuation level of the reflected waves, unlike typical absorber systems based on active noise control. Echo reduction experiments showed that the proposed piezoelectric-based multilayer sensor with an adaptive controller could attenuate reflected wave signals effectively.
Point Siamese Network for Person Tracking Using 3D Point Clouds
Authors: Cui Yubo , Fang Zheng , Zhou Sifan
Published: December 2019

Person tracking is an important issue in both computer vision and robotics. However, most existing person tracking methods using 3D point cloud are based on the Bayesian Filtering framework which are not robust in challenging scenes. In contrast with the filtering methods, in this paper, we propose a neural network to cope with person tracking using only 3D point cloud, named Point Siamese Network (PSN). PSN consists of two input branches named template and search, respectively. After finding the target person (by reading the label or using a detector), we get the inputs of the two branches and create feature spaces for them using feature extraction network. Meanwhile, a similarity map based on the feature space is proposed between them. We can obtain the target person from the map. Furthermore, we add an attention module to the template branch to guide feature extraction. To evaluate the performance of the proposed method, we compare it with the Unscented Kalman Filter (UKF) on 3 custom labeled challenging scenes and the KITTI dataset. The experimental results show that the proposed method performs better than UKF in robustness and accuracy and has a real-time speed. In addition, we publicly release our collected dataset and the labeled sequences to the research community.
Sustainable Visual Analysis for Bank Non-Performing Loans and Government Debt Distress
Authors: Liu Li , Liu Yumin , Kim Jong-Min
Published: December 2019

This article visualizes bank non-performing loans (NPLs) and government debt distress data integration and an outcome classification after the outbreak of European sovereign debt. Linear and functional principal component analysis (FPCA) and biclustering are used to show the clustering pattern of NPLs and government debt for 25 EU and BRICS countries (Brazil, Russia, India, China and South Africa) during the period of 2006 to 2017 through high-dimensional visualizations. The results demonstrate that the government debt markets of EU countries experienced a similar trend in terms of NPLs, with a similar size of NPLs across debt markets. Through visualization, we find that the government debt and NPLs of EU and BRICS countries increased drastically after the crisis, and crisis countries are contagious. However, the impact of the Greek debt crisis is lower for non-crisis countries, because the debt markets of these countries are decoupled from the Greek market. We also find that sovereign debtors in the EU countries have much closer fiscal linkages than BRICS countries. The level of crisis in the EU countries will be higher than that in the BRICS countries if crisis is driven by the common shocks of macroeconomic fundamentals.
Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network
Authors: Zhu Rongchen , Li Xin , Hu Xiaofeng , Hu Deshui
Published: December 2019

Many chemical plant explosion accidents occur along with the development of the chemical industry. Meanwhile, the interaction and influence of various factors significantly increase the uncertainty of the evolution process of such accidents. This paper presents a framework to dynamically evaluate chemical plant explosion accidents. We propose twelve nodes to represent accident evolution and establish a Bayesian network model for chemical plant explosion accidents, combining historical data with expert experience to support the prevention, management, and real-time warning. Hypothetical scenarios and catastrophic explosion scenarios were analyzed by setting different combinations of states for nodes. Moreover, the impacts of factors such as factory type, material form, accident equipment, the emergency response on casualty and property loss are evaluated. We find that sensitivity of property loss and casualties to factory type and ongoing work are less significant; the equipment factors result in more casualties than that from personnel factors; the impact of emergency response on the accident results is significant; equipment safety management and personnel safety training are the most important measures to prevent chemical plant explosion risks.
Is M&A Information Useful for Exploring Promising Industries and Technologies?
Authors: Choi Jinho , Chung Sunghun , Chang Yong Sik
Published: December 2019

Companies today that seek to diversify their business are looking for opportunities in new markets by considering their core competencies. However, companies are struggling to diversify and grow their current businesses due to a lack of information concerning diversification and a low level of capability for future commercialization. In this study, we suggest a new methodology that identifies promising industry and technology areas by examining mergers and acquisitions (M&As) transaction data. Specifically, by analyzing the extent to which firms have engaged in M&A activities, the prediction of promising industries is derived from the relationships among specific industries, as well as the M&A transactions among technology areas within a focal industry. We first theoretically test whether all M&A transactions are related to promising areas. Second, we analyze the trends of global M&As by a time-series analysis of M&A transactions by sectors over the last 15 years. Lastly, we conduct an association analysis to identify the degree of M&A connections between industry and technology areas, respectively. We hope that our results provide insights for R&D policymakers and investors who need to decide on promising industries to cultivate or invest in, and researchers who want to identify overall M&A trends and promising industries and technology areas.