bluuebunny/crossref_metadata_2025 huggingface dataset:
Co-Chaperone Bag-1 Plays a Role in the Autophagy-Dependent Cell Survival through Beclin 1 Interaction
Authors: Turk Miray , Tatli Ozge , Alkan Hamza Furkan , Ozfiliz Kilbas Pelin , Alkurt Gizem , Dinler Doganay Gizem
Published: February 2021

Expression levels of the major mammalian autophagy regulator Beclin 1 and its interaction with Bcl-2 regulate the switch between autophagic cell survival and apoptotic cell death pathways. However, some of the regulators and the precise mechanisms of these processes still remain elusive. Bag-1 (Bcl-2 associated athanogene-1), a member of BAG family proteins, is a multifunctional pro-survival molecule that possesses critical functions in vital cellular pathways. Herein, we report the role of Bag-1 on Bcl-2/Beclin 1 crosstalk through indirectly interacting with Beclin 1. Pull-down experiments suggested a molecular interaction between Bag-1 and Beclin 1 in breast cancer cell lines. On the other hand, in vitro binding assays showed that Bag-1/Beclin 1 interaction does not occur directly but occurs through a mediator molecule. Bag-1 interaction with p-Beclin 1 (T119), indicator of early autophagy, is increased during nutrient starvation suggesting involvement of Bag-1 in the autophagic regulation. Furthermore, CRISPR/Cas9-mediated Bag-1 knock-out in MCF-7 cells hampered cell survival and proliferation and resulted in decreased levels of total LC3 under starvation. Collectively, we suggest that Bag-1 modulates cell survival/death decision through maintaining macroautophagy as a component of Beclin 1-associated complexes.
Carbon Nanotubes: Probabilistic Approach for Occupational Risk Assessment
Authors: Spinazzè Andrea , Zellino Carolina , Borghi Francesca , Campagnolo Davide , Rovelli Sabrina , Keller Marta , Fanti Giacomo , Cattaneo Andrea , Cavallo Domenico M.
Published: February 2021

In this study, the occupational risk assessment of carbon nanotubes (CNTs) was performed by means of a probabilistic approach. Chronic and subchronic inhalation exposure studies were retrieved during the hazard identification phase of the study. These studies were then used to obtain a guidance value (BMCh, expressed as a lognormal distribution with geometric mean ± geometric standard deviation = 10.0 ± 4.2 µg/m3) for occupational inhalation exposure to CNTs. An exposure scenario was selected from the scientific literature: three different work events (WEs) related to the production of conductive films were considered: (WE1) manufacturing of single walled carbon nanotubes films during normal operation using local exhaust ventilation (LEV); (WE2) manufacturing of SWCNT film without LEV; and (WE3) cleaning of one of the reactors. For each WE, a probability distribution function was applied, considering exposure expressed as mass concentration, as derived from three different measurement techniques. The ratio of the exposure and the BMCh distributions (i.e., the risk characterization ratio—RCR) was used to calculate the probability of occurrence of a relevant occupational risk. All the considered WEs indicated the presence of a risk (i.e., RCR distributions ≥ 1); however, only WE2 resulted in a statistically significant level of risk.
Nanomaterial Synthesis in Ionic Liquids and Their Use on the Photocatalytic Degradation of Emerging Pollutants
Authors: Corchero Raquel , Rodil Rosario , Soto Ana , Rodil Eva
Published: February 2021

The unique properties of ionic liquids make them suitable candidates to prepare nanoscale materials. A simple method that uses exclusively a corresponding bulk material and an ionic liquid—in this case, [P6,6,6,14]Cl—was used to prepare AgCl nanoparticles and AgCl@Fe3O4 or TiO2@Fe3O4 magnetic nanocomposites. The prepared nanomaterials were characterized by X-ray powder diffraction, scanning electron microscopy, transmission electron microscopy, ultraviolet–visible spectroscopy, and X-ray photoelectron spectroscopy. The photodegradation of atenolol as a model pharmaceutical pollutant in wastewater was investigated under ultraviolet–visible light irradiation using the different synthesized nanocatalysts. In the presence of 0.75 g·L−1 AgCl nanoparticles, a practically complete degradation of 10 ppm of atenolol was obtained after 30 min, following pseudo-first-order reaction kinetics. The effect of different variables (concentrations, pH, oxidant agents, etc.) was analyzed. The recyclability of the nanocatalyst was tested and found to be successful. A degradation mechanism was also proposed. In order to improve the recovery stage of the nanocatalyst, the use of magnetic nanocomposites is proposed. Under the same experimental conditions, a slightly lower and slower degradation was achieved with an easier separation. The main conclusions of the paper are the suitability of the use of ionic liquids to prepare different nanocatalysts and the effectiveness of these at degrading an emerging pollutant in wastewater treatment.
Effect of Vitamin D Supplementation in Early Life on Children’s Growth and Body Composition: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Authors: Ma Kristine , Wei Shu Qin , Bi Wei Guang , Weiler Hope A. , Wen Shi Wu
Published: February 2021

Background: Vitamin D deficiency during pregnancy or infancy is associated with adverse growth in children. No systematic review has been conducted to summarize available evidence on the effect of vitamin D supplementation in pregnancy and infancy on growth and body composition in children. Objective: We aim to summarize the available evidence on the effect of vitamin D supplementation in pregnancy and infancy on child growth and body composition. Method: A systematic review and meta-analysis were performed on the effects of vitamin D supplementation during early life on children’s growth and body composition (bone, lean and fat). A literature search of randomized controlled trials (RCTs) was conducted to identify relevant studies on the effects of vitamin D supplementation during pregnancy and infancy on children’s body composition (bone, lean and fat) in PubMed, EMBASE and Cochrane Library from inception to 31 December 2020. A Cochrane Risk Assessment Tool was used for quality assessment. The comparison was vitamin D supplementation vs. placebo or standard care. Random-effects and fixed-effect meta-analyses were conducted. The effects are presented as mean differences (MDs) or risk ratios (RRs) with 95% confidence intervals (CIs). Results: A total of 3960 participants from eleven randomized controlled trials were eligible for inclusion. Vitamin D supplementation during pregnancy was associated with higher triceps skinfold thickness (mm) (MD 0.33, 95% CI, 0.12, 0.54; I2 = 34%) in neonates. Vitamin D supplementation during pregnancy or infancy was associated with significantly increased length for age z-score in infants at 1 year of age (MD 0.29, 95% CI, 0.03, 0.54; I2 = 0%), and was associated with lower body mass index (BMI) (kg/m2) (MD −0.19, 95% CI −0.34, −0.04; I2 = 0%) and body mass index z-score (BMIZ) (MD −0.12, 95% CI −0.21, −0.04; I2 = 0%) in offspring at 3–6 years of age. Vitamin D supplementation during early life was not observed to be associated with children’s bone, lean or fat mass. Conclusion: Vitamin D supplementation during pregnancy or infancy may be associated with reduced adiposity in childhood. Further large clinical trials of the effects of vitamin D supplementation on childhood body composition are warranted.
Effects of a Rice Bran Dietary Intervention on the Composition of the Intestinal Microbiota of Adults with a High Risk of Colorectal Cancer: A Pilot Randomised-Controlled Trial
Authors: So Winnie K. W. , Chan Judy Y. W. , Law Bernard M. H. , Choi Kai Chow , Ching Jessica Y. L. , Chan Kam Leung , Tang Raymond S. Y. , Chan Carmen W. H. , Wu Justin C. Y. , Tsui Stephen K. W.
Published: February 2021

Rice bran exhibits chemopreventive properties that may help to prevent colorectal cancer (CRC), and a short-term rice bran dietary intervention may promote intestinal health via modification of the intestinal microbiota. We conducted a pilot, double-blind, randomised placebo-controlled trial to assess the feasibility of implementing a long-term (24-week) rice bran dietary intervention in Chinese subjects with a high risk of CRC, and to examine its effects on the composition of their intestinal microbiota. Forty subjects were randomised into the intervention group (n = 19) or the control group (n = 20). The intervention participants consumed 30 g of rice bran over 24-h intervals for 24 weeks, whilst the control participants consumed 30 g of rice powder on the same schedule. High rates of retention (97.5%) and compliance (≥91.3%) were observed. No adverse effects were reported. The intervention significantly enhanced the intestinal abundance of Firmicutes and Lactobacillus, and tended to increase the Firmicutes/Bacteroidetes ratio and the intestinal abundance of Prevotella_9 and the health-promoting Lactobacillales and Bifidobacteria, but had no effect on bacterial diversity. Overall, a 24-week rice bran dietary intervention was feasible, and may increase intestinal health by inducing health-promoting modification of the intestinal microbiota. Further larger-scale studies involving a longer intervention duration and multiple follow-up outcome assessments are recommended.
Thyroid-Modulating Activities of Olive and Its Polyphenols: A Systematic Review
Authors: Pang Kok-Lun , Lumintang Johanna Nathania , Chin Kok-Yong
Published: February 2021

Olive oil, which is commonly used in the Mediterranean diet, is known for its health benefits related to the reduction of the risks of cancer, coronary heart disease, hypertension, and neurodegenerative disease. These unique properties are attributed to the phytochemicals with potent antioxidant activities in olive oil. Olive leaf also harbours similar bioactive compounds. Several studies have reported the effects of olive phenolics, olive oil, and leaf extract in the modulation of thyroid activities. A systematic review of the literature was conducted to identify relevant studies on the effects of olive derivatives on thyroid function. A comprehensive search was conducted in October 2020 using the PubMed, Scopus, and Web of Science databases. Cellular, animal, and human studies reporting the effects of olive derivatives, including olive phenolics, olive oil, and leaf extracts on thyroid function were considered. The literature search found 445 articles on this topic, but only nine articles were included based on the inclusion and exclusion criteria. All included articles were animal studies involving the administration of olive oil, olive leaf extract, or olive pomace residues orally. These olive derivatives were consistently demonstrated to have thyroid-stimulating activities in euthyroid or hypothyroid animals, but their mechanisms of action are unknown. Despite the positive results, validation of the beneficial health effects of olive derivatives in the human population is lacking. In conclusion, olive derivatives, especially olive oil and leaf extract, could stimulate thyroid function. Olive pomace residue is not suitable for pharmaceutical or health supplementation purposes. Therapeutic applications of olive oil and leaf extract, especially in individuals with hypothyroidism, require further validation through human studies.
The Mediterranean Diet in Osteoporosis Prevention: An Insight in a Peri- and Post-Menopausal Population
Authors: Quattrini Sara , Pampaloni Barbara , Gronchi Giorgio , Giusti Francesca , Brandi Maria Luisa
Published: February 2021

Osteoporosis represent a widespread public health problem. The management and prevention of osteoporosis and related low energy fractures start with a correct lifestyle and proper nutrition. Several different nutrients are essential for bone and mineral metabolism, especially calcium. Nevertheless, a well-balanced nutrition, such as Mediterranean diet (MD), proved to be beneficial for several chronic diseases and also fragility fractures resulted lower in the Mediterranean area. A prospective observational study in a population of two hundred peri- and post-menopausal women (aged 30–80 years) was developed at Careggi hospital, Florence. Both MD adherence and dietary calcium intake were evaluated in occasion of a “first visit” and a “follow-up” visit, through validated questionnaires. From a descriptive point of view, although not statistically significant, in both visits a slight increase in calcium intake was observed for high adherence to MD diet. Moreover, a short nutritional interview (20 min) was applied in our population and demonstrated to be sufficient to significantly improve MD adherence level (mean score at T0 = 6.98 ± 1.74 and T1 = 7.53 ± 1.68), opening promising paths in osteoporosis prevention.
Achievement of the Targets of the 20-Year Infancy-Onset Dietary Intervention—Association with Metabolic Profile from Childhood to Adulthood
Authors: Lehtovirta Miia , Matthews Laurie A. , Laitinen Tomi T. , Nuotio Joel , Niinikoski Harri , Rovio Suvi P. , Lagström Hanna , Viikari Jorma S. A. , Rönnemaa Tapani , Jula Antti , Ala-Korpela Mika , Raitakari Olli T. , Pahkala Katja
Published: February 2021

The Special Turku Coronary Risk Factor Intervention Project (STRIP) is a prospective infancy-onset randomized dietary intervention trial targeting dietary fat quality and cholesterol intake, and favoring consumption of vegetables, fruit, and whole-grains. Diet (food records) and circulating metabolites were studied at six time points between the ages of 9–19 years (n = 549–338). Dietary targets for this study were defined as (1) the ratio of saturated fat (SAFA) to monounsaturated and polyunsaturated fatty acids (MUFA + PUFA) < 1:2, (2) intake of SAFA < 10% of total energy intake, (3) fiber intake ≥ 80th age-specific percentile, and (4) sucrose intake ≤ 20th age-specific percentile. Metabolic biomarkers were quantified by high-throughput nuclear magnetic resonance metabolomics. Better adherence to the dietary targets, regardless of study group allocation, was assoiated with higher serum proportion of PUFAs, lower serum proportion of SAFAs, and a higher degree of unsaturation of fatty acids. Achieving ≥ 1 dietary target resulted in higher low-density lipoprotein (LDL) particle size, lower circulating LDL subclass lipid concentrations, and lower circulating lipid concentrations in medium and small high-density lipoprotein subclasses compared to meeting 0 targets. Attaining more dietary targets (≥2) was associated with a tendency to lower lipid concentrations of intermediate-density lipoprotein and very low-density lipoprotein subclasses. Thus, adherence to dietary targets is favorably associated with multiple circulating fatty acids and lipoprotein subclass lipid concentrations, indicative of better cardio-metabolic health.
Preparation and Characterization of Carboxymethyl Cellulose-Based Bioactive Composite Films Modified with Fungal Melanin and Carvacrol
Authors: Łopusiewicz Łukasz , Kwiatkowski Paweł , Drozłowska Emilia , Trocer Paulina , Kostek Mateusz , Śliwiński Mariusz , Polak-Śliwińska Magdalena , Kowalczyk Edward , Sienkiewicz Monika
Published: February 2021

Preparation of biodegradable packaging materials and valorisation of food industry residues to achieve “zero waste” goals is still a major challenge. Herein, biopolymer-based (carboxymethyl cellulose—CMC) bioactive films were prepared by the addition, alone or in combination, of carvacrol and fungal melanin isolated from champignon mushroom (Agaricus bisporus) agro-industrial residues. The mechanical, optical, thermal, water vapour, and UV-Vis barrier properties were studied. Fourier-transform infrared (FT-IR) spectroscopy studies were carried out to analyse the chemical composition of the resulting films. Antibacterial, antifungal, and antioxidant activities were also determined. Both CMC/melanin and CMC/melanin/carvacrol films showed some antimicrobial activity against Escherichia coli, Staphylococcus aureus, and Candida albicans. The addition of melanin increased the UV-blocking, mechanical, water vapour barrier, and antioxidant properties without substantially reducing the transparency of the films. The addition of carvacrol caused loss of transparency, however, composite CMC/melanin/carvacrol films showed excellent antioxidant activity and enhanced mechanical strength. The developed bioactive biopolymer films have a good potential to be green bioactive alternatives to plastic films in food packaging applications.
PET Foams Surface Treated with Graphene Nanoplatelets: Evaluation of Thermal Resistance and Flame Retardancy
Authors: Matta Samuele , Rizzi Laura Giorgia , Frache Alberto
Published: February 2021

In this work, fire-retardant systems consisting of graphene nanoplatelets (GNPs) and dispersant agents were designed and applied on polyethylene terephthalate (PET) foam. Manual deposition from three different liquid solutions was performed in order to create a protective coating on the specimen’s surface. A very low amount of coating, between 1.5 and 3.5 wt%, was chosen for the preparation of coated samples. Flammability, flame penetration, and combustion tests demonstrated the improvement provided to the foam via coating. In particular, specimens with PSS/GNPs coating, compared to neat foam, were able to interrupt the flame during horizontal and vertical flammability tests and led to longer endurance times during the flame penetration test. Furthermore, during cone calorimetry tests, the time to ignition (TTI) increased and the peak of heat release rate (pHRR) was drastically reduced by up to 60% compared to that of the uncoated PET foam. Finally, ageing for 48 and 115 h at 160 °C was performed on coated specimens to evaluate the effect on flammability and combustion behavior. Scanning electron microscopy (SEM) images proved the morphological effect of the heat treatment on the surface, showing that the coating was uniformly distributed. In this case, fire-retardant properties were enhanced, even if fewer GNPs were used.
Solubilization of Charged Porphyrins in Interpolyelectrolyte Complexes: A Computer Study
Authors: Šindelka Karel , Limpouchová Zuzana , Procházka Karel
Published: February 2021

Using coarse-grained dissipative particle dynamics (DPD) with explicit electrostatics, we performed (i) an extensive series of simulations of the electrostatic co-assembly of asymmetric oppositely charged copolymers composed of one (either positively or negatively charged) polyelectrolyte (PE) block A and one water-soluble block B and (ii) studied the solubilization of positively charged porphyrin derivatives (P+) in the interpolyelectrolyte complex (IPEC) cores of co-assembled nanoparticles. We studied the stoichiometric mixtures of 137 A10+B25 and 137 A10−B25 chains with moderately hydrophobic A blocks (DPD interaction parameter aAS=35) and hydrophilic B blocks (aBS=25) with 10 to 120 P+ added (aPS=39). The P+ interactions with other components were set to match literature information on their limited solubility and aggregation behavior. The study shows that the moderately soluble P+ molecules easily solubilize in IPEC cores, where they partly replace PE+ and electrostatically crosslink PE− blocks. As the large P+ rings are apt to aggregate, P+ molecules aggregate in IPEC cores. The aggregation, which starts at very low loadings, is promoted by increasing the number of P+ in the mixture. The positively charged copolymers repelled from the central part of IPEC core partially concentrate at the core-shell interface and partially escape into bulk solvent depending on the amount of P+ in the mixture and on their association number, AS. If AS is lower than the ensemble average ⟨AS⟩n, the copolymer chains released from IPEC preferentially concentrate at the core-shell interface, thus increasing AS, which approaches ⟨AS⟩n. If AS>⟨AS⟩n, they escape into the bulk solvent.
Performance Characteristics in Runner of an Impulse Water Turbine with Splitter Blade
Authors: Tang Lingdi , Yuan Shouqi , Tang Yue , Gao Zhijun
Published: February 2021

The impulse water turbine is a promising energy conversion device that can be used as mechanical power or a micro hydro generator, and its application can effectively ease the current energy crisis. This paper aims to clarify the mechanism of liquid acting on runner blades, the hydraulic performance, and energy conversion characteristics in the runner domain of an impulse water turbine with a splitter blade by using experimental tests and numerical simulations. The runner was divided into seven areas along the flow direction, and the power variation in the runner domain was analyzed to reflect its energy conversion characteristics. The obtained results indicate that the critical area of the runner for doing the work is in the front half of the blades, while the rear area of the blades does relatively little work and even consumes the mechanical energy of the runner to produce negative work. The high energy area is concentrated in the flow passage facing the nozzle. The energy is gradually evenly distributed from the runner inlet to the runner outlet, and the negative energy caused by flow separation with high probability is gradually reduced. The clarification of the energy conversion performance is of great significance to improve the design of impulse water turbines.
Arab Non-believers and Freethinkers on YouTube: Re-Negotiating Intellectual and Social Boundaries
Authors: Elsässer Sebastian
Published: February 2021

This article uses the increasing visibility of Arab non-believers in the virtual public sphere as an opportunity to re-examine the key issues and dividing lines between believers, sceptics, and non-believers in Arab societies. It analyzes the currently four most popular Arabic-language YouTube channels created by freethinkers, nonbelievers, and atheists and points out commonalities and differences in style, content, and message. The article argues that the sense of a lively and growing virtual community has raised the confidence of non-believers (lā-dīnīyūn) and atheists in the Arab world and made them more daring in their self-portrayal and in their demands on society. As the examples show, YouTube allows them to circumvent the hostility they face in society and in the mainstream media and to connect with an audience that numbers in the tens of thousands of people from all over the Arab world and the diaspora. The article argues that freethinking and non-belief imply an attempt to re-negotiate social and religious boundaries within Arab societies and could—in the long run—have an impact on legal and constitutional questions as well, such as family law and the prerogatives of religious authorities.
An Improved Algorithm Robust to Illumination Variations for Reconstructing Point Cloud Models from Images
Authors: Luo Nan , Huang Ling , Wang Quan , Liu Gang
Published: February 2021

Reconstructing 3D point cloud models from image sequences tends to be impacted by illumination variations and textureless cases in images, resulting in missing parts or uneven distribution of retrieved points. To improve the reconstructing completeness, this work proposes an enhanced similarity metric which is robust to illumination variations among images during the dense diffusions to push the seed-and-expand reconstructing scheme to a further extent. This metric integrates the zero-mean normalized cross-correlation coefficient of illumination and that of texture information which respectively weakens the influence of illumination variations and textureless cases. Incorporated with disparity gradient and confidence constraints, the candidate image features are diffused to their neighborhoods for dense 3D points recovering. We illustrate the two-phase results of multiple datasets and evaluate the robustness of proposed algorithm to illumination variations. Experiments show that ours recovers 10.0% more points, on average, than comparing methods in illumination varying scenarios and achieves better completeness with comparative accuracy.
Rotation Invariance Regularization for Remote Sensing Image Scene Classification with Convolutional Neural Networks
Authors: Qi Kunlun , Yang Chao , Hu Chuli , Shen Yonglin , Shen Shengyu , Wu Huayi
Published: February 2021

Deep convolutional neural networks (DCNNs) have shown significant improvements in remote sensing image scene classification for powerful feature representations. However, because of the high variance and volume limitations of the available remote sensing datasets, DCNNs are prone to overfit the data used for their training. To address this problem, this paper proposes a novel scene classification framework based on a deep Siamese convolutional network with rotation invariance regularization. Specifically, we design a data augmentation strategy for the Siamese model to learn a rotation invariance DCNN model that is achieved by directly enforcing the labels of the training samples before and after rotating to be mapped close to each other. In addition to the cross-entropy cost function for the traditional CNN models, we impose a rotation invariance regularization constraint on the objective function of our proposed model. The experimental results obtained using three publicly-available scene classification datasets show that the proposed method can generally improve the classification performance by 2~3% and achieves satisfactory classification performance compared with some state-of-the-art methods.
Evaluation of the Land GNSS-Reflected DDM Coherence on Soil Moisture Estimation from CYGNSS Data
Authors: Dong Zhounan , Jin Shuanggen
Published: February 2021

With the development of spaceborne global navigation satellite system-reflectometry (GNSS-R), it can be used for terrestrial applications as a promising remote sensing tool, such as soil moisture (SM) retrieval. The reflected L-band GNSS signal from the land surface can simultaneously generate coherent and incoherent scattering, depending on surface roughness. However, the contribution of the incoherent component was directly ignored in previous GNSS-R land soil moisture content retrieval due to the hypothesis of its relatively small proportion. In this paper, a detection method is proposed to distinguish the coherence of land GNSS-R delay-Doppler map (DDM) from the cyclone global navigation satellite system (CYGNSS) mission in terms of DDM power-spreading features, which are characterized by different classification estimators. The results show that the trailing edge slope of normalized integrated time-delay waveform presents a better performance to recognize coherent and incoherent dominated observations, indicating that 89.6% of CYGNSS land observations are dominated by the coherent component. Furthermore, the impact of the land GNSS-Reflected DDM coherence on soil moisture retrieval is evaluated from 19-month CYGNSS data. The experiment results show that the influence of incoherent component and incoherent observations is marginal for CYGNSS soil moisture retrieval, and the RMSE of GNSS-R derived soil moisture reaches 0.04 cm3/cm3.
Spatial Retrievals of Atmospheric Carbon Dioxide from Satellite Observations
Authors: Hobbs Jonathan , Katzfuss Matthias , Zilber Daniel , Brynjarsdóttir Jenný , Mondal Anirban , Berrocal Veronica
Published: February 2021

Modern remote-sensing retrievals often invoke a Bayesian approach to infer atmospheric properties from observed radiances. In this approach, plausible mean states and variability for the quantities of interest are encoded in a prior distribution. Recent developments have devised prior assumptions for the correlation among atmospheric constituents and across observing locations. This work formulates a spatial statistical framework for simultaneous multi-footprint retrievals of carbon dioxide (CO2) with application to the Orbiting Carbon Observatory-2/3 (OCO-2/3). Formally, the retrieval state vector is extended to include atmospheric and surface conditions at many footprints in a small region, and a prior distribution that assumes spatial correlation across these locations is assumed. This spatial prior allows the length-scale, or range, of spatial correlation to vary between different elements of the state vector. Various single- and multi-footprint retrievals are compared in a simulation study. A spatial prior that also includes relatively large prior variances for CO2 results in posterior inferences that most accurately represent the true state and that reduce the correlation in retrieval error across locations.
Operationalization of Remote Sensing Solutions for Sustainable Forest Management
Authors: Mozgeris Gintautas , Balenović Ivan
Published: February 2021

The pre-requisite for sustainable management of natural resources is the availability of timely, cost-effective, and comprehensive information on the status and development trends of the management object [...]
MultEYE: Monitoring System for Real-Time Vehicle Detection, Tracking and Speed Estimation from UAV Imagery on Edge-Computing Platforms
Authors: Balamuralidhar Navaneeth , Tilon Sofia , Nex Francesco
Published: February 2021

We present MultEYE, a traffic monitoring system that can detect, track, and estimate the velocity of vehicles in a sequence of aerial images. The presented solution has been optimized to execute these tasks in real-time on an embedded computer installed on an Unmanned Aerial Vehicle (UAV). In order to overcome the limitation of existing object detection architectures related to accuracy and computational overhead, a multi-task learning methodology was employed by adding a segmentation head to an object detector backbone resulting in the MultEYE object detection architecture. On a custom dataset, it achieved 4.8% higher mean Average Precision (mAP) score, while being 91.4% faster than the state-of-the-art model and while being able to generalize to different real-world traffic scenes. Dedicated object tracking and speed estimation algorithms have been then optimized to track reliably objects from an UAV with limited computational effort. Different strategies to combine object detection, tracking, and speed estimation are discussed, too. From our experiments, the optimized detector runs at an average frame-rate of up to 29 frames per second (FPS) on frame resolution 512 × 320 on a Nvidia Xavier NX board, while the optimally combined detector, tracker and speed estimator pipeline achieves speeds of up to 33 FPS on an image of resolution 3072 × 1728. To our knowledge, the MultEYE system is one of the first traffic monitoring systems that was specifically designed and optimized for an UAV platform under real-world constraints.
Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data
Authors: Harfenmeister Katharina , Itzerott Sibylle , Weltzien Cornelia , Spengler Daniel
Published: February 2021

The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric parameters and biophysical parameters observed in the field is investigated using linear and exponential regression models that are evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). The performance of single regression models is furthermore compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types. The regression models perform best for data from the phenological growth stages tillering to booting. The highest R2 values of the single regression models are reached for the plant height of wheat related to entropy and anisotropy with R2 values of 0.64 and 0.61, respectively. The multiple regression models of VH, VV, entropy, and alpha outperform single regression models in most cases. R2 values of multiple regression models of plant height (0.76), wet biomass (0.7), dry biomass (0.7), and vegetation water content (0.69) improve those of single regression models slightly by up to 0.05. Additionally, the RMSE values of the multiple regression models are around 10% lower compared to those of single regression models. The results indicate the capability of dual-polarimetric decomposition parameters in serving as meaningful input parameters for multiple regression models to improve the prediction of biophysical parameters. Additionally, their temporal profiles indicate phenological development dependent on meteorological conditions. Knowledge about biophysical parameter development and phenology is important for farmers to monitor crop growth variability during the vegetation period to adapt and to optimize field management.
Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor
Authors: Olsson Per-Ola , Vivekar Ashish , Adler Karl , Garcia Millan Virginia E. , Koc Alexander , Alamrani Marwan , Eklundh Lars
Published: February 2021

Unmanned aerial systems (UAS) carrying commercially sold multispectral sensors equipped with a sunshine sensor, such as Parrot Sequoia, enable mapping of vegetation at high spatial resolution with a large degree of flexibility in planning data collection. It is, however, a challenge to perform radiometric correction of the images to create reflectance maps (orthomosaics with surface reflectance) and to compute vegetation indices with sufficient accuracy to enable comparisons between data collected at different times and locations. Studies have compared different radiometric correction methods applied to the Sequoia camera, but there is no consensus about a standard method that provides consistent results for all spectral bands and for different flight conditions. In this study, we perform experiments to assess the accuracy of the Parrot Sequoia camera and sunshine sensor to get an indication if the quality of the data collected is sufficient to create accurate reflectance maps. In addition, we study if there is an influence of the atmosphere on the images and suggest a workflow to collect and process images to create a reflectance map. The main findings are that the sensitivity of the camera is influenced by camera temperature and that the atmosphere influences the images. Hence, we suggest letting the camera warm up before image collection and capturing images of reflectance calibration panels at an elevation close to the maximum flying height to compensate for influence from the atmosphere. The results also show that there is a strong influence of the orientation of the sunshine sensor. This introduces noise and limits the use of the raw sunshine sensor data to compensate for differences in light conditions. To handle this noise, we fit smoothing functions to the sunshine sensor data before we perform irradiance normalization of the images. The developed workflow is evaluated against data from a handheld spectroradiometer, giving the highest correlation (R2 = 0.99) for the normalized difference vegetation index (NDVI). For the individual wavelength bands, R2 was 0.80–0.97 for the red-edge, near-infrared, and red bands.
CCpos: WiFi Fingerprint Indoor Positioning System Based on CDAE-CNN
Authors: Qin Feng , Zuo Tao , Wang Xing
Published: February 2021

WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is based on a convolutional denoising autoencoder (CDAE) and a convolutional neural network (CNN). In the offline stage, this system applies the K-means algorithm to extract the validation set from the all-training set. In the online stage, the RSSI is first denoised and key features are extracted by the CDAE. Then the location estimation is output by the CNN. In this paper, the Alcala Tutorial 2017 dataset and UJIIndoorLoc are adopted to verify the performance of the CCpos system. The experimental results show that our system has excellent noise immunity and generalization performance. The mean positioning errors on the Alcala Tutorial 2017 dataset and the UJIIndoorLoc are 1.05 m and 12.4 m, respectively.
A Flexible PI/Si/SiO2 Piezoresistive Microcantilever for Trace-Level Detection of Aflatoxin B1
Authors: Tian Yuan , Liu Yi , Wang Yang , Xu Jia , Yu Xiaomei
Published: February 2021

In this paper, a polyimide (PI)/Si/SiO2-based piezoresistive microcantilever biosensor was developed to achieve a trace level detection for aflatoxin B1. To take advantage of both the high piezoresistance coefficient of single-crystal silicon and the small spring constant of PI, the flexible piezoresistive microcantilever was designed using the buried oxide (BOX) layer of a silicon-on-insulator (SOI) wafer as a bottom passivation layer, the topmost single-crystal silicon layer as a piezoresistor layer, and a thin PI film as a top passivation layer. To obtain higher sensitivity and output voltage stability, four identical piezoresistors, two of which were located in the substrate and two integrated in the microcantilevers, were composed of a quarter-bridge configuration wheatstone bridge. The fabricated PI/Si/SiO2 microcantilever showed good mechanical properties with a spring constant of 21.31 nN/μm and a deflection sensitivity of 3.54 × 10−7 nm−1. The microcantilever biosensor also showed a stable voltage output in the Phosphate Buffered Saline (PBS) buffer with a fluctuation less than 1 μV @ 3 V. By functionalizing anti-aflatoxin B1 on the sensing piezoresistive microcantilever with a biotin avidin system (BAS), a linear aflatoxin B1 detection concentration resulting from 1 ng/mL to 100 ng/mL was obtained, and the toxic molecule detection also showed good specificity. The experimental results indicate that the PI/Si/SiO2 flexible piezoresistive microcantilever biosensor has excellent abilities in trace-level and specific detections of aflatoxin B1 and other biomolecules.
Performance Evaluation of Deep Learning-Based Prostate Cancer Screening Methods in Histopathological Images: Measuring the Impact of the Model’s Complexity on Its Processing Speed
Authors: Duran-Lopez Lourdes , Dominguez-Morales Juan P. , Rios-Navarro Antonio , Gutierrez-Galan Daniel , Jimenez-Fernandez Angel , Vicente-Diaz Saturnino , Linares-Barranco Alejandro
Published: February 2021

Prostate cancer (PCa) is the second most frequently diagnosed cancer among men worldwide, with almost 1.3 million new cases and 360,000 deaths in 2018. As it has been estimated, its mortality will double by 2040, mostly in countries with limited resources. These numbers suggest that recent trends in deep learning-based computer-aided diagnosis could play an important role, serving as screening methods for PCa detection. These algorithms have already been used with histopathological images in many works, in which authors tend to focus on achieving high accuracy results for classifying between malignant and normal cases. These results are commonly obtained by training very deep and complex convolutional neural networks, which require high computing power and resources not only in this process, but also in the inference step. As the number of cases rises in regions with limited resources, reducing prediction time becomes more important. In this work, we measured the performance of current state-of-the-art models for PCa detection with a novel benchmark and compared the results with PROMETEO, a custom architecture that we proposed. The results of the comprehensive comparison show that using dedicated models for specific applications could be of great importance in the future.
GEUINF: Real-Time Visualization of Indoor Facilities Using Mixed Reality
Authors: Jurado David , Jurado Juan M. , Ortega Lidia , Feito Francisco R.
Published: February 2021

Mixed reality (MR) enables a novel way to visualize virtual objects on real scenarios considering physical constraints. This technology arises with other significant advances in the field of sensors fusion for human-centric 3D capturing. Recent advances for scanning the user environment, real-time visualization and 3D vision using ubiquitous systems like smartphones allow us to capture 3D data from the real world. In this paper, a disruptive application for assessing the status of indoor infrastructures is proposed. The installation and maintenance of hidden facilities such as water pipes, electrical lines and air conditioning tubes, which are usually occluded behind the wall, supposes tedious and inefficient tasks. Most of these infrastructures are digitized but they cannot be visualized onsite. In this research, we focused on the development of a new application (GEUINF) to be launched on smartphones that are capable of capturing 3D data of the real world by depth sensing. This information is relevant to determine the user position and orientation. Although previous approaches used fixed markers for this purpose, our application enables the estimation of both parameters with a centimeter accuracy without them. This novelty is possible since our method is based on a matching process between reconstructed walls of the real world and 3D planes of the replicated world in a virtual environment. Our markerless approach is based on scanning planar surfaces of the user environment and then, these are geometrically aligned with their corresponding virtual 3D entities. In a preprocessing phase, the 2D CAD geometry available from an architectural project is used to generate 3D models of an indoor building structure. In real time, these virtual elements are tracked with the real ones modeled by using ARCore library. Once the alignment between virtual and real worlds is done, the application enables the visualization, navigation and interaction with the virtual facility networks in real-time. Thus, our method may be used by private companies and public institutions responsible of the indoor facilities management and also may be integrated with other applications focused on indoor navigation.
Detection Threshold Estimates for InSAR Time Series: A Simulation of Tropospheric Delay Approach
Authors: Havazli Emre , Wdowinski Shimon
Published: February 2021

We present a method for estimating the detection threshold of InSAR time-series products that relies on simulations of both vertical stratification and turbulence mixing components of tropospheric delay. Our simulations take into account case-specific parameters, such as topography and wet delay. We generate the time series of simulated data with given intervals (e.g., 12 and 35 days) for temporal coverages varying between 3 and 10 years. Each simulated acquisition presents the apparent noise due to tropospheric delay, which is constrained by case-specific parameters. As the calculation parameters are randomized, we carry out a large number of simulations and analyze the results statistically and we see that, as temporal coverage increases, the amount of propagated error decreases, presenting an inverse correlation. We validate our method by comparing our results with ERS and Envisat results over Socorro Magma Body, New Mexico. Our case study results indicate that Sentinel-1 can achieve ≈1 mm/yr detection level with regularly sampled data sets that have temporal coverage longer than 5 years.
Sensorless Self-Excited Vibrational Viscometer with Two Hopf Bifurcations Based on a Piezoelectric Device
Authors: Urasaki Shinpachiro , Yabuno Hiroshi , Yamamoto Yasuyuki , Matsumoto Sohei
Published: February 2021

In this study, we propose a high-sensitivity sensorless viscometer based on a piezoelectric device. Viscosity is an essential parameter frequently used in many fields. The vibration type viscometer based on self-excited oscillation generally requires displacement sensor although they can measure high viscosity without deterioration of sensitivity. The proposed viscometer utilizes the sensorless self-excited oscillation without any detection of the displacement of the cantilever, which uses the interaction between the mechanical dynamics of the cantilever and the electrical dynamics of the piezoelectric device attached to the cantilever. Since the proposed viscometer has fourth-order dynamics and two coupled oscillator systems, the systems can produce different self-excited oscillations through different Hopf bifurcations. We theoretically showed that the response frequency jumps at the two Hopf bifurcation points and this distance between them depends on the viscosity. Using this distance makes measurement highly sensitive and easier because the jump in the response frequency can be easily detected. We experimentally demonstrate the efficiency of the proposed sensorless viscometer by a macro-scale measurement system. The results show the sensitivity of the proposed method is higher than that of the previous method based on self-excited oscillation with a displacement sensor.
High Precision Outdoor and Indoor Reference State Estimation for Testing Autonomous Vehicles
Authors: Sánchez Morales Eduardo , Dauth Julian , Huber Bertold , García Higuera Andrés , Botsch Michael
Published: February 2021

A current trend in automotive research is autonomous driving. For the proper testing and validation of automated driving functions a reference vehicle state is required. Global Navigation Satellite Systems (GNSS) are useful in the automation of the vehicles because of their practicality and accuracy. However, there are situations where the satellite signal is absent or unusable. This research work presents a methodology that addresses those situations, thus largely reducing the dependency of Inertial Navigation Systems (INSs) on the SatNav. The proposed methodology includes (1) a standstill recognition based on machine learning, (2) a detailed mathematical description of the horizontation of inertial measurements, (3) sensor fusion by means of statistical filtering, (4) an outlier detection for correction data, (5) a drift detector, and (6) a novel LiDAR-based Positioning Method (LbPM) for indoor navigation. The robustness and accuracy of the methodology are validated with a state-of-the-art INS with Real-Time Kinematic (RTK) correction data. The results obtained show a great improvement in the accuracy of vehicle state estimation under adverse driving conditions, such as when the correction data is corrupted, when there are extended periods with no correction data and in the case of drifting. The proposed LbPM method achieves an accuracy closely resembling that of a system with RTK.