bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bifunctional Cs−Au/Co3O4 (Basic and Redox)‐Catalyzed Oxidative Synthesis of Aromatic Azo Compounds from Anilines
Authors: Oseghale Charles O. , Fapojuwo Dele Peter , Alimi Oyekunle Azeez , Akinnawo Christianah Aarinola , Mogudi Batsile M. , Onisuru Oluwatayo Racheal , Meijboom Reinout 
Published: August 2021

AbstractAn eco‐friendly alkali‐promoted (Cs−Au/Co3O4) catalyst, with redox and basic properties for the oxidative dehydrogenative coupling of anilines to symmetrical and unsymmetrical aromatic azo compounds, was developed. We realized a base additive‐ and molecular O2 oxidant‐free process (using air), with reasonable reusability of the catalyst achieved under milder reaction conditions. Notably, the enhanced catalytic activity was also linked to the increased basic site concentration, low reduction temperatures, and the effect of lattice oxygen on the nanomaterials. The increased basic strength of the cation‐promoted catalyst improved the electron density of the active Au species, resulting in higher yields of the desired aromatic azo compounds.
Machine learning‐based automated fungal cell counting under a complicated background with ilastik and ImageJ
Authors: Li Chenxi , Ma Xiaoyu , Deng Jing , Li Jiajia , Liu Yanjie , Zhu Xudong , Liu Jin , Zhang Ping 
Published: August 2021

AbstractMeasuring the concentration and viability of fungal cells is an important and fundamental procedure in scientific research and industrial fermentation. In consideration of the drawbacks of manual cell counting, large quantities of fungal cells require methods that provide easy, objective and reproducible high‐throughput calculations, especially for samples in complicated backgrounds. To answer this challenge, we explored and developed an easy‐to‐use fungal cell counting pipeline that combined the machine learning‐based ilastik tool with the freeware ImageJ, as well as a conventional photomicroscope. Briefly, learning from labels provided by the user, ilastik performs segmentation and classification automatically in batch processing mode and thus discriminates fungal cells from complex backgrounds. The files processed through ilastik can be recognized by ImageJ, which can compute the numeric results with the macro ‘Fungal Cell Counter’. Taking the yeast Cryptococccus deneoformans and the filamentous fungus Pestalotiopsis microspora as examples, we observed that the customizable software algorithm reduced inter‐operator errors significantly and achieved accurate and objective results, while manual counting with a haemocytometer exhibited some errors between repeats and required more time. In summary, a convenient, rapid, reproducible and extremely low‐cost method to count yeast cells and fungal spores is described here, which can be applied to multiple kinds of eucaryotic microorganisms in genetics, cell biology and industrial fermentation.
OAAE: Optimized adaptive anomaly detection ensemble—Base model boosting by parameter optimization
Authors: Bii Joash Kiprotich , Rimiru Richard , Mwangi Ronald Waweru 
Published: August 2021

AbstractIrregular data or anomalies may occur due to human error, miscalculation, or malicious system behavior. The detection of anomalies is a difficult task that requires the use of multiple strategic methods and models. The ideal detection model should assess the strengths and optimize the results of its base models before making final decisions. This task of optimizing the results of the base models contributes to the generation of more accurate results overall. This work presents an optimized adaptive anomaly detection ensemble using heterogeneous algorithms. In the first stage, it adaptively boosts the outcomes of preceding models by weighting their decisions and finding high‐performance ones, and in the second stage, it optimizes the base models by score margin maximization, which enlarges the contrast between the scores of the anomalies and other data prior to fusion to improve detection accuracy. To validate the model, baselines and test results from 10 benchmark datasets are compared. To assess its effectiveness in terms of distinguishing anomalies, the proposed model is tested and evaluated. The analyzed data are presented as cross‐tabulations, with detailed explanations and interpretations. The experiments show an improvement in results even when the least of anomalies (single cases up to 10%) are used.
Flame whirling characteristics of fire in a room with vent
Authors: Fan Wei Peng , Wang Ya Wei , Gao Ye , Chow Wan Ki , Lai Joseph Hung Kit , Chow Cheuk Lun , Cheng Chi Honn 
Published: August 2021

SummaryVelocity distribution and motion of a whirling flame in a room fire with vent were studied analytically in this article. Using Rankine vortex model, whirling flame models with and without decay of the rotation velocity were established. The whirling flame model and the cross‐wind velocity boundary conditions were used to study the velocity distribution of the rotating flame under the action of side wind. When there is a linear velocity gradient in the lateral wind, the whirling flame still revolves around the same axis at different heights in the model without attenuation of rotation velocity, but the axis becomes tilted. In the rotation velocity attenuation model, the axis of the whirling flame changes from a straight line to a curve, and the change of the shape of the whirling flame is related to the flame height, the rotation angular velocity, and the maximum wind velocity. Under the assumption of Rankine vortex model with the same rotation angular velocity, the uniform lateral wind and lateral wind with linear velocity gradient can only affect the position and shape of the whirling flame axis but not change the rotation angular velocity.
An assessment of large‐scale flood modelling based on LiDAR data
Authors: Choné Guénolé , Biron Pascale M. , Buffin‐Bélanger Thomas , Mazgareanu Iulia , Neal Jeff C. , Sampson Christopher C. 
Published: August 2021

AbstractLarge‐scale flood modelling approaches designed for regional to continental scales usually rely on relatively simple assumptions to represent the potentially highly complex river bathymetry at the watershed scale based on digital elevation models (DEMs) with a resolution in the range of 25–30 m. Here, high‐resolution (1 m) LiDAR DEMs are employed to present a novel large‐scale methodology using a more realistic estimation of bathymetry based on hydrogeomorphological GIS tools to extract water surface slope. The large‐scale 1D/2D flood model LISFLOOD‐FP is applied to validate the simulated flood levels using detailed water level data in four different watersheds in Quebec (Canada), including continuous profiles over extensive distances measured with the HydroBall technology. A GIS‐automated procedure allows to obtain the average width required to run LISFLOOD‐FP. The GIS‐automated procedure to estimate bathymetry from LiDAR water surface data uses a hydraulic inverse problem based on discharge at the time of acquisition of LiDAR data. A tiling approach, allowing several small independent hydraulic simulations to cover an entire watershed, greatly improves processing time to simulate large watersheds with a 10‐m resampled LiDAR DEM. Results show significant improvements to large‐scale flood modelling at the watershed scale with standard deviation in the range of 0.30 m and an average fit of around 90%. The main advantage of the proposed approach is to avoid the need to collect expensive bathymetry data to efficiently and accurately simulate flood levels over extensive areas.
Feature‐versus deep learning‐based approaches for the automated detection of brain tumor with magnetic resonance images: A comparative study
Authors: Raghavendra U. , Gudigar Anjan , Rao Tejaswi N. , Rajinikanth V. , Ciaccio Edward J. , Yeong Chai Hong , Satapathy Suresh Chandra , Molinari Filippo , Acharya U. Rajendra 
Published: August 2021

AbstractThe public health is significantly affected by development of brain tumors in human patients. Glioblastoma (GBM) is a relatively common, malignant form of brain tumor, which is currently challenging to treat and cure. In contrast, Lower Grade Gliomas (LGGs) originate from glial cells and can mostly be treated and cured in the initial stages if they are detected early. A computer‐aided diagnostics (CAD) tool may help to test for the presence and extent of any such tumor, and thus can be assistive in the clinical diagnostic process. Herein, we compare handcrafted versus non‐handcrafted features‐based CAD to characterize GBM and LGG. Our machine learning‐based handcrafted model uses quantitative techniques of enhanced elongated quinary patterns and entropies analysis. We have also developed a non‐handcrafted deep learning model using Visual Geometry Group‐16 architecture for segregating GBM and LGG subjects results in 94.25% accuracy using k‐nearest neighbor classifier.
Edge and anomaly detection of brain magnetic resonance images in a distributed environment
Authors: Chaoui Cherifa Nabila , Ghomari Abdelghani , Meftah Boudjelal 
Published: August 2021

AbstractToday, all scientific advancements related to medical image processing aim to develop a unique computational model. The latter will mimic the way humans interpret images. In the present paper, we propose a formal approach biologically inspired by the human natural vision system’s mechanisms. To that end, we use the spiking neural network model for edge detection in brain MRI images. The optimal results provided by this last step increase performance, and facilitate the process of anomaly detection. For that, we have developed a tool called Edge and Anomaly Detection of Brain MRI Images in Distributed Environment (EADBMIDE). It is tested on an MRI brain tumour dataset, which shows the effectiveness of the proposed methods. Each stage in this tool is compared separately with similar approaches in the literature. The obtained results show a significant improvement, making this a recommended tool for edge and anomaly detection methods in MRI images.
Direct synthesis of 3,5‐diaryl‐1,2,4‐oxadiazoles using 1‐(2‐oxo‐2‐arylethyl)pyridin‐1‐iums with benzamidines
Authors: Zhang Yue , Wu Chengjun , Wan Xinyi , Wang Cunde 
Published: August 2021

AbstractAn efficient domino protocol for the synthesis of 1,2,4‐oxadiazole derivatives from readily available 1‐(2‐oxo‐2‐arylethyl)pyridin‐1‐iums and amidine hydrochlorides was developed. In this practical approach, N‐acyl amidine precursors were formed firstly via a simple nucleophilic substitution, without the purification of N‐acylamidine intermediates, and the following intramolecularly dehydrative cyclization gave 1,2,4‐oxadiazole derivatives in the presence of I2/K2CO3/DMSO, which exhibited excellent functional group tolerance and proceeded under simple experimental conditions.
Histone methyltransferase Ezh2 negatively regulates NK cell terminal maturation and function
Authors: Yu Minghang , Su Ziyang , Huang Xuefeng , Zhou Yujie , Zhang Xulong , Wang Bingbing , Wang Zihan , Liu Yi , Xing Nianzeng , Xia Miaoran , Wang Xi 
Published: August 2021

AbstractNK cells are innate lymphoid cells that play important roles in tumor eradication and viral clearance. We previously found that deletion or inhibition of the histone methyltransferase Ezh2 (enhancer of zeste homolog 2) in hematopoietic stem and progenitor cells (HSPCs) from both mice and humans enhanced the commitment and cytotoxicity of NK cells to tumor cells. This study tested the hypothesis that inhibiting Ezh2, especially in NK lineage cells, could also affect NK cell development and function. We crossed Ezh2fl/fl mice with Ncr1iCre mice to delete the Ezh2 gene in immature NK cells and downstream progeny. Ezh2 deficiency increased the total number of NK cells and promoted NK cell terminal differentiation, as the percentages of the most mature CD27–CD11b+ subsets were increased. The NK cell cytotoxicity against tumor cells in vitro was enhanced, with increased degranulation and IFN-γ production. In addition, during the process of human NK cells differentiating from HSPCs, inhibiting EZH2 catalytic activity at day 14 (when NK lineage commitment began) also resulted in increased proportions of mature NK cells and cytotoxicity. Furthermore, RNA-seq and CUT&RUN-qPCR assays showed that the effects of Ezh2 may be based on its direct modulation of the expression of the transcription factor Pbx1 (pre-B-cell leukemia transcription factor 1), which has been reported to promote NK cell development. In summary, we demonstrate that Ezh2 is a negative regulator of NK cell terminal maturation and function.
Using UAV image data to monitor the effects of different nitrogen application rates on tea quality
Authors: Luo Danni , Gao Yuan , Wang Yu , Shi Yujie , Chen Sizhou , Ding Zhaotang , Fan Kai 
Published: August 2021

AbstractBACKGROUNDAccurate and efficient evaluation of the effect of nitrogen application rate on tea quality is of great significance for nitrogen management in a tea garden. However, previous methods were all through soil or leaf sampling, using biochemical methods for laboratory testing. These methods are not only less one‐time detection samples, but also time‐consuming, laborious and inefficient. Therefore, the development of fast, efficient and non‐destructive diagnostic methods is an important goal in this field.RESULTSWe obtained spectral information on the tea canopy using a multispectral camera carried by an unmanned aerial vehicle (UAV), and extracted the average DN value of the experimental plot by environmental visual imagery (ENVI); we finally obtained 28 spectral parameters. By analyzing the correlation between spectral parameters and ground parameters measured synchronously, five spectral parameters with high correlation were selected. Finally, the prediction models of tea nitrogen, polyphenol and amino acid content were established by using support vector machine (SVM), partial least squares and backpropagation neural network. Through modeling comparison and coefficient verification, the results show that the ground parameters measured in the laboratory were in good agreement with the results estimated by the model. The SVM model had the best performance in predicting nitrogen and tea polyphenol content, with R2 = 0.7583 and 0.7533, root mean square error of prediction (RMSEP) = 0.4086 and 0.3392, and normalized RMSEP (NRMSEP) = 1.23 and 1.28, respectively. The partial least squares regression model had the best performance in predicting amino acid content, with R2 = 0.7597, RMSEP = 0.1176 and NRMSEP = 4.10.CONCLUSIONThe results show that the model based on UAV image data and machine learning algorithm can effectively detect the main biochemical components of the tea plant, which provides an important basis for tea garden management. © 2021 Society of Chemical Industry.
Total elbow arthroplasty for tumors of the distal humerus and elbow
Authors: Kruckeberg Bradley M. , Lee Dustin R. , Barlow Jonathan D. , Morrey Mark E. , Rose Peter S. , Sanchez‐Sotelo Joaquin , Houdek Matthew T. 
Published: August 2021

AbstractIntroductionThe elbow is a rare location for primary and metastatic tumors in the upper extremity. The goal of reconstruction is to provide painless motion and stability for hand function. Total elbow arthroplasty (TEA) is commonly utilized, with either off‐the‐self components, modular segmental endoprosthesis, or allograft‐prosthesis composites (APC). The purpose of this study was to analyze and compare commonly utilized elbow reconstructions and report outcomes of (1) patient function and (2) implant survival and complications.MethodsWe reviewed 33 patients (18 females and 15 males) undergoing elbow arthroplasty for reconstruction of an underlying oncologic process including linked TEA (n = 22, 67%), APC (n = 9, 27%), and endoprosthesis (n = 2, 6%). The most common indication was metastatic disease (n = 17, 52%), with 24 patients (73%) presenting with a pathologic fracture.ResultsFive‐year implant survival was following elbow reconstruction was 88%. The mean most recent Mayo Elbow Performance Score and Musculoskeletal Tumor Society Score were 84 ± 18 and 78 ± 15%. Postoperative complications occurred in 15 elbows (45%), most commonly periprosthetic fracture (n = 5, 15%), leading to reoperation in six elbows (18%).ConclusionAlthough elbow arthroplasty is associated with a high incidence of complications, it provides a stable platform for upper extremity function in patients with oncologic processes of the elbow.
Impact of Aortic Diameter Measurements at Three Anatomical Landmarks on Left Ventricular Output Calculation in Neonates
Authors: Ficial Benjamim , Bonafiglia Elena , Gangemi Antonella , Clemente Maria , Cappelleri Alessia , Corsini Iuri , Biban Paolo 
Published: August 2021

ObjectivesTo assess reproducibility and accuracy of left ventricular output (LVO) quantifications in neonates, when left ventricular outflow tract diameter (LVOTD) was measured at the hinges of the aortic valve (AV), at the aortic sinus (AS), and at the sinotubular junction (STJ).MethodsThis was an observational study. In the first cohort of very preterm neonates, we assessed intraobserver and interobserver repeatability of LVOTD measured at the AV, AS, and STJ and of the corresponding LVO. In the second cohort of older neonates, we compared paired LVO measurements by echo and magnetic resonance imaging (MRI).ResultsIn the first cohort of 48 neonates, mean (standard deviation) weight and age at scan were 1046 (302) g and 28.1 (2.7) weeks. Interobserver bias (95% limits of agreement [LOA]) for LVOTD at the AV, AS, and STJ was 0 (−0.3 to 0.3) mm, 0 (−0.7 to 0.7) mm, and 0 (−0.8 to 0.7) mm, respectively. Interobserver bias (95% LOA) for the corresponding LVO was −1.3 (−31 to 33) ml/kg/min, −0.5 (−88 to 87) ml/kg/min, and −7.2 (−83 to 69) ml/kg/min, respectively. In the second cohort of 10 neonates, median (range) weight and age at scan were 1942 (970–3640) g and 37.2 (31.7–39.8) weeks. LVO measured at the AV showed stronger agreement with MRI: bias (LOA) −10.6 (−74 to 52) ml/kg/min, compared to LVO measured at AS and STJ: 194 (−0.5 to 388) ml/kg/min and 43 (−72 to 159) ml/kg/min respectively.ConclusionsReproducibility and accuracy of LVO quantification by echo were better when aortic diameter was measured at AV.
Contrast‐Enhanced Ultrasound for Differentiation Between Poorly Differentiated Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma
Authors: Guo Huan‐Ling , Zheng Xin , Cheng Mei‐Qing , Zeng Dan , Huang Hui , Xie Xiao‐Yan , Lu Ming‐De , Kuang Ming , Wang Wei , Xian Meng‐Fei , Chen Li‐Da 
Published: August 2021

PurposeTo evaluate the diagnostic performance of LR‐5 for diagnosing poorly differentiated hepatocellular carcinoma (p‐HCC). To build a contrast‐enhanced ultrasound (CEUS) signature for improving the differential diagnostic performance between p‐HCC and intrahepatic cholangiocarcinoma (ICC).MethodsThe B‐mode ultrasound (BUS) and CEUS features of 60 p‐HCCs and 56 ICCs were retrospectively analyzed. The CEUS LI‐RADS category was assigned according to CEUS LI‐RADS v2017. A diagnostic CEUS signature was built based on the independent significant features. An ultrasound (US) signature combining both BUS and CEUS features was also built. The diagnostic performances of the CEUS signature, US signature, and LR‐5 were evaluated by receiver operating characteristic (ROC) analysis.ResultsOne (1.7%) p‐HCC and 26 (46.4%) ICC patients presented cholangiectasis or cholangiolithiasis (P < .001). Fifty‐four (90.0%) p‐HCCs and 8 (14.3%) ICCs showed clear boundaries in the artery phase (P < .001). The washout times of p‐HCCs and ICCs were 81.0 ± 42.5 s and 34.7 ± 8.6 s, respectively (P < .001). The AUC, sensitivity, and specificity of the CEUS signature, US signature, and LR‐5 were 0.955, 91.67%, and 90.57% versus 0.976, 96.67%, and 92.45% versus 0.758, 51.67%, and 100%, respectively. The AUC and sensitivity of CEUS LI‐RADS were much lower than those of the CEUS and US signatures (P < .001).ConclusionLR‐5 had high specificity but low sensitivity in diagnosing p‐HCC. When the washout time and tumor boundary were included in the CEUS signature, the sensitivity and AUC were remarkably increased in the differentiation between p‐HCC and ICC.
Monte Carlo Assisted FTIR Spectroscopy: A Python Tool for the Determination of the Constituents in Blended Biopolymer Samples
Authors: Souza Fernando Gomes , Cunha Cláudia Duarte , Pereira Emiliane Daher , Dias Diogo Simas Bernardes , Pal Kaushik , Pereira Michelle Colão , Silva Rebecca Alves 
Published: August 2021

AbstractA massive number of biopolymers and complex polymer blends are being developed every day. One of the significant challenges related to these materials is their characterization. In more specific terms, estimating the composition of polymeric mixtures is a substantial challenge for the technologist, who must understand the nuances of the material's composition for its proper application. Thus, this work develops a Python code capable of handling the composition analysis using the FTIR spectra of some biopolymers found in the literature. Through the Monte Carlo method associated with the Mixture Rule, these spectra are combined, generating a myriad of signals referring to different compositions. These signals are compared with the one from the FTIR spectrum of the mixture, allowing the root‐mean‐square error (RMSE) calculation. Finally, minimizing the RMSE value leads to the final composition of the material, which is presented and saved in a text file.
Geopolymer Microparticles as Up‐and‐Coming H2S Sorbers
Authors: Maranhão Fabíola da Silveira , Almeida Thuanny Moraes , de Souza Fernando Gomes , Batista Daniela , de Carvalho Felipe Ferreira , Pal Kaushik , Thomas Sabu 
Published: August 2021

AbstractGeopolymers are materials composed of aluminosilicates, which upon activation by the alkaline solution, form repeating units, being classified as inorganic polymers. Geopolymers have gained prominence due to obtaining the raw material that can be natural or residual, the ease of production, and cost‐effective. These materials are widely used in civil construction for the replacement of Portland cement and also in the environmental area for the remediation of toxic compounds. However, there is a lack in the literature about the applications of these materials. Thus, this work deals with geopolymers for the sorption of hydrogen sulfide gas.
Magnetic Porous Geopolymer: A Cheaper and Efficient Environmental Tool for Heavy Metal Sorption
Authors: Maranhão Fabíola Silveira , de Souza Junior Fernando Gomes , Filho Sérgio Thode , de Oliveira Athayde Bryan Henrique , de Carvalho Felipe Ferreira , Lino Adam , Malm Olaf 
Published: August 2021

AbstractSorption of heavy metals is a crucial controller method for environmental pollution. Contamination by heavy metals is one of the most critical and discussed environmental issue nowadays that fits in the context of bioaccumulation. Some examples of these contaminants are lead, cadmium, and chromium. In order to avoid increasing the level of these contaminants in rainwater networks, the Brazilian Council of Environment (CONAMA) establishes that it is essential that industries that produce effluents remove these metals before returning the effluent to the network. Given this, a new technology is proposed for the removal of heavy metals. A magnetic nanocomposite is produced by adding maghemite nanoparticles to an aluminosilicate matrix and subjected to heavy metal‐contaminated effluents to sorption through this material. In order to evaluate the efficiency of removal of heavy metals by aluminosilicates, the effluents are analyzed by atomic absorption spectroscopy, and the results show a 99% sorption capacity. Finally, lethality tests, using Artemia salina, are performed. The best results are from the chromium and lead absorption tests.