bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of 52 240 source plasma donors of convalescent COVID‐19 plasma: Sex, ethnicity, and age association with initial antibody levels and rate of dissipation
Authors: Schmidt Amy E. , Vogel Peter , Chastain Carrie A. , Barnes Thomas , Roth Nathan J. , Simon Toby L. 
Published: July 2022

AbstractBackgroundCOVID‐19 convalescent plasma (CCP) was approved under emergency authorization to treat critically ill patients with COVID‐19 in the United States in 2020. We explored the demographics of donors contributing plasma for a hyperimmune, plasma‐derived therapy to evaluate factors that may be associated with anti‐SARS‐CoV‐2 antibody response variability and, subsequently, antibody titers.Study DesignAn electronic search of CCP donors was performed across 282 US plasma donation centers. Donations were screened for nucleocapsid protein‐binding‐IgG using the Abbott SARS‐CoV‐2 IgG assay.ResultsOverall, 52 240 donors donated 418 046 units of CCP. Donors were of various ethnicities: 43% Caucasian, 34% Hispanic, 17% African American, 2% Native American, 1% Asian, and 3% other. Females had higher initial mean anti‐SARS‐CoV‐2 antibody titers but an overall faster rate of decline (P < .0001). Initial antibody titers increased with age: individuals aged 55 to 66 years had elevated anti‐SARS‐CoV‐2 titers for longer periods compared with other ages (P = .0004). African American donors had the lowest initial antibody titers but a slower rate of decline (P < .0001), while Caucasian (P = .0088) and Hispanic (P = .0193) groups had the fastest rates of decline. Most donor antibody levels decreased below the inclusion criteria (≥1.50) within 30 to 100 days of first donation, but donation frequency did not appear to be associated with rate of decline.ConclusionSeveral factors may be associated with anti‐SARS‐CoV‐2 antibody response including donor age and sex. Evaluating these factors during development of future hyperimmune globulin products may help generation of therapies with optimal efficacy.
Model application to quantitatively evaluate placental features from ultrasound images with gestational diabetes
Authors: Sun Hongshuang , Jiao Jing , Ren Yunyun , Guo Yi , Wang Yuanyuan 
Published: July 2022

AbstractPurposeThe goal of this study was to introduce PFCnet (placental features classification network), an multimodel model for evaluating and classifying placental features in gestational diabetes mellitus (GDM) and normal late pregnancy. Deep learning algorithms could be utilized to fully automate the examination of alterations in the placenta caused by hyperglycemia.MethodsA total of 718 placental ultrasound images, including 139 cases of GDM, were collected, including gray‐scale images (GSIs) and microflow images (MFIs). Ultrasonic assessment parameters and perinatal features were recorded. We divided gestational age into two categories for analysis (37 weeks and 37 weeks) based on the cut‐off value level of placental maturity. The PFCnet model was introduced for identifying placental characteristics from normal and GDM pregnancies after extensive training and optimization. The model was scored using metrics such as sensitivity, specificity, accuracy, and the area under the curve (AUC).ResultsIn view of multimodal fusion (GSIs and MFIs) and deep network optimization training, the overall diagnostic performance of the PFCnet model depending on the region of interest (ROI) was excellent (AUC: 93%), with a sensitivity of 89%, a specificity of 92%, and an accuracy of 92% in the independent test set. The fusion features of GSIs and MFIs in the placenta showed a higher discriminative power than single‐mode features (accuracy: Fusion 92% vs. GSIs 84% vs. MFIs 82%). The independent test set at 37 weeks exhibited a better specificity (75% vs. 69%) but a lower sensitivity(95% vs. 100%).ConclusionsWith its dual channel identification of placental parenchymal and vascular lesions in obstetric complications, the PFCnet classification model has the potential to be a useful tool for detecting placental tissue abnormalities caused by hyperglycemia.
Plantation vegetation restoration enhances the relationship between rhizosphere microbial diversity and soil multifunctionality
Authors: Li Jiajia , Yang Le , Fan Miaochun , Shangguan Zhouping 
Published: July 2022

AbstractMany studies have demonstrated the importance of rhizosphere microorganisms to soil nutrient cycling and plant stress resistance. However, we still lack a comprehensive understanding of the potential relationship between rhizosphere microbial diversity and soil multifunctionality during forest vegetation restoration. In this study, the diversity, assembly processes, and co‐occurrence patterns of rhizosphere microbial communities were explored along a 45‐year chronosequence of Robinia pseudoacacia plantations in China's Loess Plateau region. Soil multifunction index was calculated based on 11 measured available nutrient properties. The results showed that both α‐ and β‐diversities of rhizosphere microbial communities were significantly correlated with the soil multifunction index. Stochastic processes dominated rhizosphere microbial community assembly. Correlations between the abundances of sensitive operational taxonomic units for different forest ages (i.e. 15, 25, 35, and 45 years old) and soil multifunctional index varied in distinct network modules, being positive in module 1 and negative in module 2. There was a positive correlation between microbial network complexity (e.g. degree) and soil multifunctional index. The contribution of rhizosphere core microbiota to soil multifunctional index was higher than that of non‐core microbiota. To sum up, plantation vegetation restoration enhances the relationship between microbial diversity and soil multifunctionality, while affecting microbial co‐occurrence patterns in the rhizosphere.
3D magnetic resonance fingerprinting for rapid simultaneous T1, T2, and T1ρ volumetric mapping of human articular cartilage at 3 T
Authors: Sharafi Azadeh , Zibetti Marcelo V. W. , Chang Gregory , Cloos Martijn , Regatte Ravinder R. 
Published: July 2022

Quantitative MRI can detect early biochemical changes in cartilage; however, the conventional techniques only measure one parameter (e.g., T1, T2, and T1ρ) at a time while also being comparatively slow. We implemented a 3D magnetic resonance fingerprinting (3D‐MRF) technique for simultaneous, volumetric mapping of T1, T2, and T1ρ in knee articular cartilage in under 9 min. It is evaluated on 11 healthy volunteers (mean age: 53 ± 9 years), five mild knee osteoarthritis (OA) patients (Kellgren‐Lawrence (KL) score: 2, mean age: 60 ± 4 years), and the National Institute of Standards and Technology (NIST)/International Society for Magnetic Resonance in Medicine (ISMRM) system phantom. Proton density image, and T1, T2, T1ρ relaxation times, and B1+ were estimated in the NIST/ISMRM system phantom as well as in the human knee medial and lateral femur, medial and lateral tibia, and patellar cartilage. The repeatability and reproducibility of the proposed technique were assessed in the phantom using analysis of the Bland–Altman plots. The intrasubject repeatability was assessed with the coefficient of variation (CV) and root mean square CV (rmsCV). The Mann–Whitney U test was used to assess the difference between healthy subjects and mild knee OA patients. The Bland–Altman plots in the NIST/ISMRM phantom demonstrated an average difference of 0.001% ± 015%, 1.2% ± 7.1%, and 0.47% ± 3% between two scans from the same 3‐T scanner (repeatability), and 0.002% ± 015%, 0.62% ± 10.5%, and 0.97% ± 14% between the scans acquired on two different 3‐T scanners (reproducibility) for T1, T2, and T1ρ, respectively. The in vivo knee study showed excellent repeatability with rmsCV less than 1%, 2%, and 1% for T1, T2, and T1ρ, respectively. T1ρ relaxation time in the mild knee OA patients was significantly higher (p < 0.05) than in healthy subjects. The proposed 3D‐MRF sequence is fast, reproducible, robust to B1+ inhomogeneity, and can simultaneously measure the T1, T2, T1ρ, and B1+ volumetric maps of the knee joint in a single scan within a clinically feasible scan time.
Indirect covariate balance and residual confounding: An applied comparison of propensity score matching and cardinality matching
Authors: Fortin Stephen P. , Schuemie Martijn 
Published: July 2022

AbstractPurposePropensity score matching (PSM) is subject to limitations associated with limited degrees of freedom and covariate overlap. Cardinality matching (CM), an optimization algorithm, overcomes these limitations by matching directly on the marginal distribution of covariates. This study compared the performance of PSM and CM.MethodsComparative cohort study of new users of angiotensin‐converting enzyme inhibitor (ACEI) and β‐blocker monotherapy identified from a large U.S. administrative claims database. One‐to‐one matching was conducted through PSM using nearest‐neighbor matching (caliper = 0.15) and CM permitting a maximum standardized mean difference (SMD) of 0, 0.01, 0.05, and 0.10 between comparison groups. Matching covariates included 37 patient demographic and clinical characteristics. Observed covariates included patient demographics, and all observed prior conditions, drug exposures, and procedures. Residual confounding was assessed based on the expected absolute systematic error of negative control outcome experiments. PSM and CM were compared in terms of post‐match patient retention, matching and observed covariate balance, and residual confounding within a 10%, 1%, 0.25% and 0.125% sample group.ResultsThe eligible study population included 182 235 (ACEI: 129363; β‐blocker: 56872) patients. CM achieved superior patient retention and matching covariate balance in all analyses. After PSM, 1.6% and 28.2% of matching covariates were imbalanced in the 10% and 0.125% sample groups, respectively. No significant difference in observed covariate balance was observed between matching techniques. CM permitting a maximum SMD <0.05 was associated with improved residual bias as compared to PSM.ConclusionWe recommend CM with more stringent balance criteria as an alternative to PSM when matching on a set of clinically relevant covariates.
Influence of nanocomposite preparation techniques on the multifunctional properties of carbon fabric‐reinforced polystyrene‐based composites with carbon nanotubes
Authors: Erkmen Berrak , Bayram Goknur 
Published: July 2022

AbstractPolystyrene (PS)‐based and carbon fabric‐reinforced polymers (CFRPs) containing elastomer were prepared using the compression molding technique. PS was also reinforced with two types of carbon nanotubes (CNTs), as received and modified with a silane‐coupling agent. Nanocomposites were fabricated with two different methods, masterbatch dilution, and direct melt mixing. The effect of the nanocomposite preparation technique on the mechanical properties, electrical resistivity, and shape memory behavior of the multilayer composites was investigated. The tensile strength and tensile modulus of the multilayer composite containing CNTs modified with silane coupling agent, and prepared with masterbatch dilution method, increased by 33% and 34%, respectively, compared with the composites prepared with melt mixing. With the masterbatch dilution technique, the electrical resistivity was lower, and slightly increased with surface modification of CNTs. In both preparation techniques, the possible combined effect of CNTs and CF on the thermal and electrical conductivity of the composites led to 100% shape recovery lower than 30 s with electrically actuated bending test.
Reliability analysis of systems with mixed uncertainties based on Bhattacharyya distance and Bayesian network
Authors: Jia Yilong , Mi Jinhua , Sun Dong , Bai Libing , Chen Kai 
Published: July 2022

AbstractThe lack of data due to the high cost of experiments, limited knowledge of system structure, and coupling failure mechanism make it necessary to consider uncertainties when analyzing the reliability of complex systems. This paper provides a comprehensive work for system reliability analysis under consideration of mixed uncertainties. First, Bayesian networks (BNs) are used to model the structure of complex systems. Second, mixed uncertainties are described via a probability box and accurately quantified by the Bhattacharyya distance, which can synthetically represent the mean and variance of two data sets. Then, the importance of components with mixed uncertainties is defined and analyzed. Finally, a comprehensive sensitivity index is proposed for sensitivity analysis and epistemic uncertainty tracing of data sources based on the Bhattacharyya distance. This method is used to analyze the reliability of an auxiliary power supply system of a train and verifies the feasibility and accuracy of the proposed method.
Parameterized Quantum Circuits for Learning Cooperative Quantum Teleportation
Authors: Feng Yan‐Y. , Zhou Jian , Zhang Dan‐B. , Shi Jin‐J. 
Published: July 2022

AbstractQuantum teleportation is an elemental process in quantum communication and many variants have been widely investigated theoretically and experimentally. Motivated by cooperation in classical communications, cooperative quantum teleportation (CQT) are developed with features of the cooperation referring to allocation of resources and operations among participants. Parameterized quantum circuit (PQC) are employed to learn the CQT protocols on account of different training scenarios with controls of gate parameters among shared entangled states, measurement, and recovery operations. Numerical results show the CQT protocols can be discovered with unit fidelity regardless of ideal or nonideal channels by training the PQCs with hybrid quantum‐classical (HQC) optimization under full cooperations of the participants. Further, the influence of quantum channel noise on the teleportation performance is explored by parameterizing the entangled states with noise and entanglement parameters under adjustable recovery operations. Simulation results indicate the trained PQC can improve the system performance, which implies the potential denoising capability of the HQC algorithms. The suggested CQT protocols satisfy the underlying properties of universality, randomness, locality and cooperation, and discussions indicate the designed quantum circuits can be optimized in view of quantum and circuit costs by appropriate optimization algorithms for achieving higher state fidelity in the future work.
A database of soil physical properties for the Kansas Mesonet
Authors: Parker Nathaniel , Kluitenberg Gerard J. , Redmond Christopher , Patrignani Andres 
Published: July 2022

AbstractThis study developed a comprehensive database of soil physical properties to expand the potential applications of soil moisture observations across the Kansas Mesonet environmental monitoring network. The database comprises 14 site‐ and depth‐specific soil hydraulic properties for 40 stations monitoring soil water content. The measured soil hydraulic properties include sand, clay, and silt contents; bulk density; particle density; total porosity; effective saturation; saturated hydraulic conductivity; and water retention at six matric potentials. The soil database spans eight soil textural classes, with silty clay loam, silt loam, and silty clay soils dominating the fine‐textured soils, and sandy loam and sandy clay loam soils comprising the only coarse‐textured soils. All the measured soil hydraulic properties showed low coefficient of variation (CV ≤10%) within soil textural classes, except for the saturated hydraulic conductivity, which varied from a median of 0.520 ± 308 cm d–1 in the clay soils to 47.1 ± 119 cm d–1 in the sandy loam soils. The uncertainty of the Kansas Mesonet soil water content observations was reduced by at least 29% using laboratory sensor calibration equations instead of the factory default sensor equation. Our soil physical property database offers new prospects to use historical and near real‐time in situ soil water content observations across the Kansas Mesonet for diverse agricultural and hydrological applications, including drought monitoring, wildfire preparedness, and estimating potential drainage and groundwater recharge rates.
Heavy metal removal and recovery as nanoparticles from multicomponent system at low pH condition by biogenic sulfide precipitation using anaerobic sulfate reducing biomass
Authors: Kumar Manoj , Pakshirajan Kannan 
Published: July 2022

AbstractAcid mine drainage (AMD) represents an environmental and human health hazard in many countries with historic or ongoing mining industries. Biological sulfide precipitation is an emerging technique for recovery of heavy metals from such wastewater. This study reports heavy metal removal and recovery from multicomponent system containing Cd2+, Cu2+, Pb2+, Zn2+, Ni2+, and Fe2+ at low pH condition by anaerobic sulfate‐reducing biomass. Plackett–Burman design of experiments was employed to carry out this mixture study and evaluate the combined effect of these metals on their recovery as nanoparticles. The maximum removal was observed in case of Cd2+ (100%) and Cu2+ (100%), followed by that for Pb2+ (99%), Zn2+ (98%), Ni2+ (88.2%), and Fe2+ (64%). The metals were recovered in the form of metal sulfide nanoparticles, and the metal recovery was in the order: Cu > Pb > Cd > Zn > Fe > Ni. Analysis of nano‐sized metal bioprecipitate by field emission scanning electron microscopy (FESEM) and energy‐dispersive X‐ray spectroscopy (EDX) confirmed the role of biogenic sulfide in the process. The size and shape of the metal nanoparticles were confirmed using field emission transmission electron microscopy, which revealed their excellent potential for industrial application. This study demonstrates successful recovery of metal sulfides in the form of nanoparticles using SRB from a multicomponent system at low pH condition.
Assessment of groundwater quality around the Pallavaram urban solid waste dumpsite using GIS
Authors: Kuchelar Prasanna , Ramesh Shiam Babu , Sudhir Shruti , Kaliaperumal Sugirthamani Anandh 
Published: July 2022

AbstractIn Pallavaram dumpsite, there is no true geo‐synthetic clay liner laid before the initiation of the dumping process in the location. Due to the unplanned dumping of solid waste, the plausibility of groundwater contamination was witnessed around the dumpsite. This study focused on groundwater quality before and after monsoon based on GIS. In the study area, it was observed that the maximum pH value in Pre‐Monsoon (Pre MN) season is 8.12 and in the Post‐Monsoon (Post MN) season is 7.89. Turbidity varies from 3.26 to 8.41 NTU in Pre MN and 1.54 to 3.54 in Post MN. Most of the turbidity values are within the permissible limits of 5 NTU in Pre MN 2017, Post MN 2017, and Post MN 2018. The total hardness varies from 619–987 mg/L in Pre MN and Post MN, to 212–765 mg/L. The total suspended solids in the Pre MN season range from 1.1 to 12.07 mg/L, whereas in Post MN, 1.03 to 5.98 mg/L. Typically, total dissolved solids are measured as 1063 to 2671 mg/L in Pre MN, and in Post MN, as 489 mg/L to 985 mg/L, and most of the total dissolved solid levels are above the acceptable limit (500 mg/L). Subsequently, BOD and COD are above zero in all groundwater samples. The sulfate varies from 15.3 to 28.2 mg/L during the pre‐ MN period and from 11.2 to 20.1 during the Post MN season. As per the Indian standards, all the parameters were within the acceptable range. However, chlorides were beyond the permissible limits of 250 mg/L.
Clinical utility of sFlt‐1 and PlGF in screening, prediction, diagnosis and monitoring of pre‐eclampsia and fetal growth restriction
Authors: Stepan H. , Galindo A. , Hund M. , Schlembach D. , Sillman J. , Surbek D. , Vatish M. 
Published: July 2022

ABSTRACTPre‐eclampsia (PE) is characterized by placental and maternal endothelial dysfunction, and associated with fetal growth restriction (FGR), placental abruption, preterm delivery and stillbirth. The angiogenic factors soluble fms‐like tyrosine kinase‐1 (sFlt‐1) and placental growth factor (PlGF) are altered in pregnancies complicated by placenta‐related disorders. In this Review, we summarize the existing knowledge, examining the performance of maternal PlGF, sFlt‐1 and the sFlt‐1/PlGF ratio for screening PE, predicting development of PE in the short term, diagnosing PE, monitoring established PE and predicting other placenta‐related disorders in singleton pregnancy. We also discuss the performance of PlGF and the sFlt‐1/PlGF ratio for predicting PE in twin pregnancy. For first‐trimester screening in singleton pregnancy, a more accurate way of identifying high‐risk women than current practice is to combine maternal PlGF levels with clinical risk factors and ultrasound markers. Later in pregnancy, the sFlt‐1/PlGF ratio has advantages over PlGF because it has a higher pooled sensitivity and specificity for diagnosing and monitoring PE. It has clinical value because it can rule out the development of PE in the 1–4‐week period after the test. Once a diagnosis of PE is established, repeat measurement of sFlt‐1 and PlGF can help monitor progression of the condition and may inform clinical decision‐making regarding the optimal time for delivery. The sFlt‐1/PlGF ratio is useful for predicting FGR and preterm delivery, but the association between stillbirth and the angiogenic factors is unclear. The sFlt‐1/PlGF ratio can be used to predict PE in twin pregnancy, although different sFlt‐1/PlGF ratio cut‐offs from those for singleton pregnancy should be applied for optimal performance. In summary, PlGF, sFlt‐1 and the sFlt‐1/PlGF ratio are useful for screening, diagnosing, predicting and monitoring placenta‐related disorders in singleton and twin pregnancy. We propose that tests for these angiogenic factors are integrated more fully into clinical practice.© 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
Nano‐diagnostics as an emerging platform for oral cancer detection: Current and emerging trends
Authors: Chakraborty Debolina , Ghosh Debayan , Kumar Sanjit , Jenkins David , Chandrasekaran Natarajan , Mukherjee Amitava 
Published: July 2022

AbstractGlobally, oral cancer kills an estimated 150,000 individuals per year, with 300,000 new cases being diagnosed annually. The high incidence rate of oral cancer among the South‐Asian and American populations is majorly due to overuse of tobacco, alcohol, and poor dental hygiene. Additionally, socio‐economic issues and lack of general awareness delay the primary screening of the disease. The availability of early screening techniques for oral cancer can help in carving out a niche for accurate disease prognosis and also its prevention. However, conventional diagnostic approaches and therapeutics are still far from optimal. Thus, enhancing the analytical performance of diagnostic platforms in terms of specificity and precision can help in understanding the disease progression paradigm. Fabrication of efficient nanoprobes that are sensitive, noninvasive, cost‐effective, and less labor‐intensive can reduce the global cancer burden. Recent advances in optical, electrochemical, and spectroscopy‐based nano biosensors that employ noble and superparamagnetic nanoparticles, have been proven to be extremely efficient. Further, these sensitive nanoprobes can also be employed for predicting disease relapse after chemotherapy, when the majority of the biomarker load is eliminated. Herein, we provide the readers with a brief summary of conventional and new‐age oral cancer detection techniques. A comprehensive understanding of the inherent challenges associated with conventional oral cancer detection techniques is discussed. We also elaborate on how nanoparticles have shown tremendous promise and effectiveness in radically transforming the approach toward oral cancer detection.This article is categorized under:
Diagnostic Tools > Biosensing
Diagnostic Tools > Diagnostic Nanodevices
Diagnostic Tools > In Vitro Nanoparticle‐Based Sensing