bluuebunny/crossref_metadata_2025 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

Background

COVID‐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 Design

An 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.

Results

Overall, 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.

Conclusion

Several 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

Purpose

The 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.

Methods

A 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).

Results

In 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%).

Conclusions

With 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.
Advances in glycopeptide enrichment methods for the analysis of protein glycosylation over the past decade
Authors: Li Jiajia , Zhang Jie , Xu Mingming , Yang Zeren , Yue Shuang , Zhou Wanlong , Gui Chunshan , Zhang Haiyang , Li Shuwei , Wang Perry G. , Yang Shuang
Published: July 2022

Advances in bioanalytical technology have accelerated the analysis of complex protein glycosylation, which is beneficial to understand glycosylation in drug discovery and disease diagnosis. Due to its biological uniqueness in the course of disease occurrence and development, disease‐specific glycosylation requires quantitative characterization of protein glycosylation. We provide a comprehensive review of recent advances in glycosylation analysis, including workflows for glycoprotein digestion, glycopeptide separation and enrichment, and mass spectrometry sequencing. We specifically focus on different strategies for glycopeptide enrichment through physical interaction, chemical oxidation, or metabolic labeling of intact glycopeptides. Recent advances and challenges of O‐glycosylation analysis are presented, and the development of improved enrichment methods combining different proteases to analyze O‐glycosylation is also proposed.
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

Many 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.
Analysis of the escape in systems with four exit channels
Authors: Navarro Juan F. , Belgharbi Ibrahim , Martínez‐Belda María del Carmen
Published: July 2022

In this paper, we have performed a numerical investigation of the escape of a particle from two different dynamical systems with the same number of exit channels. We have chosen specific values of the parameters of the systems so that the openings of the potential well in both systems are approximately of the same size. We have found that, in the galactic system, the distribution of the times of escape follows a sequential pattern that has never been detected before. Moreover, we have proved that this pattern is directly related to the geometry of the stable manifolds to the Lyapunov orbits located at the openings of the potential. Finally, we have shown that the different nature of the two systems affects the way the escape occurs, due to the difference in the geometry of the manifolds to the Lyapunov orbits in both systems.
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

Purpose

Propensity 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.

Methods

Comparative 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.

Results

The 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.

Conclusion

We 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

Polystyrene (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

The 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

Quantum 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

This 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.
Estimating cumulative spatial risk over time with low‐rank kriging multiple membership models
Authors: Boyle Joseph , Ward Mary H. , Koutros Stella , Karagas Margaret R. , Schwenn Molly , Silverman Debra , Wheeler David C.
Published: July 2022

Many health outcomes result from accumulated exposures to one or more environmental factors. Accordingly, spatial risk studies have begun to consider multiple residential locations of participants, acknowledging that participants move and thus are exposed to environmental factors in several places. However, novel methods are needed to estimate cumulative spatial risk for disease while accounting for other risk factors. To this end, we propose a Bayesian model (LRK‐MMM) that embeds a multiple membership model (MMM) into a low‐rank kriging (LRK) model in order to estimate cumulative spatial risk at the point level while allowing for multiple residential locations per subject. The LRK approach offers a more computationally efficient means to analyze spatial risk in case‐control study data at the point level compared with a Bayesian generalized additive model, and as increased precision in spatial risk estimates by analyzing point locations instead of administrative areas. Through a simulation study, we demonstrate the efficacy of the model and its improvement upon an existing multiple membership model that uses area‐level spatial random effects to estimate risk. The results show that our proposed method provides greater spatial sensitivity (improvements ranging from 0.12 to 0.54) and power (improvements ranging from 0.02 to 0.94) to detect regions of elevated risk for disease across a range of exposure scenarios. Finally, we apply our model to case‐control data from the New England bladder cancer study to estimate cumulative spatial risk while adjusting for many covariates.
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

Acid 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

In 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

Pre‐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

Globally, 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
In situ single crystal growth of tris(trimethylsilyl)phosphate, (Me3SiO)3PO
Authors: Nickelsen Jasper , van Gerven David , Wickleder Mathias S.
Published: July 2022

(Me3SiO)3PO has been prepared by the reaction of Na[H2PO4] and (Me3Si)Cl. Crystallization of the liquid was successful using an Optical Heating and Crystallization Device (OHCD) and allow the determination of the crystal structure of the compound (monoclinic, P21, Z=4), a=1016.67(6) pm, b=1053.70(6) pm, c=1759.2(1) pm, β=91.916(2)°, wR2=0.0979). The molecular structure shows the phosphorous atom tetrahedrally coordinated by three (Me3SiO) groups and one oxygen atom. The compound can be seen as a missing link between the related cation and anion [(Me3SiO)4P]+ and [(Me3SiO)2PO2]−, respectively.
How Canada’s Data Ecosystem Offers Insights on the Options for Studying Migration in an Unprecedented Era of Information
Authors: Ramos Howard , Haan Michael
Published: July 2022

In an era in which unprecedented data on migration are collected, accessing and using it, as well as understanding the kinds of questions that can be engaged with it, often are under-examined. In this chapter, using the Canadian case, we assess how census and survey data collected by national statistics agencies, administrative data, and other data sources can be used in an unprecedented era of migration and data gathering. The chapter explores issues of data access, consistency of units of analysis and concepts, technical skill deficits, and what is missed in existing data sources. Finally, we assess the need for creating data spines and common protocols. Overall, we offer insights from our navigation of the Canadian data ecosystem and a practical assessment of what can be done with different types of data regarding the researching of migration and immigrant settlement.
Emerging Technologies
Authors: Küfeoğlu Sinan
Published: July 2022

This chapter presents brief descriptions and working principles of 34 emerging technologies which have market diffusion and are commercially available. Emerging technologies are the ones whose development and application areas are still expanding fast, and their technical and value potential is still largely unrealised. In alphabetical order, the emerging technologies that we list in this chapter are 3D printing, 5G, advanced materials, artificial intelligence, autonomous things, big data, biometrics, bioplastics, biotech and biomanufacturing, blockchain, carbon capture and storage, cellular agriculture, cloud computing, crowdfunding, cybersecurity, datahubs, digital twins, distributed computing, drones, edge computing, energy storage, flexible electronics and wearables, healthcare analytics, hydrogen, Internet of Behaviours, Internet of Things, natural language processing, quantum computing, recycling, robotic process automation, robotics, soilless farming, spatial computing and wireless power transfer.
SDG-3 Good Health and Well-Being
Authors: Küfeoğlu Sinan
Published: July 2022

There is a consensus that health is a fundamental human right. The extent of the countries seeking to improve the health conditions of their people is one of the indications of sustainable development. Poor health systems jeopardise a country’s citizens’ rights, hinder their involvement in educational programs, limit their ability to participate in economic activities and engage in meaningful work fully and ultimately raise poverty regardless of gender. This chapter presents the business models of 55 companies and use cases that employ emerging technologies and create value in SDG-3, Good Health and Well-Being. We should highlight that one use case can be related to more than one SDG and it can make use of multiple emerging technologies.
SDG-7 Affordable and Clean Energy
Authors: Küfeoğlu Sinan
Published: July 2022

Reaching affordable, clean, sustainable, modern and reliable energy is the main aim of the Sustainable Development Goal 7. Energy is placed at the centre of environmental and economic issues. Despite this significance, 20% of people living worldwide cannot access electricity in 2021. Adaptation towards SDG-7, Affordable and Clean Energy, brings in new investments and creates a significant economy around it. While private investments and government spending in developed countries concentrate on achieving efficiency and renewable energy production, developing countries focus on obtaining access to electricity and clean energy sources. This chapter presents the business models of 60 companies and use cases that employ emerging technologies and create value in SDG-7. We should highlight that one use case can be related to more than one SDG and it can make use of multiple emerging technologies.
Hebel und Handlungsfelder für die digitale Transformation in der Automobilindustrie am Beispiel der Porsche AG
Authors: Maier-Borst M. , Gassert P. , Adrianowytsch K. , Floetgen R. J. , Krcmar H.
Published: July 2022

Die Porsche AG steht seit Jahrzehnten weltweit für den Bau hochwertiger Sportwagen, die mit einem einzigartigen Fahrerlebnis zu überzeugen wissen. Dennoch stehen der gesamten Automobilbranche in diesem Jahrzehnt tief greifende Veränderungen bevor. Der Megatrend des 21. Jahrhunderts, die Digitalisierung, stellt dabei sowohl die Produkte als auch etablierte Organisationsstrukturen auf den Prüfstand. Aus diesem Anlass werden in dieser Fallstudie durch die eingehende Betrachtung des Zusammenspiels zwischen der digital-affinen Tochtergesellschaft, der Porsche Digital GmbH, und dem Mutterkonzern, der Porsche AG, branchenspezifische Erkenntnisse zur Bewältigung der digitalen Transformation in der Automobilindustrie abgeleitet.
Digitale Transformation bei ARRI – Das Beispiel der Filmdistribution in der Kinobranche
Authors: Nguyen M.-L. , Faltermeier F. , Schäffner M. , Möslein D. , Pfister D. , Floetgen R. J. , Krcmar H.
Published: July 2022

Gegenstand dieses Fallbeispiels aus dem Jahr 2017 war es, dasARRI Fusion Networkzu einer zentralen Plattform in der Filmdistribution auszubauen. DasFusion Networkdient momentan hauptsächlich zur Verwaltung von Spielstätten und Filmdistribution. ARRI Media steht als Intermediär zwischen den Filmverleihern und den Spielstätten und ist verantwortlich für die Distribution der Filme. Dem Verleiher stehen zur Übertragung der Aufträge mehrere Wege, wie die Beauftragung imARRI Fusion Network,zur Verfügung. Somit ist der Verleiher der zahlende Kunde von ARRI: Die aktuelle Distribution der Filme mit einer Festplatte über einen Lieferservice ist durch den geringen Automatisierungsgrad sehr aufwendig. Filme müssen auf Festplatten kopiert, physisch versendet, am Zielort eingespielt und die Festplatte wieder zurückversendet werden. Dadurch entsteht viel manueller Aufwand, der mit einereDelivery-Lösung deutlich reduziert werden könnte. Das Versenden der Filme übereDeliverysieht den Einsatz von Internet- oder Satellitenverbindungen vor. Allerdings isteDeliveryaufgrund eines unzureichenden Breitbandausbaus in Deutschland und zu teurer Betriebskosten noch nicht rentabel. Die neue Plattform soll Filmverleiher und Kinobetreiber näher zusammenrücken lassen und die Position von ARRI in der Filmdistribution stärken.
Der Begriff der digitalen Transformation: Ein transdisziplinärer Literaturüberblick
Authors: Riasanow T. , Soto Setzke D. , Böhm M. , Krcmar H.
Published: July 2022

Wir bezeichnen den Prozess der organisatorischen Transformation durch den Einsatz digitaler Technologien zur radikalen Umgestaltung von Organisationen als digitale Transformation. Jedoch herrschen innerhalb und zwischen der Management-, Organisations- und Information-Systems-Literatur noch erhebliche Inkonsistenzen bezüglich der Merkmale der digitalen Transformation einer Organisation. Daher haben wir eine transdisziplinäre Recherche von 175 Artikeln zu digitaler Transformation durchgeführt. Als Ergebnis haben wir zwölf Denkschulen identifiziert, die das Phänomen der digitalen Transformation diskutieren. Wir zeigen, dass die digitale Transformation auf bestehenden und neuen Denkansätzen aufbaut, wie beispielsweise digitale Innovation und Ökosysteme.
Studie zu den Auswirkungen der Corona-Pandemie in gesellschaftlicher, wirtschaftlicher und technologischer Hinsicht
Authors: Krcmar H. , Wintermann O.
Published: July 2022

In einem 10-tägigen Zeitraum Mitte April 2020 nahmen insgesamt 211 Digitalisierungs-, Technologie- und KI-Experten aus Deutschland an der Online-Befragung teil.
Digitale Geschäftsmodelle
Authors: Strunz-Happe V. , Böttcher T. , Weking J. , Krcmar H.
Published: July 2022

Die digitale Transformation hat grundlegende Auswirkungen auf die Geschäftsmodelle von Unternehmen. Das Geschäftsmodell beschreibt eine taktische Ebene zwischen der Unternehmensstrategie und den Geschäftsprozessen. Es definiert die Logik, die ein Wertversprechen für den Kunden artikuliert, sowie eine tragfähige Struktur von Einnahmen und Kosten für das Unternehmen, das diesen Wert liefert. Durch die digitale Transformation werden digitale Technologien essenziell für den Unternehmenserfolg und erzeugen digitale Geschäftsmodelle, in denen digitale Technologie in der Infrastuktur, der Kundenschnittstelle und dem Wertversprechen eingesetzt wird. Um die Transformation der Geschäftsmodelle zu verstehen, definieren wir in diesem Beitrag zunächst das Geschäftsmodell und seine Elemente. Wir beschreiben danach Klassifizierungen und Erfolgsfaktoren in der Geschäftsmodellentwicklung. Daran anschließend erläutern wir, wie digitale Technologien in digitalen Geschäftsmodellen genutzt werden. Wir betrachten dann sowohl die Auswirkung der digitalen Technologien auf Geschäftsmodelle als auch die Auswirkungen digitaler Geschäftsmodelle zum Beispiel auf die Unternehmensleistung oder Industrielogiken. Abschließend erläutern wir noch die Rolle von Geschäftsmodellinnovationen, die nötig sind, um Geschäftsmodelle, zum Beispiel durch die Integration digitaler Technologien, zu verändern.
Digitale Transformation an der Charité Berlin – ERIC: Enhanced Recovery after Intensive Care
Authors: Engel P. , Hong S. L. , Mielenz M. , Vrettos K. , Floetgen R. J. , Krcmar H.
Published: July 2022

Das deutsche Gesundheitssystem steht vor dem wachsenden Problem des Post-Intensive-Care-Syndroms. Dieses Krankheitsbild führt zu signifikanten medizinischen Beeinträchtigungen bei Patienten der Intensivmedizin und dadurch zu enormen wirtschaftlichen Belastungen des deutschen Gesundheitssystems, begründet durch die Kosten der Behandlung des Post-Intensive-Care-Syndroms. Als Reaktion auf dieses Problem hat die Charité Berlin die ERIC-Initiative (Enhanced Recovery after Intensive Care) ins Leben gerufen, um über Tele-Visiten zwischen Ärzt*innen der Charité Berlin und der Belegschaft in Partnerkrankenhäusern aktuelle medizinische Evidenz an das Krankenbett zu bringen. Dadurch konnte nachweislich die Fallzahl des Post-Intensive-Care-Syndroms reduziert werden. Damit ist die ERIC-Initiative ein Vorstoß der Digitalen Transformation im deutschen Gesundheitssystem. Diese Digitalisierungsinitiative begegnet verschiedenen Herausforderungen, welche in dieser Arbeit adressiert werden. Dafür wurden 18 Interviews mit verschiedenen Akteuren der ERIC-Initiative geführt und ausgewertet sowie die Herausforderungen identifiziert. Dabei konnten gerade softwarespezifische Probleme herausgestellt werden, die sich in der heterogenen Infrastruktur der Krankenhäuser und der fehlenden technischen Standardisierung begründen. Neben diesen führen auch fehlende IT-Kompetenzen und das Übersehen wichtiger Stakeholder-Perspektiven zu Beeinträchtigungen der Nutzung des ERIC-Systems. Im Rahmen dieses Beitrags werden zunächst die Herausforderungen dargestellt, um anschließend auf die abgeleiteten Lösungen in Form von „Lessons Learned“ einzugehen, die abschließend wiederum in ein holistisches Bild eingebettet werden, um deren konkrete Implementierung zu intensivieren.
NNLO event generation for $$ pp\to Zh\to {\mathrm{\ell}}^{+}{\mathrm{\ell}}^{-}b\overline{b} $$ production in the SM effective field theory
Authors: Haisch Ulrich , Scott Darren J. , Wiesemann Marius , Zanderighi Giulia , Zanoli Silvia
Published: July 2022

Abstract

We consider associated Zh production with Z → ℓ+ℓ− and $$ h\to b\overline{b} $$
h
→
b

b
¯

decays in hadronic collisions. In the framework of the Standard Model effective field theory (SMEFT) we calculate the QCD corrections to this process and achieve next-to-next-to-leading order plus parton shower (NNLO+PS) accuracy using the MiNNLOPS method. This precision is obtained for a subset of six SMEFT operators, including the corrections from effective Yukawa- and chromomagnetic dipole-type interactions. Missing higher-order QCD effects associated with the considered dimension-six operators are estimated to have a relative numerical impact of less than a percent on the total rate once existing experimental limits on the relevant Wilson coefficients are taken into account. We provide a dedicated Monte Carlo (MC) code that evaluates the NNLO SMEFT corrections on-the-fly in the event generation. This MC generator is used to study the numerical impact of NNLO+PS corrections on the kinematic distributions in $$ pp\to Zh\to {\mathrm{\ell}}^{+}{\mathrm{\ell}}^{-}b\overline{b} $$
pp
→
Zh
→

ℓ
+

ℓ
−

b

b
¯

production employing simple SMEFT benchmark scenarios. We identify the invariant mass $$ {m}_{b\overline{b}} $$

m

b

b
¯

of the two b-tagged jets as well as the three-invariant jet mass $$ {m}_{b\overline{b}j} $$

m

b

b
¯

j

as particularly interesting observables to study SMEFT effects. These distributions receive contributions that change both their normalisation and shape with the latter modifications depending on the exact jet definition. To our knowledge SMEFT effects of this type have so far not been discussed in the literature. The presented MC generator can also serve as a starting point to obtain NNLO+PS accuracy for a suitable enlarged set of effective operators in the future.
Evaluation of validity and reliability of video analysis and live observations to assess trauma team performance
Authors: van Maarseveen Oscar E. C. , Ham Wietske H. W. , van Cruchten Stijn , Duhoky Rauand , Leenen Luke P. H.
Published: July 2022

Introduction

A trauma resuscitation is dynamic and complex process in which failures could lead to serious adverse events. In several trauma centers, evaluation of trauma resuscitation is part of a hospital’s quality assessment program. While video analysis is commonly used, some hospitals use live observations, mainly due to ethical and medicolegal concerns. The aim of this study was to compare the validity and reliability of video analysis and live observations to evaluate trauma resuscitations.

Methods

In this prospective observational study, validity was assessed by comparing the observed adherence to 28 advanced trauma life support (ATLS) guideline related tasks by video analysis to life observations. Interobserver reliability was assessed by calculating the intra class coefficient of observed ATLS related tasks by live observations and video analysis.

Results

Eleven simulated and thirteen real-life resuscitations were assessed. Overall, the percentage of observed ATLS related tasks performed during simulated resuscitations was 10.4% (P < 0.001) higher when the same resuscitations were analysed using video compared to live observations. During real-life resuscitations, 8.7% (p < 0.001) more ATLS related tasks were observed using video review compared to live observations. In absolute terms, a mean of 2.9 (during simulated resuscitations) respectively 2.5 (during actual resuscitations) ATLS-related tasks per resuscitation were not identified using live observers, that were observed through video analysis. The interobserver variability for observed ATLS related tasks was significantly higher using video analysis compared to live observations for both simulated (video analysis: ICC 0.97; 95% CI 0.97–0.98 vs. live observation: ICC 0.69; 95% CI 0.57–0.78) and real-life witnessed resuscitations (video analyse 0.99; 95% CI 0.99–1.00 vs live observers 0.86; 95% CI 0.83–0.89).

Conclusion

Video analysis of trauma resuscitations may be more valid and reliable compared to evaluation by live observers. These outcomes may guide the debate to justify video review instead of live observations.
Glycosaminoglycan signatures in body fluids of mucopolysaccharidosis type II mouse model under long-term enzyme replacement therapy
Authors: Maccari Francesca , Rigon Laura , Mantovani Veronica , Galeotti Fabio , Salvalaio Marika , D’Avanzo Francesca , Zanetti Alessandra , Capitani Federica , Gabrielli Orazio , Tomanin Rosella , Volpi Nicola
Published: July 2022

Mucopolysaccharidosis type II (MPS II) is a neurometabolic disorder, due to the deficit of the lysosomal hydrolase iduronate 2-sulfatase (IDS). This leads to a severe clinical condition caused by a multi-organ accumulation of the glycosaminoglycans (GAGs/GAG) heparan- and dermatan-sulfate, whose elevated levels can be detected in body fluids. Since 2006, enzyme replacement therapy (ERT) has been clinically applied, showing efficacy in some peripheral districts. In addition to clinical monitoring, GAG dosage has been commonly used to evaluate ERT efficacy. However, a strict long-term monitoring of GAG content and composition in body fluids has been rarely performed. Here, we report the characterization of plasma and urine GAGs in Ids knock-out (Ids-ko) compared to wild-type (WT) mice, and their changes along a 24-week follow-up, with and without ERT. The concentration of heparan-sulfate (HS), chondroitin-sulfate (CS), and dermatan-sulfate (DS), and of the non-sulfated hyaluronic acid (HA), together with their differentially sulfated species, was quantified by capillary electrophoresis with laser-induced fluorescence. In untreated Ids-ko mice, HS and CS + DS were noticeably increased at all time points, while during ERT follow-up, a substantial decrease was evidenced for HS and, to a minor extent, for CS + DS. Moreover, several structural parameters were altered in untreated ko mice and reduced after ERT, however without reaching physiological values. Among these, disaccharide B and HS 2s disaccharide showed to be the most interesting candidates as biomarkers for MPS II. GAG chemical signature here defined provides potential biomarkers useful for an early diagnosis of MPS II, a more accurate follow-up of ERT, and efficacy evaluations of newly proposed therapies.

Key messages

Plasmatic and urinary GAGs are useful markers for MPS II early diagnosis and prognosis.

CE-LIF allows GAG structural analysis and the quantification of 17 different disaccharides.

Most GAG species increase and many structural features are altered in MPS II mouse model.

GAG alterations tend to restore to wild-type levels following ERT administration.

CS+DS/HS ratio, % 2,4dis CS+DS, and % HS 2s are potential markers for MPS II pathology and ERT efficacy.
Machine learning tools in production engineering
Authors: Rom Michael , Brockmann Matthias , Herty Michael , Iacomini Elisa
Published: July 2022

Machine learning methods have shown potential for the optimization of production processes. Due to the complex relationships often inherent in those processes, the success of such methods is often uncertain and unreliable. Therefore, understanding the (algorithmic) behavior and results of machine learning methods is crucial to improve the prediction of production processes. Here, mathematical tools may help. This paper shows how efficient algorithms for the training of neural networks and their retraining in the framework of transfer learning are expressed in a discrete as well as a time-continuous formulation. The latter can be analyzed and investigated using mathematical techniques from kinetic gas dynamics. The results obtained provide a first step towards explainable artificial intelligence. Based on the mathematical description, an adapted ensemble method for retraining of neural networks is proposed and compared with backpropagation algorithms. The process of training and retraining is a common task and therefore demonstrated for two very different production processes. The first one involves the prediction of specific cutting forces and the second one the prediction of particle properties in a plasma spraying coating process. For both use cases, the presented algorithms are applied and their performance is evaluated giving thereby an indication how mathematically inspired methods improve classical tasks in production processes.