bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of Thermal Conditions of DED With FEA Metal Additive Simulation
Authors: Heinrich Lauren , Feldhausen Thomas , Saleeby Kyle S. , Saldana Christopher , Kurfess Thomas R.
Published: August 2021

This paper presents the integration of wire-arc additive manufacturing (WAAM) using Gas Metal Arc Welding (GMAW) into a machine tool to create a retrofit hybrid computer numeric control (CNC) machine tool. GMAW, along with other direct energy deposition systems, has the capacity to deposit material faster than the excess thermal energy can dissipate. This results in the need to allow the part to cool between consecutive layers, which is the most time-consuming part of the additive process. Finite element analysis (FEA) was used in conjunction with monitored build plate surface temperatures during deposition samples to improve adequate dwell time prediction and to develop a cooling system. A deposition was completed where no dwell time was used and the build plate along with the machine table temperatures were monitored. A second deposition was completed where only one bead was deposited and the traverse speed was increased. The GMAW welder was mounted on a 3-axis CNC machine where two square deposition samples were completed. A FEA model was designed and verified using the monitored samples. The model will be used to determine improved depositions speeds and whether forced cooling would allow for an increased deposition rate without structural failure. It was determined the FEA software can be used to accurately model and predict the thermal response of WAAM AM components.
Functional Fiber Junctions for Circuit Routing in E-Textiles: Deterministic Alignment of MEMS Layout With Fabric Structure
Authors: Challa Sushmita , Islam M. Shafquatul , Wei Danming , Beharic Jasmin , Popa Dan O. , Harnett C. K.
Published: August 2021

Fabrics and fibrous materials offer a soft, porous, and flexible substrate for microelectromechanical systems (MEMS) packaging in breathable, wearable formats that allow airflow. Device-on-fiber systems require developments in the field of E-Textiles including smart fibers, functional fiber intersections, textile circuit routing, and alignment methods that adapt to irregular materials. In this paper, we demonstrate a MEMS-on-fabric layout workflow that obtains fiber intersection locations from high-resolution fabric images. We implement an image processing algorithm to drive the MEMS layout software, creating an individualized MEMS “gripper” layout designed to grasp fibers on a specific fabric substrate during a wafer-to-fabric parallel transfer step. The efficiency of the algorithm in terms of a number of intersections identified on the complete image is analyzed. The specifications of the MEMS layout design such as the length of the MEMS gripper, spatial distribution, and orientation are derivable from the MATLAB routine implemented on the image. Furthermore, the alignment procedure, tolerance, and hardware setup for the alignment method of the framed sample fabric to the wafer processed using the custom gripper layout are discussed along with the challenges of the release of MEMS devices from the Si substrate to the fabric substrate.
Auto-Calibration for Vision-Based 6-D Sensing System to Support Monitoring and Health Management for Industrial Robots
Authors: Qiao Guixiu , Li Guangkun
Published: August 2021

Industrial robots play important roles in manufacturing automation for smart manufacturing. Some high-precision applications, for example, robot drilling, robot machining, robot high-precision assembly, and robot inspection, require higher robot accuracy compared with traditional part handling operations. The monitoring and assessment of robot accuracy degradation become critical for these applications. A novel vision-based sensing system for 6-D measurement (six-dimensional x, y, z, yaw, pitch, and roll) is developed at the National Institute of Standards and Technology (NIST) to measure the dynamic high accuracy movement of a robot arm. The measured 6-D information is used for robot accuracy degradation assessment and improvement. This paper presents an automatic calibration method for a vision-based 6-D sensing system. The stereo calibration is separated from the distortion calibration to speed up the on-site adjustment. Optimization algorithms are developed to achieve high calibration accuracy. The vision-based 6-D sensing system is used on a Universal Robots (UR5) to demonstrate the feasibility of using the system to assess the robot’s accuracy degradation.
Microscale 3D Printed Patterns for Nanoscale Particle Assembly
Authors: Jambhulkar Sayli , Xu Weiheng , Zhu Yuxiang , Ravichandran Dharneedar , Song Kenan
Published: August 2021

Directed particle assembly has broad applications in sensors, actuators, microelectronics, robotics, and the biomedical area. Currently available methods include external fields such as electrical or magnetic fields, surface treatment on substrates, and DNA-assisted templates. However, these methods are most efficient at the nanoscale and would lose their efficiency and scalability above microscales. We reported in this research the uses of the 3D printed surface to direct the assembly of nanoparticles. We used carbon nanofibers (CNFs) as an example to show the long-range orders after dipping the 3D printed substrates in CNFs suspensions. The anchoring of CNFs at the solid-liquid-air contact lines will initiate the assembly procedure and further induced the neighboring CNFs because of the van der Waals forces. As a result, the CNFs formed well-regulated bands with controlled spacing and close-packing. These assembled CNFs were demonstrated in sensing applications. A gauge factor regulated the methanol at different concentrations and temperatures to pass the sensor, with the device resistivity change. In this way, the sensitivity as a function of analyte concentration and temperatures was obtained. This research studied nanoparticles’ microscale assembly based on a simple 3D printing surface and shed light on a new hybrid manufacturing for nanoparticle assembly.
SQL and NoSQL Databases for Cyber Physical Production Systems in Internet of Things for Manufacturing (IoTfM)
Authors: Gamero David , Dugenske Andrew , Kurfess Thomas , Saldana Christopher , Fu Katherine
Published: August 2021

In this paper, the design and performance differences between Relational Database Management Systems (RDBMS) and NoSQL Database Systems are examined, with attention to their applicability for real-world Internet of Things for manufacturing (IoTfM) data. While previous work has extensively compared SQL and NoSQL for both generalized and IoT uses, this work specifically examines the tradeoffs and performance differences for manufacturing applications by using a high-fidelity data set collected from a large US manufacturing firm. Growing an IoT system beyond the pilot stage requires scalable data storage; this work seeks to determine the impact of selected database systems on data write performance at scale. Payload size and message frequency were used as the primary characteristics to maintain model fidelity in simulated clients. As the number of simulated asset clients grow, the data write latency was calculated to determine how both database systems’ performance were affected. To isolate the RDBMS and NoSQL differences, a cloud environment was created using Amazon Web Services (AWS) with two identical data ingestion pipelines: writing data to an RDMBS (1) using AWS Aurora MySQL, and (2) using AWS DynamoDB NoSQL. The findings may provide guidance for further experimentation in large-scale manufacturing IoT implementations.
Forecasting Repair and Maintenance Services of Medical Devices Using Support Vector Machine
Authors: Liao Hao-yu , Cade Willie , Behdad Sara
Published: August 2021

Accurate prediction of product failures and the need for repair services become critical for various reasons, including understanding the warranty performance of manufacturers, defining cost-efficient repair strategies, and compliance with safety standards. The purpose of this study is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service system, including the number of repairs, the time of the next repair ticket or product failure, and the time to repair. A large dataset of over 530,000 repairs and maintenance of medical devices has been investigated by employing the Support Vector Machine (SVM) tool. SVM with four kernel functions is used to forecast the timing of the next failure or repair request in the system for two different products and two different failure types, namely random failure and physical damage. A frequency analysis is also conducted to explore the product quality level based on product failure and the time to repair it. Besides, the best probability distributions are fitted for the number of failures, the time between failures, and the time to repair. The results reveal the value of data analytics and machine learning tools in analyzing post-market product performance and the cost of repair and maintenance operations.
Impact of Process Parameters on the Tensile Properties of DLP Additively Manufactured ELAST-BLK 10 UV-Curable Elastomer
Authors: Meem Asma Ul Hosna , Rudolph Kyle , Cox Allyson , Andwan Austin , Osborn Timothy , Lowe Robert
Published: August 2021

Digital light processing (DLP) is an emerging vatphotopolymerization-based 3D-printing technology where full layers of photosensitive resin are irradiated and cured with projected ultraviolet (UV) light to create a three-dimensional part layer-by-layer. Recent breakthroughs in polymer chemistry have led to a growing number of UV-curable elastomeric photoresins developed exclusively for vat photopolymerization additive manufacturing (AM). Coupled with the practical manufacturing advantages of DLP AM (e.g., industry-leading print speeds and sub-micron-level print resolution), these novel elastomeric photoresins are compelling candidates for emerging applications requiring extreme flexibility, stretchability, conformability, and mechanically-tunable stiffness (e.g., soft robotic actuators and stretchable electronics). To advance the role of DLP AM in these novel and promising technological spaces, a fundamental understanding of the impact of DLP manufacturing process parameters on mechanical properties is requisite. This paper highlights our recent efforts to explore the process-property relationship for ELAST-BLK 10, a new commercially-available UV-curable elastomer for DLP AM. A full factorial design of experiments is used to investigate the effect of build orientation and layer thickness on the quasi-static tensile properties (i.e., small-strain elastic modulus, ultimate tensile strength, and elongation at fracture) of ELAST-BLK 10. Statistical results, based on a general linear model via ANOVA methods, indicate that specimens with a flat build orientation exhibit the highest elastic modulus, ultimate tensile strength, and elongation at fracture, likely due to a larger surface area that enhances crosslink density during the curing process. Several popular hyperelastic constitutive models (e.g., Mooney-Rivlin, Yeoh, and Gent) are calibrated to our quasi-static tensile data to facilitate component-level predictive analyses (e.g., finite-element modeling) of soft robotic actuators and other emerging soft-matter applications.
The Cooling Rate Effect on Graphene Synthesis Using Low Pressure Chemical Vapor Deposition
Authors: Lee Byoungdo , Chu Weishen , Li Wei
Published: August 2021

Low-pressure chemical vapor deposition (LPCVD) is the most efficient method to synthesize large-scale, high-quality graphene for many potential applications such as flexible electronics, solar cells, and separation membranes. The quality of LPCVD is affected by process variables including methane/hydrogen (CH4/H2) ratio, time, pressure, temperature, and cooling rate. The cooling rate has been recognized as one of the most important process variables affecting the amount of carbon source, nucleation, reaction time, and thus the quality of the LPCVD. In this research, we investigate the effect of cooling rate on the quality of graphene synthesize by changing the cooling rate and the gas feeding time. Graphene coverage is measured by Raman mapping. It is found that fast cooling rate leads to decreased carbon source reaction time, which in turn results in higher coverage by monolayer graphene. The temperature-dependent gas feeding time corresponding to different cooling rates can be used to properly supply the carbon source onto the copper surface, also leading to a higher graphene coverage.
A Deep Neural Network Model for Learning Runtime Frequency Response Function Using Sensor Measurements
Authors: Qu Yongzhi , Vogl Gregory W. , Wang Zechao
Published: August 2021

The frequency response function (FRF), defined as the ratio between the Fourier transform of the time-domain output and the Fourier transform of the time-domain input, is a common tool to analyze the relationships between inputs and outputs of a mechanical system. Learning the FRF for mechanical systems can facilitate system identification, condition-based health monitoring, and improve performance metrics, by providing an input-output model that describes the system dynamics. Existing FRF identification assumes there is a one-to-one mapping between each input frequency component and output frequency component. However, during dynamic operations, the FRF can present complex dependencies with frequency cross-correlations due to modulation effects, nonlinearities, and mechanical noise. Furthermore, existing FRFs assume linearity between input-output spectrums with varying mechanical loads, while in practice FRFs can depend on the operating conditions and show high nonlinearities. Outputs of existing neural networks are typically low-dimensional labels rather than real-time high-dimensional measurements. This paper proposes a vector regression method based on deep neural networks for the learning of runtime FRFs from measurement data under different operating conditions. More specifically, a neural network based on an encoder-decoder with a symmetric compression structure is proposed. The deep encoder-decoder network features simultaneous learning of the regression relationship between input and output embeddings, as well as a discriminative model for output spectrum classification under different operating conditions. The learning model is validated using experimental data from a high-pressure hydraulic test rig. The results show that the proposed model can learn the FRF between sensor measurements under different operating conditions with high accuracy and denoising capability. The learned FRF model provides an estimation for sensor measurements when a physical sensor is not feasible and can be used for operating condition recognition.
Solid Transfer of Large Particles by Dipping in a Heterogeneous Mixture
Authors: Khoda Bashir , Nazmul Ahsan A. M. M. , Shovon S. M. Naser
Published: August 2021

Solid transfer technology from mixtures is gaining ever-increasing attention from both materials scientists and production engineers due to their high potential in near net shape production of cost-effective engineering components. Dip coating, a wet deposition method is an effective and straightforward way of thin-film/layers formation. It is extensively used as a coating method due to its simplicity, low cost, and reasonable control over the thickness. The dipping mixture can be homogeneous, composite, hybrid, or heterogeneous. The mixtures are often embedded with inorganic fillers, nanoparticles, or clusters (d < 30 nm) that produce a thin film ranging from nm to couple microns. An increase in the volume of solid transfer by the dipping process can open-up a novel technique for the 3D near-net-shape production process via sintering, robocasting or additive manufacturing, and material joining. Adding larger inorganic particle size (> 1μm) and/or by adding higher solid fraction will increase the solid transfer but may result in a multi-phase heterogeneous mixture or slurry.

In this work, the physical mechanism of an increased volume of solid transfer with a larger particle size (> 5 μm) is investigated. The metallic particles are spherical in shape with an average diameter of 5.69 μm is considered as the coating material. Polymer-based glue and evaporating solvent are mixed to construct the liquid carrier system (LCS) for large inorganic hard particles. Moderate volume fraction (VF) of inorganic particles (20% < ϕp < 50%) are added into the LCS solution as solid loading. Cylindrical AISI 304 steel wire with dia 0.81 mm is used as the substrate for dipping and coating. The coating thickness (CT) and the surface packing coverage by the particles are measured in our lab. The results presented the influence of volume fraction of inorganic particle and glue composition on the solid transfer from the heterogeneous mixture.
Collaborative Platform for Remote Manufacturing Systems Using Industrial Internet and Digital Twin in the COVID-19 Era
Authors: Lee Jay , Jia Xiaodong , Yang Qibo , Sun Keyi , Li Xiang
Published: August 2021

In the wake of COVID-19, significant influence on the manufacturing industries has been observed in the past year due to the restrictions of in-person communications and interactions. As a consequence, manufacturing efficiency has reduced remarkably all over the world. Despite the great harm to the industrial operations under the pandemic, the opportunities for remote collaborative manufacturing system also arise. Effective and efficient remote manufacturing systems for the real industries have been highly demanded. Through the integration of industrial internet and digital twin systems, the remote manufacturing system can be largely facilitated. This paper proposes a general framework for the remote manufacturing system during the COVID-19 era. The concept of the intelligent collaborative remote manufacturing system is firstly reviewed, as well as discussions of the current pandemic situation and its influence on the industries. The current commercial platforms of the systems are also presented. A case study on the lighthouse factories at the Foxconn Technology Group is finally presented for understanding the implementation of the proposed strategy. The effectiveness of the framework has been validated in the real industrial scenarios, and great economic and operational benefits have been obtained. The proposed framework offers a promising solution for the remote manufacturing system under the current pandemic.
Human Motion Position Prediction for Human-Robot Collaboration in Manufacturing Considering Point Cloud Completion Of Human Joint
Authors: Hu Yang , Liu Zitong , Xu Feng , Liu Jiayi , Xu Wenjun , Feng Hao
Published: August 2021

The research of human-robot collaboration for intelligent manufacturing is being paid gradually increasing attention due to high flexibility and high manufacturing efficiency. Comparing with the traditional manufacturing with low flexibility, human-robot collaboration in manufacturing system provides more personalized and flexible way to cover the shortages of traditional manufacturing mode. In human-robot collaboration system, human motion position prediction in the collaborative space is an essential prerequisite for ensuring the safety of workers. In this paper, 3D sensor Kinect is utilized to directly obtain human joint information. A partial circle delimitation method is used to solve the offset phenomenon of human joint obtained by Kinect, so as to achieve accurate estimation of human joint points. On this basis, an algorithm combing multilayer perceptron and long short-term memory network is explored to predict human motion position accurately. It not only helps to avoid complex feature extraction due to its end-to-end characteristic, but also provide natural interaction manner between human and robot without wearable devices or tags that may become a burden for the former. After that, the experimental results demonstrate that the proposed method makes predicting results accurate, and provides the reliable basis for human position prediction in the human-robot collaboration. This research could be applied to the human motion position prediction in human-robot collaboration process.
Edge Computing Enhanced Digital Twins for Smart Manufacturing
Authors: Huang Huiyue , Xu Xun
Published: August 2021

Digital Twin is one of the key enabling technologies for smart manufacturing in the context of Industry 4.0. The combination with advanced data analytics and information and communication technologies allows Digital Twins to perform real-time simulation, optimization and prediction to their physical counterparts. Efficient bi-directional data exchange is the foundation for Digital Twin implementation. However, the widely mentioned cloud-based architecture has disadvantages, such as high pressure on bandwidth and long latency time, which limit Digital Twins to provide real-time operating responses in dynamic manufacturing processes. Edge computing has the characteristics of low connectivity, the capability of immediate analysis and access to temporal data for real-time analytics, which makes it a fit-for-purpose technology for Digital Twin development. In this paper, the benefits of edge computing to Digital Twin are first explained through the reviews of the two technologies. The Digital Twin functions to be performed at the edge are then elaborated. After that, how the data model will be used in the edge for data mapping to realize the Digital Twin is illustrated and the data mapping strategy based on the EXPRESS schemas is discussed. Finally, a case study is carried out to verify the data mapping strategy based on EXPRESS schema. This research work refers to ISO/DIS 23247 Automation systems and integration — Digital Twin framework for manufacturing.
“A Blank Slate”: Preparing for Tokyo 2021 During COVID-19
Authors: Wadsworth Nick , Hargreaves Adam
Published: August 2021

This article presents a case study of an applied consultancy experience with WL, an Olympic athlete preparing for Tokyo 2021. WL sought psychological support after decreases in performance and well-being forced them to consider their future as an athlete. COVID-19 and the lockdown of the United Kingdom were highly influential to the consultancy process, providing WL with the opportunity to explore their identity in the absence of sport. WL framed their emergence from the lockdown as a “Blank Slate,” which was a critical moment allowing them to “find themselves on and off the mat.” The sport psychologist’s existential philosophy is presented and discussed in detail. Furthermore, reflections are provided by WL’s strength and conditioning coach about the referral process and by WL themself about the efficacy of the interventions. The importance of supporting both the person and the performer when working with aspiring Olympic athletes is also discussed.
Feasibility and Provisional Efficacy of Embedding High-Intensity Interval Training Into Physical Education Lessons: A Pilot Cluster-Randomized Controlled Trial
Authors: Harris Nigel , Warbrick Isaac , Atkins Denise , Vandal Alain , Plank Lindsay , Lubans David R.
Published: August 2021

Purpose: The aim of this study was to determine the feasibility of generalist school teachers delivering curriculum connected high-intensity interval training in a school’s physical education class time. Method: Two schools volunteered to participate. A total of 84 students (11.9 [0.5] y, M = 64 and F = 19) volunteered to participate. Four classes from 2 schools were randomized to either intervention (n = 53) or control (n = 31) for one school term (8 wk). Intervention class teachers participated in a 1-day workshop instructing them how to deliver twice weekly, high-intensity interval training sessions. The control classes continued with their usual physical education curriculum. Recruitment, intervention fidelity, and program satisfaction were assessed. Preliminary efficacy (primary outcome cardiorespiratory fitness) was quantified using generalized linear mixed models, expressed as effect size. A range of secondary outcomes was also assessed. Results: The recruitment rate was 88%. About 84% of the sessions were delivered. The heart rate peak over all sessions was 89.6% (13%) of the predicted maximum. The intervention teachers reported high levels of satisfaction. Almost all student participants were positive about participating. No adverse events occurred. The adjusted between-group difference for cardiorespiratory fitness was trivial (effect size 0.02). Conclusions: This teacher-delivered high-intensity interval training program was feasible and acceptable to both teachers and student participants. It is therefore potentially scalable.
GRB2 enforces homology-directed repair initiation by MRE11
Authors: Ye Zu , Xu Shengfeng , Shi Yin , Bacolla Albino , Syed Aleem , Moiani Davide , Tsai Chi-Lin , Shen Qiang , Peng Guang , Leonard Paul G. , Jones Darin E. , Wang Bin , Tainer John A. , Ahmed Zamal
Published: August 2021

GRB2 controls radiation-induced DNA damage repair—a predictive biomarker for PARPi and radiation therapy outcome.
Cointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware
Authors: Han Joon-Kyu , Oh Jungyeop , Yun Gyeong-Jun , Yoo Dongeun , Kim Myung-Su , Yu Ji-Man , Choi Sung-Yool , Choi Yang-Kyu
Published: August 2021

Cointegration of single-transistor neurons and synapses for highly scalable neuromorphic hardware is demonstrated.
Early evidence for historical overfishing in the Gulf of Mexico
Authors: Guiry Eric J. , Kennedy Jonathan R. , O’Connell Martin T. , Gray D. Ryan , Grant Christopher , Szpak Paul
Published: August 2021

Early overfishing in the Gulf of Mexico highlights vulnerability of one of the world’s most productive marine fishery areas.
Oxygen-deficient metal oxides supported nano-intermetallic InNi
3
C
0.5
toward efficient CO
2
hydrogenation to methanol
Authors: Meng Chao , Zhao Guofeng , Shi Xue-Rong , Chen Pengjing , Liu Ye , Lu Yong
Published: August 2021

Oxygen-deficient oxide supports enhance the electron density of InNi
3
C
0.5
, enabling efficient CO
2
hydrogenation to methanol.
Controlling inflammation in the elderly with BCG vaccination
Authors: Koeken Valerie A. C. M.
Published: August 2021

The tuberculosis vaccine BCG may protect against inflammation in the elderly as well as offer an option for protection from SARS-CoV-2 in developing countries.
Formyl peptide receptor 1 signaling potentiates inflammatory brain injury
Authors: Li Zhiguo , Li Yulin , Han Jinrui , Zhu Zilong , Li Minshu , Liu Qiang , Wang Yongjun , Shi Fu-Dong
Published: August 2021

Formyl peptide receptor 1 facilitates brain inflammation and can be targeted to attenuate acute neural injury.
Extended-Spectrum β-Lactamase-Producing and
mcr-1
-Positive Escherichia coli from the Gut Microbiota of Healthy Singaporeans
Authors: Ding Yichen , Saw Woei-Yuh , Tan Linda Wei Lin , Moong Don Kyin Nwe , Nagarajan Niranjan , Teo Yik Ying , Seedorf Henning
Published: August 2021

The human gut can harbor both antibiotic-resistant and virulent
Escherichia coli
which may subsequently cause infections. In this study, we found that multidrug-resistant (MDR)
E. coli
isolates from the gut of healthy Singaporeans carry a diverse range of antibiotic resistance mechanisms and virulence factor genes and are highly diverse.
Positive Interactions between Lactic Acid Bacteria Promoted by Nitrogen-Based Nutritional Dependencies
Authors: Canon Fanny , Maillard Marie-Bernadette , Henry Gwénaële , Thierry Anne , Gagnaire Valérie
Published: August 2021

Interactions of lactic acid bacteria (LAB) are often studied in association with yeasts or propionibacteria in various fermented food products, and the mechanisms underlying their interactions are being quite well characterized. Concerning interactions between LAB, they have mainly been investigated to test antagonistic interactions.
Impact of River Channel Lateral Migration on Microbial Communities across a Discontinuous Permafrost Floodplain
Authors: Douglas Madison M. , Lingappa Usha F. , Lamb Michael P. , Rowland Joel C. , West A. Joshua , Li Gen , Kemeny Preston C. , Chadwick Austin J. , Piliouras Anastasia , Schwenk Jon , Fischer Woodward W.
Published: August 2021

Arctic lowlands contain large quantities of soil organic carbon that is currently sequestered in permafrost. With rising temperatures, permafrost thaw may allow this carbon to be consumed by microbial communities and released to the atmosphere as carbon dioxide or methane.
Update from the European Committee on Antimicrobial Susceptibility Testing (EUCAST)
Authors: Giske Christian G. , Turnidge John , Cantón Rafael , Kahlmeter Gunnar
Published: August 2021

The European Committee on Antimicrobial Susceptibility Testing (EUCAST) is an international susceptibility testing committee, organized by the European Society for Clinical Microbiology and Infectious Diseases (ESCMID) and functioning as the breakpoint advisory committee of the European Medicines Agency (EMA). The original remit of EUCAST was to harmonize European clinical breakpoints, but very soon, the activities expanded beyond the borders of Europe and included newly licensed agents in Europe.
Evolutionary and Functional Analysis of Coagulase Positivity among the Staphylococci
Authors: Pickering Amy C. , Yebra Gonzalo , Gong Xiangyu , Goncheva Mariya I. , Wee Bryan A. , MacFadyen Alison C. , Muehlbauer Lukas F. , Alves Joana , Cartwright Robyn A. , Paterson Gavin K. , Fitzgerald J. Ross
Published: August 2021

The ability of some species of staphylococci to promote coagulation of plasma is a key pathogenic and diagnostic trait. Here, we provide a comprehensive analysis of the coagulase positivity of the staphylococci and its evolutionary genetic basis.
Overlapping of Independent SARS-CoV-2 Nosocomial Transmissions in a Complex Outbreak
Authors: Pérez-Lago Laura , Martínez-Lozano Helena , Pajares-Díaz Jose Antonio , Díaz-Gómez Arantxa , Machado Marina , Sola-Campoy Pedro J. , Herranz Marta , Valerio Maricela , Olmedo María , Suárez-González Julia , Quesada-Cubo Víctor , Gómez-Ruiz Maria del Mar , López-Fresneña Nieves , Sánchez-Arcilla Ignacio , Comas Iñaki , González-Candelas Fernando , García de San José Sonia , Bañares Rafael , Catalán Pilar , Muñoz Patricia , García de Viedma, Darío
Published: August 2021

We report a complex epidemiological scenario of a nosocomial COVID-19 outbreak in the second wave, based on WGS analysis. Initially, standard epidemiological findings led to the assumption of a homogeneous outbreak caused by a single SARS-CoV-2 strain.
Transformations, Lineage Comparisons, and Analysis of Down-to-Up Protomer States of Variants of the SARS-CoV-2 Prefusion Spike Protein, Including the UK Variant B.1.1.7
Authors: Peters Michael H. , Bastidas Oscar , Kokron Daniel S. , Henze Christopher E.
Published: August 2021

Viral variants represent a major challenge to monitoring viral outbreaks and formulating strategic health care responses. Variants represent transmitting viruses that have specific mutations and deletions associated with their genome.
SARS-CoV-2 Quasispecies Provides an Advantage Mutation Pool for the Epidemic Variants
Authors: Sun Fengming , Wang Xiuhua , Tan Shun , Dan Yunjie , Lu Yanqiu , Zhang Juan , Xu Junli , Tan Zhaoxia , Xiang Xiaomei , Zhou Yi , He Weiwei , Wan Xing , Zhang Wei , Chen Yaokai , Tan Wenting , Deng Guohong
Published: August 2021

RNA virus population in a host does not consist of a consensus single haplotype but rather an ensemble of related sequences termed quasispecies. The dynamics of quasispecies afford SARS-CoV-2 a great ability on genetic fitness during intrahost evolution.
COVID-19 Cases from the First Local Outbreak of the SARS-CoV-2 B.1.1.7 Variant in China May Present More Serious Clinical Features: A Prospective, Comparative Cohort Study
Authors: Song Yang , Ge Ziruo , Cui Shuping , Tian Di , Wan Gang , Zhu Shuangli , Wang Xianbo , Wang Yu , Zhao Xiang , Xiang Pan , Xu Yanli , Zhang Tingyu , Liu Long , Liu Gang , Wang Yanhai , Tan Jianbo , Zhang Wei , Xu Wenbo , Chen Zhihai
Published: August 2021

The SARS-CoV-2 B.1.1.7 variant, which was first identified in the United Kingdom, has increased sharply in number of cases worldwide and was reported to be more contagious than the nonvariant. To our knowledge, no studies investigating the detailed clinical features of COVID-19 cases infected with the B.1.1.7 variant have been published.
Adrenal Cushing syndrome in a patient with corticosteroid-treated asthma and worsening diabetes mellitus
Authors: Fukuda Mari , Hamada Keiko , Shimizu Yuki , Tanaka Tomohiro
Published: August 2021

A 55-year-old woman with poor diabetic control and a long history of corticosteroid-treated asthma was admitted. Hypertension and dyslipidaemia developed 9 and 6 years ago, respectively, and both were poorly controlled. Three years ago, her asthma control improved, and oral/intravenous steroids were switched to inhalers. Around this time, she was diagnosed as diabetes mellitus and heavily treated with insulin and other drugs thereafter. Physical examination showed central obesity, moon face appearance, abdominal striae and purpura. Endocrinological examination revealed suppressed adrenocorticotropic hormone, but unsuppressed endogenous cortisol levels. Right adrenal mass with isotope uptake revealed by CT scan and131I-adosterol scintigraphy was compatible with cortisol-producing adenoma, leading to the diagnosis of adrenal Cushing syndrome. A history of corticosteroid usage sometimes prevents us from the timely detection of endogenous cortisol excess. Our current case tells us a lesson of the importance of suspecting non-iatrogenic causes of Cushing syndrome even in patients heavily treated with corticosteroids.
Development and validation of a deep learning system to classify aetiology and predict anatomical outcomes of macular hole
Authors: Xiao Yu , Hu Yijun , Quan Wuxiu , Yang Yahan , Lai Weiyi , Wang Xun , Zhang Xiayin , Zhang Bin , Wu Yuqing , Wu Qiaowei , Liu Baoyi , Zeng Xiaomin , Lin Zhanjie , Fang Ying , Hu Yu , Feng Songfu , Yuan Ling , Cai Hongmin , Li Tao , Lin Haotian , Yu Honghua
Published: August 2021

Aims

To develop a deep learning (DL) model for automatic classification of macular hole (MH) aetiology (idiopathic or secondary), and a multimodal deep fusion network (MDFN) model for reliable prediction of MH status (closed or open) at 1 month after vitrectomy and internal limiting membrane peeling (VILMP).

Methods

In this multicentre retrospective cohort study, a total of 330 MH eyes with 1082 optical coherence tomography (OCT) images and 3300 clinical data enrolled from four ophthalmic centres were used to train, validate and externally test the DL and MDFN models. 266 eyes from three centres were randomly split by eye-level into a training set (80%) and a validation set (20%). In the external testing dataset, 64 eyes were included from the remaining centre. All eyes underwent macular OCT scanning at baseline and 1 month after VILMP. The area under the receiver operated characteristic curve (AUC), accuracy, speciﬁcity and sensitivity were used to evaluate the performance of the models.

Results

In the external testing set, the AUC, accuracy, specificity and sensitivity of the MH aetiology classification model were 0.965, 0.950, 0.870 and 0.938, respectively; the AUC, accuracy, specificity and sensitivity of the postoperative MH status prediction model were 0.904, 0.825, 0.977 and 0.766, respectively; the AUC, accuracy, specificity and sensitivity of the postoperative idiopathic MH status prediction model were 0.947, 0.875, 0.815 and 0.979, respectively.

Conclusion

Our DL-based models can accurately classify the MH aetiology and predict the MH status after VILMP. These models would help ophthalmologists in diagnosis and surgical planning of MH.
Zinc transporter 8 autoantibody testing requires age-related cut-offs
Authors: Grace Sian Louise , Cooper Angela , Jones Angus G , McDonald Timothy James
Published: August 2021

Introduction

Zinc transporter 8 autoantibodies (ZnT8A) are biomarkers of beta cell autoimmunity in type 1 diabetes that have become more widely available to clinicians in recent years. Robust control population-defined thresholds are essential to ensure high clinical specificity in islet autoantibody testing. We aimed to determine the optimal cut-offs for ZnT8A testing.

Research design and methods

97.5th and 99th centile cut-offs were determined using residual clinical sera from 1559 controls aged between 0 and 83 years with no history of diabetes and a hemoglobin A1c level of less than 6.0% (<42 mmol/mol). ZnT8A were measured by ELISA (RSR, Cardiff, UK) on a Dynex DS2 ELISA robot (Dynex, Preston, UK). We assessed the impact of age-related cut-offs in comparison with the manufacturer’s recommended threshold in a mixed cohort of young-onset (<age 30) diabetes (UNITED study (Using pharmacogeNetics to Improve Treatment in Early-onset Diabetes), n=145).

Results

Using the manufacturer’s limit of detection, 6 WHO U/mL, 16.2% of people in the control cohort had detectable levels of ZnT8A and those who had detectable ZnT8A were much more likely to be younger (p<0.0001). The 97.5th and 99th centile thresholds were substantially higher in younger participants: 18 and 127 WHO U/mL (tested under 30 years) in comparison with 9 and 21 WHO U/mL (tested 30 years and over). In the UNITED cohort some of those found to be ZnT8A-positive by the manufacturer’s threshold but negative using the appropriate 99% centile cut-off (127 WHO U/mL) displayed characteristics suggestive of type 2 diabetes.

Conclusions

Age-related thresholds are needed for ZnT8A testing. In those aged <30 years, use of manufacturers’ recommended cut-offs may result in low test specificity and potentially high rates of false positive test results in patients who do not have autoimmune diabetes.
The Chronic Kidney Disease in Africa (CKD-Africa) collaboration: lessons from a new pan-African network
Authors: George Cindy , Stoker Suzaan , Okpechi Ikechi , Woodward Mark , Kengne Andre
Published: August 2021

Chronic kidney disease (CKD) is a global public health problem, seemingly affecting individuals from low-income and-middle-income countries (LMICs) disproportionately, especially in sub-Saharan Africa. Despite the growing evidence pointing to an increasing prevalence of CKD across Africa, there has not been an Africa-wide concerted effort to provide reliable estimates that could adequately inform health services planning and policy development to address the consequences of CKD. Therefore, we established the CKD in Africa (CKD-Africa) Collaboration. To date, the network has curated data from 39 studies conducted in 12 African countries, totalling 35 747 participants, of which most are from sub-Saharan Africa. We are, however, continuously seeking further collaborations with other groups who have suitable data to grow the network. Although many successful research consortia exist, few papers have been published (with none from Africa) detailing the challenges faced and lessons learnt in setting up and managing a research consortium. Drawing on our experience, we describe the steps taken and the key factors required to establish a functional collaborative consortium among researchers in Africa. In addition, we present the challenges we encountered in building our network, how we managed those challenges and the benefit of such a collaboration for Africa. Although the CKD-Africa Collaboration is focused primarily on CKD research, many of the lessons learnt can be applied more widely in public health research in LMICs.
Institutional infrastructural preconditions and current perioperative anaesthesia practice in patients undergoing transfemoral transcatheter aortic valve implantation: a cross-sectional study in German heart centres
Authors: Löser Benjamin , Haas Annika , Zitzmann Amelie , Dankert Andre , Treskatsch Sascha , Reuter Daniel A , Haas Sebastian , Glass Änne , Petzoldt Martin
Published: August 2021

Objectives

Transfemoral transcatheter aortic valve implantation (TF-TAVI) is an established therapy for patients with symptomatic aortic stenosis, which requires periprocedural anaesthesia care. In 2015, the German Federal Joint Committee released a directive on minimally invasive heart valve interventions which defines institutional infrastructural requirements in German heart centres. But still generally accepted expert consensus recommendations or national or international guidelines regarding periprocedural anaesthesia management for TF-TAVI are lacking. This nationwide cross-sectional study had two major objectives: first to assess the concordance with existing national regulations regarding infrastructural requirements and second to evaluate the status quo of periprocedural anaesthesia management for patients undergoing TF-TAVI in German heart centres.

Design

Multicentre cross-sectional online study to evaluate the periprocedural anaesthesia management.

Setting

In this nationwide cross-sectional study, electronic questionnaires were sent out to anaesthesia departments at TF-TAVI-performing centres in Germany in March 2019.

Participants

78 anaesthesia departments of German heart centres.

Results

54 (69.2%) centres returned the questionnaire of which 94.4% stated to hold regular Heart Team meetings, 75.9% to have ready-to-use heart-lung machines available on-site, 77.8% to have cardiac surgeons and 66.7% to have perfusionists routinely attending throughout TF-TAVI procedures. Regarding periprocedural anaesthesia management, 41 (75.9%) of the participating centres reported to predominantly use ‘monitored anaesthesia care’ and 13 (24.1%) to favour general anaesthesia. 49 (90.7%) centres stated to use institutional standard operating procedures for anaesthesia. Five-lead ECG, central venous lines, capnometry and intraprocedural echocardiography were reported to be routine measures in 85.2%, 83.3%, 77.8% and 51.9% of the surveyed heart centres.

Conclusions

The concordance with national regulations, anaesthesia management and in-house standards for TF-TAVI vary broadly among German heart centres. According to the opinion of the authors, international expert consensus recommendations and/or guidelines would be helpful to standardise peri interventional anaesthesia care.
Exploring the factors that promote or diminish a psychologically safe environment: a qualitative interview study with critical care staff
Authors: Grailey Kate , Leon-Villapalos Clare , Murray Eleanor , Brett Stephen
Published: August 2021

Objectives

This study aimed to quantify the presence of psychological safety (defined as an environment ‘safe for interpersonal risk taking’) in critical care staff, exploring the ways in which this manifested.

Design

Qualitative interview study incorporating a short quantitative survey.

Setting

Three intensive care units within one National Health Service Trust in London.

Participants

Thirty participants were recruited from all levels of seniority and roles within the multidisciplinary team. A purposive sampling technique was used, with recruitment ceasing at the point of thematic saturation.

Interventions

Semistructured interviews explored attitudes towards psychological safety and contained a quantitative assessment measuring the climate of psychological safety present.

Results

Twenty-eight participants agreed that it was easy to ask for help, with 20 agreeing it is safe to take a risk on the team, demonstrating a strong perception of psychological safety in this group.

Our thematic analysis highlighted areas where the context influenced an individual’s psychological safety including personality, culture and leadership. Possible negative consequences of psychological safety included distraction and fatigue for the team leader. We demonstrated that speaking up can be influenced by motivations other than patient safety, such as undermining or self-promotion.

Conclusions

Our data demonstrate reassuring levels of psychological safety within the participants studied. This allowed us to explore in depth the participant experience of working within a psychologically safe environment. We add to the current literature by uniquely demonstrating there can be negative consequences to a psychologically safe environment in the healthcare setting. We expand on the influence of context on psychological safety by developing a model, allowing leaders to identify which elements of context can be modified in order to promote speaking up. Team leaders can use these data to help foster a culture of openness, innovation and error prevention while minimising the risk of negative implications
Adolescent anxiety disorders and the developing brain: comparing neuroimaging findings in adolescents and adults
Authors: Xie Shuqi , Zhang Xiaochen , Cheng Wenhong , Yang Zhi
Published: August 2021

Adolescence is the peak period for the incidence of anxiety disorders. Recent findings have revealed the immaturity of neural networks underlying emotional regulation in this population. Brain vulnerability to anxiety in adolescence is related to the unsynchronised development of anxiety-relevant brain functional systems. However, our current knowledge on brain deficits in adolescent anxiety is mainly borrowed from studies on adults. Understanding adolescent-specific brain deficits is essential for developing biomarkers and brain-based therapies targeting adolescent anxiety. This article reviews and compares recent neuroimaging literature on anxiety-related brain structural and functional deficits between adolescent and adult populations, and proposes a model highlighting the differences between adolescence and adulthood in anxiety-related brain networks. This model emphasises that in adolescence the emotional control system tends to be hypoactivated, the fear conditioning system is immature, and the reward and stress response systems are hypersensitive. Furthermore, the striatum’s functional links to the amygdala and the prefrontal cortex are strengthened, while the link between the prefrontal cortex and the amygdala is weakened in adolescence. This model helps to explain why adolescents are vulnerable to anxiety disorders and provides insights into potential brain-based approaches to intervene in adolescent anxiety disorders.