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A history of low back pain affects pelvis and trunk coordination during a sustained manual materials handling task

Journal of Sport and Health Science 2016年第1期5卷 52-60页

作者： Joseph F.Seay Shane G.Sauer Tejash Patel Tanja C.Roy Military Performance division US. Army Research Institute of Environmental Medicine Natick MA 01760 USA biophysics ancl biomedical modelling division US. Army Research Institute of Environmental Medicine Natick MA O1760 USA

Purpose:The purpose of this study was to compare the coordination between the trunk and the pelvis during a sustained asymmetric repetitive lifting task between a group with a history of low back pain(LBP;HBP) and a group with no history of LBP(NBP).Methods:Volunteers lifted a 11-kg box from ankle height in front to a shelf 45° off-center at waist height,and lowered it to the start position at12 cycles/min for 10 *** side was alternated during the *** relative phase was used to calculate coordination between the pelvis and trunk rotation at the beginning(Min 1),middle(Min 5),and end of the bout(Min 9).Results:While there were no main effects for group,a significant interaction between time and group indicated that,in the frontal plane,the NBP group coordination was more anti-phase toward the end of the bout,with no such differences for the HBP *** of sagittal-axial(bend and twist) coordination revealed the HBP group coordination was more in-phase at the end of the bout over the entire cycle and for the lifting phase alone,with no such differences for the NBP ***:Differences between groups demonstrate residual consequences of LBP in an occupational scenario,even though the HBP group was pain-free for >6 months prior to data *** in-phase coordination in the HBP group may represent a coordination pattern analogous to'guarded gait' which has been observed in other studies,and may lend insight as to why these individuals are at increased risk for re-injury.

关键词： Lifting Lowering Occupational biomechanics Relative phase Relative phase variability

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Biomaterials / bioinks and extrusion bioprinting

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Bioactive Materials 2023年第10期28卷 511-536页

作者： X.B.Chen A.Fazel Anvari-Yazdi X.Duan A.Zimmerling R.Gharraei N.K.Sharma S.Sweilem L.Ning Department of Mechanical Engineering University of Saskatchewan57 Campus DrS7K 5A9SaskatoonCanada b Division of Biomedical EngineeringUniversity of Saskatchewan57 Campus DrSaskatoonS7K 5A9Canada c Department of Mechanical EngineeringCleveland State UniversityClevelandOH44115USA

Bioinks are formulations of biomaterials and living cells, sometimes with growth factors or other biomolecules, while extrusion bioprinting is an emerging technique to apply or deposit these bioinks or biomaterial solutions to create three-dimensional (3D) constructs with architectures and mechanical/biological properties that mimic those of native human tissue or organs. Printed constructs have found wide applications in tissue engineering for repairing or treating tissue/organ injuries, as well as in vitro tissue modelling for testing or validating newly developed therapeutics and vaccines prior to their use in humans. Successful printing of constructs and their subsequent applications rely on the properties of the formulated bioinks, including the rheological, mechanical, and biological properties, as well as the printing process. This article critically reviews the latest developments in bioinks and biomaterial solutions for extrusion bioprinting, focusing on bioink synthesis and characterization, as well as the influence of bioink properties on the printing process. Key issues and challenges are also discussed along with recommendations for future research.

关键词： 3D bioprinting Extrusion Bioinks Biomaterials Tissue engineering

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Deep Learning Predicts Stress–Strain Relations of Granular Materials Based on Triaxial Testing Data

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Computer Modeling in Engineering & Sciences 2021年第7期128卷 129-144页

作者： Tongming Qu Shaocheng Di Y.T.Feng Min Wang Tingting Zhao Mengqi Wang Zienkiewicz Centre for Computational Engineering College of EngineeringSwansea UniversitySwanseaWalesSA18EPUK College of Shipbuilding Engineering Harbin Engineering UniversityHarbin150001China Institute of Applied Mechanics and biomedical Engineering Taiyuan University of TechnologyTaiyuan030024China Fluid Dynamics and Solid Mechanics Group Theoretical DivisionLos Alamos National LaboratoryLos AlamosNM 87545USA

This study presents an AI-based constitutive modelling framework wherein the prediction model directly learns from triaxial testing data by combining discrete element modelling(DEM)and deep learning.A constitutive learning strategy is proposed based on the generally accepted frame-indifference assumption in constructing material constitutive *** low-dimensional principal stress-strain sequence pairs,measured from discrete element modelling of triaxial testing,are used to train recurrent neural networks,and then the predicted principal stress sequence is augmented to other high-dimensional or general stress tensor via coordinate *** detailed hyperparameter investigations,it is found that long short-term memory(LSTM)and gated recurrent unit(GRU)networks have similar prediction performance in constitutive modelling problems,and both satisfactorily predict the stress responses of granular materials subjected to a given unseen strain ***,the unique merits and ongoing challenges of data-driven constitutive models for granular materials are discussed.

关键词： Deep learning granular materials constitutive modelling discrete element modelling coordinate transformation LSTM GRU

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Photocatalytic degradation of antibiotic sulfamethizole by visible light activated perovskite LaZnO_(3)

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Journal of Environmental Sciences 2024年第10期144卷 212-224页

作者： Bui The Huy XCuong Nguyen Vu Khac Hoang Bui Nguyen Ngoc Tri Iqra Rabani Nhu Hoa Thi Tran Quang Viet Ly Hai Bang Truong Major of biomedical Engineering Division of Smart HealthcareCollege of Information Technology and ConvergencePukyong National UniversityBusan 48513Korea Center for Advanced Chemistry Institute of Research and DevelopmentDuy Tan UniversityDa Nang 550000Viet Nam Faculty of Environmental and Chemical Engineering Duy Tan UniversityDa Nang 550000Viet Nam Department of Environment and Energy Sejong UniversitySeoul 05006Korea Lab of Computational Chemistry and modelling Department of ChemistryFaculty of Natural SciencesQuy Nhon UniversityQuy NhonViet Nam Department of Nanotechnology and Advanced Materials Engineering Sejong UniversitySeoul 05006Korea Faculty of Materials Science and Technology University of ScienceHo Chi Minh City 700000Viet Nam Vietnam National University Ho Chi Minh City 700000Viet Nam Department of Environmental Engineering Seoul National University of Science and TechnologySeoul 01811Korea Optical Materials Research Group Science and Technology Advanced InstituteVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Applied Technology School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam

In thiswork,the perovskite LaZnO_(3) was synthesized via sol-gel method and applied for photocatalytic treatment of sulfamethizole(SMZ)antibiotics under visible light *** was almost completely degraded(99.2%±0.3%)within 4 hr by photocatalyst LaZnO_(3) at the optimal dosage of 1.1 g/L,with amineralization proportion of 58.7%±0.4%.The efficient performance of LaZnO_(3) can be attributed to itswide-range light absorption and the appropriate energy band edge levels,which facilitate the formation of active agents such as·O_(2)^(−),h^(+),and·*** integration of RP-HPLC/Q-TOF-MS and DFT-based computational techniques revealed three degradation pathways of SMZ,which were initiated by the deamination reaction at the aniline ring,the breakdown of the sulfonamidemoieties,and a process known as Smile-type rearrangement and SO2 *** toxicity of SMZ and the intermediateswere analyzed by quantitative structure activity relationship(QSAR),indicating the effectiveness of LaZnO_(3)-based photocatalysis in preventing secondary pollution of the intermediates to the ecosystem during the degradation *** visible-light-activated photocatalyst LaZnO_(3) exhibited efficient performance in the occurrence of inorganic anions and maintained high durability across multiple recycling tests,making it a promising candidate for practical antibiotic treatment.

关键词： Perovskite Antibiotics Photocatalysis Density functional theory Toxicity

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Support vector machine applied in QSAR modelling

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Chinese Science Bulletin 2005年第20期50卷 2291-2296页

作者： MEI Hu ZHOU Yuan LIANG Guizhao LI Zhiliang College of Chemistry and Chemical Engineering Chongqing University Chongqing 400044 China Key Laboratory of biomedical Engineering of Ministry of Education and Chongqing City Chongqing 400044 China College of Bioengineering. Chongqing University Chongqing 400044 China

Support vector machine (SVM), partial least squares (PLS), and Back-Propagation artificial neural net- work (ANN) were employed to establish QSAR models of 2 dipeptide datasets. In order to validate predictive capabilities on external dataset of the resulting models, both internal and external validations were performed. The division of dataset into both training and test sets was carried out by D-optimal design. The results showed that support vector machine (SVM) behaved well in both calibration and prediction. For the dataset of 48 bitter tasting dipeptides (BTD), the results obtained by support vector regression (SVR) were superior to that by PLS in both calibration and prediction. When compared with BP artificial neural network, SVR showed less calibration power but more predictive capability. For the dataset of angiotensin-converting enzyme (ACE) inhibitors, the results obtained by support vector machine (SVM) re- gression were equivalent to those by PLS and BP artificial neural network. In both datasets, SVR using linear kernel function behaved well as that using radial basis kernel func- tion. The results showed that there is wide prospect for the application of support vector machine (SVM) into QSAR modeling.

关键词：支撑向量装置 SVM 最小二乘法 QSAR模型人工神经网络

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Using statistics and mathematical modelling to understand infectious disease outbreaks:COVID-19 as an example

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Infectious Disease modelling 2020年第1期5卷 409-441页

作者： Christopher E.Overton Helena B.Stage Shazaad Ahmad Jacob Curran-Sebastian Paul Dark Rajenki Das Elizabeth Fearon Timothy Felton Martyn Fyles Nick Gent Ian Hall Thomas House Hugo Lewkowicz Xiaoxi Pang Lorenzo Pellis Robert Sawko Andrew Ustianowski Bindu Vekaria Luke Webb Department of Mathematics University of ManchesterUK IBM Research Hartree CentreSciTech DaresburyUK Department of Global Health and Development London School of Hygiene and Tropical MedicineUK Emergency Response Department Public Health EnglandUK division of Infection Immunity and Respiratory MedicineNIHR Biomedical Research CentreUniversity of ManchesterUK Department of Virology Manchester Medical Microbiology PartnershipManchester Foundation TrustUK Regional Infectious Diseases Unit North Manchester General HospitalUK School of Medical Sciences University of ManchesterUK Intensive Care Unit Wythenshawe HospitalManchester University NHS Foundation TrustUK Department of Health Sciences University of ManchesterUK Department of Mathematical Sciences University of LiverpoolUK The Alan Turing Institute UK Manchester Academic Health Sciences Centre UK Critical Care Unit Salford Royal HospitalNorthern Care Alliance NHS GroupUK

During an infectious disease outbreak,biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing *** by the ongoing response to COVID-19,we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing *** particular,we focus on parameter estimation in the presence of known biases in the data,and the effect of non-pharmaceutical interventions in enclosed subpopulations,such as households and care *** illustrate these methods by applying them to the COVID-19 pandemic.

关键词： COVID-19 Epidemic modelling Parameter estimation Outbreak Bias Intervention

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Immunogenicity, safety, and reactogenicity of heterologous COVID-19 primary vaccination incorporating mRNA, viral-vector, and protein-adjuvant vaccines in the UK (Com-COV2): a single-blind, randomised, phase 2, non-inferiority trial

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四川生理科学杂志 2021年第10期43卷 1819-1819页

作者： Arabella S V Stuart [a]Oxford Vaccine Group Department of Paediatrics University of Oxford Oxford UK [b]Chinese Academy of Medical Science Oxford Institute University of Oxford Oxford UK [c]Wellcome Centre for Human Genetics Nuffield Department of Medicine University of Oxford Oxford UK [d]Oxford University Hospitals NHS Foundation Trust Oxford UK [e]Statistics Modelling and Economics Department UK Health Security Agency London UK [f]Immunisation and Countermeasures division National Infection Service UK Health Security Agency London UK [g]Public Health Scotland Glasgow Scotland UK [h]UK Health Security Agency Porton Down Salisbury UK [i]Liverpool School of Tropical Medicine Liverpool UK [j]Department of Infection Immunity and Cardiovascular Disease University of Sheffield Sheffield UK [k]Department of Infection and Tropical Medicine Sheffield Teaching Hospitals NHS Foundation Trust Sheffield UK [l]The Newcastle upon Tyne Hospitals NHS Foundation Trust Newcastle upon Tyne UK [m]Translational and Clinical Research Institute Newcastle University Newcastle UK [n]NIHR Southampton Clinical Research Facility and biomedical Research Centre University Hospital Southampton NHS Foundation Trust Southampton UK [o]Faculty of Medicine and Institute for Life Sciences University of Southampton Southampton UK [p]School of Population Health Sciences and School of Cellular and Molecular Medicine University of Bristol Bristol UK [q]Department of Infection and NIHR BRC Guy's and St Thomas' NHS Foundation Trust London UK [r]MRC Clinical Trials Unit University College London London UK [s]NIHR/Wellcome Trust Clinical Research Facility University Hospitals Birmingham NHS Foundation Trust Birmingham UK [t]Institute of Microbiology and Infection University of Birmingham Birmingham UK [u]The Vaccine Institute St George's University of London London UK [v]North Bristol NHS Trust Bristol UK [w]NIHR UCLH Clinical Research Facility and NIHR UCLH biomedical Research Centre University College Lo

Background:Given the importance of flexible use of different COVID-19 vaccines within the same schedule to facilitate rapid deployment,we studied mixed priming schedules incorporating an adenoviral-vectored vaccine(ChAdOx1 nCoV-19[ChAd],AstraZeneca),two mRNA vaccines(BNT162b2[BNT],Pfizer-BioNTech,and mRNA-1273[m1273],Moderna)and a nanoparticle vaccine containing SARS-CoV-2 spike glycoprotein and Matrix-M adjuvant(NVX-CoV2373[NVX],Novavax).

关键词： Pfizer schedule COV

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