bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tissue-specific 3 prime-end adenylation of miR-125b mediates cell survival
Authors: Blahna Matthew T. , Neel Jean-Charles , Yu Ge , Louie Justin , Ghatpande Prajakta , Lagna Giorgio , Hata Akiko 
Published: June 2019

AbstractNext-generation sequencing has uncovered microRNAs (miRNAs) that undergo sequence modifications, known as isomiRs. Their physiological significance, however, remains uncertain, partly because they generally comprise a small fraction of total miRNAs. Here we report that more than 60% of miR-125b, one of the most abundant miRNAs in vascular smooth muscle cells (vSMC), exists as an edited isoform containing a non-templated adenosine residue at the 3 prime-end (miR-125b+A). The properties of miR-125b+A, such as stability and subcellular localization, are similar to those of canonical miR-125b, but miR-125b+A more potently inhibits the expression of a subgroup of targets, including the apoptosis effector Caspase-6 (CASP6). In the CASP6 transcript, adenylated miR125b preferentially targets a conserved, atypical site, with an unusual 36 nucleotides loop between seed sequence and 3 prime-end supplementary site. PAP associated domain containing 2 (PAPD2) is responsible for monoadenylation of miR-125b. Downregulation of PAPD2 results in the conversion of miR-125b+A to miR-125b, derepression of CASP6, and sensitization of vSMC to apoptotic stimuli. Thus, atypical site recognition by a tissue-specific isomiR fulfills a pro-survival role.
Novel syngeneic animal model of tobacco-associated oral cancer reveals the activity of in situ anti-CTLA-4
Authors: Wang Zhiyong , Wu Victoria H. , Allevato Michael M. , Gilardi Mara , He Yudou , Callejas-Valera Juan Luis , Vitale-Cross Lynn , Martin Daniel , Amornphimoltham Panomwat , Mcdermott James , Goto Yusuke , Molinolo Alfredo A. , Sharabi Andrew B. , Cohen Ezra E. W. , Chen Qianming , Lyons J. Guy , Alexandrov Ludmil B. , Gutkind J. Silvio 
Published: June 2019

AbstractHead and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. Tobacco use is the main risk factor for HNSCC, and tobacco-associated HNSCCs have poor prognosis and response to available treatments. Recently approved anti-PD-1 immune checkpoint inhibitors showed limited activity (≤20%) in HNSCC, highlighting the need to identify new therapeutic options. For this, mouse models that accurately reflect the complexity of the HNSCC mutational landscape and tumor immune environment are urgently needed. Here, we report the first mouse HNSCC model system that recapitulates the human tobacco-related HNSCC mutanome, in which tumors grow when implanted in the tongue of immunocompetent mice. These HNSCC lesions have similar immune infiltration and response rates to anti-PD-1 (≤20%) immunotherapy as human HNSCCs. Remarkably, we found that >70% of HNSCC lesions respond to intratumoral anti-CTLA-4. This syngeneic HNSCC mouse model provides a platform for the development of novel immunotherapeutic options for HNSCC.
Neural patterns reveal single-trial information on absolute pitch and relative pitch perception
Authors: Leipold Simon , Greber Marielle , Sele Silvano , Jäncke Lutz 
Published: June 2019

AbstractPitch is a fundamental attribute of sounds and yet is not perceived equally by all humans. Absolute pitch (AP) musicians perceive, recognize, and name pitches in absolute terms, whereas relative pitch (RP) musicians, representing the large majority of musicians, perceive pitches in relation to other pitches. In this study, we used electroencephalography (EEG) to investigate the neural representations underlying tone listening and tone labeling in a large sample of musicians (n = 105). Participants performed a pitch processing task with a listening and a labeling condition during EEG acquisition. Using a brain-decoding framework, we tested a prediction derived from both theoretical and empirical accounts of AP, namely that the representational similarity of listening and labeling is higher in AP musicians than in RP musicians. Consistent with the prediction, time-resolved single-trial EEG decoding revealed a higher representational similarity in AP musicians during late stages of pitch perception. Time-frequency-resolved EEG decoding further showed that the higher representational similarity was present in oscillations in the theta and beta frequency bands. Supplemental univariate analyses were less sensitive in detecting subtle group differences in the frequency domain. Taken together, the results suggest differences between AP and RP musicians in late pitch processing stages associated with cognition, rather than in early processing stages associated with perception.
On the Turbulence Modeling of Blood Flow in a Stenotic Vessel
Authors: Lui Mattia , Martino Savino , Salerno Mario , Quadrio Maurizio 
Published: June 2019

AbstractBlood flow dynamics in a stenosed, subject-specific carotid bifurcation is numerically simulated using direct numerical simulation (DNS) and Reynolds-averaged Navier–Stokes (RANS) equations closed with turbulence models. DNS is meant to provide a term of comparison for the RANS calculations, which include classic two-equations models (k–ε and k–ω) as well as a transitional three-equations eddy-viscosity model (kT−kL−ω). Pulsatile inlet conditions based on in vivo ultrasound measurements of blood velocity are used. The blood is modeled as a Newtonian fluid, and the vessel walls are rigid. The main purpose of this work is to highlight the problems related to the use of classic RANS models in the numerical simulation of such flows. The time-averaged DNS results, interpreted in view of their finite-time averaging error, are used to demonstrate the superiority of the transitional RANS model, which is found to provide results closer to DNS than those of conventional models. The transitional model shows better predictive capabilities in terms of turbulence intensity, temporal evolution of the pressure along the cardiac cycle, and the oscillatory shear index (OSI). Indeed, DNS brings to light the locally transitional or weakly turbulent state of the blood flow, which presents velocity and pressure fluctuations only in the poststenotic region of the internal carotid artery during systole, while the flow is laminar during diastole.
Longitudinal Reinforcement of Acute Myocardial Infarcts Improves Function by Transmurally Redistributing Stretch and Stress
Authors: Estrada Ana Cristina , Yoshida Kyoko , Clarke Samantha A. , Holmes Jeffrey W. 
Published: June 2019

Abstract
A wide range of emerging therapies, from surgical restraint to biomaterial injection to tissue engineering, aim to improve heart function and limit adverse remodeling following myocardial infarction (MI). We previously showed that longitudinal surgical reinforcement of large anterior infarcts in dogs could significantly enhance systolic function without restricting diastolic function, but the underlying mechanisms for this improvement are poorly understood. The goal of this study was to construct a finite element model that could match our previously published data on changes in regional strains and left ventricular function following longitudinal surgical reinforcement, then use the model to explore potential mechanisms for the improvement in systolic function we observed. The model presented here, implemented in febio, matches all the key features of our experiments, including diastolic remodeling strains in the ischemic region, small shifts in the end-diastolic pressure–volume relationship (EDPVR), and large changes in the end-systolic pressure–volume relationship (ESPVR) in response to ischemia and to patch application. Detailed examination of model strains and stresses suggests that longitudinal reinforcement reduces peak diastolic fiber stretch and systolic fiber stress in the remote myocardium and shifts those peaks away from the endocardial surface by reshaping the left ventricle (LV). These findings could help to guide the development of novel therapies to improve post-MI function by providing specific design objectives.
The Effect of Articular Cartilage Focal Defect Size and Location in Whole Knee Biomechanics Models
Authors: Marchi Benjamin C. , Arruda Ellen M. , Coleman Rhima M. 
Published: June 2019

Abstract
Articular cartilage focal defects are common soft tissue injuries potentially linked to osteoarthritis (OA) development. Although several defect characteristics likely contribute to osteoarthritis, their relationship to local tissue deformation remains unclear. Using finite element models with various femoral cartilage geometries, we explore how defects change cartilage deformation and joint kinematics assuming loading representative of the maximum joint compression during the stance phase of gait. We show how defects, in combination with location-dependent cartilage mechanics, alter deformation in affected and opposing cartilages, as well as joint kinematics. Small and average sized defects increased maximum compressive strains by approximately 50% and 100%, respectively, compared to healthy cartilage. Shifts in the spatial locations of maximum compressive strains of defect containing models were also observed, resulting in loading of cartilage regions with reduced initial stiffnesses supporting the new, elevated loading environments. Simulated osteoarthritis (modeled as a global reduction in mean cartilage stiffness) did not significantly alter joint kinematics, but exacerbated tissue deformation. Femoral defects were also found to affect healthy tibial cartilage deformations. Lateral femoral defects increased tibial cartilage maximum compressive strains by 25%, while small and average sized medial defects exhibited decreases of 6% and 15%, respectively, compared to healthy cartilage. Femoral defects also affected the spatial distributions of deformation across the articular surfaces. These deviations are especially meaningful in the context of cartilage with location-dependent mechanics, leading to increases in peak contact stresses supported by the cartilage of between 11% and 34% over healthy cartilage.
A Reduced-Order Flow Model for Fluid–Structure Interaction Simulation of Vocal Fold Vibration
Authors: Li Zheng , Chen Ye , Chang Siyuan , Luo Haoxiang 
Published: June 2019

Abstract
We present a novel reduced-order glottal airflow model that can be coupled with the three-dimensional (3D) solid mechanics model of the vocal fold tissue to simulate the fluid–structure interaction (FSI) during voice production. This type of hybrid FSI models have potential applications in the estimation of the tissue properties that are unknown due to patient variations and/or neuromuscular activities. In this work, the flow is simplified to a one-dimensional (1D) momentum equation-based model incorporating the entrance effect and energy loss in the glottis. The performance of the flow model is assessed using a simplified yet 3D vocal fold configuration. We use the immersed-boundary method-based 3D FSI simulation as a benchmark to evaluate the momentum-based model as well as the Bernoulli-based 1D flow models. The results show that the new model has significantly better performance than the Bernoulli models in terms of prediction about the vocal fold vibration frequency, amplitude, and phase delay. Furthermore, the comparison results are consistent for different medial thicknesses of the vocal fold, subglottal pressures, and tissue material behaviors, indicating that the new model has better robustness than previous reduced-order models.
Subject-Specific Finite Element Models of the Tibia With Realistic Boundary Conditions Predict Bending Deformations Consistent With In Vivo Measurement
Authors: Haider Ifaz T. , Baggaley Michael , Brent Edwards W. 
Published: June 2019

Abstract
Understanding the structural response of bone during locomotion may help understand the etiology of stress fracture. This can be done in a subject-specific manner using finite element (FE) modeling, but care is needed to ensure that modeling assumptions reflect the in vivo environment. Here, we explored the influence of loading and boundary conditions (BC), and compared predictions to previous in vivo measurements. Data were collected from a female participant who walked/ran on an instrumented treadmill while motion data were captured. Inverse dynamics of the leg (foot, shank, and thigh segments) was combined with a musculoskeletal (MSK) model to estimate muscle and joint contact forces. These forces were applied to an FE model of the tibia, generated from computed tomography (CT). Eight conditions varying loading/BCs were investigated. We found that modeling the fibula was necessary to predict realistic tibia bending. Applying joint moments from the MSK model to the FE model was also needed to predict torsional deformation. During walking, the most complex model predicted deformation of 0.5 deg posterior, 0.8 deg medial, and 1.4 deg internal rotation, comparable to in vivo measurements of 0.5–1 deg, 0.15–0.7 deg, and 0.75–2.2 deg, respectively. During running, predicted deformations of 0.3 deg posterior, 0.3 deg medial, and 0.5 deg internal rotation somewhat underestimated in vivo measures of 0.85–1.9 deg, 0.3–0.9 deg, 0.65–1.72 deg, respectively. Overall, these models may be sufficiently realistic to be used in future investigations of tibial stress fracture.
Resilience of Metrorail Networks: Quantification With Washington, DC as a Case Study
Authors: Saadat Yalda , Ayyub Bilal M. , Zhang Yanjie , Zhang Dongming , Huang Hongwei 
Published: June 2019

Abstract
According to the United Nation's Department of Economic and Social Affairs Population Division, 66% of the world's population will reside in urban areas by 2050, a boost from 30% in 1950. Urbanization has indeed triumphed and has increased the demand for infrastructure systems including metrorail networks, one of the most tangible examples of complex transportation infrastructure systems. A synergistic rapid growth of urban population concurrent with great increases in metrorail use may lead to perturbations in the state of such a system. Taking into consideration of such perturbations and developing appropriate resilience measurements are paramount to the design of sustainable, robust, and resilient systems capable of performing satisfactorily through intentional and/or stochastic disruptions. As such, developing resilience metrics in a structured and appropriate manner is necessary. Using Washington, DC Metro as a case study, this paper examines metrorail network resilience and associated metrics with well-defined relationships to vulnerability and tied to efficiency. This examination includes developing a metro rail model in a graph form and obtaining its basic features by network topology analysis. The analytical work demonstrates that the Washington, DC Metro with its 91 stations and 140 links is a small-world network with the presence of a scale-free phenomenon and of a high level of vulnerability in the case of disruptive events. Evaluation of the efficiency and vulnerability of a metrorail network after disruption requires the consideration of two primary failure events, i.e., the failure of a metro station and the failure of a metro segment between stations. Vulnerability evaluation identifies the most critical stations and rail segments as illustrated using the Washington, DC Metro network. Such an assessment offers a logical basis to enhance system resilience and develop postfailure recovery strategies.
Evaluation of Parametric Models Dedicated to a Magnetorheological Actuator Including Uncertainty and Sensitivity Analyses
Authors: Teixeira Philippe C. Fernandes , Saad Núbia dos Santos , Lara-Molina Fabian Andres , Cavalini Aldemir Ap , Steffen Valder 
Published: June 2019

Abstract
Semi-active actuators have been used in engineering systems for vibration control purposes. For instance, magnetorheological (MR) dampers are applied in support of vehicle seats and smart suspensions of bridges and buildings. Parametric and nonparametric approaches were developed to model MR actuators, in which the former presents well-established and representative models. In this context, this work aims at comparing the so-called Bingham, modified Bouc-Wen (BW), and hysteretic models dedicated to MR actuators. Typical inverse problems were solved to minimize the difference between the forces determined by using these models and experimental data. The obtained results demonstrated that the hysteretic model is better adapted to represent the considered MR actuator, presenting lower computational cost and easy implementation. Additionally, uncertainty and sensitivity analyses based on the interval approach were applied on the updated MR models aiming to determine the working envelopes associated with the most important parameters of the models.
Structural Reliability Analysis Using Adaptive Artificial Neural Networks
Authors: de Santana Gomes Wellison José 
Published: June 2019

Abstract
Structural reliability theory has been applied to many engineering problems in the last decades, with the primary objective of quantifying the safety of such structures. Although in some cases approximated methods may be used, many times the only alternatives are those involving more demanding approaches, such as Monte Carlo simulation (MCS). In this context, surrogate models have been widely employed as an attempt to keep the computational effort acceptable. In this paper, an adaptive approach for reliability analysis using surrogate models, proposed in the literature in the context of Kriging and polynomial chaos expansions (PCEs), is adapted for the case of multilayer perceptron (MLP) artificial neural networks (ANNs). The methodology is employed in the solution of three benchmark reliability problems and compared to MCS and other methods from the literature. In all cases, the ANNs led to results very close to those obtained by MCS and required much less limit state function evaluations. Also, the performance of the ANNs was found comparable, in terms of accuracy and efficiency, to the performance of the other methods.
Comparing Principal Component Analysis and Mahalanobis–Taguchi System to Detect Unbalance in a Centrifugal Compressor in a Floating Production Storage and Offloading
Authors: de Carvalho Michalski Miguel Angelo , de Melo Italo Skovroski , de Souza Gilberto Francisco Martha 
Published: June 2019

Abstract
Centrifugal compressors used in floating production storage and offloading (FPSO) have special characteristics, forcing manufacturers to build machines that operate at higher speeds in order to achieve the required process performance. Under these operating and design conditions, a special balance procedure called high-speed balancing may be necessary. This procedure is generally done in a special workbench of specialized companies and carrying out a field balance of these machines is an unlikely alternative. Therefore, the development of techniques to identify unbalance malfunction prior to reaching the machine vibration trip limits is essential to minimize the unit downtime. Data-based techniques are strong candidates for the task, since they can take full advantage of huge amounts of available data, allowing the information to be used in an effective way. In this work, two different techniques were chosen to improve unbalance identification: Mahalanobis–Taguchi system (MTS) and principal component analysis (PCA). Considering a real machine, several unbalance conditions were simulated and the results are presented and discussed. Both methods were able to detect unbalance, with a slightly advantage for PCA for the considered conditions.
Dynamic Risk Analysis Using Imprecise and Incomplete Information
Authors: Kamil Mohammad Zaid , Khan Faisal , Song Guozheng , Ahmed Salim 
Published: June 2019

Abstract
Accident modeling is a vital step, which helps in designing preventive measures to avoid future accidents, and thus, to enhance process safety. Bayesian networks (BN) are widely used in accident modeling due to its capability to represent accident scenarios from their causes to likely consequences. However, to assess likelihood of an accident using the BN, it requires exact basic event probabilities, which are often obtained from expert opinions. Such subjective opinions are often inconsistent and sometimes conflicting and/or incomplete. In this work, evidence theory has been coupled with BN to address inconsistency, conflict and incompleteness in the expert opinions. It combines the acquired knowledge from various subjective sources, thereby rendering accuracy in probability estimation. Another source of uncertainty in BN is model uncertainty. To represent multiple interactions of a cause–effect relationship Noisy-OR and leaky Noisy-AND gates are explored in the study. Conventional logic gates, i.e., OR/AND gates can only provide a linear interaction of cause–effect relationship hence introduces uncertainty in the assessment. The proposed methodology provides an impression how dynamic risk assessment could be conducted when the sufficient information about a process system is unavailable. To illustrate the execution of a proposed methodology, a tank equipped with a basic process control system has been used as an example. A real-life case study has also been used to validate the proposed model and compare its results with those using a deterministic approach.
Maximizing Flexibility for Complex Systems Design to Compensate Lack-of-Knowledge Uncertainty
Authors: Daub Marco , Duddeck Fabian 
Published: June 2019

Abstract
The consideration of uncertainty is especially important for the design of complex systems. Because of high complexity, the total system is normally divided into subsystems, which are treated in a hierarchical and ideally independent manner. In recent publications, e.g., (Zimmermann, M., and von Hoessle, J. E., 2013, “Computing Solution Spaces for Robust Design,” Int. J. Numer. Methods Eng., 94(3), pp. 290–307; Fender, J., Duddeck, F., and Zimmermann, M., 2017, “Direct Computation of Solution Spaces,” Struct. Multidiscip. Optim., 55(5), pp. 1787–1796), a decoupling strategy is realized via first the identification of the complete solution space (solutions not violating any design constraints) and second via derivation of a subset, a so-called box-shaped solution space, which allows for decoupling and therefore independent development of subsystems. By analyzing types of uncertainties occurring in early design stages, it becomes clear that especially lack-of-knowledge uncertainty dominates. Often, there is missing knowledge about overall manufacturing tolerances like limitations in production or subsystems are not even completely defined. Furthermore, flexibility is required to handle new requirements and shifting preferences concerning single subsystems arising later in the development. Hence, a set-based approach using intervals for design variables (i.e., interaction quantities between subsystems and the total system) is useful. Because in the published approaches, no uncertainty consideration was taken into account for the computation of these intervals, they can possibly have inappropriate size, i.e., being too narrow. The work presented here proposes to include these uncertainties related to design variables. This allows now to consider lack-of-knowledge uncertainty specific for early phase developments in the framework of complex systems design. An example taken from a standard crash load case (frontal impact against a rigid wall) illustrates the proposed methodology.