Quantification Of Parameters Research Articles

Microsurgery in Motion: An Objective Assessment of Microsurgical Skill and Efficiency

Abstract Background High levels of precision, as well as controlled, efficient motions, are important components of microsurgical technique and success. An accurate and objective means of skill assessment is lacking in resident microsurgical education. Here we employ three-dimensional, real-time motion-tracking technology to analyze hand and instrument motion during microsurgical anastomoses. We hypothesize that motion metrics can objectively quantify microsurgical skill and predict the overall level of expertise. Methods Seventeen participants including medical students, plastic surgery residents, and attendings performed two end-to-end arterial microsurgical anastomoses in a laboratory setting. Motion tracking sensors were applied to standardized positions on participants' hands and microsurgical instruments. Motion and time parameters were abstracted using sensor-derived position data. Results A total of 32 anastomoses were completed and analyzed. There were significant differences in time for task completion and idle time between attendings and junior residents (post-graduate year (PGY)1–3). Path length and working volume consistently differentiated between students and attendings for all phases of an anastomosis. Motion and time data were less able to consistently distinguish attendings from residents stratified by laboratory anastomosis experience. Conclusion Quantifiable motion parameters provide objective data regarding the efficiency of microsurgical techniques in surgical trainees. These data provide a basis for microsurgical competency assessments and may inform future structured feedback through instruction, instruments, and technological interfaces.

ORACLE: An analytical approach for T1, T2, proton density, and off-resonance mapping with phase-cycled balanced steady-state free precession.

To develop and validate a novel analytical approach simplifying , , proton density (PD), and off-resonance quantifications from phase-cycled balanced steady-state free precession (bSSFP) data. Additionally, to introduce a method to correct aliasing effects in undersampled bSSFP profiles. Off-resonant-encoded analytical parameter quantification using complex linearized equations (ORACLE) provides analytical solutions for bSSFP profiles. which instantaneously quantify , , proton density (PD), and . An aliasing correction formalism was derived to allow undersampling of bSSFP profiles. ORACLE was used to quantify , , PD, / , and based on fully sampled ( ) bSSFP profiles from numerical simulations and 3T MRI experiments in phantom and 10 healthy subjects' brains. Obtained values were compared with reference scans in the same scan session. Aliasing correction was validated in subsampled ( ) bSSFP profiles in numerical simulations and human brains. ORACLE quantifications agreed well with input values from simulations and phantom reference values (R2 = 0.99). In human brains, and quantifications when compared with reference methods showed coefficients of variation below 2.9% and 3.9%, biases of 182 and 16.6 ms, and mean white-matter values of 642 and 51 ms using ORACLE. The quantification differed less than 3 Hz between both methods. PD and maps had comparable histograms. The maps effectively identified cerebrospinal fluid. Aliasing correction removed aliasing-related quantification errors in undersampled bSSFP profiles, significantly reducing scan time. ORACLE enables simplified and rapid quantification of , , PD, and from phase-cycled bSSFP profiles, reducing acquisition time and eliminating biomarker maps' coregistration issues.

Nuclear Data Uncertainty Propagation in the PWR MOX/UO2 Core Transient Benchmark

In light water reactor (LWR) transient analyses using deterministic simulation codes, best-estimate plus uncertainty (BEPU) approaches complement the results obtained from simulation codes with uncertainty quantification. In this study, a stochastic sampling–based uncertainty analysis function was developed under the framework of BEPU, enabling uncertainty quantification of important parameters in LWR steady-state and transient analyses using SIMULATE5 and SIMULATE5-K. In the uncertainty quantification of the dynamic behavior of nuclear reactors, the uncertainty knowledge of thermal-hydraulic parameters was highlighted; however, the nuclear data uncertainty propagation to dynamic behavior was insufficient. To bridge the knowledge gap, the uncertainties of interest parameters in the Organisation for Economic Co-operation and Development/Nuclear Energy Agency and the U.S. Nuclear Regulatory Commission PWR MOX/UO2 Core Transient Benchmark were quantified considering cross-section and kinetics data uncertainties. Consequently, the uncertainties of the maximum fuel enthalpy important for rod ejection transient analyses evaluated on the basis of the generalized extreme value distribution were equally contributed by cross-section and kinetics data uncertainties.

Combination of deep learning reconstruction and quantification for dynamic contrast-enhanced (DCE) MRI.

Dynamic contrast-enhanced (DCE) MRI is an important imaging tool for evaluating tumor vascularity that can lead to improved characterization of tumor extent and heterogeneity, and for early assessment of treatment response. However, clinical adoption of quantitative DCE-MRI remains limited due to challenges in acquisition and quantification performance, and lack of automated tools. This study presents an end-to-end deep learning pipeline that exploits a novel deep reconstruction network called DCE-Movienet with a previously developed deep quantification network called DCE-Qnet for fast and quantitative DCE-MRI. DCE-Movienet offers rapid reconstruction of high spatiotemporal resolution 4D MRI data, reducing reconstruction time of the full acquisition to only 0.66 s, which is significantly shorter than compressed sensing's order of 10 min-long reconstructions, without affecting image quality. DCE-Qnet can then perform comprehensive quantification of perfusion parameter maps (Ktrans, vp, ve), and other parameters affecting quantification (T1, B1, and BAT) from a single contrast-enhanced acquisition. The end-to-end deep learning pipeline was implemented to process data acquired with a golden-angle stack-of-stars k-space trajectory and validated on healthy volunteers and a cervical cancer patient against a compressed sensing reconstruction. The end-to-end deep learning DCE-MRI technique addresses key limitations in DCE-MRI in terms of speed and quantification robustness, which is expected to improve the performance of DCE-MRI in a clinical setting.

Evaluation of Aortic Diseases Using Four-Dimensional Flow Magnetic Resonance Imaging.

The complex hemodynamic environment within the aortic lumen plays a crucial role in the progression of aortic diseases such as aneurysms and dissections. Traditional imaging modalities often fail to provide comprehensive flow dynamics that are essential for precise risk assessment and timely intervention. The advent of time-resolved, three-dimensional (3D) phase-contrast magnetic resonance imaging (4D flow MRI) has revolutionized the evaluation of aortic diseases by allowing a detailed visualizations of flow patterns and quantification of hemodynamic parameters. This review explores the utility of 4D flow MRI in the assessment of thoracic aortic diseases, highlighting the key hemodynamic parameters, including flow velocity, wall shear stress, oscillatory shear index, relative residence time, vortex, turbulent kinetic energy, flow displacement, pulse wave velocity, aortic distensibility, energy loss, and stasis. We elucidate the significant findings of studies utilizing 4D flow MRI in the context of aortic aneurysms and dissections, highlighting its role in enhancing our understanding of disease mechanisms and improving clinical outcomes. This review underscores the potential of 4D flow MRI to refine risk stratification and guide therapeutic decisions, ultimately contributing to better management of aortic diseases.

A Water Relaxation Atlas for Age- and Region-Specific Metabolite Concentration Correction at 3 T.

Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation-time differences between metabolites and water. One common approach is to correct the reference signal for differential relaxation within three tissue compartments (gray matter, white matter, and cerebrospinal fluid) using fixed literature values. However, water relaxation times (T1 and T2) vary between brain locations and with age. MRS studies, even those measuring metabolite levels across the lifespan, often ignore these effects, because of a lack of reference data. The purpose of this study is to develop a water relaxometry atlas and to integrate location- and age-appropriate relaxation values into the MRS analysis workflow. One hundred one volunteers (51 men, 50 women; ~10 male, and 10 female participants per decade from the 20s to 60s) were recruited. T1-weighted MPRAGE images ((1-mm)3 isotropic resolution) were acquired. Whole-brain water T1 and T2 measurements were made with DESPOT ((1.4 mm)3 isotropic resolution) at 3T. T1 and T2 maps were registered to the JHU MNI-SS/EVE atlas using affine and LDDMM transformation. The atlas's 268 parcels were reduced to 130 by combining homologous parcels. Mean T1 and T2 values were calculated for each parcel in each subject. Linear models of T1 and T2 as functions of age were computed, using age - 30 as the predictor. Reference atlases of "age-30-intercept" and age-slope for T1 and T2 were generated. The atlas-based workflow was integrated into Osprey, which co-registers MRS voxels to the atlas and calculates location- and age-appropriate water relaxation parameters for quantification. The water relaxation aging atlas revealed significant regional and tissue differences in water relaxation behavior across adulthood. Using location- and subject-appropriate reference values in the MRS analysis workflow removes a current methodological limitation and is expected to reduce quantification biases associated with water-referenced tissue correction, especially for studies of aging.

High-Entropy Zeolitic Imidazolate Frameworks for Dynamic Hydrogen Isotope Separation.

Entropy-driven strategy enables the systematic design of complex systems by using entropy as a quantifiable design parameter for the degree of mixing. In this study, we present mixed-linker zeolitic imidazolate frameworks (ZIFs), sod-ZIF-1 series, that features two types of six-membered rings (6MRs) with aperture sizes of 3.4 Å and 1.7 Å. By adjusting the configurational entropy, the ratio of these 6MRs can be systematically controlled, which significantly influences adsorptive properties, particularly improving the H2 affinity about 3 times throughout the series. This results in the significant enhancement of retention times in dynamic separation of hydrogen isotopes (D2/H2), even over LNG liquefaction temperature. This approach to entropy-driven pore engineering provides new opportunities for enhancing gas adsorption and separation processes.

High‐Entropy Zeolitic Imidazolate Frameworks for Dynamic Hydrogen Isotope Separation

Entropy‐driven strategy enables the systematic design of complex systems by using entropy as a quantifiable design parameter for the degree of mixing. In this study, we present mixed‐linker zeolitic imidazolate frameworks (ZIFs), sod‐ZIF‐1 series, that features two types of six‐membered rings (6MRs) with aperture sizes of 3.4 Å and 1.7 Å. By adjusting the configurational entropy, the ratio of these 6MRs can be systematically controlled, which significantly influences adsorptive properties, particularly improving the H2 affinity about 3 times throughout the series. This results in the significant enhancement of retention times in dynamic separation of hydrogen isotopes (D2/H2), even over LNG liquefaction temperature. This approach to entropy‐driven pore engineering provides new opportunities for enhancing gas adsorption and separation processes.

Towards Technological Ecologies as Compositional Environments in the Pedagogy of Acoustic Composition

Over the last few decades, the use of engraving software in acoustic composition pedagogy has become near ubiquitous. Numerous studies, such as those by Chen and O’Neill, Owlabi, and Nielsen, show that the use of both notation software and technology more broadly in composition aids both the creative process and the development of musical skills. However the paradigms of thought offered by traditional engraving software such as Sibelius and Finale arguably discourage approaches to acoustic composition beyond the quantifiable parameters it encodes visually and represents in playback, leading to the decentring of many of the musical features prevalent in contemporary art music. Using a framework informed by ecosystem-oriented analyses of creativity and Sara Ahmed’s queer phenomenology of disorientation, this paper will interrogate the potential for practical applications of new technology to broaden student composers’ toolkits when used in addition to notation software. As such, it draws from a range of real-world examples in order to offer educators some achievable means by which they can encourage student composers to think beyond notation software, as well a number of suggestions for future research, and furnish them with a basis from which to consider their music in terms that matter most to them.

Time-Resolved X-Ray Fluorescence Analysis of Cerium Ion Transport in Polymer Electrolyte Membranes Under Humidity Gradient

The durability of more than 50,000 hours is required for the widespread application of proton exchange membrane fuel cells [1]. Chemical degradation of perfluorosulfonic acid (PFSA) membrane electrolytes by radicals is one of the degradation factors [2]. To quench the radicals, cerium ions are incorporated into the membrane electrode assembly (MEA) as radical scavengers. However, cerium ions move within the plane of MEA due to humidity gradients [3]. The chemical degradation by the radicals occurs in the depleted areas of cerium ion. Understanding the in-plane transport mechanism of cerium ions under operational conditions, including quantifying parameters like diffusion coefficients, is crucial for developing MEAs with enhanced durability. This study introduces an operando measurement technique employing a custom-built cell to monitor the spatial distribution changes of cerium ions over time. This technique utilizes X-ray fluorescence spectroscopy (XRF) with synchrotron high-energy X-rays, allowing for the analysis of cerium ion distribution under operational conditions while controlling the in-plane humidity gradient. By analyzing the data, we explore the phenomena governing the in-plane transport of cerium ions, driven by humidity and concentration gradients.Two gas inlets were connected to each end plate with an X-ray transmission window to control two types of humidity environments on the left and right side with the in-plane direction of the MEA. The humidity at each gas line was monitored by using dew point hygrometers placed on gas outlets. For the catalyst-coated membranes (CCMs), a platinum catalyst layer was applied to a PFSA-based PEM with approximately 20% sulfonic acid substituted with cerium ions. Gas diffusion layers (GDLs) with microporous layer (MPL) were used. XRF measurements were performed at beamline BL37XU on SPring-8. The incident energy was 60 keV, and Ce-Kα fluorescence X-rays were counted. The beam size was 0.5×0.5 mm2. The cell temperature was maintained at 80°C, and different relative humidity (RH) in the in-plane direction were established using N2 gas (80% on left side and 50% on right side), and then changed to the entire RH 50% to observe cerium ion relaxation behavior.Figure 1 shows the Ce-Kα intensity change of the MEA under the condition of humidity difference with in-plane direction. The x-axis corresponds to the horizontal position of the MEA, with RH 80% on the left side and RH 50% on the right side. The y-axis corresponds to the time under the humidity gradient. The darker blue indicates weaker intensity of the Ce-Kα fluorescence X-ray and the red indicates stronger intensity. This suggests that cerium ions move from the high humidity side to the low humidity side with the humidity gradient as the driving force. The time scale for this transport of a few millimeters is shown to be about tens of minutes. Figure 2 compares the Ce-Kα intensity after the humidity gradient test and the relaxation in homogeneous RH 50% for 10 hours. After the humidity gradient test, cerium ion is concentrated on the right side closed to the center. The concentrated cerium ion is slightly diffused into the left part after the relaxation for 10 hours. However, the initial distribution state does not completely disappear, indicating that the driving force of the humidity gradient exceeds that of the concentration gradient at RH 50%. Reference [1] Y. Fujii, ECS Trans., 111(6), 21 (2023).[2] R. Borup, et al., Chem. Rev., 107(10), 3904 (2007).[3] Y.-H. Lai, et al., J. Electrochem. Soc., 165(6), F3217 (2018).[4] K. Morita, et al., ECS Trans., submitted. Acknowledgements: The authors would like to thank M. Toida and T. Tambo (Toyota Motor Corporation) for providing samples and for in-depth discussions. Figure 1

Time-resolved MR fingerprinting for T2* signal extraction: MR fingerprinting meets echo planar time-resolved imaging.

This study leverages the echo planar time-resolved imaging (EPTI) concept in MR fingerprinting (MRF) framework for a new time-resolved MRF (TRMRF) approach, and explores its capability for fast simultaneous quantification of multiple MR parameters including T1, T2, T2*, proton density, off resonance, and B1 +. The proposed TRMRF method uses the concept of EPTI to track the signal change along the EPI echo train for T2* weighting with a k-t Poisson-based sampling order designed for acquisition. A two-dimensional decomposition algorithm was designed for the image reconstruction, enabling fast and precise subspace modeling. The accuracy of proposed method was evaluated by a T1/T2 phantom. The feasibility was demonstrated through 5 healthy volunteer brain studies. In the phantom studies, T1, T2, and T2* maps of TRMRF correlated strongly with gold-standard methods. The concordance correlation coefficients are 0.9999, 0.9984 and 0.9978, and R2s are 0.9998, 0.9971, and 0.9983. In the in vivo studies, quantitative maps were acquired with 5 healthy volunteers. TRMRF was demonstrated to have comparable results with spiral MRF and gradient-echo EPTI. TRMRF scans using 16, 10, and 6 s per slice were also evaluated to demonstrate the capability of shorter scan times. A new approach is proposed to exploit the advantage of EPTI in the MRF framework. We demonstrate in phantom and in vivo experiments that T1, T2, T2*, proton density, off resonance, and B1 + can be simultaneously quantified within 6 s/slice by TRMRF.

Automated optimization and uncertainty quantification of convergence parameters in plane wave density functional theory calculations

First principles approaches have revolutionized our ability in using computers to predict, explore, and design materials. A major advantage commonly associated with these approaches is that they are fully parameter-free. However, numerically solving the underlying equations requires to choose a set of convergence parameters. With the advent of high-throughput calculations, it becomes exceedingly important to achieve a truly parameter-free approach. Utilizing uncertainty quantification (UQ) and linear decomposition we derive a numerically highly efficient representation of the statistical and systematic error in the multidimensional space of the convergence parameters for plane wave density functional theory (DFT) calculations. Based on this formalism we implement a fully automated approach that requires as input the target precision rather than convergence parameters. The performance and robustness of the approach are shown by applying it to a large set of elements crystallizing in a cubic fcc lattice.

Cardiac Magnetic Resonance Imaging in the Evaluation of Functional Impairments in the Right Heart.

Cardiac magnetic resonance (cMRI) imaging has recently become essential in cardiology. cMRI is widely recognized as the most reliable imaging technique for assessing the size and performance of the right ventricle. It allows for objective and functional cardiac tissue evaluations. Early in disease progression, cardiac structure and activity decrease subclinically. Late-phase clinically visible signs have been associated with less favourable outcomes. Subclinical alterations ought to be recognized for rapid evaluations and accurate treatment. An increasing amount of evidence supports cMRI deformation parameter quantification. Strain imaging enables cardiologists to assess heart function beyond traditional measurements. Prognostic information for cardiovascular disease patients is obtained through the right ventricle (RV) strain, including information primarily about the left ventricle (LV). Right atrial (RA) function evaluations using RA strain have been promising in recent studies. Therefore, this narrative review aims to present an overview of the data that are currently available for assessing right myocardial strain and biomechanics using cMRI.

The Effect of Micro-Computed Tomography Thresholding Methods on Bone Micromorphometric Analysis.

Bone micromorphometric parameters are generally analyzed with micro CT to reveal two- and three-dimensional structures. These parameters are generally used for new bone formation studies such as tissue engineering and biomaterials studies. Different threshold methods are used for the image segmentation of bone micromorphometric parameters. However, these different threshold methods provide different results for the bones analyzed. This study aimed to compare thresholding methods to evaluate bone micromorphometric parameters in the mouse bone. A dataset containing 15 mouse tibia was used to analyze the different thresholding methods for bone micromorphometric parameter analysis. These threshold methods were used to analyze the mouse tibia (n = 15) with thresholded bones. The threshold methods and the analysis were used directly from CTAn (Bruker Micro-CT). The results were compared between the threshold methods, which included bone volume, trabecular number, connectivity, trabecular separation, and other parameters. There was agreement to some extent for all bone micromorphometric analyses using the different thresholding methods. The results showed that the thresholding method showed good agreement for connectivity and trabecular thickness, but the other parameters showed limited agreement. The evaluation of threshold methods allows for the comparison of image segmentation and the quantification of mouse tibia micromorphometric parameters. This study may enable the analysis of bone micromorphometric parameters using the relatively close threshold method in image segmentation across different research groups.

Orientation-independent quantification of macromolecular proton fraction in tissues with suppression of residual dipolar coupling.

Quantitative magnetization transfer (MT) imaging enables noninvasive characterization of the macromolecular environment of tissues. However, recent work has highlighted that the quantification of MT parameters using saturation radiofrequency (RF) pulses exhibits orientation dependence in ordered tissue structures, potentially confounding its clinical applications. Notably, in tissues with ordered structures, such as articular cartilage and myelin, the residual dipolar coupling (RDC) effect can arise owing to incomplete averaging of dipolar-dipolar interactions of water protons. In this study, we demonstrated the confounding effect of RDC on quantitative MT imaging in ordered tissues can be suppressed by using an emerging technique known as macromolecular proton fraction mapping based on spin-lock (MPF-SL). The off-resonance spin-lock RF pulse in MPF-SL could be designed to generate a strong effective spin-lock field to suppress RDC without violating the specific absorption rate and hardware limitations in clinical scans. Furthermore, suppressing the water pool contribution in MPF-SL enabled the application of a strong effective spin-lock field without confounding effects from direct water saturation. Our findings were experimentally validated using human knee specimens and healthy human cartilage. The results demonstrated that MPF-SL exhibits lower sensitivity to tissue orientation compared with , , and saturation-pulse-based MT imaging. Consequently, MPF-SL could serve as a valuable orientation-independent technique for the quantification of MPF.

Retrieve water quality parameters of urban rivers from hyperspectral images through feature interaction based two-stage method fused with spectral unmixing and spatial relatedness

Monitoring water quality through efficient quantification of water quality parameters (WQPs) is of paramount importance to environmental management of urban rivers. Unmanned aerial vehicle (UAV) remote sensing techniques posed a great opportunity for visualizing spatial distributions of WQPs concentrations with higher flexibility and monitoring frequency compared to satellite remote sensing techniques, assisting to trace potential contamination sources and prevent water quality from degradation. However, current methods of water quality monitoring usually involved large masses of water samples as training data to keep calculation accuracy every time their study area changed, increasing financial cost and incurring time delay in monitoring and evaluating water quality. This study proposed a UAV-based two-stage multidirectional fusion with probabilistic matrix factorization (TSMF-PMF) method in unified framework, to effectively quantify WQPs including chemical oxygen demand (COD), biochemical oxygen demand (BOD), and turbidity from UAV hyperspectral images. TSMF-PMF established feature interaction and outlier feedback module, ensuring relatively high calculation stability with less training samples. The experimental results showed that TSMF-PMF achieved good performance on predicting concentrations of WQPs with coefficient of determination (R2) and mean absolute percentage error (MAPE) ranging from 0.82 to 0.91 and from 5.35 % to 10.23 %. This study established theoretical and technical foundation to optimize environmental management scheme of urban rivers and provided an application demonstration.

Quantitative modeling of lenticulostriate arteries on 7-T TOF-MRA for cerebral small vessel disease.

We developed a framework for segmenting and modeling lenticulostriate arteries (LSAs) on 7-T time-of-flight magnetic resonance angiography and tested its performance on cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) patients and controls. We prospectively included 29 CADASIL patients and 21 controls. The framework includes a small-patch convolutional neural network (SP-CNN) for fine segmentation, a random forest for modeling LSAs, and a screening model for removing wrong branches. The segmentation performance of our SP-CNN was compared to competitive networks. External validation with different resolution was performed on ten patients with aneurysms. Dice similarity coefficient (DSC) and Hausdorff distance (HD) between each network and manual segmentation were calculated. The modeling results of the centerlines, diameters, and lengths of LSAs were compared against manual labeling by four neurologists. The SP-CNN achieved higher DSC (92.741 ± 2.789, mean ± standard deviation) and lower HD (0.610 ± 0.141 mm) in the segmentation of LSAs. It also outperformed competitive networks in the external validation (DSC 82.6 ± 5.5, HD 0.829 ± 0.143 mm). The framework versus manual difference was lower than the manual inter-observer difference for the vessel length of primary branches (median -0.040 mm, interquartile range -0.209 to 0.059 mm) and secondary branches (0.202 mm, 0.016-0.537 mm), as well as for the offset of centerlines of primary branches (0.071 mm, 0.065-0.078 mm) and secondary branches (0.072, 0.064-0.080 mm), with p < 0.001 for all comparisons. Our framework for LSAs modeling/quantification demonstrated high reliability and accuracy when compared to manual labeling. NCT05902039 ( https://clinicaltrials.gov/study/NCT05902039?cond=NCT05902039 ). The proposed automatic segmentation and modeling framework offers precise quantification of the morphological parameters of lenticulostriate arteries. This innovative technology streamlines diagnosis and research of cerebral small vessel disease, eliminating the burden of manual labeling, facilitating cohort studies and clinical diagnosis. The morphology of LSAs is important in the diagnosis of CSVD but difficult to quantify. The proposed algorithm achieved the performance equivalent to manual labeling by neurologists. Our method can provide standardized quantitative results, reducing radiologists' workload in cohort studies.

Echocardiographic Assessment of Cardiac Remodeling According to Obesity Class

Evidence supports the existence of cardiac remodeling in obesity, but no standard diagnostic criteria has been proposed or validated.The objective of this study was to identify echocardiographic features of cardiac remodeling according to obesity class and assess the effect of non-surgical weight loss on cardiac structure and function.A total of 120 patients were divided according to their obesity class (Group 1: BMI 18.5-24.9; Group 2: 25-29.9; Group 3: 30-39.9; Group 4: >40) and underwent cross-sectional transthoracic echocardiography. Echocardiographic parameters of cardiac chamber quantification and function were compared between the 4 groups. Echocardiographic parameters were compared pre- and post-non-surgical weight loss in a subgroup of patients. Overall, there was an incremental increase in left ventricular (LV), left atrial (LA), and right ventricular dimensions, LV mass and LV stroke volume (all p<0.0001) across obesity classes. There was no significant difference in LV ejection fraction or right ventricular systolic function as assessed by tricuspid annular plane systolic excursion (TAPSE) but a significant decrease in global longitudinal strain (BMI 18.5-24.9: 22.8%±1.7%; BMI 25-29.9: 22.0%±1.4%; BMI 30-39.9: 20.8%±1.1%; BMI >40: 20.6%±1.3%, p<0.0001) and left atrial strain (BMI 18.5-24.9: 37.7%±2.3%; BMI 25-29.9: 32.8%±2.1%; BMI 30-39.9: 31.5%±1.8%; BMI >40: 29.0%±2.8%, p<0.0001). Allometric height-indexed LV and LA dimensions increased with increasing BMI class (p<0.0001). Echocardiographic parameters did not change significantly after non-surgical weight loss.In conclusion, echocardiographic features can be described according to obesity class. Allometric height indexation may better reflect cardiac remodeling in obesity in comparison to BSA indexation. Non-surgical weight loss was not associated with significant changes in cardiac chamber dimensions and function.

Multifidelity Rotor Wake Modeling with Uncertainty Quantification of Transitioning Tilt-Propeller Aircraft Performance

A framework for efficient multifidelity modeling of tilt-propeller aircraft performance is developed, with an emphasis on accurate modeling of transition maneuvers between vertical and forward flight. Low- and midfidelity vortex models are used for the propeller aerodynamic data sources and lifting surface analyses. Uncertainty quantification of propeller model parameters is conducted and used as the input uncertainty of the source data over the operational domain. A multifidelity approach is proposed, using active learning of Gaussian processes (GPs) that are sequentially and adaptively enhanced with additional higher-fidelity data queried at strategic points selected from an acquisition function. This process is applied to the buildup of single- and multifidelity GPs, forming surrogate models of propeller performance over the operational domain. Each surrogate model is used in the full aircraft mission analysis of a tilt-propeller aircraft and provides quantified uncertainty in aircraft performance over the full flight envelope. Optimal maneuvers are also explored, balancing the tradeoff between maximum power, tilt rates, and time efficiency of conversion. The developed methods are applicable to many distributed electric propulsion aircraft configurations and are applied in this paper to a pusher-configured tilt-propeller aircraft.

Optimizing SureQuant for Targeted Peptide Quantification: a Technical Comparison with PRM and SWATH-MS Methods.

Bacterial infections are a major threat to human health worldwide. A better understanding of the properties and physiology of bacterial pathogens in human tissues is required to develop urgently needed novel control strategies. Mass spectrometry-based proteomics could yield such data, but identifying and quantifying scarce bacterial proteins against a preponderance of human proteins is challenging. Here, we explored the recently introduced SureQuant method for highly sensitive targeted mass spectrometry. Using a major human pathogen, the Gram-positive bacteria Staphylococcus aureus, as an example, we evaluated several parameters, including the number of targets and intensity thresholds, for optimal qualitative and quantitative protein analysis. By comparison, we found that SureQuant achieved the same quantitative performance as standard parallel reaction monitoring while allowing accurate and precise quantification of up to 400 targets. SureQuant also surpassed the sensitivity and quantification capabilities of global data-independent acquisition methods. Finally, to facilitate method development, we provide optimized MS parameters for the sensitive quantification of different peptide panel sizes. This study provides a foundation for the broader application of SureQuant in the analysis of clinical specimens containing trace amounts of bacterial proteins as well as other studies requiring ultrasensitive detection of low-abundant proteins.