Oconnormelgaard0662
This method that is computationally efficient and, thus, scalable to large data sets may augment the abilities of SR-US in imaging microvascular structure and function.We numerically and experimentally investigate the dispersion properties of leaky Lamb waves in the cranial bone. see more Cranial Lamb waves leak energy from the skull into the brain when propagating at speeds higher than the speed of sound in the surrounding fluid. The understanding of their radiation mechanism is significantly complicated by the geometric and mechanical characteristics of the cortical tables and the trabecular bone (diploë). Toward such understanding, we here analyze the sub-1.0 MHz radiation angle dispersion spectrum of porous bone phantoms and parietal bone geometries obtained from μ CT scans. Our numerical results show that, when diploic pores are physically modeled, leakage angles computed from time transient finite-element analyses correspond to those predicted by an equivalent three-layered fluid-loaded waveguide model. For the bone geometries analyzed, two main leaky branches are observed in the near-field dispersion spectrum a fast wave radiated at small angles, which is related to the fastest fundamental Lamb mode supported by the cranial bone, and a slower wave radiated at larger angles. This observation is also confirmed by experimental tests carried out on an immersed parietal bone.Modeling of hemodynamics and artificial intelligence have great potential to support clinical diagnosis and decision making. While hemodynamics modeling is extremely time- and resource-consuming, machine learning (ML) typically requires large training data that are often unavailable. The aim of this study was to develop and evaluate a novel methodology generating a large database of synthetic cases with characteristics similar to clinical cohorts of patients with coarctation of the aorta (CoA), a congenital heart disease associated with abnormal hemodynamics. Synthetic data allows use of ML approaches to investigate aortic morphometric pathology and its influence on hemodynamics. Magnetic resonance imaging data (154 patients as well as of healthy subjects) of aortic shape and flow were used to statistically characterize the clinical cohort. The methodology generating the synthetic cohort combined statistical shape modeling of aortic morphometry and aorta inlet flow fields and numerical flow simulations. Hierarchical clustering and non-linear regression analysis were successfully used to investigate the relationship between morphometry and hemodynamics and to demonstrate credibility of the synthetic cohort by comparison with a clinical cohort. A database of 2652 synthetic cases with realistic shape and hemodynamic properties was generated. Three shape clusters and respective differences in hemodynamics were identified. The novel model predicts the CoA pressure gradient with a root mean square error of 4.6 mmHg. In conclusion, synthetic data for anatomy and hemodynamics is a suitable means to address the lack of large datasets and provide a powerful basis for ML to gain new insights into cardiovascular diseases.Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase. The resulting network resembles a static entity of knowledge, with endeavours to extend this knowledge without targeting the original task resulting in a catastrophic forgetting. Continual learning shifts this paradigm towards networks that can continually accumulate knowledge over different tasks without the need to retrain from scratch. We focus on task incremental classification, where tasks arrive sequentially and are delineated by clear boundaries. Our main contributions concern 1) a taxonomy and extensive overview of the state-of-the-art, 2) a novel framework to continually determine the stability-plasticity trade-off of the continual learner, 3) a comprehensive experimental comparison of 11 state-of-the-art continual learning methods and 4 baselines. We empirically scrutinize method strengths and weaknesses on three benchmarks, considering Tiny Imagenet and large-scale unbalanced iNaturalist and a sequence of recognition datasets. We study the influence of model capacity, weight decay and dropout regularization, and the order in which the tasks are presented, and qualitatively compare methods in terms of required memory, computation time and storage.
To reveal the possible contribution of changes in membrane ion concentration gradients and ion pump activity to axonal conduction/block induced by long-duration electrical stimulation.
A new model for conduction and block of unmyelinated axons based on the classical Hodgkin-Huxley (HH) equations is developed to include changes in Na+ and K+ concentrations and ion pumps. The effects of long-duration stimulation on axonal conduction/block is analyzed by computer simulation using this new model.
The new model successfully simulates initiation, propagation, and block of action potentials induced by short-duration (multiple milliseconds) stimulations that do not significantly change the ion concentrations in the classical HH model. In addition, the activity-dependent effects such as action potential attenuation and broadening observed in animal studies are also successfully simulated by the new model. Finally, the model successfully simulates axonal block occurring after terminating a long-duration (multiple different neurostimulation therapies.
Infant feeding practices are thought to shape food acceptance and preferences. However, few studies have evaluated whether these affect child diet later in life.
The study objective was to examine the association between infant feeding practices and dietary patterns (DPs) in school-aged children.
A secondary analysis of data from a diverse prospective birth cohort with 10 years of follow-up (WHEALS [Wayne County Health Environment Allergy and Asthma Longitudinal Study]) was conducted.
Children from the WHEALS (Detroit, MI, born 2003 through 2007) who completed a food screener at age 10 years were included (471 of 1,258 original participants).
The main outcome was DPs at age 10 years, identified using the Block Kids Food Screener.
Latent class analysis was applied for DP identification. Breastfeeding and age at solid food introduction were associated with DPs using a 3-step approach for latent class modeling based on multinomial logistic regression models.
The following childhood DPs were identified processed/energy-dense food (35%), variety plus high intake (41%), and healthy (24%).
