Quantitative and Kinetic Proteomics Reveal ApoE Isoform-dependent Proteostasis Adaptations in Mouse Brain.

Apolipoprotein E (ApoE) polymorphisms modify the risk of Alzheimer's disease with ApoE4 strongly increasing and ApoE2 modestly decreasing risk relative to the control ApoE3. To investigate how ApoE isoforms alter risk, we measured changes in proteome homeostasis in transgenic mice expressing a human ApoE gene (isoform 2, 3, or 4). The regulation of each protein's homeostasis is observed by measuring turnover rate and abundance for that protein.

Tibiopelvic Rotational Arthroplasty for Pediatric Gorham-Stout Disease: A Case Report and Surgical Technique.

Case: This is a case of a 6-year-old patient diagnosed with Gorham-Stout disease (GSD), a rare lymphangiogenic skeletal disorder, localized to the left femur. Initial nonoperative treatment with pharmaceuticals and bracing was unsuccessful. We describe a definitive operative treatment with radical femoral resection and a modified rotationplasty technique through a tibiopelvic rotational hip arthroplasty.

Toward a dynamic national transportation noise map: Modeling temporal variability of spectral traffic noise emission levels.

The National Transportation Noise Map predicts time-averaged road traffic noise across the continental United States (CONUS) based on annual average daily traffic counts. However, traffic noise can vary greatly with time. This paper outlines a method for predicting nationwide hourly varying source traffic sound emissions called the Vehicular Reduced-Order Observation-based Model (VROOM).

Quantitative and Kinetic Proteomics Reveal ApoE Isoform-dependent Proteostasis Adaptations in Mouse Brain.

Apolipoprotein E (ApoE) polymorphisms modify the risk of neurodegenerative disease with the ApoE4 isoform increasing and ApoE2 isoform decreasing risk relative to the 'wild-type control' ApoE3 isoform. To elucidate how ApoE isoforms alter the proteome, we measured relative protein abundance and turnover in transgenic mice expressing a human ApoE gene (isoform 2, 3, or 4). This data provides insight into how ApoE isoforms affect the synthesis and degradation of a wide variety of proteins.

Predictors Associated with the Need for Open Reduction of Pediatric Supracondylar Humerus Fractures: A Meta-analysis of the Recent Literature.

Background: Supracondylar humerus (SCH) fractures are some of the most common fractures in pediatric patients with surgery typically consisting of either open or closed reduction with internal fixation. The aim of this meta-analysis was to identify patient, injury, and administrative factors that are associated with treating pediatric SCH fractures with open techniques.

Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, PubMed and CINAHL database searches were conducted for studies from 2010 to 2023 that made direct comparisons between open reduction and internal fixation (ORIF) and closed reduction and percutaneous pinning (CRPP) for treating SCH fractures in the pediatric population.

Information geometry analysis example for absolute and relative transmission loss in a shallow ocean.

The model manifold, an information geometry tool, is a geometric representation of a model that can quantify the expected information content of modeling parameters. For a normal-mode sound propagation model in a shallow ocean environment, transmission loss (TL) is calculated for a vertical line array and model manifolds are constructed for both absolute and relative TL. For the example presented in this paper, relative TL yields more compact model manifolds with seabed environments that are less statistically distinguishable than manifolds of absolute TL.

How Long Can You Delay? Curve Progression While Awaiting Vertebral Body Tethering Surgery.

: The implications of delaying surgical intervention for patients with adolescent idiopathic scoliosis (AIS) wishing to undergo vertebral body tethering (VBT) have not yet been explored. It is important to understand how these delays can impact surgical planning and patient outcomes. : This was a retrospective review that analyzed all AIS patients treated between 2015 and 2021 at a single tertiary center.

The training process of many deep networks explores the same low-dimensional manifold.

We develop information-geometric techniques to analyze the trajectories of the predictions of deep networks during training. By examining the underlying high-dimensional probabilistic models, we reveal that the training process explores an effectively low-dimensional manifold. Networks with a wide range of architectures, sizes, trained using different optimization methods, regularization techniques, data augmentation techniques, and weight initializations lie on the same manifold in the prediction space.

Data-driven decomposition of crowd noise from indoor sporting events.

Separating crowd responses from raw acoustic signals at sporting events is challenging because recordings contain complex combinations of acoustic sources, including crowd noise, music, individual voices, and public address (PA) systems. This paper presents a data-driven decomposition of recordings of 30 collegiate sporting events. The decomposition uses machine-learning methods to find three principal spectral shapes that separate various acoustic sources.

Sloppy model analysis identifies bifurcation parameters without normal form analysis.

Bifurcation phenomena are common in multidimensional multiparameter dynamical systems. Normal form theory suggests that bifurcations are driven by relatively few combinations of parameters. Models of complex systems, however, rarely appear in normal form, and bifurcations are controlled by nonlinear combinations of the bare parameters of differential equations.

Toward a dynamic national transportation noise map: Modeling temporal variability of traffic volume.

The National Transportation Noise Map (NTNM) gives time-averaged traffic noise across the continental United States (CONUS) using annual average daily traffic. However, traffic noise varies significantly with time. This paper outlines the development and utility of a traffic volume model which is part of VROOM, the Vehicular Reduced-Order Observation-based model, which, using hourly traffic volume data from thousands of traffic monitoring stations across CONUS, predicts nationwide hourly varying traffic source noise.

Feature selection for a continental-scale geospatial model of environmental sound levels.

Modeling environmental sound levels over continental scales is difficult due to the variety of geospatial environments. Moreover, current continental-scale models depend upon machine learning and therefore face additional challenges due to limited acoustic training data. In previous work, an ensemble of machine learning models was used to predict environmental sound levels in the contiguous United States using a training set composed of 51 geospatial layers (downselected from 120) and acoustic data from 496 geographic sites from Pedersen, Transtrum, Gee, Lympany, James, and Salton [JASA Express Lett.

Information geometry for multiparameter models: new perspectives on the origin of simplicity.

Complex models in physics, biology, economics, and engineering are often, meaning that the model parameters are not well determined by the model predictions for collective behavior. Many parameter combinations can vary over decades without significant changes in the predictions. This review uses information geometry to explore sloppiness and its deep relation to emergent theories.

Validating two geospatial models of continental-scale environmental sound levels.

Modeling outdoor environmental sound levels is a challenging problem. This paper reports on a validation study of two continental-scale machine learning models using geospatial layers as inputs and the summer daytime A-weighted L as a validation metric. The first model was developed by the National Park Service while the second was developed by the present authors.

Utilizing Nonequilibrium Isotope Enrichments to Dramatically Increase Turnover Measurement Ranges in Single Biopsy Samples from Humans.

The synthesis of new proteins and the degradation of old proteins in vivo can be quantified in serial samples using metabolic isotope labeling to measure turnover. Because serial biopsies in humans are impractical, we set out to develop a method to calculate the turnover rates of proteins from single human biopsies. This method involved a new metabolic labeling approach and adjustments to the calculations used in previous work to calculate protein turnover.

Bayesian, frequentist, and information geometric approaches to parametric uncertainty quantification of classical empirical interatomic potentials.

In this paper, we consider the problem of quantifying parametric uncertainty in classical empirical interatomic potentials (IPs) using both Bayesian (Markov Chain Monte Carlo) and frequentist (profile likelihood) methods. We interface these tools with the Open Knowledgebase of Interatomic Models and study three models based on the Lennard-Jones, Morse, and Stillinger-Weber potentials. We confirm that IPs are typically sloppy, i.

Automatic classification and reduction of wind noise in spectral data.

Outdoor acoustic data often include non-acoustic pressures caused by atmospheric turbulence, particularly below a few hundred Hz in frequency, even when using microphone windscreens. This paper describes a method for automatic wind-noise classification and reduction in spectral data without requiring measured wind speeds. The method finds individual frequency bands matching the characteristic decreasing spectral slope of wind noise.

Unwinding the model manifold: Choosing similarity measures to remove local minima in sloppy dynamical systems.

In this paper, we consider the problem of parameter sensitivity in models of complex dynamical systems through the lens of information geometry. We calculate the sensitivity of model behavior to variations in parameters. In most cases, models are sloppy, that is, exhibit an exponential hierarchy of parameter sensitivities.

The Spectrum of Mechanism-Oriented Models and Methods for Explanations of Biological Phenomena.

Developing and improving mechanism-oriented computational models to better explain biological phenomena is a dynamic and expanding frontier. As the complexity of targeted phenomena has increased, so too has the diversity in methods and terminologies, often at the expense of clarity, which can make reproduction challenging, even problematic. To encourage improved semantic and methodological clarity, we describe the spectrum of Mechanism-oriented Models being used to develop explanations of biological phenomena.

Maximizing the information learned from finite data selects a simple model.

We use the language of uninformative Bayesian prior choice to study the selection of appropriately simple effective models. We advocate for the prior which maximizes the mutual information between parameters and predictions, learning as much as possible from limited data. When many parameters are poorly constrained by the available data, we find that this prior puts weight only on boundaries of the parameter space.

Calorimetric Methods for Measuring Stability and Reusability of Membrane Immobilized Enzymes.

Unlabelled: The aim of this work is to develop calorimetric methods for characterizing the activity and stability of membrane immobilized enzymes. Invertase immobilized on a nylon-6 nanofiber membrane is used as a test case. The stability of both immobilized and free invertase activity was measured by spectrophotometry and isothermal titration calorimetry (ITC).

Mechanisms of In Vivo Ribosome Maintenance Change in Response to Nutrient Signals.

Control of protein homeostasis is fundamental to the health and longevity of all organisms. Because the rate of protein synthesis by ribosomes is a central control point in this process, regulation, and maintenance of ribosome function could have amplified importance in the overall regulatory circuit. Indeed, ribosomal defects are commonly associated with loss of protein homeostasis, aging, and disease (1-4), whereas improved protein homeostasis, implying optimal ribosomal function, is associated with disease resistance and increased lifespan (5-7).

The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems.

We explore the relationship among experimental design, parameter estimation, and systematic error in sloppy models. We show that the approximate nature of mathematical models poses challenges for experimental design in sloppy models. In many models of complex biological processes it is unknown what are the relevant physical mechanisms that must be included to explain system behaviors.

Bridging Mechanistic and Phenomenological Models of Complex Biological Systems.

The inherent complexity of biological systems gives rise to complicated mechanistic models with a large number of parameters. On the other hand, the collective behavior of these systems can often be characterized by a relatively small number of phenomenological parameters. We use the Manifold Boundary Approximation Method (MBAM) as a tool for deriving simple phenomenological models from complicated mechanistic models.