The Value of Information for Populations in Varying Environments

The notion of information pervades informal descriptions of biological systems, but formal treatments face the problem of defining a quantitative measure of information rooted in a concept of fitness, which is itself an elusive notion. Here, we present a model of population dynamics where this problem is amenable to a mathematical analysis. In the limit where any information about future environmental variations is common to the members of the population, our model is equivalent to known models of financial investment. In this case, the population can be interpreted as a portfolio of financial assets and previous analyses have shown that a key quantity of Shannon's communication theory, the mutual information, sets a fundamental limit on the value of information. We show that this bound can be violated when accounting for features that are irrelevant in finance but inherent to biological systems, such as the stochasticity present at the individual level. This leads us to generalize the measures of uncertainty and information usually encountered in information theory

Using Flexible Criteria to Improve Manufacturing Validation During Product Development

In order to improve engineering product designs and reduce the number of problems that occur during a new product launch, firms have focused on integrating downstream product development processes into the design phases. Unfortunately, this resource inte gration to solve problems has been less common in the back-end of product development, particularly for complex products involving many components such as an automotive body. Here, manufacturing firms use sequential validation procedures first to approve compo nents, then subassemblies, and finally the end product. One trend has been to tighten component tolerances in efforts to avoid or mini mize downstream assembly problems. These stricter component requirements, however, often result in timing delays and cost overruns due to unnecessary rework of components. For complex-assemblies, the use of "flexible criteria" and an approach called "functional build" can significantly reduce validation time and costs yet still meet end product quality objectives. This paper examines this functional build approach to manufacturing validation and demonstrates its effectiveness with an automotive case example.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66875/2/10.1177_1063293X9900700404.pd

Cervical cord myelin water imaging shows degenerative changes over one year in multiple sclerosis but not neuromyelitis optica spectrum disorder

Spinal cord pathology is a feature of both neuromyelitis optica spectrum disorder (NMOSD) and relapsing-remitting multiple sclerosis (MS). While subclinical disease activity has been described in MS using quantitative magnetic resonance imaging measures, current evidence suggests that neurodegeneration is absent between relapses in NMOSD, although most evidence comes from brain studies. We aimed to assess cross-sectional differences and longitudinal changes in myelin integrity in relapse-free MS and NMOSD subjects over one year. 15 NMOSD, 15 MS subjects, and 17 healthy controls were scanned at 3T using a cervical cord mcDESPOT protocol. A subset of 8 NMOSD, 11 MS subjects and 14 controls completed follow-up. Measures of the myelin water fraction (fM) within lesioned and non-lesioned cord segments were collected. At baseline, fM in lesioned and non-lesioned segments was significantly reduced in MS (lesioned: p=0.002; non-lesioned: p=0.03) and NMOSD (lesioned: p=0.0007; non-lesioned: p=0.002) compared to controls. Longitudinally, fM decreased within non-lesioned cord segments in the MS group (−7.3%, p=0.02), but not in NMOSD (+5.8%, p=0.1), while change in lesioned segments fM did not differ from controls' in either patient group. These results suggest that degenerative changes outside of lesioned areas can be observed over a short time frame in MS, but not NMOSD, and support the use of longitudinal myelin water imaging for the assessment of pathological changes in the cervical cord in demyelinating diseases. Keywords: Multiple sclerosis, Neuromyelitis optica, Spinal cord, Magnetic resonance imaging, Myelin water imaging, Longitudinal stud

Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls

Myelin imaging is a form of quantitative magnetic resonance imaging (MRI) that measures myelin content and can potentially allow demyelinating diseases such as multiple sclerosis (MS) to be detected earlier. Although focal lesions are the most visible signs of MS pathology on conventional MRI, it has been shown that even tissues that appear normal may exhibit decreased myelin content as revealed by myelin-specific images (i.e., myelin maps). Current methods for analyzing myelin maps typically use global or regional mean myelin measurements to detect abnormalities, but ignore finer spatial patterns that may be characteristic of MS. In this paper, we present a machine learning method to automatically learn, from multimodal MR images, latent spatial features that can potentially improve the detection of MS pathology at early stage. More specifically, 3D image patches are extracted from myelin maps and the corresponding T1-weighted (T1w) MRIs, and are used to learn a latent joint myelin-T1w feature representation via unsupervised deep learning. Using a data set of images from MS patients and healthy controls, a common set of patches are selected via a voxel-wise t-test performed between the two groups. In each MS image, any patches overlapping with focal lesions are excluded, and a feature imputation method is used to fill in the missing values. A feature selection process (LASSO) is then utilized to construct a sparse representation. The resulting normal-appearing features are used to train a random forest classifier. Using the myelin and T1w images of 55 relapse-remitting MS patients and 44 healthy controls in an 11-fold cross-validation experiment, the proposed method achieved an average classification accuracy of 87.9% (SD=8.4%), which is higher and more consistent across folds than those attained by regional mean myelin (73.7%, SD=13.7%) and T1w measurements (66.7%, SD=10.6%), or deep-learned features in either the myelin (83.8%, SD=11.0%) or T1w (70.1%, SD=13.6%) images alone, suggesting that the proposed method has strong potential for identifying image features that are more sensitive and specific to MS pathology in normal-appearing brain tissues. Keywords: Deep learning, Multiple sclerosis, Myelin water imaging, Machine learning, Magnetic resonance imagin

Myelin water imaging in relapsing multiple sclerosis treated with ocrelizumab and interferon beta-1a

Background: Myelin water imaging is a magnetic resonance imaging (MRI) technique that quantifies myelin damage and repair in multiple sclerosis (MS) via the myelin water fraction (MWF). Objective: In this substudy of a phase 3 therapeutic trial, OPERA II, MWF was assessed in relapsing MS participants assigned to interferon beta-1a (IFNb-1a) or ocrelizumab (OCR) during a two-year double-blind period (DBP) followed by a two-year open label extension (OLE) with ocrelizumab treatment. Methods: MWF in normal appearing white matter (NAWM), including both whole brain NAWM and 5 white matter structures, and chronic lesions, was assessed in 29 OCR and 26 IFNb-1a treated participants at weeks 0, 24, 48 and 96 (DBP), and weeks 144 and 192 (OLE), and in white matter for 23 healthy control participants at weeks 0, 48 and 96. Results: Linear mixed-effects models of data from baseline to week 96 showed a difference in the change in MWF over time favouring ocrelizumab in all NAWM regions. At week 192, lesion MWF was lower for participants originally randomised to IFNb-1a compared to those originally randomised to OCR. Controls showed no change in MWF over 96 weeks in any region. Conclusion: Ocrelizumab appears to protect against demyelination in MS NAWM and chronic lesions and may allow for a more permissive micro environment for remyelination to occur in focal and diffusely damaged tissue

Quantifying visual pathway axonal and myelin loss in multiple sclerosis and neuromyelitis optica

AbstractBackgroundThe optic nerve is frequently injured in multiple sclerosis and neuromyelitis optica, resulting in visual dysfunction, which may be reflected by measures distant from the site of injury.ObjectiveTo determine how retinal nerve fiber layer as a measure of axonal health, and macular volume as a measure of neuronal health are related to changes in myelin water fraction in the optic radiations of multiple sclerosis and neuromyelitis optica participants with and without optic neuritis and compared to healthy controls.Methods12 healthy controls, 42 multiple sclerosis (16 with optic neuritis), and 10 neuromyelitis optica participants (8 with optic neuritis) were included in this study. Optical coherence tomography assessment involved measurements of the segmented macular layers (total macular, ganglion cell layer, inner plexiform layer, and inner nuclear layer volume) and paripapillary retinal nerve fiber layer thickness. The MRI protocol included a 32-echo T2-relaxation GRASE sequence. Average myelin water fraction values were calculated within the optic radiations as a measure of myelin density.ResultsMultiple sclerosis and neuromyelitis optica eyes with optic neuritis history had lower retinal nerve fiber layer thickness, total macular, ganglion cell and inner plexiform layer volumes compared to eyes without optic neuritis history and controls. Inner nuclear layer volume increased in multiple sclerosis with optic neuritis history (mean=0.99mm3, SD=0.06) compared to those without (mean=0.97mm3, SD=0.06; p=0.003). Mean myelin water fraction in the optic radiations was significantly lower in demyelinating diseases (neuromyelitis optica: mean=0.098, SD=0.01, multiple sclerosis with optic neuritis history: mean=0.096, SD=0.01, multiple sclerosis without optic neuritis history: mean=0.098, SD=0.02; F3,55=3.35, p=0.03) compared to controls. Positive correlations between MRI and optical coherence tomography measures were also apparent (retinal nerve fiber layer thickness and ganglion cell layer thickness: r=0.25, p=0.05, total macular volume and inner plexiform layer volume: r=0.27, p=0.04).ConclusionsThe relationship between reductions in OCT measures of neuro-axonal health in the anterior visual pathway and MRI-based measures of myelin health in the posterior visual pathway suggests that these measures may be linked through bidirectional axonal degeneration