Biomarker-based prediction of progression in MCI: Comparison of AD signature and hippocampal volume with spinal fluid amyloid-β and tau

Objective: New diagnostic criteria for mild cognitive impairment (MCI) due to Alzheimer's disease (AD) have been developed using biomarkers aiming to establish whether the clinical syndrome is likely due to underlying AD. We investigated the utility of magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) biomarkers in predicting progression from amnesic MCI to dementia, testing the hypotheses that (1) markers of amyloid and neurodegeneration provide distinct and complementary prognostic information over different time intervals, and that (2) evidence of neurodegeneration in amyloid-negative MCI individuals would be useful prognostically. Methods: Data were obtained from the ADNI-1 (Alzheimer's Disease Neuroimaging Initiative Phase 1) database on all individuals with a baseline diagnosis of MCI, baseline MRI and CSF data, and at least one follow-up visit. MRI data were processed using a published set of a priori regions of interest to derive a measure known as the ``AD signature,'' as well as hippocampal volume. The CSF biomarkers amyloid-β, total tau, and phospho tau were also examined. We performed logistic regression analyses to identify the best baseline biomarker predictors of progression to dementia over 1 or 3 years, and Cox regression models to test the utility of these markers for predicting time-to-dementia. Results: For prediction of dementia in MCI, the AD signature cortical thickness biomarker performed better than hippocampal volume. Although CSF tau measures were better than CSF amyloid-β at predicting dementia within 1 year, the AD signature was better than all CSF measures at prediction over this relatively short-term interval. CSF amyloid-β was superior to tau and AD signature at predicting dementia over 3 years. When CSF amyloid-β was dichotomized using previously published cutoff values and treated as a categorical variable, a multivariate stepwise Cox regression model indicated that both the AD signature MRI marker and the categorical CSF amyloid-β marker were useful in predicting time-to-event diagnosis of AD dementia. Conclusion: In amnesic MCI, short-term (1 year) prognosis of progression to dementia relates strongly to baseline markers of neurodegeneration, with the AD signature MRI biomarker of cortical thickness performing the best among MRI and CSF markers studied here. Longer-term (3 year) prognosis in these individuals was better predicted by a marker indicative of brain amyloid. Prediction of time-to-event in a survival model was predicted by the combination of these biomarkers. These results provide further support for emerging models of the temporal relationship of pathophysiologic events in AD and demonstrate the utility of these biomarkers at the prodromal stage of the illness

LLV - Lunar Logistic Vehicle Final report

Evaluation of systems design training institute for engineering facult

Enhancement of Rabi Splitting in a Microcavity with an Embedded Superlattice

We have observed a large coupling between the excitonic and photonic modes of an AlAs/AlGaAs microcavity filled with an 84-({\rm {\AA}})/20({\rm {\AA}}) GaAs/AlGaAs superlattice. Reflectivity measurements on the coupled cavity-superlattice system in the presence of a moderate electric field yielded a Rabi splitting of 9.5 meV at T = 238 K. This splitting is almost 50% larger than that found in comparable microcavities with quantum wells placed at the antinodes only. We explain the enhancement by the larger density of optical absorbers in the superlattice, combined with the quasi-two-dimensional binding energy of field-localized excitons.Comment: 5 pages, 4 figures, submitted to PR

Calculations of Neutron Reflectivity in the eV Energy Range from Mirrors made of Heavy Nuclei with Neutron-Nucleus Resonances

We evaluate the reflectivity of neutron mirrors composed of certain heavy nuclei which possess strong neutron-nucleus resonances in the eV energy range. We show that the reflectivity of such a mirror for some nuclei can in principle be high enough near energies corresponding to compound neutron-nucleus resonances to be of interest for certain scientific applications in non-destructive evaluation of subsurface material composition and in the theory of neutron optics beyond the kinematic limit.Comment: 18 pages, 5 figures, 1 tabl

Heat conductivity of DNA double helix

Thermal conductivity of isolated single molecule DNA fragments is of importance for nanotechnology, but has not yet been measured experimentally. Theoretical estimates based on simplified (1D) models predict anomalously high thermal conductivity. To investigate thermal properties of single molecule DNA we have developed a 3D coarse-grained (CG) model that retains the realism of the full all-atom description, but is significantly more efficient. Within the proposed model each nucleotide is represented by 6 particles or grains; the grains interact via effective potentials inferred from classical molecular dynamics (MD) trajectories based on a well-established all-atom potential function. Comparisons of 10 ns long MD trajectories between the CG and the corresponding all-atom model show similar root-mean-square deviations from the canonical B-form DNA, and similar structural fluctuations. At the same time, the CG model is 10 to 100 times faster depending on the length of the DNA fragment in the simulation. Analysis of dispersion curves derived from the CG model yields longitudinal sound velocity and torsional stiffness in close agreement with existing experiments. The computational efficiency of the CG model makes it possible to calculate thermal conductivity of a single DNA molecule not yet available experimentally. For a uniform (polyG-polyC) DNA, the estimated conductivity coefficient is 0.3 W/mK which is half the value of thermal conductivity for water. This result is in stark contrast with estimates of thermal conductivity for simplified, effectively 1D chains ("beads on a spring") that predict anomalous (infinite) thermal conductivity. Thus, full 3D character of DNA double-helix retained in the proposed model appears to be essential for describing its thermal properties at a single molecule level.Comment: 16 pages, 12 figure

Do Invariances in Deep Neural Networks Align with Human Perception?

An evaluation criterion for safe and trustworthy deep learning is how well the invariances captured by representations of deep neural networks (DNNs) are shared with humans. We identify challenges in measuring these invariances. Prior works used gradient-based methods to generate identically represented inputs (IRIs), i.e., inputs which have identical representations (on a given layer) of a neural network, and thus capture invariances of a given network. One necessary criterion for a network's invariances to align with human perception is for its IRIs look “similar” to humans. Prior works, however, have mixed takeaways; some argue that later layers of DNNs do not learn human-like invariances yet others seem to indicate otherwise. We argue that the loss function used to generate IRIs can heavily affect takeaways about invariances of the network and is the primary reason for these conflicting findings. We propose an adversarial regularizer on the IRI-generation loss that finds IRIs that make any model appear to have very little shared invariance with humans. Based on this evidence, we argue that there is scope for improving models to have human-like invariances, and further, to have meaningful comparisons between models one should use IRIs generated using the regularizer-free loss. We then conduct an in-depth investigation of how different components (e.g. architectures, training losses, data augmentations) of the deep learning pipeline contribute to learning models that have good alignment with humans. We find that architectures with residual connections trained using a (self-supervised) contrastive loss with `p ball adversarial data augmentation tend to learn invariances that are most aligned with humans. Code: github.com/nvedant07/Human-NN-Alignment. We strongly recommend reading the arxiv version of this paper: https://arxiv.org/abs/2111.14726

Drawing Trees with Perfect Angular Resolution and Polynomial Area

We study methods for drawing trees with perfect angular resolution, i.e., with angles at each node v equal to 2{\pi}/d(v). We show: 1. Any unordered tree has a crossing-free straight-line drawing with perfect angular resolution and polynomial area. 2. There are ordered trees that require exponential area for any crossing-free straight-line drawing having perfect angular resolution. 3. Any ordered tree has a crossing-free Lombardi-style drawing (where each edge is represented by a circular arc) with perfect angular resolution and polynomial area. Thus, our results explore what is achievable with straight-line drawings and what more is achievable with Lombardi-style drawings, with respect to drawings of trees with perfect angular resolution.Comment: 30 pages, 17 figure

Two Decades of Huntington Disease Testing: Patient’s Demographics and Reproductive Choices

Predictive testing for Huntington disease (HD) has been available in the United States (US) since 1987, and the Indiana University Predictive Testing Program has been providing this testing since 1990. To date there has been no published description of those who present for such testing in the US. Here we describe demographics of 141 individuals and reproductive decision making of a subset of 16 of those individuals who underwent predictive HD testing between 1990 and 2010 at one site in the US. This study is a retrospective chart review of the “Personal History Questionnaire” participants completed prior to testing. As seen in other studies, most participants were female (64.5 %), in their mid-30s (mean = 34), and had at least one child prior to testing (54 %). Multiple demographic datum points are described, and the reproductive decision making of these at-risk individuals was analyzed using Fisher’s Exact Tests. Of those women who had children before learning of their risk to inherit HD, those who attended church more frequently, had three or more children total, or whose mother was affected with HD were more likely to be comfortable with their choice to have children. We conclude that these demographic factors influence the reproductive decision-making of individuals at risk for HD. Psychologists, clinical geneticists, and genetic counselors may be able to use this information to help counsel at-risk patients regarding current or past reproductive decision making