Stability of exploratory multivariate data modeling in longitudinal data

Exploratory data-driven multivariate analysis provides a means of investigating underlying structure in complex data. To explore the stability of multivariate data modeling, we have applied a common method of multivariate modeling (factor analysis) to the Genetic Analysis Workshop 13 (GAW13) Framingham Heart Study data. Given the longitudinal nature of the data, multivariate models were generated independently for a number of different time points (corresponding to cross-sectional clinic visits for the two cohorts), and compared. In addition, each multivariate model was used to generate factor scores, which were then used as a quantitative trait in variance component-based linkage analysis to investigate the stability of linkage signals over time. We found surprisingly good correlation between factor models (i.e., predicted factor structures), maximum LOD scores, and locations of maximum LOD scores (0.81< ρ <0.94 for factor scores; ρ >0.99 for peak locations; and 0.67< ρ <0.93 for peak LOD scores). Furthermore, the regions implicated by linkage analysis with these factor scores have also been observed in other studies, further validating our exploratory modeling

A regularized procedure to generate a deep learning model for topology optimization of electromagnetic devices

The use of behavioral models based on deep learning (DL) to accelerate electromagnetic field computations has recently been proposed to solve complex electromagnetic problems. Such problems usually require time-consuming numerical analysis, while DL allows achieving the topo-logically optimized design of electromagnetic devices using desktop class computers and reasonable computation times. An unparametrized bitmap representation of the geometries to be optimized, which is a highly desirable feature needed to discover completely new solutions, is perfectly managed by DL models. On the other hand, optimization algorithms do not easily cope with high dimensional input data, particularly because it is difficult to enforce the searched solutions as feasible and make them belong to expected manifolds. In this work, we propose the use of a variational autoencoder as a data regularization/augmentation tool in the context of topology optimization. The optimization was carried out using a gradient descent algorithm, and the DL neural network was used as a surrogate model to accelerate the resolution of single trial cases in the due course of optimization. The varia-tional autoencoder and the surrogate model were simultaneously trained in a multi-model custom training loop that minimizes total loss—which is the combination of the two models’ losses. In this paper, using the TEAM 25 problem (a benchmark problem for the assessment of electromagnetic numerical field analysis) as a test bench, we will provide a comparison between the computational times and design quality for a “classical” approach and the DL-based approach. Preliminary results show that the variational autoencoder manages regularizing the resolution process and transforms a constrained optimization into an unconstrained one, improving both the quality of the final solution and the performance of the resolution process

Analysis of PCB discontinuities using FD-TD and wavelets

A wavelet-based technique is proposed for the extraction of a two-port equivalent circuit of typical printed circuit board (PCB) discontinuities from full-wave FD-TD results. An equivalent representation in the wavelet domain of the discontinuity is obtained by expanding the computed time domain quantities. Hence the wavelet transformed scattering parameters can be included in a wavelet equivalent of TEM wave propagation paths along the PCB in order to obtain an overall equivalent of the entire structure. The wavelet representation drastically minimizes the CPU time and computer storage requirements while maintaining excellent accuracy, so that it proves to be a very useful modeling tool especially in the design stage

Autoencoder Based Optimization for Electromagnetics Problems

In this work a novel approach is presented for topology optimization of electromagnetic devices. In particular a surrogate model based on Deep Neural Networks with encoder-decoder architecture is introduced. A first autoencoder learns to represent the input images that describe the topology, i.e., geometry and materials. The novel idea is to use the low dimensional latent space (i.e., the output space of the encoder) as the search space of the optimization algorithm, instead of using the higher dimensional space represented by the input images. A second neural network learns the relationship between the encoder outputs and the objective function (i.e., an electromagnetic quantity that is crucial for the design of the device) which is calculated by means of a numerical analysis. The calculation time for the optimization is greatly improved by reducing the dimensionality of the search space, and by introducing the surrogate model, whereas the quality of the result is slightly affected

Primary cup stability in THA with augmentation of acetabular defect. A comparison of healthy and osteoporotic bone

AbstractBackground contextReconstruction of acetabular defect has been advocated as standard procedure in total hip arthroplasty. The presence of bony defects at the acetabulum is viewed as a cause of instability and acetabular wall augmentation is often used without proper consideration of surrounding bone density. The initial cup-bone stability is, however, a challenge and a number of studies supported by clinical follow-ups of patients suggested that if the structural graft needs supporting more than 50% of the acetabular component, a reconstruction cage device spanning ilium to ischium should be preferred to protect the graft and provide structural stability. This study aims to (1) investigate the relationship between cup motion and bone density and (2) quantify the re-distribution of stress at the defect site after augmentation.HyphotesisPaprosky type I or II, acetabular defects, when reconstructed with bone screws supported by bioabsorbable calcified triglyceride bone cement are significantly less effective for osteoporotic bone than healthy bone.Materials and methodsAcetabular wall defects were reconstructed on six cadaveric subjects with bioabsorbable calcified triglyceride bone cement using a re-bar technique. Data of the specimen with higher bone density was used to validate a Finite Element Model. Values of bone apparent density ranging from healthy to osteoporotic were simulated to evaluate (1) the cup motion, through both displacement and rotation, (2) and the von Mises stress distribution.ResultsDefect reconstruction with bone screws and bioabsorbable calcified triglyceride bone cement results in a re-distribution of stress at the defect site. For a reduction of 65% in bone density, the cup displacement was similar to a healthy bone for loads not exceeding 300N, as load progressed up to 1500N, the reconstructed defect showed increase of 99μm (128%) in displacement and of 0.08° in rotation angle.ConclusionsBased on the results, we suggest that an alternative solution to wall defect augmentation with bone screws supported by bioabsorbable calcified triglyceride bone cement, be used for osteoporotic bone.Level of evidenceLevel IV, experimental and cadaveric study

Enhanced Detection of Expanded Repeat mRNA Foci with Hybridization Chain Reaction

Transcribed nucleotide repeat expansions form detectable RNA foci in patient cells that contribute to disease pathogenesis. The most widely used method for detecting RNA foci, fluorescence in situ hybridization (FISH), is powerful but can suffer from issues related to signal above background. Here we developed a repeat-specific form of hybridization chain reaction (R-HCR) as an alternative method for detection of repeat RNA foci in two neurodegenerative disorders: C9orf72 associated ALS and frontotemporal dementia (C9 ALS/FTD) and Fragile X-associated tremor/ataxia syndrome. R-HCR to both G4C2 and CGG repeats exhibited comparable specificity but \u3e 40 × sensitivity compared to FISH, with better detection of both nuclear and cytoplasmic foci in human C9 ALS/FTD fibroblasts, patient iPSC derived neurons, and patient brain samples. Using R-HCR, we observed that integrated stress response (ISR) activation significantly increased the number of endogenous G4C2 repeat RNA foci and triggered their selective nuclear accumulation without evidence of stress granule co-localization in patient fibroblasts and patient derived neurons. These data suggest that R-HCR can be a useful tool for tracking the behavior of repeat expansion mRNA in C9 ALS/FTD and other repeat expansion disorders

Does the pain-protective GTP cyclohydrolase haplotype significantly alter the pattern or severity of pain in humans with chronic pancreatitis?

<p>Abstract</p> <p>Background</p> <p>Pain is often a dominant clinical feature of chronic pancreatitis but the frequency and severity is highly variable between subjects. We hypothesized that genetic polymorphisms contribute to variations in clinical pain patterns. Since genetic variations in the GTP cyclohydrolase (GCH1) gene have been reported to protect some patients from pain, we investigated the effect of the "pain protective haplotype" in well characterized patients with chronic pancreatitis (CP) or recurrent acute pancreatitis (RAP) from the North American Pancreatitis Study 2 (NAPS2).</p> <p>Results</p> <p>Subjects in the NAPS2 study were asked to rank their pain in one of 5 categories reflecting different levels of pain frequency and severity. All subjects were genotyped at rs8007267 and rs3783641 to determine the frequency of the GCH1 pain-protective haplotype. In Caucasian subjects the frequency of the pain-protective GCH1 haplotype was no different in the control group (n = 236), CP patients (n = 265), RAP patients (N = 131), or in CP patients subclassified by pain category compared to previously reported haplotype frequencies in the general Caucasian population.</p> <p>Conclusion</p> <p>The GCH1 pain-protective haplotype does not have a significant effect on pain patterns or severity in RAP or CP. These results are important for helping to define the regulators of visceral pain, and to distinguish different mechanisms of pain.</p

A Bayesian method for evaluating and discovering disease loci associations, PLoS One

Abstract Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multilocus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need

RAN Translation at \u3cem\u3eC9orf72\u3c/em\u3e-Associated Repeat Expansions is Selectively Enhanced by the Integrated Stress Response

Repeat-associated non-AUG (RAN) translation allows for unconventional initiation at disease-causing repeat expansions. As RAN translation contributes to pathogenesis in multiple neurodegenerative disorders, determining its mechanistic underpinnings may inform therapeutic development. Here we analyze RAN translation at G4C2 repeat expansions that cause C9orf72-associated amyotrophic lateral sclerosis and frontotemporal dementia (C9RAN) and at CGG repeats that cause fragile X-associated tremor/ataxia syndrome. We find that C9RAN translation initiates through a cap- and eIF4A-dependent mechanism that utilizes a CUG start codon. C9RAN and CGG RAN are both selectively enhanced by integrated stress response (ISR) activation. ISR-enhanced RAN translation requires an eIF2α phosphorylation-dependent alteration in start codon fidelity. In parallel, both CGG and G4C2 repeats trigger phosphorylated-eIF2α-dependent stress granule formation and global translational suppression. These findings support a model whereby repeat expansions elicit cellular stress conditions that favor RAN translation of toxic proteins, creating a potential feed-forward loop that contributes to neurodegeneration