Experimental Study of Selective Laser Sintering of Parmax

Selective Laser Sintering (SLS) has been used to successfully process crystalline polymers, ceramics and metals. However, wide range of materials available and their applications have acted as an impetus to expand the applications of SLS to different material systems. This paper presents one such experimental study undertaken to explore the application of SLS to process Parmax® polymers – a family of high performance amorphous poly pphenylenes. As a part of the collaborative study, various process parameters and material formulations were tried out to ascertain the feasibility of the process and the initial results obtained look promising. This paper lists the material formulations tested and process parameters controlled. Also, analysis of the results of the experimental study and the outline of the next phase of research to be undertaken have been described. This study was motivated by the numerous applications of Parmax® in the electronics, defense and aerospace industries as well as the goal to expand the applications and utility of SLS.Mechanical Engineerin

Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition

When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is error-prone and can impact target model accuracy. To identify the extent of such impact, we perform and briefly present a differential analysis against three DNNs widely used for image recognition (MobileNetV2, ResNet101, and InceptionV3) converted across four well-known deep learning frameworks (PyTorch, Keras, TensorFlow (TF), and TFLite), which revealed numerous model crashes and output label discrepancies of up to 72%. To mitigate such errors, we present a novel approach towards fault localization and repair of buggy deep learning framework conversions, focusing on pre-trained image recognition models. Our technique consists of four stages of analysis: 1) conversion tools, 2) model parameters, 3) model hyperparameters, and 4) graph representation. In addition, we propose various strategies towards fault repair of the faults detected. We implement our technique on top of the Apache TVM deep learning compiler, and we test it by conducting a preliminary fault localization analysis for the conversion of InceptionV3 from TF to TFLite. Our approach detected a fault in a common DNN converter tool, which introduced precision errors in weights, reducing model accuracy. After our fault localization, we repaired the issue, reducing our conversion error to zero

DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models

Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal mapping on hardware accelerators during model deployment, which may lead to timing uncertainty and erroneous behavior. Mapping on hardware accelerators is done using multiple software components like deep learning frameworks, compilers, and device libraries, that we refer to as the computational environment. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment, as the impact of parameters like deep learning frameworks, compiler optimizations, and hardware devices on model performance and correctness is not yet well understood. In this paper we present a differential testing framework, DeltaNN, that allows us to assess the impact of different computational environment parameters on the performance of image recognition models during deployment, post training. DeltaNN generates different implementations of a given image recognition model for variations in environment parameters, namely, deep learning frameworks, compiler optimizations and hardware devices and analyzes differences in model performance as a result. Using DeltaNN, we conduct an empirical study of robustness analysis of three popular image recognition models using the ImageNet dataset. We report the impact in terms of misclassifications and inference time differences across different settings. In total, we observed up to 72% output label differences across deep learning frameworks, and up to 81% unexpected performance degradation in terms of inference time, when applying compiler optimizations

Patient-specific computational fluid dynamics-assessment of aortic hemodynamics in a spectrum of aortic valve pathologies.

OBJECTIVES: The complexity of aortic disease is not fully exposed by aortic dimensions alone, and morbidity or mortality can occur before intervention thresholds are met. Patient-specific computational fluid dynamics (CFD) were used to assess the effect of different aortic valve morphologies on velocity profiles, flow patterns, helicity, wall shear stress (WSS), and oscillatory shear index (OSI) in the thoracic aorta. METHODS: A total of 45 subjects were divided into 5 groups: volunteers, aortic regurgitation-tricuspid aortic valve (AR-TAV), aortic stenosis-tricuspid aortic valve (AS-TAV), aortic stenosis-bicuspid aortic valve right-left cusp fusion (BAV[RL]), and aortic stenosis-right-non cusp fusion (AS-BAV[RN]). Subjects underwent magnetic resonance angiography, with phase-contrast magnetic resonance imaging at the sino-tubular junction to define patient-specific inflow velocity profiles. Hemodynamic recordings were used alongside magnetic resonance imaging angiographic data to run patient-specific CFD. RESULTS: The BAV groups had larger mid-ascending aorta diameters (P < .05). Ascending aorta flow was more eccentric in BAV (flow asymmetry = 78.9% ± 6.5% for AS-BAV(RN), compared with 4.7% ± 2.1% for volunteers, P < .05). Helicity was greater in AS-BAV(RL) (P < .05). Mean WSS was elevated in AS groups, greatest in AS-BAV(RN) (37.1 ± 4.0 dyn/cm2, compared with 9.8 ± 5.4 for volunteers, P < .05). The greater curvature of the ascending aorta experienced highest WSS and lowest OSI in AS patients, most significant in AS-BAV(RN) (P < .05). CONCLUSIONS: BAV displays eccentric flow with high helicity. The presence of AS, particularly in BAV-RN, led to greater WSS and lower OSI in the greater curvature of the ascending aorta. Patient-specific CFD provides noninvasive functional assessment of the thoracic aorta, and may enable development of a personalized approach to diagnosis and management of aortic disease beyond traditional guidelines

Large scale meta-analysis characterizes genetic architecture for common psoriasis associated variants

Psoriasis is a complex disease of skin with a prevalence of about 2%. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for psoriasis to date, including data from eight different Caucasian cohorts, with a combined effective sample size amp;gt;39,000 individuals. We identified 16 additional psoriasis susceptibility loci achieving genome-wide significance, increasing the number of identified loci to 63 for European-origin individuals. Functional analysis highlighted the roles of interferon signalling and the NFkB cascade, and we showed that the psoriasis signals are enriched in regulatory elements from different T cells (CD8(+) T-cells and CD4(+) T-cells including T(H)0, T(H)1 and T(H)17). The identified loci explain similar to 28% of the genetic heritability and generate a discriminatory genetic risk score (AUC = 0.76 in our sample) that is significantly correlated with age at onset (p = 2 x 10(-89)). This study provides a comprehensive layout for the genetic architecture of common variants for psoriasis.Funding Agencies|National Institutes of Health [R01AR042742, R01AR050511, R01AR054966, R01AR063611, R01AR065183]; Foundation for the National Institutes of Health; Dermatology Foundation; National Psoriasis Foundation; Arthritis National Research Foundation; Ann Arbor Veterans Affairs Hospital; Dawn and Dudley Holmes Foundation; Babcock Memorial Trust; Medical Research Council [MR/L011808/1]; German Ministry of Education and Research (BMBF); Doris Duke Foundation [2013106]; National Institute of Health [K08AR060802, R01AR06907]; Taubman Medical Research Institute; Department of Health via the NIHR comprehensive Biomedical Research Center; Kings College London; KCH NHS Foundation Trust; Barbara and Neal Henschel Charitable Foundation; Heinz Nixdorf Foundation; Estonian Ministry of Education and Research [IUT20-46]; Centre of Translational Genomics of University of Tartu (SP1GVARENG); European Regional Development Fund (Centre of Translational Medicine, University of Tartu); German Federal Ministry of Education and Research (BMBF); National Human Genome Research Institute of the National Institutes of Health [R44HG006981]; International Psoriasis Council</p

TP53 outperforms other androgen receptor biomarkers to predict abiraterone or enzalutamide outcome in metastatic castration-resistant prostate cancer

Purpose: To infer the prognostic value of simultaneous androgen receptor (AR) and TP53 profiling in liquid biopsies from patients with metastatic castration-resistant prostate cancer (mCRPC) starting a new line of AR signaling inhibitors (ARSi). Experimental Design: Between March 2014 and April 2017, we recruited patients with mCRPC (n = 168) prior to ARSi in a cohort study encompassing 10 European centers. Blood samples were collected for comprehensive profiling of Cell Search-enriched circulating tumor cells (CTC) and circulating tumor DNA (ctDNA). Targeted CTC RNA sequencing (RNA-seq) allowed the detection of eight AR splice variants (ARV). Low-pass whole-genome and targeted gene-body sequencing of AR and TP53 was applied to identify amplifications, loss of heterozygosity, mutations, and structural rearrangements in ctDNA. Clinical or radiologic progression-free survival (PFS) was estimated by Kaplan-Meier analysis, and independent associations were determined using multivariable Cox regression models. Results: Overall, no single AR perturbation remained associated with adverse prognosis after multivariable analysis. Instead, tumor burden estimates (CTC counts, ctDNA fraction, and visceral metastases) were significantly associated with PFS. TP53 inactivation harbored independent prognostic value [HR 1.88; 95% confidence interval (CI), 1.18-3.00; P = 0.008], and outperformed ARV expression and detection of genomic AR alterations. Using Cox coefficient analysis of clinical parameters and TP53 status, we identified three prognostic groups with differing PFS estimates (median, 14.7 vs. 7.51 vs. 2.62 months; P < 0.0001), which was validated in an independent mCRPC cohort (n = 202) starting first-line ARSi (median, 14.3 vs. 6.39 vs. 2.23 months; P < 0.0001). Conclusions: In an all-comer cohort, tumor burden estimates and TP53 outperform any AR perturbation to infer prognosis. See related commentary by Rebello et al., p. 169

Riboflavin/UVA Collagen Cross-Linking-Induced Changes in Normal and Keratoconus Corneal Stroma

Purpose To determine the effect of Ultraviolet-A collagen cross-linking with hypo-osmolar and iso-osmolar riboflavin solutions on stromal collagen ultrastructure in normal and keratoconus ex vivo human corneas. Methods Using small-angle X-ray scattering, measurements of collagen D-periodicity, fibril diameter and interfibrillar spacing were made at 1 mm intervals across six normal post-mortem corneas (two above physiological hydration (swollen) and four below (unswollen)) and two post-transplant keratoconus corneal buttons (one swollen; one unswollen), before and after hypo-osmolar cross-linking. The same parameters were measured in three other unswollen normal corneas before and after iso-osmolar cross-linking and in three pairs of swollen normal corneas, in which only the left was cross-linked (with iso-osmolar riboflavin). Results Hypo-osmolar cross-linking resulted in an increase in corneal hydration in all corneas. In the keratoconus corneas and unswollen normal corneas, this was accompanied by an increase in collagen interfibrillar spacing (p<0.001); an increase in fibril diameter was also seen in two out of four unswollen normal corneas and one unswollen keratoconus cornea (p<0.001). Iso-osmolar cross-linking resulted in a decrease in tissue hydration in the swollen normal corneas only. Although there was no consistent treatment-induced change in hydration in the unswollen normal samples, iso-osmolar cross-linking of these corneas did result in a compaction of collagen fibrils and a reduced fibril diameter (p<0.001); these changes were not seen in the swollen normal corneas. Collagen D-periodicity was not affected by either treatment. Conclusion The observed structural changes following Ultraviolet-A cross-linking with hypo-osmolar or iso-osmolar riboflavin solutions are more likely a consequence of treatment-induced changes in tissue hydration rather than cross-linking