Crashworthiness simulation of composite automotive structures

In 1990 the Automotive Composites Consortium (ACC) began the investigation of crash worthiness simulation methods for composite materials. A contract was given to Livermore Software Technology Corporation (LSTC) to implement a new damage model in LS-DYNA3DTM specifically for composite structures. This model is in LS-DYNA3DTM and is in use by the ACC partners. In 1994 USCAR, a partnership of American auto companies, entered into a partnership called SCAAP (Super Computing Automotive Applications Partnership) for the express purpose of working with the National Labs on computational oriented research. A CRADA (Cooperative Research and Development Agreement) was signed with Lawrence Livermore National Laboratory, Oak Ridge National Laboratory, Sandia National Laboratory, Argonne National Laboratory, and Los Alamos National Laboratory to work in three distinctly different technical areas, one of which was composites material modeling for crash worthiness. Each Laboratory was assigned a specific modeling task. The ACC was responsible for the technical direction of the composites project and provided all test data for code verification. All new models were to be implemented in DYNA3D and periodically distributed to all partners for testing. Several new models have been developed and implemented. Excellent agreement has been shown between tube crush simulation and experiments

Liposome Co-sedimentation and Co-flotation Assays to Study Lipid-Protein Interactions

A large proportion of proteins are expected to interact with cellular membranes to carry out their physiological functions in processes such as membrane transport, morphogenesis, cytoskeletal organization, and signal transduction. The recruitment of proteins at the membrane-cytoplasm interface and their activities are precisely regulated by phosphoinositides, which are negatively charged phospholipids found on the cytoplasmic leaflet of cellular membranes and play critical roles in membrane homeostasis and cellular signaling. Thus, it is important to reveal which proteins interact with phosphoinositides and to elucidate the underlying mechanisms. Here, we present two standard in vitro methods, liposome co-sedimentation and co-flotation assays, to study lipid-protein interactions. Liposomes can mimic various biological membranes in these assays because their lipid compositions and concentrations can be varied. Thus, in addition to mechanisms of lipid-protein interactions, these methods provide information on the possible specificities of proteins toward certain lipids such as specific phosphoinositide species and can hence shed light on the roles of membrane interactions on the functions of membrane-associated proteins.Peer reviewe

Glaucoma diagnosis using multi-feature analysis and a deep learning technique

AbstractIn this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 for both glaucoma and control) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed, and the most significant four features were used to train machine learning (ML) algorithms. Two ML algorithms: deep learning (DL) and logistic regression (LR) were compared in terms of the classification accuracy for automated glaucoma detection. The performance of the ML models was evaluated on unseen test data, n = 55. An image segmentation pilot study was then performed on cross-sectional OCT scans. The ONH cup area was extracted, analyzed, and a new DL model was trained for glaucoma prediction. The DL model was estimated using five-fold cross-validation and compared with two pre-trained models. The DL model trained from the optimal features achieved significantly higher diagnostic performance (area under the receiver operating characteristic curve (AUC) 0.98 and accuracy of 97% on validation data and 96% on test data) compared to previous studies for automated glaucoma detection. The second DL model used in the pilot study also showed promising outcomes (AUC 0.99 and accuracy of 98.6%) to detect glaucoma compared to two pre-trained models. In combination, the result of the two studies strongly suggests the four features and the cross-sectional ONH cup area trained using deep learning have a great potential for use as an initial screening tool for glaucoma which will assist clinicians in making a precise decision.</jats:p

Morphology of two dimensional fracture surface

We consider the morphology of two dimensional cracks observed in experimental results obtained from paper samples and compare these results with the numerical simulations of the random fuse model (RFM). We demonstrate that the data obey multiscaling at small scales but cross over to self-affine scaling at larger scales. Next, we show that the roughness exponent of the random fuse model is recovered by a simpler model that produces a connected crack, while a directed crack yields a different result, close to a random walk. We discuss the multiscaling behavior of all these models.Comment: slightly revise

Do EnChroma glasses improve color vision for colorblind subjects?

The commercialization of EnChroma glasses has generated great expectations for people to be able to see new colors or even correct color vision deficiency (CVD). We evaluate the effectiveness of these glasses using two complementary strategies for the first time. The first consists of using the three classical types of tests — recognition, arrangement and discrimination — with and without glasses, with a high number of individuals. In the second, we use the spectral transmittance of the glasses to simulate the appearance of stimuli in a set of scenes for normal observers and observers with CVD. The results show that the glasses introduce a variation of the perceived color, but neither improve results in the diagnosis tests nor allow the observers with CVD to have a more normal color vision.Spanish State Agency of Research (AEI); Ministry for Economy, Industry and Competitiveness (MIMECO) (Grant numbers FIS2017-89258-P and DPI 2015-64571-R); European Union FEDER (European Regional Development Funds)

Digital Evaluation of Nitrite-Reduced “Kulen” Fermented Sausage Quality

This study aimed to evaluate nitrite reduction impact on geometry, colour, chemical, microbiological, and sensory traits of dry sausage (kulen) traditionally prepared with red hot paprika powder. Three batches of kulen with different nitrite levels were produced and assessed: N110 (control with 110 mg/kg of sodium nitrite), N55 (55 mg/kg of sodium nitrite), and NF (without sodium nitrite). Samples for the analyses were taken on production day, after 8, 16, 24, 32, and 40 days of ripening and after 50 and 100 days of storage. Four novel digital methods for quality assessment were deployed such as computer vision system (CVS), three-dimensional (3D) laser imaging, oral processing, and temporal dominance of sensations (TDS). Reduction and removal of nitrites from the formulation of kulen did not result in significant () differences in lightness (), redness (), and yellowness () of the sausage surface, meat, and fat parts that were measured independently by means CVS. Sausages produced by 50% nitrite reduction (N55) showed no significant () differences in terms of geometrical, chemical, colour, microbiological, and oral processing parameters compared with the control (N110) batch. On the other hand, the complete removal of nitrites from kulen formulation negatively affected biogenic amine levels and oral processing properties of the product. Nitrite reduction showed no significant effect on TDS curves among the batches. The results of this study indicate that nitrite content in traditional kulen can be reduced by 50% (55 mg/kg of sodium nitrite) without adversely affecting the various quality properties of the product

Assessment of VINO filters for correcting redgreen Color Vision Deficiency

In our ongoing research on the effectiveness of different passive tools for aiding Color Vision Deficiency (CVD) subjects, we have analyzed the VINO 02 Amp Oxy-Iso glasses using two strategies: 1) 52 observers were studied using four color tests (recognition, arrangement, discrimination, and color-naming); 2) the spectral transmittance of the lenses were used to model the color appearance of natural scenes for different simulated CVD subjects. We have also compared VINO and EnChroma glasses. The spectral transmission of the VINO glasses significantly changed color appearance. This change would allow some CVD subjects, above all the deutan ones, to be able to pass recognition tests but not the arrangement tests. To sum up, our results support the hypothesis that glasses with filters are unable to effectively resolve the problems related to color vision deficiency.The Spanish State Agency of Research (AEI); the Ministry for Economy, Industry and Competitiveness (MIMECO) (Grant numbers FIS2017-89258-P and DPI 2015-64571-R); European Union FEDER (European Regional Development Funds)

Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents

<p>Abstract</p> <p>Background</p> <p>Quantiles are a staple of epidemiologic research: in contemporary epidemiologic practice, continuous variables are typically categorized into tertiles, quartiles and quintiles as a means to illustrate the relationship between a continuous exposure and a binary outcome.</p> <p>Discussion</p> <p>In this paper we argue that this approach is highly problematic and present several potential alternatives. We also discuss the perceived drawbacks of these newer statistical methods and the possible reasons for their slow adoption by epidemiologists.</p> <p>Summary</p> <p>The use of quantiles is often inadequate for epidemiologic research with continuous variables.</p