Structural panels

Vinyl pyridines including vinyl stilbazole materials and vinyl styrylpyridine oligomer materials are disclosed. These vinylpyridines form copolymers with bismaleimides which copolymers have good fire retardancy and decreased brittleness. The cure temperatures of the copolymers are substantially below the cure temperatures of the bismaleimides alone. Reinforced composites made from the cured copolymers are disclosed as well

Vinyl stilbazoles

Vinyl pyridines including vinyl stilbazole materials and vinyl styrylpyridine oligomer materials are disclosed. These vinylpyridines form copolymers with bismaleimides which copolymers have good fire retardancy and decreased brittleness. The cure temperatures of the copolymers are substantially below the cure temperatures of the bismaleimides alone. Reinforced composites made from the cured copolymers are disclosed as well

Development and validation of a deep learning model to quantify glomerulosclerosis in kidney biopsy specimens

Importance: A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring percent global glomerulosclerosis (an important predictor of outcome) on images of kidney biopsy specimens could enable pathologists to more reproducibly and accurately quantify percent global glomerulosclerosis, potentially saving organs that would have been discarded. Objective: To compare the performances of pathologists with a deep learning model on quantification of percent global glomerulosclerosis in whole-slide images of donor kidney biopsy specimens, and to determine the potential benefit of a deep learning model on organ discard rates. Design, Setting, and Participants: This prognostic study used whole-slide images acquired from 98 hematoxylin-eosin-stained frozen and 51 permanent donor biopsy specimen sections retrieved from 83 kidneys. Serial annotation by 3 board-certified pathologists served as ground truth for model training and for evaluation. Images of kidney biopsy specimens were obtained from the Washington University database (retrieved between June 2015 and June 2017). Cases were selected randomly from a database of more than 1000 cases to include biopsy specimens representing an equitable distribution within 0% to 5%, 6% to 10%, 11% to 15%, 16% to 20%, and more than 20% global glomerulosclerosis. Main Outcomes and Measures: Correlation coefficient (r) and root-mean-square error (RMSE) with respect to annotations were computed for cross-validated model predictions and on-call pathologists\u27 estimates of percent global glomerulosclerosis when using individual and pooled slide results. Data were analyzed from March 2018 to August 2020. Results: The cross-validated model results of section images retrieved from 83 donor kidneys showed higher correlation with annotations (r = 0.916; 95% CI, 0.886-0.939) than on-call pathologists (r = 0.884; 95% CI, 0.825-0.923) that was enhanced when pooling glomeruli counts from multiple levels (r = 0.933; 95% CI, 0.898-0.956). Model prediction error for single levels (RMSE, 5.631; 95% CI, 4.735-6.517) was 14% lower than on-call pathologists (RMSE, 6.523; 95% CI, 5.191-7.783), improving to 22% with multiple levels (RMSE, 5.094; 95% CI, 3.972-6.301). The model decreased the likelihood of unnecessary organ discard by 37% compared with pathologists. Conclusions and Relevance: The findings of this prognostic study suggest that this deep learning model provided a scalable and robust method to quantify percent global glomerulosclerosis in whole-slide images of donor kidneys. The model performance improved by analyzing multiple levels of a section, surpassing the capacity of pathologists in the time-sensitive setting of examining donor biopsy specimens. The results indicate the potential of a deep learning model to prevent erroneous donor organ discard

The Deep Poincare Map: A Novel Approach for Left Ventricle Segmentation

Precise segmentation of the left ventricle (LV) within cardiac MRI images is a prerequisite for the quantitative measurement of heart function. However, this task is challenging due to the limited availability of labeled data and motion artifacts from cardiac imaging. In this work, we present an iterative segmentation algorithm for LV delineation. By coupling deep learning with a novel dynamic-based labeling scheme, we present a new methodology where a policy model is learned to guide an agent to travel over the image, tracing out a boundary of the ROI – using the magnitude difference of the Poincaré map as a stopping criterion. Our method is evaluated on two datasets, namely the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV segmentation challenge. Our method outperforms the previous research over many metrics. In order to demonstrate the transferability of our method we present encouraging results over the STACOM 2011 data, when using a model trained on the SCD dataset

Polyandrous females avoid costs of inbreeding

Why do females typically mate with more than one male? Female mating patterns have broad implications for sexual selection, speciation and conflicts of interest between the sexes, and yet they are poorly understood. Matings inevitably have costs, and for females, the benefits of taking more than one mate are rarely obvious. One possible explanation is that females gain benefits because they can avoid using sperm from genetically incompatible males, or invest less in the offspring of such males. It has been shown that mating with more than one male can increase offspring viability, but we present the first clear demonstration that this occurs because females with several mates avoid the negative effects of genetic incompatibility. We show that in crickets, the eggs of females that mate only with siblings have decreased hatching success. However, if females mate with both a sibling and a non-sibling they avoid altogether the low egg viability associated with sibling matings. If similar effects occur in other species, inbreeding avoidance may be important in understanding the prevalence of multiple mating

Origin of Cosmic Magnetic Fields

We propose that the overlapping shock fronts from young supernova remnants produce a locally unsteady, but globally steady large scale spiral shock front in spiral galaxies, where star formation and therefore massive star explosions correlate geometrically with spiral structure. This global shock front with its steep gradients in temperature, pressure and associated electric fields will produce drifts, which in turn give rise to a strong sheet-like electric current, we propose. This sheet current then produces a large scale magnetic field, which is regular, and connected to the overall spiral structure. This rejuvenates the overall magnetic field continuously, and also allows to understand that there is a regular field at all in disk galaxies. This proposal connects the existence of magnetic fields to accretion in disks. We not yet address all the symmetries of the magnetic field here; the picture proposed here is not complete. X-ray observations may be able to test it already.Comment: 18 pages, no figures; to be published in Proc. Palermo Meeting Sept. 2002, Eds. N. G. Sanchez et al., The Early Universe and the Cosmic Microwave Background: Theory and Observation

Functioning styles of personality disorders and five-factor normal personality traits: a correlation study in Chinese students

BACKGROUND: Previous studies show that both the categorical and dimensional descriptors of personality disorders are correlated with normal personality traits. Recently, a 92-item inventory, the Parker Personality Measure (PERM) was designed as a more efficient and precise first-level assessment of personality disorders. Whether the PERM constructs are correlated with those of the five-factor models of personality needs to be clarified. METHODS: We therefore invited 913 students from poly-technical schools and colleges in China to answer the PERM, the Five-Factor Nonverbal Personality Questionnaire (FFNPQ), and the Zuckerman-Kuhlman Personality Questionnaire (ZKPQ). RESULTS: Most personality constructs had satisfactory internal alphas. PERM constructs were loaded with FFNPQ and ZKPQ traits clearly on four factors, which can be labelled as Dissocial, Emotional Dysregulation, Inhibition and Compulsivity, as reported previously. FFNPQ Openness to Experience, Conscientiousness and Extraversion formed another Factor, named Experience Hunting, which was not clearly covered by PERM or ZKPQ. CONCLUSION: The PERM constructs were loaded in a predictable way on the disordered super-traits, suggesting the PERM might offer assistance measuring personality function in clinical practice

What lies beneath: exploring links between asylum policy and hate crime in the UK

This paper explores the link between increasing incidents of hate crime and the asylum policy of successive British governments with its central emphasis on deterrence. The constant problematisation of asylum seekers in the media and political discourse ensures that 'anti-immigrant' prejudice becomes mainstr earned as a common-sense response. The victims are not only the asylum seekers hoping for a better life but democratic society itself with its inherent values of pluralism and tolerance debased and destabilised