Crosslinking of aromatic polyamides via pendant propargyl groups

Methods for crosslinking N-methyl substituted aromatic polyamides were investigated in an effort to improve the applicability of these polymers as matrix resins for Kavlar trademark fiber composites. High molecular weight polymers were prepared from isophthaloyl dichloride and 4,4'- bis(methylamino)diphenylmethane with varying proportions of the N,N'bispropargyl diamine incorporated as a crosslinking agent. The propargylcontaining diamines were crosslinked thermally and characterized by infrared spectroscopy, differential scanning calorimetry, and thermogravimetric analysis. Attempts were also made to crosslink polyamide films by exposure to ultraviolet light, electron beam, and gamma radiation

Reduced anterior cingulate grey matter volume in painful hand osteoarthritis

Objective Increasing evidence supports the role of central sensitisation in osteoarthritis (OA) pain. In this study, we used neuroimaging to compare pain-processing regions of the brain in participants with and without hand OA. We then assessed for volumetric changes in these brain regions following treatment with centrally acting analgesics. Methods Participants with hand OA (n = 28) underwent T1-weighted MRI of the brain before and after 12 weeks of treatment with pregabalin, duloxetine or placebo. Grey matter volume in the anterior cingulate cortex (ACC), insular cortex and thalamus was compared to non-OA control subjects (n = 11) using FreeSurfer regional volumetric analysis and voxel-based morphometry, and evaluated for differences pre- and post-treatment. Results Relative to non-OA controls, hand OA participants had areas of reduced grey matter volume in the ACC at baseline (p = 0.007). Regional volumetric differences in the ACC persisted after 13 weeks’ treatment with pregabalin or duloxetine (p = 0.004) with no significant differences between treatment cohorts, despite improvements in NRS pain scores for pregabalin (p = 0.005) and duloxetine (p = 0.050). The ACC grey matter changes persisted despite a significant improvement in pain in the pregabalin and duloxetine groups vs. placebo. No structural differences were observed in the insular cortex or thalamus at baseline or following treatment. Conclusion Our study found evidence of reduced ACC grey matter volume in participants with hand arthritis that persisted after treatment with centrally acting analgesics pregabalin and duloxetine, respectively. The sustained changes observed in the ACC in our study could reflect the relatively short duration of treatment, or that the differences observed are irreversible volume changes due to chronic pain that are established over time

Definitive observation of the dark triplet ground state of charged excitons in high magnetic fields

The ground state of negatively charged excitons (trions) in high magnetic fields is shown to be a dark triplet state, confirming long-standing theoretical predictions. Photoluminescence (PL), reflection, and PL excitation spectroscopy of CdTe quantum wells reveal that the dark triplet trion has lower energy than the singlet trion above 24 Tesla. The singlet-triplet crossover is "hidden" (i.e., the spectral lines themselves do not cross due to different Zeeman energies), but is confirmed by temperature-dependent PL above and below 24 T. The data also show two bright triplet states.Comment: 4 figure

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels

BACKGROUND: Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. METHODS: We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. RESULTS: The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. CONCLUSION: The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management

Pennsylvania Folklife Vol. 25, No. 3

• The Pennsylvania Germans and the American Revolution • The Blooming Grove Colony • The Salebill • The Schlegel Family and the Rosicrucian Movement • A Log Settler\u27s Fort/Home • Pennsylvania Dutch Studies at Ursinus College, 1976 • The Country Sale: Folk-Cultural Questionnaire No. 43https://digitalcommons.ursinus.edu/pafolklifemag/1067/thumbnail.jp

Accumulation of driver and passenger mutations during tumor progression

Major efforts to sequence cancer genomes are now occurring throughout the world. Though the emerging data from these studies are illuminating, their reconciliation with epidemiologic and clinical observations poses a major challenge. In the current study, we provide a novel mathematical model that begins to address this challenge. We model tumors as a discrete time branching process that starts with a single driver mutation and proceeds as each new driver mutation leads to a slightly increased rate of clonal expansion. Using the model, we observe tremendous variation in the rate of tumor development - providing an understanding of the heterogeneity in tumor sizes and development times that have been observed by epidemiologists and clinicians. Furthermore, the model provides a simple formula for the number of driver mutations as a function of the total number of mutations in the tumor. Finally, when applied to recent experimental data, the model allows us to calculate, for the first time, the actual selective advantage provided by typical somatic mutations in human tumors in situ. This selective advantage is surprisingly small, 0.005 +- 0.0005, and has major implications for experimental cancer research

A single-sample method for normalizing and combining full-resolution copy numbers from multiple platforms, labs and analysis methods

Motivation: The rapid expansion of whole-genome copy number (CN) studies brings a demand for increased precision and resolution of CN estimates. Recent studies have obtained CN estimates from more than one platform for the same set of samples, and it is natural to want to combine the different estimates in order to meet this demand. Estimates from different platforms show different degrees of attenuation of the true CN changes. Similar differences can be observed in CNs from the same platform run in different labs, or in the same lab, with different analytical methods. This is the reason why it is not straightforward to combine CN estimates from different sources (platforms, labs and analysis methods)