Fibulin-4 is essential for maintaining arterial wall integrity in conduit but not muscular arteries

Homozygous or compound heterozygous mutations in fibulin-4 (FBLN4) lead to autosomal recessive cutis laxa type 1B (ARCL1B), a multisystem disorder characterized by significant cardiovascular abnormalities, including abnormal elastin assembly, arterial tortuosity, and aortic aneurysms. We sought to determine the consequences of a human disease-causing mutation in FBLN4 (E57K) on the cardiovascular system and vascular elastic fibers in a mouse model of ARCL1B. Fbln4E57K/E57K mice were hypertensive and developed arterial elongation, tortuosity, and ascending aortic aneurysms. Smooth muscle cell organization within the arterial wall of large conducting vessels was abnormal, and elastic fibers were fragmented and had a moth-eaten appearance. In contrast, vessel wall structure and elastic fiber integrity were normal in resistance/muscular arteries (renal, mesenteric, and saphenous). Elastin cross-linking and total elastin content were unchanged in large or small arteries, whereas elastic fiber architecture was abnormal in large vessels. While the E57K mutation did not affect Fbln4 mRNA levels, FBLN4 protein was lower in the ascending aorta of mutant animals compared to wild-type arteries but equivalent in mesenteric arteries. We found a differential role of FBLN4 in elastic fiber assembly, where it functions mainly in large conduit arteries. These results suggest that elastin assembly has different requirements depending on vessel type. Normal levels of elastin cross-links in mutant tissue call into question FBLN4\u27s suggested role in mediating lysyl oxidase-elastin interactions. Future studies investigating tissuespecific elastic fiber assembly may lead to novel therapeutic interventions for ARCL1B and other disorders of elastic fiber assembly. 2017 © The Authors, some rights reserved

Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection

Frequent mutation of receptor protein tyrosine phosphatases provides a mechanism for STAT3 hyperactivation in head and neck cancer

The underpinnings of STAT3 hyperphosphorylation resulting in enhanced signaling and cancer progression are incompletely understood. Loss-of-function mutations of enzymes that dephosphorylate STAT3, such as receptor protein tyrosine phosphatases, which are encoded by the PTPR gene family, represent a plausible mechanism of STAT3 hyperactivation. We analyzed whole exome sequencing (n = 374) and reverse-phase protein array data (n = 212) from head and neck squamous cell carcinomas (HNSCCs). PTPR mutations are most common and are associated with significantly increased phospho-STAT3 expression in HNSCC tumors. Expression of receptor-like protein tyrosine phosphatase T (PTPRT) mutant proteins induces STAT3 phosphorylation and cell survival, consistent with a “driver” phenotype. Computational modeling reveals functional consequences of PTPRT mutations on phospho-tyrosine–substrate interactions. A high mutation rate (30%) of PTPRs was found in HNSCC and 14 other solid tumors, suggesting that PTPR alterations, in particular PTPRT mutations, may define a subset of patients where STAT3 pathway inhibitors hold particular promise as effective therapeutic agents.Fil: Lui, Vivian Wai Yan. University of Pittsburgh; Estados UnidosFil: Peyser, Noah D.. University of Pittsburgh; Estados UnidosFil: Ng, Patrick Kwok-Shing. University Of Texas Md Anderson Cancer Center;Fil: Hritz, Jozef. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados Unidos. Masaryk University; República ChecaFil: Zeng, Yan. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Lu, Yiling. University Of Texas Md Anderson Cancer Center;Fil: Li, Hua. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Wang, Lin. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Gilbert, Breean R.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: General, Ignacio. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Bahar, Ivet. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Ju, Zhenlin. University Of Texas Md Anderson Cancer Center;Fil: Wang, Zhenghe. Case Western Reserve University; Estados UnidosFil: Pendleton, Kelsey P.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Xiao, Xiao. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Du, Yu. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Vries, John K.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Hammerman, Peter S.. Harvard Medical School; Estados UnidosFil: Garraway, Levi A.. Harvard Medical School; Estados UnidosFil: Mills, Gordon B.. University Of Texas Md Anderson Cancer Center;Fil: Johnson, Daniel E.. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Grandis, Jennifer R.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados Unido

The Mechanism Of Tobacco-Induced Decrements In Mucociliary Clearance

Chronic obstructive pulmonary disease (COPD), characterized by progressive loss of lung function including mucus obstruction and airway remodeling, is primarily caused by cigarette smoking. This pulmonary phenotype resembles that of cystic fibrosis (CF), which results from genetic mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an ion channel important in maintaining airway surface hydration and mucus clearance. Cigarette smoke exposure has been found to induce acquired CFTR dysfunction, leading to airway dehydration, abnormal mucin production, and impaired mucus transport. Smoke exposure also induces mucus hypersecretion and ciliary dysfunction; together, these changes result in defective mucus clearance and subsequent muco-obstruction. E-cigarettes, a popular alternative to traditional cigarettes, are commonly perceived to be safer than smoking. However, evidence has shown that e-cigarette components and vapor can cause defects in the airways similar to tobacco smoke. Thus, we hypothesized that e-cigarette use can induce acquired CFTR dysfunction in a similar manner to cigarette smoking, which may contribute to COPD pathogenesis. We demonstrate that e-cigarette vapor not only induces acquired CFTR dysfunction, but it also contains respiratory toxicants known to impair CFTR activity, suggesting that e-cigarettes may still lead to pulmonary decrements with chronic use. Additionally, muco-obstruction paired with observations of mucus abnormalities in CF and cigarette smoke-induced acquired CFTR dysfunction, led to the hypothesis that cigarette smoke exposure alters the airway functional microanatomy and mucus viscoelasticity, contributing to mucus transport decrements. Using real-time imaging that allows co-localized measures of the mucociliary transport (MCT) apparatus in conjunction with a novel COPD animal model, we establish that chronic smoke exposure impairs MCT in a manner that is dependent on the functional microanatomy as well as mucus properties. Here, we provide evidence that cigarette alternatives can induce airway epithelial ion transport defects that may contribute to COPD pathogenesis, and that mucociliary dysfunction and mucus abnormalities are important in COPD. Based on our data, augmenting secretion by way of CFTR channel activation or by cholinergic stimulation may improve ion transport and overcome airway surface dehydration and MCT decrements, respectively; these, along with muco-active agents to reduce mucus viscoelasticity, are promising therapeutic avenues for slowing COPD progression

Light trapping in ultrathin plasmonic solar cells

We report on the design, fabrication, and measurement of ultrathin film a-Si:H solar cells with nanostructured plasmonic back contacts, which demonstrate enhanced short circuit current densities compared to cells having flat or randomly textured back contacts. The primary photocurrent enhancement occurs in the spectral range from 550 nm to 800 nm. We use angle-resolved photocurrent spectroscopy to confirm that the enhanced absorption is due to coupling to guided modes supported by the cell. Full-field electromagnetic simulation of the absorption in the active a-Si:H layer agrees well with the experimental results. Furthermore, the nanopatterns were fabricated via an inexpensive, scalable, and precise nanopatterning method. These results should guide design of optimized, non-random nanostructured back reflectors for thin film solar cells

Rapid detection of BRCA1/2 recurrent mutations in Chinese breast and ovarian cancer patients with multiplex SNaPshot genotyping panels.

BRCA1/2 mutations are significant risk factors for hereditary breast and ovarian cancer (HBOC), its mutation frequency in HBOC of Chinese ethnicity is around 9%, in which nearly half are recurrent mutations. In Hong Kong and China, genetic testing and counseling are not as common as in the West. To reduce the barrier of testing, a multiplex SNaPshot genotyping panel that targeted 25 Chinese BRCA1/2 mutation hotspots was developed, and its feasibility was evaluated in a local cohort of 441 breast and 155 ovarian cancer patients. For those who tested negative, they were then subjected to full-gene testing with next-generation sequencing (NGS). BRCA mutation prevalence in this cohort was 8.05% and the yield of the recurrent panel was 3.52%, identifying over 40% of the mutation carriers. Moreover, from 79 Chinese breast cancer cases recruited overseas, 2 recurrent mutations and one novel BRCA2 mutation were detected by the panel and NGS respectively. The developed genotyping panel showed to be an easy-to-perform and more affordable testing tool that can provide important contributions to improve the healthcare of Chinese women with cancer as well as family members that harbor high risk mutations for HBOC

Software Requirements Classification Using Word Embeddings and Convolutional Neural Networks

Software requirements classification, the practice of categorizing requirements by their type or purpose, can improve organization and transparency in the requirements engineering process and thus promote requirement fulfillment and software project completion. Requirements classification automation is a prominent area of research as automation can alleviate the tediousness of manual labeling and loosen its necessity for domain-expertise. This thesis explores the application of deep learning techniques on software requirements classification, specifically the use of word embeddings for document representation when training a convolutional neural network (CNN). As past research endeavors mainly utilize information retrieval and traditional machine learning techniques, we entertain the potential of deep learning on this particular task. With the support of learning libraries such as TensorFlow and Scikit-Learn and word embedding models such as word2vec and fastText, we build a Python system that trains and validates configurations of Naïve Bayes and CNN requirements classifiers. Applying our system to a suite of experiments on two well-studied requirements datasets, we recreate or establish the Naïve Bayes baselines and evaluate the impact of CNNs equipped with word embeddings trained from scratch versus word embeddings pre-trained on Big Data

The role of SHIP in the development and activation of mouse mucosal and connective tissue mast cells

Although SHIP is a well-established suppressor of IgE plus Ag-induced degranulation and cytokine production in bone marrow-derived mast cells (BMMCs), little is known about its role in connective tissue (CTMCs) or mucosal (MMCs) mast cells. In this study, we compared SHIP's role in the development as well as the IgE plus Ag and TLR-induced activation of CTMCs, MMCs, and BMMCs and found that SHIP delays the maturation of all three mast cell subsets and, surprisingly, that it is a positive regulator of IgE-induced BMMC survival. We also found that SHIP represses IgE plus Ag-induced degranulation of all three mast cell subsets and that TLR agonists do not trigger their degranulation, whether SHIP is present or not, nor do they enhance IgE plus Ag-induced degranulation. In terms of cytokine production, we found that in MMCs and BMMCs, which are poor producers of TLR-induced cytokines, SHIP is a potent negative regulator of IgE plus Ag-induced IL-6 and TNF-α production. Surprisingly, however, in splenic or peritoneal derived CTMCs, which are poor producers of IgE plus Ag-induced cytokines, SHIP is a potent positive regulator of TLR-induced cytokine production. Lastly, cell signaling and cytokine production studies with and without LY294002, wortmannin, and PI3Kα inhibitor-2, as well as with PI3K p85α(-/-) BMMCs and CTMCs, are consistent with SHIP positively regulating TLR-induced cytokine production via an adaptor-mediated pathway while negatively regulating IgE plus Ag-induced cytokine production by repressing the PI3K pathway

Two-photon fluorescence imaging of intracellular hydrogen peroxide with chemoselective fluorescent probes

Abstract. We present the application of two-photon fluorescence (TPF) imaging to monitor intracellular hydrogen peroxide (H2O2) production in brain cells. For selective imaging of H2O2 over other reactive oxygen species, we employed small-molecule fluorescent probes that utilize a chemoselective boronate deprotection mechanism. Peroxyfluor-6 acetoxymethyl ester detects global cellular H2O2 and mitochondria peroxy yellow 1 detects mitochondrial H2O2. Two-photon absorption cross sections for these H2O2 probes are measured with a mode-locked Ti:sapphire laser in the wavelength range of 720 to 1040 nm. TPF imaging is demonstrated in the HT22 cell line to monitor both cytoplasmic H2O2 and localized H2O2 production in mitochondria. Endogenous cytoplasmic H2O2 production is detected with TPF imaging in rat astrocytes modified with d-amino acid oxidase. The TPF H2O2 imaging demonstrated that these chemoselective probes are powerful tools for the detection of intracellular H2O2