Lens ER-stress response during cataract development in Mip-mutant mice

AbstractMajor intrinsic protein (MIP) is a functional water-channel (AQP0) that also plays a key role in establishing lens fiber cell architecture. Genetic variants of MIP have been associated with inherited and age-related forms of cataract; however, the underlying pathogenic mechanisms are unclear. Here we have used lens transcriptome profiling by microarray-hybridization and qPCR to identify pathogenic changes during cataract development in Mip-mutant (Lop/+) mice. In postnatal Lop/+ lenses (P7) 99 genes were up-regulated and 75 were down-regulated (>2-fold, p=<0.05) when compared with wild-type. A pathway analysis of up-regulated genes in the Lop/+ lens (P7) was consistent with endoplasmic reticulum (ER)-stress and activation of the unfolded protein response (UPR). The most up-regulated UPR genes (>4-fold) in the Lop/+ lens included Chac1>Ddit3>Atf3>Trib3>Xbp1 and the most down-regulated genes (>5-fold) included two anti-oxidant genes, Hspb1 and Hmox1. Lop/+ lenses were further characterized by abundant TUNEL-positive nuclei within central degenerating fiber cells, glutathione depletion, free-radical overproduction, and calpain hyper-activation. These data suggest that Lop/+ lenses undergo proteotoxic ER-stress induced cell-death resulting from prolonged activation of the Eif2ak3/Perk-Atf4-Ddit3-Chac1 branch of the UPR coupled with severe oxidative-stress

Reuters: China wages to rise as labor shortages grow

This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.CLW_2010_Report_China_Reuters_china_wages.pdf: 24 downloads, before Oct. 1, 2020

Solution conductivity dependent crack size effect in stress corrosion cracking and corrosion fatigue

The chemical crack size effect on environmentally assisted crack growth was first demonstrated experimentally by Gangloff [1] and supported on a more robust theoretical framework by Turnbull et al. [2,3]. It is probably better dubbed the electrochemical crack size effect since the potential drop in the crack was a critical factor in determining the solution chemistry and the sensitivity to crack size. In recent experimental studies [4] we have focused on the growth rate of small and long stress corrosion and corrosion fatigue cracks in 12Cr steam turbine blade steels in low conductivity water containing 35 ppm Cl- (simulating upset steam condensate chemistry). A large effect of crack size on growth rate was observed for the same mechanical driving force. However, the crack-size effect disappeared in lower conductivity solution, 300 ppb Cl- and 300 ppb SO42- (corresponding to normal steam condensate chemistry). Furthermore, corrosion fatigue long crack growth rates were the same in aerated and in deaerated solutions for the two environments but stress corrosion cracks arrested in deaerated solution. An explanation for these varied results will be presented based on the concept of the solution conductivity dependent crack size effect and its impact on potential drop and the crack-tip potential. To underpin this conceptual idea and to explore further the scale of this effect for varied crack size and solution conductivity combinations, modelling of crack electrochemistry is being undertaken and the preliminary results will be discussed. R.P. Gangloff, The criticality of crack size in aqueous corrosion fatigue, Res. Mech. Let., 1981, 1, 299-306. A. Turnbull and J.G.N. Thomas, A model of crack electrochemistry for steels in the active state based on mass transport by diffusion and ion migration , J Electrochemical Society, 129 (7), 1412-1422, 1982. A. Turnbull and D.H. Ferriss, Mathematical modelling of the electrochemistry in corrosion fatigue cracks in steel corroding in sea water . Corrosion Science, 27 (12), 1323-1350, 1987. S. Zhou, M. Lukaszewicz and A. Turnbull, Small and short crack growth and the solution-conductivity dependent electrochemical crack size effect, Corros. Sci., 97 (2015) 25-37

Modeling speech recognition and synthesis simultaneously: Encoding and decoding lexical and sublexical semantic information into speech with no direct access to speech data

Human speakers encode information into raw speech which is then decoded by the listeners. This complex relationship between encoding (production) and decoding (perception) is often modeled separately. Here, we test how encoding and decoding of lexical semantic information can emerge automatically from raw speech in unsupervised generative deep convolutional networks that combine the production and perception principles of speech. We introduce, to our knowledge, the most challenging objective in unsupervised lexical learning: a network that must learn unique representations for lexical items with no direct access to training data. We train several models (ciwGAN and fiwGAN arXiv:2006.02951) and test how the networks classify acoustic lexical items in unobserved test data. Strong evidence in favor of lexical learning and a causal relationship between latent codes and meaningful sublexical units emerge. The architecture that combines the production and perception principles is thus able to learn to decode unique information from raw acoustic data without accessing real training data directly. We propose a technique to explore lexical (holistic) and sublexical (featural) learned representations in the classifier network. The results bear implications for unsupervised speech technology, as well as for unsupervised semantic modeling as language models increasingly bypass text and operate from raw acoustics.Comment: Submitted to Interspeech 202

Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation

We aim at segmenting small organs (e.g., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by the complex and variable background. To alleviate this, researchers proposed a coarse-to-fine approach, which used prediction from the first (coarse) stage to indicate a smaller input region for the second (fine) stage. Despite its effectiveness, this algorithm dealt with two stages individually, which lacked optimizing a global energy function, and limited its ability to incorporate multi-stage visual cues. Missing contextual information led to unsatisfying convergence in iterations, and that the fine stage sometimes produced even lower segmentation accuracy than the coarse stage. This paper presents a Recurrent Saliency Transformation Network. The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration. This brings us two-fold benefits. In training, it allows joint optimization over the deep networks dealing with different input scales. In testing, it propagates multi-stage visual information throughout iterations to improve segmentation accuracy. Experiments in the NIH pancreas segmentation dataset demonstrate the state-of-the-art accuracy, which outperforms the previous best by an average of over 2%. Much higher accuracies are also reported on several small organs in a larger dataset collected by ourselves. In addition, our approach enjoys better convergence properties, making it more efficient and reliable in practice.Comment: Accepted to CVPR 2018 (10 pages, 6 figures

A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans

Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans. However, the segmentation accuracy of some small organs (e.g., the pancreas) is sometimes below satisfaction, arguably because deep networks are easily disrupted by the complex and variable background regions which occupies a large fraction of the input volume. In this paper, we formulate this problem into a fixed-point model which uses a predicted segmentation mask to shrink the input region. This is motivated by the fact that a smaller input region often leads to more accurate segmentation. In the training process, we use the ground-truth annotation to generate accurate input regions and optimize network weights. On the testing stage, we fix the network parameters and update the segmentation results in an iterative manner. We evaluate our approach on the NIH pancreas segmentation dataset, and outperform the state-of-the-art by more than 4%, measured by the average Dice-S{\o}rensen Coefficient (DSC). In addition, we report 62.43% DSC in the worst case, which guarantees the reliability of our approach in clinical applications.Comment: Accepted to MICCAI 2017 (8 pages, 3 figures