Breeding for quantitative variables. Part 4: Breeding for nutritional quality traits

Yusuf Genc, Julia M. Humphries, Graham H. Lyons and Robin D. Grahamhttp://www.fao.org/docrep/012/i1070e/i1070e00.ht

MCTP is an ER-resident calcium sensor that stabilizes synaptic transmission and homeostatic plasticity.

Presynaptic homeostatic plasticity (PHP) controls synaptic transmission in organisms from Drosophila to human and is hypothesized to be relevant to the cause of human disease. However, the underlying molecular mechanisms of PHP are just emerging and direct disease associations remain obscure. In a forward genetic screen for mutations that block PHP we identified mctp (Multiple C2 Domain Proteins with Two Transmembrane Regions). Here we show that MCTP localizes to the membranes of the endoplasmic reticulum (ER) that elaborate throughout the soma, dendrites, axon and presynaptic terminal. Then, we demonstrate that MCTP functions downstream of presynaptic calcium influx with separable activities to stabilize baseline transmission, short-term release dynamics and PHP. Notably, PHP specifically requires the calcium coordinating residues in each of the three C2 domains of MCTP. Thus, we propose MCTP as a novel, ER-localized calcium sensor and a source of calcium-dependent feedback for the homeostatic stabilization of neurotransmission

Universal localisations and tilting modules for finite dimensional algebras

We study universal localisations, in the sense of Cohn and Schofield, for finite dimensional algebras and classify them by certain subcategories of our initial module category. A complete classification is presented in the hereditary case as well as for Nakayama algebras and local algebras. Furthermore, for hereditary algebras, we establish a correspondence between finite dimensional universal localisations and finitely generated support tilting modules. In the Nakayama case, we get a similar result using $\tau$-tilting modules, which were recently introduced by Adachi, Iyama and Reiten

GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis

Histopathological cancer diagnosis is based on visual examination of stained tissue slides. Hematoxylin and eosin (H\&E) is a standard stain routinely employed worldwide. It is easy to acquire and cost effective, but cells and tissue components show low-contrast with varying tones of dark blue and pink, which makes difficult visual assessments, digital image analysis, and quantifications. These limitations can be overcome by IHC staining of target proteins of the tissue slide. IHC provides a selective, high-contrast imaging of cells and tissue components, but their use is largely limited by a significantly more complex laboratory processing and high cost. We proposed a conditional CycleGAN (cCGAN) network to transform the H\&E stained images into IHC stained images, facilitating virtual IHC staining on the same slide. This data-driven method requires only a limited amount of labelled data but will generate pixel level segmentation results. The proposed cCGAN model improves the original network \cite{zhu_unpaired_2017} by adding category conditions and introducing two structural loss functions, which realize a multi-subdomain translation and improve the translation accuracy as well. % need to give reasons here. Experiments demonstrate that the proposed model outperforms the original method in unpaired image translation with multi-subdomains. We also explore the potential of unpaired images to image translation method applied on other histology images related tasks with different staining techniques

Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis

Cross-domain synthesizing realistic faces to learn deep models has attracted increasing attention for facial expression analysis as it helps to improve the performance of expression recognition accuracy despite having small number of real training images. However, learning from synthetic face images can be problematic due to the distribution discrepancy between low-quality synthetic images and real face images and may not achieve the desired performance when the learned model applies to real world scenarios. To this end, we propose a new attribute guided face image synthesis to perform a translation between multiple image domains using a single model. In addition, we adopt the proposed model to learn from synthetic faces by matching the feature distributions between different domains while preserving each domain's characteristics. We evaluate the effectiveness of the proposed approach on several face datasets on generating realistic face images. We demonstrate that the expression recognition performance can be enhanced by benefiting from our face synthesis model. Moreover, we also conduct experiments on a near-infrared dataset containing facial expression videos of drivers to assess the performance using in-the-wild data for driver emotion recognition.Comment: 8 pages, 8 figures, 5 tables, accepted by FG 2019. arXiv admin note: substantial text overlap with arXiv:1905.0028

Learn to synthesize and synthesize to learn

Attribute guided face image synthesis aims to manipulate attributes on a face image. Most existing methods for image-to-image translation can either perform a fixed translation between any two image domains using a single attribute or require training data with the attributes of interest for each subject. Therefore, these methods could only train one specific model for each pair of image domains, which limits their ability in dealing with more than two domains. Another disadvantage of these methods is that they often suffer from the common problem of mode collapse that degrades the quality of the generated images. To overcome these shortcomings, we propose attribute guided face image generation method using a single model, which is capable to synthesize multiple photo-realistic face images conditioned on the attributes of interest. In addition, we adopt the proposed model to increase the realism of the simulated face images while preserving the face characteristics. Compared to existing models, synthetic face images generated by our method present a good photorealistic quality on several face datasets. Finally, we demonstrate that generated facial images can be used for synthetic data augmentation, and improve the performance of the classifier used for facial expression recognition.Comment: Accepted to Computer Vision and Image Understanding (CVIU