Circulating tumor cells isolation: The “post-EpCAM era”

Circulating tumor cells (CTCs) represent a submicroscopic fraction detached from a primary tumor and in transit to a secondary site. The prognostic significance of CTCs in metastatic cancer patients was demonstrated for the first time more than ten years ago. To date, it seems clear enough that CTCs are highly heterogeneous and dynamically change their shape. Thus, the inadequacy of epithelial cell adhesion molecule (EpCAM) as universal marker for CTCs detection seems unquestionable and alternative methods able to recognize a broader spectrum of phenotypes are definitely needed. In this review the pleiotropic functions of EpCAM are discussed in detail and the role of the molecule in the biology of CTCs is critically dissected

Oyster RNA-seq data support the development of Malacoherpesviridae genomics

The family of double-stranded DNA (dsDNA) Malacoherpesviridae includes viruses able to infect marine mollusks and detrimental for worldwide aquaculture production. Due to fast-occurring mortality and a lack of permissive cell lines, the available data on the few known Malacoherpesviridae provide only partial support for the study of molecular virus features, life cycle, and evolutionary history. Following thorough data mining of bivalve and gastropod RNA-seq experiments, we used more than five million Malacoherpesviridae reads to improve the annotation of viral genomes and to characterize viral InDels, nucleotide stretches, and SNPs. Both genome and protein domain analyses confirmed the evolutionary diversification and gene uniqueness of known Malacoherpesviridae. However, the presence of Malacoherpesviridae-like sequences integrated within genomes of phylogenetically distant invertebrates indicates broad diffusion of these viruses and indicates the need for confirmatory investigations. The manifest co-occurrence of OsHV-1 genotype variants in single RNA-seq samples of Crassostrea gigas provide further support for the Malacoherpesviridae diversification. In addition to simple sequence motifs inter-punctuating viral ORFs, recombination-inducing sequences were found to be enriched in the OsHV-1 and AbHV1-AUS genomes. Finally, the highly correlated expression of most viral ORFs in multiple oyster samples is consistent with the burst of viral proteins during the lytic phase

Linguistic Diversity on the Internet: Arabic, Chinese and Cyrillic Script Top-Level Domain Names

The deployment of Arabic, Chinese, and Cyrillic top-level domain names is explored in this research by analyzing technical and policy documents of the Internet Corporation for Assigned Names and Numbers (ICANN), as well as newspaper articles in the respective language regions. The tension between English uniformity at the root level of the Internet׳s domain names system, and language diversity in the global Internet community, has resulted in various technological solutions surrounding Arabic, Chinese, and Cyrillic language domain names. These standards and technological solutions ensure the security and stability of the Internet; however, they do not comprehensively address the linguistic diversity needs of the Internet. ICANN has been transforming into an international policy organization, yet its linguistic diversity policies appear disconnected from the diversity policies of the United Nations, and remain technically oriented. Linguistic diversity in relation to IDNs at this stage mostly focus on the language representation of major languages that are spoken in powerful nation-states, who use the rhetoric of national pride, local business branding, and inclusion of non-English speakers. This situation surfaces the tension between nation-states and the new international governing institution ICANN

Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers

In image restoration tasks, like denoising and super resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods. Learning from discrete and fixed restoration levels, deep models cannot be easily generalized to data of continuous and unseen levels. This topic is rarely touched in literature, due to the difficulty of modulating well-trained models with certain hyper-parameters. We make a step forward by proposing a unified CNN framework that consists of few additional parameters than a single-level model yet could handle arbitrary restoration levels between a start and an end level. The additional module, namely AdaFM layer, performs channel-wise feature modification, and can adapt a model to another restoration level with high accuracy. By simply tweaking an interpolation coefficient, the intermediate model - AdaFM-Net could generate smooth and continuous restoration effects without artifacts. Extensive experiments on three image restoration tasks demonstrate the effectiveness of both model training and modulation testing. Besides, we carefully investigate the properties of AdaFM layers, providing a detailed guidance on the usage of the proposed method.Comment: Accepted by CVPR 2019 (oral); code is available: https://github.com/hejingwenhejingwen/AdaF

Sign language recognition with transformer networks

Sign languages are complex languages. Research into them is ongoing, supported by large video corpora of which only small parts are annotated. Sign language recognition can be used to speed up the annotation process of these corpora, in order to aid research into sign languages and sign language recognition. Previous research has approached sign language recognition in various ways, using feature extraction techniques or end-to-end deep learning. In this work, we apply a combination of feature extraction using OpenPose for human keypoint estimation and end-to-end feature learning with Convolutional Neural Networks. The proven multi-head attention mechanism used in transformers is applied to recognize isolated signs in the Flemish Sign Language corpus. Our proposed method significantly outperforms the previous state of the art of sign language recognition on the Flemish Sign Language corpus: we obtain an accuracy of 74.7% on a vocabulary of 100 classes. Our results will be implemented as a suggestion system for sign language corpus annotation