The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is the community resource for genomic, gene and protein information about the budding yeast Saccharomyces cerevisiae, containing a variety of functional information about each yeast gene and gene product. We have recently added regulatory information to SGD and present it on a new tabbed section of the Locus Summary entitled 'R...

Vol. 29, No. 6, 2017 801 Received July 27, 2016, Revised October 10, 2016, Accepted for publication October 31, 2016 Corresponding author: Yanhua Liang, Department of Dermatology, Shenzhen Hospital of Southern Medical University, 1333 Xinhu Road, Shenzhen, Guangdong 518100, China. Tel: 86-755-23360448, Fax: 86-755-23360448, E-mail: [email protected] This is an Open Access article distributed ...

Submarine groundwater (SGD) is one of the most significant pathways for the exchange of groundwater and/or source of nutrients, metals and carbon to the ocean, subsequently cause deleterious impacts on the coastal ecosystems. Microorganisms have been recognized as the important participators in the biogeochemical processes in the SGD. In this study, by utilizing 16S rRNA-based Illumina Miseq se...

Radiotherapy (RT) used for head and neck cancers causes permanent salivary gland dysfunction (SGD). Previous short-term studies have demonstrated that pre-RT salivary flow rates and the amount of radiation exposure to parotid glands influence the amount of RT-induced SGD. The purpose of this study was to determine which variables are related to the development of long-term post-RT SGD. Parotid ...

We study the problem of how to distribute the training of large-scale deep learning models in the parallel computing environment. We propose a new distributed stochastic optimization method called Elastic Averaging SGD (EASGD). We analyze the convergence rate of the EASGD method in the synchronous scenario and compare its stability condition with the existing ADMM method in the round-robin sche...

This paper proposes a new optimization algorithm called Entropy-SGD for training deep neural networks that is motivated by the local geometry of the energy landscape. Local extrema with low generalization error have a large proportion of almost-zero eigenvalues in the Hessian with very few positive or negative eigenvalues. We leverage upon this observation to construct a local-entropy-based obj...

Large-scale learning problems require algorithms that scale benignly with respect to the size of the dataset and the number of parameters to be trained; leading numerous practitioners to favor the classic stochastic gradient descent (SGD [1, 2, 3]) over more sophisticated methods. Besides its fast convergence, SGD has been observed to sometimes lead to signi cantly better generalization perform...

We propose a novel method for speeding up stochastic optimization algorithms via sketching methods, which recently became a powerful tool for accelerating algorithms for numerical linear algebra. We revisit the method of conditioning for accelerating first-order methods and suggest the use of sketching methods for constructing a cheap conditioner that attains a significant speedup with respect ...

Neutrophil-specific granule deficiency (SGD) is a rare congenital disorder. The neutrophils of individuals with SGD display atypical bi-lobed nuclei, lack expression of all secondary and tertiary granule proteins, and possess defects in chemotaxis, disaggregation, receptor up-regulation, and bactericidal activity, resulting in frequent and severe bacterial infections. Previously, a homozygous m...

The Saccharomyces Genome Database (SGD) is a scientific database that provides researchers with high-quality curated data about the genes and gene products of Saccharomyces cerevisiae. To provide instant and easy access to this information on mobile devices, we have developed YeastGenome, a native application for the Apple iPhone and iPad. YeastGenome can be used to quickly find basic informati...