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Abstract Background Deer mice (genus Peromyscus ) are the most common rodents in North America. Despite the availability of reference genomes for some species, a comprehensive database of polymorphisms, especially in those maintained as living stocks and distributed to academic investigators, is missing. In the present study we surveyed two populations of P. maniculatus that are maintained at the Peromyscus Genetic Stock Center (PGSC) for polymorphisms across their 2.5 × 10 9 bp genome. Results High density of variation was identified, corresponding to one SNP every 55 bp for the high altitude stock (SM2) or 207 bp for the low altitude stock (BW) using snpEff (v4.3). Indels were detected every 1157 bp for BW or 311 bp for SM2. The average Watterson estimator for the BW and SM2 populations is 248813.70388 and 869071.7671 respectively. Some differences in the distribution of missense, nonsense and silent mutations were identified between the stocks, as well as polymorphisms in genes associated with inflammation (NFATC2), hypoxia (HIF1a) and cholesterol metabolism (INSIG1) and may possess value in modeling pathology. Conclusions This genomic resource, in combination with the availability of P. maniculatus from the PGSC, is expected to promote genetic and genomic studies with this animal model. 

Building science gateways for humanities content poses new challenges to the science gateway community. Compared with science gateways devoted to scientific content, humanities-related projects usually require 1) processing data in various formats, such as text, image, video, etc., 2) constant public access from a broad audience, and therefore 3) reliable security, ideally with low maintenance. Many traditional science gateways are monolithic in design, which makes them easier to write, but they can be computationally inefficient when integrated with numerous scientific packages for data capture and pipeline processing. Since these packages tend to be single-threaded or nonmodular, they can create traffic bottlenecks when processing large numbers of requests. Moreover, these science gateways are usually challenging to resume development on due to long gaps between funding periods and the aging of the integrated scientific packages. In this paper, we study the problem of building science gateways for humanities projects by developing a service-based architecture, and present two such science gateways: the Moving Image Research Collections (MIRC) – a science gateway focusing on image analysis for digital surrogates of historical motion picture film, and SnowVision - a science gateway for studying pottery fragments in southeastern North America. For each science gateway, we present an overview of the background of the projects, and some unique challenges in their design and implementation. These two science gateways are deployed on XSEDE’s Jetstream academic clouding computing resource and are accessed through web interfaces. Apache Airavata middleware is used to manage the interactions between the web interface and the deep-learning-based (DL) backend service running on the Bridges graphics processing unit (GPU) cluster. 

A challenging problem in modern archaeology is to automatically identify fragmented heritage objects by their decorative full designs, such as the pottery sherds from Southeastern America. The difficulties of this problem lie in: 1) these pottery sherds are usually fragmented so that each sherd only covers a small portion of its underlying full design; 2) these sherds can be so highly degraded that curves may contain missing segments or become very shallow; and 3) curve patterns may overlap with each other from the making of these potteries. This paper presents a deep-learning based framework for matching a sherd with a database of known designs to find its underlying design. This framework contains three steps: 1) extracting curve pattern using an FCN-based curve pattern segmentation method from the digitized sherd's depth map, 2) matching a sherd with a non-composite (single copy of a design) pattern combining template matching algorithm with a dual-source CNN re-ranking method to find its underlying design, and 3) matching a sherd with a composite (multiple copies of a design) pattern using a Chamfer Matching based method. The framework was evaluated on a set of sherds from the heartland of the paddle-stamping tradition with a subset of known paddle-stamped designs of Pre-colonial southeastern North America. Extensive experimental results show the effectiveness of the proposed framework and algorithms.