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JupyterHub [1] has become a popular choice in many scientific communities, offering an easy-to-use interface for users with little to no frontend development work while promoting reproducible and replicable (R&R) science [2]. In the broad geospatial science community, CyberGISX [3] provides such a gateway environment with many cyberGIS (i.e., geospatial information science and systems based on advanced cyberinfrastructure) and geospatial software packages prebuilt and ready to use. Like other JupyterHub-based solutions, CyberGISX also provides container-based access for its users and must balance a trade-off between providing a static compute environment which enhances R&R and continuously updating the software environment to keep up with advances in scientific software. Solutions such as Binder [4] have attempted to address this trade-off by having required dependencies encoded in the package and building the software environment at the time of use. However, such a solution comes with two major disadvantages: (a) software is built at the time it is needed, increasing startup time and introducing the possibility that some of the dependencies of the environment are no longer available or have changed; and (b) the onus of specifying and managing software installations is passed to notebook developers, many of whom are domain scientists and not comfortable with such responsibilities. To address these challenges and enhance R&R with minimal effort from end-users, we have designed and implemented a solution on CyberGISX that allows software to be kept on an external file server mounted into each user's environment. Scientific software is installed with Easybuild [5] and managed by Lmod [6] giving a variety of benefits: (1) the compute environment is more standardized and easily reproducible outside of the gateway; (2) multiple versions of software can be made available to users without increasing container size; and (3) the exact copies of software are always available on the gateway instead of being rebuilt for every release, further enhancing R&R. We also employ an Easybuild-installed Anaconda [7] to create and manage conda environments on the file server. The combination of the software stack from Easybuild and Python environment from conda provides end-users with kernels for their Jupyter notebooks which are persistent and unchanged as the gateway's container updates. This design enhances R&R and adds functionality for advanced users without introducing technical barriers to non-technical end-users. As such, domain scientists using this solution need not build their own software and specify dependencies, which helps prevent the notebooks they have developed from getting broken by the next software release. This talk explores the new architecture and applications of this solution to CyberGISX [3] and CyberGIS-Jupyter for Water (CJW) [8].

Geospatial research and education have become increasingly dependent on cyberGIS, defined as geographic information science and systems based on advanced cyberinfrastructure (CI), [1] to tackle computation and data challenges. However, the use of advanced cyberGIS capabilities has typically been constrained to a small set of research groups who have the technical expertise of using CI resources. Over the past few years CyberGIS-Jupyter [2,3] has been developed to provide access to cyberGIS capabilities through an easy-to-use Jupyter Notebook interface which has made cyberGIS more accessible. For many cyberGIS and geospatial applications accessing CI resources needed for solving complex problems at scale. However, leveraging CI resources for geospatial application is challenging both due to the steep learning curve and lack of appropriate tools. CyberGIS-Compute fills this gap by providing an easy-to-use middleware tool for using and contributing geospatial application codes that leverage CI resources. This substantially lowers the learning curve for both geospatial users and developers to access cyberGIS capabilities at scale. CyberGIS-Compute is backed by Virtual ROGER (Resourcing Open Geospatial Education and Research); a geospatial supercomputer with access to a number of readily available popular geospatial libraries. With CyberGIS-Compute we have designed an easy-to-use middleware and associated Python SDK to provide access to CyberGIS capabilities, allowing geospatial applications to easily scale and employ advanced cyberinfrastructure resources. This presentation will first describe the basics of CyberGIS-Jupyter and CyberGIS-Compute, then introduce the Python SDK for CyberGIS-Compute with a simple example. Then, we will take multiple real-world geospatial applications use-cases like spatial accessibility and wildfire evacuation simulation using agent based modeling. Lastly, we will also descrive mechanism to contribute applications to the CyberGIS-Compute framework.

Spatial data often embedded with geographic references are important to numerous scientific domains (e.g., ecology, geography and spatial sciences, geosciences, and social sciences, to name just a few), and also beneficial to solving many critical societal problems (e.g., environmental and urban sustainability). In recent years, this type of data has exploded to massive size and significant complexity as increasingly sophisticated location-based sensors and devices (e.g., social networks, smartphones, and environmental sensors) are widely deployed and used. However, the tools and computational platforms available for processing synthesizing such data remain limited. Over the past couple of years, this project has helped establish CyberGIS-Jupyter as a platform for making geospatial data processing and analytics capabilities accessible. CyberGIS-Jupyter is an online geospatial computation platform for a large number of users to conduct and share scalable cyberGIS analytics via Jupyter Notebooks supported by advanced cyberinfrastructure resources such as those provisioned by the Extreme Science and Engineering Discovery Environment (XSEDE). This poster presents details of both CyberGIS-Jupyter platform in terms of both technical progress made and the enabling role it plays in enhancing cyberGIS research and educationThis poster was developed for 2020 NSF CSSI PI meeting

We examined the role of the mammalian target of rapamycin (mTOR) in hepatic cell growth. To dissociate cell growth from cell proliferation, we employed an in vivo model of nonproliferative liver growth in rats, refeeding after 48 h of food deprivation. Starvation resulted in a decrease in liver mass, liver protein, and cell size, all of which were largely restored after 24 h of refeeding. Administration of the mTOR inhibitor, rapamycin, before the refeeding period partially inhibited the restoration of liver protein content. Refeeding was also associated with an increase in ribosomal protein S6 phosphorylation and phosphorylation of the eukaryotic initiation factor (eIF) 4E binding protein 1 (4E-BP1). 4E-BP1 phosphorylation was accompanied by a decrease in the abundance of the complex containing 4E-BP1 with eIF4E. These changes were prevented by rapamycin administration. However, association of eIF4E and eIF4G and eIF2alpha phosphorylation, both of which are stimulated by refeeding, were insensitive to rapamycin. The functional importance of these observations was confirmed by polysome fractionation, which showed that translation initiation of 5' oligopyrimidine tract-containing mRNAs, which encode ribosomal proteins, was inhibited by rapamycin, whereas translation of signal transducer and activator of transcription 1 (STAT1), a cap-dependent mRNA, was unaffected. The abundance of ribosomal proteins paralleled total protein content during refeeding in both control and rapamycin-injected rats. We conclude that accretion of liver protein during refeeding is dependent on mTOR-mediated activation of the translation of ribosomal proteins but not dependent on mTOR-mediated activation of cap-dependent translation initiation.

We have studied translational control in the model of 48 h of fasting in the rat. Our initial observations showed a paradoxical increase in ribosomal protein S6 (rpS6) phosphorylation and a decrease in eukaryotic initiation factor 2alpha (eIF2alpha) phosphorylation. These effects, which would favor an increase in protein synthesis, could be attributed to increased circulating concentrations of branched-chain amino acids in fasting. To determine what mechanisms might account for decreased hepatic translation in fasting, we examined the cap binding complex. eIF4E-bound 4E-BP1 did not increase. However, eIF4E-bound eIF4G and total cellular eIF4G were profoundly decreased in fasted liver. eIF4G mRNA levels were not lower after fasting. Based on the hypothesis that decreased eIF4G translation might account for the reduced eIF4G content, we fractionated ribosomes by sucrose density centrifugation. Immunoblotting for rpS6 showed modest polysomal disaggregation upon fasting. PCR analysis of polysome profiles revealed that a spectrum of mRNAs undergo different translational regulation in the fasted state. In particular, eIF4G was minimally affected by fasting. This indicated that reduced eIF4G abundance in fasting may be a function of its stability, whereas its recovery upon refeeding is necessarily independent of its own involvement in the cap binding complex. Western immunoblotting of polysome fractions showed that phosphorylated rpS6 was disproportionately present in translating polysomes in fed and fasted animals, consistent with a role in translational control. However, the translation of rpS8, an mRNA with a 5'-oligopyrimidine tract, did not coincide with rpS6 phosphorylation, thus dissociating rpS6 phosphorylation from the translational control of this subset of mRNAs.

Fetal growth retardation, a common end point for a variety of conditions affecting mother and fetus, is associated with reduced liver mass. We have performed studies to determine the mechanism for decreased liver mass in a maternal starvation model of fetal growth restriction in the rat. Pregnant dams were deprived of food for 48 h before delivery on embryonic day 19 (E19). Fetal body weight was not affected. However, fetal liver weight was reduced by approximately 15%. Immunostaining of fetal liver for proliferating cell nuclear antigen and flow cytometry on isolated fetal hepatocytes showed G1 cell cycle arrest in samples from starved dams. Based on our prior studies showing attenuated hepatic insulin signaling in the late gestation fetal rat, we tested the hypothesis that G1 arrest in our model might be due to altered nutrient signaling. Fetal plasma amino acid analyses showed no decrease in branched-chain amino acids, but arginine concentrations were decreased in fetuses of fasted mothers. Reduced arginine in E19 fetal hepatocyte culture media was associated with decreased DNA synthesis. Whereas levels of cyclins D and E were unchanged in fetal hepatocytes exposed to low arginine, cyclin E-dependent kinase activity was reduced. Low arginine also induced changes in the translational machinery, indicative of impaired signaling through the nutrient sensing kinase mammalian target of rapamycin. Our results are consistent with the hypothesis that restricted nutrient availability signals to the hepatocyte cell cycle in fetuses of fasted mothers, thereby accounting for decreased hepatocyte proliferation and liver mass.

Insulin has long been assigned a key role in the regulation of growth and metabolism during fetal life. Our prior observations indicated that hepatic insulin signaling is attenuated in the late-gestation fetal rat. Therefore, we studied the perinatal ontogeny of hepatic insulin signaling extending from phosphatidylinositol 3-kinase (PI3K) to the ribosome. Initial studies demonstrated markedly decreased insulin-mediated activation of ribosomal protein S6 kinase 1 (S6K1) in the fetus. We found a similar pattern in the regulation of Akt, a kinase upstream from S6K1. Insulin produced minimal activation of insulin receptor substrate (IRS)-1-associated PI3K activity in fetal liver. A modest IRS-2-associated response was seen in the fetus. However, levels of both IRS-1 and IRS-2 were very low in fetal liver relative to adult liver. IRS-1 content and insulin responsiveness of PI3K, Akt, and S6K1 showed a transition to the adult phenotype during the first several postnatal weeks. Examination of downstream insulin signaling to the translational apparatus showed marked attenuation, relative to the adult, of fetal hepatic insulin-mediated phosphorylation of 4E-BP1, the regulatory protein for the eukaryotic initiation factor eIF4E, and ribosomal protein S6. The mammalian target of rapamycin (mTOR), a key integrator of nutritional and metabolic regulation of translation, was present in low amounts, was hypophosphorylated, and was not insulin sensitive in the fetus. Our results indicate that protein synthesis during late-gestation liver development may be mTOR and insulin independent. Reexamination of the role of insulin in fetal liver physiology may be warranted.