Neuroblastoma patient outcomes, tumor differentiation, and ERK activation are correlated with expression levels of the ubiquitin ligase UBE4B.

BackgroundUBE4B is an E3/E4 ubiquitin ligase whose gene is located in chromosome 1p36.22. We analyzed the associations of UBE4B gene and protein expression with neuroblastoma patient outcomes and with tumor prognostic features and histology.MethodsWe evaluated the association of UBE4B gene expression with neuroblastoma patient outcomes using the R2 Platform. We screened neuroblastoma tumor samples for UBE4B protein expression using immunohistochemistry. FISH for UBE4B and 1p36 deletion was performed on tumor samples. We then evaluated UBE4B expression for associations with prognostic factors and with levels of phosphorylated ERK in neuroblastoma tumors and cell lines.ResultsLow UBE4B gene expression is associated with poor outcomes in patients with neuroblastoma and with worse outcomes in all patient subgroups. UBE4B protein expression was associated with neuroblastoma tumor differentiation, and decreased UBE4B protein levels were associated with high-risk features. UBE4B protein levels were also associated with levels of phosphorylated ERK.ConclusionsWe have demonstrated associations between UBE4B gene expression and neuroblastoma patient outcomes and prognostic features. Reduced UBE4B protein expression in neuroblastoma tumors was associated with high-risk features, a lack of differentiation, and with ERK activation. These results suggest UBE4B may contribute to the poor prognosis of neuroblastoma tumors with 1p36 deletions and that UBE4B expression may mediate neuroblastoma differentiation

Archiving and disseminating integrative structure models.

Limitations in the applicability, accuracy, and precision of individual structure characterization methods can sometimes be overcome via an integrative modeling approach that relies on information from all available sources, including all available experimental data and prior models. The open-source Integrative Modeling Platform (IMP) is one piece of software that implements all computational aspects of integrative modeling. To maximize the impact of integrative structures, the coordinates should be made publicly available, as is already the case for structures based on X-ray crystallography, NMR spectroscopy, and electron microscopy. Moreover, the associated experimental data and modeling protocols should also be archived, such that the original results can easily be reproduced. Finally, it is essential that the integrative structures are validated as part of their publication and deposition. A number of research groups have already developed software to implement integrative modeling and have generated a number of structures, prompting the formation of an Integrative/Hybrid Methods Task Force. Following the recommendations of this task force, the existing PDBx/mmCIF data representation used for atomic PDB structures has been extended to address the requirements for archiving integrative structural models. This IHM-dictionary adds a flexible model representation, including coarse graining, models in multiple states and/or related by time or other order, and multiple input experimental information sources. A prototype archiving system called PDB-Dev ( https://pdb-dev.wwpdb.org ) has also been created to archive integrative structural models, together with a Python library to facilitate handling of integrative models in PDBx/mmCIF format

A case study in open source innovation: developing the Tidepool Platform for interoperability in type 1 diabetes management.

OBJECTIVE:Develop a device-agnostic cloud platform to host diabetes device data and catalyze an ecosystem of software innovation for type 1 diabetes (T1D) management. MATERIALS AND METHODS:An interdisciplinary team decided to establish a nonprofit company, Tidepool, and build open-source software. RESULTS:Through a user-centered design process, the authors created a software platform, the Tidepool Platform, to upload and host T1D device data in an integrated, device-agnostic fashion, as well as an application ("app"), Blip, to visualize the data. Tidepool's software utilizes the principles of modular components, modern web design including REST APIs and JavaScript, cloud computing, agile development methodology, and robust privacy and security. DISCUSSION:By consolidating the currently scattered and siloed T1D device data ecosystem into one open platform, Tidepool can improve access to the data and enable new possibilities and efficiencies in T1D clinical care and research. The Tidepool Platform decouples diabetes apps from diabetes devices, allowing software developers to build innovative apps without requiring them to design a unique back-end (e.g., database and security) or unique ways of ingesting device data. It allows people with T1D to choose to use any preferred app regardless of which device(s) they use. CONCLUSION:The authors believe that the Tidepool Platform can solve two current problems in the T1D device landscape: 1) limited access to T1D device data and 2) poor interoperability of data from different devices. If proven effective, Tidepool's open source, cloud model for health data interoperability is applicable to other healthcare use cases

Revealing nascent proteomics in signaling pathways and cell differentiation.

Regulation of gene expression at the level of protein synthesis is a crucial element in driving how the genetic landscape is expressed. However, we are still limited in technologies that can quantitatively capture the immediate proteomic changes that allow cells to respond to specific stimuli. Here, we present a method to capture and identify nascent proteomes in situ across different cell types without disturbing normal growth conditions, using O-propargyl-puromycin (OPP). Cell-permeable OPP rapidly labels nascent elongating polypeptides, which are subsequently conjugated to biotin-azide, using click chemistry, and captured with streptavidin beads, followed by digestion and analysis, using liquid chromatography-tandem mass spectrometry. Our technique of OPP-mediated identification (OPP-ID) allows detection of widespread proteomic changes within a short 2-hour pulse of OPP. We illustrate our technique by recapitulating alterations of proteomic networks induced by a potent mammalian target of rapamycin inhibitor, MLN128. In addition, by employing OPP-ID, we identify more than 2,100 proteins and uncover distinct protein networks underlying early erythroid progenitor and differentiation states not amenable to alternative approaches such as amino acid analog labeling. We present OPP-ID as a method to quantitatively identify nascent proteomes across an array of biological contexts while preserving the subtleties directing signaling in the native cellular environment

Large-Scale Mapping of Human Activity using Geo-Tagged Videos

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to accurately map activities both spatially and temporally. We also demonstrate the advantages of using the visual content over the tags/titles.Comment: Accepted at ACM SIGSPATIAL 201