With the advent of the Heliophysics/Geospace System Observatory (H/GSO), a complement of multi-spacecraft missions and ground-based observatories to study the space environment, data retrieval, analysis, and visualization of space physics data can be daunting. The Space Physics Environment Data Analysis System (SPEDAS), a grass-roots software development platform ( www.spedas.org ), is now officially supported by NASA Heliophysics as part of its data environment infrastructure. It serves more than a dozen space missions and ground observatories and can integrate the full complement of past and upcoming space physics missions with minimal resources, following clear, simple, and well-proven guidelines. Free, modular and configurable to the needs of individual missions, it works in both command-line (ideal for experienced users) and Graphical User Interface (GUI) mode (reducing the learning curve for first-time users). Both options have “crib-sheets,” user-command sequences in ASCII format that can facilitate record-and-repeat actions, especially for complex operations and plotting. Crib-sheets enhance scientific interactions, as users can move rapidly and accurately from exchanges of technical information on data processing to efficient discussions regarding data interpretation and science. SPEDAS can readily query and ingest all International Solar Terrestrial Physics (ISTP)-compatible products from the Space Physics Data Facility (SPDF), enabling access to a vast collection of historic and current mission data. The planned incorporation of Heliophysics Application Programmer’s Interface (HAPI) standards will facilitate data ingestion from distributed datasets that adhere to these standards. Although SPEDAS is currently Interactive Data Language (IDL)-based (and interfaces to Java-based tools such as Autoplot), efforts are under-way to expand it further to work with python (first as an interface tool and potentially even receiving an under-the-hood replacement). We review the SPEDAS development history, goals, and current implementation. We explain its “modes of use” with examples geared for users and outline its technical implementation and requirements with software developers in mind. We also describe SPEDAS personnel and software management, interfaces with other organizations, resources and support structure available to the community, and future development plans. Electronic Supplementary Material The online version of this article (10.1007/s11214-018-0576-4) contains supplementary material, which is available to authorized users.

Treatment with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) improves the overall survival of patients with EGFR-mutated non-small-cell lung cancer (NSCLC). First-generation EGFR-TKIs (e.g., gefitinib and erlotinib) or second-generation EGFR-TKIs (e.g., afatinib and dacomitinib) are effective for the treatment of EGFR-mutated NSCLC, especially in patients with EGFR exon 19 deletions or an exon 21 L858R mutation. However, almost all cases experience disease recurrence after 1 to 2 years due to acquired resistance. The EGFR T790M mutation in exon 20 is the most frequent alteration associated with the development of acquired resistance. Osimertinib—a third-generation EGFR-TKI—targets the T790M mutation and has demonstrated high efficacy against EGFR-mutated lung cancer. However, the development of acquired resistance to third-generation EGFR-TKI, involving the cysteine residue at codon 797 mutation, has been observed. Other mechanisms of acquired resistance include the activation of alternative pathways or downstream targets and histological transformation (i.e., epithelial–mesenchymal transition or conversion to small-cell lung cancer). Furthermore, the development of primary resistance through overexpression of the hepatocyte growth factor and suppression of Bcl-2-like protein 11 expression may lead to problems. In this report, we review these mechanisms and discuss therapeutic strategies to overcome resistance to EGFR-TKIs.

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Transforming growth factor beta (TGF-beta) contributes to the progression of pulmonary fibrosis through up-regulation of alpha-smooth muscle actin (alpha-SMA) as lung myofibroblast differentiation. Bioactive sphingosine 1-phosphate (S1P) has been shown to mimic TGF-beta signals; however, the function of S1P in lung fibrotic process has not been well documented. We found, in a mouse model of bleomycin lung fibrosis, that SPHK1 and alpha-SMA were colocalized within lung fibrotic foci and that these expressions were significantly increased in primary cultured fibroblasts. Using human lung fibroblasts WI-38, we explored the rationale of sphingosine kinase (SPHK) with TGF-beta1 stimulation. SPHK inhibitors and small interference RNA (siRNA) targeted SPHK1 decreased alpha-SMA and fibronectin expression up-regulated by TGF-beta1. In the meantime, SPHK1 inhibition did not affect smad2 phosphorylation in response to TGF-beta1. Then we examined whether S1P receptors transactivation may affect TGF-beta signals. siRNA against S1P(2) and S1P(3), but not S1P(1), reduced alpha-SMA expression as well as Y-27632, Rho kinase inhibitor. We also detected activation of Rho GTPase upon stimulation of TGF-beta1 on the cell membrane where S1P(2) or S1P(3) was overexpressed. These data suggested that SPHK1 activation by TGF-beta1 leads to Rho-associated myofibroblasts differentiation mediated by transactivated S1P receptors in the lung fibrogenic process.

BACKGROUND:The secondary epidermal growth factor receptor (EGFR) mutation Thr790Met (T790M) accounts for approximately half of acquired resistances to EGFR-tyrosine kinase inhibitor (TKI). Recent reports have demonstrated that the emergence of T790M predicts a favorable prognosis and indolent progression. However, rebiopsy to confirm T790M status can be challenging due to limited tissue availability and procedural feasibility, and little is known regarding the differences among patients with or without T790M mutation. METHODS: The study investigated 78 EGFR-mutant patients who had undergone rebiopsy after TKI failure. The peptide nucleic acid-locked nucleic acid polymerase chain reaction clamp method was used in EGFR mutational analyses. Various patient characteristics and postprogression survivals (PPSs) after initial TKI failure were retrospectively compared in patients with and without T790M. RESULTS: The T790M mutation was identified in 4 (17%) of 24 central nervous system lesions, and in 22 (41%) of 54 other lesions (P 5.0417). No other characteristics had a statistical association with T790M prevalence. Median PPS was 31.4 months in 26 patients with T790M, and 11.4 months in 52 patients without T790M (P 5.0017). In the multivariate analysis, statistically significant factors for longer PPS included T790M-positive, good performance status, and no carcinomatous meningitis. CONCLUSIONS: The emergence of T790M in central nervous system lesions was rare, compared with other lesions. Patients with T790M after TKI failure appear to have better prognoses than those without T790M. TKI rechallenge or continuous administration beyond progression may be effective after initial TKI failure. Cancer 2013;119:4325-32.

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10100245/-/DC1.

Quantitative and qualitative analyses of STIR TSE MR images enable differentiation of lymph nodes with metastasis from those without. Qualitative analysis can substitute for quantitative analysis of STIR TSE MR imaging data.

T790M status in an individual patient can be spatiotemporally heterogeneous because of selective pressure from EGFR-TKI.