Spectroscopic and Photometric Study of the Contact Binary BO CVn

We present the results of the study of the contact binary system BO CVn. We have obtained physical parameters of the components based on combined analysis of new, multi-color light curves and spectroscopic mass ratio. This is the first time the latter has been determined for this object. We derived the contact configuration for the system with a very high filling factor of about 88 percent. We were able to reproduce the observed light curve, namely the flat bottom of the secondary minimum, only if a third light has been added into the list of free parameters. The resulting third light contribution is significant, about 20-24 percent, while the absolute parameters of components are: M1=1.16, M2=0.39, R1=1.62 and R2=1.00 (in solar units). The O-C diagram shows an upward parabola which, under the conservative mass transfer assumption, would correspond to a mass transfer rate of dM/dt = 6.3 \times 10-8M\odot/yr, matter being transferred from the less massive component to the more massive one. No cyclic, short-period variations have been found in the O-C diagram (but longer-term variations remain a possibility)Comment: 16 pages, 5 figures, 5 tables, accepted for publication by New Astronom

The Proneural Molecular Signature Is Enriched in Oligodendrogliomas and Predicts Improved Survival among Diffuse Gliomas

The Cancer Genome Atlas Project (TCGA) has produced an extensive collection of ‘-omic’ data on glioblastoma (GBM), resulting in several key insights on expression signatures. Despite the richness of TCGA GBM data, the absence of lower grade gliomas in this data set prevents analysis genes related to progression and the uncovering of predictive signatures. A complementary dataset exists in the form of the NCI Repository for Molecular Brain Neoplasia Data (Rembrandt), which contains molecular and clinical data for diffuse gliomas across the full spectrum of histologic class and grade. Here we present an investigation of the significance of the TCGA consortium's expression classification when applied to Rembrandt gliomas. We demonstrate that the proneural signature predicts improved clinical outcome among 176 Rembrandt gliomas that includes all histologies and grades, including GBMs (log rank test p = 1.16e-6), but also among 75 grade II and grade III samples (p = 2.65e-4). This gene expression signature was enriched in tumors with oligodendroglioma histology and also predicted improved survival in this tumor type (n = 43, p = 1.25e-4). Thus, expression signatures identified in the TCGA analysis of GBMs also have intrinsic prognostic value for lower grade oligodendrogliomas, and likely represent important differences in tumor biology with implications for treatment and therapy. Integrated DNA and RNA analysis of low-grade and high-grade proneural gliomas identified increased expression and gene amplification of several genes including GLIS3, TGFB2, TNC, AURKA, and VEGFA in proneural GBMs, with corresponding loss of DLL3 and HEY2. Pathway analysis highlights the importance of the Notch and Hedgehog pathways in the proneural subtype. This demonstrates that the expression signatures identified in the TCGA analysis of GBMs also have intrinsic prognostic value for low-grade oligodendrogliomas, and likely represent important differences in tumor biology with implications for treatment and therapy

Program Model Checking: A Practitioner's Guide

Program model checking is a verification technology that uses state-space exploration to evaluate large numbers of potential program executions. Program model checking provides improved coverage over testing by systematically evaluating all possible test inputs and all possible interleavings of threads in a multithreaded system. Model-checking algorithms use several classes of optimizations to reduce the time and memory requirements for analysis, as well as heuristics for meaningful analysis of partial areas of the state space Our goal in this guidebook is to assemble, distill, and demonstrate emerging best practices for applying program model checking. We offer it as a starting point and introduction for those who want to apply model checking to software verification and validation. The guidebook will not discuss any specific tool in great detail, but we provide references for specific tools

Reversal of cancer gene expression identifies repurposed drugs for diffuse intrinsic pontine glioma

Diffuse intrinsic pontine glioma (DIPG) is an aggressive incurable brainstem tumor that targets young children. Complete resection is not possible, and chemotherapy and radiotherapy are currently only palliative. This study aimed to identify potential therapeutic agents using a computational pipeline to perform an in silico screen for novel drugs. We then tested the identified drugs against a panel of patient-derived DIPG cell lines. Using a systematic computational approach with publicly available databases of gene signature in DIPG patients and cancer cell lines treated with a library of clinically available drugs, we identified drug hits with the ability to reverse a DIPG gene signature to one that matches normal tissue background. The biological and molecular effects of drug treatment was analyzed by cell viability assay and RNA sequence. In vivo DIPG mouse model survival studies were also conducted. As a result, two of three identified drugs showed potency against the DIPG cell lines Triptolide and mycophenolate mofetil (MMF) demonstrated significant inhibition of cell viability in DIPG cell lines. Guanosine rescued reduced cell viability induced by MMF. In vivo, MMF treatment significantly inhibited tumor growth in subcutaneous xenograft mice models. In conclusion, we identified clinically available drugs with the ability to reverse DIPG gene signatures and anti-DIPG activity in vitro and in vivo. This novel approach can repurpose drugs and significantly decrease the cost and time normally required in drug discovery

A highly invasive human glioblastoma pre-clinical model for testing therapeutics

Animal models greatly facilitate understanding of cancer and importantly, serve pre-clinically for evaluating potential anti-cancer therapies. We developed an invasive orthotopic human glioblastoma multiforme (GBM) mouse model that enables real-time tumor ultrasound imaging and pre-clinical evaluation of anti-neoplastic drugs such as 17-(allylamino)-17-demethoxy geldanamycin (17AAG). Clinically, GBM metastasis rarely happen, but unexpectedly most human GBM tumor cell lines intrinsically possess metastatic potential. We used an experimental lung metastasis assay (ELM) to enrich for metastatic cells and three of four commonly used GBM lines were highly metastatic after repeated ELM selection (M2). These GBM-M2 lines grew more aggressively orthotopically and all showed dramatic multifold increases in IL6, IL8, MCP-1 and GM-CSF expression, cytokines and factors that are associated with GBM and poor prognosis. DBM2 cells, which were derived from the DBTRG-05MG cell line were used to test the efficacy of 17AAG for treatment of intracranial tumors. The DMB2 orthotopic xenografts form highly invasive tumors with areas of central necrosis, vascular hyperplasia and intracranial dissemination. In addition, the orthotopic tumors caused osteolysis and the skull opening correlated to the tumor size, permitting the use of real-time ultrasound imaging to evaluate antitumor drug activity. We show that 17AAG significantly inhibits DBM2 tumor growth with significant drug responses in subcutaneous, lung and orthotopic tumor locations. This model has multiple unique features for investigating the pathobiology of intracranial tumor growth and for monitoring systemic and intracranial responses to antitumor agents

Molecular Analysis of Precursor Lesions in Familial Pancreatic Cancer

PMCID: PMC3553106This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

A foundation for runtime monitoring

Runtime Verification is a lightweight technique that complements other verification methods in an effort to ensure software correctness. The technique poses novel questions to software engineers: it is not easy to identify which specifications are amenable to runtime monitor-ing, nor is it clear which monitors effect the required runtime analysis correctly. This exposition targets a foundational understanding of these questions. Particularly, it considers an expressive specification logic (a syntactic variant of the modal μ-calculus) that is agnostic of the verification method used, together with an elemental framework providing an operational semantics for the runtime analysis performed by monitors. The correspondence between the property satisfactions in the logic on the one hand, and the verdicts reached by the monitors performing the analysis on the other, is a central theme of the study. Such a correspondence underpins the concept of monitorability, used to identify the subsets of the logic that can be adequately monitored for by RV. Another theme of the study is that of understanding what should be expected of a monitor in order for the verification process to be correct. We show how the monitor framework considered can constitute a basis whereby various notions of monitor correctness may be defined and investigated.peer-reviewe

Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution

Glioblastoma (GBM) is a prototypical heterogeneous brain tumor refractory to conventional therapy. A small residual population of cells escapes surgery and chemoradiation, resulting in a typically fatal tumor recurrence ~7 mo after diagnosis. Understanding the molecular architecture of this residual population is critical for the development of successful therapies. We used whole-genome sequencing and whole-exome sequencing of multiple sectors from primary and paired recurrent GBM tumors to reconstruct the genomic profile of residual, therapy resistant tumor initiating cells. We found that genetic alteration of the p53 pathway is a primary molecular event predictive of a high number of subclonal mutations in glioblastoma. The genomic road leading to recurrence is highly idiosyncratic but can be broadly classified into linear recurrences that share extensive genetic similarity with the primary tumor and can be directly traced to one of its specific sectors, and divergent recurrences that share few genetic alterations with the primary tumor and originate from cells that branched off early during tumorigenesis. Our study provides mechanistic insights into how genetic alterations in primary tumors impact the ensuing evolution of tumor cells and the emergence of subclonal heterogeneity