Application of new dynamical spectra of orbits in Hamiltonian systems

In the present article, we investigate the properties of motion in Hamiltonian systems of two and three degrees of freedom, using the distribution of the values of two new dynamical parameters. The distribution functions of the new parameters, define the S(g) and the S(w) dynamical spectra. The first spectrum definition, that is the S(g) spectrum, will be applied in a Hamiltonian system of two degrees of freedom (2D), while the S(w) dynamical spectrum will be deployed in a Hamiltonian system of three degrees of freedom (3D). Both Hamiltonian systems, describe a very interesting dynamical system which displays a large variety of resonant orbits, different chaotic components and also several sticky regions. We test and prove the efficiency and the reliability of these new dynamical spectra, in detecting tiny ordered domains embedded in the chaotic sea, corresponding to complicated resonant orbits of higher multiplicity. The results of our extensive numerical calculations, suggest that both dynamical spectra are fast and reliable discriminants between different types of orbits in Hamiltonian systems, while requiring very short computation time in order to provide solid and conclusive evidence regarding the nature of an orbit. Furthermore, we establish numerical criteria in order to quantify the results obtained from our new dynamical spectra. A comparison to other previously used dynamical indicators, reveals the leading role of the new spectra.Comment: Published in Nonlinear Dynamics (NODY) journal. arXiv admin note: text overlap with arXiv:1009.1993 by other author

Cassini UVIS Observations of the Io Plasma Torus. IV. Modeling Temporal and Azimuthal Variability

In this fourth paper in a series, we present a model of the remarkable temporal and azimuthal variability of the Io plasma torus observed during the Cassini encounter with Jupiter. Over a period of three months, the Cassini Ultraviolet Imaging Spectrograph (UVIS) observed a dramatic variation in the average torus composition. Superimposed on this long-term variation, is a 10.07-hour periodicity caused by an azimuthal variation in plasma composition subcorotating relative to System III longitude. Quite surprisingly, the amplitude of the azimuthal variation appears to be modulated at the beat frequency between the System III period and the observed 10.07-hour period. Previously, we have successfully modeled the months-long compositional change by supposing a factor of three increase in the amount of material supplied to Io's extended neutral clouds. Here, we extend our torus chemistry model to include an azimuthal dimension. We postulate the existence of two azimuthal variations in the number of super-thermal electrons in the torus: a primary variation that subcorotates with a period of 10.07 hours and a secondary variation that remains fixed in System III longitude. Using these two hot electron variations, our model can reproduce the observed temporal and azimuthal variations observed by Cassini UVIS.Comment: Revised 24 August 2007 Accepted by Icarus, 50 pages, 2 Tables, 8 figure

Financial Fraud of Older Adults During the Early Months of the COVID-19 Pandemic

BACKGROUND AND OBJECTIVES: Coronavirus disease 2019 (COVID-19) created a "perfect storm" for financial fraud targeting older adults. Guided by the Contextual Theory of Elder Abuse, we focused on individual and systemic contexts to examine how older adults became prey to financial fraud. RESEARCH DESIGN AND METHODS: In July 2020, 998 adults who were 60-98 years of age (93% White; 64% female) completed an online survey about experiences with financial fraud. Participants were recruited from gerontology research registries at Florida State University, University of Pittsburg, Virginia Tech, and Wayne State University. RESULTS: Over half (65.9%) of the respondents experienced a COVID-19-related scam attempt, with charity contributions (49%) and COVID-19 treatments (42%) being the most common. Perpetrators commonly contacted older adults electronically (47%) two or more times (64%). Although most respondents ignored the request (i.e., hung up the phone and deleted text/e-mail), 11.3% sent a requested payment, and 5.3% provided personal information. Predictors of vulnerability included contentment with financial situation, concern about finances in the aftermath of the pandemic, and wishing to talk to someone about financial decisions. Respondents targeted for a non-COVID-19 scam attempt were less likely to be targets of a COVID-19-related scam. DISCUSSION AND IMPLICATIONS: Older adults who were financially secure, worried about their financial situation, or wished they could speak with someone about their financial decisions appeared susceptible to falling victim to a fraud attempt. The high number of attempts indicates a need for a measurable and concerted effort to prevent the financial fraud of older adults

Mass Splitting and Production of Σc0\Sigma_c^0 and Σc++\Sigma_c^{++} Measured in 500GeV500 {GeV} π−−\pi^- -N Interactions

From a sample of $2722 \pm 78$ $\Lambda_c^+$ decaying to the $pK^-\pi^+$ final state, we have observed, in the hadroproduction experiment E791 at Fermilab, $143 \pm 20$ $\Sigma_c^0$ and $122 \pm 18$ $\Sigma_c^{++}$ through their decays to $\Lambda_c^+ \pi^{\pm}$. The mass difference $M(\Sigma_c^0) - M(\Lambda_c^+$) is measured to be $(167.38\pm 0.29\pm 0.15) {MeV}$; for $M(\Sigma_c^{++}) - M(\Lambda_c^+)$, we find $(167.76\pm 0.29\pm0.15) {MeV}$. The rate of $\Lambda_c^+$ production from decays of the $\Sigma_c$ triplet is (22\pm 2\pm 3) {%} of the total $\Lambda_c^+$ production assuming equal rate of production from all three, as measured for $\Sigma_c^0$ and $\Sigma_c^{++}$. We do not observe a statistically significant $\Sigma_c$ baryon-antibaryon production asymmetry. The $x_F$ and $p_t^2$ spectra of $\Lambda_c^+$ from $\Sigma_c$ decays are observed to be similar to those for all $\Lambda_c^+$'s produced.Comment: 15 pages, uuencoded postscript 3 figures uuencoded, tar-compressed fil

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Enhancing the effectiveness of interdisciplinary mental health treatment teams

Mental health administrators often lack guidelines for promoting and evaluating the effectiveness of interdisciplinary clinical treatment teams. This article describes the use of a model of group effectiveness that elucidates several aspects of team effectiveness. Also discussed are how administrators can support such teams by reviewing their initial set-up, how the organization influences the team's productivity and longevity, and how team members can better understand one another's personal and professional frames of reference to improve mutual collaboration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44090/1/10488_2005_Article_BF02106536.pd

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

lncRNA Epigenetic Landscape Analysis Identifies EPIC1 as an Oncogenic lncRNA that Interacts with MYC and Promotes Cell-Cycle Progression in Cancer

We characterized the epigenetic landscape of genes encoding long noncoding RNAs (lncRNAs) across 6,475 tumors and 455 cancer cell lines. In stark contrast to the CpG island hypermethylation phenotype in cancer, we observed a recurrent hypomethylation of 1,006 lncRNA genes in cancer, including EPIC1 (epigenetically-induced lncRNA1). Overexpression of EPIC1 is associated with poor prognosis in luminal B breast cancer patients and enhances tumor growth in vitro and in vivo. Mechanistically, EPIC1 promotes cell-cycle progression by interacting with MYC through EPIC1's 129\u2013283 nt region. EPIC1 knockdown reduces the occupancy of MYC to its target genes (e.g., CDKN1A, CCNA2, CDC20, and CDC45). MYC depletion abolishes EPIC1's regulation of MYC target and luminal breast cancer tumorigenesis in vitro and in vivo. Wang et al. characterize the epigenetic landscape of lncRNAs genes across a large number of human tumors and cancer cell lines and observe recurrent hypomethylation of lncRNA genes, including EPIC1. EPIC1 RNA promotes cell-cycle progression by interacting with MYC and enhancing its binding to target genes

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines

The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects