Characterizing the Low-Redshift Intergalactic Medium towards PKS1302-102

We present a detailed analysis of the intergalactic metal-line absorption systems in the archival HST/STIS and FUSE ultraviolet spectra of the low-redshift quasar PKS1302-102 (z_QSO = 0.2784). We supplement the archive data with CLOUDY ionization models and a survey of galaxies in the quasar field. There are 15 strong Lya absorbers with column densities logN_HI > 14. Of these, six are associated with at least CIII 977 absorption (logN(C^++) > 13); this implies a redshift density dN_CIII/dz = 36+13/-9 (68% confidence limits) for the five detections with rest equivalent width W_r > 50 mA. Two systems show OVI 1031,1037 absorption in addition to CIII (logN(O^+5) > 14). One is a partial Lyman limit system (logN_HI = 17) with associated CIII, OVI, and SiIII 1206 absorption. There are three tentative OVI systems that do not have CIII detected. For one OVI doublet with both lines detected at 3 sigma with W_r > 50 mA, dN_OVI/dz = 7+9/-4. We also search for OVI doublets without Lya absorption but identify none. From CLOUDY modeling, these metal-line systems have metallicities spanning the range -4 < [M/H] < -0.3. The two OVI systems with associated CIII absorption cannot be single-phase, collisionally-ionized media based on the relative abundances of the metals and kinematic arguments. From the galaxy survey, we discover that the absorption systems are in a diverse set of galactic environments. Each metal-line system has at least one galaxy within 500 km/s and 600 h^-1 kpc with L > 0.1 L_*.Comment: 21 pages in emulatepj form, 24 figures, 10 tables, accepted to Ap

Optimal Search Based Gene Selection for Cancer Prognosis

Gene array data have been widely used for cancer diagnosis in recent years. However, high dimensionality has been a major problem for gene array-based classification. Gene selection is critical for accurate classification and for identifying the marker genes to discriminate different tumor types. This paper created a framework of gene selection methods based on previous studies. We focused on optimal search-based gene subset selection methods that evaluate the group performance of genes and help to pinpoint global optimal set of marker genes. Notably, this study is the first to introduce tabu search to gene selection from high dimensional gene array data. Experimental studies on several gene array datasets demonstrated the effectiveness of optimal search-based gene subset selection to identify marker genes

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice

Motivation and Beliefs about the Nature of Scientific Knowledge Within an Immersive Virtual Ecosystems Environment

We explored Grade 6 students‘ (n = 202) self-efficacy, epistemic beliefs, and science interest over a 10-day virtual ecology curriculum. Pre- and post-surveys were administered, and analyses revealed that (1) students became more self-efficacious about inquiring scientifically after participating in the activity; (2) students on average evinced a shift toward more constructivist views about the role of authority in justifying scientific claims; (3) students who identified more strongly with being a science person evinced greater gains in self efficacy, developed a less constructivist view about the role of authority in justifying claims, and became more interested in science overall; and (4) students who held an incremental theory of ability evinced greater gains in self-efficacy. We discuss the implications of these findings for science educators and instructional designers in the design and use of immersive virtual worlds for middle school science students

Human papillomavirus E7 induces rereplication in response to DNA damage

Human papillomavirus (HPV) infection is necessary but not sufficient for cervical carcinogenesis. Genomic instability caused by HPV allows cells to acquire additional mutations required for malignant transformation. Genomic instability in the form of polyploidy has been demonstrated to play an important role in cervical carcinogenesis. We have recently found that HPV-16 E7 oncogene induces polyploidy in response to DNA damage; however, the mechanism is not known. Here we present evidence demonstrating that HPV-16 E7-expressing cells have an intact G(2) checkpoint. Upon DNA damage, HPV-16 E7-expressing cells arrest at the G(2) checkpoint and then undergo rereplication, a process of successive rounds of host DNA replication without entering mitosis. Interestingly, the DNA replication initiation factor Cdt1, whose uncontrolled expression induces rereplication in human cancer cells, is upregulated in E7-expressing cells. Moreover, downregulation of Cdt1 impairs the ability of E7 to induce rereplication. These results demonstrate an important role for Cdt1 in HPV E7-induced rereplication and shed light on mechanisms by which HPV induces genomic instability