Single-qubit rotations in two-dimensional optical lattices with multiqubit addressing

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Categorization of species as native or nonnative using DNA sequence signatures without a complete reference library.

New genetic diagnostic approaches have greatly aided efforts to document global biodiversity and improve biosecurity. This is especially true for organismal groups in which species diversity has been underestimated historically due to difficulties associated with sampling, the lack of clear morphological characteristics, and/or limited availability of taxonomic expertise. Among these methods, DNA sequence barcoding (also known as "DNA barcoding") and by extension, meta-barcoding for biological communities, has emerged as one of the most frequently utilized methods for DNA-based species identifications. Unfortunately, the use of DNA barcoding is limited by the availability of complete reference libraries (i.e., a collection of DNA sequences from morphologically identified species), and by the fact that the vast majority of species do not have sequences present in reference databases. Such conditions are critical especially in tropical locations that are simultaneously biodiversity rich and suffer from a lack of exploration and DNA characterization by trained taxonomic specialists. To facilitate efforts to document biodiversity in regions lacking complete reference libraries, we developed a novel statistical approach that categorizes unidentified species as being either likely native or likely nonnative based solely on measures of nucleotide diversity. We demonstrate the utility of this approach by categorizing a large sample of specimens of terrestrial insects and spiders (collected as part of the Moorea BioCode project) using a generalized linear mixed model (GLMM). Using a training data set of known endemic (n = 45) and known introduced species (n = 102), we then estimated the likely native/nonnative status for 4,663 specimens representing an estimated 1,288 species (412 identified species), including both those specimens that were either unidentified or whose endemic/introduced status was uncertain. Using this approach, we were able to increase the number of categorized specimens by a factor of 4.4 (from 794 to 3,497), and the number of categorized species by a factor of 4.8 from (147 to 707) at a rate much greater than chance (77.6% accuracy). The study identifies phylogenetic signatures of both native and nonnative species and suggests several practical applications for this approach including monitoring biodiversity and facilitating biosecurity

A Herschel study of Planetary Nebulae

We present Herschel PACS and SPIRE images of the dust shells around the planetary nebulae NGC 650, NGC 6853, and NGC 6720, as well as images showing the dust temperature in their shells. The latter shows a rich structure, which indicates that internal extinction in the UV is important despite the highly evolved status of the nebulae.Comment: 2 pages, 1 figure, 2012, proceedings IAU Symposium 283 Planetary Nebulae: An Eye to the Futur

Oil spill source identification using colorimetric detection

The colorimetric detection of polycyclic aromatic hydrocarbons (PAHs) was investigated for the quick and easy identification of likely oil spill offenders. In this new technology, photochromic compounds were used to sense PAHs by varying their photoswitching capacity. To that end, three photochromes were designed and showed varying degrees of photoswitching inhibition depending on PAH analyte, photochrome and excitation wavelength. PAH mixtures that mimic oil spills showed the same varying response and demonstrated the accuracy of this technology. To prove the applicability of this technology, an array was assembled using the three photochromes at three excitation wavelengths and tested against authentic crude oil samples. Not only could these samples be differentiated, weathering of two distinctly different oil samples showed limited variation in response, demonstrating that this may be a viable technique for in situ oil identification

Low-level cadmium exposure and cardiovascular outcomes in elderly Australian women: A cohort study

Background Cadmium has been associated with increased risk of cardiovascular disease (CVD) in observational studies, however there has been a limited focus on this relationship in women. Objectives This study investigated the association of urinary cadmium (UCd) concentrations with CVD outcomes and all-cause mortality in elderly Western Australian (WA) women. Methods UCd excretion was measured at baseline in 1359 women, mean age 75.2 ± 2.7 years and 14.5 years of atherosclerotic vascular disease (ASVD) hospitalisations and deaths, including both the principle cause of death and all associated causes of death. Health outcome data were retrieved from the Western Australian Data Linkage System. Cox regression analysis was used to estimate hazard ratios of ASVD and all-cause mortality. UCd was ln-transformed and models were adjusted for demographic and CVD risk factors. Results Median (IQR) concentration of UCd was 0.18 (0.09–0.32) μg/L. In multivariable-adjusted analyses per ln unit (equivalent to ∼2.7 fold) increase in UCd, there was a 36% increase in the risk of death from heart failure and 17% increase in the risk of a heart failure event, respectively (HR = 1.36, 95% CI 1.11–1.67; HR = 1.17, 95% CI 1.01–1.35). When analyses were restricted to never smokers the relationship between UCd and death from heart failure remained (HR 1.29, 95% CI 1.01–1.63). Conclusions This study suggests that even at low levels of exposure cadmium may be associated with heart failure hospitalisations and deaths in older women, however given the dilute nature of these urine samples, the results must be interpreted with caution

IP-10/CXCL10 induction in human pancreatic cancer stroma influences lymphocytes recruitment and correlates with poor survival

Pancreatic ductal adenocarcinoma (PDAC) is characterized by an abundant desmoplastic reaction driven by pancreatic stellate cells (PSCs) that contributes to tumor progression. Here we sought to characterize the interactions between pancreatic cancer cells (PCCs) and PSCs that affect the inflammatory and immune response in pancreatic tumors. Conditioned media from mono- and cocultures of PSCs and PCCs were assayed for expression of cytokines and growth factors. IP-10/CXCL10 was the most highly induced chemokine in coculture of PSCs and PCCs. Its expression was induced in the PSCs by PCCs. IP-10 was elevated in human PDAC specimens, and positively correlated with high stroma content. Furthermore, gene expression of IP-10 and its receptor CXCR3 were significantly associated with the intratumoral presence of regulatory T cells (Tregs). In an independent cohort of 48 patients with resectable pancreatic ductal adenocarcinoma, high IP-10 expression levels correlated with decreased median overall survival. Finally, IP-10 stimulated the ex vivo recruitment of CXCR3+ effector T cells as well as CXCR3+ Tregs derived from patients with PDAC. Our findings suggest that, in pancreatic cancer, CXCR3+ Tregs can be recruited by IP-10 expressed by PSCs in the tumor stroma, leading to immunosuppressive and tumor-promoting effects

Synergistic drug combinations from electronic health records and gene expression.

ObjectiveUsing electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding.MethodWe applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis.ResultsFrom EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence.ConclusionsThis is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing

A phantom assessment of achievable contouring concordance across multiple treatment planning systems

In this paper, the highest level of inter- and intra-observer conformity achievable with different treatment planning systems (TPSs), contouring tools, shapes, and sites have been established for metrics including the Dice similarity coefficient (DICE) and Hausdorff Distance. High conformity values, e.g. DICEBreast_Shape = 0.99 ± 0.01, were achieved. Decreasing image resolution decreased contouring conformity