Cyphastrea salae, a new species of hard coral from Lord Howe Island, Australia (Scleractinia, Merulinidae)

A new zooxanthellate reef-dwelling scleractinian coral species, Cyphastrea salae sp. n. (Scleractinia, Merulinidae), is described from Lord Howe Island Australia. The new species can be distinguished morphologically from the only other congeneric species on Lord Howe Island, C. microphthalma, by the number of primary septa (12 vs. 10) and the much taller corallites (mean +/- SE: 1.0 +/- 0.07 mm v 0.4 +/- 0.04 mm). The relationship of C. salae to four of the other eleven currently accepted species in the genus was explored through analyses of nuclear (28S rDNA) and mitochondrial (noncoding intergenic region) gene sequences. Cyphastrea salae sp. n. forms a strongly supported clade that is distinct from a clade containing three species found commonly in Australia, C. chalcidicum, C. serailia, and C. microphthalma. One specimen was also found in the Solitary Islands, another high latitude location in south-eastern Australia. The discovery of a new species in the genus Cyphastrea on high latitude reefs in south-eastern Australia suggests that other new species might be found among more diverse genera represented here and that the scleractinian fauna of these isolated locations is more distinct than previously recognised

EST analysis of gene expression in early cleavage-stage sea urchin embryos

A set of 956 expressed sequence tags derived from 7-hour (mid-cleavage) sea urchin embryos was analyzed to assess biosynthetic functions and to illuminate the structure of the message population at this stage. About a quarter of the expressed sequence tags represented repetitive sequence transcripts typical of early embryos, or ribosomal and mitochondrial RNAs, while a majority of the remainder contained significant open reading frames. A total of 232 sequences, including 153 different proteins, produced significant matches when compared against GenBank. The majority of these identified sequences represented ‘housekeeping’ proteins, i.e., cytoskeletal proteins, metabolic enzymes, transporters and proteins involved in cell division. The most interesting finds were components of signaling systems and transcription factors not previously reported in early sea urchin embryos, including components of Notch and TGF signal transduction pathways. As expected from earlier kinetic analyses of the embryo mRNA populations, no very prevalent protein-coding species were encountered; the most highly represented such sequences were cDNAs encoding cyclins A and B. The frequency of occurrence of all sequences within the database was used to construct a sequence prevalence distribution. The result, confirming earlier mRNA population analyses, indicated that the poly(A) RNA of the early embryo consists mainly of a very complex set of low-copy-number transcripts

Generalized Pseudopotentials for Higher Partial Wave Scattering

We derive a generalized zero-range pseudopotential applicable to all partial wave solutions to the Schroedinger equation based on a delta-shell potential in the limit that the shell radius approaches zero. This properly models all higher order multipole moments not accounted for with a monopolar delta function at the origin, as used in the familiar Fermi pseudopotential for s-wave scattering. By making the strength of the potential energy dependent, we derive self-consistent solutions for the entire energy spectrum of the realistic potential. We apply this to study two particles in an isotropic harmonic trap, interacting through a central potential, and derive analytic expressions for the energy eigenstates and eigenvalues.Comment: RevTeX 4 pages, 1 figure, final published versio

Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease

The joint analysis of biomedical data in Alzheimer's Disease (AD) is important for better clinical diagnosis and to understand the relationship between biomarkers. However, jointly accounting for heterogeneous measures poses important challenges related to the modeling of the variability and the interpretability of the results. These issues are here addressed by proposing a novel multi-channel stochastic generative model. We assume that a latent variable generates the data observed through different channels (e.g., clinical scores, imaging, ...) and describe an efficient way to estimate jointly the distribution of both latent variable and data generative process. Experiments on synthetic data show that the multi-channel formulation allows superior data reconstruction as opposed to the single channel one. Moreover, the derived lower bound of the model evidence represents a promising model selection criterion. Experiments on AD data show that the model parameters can be used for unsupervised patient stratification and for the joint interpretation of the heterogeneous observations. Because of its general and flexible formulation, we believe that the proposed method can find important applications as a general data fusion technique.Comment: accepted for presentation at MLCN 2018 workshop, in Conjunction with MICCAI 2018, September 20, Granada, Spai

Impact of sudden stratospheric warmings on United Kingdom mortality

Sudden stratospheric warmings (SSWs) during boreal winter are one of the main drivers of sub‐seasonal climate variability in the Northern Hemisphere. Although the impact of SSW events on surface climate and climate extremes has been clearly demonstrated, the impact of the resulting climate anomalies on society has not been so widely considered. In the United Kingdom (UK), SSWs are associated with cold weather, which is linked to significant increases in mortality. This study demonstrates, for the first time, that SSWs are linked to increases in mortality in the UK. A distributed lag nonlinear model and standard parameter settings from the literature is used to construct a daily time series of UK deaths attributable to cold weather between 1991 and 2018. Weekly mortality associated with SSWs is diagnosed using a superposed epoch analysis of attributed mortality for the 15 SSW events in this period. SSW associated mortality peaks between 3 and 5 weeks after SSW central date and leads to, on average, 620 additional deaths in the same period. Given that the impacts of SSWs can be skilfully predicted on sub‐seasonal timescales, this suggests that health and social care systems could derive substantial benefit from sub‐seasonal forecasts during SSWs

Spin coherent quantum transport of electrons between defects in diamond

The nitrogen-vacancy color center in diamond has rapidly emerged as an important solid-state system for quantum information processing. While individual spin registers have been used to implement small-scale diamond quantum computing, the realization of a large-scale device requires development of an on-chip quantum bus for transporting information between distant qubits. Here we propose a method for coherent quantum transport of an electron and its spin state between distant NV centers. Transport is achieved by the implementation of spatial stimulated adiabatic Raman passage through the optical control of the NV center charge states and the confined conduction states of a diamond nanostructure. Our models show that for two NV centers in a diamond nanowire, high fidelity transport can be achieved over distances of order hundreds of nanometres in timescales of order hundreds of nanoseconds. Spatial adiabatic passage is therefore a promising option for realizing an on-chip spin quantum bus

Supervised Learning-Based tagSNP Selection for Genome-Wide Disease Classifications

Background: Comprehensive evaluation of common genetic variations through association of single nucleotide polymorphisms (SNPs) with complex human diseases on the genome-wide scale is an active area in human genome research. One of the fundamental questions in a SNP-disease association study is to find an optimal subset of SNPs with predicting power for disease status. To find that subset while reducing study burden in terms of time and costs, one can potentially reconcile information redundancy from associations between SNP markers. Results: We have developed a feature selection method named Supervised Recursive Feature Addition (SRFA). This method combines supervised learning and statistical measures for the chosen candidate features/SNPs to reconcile the redundancy information and, in doing so, improve the classification performance in association studies. Additionally, we have proposed a Support Vector based Recursive Feature Addition (SVRFA) scheme in SNP-disease association analysis. Conclusions: We have proposed using SRFA with different statistical learning classifiers and SVRFA for both SNP selection and disease classification and then applying them to two complex disease data sets. In general, our approaches outperform the well-known feature selection method of Support Vector Machine Recursive Feature Elimination and logic regression-based SNP selection for disease classification in genetic association studies. Our study further indicates that both genetic and environmental variables should be taken into account when doing disease predictions and classifications for the most complex human diseases that have gene-environment interactions