ALBERT

Description: We present optical photometric and spectroscopic coverage of the superluminous supernova (SLSN) PS1-11ap, discovered with the Pan-STARRS1 Medium Deep Survey at z  = 0.524. This intrinsically blue transient rose slowly to reach a peak magnitude of M u  = –21.4 mag and bolometric luminosity of 8 10 43 erg s –1 before settling on to a relatively shallow gradient of decline. The observed decline is significantly slower than those of the SLSNe-Ic which have been the focus of much recent attention. Spectroscopic similarities with the lower redshift SN2007bi and a decline rate similar to 56 Co decay time-scale initially indicated that this transient could be a candidate for a pair instability supernova (PISN) explosion. Overall the transient appears quite similar to SN2007bi and the lower redshift object PTF12dam. The extensive data set, from 30 d before peak to 230 d after, allows a detailed and quantitative comparison with published models of PISN explosions. We find that the PS1-11ap data do not match these model explosion parameters well, supporting the recent claim that these SNe are not pair instability explosions. We show that PS1-11ap has many features in common with the faster declining SLSNe-Ic, and the light-curve evolution can also be quantitatively explained by the magnetar spin-down model. At a redshift of z  = 0.524, the observer-frame optical coverage provides comprehensive rest-frame UV data and allows us to compare it with the SLSNe recently found at high redshifts between z  = 2 and 4. While these high- z explosions are still plausible PISN candidates, they match the photometric evolution of PS1-11ap and hence could be counterparts to this lower redshift transient.

Description: Motivation: Imputation using external reference panels (e.g. 1000 Genomes) is a widely used approach for increasing power in genome-wide association studies and meta-analysis. Existing hidden Markov models (HMM)-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available. Results: In simulations using 1000 Genomes (1000G) data, this method recovers 84% (54%) of the effective sample size for common (〉5%) and low-frequency (1–5%) variants [increasing to 87% (60%) when summary linkage disequilibrium information is available from target samples] versus the gold standard of 89% (67%) for HMM-based imputation, which cannot be applied to summary statistics. Our approach accounts for the limited sample size of the reference panel, a crucial step to eliminate false-positive associations, and it is computationally very fast. As an empirical demonstration, we apply our method to seven case–control phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) data and a study of height in the British 1958 birth cohort (1958BC). Gaussian imputation from summary statistics recovers 95% (105%) of the effective sample size (as quantified by the ratio of $${\chi }^{2}$$ association statistics) compared with HMM-based imputation from individual-level genotypes at the 227 (176) published single nucleotide polymorphisms (SNPs) in the WTCCC (1958BC height) data. In addition, for publicly available summary statistics from large meta-analyses of four lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have been obtained using previously published methods, and demonstrate their accuracy by masking subsets of the data. We show that 1000G imputation using our approach increases the magnitude and statistical evidence of enrichment at genic versus non-genic loci for these traits, as compared with an analysis without 1000G imputation. Thus, imputation of summary statistics will be a valuable tool in future functional enrichment analyses. Availability and implementation: Publicly available software package available at http://bogdan.bioinformatics.ucla.edu/software/ . Contact: bpasaniuc@mednet.ucla.edu or aprice@hsph.harvard.edu Supplementary information: Supplementary materials are available at Bioinformatics online.

Description: Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images that are produced by most major ground-based time-domain surveys with large format CCD cameras. This dependence on humans to reject bogus detections is unsustainable for next generation all-sky surveys and significant effort is now being invested to solve the problem computationally. In this paper, we explore a simple machine learning approach to real–bogus classification by constructing a training set from the image data of ~32 000 real astrophysical transients and bogus detections from the Pan-STARRS1 Medium Deep Survey. We derive our feature representation from the pixel intensity values of a 20 x 20 pixel stamp around the centre of the candidates. This differs from previous work in that it works directly on the pixels rather than catalogued domain knowledge for feature design or selection. Three machine learning algorithms are trained (artificial neural networks, support vector machines and random forests) and their performances are tested on a held-out subset of 25 per cent of the training data. We find the best results from the random forest classifier and demonstrate that by accepting a false positive rate of 1 per cent, the classifier initially suggests a missed detection rate of around 10 per cent. However, we also find that a combination of bright star variability, nuclear transients and uncertainty in human labelling means that our best estimate of the missed detection rate is approximately 6 per cent.

Description: The sea slug Elysia chlorotica offers a unique opportunity to study the evolution of a novel function (photosynthesis) in a complex multicellular host. Elysia chlorotica harvests plastids (absent of nuclei) from its heterokont algal prey, Vaucheria litorea . The "stolen" plastids are maintained for several months in cells of the digestive tract and are essential for animal development. The basis of long-term maintenance of photosynthesis in this sea slug was thought to be explained by extensive horizontal gene transfer (HGT) from the nucleus of the alga to the animal nucleus, followed by expression of algal genes in the gut to provide essential plastid-destined proteins. Early studies of target genes and proteins supported the HGT hypothesis, but more recent genome-wide data provide conflicting results. Here, we generated significant genome data from the E. chlorotica germ line (egg DNA) and from V. litorea to test the HGT hypothesis. Our comprehensive analyses fail to provide evidence for alga-derived HGT into the germ line of the sea slug. Polymerase chain reaction analyses of genomic DNA and cDNA from different individual E. chlorotica suggest, however, that algal nuclear genes (or gene fragments) are present in the adult slug. We suggest that these nucleic acids may derive from and/or reside in extrachromosomal DNAs that are made available to the animal through contact with the alga. These data resolve a long-standing issue and suggest that HGT is not the primary reason underlying long-term maintenance of photosynthesis in E. chlorotica. Therefore, sea slug photosynthesis is sustained in as yet unexplained ways that do not appear to endanger the animal germ line through the introduction of dozens of foreign genes.

Description: We present the results of a pilot study for the extended Massive Cluster Survey (eMACS), a comprehensive search for distant, X-ray luminous galaxy clusters at z 〉 0.5. Our pilot study applies the eMACS concept to the 71 deg 2 area extended by the 10 fields of the Pan-STARRS1 (PS1) Medium Deep Survey (MDS). Candidate clusters are identified by visual inspection of PS1 images in the g , r , i and z bands in a 5  x 5 arcmin 2 region around X-ray sources detected in the ROSAT All-Sky Survey (RASS). To test and optimize the eMACS X-ray selection criteria, our pilot study uses the largest possible RASS data base, i.e. all RASS sources listed in the Bright and Faint Source Catalogues (BSC and FSC) that fall within the MDS footprint. We apply no additional constraints regarding X-ray flux, spectral hardness ratio or photon statistics and lower the redshift threshold to z 〉 0.3 to extend the probed luminosity range to poorer systems. Scrutiny of PS1/MDS images for 41 BSC and 200 FSC sources combined with dedicated spectroscopic follow-up observations results in a sample of 11 clusters with estimated or spectroscopic redshifts of z 〉 0.3. In order to assess and quantify the degree of point source contamination of the observed RASS fluxes, we examine archival Chandra data obtained in targeted and serendipitous observations of six of the 11 clusters found. As expected, the diffuse emission from all six systems is contaminated by point sources to some degree, and for half of them active galactic nucleus emission dominates. X-ray follow-up observations will thus be crucial in order to establish robust cluster luminosities for eMACS clusters. Although the small number of distant X-ray luminous clusters in the MDS does not allow us to make firm predictions for the over 20 000 deg 2 of extragalactic sky covered by eMACS, the identification of two extremely promising eMACS cluster candidates at z 0.6 (both yet to be observed with Chandra ) in such a small solid angle is encouraging. Representing a tremendous gain over the presently known two dozen such systems from X-ray, optical and Sunyaev–Zel’dovich cluster surveys combined, the sample of over 100 extremely massive clusters at z 〉 0.5 expected from eMACS would be invaluable for the identification of the most powerful gravitational lenses in the Universe, as well as for in-depth and statistical studies of the physical properties of the most massive galaxy clusters out to z  ~ 1.

Description: A new type of interstrand DNA–DNA cross-link between abasic (Ap) sites and 2'-deoxyadenosine (dA) residues was recently reported, but the chemical structure and properties of this lesion were not rigorously established. Here we characterized the nucleoside cross-link remnant released by enzymatic digestion of duplex DNA containing the dA-Ap cross-link. A synthetic standard was prepared for the putative nucleoside cross-link remnant 6 in which the anomeric carbon of the 2-deoxyribose residue was connected to the exocyclic N 6 -amino group of dA. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis showed that the synthetic material 6 matched the authentic cross-link remnant released by enzymatic digestion of cross-linked DNA. These findings establish the chemical structure of the dA-Ap cross-link released from duplex DNA and may provide methods for the detection of this lesion in cellular DNA. Both the nucleoside cross-link remnant 6 and the cross-link in duplex DNA were quite stable at pH 7 and 37°C, suggesting that the dA-Ap cross-link could be a persistent lesion with the potential to block the action of various DNA processing enzymes.

Description: Anthropogenic carbon dioxide (CO 2 ) emissions simultaneously increase ocean temperatures and reduce ocean surface pH, a process termed ocean acidification (OA). OA is expected to negatively affect the growth and physiology of many calcified organisms, but the response of non-calcified (fleshy) organisms is less well understood. Rising temperatures and p CO 2 can enhance photosynthetic rates (within tolerance limits). Therefore, warming may interact with OA to alter biological responses of macroalgae in complicated ways. Beyond thresholds of physiological tolerance, however, rising temperatures could further exacerbate negative responses to OA. Many studies have investigated the effects of OA or warming independently of each other, but few studies have quantified the interactive effects of OA and warming on marine organisms. We conducted four short-term independent factorial CO 2 enrichment and warming experiments on six common species of calcified and fleshy macroalgae from southern California to investigate the independent and interactive effects of CO 2 and warming on growth, carbonic anhydrase (CA) enzyme activity, pigment concentrations, and photosynthetic efficiency. There was no effect of elevated p CO 2 on CA activity, pigment concentration, and photosynthetic efficiency in the macroalgal species studies. However, we found that calcareous algae suffered reduced growth rates under high p CO 2 conditions alone, although the magnitude of the effect varied by species. Fleshy algae had mixed responses of growth rates to high p CO 2 , indicating that the effects of p CO 2 enrichment are inconsistent across species. The combined effects of elevated p CO 2 and warming had a significantly negative impact on growth for both fleshy and calcareous algae; calcareous algae experienced five times more weight loss than specimens in ambient control conditions and fleshy growth was reduced by 76%. Our results demonstrate the need to study the interactive effects of multiple stressors associated with global change on marine communities.

Description: The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.

Description: Bacterial protein glycosylation systems from varying species have been functionally reconstituted in Escherichia coli . Both N- and O-linked glycosylation pathways, in which the glycans are first assembled onto lipid carriers and subsequently transferred to acceptor proteins by an oligosaccharyltransferase (OTase), have been documented in bacteria. The identification and characterization of novel OTases with different properties may provide new tools for engineering glycoproteins of biotechnological interest. In the case of OTases involved in O-glycosylation (O-OTases), there is very low sequence homology between those from different bacterial species. The Wzy_C signature domain common to these enzymes is also present in WaaL ligases; enzymes involved in lipopolysaccharide biosynthesis. Therefore, the identification of O-OTases using solely bioinformatic methods is problematic. The hypothetical proteins BTH_I0650 from Burkholderia thailandensis E264 and VC0393 from Vibrio cholerae N16961 contain the Wzy_C domain. In this work, we demonstrate that both proteins have O-OTase activity and renamed them PglL Bt and PglL Vc , respectively, similar to the Neisseria meningitidis counterpart (PglL Nm ). In E. coli , PglL Bt and PglL Vc display relaxed glycan and protein specificity. However, effective glycosylation depends upon a specific combination of the protein acceptor, glycan and O-OTase analyzed. This knowledge has important implications in the design of glycoconjugates and provides novel tools for use in glycoengineering applications. The codification of enzymatically active O-OTase in the genomes of members of the Vibrio and Burkholderia genera suggests the presence of still unknown O-glycoproteins in these organisms, which might have a role in bacterial physiology or pathogenesis.

Description: A recent slew of ENCyclopedia Of DNA Elements (ENCODE) Consortium publications, specifically the article signed by all Consortium members, put forward the idea that more than 80% of the human genome is functional. This claim flies in the face of current estimates according to which the fraction of the genome that is evolutionarily conserved through purifying selection is less than 10%. Thus, according to the ENCODE Consortium, a biological function can be maintained indefinitely without selection, which implies that at least 80 – 10 = 70% of the genome is perfectly invulnerable to deleterious mutations, either because no mutation can ever occur in these "functional" regions or because no mutation in these regions can ever be deleterious. This absurd conclusion was reached through various means, chiefly by employing the seldom used "causal role" definition of biological function and then applying it inconsistently to different biochemical properties, by committing a logical fallacy known as "affirming the consequent," by failing to appreciate the crucial difference between "junk DNA" and "garbage DNA," by using analytical methods that yield biased errors and inflate estimates of functionality, by favoring statistical sensitivity over specificity, and by emphasizing statistical significance rather than the magnitude of the effect. Here, we detail the many logical and methodological transgressions involved in assigning functionality to almost every nucleotide in the human genome. The ENCODE results were predicted by one of its authors to necessitate the rewriting of textbooks. We agree, many textbooks dealing with marketing, mass-media hype, and public relations may well have to be rewritten.