BFKL and CCFM final states

I give a brief presentation of recent results on the equivalence of BFKL and CCFM small-x final states, and discuss their implications for phenomenology.Comment: 4 pages. Talk presented at 7th International Workshop on Deep Inelastic Scattering, Zeuthen, Germany, April 199

Large-scale inference and graph theoretical analysis of gene-regulatory networks in B. stubtilis

We present the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B. subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. By employing a computational, linear correlative procedure to generate these networks, and by analyzing the networks from a graph theoretical perspective, we are able to verify the biological viability of our inferred networks, and we demonstrate that our networks' graph theoretical properties are remarkably similar to those of other biological systems. In addition, by comparing our inferred networks to those of a previous, noisier implementation of the linear inference process [17], we are able to identify trends in graph theoretical behavior that occur both in our networks as well as in their perturbed counterparts. These commonalities in behavior at multiple levels of complexity allow us to ascertain the level of complexity to which our process is robust to noise.Comment: 22 pages, 4 figures, accepted for publication in Physica A (2006

Evolution of star formation in the UKIDSS ultra deep survey field-I. Luminosity functions and cosmic star formation rate out to z = 1.6

We present new results on the cosmic star formation history in the Subaru/XMM-Newton Deep Survey (SXDS)-Ultra Deep Survey (UDS) field out to z = 1.6. We compile narrowband data from the Subaru Telescope and the Visible and Infrared Survey Telescope forAstronomy (VISTA) in conjunction with broad-band data from the SXDS and UDS, to makea selection of 5725 emission-line galaxies in 12 redshift slices, spanning 10 Gyr of cosmictime. We determine photometric redshifts for the sample using 11-band photometry, and usea spectroscopically confirmed subset to fine tune the resultant redshift distribution. We usethe maximum-likelihood technique to determine luminosity functions in each redshift slice and model the selection effects inherent in any narrow-band selection statistically, to obviatethe retrospective corrections ordinarily required. The deep narrow-band data are sensitive tovery low star formation rates (SFRs), and allow an accurate evaluation of the faint end slopeof the Schechter function, α We find that a is particularly sensitive to the assumed faintest broad-band magnitude of a galaxy capable of hosting an emission line, and propose thatthis limit should be empirically motivated. For this analysis, we base our threshold on thelimiting observed equivalent widths of emission lines in the local Universe. We compute thecharacteristic SFR of galaxies in each redshift slice, and the integrated SFR density,ρ SFR. Wefind our results to be in good agreement with the literature and parametrize the evolution of the SFR density as ρ SFR α(1 + z)4.58 confirming a steep decline in star formation activity since z ~ 1.6.Peer reviewe

Hubs with Network Motifs Organize Modularity Dynamically in the Protein-Protein Interaction Network of Yeast

BACKGROUND: It has been recognized that modular organization pervades biological complexity. Based on network analysis, 'party hubs' and 'date hubs' were proposed to understand the basic principle of module organization of biomolecular networks. However, recent study on hubs has suggested that there is no clear evidence for coexistence of 'party hubs' and 'date hubs'. Thus, an open question has been raised as to whether or not 'party hubs' and 'date hubs' truly exist in yeast interactome. METHODOLOGY: In contrast to previous studies focusing on the partners of a hub or the individual proteins around the hub, our work aims to study the network motifs of a hub or interactions among individual proteins including the hub and its neighbors. Depending on the relationship between a hub's network motifs and protein complexes, we define two new types of hubs, 'motif party hubs' and 'motif date hubs', which have the same characteristics as the original 'party hubs' and 'date hubs' respectively. The network motifs of these two types of hubs display significantly different features in spatial distribution (or cellular localizations), co-expression in microarray data, controlling topological structure of network, and organizing modularity. CONCLUSION: By virtue of network motifs, we basically solved the open question about 'party hubs' and 'date hubs' which was raised by previous studies. Specifically, at the level of network motifs instead of individual proteins, we found two types of hubs, motif party hubs (mPHs) and motif date hubs (mDHs), whose network motifs display distinct characteristics on biological functions. In addition, in this paper we studied network motifs from a different viewpoint. That is, we show that a network motif should not be merely considered as an interaction pattern but be considered as an essential function unit in organizing modules of networks

Effects of elevated CO2 on phytoplankton community biomass and species composition during a spring Phaeocystis spp. bloom in the western English Channel

A 21-year time series of phytoplankton community structure was analysed in relation to Phaeocystis spp. to elucidate its contribution to the annual carbon budget at station L4 in the western English Channel (WEC). Between 1993–2014 Phaeocystis spp. contributed ∼4.6% of the annual phytoplankton carbon and during the March − May spring bloom, the mean Phaeocystis spp. biomass constituted 17% with a maximal contribution of 47% in 2001. Upper maximal weekly values above the time series mean ranged from 63 to 82% of the total phytoplankton carbon (∼42–137 mg carbon (C) m −3 ) with significant inter-annual variability in Phaeocystis spp. Maximal biomass usually occurred by the end of April, although in some cases as early as mid-April (2007) and as late as late May (2013). The effects of elevated pCO 2 on the Phaeocystis spp. spring bloom were investigated during a fifteen-day semi-continuous microcosm experiment. The phytoplankton community biomass was estimated at ∼160 mg C m −3 and was dominated by nanophytoplankton (40%, excluding Phaeocystis spp.), Phaeocystis spp. (30%) and cryptophytes (12%). The smaller fraction of the community biomass comprised picophytoplankton (9%), coccolithophores (3%), Synechococcus (3%), dinoflagellates (1.5%), ciliates (1%) and diatoms (0.5%). Over the experimental period, total biomass increased significantly by 90% to ∼305 mg C m −3 in the high CO 2 treatment while the ambient pCO 2 control showed no net gains. Phaeocystis spp. exhibited the greatest response to the high CO 2 treatment, increasing by 330%, from ∼50 mg C m −3 to over 200 mg C m −3 and contributing ∼70% of the total biomass. Taken together, the results of our microcosm experiment and analysis of the time series suggest that a future high CO 2 scenario may favour dominance of Phaeocystis spp. during the spring bloom. This has significant implications for the formation of hypoxic zones and the alteration of food web structure including inhibitory feeding effects and lowered fecundity in many copepod species

A diagnostic algorithm combining clinical and molecular data distinguishes Kawasaki disease from other febrile illnesses

<p>Abstract</p> <p>Background</p> <p>Kawasaki disease is an acute vasculitis of infants and young children that is recognized through a constellation of clinical signs that can mimic other benign conditions of childhood. The etiology remains unknown and there is no specific laboratory-based test to identify patients with Kawasaki disease. Treatment to prevent the complication of coronary artery aneurysms is most effective if administered early in the course of the illness. We sought to develop a diagnostic algorithm to help clinicians distinguish Kawasaki disease patients from febrile controls to allow timely initiation of treatment.</p> <p>Methods</p> <p>Urine peptidome profiling and whole blood cell type-specific gene expression analyses were integrated with clinical multivariate analysis to improve differentiation of Kawasaki disease subjects from febrile controls.</p> <p>Results</p> <p>Comparative analyses of multidimensional protein identification using 23 pooled Kawasaki disease and 23 pooled febrile control urine peptide samples revealed 139 candidate markers, of which 13 were confirmed (area under the receiver operating characteristic curve (ROC AUC 0.919)) in an independent cohort of 30 Kawasaki disease and 30 febrile control urine peptidomes. Cell type-specific analysis of microarrays (csSAM) on 26 Kawasaki disease and 13 febrile control whole blood samples revealed a 32-lymphocyte-specific-gene panel (ROC AUC 0.969). The integration of the urine/blood based biomarker panels and a multivariate analysis of 7 clinical parameters (ROC AUC 0.803) effectively stratified 441 Kawasaki disease and 342 febrile control subjects to diagnose Kawasaki disease.</p> <p>Conclusions</p> <p>A hybrid approach using a multi-step diagnostic algorithm integrating both clinical and molecular findings was successful in differentiating children with acute Kawasaki disease from febrile controls.</p