Emergence of structural and dynamical properties of ecological mutualistic networks

Mutualistic networks are formed when the interactions between two classes of species are mutually beneficial. They are important examples of cooperation shaped by evolution. Mutualism between animals and plants plays a key role in the organization of ecological communities. Such networks in ecology have generically evolved a nested architecture independent of species composition and latitude - specialists interact with proper subsets of the nodes with whom generalists interact. Despite sustained efforts to explain observed network structure on the basis of community-level stability or persistence, such correlative studies have reached minimal consensus. Here we demonstrate that nested interaction networks could emerge as a consequence of an optimization principle aimed at maximizing the species abundance in mutualistic communities. Using analytical and numerical approaches, we show that because of the mutualistic interactions, an increase in abundance of a given species results in a corresponding increase in the total number of individuals in the community, as also the nestedness of the interaction matrix. Indeed, the species abundances and the nestedness of the interaction matrix are correlated by an amount that depends on the strength of the mutualistic interactions. Nestedness and the observed spontaneous emergence of generalist and specialist species occur for several dynamical implementations of the variational principle under stationary conditions. Optimized networks, while remaining stable, tend to be less resilient than their counterparts with randomly assigned interactions. In particular, we analytically show that the abundance of the rarest species is directly linked to the resilience of the community. Our work provides a unifying framework for studying the emergent structural and dynamical properties of ecological mutualistic networks.Comment: 10 pages, 4 figure

Linkage analysis of longitudinal data and design consideration

BACKGROUND: Statistical methods have been proposed recently to analyze longitudinal data in genetic studies. So far, little attention has been paid to examine the relationship among key factors in genetic longitudinal studies including power, the number of families or sibships, and the number of repeated measures per individual subjects. RESULTS: We proposed a variance component model that extends classic variance component models for a single quantitative trait to mapping longitudinal traits. Our model includes covariate effects and allows genetic effects to vary over time. Using our proposed model, we examined the power, pedigree structures, and sample size through simulation experiments. CONCLUSION: Our simulation results provide useful insights into the study design for genetic, longitudinal studies. For example, collecting a small number of large sibships is much more powerful than collecting a large number of small sibships or increasing the number of repeated measures, when the total number of measurements is comparable

Cooperative coupling of ultracold atoms and surface plasmons

Cooperative coupling between optical emitters and light fields is one of the outstanding goals in quantum technology. It is both fundamentally interesting for the extraordinary radiation properties of the participating emitters and has many potential applications in photonics. While this goal has been achieved using high-finesse optical cavities, cavity-free approaches that are broadband and easy to build have attracted much attention recently. Here we demonstrate cooperative coupling of ultracold atoms with surface plasmons propagating on a plane gold surface. While the atoms are moving towards the surface they are excited by an external laser pulse. Excited surface plasmons are detected via leakage radiation into the substrate of the gold layer. A maximum Purcell factor of $\eta_\mathrm{P}=4.9$ is reached at an optimum distance of $z=250~\mathrm{nm}$ from the surface. The coupling leads to the observation of a Fano-like resonance in the spectrum.Comment: 9 pages, 4 figure

Predicting residents' performance: A prospective study

BACKGROUND: Objective criteria for predicting residents' performance do not exist. The purpose of this study was to test the hypothesis that global assessment by an intern selection committee (ISC) would correlate with the future performance of residents. METHODS: A prospective study of 277 residents between 1992 and 1999. Global assessment at the time of interview was compared to subsequent clinical (assessed by chief residents) and cognitive performance (assessed by the American Board of Pediatrics in-service training examination). RESULTS: ISC ratings correlated significantly with clinical performance at 24 and 36 months of training (r = 0.58, P < .001; and r = 0.60, P < .001 respectively). ISC ratings also correlated significantly with in-service exam scores in the 1(st), 2(nd), and 3(rd) years of training (r = 0.35, P = .0016; r = 0.39, P = 0.0003; r = 0.50, P = 0.005 respectively). CONCLUSIONS: Global assessment by an ISC predicted residents' clinical and cognitive performances

Comparison of Pathway Analysis Approaches Using Lung Cancer GWAS Data Sets

Pathway analysis has been proposed as a complement to single SNP analyses in GWAS. This study compared pathway analysis methods using two lung cancer GWAS data sets based on four studies: one a combined data set from Central Europe and Toronto (CETO); the other a combined data set from Germany and MD Anderson (GRMD). We searched the literature for pathway analysis methods that were widely used, representative of other methods, and had available software for performing analysis. We selected the programs EASE, which uses a modified Fishers Exact calculation to test for pathway associations, GenGen (a version of Gene Set Enrichment Analysis (GSEA)), which uses a Kolmogorov-Smirnov-like running sum statistic as the test statistic, and SLAT, which uses a p-value combination approach. We also included a modified version of the SUMSTAT method (mSUMSTAT), which tests for association by averaging χ2 statistics from genotype association tests. There were nearly 18000 genes available for analysis, following mapping of more than 300,000 SNPs from each data set. These were mapped to 421 GO level 4 gene sets for pathway analysis. Among the methods designed to be robust to biases related to gene size and pathway SNP correlation (GenGen, mSUMSTAT and SLAT), the mSUMSTAT approach identified the most significant pathways (8 in CETO and 1 in GRMD). This included a highly plausible association for the acetylcholine receptor activity pathway in both CETO (FDR≤0.001) and GRMD (FDR = 0.009), although two strong association signals at a single gene cluster (CHRNA3-CHRNA5-CHRNB4) drive this result, complicating its interpretation. Few other replicated associations were found using any of these methods. Difficulty in replicating associations hindered our comparison, but results suggest mSUMSTAT has advantages over the other approaches, and may be a useful pathway analysis tool to use alongside other methods such as the commonly used GSEA (GenGen) approach

Advanced optical imaging in living embryos

Developmental biology investigations have evolved from static studies of embryo anatomy and into dynamic studies of the genetic and cellular mechanisms responsible for shaping the embryo anatomy. With the advancement of fluorescent protein fusions, the ability to visualize and comprehend how thousands to millions of cells interact with one another to form tissues and organs in three dimensions (xyz) over time (t) is just beginning to be realized and exploited. In this review, we explore recent advances utilizing confocal and multi-photon time-lapse microscopy to capture gene expression, cell behavior, and embryo development. From choosing the appropriate fluorophore, to labeling strategy, to experimental set-up, and data pipeline handling, this review covers the various aspects related to acquiring and analyzing multi-dimensional data sets. These innovative techniques in multi-dimensional imaging and analysis can be applied across a number of fields in time and space including protein dynamics to cell biology to morphogenesis

Quasar Sightline and Galaxy Evolution (QSAGE) - III. The mass-metallicity and fundamental metallicity relation of z ≈ 2.2 galaxies

We present analysis of the mass-metallicity relation (MZR) for a sample of 67 [O iii]-selected star-forming (SF) galaxies at a redshift range of z = 1.99-2.32 (zmed = 2.16) using Hubble Space Telescope Wide Field Camera 3 grism spectroscopy from the Quasar Sightline and Galaxy Evolution survey. Metallicities were determined using empirical gas-phase metallicity calibrations based on the strong emission lines [O ii]3727, 3729, [O iii]4959, 5007 and Hβ. SF galaxies were identified, and distinguished from active-galactic nuclei, via Mass-Excitation diagrams. Using z ∼0 metallicity calibrations, we observe a negative offset in the z = 2.2 MZR of ≈-0.51 dex in metallicity when compared to locally derived relationships, in agreement with previous literature analysis. A similar offset of ≈-0.46 dex in metallicity is found when using empirical metallicity calibrations that are suitable out to z ∼5, though our z = 2.2 MZR, in this case, has a shallower slope. We find agreement between our MZR and those predicted from various galaxy evolution models and simulations. Additionally, we explore the extended fundamental metallicity relation (FMR) which includes an additional dependence on star formation rate. Our results consistently support the existence of the FMR, as well as revealing an offset of 0.28 ± 0.04 dex in metallicity compared to locally derived relationships, consistent with previous studies at similar redshifts. We interpret the negative correlation with SFR at fixed mass, inferred from an FMR existing for our sample, as being caused by the efficient accretion of metal-poor gas fuelling SFR at cosmic noon

Human Mobility in a Continuum Approach

Human mobility is investigated using a continuum approach that allows to calculate the probability to observe a trip to anyarbitrary region, and the fluxes between any two regions. The considered description offers a general and unified framework, in which previously proposed mobility models like the gravity model, the intervening opportunities model, and the recently introduced radiation model are naturally resulting as special cases. A new form of radiation model is derived and its validity is investigated using observational data offered by commuting trips obtained from the United States census data set, and the mobility fluxesextracted from mobile phone data collected in a western European country. The new modeling paradigm offered by this description suggests that the complex topological features observed in large mobility and transportation networks may be the result of a simple stochastic process taking place on an inhomogeneous landscape.Comment: 13 pages, 3 figure