University of Colorado Boulder

About: University of Colorado Boulder is a education organization based out in Boulder, Colorado, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 48794 authors who have published 115151 publications receiving 5387328 citations. The organization is also known as: CU Boulder & UCB.

Antidepressant Drug Effects and Depression Severity: A Patient-Level Meta-analysis

Abstract: Context Antidepressant medications represent the best established treatment for major depressive disorder, but there is little evidence that they have a specific pharmacological effect relative to pill placebo for patients with less severe depression. Objective To estimate the relative benefit of medication vs placebo across a wide range of initial symptom severity in patients diagnosed with depression. Data Sources PubMed, PsycINFO, and the Cochrane Library databases were searched from January 1980 through March 2009, along with references from meta-analyses and reviews. Study Selection Randomized placebo-controlled trials of antidepressants approved by the Food and Drug Administration in the treatment of major or minor depressive disorder were selected. Studies were included if their authors provided the requisite original data, they comprised adult outpatients, they included a medication vs placebo comparison for at least 6 weeks, they did not exclude patients on the basis of a placebo washout period, and they used the Hamilton Depression Rating Scale (HDRS). Data from 6 studies (718 patients) were included. Data Extraction Individual patient-level data were obtained from study authors. Results Medication vs placebo differences varied substantially as a function of baseline severity. Among patients with HDRS scores below 23, Cohen d effect sizes for the difference between medication and placebo were estimated to be less than 0.20 (a standard definition of a small effect). Estimates of the magnitude of the superiority of medication over placebo increased with increases in baseline depression severity and crossed the threshold defined by the National Institute for Clinical Excellence for a clinically significant difference at a baseline HDRS score of 25. Conclusions The magnitude of benefit of antidepressant medication compared with placebo increases with severity of depression symptoms and may be minimal or nonexistent, on average, in patients with mild or moderate symptoms. For patients with very severe depression, the benefit of medications over placebo is substantial.

High-quality electron beams from a laser wakefield accelerator using plasma-channel guiding

Abstract: Laser-driven accelerators, in which particles are accelerated by the electric field of a plasma wave (the wakefield) driven by an intense laser, have demonstrated accelerating electric fields of hundreds of GV m-1 (refs 1–3) These fields are thousands of times greater than those achievable in conventional radio-frequency accelerators, spurring interest in laser accelerators4,5 as compact next-generation sources of energetic electrons and radiation To date, however, acceleration distances have been severely limited by the lack of a controllable method for extending the propagation distance of the focused laser pulse The ensuing short acceleration distance results in low-energy beams with 100 per cent electron energy spread1,2,3, which limits potential applications Here we demonstrate a laser accelerator that produces electron beams with an energy spread of a few per cent, low emittance and increased energy (more than 109 electrons above 80 MeV) Our technique involves the use of a preformed plasma density channel to guide a relativistically intense laser, resulting in a longer propagation distance The results open the way for compact and tunable high-brightness sources of electrons and radiation

The three-dimensional power spectrum of galaxies from the sloan digital sky survey

Abstract: We measure the large-scale real-space power spectrum P(k) by using a sample of 205,443 galaxies from the Sloan Digital Sky Survey, covering 2417 effective square degrees with mean redshift z ≈ 0.1. We employ a matrix-based method using pseudo-Karhunen-Loeve eigenmodes, producing uncorrelated minimum-variance measurements in 22 k-bands of both the clustering power and its anisotropy due to redshift-space distortions, with narrow and well-behaved window functions in the range 0.02 h Mpc-1 < k < 0.3 h Mpc-1. We pay particular attention to modeling, quantifying, and correcting for potential systematic errors, nonlinear redshift distortions, and the artificial red-tilt caused by luminosity-dependent bias. Our results are robust to omitting angular and radial density fluctuations and are consistent between different parts of the sky. Our final result is a measurement of the real-space matter power spectrum P(k) up to an unknown overall multiplicative bias factor. Our calculations suggest that this bias factor is independent of scale to better than a few percent for k < 0.1 h Mpc-1, thereby making our results useful for precision measurements of cosmological parameters in conjunction with data from other experiments such as the Wilkinson Microwave Anisotropy Probe satellite. The power spectrum is not well-characterized by a single power law but unambiguously shows curvature. As a simple characterization of the data, our measurements are well fitted by a flat scale-invariant adiabatic cosmological model with h Ωm = 0.213 ± 0.023 and σ8 = 0.89 ± 0.02 for L* galaxies, when fixing the baryon fraction Ωb/Ωm = 0.17 and the Hubble parameter h = 0.72; cosmological interpretation is given in a companion paper.

Using network analysis to explore co-occurrence patterns in soil microbial communities

Abstract: Exploring large environmental datasets generated by high-throughput DNA sequencing technologies requires new analytical approaches to move beyond the basic inventory descriptions of the composition and diversity of natural microbial communities. In order to investigate potential interactions between microbial taxa, network analysis of significant taxon co-occurrence patterns may help to decipher the structure of complex microbial communities across spatial or temporal gradients. Here, we calculated associations between microbial taxa and applied network analysis approaches to a 16S rRNA gene barcoded pyrosequencing dataset containing >160 000 bacterial and archaeal sequences from 151 soil samples from a broad range of ecosystem types. We described the topology of the resulting network and defined operational taxonomic unit categories based on abundance and occupancy (that is, habitat generalists and habitat specialists). Co-occurrence patterns were readily revealed, including general non-random association, common life history strategies at broad taxonomic levels and unexpected relationships between community members. Overall, we demonstrated the potential of exploring inter-taxa correlations to gain a more integrated understanding of microbial community structure and the ecological rules guiding community assembly.

Embracing the unknown: disentangling the complexities of the soil microbiome.

Abstract: Soil microorganisms are clearly a key component of both natural and managed ecosystems. Despite the challenges of surviving in soil, a gram of soil can contain thousands of individual microbial taxa, including viruses and members of all three domains of life. Recent advances in marker gene, genomic and metagenomic analyses have greatly expanded our ability to characterize the soil microbiome and identify the factors that shape soil microbial communities across space and time. However, although most soil microorganisms remain undescribed, we can begin to categorize soil microorganisms on the basis of their ecological strategies. This is an approach that should prove fruitful for leveraging genomic information to predict the functional attributes of individual taxa. The field is now poised to identify how we can manipulate and manage the soil microbiome to increase soil fertility, improve crop production and improve our understanding of how terrestrial ecosystems will respond to environmental change.