Alliances, assemblages, and affects : three moments of building collective working-class literacies

This article explores how assemblage and affect theories can enable research into the formation of a collective working-class identity, inclusive of written, print, publication, and organizational literacies through the origins of the Federation of Worker Writer and Community Publishers, an organization that expanded its collectivity as new heritages, ethnicities, and immigrant identities altered the organization’s membership and "class" identity

PhylOTU: a high-throughput procedure quantifies microbial community diversity and resolves novel taxa from metagenomic data.

Microbial diversity is typically characterized by clustering ribosomal RNA (SSU-rRNA) sequences into operational taxonomic units (OTUs). Targeted sequencing of environmental SSU-rRNA markers via PCR may fail to detect OTUs due to biases in priming and amplification. Analysis of shotgun sequenced environmental DNA, known as metagenomics, avoids amplification bias but generates fragmentary, non-overlapping sequence reads that cannot be clustered by existing OTU-finding methods. To circumvent these limitations, we developed PhylOTU, a computational workflow that identifies OTUs from metagenomic SSU-rRNA sequence data through the use of phylogenetic principles and probabilistic sequence profiles. Using simulated metagenomic data, we quantified the accuracy with which PhylOTU clusters reads into OTUs. Comparisons of PCR and shotgun sequenced SSU-rRNA markers derived from the global open ocean revealed that while PCR libraries identify more OTUs per sequenced residue, metagenomic libraries recover a greater taxonomic diversity of OTUs. In addition, we discover novel species, genera and families in the metagenomic libraries, including OTUs from phyla missed by analysis of PCR sequences. Taken together, these results suggest that PhylOTU enables characterization of part of the biosphere currently hidden from PCR-based surveys of diversity

A new approach to the chronology of caves 268/272/275 in the Dunhuang Mogao Grottoes: combining radiocarbon dates and archaeological information within a Bayesian statistical framework

The construction chronology of three of the earliest Dunhuang Mogao Grottoes (Caves 268, 272, and 275) has been the subject of ongoing debate for over half a century. This chronology is a crucial topic in terms of further understanding of the establishment of the Dunhuang Mogao Grottoes, early Buddhism in the Gansu corridor, and its relationship with Buddhism developed in the Central Plains. Building upon archaeological, art historical and radiocarbon (14C) dating studies, we integrate new 14C data with these previously published findings utilizing Bayesian statistical modeling to improve the chronological resolution of this issue. Thus, we determine that all three of these caves were constructed around AD 410–440, suggesting coeval rather than sequential construction

Global marine bacterial diversity peaks at high latitudes in winter.

Genomic approaches to characterizing bacterial communities are revealing significant differences in diversity and composition between environments. But bacterial distributions have not been mapped at a global scale. Although current community surveys are way too sparse to map global diversity patterns directly, there is now sufficient data to fit accurate models of how bacterial distributions vary across different environments and to make global scale maps from these models. We apply this approach to map the global distributions of bacteria in marine surface waters. Our spatially and temporally explicit predictions suggest that bacterial diversity peaks in temperate latitudes across the world's oceans. These global peaks are seasonal, occurring 6 months apart in the two hemispheres, in the boreal and austral winters. This pattern is quite different from the tropical, seasonally consistent diversity patterns observed for most macroorganisms. However, like other marine organisms, surface water bacteria are particularly diverse in regions of high human environmental impacts on the oceans. Our maps provide the first picture of bacterial distributions at a global scale and suggest important differences between the diversity patterns of bacteria compared with other organisms

The Phylogenetic Diversity of Metagenomes

Phylogenetic diversity—patterns of phylogenetic relatedness among organisms in ecological communities—provides important insights into the mechanisms underlying community assembly. Studies that measure phylogenetic diversity in microbial communities have primarily been limited to a single marker gene approach, using the small subunit of the rRNA gene (SSU-rRNA) to quantify phylogenetic relationships among microbial taxa. In this study, we present an approach for inferring phylogenetic relationships among microorganisms based on the random metagenomic sequencing of DNA fragments. To overcome challenges caused by the fragmentary nature of metagenomic data, we leveraged fully sequenced bacterial genomes as a scaffold to enable inference of phylogenetic relationships among metagenomic sequences from multiple phylogenetic marker gene families. The resulting metagenomic phylogeny can be used to quantify the phylogenetic diversity of microbial communities based on metagenomic data sets. We applied this method to understand patterns of microbial phylogenetic diversity and community assembly along an oceanic depth gradient, and compared our findings to previous studies of this gradient using SSU-rRNA gene and metagenomic analyses. Bacterial phylogenetic diversity was highest at intermediate depths beneath the ocean surface, whereas taxonomic diversity (diversity measured by binning sequences into taxonomically similar groups) showed no relationship with depth. Phylogenetic diversity estimates based on the SSU-rRNA gene and the multi-gene metagenomic phylogeny were broadly concordant, suggesting that our approach will be applicable to other metagenomic data sets for which corresponding SSU-rRNA gene sequences are unavailable. Our approach opens up the possibility of using metagenomic data to study microbial diversity in a phylogenetic context

Tracing the flows of copper and copper alloys in the Early Iron Age societies of the eastern Eurasian steppe

Abstract</jats:p

Pharmacokinetic profile of enrofloxacin and its metabolite ciprofloxacin in Asian house geckos (Hemidactylus frenatus) after single-dose oral administration of enrofloxacin

The pharmacokinetics of enrofloxacin and its active metabolite ciprofloxacin were determined following oral administration in 21 Asian house geckos (Hemidactylus frenatus) at a dose of 10 mg/kg. Changes in enrofloxacin and ciprofloxacin plasma concentrations were quantified at regular intervals over 72 h (1, 2, 6, 12, 24, 48, and 72 h). Samples were analysed by high-pressure liquid chromatography (HPLC) and the enrofloxacin pharmacokinetic data underwent a two-compartment analysis. Due to the limited ciprofloxacin plasma concentrations above the lower limit of quantification (LLOQ), the ciprofloxacin data underwent non-compartment analysis and the half-life was determined by the Lineweaver-Burke plot and analysis. The enrofloxacin and ciprofloxacin mean half-lives (t½) were 0.95 h (α) / 24.36 h (β), and 11.06 h respectively, area under the curve (AUC0-24h) were 60.56 and 3.14 µg/mL*h, respectively, maximum concentrations (Cmax) were 12.31 and 0.24 µg/mL, respectively, and time required to reach the Cmax (Tmax) were 1 and 2 h respectively. Enrofloxacin was minimally converted to the active metabolite ciprofloxacin, with ciprofloxacin concentrations contributing only 4.91% of the total fluoroquinolone concentrations (AUC0-24h). Based on the pharmacokinetic indices when using susceptibility breakpoints when determined at mammalian body temperature it is predicted that single oral administration of enrofloxacin (10 mg/kg) would result in plasma concentrations effective against susceptible bacterial species inhibited by an enrofloxacin MIC ≤ 0.5 µg/mL in vitro, but additional studies will be required to determine its efficacy in vivo

The cytokine temporal profile in rat cortex after controlled cortical impact

Cerebral inflammatory responses may initiate secondary cascades following traumatic brain injury (TBI). Changes in the expression of both cytokines and chemokines may activate, regulate, and recruit innate and adaptive immune cells associated with secondary degeneration, as well as alter a host of other cellular processes. In this study, we quantified the temporal expression of a large set of inflammatory mediators in rat cortical tissue after brain injury. Following a controlled cortical impact (CCI) on young adult male rats, cortical and hippocampal tissue of the injured hemisphere and matching contralateral material was harvested at early (4, 12, and 24 hours) and extended (3 and 7 days) time points post-procedure. Naïve rats that received only anesthesia were used as controls. Processed brain homogenates were assayed for chemokine and cytokine levels utilizing an electrochemiluminescence-based multiplex ELISA platform. The temporal profile of cortical tissue samples revealed a multi-phasic injury response following brain injury. CXCL1, IFN-γ, TNF-α levels significantly peaked at four hours post-injury compared to levels found in naïve or contralateral tissue. CXCL1, IFN-γ, and TNF-α levels were then observed to decrease at least 3-fold by 12 hours post-injury. IL-1β, IL-4, and IL-13 levels were also significantly elevated at four hours post-injury although their expression did not decrease more than 3-fold for up to 24 hours post-injury. Additionally, IL-1β and IL-4 levels displayed a biphasic temporal profile in response to injury, which may suggest their involvement in adaptive immune responses. Interestingly, peak levels of CCL2 and CCL20 were not observed until after four hours post-injury. CCL2 levels in injured cortical tissue were significantly higher than peak levels of any other inflammatory mediator measured, thus suggesting a possible use as a biomarker. Fully elucidating chemokine and cytokine signaling properties after brain injury may provide increased insight into a number of secondary cascade events that are initiated or regulated by inflammatory responses

Toward a predictive understanding of Earth’s microbiomes to address 21st century challenges

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in mBio 7 (2016): e00714-16, doi:10.1128/mBio.00714-16.Microorganisms have shaped our planet and its inhabitants for over 3.5 billion years. Humankind has had a profound influence on the biosphere, manifested as global climate and land use changes, and extensive urbanization in response to a growing population. The challenges we face to supply food, energy, and clean water while maintaining and improving the health of our population and ecosystems are significant. Given the extensive influence of microorganisms across our biosphere, we propose that a coordinated, cross-disciplinary effort is required to understand, predict, and harness microbiome function. From the parallelization of gene function testing to precision manipulation of genes, communities, and model ecosystems and development of novel analytical and simulation approaches, we outline strategies to move microbiome research into an era of causality. These efforts will improve prediction of ecosystem response and enable the development of new, responsible, microbiome-based solutions to significant challenges of our time.E.L.B. is supported by the Genomes-to-Watersheds Subsurface Biogeochemical Research Scientific Focus Area, and T.R.N. is supported by ENIGMA-Ecosystems and Networks Integrated with Genes and Molecular Assemblies (http://enigma.lbl.gov) Scientific Focus Area, funded by the U.S. Department of Energy (US DOE), Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02- 05CH11231 to Lawrence Berkeley National Laboratory (LBNL). M.E.M. is also supported by the US DOE, Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02-05CH11231. Z.G.C. is supported by National Science Foundation Integrative Organismal Systems grant #1355085, and by US DOE, Office of Biological and Environmental Research grant # DE-SC0008182 ER65389 from the Terrestrial Ecosystem Science Program. M.J.B. is supported by R01 DK 090989 from the NIH. T.J.D. is supported by the US DOE Office of Science’s Great Lakes Bioenergy Research Center, grant DE-FC02- 07ER64494. J.L.G. is supported by Alfred P. Sloan Foundation G 2-15-14023. R.K. is supported by grants from the NSF (DBI-1565057) and NIH (U01AI24316, U19AI113048, P01DK078669, 1U54DE023789, U01HG006537). K.S.P. is supported by grants from the NSF DMS- 1069303 and the Gordon & Betty Moore Foundation (#3300)