Silicon implantation in GaAs

The electrical properties of room-temperature Si implants in GaAs have been studied. The implantations were done at 300 keV with doses ranging from 1.7×10^13 to 1.7×10^15 cm^–2. The implanted samples were annealed with silicon nitride encapsulants in H2 atmosphere for 30 min at temperatures ranging from 800 to 900°C to electrically activate the implanted ions. Results show that the implanted layers are n type, which implies that the Si ions preferentially go into Ga sites substitutionally. For low-dose implants, high (~90%) electrical activation of the implanted ions is achieved and the depth distribution of the free-electron concentration in the implanted layer roughly follows a Gaussian. However, for high-dose implants, the activation is poor (<15% for a 900 °C anneal) and the electron concentration profile is flat and deeper than the expected range

Characterization of shifts of koala (Phascolarctos cinereus) intestinal microbial communities associated with antibiotic treatment.

Koalas (Phascolarctos cinereus) are arboreal marsupials native to Australia that eat a specialized diet of almost exclusively eucalyptus leaves. Microbes in koala intestines are known to break down otherwise toxic compounds, such as tannins, in eucalyptus leaves. Infections by Chlamydia, obligate intracellular bacterial pathogens, are highly prevalent in koala populations. If animals with Chlamydia infections are received by wildlife hospitals, a range of antibiotics can be used to treat them. However, previous studies suggested that koalas can suffer adverse side effects during antibiotic treatment. This study aimed to use 16S rRNA gene sequences derived from koala feces to characterize the intestinal microbiome of koalas throughout antibiotic treatment and identify specific taxa associated with koala health after treatment. Although differences in the alpha diversity were observed in the intestinal flora between treated and untreated koalas and between koalas treated with different antibiotics, these differences were not statistically significant. The alpha diversity of microbial communities from koalas that lived through antibiotic treatment versus those who did not was significantly greater, however. Beta diversity analysis largely confirmed the latter observation, revealing that the overall communities were different between koalas on antibiotics that died versus those that survived or never received antibiotics. Using both machine learning and OTU (operational taxonomic unit) co-occurrence network analyses, we found that OTUs that are very closely related to Lonepinella koalarum, a known tannin degrader found by culture-based methods to be present in koala intestines, was correlated with a koala's health status. This is the first study to characterize the time course of effects of antibiotics on koala intestinal microbiomes. Our results suggest it may be useful to pursue alternative treatments for Chlamydia infections without the use of antibiotics or the development of Chlamydia-specific antimicrobial compounds that do not broadly affect microbial communities

Whole genome sequence analysis reveals the broad distribution of the RtxA type 1 secretion system and four novel putative type 1 secretion systems throughout the Legionella genus.

Type 1 secretion systems (T1SSs) are broadly distributed among bacteria and translocate effectors with diverse function across the bacterial cell membrane. Legionella pneumophila, the species most commonly associated with Legionellosis, encodes a T1SS at the lssXYZABD locus which is responsible for the secretion of the virulence factor RtxA. Many investigations have failed to detect lssD, the gene encoding the membrane fusion protein of the RtxA T1SS, in non-pneumophila Legionella, which has led to the assumption that this system is a virulence factor exclusively possessed by L. pneumophila. Here we discovered RtxA and its associated T1SS in a novel Legionella taurinensis strain, leading us to question whether this system may be more widespread than previously thought. Through a bioinformatic analysis of publicly available data, we classified and determined the distribution of four T1SSs including the RtxA T1SS and four novel T1SSs among diverse Legionella spp. The ABC transporter of the novel Legionella T1SS Legionella repeat protein secretion system shares structural similarity to those of diverse T1SS families, including the alkaline protease T1SS in Pseudomonas aeruginosa. The Legionella bacteriocin (1-3) secretion systems T1SSs are novel putative bacteriocin transporting T1SSs as their ABC transporters include C-39 peptidase domains in their N-terminal regions, with LB2SS and LB3SS likely constituting a nitrile hydratase leader peptide transport T1SSs. The LB1SS is more closely related to the colicin V T1SS in Escherichia coli. Of 45 Legionella spp. whole genomes examined, 19 (42%) were determined to possess lssB and lssD homologs. Of these 19, only 7 (37%) are known pathogens. There was no difference in the proportions of disease associated and non-disease associated species that possessed the RtxA T1SS (p = 0.4), contrary to the current consensus regarding the RtxA T1SS. These results draw into question the nature of RtxA and its T1SS as a singular virulence factor. Future studies should investigate mechanistic explanations for the association of RtxA with virulence

Laser pulse annealing of ion-implanted GaAs

GaAs single-crystals wafers are implanted at room temperature with 400-keV Te + ions to a dose of 1×10^15 cm^–2 to form an amorphous surface layer. The recrystallization of this layer is investigated by backscattering spectrometry and transmission electron microscopy after transient annealing by Q-switched ruby laser irradiation. An energy density threshold of about 1.0 J/cm^2 exists above which the layer regrows epitaxially. Below the threshold the layer is polycrystalline; the grain size increases as the energy density approaches threshold. The results are analogous to those reported for the elemental semiconductors, Si and Ge. The threshold value observed is in good agreement with that predicted by the simple model successfully applied previously to Si and Ge

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

Energetic and Environmental Constraints on the Community Structure of Benthic Microbial Mats in Lake Fryxell, Antarctica.

Ecological communities are regulated by the flow of energy through environments. Energy flow is typically limited by access to photosynthetically active radiation (PAR) and oxygen concentration (O2). The microbial mats growing on the bottom of Lake Fryxell, Antarctica, have well-defined environmental gradients in PAR and (O2). We analyzed the metagenomes of layers from these microbial mats to test the extent to which access to oxygen and light controls community structure. We found variation in the diversity and relative abundances of Archaea, Bacteria and Eukaryotes across three (O2) and PAR conditions: high (O2) and maximum PAR, variable (O2) with lower maximum PAR, and low (O2) and maximum PAR. We found distinct communities structured by the optimization of energy use on a millimeter-scale across these conditions. In mat layers where (O2) was saturated, PAR structured the community. In contrast, (O2) positively correlated with diversity and affected the distribution of dominant populations across the three habitats, suggesting that meter-scale diversity is structured by energy availability. Microbial communities changed across covarying gradients of PAR and (O2). The comprehensive metagenomic analysis suggests that the benthic microbial communities in Lake Fryxell are structured by energy flow across both meter- and millimeter-scales

Application of regulatory sequence analysis and metabolic network analysis to the interpretation of gene expression data

We present two complementary approaches for the interpretation of clusters of co-regulated genes, such as those obtained from DNA chips and related methods. Starting from a cluster of genes with similar expression profiles, two basic questions can be asked: 1. Which mechanism is responsible for the coordinated transcriptional response of the genes? This question is approached by extracting motifs that are shared between the upstream sequences of these genes. The motifs extracted are putative cis-acting regulatory elements. 2. What is the physiological meaning for the cell to express together these genes? One way to answer the question is to search for potential metabolic pathways that could be catalyzed by the products of the genes. This can be done by selecting the genes from the cluster that code for enzymes, and trying to assemble the catalyzed reactions to form metabolic pathways. We present tools to answer these two questions, and we illustrate their use with selected examples in the yeast Saccharomyces cerevisiae. The tools are available on the web (http://ucmb.ulb.ac.be/bioinformatics/rsa-tools/; http://www.ebi.ac.uk/research/pfbp/; http://www.soi.city.ac.uk/~msch/)