Microbiome profiling by Illumina sequencing of combinatorial sequence-tagged PCR products

We developed a low-cost, high-throughput microbiome profiling method that uses combinatorial sequence tags attached to PCR primers that amplify the rRNA V6 region. Amplified PCR products are sequenced using an Illumina paired-end protocol to generate millions of overlapping reads. Combinatorial sequence tagging can be used to examine hundreds of samples with far fewer primers than is required when sequence tags are incorporated at only a single end. The number of reads generated permitted saturating or near-saturating analysis of samples of the vaginal microbiome. The large number of reads al- lowed an in-depth analysis of errors, and we found that PCR-induced errors composed the vast majority of non-organism derived species variants, an ob- servation that has significant implications for sequence clustering of similar high-throughput data. We show that the short reads are sufficient to assign organisms to the genus or species level in most cases. We suggest that this method will be useful for the deep sequencing of any short nucleotide region that is taxonomically informative; these include the V3, V5 regions of the bac- terial 16S rRNA genes and the eukaryotic V9 region that is gaining popularity for sampling protist diversity.Comment: 28 pages, 13 figure

Adsorption of hydroxamate siderophores and EDTA on goethite in the presence of the surfactant sodium dodecyl sulfate

Siderophore-promoted iron acquisition by microorganisms usually occurs in the presence of other organic molecules, including biosurfactants. We have investigated the influence of the anionic surfactant sodium dodecyl sulfate (SDS) on the adsorption of the siderophores DFOB (cationic) and DFOD (neutral) and the ligand EDTA (anionic) onto goethite (α-FeOOH) at pH 6. We also studied the adsorption of the corresponding 1:1 Fe(III)-ligand complexes, which are products of the dissolution process. Adsorption of the two free siderophores increased in a similar fashion with increasing SDS concentration, despite their difference in molecule charge. In contrast, SDS had little effect on the adsorption of EDTA. Adsorption of the Fe-DFOB and Fe-DFOD complexes also increased with increasing SDS concentrations, while adsorption of Fe-EDTA decreased. Our results suggest that hydrophobic interactions between adsorbed surfactants and siderophores are more important than electrostatic interactions. However, for strongly hydrophilic molecules, such as EDTA and its iron complex, the influence of SDS on their adsorption seems to depend on their tendency to form inner-sphere or outer-sphere surface complexes. Our results demonstrate that surfactants have a strong influence on the adsorption of siderophores to Fe oxides, which has important implications for siderophore-promoted dissolution of iron oxides and biological iron acquisition

Detection of Epileptogenic Cortical Malformations with Surface-Based MRI Morphometry

Magnetic resonance imaging has revolutionized the detection of structural abnormalities in patients with epilepsy. However, many focal abnormalities remain undetected in routine visual inspection. Here we use an automated, surface-based method for quantifying morphometric features related to epileptogenic cortical malformations to detect abnormal cortical thickness and blurred gray-white matter boundaries. Using MRI morphometry at 3T with surface-based spherical averaging techniques that precisely align anatomical structures between individual brains, we compared single patients with known lesions to a large normal control group to detect clusters of abnormal cortical thickness, gray-white matter contrast, local gyrification, sulcal depth, jacobian distance and curvature. To assess the effects of threshold and smoothing on detection sensitivity and specificity, we systematically varied these parameters with different thresholds and smoothing levels. To test the effectiveness of the technique to detect lesions of epileptogenic character, we compared the detected structural abnormalities to expert-tracings, intracranial EEG, pathology and surgical outcome in a homogeneous patient sample. With optimal parameters and by combining thickness and GWC, the surface-based detection method identified 92% of cortical lesions (sensitivity) with few false positives (96% specificity), successfully discriminating patients from controls 94% of the time. The detected structural abnormalities were related to the seizure onset zones, abnormal histology and positive outcome in all surgical patients. However, the method failed to adequately describe lesion extent in most cases. Automated surface-based MRI morphometry, if used with optimized parameters, may be a valuable additional clinical tool to improve the detection of subtle or previously occult malformations and therefore could improve identification of patients with intractable focal epilepsy who may benefit from surgery

Transformational school leadership as a key factor for teachers’ job attitudes during their first year in the profession

Teacher attrition is a global concern that is particularly prevalent among beginning teachers. Teachers' intrinsic motivation to teach, affective organisational commitment and job satisfaction are considered job attitudes that stop them from dropping out of the profession. This study explores the interplay between factors at the school level (i.e. transformational leadership of the principal, professional collegial support) and the teacher level (i.e. self-efficacy) influencing these job attitudes. A sample of 292 first-year primary-school teachers participated. The results of the path analysis demonstrated that transformational leadership of the principal is directly related to teachers' job attitudes in a positive way. Moreover, transformational leadership of the principal is also indirectly related to these attitudes, via both professional collegial support and teachers' self-efficacy. Implications for the supportive role of the principal in the teachers' first year in the profession are discussed

U.S. medical resident familiarity with national tuberculosis guidelines

<p>Abstract</p> <p>Background</p> <p>The ability of medical residents training at U.S. urban medical centers to diagnose and manage tuberculosis cases has important public health implications. We assessed medical resident knowledge about tuberculosis diagnosis and early management based on American Thoracic Society guidelines.</p> <p>Methods</p> <p>A 20-question tuberculosis knowledge survey was administered to 131 medical residents during a single routinely scheduled teaching conference at four different urban medical centers in Baltimore and Philadelphia. Survey questions were divided into 5 different subject categories. Data was collected pertaining to institution, year of residency training, and self-reported number of patients managed for tuberculosis within the previous year. The Kruskal-Wallis test was used to detect differences in median percent of questions answered correctly based on these variables.</p> <p>Results</p> <p>The median percent of survey questions answered correctly for all participating residents was 55%. Medical resident knowledge about tuberculosis did not improve with increasing post-graduate year of training or greater number of patients managed for tuberculosis within the previous year. Common areas of knowledge deficiency included the diagnosis and management of latent tuberculosis infection (median percent correct, 40.7%), as well as the interpretation of negative acid-fast sputum smear samples.</p> <p>Conclusion</p> <p>Many medical residents lack adequate knowledge of recommended guidelines for the management of tuberculosis. Since experience during training influences future practice pattterns, education of medical residents on guidelines for detection and early management of tuberculosis may be important for future improvements in national tuberculosis control strategies.</p

Recurrent Activity in Higher Order, Modality Non-Specific Brain Regions: A Granger Causality Analysis of Autobiographic Memory Retrieval

It has been proposed that the workings of the brain are mainly intrinsically generated recurrent neuronal activity, with sensory inputs as modifiers of such activity in both sensory and higher order modality non-specific regions. This is supported by the demonstration of recurrent neuronal activity in the visual system as a response to visual stimulation. In contrast recurrent activity has never been demonstrated before in higher order modality non-specific regions. Using magneto-encephalography and Granger causality analysis, we tested in a paralimbic network the hypothesis that stimulation may enhance causal recurrent interaction between higher-order, modality non-specific regions. The network includes anterior cingulate/medial prefrontal and posterior cingulate/medial parietal cortices together with pulvinar thalami, a network known to be effective in autobiographic memory retrieval and self-awareness. Autobiographic memory retrieval of previous personal judgments of visually presented words was used as stimuli. It is demonstrated that the prestimulus condition is characterized by causal, recurrent oscillations which are maximal in the lower gamma range. When retrieving previous judgments of visually presented adjectives, this activity is dramatically increased during the stimulus task as ascertained by Granger causality analysis. Our results confirm the hypothesis that stimulation may enhance causal interaction between higher order, modality non-specific brain regions, exemplified in a network of autobiographical memory retrieval

An Australian Aboriginal birth cohort: a unique resource for a life course study of an Indigenous population. A study protocol

BACKGROUND: The global rise of Type 2 diabetes and its complications has drawn attention to the burden of non-communicable diseases on populations undergoing epidemiological transition. The life course approach of a birth cohort has the potential to increase our understanding of the development of these chronic diseases. In 1987 we sought to establish an Australian Indigenous birth cohort to be used as a resource for descriptive and analytical studies with particular attention on non-communicable diseases. The focus of this report is the methodology of recruiting and following-up an Aboriginal birth cohort of mobile subjects belonging to diverse cultural and language groups living in a large sparsely populated area in the Top End of the Northern Territory of Australia. METHODS: A prospective longitudinal study of Aboriginal singletons born at the Royal Darwin Hospital 1987–1990, with second wave cross-sectional follow-up examination of subjects 1998–2001 in over 70 different locations. A multiphase protocol was used to locate and collect data on 686 subjects with different approaches for urban and rural children. Manual chart audits, faxes to remote communities, death registries and a full time subject locator with past experience of Aboriginal communities were all used. DISCUSSION: The successful recruitment of 686 Indigenous subjects followed up 14 years later with vital status determined for 95% of subjects and examination of 86% shows an Indigenous birth cohort can be established in an environment with geographic, cultural and climatic challenges. The high rates of recruitment and follow up indicate there were effective strategies of follow-up in a supportive population

Graphical models for inferring single molecule dynamics

<p>Abstract</p> <p>Background</p> <p>The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM). The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET)<it> versus</it> time data, where the smFRET time series is modeled as a hidden Markov model (HMM) with Gaussian observables. A detailed description of smFRET is provided as well.</p> <p>Results</p> <p>The VBEM algorithm returns the model’s evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME), and the latter a description of the model’s parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML) optimized by the expectation maximization (EM) algorithm, the most important being a natural form of model selection and a well-posed (non-divergent) optimization problem.</p> <p>Conclusions</p> <p>The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics.</p