Species abundance information improves sequence taxonomy classification accuracy.

Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level classification accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments

redbiom: a Rapid Sample Discovery and Feature Characterization System.

Meta-analyses at the whole-community level have been important in microbiome studies, revealing profound features that structure Earth's microbial communities, such as the unique differentiation of microbes from the mammalian gut relative to free-living microbial communities, the separation of microbiomes in saline and nonsaline environments, and the role of pH in driving soil microbial compositions. However, our ability to identify the specific features of a microbiome that differentiate these community-level patterns have lagged behind, especially as ever-cheaper DNA sequencing has yielded increasingly large data sets. One critical gap is the ability to search for samples that contain specific features (for example, sub-operational taxonomic units [sOTUs] identified by high-resolution statistical methods for removing amplicon sequencing errors). Here we introduce redbiom, a microbiome caching layer, which allows users to rapidly query samples that contain a given feature, retrieve sample data and metadata, and search for samples that match specified metadata values or ranges (e.g., all samples with a pH of >7), implemented using an in-memory NoSQL database called Redis. By default, redbiom allows public anonymous sample access for over 100,000 publicly available samples in the Qiita database. At over 100,000 samples, the caching server requires only 35 GB of resident memory. We highlight how redbiom enables a new type of characterization of microbiome samples and provide tutorials for using redbiom with QIIME 2. redbiom is open source under the BSD license, hosted on GitHub, and can be deployed independently of Qiita to enable search of proprietary or clinically restricted microbiome databases.IMPORTANCE Although analyses that combine many microbiomes at the whole-community level have become routine, searching rapidly for microbiomes that contain a particular sequence has remained difficult. The software we present here, redbiom, dramatically accelerates this process, allowing samples that contain microbiome features to be rapidly identified. This is especially useful when taxonomic annotation is limited, allowing users to identify environments in which unannotated microbes of interest were previously observed. This approach also allows environmental or clinical factors that correlate with specific features, or vice versa, to be identified rapidly, even at a scale of billions of sequences in hundreds of thousands of samples. The software is integrated with existing analysis tools to enable fast, large-scale microbiome searches and discovery of new microbiome relationships

The role of localised prostate cancer treatment in renal transplant patients: a systematic review

Objective To systematically review and critically appraise all treatment options for localised prostate cancer in renal transplant candidates and recipients. Method A systematic review was conducted adhering to PRISMA guidelines. Searches were performed in the Cochrane Library, Embase, Medline, the Transplant Library and Trip database for studies published up to September 2022. Risk of bias was assessed with the Cochrane Risk of Bias in Non-Randomised Studies of Interventions for non-randomised studies tool. Results A total of 60 studies were identified describing 525 patients. The majority of studies were either retrospective non-randomised comparative or case series/reports of poor quality. The vast majority of studies were focussed on prostate cancer after renal transplantation. Overall, 410 (78%) patients underwent surgery, 93 (18%) patients underwent radiation therapy or brachytherapy, one patient underwent focal therapy (high-intensity frequency ultrasound) and 21 patients were placed on active surveillance. The mean age was 61 years old, the mean PSA level at diagnosis was 9.6 ng/mL and the mean follow-up time was 31 months. The majority of patients had low-risk disease with 261 patients having Gleason 6 prostate cancer (50%), followed by 220 Gleason 7 patients (42%). All prostate cancer mortality cases were in high-risk prostate cancer (≥Gleason 8). The cancer-specific survival results were similar between surgery and radiotherapy at 1 and 3 years. Conclusion Localised prostate cancer treatment in renal transplant patients should be risk stratified. Surgery and radiation treatment for localised prostate cancer in renal transplant patients appear equally efficacious. Given the limitations of this study, future research should concentrate on developing a multicentre RCT with long-term registry follow-up

Trends in silicosis prevalence and the healthy worker effect among gold miners in South Africa: a prevalence study with follow up of employment status.

BACKGROUND: Given the intimate association between silicosis and tuberculosis, understanding the epidemiology of the South African gold mining industry silicosis epidemic is essential to current initiatives to control both silicosis and tuberculosis in this population, one of the most heavily affected globally. The study's objectives were to compare the prevalence of silicosis among working black gold miners in South Africa during 2004-2009 to that of previous studies, including autopsy series, and to analyse the influence of silicosis and/or tuberculosis on exiting employment. METHODS: Routine chest radiographs from a cohort of gold miners were read for silicosis by an experienced reader (I), and a subset re-read by a B-trained reader (II). Two methods of presenting the readings were used. Additionally, with baseline status of silicosis and previous or active tuberculosis as predictors, survival analysis examined the probability of exiting the workforce for any reason during 2006-2011. RESULTS: Reader I read 11 557 chest radiographs and reader II re-read 841. Overall, silicosis prevalence (ILO ≥ 1/0: 5.7 and 6.2% depending on reader method) was similar to the age adjusted prevalence found in a large study in 1984 (5.0%). When comparison was restricted to a single mine shaft previously studied in 2000, a decline in prevalence (ILO ≥ 1/1) was suggested for one of the reading methods (duration adjusted 20.5% vs. 13.0% in the current study). These findings are discordant with a long-term rising autopsy prevalence of silicosis over this period. Overall, relative to miners with neither disease, the adjusted hazard ratio for exiting employment during the follow-up period was 1.54 for baseline silicosis [95% confidence interval (CI) 1.17, 2.04], 1.71 for tuberculosis (95% CI 1.51, 1.94) and 1.53 for combined disease (95% CI 1.20, 1.96). CONCLUSIONS: This study found, a) there was no significant decline in overall silicosis prevalence among working black miners in the South African gold mining industry between 1984 and 2004-2009, and b) a possible decline at one mine shaft more recently. In the absence of evidence of declining respirable silica concentrations between the 1980s and 2000s, the trends found are plausibly due to a healthy worker survivor effect, which may be accelerating

Understanding barriers to women seeking and receiving help for perinatal mental health problems in UK general practice: development of a questionnaire

Aim To develop a questionnaire to measure quantitatively barriers and facilitators to women’s disclosure of perinatal mental health problems in UK primary care. To pilot and evaluate the questionnaire for content validity and internal consistency. Background Around 15% of women develop a mental illness in the perinatal period, such as depression, anxiety or PTSD. In the United Kingdom, 90% of these women will be cared for in primary care, yet currently in as many as 50% of cases, no discussion of this issue takes place. One reason for this is that women experience barriers to disclosing symptoms of perinatal mental illness in primary care. These have previously been explored qualitatively, but no tool currently exists with which to measure these barriers quantitatively. Methods Questionnaire items, drawn from qualitative literature and accounts of women’s experiences, were identified, refined iteratively, and arranged in themes. The questionnaire was piloted using cognitive debriefing interviews to establish content validity. Women completed a refined version online. Responses were analysed using descriptive statistics. Internal consistency of subscales was calculated using Cronbach’s alpha. Findings Cognitive debriefing interviews with five women showed the majority of questionnaire items were relevant, appropriate and easy to understand. The final questionnaire was completed by 71 women, and the majority of subscales had good internal consistency. The barrier scoring most highly was fear and stigma, followed by willingness to seek help and logistics of attending an appointment. Family/partner support and GPs’ reaction were the lowest scoring barriers. Factors facilitating disclosure were GPs being empathetic and non-judgemental, and listening during discussions. In the future this questionnaire can be used to examine which barriers are most important for particular groups of women. This may enable development of strategies to improve acknowledgement and discussion, and prevent under-recognition and under-treatment, of perinatal mental health problems in primary care

Pathological rate matrices: from primates to pathogens

<p>Abstract</p> <p>Background</p> <p>Continuous-time Markov models allow flexible, parametrically succinct descriptions of sequence divergence. Non-reversible forms of these models are more biologically realistic but are challenging to develop. The instantaneous rate matrices defined for these models are typically transformed into substitution probability matrices using a matrix exponentiation algorithm that employs eigendecomposition, but this algorithm has characteristic vulnerabilities that lead to significant errors when a rate matrix possesses certain 'pathological' properties. Here we tested whether pathological rate matrices exist in nature, and consider the suitability of different algorithms to their computation.</p> <p>Results</p> <p>We used concatenated protein coding gene alignments from microbial genomes, primate genomes and independent intron alignments from primate genomes. The Taylor series expansion and eigendecomposition matrix exponentiation algorithms were compared to the less widely employed, but more robust, Padé with scaling and squaring algorithm for nucleotide, dinucleotide, codon and trinucleotide rate matrices. Pathological dinucleotide and trinucleotide matrices were evident in the microbial data set, affecting the eigendecomposition and Taylor algorithms respectively. Even using a conservative estimate of matrix error (occurrence of an invalid probability), both Taylor and eigendecomposition algorithms exhibited substantial error rates: ~100% of all exonic trinucleotide matrices were pathological to the Taylor algorithm while ~10% of codon positions 1 and 2 dinucleotide matrices and intronic trinucleotide matrices, and ~30% of codon matrices were pathological to eigendecomposition. The majority of Taylor algorithm errors derived from occurrence of multiple unobserved states. A small number of negative probabilities were detected from the Pad�� algorithm on trinucleotide matrices that were attributable to machine precision. Although the Padé algorithm does not facilitate caching of intermediate results, it was up to 3× faster than eigendecomposition on the same matrices.</p> <p>Conclusion</p> <p>Development of robust software for computing non-reversible dinucleotide, codon and higher evolutionary models requires implementation of the Padé with scaling and squaring algorithm.</p

Predictors of silicosis and variation in prevalence across mines among employed gold miners in South Africa

Background The stated intention to eliminate silicosis from the South African goldmining industry as well as current programmes to find and compensate ex-miners with silicosis require an understanding of variation in silicosis prevalence across the industry. We aimed to identify the predictors of radiological silicosis in a large sample of working miners across gold mines in South Africa. Methods Routine surveillance chest radiographs were collected from 15 goldmine “clusters” in a baseline survey undertaken in preparation for a separate tuberculosis isoniazid prophylaxis trial. All images were read for silicosis by a health professional experienced in using the International Labour Organisation (ILO) classification. Profusion thresholds of > 1/0 and > 1/1 were used. Demographic and occupational information was obtained by questionnaire. Predictors of silicosis were examined in a multivariable logistic regression model, including age, gender, racial ascription, country of origin, years since starting mine employment, mine shaft, skill category, underground work status and tuberculosis. Results The crude silicosis prevalence at ILO > 1/1 was 3.8% [95% confidence interval (CI) 3.5–4.1%]. The range across mine shafts was 0.8–6.9%. After adjustment for covariates, the interquartile range across shafts was reduced from 2.4 to 1.2%. Black miners [adjusted odds ratio (aOR) 2.8; 95% CI 1.1–7.2] and miners in full-time underground work (aOR 2.1; 95% CI 1.3–3.4) had substantially elevated odds of silicosis, while workers from Mozambique had lower odds (aOR 0.54; 95% CI 0.38–0.77). Silicosis odds rose sharply with both age and years since starting in the industry (p for linear trend  15 years since first exposure and 2.2% < 10 years. Conclusions In surveillance of silicosis in working gold miners time since first exposure remains a powerful predictor. Age appears to be an independent predictor, while the detection of radiological silicosis in short-service miners requires attention. Public risk reporting by mines should include factors bearing on silicosis prevalence, specifically dust concentrations, with independent verification. Studies of silicosis and tuberculosis in ex-miners are needed, supported by an accessible electronic database of the relevant medical and dust exposure records of all gold miners

Species abundance information improves sequence taxonomy classification accuracy

Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level classification accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments.QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and 1565057 to R.K. This work was supported by an NHMRC project grant APP1085372, awarded to G.A.H., J.G.C., and R.K

Detecting coevolution without phylogenetic trees? Tree-ignorant metrics of coevolution perform as well as tree-aware metrics

<p>Abstract</p> <p>Background</p> <p>Identifying coevolving positions in protein sequences has myriad applications, ranging from understanding and predicting the structure of single molecules to generating proteome-wide predictions of interactions. Algorithms for detecting coevolving positions can be classified into two categories: tree-aware, which incorporate knowledge of phylogeny, and tree-ignorant, which do not. Tree-ignorant methods are frequently orders of magnitude faster, but are widely held to be insufficiently accurate because of a confounding of shared ancestry with coevolution. We conjectured that by using a null distribution that appropriately controls for the shared-ancestry signal, tree-ignorant methods would exhibit equivalent statistical power to tree-aware methods. Using a novel t-test transformation of coevolution metrics, we systematically compared four tree-aware and five tree-ignorant coevolution algorithms, applying them to myoglobin and myosin. We further considered the influence of sequence recoding using reduced-state amino acid alphabets, a common tactic employed in coevolutionary analyses to improve both statistical and computational performance.</p> <p>Results</p> <p>Consistent with our conjecture, the transformed tree-ignorant metrics (particularly Mutual Information) often outperformed the tree-aware metrics. Our examination of the effect of recoding suggested that charge-based alphabets were generally superior for identifying the stabilizing interactions in alpha helices. Performance was not always improved by recoding however, indicating that the choice of alphabet is critical.</p> <p>Conclusion</p> <p>The results suggest that t-test transformation of tree-ignorant metrics can be sufficient to control for patterns arising from shared ancestry.</p