Timing, frequency and environmental conditions associated with mainstem-tributary movement by a lowland river fish, golden perch (Macquaria ambigua)

Tributary and mainstem connections represent important links for the movement of fish and other biota throughout river networks. We investigated the timing, frequency and environmental conditions associated with movements by adult golden perch (Macquaria ambigua) between the mainstem of the mid-Murray River and a tributary, the Goulburn River, in south-eastern Australia, using acoustic telemetry over four years (2007–2011). Fish were tagged and released in autumn 2007–2009 in the mid-Murray (n = 42) and lower Goulburn (n = 37) rivers within 3–6 km of the mid-Murray-lower Goulburn junction. 38% of tagged fish undertook mainstem–tributary movements, characterised mostly by temporary occupation followed by return of fish to the original capture river. Approximately 10% of tagged fish exhibited longer-term shifts between the mainstem and tributary. Movement of fish from the tributary into the mainstem occurred primarily during the spawning season and in some years coincided with the presence of golden perch eggs/larvae in drift samples in the mainstem. Many of the tributary-to-mainstem movements occurred during or soon after changes in flow. The movements of fish from the mainstem into the tributary were irregular and did not appear to be associated with spawning. The findings show that golden perch moved freely across the mainstem–tributary interface. This demonstrates the need to consider the spatial, behavioural and demographic interdependencies of aquatic fauna across geographic management units such as rivers

Short-term genome stability of serial Clostridium difficile ribotype 027 isolates in an experimental gut model and recurrent human disease

Copyright: © 2013 Eyre et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedClostridium difficile whole genome sequencing has the potential to identify related isolates, even among otherwise indistinguishable strains, but interpretation depends on understanding genomic variation within isolates and individuals.Serial isolates from two scenarios were whole genome sequenced. Firstly, 62 isolates from 29 timepoints from three in vitro gut models, inoculated with a NAP1/027 strain. Secondly, 122 isolates from 44 patients (2–8 samples/patient) with mostly recurrent/on-going symptomatic NAP-1/027 C. difficile infection. Reference-based mapping was used to identify single nucleotide variants (SNVs).Across three gut model inductions, two with antibiotic treatment, total 137 days, only two new SNVs became established. Pre-existing minority SNVs became dominant in two models. Several SNVs were detected, only present in the minority of colonies at one/two timepoints. The median (inter-quartile range) [range] time between patients’ first and last samples was 60 (29.5–118.5) [0–561] days. Within-patient C. difficile evolution was 0.45 SNVs/called genome/year (95%CI 0.00–1.28) and within-host diversity was 0.28 SNVs/called genome (0.05–0.53). 26/28 gut model and patient SNVs were non-synonymous, affecting a range of gene targets.The consistency of whole genome sequencing data from gut model C. difficile isolates, and the high stability of genomic sequences in isolates from patients, supports the use of whole genome sequencing in detailed transmission investigations.Peer reviewe

DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis.

MOTIVATION: Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. RESULTS: We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space. AVAILABILITY AND IMPLEMENTATION: The details of source code are provided at http://www.robots.ox.ac.uk/?davidc/code.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Decline of meticillin-resistant Staphylococcus aureus in Oxfordshire hospitals is strain-specific and preceded infection-control intensification

Background In the past, strains of Staphylococcus aureus have evolved, expanded, made a marked clinical impact and then disappeared over several years. Faced with rising meticillin-resistant S aureus (MRSA) rates, UK government-supported infection control interventions were rolled out in Oxford Radcliffe Hospitals NHS Trust from 2006 onwards. Methods Using an electronic Database, the authors identified isolation of MRS among 611 434 hospital inpatients admitted to acute hospitals in Oxford, UK, 1 April 1998 to 30 June 2010. Isolation rates were modelled using segmented negative binomial regression for three groups of isolates: from blood cultures, from samples suggesting invasion (eg, cerebrospinal fluid, joint fluid, pus samples) and from surface swabs (eg, from wounds). Findings MRSA isolation rates rose rapidly from 1998 to the end of 2003 (annual increase from blood cultures 23%, 95% CI 16% to 30%), and then declined. The decline accelerated from mid-2006 onwards (annual decrease post-2006 38% from blood cultures, 95% CI 29% to 45%, p=0.003 vs previous decline). Rates of meticillin-sensitive S aureus changed little by comparison, with no evidence for declines 2006 onward (p=0.40); by 2010, sensitive S aureus was far more common than MRSA (blood cultures: 2.9 vs 0.25; invasive samples 14.7 vs 2.0 per 10 000 bedstays). Interestingly, trends in isolation of erythromycin-sensitive and resistant MRSA differed. Erythromycin-sensitive strains rose significantly faster (eg, from blood cultures p=0.002), and declined significantly more slowly (p=0.002), than erythromycin-resistant strains (global p<0.0001). Bacterial typing suggests this reflects differential spread of two major UK MRSA strains (ST22/36), ST36 having declined markedly 2006-2010, with ST22 becoming the dominant MRSA strain. Conclusions MRSA isolation rates were falling before recent intensification of infection-control measures. This, together with strain-specific changes in MRSA isolation, strongly suggests that incompletely understood biological factors are responsible for the much recent variation in MRSA isolation. A major, mainly meticillin-sensitive, S aureus burden remains

Genomic diversity affects the accuracy of bacterial single-nucleotide polymorphism-calling pipelines

Background: Accurately identifying SNPs from bacterial sequencing data is an essential requirement for using genomics to track transmission and predict important phenotypes such as antimicrobial resistance. However, most previous performance evaluations of SNP calling have been restricted to eukaryotic (human) data. Additionally, bacterial SNP calling requires choosing an appropriate reference genome to align reads to, which, together with the bioinformatic pipeline, affects the accuracy and completeness of a set of SNP calls obtained. This study evaluates the performance of 209 SNP calling pipelines using a combination of simulated data from 254 strains of 10 clinically common bacteria and real data from environmentally-sourced and genomically diverse isolates within the genera Citrobacter, Enterobacter, Escherichia and Klebsiella. Results: We evaluated the performance of 209 SNP calling pipelines, aligning reads to genomes of the same or a divergent strain. Irrespective of pipeline, a principal determinant of reliable SNP calling was reference genome selection. Across multiple taxa, there was a strong inverse relationship between pipeline sensitivity and precision, and the Mash distance (a proxy for average nucleotide divergence) between reads and reference genome. The effect was especially pronounced for diverse, recombinogenic, bacteria such as Escherichia coli, but less dominant for clonal species such as Mycobacterium tuberculosis. Conclusions: The accuracy of SNP calling for a given species is compromised by increasing intra-species diversity. When reads were aligned to the same genome from which they were sequenced, among the highest performing pipelines was Novoalign/GATK. By contrast, when reads were aligned to particularly divergent genomes, the highest-performing pipelines often employed the aligners NextGenMap or SMALT, and/or the variant callers LoFreq, mpileup or Strelka

Strong population structure deduced from genetics, otolith chemistry and parasite abundances explains vulnerability to localized fishery collapse in a large Sciaenid fish, Protonibea diacanthus

As pressure on coastal marine resources is increasing globally, the need to quantitatively assess vulnerable fish stocks is crucial in order to avoid the ecological consequences of stock depletions. Species of Sciaenidae (croakers, drums) are important components of tropical and temperate fisheries and are especially vulnerable to exploitation. The black-spotted croaker, Protonibea diacanthus, is the only large sciaenid in coastal waters of northern Australia where it is targeted by commercial, recreational and indigenous fishers due to its food value and predictable aggregating behaviour. Localised declines in the abundance of this species have been observed, highlighting the urgent requirement by managers for information on fine and broad-scale population connectivity. This study examined the population structure of P. diacanthus across northwestern Australia using three complementary methods: genetic variation in microsatellite markers, otolith elemental composition and parasite assemblage composition. The genetic analyses demonstrated that there were at least five genetically distinct populations across the study region, with gene flow most likely restricted by inshore biogeographic barriers such as the Dampier Peninsula. The otolith chemistry and parasite analyses also revealed strong spatial variation among locations within broad-scale regions, suggesting fine-scale location fidelity within the lifetimes of individual fish. The complementarity of the three techniques elucidated patterns of connectivity over a range of spatial and temporal scales. We conclude that fisheries stock assessments and management are required at fine scales (100's km) to account for the restricted exchange among populations (stocks) and to prevent localised extirpations of this species. Realistic management arrangements may involve the successive closure and opening of fishing areas to reduce fishing pressure

Time of day of vaccination affects SARS-CoV-2 antibody responses in an observational study of health care workers

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global crisis with unprecedented challenges for public health. Vaccinations against SARS-CoV-2 have slowed the incidence of new infections and reduced disease severity. As the time of day of vaccination has been reported to influence host immune responses to multiple pathogens, we quantified the influence of SARS-CoV-2 vaccination time, vaccine type, participant age, sex, and days post-vaccination on anti-Spike antibody responses in health care workers. The magnitude of the anti-Spike antibody response is associated with the time of day of vaccination, vaccine type, participant age, sex, and days post-vaccination. These results may be relevant for optimising SARS-CoV-2 vaccine efficacy