Biofilm and planktonic bacterial and fungal communities transforming high molecular weight polycyclic aromatic hydrocarbons.

High molecular weight polycyclic aromatic hydrocarbons (HMW-PAHs) are natural components of fossil fuels that are carcinogenic and persistent in the environment, particularly in oil sands process-affected water (OSPW). Their hydrophobicity and tendency to adsorb to organic matter result in low bioavailability and high recalcitrance to degradation. Despite the importance of microbes for environmental remediation, little is known about those involved in HMW-PAH transformations. Here, we investigated the transformation of HMW-PAHs using samples of OSPW, and compared the bacterial and fungal community composition attached to hydrophobic filters and in suspension. It was anticipated that the hydrophobic filters with sorbed HMW-PAHs would select for microbes that specialise in adhesion. Over 33 days more pyrene was removed (75% ± 11.7) than the five-ring PAHs benzo[a]pyrene (44% ± 13.6) and benzo[b]fluoranthene (41% ± 12.6). For both bacteria and fungi, the addition of PAHs led to a shift in community composition, but thereafter the major factor determining the fungal community composition was whether they were in the planktonic phase or attached to filters. In contrast, the major determinant of the bacterial community composition was the nature of the PAH serving as the carbon source. The main bacteria enriched by HMW-PAHs were Pseudomonas, Bacillus and Microbacterium species. This report demonstrates that OSPW harbour microbial communities with the capacity to transform HMW-PAHs. Furthermore, the provision of suitable surfaces that encourage PAH sorption and microbial adhesion select for different fungal and bacterial species with the potential for HMW-PAH degradation

Variation of oxygenation conditions on a hydrocarbonoclastic microbial community reveals Alcanivorax and Cycloclasticus ecotypes

Deciphering the ecology of marine obligate hydrocarbonoclastic bacteria (MOHCB) is of crucial importance for understanding their success in occupying distinct niches in hydrocarbon-contaminated marine environments after oil spills. In marine coastal sediments, MOHCB are particularly subjected to extreme fluctuating conditions due to redox oscillations several times a day as a result of mechanical (tide, waves and currents) and biological (bioturbation) reworking of the sediment. The adaptation of MOHCB to the redox oscillations was investigated by an experimental ecology approach, subjecting a hydrocarbon-degrading microbial community to contrasting oxygenation regimes including permanent anoxic conditions, anoxic/oxic oscillations and permanent oxic conditions. The most ubiquitous MOHCB, Alcanivorax and Cycloclasticus, showed different behaviors, especially under anoxic/oxic oscillation conditions, which were more favorable for Alcanivorax than for Cycloclasticus. The micro-diversity of 16S rRNA gene transcripts from these genera revealed specific ecotypes for different oxygenation conditions and their dynamics. It is likely that such ecotypes allow the colonization of distinct ecological niches that may explain the success of Alcanivorax and Cycloclasticus in hydrocarbon-contaminated coastal sediments during oil-spills

Regulation of plasmid-encoded isoprene metabolism in Rhodococcus, a representative of an important link in the global isoprene cycle

Emissions of biogenic volatile organic compounds (VOCs) form an important part of the global carbon cycle, comprising a significant proportion of net ecosystem productivity. They impact atmospheric chemistry and contribute directly and indirectly to greenhouse gases. Isoprene, emitted largely from plants, comprises one third of total VOCs, yet in contrast to methane, which is released in similar quantities, we know little of its biodegradation. Here, we report the genome of an isoprene degrading isolate, Rhodococcus sp. AD45, and, using mutagenesis shows that a plasmid-encoded soluble di-iron centre isoprene monooxygenase (IsoMO) is essential for isoprene metabolism. Using RNA sequencing (RNAseq) to analyse cells exposed to isoprene or epoxyisoprene in a substrate-switch time-course experiment, we show that transcripts from 22 contiguous genes, including those encoding IsoMO, were highly upregulated, becoming among the most abundant in the cell and comprising over 25% of the entire transcriptome. Analysis of gene transcription in the wild type and an IsoMO-disrupted mutant strain showed that epoxyisoprene, or a subsequent product of isoprene metabolism, rather than isoprene itself, was the inducing molecule. We provide a foundation of molecular data for future research on the environmental biological consumption of this important, climate-active compound

Dynamics and distribution of bacterial and archaeal communities in oil-contaminated temperate coastal mudflat mesocosms

Mudflats are ecologically important habitats that are susceptible to oil pollution, but intervention is difficult in these fine-grained sediments, and so clean-up usually relies on natural attenuation. Therefore, we investigated the impact of crude oil on the bacterial, diatom and archaeal communities within the upper parts of the diatom-dominated sediment and the biofilm that detached from the surface at high tide. Biodegradation of petroleum hydrocarbons was rapid, with a 50 % decrease in concentration in the 0–2-mm section of sediment by 3 days, indicating the presence of a primed hydrocarbon-degrading community. The biggest oil-induced change was in the biofilm that detached from the sediment, with increased relative abundance of several types of diatom and of the obligately hydrocarbonoclastic Oleibacter sp., which constituted 5 % of the pyrosequences in the oiled floating biofilm on day 3 compared to 0.6 % in the non-oiled biofilm. Differences in bacterial community composition between oiled and non-oiled samples from the 0–2-mm section of sediment were only significant at days 12 to 28, and the 2–4-mm-sediment bacterial communities were not significantly affected by oil. However, specific members of the Chromatiales were detected (1 % of sequences in the 2–4-mm section) only in the oiled sediment, supporting other work that implicates them in anaerobic hydrocarbon degradation. Unlike the Bacteria, the archaeal communities were not significantly affected by oil. In fact, changes in community composition over time, perhaps caused by decreased nutrient concentration and changes in grazing pressure, overshadowed the effect of oil for both Bacteria and Archaea. Many obligate hydrocarbonoclastic and generalist oil-degrading bacteria were isolated, and there was little correspondence between the isolates and the main taxa detected by pyrosequencing of sediment-extracted DNA, except for Alcanivorax, Thalassolituus, Cycloclasticus and Roseobacter spp., which were detected by both methods

Microbial community composition of deep-sea corals from the Red Sea provides insight into functional adaption to a unique environment

Microbes associated with deep-sea corals remain poorly studied. The lack of symbiotic algae suggests that associated microbes may play a fundamental role in maintaining a viable coral host via acquisition and recycling of nutrients. Here we employed 16 S rRNA gene sequencing to study bacterial communities of three deep-sea scleractinian corals from the Red Sea, Dendrophyllia sp., Eguchipsammia fistula, and Rhizotrochus typus. We found diverse, species-specific microbiomes, distinct from the surrounding seawater. Microbiomes were comprised of few abundant bacteria, which constituted the majority of sequences (up to 58% depending on the coral species). In addition, we found a high diversity of rare bacteria (taxa at 90% of all bacteria). Interestingly, we identified anaerobic bacteria, potentially providing metabolic functions at low oxygen conditions, as well as bacteria harboring the potential to degrade crude oil components. Considering the presence of oil and gas fields in the Red Sea, these bacteria may unlock this carbon source for the coral host. In conclusion, the prevailing environmental conditions of the deep Red Sea (>20 °C, <2 mg oxygen L−1) may require distinct functional adaptations, and our data suggest that bacterial communities may contribute to coral functioning in this challenging environment.This work was supported from baseline funds to CRV and under the Center Competitive Funding (CCF) Program FCC/1/1973-18-01 by the King Abdullah University of Science and Technology (KAUST)

Survey of liver pathologists to assess attitudes towards digital pathology and artificial intelligence

\ua9 Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ. AIMS: A survey of members of the UK Liver Pathology Group (UKLPG) was conducted, comprising consultant histopathologists from across the UK who report liver specimens and participate in the UK National Liver Pathology External Quality Assurance scheme. The aim of this study was to understand attitudes and priorities of liver pathologists towards digital pathology and artificial intelligence (AI). METHODS: The survey was distributed to all full consultant members of the UKLPG via email. This comprised 50 questions, with 48 multiple choice questions and 2 free-text questions at the end, covering a range of topics and concepts pertaining to the use of digital pathology and AI in liver disease. RESULTS: Forty-two consultant histopathologists completed the survey, representing 36% of fully registered members of the UKLPG (42/116). Questions examining digital pathology showed respondents agreed with the utility of digital pathology for primary diagnosis 83% (34/41), second opinions 90% (37/41), research 85% (35/41) and training and education 95% (39/41). Fatty liver diseases were an area of demand for AI tools with 80% in agreement (33/41), followed by neoplastic liver diseases with 59% in agreement (24/41). Participants were concerned about AI development without pathologist involvement 73% (30/41), however, 63% (26/41) disagreed when asked whether AI would replace pathologists. CONCLUSIONS: This study outlines current interest, priorities for research and concerns around digital pathology and AI for liver pathologists. The majority of UK liver pathologists are in favour of the application of digital pathology and AI in clinical practice, research and education

Object-based Feedback Attention in Convolutional Neural Networks Improves Tumour Detection in Digital Pathology

Abstract Human visual attention allows prior knowledge or expectations to influence visual processing, allocating limited computational resources to only that part of the image that are likely to behaviourally important. Here, we present an image recognition system based on biological vision that guides attention to more informative locations within a larger parent image, using a sequence of saccade-like motions. We demonstrate that at the end of the saccade sequence the system has an improved classification ability compared to the convolutional neural network (CNN) that represents the feedforward part of the model. Feedback activations highlight salient image features supporting the explainability of the classification. Our attention model deviates substantially from more common feedforward attention mechanisms, which linearly reweight part of the input. This model uses several passes of feedforward and backward activation, which interact non-linearly. We apply our feedback architecture to histopathology patch images, demonstrating a 3.5% improvement in accuracy (p < 0.001) when retrospectively processing 59,057 9-class patches from 689 colorectal cancer WSIs. In the saccade implementation, overall agreement between expert-labelled patches and model prediction reached 93.23% for tumour tissue, surpassing inter-pathologist agreement. Our method is adaptable to other areas of science which rely on the analysis of extremely large-scale images

Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy

Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1–97.7) and mean specificity of 93.3% (CI 90.5–95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance

Is there a common water-activity limit for the three domains of life?

Archaea and Bacteria constitute a majority of life systems on Earth but have long been considered inferior to Eukarya in terms of solute tolerance. Whereas the most halophilic prokaryotes are known for an ability to multiply at saturated NaCl (water activity (a w) 0.755) some xerophilic fungi can germinate, usually at high-sugar concentrations, at values as low as 0.650-0.605 a w. Here, we present evidence that halophilic prokayotes can grow down to water activities of <0.755 for Halanaerobium lacusrosei (0.748), Halobacterium strain 004.1 (0.728), Halobacterium sp. NRC-1 and Halococcus morrhuae (0.717), Haloquadratum walsbyi (0.709), Halococcus salifodinae (0.693), Halobacterium noricense (0.687), Natrinema pallidum (0.681) and haloarchaeal strains GN-2 and GN-5 (0.635 a w). Furthermore, extrapolation of growth curves (prone to giving conservative estimates) indicated theoretical minima down to 0.611 a w for extreme, obligately halophilic Archaea and Bacteria. These were compared with minima for the most solute-tolerant Bacteria in high-sugar (or other non-saline) media (Mycobacterium spp., Tetragenococcus halophilus, Saccharibacter floricola, Staphylococcus aureus and so on) and eukaryotic microbes in saline (Wallemia spp., Basipetospora halophila, Dunaliella spp. and so on) and high-sugar substrates (for example, Xeromyces bisporus, Zygosaccharomyces rouxii, Aspergillus and Eurotium spp.). We also manipulated the balance of chaotropic and kosmotropic stressors for the extreme, xerophilic fungi Aspergillus penicilloides and X. bisporus and, via this approach, their established water-activity limits for mycelial growth (∼0.65) were reduced to 0.640. Furthermore, extrapolations indicated theoretical limits of 0.632 and 0.636 a w for A. penicilloides and X. bisporus, respectively. Collectively, these findings suggest that there is a common water-activity limit that is determined by physicochemical constraints for the three domains of life

Artificial intelligence in digital pathology: a diagnostic test accuracy systematic review and meta-analysis

Ensuring diagnostic performance of AI models before clinical use is key to the safe and successful adoption of these technologies. Studies reporting AI applied to digital pathology images for diagnostic purposes have rapidly increased in number in recent years. The aim of this work is to provide an overview of the diagnostic accuracy of AI in digital pathology images from all areas of pathology. This systematic review and meta-analysis included diagnostic accuracy studies using any type of artificial intelligence applied to whole slide images (WSIs) in any disease type. The reference standard was diagnosis through histopathological assessment and / or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. We identified 2976 studies, of which 100 were included in the review and 48 in the full meta-analysis. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model. 100 studies were identified for inclusion, equating to over 152,000 whole slide images (WSIs) and representing many disease types. Of these, 48 studies were included in the meta-analysis. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4) for AI. There was substantial heterogeneity in study design and all 100 studies identified for inclusion had at least one area at high or unclear risk of bias. This review provides a broad overview of AI performance across applications in whole slide imaging. However, there is huge variability in study design and available performance data, with details around the conduct of the study and make up of the datasets frequently missing. Overall, AI offers good accuracy when applied to WSIs but requires more rigorous evaluation of its performance