How Causal are Microbiomes? A Comparison with the Helicobacter pylori Explanation of Ulcers

Human microbiome research makes causal connections between entire microbial communities and a wide array of traits that range from physiological diseases to psychological states. To evaluate these causal claims, we first examine a well-known single-microbe causal explanation: of Helicobacter pylori causing ulcers. This apparently straightforward causal explanation is not so simple, however. It does not achieve a key explanatory standard in microbiology, of Koch’s postulates, which rely on manipulations of single-microorganism cultures to infer causal relationships to disease. When Koch’s postulates are framed by an interventionist causal framework, it is clearer what the H. pylori explanation achieves and where its explanatory strengths lie. After assessing this ‘simple’, single-microbe case, we apply the interventionist framework to two key areas of microbiome research, in which obesity and mental health states are purportedly explained by microbiomes. Despite the experimental data available, interventionist criteria for explanation show that many of the causal claims generated by microbiome research are weak or misleading. We focus on the stability, specificity and proportionality of proposed microbiome causal explanations, and evaluate how effectively these dimensions of causal explanation are achieved in some promising avenues of research. We suggest some conceptual and explanatory strategies to improve how causal claims about microbiomes are made

How Causal are Microbiomes? A Comparison with the Helicobacter pylori Explanation of Ulcers

Human microbiome research makes causal connections between entire microbial communities and a wide array of traits that range from physiological diseases to psychological states. To evaluate these causal claims, we first examine a well-known single-microbe causal explanation: of Helicobacter pylori causing ulcers. This apparently straightforward causal explanation is not so simple, however. It does not achieve a key explanatory standard in microbiology, of Koch’s postulates, which rely on manipulations of single-microorganism cultures to infer causal relationships to disease. When Koch’s postulates are framed by an interventionist causal framework, it is clearer what the H. pylori explanation achieves and where its explanatory strengths lie. After assessing this ‘simple’, single-microbe case, we apply the interventionist framework to two key areas of microbiome research, in which obesity and mental health states are purportedly explained by microbiomes. Despite the experimental data available, interventionist criteria for explanation show that many of the causal claims generated by microbiome research are weak or misleading. We focus on the stability, specificity and proportionality of proposed microbiome causal explanations, and evaluate how effectively these dimensions of causal explanation are achieved in some promising avenues of research. We suggest some conceptual and explanatory strategies to improve how causal claims about microbiomes are made

Evolutionary significance of maternal kinship in a long-lived mammal

Preferential treatment of kin is widespread across social species and is considered a central prerequisite to the evolution of cooperation through kin selection. Though it is well known that, among most social mammals, females will remain within their natal group and often bias social behaviour towards female maternal kin, less is known about the fitness consequences of these relationships. We test the fitness benefits of living with maternal sisters, measured by age-specific female reproduction, using an unusually large database of a semi-captive Asian elephant (Elephas maximus) population. This study system is particularly valuable to an exploration of reproductive trends in a long-lived mammal, because it includes life-history data that span multiple generations, enabling a study of the effects of kinship across a female's lifespan. We find that living near a sister significantly increased the likelihood of annual reproduction among young female elephants, and this effect was strongest when living near a sister 0-5 years younger. Our results show that fitness benefits gained from relationships with kin are age-specific, establish the basis necessary for the formation and maintenance of close social relationships with female kin, and highlight the adaptive importance of matriliny in a long-lived mammal.This article is part of the theme issue 'The evolution of female-biased kinship in humans and other mammals'.</p

Detection of Near-IR CO Absorption Bands in R Coronae Borealis Stars

R Coronae Borealis (RCB) stars are hydrogen-deficient, carbon-rich pulsating post-AGB stars that experience massive irregular declines in brightness caused by circumstellar dust formation. The mechanism of dust formation around RCB stars is not well understood. It has been proposed that CO molecules play an important role in cooling the circumstellar gas so that dust may form. We report on a survey for CO in a sample of RCB stars. We obtained H- and K-band spectra including the first and second overtone CO bands for eight RCB stars, the RCB-like star, DY Per and the final-helium-flash star, FG Sge. The first and second overtone CO bands were detected in the cooler (T(eff)<6000 K) RCB stars, Z Umi, ES Aql, SV Sge and DY Per. The bands are not present in the warmer (T(eff)>6000 K) RCB stars, R CrB, RY Sgr, SU Tau, XX Cam. In addition, first overtone bands are seen in FG Sge, a final-helium-flash star that is in an RCB-like phase at present. Effective temperatures of the eight RCB stars range from 4000 to 7250 K. The observed photospheric CO absorption bands were compared to line-blanketed model spectra of RCB stars. As predicted by the models, the CO bands are strongest in the coolest RCB stars and not present in the warmest. No correlation was found between the presence or strength of the CO bands and dust formation activity in the stars.Comment: 13 oages, 3 figures, AJ in pres

Value of combining transect counts and telemetry data to determine short-term population trends in a globally threatened species

To evaluate conservation interventions, it is necessary to obtain reliable population trends for short (<10 years) time scales. Telemetry can be used to estimate short-term survival rates and is a common tool for assessing population trends, but it has limitations and can be biased toward specific behavioral traits of tagged individuals. Encounter rates calculated from transects can be useful for assessing changes across multiple species, but they can have large confidence intervals and be affected by variations in survey conditions. The decline of African vultures has been well-documented, but understanding of recent trends is lacking. To examine population trends, we used survival estimates from telemetry data collected over 6 years (primarily for white-backed vultures [Gyps africanus]) and transect counts conducted over 8 years (for 7 scavenging raptors) in 3 large protected areas in Tanzania. Population trends were estimated using survival analysis combined with the Leslie Lefkovitch matrix model from the telemetry data and using Bayesian mixed effects generalized linear regression models from the transect data. Both methods showed significant declines for white-backed vultures in Ruaha and Nyerere National Parks. Only telemetry estimates suggested significant declines in Katavi National Park. Encounter rates calculated from transects also showed declines in Nyerere National Park for lappet-faced vultures (38% annual declines) and Bateleurs (18%) and in Ruaha National Park for white-headed vultures (Trigonoceps occipitalis) (19%). Mortality rates recorded and inferred from telemetry suggested that poisoning is prevalent. However, only 6 mortalities of the 26 presumed mortalities were confirmed to be caused by poisoning, highlighting the challenges of determining the cause of death when working across large landscapes. Despite declines, our data provide evidence that southern Tanzania has higher current encounter rates of African vultures than elsewhere in East Africa. Preventing further declines will depend greatly on mitigating poisoning. Based on our results, we suggest that the use of multiple techniques improves understanding of population trends over the short term.Vulture research in southern Tanzania was funded by the North Carolina Zoological Society and the Wildlife Conservation Society. Donor support provided by Association of Zoos and Aquariums (AZA), AZA SAFE (Saving Animals From Extinction), Dallas Zoo, Disney Conservation Fund, Leiden Conservation Foundation, National Geographic Society, Taronga Conservation Society Australia, The Mohamed bin Zayed Species Conservation Fund, and the Wyss Foundation. Research permission was granted by the Tanzania Wildlife Research Institute (TAWIRI), Tanzania Commission for Science and Technology, and Tanzania National Parks and Tanzania Wildlife Authority (TAWA). We thank Singira Ngoishiye and TAWA Selous GR for invaluable contributions to ensure successful deployment of satellite tags and rapidmobilization of rangers to poisoning events, L. Mlawila for assistance with surveys and retrieving satellite tags from mortalities, and E. Kohi (TAWIRI) for his input on the manuscript. Work in Selous Game Reserve (nowNyerere National Park) was conducted in collaboration with Frankfurt Zoological Society.https://conbio.onlinelibrary.wiley.com/journal/15231739am2024Mammal Research InstituteZoology and EntomologySDG-15:Life on lan

Accurate brain-age models for routine clinical MRI examinations

Convolutional neural networks (CNN) can accurately predict chronological age in healthy individuals from structural MRI brain scans. Potentially, these models could be applied during routine clinical examinations to detect deviations from healthy ageing, including early-stage neurodegeneration. This could have important implications for patient care, drug development, and optimising MRI data collection. However, existing brain-age models are typically optimised for scans which are not part of routine examinations (e.g., volumetric T1-weighted scans), generalise poorly (e.g., to data from different scanner vendors and hospitals etc.), or rely on computationally expensive pre-processing steps which limit real-time clinical utility. Here, we sought to develop a brain-age framework suitable for use during routine clinical head MRI examinations. Using a deep learning-based neuroradiology report classifier, we generated a dataset of 23,302 'radiologically normal for age' head MRI examinations from two large UK hospitals for model training and testing (age range = 18-95 years), and demonstrate fast (&lt; 5 seconds), accurate (mean absolute error [MAE] &lt; 4 years) age prediction from clinical-grade, minimally processed axial T2-weighted and axial diffusion-weighted scans, with generalisability between hospitals and scanner vendors (Δ MAE &lt; 1 year). The clinical relevance of these brain-age predictions was tested using 228 patients whose MRIs were reported independently by neuroradiologists as showing atrophy 'excessive for age'. These patients had systematically higher brain-predicted age than chronological age (mean predicted age difference = +5.89 years, 'radiologically normal for age' mean predicted age difference = +0.05 years, p &lt; 0.0001). Our brain-age framework demonstrates feasibility for use as a screening tool during routine hospital examinations to automatically detect older-appearing brains in real-time, with relevance for clinical decision-making and optimising patient pathways.</p

Automated triaging of head MRI examinations using convolutional neural networks

The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans around the world. For many neurological conditions, this delay can result in increased morbidity and mortality. An automated triaging tool could reduce reporting times for abnormal examinations by identifying abnormalities at the time of imaging and prioritizing the reporting of these scans. In this work, we present a convolutional neural network for detecting clinically-relevant abnormalities in $\text{T}_2$-weighted head MRI scans. Using a validated neuroradiology report classifier, we generated a labelled dataset of 43,754 scans from two large UK hospitals for model training, and demonstrate accurate classification (area under the receiver operating curve (AUC) = 0.943) on a test set of 800 scans labelled by a team of neuroradiologists. Importantly, when trained on scans from only a single hospital the model generalized to scans from the other hospital ($\Delta$AUC $\leq$ 0.02). A simulation study demonstrated that our model would reduce the mean reporting time for abnormal examinations from 28 days to 14 days and from 9 days to 5 days at the two hospitals, demonstrating feasibility for use in a clinical triage environment.Comment: Accepted as an oral presentation at Medical Imaging with Deep Learning (MIDL) 202

Labelling imaging datasets on the basis of neuroradiology reports: a validation study

Natural language processing (NLP) shows promise as a means to automate the labelling of hospital-scale neuroradiology magnetic resonance imaging (MRI) datasets for computer vision applications. To date, however, there has been no thorough investigation into the validity of this approach, including determining the accuracy of report labels compared to image labels as well as examining the performance of non-specialist labellers. In this work, we draw on the experience of a team of neuroradiologists who labelled over 5000 MRI neuroradiology reports as part of a project to build a dedicated deep learning-based neuroradiology report classifier. We show that, in our experience, assigning binary labels (i.e. normal vs abnormal) to images from reports alone is highly accurate. In contrast to the binary labels, however, the accuracy of more granular labelling is dependent on the category, and we highlight reasons for this discrepancy. We also show that downstream model performance is reduced when labelling of training reports is performed by a non-specialist. To allow other researchers to accelerate their research, we make our refined abnormality definitions and labelling rules available, as well as our easy-to-use radiology report labelling app which helps streamline this process

Welfare and enrichment of managed nocturnal species, supported by technology

This paper addresses the potential for technology to support husbandry and enrichment opportunities that enhance the welfare of zoo and sanctuary-housed nocturnal and crepuscular species. This topic was investigated through the medium of a multidisciplinary workshop (Moon Jam) that brought together species experts, zoo designers, Animal–Computer Interaction researchers and post-graduate students in collaborative discussions and design sessions. We explain the context through an examination of existing research and current practices, and report on specific challenges raised and addressed during the Moon Jam, highlighting and discussing key themes that emerged. Finally, we offer a set of guidelines to support the integration of technology into the design of animal husbandry and enrichment that support wellbeing, to advance the best practices in keeping and managing nocturnal and crepuscular animals