Deep learning evaluation of echocardiograms to identify occult atrial fibrillation.

Atrial fibrillation (AF) often escapes detection, given its frequent paroxysmal and asymptomatic presentation. Deep learning of transthoracic echocardiograms (TTEs), which have structural information, could help identify occult AF. We created a two-stage deep learning algorithm using a video-based convolutional neural network model that (1) distinguished whether TTEs were in sinus rhythm or AF and then (2) predicted which of the TTEs in sinus rhythm were in patients who had experienced AF within 90 days. Our model, trained on 111,319 TTE videos, distinguished TTEs in AF from those in sinus rhythm with high accuracy in a held-out test cohort (AUC 0.96 (0.95-0.96), AUPRC 0.91 (0.90-0.92)). Among TTEs in sinus rhythm, the model predicted the presence of concurrent paroxysmal AF (AUC 0.74 (0.71-0.77), AUPRC 0.19 (0.16-0.23)). Model discrimination remained similar in an external cohort of 10,203 TTEs (AUC of 0.69 (0.67-0.70), AUPRC 0.34 (0.31-0.36)). Performance held across patients who were women (AUC 0.76 (0.72-0.81)), older than 65 years (0.73 (0.69-0.76)), or had a CHA2DS2VASc ≥2 (0.73 (0.79-0.77)). The model performed better than using clinical risk factors (AUC 0.64 (0.62-0.67)), TTE measurements (0.64 (0.62-0.67)), left atrial size (0.63 (0.62-0.64)), or CHA2DS2VASc (0.61 (0.60-0.62)). An ensemble model in a cohort subset combining the TTE model with an electrocardiogram (ECGs) deep learning model performed better than using the ECG model alone (AUC 0.81 vs. 0.79, p = 0.01). Deep learning using TTEs can predict patients with active or occult AF and could be used for opportunistic AF screening that could lead to earlier treatment.

Emerging long-term trends and interdecadal cycles in Antarctic polynyas.

Polynyas, areas of open water embedded within sea ice, are a key component of ocean-atmosphere interactions that act as hotspots of sea-ice production, bottom-water formation, and primary productivity. The specific drivers of polynya dynamics remain, however, elusive and coupled climate models struggle to replicate Antarctic polynya activity. Here, we leverage a 44-y time series of Antarctic sea ice to elucidate long-term trends. We identify Antarctic-wide linear increases and a hitherto undescribed cyclical pattern of polynya activity across the Ross Sea region that potentially arises from interactions between the Amundsen Sea Low and Southern Annular Mode. While their specific drivers remain unknown, identifying these emerging patterns augments our capacity to understand the processes that influence sea ice. As we enter a potentially new age of Antarctic sea ice, this advance in understanding will, in turn, lead to more accurate predictions of environmental change, and its implications for Antarctic ecosystems.

Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study.

BACKGROUND: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing. METHODS: In a derivation cohort of preoperative patients with available electrocardiograms (ECGs) from Cedars-Sinai Medical Center (Los Angeles, CA, USA) between Jan 1, 2015 and Dec 31, 2019, a deep-learning algorithm was developed to leverage waveform signals to discriminate postoperative mortality. We randomly split patients (8:1:1) into subsets for training, internal validation, and final algorithm test analyses. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values in the hold-out test dataset and in two external hospital cohorts and compared with the established Revised Cardiac Risk Index (RCRI) score. The primary outcome was post-procedural mortality across three health-care systems. FINDINGS: 45 969 patients had a complete ECG waveform image available for at least one 12-lead ECG performed within the 30 days before the procedure date (59 975 inpatient procedures and 112 794 ECGs): 36 839 patients in the training dataset, 4549 in the internal validation dataset, and 4581 in the internal test dataset. In the held-out internal test cohort, the algorithm discriminates mortality with an AUC value of 0·83 (95% CI 0·79-0·87), surpassing the discrimination of the RCRI score with an AUC of 0·67 (0·61-0·72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0·79 (0·75-0·83) and 0·75 (0·74-0·76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8·83 (5·57-13·20) for postoperative mortality compared with an unadjusted OR of 2·08 (0·77-3·50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0·85 [0·77-0·92]), non-cardiac surgery (AUC 0·83 [0·79-0·88]), and catheterisation or endoscopy suite procedures (AUC 0·76 [0·72-0·81]). INTERPRETATION: A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications. FUNDING: National Heart, Lung, and Blood Institute.

Leveraging 3D Echocardiograms to Evaluate AI Model Performance in Predicting Cardiac Function on Out-of-Distribution Data.

Advancements in medical imaging and artificial intelligence (AI) have revolutionized the field of cardiac diagnostics, providing accurate and efficient tools for assessing cardiac function. AI diagnostics claims to improve upon the human-to-human variation that is known to be significant. However, when put in practice, for cardiac ultrasound, AI models are being run on images acquired by human sonographers whose quality and consistency may vary. With more variation than other medical imaging modalities, variation in image acquisition may lead to out-of-distribution (OOD) data and unpredictable performance of the AI tools. Recent advances in ultrasound technology has allowed the acquisition of both 3D as well as 2D data, however 3D has more limited temporal and spatial resolution and is still not routinely acquired. Because the training datasets used when developing AI algorithms are mostly developed using 2D images, it is difficult to determine the impact of human variation on the performance of AI tools in the real world. The objective of this project is to leverage 3D echos to simulate realistic human variation of image acquisition and better understand the OOD performance of a previously validated AI model. In doing so, we develop tools for interpreting 3D echo data and quantifiably recreating common variation in image acquisition between sonographers. We also developed a technique for finding good standard 2D views in 3D echo volumes. We found the performance of the AI model we evaluated to be as expected when the view is good, but variations in acquisition position degraded AI model performance. Performance on far from ideal views was poor, but still better than random, suggesting that there is some information being used that permeates the whole volume, not just a quality view. Additionally, we found that variations in foreshortening didn't result in the same errors that a human would make.

Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation.

Importance: Early detection of atrial fibrillation (AF) may help prevent adverse cardiovascular events such as stroke. Deep learning applied to electrocardiograms (ECGs) has been successfully used for early identification of several cardiovascular diseases. Objective: To determine whether deep learning models applied to outpatient ECGs in sinus rhythm can predict AF in a large and diverse patient population. Design, Setting, and Participants: This prognostic study was performed on ECGs acquired from January 1, 1987, to December 31, 2022, at 6 US Veterans Affairs (VA) hospital networks and 1 large non-VA academic medical center. Participants included all outpatients with 12-lead ECGs in sinus rhythm. Main Outcomes and Measures: A convolutional neural network using 12-lead ECGs from 2 US VA hospital networks was trained to predict the presence of AF within 31 days of sinus rhythm ECGs. The model was tested on ECGs held out from training at the 2 VA networks as well as 4 additional VA networks and 1 large non-VA academic medical center. Results: A total of 907 858 ECGs from patients across 6 VA sites were included in the analysis. These patients had a mean (SD) age of 62.4 (13.5) years, 6.4% were female, and 93.6% were male, with a mean (SD) CHA2DS2-VASc (congestive heart failure, hypertension, age, diabetes mellitus, prior stroke or transient ischemic attack or thromboembolism, vascular disease, age, sex category) score of 1.9 (1.6). A total of 0.2% were American Indian or Alaska Native, 2.7% were Asian, 10.7% were Black, 4.6% were Latinx, 0.7% were Native Hawaiian or Other Pacific Islander, 62.4% were White, 0.4% were of other race or ethnicity (which is not broken down into subcategories in the VA data set), and 18.4% were of unknown race or ethnicity. At the non-VA academic medical center (72 483 ECGs), the mean (SD) age was 59.5 (15.4) years and 52.5% were female, with a mean (SD) CHA2DS2-VASc score of 1.6 (1.4). A total of 0.1% were American Indian or Alaska Native, 7.9% were Asian, 9.4% were Black, 2.9% were Latinx, 0.03% were Native Hawaiian or Other Pacific Islander, 74.8% were White, 0.1% were of other race or ethnicity, and 4.7% were of unknown race or ethnicity. A deep learning model predicted the presence of AF within 31 days of a sinus rhythm ECG on held-out test ECGs at VA sites with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI, 0.85-0.86), accuracy of 0.78 (95% CI, 0.77-0.78), and F1 score of 0.30 (95% CI, 0.30-0.31). At the non-VA site, AUROC was 0.93 (95% CI, 0.93-0.94); accuracy, 0.87 (95% CI, 0.86-0.88); and F1 score, 0.46 (95% CI, 0.44-0.48). The model was well calibrated, with a Brier score of 0.02 across all sites. Among individuals deemed high risk by deep learning, the number needed to screen to detect a positive case of AF was 2.47 individuals for a testing sensitivity of 25% and 11.48 for 75%. Model performance was similar in patients who were Black, female, or younger than 65 years or who had CHA2DS2-VASc scores of 2 or greater. Conclusions and Relevance: Deep learning of outpatient sinus rhythm ECGs predicted AF within 31 days in populations with diverse demographics and comorbidities. Similar models could be used in future AF screening efforts to reduce adverse complications associated with this disease.

Macroalgal microbiome biogeography is shaped by environmental drivers rather than geographic distance.

BACKGROUND AND AIMS: Contrasting patterns of host and microbiome biogeography can provide insight into the drivers of microbial community assembly. Distance-decay relationships are a classic biogeographic pattern that are shaped by interactions between selective and non-selective processes. Joint biogeography of microbiomes and their hosts are of increasing interest due to the potential for microbiome-facilitated adaptation. METHODS: In this study we examine the coupled biogeography of the model macroalgae Durvillaea and its microbiome using a combination of Genotyping-by-Sequencing (host) and 16S rRNA amplicon sequencing (microbiome). Alongside these approaches, we employ environmental data to characterize the relationship between the microbiome, the host, and the environment. KEY RESULTS: We show that although host and microbiome exhibit shared biogeographic structure, these arise from different processes - with host biogeography showing classic signs of geographic distance decay, but the microbiome showing environmental distance decay. Examination of microbial sub communities, defined by abundance, revealed that the abundance of microbes is linked to environmental selection. As microbes become less common, the dominant ecological processes shift away from selective processes and towards neutral processes. Contrary to expectations, we found that ecological drift does not promote structuring of the microbiome. CONCLUSIONS: Our results suggest that although host macroalgae exhibit a relatively 'typical' biogeographic pattern of declining similarity with increasing geographic distance, the microbiome is more variable, and is primarily shaped by environmental conditions. Our findings suggest that the Baas Becking hypothesis of "everything is everywhere, the environment selects" may be a useful hypothesis to understand biogeography of macroalgal microbiomes. As environmental conditions change in response to anthropogenic influences, the processes structuring the microbiome of macroalgae may shift while those governing the host biogeography are less likely to change. As a result, increasingly decoupled host-microbe biogeography may be observed in response to such human influences.

Blinded, randomized trial of sonographer versus AI cardiac function assessment.

Artificial intelligence (AI) has been developed for echocardiography1-3, although it has not yet been tested with blinding and randomization. Here we designed a blinded, randomized non-inferiority clinical trial (ClinicalTrials.gov ID: NCT05140642; no outside funding) of AI versus sonographer initial assessment of left ventricular ejection fraction (LVEF) to evaluate the impact of AI in the interpretation workflow. The primary end point was the change in the LVEF between initial AI or sonographer assessment and final cardiologist assessment, evaluated by the proportion of studies with substantial change (more than 5% change). From 3,769 echocardiographic studies screened, 274 studies were excluded owing to poor image quality. The proportion of studies substantially changed was 16.8% in the AI group and 27.2% in the sonographer group (difference of -10.4%, 95% confidence interval: -13.2% to -7.7%, P < 0.001 for non-inferiority, P < 0.001 for superiority). The mean absolute difference between final cardiologist assessment and independent previous cardiologist assessment was 6.29% in the AI group and 7.23% in the sonographer group (difference of -0.96%, 95% confidence interval: -1.34% to -0.54%, P < 0.001 for superiority). The AI-guided workflow saved time for both sonographers and cardiologists, and cardiologists were not able to distinguish between the initial assessments by AI versus the sonographer (blinding index of 0.088). For patients undergoing echocardiographic quantification of cardiac function, initial assessment of LVEF by AI was non-inferior to assessment by sonographers.

Spatio-temporal development of the urban heat island in a socioeconomically diverse tropical city.

Urban heat islands, where temperatures are elevated relative to non-urban surrounds, are near-ubiquitous in cities globally. Yet, the magnitude and form of urban heat islands in the tropics, where heat has a large morbidity and mortality burden, is not well understood, especially for those of urban informal settlements. We used 29 years of Landsat satellite-derived surface temperature, corroborated by in situ temperature measurements, to provide a detailed spatial and temporal assessment of urban heat islands in Makassar, Indonesia, a city that is representative of rapidly growing urban settlements across the tropics. Our analysis identified surface urban heat islands of up to 9.2 °C in long-urbanised parts of the city and 6.3 °C in informal settlements, the seasonal patterns of which were driven by change in non-urban areas rather than in urban areas themselves. In recently urbanised areas, the majority of urban heat island increase occurred before land became 50% urbanised, whereas the established heat island in long-urbanised areas remained stable in response to urban expansion. Green and blue space protected some informal settlements from the worst urban heat islands observed across the city and maintenance of such space will be essential to mitigate the growing heat burden from urban expansion and anthropogenic climate change. Settlements further than 4 km from the coast and with Normalised Difference Vegetation Index (NDVI) less than 0.2 had higher surface temperatures, with modelled effects of more than 5 °C. Surface temperature measurements were representative of in situ heat exposure, measured in a subset of 12 informal settlements, where mean indoor temperature had the strongest relationship with surface temperature (R2 = 0.413, P = 0.001). We advocate for green space to be prioritised in urban planning, redevelopment and informal settlement upgrading programs, with consideration of the unique environmental and socioeconomic context of tropical cities.

Prediction of Coronary Artery Calcium Using Deep Learning of Echocardiograms.

BACKGROUND: Coronary artery calcification (CAC), often assessed by computed tomography (CT), is a powerful marker of coronary artery disease that can guide preventive therapies. Computed tomographies, however, are not always accessible or serially obtainable. It remains unclear whether other widespread tests such as transthoracic echocardiograms (TTEs) can be used to predict CAC. METHODS: Using a data set of 2,881 TTE videos paired with coronary calcium CTs, we trained a video-based artificial intelligence convolutional neural network to predict CAC scores from parasternal long-axis views. We evaluated the model's ability to classify patients from a held-out sample as well as an external site sample into zero CAC and high CAC (CAC ≥ 400 Agatston units) groups by receiver operating characteristic and precision-recall curves. We also investigated whether such classifications prognosticated significant differences in 1-year mortality rates by the log-rank test of Kaplan-Meier curves. RESULTS: Transthoracic echocardiogram artificial intelligence models had high discriminatory abilities in predicting zero CAC (receiver operating characteristic area under the curve [AUC] = 0.81 [95% CI, 0.74-0.88], F1 score = 0.95) and high CAC (AUC = 0.74 [0.68-0.8], F1 score = 0.74). This performance was confirmed in an external test data set of 92 TTEs (AUC = 0.75 [0.65-0.85], F1 score = 0.77; and AUC = 0.85 [0.76-0.93], F1 score = 0.59, respectively). Risk stratification by TTE-predicted CAC performed similarly to CT CAC scores in prognosticating significant differences in 1-year survival in high-CAC patients (CT CAC ≥ 400 vs CT CAC < 400, P = .03; TTE-predicted CAC ≥ 400 vs TTE-predicted CAC < 400, P = .02). CONCLUSIONS: A video-based deep learning model successfully used TTE videos to predict zero CAC and high CAC with high accuracy. Transthoracic echocardiography-predicted CAC prognosticated differences in 1-year survival similar to CT CAC. Deep learning of TTEs holds promise for future adjunctive coronary artery disease risk stratification to guide preventive therapies.

Confounders mediate AI prediction of demographics in medical imaging.

Deep learning has been shown to accurately assess "hidden" phenotypes from medical imaging beyond traditional clinician interpretation. Using large echocardiography datasets from two healthcare systems, we test whether it is possible to predict age, race, and sex from cardiac ultrasound images using deep learning algorithms and assess the impact of varying confounding variables. Using a total of 433,469 videos from Cedars-Sinai Medical Center and 99,909 videos from Stanford Medical Center, we trained video-based convolutional neural networks to predict age, sex, and race. We found that deep learning models were able to identify age and sex, while unable to reliably predict race. Without considering confounding differences between categories, the AI model predicted sex with an AUC of 0.85 (95% CI 0.84-0.86), age with a mean absolute error of 9.12 years (95% CI 9.00-9.25), and race with AUCs ranging from 0.63 to 0.71. When predicting race, we show that tuning the proportion of confounding variables (age or sex) in the training data significantly impacts model AUC (ranging from 0.53 to 0.85), while sex and age prediction was not particularly impacted by adjusting race proportion in the training dataset AUC of 0.81-0.83 and 0.80-0.84, respectively. This suggests significant proportion of AI's performance on predicting race could come from confounding features being detected. Further work remains to identify the particular imaging features that associate with demographic information and to better understand the risks of demographic identification in medical AI as it pertains to potentially perpetuating bias and disparities.

High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning.

IMPORTANCE: Early detection and characterization of increased left ventricular (LV) wall thickness can markedly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating causes of increased wall thickness, such as hypertrophy, cardiomyopathy, and cardiac amyloidosis. OBJECTIVE: To assess the accuracy of a deep learning workflow in quantifying ventricular hypertrophy and predicting the cause of increased LV wall thickness. DESIGN, SETTINGS, AND PARTICIPANTS: This cohort study included physician-curated cohorts from the Stanford Amyloid Center and Cedars-Sinai Medical Center (CSMC) Advanced Heart Disease Clinic for cardiac amyloidosis and the Stanford Center for Inherited Cardiovascular Disease and the CSMC Hypertrophic Cardiomyopathy Clinic for hypertrophic cardiomyopathy from January 1, 2008, to December 31, 2020. The deep learning algorithm was trained and tested on retrospectively obtained independent echocardiogram videos from Stanford Healthcare, CSMC, and the Unity Imaging Collaborative. MAIN OUTCOMES AND MEASURES: The main outcome was the accuracy of the deep learning algorithm in measuring left ventricular dimensions and identifying patients with increased LV wall thickness diagnosed with hypertrophic cardiomyopathy and cardiac amyloidosis. RESULTS: The study included 23 745 patients: 12 001 from Stanford Health Care (6509 [54.2%] female; mean [SD] age, 61.6 [17.4] years) and 1309 from CSMC (808 [61.7%] female; mean [SD] age, 62.8 [17.2] years) with parasternal long-axis videos and 8084 from Stanford Health Care (4201 [54.0%] female; mean [SD] age, 69.1 [16.8] years) and 2351 from CSMS (6509 [54.2%] female; mean [SD] age, 69.6 [14.7] years) with apical 4-chamber videos. The deep learning algorithm accurately measured intraventricular wall thickness (mean absolute error [MAE], 1.2 mm; 95% CI, 1.1-1.3 mm), LV diameter (MAE, 2.4 mm; 95% CI, 2.2-2.6 mm), and posterior wall thickness (MAE, 1.4 mm; 95% CI, 1.2-1.5 mm) and classified cardiac amyloidosis (area under the curve [AUC], 0.83) and hypertrophic cardiomyopathy (AUC, 0.98) separately from other causes of LV hypertrophy. In external data sets from independent domestic and international health care systems, the deep learning algorithm accurately quantified ventricular parameters (domestic: R2, 0.96; international: R2, 0.90). For the domestic data set, the MAE was 1.7 mm (95% CI, 1.6-1.8 mm) for intraventricular septum thickness, 3.8 mm (95% CI, 3.5-4.0 mm) for LV internal dimension, and 1.8 mm (95% CI, 1.7-2.0 mm) for LV posterior wall thickness. For the international data set, the MAE was 1.7 mm (95% CI, 1.5-2.0 mm) for intraventricular septum thickness, 2.9 mm (95% CI, 2.4-3.3 mm) for LV internal dimension, and 2.3 mm (95% CI, 1.9-2.7 mm) for LV posterior wall thickness. The deep learning algorithm accurately detected cardiac amyloidosis (AUC, 0.79) and hypertrophic cardiomyopathy (AUC, 0.89) in the domestic external validation site. CONCLUSIONS AND RELEVANCE: In this cohort study, the deep learning model accurately identified subtle changes in LV wall geometric measurements and the causes of hypertrophy. Unlike with human experts, the deep learning workflow is fully automated, allowing for reproducible, precise measurements, and may provide a foundation for precision diagnosis of cardiac hypertrophy.

Interpretable deep learning prediction of 3d assessment of cardiac function.

As deep learning plays an increasing role in making medical decisions, explainability is playing an increasing role in satisfying regulatory requirements and facilitating trust and transparency in deep learning approaches. In cardiac imaging, the task of accurately assessing left-ventricular function is crucial for evaluating patient risk, diagnosing cardiovascular disease, and clinical decision making. Previous video based methods to predict ejection fraction yield high accuracy but at the expense of explainability and did not utilize the standard clinical workflow. More explainable methods that match the clinical workflow, using 2D semantic segmentation, have been explored but found to have lower accuracy. To simultaneously increase accuracy and utilize an approach that matches the standard clinical workflow, we propose a frame-by-frame 3D depth-map approach that is both accurate (mean absolute error of 6.5%) and explainable, utilizing the conventional clinical workflow with method of discs evaluation of left ventricular volume. This method is more reproducible than human evaluation and generates volume predictions that can be interpreted by clinicians and provide the opportunity to intervene and adjust the deep learning prediction.

Sub-critical limits are viable alternatives to critical thermal limits.

Thermal traits are frequently used to explain variation in species distributions, abundance, and sensitivity to climate change. Due to their utility and ease of measurement, critical thermal limits in particular have proliferated across the ecophysiological literature. Critical limit assays can, however, have deleterious or even lethal effects on individuals and there is growing recognition that intermediate metrics of performance can provide a further, nuanced understanding of how species interact with their environments. Meanwhile, the scarcity of data describing sub-critical or voluntary limits, which have been proposed as alternatives to critical limits and can be collected under less extreme conditions, reduces their value in comparative analyses and broad-scale syntheses. To overcome these limitations and determine if sub-critical limits are viable proxies for upper and lower critical thermal limits we measured and compared the critical and sub-critical thermal limits of 2023 ants representing 51 species. Sub-critical limits in isolation were a satisfactory linear predictor for both individual and species critical limits and when species identity was also considered there were substantial gains in variance explained. These gains indicate that a species-specific conversion factor can further improve estimates of critical traits using sub-critical proxies. Sub-critical limits can, therefore, be integrated into broader syntheses of critical limits and confidently used to calculate common ecological metrics, such as warming tolerance, so long as uncertainty in estimates is explicitly acknowledged. Although lower thermal traits exhibited more variation than their upper counterparts, the stronger phylogenetic signal of lower thermal traits indicates that appropriate conversions for lower thermal traits can be inferred from congenerics or other closely related taxa.

Chronic heat stress in tropical urban informal settlements.

The health and economic impacts of extreme heat on humans are especially pronounced in populations without the means to adapt. We deployed a sensor network across 12 informal settlements in Makassar, Indonesia to measure the thermal environment that people experience inside and outside their homes. We calculated two metrics to assess the magnitude and frequency of heat stress conditions, wet bulb temperature and wet bulb globe temperature, and compared our in situ data to that collected by weather stations. We found that informal settlement residents experience chronic heat stress conditions, which are underestimated by weather stations. Wet bulb temperatures approached the uppermost limits of human survivability, and wet bulb globe temperatures regularly exceeded recommended physical activity thresholds, both in houses and outdoors. Under a warming climate, a growing number of people living informally will face potentially severe impacts from heat stress that have likely been previously overlooked or underestimated.

A planetary health model for reducing exposure to faecal contamination in urban informal settlements: Baseline findings from Makassar, Indonesia.

BACKGROUND: The intense interactions between people, animals and environmental systems in urban informal settlements compromise human and environmental health. Inadequate water and sanitation services, compounded by exposure to flooding and climate change risks, expose inhabitants to environmental contamination causing poor health and wellbeing and degrading ecosystems. However, the exact nature and full scope of risks and exposure pathways between human health and the environment in informal settlements are uncertain. Existing models are limited to microbiological linkages related to faecal-oral exposures at the individual level, and do not account for a broader range of human-environmental variables and interactions that affect population health and wellbeing. METHODS: We undertook a 12-month health and environmental assessment in 12 flood-prone informal settlements in Makassar, Indonesia. We obtained caregiver-reported health data, anthropometric measurements, stool and blood samples from children < 5 years, and health and wellbeing data for children 5-14 years and adult respondents. We collected environmental data including temperature, mosquito and rat species abundance, and water and sediment samples. Demographic, built environment and household asset data were also collected. We combined our data with existing literature to generate a novel planetary health model of health and environment in informal settlements. RESULTS: Across the 12 settlements, 593 households and 2764 participants were enrolled. Two-thirds (64·1%) of all houses (26·3-82·7% per settlement) had formal land tenure documentation. Cough, fever and diarrhoea in the week prior to the survey were reported among an average of 34.3%, 26.9% and 9.7% of children aged < 5 years, respectively; although proportions varied over time, prevalence among these youngest children was consistently higher than among children 5-14 years or adult respondents. Among children < 5 years, 44·3% experienced stunting, 41·1% underweight, 12.4% wasting, and 26.5% were anaemic. There was self- or carer-reported poor mental health among 16.6% of children aged 5-14 years and 13.9% of adult respondents. Rates of potential risky exposures from swimming in waterways, eating uncooked produce, and eating soil or dirt were high, as were exposures to flooding and livestock. Just over one third of households (35.3%) had access to municipal water, and contamination of well water with E. coli and nitrogen species was common. Most (79·5%) houses had an in-house toilet, but no houses were connected to a piped sewer network or safe, properly constructed septic tank. Median monthly settlement outdoor temperatures ranged from 26·2 °C to 29.3 °C, and were on average, 1·1 °C warmer inside houses than outside. Mosquito density varied over time, with Culex quinquefasciatus accounting for 94·7% of species. Framed by a planetary health lens, our model includes four thematic domains: (1) the physical/built environment; (2) the ecological environment; (3) human health; and (4) socio-economic wellbeing, and is structured at individual, household, settlement, and city/beyond spatial scales. CONCLUSIONS: Our planetary health model includes key risk factors and faecal-oral exposure pathways but extends beyond conventional microbiological faecal-oral enteropathogen exposure pathways to comprehensively account for a wider range of variables affecting health in urban informal settlements. It includes broader ecological interconnections and planetary health-related variables at the household, settlement and city levels. It proposes a composite framework of markers to assess water and sanitation challenges and flood risks in urban informal settlements for optimal design and monitoring of interventions.

Study design, rationale and methods of the Revitalising Informal Settlements and their Environments (RISE) study: a cluster randomised controlled trial to evaluate environmental and human health impacts of a water-sensitive intervention in informal settlements in Indonesia and Fiji.

INTRODUCTION: Increasing urban populations have led to the growth of informal settlements, with contaminated environments linked to poor human health through a range of interlinked pathways. Here, we describe the design and methods for the Revitalising Informal Settlements and their Environments (RISE) study, a transdisciplinary randomised trial evaluating impacts of an intervention to upgrade urban informal settlements in two Asia-Pacific countries. METHODS AND ANALYSIS: RISE is a cluster randomised controlled trial among 12 settlements in Makassar, Indonesia, and 12 in Suva, Fiji. Six settlements in each country have been randomised to receive the intervention at the outset; the remainder will serve as controls and be offered intervention delivery after trial completion. The intervention involves a water-sensitive approach, delivering site-specific, modular, decentralised infrastructure primarily aimed at improving health by decreasing exposure to environmental faecal contamination. Consenting households within each informal settlement site have been enrolled, with longitudinal assessment to involve health and well-being surveys, and human and environmental sampling. Primary outcomes will be evaluated in children under 5 years of age and include prevalence and diversity of gastrointestinal pathogens, abundance and diversity of antimicrobial resistance (AMR) genes in gastrointestinal microorganisms and markers of gastrointestinal inflammation. Diverse secondary outcomes include changes in microbial contamination; abundance and diversity of pathogens and AMR genes in environmental samples; impacts on ecological biodiversity and microclimates; mosquito vector abundance; anthropometric assessments, nutrition markers and systemic inflammation in children; caregiver-reported and self-reported health symptoms and healthcare utilisation; and measures of individual and community psychological, emotional and economic well-being. The study aims to provide proof-of-concept evidence to inform policies on upgrading of informal settlements to improve environments and human health and well-being. ETHICS: Study protocols have been approved by ethics boards at Monash University, Fiji National University and Hasanuddin University. TRIAL REGISTRATION NUMBER: ACTRN12618000633280; Pre-results.

Realised rather than fundamental thermal niches predict site occupancy: Implications for climate change forecasting.

Thermal performance traits are regularly used to make forecasts of the responses of ectotherms to anthropogenic environmental change, but such forecasts do not always differentiate between fundamental and realised thermal niches. Here we determine the relative extents to which variation in the fundamental and realised thermal niches accounts for current variation in species abundance and occupancy and assess the effects of niche-choice on future-climate response estimations. We investigated microclimate and macroclimate temperatures alongside abundance, occupancy, critical thermal limits and foraging activity of 52 ant species (accounting for >95% individuals collected) from a regional assemblage from across the Western Cape Province, South Africa, between 2003 and 2014. Capability of a species to occupy sites experiencing the most extreme temperatures, coupled with breadth of realised niche, explained most deviance in occupancy (up to 75%), while foraging temperature range and body mass explained up to 50.5% of observed variation in mean species abundance. When realised niches are used to forecast responses to climate change, large positive and negative effects among species are predicted under future conditions, in contrast to the forecasts of minimal impacts on all species that are indicated by fundamental niche predictions.

Tracheal branching in ants is area-decreasing, violating a central assumption of network transport models.

The structure of tubular transport networks is thought to underlie much of biological regularity, from individuals to ecosystems. A core assumption of transport network models is either area-preserving or area-increasing branching, such that the summed cross-sectional area of all child branches is equal to or greater than the cross-sectional area of their respective parent branch. For insects, the most diverse group of animals, the assumption of area-preserving branching of tracheae is, however, based on measurements of a single individual and an assumption of gas exchange by diffusion. Here we show that ants exhibit neither area-preserving nor area-increasing branching in their abdominal tracheal systems. We find for 20 species of ants that the sum of child tracheal cross-sectional areas is typically less than that of the parent branch (area-decreasing). The radius, rather than the area, of the parent branch is conserved across the sum of child branches. Interpretation of the tracheal system as one optimized for the release of carbon dioxide, while readily catering to oxygen demand, explains the branching pattern. Our results, together with widespread demonstration that gas exchange in insects includes, and is often dominated by, convection, indicate that for generality, network transport models must include consideration of systems with different architectures.

A widespread thermodynamic effect, but maintenance of biological rates through space across life's major domains.

For over a century, the hypothesis of temperature compensation, the maintenance of similar biological rates in species from different thermal environments, has remained controversial. An alternative idea, that fitness is greater at higher temperatures (the thermodynamic effect), has gained increasing traction. This alternative hypothesis is also being used to understand large-scale biodiversity responses to environmental change. Yet evidence in favour of each of these contrasting hypotheses continues to emerge. In consequence, the fundamental nature of organismal thermal responses and its implications remain unresolved. Here, we investigate these ideas explicitly using a global dataset of 619 observations of four categories of organismal performance, spanning 14 phyla and 403 species. In agreement with both hypotheses, we show a positive relationship between the temperature of maximal performance rate (Topt) and environmental temperature (Tenv) for developmental rate and locomotion speed, but not growth or photosynthesis rate. Next, we demonstrate that relationships between Tenv and the maximal performance rate (Umax) are rarely significant and positive, as expected if a thermodynamic effect predominates. By contrast, a positive relationship between Topt and Umax is always present, but markedly weaker than theoretically predicted. These outcomes demonstrate that while some form of thermodynamic effect exists, ample scope is present for biochemical and physiological adaptation to thermal environments in the form of temperature compensation.

High resolution temperature data for ecological research and management on the Southern Ocean Islands.

Southern Ocean Islands are globally significant conservation areas. Predicting how their terrestrial ecosystems will respond to current and forecast climate change is essential for their management and requires high-quality temperature data at fine spatial resolutions. Existing datasets are inadequate for this purpose. Remote-sensed land surface temperature (LST) observations, such as those collected by satellite-mounted spectroradiometers, can provide high-resolution, spatially-continuous data for isolated locations. These methods require a clear sightline to measure surface conditions, however, which can leave large data-gaps in temperature time series. Using a spatio-temporal gap-filling method applied to high-resolution (~1 km) LST observations for 20 Southern Ocean Islands, we compiled a complete monthly temperature dataset for a 15-year period (2001-2015). We validated results using in situ measurements of microclimate temperature. Gap-filled temperature observations described the thermal heterogeneity of the region better than existing climatology datasets, particularly for islands with steep elevational gradients and strong prevailing winds. This dataset will be especially useful for terrestrial ecologists, conservation biologists, and for developing island-specific management and mitigation strategies for environmental change.