Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning

Radiologists are in short supply globally, and deep learning models offer a promising solution to address this shortage as part of clinical decision-support systems. However, training such models often requires expensive and time-consuming manual labeling of large datasets. Automatic label extraction from radiology reports can reduce the time required to obtain labeled datasets, but this task is challenging due to semantically similar words and missing annotated data. In this work, we explore the potential of weak supervision of a deep learning-based label prediction model, using a rule-based labeler. We propose a deep learning-based CheXpert label prediction model, pre-trained on reports labeled by a rule-based German CheXpert model and fine-tuned on a small dataset of manually labeled reports. Our results demonstrate the effectiveness of our approach, which significantly outperformed the rule-based model on all three tasks. Our findings highlight the benefits of employing deep learning-based models even in scenarios with sparse data and the use of the rule-based labeler as a tool for weak supervision

Drought-Induced Accumulation of Root Exudates Supports Post-drought Recovery of Microbes in Mountain Grassland

Droughts strongly affect carbon and nitrogen cycling in grasslands, with consequences for ecosystem productivity. Therefore, we investigated how experimental grassland communities interact with groups of soil microorganisms. In particular, we explored the mechanisms of the drought-induced decoupling of plant photosynthesis and microbial carbon cycling and its recovery after rewetting. Our aim was to better understand how root exudation during drought is linked to pulses of soil microbial activity and changes in plant nitrogen uptake after rewetting. We set up a mesocosm experiment on a meadow site and used shelters to simulate drought. We performed two 13C-CO2 pulse labelings, the first at peak drought and the second in the recovery phase, and traced the flow of assimilates into the carbohydrates of plants and the water extractable organic carbon and microorganisms from the soil. Total microbial tracer uptake in the main metabolism was estimated by chloroform fumigation extraction, whereas the lipid biomarkers were used to assess differences between the microbial groups. Drought led to a reduction of aboveground versus belowground plant growth and to an increase of 13C tracer contents in the carbohydrates, particularly in the roots. Newly assimilated 13C tracer unexpectedly accumulated in the water-extractable soil organic carbon, indicating that root exudation continued during the drought. In contrast, drought strongly reduced the amount of 13C tracer assimilated into the soil microorganisms. This reduction was more severe in the growth-related lipid biomarkers than in the metabolic compounds, suggesting a slowdown of microbial processes at peak drought. Shortly after rewetting, the tracer accumulation in the belowground plant carbohydrates and in the water-extractable soil organic carbon disappeared. Interestingly, this disappearance was paralleled by a quick recovery of the carbon uptake into metabolic and growth-related compounds from the rhizospheric microorganisms, which was probably related to the higher nitrogen supply to the plant shoots. We conclude that the decoupling of plant photosynthesis and soil microbial carbon cycling during drought is due to reduced carbon uptake and metabolic turnover of rhizospheric soil microorganisms. Moreover, our study suggests that the maintenance of root exudation during drought is connected to a fast reinitiation of soil microbial activity after rewetting, supporting plant recovery through increased nitrogen availability

Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training

OBJECTIVES Diagnostic accuracy of artificial intelligence (AI) pneumothorax (PTX) detection in chest radiographs (CXR) is limited by the noisy annotation quality of public training data and confounding thoracic tubes (TT). We hypothesize that in-image annotations of the dehiscent visceral pleura for algorithm training boosts algorithm's performance and suppresses confounders. METHODS Our single-center evaluation cohort of 3062 supine CXRs includes 760 PTX-positive cases with radiological annotations of PTX size and inserted TTs. Three step-by-step improved algorithms (differing in algorithm architecture, training data from public datasets/clinical sites, and in-image annotations included in algorithm training) were characterized by area under the receiver operating characteristics (AUROC) in detailed subgroup analyses and referenced to the well-established \textquotedblCheXNet\textquotedbl algorithm. RESULTS Performances of established algorithms exclusively trained on publicly available data without in-image annotations are limited to AUROCs of 0.778 and strongly biased towards TTs that can completely eliminate algorithm's discriminative power in individual subgroups. Contrarily, our final \textquotedblalgorithm 2\textquotedbl which was trained on a lower number of images but additionally with in-image annotations of the dehiscent pleura achieved an overall AUROC of 0.877 for unilateral PTX detection with a significantly reduced TT-related confounding bias. CONCLUSIONS We demonstrated strong limitations of an established PTX-detecting AI algorithm that can be significantly reduced by designing an AI system capable of learning to both classify and localize PTX. Our results are aimed at drawing attention to the necessity of high-quality in-image localization in training data to reduce the risks of unintentionally biasing the training process of pathology-detecting AI algorithms. KEY POINTS • Established pneumothorax-detecting artificial intelligence algorithms trained on public training data are strongly limited and biased by confounding thoracic tubes. • We used high-quality in-image annotated training data to effectively boost algorithm performance and suppress the impact of confounding thoracic tubes. • Based on our results, we hypothesize that even hidden confounders might be effectively addressed by in-image annotations of pathology-related image features

Clinically focused multi-cohort benchmarking as a tool for external validation of artificial intelligence algorithm performance in basic chest radiography analysis

Artificial intelligence (AI) algorithms evaluating [supine] chest radiographs ([S]CXRs) have remarkably increased in number recently. Since training and validation are often performed on subsets of the same overall dataset, external validation is mandatory to reproduce results and reveal potential training errors. We applied a multicohort benchmarking to the publicly accessible (S)CXR analyzing AI algorithm CheXNet, comprising three clinically relevant study cohorts which differ in patient positioning ([S]CXRs), the applied reference standards (CT-/[S]CXR-based) and the possibility to also compare algorithm classification with different medical experts’ reading performance. The study cohorts include [1] a cohort, characterized by 563 CXRs acquired in the emergency unit that were evaluated by 9 readers (radiologists and non-radiologists) in terms of 4 common pathologies, [2] a collection of 6,248 SCXRs annotated by radiologists in terms of pneumothorax presence, its size and presence of inserted thoracic tube material which allowed for subgroup and confounding bias analysis and [3] a cohort consisting of 166 patients with SCXRs that were evaluated by radiologists for underlying causes of basal lung opacities, all of those cases having been correlated to a timely acquired computed tomography scan (SCXR and CT within < 90 min). CheXNet non-significantly exceeded the radiology resident (RR) consensus in the detection of suspicious lung nodules (cohort [1], AUC AI/RR: 0.851/0.839, p = 0.793) and the radiological readers in the detection of basal pneumonia (cohort [3], AUC AI/reader consensus: 0.825/0.782, p = 0.390) and basal pleural effusion (cohort [3], AUC AI/reader consensus: 0.762/0.710, p = 0.336) in SCXR, partly with AUC values higher than originally published (“Nodule”: 0.780, “Infiltration”: 0.735, “Effusion”: 0.864). The classifier “Infiltration” turned out to be very dependent on patient positioning (best in CXR, worst in SCXR). The pneumothorax SCXR cohort [2] revealed poor algorithm performance in CXRs without inserted thoracic material and in the detection of small pneumothoraces, which can be explained by a known systematic confounding error in the algorithm training process. The benefit of clinically relevant external validation is demonstrated by the differences in algorithm performance as compared to the original publication. Our multi-cohort benchmarking finally enables the consideration of confounders, different reference standards and patient positioning as well as the AI performance comparison with differentially qualified medical readers

Leaf-level metabolic changes in response to drought affect daytime CO2 emission and isoprenoid synthesis pathways

In the near future, climate change will cause enhanced frequency and/or severity of droughts in terrestrial ecosystems, including tropical forests. Drought responses by tropical trees may affect their carbon use, including production of volatile organic compounds (VOCs), with implications for carbon cycling and atmospheric chemistry that are challenging to predict. It remains unclear how metabolic adjustments by mature tropical trees in response to drought will affect their carbon fluxes associated with daytime CO2 production and VOC emission. To address this gap, we used position-specific 13C-pyruvate labeling to investigate leaf CO2 and VOC fluxes from four tropical species before and during a controlled drought in the enclosed rainforest of Biosphere 2 (B2). Overall, plants that were more drought-sensitive had greater reductions in daytime CO2 production. Although daytime CO2 production was always dominated by non-mitochondrial processes, the relative contribution of CO2 from the tricarboxylic acid cycle tended to increase under drought. A notable exception was the legume tree Clitoria fairchildiana R.A. Howard, which had less anabolic CO2 production than the other species even under pre-drought conditions, perhaps due to more efficient refixation of CO2 and anaplerotic use for amino acid synthesis. The C. fairchildiana was also the only species to allocate detectable amounts of 13C label to VOCs and was a major source of VOCs in B2. In C. fairchildiana leaves, our data indicate that intermediates from the mevalonic acid (MVA) pathway are used to produce the volatile monoterpene trans-β-ocimene, but not isoprene. This apparent crosstalk between the MVA and methylerythritol phosphate pathways for monoterpene synthesis declined with drought. Finally, although trans-β-ocimene emissions increased under drought, it was increasingly sourced from stored intermediates and not de novo synthesis. Unique metabolic responses of legumes may play a disproportionate role in the overall changes in daytime CO2 and VOC fluxes in tropical forests experiencing drought

Vorrichtung und Verfahren zum Schutz von außenexponierten Objekten

Die Erfindung betrifft eine Vorrichtung (1), zum Schutz von außenexponierten Objekten, mit zumindest einer Bodenmanschette (2), welche dazu eingerichtet ist, das zu schützende Objekt am Boden umlaufend zu umgeben und Bodenfeuchtigkeit abzuhalten, und mit einer Hülle aus Membranwerkstoff (3), welche dazu eingerichtet ist, das zu schützende Objekt von oben und von allen Seiten zu umgeben und welche an der Bodenmanschette (2) reversibel befestigbar ist und mit der Bodenmanschette (2) so abschließt, dass kein Niederschlag eindringen kann, wobei die Hülle aus Membranwerkstoff (3) transparent oder transluzent ist und die Vorrichtung (1) mindestens eine erste Lüftungsöffnung (4) für die Zuluft (5) und mindestens eine zweite Lüftungsöffnung (6) für die Abluft (7) aufweist. Weiterhin betrifft die Erfindung ein Verfahren zum Schutz von außenexponierten Objekten vor nachteiligen Witterungseinflüssen

Data from: Land use in mountain grasslands alters drought response and recovery of carbon allocation and plant-microbial interactions

1. Mountain grasslands have recently been exposed to substantial changes in land-use and climate and in the near future will likely face an increased frequency of extreme droughts. To date is not known how the drought responses of carbon (C) allocation, a key process in the C cycle, are affected by land-use changes in mountain grassland. 2. We performed an experimental summer drought on an abandoned grassland and a traditionally managed hay meadow and traced the fate of recent assimilates through the plant-soil continuum. We applied two 13CO2 pulses, at peak drought and in the recovery phase shortly after rewetting. 3. Drought decreased total C uptake in both grassland types and led to a loss of aboveground carbohydrate storage pools. The belowground C allocation to root sucrose was enhanced by drought, especially in the meadow, which also held larger root carbohydrate storage pools. 4. The microbial community of the abandoned grassland comprised more saprotrophic fungal and Gram (+) bacterial markers compared to the meadow. Drought increased the newly introduced AM and saprotrophic fungi:bacteria ratio in both grassland types. At peak drought the 13C transfer into AM fungi, saprotrophic fungi and Gram (-) bacteria was more strongly reduced in the meadow than in the abandoned grassland, which contrasted the patterns of the root carbohydrate pools. 5. In both grassland types the C allocation largely recovered after rewetting. Slowest recovery was found for AM fungi and their 13C uptake. In contrast, all bacterial markers quickly recovered C uptake. In the meadow, where plant nitrate uptake was enhanced after drought, C uptake was even higher than in control plots. 6. Synthesis. Our results suggest that resistance and resilience (i.e. recovery) of plant C dynamics and plant-microbial interactions are negatively related, i.e. high resistance is followed by slow recovery and vice versa. The abandoned grassland was more resistant to drought than the meadow and possibly had a stronger link to AM fungi that could have provided better access to water through the hyphal network. In contrast, meadow communities strongly reduced C allocation to storage and C transfer to the microbial community in the drought phase, but in the recovery phase invested C resources in the bacterial communities to gain more nutrients for regrowth. We conclude that management of mountain grasslands increases their resilience to drought

Land use and drought effects on grassland C dynamics

Data were collected from a common garden drought experiment in the Austrian Central Alps using intact vegetation-soil monoliths from an extensively managed meadow and a nearby abandoned grassland. The monoliths were installed in summer 2013 on the meadow site and pre-incubated until drought simulation was started in early summer 2014. The effects of land use on the response of plant C allocation and plant-microbial C transfer to drought and rewetting were studied by conducting two 13C pulse-chase labelling campaigns. A description of all data and abbreviations used in the excel file can be found in tab "0-README"

Data from: Land use in mountain grasslands alters drought response and recovery of carbon allocation and plant-microbial interactions

1. Mountain grasslands have recently been exposed to substantial changes in land-use and climate and in the near future will likely face an increased frequency of extreme droughts. To date is not known how the drought responses of carbon (C) allocation, a key process in the C cycle, are affected by land-use changes in mountain grassland. 2. We performed an experimental summer drought on an abandoned grassland and a traditionally managed hay meadow and traced the fate of recent assimilates through the plant-soil continuum. We applied two 13CO2 pulses, at peak drought and in the recovery phase shortly after rewetting. 3. Drought decreased total C uptake in both grassland types and led to a loss of aboveground carbohydrate storage pools. The belowground C allocation to root sucrose was enhanced by drought, especially in the meadow, which also held larger root carbohydrate storage pools. 4. The microbial community of the abandoned grassland comprised more saprotrophic fungal and Gram (+) bacterial markers compared to the meadow. Drought increased the newly introduced AM and saprotrophic fungi:bacteria ratio in both grassland types. At peak drought the 13C transfer into AM fungi, saprotrophic fungi and Gram (-) bacteria was more strongly reduced in the meadow than in the abandoned grassland, which contrasted the patterns of the root carbohydrate pools. 5. In both grassland types the C allocation largely recovered after rewetting. Slowest recovery was found for AM fungi and their 13C uptake. In contrast, all bacterial markers quickly recovered C uptake. In the meadow, where plant nitrate uptake was enhanced after drought, C uptake was even higher than in control plots. 6. Synthesis. Our results suggest that resistance and resilience (i.e. recovery) of plant C dynamics and plant-microbial interactions are negatively related, i.e. high resistance is followed by slow recovery and vice versa. The abandoned grassland was more resistant to drought than the meadow and possibly had a stronger link to AM fungi that could have provided better access to water through the hyphal network. In contrast, meadow communities strongly reduced C allocation to storage and C transfer to the microbial community in the drought phase, but in the recovery phase invested C resources in the bacterial communities to gain more nutrients for regrowth. We conclude that management of mountain grasslands increases their resilience to drought