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

The effect of natural organic matter on the adsorption of mercury to bacterial cells

We investigated the ability of non-metabolizing Bacillus subtilis, Shewanella oneidensis MR-1, and Geobacter sulfurreducens bacterial species to adsorb mercury in the absence and presence of Suwanee River fulvic acid (FA). Bulk adsorption and X-ray absorption spectroscopy (XAS) experiments were conducted at three pH conditions, and the results indicate that the presence of FA decreases the extent of Hg adsorption to biomass under all of the pH conditions studied. Hg XAS results show that the presence of FA does not alter the binding environment of Hg adsorbed onto the biomass regardless of pH or FA concentration, indicating that ternary bacteria–Hg–FA complexes do not form to an appreciable extent under the experimental conditions, and that Hg binding on the bacteria is dominated by sulfhydryl binding. We used the experimental results to calculate apparent partition coefficients, Kd, for Hg under each experimental condition. The calculations yield similar coefficients for Hg onto each of the bacterial species studies, suggesting there is no significant difference in Hg partitioning between the three bacterial species. The calculations also indicate similar coefficients for Hg–bacteria and Hg–FA complexes. S XAS measurements confirm the presence of sulfhydryl sites on both the FA and bacterial cells, and demonstrate the presence of a wide range of S moieties on the FA in contrast to the bacterial biomass, whose S sites are dominated by thiols. Our results suggest that although FA can compete with bacterial binding sites for aqueous Hg, because of the relatively similar partition coefficients for the types of sorbents, the competition is not dominated by either bacteria or FA unless the concentration of one type of site greatly exceeds that of the other

Lignin biomarkers as tracers of mercury sources in lakes water column

This study presents the role of specific terrigenous organic compounds as important vectors of mercury (Hg) transported from watersheds to lakes of the Canadian boreal forest. In order to differentiate the autochthonous from the allochthonous organic matter (OM), lignin derived biomarker signatures [Lambda, S/V, C/V, P/(V ? S), 3,5-Bd/V and (Ad/Al)v] were used. Since lignin is exclusively produced by terrigenous plants, this approach can give a non equivocal picture of the watershed inputs to the lakes. Moreover, it allows a characterization of the source of OM and its state of degradation. The water column of six lakes from the Canadian Shield was sampled monthly between June and September 2005. Lake total dissolved Hg concentrations and Lambda were positively correlated, meaning that Hg and ligneous inputs are linked (dissolved OM r2 = 0.62, p\0.0001; particulate OM r2 = 0.76, p\0.0001). Ratios of P/(V ? S) and 3,5-Bd/V from both dissolved OM and particulate OM of the water column suggest an inverse relationship between the progressive state of pedogenesis and maturation of the OM in soil before entering the lake, and the Hg concentrations in the water column. No relation was found between Hg levels in the lakes and the watershed flora composition—angiosperm versus gymnosperm or woody versus non-woody compounds. This study has significant implications for watershed management of ecosystems since limiting fresh terrestrial OM inputs should reduce Hg inputs to the aquatic systems. This is particularly the case for largescale land-use impacts, such as deforestation, agriculture and urbanization, associated to large quantities of soil OM being transferred to aquatic systems

The impact of natural and anthropogenic Dissolved Organic Carbon (DOC), and pH on the toxicity of triclosan to the crustacean Gammarus pulex (L.).

Regulatory ecotoxicology testing rarely accounts for the influence of natural water chemistry on the bioavailability and toxicity of a chemical. Therefore, this study identifies whether key omissions in relation to Dissolved Organic Carbon (DOC) and pH have an impact on measured effect concentrations (EC). Laboratory ecotoxicology tests were undertaken for the widely used antimicrobial compound triclosan, using adult Gammarus pulex (L.), a wild-type amphipod using synthetic fresh water, humic acid solutions and wastewater treatment works effluent. The toxicity of triclosan was tested at two different pHs of 7.3 and 8.4, with and without the addition of DOC and 24 and 48hour EC values with calculated 95% confidence intervals calculated. Toxicity tests undertaken at a pH above triclosan's pKa and in the presents of humic acid and effluent, containing 11 and 16mgL(-1) mean DOC concentrations respectively, resulted in significantly decreased triclosan toxicity. This was most likely a result of varying triclosan speciation and complexation due to triclosan's pKa and high hydrophobicity controlling its bioavailability. The mean 48hour EC50 values varied between 0.75±0.45 and 1.93±0.12mgL(-1) depending on conditions. These results suggest that standard ecotoxicology tests can cause inaccurate estimations of triclosan's bioavailability and subsequent toxicity in natural aquatic environments. These results highlight the need for further consideration regarding the role that water chemistry has on the toxicity of organic contaminants and how ambient environmental conditions are incorporated into the standard setting and consenting processes in the future

Stoichiometry of mercury-thiol complexes on bacterial cell envelopes

We have examined the speciation of Hg(II) complexed with intact cell suspensions (1013 cells L− 1) of Bacillus subtilis, a common gram-positive soil bacterium, Shewanella oneidensis MR-1, a facultative gram-negative aquatic organism, and Geobacter sulfurreducens, a gram-negative anaerobic bacterium capable of Hg-methylation at Hg(II) loadings spanning four orders of magnitude (120 nM to 350 μM) at pH 5.5 (± 0.2). The coordination environments of Hg on bacterial cells were analyzed using synchrotron based X-ray Absorption Near Edge Structure (XANES) and Extended X-ray Absorption Fine Structure (EXAFS) spectroscopy at the Hg LIII edge. The abundance of thiols on intact cells was determined by a fluorescence-spectroscopy based method using a soluble bromobimane, monobromo(trimethylammonio)bimane (qBBr) to block thiol sites, and potentiometric titrations of biomass with and without qBBr treatment. The chemical forms of S on intact bacterial cells were determined using S k-edge XANES spectroscopy. Hg(II) was found to complex entirely with cell bound thiols at low Hg:biomass ratios. For Bacillus subtilis and Shewanella oneidensis MR-1 cells, the Hg—S stoichiometry changed from Hg—S₃ to Hg—S₂ and Hg—S (where ‘S’ represents a thiol site such as is present on cysteine) progressively as the Hg(II) loading increased on the cells. However, Geobacter sulfurreducens did not form Hg—S₃ complexes. Because the abundance of thiol was highest for Geobacter sulfurreducens (75 μM/g wet weight) followed by Shewanella oneidensis MR-1 (50 μM/g wet weight) and Bacillus subtilis (25 μM/g wet weight), the inability of Hg(II) to form Hg—S₃ complexes on Geobacter sulfurreducens suggests that the density and reactivity of S-amino acid containing cell membrane proteins on Geobacter sulfurreducens are different from those of Bacillus subtilis and Shewanella oneidensis MR-1. Upon saturation of the high affinity thiol sites at higher Hg:biomass ratios, Hg(II) was found to form a chelate with α-hydroxy carboxylate anion. The stoichiometry of cell envelope bound Hg-thiol complexes and the associated abundance of thiols on the cell envelopes provide important insights for understanding the differences in the rate and extent of uptake and redox transformations of Hg in the environment

Checklist of the subfamily Adoncholaiminae Gerlach and Riemann, 1974 (Nematoda: Oncholaimida: Oncholaimidae) of the world: genera, species, distribution, and reference list for taxonomists and ecologists

Adoncholaiminae is one of the seven subfamilies in the free-living aquatic nematode family Oncholaimidae. Nematodes in Adoncholaiminae are found from various water environment of the world. However, a checklist of all Adoncholaiminae species including full literature, especially information of experimental (not taxonomic) works, has not been updated for more than 40 years. A revised checklist of the subfamily Adoncholaiminae of the world is provided. It contains 31 valid and 13 invalid species names in four genera with synonyms, collection records, and full literature from 1860's to 2015 for each species. A literature survey of total 477 previous papers was conducted in this work, and 362 of them are newly added to checklist

German Character Recognition Dataset

The dataset contains 282,472 grayscale images, each measuring 40 x 40 pixels, depicting a diverse range of 82 distinct German characters, digits and mathematical symbols. In contrast to the MNIST dataset, where image alignment varies, all the images in this dataset are perfectly aligned. They are centered within a 40 x 40 bounding box, ensuring they touch either the left and right sides or the top and bottom borders. This alignment significantly simplifies the training task, leading to excellent performance metrics. The training and testing data is stored in two separate CSV files. In each file, the first column represents the Unicode character, while the subsequent 1600 values correspond to the grayscale values of the flattened image. If you find any aspect unclear, please refer to our attached code, which offers a comprehensive logic for training a CNN in PyTorch. You can easily select the specific classes on which you intend to train. Notably, when exclusively training on the digits from 0 to 9, we achieved an impressive accuracy and Matthews Correlation Coefficient (MCC) of roughly 99% on the test data