Publications by authors named "M Zimmermann"

2,121 Publications

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Methodological Advances to Study Contaminant Biotransformation: New Prospects for Understanding and Reducing Environmental Persistence?

ACS ES T Water 2021 Jul 24;1(7):1541-1554. Epub 2021 Jun 24.

Chair of Urban Water Systems Engineering, Technical University of Munich, 85748 Garching, Germany.

Complex microbial communities in environmental systems play a key role in the detoxification of chemical contaminants by transforming them into less active metabolites or by complete mineralization. Biotransformation, i.e., transformation by microbes, is well understood for a number of priority pollutants, but a similar level of understanding is lacking for many emerging contaminants encountered at low concentrations and in complex mixtures across natural and engineered systems. Any advanced approaches aiming to reduce environmental exposure to such contaminants (e.g., novel engineered biological water treatment systems, design of readily degradable chemicals, or improved regulatory assessment strategies to determine contaminant persistence ) will depend on understanding the causal links among contaminant removal, the key driving agents of biotransformation at low concentrations (i.e., relevant microbes and their metabolic activities), and how their presence and activity depend on environmental conditions. In this Perspective, we present the current understanding and recent methodological advances that can help to identify such links, even in complex environmental microbiomes and for contaminants present at low concentrations in complex chemical mixtures. We discuss the ensuing insights into contaminant biotransformation across varying environments and conditions and ask how much closer we have come to designing improved approaches to reducing environmental exposure to contaminants.
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http://dx.doi.org/10.1021/acsestwater.1c00025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276273PMC
July 2021

Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization.

Nat Commun 2021 07 14;12(1):4315. Epub 2021 Jul 14.

Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany.

Unmasking the decision making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we demonstrate that adversarially trained models can significantly enhance the usability of pathology detection as compared to their standard counterparts. We let six experienced radiologists rate the interpretability of saliency maps in datasets of X-rays, computed tomography, and magnetic resonance imaging scans. Significant improvements are found for our adversarial models, which are further improved by the application of dual-batch normalization. Contrary to previous research on adversarially trained models, we find that accuracy of such models is equal to standard models, when sufficiently large datasets and dual batch norm training are used. To ensure transferability, we additionally validate our results on an external test set of 22,433 X-rays. These findings elucidate that different paths for adversarial and real images are needed during training to achieve state of the art results with superior clinical interpretability.
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http://dx.doi.org/10.1038/s41467-021-24464-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8280105PMC
July 2021

Breed and heterotic effects for mature weight in beef cattle.

J Anim Sci 2021 Jul 14. Epub 2021 Jul 14.

Department of Animal Science, University of Nebraska - Lincoln, Lincoln, Nebraska, United States.

Mature cow weight (MWT) is heritable and affects the costs and efficiency of a breeding operation. Cow weight is also influenced by the environment, and the relationship between the size and profitability of a cow varies depending on production system. Producers therefore need tools to incorporate MWT in their selection of cattle breeds and herd replacements. The objective of this study was to estimate breed and heterotic effects for MWT using weight-age data on crossbred cows. Cow's MWT at 6 yr was predicted from the estimated parameter values-asymptotic weight and maturation constant (k)-from the fit of the Brody function to their individual data. Values were obtained for 5,156 crossbred cows from the U.S. Meat Animal Research Center (USMARC) Germplasm Evaluation Program using 108,957 weight records collected from approximately weaning up to 6 yr of age. The cows were produced from crosses among 18 beef breeds. A bivariate animal model was fitted to the MWT and k obtained for each cow. The fixed effects were birth year-season contemporary group, and covariates of direct and maternal breed fractions, direct and maternal heterosis, and age at final weighing. The random effects were direct additive and residual. A maternal additive random effect also was fitted for k. In a separate analysis from that used to estimate breed effects and (co)variances, cow MWT was regressed on sire yearling weight (YWT) EPD by its addition as a covariate to the animal model fitted for MWT. That regression coefficient was then used to adjust breed solutions for sire selection in the USMARC herd. Direct heterosis was 15.3 ± 2.6 kg for MWT and 0.000118 ± 0.000029 d-1 for k. Maternal heterosis was -5.7 ± 3.0 kg for MWT and 0.000130 ± 0.000035 d-1 for k. Direct additive heritabilities were 0.56 ± 0.03 for MWT and 0.23 ± 0.03 for k. The maternal additive heritability for k was 0.11 ± 0.02. The direct additive correlation between MWT and k was negligible (0.08 ± 0.09). Adjusted for sire sampling, Angus was heaviest at maturity of the breeds compared. Deviations from Angus ranged from -8.9 kg (Charolais) to 136.7 kg (Braunvieh). Ordered by decreasing MWT, the breeds ranked Angus, Charolais, Hereford, Brahman, Salers, Santa Gertrudis, Simmental, Maine Anjou, Limousin, Red Angus, Brangus, Chiangus, Shorthorn, Gelbvieh, Beefmaster, and Braunvieh. These breed effects for MWT can inform breeding programs where cow size is considered a key component of overall profitability.
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http://dx.doi.org/10.1093/jas/skab209DOI Listing
July 2021

Inactivation of the Euchromatic Histone-Lysine -Methyltransferase 2 Pathway in Pancreatic Epithelial Cells Antagonizes Cancer Initiation and Pancreatitis-Associated Promotion by Altering Growth and Immune Gene Expression Networks.

Front Cell Dev Biol 2021 23;9:681153. Epub 2021 Jun 23.

Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, United States.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive, painful disease with a 5-year survival rate of only 9%. Recent evidence indicates that distinct epigenomic landscapes underlie PDAC progression, identifying the H3K9me pathway as important to its pathobiology. Here, we delineate the role of Euchromatic Histone-lysine -Methyltransferase 2 (EHMT2), the enzyme that generates H3K9me, as a downstream effector of oncogenic KRAS during PDAC initiation and pancreatitis-associated promotion. inactivation in pancreatic cells reduces H3K9me2 and antagonizes Kras -mediated acinar-to-ductal metaplasia (ADM) and Pancreatic Intraepithelial Neoplasia (PanIN) formation in both the and mouse models. acinar explants also show impaired EGFR-KRAS-MAPK pathway-mediated ADM upon deletion. Notably, Kras increases EHMT2 protein levels and EHMT2-EHMT1-WIZ complex formation. Transcriptome analysis reveals that inactivation upregulates a cell cycle inhibitory gene expression network that converges on the pathway. Congruently, pancreas tissue from animals with inactivation have increased P21 protein levels and enhanced senescence. Furthermore, loss of reduces inflammatory cell infiltration typically induced during Kras -mediated initiation. The inhibitory effect on Kras -induced growth is maintained in the pancreatitis-accelerated model, while simultaneously modifying immunoregulatory gene networks that also contribute to carcinogenesis. This study outlines the existence of a novel KRAS-EHMT2 pathway that is critical for mediating the growth-promoting and immunoregulatory effects of this oncogene , extending human observations to support a pathophysiological role for the H3K9me pathway in PDAC.
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http://dx.doi.org/10.3389/fcell.2021.681153DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8261250PMC
June 2021

Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia.

Clin Imaging 2021 Jul 1;80:58-66. Epub 2021 Jul 1.

Azienda Ospedaliera Universitaria di Cagliari, Cagliari, Italy.

Purpose: Comparison of deep learning algorithm, radiomics and subjective assessment of chest CT for predicting outcome (death or recovery) and intensive care unit (ICU) admission in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

Methods: The multicenter, ethical committee-approved, retrospective study included non-contrast-enhanced chest CT of 221 SARS-CoV-2 positive patients from Italy (n = 196 patients; mean age 64 ± 16 years) and Denmark (n = 25; mean age 69 ± 13 years). A thoracic radiologist graded presence, type and extent of pulmonary opacities and severity of motion artifacts in each lung lobe on all chest CTs. Thin-section CT images were processed with CT Pneumonia Analysis Prototype (Siemens Healthineers) which yielded segmentation masks from a deep learning (DL) algorithm to derive features of lung abnormalities such as opacity scores, mean HU, as well as volume and percentage of all-attenuation and high-attenuation (opacities >-200 HU) opacities. Separately, whole lung radiomics were obtained for all CT exams. Analysis of variance and multiple logistic regression were performed for data analysis.

Results: Moderate to severe respiratory motion artifacts affected nearly one-quarter of chest CTs in patients. Subjective severity assessment, DL-based features and radiomics predicted patient outcome (AUC 0.76 vs AUC 0.88 vs AUC 0.83) and need for ICU admission (AUC 0.77 vs AUC 0.0.80 vs 0.82). Excluding chest CT with motion artifacts, the performance of DL-based and radiomics features improve for predicting ICU admission.

Conclusion: DL-based and radiomics features of pulmonary opacities from chest CT were superior to subjective assessment for differentiating patients with favorable and adverse outcomes.
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http://dx.doi.org/10.1016/j.clinimag.2021.06.036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247202PMC
July 2021