The role of flood wave superposition in the severity of large floods

The severity of floods is shaped not only by eventand catchment-specific characteristics but also depends on the river network configuration. At the confluence of relevant tributaries with the main river, flood event characteristics may change depending on the magnitude and temporal match of flood waves. This superposition of flood waves may potentially increase the flood severity downstream in the main river. However, this aspect has not been analysed for a large set of river confluences to date. To fill this gap, the role of flood wave superposition in the flood severity at downstream gauges is investigated in four large river basins in Germany and Austria (the Elbe, the Danube, the Rhine and the Weser). A novel methodological approach to analyse flood wave superposition is presented and applied to mean daily discharge data from 37 triple points. A triple point consists of three gauges: one in the tributary as well as one upstream and downstream of the confluence with the main river respectively. At the triple points, differences and similarities in flood wave characteristics between the main river and the tributary are analysed in terms of the temporal match and the magnitudes of flood peaks. At many of the confluences analysed, the tributary peaks consistently arrive earlier than the main river peaks, although high variability in the time lag is generally detected. No large differences in temporal matching are detected for floods of different magnitudes. In the majority of cases, the largest floods at the downstream gauge do not occur due to perfect temporal match between the tributary and the main river. In terms of spatial variability, the impact of flood wave superposition is site-specific. Characteristic patterns of flood wave superposition are detected for flood peaks in the Danube River, where peak discharges largely increase due to inflow from alpine tributaries. Overall, we conclude that the superposition of flood waves is not the driving factor behind flood peak severity at the major confluences in Germany; however, a few confluences show the potential for strong flood magnifications if a temporal shift in flood waves was to occur

Strongyloides stercoralis infection in dogs in Austria: two case reports.

BACKGROUND Strongyloides stercoralis is endemic in tropical and subtropical regions, but reports of infections in central and northern Europe have been recently increasing. Infections occur mainly in humans and dogs. In dogs, both dog-adapted and zoonotic S. stercoralis genotypes seem to occur. Clinical manifestations mainly include gastrointestinal and respiratory signs. The severity of the disease can vary greatly and depends on the immune status of the host. The infection is potentially fatal in immunosuppressed individuals, either medically induced or due to an underlying disease, in which hyperinfections and disseminated infections with extraintestinal parasite dissemination may occur. METHODS Diagnosis was based on coproscopy, including flotation and the Baermann funnel technique, histology of small intestinal biopsies and molecular analysis of mitochondrial cytochrome oxidase subunit I (COI) and hypervariable regions I and IV (HVR I and HVR IV) of the nuclear 18S rDNA loci. RESULTS Two independent cases of severe canine S. stercoralis infection in Austria are presented. In both cases, S. stercoralis was detected in histological sections of the small intestine and with the Baermann funnel technique. Molecular analysis revealed strains with zoonotic potential. Case 1 was a 1-year-old female French bulldog with a long history of respiratory and gastrointestinal signs, severe emaciation and apathy before S. stercoralis infection was diagnosed. Treatment with moxidectin (2.5 mg/kg body weight [BW], oral route) did not eliminate the infection, but treatment with ivermectin (0.2 mg/kg BW, subcutaneously) was successful. Case 2 consisted of two 2-month-old Pomeranian puppies, one female and one male, from a litter of four, which died soon after presenting dyspnoea and haemorrhagic diarrhoea (female) or torticollis (male); S. stercoralis infection was first diagnosed post-mortem. CONCLUSION More attention should be paid to this nematode because although it appears to be rare in Austria, it is easily overlooked on standard coproscopy unless a Baermann funnel technique is used, and even then, it can be missed. Moxidectin is not always successful in eliminating the infection, and treatment with ivermectin should be considered in cases of infection

Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region

Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section. We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (five board-certified, three in training) to select a field of interest for the mitotic count. To assess the potential difference on the mitotic count, we compared the mitotic count of the selected regions to the overall distribution on the slide. Additionally, we evaluated three deep learning-based methods for the assessment of highest mitotic density: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human pathologists on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963-0.979). Further, we found considerable differences in position selection between pathologists, which could partially explain the high variance that has been reported for the manual mitotic count. To achieve better interrater agreement, we propose to use a computer-based area selection for support of the pathologist in the manual mitotic count

Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images

Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training, n = 245 whole-slide images (WSIs), validation (n = 35 WSIs), and test sets (n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types

In vitro and in vivo effects of Pelargonium sidoides DC. root extract EPs® 7630 and selected constituents against SARS-CoV-2 B.1, Delta AY.4/AY.117 and Omicron BA.2

The occurrence of immune-evasive SARS-CoV-2 strains emphasizes the importance to search for broad-acting antiviral compounds. Our previous in vitro study showed that Pelargonium sidoides DC. root extract EPs® 7630 has combined antiviral and immunomodulatory properties in SARS-CoV-2-infected human lung cells. Here we assessed in vivo effects of EPs® 7630 in SARS-CoV-2-infected hamsters, and investigated properties of EPs® 7630 and its functionally relevant constituents in context of phenotypically distinct SARS-CoV-2 variants. We show that EPs® 7630 reduced viral load early in the course of infection and displayed significant immunomodulatory properties positively modulating disease progression in hamsters. In addition, we find that EPs® 7630 differentially inhibits SARS-CoV-2 variants in nasal and bronchial human airway epithelial cells. Antiviral effects were more pronounced against Omicron BA.2 compared to B.1 and Delta, the latter two preferring TMPRSS2-mediated fusion with the plasma membrane for cell entry instead of receptor-mediated low pH-dependent endocytosis. By using SARS-CoV-2 Spike VSV-based pseudo particles (VSVpp), we confirm higher EPs® 7630 activity against Omicron Spike-VSVpp, which seems independent of the serine protease TMPRSS2, suggesting that EPs® 7630 targets endosomal entry. We identify at least two molecular constituents of EPs® 7630, i.e., (−)-epigallocatechin and taxifolin with antiviral effects on SARS-CoV-2 replication and cell entry. In summary, our study shows that EPs® 7630 ameliorates disease outcome in SARS-CoV-2-infected hamsters and has enhanced activity against Omicron, apparently by limiting late endosomal SARS-CoV-2 entry

Estimation of total collagen volume: a T1 mapping versus histological comparison study in healthy Landrace pigs

Right ventricular biopsy represents the gold standard for the assessment of myocardial fibrosis and collagen content. This invasive technique, however, is accompanied by perioperative complications and poor reproducibility. Extracellular volume (ECV) measured through cardiovascular magnetic resonance (CMR) has emerged as a valid surrogate method to assess fibrosis non-invasively. Nonetheless, ECV provides an overestimation of collagen concentration since it also considers interstitial space. Our study aims to investigate the feasibility of estimating total collagen volume (TCV) through CMR by comparing it with the TCV measured at histology. Seven healthy Landrace pigs were acutely instrumented closed-chest and transported to the MRI facility for measurements. For each protocol, CMR imaging at 3T was acquired. MEDIS software was used to analyze T1 mapping and ECV for both the left ventricular myocardium (LVmyo) and left ventricular septum (LVseptum). ECV was then used to estimate TCVCMR at LVmyo and LVseptum following previously published formulas. Tissues were prepared following an established protocol and stained with picrosirius red to analyze the TCVhisto in LVmyo and LVseptum. TCV measured at LVmyo and LVseptum with both histology (8 ± 5 ml and 7 ± 3 ml, respectively) and T1-Mapping (9 ± 5 ml and 8 ± 6 ml, respectively) did not show any regional differences. TCVhisto and TCVCMR showed a good level of data agreement by Bland–Altman analysis. Estimation of TCV through CMR may be a promising way to non-invasively assess myocardial collagen content and may be useful to track disease progression or treatment response

Nuclear Morphometry using a Deep Learning-based Algorithm has Prognostic Relevance for Canine Cutaneous Mast Cell Tumors

Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics (morphometry) can improve reproducibility, but manual methods are time consuming. In this study, we evaluated fully automated morphometry using a deep learning-based algorithm in 96 canine cutaneous mast cell tumors with information on patient survival. Algorithmic morphometry was compared with karyomegaly estimates by 11 pathologists, manual nuclear morphometry of 12 cells by 9 pathologists, and the mitotic count as a benchmark. The prognostic value of automated morphometry was high with an area under the ROC curve regarding the tumor-specific survival of 0.943 (95% CI: 0.889 - 0.996) for the standard deviation (SD) of nuclear area, which was higher than manual morphometry of all pathologists combined (0.868, 95% CI: 0.737 - 0.991) and the mitotic count (0.885, 95% CI: 0.765 - 1.00). At the proposed thresholds, the hazard ratio for algorithmic morphometry (SD of nuclear area $\geq 9.0 \mu m^2$) was 18.3 (95% CI: 5.0 - 67.1), for manual morphometry (SD of nuclear area $\geq 10.9 \mu m^2$) 9.0 (95% CI: 6.0 - 13.4), for karyomegaly estimates 7.6 (95% CI: 5.7 - 10.1), and for the mitotic count 30.5 (95% CI: 7.8 - 118.0). Inter-rater reproducibility for karyomegaly estimates was fair ($\kappa$ = 0.226) with highly variable sensitivity/specificity values for the individual pathologists. Reproducibility for manual morphometry (SD of nuclear area) was good (ICC = 0.654). This study supports the use of algorithmic morphometry as a prognostic test to overcome the limitations of estimates and manual measurements