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Simulation methods for extreme flood estimation represent an important complement to statistical flood frequency analysis because a spectrum of catchment conditions potentially leading to extreme flows can be assessed. In this paper, stochastic, semi-continuous simulation is used to estimate extreme floods in three catchments located in Norway, all of which are characterised by flood regimes in which snowmelt often has a significant role. The simulations are based on SCHADEX, which couples a precipitation probabilistic model with a hydrological simulation such that an exhaustive set of catchment conditions and responses is simulated. The precipitation probabilistic model is conditioned by regional weather patterns, and a bottom-up classification procedure was used to define a set of weather patterns producing extreme precipitation in Norway. SCHADEX estimates for the 1000-year (Q1000) discharge are compared with those of several standard methods, including event based and long-term simulations which use a single extreme precipitation sequence as input to a hydrological model, statistical flood frequency analysis based on the annual maximum series, and the GRADEX method. The comparison suggests that the combination of a precipitation probabilistic model with a long-term simulation of catchment conditions, including snowmelt, produces estimates for given return periods which are more in line with those based on statistical flood frequency analysis, as compared with the standard simulation methods, in two of the catchments. In the third case, the SCHADEX method gives higher estimates than statistical flood frequency analysis and further suggests that the seasonality of the most likely Q1000 events differs from that of the annual maximum flows. The semi-continuous stochastic simulation method highlights the importance of considering the joint probability of extreme precipitation, snowmelt rates and catchment saturation states when assigning return periods to floods estimated by precipitation runoff methods. The SCHADEX methodology, as applied here, is dependent on observed discharge data for calibration of a hydrological model, and further study to extend its application to ungauged catchments would significantly enhance its versatility. [ABSTRACT FROM AUTHOR]

Classifications of atmospheric weather patterns (WPs) are widely used for the description of the climate of a given region and are employed for many applications, such as weather forecasting, downscaling of global circulation model outputs and reconstruction of past climates. WP classifications were recently used to improve the statistical characterisation of heavy rainfall. In this context, bottom-up approaches, combining spatial distribution of heavy rainfall observations and geopotential height fields have been used to define WP classifications relevant for heavy rainfall statistical analysis. The definition of WPs at the synoptic scale creates an interesting variable which could be used as a link between the global scale of climate signals and the local scale of precipitation station measurements. We introduce here a new WP classification centred on the British Columbia Coastal region (Canada) and based on a bottom-up approach. Five contrasted WPs composed this classification, four rainy WPs and one non-rainy WP, the anticyclonic pattern. The four rainy WPs are mainly observed in the winter months (October to March), which is the period of heavy precipitation events in Coastal BC and is thus consistent with the local climatology. The combination of this WP classification with the seasonal description of rainfall is shown to be useful for splitting observed precipitation series into more homogeneous sub-samples and thus identifying, for each station, the synoptic situations that generate the highest hazard in terms of heavy rainfall events. El Niño Southern Oscillations significantly influence the frequency of occurrence of two Coastal BC WPs. Within each WP, ENSO seem to influence only the frequency of rainy events and not the magnitudes of heavy rainfall events. Consequently, MEWP heavy rainfall estimations do not show significant evolution of heavy rainfall behaviour between Niño and Niña winters. However, the WP approach captures the variability of the probability of occurrences of synoptic situations generating heavy rainfall depending on ENSO and opening interesting perspectives for the analysis of heavy rainfall distribution in a non-stationary context. [ABSTRACT FROM AUTHOR]

A reliable estimate of extreme flood characteristics has always been an active topic in hydrological research. Over the decades a large number of approaches and their modifications have been proposed and used, with various methods utilizing continuous simulation of catchment runoff, being the subject of the most intensive research in the last decade. In this paper a new and promising stochastic semi-continuous method is used to estimate extreme discharges in two mountainous Slovak catchments of the rivers Váh and Hron, in which snow-melt processes need to be taken into account. The SCHADEX method used, couples a precipitation probabilistic model with a rainfall-runoff model used to both continuously simulate catchment hydrological conditions and to transform generated synthetic rainfall events into corresponding discharges. The stochastic nature of the method means that a wide range of synthetic rainfall events were simulated on various historical catchment conditions, taking into account not only the saturation of soil, but also the amount of snow accumulated in the catchment. The results showed that the SCHADEX extreme discharge estimates with return periods of up to 100 years were comparable to those estimated by statistical approaches. In addition, two reconstructed historical floods with corresponding return periods of 100 and 1000 years were compared to the SCHADEX estimates. The results confirmed the usability of the method for estimating design discharges with a recurrence interval of more than 100 years and its applicability in Slovak conditions.

The University of Nantes has developed a 3D printing technique (BatiPrint3DTM) dedicated to the construction of the walls of a house. This innovative on site construction technique is based on the deposition of two layers of expansive foam used as a formwork for a third concrete layer. It allows to build at the same time the structure and the insulation. This new construction technology has first been developed at the laboratory, but rapidly, we decided to deploy it on site, in order to demonstrate its technical viability. We present the technology Batiprint3DTM and the demonstrator YhnovaTM, a 95m2 social dwelling built for the social landlord Nantes Métropole Habitat (NMH).

Abstract Background Extravasation of circulating cancer cells is a key event of metastatic dissemination that is initiated by the adhesion of cancer cells to endothelial cells. It requires interactions between adhesion receptors on endothelial cells and their counter-receptors on cancer cells. Notably, E-selectin, a major endothelial adhesion receptor, interacts with Death receptor-3 present on metastatic colon carcinoma cells. This interaction confers metastatic properties to colon cancer cells by promoting the adhesion of cancer cells to endothelial cells and triggering the activation of the pro-migratory p38 and pro-survival ERK pathways in the cancer cells. In the present study, we investigated further the mechanisms by which the E-selectin-activated pathways downstream of DR3 confer a survival advantage to colon cancer cells. Methods Cell survival has been ascertained by using the WST-1 assay and by evaluating the activation of the PI3 kinase/NFκB survival axis. Apoptosis has been assayed by determining DNA fragmentation by Hoechst staining and by measuring cleavage of caspases-8 and -3. DR3 isoforms have been identified by PCR. For more precise quantification, targeted PCR reactions were carried out, and the amplified products were analyzed by automated chip-based microcapillary electrophoresis on an Agilent 2100 Bioanalyzer instrument. Results Interaction between DR3-expressing HT29 colon carcinoma cells and E-selectin induces the activation of the PI3K/Akt pathway. Moreover, p65/RelA, the anti-apoptotic subunit of NFκB, is rapidly translocated to the nucleus in response to E-selectin. This translocation is impaired by the PI3K inhibitor LY294002. Furthermore, inhibition of the PI3K/Akt pathway increases the cleavage of caspase 8 in colon cancer cells treated with E-selectin and this effect is still further increased when both ERK and PI3K pathways are concomitantly inhibited. Intriguingly, metastatic colon cancer cell lines such as HT29 and SW620 express higher levels of a splice variant of DR3 that has no trans-membrane domain and no death domain. Conclusion Colon cancer cells acquire an increased capacity to survive via the activation of the PI3K/NFκB pathway following the stimulation of DR3 by E-selectin. Generation of a DR3 splice variant devoid of death domain can further contribute to protect against apoptosis.

Abstract Background Werner Syndrome (WS) is a rare disorder characterized by the premature onset of a number of age-related diseases. The gene responsible for WS encodes a DNA helicase/exonuclease protein believed to affect different aspects of transcription, replication, and/or DNA repair. In addition to genomic instability, human WS cells exhibit oxidative stress. In this report, we have examined the impact of exogenous hydrogen peroxide on the expression profile of mouse embryonic fibroblasts lacking part of the helicase domain of the WRN homologue (here referred to as WrnΔhel/Δhel). Results WrnΔhel/Δhel mutant mouse embryonic fibroblasts exhibit increased oxidative stress. This was reflected by increased intracellular reactive oxygen species (ROS), increased oxidative damage in genomic DNA, changes in ATP/ADP ratios, and a disruption of the inner mitochondrial transmembrane potential when compared to wild type mouse embryonic fibroblasts. Expression profile analyses of hydrogen peroxide-treated wild type cells have indicated significant decreases in the expression of genes involved in mitosis, glycolysis, fatty acid metabolism, nucleic acid metabolism, and cell cycle control, as well as protein modification and stability. Such decreases in these biological processes were not observed in hydrogen peroxide-treated WrnΔhel/Δhel cells. Importantly, untreated WrnΔhel/Δhel cells already exhibited down regulation of several biological processes decreased in wild type cells that had been treated with hydrogen peroxide. Conclusion Expression profiling of WrnΔhel/Δhel mutant cells revealed a very different response to exogenous addition of hydrogen peroxide in culture compared to wild type cells. This is due in part to the fact that WrnΔhel/Δhel mutant cells already exhibited a modest chronic intracellular oxidative stress.

This paper presents a new theoretical approach for the description of multidimensional objects for which 3D and 4D are particular cases. The approach is based on a curved space which is associated to each object. This curved space is characterised by Riemannian tensors from which invariant quantities are defined. A descriptor or index is constructed from those invariants for which statistical and abstract graph representations are associated. The obtained representations are invariant under general coordinate transformations. The statistical representation allows a compact description of the object while the abstract graph allows describing the relations in between the parts as well as the structure.

Our objective was to validate the possibility of detecting SARA from milk Fourier transform mid-infrared spectroscopy estimated fatty acids (FA) and machine learning. Subacute ruminal acidosis is a common condition in modern commercial dairy herds for which the diagnostic remains challenging due to its symptoms often being subtle, nonexclusive, and not immediately apparent. This observational study aimed at evaluating the possibility of predicting SARA by developing machine learning models to be applied to farm data and to provide an estimated portrait of SARA prevalence in commercial dairy herds. A first data set composed of 488 milk samples of 67 cows (initial DIM = 8.5 ± 6.18; mean ± SD) from 7 commercial dairy farms and their corresponding SARA classification (SARA+ if rumen pH <6.0 for 300 min, else SARA-) was used for the development of machine learning models. Three sets of predictive variables: i) milk major components (MMC), ii) milk FA (MFA), and iii) MMC combined with MFA (MMCFA) were submitted to 3 different algorithms, namely Elastic net (EN), Extreme gradient boosting (XGB), and Partial least squares (PLS), and evaluated using 3 different scenarios of cross-validation. Accuracy, sensitivity, and specificity of the resulting 27 models were analyzed using a linear mixed model. Model performance was not significantly affected by the choice of algorithm. Model performance was improved by including fatty acids estimations (MFA and MMCFA as opposed to MMC alone). Based on these results, one model was selected (algorithm: EN; predictive variables: MMCFA; 60.4, 65.4, and 55.3% of accuracy, sensitivity, and specificity, respectively) and applied to a large data set comprising the first test-day record (milk major components and FA within the first 70 DIM of 211,972 Holstein cows (219,503 samples) collected from 3001 commercial dairy herds. Based on this analysis, the within-herd SARA prevalence of commercial farms was estimated at 6.6 ± 5.29% ranging from 0 to 38.3%. A subsequent linear mixed model was built to investigate the herd-level factors associated to higher within-herd SARA prevalence. Milking system, proportion of primiparous cows, herd size and seasons were all herd-level factors affecting SARA prevalence. Furthermore, milk production was positively, and milk fat yield negatively associated with SARA prevalence. Due to their moderate levels of accuracy, the SARA prediction models developed in our study, using data from continuous pH measurements on commercial farms, are not suitable for diagnostic purpose. However, these models can provide valuable information at the herd level., (The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Recent breakthroughs in deep learning have revolutionized protein sequence and structure prediction. These advancements are built on decades of protein design efforts, and are overcoming traditional time and cost limitations. Diffusion models, at the forefront of these innovations, significantly enhance design efficiency by automating knowledge acquisition. In the field of de novo protein design, the goal is to create entirely novel proteins with predetermined structures. Given the arbitrary positions of proteins in 3-D space, graph representations and their properties are widely used in protein generation studies. A critical requirement in protein modelling is maintaining spatial relationships under transformations (rotations, translations, and reflections). This property, known as equivariance, ensures that predicted protein characteristics adapt seamlessly to changes in orientation or position. Equivariant graph neural networks offer a solution to this challenge. By incorporating equivariant graph neural networks to learn the score of the probability density function in diffusion models, one can generate proteins with robust 3-D structural representations. This review examines the latest deep learning advancements, specifically focusing on frameworks that combine diffusion models with equivariant graph neural networks for protein generation., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2024 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

Abstract: A 69-year-old man diagnosed with progressive bone metastatic castration-resistant prostate adenocarcinoma and concurrent alcoholic cirrhosis with multiple hepatocellular carcinoma (HCC) nodules was referred to our nuclear medicine service for 177 Lu-PSMA-617 therapy. The patient's pretreatment screening using 68 Ga-PSMA-11 PET/CT revealed high prostate-specific membrane antigen expression in both prostatic and HCC lesions. The patient underwent 2 doses of 177 Lu-PSMA-617. Subsequent imaging assessments with 68 Ga-PSMA-11 PET/CT and hepatic MRI indicated progressive HCC nodules, while showing a partial response in prostatic bone metastases. Positive clinical and biological responses were observed only in prostatic disease, but not in HCC nodules., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Protein generation has numerous applications in designing therapeutic antibodies and creating new drugs. Still, it is a demanding task due to the inherent complexities of protein structures and the limitations of current generative models. Proteins possess intricate geometry, and sampling their conformational space is challenging due to its high dimensionality. This paper introduces novel Markovian and non-Markovian generative diffusion models based on fractional stochastic differential equations and the Lévy distribution, allowing for a more effective exploration of the conformational space. The approach is applied to a dataset of 40 , 000 proteins and evaluated in terms of Fréchet distance, fidelity, and diversity, outperforming the state-of-the-art by 25.4%, 35.8%, and 11.8%, respectively., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2024 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

Objectives: To evaluate the prevalence of intra-myocardial fatty scars (IMFS) most likely indicating previous silent myocardial infarction (SMI), as detected on coronary artery calcium (CAC) computed tomography (CT) scans in diabetic patients without history of coronary heart disease (CHD)., Methods: Diabetic patients screened for silent coronary insufficiency in a tertiary-care, university hospital between Jan-2015 and Dec-2016 were categorized according to their CAC score in two groups comprising 242 patients with CACS = 0 and 145 patients with CACS ≥ 300. CAC-CT scans were retrospectively evaluated for subendorcardial and transmural IMFS of the left ventricle. Adipose remodeling, patients' characteristics, cardiovascular risk factors and metabolic profile were compared between groups., Results: Eighty-three (21%) patients with IMFS were identified, 55 (37.9%) in the group CACS ≥ 300 and 28 (11.6%) in the CACS = 0 (OR = 4.67; 95% CI = 2.78-7.84; p < 0.001). Total and average surface of IMFS and their number per patient were similar in both groups (p = 0.55; p = 0.29; p = 0.61, respectively). In the group CACS ≥ 300, patients with IMFS were older (p = 0.03) and had longer-lasting diabetes (p = 0.04). Patients with IMFS were older and had longer history of diabetes, reduced glomerular filtration rate, more coronary calcifications (all p < 0.05), and higher prevalence of carotid plaques (OR = 3.03; 95% CI = 1.43-6.39, p = 0.004). After correction for other variables, only a CACS ≥ 300 (OR = 5.12; 95% CI = 2.66-9.85; p < 0.001) was associated with an increased risk of having IMFS., Conclusions: In diabetic patients without known CHD, IMFSs were found in patients without coronary calcifications, although not as frequently as in patients with heavily calcified coronary arteries. It remains to be established if this marker translates in an upwards cardiovascular risk restratification especially in diabetic patients with CACS = 0., Clinical Relevance Statement: In diabetic patients without history of coronary heart disease, intramyocardial fatty scars, presumably of post-infarction origin, can be detected on coronary artery calcium CT scans more frequently, but not exclusively, if the coronary arteries are heavily calcified as compared to those without calcifications., Key Points: • Intramyocardial fatty scars (IMFS), presumably of post-infarction origin, can be detected on coronary artery calcium (CAC) CT scans more frequently, but not exclusively, in diabetic patients with CACS ≥ 300 as compared to patients CACS = 0. • Patients with IMFS were older and had longer history of diabetes, reduced glomerular filtration rate, and more coronary calcifications. • Carotid plaques and CACS ≥ 300 were associated with an increased risk of having IMFS, about three and five folds respectively., (© 2023. The Author(s).)

Prostate-specific membrane antigen (PSMA), whose high expression has been demonstrated in metastatic aggressive prostate adenocarcinoma, is also highly expressed in the neovessels of various solid tumors, including clear cell renal cell carcinoma (ccRCC). In the VISION phase III clinical trial, PSMA-targeted radioligand therapy (PRLT) with lutetium 177 demonstrated a 4-month overall survival OS benefit compared to the best standard of care in heavily pretreated metastatic prostate cancer. Despite the improvement in the management of metastatic clear cell renal cell carcinoma (mccRCC) with antiangiogenic tyrosine kinase inhibitor (TKI) and immunotherapy, there is still a need for new treatments for patients who progress despite these drugs. In this study, we discuss the rationale of PRLT applied to the treavtment of mccRCC.

Abstract: We reported the case of a 73-year-old man for whom a prostatic adenocarcinoma with synchronous bone metastases was diagnosed. Because his disease was progressing despite several lines of chemotherapy and hormonotherapy, he was screened with a 68 Ga-PSMA PET/CT for a possible 177 Lu-PSMA-617 therapy. The examination demonstrated an intense diffuse bone uptake related to the known bone involvement. It also showed an unexpected diffuse and intense lung uptake, secondary to an active polyangiitis granulomata. This intense lung uptake prohibits the radioligand therapy., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein's primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein's amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions., (Crown Copyright © 2023 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

The classification of deformable protein shapes, based solely on their macromolecular surfaces, is a challenging problem in protein-protein interaction prediction and protein design. Shape classification is made difficult by the fact that proteins are dynamic, flexible entities with high geometrical complexity. In this paper, we introduce a novel description for such deformable shapes. This description is based on the bifractional Fokker-Planck and Dirac-Kähler equations. These equations analyse and probe protein shapes in terms of a scalar, vectorial and non-commuting quaternionic field, allowing for a more comprehensive description of the protein shapes. An underlying non-Markovian Lévy random walk establishes geometrical relationships between distant regions while recalling previous analyses. Classification is performed with a multiobjective deep hierarchical pyramidal neural network, thus performing a multilevel analysis of the description. Our approach is applied to the SHREC'19 dataset for deformable protein shapes classification and to the SHREC'16 dataset for deformable partial shapes classification, demonstrating the effectiveness and generality of our approach.

Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2022 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

Chromosomal translocations frequently promote carcinogenesis by producing gain-of-function fusion proteins. Recent studies have identified highly recurrent chromosomal translocations in patients with endometrial stromal sarcomas (ESSs) and ossifying fibromyxoid tumors (OFMTs), leading to an in-frame fusion of PHF1 (PCL1) to six different subunits of the NuA4/TIP60 complex. While NuA4/TIP60 is a coactivator that acetylates chromatin and loads the H2A.Z histone variant, PHF1 is part of the Polycomb repressive complex 2 (PRC2) linked to transcriptional repression of key developmental genes through methylation of histone H3 on lysine 27. In this study, we characterize the fusion protein produced by the EPC1 - PHF1 translocation. The chimeric protein assembles a megacomplex harboring both NuA4/TIP60 and PRC2 activities and leads to mislocalization of chromatin marks in the genome, in particular over an entire topologically associating domain including part of the HOXD cluster. This is linked to aberrant gene expression-most notably increased expression of PRC2 target genes. Furthermore, we show that JAZF1-implicated with a PRC2 component in the most frequent translocation in ESSs, JAZF1-SUZ12 -is a potent transcription activator that physically associates with NuA4/TIP60, its fusion creating outcomes similar to those of EPC1-PHF1 Importantly, the specific increased expression of PRC2 targets/ HOX genes was also confirmed with ESS patient samples. Altogether, these results indicate that most chromosomal translocations linked to these sarcomas use the same molecular oncogenic mechanism through a physical merge of NuA4/TIP60 and PRC2 complexes, leading to mislocalization of histone marks and aberrant Polycomb target gene expression., (© 2022 Sudarshan et al.; Published by Cold Spring Harbor Laboratory Press.)