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It is essential to understand how failure mechanisms work in arch dams and, in particular, their most common manifestation: cracking. In this paper, the different types of cracking are explained in terms of their causes and consequences. Then, an exhaustive literature review is carried out that results in a detailed compilation of the characteristics of 38 cracked arch dams from all over the world, including crack characteristics (zone, position, dimensions and probable cause). This review is restricted to only those dams for which information on the position of the cracks or dam displacements is publicly available. As part of the review, a brief summary of key data for each dam is included, as well as a compilation of published crack diagrams. The positions of the cracks of all the dams are classified using diagrams in relation to the type of dam and the origin of the crack. Finally, the distribution of some dam parameters and crack features is analyzed by studying the relationships between them. [ABSTRACT FROM AUTHOR]

Dam safety monitoring is critical due to its social, environmental, and economic implications. Although conventional statistical approaches have been used for surveillance, advancements in technology, particularly in Artificial Intelligence (AI) and Machine Learning (ML), offer promising avenues for enhancing predictive capabilities. We investigate the application of ML algorithms, including Boosted Regression Trees (BRT), Random Forest (RF), and Neural Networks (NN), focussing on their combination by Stacking to improve prediction accuracy on concrete dam deformation using radial displacement data from three dams. The methodology involves training first-level models (experts) using those algorithms, and a second-level meta-learner that combines their predictions using BRT, a Linear Model (LM) and the Greedy Weighted Algorithm (GWA). A comparative analysis demonstrates the superiority of Stacking over traditional methods. The GWA emerged as the most suitable meta-learner, enhancing the optimal expert in all cases, with improvement rates reaching up to 16.12% over the optimal expert. Our study addresses critical questions regarding the GWA's expert weighting and its impact on prediction precision. The results indicate that the combination of accurate experts using the GWA improves model reliability by reducing error dispersion. However, variations in optimal weights over time necessitate robust error estimation using cross-validation by blocks. Furthermore, the assignment of weights to experts closely correlates with their precision: the more accurate a model is, the more weight that is assigned to it. The GWA improves on the optimal expert in most cases, including at extreme values of error, with improvement rates up to 41.74%. Our findings suggest that the proposed methodology significantly advances AI applications in infrastructure monitoring, with implications for dam safety. [ABSTRACT FROM AUTHOR]

DNA-encoded libraries (DELs) have demonstrated to be one of the most powerful technologies within the ligand identification toolbox, widely used either in academia or biotech and pharma companies. DEL methodology utilizes affinity selection (AS) as the approach to interrogate the protein of interest for the identification of binders. Here we present a high-throughput, fully automated AS platform developed to fulfill industrial standards and compatible with different assay formats to improve the reproducibility of the AS process for DEL binders identification. This platform is flexible enough to virtually set aside all kinds of DELs and AS methods and conditions using immobilized proteins. It bears the two main immobilization methods to support of the proteins of interest: magnetic beads or resin tip columns. A combination of a broad variety of protocol options with a wide range of different experimental conditions can be set up with a throughput of 96 samples at the same time. In addition, small modifications of the protocols provide the platform with the versatility to run not only the routine DEL screens, but also test covalent libraries, the successful immobilization of the proteins of interest, and many other experiments that may be required. This versatile AS platform for DEL can be a powerful instrument for direct application of the technology in academic and industry settings. [ABSTRACT FROM AUTHOR]

Background: Rejection continues to be the main cause of renal graft loss. Currently, the gold standard for diagnosis is an allograft biopsy; however, because it is time-consuming, costly, and invasive, the pursuit of novel biomarkers has gained interest. Variation in the expressions of miRNAs is currently considered a probable biomarker for the diagnosis of acute rejection. This study aimed to determine whether miR-150-5p in serum is related to microvascular damage in patients with acute antibody-mediated rejection (ABMR). Methods: A total of 27 patients who underwent renal transplantation (RT) with and without ABMR were included in the study. We performed the quantification of hsa-miR-150-5p, hsa-miR-155, hsa-miR-21, hsa-miR-126, and hsa-miR-1 in plasma by RT-qPCR. The expressions between the groups and their correlations with the histological characteristics of the patients with ABMR were also investigated. Results: miR-150-5p significantly increased in the plasma of patients with rejection (p < 0.05), and the changes in miR-150-5p were directly correlated with microvascular inflammation in the allograft biopsies. Clinical utility was determined by ROC analysis with an area under the curve of 0.873. Conclusions: Our results show that the patients with RT with ABMR exhibited increased expression of miR-150-5p compared to patients without rejection, which could have clinical consequences, as well as probable utility in the diagnosis of ABMR, and bioinformatics may help in unraveling the molecular mechanisms underlying ABMR conditions. [ABSTRACT FROM AUTHOR]

We present the first results concerning synthesizer-driven high-harmonic generation that reach the water-window region. This approach holds the promise of offering greater spectral tunability in the generation of isolated attosecond pulses and at the same time of achieving higher photon-flux, required for attosecondresolved soft X-ray transient absorption experiments.