Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.- Pfizer Pharmaceuticals(undefined

Beam-to-column joints in steel frames

469 σ.Tα τελευταία χρόνια αναδεικνύεται συνεχώς περισσότερο η δυνατότητα αξιοποίησης της στροφικής απόκρισης των κόμβων δοκού-υποστυλώματος στην ανάλυση και τον σχεδιασμό των μεταλλικών κατασκευών. Τα οφέλη που προκύπτουν από την εκμετάλλευση της δυνατότητας αυτής αφορούν αφενός την οικονομία των υλικών και την ταχύτητα ανέγερσης και αφετέρου την αξιοποίηση της μετελαστικής απόκρισης των κόμβων για την βελτίωση της αξιοπιστίας και αποτελεσματικότητας της κατασκευής σε περιπτώσεις σχεδιασμού με αυξημένες απαιτήσεις πλαστιμότητας. Στην παρούσα διατριβή επιχειρείται η ανάπτυξη αξιόπιστων μεθόδων εκτίμησης της μη γραμμικής στροφικής συμπεριφοράς κόμβων δοκού-υποστυλώματος, με απώτερο στόχο την διάδοση της συμμετοχής τους στην ανάλυση των κατασκευών, ως ουσιαστικών παραμέτρων σχεδιασμού. Το ενδιαφέρον επικεντρώνεται στις κοχλιωτές συνδέσεις, οι οποίες συμβάλλουν στην ευκολία ανέγερσης και επιπλέον είναι σε θέση να εξασφαλίσουν υψηλή διαθέσιμη πλαστιμότητα. Η περιοχή της εφελκυόμενης ζώνης των συνδέσεων αυτών θεωρείται κρίσιμη παράμετρος της συνολικής συμπεριφοράς του κόμβου και για την περιγραφή της απόκρισης της αναπτύσσεται ένα επαυξητικό προσομοίωμα βραχέος ταυ. Με το προσομοίωμα αυτό αντιμετωπίζονται σύνθετα χαρακτηριστικά της απόκρισης της σύνδεσης βραχέος ταυ, όπως είναι μεταξύ άλλων τα φαινόμενα επαφής, η μη γραμμικότητα υλικού και η τρισδιάστατη εντατική κατάσταση. Το προσομοίωμα αυτό ενσωματώνεται έπειτα ως συστατικό μέρος μηχανικών προσομοιωμάτων κοχλιωτών κόμβων δοκού-υποστυλώματος χρησιμοποιώντας διάφορες εναλλακτικές διασυνδέσεις ελατηρίων. Με τα συγκεκριμένα μηχανικά προσομοιώματα, υπολογίζεται η πλήρης καμπύλη ροπής-στροφής των κόμβων. Η αποτελεσματικότητα τους αξιολογείται με μεγάλο αριθμό πειραματικών δοκιμών και σύνθετων προσομοιωμάτων πεπερασμένων στοιχείων από την βιβλιογραφία. Οι μέθοδοι υπολογισμού της μη γραμμικής απόκρισης των κόμβων συνολικά και της σύνδεσης βραχέος ταυ μεμονωμένα, που αναπτύχθηκαν στην παρούσα διατριβή, αποδεικνύεται ότι οδηγούν σε βελτιωμένη και περισσότερο αξιόπιστη εκτίμηση της πραγματικής συμπεριφοράς, σε σύγκριση με εναλλακτικές μεθοδολογίες υπολογισμού. Θεωρείται κατά συνέπεια, ότι η αξιοποίηση των κόμβων δοκού-υποστυλώματος ως ουσιαστικών παραμέτρων του σχεδιασμού μεταλλικών κατασκευών καθίσταται περισσότερο προσιτή.In recent years the possibility to take into account the rotational behavior of beam-to-column joints in the analysis and design of steel structures becomes more and more prominent. The resultant benefits extend to both material savings and erection speed and also to the utilization of the post-elastic joint response, in order to enhance the reliability and the effectiveness of the structures in cases of design with increased ductility requirements. In the present dissertation, the development of reliable methods for the prediction of the nonlinear rotational joint response is undertaken, towards the long term goal for expanded consideration of joints as effective design parameters. The focus of the research is devoted to bolted connections, which promote erection ease and have the capacity to ensure increased ductility. The tension zone of these connections is considered as a crucial parameter for the complete joint behavior and thus, for the prediction of its response an incremental T-stub model is developed. Employing this model, complex response characteristics of the T-stub connection are taken into consideration, such as the contact phenomena, the material nonlinearity and the three dimensional stress state. This model is afterwards implemented as a component of mechanical models, representing bolted beam-to-column joints, featuring various alternative typologies for the spring assemblage. The full moment-rotation curve is calculated by means of these mechanical models. Their effectiveness is validated through a reasonably high number of experimental tests and advanced finite element models found in the literature. The methods for the calculation of the nonlinear response of complete joints and single T-stub connections, developed in the present dissertation, appear to lead to improved and more reliable prediction of the real behaviour, compared to alternative existing methodologies. It is considered thereafter, that the utilization of beam-to-column joints as effective parameters for the design of steel structures, is further promoted as an applicable option.Μηνάς Ε. Λεμονή

Soft computing-based models for the prediction of masonry compressive strength

Masonry is a building material that has been used in the last 10.000 years and remains competitive today for the building industry. The compressive strength of masonry is used in modern design not only for gravitational and lateral loading, but also for quality control of materials and execution. Given the large variations of geometry of units and joint thickness, materials and building practices, it is not feasible to test all possible combinations. Many researchers tried to provide relations to estimate the compressive strength of masonry from the constituents, which remains a challenge. Similarly, modern design codes provide lower bound solutions, which have been demonstrated to be weakly correlated to observed test results in many cases. The present paper adopts soft-computing techniques to address this problem and a dataset with 401 specimens is considered. The obtained results allow to identify the most relevant parameters affecting masonry compressive strength, areas in which more experimental research is needed and expressions providing better estimates when compared to formulas existing in codes or literature.publishe

Novel Fuzzy-Based Optimization Approaches for the Prediction of Ultimate Axial Load of Circular Concrete-Filled Steel Tubes

An accurate estimation of the axial compression capacity of the concrete-filled steel tubular (CFST) column is crucial for ensuring the safety of structures containing them and preventing related failures. In this article, two novel hybrid fuzzy systems (FS) were used to create a new framework for estimating the axial compression capacity of circular CCFST columns. In the hybrid models, differential evolution (DE) and firefly algorithm (FFA) techniques are employed in order to obtain the optimal membership functions of the base FS model. To train the models with the new hybrid techniques, i.e., FS-DE and FS-FFA, a substantial library of 410 experimental tests was compiled from openly available literature sources. The new model’s robustness and accuracy was assessed using a variety of statistical criteria both for model development and for model validation. The novel FS-FFA and FS-DE models were able to improve the prediction capacity of the base model by 9.68% and 6.58%, respectively. Furthermore, the proposed models exhibited considerably improved performance compared to existing design code methodologies. These models can be utilized for solving similar problems in structural engineering and concrete technology with an enhanced level of accuracy

Soft computing-based models for the prediction of masonry compressive strength

Masonry is a building material that has been used in the last 10.000 years and remains competitive today for the building industry. The compressive strength of masonry is used in modern design not only for gravitational and lateral loading, but also for quality control of materials and execution. Given the large variations of geometry of units and joint thickness, materials and building practices, it is not feasible to test all possible combinations. Many researchers tried to provide relations to estimate the compressive strength of masonry from the constit-uents, which remains a challenge. Similarly, modern design codes provide lower bound solutions, which have been demonstrated to be weakly correlated to observed test results in many cases. The present paper adopts soft-computing techniques to address this problem and a dataset with 401 specimens is considered. The obtained results allow to identify the most relevant parameters affecting masonry compressive strength, areas in which more experimental research is needed and expressions providing better estimates when compared to formulas existing in codes or literature.MU - Malayer University(undefined

Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques

In this study, a model for the estimation of the compressive strength of concretes incorporating metakaolin is developed and parametrically evaluated, using soft computing techniques. Metakaolin is a component extensively employed in recent decades as a means to reduce the requirement for cement in concrete. For the proposed models, six parameters are accounted for as input data. These are the age at testing, the metakaolin percentage in relation to the total binder, the water-to-binder ratio, the percentage of superplasticizer, the binder to sand ratio and the coarse to fine aggregate ratio. For training and verification of the developed models a database of 867 experimental specimens has been compiled, following a broad survey of the relevant published literature. A robust evaluation process has been utilized for the selection of the optimum model, which manages to estimate the concrete compressive strength, accounting for metakaolin usage, with remarkable accuracy. Using the developed model, a number of diagrams is produced that reveal the highly non-linear influence of mix components to the resulting concrete compressive strength.ZU - Zagazig University(undefined

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype