An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials.

The Open Source Malaria (OSM) consortium is developing compounds that kill the human malaria parasite, Plasmodium falciparum, by targeting PfATP4, an essential ion pump on the parasite surface. The structure of PfATP4 has not been determined. Here, we describe a public competition created to develop a predictive model for the identification of PfATP4 inhibitors, thereby reducing project costs associated with the synthesis of inactive compounds. Competition participants could see all entries as they were submitted. In the final round, featuring private sector entrants specializing in machine learning methods, the best-performing models were used to predict novel inhibitors, of which several were synthesized and evaluated against the parasite. Half possessed biological activity, with one featuring a motif that the human chemists familiar with this series would have dismissed as "ill-advised". Since all data and participant interactions remain in the public domain, this research project "lives" and may be improved by others

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Effect of deferiprone on in vivo model of autologous renal transplantation: experimental study in pigs

Introduction: Ischemia-reperfusion syndrome (IRI) represents the major non-immune factor whichleads to renal graft damage. Iron features prominently in IRI processes and several studies haveshown a beneficial role of iron-chelating agent desferoxamine. Another chelating agent deferiprone (L1)has been used in iron overload patients showing better results possibly due to its differentphysicochemical properties. Purpose: The purpose of this study was to assess any possible protective effect of L1 on a kidneyautotransplantation model by studying the damage caused to kidney graft during cold ischemia,reperfusion and post-surgery phases.Material and Method: 14 pigs (65-75kg body weight) were used and consisted the control group (7)and the study group (L1-7). Pigs in L1 group received twice daily 50mg/kg of L1 for 3 consecutive daysplus another dose just before the implantation of graft. Left nephrectomy and graft (left kidney)implantation to right renal vascular pedicle, with concomitant right nephrectomy, were done with anovel experimental model using total extra-peritoneal approaches. In both groups, after left kidneyremoval, it was flushed with Custodiol®solution and immediately was connected to an extracorporealcirculatory machine at 4oC with flow of 50-100ml/min, maintaining fluid (KPS-1®solution) pressure notto exceed 35-40 mmHg, for a period of 4h. Then it was cold stored in Custodiol®solution for another13h (total cold ischemic period: 17h). In L1 group, 0.1gr of L1 were diluted in KPS-1®and Custodiol®solutions. Laboratory and histologic markers of kidney damage were evaluated during a) coldpreservation phase (weight of graft, CK/LDH concentrations into the preservation solution), b) at30mins after reperfusion phase (8-isoprostane, biopsy), c) during post-surgery phase (Urea, Creatserum concentrations) and before sacrifice of animals at 14th postoperative day (biopsy). Biopsyspecimens were examined by two independed pathologists to assess the degree of graft damagethrough the histopathological index and expression of adhesion molecules ICAM-1 and VCAM-1.Results: All animals survived until 14th postoperative day. After 17h cold ischemia period, the meanweight of grafts and CK/LDH concentrations were lower in L1 group compared to control group(p=0.001, p=0.001 and p=0.007, respectively). At 30mins after reperfusion the mean concentration of8-isoprostanes was lower in L1 group compared to control group (p=0.007). Histopathological index at30mins after reperfusion was significantly lower in L1 group compared to control group (p=0.001) andthe same result was seen at 14th postoperative day (p=0.001). Additionally, between 30mins and 14thpostoperative day, the histopathological index was increased in control group (p=0.042), while itremained stable in L1 group (p=0.862). At 30mins after reperfusion, expression of VCAM-1 in L1 groupwas lower compared to L1 group (p=0.02). The same result between two groups was obvious forICAM-1 and VCAM-1 at 14th day (p=0.029 and p=0.04, respectively). ICAM-1 expression was reducedin both groups from 30mins to 14th postoperative day (control group: p=0.034 and L1 group: p=0.034),while VCAM-1 expression remained constant in both groups (control group: p=0.414 and L1 group:p=0.083). From 3rd up to 8th postoperative day Urea serum concentrations and from 3rd up to 7thpostoperative day Creat serum concentrations were constantly lower in L1 group compared to controlgroup (all p<0.05).Conclusions: Due to above promising results, it seems that deferiprone manifests a clearcytoprotective role in renal autotransplantation model by reducing kidney damage both at preservationperiod and after reperfusion, resulting in better graft function during postoperative phase and possiblylowering graft’s immunogenicity.Εισαγωγή: Η μη ανοσολογικής αρχής ιστική βλάβη του νεφρικού μοσχεύματος, λόγω της υποθερμικής συντήρησής του και του φαινομένου ισχαιμίας-επαναιμάτωσης μετά την εμφύτευσή του, σχετίζεται ευθέως ανάλογα με την πρώιμη και όψιμη λειτουργία του. Ο ελεύθερος εντός των κυττάρων σίδηρος συμμετέχει στη βλάβη αυτή, λόγω του κεντρικού του ρόλου στα φαινόμενα οξειδωαναγωγής και μελέτες με δεσφεροξαμίνη έχουν αποδείξει ένα σχετικά ευεργετικό ρόλο. Η δεφεριπρόνη, λόγω των φυσικοχημικών της ιδιοτήτων, φαίνεται να υπερτερεί της δεσφεροξαμίνης σε ασθενείς με αιμοσιδήρωση, δείχνοντας μεγαλύτερη δυνατότητα διαπερατότητας των κυτταρικών μεμβρανών και πιθανά μεγαλύτερη δέσμευση του ελεύθερου σιδήρου.Σκοπός: Σκοπό της μελέτης αποτέλεσε η διερεύνηση της πιθανής ευεργετικής επίδρασης της δεφεριπρόνης στη μεταμόσχευση νεφρού, μελετώντας τη βλάβη στο νεφρικό μόσχευμα κατά τη ψυχρή ισχαιμία, την επαναιμάτωση και μετεγχειρητικά.Υλικό και Μέθοδος: Χρησιμοποιήθηκαν 14 ενήλικες χοίροι φάρμας (ΣΒ: 65-75kg). Τα 7 πειραματόζωα αποτέλεσαν την ομάδα ελέγχου, ενώ στα υπόλοιπα 7 που αποτέλεσαν την ομάδα μελέτης χορηγήθηκε δεφεριπρόνη ενδοφλεβίως σε δοσολογία 50mg/kg δύο φορές την ημέρα για 3 ημέρες και μία δόση πριν τη μεταμόσχευση. Η αριστερή νεφρεκτομή και η μεταμόσχευση του αριστερού νεφρού στα δεξιά νεφρικά αγγεία, με συνοδό δεξιά νεφρεκτομή, πραγματοποιήθηκαν με ένα καινοτόμο πειραματικό μοντέλο με εξολοκλήρου εξωπεριτοναϊκή προσπέλαση. Το μόσχευμα (αριστερός νεφρός) και στις δύο ομάδες εκπλύθηκε με διάλυμα Custodiol®και αμέσως μετά συνδέθηκε σε μηχανή εξωσωματικής κυκλοφορίας στους 4οC, με ροή στα 50-100ml/min, διατηρώντας την πίεση του διαλύματος KPS-1®κάτω από 35-40mmHg για 4h. Μετά συντηρούταν το μόσχευμα σε υποθερμία σε διάλυμα Custodiol®για 13h (συνολική ψυχρή ισχαιμία: 17h). Στην ομάδα L1, 0,1gr L1 προστέθηκε στα διαλύματαCustodiol®και KPS-1®. Εργαστηριακοί και ιστολογικοί δείκτες νεφρικής βλάβης εκτιμήθηκαν κατά τη διάρκεια α) της ψυχρής συντήρησης (βάρος μοσχεύματος, CK/LDH διαλύματος συντήρησης), β) στα 30mins από την επαναιμάτωση (8-ισοπροστάνια, βιοψία), γ) κατά τη μετεγχειρητική περίοδο(συγκέντρωση ουρίας και κρεατινίνης ορού) και πριν την ευθανασία των πειραματόζωων τη 14η μετεγχειρητική ημέρα (βιοψία). Οι βιοψίες εξετάστηκαν από δύο ανεξάρτητους παθολογοανατόμους για να εκτιμήσουν τη βλάβη του μοσχεύματος μέσω του ιστοπαθολογικού δείκτη και της έκφρασης των μορίων προσκόλλησης ICAM-1 και VCAM-1.Αποτελέσματα: Όλα τα πειραματόζωα επέζησαν μέχρι τη 14η μετεγχειρητική ημέρα. Μετά από 17hψυχρής ισχαιμίας, τόσο το βάρος, όσο και οι συγκεντρώσεις των ενζύμων κυτταρικής βλάβης, ήταν στατιστικά χαμηλότερα στην ομάδα μελέτης, σε σχέση με αυτά της ομάδας ελέγχου (p=0,001 για το βάρος, p=0,001 για την CK και p=0,007 για την LDH, αντίστοιχα). Αντίστοιχα ήταν και τα αποτελέσματα για τη συγκέντρωση των 8-ισοπροστανίων στα 30mins μετά την επαναιμάτωση (p=0,007). Ο ιστοπαθολογικός δείκτης ήταν στατιστικά μικρότερος στην ομάδα μελέτης, σε σχέση με την ομάδα ελέγχου, τόσο στα 30mins από την επαναιμάτωση, όσο και κατά τη 14η μετεγχειρητική ημέρα (p=0,001και p=0,001, αντίστοιχα). Επιπλέον, ενώ αυτός αυξήθηκε στην ομάδα ελέγχου, μεταξύ των 30mins και της 14ης μτχ ημέρας, αντίθετα παρέμεινε σταθερός στην ομάδα μελέτης (p=0,042 και p=0,862,αντίστοιχα). Η έκφραση των μορίων προσκόλλησης ICAM-1 και VCAM-1 κατά την 14η μτχ ημέρα ήταν μικρότερη στην ομάδα μελέτης, σε σχέση με την ομάδα ελέγχου (p=0,029 και p=0,04, αντίστοιχα), ενώ στα 30mins αντίστοιχα μικρότερη ήταν η έκφραση του VCAM-1 (p=0,02). Η έκφραση του ICAM-1 μειώθηκε και στις δύο ομάδες από 30mins στη 14η μτχ ημέρα (ομάδα ελέγχου: p=0,034 και ομάδα μελέτης: p=0,034), ενώ η έκφραση του VCAM-1 παρέμεινε σταθερή και στις δύο ομάδες (ομάδα ελέγχου: p=0,414 και ομάδα μελέτης: p=0,083). Τέλος, από την 3η μέχρι την 8η μετεγχειρητική ημέρα η συγκέντρωση της ουρίας ορού και από την 3η μέχρι την 7η μετεγχειρητική ημέρα η συγκέντρωση της κρεατινίνης ορού ήταν μικρότερες στην ομάδα μελέτης συγκριτικά με την ομάδα ελέγχου (και τα δύοp<0,05).Συμπεράσματα: Η δεφεριπρόνη ασκεί προστατευτική δράση στο νεφρικό μόσχευμα, τόσο κατά τη φάση συντήρησής του, όσο και μετά την επαναιμάτωσή του, με αποτέλεσμα στατιστικά σημαντική βελτίωση της μετεγχειρητικής του λειτουργίας και πιθανά μικρότερης αντιγονικότητας

Online μέθοδοι προσαρµογής παραµέτρων σε πληθυσµιακούς μεταευρετικούς αλγορίθµους

Optimization problems lie in the core of scientific and technological development. They appear inalmost every decision-making process, under various types and forms. A multitude of algorithms have been proposed in relevant literature to solve optimization problems. However, theoretical evidence suggests that the development of an overall optimal algorithm is impossible. For this reason, problemspecific optimization algorithms have been developed, incorporating a variety of features and ad hoc operations that exploit specific properties of the corresponding optimization problem. Typically, optimization algorithms have control parameters that adjust their dynamic with critical impact on their performance. Thus, proper parameter tuning becomes the cornerstone of efficient problem solving. There is a continuous line of research on parameter tuning methods since the early development of optimization algorithms. The majority of these methods addresses the tuning problem offline, i.e., prior to the algorithm’s execution. Established offline methods are based on statistical methodologies to identify promising parameter configurations, and their results may be reusable in problems of similar type. However, they neglect the algorithm’s feed- back and performance fluctuations during its run. The alternative approach is the use of online methods that dynamically adapt the parameters during the algorithm’s run. These methods exploit real-time performance data and, hence, they can make informative decisions on the parameter adaptation. This usually comes at the cost of non-reusable decisions. The main goal of the present thesis is the development of new online parameter adaptation methods that can be particularly useful for the class of metaheuristic optimization algorithms. The first part of the dissertation comprises the necessary background information on the current state-of-the-art and the optimization algorithms that will be used for demonstration purpose. In the second part of the thesis, two new online parameter adaptation methods are proposed. The first method, called Grid-based Parameter Adaptation Method, is based on grid search in the parameter space. The proposed methodcan be used on any algorithm and tackles both scalar and discrete parameters (including categoricalones). The new method is demonstrated on two state-of-the-art metaheuristics. For this purpose, two established benchmark suites are also considered. The second proposed method, called Gradientbased Parameter Adaptation Method with Line Search, replaces the grid search with approximate gradient search in the parameter space. The search procedure is further equipped with a recently proposed gradient-free line search technique. These modifications offer additional performance improvement with respect to the grid-based method, as revealed by the relevant performance assessment.Τα προβλήµατα βελτιστοποίησης βρίσκονται στον πυρήνα της επιστηµονικής και τεχνολογικής έρευνας. Εµφανίζονται σχεδόν σε κάθε διαδικασία λήψης αποφάσεων, υπό διάφορους τύπους καιµορφές. Για την επίλυση προβληµάτων βελτιστοποίησης έχουν προταθεί πολλοί αλγόριθµοι στησχετική βιβλιογραφία. Ωστόσο, θεωρητικές µελέτες έδειξαν ότι είναι αδύνατη η ανάπτυξη ενός καθολικά βέλτιστου αλγορίθµου. Για το λόγο αυτό, η έρευνα επικεντρώνεται στην ανάπτυξη αλγορίθµων βελτιστοποίησης για συγκεκριµένα προβλήµατα, οι οποίοι ενσωµατώνουν ποικίλα χαρακτηριστικά και ad hoc λειτουργίες που εκµεταλλεύονται συγκεκριµένες ιδιότητες του αντίστοιχου προβλήµατος βελτιστοποίησης. Τυπικά, οι αλγόριθµοι βελτιστοποίησης έχουν παραµέτρους ελέγχου που προσαρµόζουν τη δυναµική τους µε κρίσιµο αντίκτυπο στην απόδοσή τους. Έτσι, η σωστή προσαρµογή παραµέτρων αποτελεί ακρογωνιαίο λίθο για την αποτελεσµατική επίλυση προβληµάτων. Για το λόγο αυτό, υπάρχει συνεχές και αυξανόµενο ερευνητικό ενδιαφέρον για τις µεθόδους προσαρµογής παραµέτρων. Η πλειονότητα αυτών των µεθόδων αντιµετωπίζει το πρόβληµα προσαρµογής παραµέτρων offline, δηλαδή πριν από την εκτέλεση του αλγορίθµου. Καθιερωµένες µέθοδοι αυτού του τύπου βασίζονται σε στατιστικές µεθοδολογίες και τα αποτελέσµατά τους δύνανται να επαναχρησιµοποιηθούν σε παρόµοια προβλήµατα. Ωστόσο, δεν λαµβάνουν υπόψη δεδοµένα που προκύπτουν κατά την εκτέλεση του αλγορίθµου, καθώς και πιθανές διακυµάνσεις στην απόδοσή του. Η εναλλακτική προσέγγιση είναι η χρήση online µεθόδων που προσαρµόζουν δυναµικά τις παραµέτρους κατά την εκτέλεση του αλγορίθµου. Αυτές οι µέθοδοι εκµεταλλεύονται δεδοµένα απόδοσης του αλγορίθµου που προκύπτουν σε πραγµατικό χρόνο και, ως εκ τούτου, µπορούν να ενηµερώνουν άµεσα τις παραµέτρους. Ωστόσο, τα αποτελέσµατα αυτών των µεθόδων συνήθως δεν είναι επαναχρησιµοποιήσιµα σε παρόµοια προβλήµατα. Ο κύριος στόχος της παρούσας διατριβής είναι η ανάπτυξη νέων online µεθόδων προσαρµογής παραµέτρων, µε ιδιαίτερη στόχευση στις µεταευρετικές µεθόδους βελτιστοποίησης. Το πρώτο µέρος της διατριβής περιλαµβάνει τις απαραίτητες βασικές πληροφορίες σχετικά µε το τρέχον state-of-the-art και τους αλγορίθµους βελτιστοποίησης που θα χρησιµοποιηθούν για την επίδειξη των νέων µεθόδων. Στο δεύτερο µέρος της διατριβής προτείνονται δύο νέες µέθοδοι προσαρµογής παραµέτρων. Η πρώτηµέθοδος, που ονοµάζεται Grid-based Parameter Adaptation Method, βασίζεται στην αναζήτησηπλέγµατος στο χώρο των παραµέτρων. Η προτεινόµενη µέθοδος µπορεί να χρησιµοποιηθεί σεοποιονδήποτε αλγόριθµο και αντιµετωπίζει τόσο τις πραγµατικές όσο και τις διακριτές παραµέτρους(συµπεριλαµβανοµένων των κατηγορικών παραµέτρων). Η νέα µέθοδος εφαρµόζεται σε δύοδηµοφιλείς µεταευρετικούς αλγορίθµους. Για το σκοπό αυτό, χρησιµοποιούνται δύο βασικές σουίτεςδοκιµαστικών προβληµάτων. Η δεύτερη προτεινόµενη µέθοδος, η οποία ονοµάζεται Gradient-basedParameter Adaptation Method with Line Search, αντικαθιστά την αναζήτηση πλέγµατος µεπροσεγγιστική αναζήτηση παραγώγων στο χώρο των παραµέτρων. Η διαδικασία αναζήτησης είναιεπιπλέον εφοδιασµένη µε µια πρόσφατη τεχνική ευθύγραµµης αναζήτησης χωρίς παραγώγους. Οιπαραπάνω τροποποιήσεις προσφέρουν πρόσθετη βελτίωση απόδοσης σε σχέση µε τη µέθοδοπλέγµατος, όπως αποκαλύπτεται από τη σχετική πειραµατική αξιολόγηση

On damage localization in wind turbine blades: a critical comparison and assessment of modal-based criteria

The effective localization of damage in structural systems and components remains an active research topic in the engineering community. In contrast to damage detection, for which many alternative methods of a certain degree of functionality have already been established, damage localization is considerably more complicated and, in most cases, requires the availa- bility of redundant spatial information. The localization of the exact point where damage, once detected, exists is inherently de- pendent on the adoption of appropriate damage–sensitive features. In general, these should be selected in a way, that allows for the associated feature extraction procedure to take place in a “transformed domain”, where the initial information is significantly amplified for the location of damage. In this respect, vibration–based methods develop damage-sensitive features on the basis of the modal properties of a structure (e.g. natural frequencies, damping ratios and modal and operating shapes), or quantities that are derived from these (e.g. curvatures, flexibility, strain energy, etc.). In this work, we apply and compare the most common vibration–based criteria for damage localization, by considering a small-scale wind turbine blade as a case study (Fig.1). To this end, a 3-dimensional finite element model of the blade is utilized that consists of an exterior laminate composite surface, modelled with shell elements, and an interior foam represented by solid elements. The critical assessment ranks the efficacy of each method in terms of (i) infor- mation availability (e.g. input from all degrees of freedom vs. input from a sparse subset of nodes); (ii) various scenario of damage patterns of increasing severity; and (iii) sensitivity to noise

Optimal ordering and disposal decisions for products with a fixed shelf life

In this paper, motivated by the current increasing interest and action on food waste reduction, inventory decisions of a retailer who deals with a product that has a fixed shelf life are studied. Being a common strategy of many retail stores, we assume that at a specific time instant, close to the expiration date, a price markdown is offered in order to increase demand. However, at the same time, due to customers’ attention to the freshness of the product, the demand becomes a decreasing function with respect to the time remaining before the expiration date. In accordance with the European Union food donation guidelines, we assume that if at the end of the reorder interval unsold items remain that have not exceeded their expiration date, they can be donated to non-profit organizations for human consumption. The donated products can generate direct revenue from tax deductions and indirect revenue by increasing the company’s reputation and gain of goodwill from the customers. If the unsold items have expired, they can be sold at a salvage price to the livestock market. The aim of our model is to determine the reorder interval, the time instant to markdown the product’s initial selling price and the quantity that will be donated or sold to the livestock market so that the profit of the system is maximized. Closed form solutions are obtained, which depend on specific parametric conditions, providing managerial insights

Sequential Bayesian Inference for Uncertain Nonlinear Dynamic Systems: A Tutorial

In this article, an overview of Bayesian methods for sequential simulation from posterior distributions of nonlinear and non-Gaussian dynamic systems is presented. The focus is mainly laid on sequential Monte Carlo methods, which are based on particle representations of probability densities and can be seamlessly generalized to any state-space representation. Within this context, a unified framework of the various Particle Filter (PF) alternatives is presented for the solution of state, state-parameter and input-state-parameter estimation problems on the basis of sparse measurements. The algorithmic steps of each filter are thoroughly presented and a simple illustrative example is utilized for the inference of i) unobserved states, ii) unknown system parameters and iii) unmeasured driving inputs

Neutrophil Extracellular Traps and Pancreatic Cancer Development: A Vicious Cycle

Neutrophil extracellular traps (NETs) are a neutrophil-generated extracellular network of chromatin and chromatin-bound molecules with antimicrobial potency. Recent data suggest that NETs are associated with cancer progression and cancer-associated hypercoagulability. Pancreatic adenocarcinoma (PDAC) is a lethal type of cancer in which hypercoagulability and cancer-related thrombosis are among the main complications. In the current report, we summarize the available data on the interplay between NET formation and PDAC development. We conclude that NETs support a dual role during PDAC progression and metastasis. Their formation is on the one hand an important event that shapes the cancer microenvironment to support cancer cell proliferation, invasion and metastasis. On the other hand, NETs may lead to cancer-associated thrombosis. Both mechanisms seem to be dependent on distinct molecular mechanisms that link inflammation to cancer progression. Collectively, NET formation may contribute to the pathogenesis of PDAC, while during cancer development, the proinflammatory environment enables the induction of new NETs and thrombi, forming a vicious cycle. We suggest that targeting NET formation may be an effective mechanism to inhibit both PDAC development and the accompanying hypercoagulability

Employing Classification Techniques on SmartSpeech Biometric Data towards Identification of Neurodevelopmental Disorders

Early detection and evaluation of children at risk of neurodevelopmental disorders and/or communication deficits is critical. While the current literature indicates a high prevalence of neurodevelopmental disorders, many children remain undiagnosed, resulting in missed opportunities for effective interventions that could have had a greater impact if administered earlier. Clinicians face a variety of complications during neurodevelopmental disorders’ evaluation procedures and must elevate their use of digital tools to aid in early detection efficiently. Artificial intelligence enables novelty in taking decisions, classification, and diagnosis. The current research investigates the efficacy of various machine learning approaches on the biometric SmartSpeech datasets. These datasets come from a new innovative system that includes a serious game which gathers children’s responses to specifically designed speech and language activities and their manifestations, intending to assist during the clinical evaluation of neurodevelopmental disorders. The machine learning approaches were used by utilizing the algorithms Radial Basis Function, Neural Network, Deep Learning Neural Networks, and a variation of Grammatical Evolution (GenClass). The most significant results show improved accuracy (%) when using the eye tracking dataset; more specifically: (i) for the class Disorder with GenClass (92.83%), (ii) for the class Autism Spectrum Disorders with Deep Learning Neural Networks layer 4 (86.33%), (iii) for the class Attention Deficit Hyperactivity Disorder with Deep Learning Neural Networks layer 4 (87.44%), (iv) for the class Intellectual Disability with GenClass (86.93%), (v) for the class Specific Learning Disorder with GenClass (88.88%), and (vi) for the class Communication Disorders with GenClass (88.70%). Overall, the results indicated GenClass to be nearly the top competitor, opening up additional probes for future studies toward automatically classifying and assisting clinical assessments for children with neurodevelopmental disorders

Applying Neural Networks on Biometric Datasets for Screening Speech and Language Deficiencies in Child Communication

Screening and evaluation of developmental disorders include complex and challenging procedures, exhibit uncertainties in the diagnostic fit, and require high clinical expertise. Although typically, clinicians’ evaluations rely on diagnostic instrumentation, child observations, and parents’ reports, these may occasionally result in subjective evaluation outcomes. Current advances in artificial intelligence offer new opportunities for decision making, classification, and clinical assessment. This study explores the performance of different neural network optimizers in biometric datasets for screening typically and non-typically developed children for speech and language communication deficiencies. The primary motivation was to give clinicians a robust tool to help them identify speech disorders automatically using artificial intelligence methodologies. For this reason, in this study, we use a new dataset from an innovative, recently developed serious game collecting various data on children’s speech and language responses. Specifically, we employed different neural network approaches such as Artificial Neural Networks (ANNs), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), along with state-of-the-art Optimizers, namely the Adam, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), Genetic algorithm (GAs), and Particle Swarm Optimization algorithm (PSO). The results were promising, while Integer-bounded Neural Network proved to be the best competitor, opening new inquiries for future work towards automated classification supporting clinicians’ decisions on neurodevelopmental disorders