Characteristics of drug and dietary supplement inquiries by college athletes.

BackgroundIn the United States, the National Center for Drug Free Sport manages the drug-testing programs for athletes of the National Collegiate Athletic Association (NCAA). Through its Resource Exchange Center (REC), Drug Free Sport supports athletic staff and athletes with information regarding drugs and dietary supplements.PurposeTo characterize the types of drug-related and dietary supplement-related inquiries submitted to Drug Free Sport through the REC.Study designCross-sectional study.MethodsAll inquiries submitted to the REC for the period of September 1, 2005, through June 30, 2006, were reviewed. The data were categorized by the method of inquiry submission; the name of the substance in question; the sex, sport, and NCAA division of the athlete involved; the nature of the inquiry; and the response provided by the REC regarding the NCAA's status of the substance in question.ResultsPseudoephedrine, acetaminophen/hydrocodone, and albuterol were the most commonly self-searched medications; stimulants accounted for the majority of banned medications. Dietary supplements accounted for 80% of all inquiries submitted to the REC via the Banned Drug Inquiry Form. Among all dietary supplements, creatine was the most commonly inquired. Banned substances accounted for 29% of all inquiries.ConclusionsThere were more than 10 000 inquiries regarding the status of medications, dietary supplements, and other substances for NCAA athletes during the 2005-2006 academic year. It is helpful for athletes to have resources that help them navigate banned-substance lists and so avoid the inadvertent use of banned substances.Clinical relevanceEducating athletes regarding the stimulant content of various dietary supplements and addressing the lack of clinical trials to support stated claims and safety appear critical

Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification

This paper introduces a novel approach to leverage the knowledge of existing expert models for training new Convolutional Neural Networks, on domains where task-specific data are limited or unavailable. The presented scheme is applied in offline handwritten signature verification (OffSV) which, akin to other biometric applications, suffers from inherent data limitations due to regulatory restrictions. The proposed Student-Teacher (S-T) configuration utilizes feature-based knowledge distillation (FKD), combining graph-based similarity for local activations with global similarity measures to supervise student's training, using only handwritten text data. Remarkably, the models trained using this technique exhibit comparable, if not superior, performance to the teacher model across three popular signature datasets. More importantly, these results are attained without employing any signatures during the feature extraction training process. This study demonstrates the efficacy of leveraging existing expert models to overcome data scarcity challenges in OffSV and potentially other related domains

The Relationship between Ecosystem Services, Human Health and Well-being and its Implication for Environmental Planning: An Agent-Based Model and Geosimulation

A tremendous number of studies have examined the relationship between ecosystem health and human health and well-being, especially in urban settings. The project builds upon a nation-wide Canadian study by Crouse et al. (2017) which explored the correlations between urban greenness and cause-specific mortalities using Cox proportional hazard ratios. Crouse et al. (2017) concluded that increased urban greenness in proximity to participants’ residences is associated with decreases in the risks of cause-specific mortalities. The goal of this project is to develop an agent-based model using NetLogo to explore the relationship between ecosystem services, human health and well-being in the Credit River Watershed (CRW). The model utilizes a Normalized Difference Vegetation Index (NDVI) to establish values of urban greenness in the CRW. Then, hazard ratios are calculated from these NDVI values based on the association observed in the Crouse et al. (2017) study. The model uses a tree-planting, or greening, agent that changes the values of greenness in the study area, thus decreasing hazard ratios. The greening agent is counteracted by a developer agent which converts land adjacent to residential areas into new development. Consequently, this action decreases greenness and increases hazard ratios. This interaction occurs overtime and the results are shown through geosimulation. The model also provides a set of user-defined parameters that modify the nature of agent interactions and the underlying rules governing the model. Overall, the model serves as an educational and decision-support tool for stakeholders in the CRW, including residents, municipal planners, conservation authorities, and policy makers

Νευρωνικά δίκτυα: στρατηγικές βαθιάς εκμάθησης για προβλήματα με περιορισμένο πλήθος δεδομένων

Small sample size learning (SSSL) problem arises when the available training data are limited, making it challenging for machine learning models to capture meaningful patterns and provide accurate predictions. In computer vision applications, constraints on training data are common due to data collection difficulties or high annotation costs. This PhD thesis focuses on exploring deep learning strategies tailored for addressing the SSSL problem, with a specific emphasis on developing efficient training methods for convolutional neural networks (CNNs) when only a limited amount of data are available. Different approaches exist based on the space being considered: data augmentation techniques in the input space, approximating target functions with regularization and pretraining in the model space and encoding relationships between data points within a latent feature space. In this dissertation we propose methods that address SSSL in one or multiple spaces simultaneously. The applications studied in this thesis include biometric verification in the offline signature verification (OffSV) problem, which currently lacks a large available offline signature dataset, and the biomedical problem of human epithelial type-2 (Hep-2) cell classification through indirect immunofluorescence (IIF) microscopy images, involving a challenging annotation process. Initially, shallow representation learning approaches, utilizing traditional computer vision techniques, are studied as a baseline scenario of approaching SSSL. This enabled us to gain valuable insights into the intrinsic characteristics of the studied problems and enhanced the interpretability of the results. Subsequently, a hybrid scheme combining hand-crafted descriptors with a CNN model is proposed. Hand crafted features can create representations with desired invariance characteristics, hence when used as input to a CNN, can provide a more effective starting point for training with limited samples size. A different path to address the SSSL problem studied in this dissertation involves utilizing external data from a similar domain with data abundance. These data can serve as information carriers within a sophisticated training procedure, aimed at enhancing performance in the target problem that suffers data limitations. Such methods were developed in the context of OffSV, where auxiliary handwritten text data were utilized during the training of CNNs in the writer identification task, managing to learn effective encodings of signature images by employing domain adaptation techniques, achieving comparable performance or even surpassing models trained on thousands of signature images. The first such approach proposed in this thesis is explicit domain adaptation, which encompasses metric learning using an additional transformation layer trained via contrastive loss, used to transform the outputs of a pretrained CNN model. The second proposed technique is implicit domain adaptation, implemented through teacher supervision in the Feature-based Knowledge Distillation (FKD) scheme. This method leverages both local and global information from intermediate representations of the teacher to facilitate efficient knowledge transfer. Results demonstrate that the proposed approaches effectively address the SSSL problem in the OffSV domain, operating in either the feature space or the model space, by utilizing auxiliary data in the input space to overcome the challenges posed by the data limitations.Το πρόβλημα εκμάθησης με μικρό πλήθος δεδομένων προκύπτει όταν τα διαθέσιμα δεδομένα εκπαίδευσης είναι περιορισμένα, κάτι που καθιστά δύσκολη την αποτελεσματική εκπαίδευση μοντέλων μηχανικής εκμάθησης και την πρόβλεψη ακριβών αποτελεσμάτων. Η διαθεσιμότητα μεγάλου όγκου δεδομένων συχνά αποτελεί πρόκληση, τόσο σε επίπεδο αποθήκευσης και επεξεργασίας, όσο και σε επίπεδο συλλογής, ελέγχου και χειρωνακτικής επισήμανσης των δεδομένων, ιδιαίτερα σε προβλήματα επιβλεπόμενης εκμάθησης. Η παρούσα Διδακτορική Διατριβή εστιάζει στην ανάπτυξη αποδοτικών και πρωτοποριακών τεχνικών εκμάθησης που καθιστούν εφικτή την αξιοποίηση τεχνικών αιχμής από το χώρο των βαθιών Συνελικτικών Νευρωνικών Δικτύων (ΣΝΔ) σε προβλήματα με εγγενείς περιορισμούς στα διαθέσιμα δεδομένα εκπαίδευσης, όπως π.χ. είναι οι βιομετρικές και βιο-ιατρικές εφαρμογές. Μια απλή ταξινόμηση των προσεγγίσεων για την επίλυση του προβλήματος εκμάθησης με μικρό πλήθος δεδομένων μπορεί να πραγματοποιηθεί με βάση το πεδίο που εφαρμόζονται οι διάφορες τεχνικές, αξιοποιώντας το χώρο εισόδου των δεδομένων με κυρίαρχες τις μεθόδους επαύξησης των δεδομένων, το πεδίο του μοντέλου αναζητώντας τη βέλτιστη συνάρτηση για την αποδοτική κωδικοποίηση της πληροφορίας, και την ανάπτυξη σχέσεων (αν)ομοιότητας στα εξαγόμενα αποτελέσματα του μοντέλου. Αρχικά μελετώνται μοντέλα εκμάθησης με χρήση ρηχών μεθόδων αναπαράστασης εικόνων, χρησιμοποιώντας κλασσικές τεχνικές υπολογιστικής όρασης ως βάση αναφοράς στο πρόβλημα με μικρό πλήθος δεδομένων. Αυτό είναι βοηθητικό για την κατανόηση των χαρακτηριστικών των σημάτων που μελετώνται αλλά και την καλύτερη εξήγηση των αποτελεσμάτων. Στη συνέχεια, προτείνεται μια υβριδική μέθοδος που συνδυάζει κλασσικούς περιγραφείς εικόνων με ένα ΣΝΔ. Ο κλασσικός τρόπος κωδικοποίησης της πληροφορίας εφοδιάζει τις προκύπτουσες αναπαραστάσεις της εικόνας με επιθυμητά χαρακτηριστικά, και όταν χρησιμοποιούνται ως είσοδος σε ένα ΣΝΔ, μπορούν να παρέχουν ένα πιο αποτελεσματικό σημείο εκκίνησης για την εκπαίδευση του δικτύου με περιορισμένο αριθμό δειγμάτων. Σε μια διαφορετική κατεύθυνση αντιμετώπισης του προβλήματος εκμάθησης με μικρό πλήθος δεδομένων, αξιοποιήθηκε η χρήση εξωτερικών δεδομένων από έναν παρόμοιο πρόβλημα με πληθώρα δεδομένων. Αυτά τα δεδομένα εξάχθηκαν έτσι ώστε να εξυπηρετούν ως φορείς πληροφορίας μια ειδικά σχεδιασμένη διαδικασία εκπαίδευσης, με στόχο να βελτιωθεί η απόδοση στο πρόβλημα που υποφέρει από περιορισμούς δεδομένων. Σε αυτή την περίπτωση, η αποτελεσματική προσαρμογή των δύο προβλημάτων, του προβλήματος με επάρκεια δεδομένων και του προβλήματος ενδιαφέροντος με περιορισμένα δεδομένα, πραγματοποιείται σχεδιάζοντας τη διαδικασία εκπαίδευσης τόσο άμεσα με την εκμάθηση αποστάσεων μέσω ενός πρόσθετου επιπέδου μετασχηματισμού που χρησιμοποιείται για να μετασχηματίσει τις εξόδους ενός προ-εκπαιδευμένου μοντέλου ΣΝΔ στη βάση της ομοιότητας των σημάτων που μελετώνται όσο και έμμεσα μέσω ενός σχήματος απόσταξης γνώσης μεταξύ δύο ΣΝΔ, όπου το ένα δίκτυο έχει το ρόλο του μαθητευόμενου και το άλλο του επιβλέποντος, σχηματίζοντας καινοτόμες συναρτήσεις ομοιότητας μεταξύ των ενδιάμεσων αναπαραστάσεων των δύο μοντέλων για την αποτελεσματική μεταφορά της πληροφορίας από το δίκτυο επιβλέποντα κατά τη διάρκεια της εκπαίδευσης του δικτύου μαθητευόμενου. Επομένως, στα πλαίσια αυτής της διδακτορικής διατριβής, σχεδιάστηκαν πρωτοποριακές προσεγγίσεις επίλυσης του προβλήματος εκμάθησης με περιορισμένα δεδομένα αναπτύσσοντας τεχνικές σε πολλαπλά πεδία του προβλήματος αλλά και δοκιμάζοντας διαφορετικές εφαρμογές ενδιαφέροντος

Implanon: a critical review.

To evaluate clinical information on Implanon as a long-term method of contraception and, specifically, to review the efficacy and adverse effect profile of Implanon.A MEDLINE search (from 1966 through June 1999) was performed to retrieve primary and review articles. The search was limited to data on human subjects. Some references were identified through secondary sources.Pharmacokinetic and pharmacodynamic studies, and clinical trials assessing the efficacy and safety of desogestrel implants were reviewed. Relevancy and consistency of information was assessed for each trial.Implanon is the newest contraceptive implant system that has completed worldwide Phase III clinical trials. It is a single rod that contains a core of 68 mg of 3-keto-desogestrel with a membrane of ethylene vinyl acetate. Inhibition of ovulation occurs within one day of implantation, and effective contraception lasts for three years. Fertility returns within one month after implant removal. Insertion and removal by trained medical professionals is simple, and only minor complications have been documented. Adverse effects are mild and primarily consist of abnormal bleeding, weight gain, acne, breast pain, and headache.The data on Implanon indicate that it provides effective long-term contraception with limited adverse effects. It appears to be a good addition to the currently available contraceptives

Characteristics of drug and dietary supplement inquiries by college athletes.

BackgroundIn the United States, the National Center for Drug Free Sport manages the drug-testing programs for athletes of the National Collegiate Athletic Association (NCAA). Through its Resource Exchange Center (REC), Drug Free Sport supports athletic staff and athletes with information regarding drugs and dietary supplements.PurposeTo characterize the types of drug-related and dietary supplement-related inquiries submitted to Drug Free Sport through the REC.Study designCross-sectional study.MethodsAll inquiries submitted to the REC for the period of September 1, 2005, through June 30, 2006, were reviewed. The data were categorized by the method of inquiry submission; the name of the substance in question; the sex, sport, and NCAA division of the athlete involved; the nature of the inquiry; and the response provided by the REC regarding the NCAA's status of the substance in question.ResultsPseudoephedrine, acetaminophen/hydrocodone, and albuterol were the most commonly self-searched medications; stimulants accounted for the majority of banned medications. Dietary supplements accounted for 80% of all inquiries submitted to the REC via the Banned Drug Inquiry Form. Among all dietary supplements, creatine was the most commonly inquired. Banned substances accounted for 29% of all inquiries.ConclusionsThere were more than 10 000 inquiries regarding the status of medications, dietary supplements, and other substances for NCAA athletes during the 2005-2006 academic year. It is helpful for athletes to have resources that help them navigate banned-substance lists and so avoid the inadvertent use of banned substances.Clinical relevanceEducating athletes regarding the stimulant content of various dietary supplements and addressing the lack of clinical trials to support stated claims and safety appear critical

Subscripto multiplex: A Riemannian symmetric positive definite strategy for offline signature verification

International audienceThe human handwritten signature is considered to be a significant biometric trait. In the case of offline signatures, the problem is addressed as an image recognition task. On the other hand, the visual representation of symmetric positive definitive matrices, usually by means of the covariance descriptor of the image feature maps, forms a specific Riemannian manifold with a widespread usage and a favorable performance in a plethora of applications. Surprisingly, no records of offlinesignature-verification-oriented research in the space of symmetric positive definitive matrix have been found up to now. In this work, we propose, for the first time in offline signature-verification literature, mapping of handwritten signature images in points of the tangent space of a connected symmetric positive definitive manifold for verification purposes. Furthermore, based on the principles of differential geometry, we address the notorious limited training problem of offline signature verification in this manifold by proposing two different feature augmentation methods. The efficiency of the proposed method is evaluated using three popular datasets of Western and Asian origin. Error rates against skilled and random forgery in both baselines as well augmentation scenarios are strong indicators of the informative and highly discriminative nature of symmetric positive definitive manifold oriented representation