Functional mapping of somatostatin receptors in the retina: a review

AbstractThe peptide somatostatin is one of many neuroactive agents that influence retinal physiology. It is synthesized primarily in a subclass of amacrine cells and believed to function as a neurotransmitter, neuromodulator or trophic factor. The cloning of the somatostatin receptors (sst1–5) in the early nineties provided the appropriate tools for the study of ssts in many tissues, including the retina. In this review, emphasis is given to recent studies that have provided significant information on the functional role of somatostatin in retinal circuitry and the retinal pigment epithelium. The important role of somatostatin in retinal disease therapeutics is also discussed

Deep Affordance-grounded Sensorimotor Object Recognition

It is well-established by cognitive neuroscience that human perception of objects constitutes a complex process, where object appearance information is combined with evidence about the so-called object "affordances", namely the types of actions that humans typically perform when interacting with them. This fact has recently motivated the "sensorimotor" approach to the challenging task of automatic object recognition, where both information sources are fused to improve robustness. In this work, the aforementioned paradigm is adopted, surpassing current limitations of sensorimotor object recognition research. Specifically, the deep learning paradigm is introduced to the problem for the first time, developing a number of novel neuro-biologically and neuro-physiologically inspired architectures that utilize state-of-the-art neural networks for fusing the available information sources in multiple ways. The proposed methods are evaluated using a large RGB-D corpus, which is specifically collected for the task of sensorimotor object recognition and is made publicly available. Experimental results demonstrate the utility of affordance information to object recognition, achieving an up to 29% relative error reduction by its inclusion.Comment: 9 pages, 7 figures, dataset link included, accepted to CVPR 201

Ta prolegomena of Alexander's homonoia

[Δε διατίθεται περίληψη / no abstract available][Δε διατίθεται περίληψη / no abstract available

The Evolution of Teen Pregnancy: A Comprehensive “Application” to Educate Teen Mothers

Teen pregnancy is a major concern in the United States. Although teen pregnancy rates have declined, teen pregnancy still exists and babies are still born to girls who may not be well prepared to achieve a healthy pregnancy and subsequently parent. Today, there are many pregnancy materials on the market; however, most pregnancy products are geared toward pregnant adults. This project attempts to address the access gap for teen pregnancy education. This project includes two components. The paper component, also known as the educator’s companion, which is meant to be utilized by the educator working with the teen who uses the app. Information provided will include how to assist the teen in understanding her economic and health management resources, as well as planning for family and social support in pregnancy and the immediate postpartum period. The second component is a smart phone application (app) entitled “Pregnancy Management for Teens: Center for the Expecting Teen” or “PM4Teens”. The app covers the same topics provided in the educator’s companion to provide the teen with the knowledge that she may seek out in her own time when she feels most comfortable. The purpose of this text is not to encourage teen pregnancy, but to assist the educator in providing accurate, age appropriate education to the teens who do become pregnant

A Deep Learning Approach to Object Affordance Segmentation

Learning to understand and infer object functionalities is an important step towards robust visual intelligence. Significant research efforts have recently focused on segmenting the object parts that enable specific types of human-object interaction, the so-called "object affordances". However, most works treat it as a static semantic segmentation problem, focusing solely on object appearance and relying on strong supervision and object detection. In this paper, we propose a novel approach that exploits the spatio-temporal nature of human-object interaction for affordance segmentation. In particular, we design an autoencoder that is trained using ground-truth labels of only the last frame of the sequence, and is able to infer pixel-wise affordance labels in both videos and static images. Our model surpasses the need for object labels and bounding boxes by using a soft-attention mechanism that enables the implicit localization of the interaction hotspot. For evaluation purposes, we introduce the SOR3D-AFF corpus, which consists of human-object interaction sequences and supports 9 types of affordances in terms of pixel-wise annotation, covering typical manipulations of tool-like objects. We show that our model achieves competitive results compared to strongly supervised methods on SOR3D-AFF, while being able to predict affordances for similar unseen objects in two affordance image-only datasets.Comment: 5 pages, 4 figures, ICASSP 202

Non-metallic brush seals for gas turbine bearings

A non-metallic brush seal has been developed as an oil seal for use in turbomachinary. Traditionally labyrinth-type seals with larger clearances have been used in such applications. Labyrinth seals have higher leakage rates and can undergo excessive wear in case of rotor instability. Brush seals reduce leakage by up to an order of magnitude and provide compliance against rotor instabilities. Brush seals are compact and are much less prone to degradation associated with oil sealing. This paper describes the benefits and development of the nonmetallic brush seals for oil sealing application

Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation

Generalising deep models to new data from new centres (termed here domains) remains a challenge. This is largely attributed to shifts in data statistics (domain shifts) between source and unseen domains. Recently, gradient-based meta-learning approaches where the training data are split into meta-train and meta-test sets to simulate and handle the domain shifts during training have shown improved generalisation performance. However, the current fully supervised meta-learning approaches are not scalable for medical image segmentation, where large effort is required to create pixel-wise annotations. Meanwhile, in a low data regime, the simulated domain shifts may not approximate the true domain shifts well across source and unseen domains. To address this problem, we propose a novel semi-supervised meta-learning framework with disentanglement. We explicitly model the representations related to domain shifts. Disentangling the representations and combining them to reconstruct the input image allows unlabeled data to be used to better approximate the true domain shifts for meta-learning. Hence, the model can achieve better generalisation performance, especially when there is a limited amount of labeled data. Experiments show that the proposed method is robust on different segmentation tasks and achieves state-of-the-art generalisation performance on two public benchmarks.Comment: Accepted by MICCAI 202

Συγκριτική μελέτη της ανοσοϊστοχημικής έκφρασης των μορίων BMP4 και FOXN1 σε οδοντογενείς κερατινοκύστεις και ορθοκερατινοποιημένες οδοντογενείς κύστεις

Εισαγωγή: Η Οδοντογενής Κερατινοκύστη (ΟΚΚ) και η Ορθοκερατινοποιημένη Οδοντογενής Κύστη (ΟΟΚ) αποτελούν δύο οδοντογενείς βλάβες αναπτυξιακής αιτιολογίας με πρότυπο επιθηλιακής διαφοροποίησης και κερατινοποίησης παρόμοιο με του καλυπτικού επιθηλίου και κυστικών δερματικών βλαβών, αλλά με εκ διαμέτρου αντίθετη βιολογική συμπεριφορά όπως αυτή χαρακτηρίζεται από το αυξημένο ποσοστό υποτροπής της πρώτης έναντι της δεύτερης. Η μορφογενετική πρωτεΐνη του οστού 4 (BMP4) αποτελεί κομβικό μόριο για την φυσιολογική διαδικασία της οδοντογένεσης, ρυθμίζοντας την έκφραση της πρωτεΐνης Sonic Hedgehog (SHH) ενός μορίου το οποίο φαίνεται να διαδραματίζει σημαντικό ρόλο στην παθογένεση της ΟΚΚ. Επίσης, σε ποικίλους ιστούς επιθηλιακής προέλευσης, όπως το τριχοθυλάκιο και η επιδερμίδα, επάγει την έκφραση του μεταγραφικού παράγοντα Forkhead box N1 (FOXN1), συμβάλλοντας στην ωρίμανση των επιθηλιακών κυττάρων και τον έλεγχο του κυτταρικού πολλαπλασιασμού. Σκοπός: Η ανοσοϊστοχημική διερεύνηση και συγκριτική αξιολόγηση της έκφρασης και της μικροανατομικής κατανομής των μορίων BMP4 και FOXN1 σε ΟΚΚ συγκριτικά με ΟΟΚ. Μέθοδος και Υλικά: Η έκφραση των BMP4 και FOXN1 μελετήθηκε με τη τεχνική της ανοσοϊστοχημείας σε 20 μη συνδρομικές και μη υποτροπιάζουσες ΟΚΚ προερχόμενες από 20 ασθενείς συγκριτικά με 16 περιπτώσεις ΟΟΚ. Όλα τα ιστοτεμάχια προήλθαν από το ιστοπαθολογικό αρχείο του Εργαστηρίου Στοματολογίας της Οδοντιατρικής Σχολής του Εθνικού και Καποδιστριακού Πανεπιστημίου Αθηνών. Αποτελέσματα: Παρατηρήθηκε κυτταροπλασματική έκφραση της BMP4 στο επιθήλιο 7/20 (35%) OKK έναντι 13/16 (81,25%) ΟΟΚ, με τη διαφορά αυτή να είναι στατιστικά σημαντική (p=0,006). Κατά την σύγκριση της έκφρασης της πρωτεΐνης στο συνδετικό ιστό ανάμεσα στις 2 βλάβες, δεν παρατηρήθηκε στατιστικά σημαντική διαφορά (p=0,718), ωστόσο, η ταυτόχρονη θετικότητα BMP4 σε επιθήλιο και συνδετικό ιστό παρατηρήθηκε πιο συχνά στις ΟΟΚ (p=0,02). Όσον αφορά στον FOXN1, 12/16 (75%) ΟΟΚ εμφάνιζαν θετικότητα στο επιθήλιο έναντι 6/20 (30%) ΟΚΚ ενώ ο φαινότυπος BMP4+ FOXN1+ παρατηρήθηκε σε συχνότερο βαθμό στις ΟΟΚ (p=0,007), τόσο για την επιθηλιακή όσο και για τη μεσεγχυματική εντόπιση της BMP4, με τα προαναφερθέντα αποτελέσματα να αναδεικνύονται στατιστικά σημαντικά (p=0,004). Συμπεράσματα: Η υψηλότερη ανοσοϊστοχημική έκφραση των BMP4 και FOXN1 και η μεγαλύτερη συχνότητα ταυτόχρονης θετικότητας των 2 μορίων στην ΟΟΚ έναντι της ΟΚΚ, πιθανόν, υποδηλώνουν αυξημένη δραστηριότητα του μονοπατιού BMP4-FOXN1 στην ΟΟΚ και συμμετοχή στην υψηλότερη επιθηλιακή διαφοροποίηση της συγκριτικά με την ΟΚΚ.Introduction: Odontogenic Keratocyst (OKC) and Orthokeratinized Odontogenic Cyst (OOC) are odontogenic cysts of developmental origin that share common features such as keratinized lining epithelium and histological similarity with the epidermis and cutaneous cystic lesions, albeit showing contrasting biological behavior and recurrence rate. Bone morphogenetic protein 4 (BMP4) plays a critical role in the process of odontogenesis regulating the expression of the Sonic Hedgehog (SHH) protein, a molecule implicated in the pathogenesis of OKC. Furthermore, in various tissues of epithelial origin, mainly the interfollicular epidermis and the hair follicle, BMP4 induces the expression of the transcription factor Forkhead box N1 (FOXN1) which participates in the terminal differentiation of epithelial cells as well as the control of cellular proliferation. Objectives: To investigate and compare the immunohistochemical expression of BMP4 and FOXN1 in OKCs and OOCs. Materials and Methods: BMP4 and FOXN1 expression was assessed by immunohistochemistry in 20 non-syndromic and non-recurrent OKCs in comparison to 16 OOCs. All specimens were retrieved from the archives of the Oral Pathology Laboratory of the Department of Oral Medicine and Oral Pathology, National and Kapodistrian University of Athens, Greece. Results: BMP4 epithelial expression was cytoplasmic and was detected in 7/20 (35%) OKCs compared to 13/16 (81,25%) OOCs and this observed difference was statistically significant (p=0,006). Even though no difference was noted in the expression of BMP4 in the connective tissue of the two lesions (p=0,718), a higher percentage of simultaneous positivity of the protein in the epithelium and connective tissue of OOC was seen, in a statistically significant manner (p=0,02). Regarding epithelial FOXN1 expression, it was detected in 12/16 (75%) of OOC cases versus 6/20 (30%) of OKC cases (p=0,007). The double positive phenotype of BMP4 and FOXN1 was more prevalent in OOCs in comparison to OKCs, both in cases where BMP4 was expressed in the epithelium and in cases where the protein’s expression was found in the connective tissue, with these results being statistically significant (p=0,004) Conclusion: The higher immunohistochemical expression of both BMP4 and FOXN1 as well as the more prevalent BMP4+ FOXN1+ phenotype in OOC suggest a higher activity of the BMP4-FOXN1 axis in this lesion, a finding that is possibly reflected in the more mature epithelial phenotype of OOC compared to OKC

Noise-in, Bias-out: Balanced and Real-time MoCap Solving

Real-time optical Motion Capture (MoCap) systems have not benefited from the advances in modern data-driven modeling. In this work we apply machine learning to solve noisy unstructured marker estimates in real-time and deliver robust marker-based MoCap even when using sparse affordable sensors. To achieve this we focus on a number of challenges related to model training, namely the sourcing of training data and their long-tailed distribution. Leveraging representation learning we design a technique for imbalanced regression that requires no additional data or labels and improves the performance of our model in rare and challenging poses. By relying on a unified representation, we show that training such a model is not bound to high-end MoCap training data acquisition, and exploit the advances in marker-less MoCap to acquire the necessary data. Finally, we take a step towards richer and affordable MoCap by adapting a body model-based inverse kinematics solution to account for measurement and inference uncertainty, further improving performance and robustness. Project page: https://moverseai.github.io/noise-tailComment: Project page: https://moverseai.github.io/noise-tai