RESCUR : surfing the waves - a resilience curriculum for early years and primary schools - a teacher's guide

This book is based on a European Project, RESCUR, financed by the EU LLP Comenius Programme, together with the six Universities taking part in the project, namely the University of Malta (coordinator), University of Crete, Greece, University of Lisbon, Portugal, University of Pavia, Italy, Orebro University, Sweden, and the University of Zagreb, Croatia. The Maltese version of this book is also available in this section.RESCUR Surfing The Waves is a resilience programme for early years and primary schools in Europe developed by six European universities The curriculum seeks to empower vulnerable children at risk of early school leaving, absenteeism, disengagement, bullying, social exclusion and marginalisation through a universal, whole school approach. Amongst its special features, it includes story telling making use of two specially created animal characters, mindfulness activities at the beginning of each session, ready made activities and resources for the classroom teacher, interactive multisensory activities, learners portfolio, take home activities, teacher and self assessment checklists for each theme and subtheme, finger and cloth puppets, theme posters, and activity sheets. The activities are experiential, spiral, developmental, inclusive and make use of the SAFE approach. The programme consists of a Teachers Guide, a Parents Guide, and three manuals of activities and resources for Early Years, Early Primary Years and Late Primary Years respectively. It is available in hard and soft copies and available in English and six other languages (Croatian, Greek, Italian, Maltese, Portugese and Swedish). The Maltese version of this book is also available in this section.peer-reviewe

RESCUR : surfing the waves - a resilience curriculum for early years and primary schools - a teacher's guide [Revised edition]

Lifelong Learning Programme Comenius Project. This publication is a product of the project ‘RESCUR - Developing a Resilience Curriculum for Primary Schools in Europe’ www.rescur.eu, funded by the EU Commission Lifelong Learning ProgrammeRESCUR Surfing The Waves (Revised) is a resilience programme for early years and primary schools developed by six European universities, published in 10 languages and implemented in schools across Europe and other countries such as Australia, Russia and Turkey. The programme seeks to empower children at risk of early school leaving, absenteeism, disengagement, bullying, social exclusion and marginalisation through a universal intervention implemented within an inclusive context. Amongst its special features, it includes story telling based on two specially designed characters, mindfulness activities, ready-made activities and resources for the classroom teacher, interactive multisensory activities, take home activities, and teacher and self-assessment checklists for each theme. The 2022 revised edition of the programme includes a Revised Teachers Guide, two new story books with pictures, a Revised Parents Guide, and three revised manuals of activities and resources for Early Years, Early Primary Years and Late Primary Years respectively.peer-reviewe

Toponymy of the Murter island, University of Zadar, Center of the Adriatic onomastic research, Onomastica Adriatica Series, vol. 4 (chief editor Vladimir Skračić), Zadar, 2010

[This corrects the article DOI: 10.1371/journal.pone.0189885.]

Physical and methodological perspectives on the optical properties of biological samples: A review

The optical properties of biological systems can be measured by imaging and microscopy methodologies. The use of X-rays, γ-radiation and electron microscopy provides information about the contents and functions of the systems. The need to develop imaging methods and analyses to measure these optical properties is increasing. On the other hand, biological samples are easily penetrated by a high-energy input, which has revolutionized the field of tissue optical properties and has now reached a point where light can be applied in the diagnosis and treatment of diseases. To this end, developing methodologies would allow the in-depth study of optical properties of tissues. In the present work, we review the literature focusing on optical properties of biological systems and tissues. We have reviewed the literature for related articles on biological samples’ optical properties. We have reported on the theoretical concepts and the applications of Monte Carlo simulations in the studies of optical properties of biological samples. Optical properties of biological samples are of paramount importance for the understanding of biological samples as well as for their applications in disease diagnosis and therapy. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Physical and methodological perspectives on the optical properties of biological samples: A review

The optical properties of biological systems can be measured by imaging and microscopy methodologies. The use of X-rays, γ-radiation and electron microscopy provides information about the contents and functions of the systems. The need to develop imaging methods and analyses to measure these optical properties is increasing. On the other hand, biological samples are easily penetrated by a high-energy input, which has revolutionized the field of tissue optical properties and has now reached a point where light can be applied in the diagnosis and treatment of diseases. To this end, developing methodologies would allow the in-depth study of optical properties of tissues. In the present work, we review the literature focusing on optical properties of biological systems and tissues. We have reviewed the literature for related articles on biological samples’ optical properties. We have reported on the theoretical concepts and the applications of Monte Carlo simulations in the studies of optical properties of biological samples. Optical properties of biological samples are of paramount importance for the understanding of biological samples as well as for their applications in disease diagnosis and therapy. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Deep Learning Approaches to Osteosarcoma Diagnosis and Classification: A Comparative Methodological Approach

Background: Osteosarcoma is the most common primary malignancy of the bone, being most prevalent in childhood and adolescence. Despite recent progress in diagnostic methods, histopathology remains the gold standard for disease staging and therapy decisions. Machine learning and deep learning methods have shown potential for evaluating and classifying histopathological cross-sections. Methods: This study used publicly available images of osteosarcoma cross-sections to analyze and compare the performance of state-of-the-art deep neural networks for histopathological evaluation of osteosarcomas. Results: The classification performance did not necessarily improve when using larger networks on our dataset. In fact, the smallest network combined with the smallest image input size achieved the best overall performance. When trained using 5-fold cross-validation, the MobileNetV2 network achieved 91% overall accuracy. Conclusions: The present study highlights the importance of careful selection of network and input image size. Our results indicate that a larger number of parameters is not always better, and the best results can be achieved on smaller and more efficient networks. The identification of an optimal network and training configuration could greatly improve the accuracy of osteosarcoma diagnoses and ultimately lead to better disease outcomes for patients

Serum Biomarkers and Classification and Regression Trees Can Discriminate Symptomatic from Asymptomatic Carotid Artery Disease Patients

Objective: To assess biomarkers between symptomatic and asymptomatic patients, and to construct a classification and regression tree (CART) algorithm for their discrimination. Patients and Methods: 136 patients were enrolled. They were symptomatic (high risk) (N = 82, stenosis degree ≥ 50%, proven to be responsible for ischemic stroke the last six months) and asymptomatic (low risk) (N = 54, stenosis degree ≤ 50%). Levels of fibrinogen, matrix metalloproteinase-1 (MMP-1), tissue inhibitor of metalloproteinase-1 (TIMP-1), soluble intercellular adhesion molecule (SiCAM), soluble vascular cell adhesion molecule (SvCAM), adiponectin and insulin were measured on a Luminex 3D platform and their differences were evaluated; subsequently, a CART model was created and evaluated. Results: All measured biomarkers, except adiponectin, had significantly higher levels in symptomatic patients. The constructed CART prognostic model had 97.6% discrimination accuracy on symptomatic patients and 79.6% on asymptomatic, while the overall accuracy was 90.4%. Moreover, the population was split into training and test sets for CART validation. Conclusion: Significant differences were found in the biomarkers between symptomatic and asymptomatic patients. The CART model proved to be a simple decision-making algorithm linked with risk probabilities and provided evidence to identify and, therefore, treat patients being at high risk for cardiovascular disease. © 2021, The Author(s)