Elastic Registration of Geodesic Vascular Graphs

Vascular graphs can embed a number of high-level features, from morphological parameters, to functional biomarkers, and represent an invaluable tool for longitudinal and cross-sectional clinical inference. This, however, is only feasible when graphs are co-registered together, allowing coherent multiple comparisons. The robust registration of vascular topologies stands therefore as key enabling technology for group-wise analyses. In this work, we present an end-to-end vascular graph registration approach, that aligns networks with non-linear geometries and topological deformations, by introducing a novel overconnected geodesic vascular graph formulation, and without enforcing any anatomical prior constraint. The 3D elastic graph registration is then performed with state-of-the-art graph matching methods used in computer vision. Promising results of vascular matching are found using graphs from synthetic and real angiographies. Observations and future designs are discussed towards potential clinical applications

Programming for young children using tangible tiles and camera-enabled handheld devices

Schools are trying to teach programming at an earlier age, but there are some difficulties, namely the cost of having enough computer stations for the kids. We present the tangible system Tactode for young students to learn to program in the classroom, using handheld camera devices. The system was tested with a small focus group of students between 10 and 12 years old, that were asked to draw a regular polygon using the Scratch cat. All students completed the required task although some required help. Both students and teachers reported that they thoroughly enjoyed the experience and would like to repeat. In questionaries following the activities, the students declared that they found the language easy to use, with only 14% deeming it somewhat difficult. We consider these early results encouraging as well as informative for future developments

Structural characterization of lead metaniobate thin films deposited by pulsed laser ablation

The ferroelectric polymorph of lead metaniobate (PbNb2O6) presents an orthorhombic structure that is metastable at room temperature. This phase is obtained by quenching from high temperature. The fabrication of lead niobate thin films with this orthorhombic form has been reported to be difficult due to the presence of phases with the rhombohedric form or other non-stoichimetric phases. In this work, lead niobate thin films have been prepared by laser ablation, at different oxygen pressures and with different substrate temperatures. Their structure was studied by X-ray diffraction and their surface was examined by scanning electron microscopy (SEM). The results show that for low deposition temperatures the films presented a rhombohedric-PbNb2O6 structural phase. As Tdep increases the films started to develop an orthorhombic- PbNb2O6 structure that appeared at 400ºC and remains up to 600ºC. For lower oxygen pressure during deposition, a mixture of this phase and other orthorhombic lead deficient phases are present in the films. On the other hand, by increasing the oxygen pressure the lead deficient phases are strongly reduced and the films present only the orthorhombic- PbNb2O6 structure.(undefined

Temporal patterns of TV watching for Portuguese viewers

Audiometer systems provide enormous amounts of detailed TV watching data. Several relevant and interdependent factors may influence TV viewers' behavior. In this work we focus on the time factor and derive Temporal Patterns of TV watching, based on panel data. Clustering base attributes are originated from 1440 binary minute-related attributes, capturing the TV watching status (watch/not watch). Since there are around 2500 panel viewers a data reduction procedure is first performed. K-Means algorithm is used to obtain daily clusters of viewers. Weekly patterns are then derived which rely on daily patterns. The obtained solutions are tested for consistency and stability. Temporal TV watching patterns provide new insights concerning Portuguese TV viewers' behavior

Extracellular matrix in skin diseases: the road to new therapies

"Article in Press"Background: The extracellular matrix (ECM) is a vital structure with a dynamic and complex organization that plays an essential role in tissue homeostasis. In the skin, the ECM is arranged into two types of com- partments: interstitial dermal matrix and basement membrane (BM). All evidence in the literature sup- ports the notion that direct dysregulation of the composition, abundance or structure of one of these types of ECM, or indirect modifications in proteins that interact with them is linked to a wide range of human skin pathologies, including hereditary, autoimmune, and neoplastic diseases. Even though the ECMâ s key role in these pathologies has been widely documented, its potential as a therapeutic target has been overlooked. Aim of review: This review discusses the molecular mechanisms involved in three groups of skin ECM- related diseases - genetic, autoimmune, and neoplastic â and the recent therapeutic progress and oppor- tunities targeting ECM. Key scientific concepts of review: This article describes the implications of alterations in ECM components and in BM-associated molecules that are determinant for guaranteeing its function in different skin dis- orders. Also, ongoing clinical trials on ECM-targeted therapies are discussed together with future oppor- tunities that may open new avenues for treating ECM-associated skin diseases.This work was supported by ERC Consolidator Grant – ECM_INK (ERC-2016-COG-726061) (A. P. Marques and FCT with grant SFRH/ BD/137766/2018 (M. D. Malta) and contract from Norte-01-0145- FEDER-02219015 (M. T. Cerqueira)

Privacy Distillation:Reducing Re-identification Risk of Multimodal Diffusion Models

Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself. We introduce Privacy Distillation, a framework that allows a text-to-image generative model to teach another model without exposing it to identifiable data. Here, we are interested in the privacy issue faced by a data provider who wishes to share their data via a multimodal generative model. A question that immediately arises is ``How can a data provider ensure that the generative model is not leaking identifiable information about a patient?''. Our solution consists of (1) training a first diffusion model on real data (2) generating a synthetic dataset using this model and filtering it to exclude images with a re-identifiability risk (3) training a second diffusion model on the filtered synthetic data only. We showcase that datasets sampled from models trained with privacy distillation can effectively reduce re-identification risk whilst maintaining downstream performance

Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models

Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself. We introduce Privacy Distillation, a framework that allows a text-to-image generative model to teach another model without exposing it to identifiable data. Here, we are interested in the privacy issue faced by a data provider who wishes to share their data via a multimodal generative model. A question that immediately arises is ``How can a data provider ensure that the generative model is not leaking identifiable information about a patient?''. Our solution consists of (1) training a first diffusion model on real data (2) generating a synthetic dataset using this model and filtering it to exclude images with a re-identifiability risk (3) training a second diffusion model on the filtered synthetic data only. We showcase that datasets sampled from models trained with privacy distillation can effectively reduce re-identification risk whilst maintaining downstream performance

A spatial scale assessment of habitat effects on arthropod communities of an oceanic island

Copyright © 2009 Elsevier Masson SAS. All rights reserved.Most habitats in the Azores have undergone substantial land-use changes and anthropogenic disturbance during the last six centuries. In this study we assessed how the richness, abundance and composition of arthropod communities change with: (1) habitat type and (2) the surrounding land-use at different spatial scales. The research was conducted in Terceira Island, Azores. In eighty-one sites of four different habitat types (natural and exotic forests, semi-natural and intensively managed pastures), epigaeic arthropods were captured with pitfall traps and classified as endemic, native or introduced. The land-use surrounding each site was characterized within a radius ranging from 100 to 5000 m. Non-parametric tests were used to identify differences in species richness, abundance and composition between habitat types at different spatial scales. Endemic and native species were more abundant in natural forests, while introduced species were more abundant in intensively managed pastures. Natural forests and intensively managed pastures influenced arthropod species richness and composition at all spatial scales. Exotic forests and semi-natural pastures, however, influenced the composition of arthropod communities at larger scales, promoting the connectivity of endemic and native species populations. Local species richness, abundance and composition of arthropod communities are mostly determined by the presence of nearby natural forests and/or intensively managed pastures. However, semi-natural pastures and exotic forests seem to play an important role as corridors between natural forests for both endemic and native species. Furthermore, exotic forests may serve as a refuge for some native species