pinecone/core-2020-05-10-deduplication huggingface dataset:
Topological analysis of the vasculature of angiopoietin-expressing tumours through scale-space tracing

This work describes the topological analysis of the vasculature of tumours. The analysis is performed with a scale-space technique, which traces the centrelines of vessels as topological ridges of the image intensities and then obtains a series of measurements, which are used to compare the vasculatures. Besides the measurements directly associated with the centrelines, the scales obtained allow the estimation of width andthusareacoveredwithvessels. Tumours of SW1222 human colorectal carcinoma xenografts were observed when growing in dorsal skin-fold window chambers in mice. Three variants of the tumours expressing either endogenous levels of angiopoietins (WT) or over-expressing either angiopoietin-1 (Ang-1) or angiopoietin-2 (Ang-2) were assessed with/without vascular targeted therapy. The scale-space technique was able to discriminate between the vasculatures of the three different tumour types prior to treatment. Results also suggested that over-expression of Ang-2 was associated with susceptibility of the tumour vasculature to the vascular disrupting agent, combretastatin A4 phosphate (CA4P). Substantiation of this finding would point to the potential of tumour Ang-2 expression as a predictive bio-marker for response to CA4P
Can Planning Images Reduce Scatter in Follow-Up Cone-Beam CT?

Due to its wide field of view, cone-beam computed tomography (CBCT) is
plagued by large amounts of scatter, where attenuated photons hit the detector,
and corrupt the linear models used for reconstruction. Given that one can
generate a good estimate of scatter however, then image accuracy can be
retained. In the context of adaptive radiotherapy, one usually has a
low-scatter planning CT image of the same patient at an earlier time.
Correcting for scatter in the subsequent CBCT scan can either be self
consistent with the new measurements or exploit the prior image, and there are
several recent methods that report high accuracy with the latter. In this
study, we will look at the accuracy of various scatter estimation methods, how
they can be effectively incorporated into a statistical reconstruction
algorithm, along with introducing a method for matching off-line Monte-Carlo
(MC) prior estimates to the new measurements. Conclusions we draw from testing
on a neck cancer patient are: statistical reconstruction that incorporates the
scatter estimate significantly outperforms analytic and iterative methods with
pre-correction; and although the most accurate scatter estimates can be made
from the MC on planning image, they only offer a slight advantage over the
measurement based scatter kernel superposition (SKS) in reconstruction error.Comment: 13 pages, submitted to Medical Image Understanding and Analysis
  (MIUA) 2017 conferenc
Model-based correction of segmentation errors in digitised histological images

This is the final version of the article. Available from Springer via the DOI in this record.This paper describes an application of topological, model-based methods for the algorithmic correction of segmentation errors in digitised histological images. The topological analysis is provided by the spatial logic Discrete Mereotopology and integrates qualitative spatial reasoning and constraint satisfaction methods with classical image processing methods. A set of eight topological relations defined on binary segmented regions are factored out and reworked as nodes of a set of directed graphs. The graphs encode and constrain a set of set-theoretic and topological segmentation operations on regions, so that the interpreted images and any proposed changes made to the regions can be made to conform to a valid histological model. Worked examples are given using images of H & E stained H400 cell line cultures.This work was supported by the EPSRC through funding under grant EP/M023869/1, “Novel context-based segmentation algorithms for intelligent microscopy”
Automatic Hotspots Detection for Intracellular Calcium Analysis in Fluorescence Microscopic Videos

In recent years, life-cell imaging techniques and their software applications have become powerful tools to investigate complex biological mechanisms such as calcium signalling. In this paper, we propose an automated framework to detect are-as inside cells that show changes in their calcium concentration i.e. the regions of interests or hotspots, based on videos taken after loading living mouse cardiomyocytes with fluorescent calcium reporter dyes. The proposed system allows an objective and efficient analysis through the following four key stages: 1) Pre-processing to enhance video quality, 2) First level segmentation to detect candidate hotspots based on adaptive thresholding on the frame level, 3) Second-level segmentation to fuse and identify the best hotspots from the entire video by pro-posing the concept of calcium fluorescence hit-ratio, and 4) Extraction of the changes of calcium fluorescence over time per hotspot. From the extracted signals, different measurements are calculated such as maximum peak amplitude, area under the curve, peak frequency, and inter-spike interval of calcium changes. The system was tested using calcium imaging data collected from Heart muscle cells. The paper argues that the automated proposal offers biologists a tool to speed up the processing time and mitigate the consequences of inter-intra observer variability
Multi-level Trainable Segmentation for Measuring Gestational and Yolk Sacs from Ultrasound Images

As a non-hazardous and non-invasive approach to medical diagnostic imaging, ultrasound serves as an ideal candidate for tracking and monitoring pregnancy development. One critical assessment during the first trimester of the pregnancy is the size measurements of the Gestation Sac (GS) and the Yolk Sac (YS) from ultrasound images. Such measurements tend to give a strong indication on the viability of the pregnancy. This paper proposes a novel multi-level trainable segmentation method to achieve three objectives in the following order: (1) segmenting and measuring the GS, (2) automatically identifying the stage of pregnancy, and (3) segmenting and measuring the YS. The first level segmentation employs a trainable segmentation technique based on the histogram of oriented gradients to segment the GS and estimate its size. This is then followed by an automatic identification of the pregnancy stage based on histogram analysis of the content of the segmented GS. The second level segmentation is used after that to detect the YS and extract its relevant size measurements. A trained neural network classifier is employed to perform the segmentation at both levels. The effectiveness of the proposed solution has been evaluated by comparing the automatic size measurements of the GS and YS against the ones obtained gynaecologist. Experimental results on 199 ultrasound images demonstrate the effectiveness of the proposal in producing accurate measurements as well as identifying the correct stage of pregnancy
Automatic Quantification of Epidermis Curvature in H&E Stained Microscopic Skin Image of Mice

Changes in the curvature of the epidermis layer is often associated with many skin disorders, such as ichthyoses and generic effects of ageing. Therefore, methods to quantify changes in the curvature are of a scientific and clinical interest. Manual methods to determine curvature are both laborious and intractable to large scale investigations. This paper proposes an automatic algorithm to quantify curvature of microscope images of H&E-stained murine skin. The algorithm can be divided into three key stages. First, skin layers segmentation based on colour deconvolution to separate the original image into three channels of different representations to facilitate segmenting the image into multiple layers, namely epidermis, dermis and subcutaneous layers. The algorithm then further segments the epidermis layer into cornified and basal sub-layers. Secondly, it quantifies the curvature of the epidermis layer by measuring the difference between the epidermis edge and a straight line (theoretical reference line) connecting the two far sides of the epidermis edge. Finally, the curvature measurements extracted from a large number of images of mutant mice are used to identify a list of genes responsible for changes in the epidermis curvature. A dataset of 5714 H&E microscopic images of mutant and wild type mice were used to evaluate the effectiveness of the algorithm
Data Integration for Open Data on the Web

In this lecture we will discuss and introduce challenges of\ud
integrating openly available Web data and how to solve them. Firstly,\ud
while we will address this topic from the viewpoint of Semantic Web\ud
research, not all data is readily available as RDF or Linked Data, so\ud
we will give an introduction to different data formats prevalent on the\ud
Web, namely, standard formats for publishing and exchanging tabular,\ud
tree-shaped, and graph data. Secondly, not all Open Data is really completely\ud
open, so we will discuss and address issues around licences, terms\ud
of usage associated with Open Data, as well as documentation of data\ud
provenance. Thirdly, we will discuss issues connected with (meta-)data\ud
quality issues associated with Open Data on the Web and how Semantic\ud
Web techniques and vocabularies can be used to describe and remedy\ud
them. Fourth, we will address issues about searchability and integration\ud
of Open Data and discuss in how far semantic search can help to overcome\ud
these. We close with briefly summarizing further issues not covered\ud
explicitly herein, such as multi-linguality, temporal aspects (archiving,\ud
evolution, temporal querying), as well as how/whether OWL and RDFS\ud
reasoning on top of integrated open data could be help
A task recommendation system for children and youth with autism spectrum disorder

Current studies indicate that 1 in 68 children have Autism Spectrum Disease. It is known that early diagnosis and intervention can alter the course of development and significantly improve the prognosis of the disease. It is our intention to develop a task Recommendation System, which will use a Case-based Reasoning machine learning technique, in order to supplement the child’s regular therapy. Besides the tasks’ recommendation, this application will allow a closer monitoring by parents and a better coordination with the therapists, contributing to improve the results on child’s development.(UID/CEC/00319/2013)info:eu-repo/semantics/publishedVersio
Adapting the user path through trajectory data mining

The pervasiveness of location based services such the GPS on
mobile devices enabled the gathering of massive spatial-temporal data.
These databases enabled the mining of new data in order to calculate
frequent patterns and predict the movement of the objects. In the development
of our system for guiding the user with cognitive disabilities
(CogHelper) we are applying a trajectory data mining to adapt and adjust
the path to user preferences. Indeed, the guiding process may be
more useful and it increases the quality of life of the user through this
new functionality (conciliated with the speculative computation module).
Thus, instead of the user having to adapt to the application we are
developing a system that adapts the path to the user. Rather than being
guided through the shortest path he may be oriented by a longer but
preferred path. The main contribution of this paper is the specification
of the trajectory data mining which is incorporated in CogHelper system.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e a Tecnologia within the Project Scope: UID/CEC/00319/2013. The work of João Ramos is supported by a FCT doctoral grant with the reference SFRH/BD/89530/2012.info:eu-repo/semantics/publishedVersio
Modelling an intelligent interaction system for increasing the level of attention

Learning activities using technologies is one of the common education
methods. Its advantages allow that students can learn with concepts more
practical’s. However in this environment not all the students can be attentive. In
this research an Ambient Intelligent System has been designed using biometrics
behaviors for detecting learner inattentiveness. The learning attentiveness of a
student can be determined precisely and the teacher has access to these results
and might improve news strategies.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and
FCT – Fundação para a Ciência e Tecnologia within the Project Scope:
UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio
An environment for studying visual emotion perception

Visual emotion perception is the ability of recognizing and identifying emotions through the visual interpretation of a situation or environment. In this paper we propose an innovative environment for supporting this type of studies, aimed at replacing current pencil-and-paper approaches. Besides automatizing the whole process, this environment provides new features that can enrich the study of emotion perception. These new features are especially interesting for the field of Human-Compute Interaction and Affective computing as they quantify the effects of experiencing different emotional dimensions on the individual’s interaction with the computer.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT   Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio
Defining an architecture for a ubiquitous group decision support system

Supporting group decision-making in ubiquitous contexts is fundamental
while developing Group Decision Support Systems (GDSS). Here we propose
an architecture that assures ubiquity and allows the development of a system
which can be used anywhere at any time and through almost any sort of electronic
device. Our approach can be used by developers that intend to build Ubiquitous
Group Decision Support Systems (UbiGDSS). It uses three main components that
are interconnected and that will allow to collect and preserve the amount and
quality of intelligence generated in face-to-face meetings.This work was supported by COMPETE Programme (operational programme for
competitiveness) within Project POCI-01-0145-FEDER-007043, by National Funds
through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for
Science and Technology) within the Projects UID/CEC/00319/2013,
UID/EEA/00760/2013, and the João Carneiro PhD Grant with the Reference
SFRH/BD/89697/2012.info:eu-repo/semantics/publishedVersio
Representation learning for cross-modality classification

Differences in scanning parameters or modalities can complicate image analysis based on supervised classification. This paper presents two representation learning approaches, based on autoencoders, that address this problem by learning representations that are similar across domains. Both approaches use, next to the data representation objective, a similarity objective to minimise the difference between representations of corresponding patches from each domain. We evaluated the methods in transfer learning experiments on multi-modal brain MRI data and on synthetic data. After transforming training and test data from different modalities to the common representations learned by our methods, we trained classifiers for each of pair of modalities. We found that adding the similarity term to the standard objective can produce representations that are more similar and can give a higher accuracy in these cross-modality classification experiments
Using AgMIP Regional Integrated Assessment Methods to Evaluate Vulnerability, Resilience and Adaptive Capacity for Climate Smart Agricultural Systems

The predicted effects of climate change call for a multi-dimensional method to assess the performance of various agricultural systems across economic, environmental and social dimensions. Climate smart agriculture (CSA) recognizes that the three goals of climate adaptation, mitigation and resilience must be integrated into the framework of a sustainable agricultural system. However, current methods to determine a systems’ ability to achieve CSA goals are lacking. This paper presents a new simulation-based method based on the Regional Integrated Assessment (RIA) methods developed by the Agricultural Model Inter-comparison and Improvement Project (AgMIP) for climate impact assessment. This method combines available data, field- and stakeholder-based surveys, biophysical and economic models, and future climate and socio-economic scenarios. It features an integrated farm and household approach and accounts for heterogeneity across biophysical and socioeconomic variables as well as temporal variability of climate indicators. This method allows for assessment of the technologies and practices of an agricultural system to achieve the three goals of CSA. The case study of a mixed crop livestock system in western Zimbabwe is highlighted as a typical smallholder agricultural systems in Africa
Devising effective strategies and policies for CSA: insights from a panel of global policy experts

In this section, we present the results of a consultation with a panel of leading thinkers on agricultural and climate change policy. We interviewed these experts using a set of questions based on the main findings, conclusions, insights and questions that emerged from our set of case studies and conceptual papers. This section is divided into four parts, each focusing on a set of questions relating to the conclusions that emerged from the case study and conceptual chapters. (i) Focus on changes in production systems as adaptation: priorities and policy actions; (ii) Incorporating climate change into agricultural research and extension; (iii) Taking a close look at national policies affecting risk management: index insurance, safety nets and input subsidies and (iv) Priorities for the future and summary of main points. Overall, there is a fairly high level of agreement amongst the panel members in responding to most of the interview questions, although with some difference in emphasis or applications. However there are also some differences of opinion that emerge from their responses. In this chapter, we discuss the main points made on each of the issues addressed, highlighting the areas of agreement, as well as differences. (Résumé d'auteur
Extrasolar Planets and Their Host Stars

In order to understand the exoplanet, you need to understand its parent star.
Astrophysical parameters of extrasolar planets are directly and indirectly
dependent on the properties of their respective host stars. These host stars
are very frequently the only visible component in the systems. This book
describes our work in the field of characterization of exoplanet host stars
using interferometry to determine angular diameters, trigonometric parallax to
determine physical radii, and SED fitting to determine effective temperatures
and luminosities. The interferometry data are based on our decade-long survey
using the CHARA Array. We describe our methods and give an update on the status
of the field, including a table with the astrophysical properties of all stars
with high-precision interferometric diameters out to 150 pc (status Nov 2016).
In addition, we elaborate in more detail on a number of particularly
significant or important exoplanet systems, particularly with respect to (1)
insights gained from transiting exoplanets, (2) the determination of system
habitable zones, and (3) the discrepancy between directly determined and
model-based stellar radii. Finally, we discuss current and future work
including the calibration of semi-empirical methods based on interferometric
data.Comment: 80 pages in SpringerBrief format containing a few blank pages, 16
  figures, 1 table of all stars with high-precision interferometric diameters,
  glossary of commonly encountered terms, SpringerBrief 2017, ISBN
  978-3-319-61198-
Hybrid ASP-based approach to pattern mining

Detecting small sets of relevant patterns from a given dataset is a
central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered
relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for specifying such criteria in a form of constraints. Although progress has
been made, on the one hand, on solving individual mining problems and, on the
other hand, developing generic mining systems, the existing methods either focus
on scalability or on generality. In this paper we make steps towards combining
local (frequency, size, cost) and global (various condensed representations like
maximal, closed, skyline) constraints in a generic and efficient way. We present a
hybrid approach for itemset and sequence mining which exploits dedicated highly
optimized mining systems to detect frequent patterns and then filters the results
using declarative ASP. Experiments on real-world datasets show the effectiveness
of the proposed method and computational gains both for itemset and sequence
mining.status: publishe
Three methods for revising hybrid knowledge bases

Contemporary approaches for the Semantic Web include hybrid knowledge bases that combine ontologies with rule-based languages. Despite a number of existing combination approaches, little attention has been given to change mechanisms for hybrid knowledge bases that can appropriately handle the dynamics of information on the Web. We present here three methods for revising hybrid knowledge bases in light of new information. We show by means of representation theorems that two of them fit properly into the classic belief change framework and that each of the two generalises the third method.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Modeling, simulation and control of a 4WD electric vehicle with in-wheel motors

A relatively new technology for the electric vehicles considers the use of brushless permanent magnet motors directly connected to the car wheels (in-wheel motors or hub motors). In order to evaluate the performance that can be obtained, a complete dynamic model of a four-wheel drive (4WD) electric vehicle equipped with four in-wheel motors is developed and a correspondent parametric simulator is implemented in Matlab/Simulink™. The simulator is also employed for designing, testing and comparing various control logics which reproduce the handling behavior of a real vehicle
A Study of Learners' Behaviors in Hands-On Learning Situations and Their Correlation with Academic Performance

This study analyzes students’ behavior in our remote laboratory environment and aims at identifying behavioural patterns during a practical session that lead to better learning outcomes, in order to predict learners’ performance and to automatically guide students who might need more support. Based on data collected from an experimentation conducted in an authentic learning context, we discover recurrent sequential patterns of actions that lead us to the definition of learning strategies as indicators of higher level of abstraction. Results show that some of the strategies are correlated to the learners’ performance at the final assessment test. For instance, construction of a complex action step by step, or reflexion before submitting an action, are two strategies applied more often by learners of a higher level of performance than by others. These findings led us to instrument for both students and instructors new guiding and tutoring tools in our remote lab environment