Comparing empirical ROC curves using a Java application: CERCUS

Receiver Operating Characteristic (ROC) analysis is a methodology that has gained much popularity in our days, especially in Medicine, since through the ROC curves, it provides a useful tool to evaluate and specify problems in the performance of a diagnostic indicator. The area under empirical ROC curve (AUC) it’s an indicator that can be used to compare two or more ROC curves. This work arose from the necessity of the existence of software that allows the calculation of the necessary measures to compare systems based on ROC curves. Several software, commercial and non-commercial, are available to perform the calculation of the measures associated to the ROC analysis. However, they present some flaws, especially when there is a need to compare independent samples with different dimensions, or also to compare two ROC curves that intersect. In this paper is presented a new application called CERCUS (Comparison of Empirical ROC Curves). This was developed using a programming language (Java) and stands out for the possibility of comparing two or more ROC curves that cross each other. The main objective of CERCUS is the calculation of several ROC estimates using different methods and make the ROC curves comparison, even if there is an intersection, either for independent or paired samples. It also allows the graph representation of the ROC curve in a unitary plan as well the graph of the area between curves in comparison. This paper presents the program’s versatility in data entry, test menus and visualization of graphs and results.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/201

A Model of Mixed Signals With Applications to Countersignalling

We develop a job-market signalling model where signals convey two pieces of information. This model is employed to study countersignalling (signals nonmonotonic in ability) and the GED exam. A result of the model is that countersignalling is more likely to occur in jobs that require a combination of skills that differs from the combination used in the schooling process. The model also produces testable implications consistent with evidence on the GED: (i) it signals both high cognitive and low noncognitive skills and (ii) it does not affect wages

An Improved Algorithm for Generating Database Transactions from Relational Algebra Specifications

Alloy is a lightweight modeling formalism based on relational algebra. In prior work with Fisler, Giannakopoulos, Krishnamurthi, and Yoo, we have presented a tool, Alchemy, that compiles Alloy specifications into implementations that execute against persistent databases. The foundation of Alchemy is an algorithm for rewriting relational algebra formulas into code for database transactions. In this paper we report on recent progress in improving the robustness and efficiency of this transformation

Unravelling the path to create a cell sheet-based model of skin scar-like tissue

Regardless of the advances in understanding the mechanisms and the pathophysiology behind skin deformities, scaring continues to be an unsolved clinical problem. The underlying wound healing process involves a series of key cells which play different key roles. Fibroblasts are known to suffer the influence of local biochemical (e.g TGF-B1) and biomechanical signaling upon a wound scenario leading to a phenotypical change into myofibroblasts. The latter enhance immature extracellular matrix (ECM) synthesis and generate tensional forces that leads to ECM reorganization. Certain skin pathologies (e.g hypertrophic scars) rise from a dysfunction of this underlying regulatory mechanism which in turn drives myofibroblast persistence in the wound. When trying to study the mechanisms behind scarring human ex vivo samples are many times scarce and most of the current in vitro systems rely on standard 2D cultures of keloid/hypertrophic scar fibroblasts. Taking all of this into consideration we propose the use of cell sheet technology to create an in vitro 3D scar model. Herein we report the effect of TGF-B1 in human dermal fibroblast cell sheets as the first step to attain cell sheets with a myofibroblast-like phenotype in which cells are embedded in a scar-like ECM. To further strengthen our concept we performed the stacking of pre-formed cell sheets generating a cohesive 3D scar-like tissue. Human dermal fibroblast (hDFbs) cell sheets were produced as previously described1, and stimulated with TGF-B1 (10ng/ml) over 7, 14 and 21 days. Following phenotype and ECM characterization, cell sheets were stacked in order to obtain a 3D structure composed of 2 or 3 cell-sheets. The analysis of key genes (q-PCR) and proteins (Western blot and immunocytochemistry) showed that hDFbs cell sheets, when stimulated with TGF-B1 present an increased expression of a-SMA, fibronectin (FN) ED- A and FN ED-B, characteristic of a myofibroblast-like phenotype. When looking into the expression of scar ECM-associated proteins, hDFbs cell sheets obtained in the presence of TGF-B1 produced higher amounts of fibronectin and collagen I. Stable 3D constructs with a noticeable level of integration after a total of 21 days of culture, were further created upon stacking of the cell sheets obtained after 7days of culture in the presence of TGF-B1. In conclusion, this work suggested that it is possible to promote the secretion of scar-like ECM in hDFbs cell sheets due to phenotypic changes into myofibroblast-like cells when stimulated with TGF-B1. Cohesive 3D scar-like tissue structures were obtained which opens the possibility to develop a highly accurate in vitro 3D scar model to study underlying cellular mechanisms involved in the wound healing deregulation. Grant IF/00945/2014 funded by FCT/MCTES, Project “NORTE-08-5369-FSE-000044”, funded by Programa Operacional Norte 2020 Fundo Social Europeu, and GENE2SKIN Twinning Project, Horizon 2020, funded by the European Commissioninfo:eu-repo/semantics/publishedVersio

In vitro 3D cell sheet-based model for unraveling scar pathophysiology

Fibroblasts are key players in the scarring process. In hypertrophic scars, fibroblasts suffer phenotypical changes into myofibroblasts persisting in the wound under the influence of local biochemical (TGFb1) and biomechanical signaling leading to enhanced immature extracellular matrix (ECM) synthesis. Benchtop models of hypertrophic scars rely on scarce human ex vivo samples or standard 2D cultures of hypertrophic scar fibroblasts. We therefore propose the use of human dermal fibroblast cell sheets (hDFbsCS) as the first step to attain cell sheets with a myofibroblast-like phenotype to generate cohesive in vitro 3D scar-like tissues. hDFbsCS were produced as previously described (Cerqueira, 2014), and stimulated with TGFb1 up to 21 days. Following phenotype and ECM characterization, 3 hDFbsCS were stacked to obtain a 3D structure. Gene and protein analysis showed that upon TGFb1 stimulation, hDFbsCS present a higher expression of aSMA, fibronectin EDA and EDB, characteristic of amyofibroblast-like phenotype. Regarding the expression of scar ECM-associated proteins, TGFb1 stimulated hDFbsCS produced increased fibronectin and collagen I. Upon stacking of hDFbsCS obtained after 7 days of culture in the presence of TGFb1, stable and integrated 3D constructs were obtained. This work suggests that it is possible to create cohesive 3D scar-like tissue structures from hDFbsCS opening the possibility to develop in vitro 3D scar models to study wound healing deregulation pathophysiology. Acknowledgments: Grant IF.00945.2014 and SFRH.BD. 119756.2016 (FCT MCTES), NORTE.08.5369.FSE.000044 (funded by Programa Operacional Norte 2020 Fundo Social Europeu), GENE2SKIN Twinning Project, Horizon 2020 (European Commission).Grant IF.00945.2014 and SFRH.BD.119756.2016 (FCT_MCTES), NORTE.08.5369.FSE.000044 (funded by Programa_Operacional_Norte_2020 Fundo Social Europeu), GENE2SKIN Twinning Project, Horizon_2020 (European Commission).info:eu-repo/semantics/publishedVersio

A Benchmark for Iris Location and a Deep Learning Detector Evaluation

The iris is considered as the biometric trait with the highest unique probability. The iris location is an important task for biometrics systems, affecting directly the results obtained in specific applications such as iris recognition, spoofing and contact lenses detection, among others. This work defines the iris location problem as the delimitation of the smallest squared window that encompasses the iris region. In order to build a benchmark for iris location we annotate (iris squared bounding boxes) four databases from different biometric applications and make them publicly available to the community. Besides these 4 annotated databases, we include 2 others from the literature. We perform experiments on these six databases, five obtained with near infra-red sensors and one with visible light sensor. We compare the classical and outstanding Daugman iris location approach with two window based detectors: 1) a sliding window detector based on features from Histogram of Oriented Gradients (HOG) and a linear Support Vector Machines (SVM) classifier; 2) a deep learning based detector fine-tuned from YOLO object detector. Experimental results showed that the deep learning based detector outperforms the other ones in terms of accuracy and runtime (GPUs version) and should be chosen whenever possible.Comment: Accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) 201

Enzymatically activated emulsions stabilised by interfacial nanofibre networks

We report on-demand formation of emulsions stabilised by interfacial nanoscale networks. These are formed through biocatalytic dephosphorylation and self-assembly of Fmoc(9-fluorenylmethoxycarbonyl)-dipeptide amphiphiles in aqueous/organic mixtures. This is achieved by using alkaline phosphatase which transforms surfactant-like phosphorylated precursors into self-assembling aromatic peptide amphiphiles (Fmoc-tyrosine-leucine, Fmoc-YL) that form nanofibrous networks. In biphasic organic/aqueous systems, these networks form preferentially at the interface thus providing a means of emulsion stabilisation. We demonstrate on-demand emulsification by enzyme addition, even after storage of the biphasic mixture for several weeks. Experimental (Fluorescence, FTIR spectroscopy, fluorescence microscopy, electron microscopy, atomic force microscopy) and computational techniques (atomistic molecular dynamics) are used to characterise the interfacial self-assembly process

A Solution to the Challenge of Optimization on ''Golf-Course''-Like Fitness Landscapes

Genetic algorithms (GAs) have been used to find efficient solutions to numerous fundamental and applied problems. While GAs are a robust and flexible approach to solve complex problems, there are some situations under which they perform poorly. Here, we introduce a genetic algorithm approach that is able to solve complex tasks plagued by so-called ''golf-course''-like fitness landscapes. Our approach, which we denote variable environment genetic algorithms (VEGAs), is able to find highly efficient solutions by inducing environmental changes that require more complex solutions and thus creating an evolutionary drive. Using the density classification task, a paradigmatic computer science problem, as a case study, we show that more complex rules that preserve information about the solution to simpler tasks can adapt to more challenging environments. Interestingly, we find that conservative strategies, which have a bias toward the current state, evolve naturally as a highly efficient solution to the density classification task under noisy conditions