175 research outputs found

    Collaborative method to maintain business process models updated

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    Business process models are often forgotten after their creation and its representation is not usually updated. This appears to be negative as processes evolve over time. This paper discusses the issue of business process models maintenance through the definition of a collaborative method that creates interaction contexts enabling business actors to discuss about business processes, sharing business knowledge. The collaboration method extends the discussion about existing process representations to all stakeholders promoting their update. This collaborative method contributes to improve business process models, allowing updates based in change proposals and discussions, using a groupware tool that was developed. Four case studies were developed in real organizational environment. We came to the conclusion that the defined method and the developed tool can help organizations to maintain a business process model updated based on the inputs and consequent discussions taken by the organizational actors who participate in the processes.info:eu-repo/semantics/publishedVersio

    Pediatric ocular rosacea, a misdiagnosed disease with high morbidity: Proposed diagnostic criteria

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    Ocular rosacea is an important and underdiagnosed chronic inflammatory disorder observed in children. A clinical spectrum ranging from chronic eyelid inflammation, recurrent ocular redness, photophobia and/or hordeola/chalazions and conjunctival/corneal phlyctenules evolving to neovascularization and scarring may occur. Visual impairment and consequent amblyopia are frequent and corneal perforation although rare is the most feared complication. Ocular manifestations usually precede cutaneous lesions. Although few cases of pediatric ocular rosacea (POR) have been reported in the literature, many cases must have been underdiagnosed or misdiagnosed. The delay in diagnosis is greater than one year in the large majority of cases and may lead to serious ocular sequelae. This review aims to highlight the clinical features of POR, its epidemiology, easy diagnosis and effective treatment. We also propose new diagnostic criteria, in which at least three of the five clinical criteria must be present: (1) Chronic or recurrent keratoconjunctivitis and/or red eye and/or photophobia; (2) Chronic or recurrent blepharitis and/or chalazia/ hordeola; (3) Eyelid telangiectasia documented by an ophthalmologist; (4) Primary periorificial dermatitis and/ or primary features of rosacea; and (5) Positive familial history of cutaneous and/or ocular rosacea

    BV equivalence with boundary

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    An extension of the notion of classical equivalence of equivalence in the Batalin–Vilkovisky (BV) and Batalin–Fradkin–Vilkovisky (BFV) frameworks for local Lagrangian field theory on manifolds possibly with boundary is discussed. Equivalence is phrased in both a strict and a lax sense, distinguished by the compatibility between the BV data for a field theory and its boundary BFV data, necessary for quantisation. In this context, the first- and second-order formulations of nonabelian Yang–Mills and of classical mechanics on curved backgrounds, all of which admit a strict BV–BFV description, are shown to be pairwise equivalent as strict BV–BFV theories. This in particular implies that their BV complexes are quasi-isomorphic. Furthermore, Jacobi theory and one-dimensional gravity coupled with scalar matter are compared as classically equivalent reparametrisation-invariant versions of classical mechanics, but such that only the latter admits a strict BV–BFV formulation. They are shown to be equivalent as lax BV–BFV theories and to have isomorphic BV cohomologies. This shows that strict BV–BFV equivalence is a strictly finer notion of equivalence of theories

    Risk of low energy availability among female and male elite runners competing at the 26th European cross-country championships

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    Low energy availability (LEA) causes impaired physiological functioning. Cross-country running is a weight-sensitive sport, making athletes more prone to LEA. We aimed to estimate the prevalence of elite European cross-country athletes at risk of LEA using the LEA in Females Questionnaire (LEAF-Q) and to analyze demographic and physical characteristics that are associated with LEA. Athletes ≥ 18 years competing at the 26th European Cross-Country Championships (n = 602) were invited to complete a questionnaire (sociodemographic, training, anthropometric characteristics, and LEAF-Q). A total of 207 valid surveys were collected (83 females, 22.1 (4.0) years, and 124 males, 22.3 (4.1) years), and 16 surveys were excluded. A high prevalence of athletes at risk of LEA (64.3%) was observed, being higher in females than in males (79.5 and 54.0% respectively, p < 0.001). More than half of athletes (54.1%, n = 112) reported bowel movements once a week or more rarely, while 33 female athletes (41.3%) did not report normal menstruation. Overall, cross-country athletes are at high risk of LEA. Moreover, a high prevalence of gastrointestinal and menstrual impairments was reported. Hence, athletes should be followed by multidisciplinary teams to inform, prevent, and treat LEA and its effects.publishersversionpublishe

    Deep Learning for Identification of Acute Illness and Facial Cues of Illness

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    Background: The inclusion of facial and bodily cues (clinical gestalt) in machine learning (ML) models improves the assessment of patients' health status, as shown in genetic syndromes and acute coronary syndrome. It is unknown if the inclusion of clinical gestalt improves ML-based classification of acutely ill patients. As in previous research in ML analysis of medical images, simulated or augmented data may be used to assess the usability of clinical gestalt. Objective: To assess whether a deep learning algorithm trained on a dataset of simulated and augmented facial photographs reflecting acutely ill patients can distinguish between healthy and LPS-infused, acutely ill individuals. Methods: Photographs from twenty-six volunteers whose facial features were manipulated to resemble a state of acute illness were used to extract features of illness and generate a synthetic dataset of acutely ill photographs, using a neural transfer convolutional neural network (NT-CNN) for data augmentation. Then, four distinct CNNs were trained on different parts of the facial photographs and concatenated into one final, stacked CNN which classified individuals as healthy or acutely ill. Finally, the stacked CNN was validated in an external dataset of volunteers injected with lipopolysaccharide (LPS). Results: In the external validation set, the four individual feature models distinguished acutely ill patients with sensitivities ranging from 10.5% (95% CI, 1.3–33.1% for the skin model) to 89.4% (66.9–98.7%, for the nose model). Specificity ranged from 42.1% (20.3–66.5%) for the nose model and 94.7% (73.9–99.9%) for skin. The stacked model combining all four facial features achieved an area under the receiver characteristic operating curve (AUROC) of 0.67 (0.62–0.71) and distinguished acutely ill patients with a sensitivity of 100% (82.35–100.00%) and specificity of 42.11% (20.25–66.50%). Conclusion: A deep learning algorithm trained on a synthetic, augmented dataset of facial photographs distinguished between healthy and simulated acutely ill individuals, demonstrating that synthetically generated data can be used to develop algorithms for health conditions in which large datasets are difficult to obtain. These results support the potential of facial feature analysis algorithms to support the diagnosis of acute illness

    Extracting Dielectric Properties for MRI-based Phantoms for Axillary Microwave Imaging Device

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    Microwave Imaging (MWI) is an emerging medical imaging technique, which has been studied to aid breast cancer diagnosis in the frequency range from 0.5 to 30 GHz. The information about the dielectric properties of each tissue is essential to assess the viability of this type of systems. However, accurate measurements of heterogeneous tissues can be very challenging, and the current available information is still very limited. In this paper, we present a methodology for extracting dielectric properties to create anatomical models of the axillary region. These models will be used in a MWI device to aid breast cancer diagnosis through the detection of metastasised axillary lymph nodes. We apply segmentation tools to Magnetic Resonance Images (MRI) of the breast and assign dielectric properties to each tissue, extracting preliminary information about the properties of axillary lymph nodes. This study may open a way to more quickly extract dielectric properties of tissues and/or validate measurements, accelerating the development of microwave-based medical devices.The authors would like to acknowledge the study with ref. CES/44/2019/ME in Hospital da Luz Lisboa (19/09/2019).info:eu-repo/semantics/publishedVersio

    Dopamine neuron activity encodes the length of upcoming contralateral movement sequences

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    Funding Information: We thank Ana Vaz and Catarina Carvalho for mouse colony management, Thomas Akam and Hélio Rodrigues for help in behavioral box development and implementation, and the Champalimaud Hardware Platform (Filipe Carvalho, Artur Silva, and Dário Bento) for support in the development of the behavioral hardware setup. We thank Cristina Alcácer and Nuno Loureiro for their contributions during the 6-OHDA experiments. This work was supported by Fundação Para a Ciência e Tecnologia (FCT) through a doctoral fellowship ( SFRH/BD/119623/2016 to M.D.M.) and Fundação Luso-Americana para o Desenvolvimento (FLAD) by a visiting student fellowship ( 2018/31 to M.D.M.); by a doctoral fellowship from the Gulbenkian Foundation (to J.A.d.S.), a Marie Curie Fellowship ( MSCA-IF-RI 2016 to L.F.H.), and a Spanish Ministry of Innovation and Sciences doctoral fellowship ( BES-2016-077493 to I.C.); and by ERA-NET , ERC ( COG 617142 ), HHMI ( IEC 55007415 ), National Institutes of Health ( 5U19NS104649 ), and the Simons-Emory International Consortium on Motor Control and the Aligning Science Across Parkinson’s ( ASAP-020551 ) through the Michael J. Fox Foundation for Parkinson’s Research (MJFF) to R.M.C. Further support was obtained from the research infrastructure Congento, co-funded by Lisboa2020 and FCT ( LISBOA-01-0145-FEDER-022170 ). Publisher Copyright: © 2024 The AuthorsDopaminergic neurons (DANs) in the substantia nigra pars compacta (SNc) have been related to movement speed, and loss of these neurons leads to bradykinesia in Parkinson's disease (PD). However, other aspects of movement vigor are also affected in PD; for example, movement sequences are typically shorter. However, the relationship between the activity of DANs and the length of movement sequences is unknown. We imaged activity of SNc DANs in mice trained in a freely moving operant task, which relies on individual forelimb sequences. We uncovered a similar proportion of SNc DANs increasing their activity before either ipsilateral or contralateral sequences. However, the magnitude of this activity was higher for contralateral actions and was related to contralateral but not ipsilateral sequence length. In contrast, the activity of reward-modulated DANs, largely distinct from those modulated by movement, was not lateralized. Finally, unilateral dopamine depletion impaired contralateral, but not ipsilateral, sequence length. These results indicate that movement-initiation DANs encode more than a general motivation signal and invigorate aspects of contralateral movements.publishersversionpublishe

    Development of MRI‐based axillary numerical models and estimation of axillary lymph node dielectric properties for microwave imaging

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    Purpose: Microwave imaging (MWI) has been studied as a complementary imaging modality to improve sensitivity and specificity of diagnosis of axillary lymph nodes (ALNs), which can be metastasized by breast cancer. The feasibility of such a system is based on the dielectric contrast between healthy and metastasized ALNs. However, reliable information such as anatomically realistic numerical models and matching dielectric properties of the axillary region and ALNs, which are crucial to develop MWI systems, are still limited in the literature. The purpose of this work is to develop a methodology to infer dielectric properties of structures from magnetic resonance imaging (MRI), in particular, ALNs. We further use this methodology, which is tailored for structures farther away from MR coils, to create MRI- based numerical models of the axillary region and share them with the scientific community, through an open- access repository. Methods: We use a dataset of breast MRI scans of 40 patients, 15 of them with metastasized ALNs. We apply image processing techniques to minimize the artifacts in MR images and segment the tissues of interest. The background, lung cavity, and skin are segmented using thresholding techniques and the remaining tissues are segmented using a K- means clustering algorithm. The ALNs are segmented combining the clustering results of two MRI sequences. The performance of this methodology was evaluated using qualitative criteria. We then apply a piecewise linear interpolation between voxel signal intensities and known dielectric properties, which allow us to create dielectric property maps within an MRI and consequently infer ALN properties. Finally, we compare healthy and metastasized ALN dielectric properties within and between patients, and we create an open- access repository of numerical axillary region numerical models which can be used for electromagnetic simulations. Results: The proposed methodology allowed creating anatomically realistic models of the axillary region, segmenting 80 ALNs and analyzing the corresponding dielectric properties. The estimated relative permittivity of those ALNs ranged from 16.6 to 49.3 at 5 GHz. We observe there is a high variability of dielectric properties of ALNs, which can be mainly related to the ALN size and, consequently, its composition. We verified an average dielectric contrast of 29% between healthy and metastasized ALNs. Our repository comprises 10 numerical models of the axillary region, from five patients, with variable number of metastasized ALNs and body mass index. Conclusions: The observed contrast between healthy and metastasized ALNs is a good indicator for the feasibility of a MWI system aiming to diagnose ALNs. This paper presents new contributions regarding anatomical modeling and dielectric properties' characterization, in particular for axillary region applications.info:eu-repo/semantics/publishedVersio

    Student perspectives on the relationship between a curve and its tangent in the transition from Euclidean Geometry to Analysis

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    The tangent line is a central concept in many mathematics and science courses. In this paper we describe a model of students’ thinking – concept images as well as ability in symbolic manipulation – about the tangent line of a curve as it has developed through students’ experiences in Euclidean Geometry and Analysis courses. Data was collected through a questionnaire administered to 196 Year 12 students. Through Latent Class Analysis, the participants were classified in three hierarchical groups representing the transition from a Geometrical Global perspective on the tangent line to an Analytical Local perspective. In the light of this classification, and through qualitative explanations of the students’ responses, we describe students’ thinking about tangents in terms of seven factors. We confirm the model constituted by these seven factors through Confirmatory Factor Analysis

    Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis

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    Breast cancer diagnosis using radar-based medical MicroWave Imaging (MWI) has been studied in recent years. Realistic numerical and physical models of the breast are needed for simulation and experimental testing of MWI prototypes. We aim to provide the scientific community with an online repository of multiple accurate realistic breast tissue models derived from Magnetic Resonance Imaging (MRI), including benign and malignant tumours. Such models are suitable for 3D printing, leveraging experimental MWI testing. We propose a pre-processing pipeline, which includes image registration, bias field correction, data normalisation, background subtraction, and median filtering. We segmented the fat tissue with the region growing algorithm in fat-weighted Dixon images. Skin, fibroglandular tissue, and the chest wall boundary were segmented from water-weighted Dixon images. Then, we applied a 3D region growing and Hoshen-Kopelman algorithms for tumour segmentation. The developed semi-automatic segmentation procedure is suitable to segment tissues with a varying level of heterogeneity regarding voxel intensity. Two accurate breast models with benign and malignant tumours, with dielectric properties at 3, 6, and 9 GHz frequencies have been made available to the research community. These are suitable for microwave diagnosis, i.e., imaging and classification, and can be easily adapted to other imaging modalities.info:eu-repo/semantics/publishedVersio
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