17,186 research outputs found
The effects of peer influence on adolescent pedestrian road-crossing decisions
Objective: Adolescence is a high-risk period for pedestrian injury. It is also a time of heightened susceptibility to peer influence. The aim of this research was to examine the effects of peer influence on the pedestrian road-crossing decisions of adolescents.
Methods: Using 10 videos of road-crossing sites, 80 16- to 18-year-olds were asked to make pedestrian road-crossing decisions. Participants were assigned to one of 4 experimental conditions: negative peer (influencing unsafe decisions), positive peer (influencing cautious decisions), silent peer (who observed but did not comment), and no peer (the participant completed the task alone). Peers from the adolescent’s own friendship group were recruited to influence either an unsafe or a cautious decision.
Results: Statistically significant differences were found between peer conditions. Participants least often identified safe road-crossing
sites when accompanied by a negative peer and more frequently identified dangerous road-crossing sites when accompanied by a positive peer. Both cautious and unsafe comments from a peer influenced adolescent pedestrians’ decisions.
Conclusions: These findings showed that road-crossing decisions of adolescents were influenced by both unsafe and cautious comments from their peers. The discussion highlighted the role that peers can play in both increasing and reducing adolescent risk-taking
Fiber Orientation Estimation Guided by a Deep Network
Diffusion magnetic resonance imaging (dMRI) is currently the only tool for
noninvasively imaging the brain's white matter tracts. The fiber orientation
(FO) is a key feature computed from dMRI for fiber tract reconstruction.
Because the number of FOs in a voxel is usually small, dictionary-based sparse
reconstruction has been used to estimate FOs with a relatively small number of
diffusion gradients. However, accurate FO estimation in regions with complex FO
configurations in the presence of noise can still be challenging. In this work
we explore the use of a deep network for FO estimation in a dictionary-based
framework and propose an algorithm named Fiber Orientation Reconstruction
guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a
smaller dictionary encoding coarse basis FOs to represent the diffusion
signals. To estimate the mixture fractions of the dictionary atoms (and thus
coarse FOs), a deep network is designed specifically for solving the sparse
reconstruction problem. Here, the smaller dictionary is used to reduce the
computational cost of training. Second, the coarse FOs inform the final FO
estimation, where a larger dictionary encoding dense basis FOs is used and a
weighted l1-norm regularized least squares problem is solved to encourage FOs
that are consistent with the network output. FORDN was evaluated and compared
with state-of-the-art algorithms that estimate FOs using sparse reconstruction
on simulated and real dMRI data, and the results demonstrate the benefit of
using a deep network for FO estimation.Comment: A shorter version is accepted by MICCAI 201
DeepCare: A Deep Dynamic Memory Model for Predictive Medicine
Personalized predictive medicine necessitates the modeling of patient illness
and care processes, which inherently have long-term temporal dependencies.
Healthcare observations, recorded in electronic medical records, are episodic
and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural
network that reads medical records, stores previous illness history, infers
current illness states and predicts future medical outcomes. At the data level,
DeepCare represents care episodes as vectors in space, models patient health
state trajectories through explicit memory of historical records. Built on Long
Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle
irregular timed events by moderating the forgetting and consolidation of memory
cells. DeepCare also incorporates medical interventions that change the course
of illness and shape future medical risk. Moving up to the health state level,
historical and present health states are then aggregated through multiscale
temporal pooling, before passing through a neural network that estimates future
outcomes. We demonstrate the efficacy of DeepCare for disease progression
modeling, intervention recommendation, and future risk prediction. On two
important cohorts with heavy social and economic burden -- diabetes and mental
health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare
trajectories from medical records: A deep learning approach
Endoscopic navigation in the absence of CT imaging
Clinical examinations that involve endoscopic exploration of the nasal cavity
and sinuses often do not have a reference image to provide structural context
to the clinician. In this paper, we present a system for navigation during
clinical endoscopic exploration in the absence of computed tomography (CT)
scans by making use of shape statistics from past CT scans. Using a deformable
registration algorithm along with dense reconstructions from video, we show
that we are able to achieve submillimeter registrations in in-vivo clinical
data and are able to assign confidence to these registrations using confidence
criteria established using simulated data.Comment: 8 pages, 3 figures, MICCAI 201
Sudden drop of fractal dimension of electromagnetic emissions recorded prior to significant earthquake
The variation of fractal dimension and entropy during a damage evolution
process, especially approaching critical failure, has been recently
investigated. A sudden drop of fractal dimension has been proposed as a
quantitative indicator of damage localization or a likely precursor of an
impending catastrophic failure. In this contribution, electromagnetic emissions
recorded prior to significant earthquake are analysed to investigate whether
they also present such sudden fractal dimension and entropy drops as the main
catastrophic event is approaching. The pre-earthquake electromagnetic time
series analysis results reveal a good agreement to the theoretically expected
ones indicating that the critical fracture is approaching
La métaplasie malpighienne dans le carcinome papillaire de la thyroïde
Introduction : la métaplasie malpighienne est rare au niveau de la thyroïde. Elle peut être associée à un processus pathologique tumoral ou inflammatoire.Matériels et méthodes : les auteurs se proposent de rapporter une observation de métaplasie malpighienne de la thyroïde associée à un carcinome papillaire diagnostiqué au service d’Anatomie et de Cytologie pathologiques du CHU Farhat Hached de Sousse et d’en discuter la pathogénie de cette métaplasie, ses circonstances de survenue et ses difficultés diagnostiques.Résultats : il s’agissait d’une fille âgée de 9 ans ayant consulté pour un nodule de la thyroïde. Une cytoponction de ce nodule était pratiquée et avait montré la présence de cellules tumorales d’un carcinome papillaire. Une thyroïdectomie totale avec curage triangulaire fonctionnel a été réalisée. L’examen anatomo-pathologique de la pièce a confirmé la présence d’un carcinome papillaire de la thyroïde avec présence au voisinage de la tumeur de plages de cellules malpighiennes d’allure non tumorale.Conclusion : bien que rare, la métaplasie malpighienne peut se voir dans la thyroïde. Elle doit être distinguée d’un carcinome épidermoïde de la thyroïde par la recherche systématique, devant tout foyer de métaplasie malpighienne, des signes de malignité.Mots clés : métaplasie malpighienne, carcinome papillaire, glande thyroïde
Land subsidence susceptibility mapping in South Korea using machine learning algorithms
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results
An overview of the tapeworms of vertebrate bowels of the earth
entire volume OA; selected chapter posted hereCopyright: © The University of Kansas, Natural History Museum. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
Brucellosis remains a neglected disease inthe developing world: a call forinterdisciplinary action
Brucellosis places significant burdens on the human healthcare system and limits the economic growth of individuals, communities, and nations where such development is especially important to diminish the prevalence of poverty. The implementation of public policy focused on mitigating the socioeconomic effects of brucellosis in human and animal populations is desperately needed. When developing a plan to mitigate the associated consequences, it is vital to consider both the abstract and quantifiable effects. This requires an interdisciplinary and collaborative, or One Health, approach that consists of public education, the development of an infrastructure for disease surveillance and reporting in both veterinary and medical fields, and campaigns for control in livestock and wildlife species
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