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

Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm

Functional endoscopic sinus surgery (FESS) is a surgical procedure used to treat acute cases of sinusitis and other sinus diseases. FESS is fast becoming the preferred choice of treatment due to its minimally invasive nature. However, due to the limited field of view of the endoscope, surgeons rely on navigation systems to guide them within the nasal cavity. State of the art navigation systems report registration accuracy of over 1mm, which is large compared to the size of the nasal airways. We present an anatomically constrained video-CT registration algorithm that incorporates multiple video features. Our algorithm is robust in the presence of outliers. We also test our algorithm on simulated and in-vivo data, and test its accuracy against degrading initializations.Comment: 8 pages, 4 figures, MICCA

Urinary catheter-associated microbiota change in accordance with treatment and infection status

© 2017 Bossa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The use of long-term catheterisation to manage insensate bladders, often associated with spinal cord injury (SCI), increases the risk of microbial colonisation and infection of the urinary tract. Urinary tract infection (UTI) is typically diagnosed and treated based on the culturing of organisms from the urine, although this approach overlooks low titer, slow growing and non-traditional pathogens. Here, we present an investigation of the urinary tract microbiome in catheterised SCI individuals, using T-RFLP and metagenomic sequencing of the microbial community. We monitored three neurogenic patients over a period of 12 months, who were part of a larger study investigating the efficacy of probiotics in controlling UTIs, to determine how their urinary tract microbial community composition changed over time and in relation to probiotic treatment regimens. Bacterial biofilms adherent to urinary catheters were examined as a proxy for bladder microbes. The microbial community composition of the urinary tract differed significantly between individuals. Probiotic therapy resulted in a significant change in the microbial community associated with the catheters. The community also changed as a consequence of UTI and this shift in community composition preceded the clinical diagnosis of infection. Changes in the microbiota due to probiotic treatment or infection were transient, resolving to microbial communities similar to their pre-treatment communities, suggesting that the native community was highly resilient. Based on these results, we propose that monitoring a patient’s microbial community can be used to track the health of chronically catheterized patients and thus, can be used as part of a health-status monitoring program

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

A new purple sulfur bacterium from saline littoral sediments, Thiorhodotvibrio winogradskyi gen. nov. and sp. nov.

Two strains of a new purple sulfur bacterium were isolated in pure culture from the littoral sediment of a saline lake (Mahoney Lake, Canada) and a marine microbial mat from the North Sea island of Mellum, respectively. Single cells were vibrioid-to spirilloid-shaped and motile by means of single polar flagella. Intracellular photosynthetic membranes were of the vesicular type. As photosynthetic pigments, bacteriochlorophyll a and the carotenoids lycopene, rhodopin, anhydrorhodovibrin, rhodovibrin and spirilloxanthin were present. Hydrogen sulfide and elemental sulfur were used under anoxic conditions for phototrophic growth. In addition one strain (06511) used thiosulfate. Carbon dioxide, acetate and pyruvate were utilized by both strains as carbon sources. Depending on the strain propionate, succinate, fumarate, malate, tartrate, malonate, glycerol or peptone may additionally serve as carbon sources in the light. Optimum growth rates were obtained at pH 7.2, 33 °C, 50 mol m-2 s-1 intensity of daylight fluorescent tubes and a salinity of 2.2–3.2% NaCl. During growth on sulfide, up to ten small sulfur globules were formed inside the cells. The strains grew microaerophilic in the dark and exhibited high specific respiration rates. No vitamins were required for growth. The DNA base composition was 61.0–62.4 mol% G+C. The newly isolated bacterium belongs to the family chromatiaceae and is described as a member of a new genus and species, Thiorhodovibrio winogradskyi gen. nov. and sp. nov. with the type strain SSP1, DSM No. 6702

Some considerations for the communication of results of air pollution health effects tracking

Communicating effectively and efficiently on air quality and its health impacts is an important but difficult and complex task. It requires careful consideration of the audience one wants to reach, the messages one is trying to present, the venue through which the message will be delivered. The audience, context, technique, and content factors may affect how well it is heard and how appropriately it is interpreted. In this short paper, I describe many of these concerns and provide some suggestions for how best to address them. However, since every audience differs in goals, characteristics, and nature, what is most important is implementing an effective communications program. This program should include frequent two-way communication, repeated and on-going evaluation of how well the audience understands the messages, and consideration of how to improve the delivery

Effects of metronidazole and probiotics oligosaccharide on bacterial translocation in protein malnutrition

The present study aims to evaluate the effects of metronidazole, probiotics oligosaccharide on indigenous microflora and bacterial translocation (BT) in protein malnourished rats. Thirty male Wistarrats were divided into three groups: protein malnourished rats PM (group1, n = 10) were fed with maize only, protein malnourished rats (group 2, n = 10) were received metronidazole and protein malnourished rats (group 3, n = 10) were received both metronidazole and probiotics-oligosaccharide for fifteen days. Metronidazole (1000 mg/kg/day) was given via an orogastric feeding tube to the second and third groups. Lyophilized probiotics-oligosaccharide (0.5 mg/g body weight/day) was given in two doses via the same route to the third group. All animals were sacrificed after fifteen days of protein malnutrition and cultures of the mesenteric lymph nodes (MLNs), liver, spleen and cecal contents were done. Theincidence of bacterial translocation (BT) was 30% (3/10) in protein malnourished group 1,60% (06/10) in group 2 where protein malnutrition was associated with metronidazole and 25% (2.5/10) in group 3whose animals were subjected to protein malnutrition associated with metronidazole and probiotics oligosaccharide. A significant increase in the BT incidence was found in group 2 (P < 0.05), while a significant decrease was found in group 3 when compared to group 1. The total bacterial count of cecal flora was significantly low in group 3 than in group 1 (P < 0.01). These results suggest that the incidence of BT in protein malnutrition is increased by using an antibiotic while probioticsoligosaccharide decreases this incidence in protein malnutrition induced by antibiotic. Thus, weconclude that probiotics-oligosaccharide can effectively protect the intestinal mucosa and prevent BT in protein malnourished infants

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