Rhapso : automatic stitching of mass segments from fourier transform ion cyclotron resonance mass spectra

Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) provides the resolution and mass accuracy needed to analyze complex mixtures such as crude oil. When mixtures contain many different components, a competitive effect within the ICR cell takes place that hampers the detection of a potentially large fraction of the components. Recently, a new data collection technique, which consists of acquiring several spectra of small mass ranges and assembling a complete spectrum afterward, enabled the observation of a record number of peaks with greater accuracy compared to broadband methods. There is a need for statistical methods to combine and preprocess segmented acquisition data. A particular challenge of quadrupole isolation is that near the window edges there is a drop in intensity, hampering the stitching of consecutive windows. We developed an algorithm called Rhapso to stitch peak lists corresponding to multiple different m/z regions from crude oil samples. Rhapso corrects potential edge effects to enable the use of smaller windows and reduce the required overlap between windows, corrects mass shifts between windows, and generates a single peak list for the full spectrum. Relative to a stitching performed manually, Rhapso increased the data processing speed and avoided potential human errors, simplifying the subsequent chemical analysis of the sample. Relative to a broadband spectrum, the stitched output showed an over 2-fold increase in assigned peaks and reduced mass error by a factor of 2. Rhapso is expected to enable routine use of this spectral stitching method for ultracomplex samples, giving a more detailed characterization of existing samples and enabling the characterization of samples that were previously too complex to analyze

Risk factors for major joints heterotopic ossification after thermal injury

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Radon, From the Ground into Our Schools: Parent/Guardian Awareness of Radon Levels in Vermont Schools

Introduction. Radon is the leading cause of lung cancer among non-smokers. Ex- posure to radon in schools may be harmful to schoolchildren, faculty, and staff, but there is currently no legislation mandating testing or mitigation of radon levels in Vermont schools. Objectives. The goal of our study was to assess Vermont parents’ awareness of radon’s harmful effects, as well as awareness of and support for testing and mitigation of radon levels in their children’s schools. Methods. We distributed paper and online surveys to Vermont parents of children grades K-12. 126 surveys were received and quantitatively analyzed. We held a focus group of two Vermont parents to gather qualitative data. Results. Most surveyed parents demonstrated general knowledge of radon, but only 51% believed that radon affects the lungs. 8% were confident that their children’s schools had informed them about radon levels. 91.2% believe their children’s schools should take action to address elevated radon levels and 87% would support mandated mitigation. There is some concern and lack of knowledge about the financial implications of radon mitigation. Conclusions. Most Vermont parents of children grades K-12 are unaware that radon is a lung carcinogen and do not know their children’s school’s radon levels or mitigation status. However, most are in favor of legislation that would require testing and dis- closure of schools’ high radon levels. Educating parents about school radon levels and their association with lung cancer could be a foundation for community support of legislation that mandates testing and mitigation of radon in Vermont schools.https://scholarworks.uvm.edu/comphp_gallery/1252/thumbnail.jp

Biomechanics and energetics of walking in powered ankle exoskeletons using myoelectric control versus mechanically intrinsic control

Abstract Background Controllers for assistive robotic devices can be divided into two main categories: controllers using neural signals and controllers using mechanically intrinsic signals. Both approaches are prevalent in research devices, but a direct comparison between the two could provide insight into their relative advantages and disadvantages. We studied subjects walking with robotic ankle exoskeletons using two different control modes: dynamic gain proportional myoelectric control based on soleus muscle activity (neural signal), and timing-based mechanically intrinsic control based on gait events (mechanically intrinsic signal). We hypothesized that subjects would have different measures of metabolic work rate between the two controllers as we predicted subjects would use each controller in a unique manner due to one being dependent on muscle recruitment and the other not. Methods The two controllers had the same average actuation signal as we used the control signals from walking with the myoelectric controller to shape the mechanically intrinsic control signal. The difference being the myoelectric controller allowed step-to-step variation in the actuation signals controlled by the user’s soleus muscle recruitment while the timing-based controller had the same actuation signal with each step regardless of muscle recruitment. Results We observed no statistically significant difference in metabolic work rate between the two controllers. Subjects walked with 11% less soleus activity during mid and late stance and significantly less peak soleus recruitment when using the timing-based controller than when using the myoelectric controller. While walking with the myoelectric controller, subjects walked with significantly higher average positive and negative total ankle power compared to walking with the timing-based controller. Conclusions We interpret the reduced ankle power and muscle activity with the timing-based controller relative to the myoelectric controller to result from greater slacking effects. Subjects were able to be less engaged on a muscle level when using a controller driven by mechanically intrinsic signals than when using a controller driven by neural signals, but this had no affect on their metabolic work rate. These results suggest that the type of controller (neural vs. mechanical) is likely to affect how individuals use robotic exoskeletons for therapeutic rehabilitation or human performance augmentation.https://deepblue.lib.umich.edu/bitstream/2027.42/143850/1/12984_2018_Article_379.pd