System and Methods for Deploying Payloads

Embodiments of the present invention include systems for launching primary or secondary payloads or actuating other launch vehicle or payload or instrumentation devices. The system includes an adapter assembly and at least one sequencer mounted to the adapter assembly. The sequencer includes: controller boards, each of the controller boards having a controller for controlling deployment of the payloads and data files; output ports coupled to the controller boards and configured to transmit signals from the controller boards to dispensers therethrough, deployment mechanisms containing the payloads, the adapter assembly having channels for accommodating the dispensers; and a detector coupled to the controller boards and adapted to detect an external signal and, in response to the external signal, to send an initiation signal to the controller boards. The system also includes at least one power supply coupled to the sequencer and adapted to provide an electrical power to the sequencer

Driver glance behaviors and scanning patterns: Applying static and dynamic glance measures to the analysis of curve driving with secondary tasks

Performing secondary tasks (or non‐driving‐related tasks) while driving on curved roads may be risky and unsafe. The purpose of this study was to explore whether driving safety in situations involving curved roads and secondary tasks can be evaluated using multiple measures of eye movement. We adopted Markov‐based transition algorithms (i.e., transition/stationary probabilities, entropy) to quantify drivers’ dynamic eye movement patterns, in addition to typical static visual measures, such as frequency and duration of glances. The algorithms were evaluated with data from an experiment (Jeong & Liu, 2019) involving multiple road curvatures and stimulus‐response secondary task types. Drivers were more likely to scan only a few areas of interest with a long duration in sharper curves. Total head‐down glance time was longer in less sharp curves in the experiment, but the probability of head‐down glances was higher in sharper curves over the long run. The number of reliable transitions between areas of interest varied with the secondary task type. The visual scanning patterns for visually undemanding tasks were as random as those for visually demanding tasks. Markov‐based measures of dynamic eye movements provided insights to better understand drivers’ underlying mental processes and scanning strategies, compared with typical static measures. The presented methods and results can be useful for in‐vehicle systems design and for further analysis of visual scanning patterns in the transportation domain.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151975/1/hfm20798_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151975/2/hfm20798.pd

Clinical Validation of a Sensitive Test for Saliva Collected in Healthcare and Community Settings with Pooling Utility for Severe Acute Respiratory Syndrome Coronavirus 2 Mass Surveillance

The clinical performance of saliva compared with nasopharyngeal swabs (NPSs) has shown conflicting results in healthcare and community settings. In the present study, a total of 429 matched NPS and saliva sample pairs, collected in either healthcare or community setting, were evaluated. Phase-1 (protocol U) tested 240 matched NPS and saliva sample pairs; phase 2 (SalivaAll protocol) tested 189 matched NPS and saliva sample pairs, with an additional sample homogenization step before RNA extraction. A total of 85 saliva samples were evaluated with both protocols. In phase-1, 28.3% (68/240) samples tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from saliva, NPS, or both. The detection rate from saliva was lower compared with that from NPS samples (50.0% versus 89.7%). In phase-2, 50.2% (95/189) samples tested positive for SARS-CoV-2 from saliva, NPS, or both. The detection rate from saliva was higher compared with that from NPS samples (97.8% versus 78.9%). Of the 85 saliva samples evaluated with both protocols, the detection rate was 100% for samples tested with SalivaAll, and 36.7% with protocol U. The limit of detection with SalivaAll protocol was 20 to 60 copies/mL. The pooled testing approach demonstrated a 95% positive and 100% negative percentage agreement. This protocol for saliva samples results in higher sensitivity compared with NPS samples and breaks the barrier to using pooled saliva for SARS-CoV-2 testing

Effect of developmental stage of HSC and recipient on transplant outcomes

The first hematopoietic stem cells (HSCs) that engraft irradiated adult mice arise in the aorta-gonad-mesonephros (AGM) on embryonic day 11.5 (E11.5). However, at this stage, there is a discrepancy between the apparent frequency of HSCs depicted with imaging and their rarity when measured with limiting dilution transplant. We have attempted to reconcile this difference using neonatal recipients, which are more permissive for embryonic HSC engraftment. We found that embryonic HSCs from E9.5 and E10.5 preferentially engrafted neonates, whereas developmentally mature, definitive HSCs from E14.5 fetal liver or adult bone marrow (BM) more robustly engrafted adults. Neonatal engraftment was enhanced after treating adult BM-derived HSCs with interferon. Adult BM-derived HSCs preferentially homed to the liver in neonatal mice yet showed balanced homing to the liver and spleen in adults. These findings emphasize the functional differences between nascent and mature definitive HSCs

Training the workforce in evidence-based public health: An evaluation of impact among US and international practitioners

INTRODUCTION: The Prevention Research Center in St. Louis developed a course on evidence-based public health in 1997 to train the public health workforce in implementation of evidence-based public health. The objective of this study was to assess use and benefits of the course and identify barriers to using evidence-based public health skills as well as ways to improve the course. METHODS: We used a mixed-method design incorporating on-site pre- and post-evaluations among US and international course participants who attended from 2008 through 2011 and web-based follow-up surveys among course participants who attended from 2005 through 2011 (n = 626). Respondents included managers, specialists, and academics at state health departments, local health departments, universities, and national/regional health departments. RESULTS: We found significant improvement from pre- to post-evaluation for 11 measures of knowledge, skill, and ability. Follow-up survey results showed at least quarterly use of course skills in most categories, majority endorsement of most course benefits, and lack of funding and coworkers who do not have evidence-based public health training as the most significant barriers to implementation of evidence-based public health. Respondents suggested ways to increase evidence-based decision making at their organization, focusing on organizational support and continued access to training. CONCLUSION: Although the evidence-based public health course is effective in improving self-reported measures of knowledge, skill, and ability, barriers remain to the implementation of evidence-based decision making, demonstrating the importance of continuing to offer and expand training in evidence-based public health

Seeded Bayesian Networks: Constructing genetic networks from microarray data

<p>Abstract</p> <p>Background</p> <p>DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes – often represented as networks – in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results.</p> <p>Results</p> <p>Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data.</p> <p>Conclusion</p> <p>The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.</p

Characterization of complex networks: A survey of measurements

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements.Comment: A working manuscript with 78 pages, 32 figures. Suggestions of measurements for inclusion are welcomed by the author

Involvement in teaching improves learning in medical students: a randomized cross-over study

<p>Abstract</p> <p>Background</p> <p>Peer-assisted learning has many purported benefits including preparing students as educators, improving communication skills and reducing faculty teaching burden. But comparatively little is known about the effects of teaching on learning outcomes of peer educators in medical education.</p> <p>Methods</p> <p>One hundred and thirty-five first year medical students were randomly allocated to 11 small groups for the Gastroenterology/Hematology Course at the University of Calgary. For each of 22 sessions, two students were randomly selected from each group to be peer educators. Students were surveyed to estimate time spent preparing as peer educator versus group member. Students completed an end-of-course 94 question multiple choice exam. A paired t-test was used to compare performance on clinical presentations for which students were peer educators to those for which they were not.</p> <p>Results</p> <p>Preparation time increased from a mean (SD) of 36 (33) minutes baseline to 99 (60) minutes when peer educators (Cohen's <it>d </it>= 1.3; p < 0.001). The mean score (SD) for clinical presentations in which students were peer educators was 80.7% (11.8) compared to77.6% (6.9) for those which they were not (<it>d </it>= 0.33; <it>p </it>< 0.01).</p> <p>Conclusion</p> <p>Our results suggest that involvement in teaching small group sessions improves medical students' knowledge acquisition and retention.</p