Construction and Calibration of a Streaked Optical Spectrometer for Shock Temperature

Here we describe the implementation and calibration of a streaked visible spectrometer (SVS) for optical pyrometry and emission/absorption spectroscopy on light gas gun platforms in the UC Davis Shock Compression Laboratory. The diagnostic consists of an optical streak camera coupled to a spectrometer to provide temporally and spectrally-resolved records of visible emission from dynamically-compressed materials. Fiber optic coupling to the sample enables a small diagnostic footprint on the target face and flexibility of operation on multiple launch systems without the need for open optics. We present the details of calibration (time, wavelength and spectral radiance) for absolute temperature determination and present benchmark measurements of system performance.Comment: 6 pages, 3 figures Davies, E., et al. (accepted). In J. Lane, T. Germann, and M. Armstrong (Eds.), 21st Biennial APS Conference on Shock Compression of Condensed Matter (SCCM19). AIP Publishin

Effects of Thinning Intensity, Prescribed Fire, and Herbicide on Wildlife Habitat in Mid-rotation Loblolly Pine Stands

Pine (Pinus spp.) plantations cover 16.8 MM ha across the southeastern United States. Many forest owners are interested in managing their forests for multiple objectives, including timber production and wildlife habitat for both game (e.g., white-tailed deer [Odocoileus virginianus]) and nongame species. Commercial thinning and application of herbicide or prescribed fire at mid-rotation can help landowners meet these objectives. However, information is lacking on thinning prescriptions that reduce residual basal area beyond industry standards, as well as the effects of common herbicide tank mixtures (i.e., imazapyr + metsulfuron methyl) on habitat quality for open forest specialists and deer. Therefore, we initiated an operational-scale, manipulative, experiment to quantify the effects of thinning to 9, 14, and 18 m2 ha-1, with and without prescribed fire and herbicide, on habitat quality for open forest specialists and nutritional carrying capacity (deer days/ha) for deer in mid-rotation loblolly pine stands

Violence and affective states in contemporary Latin America

This special issue brings together scholars interested in the analysis of the social, cultural and affective dimensions of violence. The contributions explore the connections between situated experiences of violence and shifting affective states, relations, sensations and contingencies in contemporary Latin America. The articles consider how violence might constitute a nexus for the production of subjectivities and forms of identification, relationality and community, alterity and belonging, in a range of Latin American contexts including Argentina, Brazil, Guatemala, Mexico and in the Mexican diaspora in Spain

Wrist Accelerometer Cut Points for Classifying Sedentary Behavior in Children.

INTRODUCTION: This study aimed to examine the validity and accuracy of wrist accelerometers for classifying sedentary behavior (SB) in children. METHODS: Fifty-seven children (5-8 and 9-12 yr) completed an ~170-min protocol, including 15 semistructured activities and transitions. Nine ActiGraph (GT3X+) and two GENEActiv wrist cut points were evaluated. Direct observation was the criterion measure. The accuracy of wrist cut points was compared with that achieved by the ActiGraph hip cut point (≤25 counts per 15 s) and the thigh-mounted activPAL3. Analyses included equivalence testing, Bland-Altman procedures, and area under the receiver operating curve (ROC-AUC). RESULTS: The most accurate ActiGraph wrist cut points (Kim; vector magnitude, ≤3958 counts per 60 s; vertical axis, ≤1756 counts per 60 s) demonstrated good classification accuracy (ROC-AUC = 0.85-0.86) and accurately estimated SB time in 5-8 yr (equivalence P = 0.02; mean bias = 4.1%, limits of agreement = -20.1% to 28.4%) and 9-12 yr (equivalence P 0.05) and classification accuracy (ROC-AUC = 0.79-0.80) was lower than for ActiGraph hip and activPAL3. CONCLUSION: The most accurate SB ActiGraph (Kim) and GENEActiv (Schaefer) wrist cut points can be applied in children with similar confidence as the ActiGraph hip cut point (≤25 counts per 15 s), although activPAL3 was generally more accurate.This study was funded by the National Heart Foundation of Australia (G11S5975). DPC is supported by an Australian Research Council Discovery Early Career Researcher Award (DE140101588). ADO is supported by a National Heart Foundation of Australia Career Development Fellowship (CR11S 6099). TH is funded by a National Health and Medical Research Council Early Career Fellowship (APP1070571). The work of UE and SB is funded by the UK Medical Research Council (MC_UU_12015/3). ST is supported by the National Health and Medical Research Council Centre of Research Excellence on Sitting Time and Chronic Disease Prevention (APP1057608)

Increasing physical activity among young children from disadvantaged communities: Study protocol of a group randomised controlled effectiveness trial

Background: Participation in regular physical activity (PA) during the early years helps children achieve healthy body weight and can substantially improve motor development, bone health, psychosocial health and cognitive development. Despite common assumptions that young children are naturally active, evidence shows that they are insufficiently active for health and developmental benefits. Exploring strategies to increase physical activity in young children is a public health and research priority. Methods: Jump Start is a multi-component, multi-setting PA and gross motor skill intervention for young children aged 3–5 years in disadvantaged areas of New South Wales, Australia. The intervention will be evaluated using a two-arm, parallel group, randomised cluster trial. The Jump Start protocol was based on Social Cognitive Theory and includes five components: a structured gross motor skill lesson (Jump In); unstructured outdoor PA and gross motor skill time (Jump Out); energy breaks (Jump Up); activities connecting movement to learning experiences (Jump Through); and a home-based family component to promote PA and gross motor skill (Jump Home). Early childhood education and care centres will be demographically matched and randomised to Jump Start (intervention) or usual practice (comparison) group. The intervention group receive Jump Start professional development, program resources, monthly newsletters and ongoing intervention support. Outcomes include change in total PA (accelerometers) within centre hours, gross motor skill development (Test of Gross Motor Development-2), weight status (body mass index), bone strength (Sunlight MiniOmni Ultrasound Bone Sonometer), self-regulation (Heads-Toes-Knees-Shoulders, executive function tasks, and proxy-report Temperament and Approaches to learning scales), and educator and parent self-efficacy. Extensive quantitative and qualitative process evaluation and a cost-effectiveness evaluation will be conducted. Discussion: The Jump Start intervention is a unique program to address low levels of PA and gross motor skill proficiency, and support healthy lifestyle behaviours among young children in disadvantaged communities. If shown to be efficacious, the Jump Start approach can be expected to have implications for early childhood education and care policies and practices, and ultimately a positive effect on the health and development across the life course

Automating Scanning Tunnelling Microscopy: A Comparative Study of Machine Learning and Deterministic Methods

Scanning probe microscopy (SPM) methods allow for atomic scale investigation of material surfaces in real space and provide the potential to construct atomically precise structures atom-by-atom. Although these techniques have been available for decades, full automation of the processes involved in these experiments has not yet been fully realised. Currently, the process of setting up, running, and maintaining the SPM remains a laborious, time-consuming process, often as a result of the constantly changing shape of the probe tip. Manual identification and correction of the state of the probe tip in situ requires a human operator to compare the probe quality via manual inspection of topographical images after any change in the probe. Previous attempts to automate the classification of the scanning probe state have predominantly relied on machine learning (ML) techniques. However, training these models demands large, labelled datasets for each surface under study. These datasets are extremely time-consuming to create, and are not always available, especially when considering a new substrate or adsorbate system. This thesis focuses on automating the classification of the probe tip in scanning tunnelling microscopy (STM), addressing both imaging and spectroscopic applications. The use of ML in this automation is compared to less data-intensive, deterministic techniques, exploring the broader need for ML in autonomous scripting. We find that using these deterministic methods, comparable results in classifications can be achieved to those obtained with the use of ML. ML models were trained when possible to demonstrate the efficacy of the deterministic methods, via direct comparisons between the two classification techniques. The applicability of deterministic approaches is further validated through the utilisation of these classifiers in automated experiments on various substrate systems. In addition to this, a scale-invariant method for surface mapping using Fourier space analysis is presented, which could aid in further automated experimentation

A Communication Efficient Framework for Finding Outliers in Wireless Sensor Networks

Outlier detection is a well-studied problem in various fields. the unique challenges of wireless sensor networks make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we present a new communication technique to find outliers in a wireless sensor network. Communication is minimized through controlling sensor when sensors are allowed to communicate. at the same time, minimal assumptions are made about the nature of the data set as to minimize the loss of generality in the architecture. © 2010 IEEE

A Survey of Methods for Finding Outliers in Wireless Sensor Networks

Outlier detection is a well-studied problem in various fields. The unique characteristics and constraints of wireless sensor networks (WSN) make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we survey the current state of research in this area, compare them and present some future directions for smarter handling of outliers in WSN

Aptamer-based multiplexed proteomic technology for biomarker discovery

Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine

Methods for Estimating Kidney Disease Stage Transition Probabilities Using Electronic Medical Records

Chronic diseases are often described by stages of severity. Clinical decisions about what to do are influenced by the stage, whether a patient is progressing, and the rate of progression. For chronic kidney disease (CKD), relatively little is known about the transition rates between stages. To address this, we used electronic health records (EHR) data on a large primary care population, which should have the advantage of having both sufficient follow-up time and sample size to reliably estimate transition rates for CKD. However, EHR data have some features that threaten the validity of any analysis. In particular, the timing and frequency of labratory values and clinical measurements are not determined a priori by research investigators, but rather, depend on many factors, including the current health of the patient. We developed an approach for estimatating CKD stage transition rates using hidden Markov models (HMMs), when the level of information and observation time vary among individuals. To estimate the HMMs in a computationally manageable way, we used a “discretization” method to transform daily data into intervals of 30 days, 90 days, or 180 days. We assessed the accuracy and computation time of this method via simulation studies. We also used simulations to study the effect of informative observation times on the estimated transition rates. Our simulation results showed good performance of the method, even when missing data are non-ignorable. We applied the methods to EHR data from over 60,000 primary care patients who have chronic kidney disease (stage 2 and above). We estimated transition rates between six underlying disease states. The results were similar for men and women