Texas 4-H Members’ Sense of Community Engagement and Attachment

Previous researchers found that youth in 4-H were four times more likely to actively contribute to their communities, two times more likely to be civically active, and five times more likely to graduate from college than non-4- H members. In addition, youth who were more actively involved in community engagement tended to perform at an increased academic achievement level and were more likely to go to college, according to previous studies. The results of the research reported here described participants’ community service and engagement activities both in and outside of 4-H and their attachment to their home communities. Respondents were mostly residents of rural areas, farms, or small towns and cities. They were satisfied with where they lived, and they reported that contributing to their community was important to them and believed it made a positive influence on their life. Most participants also indicated that the community in which they lived and the people closest to them were important parts of their lives and contributed positively to their development. By determining current 4-H members’ level of community attachment, Extension professionals can better understand the influence a community and its stakeholders have in a young person’s leadership development and aspirations

High-precision frequency measurements: indispensable tools at the core of the molecular-level analysis of complex systems

This perspective article provides an assessment of the state-of-the-art in the molecular-resolution analysis of complex organic materials. These materials can be divided into biomolecules in complex mixtures (which are amenable to successful separation into unambiguously defined molecular fractions) and complex nonrepetitive materials (which cannot be purified in the conventional sense because they are even more intricate). Molecular-level analyses of these complex systems critically depend on the integrated use of high-performance separation, high-resolution organic structural spectroscopy and mathematical data treatment. At present, only high-precision frequency-derived data exhibit sufficient resolution to overcome the otherwise common and detrimental effects of intrinsic averaging, which deteriorate spectral resolution to the degree of bulk-level rather than molecular-resolution analysis. High-precision frequency measurements are integral to the two most influential organic structural spectroscopic methods for the investigation of complex materials—NMR spectroscopy (which provides unsurpassed detail on close-range molecular order) and FTICR mass spectrometry (which provides unrivalled resolution)—and they can be translated into isotope-specific molecular-resolution data of unprecedented significance and richness. The quality of this standalone de novo molecular-level resolution data is of unparalleled mechanistic relevance and is sufficient to fundamentally advance our understanding of the structures and functions of complex biomolecular mixtures and nonrepetitive complex materials, such as natural organic matter (NOM), aerosols, and soil, plant and microbial extracts, all of which are currently poorly amenable to meaningful target analysis. The discrete analytical volumetric pixel space that is presently available to describe complex systems (defined by NMR, FT mass spectrometry and separation technologies) is in the range of 108–14 voxels, and is therefore capable of providing the necessary detail for a meaningful molecular-level analysis of very complex mixtures. Nonrepetitive complex materials exhibit mass spectral signatures in which the signal intensity often follows the number of chemically feasible isomers. This suggests that even the most strongly resolved FTICR mass spectra of complex materials represent simplified (e.g. isomer-filtered) projections of structural space

Spectroscopic Characterization of Oceanic Dissolved Organic Matter Isolated By Reverse Osmosis Coupled With Electrodialysis

Oceanic dissolved organic matter (DOM) is one of the largest pools of reduced carbon on Earth, yet DOM remains poorly chemically characterized. Studies to determine the chemical nature of oceanic DOM have been impeded by the lack of efficient and non-fractioning methods to recover oceanic DOM. Here, a DOM fraction (~40 to 86% recovery) was isolated using reverse osmosis/electrodialysis (RO/ED) and analyzed by solid-state 13C nuclear magnetic resonance (NMR) spectroscopy. Samples were obtained from biogeochemically distinct environments: photobleached surface gyre, productive coastal upwelling zone, oxygen minimum, North Atlantic Deep Water, and North Pacific Deep Water. A ubiquitous ‘background’ refractory carbon pool was apparent throughout the ocean and dominated in the deep Pacific Ocean. Advanced NMR spectral editing revealed that condensed aromatic and quaternary anomeric carbons contribute to this deep refractory DOC pool, the quaternary anomeric carbons being a newly identified and potentially important component of bio-refractory carbohydrate-like carbon. Additionally, our results support the multi-pool (e.g. 3-pool: labile, semi-labile, and refractory) conceptual model of marine DOM biogeochemistry. Surface samples, hypothesized to be enriched in labile and semi-labile DOM, were enriched in carbohydrate-like material consistent with prior studies. High carboxyl signals in the deep Pacific support the hypothesis that a major fraction of the refractory pool consists of carboxyl-rich alicyclic molecules (CRAM)

Innovative Self-Regulation Strategies to Reduce Weight Gain in Young Adults: The Study of Novel Approaches to Weight Gain Prevention (SNAP) Randomized Clinical Trial

Weight gain occurs commonly in young adults and has adverse effects on health. Reducing weight gain in young adults would have significant public health impact

Age-Associated Changes in Hearts of Male Fischer 344/Brown Norway F1 Rats

Aging is associated with left ventricular hypertrophy, dilatation, and fibrosis of the heart. The Fischer 344/Brown Norway F1 (F344/BNF1) rat is recommended for age-related studies by the National Institutes on Aging because this hybrid rat lives longer and has a lower rate of pathological conditions than inbred rats. However, little is known about age-associated changes in cardiac and aortic function and structure in this model. This study evaluated age-related cardiac changes in male F344/BNF1 rats using ECHO, gross, and microscopic examinations. Rats aged 6-, 30-, and 36-mo were anesthetized and two-dimensional ECHO measurements, two-dimensional guided M-mode, Doppler M-mode, and other recordings from parasternal long- and short-axis views were obtained using a Phillips 5500 ECHO system with a 12 megahertz transducer. Hearts and aortas from sacrificed rats were evaluated grossly and microscopically. The ECHO studies revealed persistent cardiac arrhythmias (chiefly PVCs) in 72% (13/18) of 36-mo rats, 10% (1/10) of 30-mo rats, and none in 6-mo rats (0/16). Gross and microscopic studies showed left ventricular (LV) dilatation, borderline to mild hypertrophy, and areas of fibrosis that were common in 36-mo rats, less evident in 30-mo rats, and absent in 6-mo rats. Aging was associated with mild to moderate decreases of LV diastolic and systolic function. Thus, male F344/BN F1 rats demonstrated progressive age-related (a) decline in cardiac function (diastolic and systolic indices), (b) LV structural changes (chamber dimensions, volumes, and wall thicknesses), and (c) persistent arrhythmias. These changes are consistent with those in humans. The noninvasive ECHO technique offers a means to monitor serial age-related cardiac failure and therapeutic responses in the same rats over designated time intervals

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. $A$-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.Comment: 41 page