Borrowed From the Future: Challenges and Guidelines for Community-Based Natural Resource Management

Identifies some of the obstacles that thwart the success of community-based programs, in a search for feasible solutions to the combined problems of environmental degradation and increasing human poverty and inequality

The feedback-related negativity reflects the binary evaluation of good versus bad outcomes

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It's worse than you thought : the feedback negativity and violations of reward prediction in gambling tasks

The reinforcement learning theory suggests that the feedback negativity should be larger when feedback is unexpected. Two recent studies found, however, that the feedback negativity was unaffected by outcome probability. To further examine this issue, participants in the present studies made reward predictions on each trial of a gambling task where objective reward probability was indicated by a cue. In Study 1, participants made reward predictions following the cue, but prior to their gambling choice; in Study 2, predictions were made following their gambling choice. Predicted and unpredicted outcomes were associated with equivalent feedback negativities in Study 1. In Study 2, however, the feedback negativity was larger for unpredicted outcomes. These data suggest that the magnitude of the feedback negativity is sensitive to violations of reward prediction, but that this effect may depend on the close coupling of prediction and outcome

Mechanisms and regulation of surface interactions and biofilm formation in Agrobacterium

For many pathogenic bacteria surface attachment is a required first step during host interactions. Attachment can proceed to invasion of host tissue or cells or to establishment of a multicellular bacterial community known as a biofilm. The transition from a unicellular, often motile, state to a sessile, multicellular, biofilm-associated state is one of the most important developmental decisions for bacteria. Agrobacterium tumefaciens genetically transforms plant cells by transfer and integration of a segment of plasmid-encoded transferred DNA (T-DNA) into the host genome, and has also been a valuable tool for plant geneticists. A. tumefaciens attaches to and forms a complex biofilm on a variety of biotic and abiotic substrates in vitro. Although rarely studied in situ, it is hypothesized that the biofilm state plays an important functional role in the ecology of this organism. Surface attachment, motility, and cell division are coordinated through a complex regulatory network that imparts an unexpected asymmetry to the A. tumefaciens life cycle. In this review we describe the mechanisms by which A. tumefaciens associates with surfaces, and regulation of this process. We focus on the transition between flagellar-based motility and surface attachment, and on the composition, production, and secretion of multiple extracellular components that contribute to the biofilm matrix. Biofilm formation by A. tumefaciens is linked with virulence both mechanistically and through shared regulatory molecules. We detail our current understanding of these and other regulatory schemes, as well as the internal and external (environmental) cues mediating development of the biofilm state, including the second messenger cyclic-di-GMP, nutrient levels, and the role of the plant host in influencing attachment and biofilm formation. A. tumefaciens is an important model system contributing to our understanding of developmental transitions, bacterial cell biology, and biofilm formation

Identification and Quantification of Cotton Yield Monitor Errors

Cotton yield monitors are an important part of a precision agriculture program and are becoming widely used by cotton producers for making management decisions. Members of the cotton industry have shown interest in using cotton yield monitors for collecting data from production scale variety yield trials (experiments that test yield performance for numerous varieties). Weighing boll buggies are the current industry standard for measuring yield in variety trials. This process is time consuming and requires extra equipment and labor. The ability to use a yield monitor for measuring yield would streamline variety trial harvesting. Recommendations for the Ag Leader cotton yield monitor state that the monitor should be recalibrated when harvesting a new variety. This poses a problem for collecting yield data from a variety trial due to the numerous calibrations that would be required. The primary objective of this research is to evaluate and enhance monitor performance in order to use it for collecting variety trial data. This will be done using different calibration techniques and post-processing models developed using measured gin turnout and environmental variables. Data were collected in 2007 and 2008 at the Milan Research and Education Center in Milan, TN. Monitor weights were compared to boll buggy weights to determine variation between these two yield estimation techniques. This measured variation is defined as Yield Prediction Error (YPE). Before calibration, yield explained 44% of the variation in YPE. After post-calibration, moisture and yield explained 48% of the variation in YPE. Post-processing models were developed using these types of relationships but were unsuccessful as they introduced more variation into the data set. The relationship of YPE to moisture suggests that boll buggy weights should be adjusted to a common moisture content. The relationship of YPE to yield suggests that improvements could be made to the monitor. Post-processing the data using yield in the model was able to reduce the mean absolute error to 2.5% from 3.3% using only calibration C (recalibrating when weather or other events cause a multiple day stoppage in harvesting). Tukey’s mean separation test was used for both yield measurement techniques to determine differences in variety trial results. In both 2007 and 2008, the variety trial results returned the same differences for both yield estimation techniques. This dataset supports that with proper calibration, the yield monitor can be used to collect yield data for cotton variety trials

A three-dimensional, dynamic model of the human body for lifting motions

Lower back pain is prevalent in society and manual lifting has been linked as one potential cause of these types of injuries. Therefore, the 3dLift biomechanical model was developed in this research with the goal of quantitatively analyzing lifting motions. The model divided the body into fifteen segments that were connected by fourteen anatomical joints. During experimental trials, a volunteer subject lifted an object using four different lifting combinations: symmetric leglifts, asymmetric leglifts, symmetric backlifts, and asymmetric backlifts. In order to individualize the 3dLift model, anthropometric parameters were estimated using measurements taken on the subject. During the lifting trials, the subject wore reflective markers placed on anatomical landmarks, the motions of which were tracked by five video cameras. The subject also stood with each foot on a separate force platform that was used to determine ground reaction forces and centers of pressure. Signal processing methods were utilized to predict the marker positions that were obscured during the lifting trials, and digital filtering was implemented to attenuate noise in the data. After reducing the experimental errors, the segment coordinate axes, Cardan angles, joint center positions, and mass center positions were calculated. The changes in the segment orientations with respect to time were then analyzed to determine the three-dimensional kinematics of the segments. Anthropometric, video, and force platform information were combined in equations of motion that were derived to predict the forces and moments occurring at the joints during the lifting motions. A lower body formulation was developed that started with the measured ground reactions at the feet and proceeded through the segments to the T10/T11 intervertebral joint. Similarly, an upper body formulation was derived that began with a known lifted load at the hands and continued through the segments to the same T10/T11 intervertebral joint. While predicting joint forces and moments, the two formulations also served as a means of validating the 3dLift model by comparing the results at the T10/T11 joint. While there is much work yet to be done in this research area, the 3dLift model takes the first steps by developing a systematic methodology for studying lifting motions

Adipocyte lipid synthesis coupled to neuronal control of thermogenic programming

BACKGROUND: The de novo biosynthesis of fatty acids (DNL) through fatty acid synthase (FASN) in adipocytes is exquisitely regulated by nutrients, hormones, fasting, and obesity in mice and humans. However, the functions of DNL in adipocyte biology and in the regulation of systemic glucose homeostasis are not fully understood. METHODS and RESULTS: Here we show adipocyte DNL controls crosstalk to localized sympathetic neurons that mediate expansion of beige/brite adipocytes within inguinal white adipose tissue (iWAT). Induced deletion of FASN in white and brown adipocytes of mature mice (iAdFASNKO mice) enhanced glucose tolerance, UCP1 expression, and cAMP signaling in iWAT. Consistent with induction of adipose sympathetic nerve activity, iAdFASNKO mice displayed markedly increased neuronal tyrosine hydroxylase (TH) and neuropeptide Y (NPY) content in iWAT. In contrast, brown adipose tissue (BAT) of iAdFASNKO mice showed no increase in TH or NPY, nor did FASN deletion selectively in brown adipocytes (UCP1-FASNKO mice) cause these effects in iWAT. CONCLUSIONS: These results demonstrate that downregulation of fatty acid synthesis via FASN depletion in white adipocytes of mature mice can stimulate neuronal signaling to control thermogenic programming in iWAT