Critical time window for NO-cGMP-dependent long-term memory formation after one-trial appetitive conditioning

The nitric oxide (NO)-cGMP signaling pathway is implicated in an increasing number of experimental models of plasticity. Here, in a behavioral analysis using one-trial appetitive associative conditioning, we show that there is an obligatory requirement for this pathway in the formation of long-term memory (LTM). Moreover, we demonstrate that this requirement lasts for a critical period of ~5 hr after training. Specifically, we trained intact specimens of the snail Lymnaea stagnalis in a single conditioning trial using a conditioned stimulus, amyl-acetate, paired with a salient unconditioned stimulus, sucrose, for feeding. Long-term associative memory induced by a single associative trial was demonstrated at 24 hr and shown to last at least 14 d after training. Tests for LTM and its dependence on NO were performed routinely 24 hr after training. The critical period when NO was needed for memory formation was established by transiently depleting it from the animals at a series of time points after training by the injection of the NO-scavenger 2-phenyl-4,4,5,5-tetramethyl-imidazoline-1-oxyl 3-oxide (PTIO).By blocking the activity of NO synthase and soluble guanylyl cyclase enzymes after training, we provided further evidence that LTM formation depends on an intact NO-cGMP pathway. An electrophysiological correlate of LTM was also blocked by PTIO, showing that the dependence of LTM on NO is amenable to analysis at the cellular level in vitro. This represents the first demonstration that associative memory formation after single-trial appetitive classical conditioning is dependent on an intact NO-cGMP signaling pathway

The Self-Pollination of Amur Honeysuckle

Amur honeysuckle (Lonicera maackii) poses a dire threat to the health of forests throughout the eastern United States. While self-pollination has been identified as an important trait of invasive plant species, this trait is understudied, and Amur honeysuckle is anecdotally described as lacking this characteristic. To examine the ability of Amur honeysuckle to self-pollinate, we selected 171 individual shrubs distributed across 9 sites. We compared the number of berries, seeds per berry, and seed germination rates of self- and cross-pollinated flowers by pairing branches covered with pollination bags prior to flower emergence with uncovered branches on the same individual shrub. Out of 171 individuals, 48 produced self-pollinated berries within pollination bags (28%), with 48% of bagged branches exhibiting necrosis due to increased temperature and humidity. Self-pollinated berries produced 1.5 ± 1.4 ( mean ± 1 SD) seeds per berry, whereas cross-pollinated berries produced 3.3 ± 1.5 seeds per berry. In a germination trial, 47.3% of self-pollinated seeds have germinated compared to 41.7% of crosspollinated seeds. This study has shown that Amur honeysuckle can self-pollinate and set viable seed, providing the species with an important mechanism to increase population abundance during early stages of invasion

Who\u27s Ready to Lead? The Impact of Developmental Readiness on a State Leadership Development Program

According to McKinsey & Company (2014), US companies are spending almost $14 billion annually on leadership development, only 7% of senior managers think their companies are effectively developing their leaders, and 30% of US companies believe their leaders lack the right capabilities. The current research addresses the leadership development issue from the antecedent perspective, and we are extending Avolio & Hannah’s (2008) theory of developmental readiness. They argue that individuals who possess higher levels of developmental readiness will be more likely to maximize their development when exposed to a developmental experience. However, there has been little empirical research on the combined components of developmental readiness in a true representative sample of leaders. This study is looking to add empirical findings to this theory demonstrating that leaders who score higher on scales of developmental readiness components benefit more from leadership training. More specifically, we will be assessing trainees from a Tennessee state leadership program (LEAD TN) on six different scales related to developmental readiness. We will also record levels of perceived improvement across the course of the program in attempt to find positive relationships between developmental readiness and this criterion. If the relationships found between developmental readiness and trainee perceived improvement are significant, it should inform organizations about the importance of assessing these characteristics as antecedents to leadership growth. The results should answer the following question: why invest time and money into developing a leader if he or is she is not ready, willing, or able to engage in such development? Best practice would be to assess the leaders’ developmental readiness using a standardized tool of validated measures and provide these leaders with individualized feedback before beginning the developmental experience. Therefore, they would have time to work on their individual motivation and abilities needed to have a positive training experience

Medicare Reimbursement for Total Joint Arthroplasty: The Driving Forces.

BACKGROUND: Total joint arthroplasty is a large and growing part of the U.S. Medicare budget, drawing attention to how much providers are paid for their services. The purpose of this study was to examine the variables that affect total joint arthroplasty reimbursement. Along with standard economic variables, we include unique health-care variables. Given the focus on value in the Affordable Care Act, the model examines the relationship of the quality of care to total joint arthroplasty reimbursement. We hoped to find that reimbursement patterns reward quality and reflect standard economic principles. METHODS: Multivariable regression was performed to identify variables that correlate with Medicare reimbursement for total joint arthroplasty. Inpatient charge or reimbursement data on Medicare reimbursements were available for 2,750 hospitals with at least 10 discharges for uncomplicated total joint arthroplasty from the Centers for Medicare & Medicaid Services (CMS) for fiscal year 2011. Reimbursement variability was examined by using the Dartmouth Atlas to group institutions into hospital referral regions and hospital service areas. Independent variables were taken from the Dartmouth Atlas, CMS, the WWAMI (Washington, Wyoming, Alaska, Montana, Idaho) Rural Health Research Center, and the United States Census. RESULTS: There were 427,207 total joint arthroplasties identified, with a weighted mean reimbursement of $14,324.84 (range,$9,103 to $38,686). Nationally, the coefficient of variation for reimbursements was 0.19. The regression model accounted for 52.5% of reimbursement variation among providers. The total joint arthroplasty provider volume (p \u3c 0.001) and patient satisfaction (p \u3c 0.001) were negatively correlated with reimbursement. Government ownership of a hospital (p \u3c 0.001) and higher Medicare costs (p \u3c 0.001) correlated positively with reimbursement. CONCLUSIONS: Medicare reimbursements for total joint arthroplasty are highly variable. Greater reimbursement was associated with lower patient volume, lower patient satisfaction, a healthier patient population, and government ownership of a hospital. As value-based reimbursement provisions of the Affordable Care Act are implemented, there will be dramatic changes in total joint arthroplasty reimbursements. To meet these changes, providers should expect qualities such as high patient volume, willingness to care for sicker patient populations, patient satisfaction, safe outcomes, and procedural demand to correlate with their reimbursement. CLINICAL RELEVANCE: Practicing orthopaedic surgeons and hospital administrators should be aware of discrepancies in inpatient reimbursement for total joint arthroplasty from Medicare. Furthermore, these discrepancies are not associated with typical economic factors. These findings warrant further investigation and collaboration between policymakers and providers to develop value-based reimbursement

An Overview of MOOS-IvP and a Users Guide to the IvP Helm - Release 4.2.1

This document describes the IvP Helm - an Open Source behavior-based autonomy application for unmanned vehicles. IvP is short for interval programming - a technique for representing and solving multi-objective optimizations problems. Behaviors in the IvP Helm are reconciled using multi-objective optimization when in competition with each other for influence of the vehicle. The IvP Helm is written as a MOOS application where MOOS is a set of Open Source publish-subscribe autonomy middleware tools. This document describes the configuration and use of the IvP Helm, provides examples of simple missions and information on how to download and build the software from the MOOS-IvP server at www.moos-ivp.org.United States. Office of Naval Research (Code 311

Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies

While reinforcement learning has achieved remarkable successes in several domains, its real-world application is limited due to many methods failing to generalise to unfamiliar conditions. In this work, we consider the problem of generalising to new transition dynamics, corresponding to cases in which the environment's response to the agent's actions differs. For example, the gravitational force exerted on a robot depends on its mass and changes the robot's mobility. Consequently, in such cases, it is necessary to condition an agent's actions on extrinsic state information and pertinent contextual information reflecting how the environment responds. While the need for context-sensitive policies has been established, the manner in which context is incorporated architecturally has received less attention. Thus, in this work, we present an investigation into how context information should be incorporated into behaviour learning to improve generalisation. To this end, we introduce a neural network architecture, the Decision Adapter, which generates the weights of an adapter module and conditions the behaviour of an agent on the context information. We show that the Decision Adapter is a useful generalisation of a previously proposed architecture and empirically demonstrate that it results in superior generalisation performance compared to previous approaches in several environments. Beyond this, the Decision Adapter is more robust to irrelevant distractor variables than several alternative methods.Comment: Accepted to NeurIPS 202