Optimization of Application Timing and Frequency of Microbial Inoculants for Turfgrass Disease Control

NYS IPM Type: Project ReportConsiderable information is now available concerning the use of microbial inoculants for the control of turfgrass diseases. However, despite positive experimental results, few microbial inoculants have been highly effective in field studies or in commercial use on golf courses. A number of studies have shown that microbial agents perform most effectively when populations can be maintained at high levels, usually at populations exceeding 107 cells/g soil. However, applications made during the daytime hours may limit population development due to UV exposure or to desiccation. The limited number of success stories of biological control on golf courses have been from sites where applications of biological control organisms were applied during the overnight hours. Technology is now available for golf course superintendents to culture microbial inoculants on site at the golf course and apply inoculants directly through the irrigation system at any time of the day or night. It is now feasible for superintendents to make applications on a daily basis. This study was designed to evaluate the impacts of application scheduling on the disease control efficacy of introduced microbial inoculants

Cyclic Ovarian Hormone Modulation of Supraspinal Δ\u3csup\u3e9\u3c/sup\u3e-tetrahydrocannabinol-induced Antinociception and Cannabinoid Receptor Binding in the Female Rat

Estrous cycle-related fluctuations in delta-9-tetrahydrocannabinol (THC)-induced antinociception have been observed in the rat. The aim of this study was to determine which major ovarian hormone modulates the antinociceptive effects of i.c.v. THC, and whether hormone modulation of THC\u27s behavioral effects could be due to changes in brain cannabinoid receptors (CBr). Vehicle (oil) or hormones (estradiol or progesterone, or both) were administered to female rats on days 3 and 7 post-ovariectomy. On the morning or afternoon of day 8 or day 9, vehicle or THC (100 μg) was administered i.c.v. Paw pressure, tail withdrawal, locomotor activity and catalepsy tests were conducted over a 3-h period. Estradiol (with and without progesterone) enhanced THC-induced paw pressure antinociception only. Ovarian hormones time-dependently modulated CBr in brain structures that mediate antinociception and locomotor activity, but the changes observed in CBr did not parallel changes in behavior. However, the time course of CBr changes must be further elucidated to determine the functional relationship between receptor changes and antinociceptive sensitivity to THC

Asset liability modelling and pension schemes: the application of robust optimization to USS

This paper uses a novel numerical optimization technique - robust optimization - that is well suited to solving the asset-liability management (ALM) problem for pension schemes. It requires the estimation of fewer stochastic parameters, reduces estimation risk and adopts a prudent approach to asset allocation. This study is the first to apply it to a real-world pension scheme, and the first ALM model of a pension scheme to maximise the Sharpe ratio. We disaggregate pension liabilities into three components - active members, deferred members and pensioners, and transform the optimal asset allocation into the scheme’s projected contribution rate. The robust optimization model is extended to include liabilities and used to derive optimal investment policies for the Universities Superannuation Scheme (USS), benchmarked against the Sharpe and Tint, Bayes-Stein, and Black-Litterman models as well as the actual USS investment decisions. Over a 144 month out-of-sample period robust optimization is superior to the four benchmarks across 20 performance criteria, and has a remarkably stable asset allocation – essentially fix-mix. These conclusions are supported by six robustness checks

Optimal partial-arcs in VMAT treatment planning

Purpose: To improve the delivery efficiency of VMAT by extending the recently published VMAT treatment planning algorithm vmerge to automatically generate optimal partial-arc plans. Methods and materials: A high-quality initial plan is created by solving a convex multicriteria optimization problem using 180 equi-spaced beams. This initial plan is used to form a set of dose constraints, and a set of partial-arc plans is created by searching the space of all possible partial-arc plans that satisfy these constraints. For each partial-arc, an iterative fluence map merging and sequencing algorithm (vmerge) is used to improve the delivery efficiency. Merging continues as long as the dose quality is maintained above a user-defined threshold. The final plan is selected as the partial arc with the lowest treatment time. The complete algorithm is called pmerge. Results: Partial-arc plans are created using pmerge for a lung, liver and prostate case, with final treatment times of 127, 245 and 147 seconds. Treatment times using full arcs with vmerge are 211, 357 and 178 seconds. Dose quality is maintained across the initial, vmerge, and pmerge plans to within 5% of the mean doses to the critical organs-at-risk and with target coverage above 98%. Additionally, we find that the angular distribution of fluence in the initial plans is predictive of the start and end angles of the optimal partial-arc. Conclusions: The pmerge algorithm is an extension to vmerge that automatically finds the partial-arc plan that minimizes the treatment time. VMAT delivery efficiency can be improved by employing partial-arcs without compromising dose quality. Partial arcs are most applicable to cases with non-centralized targets, where the time savings is greatest

A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts

Unraveling the drift behaviour of the remarkable pulsar PSR B0826-34

We present new results from high sensitivity GMRT observations of PSR B0826-34. We provide a model to explain the observed subpulse drift properties of this pulsar, including the apparent reversals of the drift direction. In this model, PSR B0826-34 is close to being an aligned rotator. We solve for the emission geometry of this pulsar and show that the angle between the rotation and the magnetic axes is less than 5 deg. We see evidence for as many as 6 to 7 drifting bands in the main pulse at 318 MHz, which are part of a circulating system of about 15 spark-associated subpulse emission beams. We provide quantitative treatments of the aliasing problem and various effects of geometry. The observed drift rate is an aliased version of the true drift rate, such that a subpulse drifts to the location of the adjacent subpulse (or a multiple thereof) in about one pulsar period. We show that small variations, of the order of 3-8%, in the mean drift rate are then enough to explain the apparent reversals of drift direction. We find the mean circulation time of the drift pattern to be significantly longer than the predictions of the original RS75 model and propose an explanation for this, based on modified models with temperature regulated partial ion flow in the polar vacuum gap. From the variation of the mean subpulse separation across the main pulse window, we show that the spark pattern is not centred around the dipole axis, but around a point much closer (within a degree or so) to the rotation axis -- we discuss the implication of this.Comment: 23 pages (including 9 figure). Submitted to Astronomy and Astrophysics on November 11, 200