Alien Registration- Mcaloon, James J. (Kingman, Penobscot County)

https://digitalmaine.com/alien_docs/15665/thumbnail.jp

Preventative intervention for social, emotional and behavioural difficulties in toddlers and their families: A pilot study

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Preventative intervention early in life is key to interrupting trajectories toward subsequent emotional and behavioural problems later in life. This study examined the effectiveness of the Holding Hands program, an innovative program of preventative intervention aimed at improving the behavioural and emotional functioning of 12 to 48-month-old toddlers, and the wellbeing of their parents. This program seeks to synthesise the existing evidence in four ways; it incorporates both traditional Parent Management Training and Direct Coaching methods. It is intensive, significantly reducing session numbers and it explicitly addresses parental emotion regulation. The program also utilises operant learning principals in an effort to contingently reinforce behaviour that parents want to see more of, without employing exclusionary strategies in response to behavior that parents want to see less of. Thirty-one families, with a toddler who met clinical or sub-clinical cut-offs for externalising or internalising problems, were self- or externally-referred to the six- to eight-week program. Results indicated statistically significant improvement in toddler emotional and behavioural functioning, and parent well-being on a range of psychometric measures from pre- to post-treatment. Treatment gains were maintained for parents and children at follow-up. Implications of these findings for clinical practice and suggestions for future research are discussed

Enzymatic corn wet milling: engineering process and cost model

<p>Abstract</p> <p>Background</p> <p>Enzymatic corn wet milling (E-milling) is a process derived from conventional wet milling for the recovery and purification of starch and co-products using proteases to eliminate the need for sulfites and decrease the steeping time. In 2006, the total starch production in USA by conventional wet milling equaled 23 billion kilograms, including modified starches and starches used for sweeteners and ethanol production <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>. Process engineering and cost models for an E-milling process have been developed for a processing plant with a capacity of 2.54 million kg of corn per day (100,000 bu/day). These models are based on the previously published models for a traditional wet milling plant with the same capacity. The E-milling process includes grain cleaning, pretreatment, enzymatic treatment, germ separation and recovery, fiber separation and recovery, gluten separation and recovery and starch separation. Information for the development of the conventional models was obtained from a variety of technical sources including commercial wet milling companies, industry experts and equipment suppliers. Additional information for the present models was obtained from our own experience with the development of the E-milling process and trials in the laboratory and at the pilot plant scale. The models were developed using process and cost simulation software (SuperPro Designer^®) and include processing information such as composition and flow rates of the various process streams, descriptions of the various unit operations and detailed breakdowns of the operating and capital cost of the facility.</p> <p>Results</p> <p>Based on the information from the model, we can estimate the cost of production per kilogram of starch using the input prices for corn, enzyme and other wet milling co-products. The work presented here describes the E-milling process and compares the process, the operation and costs with the conventional process.</p> <p>Conclusion</p> <p>The E-milling process was found to be cost competitive with the conventional process during periods of high corn feedstock costs since the enzymatic process enhances the yields of the products in a corn wet milling process. This model is available upon request from the authors for educational, research and non-commercial uses.</p

Relative importance of herd-level risk factors for probability of infection with paratuberculosis in Irish dairy herds

Control of paratuberculosis is challenging due to the relatively poor performance of diagnostic tests, a prolonged incubation period, and protracted environmental survival. Prioritization of herd-level interventions is not possible because putative risk factors are often not supported by risk factor studies. The objective for this study was to investigate the relative importance of risk factors for an increased probability of herd paratuberculosis infection. Risk assessment data, comprehensive animal purchase history, and diagnostic test data were available for 936 Irish dairy herds. Both logistic regression and a Bayesian β regression on the outcome of a latent class analysis were conducted. Population attributable fractions and proportional reduction in variance explained were calculated for each variable in the logistic and Bayesian models, respectively. Routine use of the calving area for sick or lame cows was found to be a significant explanatory covariate in both models. Purchasing behavior for the previous 10 yr was not found to be significant. For the logistic model, length of time calves spend in the calving pen (25%) and routine use of the calving pen for sick or lame animals (14%) had the highest attributable fractions. For the Bayesian model, the overall R2 was 16%. Dry cow cleanliness (7%) and routine use of the calving area for sick or lame cows (6%) and had the highest proportional reduction in variance explained. These findings provide support for several management practices commonly recommended as part of paratuberculosis control programs; however, a large proportion of the observed variation in probability of infection remained unexplained, suggesting other important risks factors may exist

Low accuracy of Bayesian latent class analysis for estimation of herd-level true prevalence under certain disease characteristics—An analysis using simulated data

Estimation of the true prevalence of infected individuals involves the application of a diagnostic test to a population and adjusting according to test performance, sensitivity and specificity. Bayesian latent class analysis for the estimation of herd and animal-level true prevalence, has become increasingly used in veterinary epidemiology and is particularly useful in incorporating uncertainty and variability into analyses in a flexible framework. However, the approach has not yet been evaluated using simulated data where the true prevalence is known. Furthermore, using this approach, the within-herd true prevalence is often assumed to follow a beta distribution, the parameters of which may be modelled using hyperpriors to incorporate both uncertainty and variability associated with this parameter. Recently however, the authors of the current study highlighted a potential issue with this approach, in particular, with fitting the distributions and a tendency for the resulting distribution to invert and become clustered at zero. Therefore, the objective of the present study was to evaluate commonly specified models using simulated datasets where the herd-level true prevalence was known. The specific purpose was to compare findings from models using hyperpriors to those using a simple beta distribution to model within-herd prevalence. A second objective was to investigate sources of error by varying characteristics of the simulated dataset. Mycobacterium avium subspecies paratuberculosis infection was used as an example for the baseline dataset. Data were simulated for 1000 herds across a range of herd-level true prevalence scenarios, and models were fitted using priors from recently published studies. The results demonstrated poor performance of these latent class models for diseases characterised by poor diagnostic test sensitivity and low within-herd true prevalence. All variations of the model appeared to be sensitive to the prior and tended to overestimate herd-level true prevalence. Estimates were substantially improved in different infection scenarios by increasing test sensitivity and within-herd true prevalence. The results of this study raise questions about the accuracy of published estimates for the herd-level true prevalence of paratuberculosis based on serological testing, using latent class analysis. This study highlights the importance of conducting more rigorous sensitivity analyses than have been carried out in previous analyses published to date

A review of paratuberculosis in dairy herds — Part 1: Epidemiology

Bovine paratuberculosis is a chronic infectious disease of cattle caused by Mycobacterium avium subspecies paratuberculosis (MAP). This is the first in a two-part review of the epidemiology and control of paratuberculosis in dairy herds. Paratuberculosis was originally described in 1895 and is now considered endemic among farmed cattle worldwide. MAP has been isolated from a wide range of non-ruminant wildlife as well as humans and non-human primates. In dairy herds, MAP is assumed to be introduced predominantly through the purchase of infected stock with additional factors modulating the risk of persistence or fade-out once an infected animal is introduced. Faecal shedding may vary widely between individuals and recent modelling work has shed some light on the role of super-shedding animals in the transmission of MAP within herds. Recent experimental work has revisited many of the assumptions around age susceptibility, faecal shedding in calves and calf-to-calf transmission. Further efforts to elucidate the relative contributions of different transmission routes to the dissemination of infection in endemic herds will aid in the prioritisation of efforts for control on farm