Response to novel objects and foraging tasks by common marmoset (Callithrix Jacchus) female Pairs

Many studies have shown that environmental enrichment can significantly improve the psychological well-being of captive primates, increasing the occurrence of explorative behavior and thus reducing boredom. The response of primates to enrichment devices may be affected by many factors such as species, sex, age, personality and social context. Environmental enrichment is particularly important for social primates living in unnatural social groupings (i.e. same-sex pairs or singly housed animals), who have very few, or no, benefits from the presence of social companions in addition to all the problems related to captivity (e.g. increased inactivity). This study analyses the effects of enrichment devices (i.e. novel objects and foraging tasks) on the behavior of common marmoset (Callithrix jacchus) female pairs, a species that usually lives in family groups. It aims to determine which aspects of an enrichment device are more likely to elicit explorative behaviors, and how aggressive and stress-related behaviors are affected by its presence. Overall, the marmosets explored foraging tasks significantly longer than novel objects. The type of object, which varied in size, shape and aural responsiveness (i.e. they made a noise when the monkey touched them), did not affect the response of the monkeys, but they explored objects that were placed higher in the enclosure more than those placed lower down.Younger monkeys were more attracted to the enrichment devices than the older ones. Finally, stress-related behavior (i.e. scratching) significantly decreased when the monkeys were presented with the objects; aggressive behavior as unaffected. This study supports the importance of environmental enrichment for captive primates and shows that in marmosets its effectiveness strongly depends upon the height of the device in the enclosure and the presence of hidden food. The findings can be explained ifone considers the foraging behavior of wild common marmosets. Broader applications for the research findings are suggested in relation to enrichment

Alternative regression models to assess increase in childhood BMI

<p>Abstract</p> <p>Background</p> <p>Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations.</p> <p>Methods</p> <p>Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity.</p> <p>Results</p> <p>GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models.</p> <p>Conclusion</p> <p>GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.</p

Design of Experiments for Screening

The aim of this paper is to review methods of designing screening experiments, ranging from designs originally developed for physical experiments to those especially tailored to experiments on numerical models. The strengths and weaknesses of the various designs for screening variables in numerical models are discussed. First, classes of factorial designs for experiments to estimate main effects and interactions through a linear statistical model are described, specifically regular and nonregular fractional factorial designs, supersaturated designs and systematic fractional replicate designs. Generic issues of aliasing, bias and cancellation of factorial effects are discussed. Second, group screening experiments are considered including factorial group screening and sequential bifurcation. Third, random sampling plans are discussed including Latin hypercube sampling and sampling plans to estimate elementary effects. Fourth, a variety of modelling methods commonly employed with screening designs are briefly described. Finally, a novel study demonstrates six screening methods on two frequently-used exemplars, and their performances are compared

Decolorization of synthetic melanoidins-containing wastewater by a bacterial consortium

The presence of melanoidins in molasses wastewater leads to water pollution both due to its dark brown color and its COD contents. In this study, a bacterial consortium isolated from waterfall sediment was tested for its decolorization. The identification of culturable bacteria by 16S rDNA based approach showed that the consortium composed of Klebsiella oxytoca, Serratia mercescens, Citrobacter sp. and unknown bacterium. In the context of academic study, prevention on the difficulties of providing effluent as well as its variations in compositions, several synthetic media prepared with respect to color and COD contents based on analysis of molasses wastewater, i.e., Viandox sauce (13.5% v/v), caramel (30% w/v), beet molasses wastewater (41.5% v/v) and sugarcane molasses wastewater (20% v/v) were used for decolorization using consortium with color removal 9.5, 1.13, 8.02 and 17.5%, respectively, within 2 days. However, Viandox sauce was retained for further study. The effect of initial pH and Viandox concentration on decolorization and growth of bacterial consortium were further determined. The highest decolorization of 18.3% was achieved at pH 4 after 2 day of incubation. Experiments on fresh or used medium and used or fresh bacterial cells, led to conclusion that the limitation of decolorization was due to nutritional deficiency. The effect of aeration on decolorization was also carried out in 2 L laboratory-scale suspended cell bioreactor. The maximum decolorization was 19.3% with aeration at KLa = 2.5836 h-1 (0.1 vvm)

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Development and validation of A quasi-dimensional model for (M)Ethanol-Fuelled SI engines

RESEARCH OBJECTIVE - The use of methanol and ethanol in spark-ignition engines forms an interesting approach to decarbonizing transport and securing domestic energy supply. Experimental work has produced promising results, however, the full potential of light alcohols in modern engine technology remains to be explored. Today, this can be addressed at low cost using system simulations of the whole engine, provided that the employed models account for the effect of the fuel on engine operation. The goal of current work is to develop an engine cycle model that can accurately predict performance, efficiency, pollutant emissions and knock onset in state-of-the-art neat alcohol engines. METHODOLOGY - Two-zone thermodynamic engine modeling, in combination with 1D gas dynamics, is put forward as a useful tool for cheap and fast optimization of engines. Typically, this model class derives the mass burning rate of fuel from turbulent combustion models. A fundamental building block of turbulent combustion models is an expression for the laminar burning velocity of the fuel-air-residuals mixture at instantaneous cylinder pressure and temperature. This physicochemical property basically groups the contribution of the chemical reactions (of the fuel) to combustion. Consequently, an important part of our study consisted of calculating (using chemical kinetics) and measuring the laminar burning velocity of methanol and ethanol at engine-like conditions. In order to validate the developed engine model, its predictions were compared against a database of experimental results obtained on three different flex-fuel and dedicated alcohol engines. RESULTS - Comparison of the experimental and simulated cylinder, intake and exhaust pressure traces confirmed the predictive power of our engine model for methanol-fuelled engines. A wide variety of engine operating points were accurately reproduced thanks to a new laminar burning velocity correlation, which correctly accounts for changes in pressure, temperature, mixture richness and residual ratio. The Flame Closure Model of Zimont-Lipatnikov emerged as the most widely applicable model from a comparison of several turbulent combustion models. With regard to the gas dynamics it proved necessary to include a fuel puddling submodel to take the cooling effect due to alcohol injection into consideration. LIMITATIONS - The developed model was successfully validated for normal combustion in port-injected neat methanol engines. The validation of the routines for ethanol combustion and engines with direct injection is part of ongoing work. Now that normal combustion can be accurately simulated, further work will look at the prediction of pollutant emissions and knock onset in these engines. NOVELTY - This paper presents the first recent attempt to model the application of neat alcohols in modern and anticipated future engine technologies. Compared to previous work the effects of in-cylinder and mixture conditions on the combustion are more accurately predicted thanks to the inclusion of a new and widely validated laminar burning velocity correlation. In contrast to other studies, the current experimental database also includes measurements on turbocharged, high compression ratio engines with elevated amounts of EGR, which is representative of future dedicated alcohol engines. CONCLUSIONS - The current work focused on adapting the various submodels of quasi-dimensional engine codes to the properties of light alcohols. The developed simulation tools can be used with confidence to optimize current and future engines running on neat methanol and ethanol. This work also forms the starting point for an extension of the modelling concepts to alcohol-gasoline blends, which hold more industrial relevance

Malaria paediatric hospitalization between 1999 and 2008 across Kenya

<p>Abstract</p> <p>Background</p> <p>Intervention coverage and funding for the control of malaria in Africa has increased in recent years, however, there are few descriptions of changing disease burden and the few reports available are from isolated, single site observations or are of reports at country-level. Here we present a nationwide assessment of changes over 10 years in paediatric malaria hospitalization across Kenya.</p> <p>Methods</p> <p>Paediatric admission data on malaria and non-malaria diagnoses were assembled for the period 1999 to 2008 from in-patient registers at 17 district hospitals in Kenya and represented the diverse malaria ecology of the country. These data were then analysed using autoregressive moving average time series models with malaria and all-cause admissions as the main outcomes adjusted for rainfall, changes in service use and populations-at-risk within each hospital's catchment to establish whether there has been a statistically significant decline in paediatric malaria hospitalization during the observation period.</p> <p>Results</p> <p>Among the 17 hospital sites, adjusted paediatric malaria admissions had significantly declined at 10 hospitals over 10 years since 1999; had significantly increased at four hospitals, and remained unchanged in three hospitals. The overall estimated average reduction in malaria admission rates was 0.0063 cases per 1,000 children aged 0 to 14 years per month representing an average percentage reduction of 49% across the 10 hospitals registering a significant decline by the end of 2008. Paediatric admissions for all-causes had declined significantly with a reduction in admission rates of greater than 0.0050 cases per 1,000 children aged 0 to 14 years per month at 6 of 17 hospitals. Where malaria admissions had increased three of the four sites were located in Western Kenya close to Lake Victoria. Conversely there was an indication that areas with the largest declines in malaria admission rates were areas located along the Kenyan coast and some sites in the highlands of Kenya.</p> <p>Conclusion</p> <p>A country-wide assessment of trends in malaria hospitalizations indicates that all is not equal, important variations exist in the temporal pattern of malaria admissions between sites and these differences require more detailed investigation to understand what is required to promote a clinical transition across Africa.</p

Changing malaria intervention coverage, transmission and hospitalization in Kenya

<p>Abstract</p> <p>Background</p> <p>Reports of declining incidence of malaria disease burden across several countries in Africa suggest that the epidemiology of malaria across the continent is in transition. Whether this transition is directly related to the scaling of intervention coverage remains a moot point.</p> <p>Methods</p> <p>Paediatric admission data from eight Kenyan hospitals and their catchments have been assembled across two three-year time periods: September 2003 to August 2006 (pre-scaled intervention) and September 2006 to August 2009 (post-scaled intervention). Interrupted time series (ITS) models were developed adjusting for variations in rainfall and hospital use by surrounding communities to show changes in malaria hospitalization over the two periods. The temporal changes in factors that might explain changes in disease incidence were examined sequentially for each hospital setting, compared between hospital settings and ranked according to plausible explanatory factors.</p> <p>Results</p> <p>In six out of eight sites there was a decline in Malaria admission rates with declines between 18% and 69%. At two sites malaria admissions rates increased by 55% and 35%. Results from the ITS models indicate that before scaled intervention in September 2006, there was a significant month-to-month decline in the mean malaria admission rates at four hospitals (trend P < 0.05). At the point of scaled intervention, the estimated mean admission rates for malaria was significantly less at four sites compared to the pre-scaled period baseline. Following scaled intervention there was a significant change in the month-to-month trend in the mean malaria admission rates in some but not all of the sites. Plausibility assessment of possible drivers of change pre- versus post-scaled intervention showed inconsistent patterns however, allowing for the increase in rainfall in the second period, there is a suggestion that starting transmission intensity and the scale of change in ITN coverage might explain some but not all of the variation in effect size. At most sites where declines between observation periods were documented admission rates were changing before free mass ITN distribution and prior to the implementation of ACT across Kenya.</p> <p>Conclusion</p> <p>This study provides evidence of significant within and between location heterogeneity in temporal trends of malaria disease burden. Plausible drivers for changing disease incidence suggest a complex combination of mechanisms, not easily measured retrospectively.</p

Genetic Evaluation of Hip Score in UK Labrador Retrievers

Hip dysplasia is an important and complex genetic disease in dogs with both genetic and environmental influences. Since the osteoarthritis that develops is irreversible the only way to improve welfare, through reducing the prevalence, is through genetic selection. This study aimed to evaluate the progress of selection against hip dysplasia, to quantify potential improvements in the response to selection via use of genetic information and increases in selection intensity, and to prepare for public provision of estimated breeding values (EBV) for hip dysplasia in the UK. Data consisted of 25,243 single records of hip scores of Labrador Retrievers between one and four years old, from radiographs evaluated between 2000 and 2007 as part of the British Veterinary Association (BVA) hip score scheme. A natural logarithm transformation was applied to improve normality and linear mixed models were evaluated using ASREML. Genetic correlations between left and right scores, and total hip scores at one, two and three years of age were found to be close to one, endorsing analysis of total hip score in dogs aged one to three as an appropriate approach. A heritability of 0.35±0.016 and small but significant litter effect (0.07±0.009) were estimated. The observed trends in both mean hip score and mean EBV over year of birth indicate that a small genetic improvement has been taking place, approximately equivalent to avoiding those dogs with the worst 15% of scores. Deterministic analysis supported by simulations showed that a 19% greater response could be achieved using EBV compared to phenotype through increases in accuracy alone. This study establishes that consistent but slow genetic improvement in the hip score of UK Labrador Retrievers has been achieved over the previous decade, and demonstrates that progress may be easily enhanced through the use of EBVs and more intense selection