Improved Model Fit Research Articles

Covariates in population pharmacokinetic studies of critically ill adults receiving β-lactam antimicrobials: a systematic review and narrative synthesis.

Population pharmacokinetic studies of β-lactam antimicrobials in critically ill patients derive models that inform their dosing. In non-linear mixed-effects modelling, covariates are often used to improve model fit and explain variability. We aimed to investigate which covariates are most commonly assessed and which are found to be significant, along with global patterns of publication. We conducted a systematic review, searching MEDLINE, Embase, CENTRAL and Web of Science on 01 March 2023, including studies of critically ill adults receiving β-lactam antimicrobials who underwent blood sampling for population pharmacokinetic studies. We extracted and categorized all reported covariates and assessed reporting quality using the ClinPK checklist. Our search identified 151 studies with 6018 participants. Most studies reported observational cohorts (120 studies, 80%), with the majority conducted in high-income settings (136 studies, 90%). Of the 1083 identified covariate instances, 237 were unique; the most common categories were patient characteristics (n = 404), biomarkers (n = 206) and physiological parameters (n = 163). Only seven distinct commonly reported covariates (CLCR, weight, glomerular filtration rate, diuresis, need for renal replacement, serum albumin and C-reactive protein) were significant more than 20% of the time. Covariates are most commonly chosen based on biological plausibility, with patient characteristics and biomarkers the most frequently investigated. We developed an openly accessible database of reported covariates to aid investigators with covariate selection when designing population pharmacokinetic studies. Novel covariates, such as sepsis subphenotypes, have not been explored yet, leaving a research gap for future work.

EXPLORING SOME SPATIALLY CONSTRAINED DELINEATION METHODS IN SEGMENTING THE MALAYSIAN COMMERCIAL PROPERTY MARKET

This study delves into the property submarket in Kuala Lumpur and Selangor, Malaysia. The submarket is anticipated to be simple, uniform, and dense, making it highly influenced by neighbouring properties. However, traditional data-driven methods that overlook spatial contiguity disregard this density condition. To tackle this problem, the study investigates spatially constrained data-driven methods utilizing Principal Component Analysis (PCA) and cluster analysis. The findings reveal that spatially constrained methods outperform traditional methods by minimizing errors and enhancing model fit. Specifically, the two-step cluster method and k-means cluster method reduce errors by 6.96% and 7.22%, respectively, but at the cost of model fit by 11.23% and 13.94%. Conversely, the spatial k-means and spatial agglomerative hierarchical cluster methods reduce errors by 8.68% and 8.17%, respectively, while improving model fit by 7.1% and 6.35%. Hence, the study concludes that spatially constrained data-driven methods are more effective in differentiating commercial property submarkets than traditional methods.

Climate-Sensitive Diameter Growth Models for White Spruce and White Pine Plantations

Global change in the climate is affecting tree/forest growth. There have been many studies that analyzed climate effects on tree growth. Results presented in these studies showed that the climate had both positive and negative effects on tree growth. The nature (positive/negative) and magnitude of the effects and the climate variables affecting growth depended on tree species. Climate-sensitive diameter growth models are not available for white pine (Pinus strobus L.) and white spruce (Picea glauca (Moench) Voss) plantations. These models are needed to project forest growth and yield and develop forest management plans. Therefore, diameter growth models were developed for white pine and white spruce plantations by incorporating climate variables. Four hundred white pine and white spruce trees (200 per species) were sampled from 80 (40 per species) even-aged monospecific plantations (five trees per plantation) across Ontario, Canada. Diameter–age pairs were obtained from these trees using stem analysis. A nonlinear mixed-effects modeling approach was used to develop diameter growth models. To make the models climate sensitive, model parameters were expressed in term of climate variables. Inclusion of climate variables significantly improved model fit statistics and predictive accuracy. For evaluation, diameters (inside bark) at breast height were estimated for three geographic locations (east, west, and south) across Ontario for an 80-year growth period (2021–2100) under three climate change (emissions) scenarios (representative concentration pathway or RCP 2.6, 4.5, and 8.5 watts m−2). For both species, the overall climate effects were negative. For white spruce, the maximum pronounced difference in projected diameters after the 80-year growth period was in the west. At this location, compared to the no climate change scenario, projected spruce diameters under RCPs 2.6 and 8.5 were thinner by 4.64 (15.99%) and 3.72 (12.80%) cm, respectively. For white pine, the maximum difference was in the south. Compared to the no climate change scenario, projected pine diameters at age 80 under RCPs 2.6 and 8.5 at this location were narrower by 4.54 (13.99%) and 7.60 (23.43%) cm, respectively. For both species, climate effects on diameter growth were less evident at other locations. If the values of climate variables are unavailable, models fitted without climate variables can be used to estimate these diameters for both species.

Landscape-wide pulse events predict trait-based responses among wetland birds in perennial channels of a dryland wetland.

Wetlands in arid or semiarid zones are vital for maintaining biodiversity but face growing threats. Flooding regime variability is a key driver of ecological dynamism in these systems, dictating primary productivity on a large spatial scale. The functional composition or diversity of wetland-dependent bird species has been found to be sensitive to fluctuations in hydrological regimes and can thus be indicative of cascading ecosystem responses associated with climate change. In this paper, we investigate whether large-scale changes in inundation and fire-a significant additional biodiversity determinant in (semi-)arid landscapes-are reliable predictors of functional group responses of wetland-dependent birds along a perennial channel of the Okavango Delta, Botswana. We fit generalized additive models (GAMs) to 6 years of bird survey data collected along ~190-km-long annual transects and use remotely sensed landscape-level inundation estimates, as well as spatiotemporal distance to fire, to predict the responsiveness of seven trait-based functional group abundances. During the surveys, a total of 89 different wetland-dependent bird species were recorded, including 76 residents, across all years, with below-surface feeding waders consistently the most abundant functional group. Including estimated spatiotemporal variability in flooding and fire, as well as their interactions, improved model fit for all seven functional groups, explaining between 46.8% and 68.3% of variability in functional group abundances. Covariates representing longer-term variability in inundation generally performed better than shorter-term ones. For example, variability in inundation over the 5 months preceding a survey best predicted the responses of all functional groups, which also all exhibited responsiveness to the interaction between flooding and fire. We were able to interpret the responses of individual functional groups, based on the resource exploitation assumption. Overall, our results suggest that perennial waters in dryland wetlands offer functional refugia to wetland-dependent birds and highlight the indicative power of large-scale trait-based bird monitoring. Our findings demonstrate the potential utility of such a monitoring regime for dryland wetland ecosystems vulnerable to industrial-scale anthropogenic pressure and associated climate change.

Marginal Distribution Fitting Method for Modelling Flood Extremes on a River Network

This study utilized a max-stable process (MSP) model with a dependence structure defined via a non-Euclidean distance metric, with the goal of modelling extreme flood data on a river network. The dataset was composed of mean daily discharge observations from 22 United States Geological Survey streamflow gaging stations for river basins in Missouri and Arkansas. The analysis included the application of the elastic-net penalty to automatically build spatially varying trend surfaces to model the marginal distributions. The dependence model accounted for the river distance between hydrologically connected gaging sites and the hydrologic distance, defined as the Euclidean distance between the centers of site’s associated drainage areas, for all stations. Modelling the marginal distributions and spatial dependence among the extremes are two key components for spatially modelling extremes. Among the 16 covariates evaluated for marginal fitting, 7 were selected to spatially model the generalized extreme value (GEV) location parameter (for each gaging station’s contributing drainage basin, its outlet elevation, centroid x coordinate, centroid elevation, area, average basin width, elevation range, and median land surface slope). The three covariates selected for the GEV scale parameter included the area, average basin width, and median land surface slope. The GEV shape parameter was assumed to be constant throughout the entire study area. Comparisons of estimates obtained from the spatial covariate model with their corresponding “at-site” estimates resulted in computed values of 0.95, 0.95, 0.94 and 0.85, 0.84, 0.90 for the coefficient of determination, Nash–Sutcliffe efficiency, and Kling–Gupta efficiency for the GEV location and scale parameters, respectively. Brown–Resnick MSP models were fit to independent multivariate events extracted from a set of common discharge data, transformed to unit Fréchet margins while considering different permutations of the non-Euclidean dependence model. Each of the fitted model’s log-likelihood values indicated improved fits when using hydrologic distance rather than Euclidean distance. They also demonstrated that accounting for flow-connected dependence and anisotropy further improved model fit. In this study, the results from both parts were illustrative; however, further research with larger datasets and more heterogeneous systems is recommended.

A comparison of retrospectively reported and ecological momentary assessment-reported perceived social support in predicting ecological momentary assessment-reported non-suicidal self-injury.

Non-suicidal self-injury (NSSI) urges and behaviors are associated with lower perceived social support and related constructs (e.g., perceived rejection). However, no studies have examined the concordance of retrospective (baseline) and ecological momentary assessment (EMA) perceived social support assessments. Retrospective and EMA reports are often only weakly to moderately correlated; measurement approaches may, therefore, impact observed associations between variables. We tested whether average EMA-reported perceived emotional social support uniquely predicts EMA-reported NSSI urges and behaviors above baseline-reported retrospective self-report of perceived emotional social support alone. 93 young adults (ages 18-34) with past-month NSSI urges or behaviors and lifetime NSSI behaviors completed a semi-structured interview, self-report surveys, and a 2-week EMA protocol. Baseline- and EMA-reported perceived emotional social support were positively correlated (Kendall's tau-b = 0.51). Average EMA-reported social support was uniquely associated with EMA-reported NSSI urges but not NSSI behaviors. EMA-reported perceived emotional social support captured information not represented by baseline reports alone, but improvement in model fit was modest. EMA-reported social support may further improve the estimation of EMA-reported NSSI urges if modeled as a proximal predictor of NSSI. Further work is needed to clarify temporal directions between social support and NSSI urges. Limitations are discussed.

Which fatigue scale should I use? A Rasch analysis of two fatigue scales in inflammatory conditions

Abstract Objectives This study aimed to assess the psychometric properties of two widely used fatigue scales in a sample of patients with inflammatory conditions. Methods Rasch analysis was used to examine scale reliability, item bias, unidimensionality and overall fit to the Rasch model. Subtest methodology was utilized to attempt to improve model fit for the Chalder Fatigue Scale (CFQ) and Bristol Rheumatoid Arthritis Fatigue Multidimensional Questionnaire (BRAF-MDQ). Results Initial analysis displayed strong reliability (person separation index 0.89–0.96) alongside a lack of item bias in both scales. However, evidence for unidimensionality was not found for either scale. Overall fit to the Rasch model was marginal for the CFQ and misfitting for the BRAF-MDQ. Local dependency was observed as well as significant item misfit for both scales. Subtest modifications resulted in the best model fit for the BRAF-MDQ [χ2(16) = 15.77, P = 0.469] and the CFQ [χ2(25) = 15.49, P = 0.929]. Modifications resulted in improved fit, reductions in measurement error and the production of ordinal-to-interval conversion tables for both scales. Conversion tables apply the benefits of enhanced measurement accuracy, valid comparison of BRAF-MDQ and CFQ scores to other interval-level data, appropriate use in parametric statistics and enhanced precision in clinical cut-off scores—without the need to change administration format. Conclusion The BRAF-MDQ and CFQ are valid, reliable tools for fatigue assessment. Psychometric indices and content factors suggest the CFQ is suited to measuring general fatigue, particularly when response burden is a concern, while the BRAF-MDQ should be used in clinical presentations where other symptoms are severe and the impact of fatigue on daily living and emotional and social well-being is of interest.

What determines mpox vaccination uptake? Assessing the effect of intent-to-vaccinate versus other determinants among men who have sex with men

BackgroundIn response to the mpox outbreak, vaccination was offered in the Netherlands to men who have sex with men (MSM) at increased risk for mpox. Successful vaccination campaigns are leveraged by high intent-to-vaccinate, yet intent might not always lead to uptake. Therefore, we assessed the impact of intent-to-vaccinate and other factors on vaccination uptake among participants of the Amsterdam Cohort Studies (ACS). MethodIn July 2022, prior to the mpox vaccination campaign, we distributed an online survey regarding mpox intent-to-vaccinate, as well as e.g. beliefs, attitude, subjective norms, and perception of risk among ACS participants (all MSM). Vaccination uptake was self-reported during study visits after August 2022. The association between vaccination intent and uptake, and determinants of intent, was jointly assessed using a structural equation model (SEM) based on components of the Theory of Planned Behavior (TPB). In a second SEM, determinants of intent were allowed to have a direct effect on vaccination uptake. Results492 MSM (median age = 46 years) were included in analyses. 380 (77%) had high intent-to-vaccinate and 238 (48%) received at least one vaccine dose. In the first model with a direct relation between intent and uptake only, TBP components predicted intent as expected, and high intent-to-vaccinate was significantly associated with getting vaccinated (β = 1.1, 95%CI = 0.6–1.5). However, 175/380 (46%) participants with high intent-to-vaccinate did not get vaccinated. The second model had an improved model fit compared to the first model. The effect of intent on uptake was non-significant, and only perceiving to be at higher risk of infection significantly increased vaccination uptake later on (β = 0.42, 95%CI = 0.26–0.59). Having a steady relationship decreased the probability of vaccination (β = -0.59, 95%CI = -1.0- −0.18). ConclusionsWhile intent-to-vaccinate for mpox was high among MSM, high intent did not necessarily result in vaccine uptake. Mpox risk perception might have played a more pivotal role in getting vaccinated, which may be related to the evolution of vaccination eligibility criteria and accessibility to the vaccine.

Agroecologies defined by species distribution models improve model fit of genotype by environment interactions to identify the best performing chicken breeds for smallholder systems

Animal performance is an outcome of genetic effects, environmental influences, and their interaction. Understanding the influences of the environment on performance is important to identify the right breeds for a given environment. Agroecological zonation is commonly used to classify environments and compare the performance of breeds before their wider introduction into a new environment. Environmental classes, also referred to as agroecologies, are traditionally defined based on agronomically important environmental predictors. We hypothesized that our own classification of agroecologies for livestock at a species level and incorporating the most important environmental predictors may improve genotype by environment interactions (GxE) estimations over conventional methodology. We collected growth performance data on improved chicken breeds distributed to multiple environments in Ethiopia. We applied species distribution models (SDMs) to identify the most relevant environmental predictors and to group chicken performance testing sites into agroecologies. We fitted linear mixed-effects models (LMM) to make model comparisons between conventional and SDM-defined agroecologies. Then we used Generalized Additive Models (GAMs) to visualize the influences of SDM-identified environmental predictors on the live body weight of chickens at species level. The model fit in LMM for GxE prediction improved when agroecologies were defined based on SDM-identified environmental predictors. Partial dependence plots (PDPs) produced by GAMs showed complex relationships between environmental predictors and body weight. Our findings suggest that multi-environment performance evaluations of candidate breeds should be based on SDM-defined environmental classes or agroecologies. Moreover, our study shows that GAMs are well-suited to visualizing the influences of bioclimatic factors on livestock performance.

Evaluating the indoor radon concentrations in the Swedish building stock using statistical and machine learning

Exposure to excessive indoor radon causes around 500 lung cancer deaths in Sweden annually. However, until 2020, indoor radon measurements were only conducted in around 16% of Swedish single-family houses and 17% of multifamily houses. It is estimated that approximately 16% of single-family houses exceed the indoor radon reference level of 200 Bq/m3, and the corresponding situation in multifamily houses is unknown. Measuring indoor radon on an urban scale is complicated and costly. Statistical and machine learning, exploiting historical data for pattern identification, provides alternative approaches for assessing indoor radon risk in existing dwellings. By training MARS (Multivariate Adaptive Regression Splines) and Random Forest (RF) regression models with the data labels from the radon measurement records in the Swedish Energy Performance Certification registers, property registers, soil maps, and the radiometric grids, the correlations between response and predictive variables can be untangled. The interplay of the key features, including uranium and thorium concentrations, ventilation systems, construction year, basements, and the number of floors, and their impact magnitudes on indoor radon concentrations, are investigated in the study. The regression models tailored for different building classes were developed and evaluated. Despite the data complexity, the RF models can explain 28% of the variance in multifamily houses, 24% in all buildings, and 21% in single-family houses. To improve model fitting, more intricate supervised learning algorithms should be explored in the future. The study outcomes can contribute to prioritizing remediation measures for building stocks suspected of high indoor radon risk.

Association of genetic and sulcal traits with executive function in congenital heart disease.

Persons with congenital heart disease (CHD) are at increased risk of neurodevelopmental disabilities, including impairments to executive function. Sulcal pattern features correlate with executive function in adolescents with single-ventricle heart disease and tetralogy of Fallot. However, the interaction of sulcal pattern features with genetic and participant factors in predicting executive dysfunction is unknown. We studied sulcal pattern features, participant factors, and genetic risk for executive function impairment in a cohort with multiple CHD types using stepwise linear regression and machine learning. Genetic factors, including predicted damaging de novo or rare inherited variants in neurodevelopmental disabilitiesrisk genes, apolipoprotein E genotype, and principal components of sulcal pattern features were associated with executive function measures after adjusting for age at testing, sex, mother's education, and biventricular versus single-ventricle CHD in a linear regression model. Using regression trees and bootstrap validation, younger participant age and larger alterations in sulcal pattern features were consistently identified as important predictors of decreased cognitive flexibility with left hemisphere graph topology often selected as the most important predictor. Inclusion of both sulcal pattern and genetic factors improved model fit compared to either alone. We conclude that sulcal measures remain important predictors of cognitive flexibility, and the model predicting executive outcomes is improved by inclusion of potential genetic sources of neurodevelopmental risk. If confirmed, measures of sulcal patterning may serve as early imaging biomarkers to identify those at heightened risk for future neurodevelopmental disabilities.

Applying the heteroskedastic ordered probit model on injury severity for improved age and gender estimation

Objective Driver characteristics have been linked to the frequency and severity of car crashes. Among these, age and gender have been shown to impact both the possibility and severity of a crash. Previous studies have used standard ordered probit (OP) models to analyze crash data, and some research has suggested heteroskedastic ordered probit (HETOP) could provide improved model fit. The objective of this paper is to evaluate potential improvements of the heteroskedastic ordered probit (HETOP) model compared to the standard ordered probit (OP) model in crash analysis, by examining the effect of gender across age on injury severity among drivers. This paper hypothesizes that the HETOP model can provide a better fit to crash data, by allowing heteroskedasticity in the distribution of injury severity across driver age and gender. Methods Data for 20,222 crashes were analyzed for North Carolina from 2016 to 2018, which represents the state with the highest number of fatalities per 100 million vehicle miles traveled amongst available crash data from the Highway Safety Information System. Results Darker lighting conditions, severe road surface conditions, and less severe weather were associated with increased injury severity. For driver demographics, the probability of severe injuries increased with age and for male drivers. Moreover, the variance of severity increased with age disproportionately within and across genders, and the HETOP was able to account for this. Conclusions The results of the two applied approaches revealed that HETOP model outperformed the standard OP model when measuring the effects of age and gender together in injury severity analysis, due to the heteroskedasticity in injury severity within gender and age. The HETOP statistical method presented in this paper can be more broadly applied across other contexts and combinations of independent variables for improved model prediction and accuracy of causal variables in traffic safety.

Temporal effect of imatinib adherence on time to remission in chronic myeloid leukemia patients.

Adherence to imatinib in chronic myeloid leukemia (CML) patients is estimated to be as low as 70% despite its clinical benefit, and our understanding of the impact of nonadherence in this population is limited. This study presents a novel application of the Alternating Conditional Estimation (ACE) algorithm in newly diagnosed CML patients to map the full dose-response curve (DRC) and determine how the strength of this curve varies over time. We applied the ACE algorithm alongside a backward elimination procedure to detect the presence of time dependence and nonlinearity in the relationship between imatinib adherence and time-to-remission. An extended Cox model allowing for the flexible modeling of identified effects via unpenalized B-splines was subsequently fit and assessed. The substantial improvement in model fit associated with the extended Cox approach suggests that traditional Cox proportional hazards model assumptions do not hold in this setting. Results indicate that the DRC for imatinib is non-linearly increasing, with an attenuated effect above a 74% adherence rate. The strength of this effect on remission varied over time and was strongest in the initial months of treatment, reaching a peak around 90 days post-initiation (log hazard ratio: 2.12, 95% confidence interval: 1.47 to 2.66). Most patients that achieved remission did so by 4 months (120 days) with consistently high adherence, suggesting that this could be a critical time and duration for realizing treatment benefit and patient monitoring. Findings regarding the relationship between adherence and remission can additionally help guide the design of future studies.

PSVII-7 Genetic Evaluation of Feed and Water Intake Behavior Traits in Feedlot Cattle

Abstract Feed and water intake are two economically relevant traits of cattle production. These traits can impact producer profitability and efficiency. Feed intake and, to a lesser degree, water intake have been studied previously. However, there is limited research on the animal behaviors associated with intake and the literature lacks standardized phenotypes for these behaviors. The objective of this study was to estimate heritabilities and variance components for feed and water intake behavior traits in feedlot steers. An Insentec system (Hokofarm Group, The Netherlands) was used to obtain feed (n = 824) and water (n = 786) intakes from crossbred steers for 70 days. Daily dry matter intake (DMI) and daily water intake (DWI) records were used to compute five behaviors for both feed and water intake: number of sessions (NS), intake rate (IR), session size (SS), time per session (TS), and session interval (SI). A mixed linear model was fit for all traits to estimate variance components and heritabilities with a base model including the fixed effects group(pen) and breed composition and the random effect animal. Additional covariates were tested and added to the base model if improved model fit. Heritabilities for feed intake behaviors were 0.50 ± 0.12, 0.63 ± 0.12, 0.40 ± 0.13, 0.35 ± 0.12, and 0.60 ± 0.12 for NS, IR, SS, TS, and SI, respectively. Heritabilities for water intake behaviors were 0.56 ±0.11, 0.88 ± 0.07, 0.70 ± 0.11, 0.54 ± 0.12, and 0.80 ± 0.10 for NS, IR, SS, TS, and SI, respectively. Dry matter intake and DWI had heritabilities of 0.57 ± 0.11 and 0.44 ± 0.11, respectively. Bivariate models including feed intake behaviors and DMI as well as bivariate models including water intake behaviors and DWI were used to obtain correlations between intake and intake behaviors. Phenotypic correlations varied widely between pairs of traits for both feed and water intake behaviors (-0.83 to 0.82). Genetic correlations between DMI and feed intake behaviors were moderate to high (-0.64 ± 0.34 to 0.49 ± 0.16), while genetic correlations between DWI and water intake behaviors were low to moderate (-0.37 ± 0.14 to 0.48 ± 0.15). Significant (log10P-value ≥ 4.5) SNPs were identified for the feed and water intake behaviors. The number of significant SNPs identified for feed intake traits, NS, IR, SS, TS, SI, and DMI were 3, 10, 8, 8, 5, and 5, respectively. The number of significant SNPs identified for water intake traits, NS, IR, SS, TS, SI, and DWI were 4, 8, 3, 2, 5, and 4, respectively. Candidate genes and previously reported quantitative trait loci near significant SNPs were identified. The results indicated that feed and water intake behaviors are heritable and may be one additional route to evaluating feed and water intake.

222 Effects of Nutritional Strategies of Growth Rate Manipulation During the Gilt Development Phases on Growth and Reproductive Performance

Abstract Our objective was to evaluate the effects of nutritional interventions for growth rate manipulation during the gilt development phases on growth and reproductive performance. A total of 810 Camborough L42 gilts from three gilt groups were received between 9 to 11 weeks of age. Gilts were individually weighed, and pens of gilts were allotted to one of two dietary treatments balanced by lot (birth week) and pen weight for a total of 24 pens/treatment. Treatments consisted of two levels of dietary nutrients:1) diets based on corn, soybean meal, and wheat middlings formulated to meet or exceed the PIC standardized ileal digestible (SID) lysine (Lys) recommendations with a total dietary fiber content (TDF) of 10, 10, and 11% (Adeq); and2) slower growing diets based on corn, soybean meal, wheat middlings, and corn germ where SID Lys was reduced by 6, 11, and 11%, dietary metabolizable energy level was reduced by 2.7, 4.6, and 4.7%, and TDF content was increased to 15, 18, and 20% (Low). Diets were fed in three phases as 27.2 to 54.5 kg, 54.5 to 81.8 kg, and 81.8 to 113.6 kg of body weight (BW). At the end of phase 3, gilts were individually weighed and the number of selected gilts per pen was recorded. After selection, gilts were heat checked with the date of heat-no-service (HNS) recorded and then sent to sow farms for breeding in the next recorded heat. Data were analyzed using generalized linear mixed models considering treatment as fixed effect, lot as random effect, and covariates were included if they were statistically significant and improved model fit. Gilts fed the Low diets had less overall average daily gain compared with gilts fed Adeq diets (P < 0.05; Table 1). This response was mainly driven by a marginal significant reduction (P < 0.10) in average daily feed intake for the gilts fed Low diets compared with Adeq, with no evidence for differences (P > 0.10) in overall feed efficiency. As a result of the reduction in growth rate, gilts fed the Low diets were 4.8 kg lighter (P < 0.05) at selection compared with gilts fed Adeq diets. There was no evidence for treatment differences in selection rate, breeding rate, age at first HNS nor age at first breeding (P > 0.10). In summary, results from this study demonstrated that nutritional strategies can be applied to reduce developing gilts growth rate without negatively affecting puberty, selection rate, or age at first breeding.

220 Impact of Bump Feeding During Late Gestation on Reproductive Performance of Over-Conditioned Sows

Abstract Our objective was to evaluate the effect of bump-feeding during late gestation on reproductive performance of multiparous over-conditioned sows. A total of 220 PIC Camborough sows were assigned to one of three feeding levels in a completely randomized design. Caliper was individually collected at d 90 of gestation from a larger group of sows to assess body condition, and only sows from parity 1+ and considered over-conditioned by the sow caliper were selected for the study. Sows were equally balanced by parity (P1 and P2 as young, and P3+ as mature) when allocated to treatments. All sows were fed a common gestation diet containing 3.20 Mcal of metabolizable energy (ME) per kg and 0.56% standardized ileal digestible lysine, but feed drop-boxes were adjusted to different feeding levels according to treatment. Treatments consisted of 5.09 Mcal of ME/d (1.59 kg/d; CONTROL), 6.54 Mcal of ME/d (2.04 kg/d; 0.45kg-BUMP), or 8.00 Mcal of ME/d (2.49 kg/d; 0.90kg-BUMP); and fed to sows from d 90 of gestation until they were moved to the farrowing room. Sows were housed in crates during gestation. Sow body weight (BW) and caliper measurements were collected at pre-farrowing and weaning, and their difference was calculated. Additionally, post-farrowing 48h BW was predicted using the following equation: 48h post-farrow BW = -20.81 + (0.98 × pre-farrow BW). Total born, born alive (BA), stillborn (SB), and mummified were collected for each sow at farrowing. Individual piglet BW were recorded at birth, at cross-foster, and at weaning. Data were analyzed using a generalized linear model considering sow as the experimental unit. Models included treatment, parity group, and their interaction as fixed effects. Covariates were included if they were statistically significant and improved model fit. There was no treatment by parity group interaction for any response criteria. There was no evidence for differences between treatments for caliper at d 90 of gestation, caliper pre-farrow, caliper at weaning, or caliper change between d 90 to pre-farrow and pre-farrow to weaning (P >0.10; Table 1). Sows that received 0.90 kg-BUMP were marginally heavier at farrow than CONTROL sows (P < 0.10), with those receiving 0.45 kg-BUMP intermediate. CONTROL sows lost less BW during lactation compared with 0.45kg-BUMP sows (P < 0.05), with 0.90 kg-BUMP sows intermediate. Sows receiving 0.45kg-BUMP from d90 of gestation to farrowing had greater percentage BA piglets and decreased percentage SB compared with sows receiving 0.90 kg-BUMP (P < 0.05), with CONTROL sows intermediate. There was no evidence (P >0.10) for treatment differences for piglet birth weight, litter and piglet BW gain, or percentage weaned piglets. In summary, increasing the feeding level for over-conditioned sows during late gestation did not impact sow reproductive or litter performances.

Serotonin Degeneration and Amyloid-β Deposition in Mild Cognitive Impairment: Relationship to Cognitive Deficits.

Neuropathological and neuroimaging studies have demonstrated degeneration of the serotonin system in Alzheimer's disease (AD). Neuroimaging studies have extended these observations to the preclinical stages of AD, mild cognitive impairment (MCI). Serotonin degeneration has been observed also in transgenic amyloid mouse models, prior to widespread cortical distribution of amyloid-β (Aβ). The present study evaluated the regional distribution of the serotonin transporter (5-HTT) and of Aβ in individuals with MCI and healthy older controls, as well as the contribution of 5-HTT and Aβ to cognitive deficits. Forty-nine MCI participants and 45 healthy older controls underwent positron emission tomography (PET) imaging of 5-HTT and Aβ, structural magnetic resonance imaging and neuropsychological assessments. Lower cortical, striatal, and limbic 5-HTT and higher cortical Aβ was observed in MCIs relative to healthy controls. Lower 5-HTT, mainly in limbic regions, was correlated with greater deficits in auditory-verbal and visual-spatial memory and semantic, not phonemic fluency. Higher cortical A β was associated with greater deficits in auditory-verbal and visual-spatial memory and in semantic, not phonemic fluency. When modeling the association between cognition, gray matter volumes and Aβ, inclusion of 5-HTT in limbic and in select cortical regions significantly improved model fit for auditory-verbal and visual-spatial memory and semantic, but not phonemic fluency. These results support the role of serotonin degeneration in the memory and semantic fluency deficits observed in MCI.

The impact of dual VA-Medicare use on a data-driven clinical management tool for older Veterans with multimorbidity.

Healthcare systems are increasingly turning to data-driven approaches, such as clustering techniques, to inform interventions for medically complex older adults. However, patients seeking care in multiple healthcare systems may have missing diagnoses across systems, leading to misclassification of resulting groups. We evaluated the impact of multi-system use on the accuracy and composition of multimorbidity groups among older adults in the Veterans Health Administration (VA). Eligible patients were VA primary care users aged ≥65 years and in the top decile of predicted 1-year hospitalization risk in 2018 (n = 558,864). Diagnoses of 26 chronic conditions were coded using a 24-month lookback period and input into latent class analysis (LCA) models. In a random 10% sample (n = 56,008), we compared the resulting model fit, class profiles, and patient assignments from models using only VA system data versus VA with Medicare data. LCA identified six patient comorbidity groups using VA system data. We labeled groups based on diagnoses with higher within-group prevalence relative to the average: Substance Use Disorders (7% of patients), Mental Health (15%), Heart Disease (22%), Diabetes (16%), Tumor (14%), and High Complexity (10%). VA with Medicare data showed improved model fit and assigned more patients with high accuracy. Over 70% of patients assigned to the Substance, Mental Health, High Complexity, and Tumor groups using VA data were assigned to the same group in VA with Medicare data. However, 41.9% of the Heart Disease group and 14.7% of the Diabetes group were reassigned to a new group characterized by multiple cardiometabolic conditions. The addition of Medicare data to VA data for older high-risk adults improved clustering model accuracy and altered the clinical profiles of groups. Accessing or accounting for multi-system data is key to the success of interventions based on empiric grouping in populations with dual-system use.

Early exposure to added sugars via infant formula may explain high intakes of added sugars during complementary feeding beyond maternal modeling.

Research has shown that early exposure to added sugars from table food is related to increased intake of added sugars in later childhood. The earliest window of exposure to added sugars may be in infancy via infant formula. However, beyond the well-established factors of maternal lifestyle and modeling, there is a lack of research examining how exposure to added sugars from infant formula influences infant/toddler added sugar intakes from table foods and sugar sweetened beverages (SSB). While accounting factors previously associated with infant/toddler added sugar intakes and maternal SSB consumption (proximal measure of maternal modeling), this study aims to examine if there is an association between added sugars in infant formula and added sugar intakes from table foods and SSB during the complementary feeding period. This is a secondary, cross-sectional analysis using three-day caregiver-reported 24-h dietary recalls in a cohort of infant/toddlers (n = 95), ages 9- < 16 mos., enrolled in a music intervention trial. Hierarchical stepwise regression was used to estimate the association between exposure to added sugars from infant formula and (1) intake of added sugars from table food and (2) SSB consumption. Infant/toddler SSB consumption was transformed to account for distributional properties. We performed incremental F-tests to determine whether the addition of each step improved model fit (R2). Early exposure to added sugars via infant formula was associated with infant/toddler SSB (ΔR2 = 0.044, Finc (1, 87) =6.009, p = 0.016) beyond sociodemographic and maternal SSB consumption, but not with infant/toddler added sugar intakes from table foods (ΔR2 = 0.02, Finc (1, 87) =3.308, p = 0.072). While past studies have identified circumstantial (i.e., sociodemographic), or indirect (i.e., maternal lifestyle and modeling), mechanisms contributing to higher infant/toddler added sugar intakes, this study identifies exposure to added sugars from infant formula as a possible direct mechanism explaining why some infants/toddlers consume more added sugars.

Effects of Traffic and Road Contextual Language on Survey Design: A Comparison of Two Emotional Intelligence Scales

Research has found that emotional intelligence (EI) can predict dangerous driving behaviors. However, EI measures have not been explicitly designed for driving and there exists a need to develop an EI scale to better measure drivers’ EI relevant to driving situations. The objective of this study was to evaluate how adapting survey language to the driving context effected the predictive power of driver EI on dangerous driving. Two surveys were conducted, where the baseline survey used the original survey language and the second rephrased every question to provide driving specific context. The two EI scales used in both surveys were the Trait Emotional Intelligence Questionnaire (TEIQue) and Genos EI. Correlation and regression analyses revealed that TEIQue, compared to Genos, has a stronger ability to assess drivers’ EI and predict dangerous behaviors. Changing language to the traffic context significantly improved model fit between driving behaviors and TEIQue, but not Genos.