Linear Model Fit Research Articles

PartR2: partitioning R2 in generalized linear mixed models.

The coefficient of determination R2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. The R2 of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part) R2 and structure coefficients, but this is rarely done due to a lack of software implementing these statistics. Here, we introduce partR2, an R package that quantifies part R2 for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest from the model and monitors the change in the variance of the linear predictor. The difference to the full model gives a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors. partR2 also estimates structure coefficients as the correlation between a predictor and fitted values, which provide an estimate of the total contribution of a fixed effect to the overall prediction, independent of other predictors. Structure coefficients can be converted to the total variance explained by a predictor, here called ‘inclusive’ R2, as the square of the structure coefficients times total R2. Furthermore, the package reports beta weights (standardized regression coefficients). Finally, partR2 implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR2 with real example datasets for Gaussian and binomial GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors.

A simple and reliable proposal to determine the technical feasibility of biogas use and the energetic self-sustainability in UASB-based sewage treatment plants.

The biogas produced in UASB-based sewage treatment plants (STPs) is rarely used for energy purposes and its potential is often unknown. This study aimed to propose a simple and reliable method based on energy balance to determine the technical feasibility of biogas use and the energy self-sufficiency of UASB reactors. To this end, we considered (i) electric power production (E) and (ii) electric power consumption (Econ) ascribed to sewage pumping stations (SPSs) at different pressure heads (0 to 4 m, 4.1 to 8 m, 8.1 to 12 m, and 12.1 to 16 m). The technical feasibility of biogas use was assessed by evaluating if the flow of biogas produced in the STPs would be sufficient for the functioning of a commercial motor-generator. The linear model fit to estimate the biogas energy potential (y-axis) in STPs and the sewage flow (x-axis) is represented by y = 122.65x (R2 = 0.64). In total, 1,054 STPs in Brazil use UASB reactors as treatment units, of which nearly 31% are located in the southeast. However, only 11.2% of these STPs, which serve populations of over 29,981, presented technical feasibility to recover biogas. The mathematical equations proposed in this study to estimate the net electric power production in UASB-based STPs are relevant tools for sanitation companies and can enable studies to be performed for the implementation of energy self-sufficiency projects in Brazil.

Right Ventricle Pressure Volume Loop Measurement Trends Predicted by End‐Diastolic Volume

To examine Right Ventricle (RV) size as a predicting factor of pressure volume loop (PV loop) measurement, we assessed PV loop functional dependence on end-diastolic volume (EDV) from twenty animal and human RV datasets [1]. Methods PV loop sequences were processed to compute end-systolic elastance (Ees), ejecting pressure-volume-area (PVA), isovolumic mode pressure-volume-area (PVAiso), and isovolumic mode peak developed pressure (Piso). The outcomes of Ees, Piso, PVA, and PVAiso were then applied as dependent variables of EDV predicted trends with linear and power regression model fitting. Results Over the EDV range from 25 mL to 150 mL, the PVA and PVAiso measures were positively correlated to EDV. The Ees elastance measures were inversely EDV correlated, and Piso pressures were uncorrelated to EDV. Results of regression shown in the table, indicate PVA (R2 =.74) and PVAiso (R2 =.74) measures of contractile mechanism energy output were the optimally fitted indices by EDV prediction. Conclusions Decreasing RV elastance with increasing EDV size, may reflect a structural adaptation of larger chambers to a more extensive pulmonary vascular network. Positively correlated trends of PVA and PVAiso to the RV size, also suggest these measures form a promising basis to interpret ranges of normal function during different stages of pediatric patient growth. [1] E .N. Marcus, A Study of Cardiac Ventricle Performance Indices Estimated with Data from Single-Beat Pressure Volume Loops, Sensors & Transducers, Vol. 244, Issue 5, September 2020, pp. 1-6.

Carboxymethyl cellulose-chitosan composite hydrogel: Modelling and experimental study of the effect of composition on microstructure and swelling response

Molecular recognition is essential for the advancement of functional supramolecular natural polymer-based hydrogels. First, a series of carboxymethyl cellulose (CMC)-chitosan (CSN) hydrogels crosslinked with fumaric acid are studied, where the influence of composition on microstructure and swelling is investigated using mathematical modelling and experiment and the hydrolytic properties, microstructure parameters and physicochemical properties are examined. Second, best fit values for the responses are obtained using multiple linear regression and MATLAB R2020a curve fitting and predictive models are generated. Third, the optimum microstructure is loaded with polyethylene glycol (PEG) and bismuth telluride (Bi2Te3) and coated on fabric for imparting thermal sensitivity. The results show that (1) optimum microstructure (25.65 ± 1.86 nm mesh size, 116.25 ± 0.00 μmol/cm3 effective crosslinking-density, 348.03 ± 10.81% swelling, and 62.86 ± 1.11% gel fraction) is found at CMC:CSN = 1:3 for G3; (2) the model shows good agreement with experimental data demonstrating potential for estimating hydrogel swelling and microstructure; and (3) G3/PEG and G3/PEG/Bi2Te3 enhance thermal conductivity of fabric at ambient, body, and elevated temperatures. The study demonstrates the potential of the generated model in predicting CMC-CSN swelling and G3 as an ideal host matrix for wearable textiles/devices.

Interpreting the variation phenomena of B2 1633+382 via the two-component model

ABSTRACT Blazars are variable targets in the sky, whose variation mechanism remains an open question. In this work, we make a comprehensive study of the variation phenomena of the spectral index and polarization degree to deeply understand the variation mechanism of B2 1633+382 (4C 38.41). We use the local cross-correlation function to perform a correlation analysis between multiwavelength light curves. We find that both the γ-ray and the optical V band are correlated with the 15-GHz radio at beyond the 3σ confidence level. Based on the lag analysis, the emitting regions of the γ-ray and optical are located at $14.2_{-2.4}^{+0}$ and $14.2_{-8.3}^{+8.3}$ pc upstream of the core region of the15-GHz radio, and are far away from the broad-line region. The broad lines in the spectrum indicate the existence of an accretion disc component in the radiation. Thus, we consider a two-component model, which includes the relative constant background component and the varying jet component to study the variation behaviours. The Markov Chain Monte Carlo procedure is adopted to study the physical parameters of the jet and the background components. To some extent, the study of normalized residuals indicates that the two-component model fits better than the linear fitting model. The jet with a helical magnetic field is promising for explaining the variation, and the shock-in-jet model is not completely ruled out.

Greenness, texture, and spatial relationships predict floristic diversity across wetlands of the conterminous United States

Plant diversity safeguards wetland ecosystem functions, stability, and resilience, but is threatened by habitat loss and degradation. Remote sensing could support the cost-effective management of biodiversity by providing consistent and frequent data at large scales. While identifying individual species from remote sensing datasets with low spatial and spectral resolution is challenging, studies can focus on factors known to correlate with or promote diversity. We tested the predictive potential of such factors — maximum annual greenness as an indicator of productivity, texture (i.e., spatial arrangement of grey tones) as a proxy for habitat heterogeneity, and spatial autocorrelation — across a dataset of 1115 wetlands in the conterminous United States surveyed by the EPA’s National Wetland Condition Assessment. We used multivariate linear regressions to test whether spectral and spatial metrics derived from two open-source datasets — NASA’s Landsat 5 TM and 7 ETM+ (30 m, 16-day revisit) and USDA’s National Agriculture Inventory Program (1 m, biennial) — can predict wetland plant diversity and richness. Individual texture metrics showed different sensitivity to vegetation evenness, growth form, and spatial distribution and could together predict 35–36% of site variation in richness and diversity. This highlights the impact of habitat heterogeneity on species diversity and spectral variability. While maximum annual greenness and texture metrics had similar predictive capacity, their interactions and combined effects improved the fit of linear models by 11–14%, demonstrating their complementarity. Best results were achieved when including distance-based Moran's Eigenvector Maps (dbMEMs) describing spatial relations among sites at multiple scales and reflecting the role of spatially structured factors (e.g., climate, topography, dispersal) on diversity. Together greenness, texture, and dbMEMs could predict 59% of plant richness and 50% of plant diversity across the entire dataset and up to 71% of the richness of least disturbed sites. These results show the potential of open-source remote sensing datasets to monitor biodiversity resources at a large scale and prioritize the protection and field monitoring of wetlands.

Distortion Robust Relative Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Using Image Features

In this article, we propose a novel framework to radiometrically correct unregistered multisensor image pairs based on the extracted feature points with the KAZE detector and the conditional probability (CP) process in the linear model fitting. In this method, the scale, rotation, and illumination invariant radiometric control set samples (SRII-RCSS) are first extracted by the blockwise KAZE strategy. They are then distributed uniformly over both textured and texture-less land use/land cover (LULC) using grid interpolation and a set of nearest-neighbors. Subsequently, SRII-RCSS are scored by a similarity measure, and the histogram of the scores is then used to refine SRII-RCSS. The normalized subject image is produced by adjusting the subject image to the reference image using the CP-based linear regression (CPLR) based on the optimal SRII-RCSS. The registered normalized image is finally generated by registration of the normalized subject image to the reference image through a two-pass registration method, namely affine-B-spline and, then, it is enhanced by updating the normalization coefficient of CPLR based on the SRII-RCSS. In this study, eight multitemporal data sets acquired by inter/intra satellite sensors were used in tests to comprehensively assess the efficiency of the proposed method. Experimental results show that the proposed method outperforms the existing state-of-the-art relative radiometric normalization (RRN) methods both qualitatively and quantitatively, indicating its capability for RRN of unregistered multisensor image pairs.

The Thermal-Oxidation Behavior of Pristine and Doped Magnéli Phase Titanium Oxides

Magnéli phase titanium oxides (Ti n O2n−1, 4 ≤ n ≤ 10) are important materials for solid state and electrochemical technologies such as memristors, batteries, fuel cells, and electrochemical devices for water treatment. Developing an understanding of transitions between Ti n O2n−1 and its product of oxidation, titanium(IV) oxide (TiO2), as well as strategies such as doping to modulate the conditions for such changes will enable the development of more effective devices. To elucidate a mechanism for their thermal oxidation and investigate the influence of doping, the thermal-oxidation behavior in air of Ti4O7 doped with vanadium, chromium, and iron were investigated by thermogravimetric analysis (TGA). These powders prepared by high-temperature H2 reduction of dopant-containing TiO2 were characterized by scanning electron microscopy (SEM), gas adsorption analysis, X-ray fluorescence (XRF), energy-dispersive X-ray (EDX) spectroscopy, X-ray photoelectron spectroscopy (XPS), and powder X-ray diffraction (PXRD). V- and Fe-‍doping improved the thermal stability of Ti4O7 as evidenced by higher onset temperatures in their thermograms. Three-dimensional diffusion reaction models adequately describe the solid-state kinetics of thermal oxidation of Ti4O7 in air as demonstrated by linear model-fitting. Doping shows a mixed influence on the kinetics for thermal oxidation in air reducing both the Arrhenius pre-exponential factor and the activation energy.

Efficient estimation of chlorophyll a concentration in artificial upwelling

Artificial upwelling is an effective method to boost the primary productivity in the sea area, which provides a strong support for fishery. The chlorophyll a concentration is a key indicator to evaluate the marine productivity. This paper aims to provide a high-precision model to approximate the chlorophyll a concentration of artificial upwelling. After eliminating the abnormal values from the experimental data, we first used the global linear fitting (GLF) and the global quadratic fitting (GQF) models respectively to obtain reference errors. In order to improve the precision, we introduced the idea of the piecewise fitting to the model. The training set was partitioned into two, four and eight segments to find the optimal number of segments that the model can achieve the best precision. These models calculate the least squares solutions of the error function on each segment. To satisfy the engineering requirement, which is the continuity at boundary points, constraints were added to the boundary points of adjacent segments. Further, the quadratic fitting was proposed and applied on segments where the errors of fitting were relatively large when applying the constrained piecewise linear fitting (CPLF) model, namely the local quadratic fitting (LQF) model. Finally, we partitioned one more variable on the piecewise linear fitting model to examine if the predictive precision can be enhanced. Compared with the work of predecessors, where the GLF, GQF models and intelligent algorithms were applied to this issue, the results of our tested models showed higher precision in predicting chlorophyll a concentration and less complexity in model operation.

Age- and sex-dependent regression parameters for estimating live weight frim body measurements of West African Dwarf sheep

The relationships between live weight and eight body measurements of West African Dwarf (WAD) sheep were studied using 300 animals under farm condition. The animals were categorized based on age and sex. Data obtained on height at withers (HW), heart girth (HG), body length (BL), head length (HL), length of hindquarter (LHQ), width of hindquarter (WHQ), head width(HDW), and loin girth (LG) were fitted into simple linear (change in body measurement is directly proportional to weight or body size), allometric (body measurements do not necessarily change in direct proportion to weight or body size), and multiple linear regression models to predict live weight from the body measurements according to age group and sex. Results showed that live weight and body measurements of ewe were higher than that of the ram. Live weight, HG, HW, WHQ, LG, BL, LHQ, HL, and HW increased with the age of the animals. In multiple linear regression model, WHQ, LHQ, HW, HL and HDW best fit the model for sheep aged ≤1; HG, LG, BL and HDW for 2 year-old sheep; HG, BL, and HL best fit the model for sheep 3 years age group; LHQ best fit the model for sheep of 4 years of age; while HL best fits sheep that were in 5 year age category. Coefficients of determination (R2) values for linear and allometric models for predicting the live weight of WAD sheep increased with age in all the body measurements (HW, HG, BL, HL, LHQ, WHQ, HDW and LG). Sex had significant influence on the model with R2 values consistently higher in females except the models for LHQ, WHQ, LG and BL were they the same with the males. Based on R2 values, it was concluded that both linear and allometric regression models could be used to predict live weight from body measurements of WAD sheep. 

Predictors of timely diagnostic follow-up after an abnormal Pap test among Hispanic women seeking care in El Paso, Texas

BackgroundDiagnostic follow-up of women with an abnormal Pap test is necessary to resolve the risk developing cervical cancer. The purpose of this study is to describe patient characteristics associated with timely receipt of a diagnostic colposcopy after an abnormal Pap test among Hispanic women in El Paso, a Texas-Mexico border city.MethodsWe conducted a retrospective chart review of Hispanic patients seen at an academic colposcopy clinic following an abnormal Pap test. An optimal diagnostic interval to colposcopy was based on a National Breast and Cervical Cancer Early Detection Program (NBCCEDP) quality indicator and was defined as receipt of colposcopy within 90 days or less from the date of an abnormal Pap test. Risk ratios (RR) were calculated by building a generalized linear model fit using a Poisson distribution, log link, and robust variance.ResultsOverall, 177 of the 270 women (65.6%) received follow-up within an optimal diagnostic interval. After adjusting for other variables in the model, women who were 30 years of age or older were 32% more likely to have an optimal interval than younger women (adjusted RR = 1.32, P < 0.01). High school graduates were less likely than more educated women to have an optimal interval (adjusted RR = 0.68, P < 0.01). Participation in the NBCCEDP was not associated with receipt of follow-up within an optimal diagnostic interval.ConclusionsCompared with women with greater educational attainment, high school graduates were less likely to receive follow-up within an optimal diagnostic interval, as were younger (< 30 years) women compared with older women. Participation in the NBCCEDP was not associated with receipt of care within an optimal diagnostic interval.

Defining maximal outcome improvement thresholds for patient satisfaction after reverse total shoulder arthroplasty: a minimum two-year follow-up study

BackgroundPrevious research has investigated the percentage of maximal outcome improvement (MOI) for the American Shoulder and Elbow Surgeons (ASES) and Simple Shoulder Test scores after reverse total shoulder arthroplasty (rTSA); however, few other outcome scores have been validated. The purpose of this study was to establish thresholds of MOI percentage for the Single Assessment Numeric Evaluation (SANE) and subjective Constant-Murley (Constant) scores associated with patient satisfaction at 2 years following rTSA. MethodsA retrospective review of institutional registry data for all patients who underwent primary rTSA between 09/2016 and 02/2018 was performed. All patients completed the ASES, SANE, and Constant outcome measures preoperatively and at a minimum of 2 years postoperatively. Changes were assessed with Wilcoxon signed-rank tests. MOI percentage was defined as the score improvement from baseline divided by the highest possible improvement from baseline based on the maximum value of the outcome score used. Receiver operating characteristic (ROC) analyses were used to determine optimal MOI percentage thresholds associated with postoperative satisfaction. Stepwise multivariate logistic regression was performed to identify variables associated with achieving the MOI. ResultsA total of 105 patients were included in the final analysis, with a mean (±standard deviation) age of 70.0 ± 7.5 years and BMI of 28.9 ± 5.6 kg/m2. The majority of patients were satisfied postoperatively (81.0%). Statistically significant increases were observed from baseline to 2 years postoperatively for the outcome measures assessed (P < .001 for all). ROC analysis demonstrated that the optimal MOI percentage threshold for achieving satisfaction was 66.9% for ASES ([Area under curve, AUC]: 0.87), 71.0% for SANE (AUC: 0.89), and 37.5% for Constant (AUC: 0.85). The linear regression model fit between the proportion of satisfied patients and the calculated MOI percentages was moderate for SANE (r2 = 0.313, P < .001) and weak for Constant (r2 = 0.228, P < .001). Multivariate logistic regression demonstrated that comorbid diabetes was associated with a decreased likelihood of MOI achievement for SANE ([Odds Ratio, OR]: 0.66, P = .020) and Constant (OR: 0.72, P = .027). Preoperative diagnosis of rotator cuff arthropathy (OR: 0.84, P = 0.049) was associated with a decreased likelihood of MOI achievement for Constant. No factors were significantly associated with MOI achievement for ASES. ConclusionAchieving 66.9%, 71.0%, and 37.5% of the MOI for the ASES, SANE, and Constant scores is associated with postoperative satisfaction following rTSA. Comorbid diabetes and diagnosis of rotator cuff arthropathy were significantly associated with a decreased likelihood of MOI achievement. Level of evidenceLevel III; Retrospective Case Series (Prognostic)

Linear and non-linear regression model fitting of testicular three-dimensional growth in Miranda donkeys

In endangered donkey populations, reproductive performance is one the most important areas to study, provided its connection with conservation opportunities. Among reproductive traits, testicular dimensions, their relationship with body weight and seminal qualitative and quantitative parameters is particularly notable. The aim of the present study was to model the evolution of body weight, testicular length, width and height, volume and gonadosomatic index in Miranda donkeys using in vivo ultrasonography. Data on the aforementioned variables of 23 Miranda donkeys, from 4 to 259 months of age, were fitted to eleven linear and nonlinear models. Cubic function modelling reported the best-fitting properties for almost all variables, except for body weight (sigmoid curve model). Cubic model presented a higher ability to capture interindividual variability (Adj. R2 from 0.795 to 0.922) for testicular height, width, length and volume measurements than the rest of fucntions tested. Bayesian information criterion (BIC) values suggest in vivo ultrasonography may be a rather efficient and accurate tool when predicting for the evolution of testicular volume or gonadosomatic index than three-dimensional testicular measurements. Highlights Cubic functions efficiently model testicular dimensions, volume and composite indices evolution. Ultrasonography objectively, accurately and reproducibly determines testicular volume. Testicular asymmetry physiologically occurs to balance bilateral testicular activity.

Abnormal large-scale structural rich club organization in Leber's hereditary optic neuropathy

ObjectiveThe purpose of this study was to investigate whether the large-scale structural rich club organization was abnormal in patients with Leber's hereditary optic neuropathy (LHON) using diffusion tensor imaging (DTI), and the associations among disrupted brain structural connectivity, disease duration, and neuro-ophthalmological impairment. MethodsNineteen acute, 34 chronic LHON patients, and 36 healthy controls (HC) underwent DTI and neuro-ophthalmological measurements. The brain structural network and rich club organization were constructed based on deterministic fiber tracking at the individual level. Then intergroup differences among the acute, chronic LHON patients and healthy controls (HC) in three types of structural connections, including rich club, feeder, and local ones, were compared. Network-based Statistics (NBS) was also used to test the intergroup connectivity differences for each fiber. Several linear and nonlinear curve fit models were applied to explore the associations among large-scale brain structural connectivity, disease duration, and neuro-ophthalmological metrics. ResultsCompared to the HC, both the acute and chronic LHON patients had consistently significantly lower fractional anisotropy (FA) and higher radial diffusion (RD) for feeder connections (p < 0.05, FDR correction). Acute LHON patients had significantly lower FA and higher RD for local connections (p < 0.05, FDR correction). There was no significant difference in large-scale brain structural connectivity between acute and chronic LHON (p > 0.05, FDR correction). NBS also identified reduced FA of three feeder connections and five local ones linking visual, auditory, and basal ganglia areas in LHON patients (p < 0.05, FDR correction). No structural connections showed linear or nonlinear association with either disease duration or neuro-ophthalmological indicators (p > 0.05, FDR correction). A significant negative correlation was shown between the retinal nerve fiber layer (RNFL) thickness and disease duration (p < 0.05, FDR correction). ConclusionsAbnormal rich club organization of the structural network was identified in both the acute and chronic LHON. Furthermore, our findings suggest the coexistence of both primary and secondary connectivity damage in the LHON.

Software for Bayesian Statistics

In this summary we introduce the papers published in the special issue on Bayesian statistics. This special issue comprises 20 papers on Bayesian statistics and Bayesian inference on different topics such as general packages for hierarchical linear model fitting, survival models, clinical trials, missing values, time series, hypothesis testing, priors, approximate Bayesian computation, and others.

Experimental investigation of surface roughness and cutting force during machining of SS43100 by using coated and uncoated carbide inserts

Machining processes are the accurate metal removal processes for shaping components in the manufacturing industry. The main challenges of the manufacturing industry are to increase productivity and quality. Manufacturing industries are trying to reduce the cost of machined parts which are manufactured by different machined methods. Machining hard metals is a challenging task. In the presented research work machining parameters were investigated for machining of SS43100 by using coated and uncoated carbide tool inserts. Carbide tool inserts were coated by Ti16Zr using the PVD method of coating. Experiments were performed during the turning operation of SS43100 by coated and uncoated tool inserts. Cutting speed range were 150r.p.m, 250 r.p.m., 350 r.p.m., feed were 0.085 mm/rev, 0.095 mm/rev, 0.1 mm/rev. and depth of cut was 0.1 mm, 0.15 mm, and 0.2 mm selected as controlling factors. Surface roughness and cutting force were selected as response factors. Digital lathe tool dynamometer was used for cutting force measurement and ITI surface tester RS232 was used for surface roughness measurement. In the experimental study and analysis of output response parameters, Taghuchi L9 orthogonal array model has used The results obtained were optimized using the Taghuchi technique for cutting forces and surface roughness. The Taghuchi technique was performed for combinations of each input variable in linear fit model multivariate analytics. Experimental results were compared and models established supported by evidence.

Effects of Standardized Ileal Digestible Lysine Level on Growth Performance in 120 to 170 lb DNA Finishing Pigs

The objective of this study was to estimate the SID Lys requirement for growth and feed efficiency of 120- to 170-lb finishing pigs. A total of 700 barrows and gilts (600 × 241, DNA; initial BW of 117.2 ± 1.89 lb) were used in two separate studies, each lasting 21-d. Pens of pigs were blocked by BW and randomly allotted to 1 of 6 dietary treatments with 8 to 10 pigs per pen in a randomized complete block design. A similar number of barrows and gilts were placed in each pen. Dietary treatments were corn-soybean meal-based and formulated to 0.65, 0.72, 0.79, 0.86, 0.93, and 1.00% SID Lys, with 12 replications per treatment. Increasing SID Lys increased (linear, P < 0.001) ADG, with pigs fed 1.00% SID Lys having the greatest final BW. In addition, increasing SID Lys decreased (quadratic, P = 0.004) ADFI. Feed efficiency improved, while Lys intake/d, and Lys intake/kg of gain increased (quadratic, P < 0.005), with increasing SID Lys. At both high and low ingredient and pig prices, feed cost/pig increased (quadratic, P < 0.05) as SID Lys increased, while feed cost/lb of gain decreased (quadratic, P < 0.001), with pigs fed 0.79% SID Lys having the lowest feed cost/lb of gain. At high and low feed prices, increasing SID Lys increased (linear, P < 0.002) IOFC. A linear model resulted in the best fit for ADG and predicted that the maximum ADG response was beyond 1.00% SID Lys. For F/G, the quadratic polynomial model predicted a requirement of 0.97% SID Lys. At high ingredient and pig prices, the broken-line linear model to maximize IOFC predicted that there was no further significant improvement to IOFC past 0.76% SID Lys. Meanwhile, at low ingredient and pig prices the quadratic polynomial model predicted a requirement of 0.91% SID Lys to maximize IOFC, however, a similar fitting linear model predicted maximum IOFC response at greater than 1.00% SID Lys. In summary, the optimal SID Lys level for 120- to 170-lb finishing pigs depends upon the response criteria, with growth performance optimized at or greater than 0.97% SID Lys and IOFC maximized between 0.76 to 0.91% SID Lys.

Effects of Standardized Ileal Digestible Lysine Level on Growth Performance in 170 to 220 lb DNA Finishing Pigs

The objective of this study was to estimate the SID Lys requirement for growth and feed efficiency of 170- to 220-lb finishing pigs. A total of 616 barrows and gilts (600 × 241, DNA; initially 168.3 ± 2.7 lb BW) were used in two separate studies each lasting 21 d. Pens of pigs were blocked by BW and randomly allotted to 1 of 7 dietary treatments with 8 to 10 pigs per pen in a randomized complete block design. A similar number of barrows and gilts were placed in each pen. Dietary treatments were corn-soybean meal-based and formulated to 0.58, 0.65, 0.72, 0.79, 0.86, 0.92, and 1.00% SID Lys. There were 11 replications per treatment for levels of 0.65, 0.72, 0.79, 0.86, and 0.92% SID Lys; 6 replications for the 0.58% SID Lys treatment; and 5 replications for the 1.00% SID Lys treatment. Increasing SID Lys increased (linear, P = 0.022) ADG. Feed efficiency (quadratic, P = 0.034) improved, while Lys intake/d, and Lys intake/kg of gain increased (linear, P < 0.001) with increasing SID Lys. At both high and low ingredient and pig prices, feed cost per pig (linear, P < 0.001) and feed cost/lb gain increased (quadratic, P < 0.05), and total revenue per pig tended to increase (linear, P = 0.051) as SID Lys increased. However, at both high and low ingredient and pig prices, there were no differences in income over feed cost (IOFC) among dietary treatments. The broken-line linear model to maximize ADG predicted that there was no further improvement to ADG past 0.83% and for F/G, the quadratic polynomial model predicted a requirement of 0.90% SID Lys. However, similar fitting linear models predicted maximum ADG and F/G greater than 1.00% SID Lys. Income over feed cost at high ingredient and pig prices was predicted by the QP model to be maximized at 0.78% SID Lys. Meanwhile, at low ingredient and pig prices, the BLL model predicted maximum IOFC at 0.76% SID Lys, or lower. In summary, the optimal SID Lys level for finishing pigs from 170 to 220 lb depends upon the response criteria, with growth performance maximized between 0.83 to 0.90% SID Lys. Income over feed cost was maximized at 0.78% SID Lys or lower.

946. Risk factors and Metabolic Implications of Integrase Inhibitor Associated Weight Gain

BackgroundExcessive weight gain associated with integrase strand transfer inhibitor (InSTI) antiretrovirals is an emerging issue in the current antiretroviral treatment (ART) era. Despite the known association between excess weight and impaired glucose tolerance in the general population, the metabolic implications of InSTI-associated weight gain have not been established. The objective of this study was to evaluate the presence of InSTI-associated weight gain amongst a diverse, urban population and to investigate potential risk factors and metabolic implications.MethodsWe obtained demographic, pharmacy and laboratory data from the hospital clinical data warehouse for ART-naïve adult HIV+ patients at Boston Medical Center between fiscal year (FY) 2007-2017. We compared patients who initiated on an InSTI to those with an alternate regimen (i.e. PI, NNRTI) who remained on their initial regimen for at least 18 months. Individuals with diabetes mellitus (DM) prescription or ICD 9/10 code prior to ART initiation were excluded. Our primary outcome was percent weight change in the first 24 months of ART estimated by linear mixed effects model fit by restricted maximum likelihood. Our secondary outcome was incident DM diagnosis in the 18 months after ART using progression-free survival (PFS). PFS rates were estimated by the Kaplan-Meier method and log-rank test, and Cox proportional-hazards model were determined, all using R v3.6.2.ResultsBetween FY 2007-2017, 139 patients were initiated on InSTIs and 1117 were initiated on alternative anchor regimens. Approximately, one third of the cohort was female and more than 75% were non-white. InSTI use in women was associated with increased weight gain in the first 24 months of ART compared to non-InSTIs (+9.57%, p = 0.002, Figure 1). InSTI use was associated with more incident DM diagnoses in the first 18 months of ART compared to non-InSTI regimens (HR = 3.27, p = 0.014).Figure 1: Percentage weight change from baseline at ART initiation between InSTI and non-InSTI regimens ConclusionFemales have higher InSTI-associated weight gain which suggests they may be more susceptible to adverse metabolic issues. InSTI use appears to be associated with an increased incidence of DM diagnoses following ART initiation. Further prospective and controlled studies will be necessary to describe the mechanism of this effect and refine HIV management strategies.Disclosures Archana Asundi, MD, Gilead (Scientific Research Study Investigator)Merck (Scientific Research Study Investigator)ViiV (Scientific Research Study Investigator) Nina H. Lin, MD, Gilead Sciences (Scientific Research Study Investigator)ViiV (Scientific Research Study Investigator)

Development of normative data for the Five Point Test using the cognitive reliability and time to failure rate theory

In this study, several theoretically based curve fitting nonlinear models for analyzing the Five Point Test (FPT), a nonverbal fluency test, were examined. One thousand two hundred and one participants from the general population of Germany and the USA completed the FPT. The test scores were analyzed using three process indexes; the number of unique designs, strategy, and repetitions. Participants were stratified by age and level of education, and the resulting data were examined using best-fit nonlinear distribution equations. The study shows that the Weibull Growth three-factor curve fitting model provides equivalent fit as four-factor quadratic or linear curve fitting models for estimates continue normative data the Reliability and Time to Failure Theory of Cognitive Functions. Namely, that neurocognitive abilities remain stable during young adulthood and after reaching a threshold, starts to linearly or exponentially decline. The study also shows that education differentially impacts the asymptote, threshold, and decline rate of the FPT indexes confirming the importance of isolating specific cognitive domains within neuropsychological tests as these indexes might be related to the activation of particular cell assemblies, which in turn are differentially impacted by aging.