Nonlinear Mixed-effects Regression Research Articles

Lamivudine dosing for preterm infants exposed to HIV: a population pharmacokinetic modelling and simulation study.

To develop a pragmatic twice daily lamivudine dosing strategy for preterm infants from 24 to 37 completed weeks of gestation. Data were combined from eight pharmacokinetic studies in neonates and infants receiving lamivudine oral solution. A population pharmacokinetic model was developed using non-linear mixed effects regression. Different lamivudine dosing strategies, stratified by gestational age at birth (GA) bands, were simulated in a virtual population of preterm infants, aimed at maintaining lamivudine drug exposures (AUC0-12) within a reference target range of 2.95 to 13.25 µg·h/mL, prior to switching to WHO-weight band doses when ≥4 weeks of age and weighing ≥3 kg. A total of 154 infants (59% female) contributed 858 lamivudine plasma concentrations. Median (range) GA at birth was 38 (27-41) weeks. At the time of first pharmacokinetic sampling infants were older with median postnatal age (PNA) of 6.3 (0.52-26.6) weeks. Lamivudine concentrations were described by a one-compartment model, with CL/F and V/F allometrically scaled to weight. Maturation of CL/F was described using an Emax model based on PNA. CL/F was also adjusted on GA to allow extrapolation for extreme prematurity. Simulations predicted an optimal lamivudine dosing for infants GA ≥24 to <30 weeks of 2 mg/kg twice daily from birth until weighing 3 kg; and for GA ≥30 to <37 weeks, 2 mg/kg twice daily for the first 4 weeks of life, followed by 4 mg/kg twice daily until weighing 3 kg. Model-based predictions support twice daily pragmatic GA band dosing of lamivudine for preterm infants, but clinical validation is warranted.

Evaluation of the management potential of timber resources in clearwater floodplain forests in the Amazon using growth models

The Amazonian clearwater igapós are poorly studied floodplain ecosystems that are mainly covered by forests and are undergoing massive threats due to changes in land use and climate. Their hydrochemical characteristics and edaphic conditions fall between those of the eutrophic várzea floodplains on whitewater rivers and those of the oligotrophic igapós on blackwater rivers. Previous studies have indicated the management potential of timber species in the highly dynamic várzea floodplains due to the fast tree growth and high forest productivity. Timber resource management, however, is not recommended for the blackwater ecosystem because of its slow dynamics and high vulnerability to disturbances. For clearwater igapós, information on the potential for sustainable management of timber resources is lacking. In this study, we modeled the growth in diameter, height, and volume to derive species-specific minimum logging diameters (MLD) and felling cycles (FC) for eight merchantable species in the clearwater igapós of the Branco and Tapajós rivers in the northern and southern Amazon Basin, respectively. Diameter growth was modeled by analyzing the tree rings that are annually formed in the Amazonian floodplains as a consequence of the regular and predicable long-term flooding. Growth modeling followed the guidelines of the Growth-Oriented Logging (GOL) concept, with the adjustment of diameter growth improved by applying nonlinear mixed-effects regression. MLDs varied from 36 to 90 cm and FCs ranged from 6 to 21 years, which diverges from the standards of Brazilian logging regulations (MLD: 50 cm; FC: 25–35 years). This indicates the potential for timber resource management, which should be tested and introduced at small scales, integrated in protected areas to stepwise promote the sustainable management of these natural resources by traditional communities to increase their income and the conservation of this ecosystem.

Motor Development in Children with Cerebral Palsy in Sweden-A Population-Based Longitudinal Register Study.

The aim was to explore longitudinal motor development in children with cerebral palsy (CP) in Sweden with respect to the Gross Motor Function Classification System (GMFCS). In this national CP registry-based study, 2138 children aged 0.5-19 years participated (42% girls). The distribution with respect to GMFCS was I: 49%, II: 16%, III: 10%, IV: 14%, and V: 11%. In total, 5538 assessments (mean 2.7, min-max: 1-9) with the Gross Motor Function Measure-66 were included. Data were analysed using non-linear mixed-effects regression models, and the Stable Limit Model was selected to fit data. Five distinct curves of predicted gross motor development with respect to GMFCS levels were obtained. The achieved motor development was maintained over time. The estimated average GMFM-66 limit and the average age when 90% of the expected limits were reached were at GMFCS I: 88 at age 4.5; GMFCS II: 71 at age 4.2; GMFCS III: 54 at age 3.1; GMFCS IV: 38 at age 2.6, and at GMFCS V: 18 at age 0.9. In conclusion, this is the first national population-based study following motor development in CP. Five distinct curves reported in previous controlled research studies were confirmed. Our study adds knowledge about motor development captured in children's everyday context.

Comparing the impact of atezolizumab plus bevacizumab and lenvatinib on the liver function in hepatocellular carcinoma patients: A mixed-effects regression model approach.

This retrospective study compared the impact of atezolizumab plus bevacizumab (Atez/Bev) and lenvatinib (LEN) on the liver function in patients with hepatocellular carcinoma. We included 526 patients who received Atez/Bev and 731 who received LEN March 2018 and July 2022 in this study. We conducted a 1:1 propensity-score-matched analysis and identified 324 patients in each group for inclusion in the present analysis. Nonlinear mixed-effects regression models were employed, allowing for the evaluation and inclusion of cases where treatment was interrupted due to disease progression, adverse events, or loss to follow-up. These models were used to compare the ALBI score between the Atez/Bev and LEN groups. Following propensity score matching, the mean ALBI scores in the Atez/Bev and LEN groups were -2.41 ± 0.40 and -2.44 ± 0.42 at baseline, and -2.17 ± 0.56 and -2.19 ± 0.58 at 12 weeks, respectively. Although the ALBI score significantly worsened during treatment in both groups (p < 0.001), there was no significant difference in the rate of ALBI score deterioration between the groups (p = 0.06). Subgroup analyses showed that LEN-treated patients with BCLC advanced stage (p = 0.02) and those who initially received the full dose (p < 0.001) had a significantly greater worsening of ALBI score compared to Atez/Bev. Using a nonlinear mixed-effects regression approach, which allowed for the inclusion of cases with treatment interruption, we found no significant difference in the trend of liver function deterioration between the Atez/Bev and LEN groups. Caution should be exercised for LEN-treated patients with BCLC advanced stage or those receiving the full dose of LEN.

Genetically Predicted Blood Pressure and Cognition in Midlife: A UK Biobank Study.

This UK Biobank study uses a mendelian randomization approach to mitigate the variability and confounding that has affected previous analyses of the relationship between measured blood pressure (BP) and cognition and thus delineate the true association between the two. Systolic BP (SBP) and diastolic BP polygenic risk scores (PRSs) were calculated using summary statistics from the International Consortium of Blood Pressure-Genome Wide Association Study (n=299 024). Adjusted nonlinear mixed-effects regression models were used, including a natural splines term for BP-PRS with outcomes of fluid intelligence, reaction time (RT), and composite attention score. Moderating effects of age, sex, and antihypertensive use were assessed in separate models. There were 448 575 participants (mean age, 56.3 years; age range, 37-72 years) included in the analysis after genetic and neurological disease exclusions. Genetic propensity for high SBP had an approximately linear association with worsened fluid intelligence (P=0.0018). This relationship was significantly moderated by age (P<0.0001). By contrast, genetic propensity for high and low SBP and diastolic BP predicted worse attention function (P=0.0099 and P=0.0019), with high PRSs predicting worse function than low PRSs. Genetic propensity for low SBP and diastolic BP was associated with considerably worse RTs, while for high SBP-PRSs, the RT plateaued (P<0.0001). The relationships between RT and the PRSs were significantly moderated by sex (P<0.0001) and antihypertensive use (P<0.0001). Genetic propensity for high and low BP impacts on midlife cognition in subtle ways and differentially affects cognitive domains. While a genetic propensity to low BP may preserve nontimed tests in midlife, it may come at a trade-off with worsened attention scores and RT.

Region Specific Ground-Motion Predictive Models for Shallow Active Regions

ABSTRACT Using a global dataset, we propose a series of ground-motion predictive models for acceleration response spectra. The proposed models include regional adjustments to source, path, and site terms with nonlinear soil behavior. The regression coefficients are computed using linear and nonlinear mixed-effect regression algorithms. Furthermore, we develop heteroscedastic variability models for between-event, site-to-site, and single-site standard deviations. The between-event sigma model depends solely on magnitude, but the single-site standard deviation model depends on both magnitude and distance. Finally, the site-to-site standard deviation model is given in terms of VS30 and spectral acceleration at rock site condition.

A data-driven artificial neural network model for the prediction of ground motion from induced seismicity: The case of The Geysers geothermal field

Ground-motion models have gained foremost attention during recent years for being capable of predicting ground-motion intensity levels for future seismic scenarios. They are a key element for estimating seismic hazard and always demand timely refinement in order to improve the reliability of seismic hazard maps. In the present study, we propose a ground motion prediction model for induced earthquakes recorded in The Geysers geothermal area. We use a fully connected data-driven artificial neural network (ANN) model to fit ground motion parameters. Especially, we used data from 212 earthquakes recorded at 29 stations of the Berkeley–Geysers network between September 2009 and November 2010. The magnitude range is 1.3 and 3.3 moment magnitude (Mw), whereas the hypocentral distance range is between 0.5 and 20 km. The ground motions are predicted in terms of peak ground acceleration (PGA), peak ground velocity (PGV), and 5% damped spectral acceleration (SA) at T=0.2, 0.5, and 1 s. The predicted values from our deep learning model are compared with observed data and the predictions made by empirical ground motion prediction equations developed bySharma et al. (2013)for the same data set by using the nonlinear mixed-effect (NLME) regression technique. For validation of the approach, we compared the models on a separate data made of 25 earthquakes in the same region, with magnitudes ranging between 1.0 and 3.1 and hypocentral distances ranging between 1.2 and 15.5 km, with the ANN model providing a 3% improvement compared to the baseline GMM model. The results obtained in the present study show a moderate improvement in ground motion predictions and unravel modeling features that were not taken into account by the empirical model. The comparison is measured in terms of both theR2statistic and the total standard deviation, together with inter-event and intra-event components.

Determination of the effect of iatrogenic blood contamination on lactate dehydrogenase and creatine kinase activity in canine cerebrospinal fluid.

Lactate dehydrogenase (LDH) and creatine kinase (CK) have differential tissue activity and isoenzyme profiles. LDH and CK exist as 5 and 3 isoenzymes, respectively, in both serum and cerebrospinal fluid (CSF). Studies have demonstrated that measuring LDH, CK, and their isoenzymes in CSF has diagnostic and prognostic values for dogs and people with neurologic disorders. Iatrogenic blood contamination can distort the results of CSF analysis. The purpose of this study was to determine allowable thresholds of blood contamination (RBC/μL) for accurate measurement of LDH, CK, and their isoenzymes in canine CSF. Venous blood and CSF were collected from healthy dogs. Total LDH and CK activity were measured spectrophotometrically. Isoenzyme profiles were determined using gel electrophoresis and densitometric scanning. All samples were analyzed within 6hours of collection. A nonlinear mixed effects regression model was used to estimate the allowable thresholds of blood contamination for accurate measurement of LDH, CK, and their isoenzymes in canine CSF. The threshold of iatrogenic blood contamination for total LDH and total CK in healthy dogs are 6696 RBC/μL (95% CI 3879-11 187) and 5961 RBC/μL (95% CI 2939-12 085), respectively. LDH-1 is the most sensitive isoenzyme to iatrogenic blood contamination, while LDH-4 is the least sensitive. These results are important for the interpretation of LDH, CK, and their isoenzymes in canine CSF. Additionally, our methodology is translatable for determining thresholds of acceptable iatrogenic blood contamination in CSF for other diagnostic and prognostic biomarkers of neurologic disease.

SOLUBLE GUANYLYL CYCLASE ACTIVATION RESCUES HYPEROXIA-INDUCED DYSFUNCTION OF VASCULAR RELAXATION.

Introduction: Perioperative alterations in perfusion lead to ischemia and reperfusion injury, and supplemental oxygen is administered during surgery to limit hypoxic injury but can lead to hyperoxia. We hypothesized that hyperoxia impairs endothelium-dependent and endothelium-independent vasodilation but not the vasodilatory response to heme-independent soluble guanylyl cyclase activation. Methods: We measured the effect of oxygen on vascular reactivity in mouse aortas. Mice were ventilated with 21% (normoxia), 60% (moderate hyperoxia), or 100% (severe hyperoxia) oxygen during 30 minutes of renal ischemia and 30 minutes of reperfusion. After sacrifice, the thoracic aorta was isolated, and segments mounted on a wire myograph. We measured endothelium-dependent and endothelium-independent vasodilation with escalating concentrations of acetylcholine (ACh) and sodium nitroprusside (SNP), respectively, and we measured the response to heme-independent soluble guanylyl cyclase activation with cinaciguat. Vasodilator responses to each agonist were quantified as the maximal theoretical response ( Emax ) and the effective concentration to elicit 50% relaxation (EC 50 ) using a sigmoid model and nonlinear mixed-effects regression. Aortic superoxide was measured with dihydroethidium probe and high-performance liquid chromatography quantification of the specific superoxide product 2-hydroxyethidium. Results: Hyperoxia impaired endothelium-dependent (ACh) and endothelium-independent (SNP) vasodilation compared with normoxia and had no effect on cinaciguat-induced vasodilation. The median ACh Emax was 76.4% (95% confidence interval = 69.6 to 83.3) in the normoxia group, 53.5% (46.7 to 60.3) in the moderate hyperoxia group, and 53.1% (46.3 to 60.0) in the severe hyperoxia group ( P &lt; 0.001, effect across groups), while the ACh EC 50 was not different among groups. The SNP Emax was 133.1% (122.9 to 143.3) in normoxia, 128.3% (118.1 to 138.6) in moderate hyperoxia, and 114.8% (104.6 to 125.0) in severe hyperoxia ( P &lt; 0.001, effect across groups), and the SNP EC 50 was 0.38 log M greater in moderate hyperoxia than in normoxia (95% confidence interval = 0.18 to 0.58, P &lt; 0.001). Cinaciguat Emax and EC 50 were not different among oxygen treatment groups (median range Emax = 78.0% to 79.4% and EC 50 = -18.0 to -18.2 log M across oxygen groups). Aorta 2-hydroxyethidium was 1419 pmol/mg of protein (25th-75th percentile = 1178-1513) in normoxia, 1993 (1831-2473) in moderate hyperoxia, and 2078 (1936-2922) in severe hyperoxia ( P = 0.008, effect across groups). Conclusions: Hyperoxia, compared with normoxia, impaired endothelium-dependent and endothelium-independent vasodilation but not the response to heme-independent soluble guanylyl cyclase activation, and hyperoxia increased vascular superoxide production. Results from this study could have important implications for patients receiving high concentrations of oxygen and at risk for ischemia reperfusion-mediated organ injury.

0217 Is the Timing of the Endogenous Circadian Rhythm of Neurobehavioral Functioning Inherently Task-Dependent?

Abstract Introduction Changes in waking neurobehavioral functioning (NF) over time are governed by homeostatic and circadian processes. It has been reported that peak circadian timing varies inherently between tasks, such that the optimal timing for NF would be task-dependent. Here we investigated this idea with a simulated shift work study protocol followed by a 24h constant routine (CR) protocol to experimentally and statistically separate the circadian from the homeostatic process. Methods N=13 healthy adults (ages 25.5±3.2y; 9 men) completed a 7-day/6-night in-laboratory study. They were randomized to a 3-day simulated day shift condition (n=7) with nighttime sleep (22:00–06:00) or a 3-day simulated night shift condition (n=6) with daytime sleep (10:00–18:00). They then underwent a 24h CR protocol, during which they stayed awake under constant behavioral and environmental conditions and blood was collected at 1–3h intervals for the assessment of dim light melatonin onset (DLMO). During scheduled wakefulness, subjects completed three functionally distinct NF tasks at ~2h intervals: the Karolinska Sleepiness Scale (KSS), Digit Symbol Substitution Test (DSST), and Psychomotor Vigilance Test (PVT). Data from these tasks taken during the CR protocol were analyzed with non-linear mixed-effects regression to separate endogenous circadian effects from the homeostatic process. Results Following simulated night shift, compared to simulated day shift, on average there was a modest, 1.4h (±0.8h SE) delay in DLMO (F1,11=3.68, p=0.082). As such, the simulated night shift condition produced a 10.6h shift in alignment of the homeostatic process relative to the circadian process. Regardless of prior shift condition, the peak of the circadian rhythm effect on NF occurred post-DLMO by 16.8h (±1.0h) for KSS, 15.9h (±1.4h) for DSST, and 18.6h (±1.0h) for PVT, which was not significantly different (F2,9=1.55, p=0.26). Conclusion As proof of principle, we studied three distinct NF assays, and found only small, non-significant differences between them in the timing of underlying circadian rhythmicity. While a larger sample could have yielded statistical significance in our comparison of circadian peak times, the small magnitude of the observed difference does not support the idea of inherent task-dependent differences in the timing of the endogenous circadian rhythm’s influence on NF. Support (If Any) CDMRP W81XWH-16-1-0319 and W81XWH-20-1-0442

Quantitative clinical pharmacology and patient-centered healthcare technologies: perspectives 2030

One of the most promising trends in clinical pharmacology is pharmacometrics, a combination of pharmacology and statistics that implements quantitative approaches for characterising dose–response relationships and predicting the variability of these relationships attributable to patient-specific characteristics (covariates). The aim of the study was to evaluate the significance of quantitative clinical pharmacology and discuss opportunities for its development in the context of health systems moving towards the value-based care model. The study showed that two key prerequisites for pharmacometrics development were the advancements in mathematical and statistical methodology based upon non-linear mixed effects regression modelling and the emergence of a personalised medicine paradigm aimed at creation of strategies for individualised prescribing of medicinal products. The study demonstrated the necessity for using the dose–response relationship information obtained by exploratory analysis of data stored in existing and newly created bases. Further integration of pharmacostatistical modelling and real-world data processing technologies, as well as their incorporation into clinical and economic evaluation of health technologies, will streamline decision making and, thus, facilitate the transition of health systems to the value-based model.

Gap detection responses modelled using the Hill equation in adults with well-controlled HIV

Objective This study’s objective was determining whether gap detection deficits are present in a longstanding cohort of people living with HIV (PLWH) compared to those living without HIV (PLWOH) using a new gap detection modelling technique (i.e. fitting gap responses using the Hill equation and analysing the individual gap detection resulting curves with non-linear statistics). This approach provides a measure of both gap threshold and the steepness of the gap length/correct detection relationship. Design The relationship between the correct identification rate at each gap length was modelled using the Hill equation. Results were analysed using a nonlinear mixed-effect regression model. Study sample 45 PLWH (age range 41–78) and 39 PLWOH (age range 38–79) were enrolled and completed gap detection testing. Results The likelihood ratio statistic comparing the full regression model with the HIV effects to the null model, assuming one population curve for both groups, was highly significant (p < 0.001), suggesting a less precise relationship between gap length and correct detection in PLWH. Conclusions PLWH showed degraded gap detection ability compared to PLWOH, likely due to central nervous system effects of HIV infection or treatment. The Hill equation provided a new approach for modelling gap detection ability.

A New Model for Vertical-to-Horizontal Response Spectral Ratios for Central and Eastern North America

ABSTRACT It is a well-known fact that critical structures are required to be designed for the vertical effects of earthquake ground motions as well as the horizontal effects. We developed a new model for the spectral ratio of vertical-to-horizontal components of earthquakes (V/H ratio) for central and eastern North America (CENA). The proposed V/H ratio model has the advantage of considering the earthquake magnitude, source-to-site distance, and the shear-wave velocity of soil deposits in the upper 30 m of the site for peak ground acceleration and a wide range of spectral periods (0.01–10.0 s). The model evaluation is based on a comprehensive set of regression analysis of the compiled Next Generation Attenuation-East database of the available CENA recordings with M ≥ 3.4 and Rrup&amp;lt;1000 km. The median value of the geometric mean of the orthogonal horizontal motions rotated through all possible nonredundant rotation angles, known as the RotD50, is used along with the vertical component to perform regression using the nonlinear mixed-effect regression. We excluded the earthquakes and recording stations in the Gulf Coast region due to their different ground motion attenuation (Dreiling et al., 2014). To compute V/H ratios for the Gulf Coast region, we refer the readers to the study of Haji-Soltani et al. (2017). Moreover, we excluded the National Earthquake Hazard Reduction Program site class E sites from consideration because of their complex site response characteristics and their potential for nonlinear site effects. The predicted V/H ratios from the proposed model are compared with recently published V/H ratio models. We suggest our model to be used for developing the vertical response spectra for CENA sites.

Abstract 9356: The Effect of Intraoperative Oxygenation on Vascular Reactivity in Human Resistance Arterioles

Introduction: Hyperoxia during surgery is common and may promote oxidative stress. Oxidative stress uncouples endothelial nitric oxide synthase and may impair vascular reactivity, a key regulator of tissue perfusion. Hypothesis: Hyperoxia during cardiac surgery impairs vascular reactivity compared to normoxia. Methods: We isolated resistance-level arterioles from mediastinal fat collected from subjects in a clinical trial of hyperoxia versus normoxia during cardiac surgery. Tissue was obtained at the end of surgery, and arterioles from 24 subjects were mounted on a wire myograph. After norepinephrine contraction, we measured the endothelium-dependent response to acetylcholine and the endothelium-independent response to sodium nitroprusside. To further identify if hyperoxygenation impairs nitric oxide mediated vasodilation via oxidation of the soluble guanylyl cyclase heme moiety, we measured the response to the heme-independent soluble guanylyl cyclase activator cinaciguat. We compared relaxation to increasing doses of each vasodilator in normoxia vs. hyperoxia treated arterioles using repeated measures ANOVA and nonlinear mixed effects regression. Results: The acetylcholine response was uniformly impaired (P=0.98, Figure ). The sodium nitroprusside response was selectively impaired in hyperoxia treatment vs. normoxia as reflected by a higher EC50 (EC50: 50.1±6.3 x10-8 M for hyperoxia vs. 7.9 ± 0.8 x 10-8 M for normoxia, P=0.02) and decreased peak response. In striking contrast to the response to sodium nitroprusside, vasodilation evoked by cinaciguat was not impaired and was slightly enhanced for hyperoxia vs. normoxia (EC50: 7.9±0.8 x10-8 M vs. 15.8±6.2 x10-8 M; P=0.07, Figure). Conclusions: Intraoperative hyperoxia impaired human arteriole vasodilation likely via oxidation of the soluble guanylyl cyclase heme moiety. These data provide additional mechanistic insight into potential harmful effects of intraoperative hyperoxia.

Recycled two-stage estimation in nonlinear mixed effects regression models

We consider a re-sampling scheme for estimation of the population parameters in the mixed-effects nonlinear regression models of the type used, for example, in clinical pharmacokinetics. We provide a two-stage estimation procedure which resamples (or recycles), via random weightings, the various parameter's estimates to construct consistent estimates of their respective sampling distributions. In particular, we establish under rather general distribution-free assumptions, the asymptotic normality and consistency of the standard two-stage estimates and of their resampled version and demonstrate the applicability of our proposed resampling methodology in a small simulation study. A detailed example based on real clinical pharmacokinetic data is also provided.

Trait Interindividual Differences in the Magnitude of Subjective Sleepiness from Sleep Inertia.

In shift work settings and on-call operations, workers may be at risk of sleep inertia when called to action immediately after awakening from sleep. However, individuals may differ substantially in their susceptibility to sleep inertia. We investigated this using data from a laboratory study in which 20 healthy young adults were each exposed to 36 h of total sleep deprivation, preceded by a baseline sleep period and followed by a recovery sleep period, on three separate occasions. In the week prior to each laboratory session and on the corresponding baseline night in the laboratory, participants either extended their sleep period to 12 h/day or restricted it to 6 h/day. During periods of wakefulness in the laboratory, starting right after scheduled awakening, participants completed neurobehavioral tests every 2 h. Testing included the Karolinska Sleepiness Scale to measure subjective sleepiness, for which the data were analyzed with nonlinear mixed-effects regression to quantify sleep inertia. This revealed considerable interindividual differences in the magnitude of sleep inertia, which were highly stable within individuals after both baseline and recovery sleep periods, regardless of study condition. Our results demonstrate that interindividual differences in subjective sleepiness due to sleep inertia are substantial and constitute a trait.

Omnibus Modeling of Listeria monocytogenes Growth Rates at Low Temperatures

Listeria monocytogenes is a pathogen of considerable public health importance with a high case fatality. L. monocytogenes can grow at refrigeration temperatures and is of particular concern for ready-to-eat foods that require refrigeration. There is substantial interest in conducting and modeling shelf-life studies on L. monocytogenes, especially relating to storage temperature. Growth model parameters are generally estimated from constant-temperature growth experiments. Traditionally, first-order and second-order modeling (or primary and secondary) of growth data has been done sequentially. However, omnibus modeling, using a mixed-effects nonlinear regression approach, can model a full dataset covering all experimental conditions in one step. This study compared omnibus modeling to conventional sequential first-order/second-order modeling of growth data for five strains of L. monocytogenes. The omnibus model coupled a Huang primary model for growth with secondary models for growth rate and lag phase duration. First-order modeling indicated there were small significant differences in growth rate depending on the strain at all temperatures. Omnibus modeling indicated smaller differences. Overall, there was broad agreement between the estimates of growth rate obtained by the first-order and omnibus modeling. Through an appropriate choice of fixed and random effects incorporated in the omnibus model, potential errors in a dataset from one environmental condition can be identified and explored.

075 Systematic Individual Differences in Vulnerability to Sleep Inertia

Abstract Introduction Sleep inertia (SI), the transient grogginess and disorientation that occurs upon awakening, may be particularly problematic in on-call operations that require safety-sensitive and time-critical action, such as healthcare and emergency response. The magnitude of SI is determined by multiple factors, including sleep history, and anecdotal evidence suggests individuals may differ considerably in susceptibility to impairment from SI. Methods As part of a larger study investigating individual differences in neurobehavioral impairment, N=21 healthy adults (aged 21–38y; 9 females) completed three laboratory-based neurobehavioral testing sessions, each preceded by baseline sleep. Baseline sleep and nightly at-home sleep opportunities during the week prior were either 12h (extended) or 6h (restricted); two sessions involved extension and one involved restriction in randomized, counterbalanced order. Baseline sleep opportunities ended at 10:00, whereupon subjects completed a 60min neurobehavioral test battery, which began with the Karolinska Sleepiness Scale (KSS). The test battery was repeated every 2h throughout the testing sessions. Results A nonlinear mixed-effects regression, controlling for prior sleep restriction/extension and session number, was used to estimate the subjective magnitude of SI immediately upon awakening from baseline sleep, as measured by KSS scores, and the exponential dissipation rate of the effect relative to KSS scores later in the day (12:00-20:00). Following prior sleep extension, SI was associated with a 1.82±0.59 KSS score increase (p=0.006), which subsequently dissipated from a level of 4.80±0.65 to 2.98±0.29 (p=&amp;lt;0.001) later in the day. Following prior sleep restriction, SI was associated with a 1.58±0.58 KSS score increase (p=0.014), which subsequently dissipated from a level of 5.39±0.63 to 3.80±0.30 later in the day. SI took ~45min to dissipate to a negligible level based on a time constant estimate of 23.6±15.8min. Importantly, there were substantial, systematic individual differences in the magnitude of SI, with a between-subjects standard deviation of 1.15±0.42 points on the KSS (ICC=0.51, F=19.5, p&amp;lt;0.001). Conclusion We observed sizeable, systematic individual differences in subjective sleepiness due to sleep inertia. To what degree these individual differences predict objective performance deficits remains to be investigated. Support (if any) NASA grant NAG9-1161 and CDMRP grant W81XWH-20-1-0442

Artificial intelligence with deep learning algorithms to model relationships between total tree height and diameter at breast height

Aim of Study: As an innovative prediction technique, Artificial Intelligence technique based on a Deep Learning Algorithm (DLA) with various numbers of neurons and hidden layer alternatives were trained and evaluated to predict the relationships between total tree height (TTH) and diameter at breast height (DBH) with nonlinear least squared (NLS) regression models and nonlinear mixed effect (NLME) regression models.Area of Study: The data of this study were measured from even-aged, pure Turkish Pine (Pinus brutia Ten.) stands in the Kestel Forests located in the Bursa region of northwestern Turkey.Material and Methods: 1132 pairs of TTH-DBH measurements from 132 sample plots were used for modeling relationships between TTH, DBH, and stand attributes such as dominant height (Ho) and diameter (Do).Main Results: The combination of 100 # neurons and 8 # hidden layer in DLA resulted in the best predictive total height prediction values with Average Absolute Error (0.4188), max. Average Absolute Error (3.7598), Root Mean Squared Error (0.6942), Root Mean Squared error % (5.2164), Akaike Information Criteria (-345.4465), Bayesian Information Criterion (-330.836), the average Bias (0.0288) and the average Bias % (0.2166), and fitting abilities with r (0.9842) and Fit Index (0.9684). Also, the results of equivalence tests showed that the DLA technique successfully predicted the TTH in the validation dataset.Research highlights: These superior fitting scores coupled with the validation results in TTH predictions suggested that deep learning network models should be considered an alternative to the traditional nonlinear regression techniques and should be given importance as an innovative prediction technique.Keywords: Prediction; artificial intelligence; deep learning algorithms; number of neurons; hidden layer alternatives.Abbreviations: TTH (total tree height), DBH (diameter at breast height), OLS (ordinary least squares), NLME (nonlinear mixed effect), AIT (Artificial Intelligence Techniques), ANN (Artificial Neural Network), DLA (Deep Learning Algorithm), GPU (Graphical Processing Units), NLS (nonlinear least squared), RMSE (root mean squared error), AIC (Akaike information criteria), BIC (Bayesian information criterion), FI (fit index), AAE (average absolute error), BLUP (best linear unbiased predictor), TOST (two one-sided test method).

The Use of System Dynamics Methodology in Building a COVID-19 Confirmed Case Model.

Researchers used a hybrid model (a combination of health resource demand model and disease transmission model), Bayesian model, and susceptible-exposed-infectious-removed (SEIR) model to predict health service utilization and deaths and mixed-effect nonlinear regression. Further, they used the mixture model to predict the number of confirmed cases and deaths or to predict when the curve would flatten. In this article, we show, through scenarios developed using system dynamics methodology, besides close to real-world results, the detrimental effects of ignoring social distancing guidelines (in terms of the number of people infected, which decreased as the percentage of noncompliance decreased).