Linear Model Fit Research Articles

Impacts on study design when implementing digital measures in Parkinson's disease-modifying therapy trials.

Parkinson's Disease affects over 8.5 million people and there are currently no medications approved to treat underlying disease. Clinical trials for disease modifying therapies (DMT) are hampered by a lack of sufficiently sensitive measures to detect treatment effect. Reliable digital assessments of motor function allow for frequent at-home measurements that may be able to sensitively detect disease progression. Here, we estimate the test-retest reliability of a suite of at-home motor measures derived from raw triaxial accelerometry data collected from 44 participants (21 with confirmed PD) and use the estimates to simulate digital measures in DMT trials. We consider three schedules of assessments and fit linear mixed models to the simulated data to determine whether a treatment effect can be detected. We find at-home measures vary in reliability; many have ICCs as high as or higher than MDS-UPDRS part III total score. Compared with quarterly in-clinic assessments, frequent at-home measures reduce the sample size needed to detect a 30% reduction in disease progression from over 300 per study arm to 150 or less than 100 for bursts and evenly spaced at-home assessments, respectively. The results regarding superiority of at-home assessments for detecting change over time are robust to relaxing assumptions regarding the responsiveness to disease progression and variability in progression rates. Overall, at-home measures have a favorable reliability profile for sensitive detection of treatment effects in DMT trials. Future work is needed to better understand the causes of variability in PD progression and identify the most appropriate statistical methods for effect detection.

An interactive R-based custom quantification program for semi-quantitative analysis of triacylglycerols in bovine milk.

The R programming language, RStudio, and open-source software solutions for analysis of liquid chromatography-mass spectrometry (LC-MS) data have been used with user-written R-based custom quantification programs (CQP) for semi-quantification of triacylglycerols (TAGs) in bovine milk lipid extracts. Using the peak-finding capabilities of the package "xcms" in RStudio, peaks were integrated, and retention times aligned, normalized, and then used for semi-quantitative analysis of a custom set of four extraction internal standards (EISs) and 29 TAG regioisomers using the choice of four analytical internal standards (AISs). Alternating stereospecific numbering (sn) 1,3 TAG regioisomers (standards 1, 3, and 5 of six calibration standards) and sn-1,2 TAG regioisomers (standards 2, 4, and 6 of six standards) were used to make a set of six calibration standards, which were used for quantification using a linear fit model, polynomial fit model, power fit model, level-bracketed linear fit, replicate-bracketed polynomial fit, replicate-bracketed power fit, and replicate- and level-bracketed linear fit and response factors. For example, the linear fit for EIS1 gave an unacceptable coefficient of determination (CoD), r2 = 0.9616, whereas the polynomial fit gave r2 = 0.9908 and the power fit gave r2 = 0.9928, while the double-bracketed linear fit gave CoDs of r2 = 0.9960, 0.9848, and 0.9781 for the three brackets, yet gave the least % difference to known calibration concentrations. For unparalleled transparency, the CQP produced webpages that allowed every step in the data processing and quantification sequence to be verified and reproduced, and contained interactive figures. The data are publicly available using a digital object identifier (DOI). The R code can be downloaded and used with the downloadable data to reproduce the results, to modify the code and further customize the results, or to copy and paste and adapt the code to other quantification applications.

Use of Phenomics in the Selection of UAV-Based Vegetation Indices and Prediction of Agronomic Traits in Soybean Subjected to Flooding

Flooding is a frequent environmental stress that reduces soybean growth and grain yield in many producing areas in the world, such as the United States, Southeast Asia, and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas in crop rotation with rice, which provides numerous technical, economic, and environmental benefits. In this context, the identification of the most important spectral variables for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics, with faster and more reliable results enabled using multispectral sensors mounted on unmanned aerial vehicles (UAVs). Accordingly, this research aimed to identify the optimal UAV-based multispectral vegetation indices for characterizing the response of soybean genotypes subjected to flooding and to test the best linear model fit in predicting tolerance scores, relative maturity group, biomass, and grain yield based on phenomics analysis. Forty-eight soybean cultivars were sown in two environments (flooded and non-flooded). Ground evaluations and UAV-image acquisition were conducted at 13, 38, and 69 days after flooding and at grain harvest, corresponding to the phenological stages V8, R1, R3, and R8, respectively. Data were subjected to variance component analysis and genetic parameters were estimated, with stepwise regression applied for each agronomic variable of interest. Our results showed that vegetation indices behave differently in their suitability for more tolerant genotype selection. Using this approach, phenomics analysis efficiently identified indices with high heritability, accuracy, and genetic variation (>80%), as observed for MSAVI, NDVI, OSAVI, SAVI, VEG, MGRVI, EVI2, NDRE, GRVI, BNDVI, and RGB index. Additionally, variables predicted based on estimated genetic data via phenomics had determination coefficients above 0.90, enabling the reduction in the number of important variables within the linear model.

Study on the applicability of FAI linear fitting model in the extraction of cyanobacterial blooms.

Currently, more and more lakes around the world are experiencing outbreaks of cyanobacterial blooms, and high-precision and rapid monitoring of the spatial distribution of algae in water bodies is an important task. Remote sensing technology is one of the effective means for monitoring algae in water bodies. Studies have shown that the Floating Algae Index (FAI) is superior to methods such as the Standardized Differential Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in monitoring cyanobacterial blooms. However, compared to the NDVI method, the FAI method has difficulty in determining the threshold, and how to choose the threshold with the highest classification accuracy is challenging. In this study, FAI linear fitting model (FAI-L) is selected to solve the problem that FAI threshold is difficult to determine. Innovatively combine FAI index and NDVI index, and use NDVI index to find the threshold of FAI index. In order to analyze the applicability of FAI-L to extract cyanobacterial blooms, this paper selected multi-temporal Landsat8, HJ-1B, and Sentinel-2 remote sensing images as data sources, and took Chaohu Lake and Taihu Lake in China as research areas to extract cyanobacterial blooms. The results show that (1) the accuracy of extracting cyanobacterial bloom by FAI-L method is generally higher than that by NDVI and FAI. Under different data sources and different research areas, the average accuracy of extracting cyanobacterial blooms by FAI-L method is 95.13%, which is 6.98% and 18.43% higher than that by NDVI and FAI respectively. (2) The average accuracy of FAI-L method for extracting cyanobacterial blooms varies from 84.09 to 99.03%, with a standard deviation of 4.04, which is highly stable and applicable. (3) For simultaneous multi-source image data, the FAI-L method has the highest average accuracy in extracting cyanobacterial blooms, at 95.93%, which is 6.77% and 13.26% higher than NDVI and FAI methods, respectively. In this paper, it is found that FAI-L method shows high accuracy and stability in extracting cyanobacterial blooms, and it can extract the spatial distribution of cyanobacterial blooms well, which can provide a new method for monitoring cyanobacterial blooms.

Abstract P137: Real-World Impact of Semaglutide on Cardiometabolic Risk Factors in Patients without Diabetes: A Multi-Center Analysis of Electronic Health Records

Introduction: In randomized controlled trials, semaglutide reduces cardiovascular risks in individuals with obesity/overweight without diabetes. However, its real-world impact on cardiometabolic risk factors remains unclear in this group. Methods: We conducted a multi-center analysis of electronic health records data from Yale New Haven Health (01/2018-12/2022) and Sentara Health (01/2018-06/2023). The study population included adults without diabetes mellitus who were prescribed semaglutide and had regular follow-ups over 24 months. The index date was the date of the first prescription of semaglutide. The percent (%) change in weight from baseline and blood pressure (BP) (mmHg) were fit in separate linear regression models. A random-effects meta-analysis was used to synthesize the results from different sites. Results: A total of 523 patients without diabetes prescribed semaglutide at Yale and Sentara were included in the analyses. The study population had a mean (SD) age of 56.6 (12.6) years, 69.6% were female, and the mean (SD) BMI was 37.9 (8.4) kg/m 2 . We observed a continuous decrease in body weight over 24 months (Figure 1). At 18-23 months, body weight among these patients was significantly reduced by 4.8 % (2.2, 7.4%). Systolic BP was significantly reduced by 2.03 mmHg (0.4, 3.7 mmHg) at 18-23 months, while there was no significant change in diastolic BP at the end of the study period. Conclusions: This multi-center analysis provided preliminary results of the impact of semaglutide on body weight and BP in real-world settings in individuals without diabetes. These findings highlight the importance of post-market surveillance of novel anti-obesity medications.

Improvements in precision and accuracy of complex- relative to real-domain linear combination model spectral fitting not necessarily recovered by zero filling.

Although the information obtained from in vivo proton magnetic resonance spectroscopy (1H MRS) presents a complex-valued spectrum, spectral quantification generally employs linear combination model (LCM) fitting using the real spectrum alone. There is currently no known investigation comparing fit results obtained from LCM fitting over the full complex data versus the real data and how these results might be affected by common spectral preprocessing procedure zero filling. Here, we employ linear combination modeling of simulated and measured spectral data to examine two major ideas: first, whether use of the full complex rather than real-only data can provide improvements in quantification by linear combination modeling and, second, to what extent zero filling might influence these improvements. We examine these questions by evaluating the errors of linear combination model fits in the complex versus real domains against three classes of synthetic data: simulated Lorentzian singlets, simulated metabolite spectra excluding the baseline, and simulated metabolite spectra including measured in vivo baselines. We observed that complex fitting provides consistent improvements in fit accuracy and precision across all three data types. While zero filling obviates the accuracy and precision benefit of complex fitting for Lorentzian singlets and metabolite spectra lacking baselines, it does not necessarily do so for complex spectra including measured in vivo baselines. Overall, performing linear combination modeling in the complex domain can improve metabolite quantification accuracy relative to real fits alone. While this benefit can be similarly achieved via zero filling for some spectra with flat baselines, this is not invariably the case for all baseline types exhibited by measured in vivo data.

Modelling and optimization of epoxy-PMMA microcapsule synthesis parameter: A response surface methodology approach

The efficiency of self-healing microcapsule in restoring damages incurred by polymeric or composite materials is heavily dependent on modelling of encapsulation conditions to achieve optimized microcapsule with desired characteristics. This study modelled the effects of encapsulation conditions (core–shell ratio, agitation rate, and temperature) on the morphological, chemical, and thermal characteristics of epoxy-polymethylmethacrylate (epoxy-PMMA) microcapsules using response surface methodology (RSM). Epoxy-PMMA microcapsules were synthesized by encapsulating epoxy resin in polymethylmethacrylate (PMMA) at varied encapsulation conditions using solvent evaporation method. The morphology of the synthesized microcapsule using optical microscope (OP) revealed that the microcapsules are either mononuclear or irregular capsule types. The modelled effect showed that microcapsule percentage yield varied between 74.96 to 96.56%, was highly influenced by core–shell ratio and the effect of studied encapsulation conditions on percentage yield was best described by quadratic model. The core content of the microcapsules varied between 54.8 to 67.2%, observed to be highly influenced by both core–shell ratio and agitation rate which fit into linear model. The microcapsule average diameter was between 26 to 74 μm, highly influenced by agitation rate and fit linear model. Fourier transform infrared (FTIR) spectra of synthesized microcapsules revealed epoxy characteristic peak of C–O–C at 913 cm−1 and C–O-ph stretching at 1032 cm−1. C–O doublet of PMMA was observed at 1386 cm−1 and 1189 cm−1. Thermogravimetric analysis (TGA) of epoxy-PMMA microcapsule showed three stages of decomposition attributed to water evaporation, epoxy degradation, and PMMA shell degradation. Lastly, optimization process to achieve maximum yield, maximum core content and minimum capsule diameter was obtained with core–shell ratio of 1.5:3 and agitation rate of 1000 rpm at 40 °C. The synthesized epoxy-PMMA microcapsules exhibited chemical, thermal, morphological stability and the models can be optimized to achieve microcapsule with desired characteristics.

Association of amisulpride and recovery room length of stay among patients with postoperative nausea and vomiting following outpatient surgery

Study objectivePostoperative nausea and vomiting (PONV) is a common sequela of surgery in patients undergoing general anesthesia. Amisulpride has shown promise in its ability to treat PONV. The objective of this study was to determine if amisulpride is associated with significant changes in PACU efficiency within a fast-paced ambulatory surgery center. MethodsThis was a retrospective cohort study of 816 patients at a single ambulatory surgery center who experienced PONV between 2018 and 2023. The two cohorts analyzed were patients who did or did not have amisulpride among their anti-emetic regimens in the PACU during two distinct time periods (before and after amisulpride was introduced). The primary outcome of the study was PACU length of stay. Both unmatched analysis and a linear multivariable mixed-effects model fit by restricted maximum likelihood (random effect being surgical procedure) were used to analyze the association between amisulpride and PACU length of stay. We performed segmented regression to account for cohorts occurring during two time periods. ResultsUnmatched univariate analysis revealed no significant difference in PACU length of stay (minutes) between the amisulpride and no amisulpride cohorts (115 min vs 119 min, respectively; P = 0.07). However, when addressing confounders by means of the mixed-effects multivariable segmented regression, the amisulpride cohort was associated with a statistically significant reduction in PACU length of stay by 26.1 min (P < 0.001). ConclusionsThis study demonstrated that amisulpride was associated with a significant decrease in PACU length of stay among patients with PONV in a single outpatient surgery center. The downstream cost-savings and operational efficiency gained from this drug's implementation may serve as a useful lens through which this drug's widespread implementation may further be rationalized.

Rapid Estimation of Soil Erosion Rate from Exhumed Roots (Xiaolong Mts, China)

Soil erosion is a challenge worldwide, including in China. The dendrogeomorphic method was applied, for the first time, at Xiaolong Mts in Gansu Province to obtain a quantitative estimate of the soil erosion rate. The dataset built in this pilot study allowed the identification of exhumation texture in exposed roots between 1967 and 2002. The calculated mean erosion rate estimates (Era) ranged from 2.6 to 16.5 mm yr−1 and showed an increase with the slope steepness (s). The best fitting linear model (Era = 0.043(±0.017) × s + 3.09(±1.04); R² = 0.20; R²adj = 0.16; F = 6.18; p = 0.02) could be used in future research to determine and to map soil denudation in this part of the Xiaolong Mts. Notable associations were found between erosive rainfalls and root exhumation events. Daily (Rx1day) and 5-day (Rx5day) precipitation totals of 56 and 73 mm, respectively, seem to be critical thresholds which if exceeded will always induce root exhumation in the same year or in the consecutive season in the forest of the Xiaolong Mts in the studied period.

Exploring an EM-algorithm for banded regression in computational neuroscience

Abstract Regression is a principal tool for relating brain responses to stimuli or tasks in computational neuroscience. This often involves fitting linear models with predictors that can be divided into groups, such as distinct stimulus feature subsets in encoding models or features of different neural response channels in decoding models. When fitting such models, it can be relevant to allow differential shrinkage of the different groups of regression weights. Here, we explore a framework that allows for straightforward definition and estimation of such models. We present an expectation-maximization algorithm for tuning hyperparameters that control shrinkage of groups of weights. We highlight properties, limitations, and potential use-cases of the model using simulated data. Next, we explore the model in the context of a BOLD fMRI encoding analysis and an EEG decoding analysis. Finally, we discuss cases where the model can be useful and scenarios where regularization procedures complicate model interpretation.

Accelerated free-breathing liver fat and quantification using multi-echo stack-of-radial MRI with motion-resolved multidimensional regularized reconstruction: Initial retrospective evaluation.

To improve image quality, mitigate quantification biases and variations for free-breathing liver proton density fat fraction (PDFF) and quantification accelerated by radial k-space undersampling. A free-breathing multi-echo stack-of-radial MRI method was developed with compressed sensing with multidimensional regularization. It was validated in motion phantoms with reference acquisitions without motion and in 11 subjects (6 patients with nonalcoholic fatty liver disease) with reference breath-hold Cartesian acquisitions. Images, PDFF, and maps were reconstructed using different radial view k-space sampling factors and reconstruction settings. Results were compared with reference-standard results using Bland-Altman analysis. Using linear mixed-effects model fitting (p < 0.05 considered significant), mean and SD were evaluated for biases and variations of PDFF and , respectively, and coefficient of variation on the first echo image was evaluated as a surrogate for image quality. Using the empirically determined optimal sampling factor of 0.25 in the accelerated in vivo protocols, mean differences and limits of agreement for the proposed method were [-0.5; -33.6, 32.7] s-1 for and [-1.0%; -5.8%, 3.8%] for PDFF, close to those of a previous self-gating method using fully sampled radial views: [-0.1; -27.1, 27.0] s-1 for and [-0.4%; -4.5%, 3.7%] for PDFF. The proposed method had significantly lower coefficient of variation than other methods (p < 0.001). Effective acquisition time of 64 s or 59 s was achieved, compared with 171 s or 153 s for two baseline protocols with different radial views corresponding to sampling factor of 1.0. This proposed method may allow accelerated free-breathing liver PDFF and mapping with reduced biases and variations.

Reflective microring-resonator-based microwave photonic sensor incorporating a self-attention assisted convolutional neural network

In this paper, a reflective microring resonator (MRR)-based microwave photonic (MWP) sensor incorporating a self-attention convolutional neural network (CNN) is presented. An MRR cascaded with an inverse-designed optical reflector is adopted as the sensor probe to allow for utilizing the responses generated from both the clockwise and counterclockwise resonant modes. Through the MWP interrogation, the cascaded resonant modes can be transformed into distinctive deep radio-frequency (RF) spectral notches under different modulator bias conditions. By using a self-attention assisted CNN processing to leverage both the local and global features of the RF spectra, a sensing model with improved accuracy can be established. As a proof of concept, the proposed scheme is experimentally demonstrated in temperature sensing. Even with a small dataset, the root-mean-square error of the sensing model established after training is achieved at 0.026°C, which shows a 10-fold improvement in sensing accuracy compared to that of the traditional linear fitting model.

Effects of Pregnancy Prevention on Brucella abortus Shedding in American bison (Bison bison).

Products of parturition are the predominant source of Brucella abortus for transmission in bison (Bison bison). Our objective was to assess whether preventing pregnancy in Brucella-seropositive bison reduced B. abortus shedding. Brucella-seropositive and -seronegative bison from Yellowstone National Park, Wyoming, USA were used in a replicated experiment. Each of two replicates (rep1, rep2) included a group of seropositive females treated with a single dose of gonadotropin-releasing hormone-based immunocontraceptive (Treatment rep1, n=15; Treatment rep2, n=20) and an untreated group (Control rep1, n=14; Control rep2, n=16) housed separately. Seronegative sentinel females were placed in each group to monitor horizontal transmission. Seronegative males were co-mingled for breeding each year. Pregnant females were removed from treatment groups in the first year, but not thereafter. Each January-June we monitored for B. abortus shedding events-any parturition associated with culture-positive fluids or tissues. We analyzed probability of shedding events using a negative binomial generalized linear mixed model fit by maximum likelihood using Laplace approximation. Over 5 yr, we observed zero shedding events in Treatment rep1 vs. 12 in Control rep1. All five Control rep1 sentinels but zero (0/5) Treatment rep1 sentinels seroconverted. In the second replicate, Treatment rep2 had two shedding events over 3 yr and Control rep2 had five events over 2 yr. Sentinels in both Control rep2 (3/6) and Treatment rep2 (5/6) seroconverted by trial endpoint. Treatment rep1 showed a reduced shedding probability relative to Control rep1, Treatment rep2, and Control rep2 (log odds value -25.36 vs. -1.71, -1.39, and -0.23, respectively). Fixed effect predictor covariates, year and age, had no explanatory value. These data suggest that successful contraception of brucellosis-seropositive female bison prevents shedding of B. abortus by individual animals. However, contraceptive treatment may or may not sufficiently reduce disease transmission to reduce brucellosis prevalence in an affected herd.

Elevated tumor necrosis factor alpha and vascular endothelial growth factor in intermediate age-related macular degeneration and geographic atrophy.

Tumor necrosis factor alpha (TNF-α) is an inflammatory cytokine implicated in pathological changes to the retinal pigment epithelium that are similar to changes in geographic atrophy (GA), an advanced form of age related macular degeneration (AMD). TNF-α also modulates expression of other cytokines including vascular endothelial growth factor (VEGF), leading to choroidal atrophy in models of AMD. The purpose of this study was to investigate systemic TNF-α and VEGF in patients with GA and intermediate AMD (iAMD) compared to controls without AMD. We examined plasma levels of TNF-α and VEGF in patients with GA, iAMD, and controls without AMD from the University of Colorado AMD registry (2014 to 2021). Cases and controls were characterized by multimodal imaging. TNF-α and VEGF were measured via multiplex immunoassay and data were analyzed using a non-parametric rank based linear regression model fit to plasma biomarkers. There were 97 GA, 199 iAMD patients and 139 controls. TNF-α was significantly increased in GA (Median:9.9pg/ml, IQR:7.3-11.8) compared to iAMD (Median:7.4, IQR:5.3-9.1) and in both GA and iAMD compared to controls (Median:6.4, IQR:5.3-7.8), p<0.01 for all comparisons. VEGF was significantly increased in iAMD (Median:8.9, IQR:4.8-14.3) compared to controls (Median:7.7, IQR:4.6-11.1), p<0.01. There was a significant positive correlation between TNF-α and VEGF in GA (0.46, p<0.01), and iAMD (0.20, p=0.01) with no significant interaction between TNF-α and VEGF in any group. These findings suggest TNF-α and VEGF may contribute to systemic inflammatory processes associated with iAMD and GA. TNF-α and VEGF may function as systemic biomarkers for disease development.

Experimental and theoretical study on mechanical performance of Fe-SMA/steel single lap joints

The activated iron-based shape memory alloy (Fe-SMA) can introduce considerable prestress in the damaged zone of deteriorated steel structures through bonding technologies. In China, it has been successfully applied to several kilometer-span steel bridges, such as Sutong Bridge and Hangzhou Bay Bridge. The shear and interfacial performance of steel-to-Fe-SMA plates are crucial issues in bonding Fe-SMA to reinforce deteriorated steel structures, which remain unclear. This paper investigates the shear characteristics of Fe-SMA/steel single lap joints (SLJs), considering the effects of adhesive types, adhesive layer thicknesses and overlap lengths. The stress distribution and the interfacial shear stress at the overlap zone are obtained through experiments, and the damage evolution and load-transfer mechanisms of adhesive layers are analyzed. The experimental outcomes indicate that the maximum tensile stresses of Fe-SMA plates can reach 61 % of their ultimate strength. The failure modes and ultimate load of Fe-SMA/steel SLJs are affected by adhesive types. Interface failure occurs in CH 120 and PCMdur™-15 specimens, whereas interface, cohesive and mixing failure are formed in Sika 31 specimens. Due to the adverse effects of adhesive layer defects, out-of-plane bending moment, and damage accumulation of the adhesive layers, the distribution of surface strain and interfacial shear stress of all specimens are mainly at the effective overlap zone. For an efficient engineering design, the linear and polynomial fitting models of cohesive zone models with different adhesive types and adhesive layer thicknesses are proposed based on experiments. The experimental and theoretical achievements can provide design recommendations for deteriorated steel structures rehabilitated by bonding Fe-SMA.

Study of Linear Model Fitting and Distribution Analysis on Random Numbers Generated in Fortran

In this study, we employed Fortran 90 programming to generate pseudorandom numbers ranging from 0 to 1 at various sample sizes (n = 1000, 500, 250, 125, 63, 32, 16, 8). Subsequently, linear fitting models were applied to the generated data. Our findings indicated that the higher sample size of n=1000 yielded the least asymptotic standard error for both slope ‘a’ and intercept ‘b’ parameters in the linear equation. We observed a decrease in the sum of squares of residuals as the sample size (n) decreased, indicating that the linear model also provided a better fit to the data with smaller sample sizes. The consistent nature of the kernel density estimate plots suggests that as the sample size increases, the estimation becomes more precise and less affected by random noise or sampling variability, further enhancing the reliability of the estimated PDF.

Estimation and interpretation problems and solutions when using proportion covariates in linear regression models.

Proportion variables, also known as compositional data, are very common in ecology. Unfortunately, few scientists are aware of how compositional data, when used as covariates, can adversely impact statistical analysis. We describe here how proportion covariates result in multicollinearity and parameter identifiability problems. Using simulated data on bird species richness as a function of land use, we show how these problems manifest when fitting a wide range of models in R, both in a frequentist and Bayesian framework. In particular, we show that similar models can often generate substantially different parameter estimates, leading to very different conclusions. Dropping a covariate or the intercept from the model can solve the multicollinearity and parameter identifiability problems. Unfortunately, these solutions do not fix the inherent challenges associated with interpreting parameter estimates. To this end, we propose focusing the interpretation on the difference of slope parameters to avoid the inherent unidentifiability of individual parameters. We also propose conditional plots with two x-axes and marginal plots as visualization techniques that can help users better interpret their modeling results. We illustrate these problems and proposed solutions using empirical data from the North American Breeding Bird Survey. The practical and straightforward approaches suggested in this article will help the fitting of linear models and interpretation of its results when some of the covariates are proportions.

English Prosodic Focus Marking by Cantonese Trilingual Children With and Without Autism Spectrum Disorder.

The current study investigated English prosodic focus marking by autistic and typically developing (TD) Cantonese trilingual children, and examined the potential differences in this regard compared to native English-speaking children. Forty-eight participants were recruited with 16 speakers for each of the three groups (Cantonese-speaking autistic [CASD], Cantonese-speaking TD [CTD], and English-speaking TD [ETD] children), and prompt questions were designed to elicit desired focus type (i.e., broad, narrow, and contrastive focus). Mean duration, mean fundamental frequency (F0), F0 range, mean intensity, and F0 curves were used as the acoustic correlates for linear mixed-effects model fitting and functional data analyses in relation to groups and focus conditions (i.e., broad, narrow, and contrastive pre-, on-, and post-focus). The CTD group had post-focus compression (PFC) patterns via reducing mean duration, narrowing F0 range, and lowering mean F0, F0 curve, and mean intensity for words under both narrow and contrastive post-focus conditions, while the CASD group only had shortened mean duration and lowered F0 curves. However, neither the CTD group nor CASD group showed much of on-focus expansion (OFE) patterns. The ETD group marked OFE by increasing mean duration, mean F0, mean intensity, and higher F0 curve for words under on-focus conditions. The CTD group utilized more acoustic cues than the CASD group when it comes to PFC. The ETD group differed from the CASD and CTD groups in the use of OFE. Furthermore, both the CASD and CTD groups showed positive first language transfer in the use of duration and intensity and, potentially, successful acquisition in the use of F0 for prosodic focus marking. Meanwhile, the differences in the use of OFE between the Cantonese-speaking and English-speaking groups, not PFC, might indicate that Cantonese-speaking children acquire PFC prior to OFE.

Accurate correction model of blood potassium concentration in hemolytic specimens

Background and aimsThe results of blood potassium can be seriously affected by specimen hemolysis which may interfere with clinicians’ interpretation of test results. Redrawing blood and retesting may delay treatment time and it is not feasible for critically ill patients with difficulty in specimen collection. Therefore, it is significant to establish a mathematical model that can quickly correct the blood potassium concentration of hemolytic specimens. Materials and methodsThe residual blood samples from 107 patients at Peking University Third Hospital were collected to establish potassium correction model. Samples with different hemolysis indexes were obtained by ultrasonic crushing method. Blood potassium correction models of hemolysis specimens were established by linear regression and curve fitting using SPSS and MATLAB, respectively. In addition, blood samples from another 85 patients were used to verify the accuracy of the models and determine the optimal model. ResultsVariation of potassium (ΔK) was 0.003HI-0.03 (R2 = 0.9749) in linear regression model which had high correlation in ΔK and HI, and the correction formula was Kcorrection = Khemolysis-0.003 × HI + 0.03. Average rate of potassium change (αaverage) was 0.003 ± 0.0002 mmol/L in curve fitting model, and correction formula was Kcorrection = Khemolysis-0.003 × HI, and both men and women can use the same correction model. The accuracy of linear regression model was 96.5 %, and there was statistical difference between the verification results and the measured values (p < 0.05), while the accuracy of curve fitting model was 100 %, and there was no statistical difference between the verification results and the measured values (p = 0.552). The model was validated in an independent set of samples and all were within the TEa of 6 % and the accuracy of 100 %. ConclusionsBoth linear regression and curve fitting models of potassium correction had high accuracy, and can effectively correct the potassium concentration of hemolytic specimens, while the curve fitting model have superior accuracy.

Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data.

Crop improvement efforts have exploited new methods for modeling spatial trends using the arrangement of the experimental units in the field. These methods have shown improvement in predicting the genetic potential of evaluated genotypes. However, the use of these tools may be limited by the exposure and accessibility to these products. In addition, these new methodologies often require plant scientists to be familiar with the programming environment used to implement them; constraints that limit data analysis efficiency for decision-making. These challenges have led to the development of Mr.Bean, an accessible and user-friendly tool with a comprehensive graphical visualization interface. The application integrates descriptive analysis, measures of dispersion and centralization, linear mixed model fitting, multi-environment trial analysis, factor analytic models, and genomic analysis. All these capabilities are designed to help plant breeders and scientist working with agricultural field trials make informed decisions more quickly. Mr.Bean is available for download at https://github.com/AparicioJohan/MrBeanApp.