Best-fit Linear Model Research Articles

Quantitative Foveal Structural Metrics as Predictors of Visual Acuity in Human Albinism.

To assess the degree to which quantitative foveal structural measurements account for variation in best-corrected visual acuity (BCVA) in human albinism. BCVA was measured and spectral domain optical coherence tomography (SD-OCT) images were acquired for 74 individuals with albinism. Categorical foveal hypoplasia grades were assessed using the Leicester Grading System for Foveal Hypoplasia. Foveal anatomical specialization (foveal versus parafoveal value) was quantified for inner retinal layer (IRL) thickness, outer segment (OS) length, and outer nuclear layer (ONL) thickness. These metrics, participant sex, and age were used to build a multiple linear regression of BCVA. This combined linear model's predictive properties were compared to those of categorical foveal hypoplasia grading. The cohort included three participants with type 1a foveal hypoplasia, 23 participants with type 1b, 33 with type 2, ten with type 3, and five with type 4. BCVA ranged from 0.08 to 1.00 logMAR (mean ± SD: 0.53 ± 0.21). IRL ratio, OS ratio, and ONL ratio were measured in all participants and decreased with increasing severity of foveal hypoplasia. The best-fit combined linear model included all three quantitative metrics and participant age expressed as a binary variable (divided into 0-18years and 19years or older; adjusted R2 = 0.500). This model predicted BCVA more accurately than a categorical foveal hypoplasia model (adjusted R2 = 0.352). A quantitative model of foveal specialization accounts for more variance in BCVA in albinism than categorical foveal hypoplasia grading. Other factors, such as optical aberrations and eye movements, may account for the remaining unexplained variance.

Dynamic mode decomposition for data-driven analysis and reduced-order modeling of E × B plasmas: I. Extraction of spatiotemporally coherent patterns

The advent of data-driven/machine-learning based methods and the increase in data available from high-fidelity simulations and experiments has opened new pathways toward realizing reduced-order models for plasma systems that can aid in explaining the complex, multi-dimensional phenomena and enable forecasting and prediction of the systems’ behavior. In this two-part article, we evaluate the utility and the generalizability of the dynamic mode decomposition (DMD) algorithm for data-driven analysis and reduced-order modeling of plasma dynamics in cross-field E × B configurations. The DMD algorithm is an interpretable data-driven method that finds a best-fit linear model describing the time evolution of spatiotemporally coherent structures (patterns) in data. We have applied the DMD to extensive high-fidelity datasets generated using a particle-in-cell (PIC) code based on the cost-efficient reduced-order PIC scheme. In this part, we first provide an overview of the concept of DMD and its underpinning proper orthogonal and singular value decomposition methods. Two of the main DMD variants are next introduced. We then present and discuss the results of the DMD application in terms of the identification and extraction of the dominant spatiotemporal modes from high-fidelity data over a range of simulation conditions. We demonstrate that the DMD variant based on variable projection optimization (OPT-DMD) outperforms the basic DMD method in identification of the modes underlying the data, leading to notably more reliable reconstruction of the ground-truth. Furthermore, we show in multiple test cases that the discrete frequency spectrum of OPT-DMD-extracted modes is consistent with the temporal spectrum from the fast Fourier transform of the data. This observation implies that the OPT-DMD augments the conventional spectral analyses by being able to uniquely reveal the spatial structure of the dominant modes in the frequency spectra, thus, yielding more accessible, comprehensive information on the spatiotemporal characteristics of the plasma phenomena.

Spatiotemporal Variation and Pattern Analysis of Air Pollution and Its Correlation with NDVI in Nanjing City, China: A Landsat-Based Study

The rapid socio-economic development and urbanization in China have led to a decline in air quality. Therefore, the spatial and temporal distribution patterns of urban air pollution, as well as its formation mechanisms and influencing factors, have become important areas of research in atmospheric environment studies. This paper focuses on nine monitoring sites in Nanjing, where concentration data for six air pollutants and vegetation index data were collected from 2013 to 2021. The objective of this study is to investigate the changes in air pollutants and vegetation index over time and space, as well as their relationship with each other, and to assess the social and environmental impacts of air pollution. The findings reveal a spatial distribution pattern of air pollution in Nanjing that exhibits significant variability, with pollutant concentrations decreasing from the city center towards the surrounding areas. Notably, the main urban area has lower air quality compared to the peripheral regions. The results obtained from best-fit linear regression models and correlation heatmaps demonstrate a strong correlation (coefficient of determination, R2 > 0.5) between the normalized difference vegetation index (NDVI) and pollutants such as SO2, NO2, PM2.5, PM10, and O3 within a radial distance of 2 km from the air pollutant monitoring sites. These findings indicate that NDVI can be an effective indicator for assessing the distribution and concentrations of air pollutants. Negative correlations between NDVI and socio-economic indicators are observed under relatively consistent natural conditions, including climate and terrain. Therefore, the spatiotemporal distribution patterns of NDVI can provide valuable insights not only into socio-economic growth but also into the levels and locations of air pollution concentrations.

Tracing isotope precipitation patterns across Mexico

Mexico encompasses a large spectrum of landscapes with topographic, geographic, and climatic factors interacting in a complex ecohydrological setting. For decades, isotope hydrogeological tools have been applied in Mexico using short-term or seasonal local meteoric water lines as valid input functions. Yet, a systematic evaluation of meteoric isotope characteristics is still lacking. Here we report on the spatial and temporal isotope variations of 21 precipitation monitoring stations across Mexico. Our database includes 608 monthly samples collected from 2018 to 2021 over four regions (between 5 and 2,365 m asl): the Pacific coast, the Gulf of Mexico/Caribbean Sea region, and the Central and Northern plateaus. Precipitation δ18O seasonality from the dry (winter) to the wet season (summer) was characterized by a notable W-shaped variability. Monthly precipitation amounts and δ18O compositions exhibited poor to strong linear regressions (Adj. r2<0.01 to 0.75), with inverse (positive) relationships over the northern monsoon-affected region. Low d-excess (5.1 to 9.7‰) corresponded with greater terrestrial moisture contributions (20.5%) over the arid northern regions. Moisture inputs from the Gulf of Mexico/Caribbean Sea and the Pacific Ocean were associated with near-equilibrium or greater d-excess values (8.8 to 14.3‰), respectively. The best-fit linear models for δ18O (Adj. r2 = 0.85) and δ2H (Adj. r2 = 0.88) were determined for topographic and geographical predictors, resulting in an updated high-resolution precipitation isoscape (100 m2 grid) for Mexico. Orographic barriers (-2.10‰ in δ18O/km) coupled with the interaction of tropical cyclones and cold fronts, the evolution of the North American Monsoon system, and the passage of easterly trade winds play a remarkable role in controlling the spatial isotope rainfall variability. Our findings provide a robust baseline for ecohydrological, climatic, forensic, archeological, and paleoclimate studies in North America.

Pilot-wave dynamics: Using dynamic mode decomposition to characterize bifurcations, routes to chaos, and emergent statistics.

We develop a data-driven characterization of the pilot-wave hydrodynamic system in which a bouncing droplet self-propels along the surface of a vibrating bath. We consider drop motion in a confined one-dimensional geometry and apply the dynamic mode decomposition (DMD) in order to characterize the evolution of the wave field as the bath's vibrational acceleration is increased progressively. Dynamic mode decomposition provides a regression framework for adaptively learning a best-fit linear dynamics model over snapshots of spatiotemporal data. Thus, DMD reduces the complex nonlinear interactions between pilot waves and droplet to a low-dimensional linear superposition of DMD modes characterizing the wave field. In particular, it provides a low-dimensional characterization of the bifurcation structure of the pilot-wave physics, wherein the excitation and recruitment of additional modes in the linear superposition models the bifurcation sequence. This DMD characterization yields a fresh perspective on the bouncing-droplet problem that forges valuable new links with the mathematical machinery of quantum mechanics. Specifically, the analysis shows that as the vibrational acceleration is increased, the pilot-wave field undergoes a series of Hopf bifurcations that ultimately lead to a chaotic wave field. The established relation between the mean pilot-wave field and the droplet statistics allows us to characterize the evolution of the emergent statistics with increased vibrational forcing from the evolution of the pilot-wave field. We thus develop a numerical framework with the same basic structure as quantum mechanics, specifically a wave theory that predicts particle statistics.

Constraining the Size of the Circumgalactic Medium Using the Transverse Autocorrelation Function of C iv Absorbers in Paired Quasar Spectra

The circumgalactic medium (CGM) plays a vital role in the formation and evolution of galaxies, acting as a lifeline between galaxies and the surrounding intergalactic medium. In this study, we leverage a unique sample of quasar pairs to investigate the properties of the CGM with absorption line tomography. We present a new sample of medium-resolution Keck/ESI, Magellan/MagE, and VLT/XSHOOTER spectra of 29 quasar pairs at redshift 2 < z < 3. We supplement the sample with additional spectra of 32 pairs from the literature, creating a catalog of 61 quasar pairs with angular separations between 1.″7 and 132.″9 and projected physical separations (r ⊥) between 14 kpc and 887 kpc. We construct a catalog of 906 metal-line absorption doublets of C iv (λλ1548, 1550) with equivalent widths ranging from 6 m Å ≤ W r,1550 ≤ 2053 m Å. The best-fit linear model to the log-space equivalent width frequency distribution () of the sample yields coefficients of m = −1.44 ± 0.16 and b = −0.43 ± 0.16. To constrain the projected extent of C iv, we calculate the transverse autocorrelation function. The flattening of the autocorrelation function at low r ⊥ provides a lower limit for the coherence length of the metal enriched CGM—on the order of 200 h −1 comoving kpc. This physical size constraint allows us to refine our understanding of the metals in the CGM, where the extent of C iv in the CGM depends on gas flows, feedback, timescale of metal injection and mixing, and the mass of the host galaxies.

Bagging, optimized dynamic mode decomposition for robust, stable forecasting with spatial and temporal uncertainty quantification.

Dynamic mode decomposition (DMD) provides a regression framework for adaptively learning a best-fit linear dynamics model over snapshots of temporal, or spatio-temporal, data. A variety of regression techniques have been developed for producing the linear model approximation whose solutions are exponentials in time. For spatio-temporal data, DMD provides low-rank and interpretable models in the form of dominant modal structures along with their exponential/oscillatory behaviour in time. The majority of DMD algorithms, however, are prone to bias errors from noisy measurements of the dynamics, leading to poor model fits and unstable forecasting capabilities. The optimized DMD algorithm minimizes the model bias with a variable projection optimization, thus leading to stabilized forecasting capabilities. Here, the optimized DMD algorithm is improved by using statistical bagging methods whereby a single set of snapshots is used to produce an ensemble of optimized DMD models. The outputs of these models are averaged to produce a bagging, optimized dynamic mode decomposition (BOP-DMD). BOP-DMD improves performance by stabilizing and cross-validating the DMD model by ensembling; it also robustifies the model and provides both spatial and temporal uncertainty quantification (UQ). Thus, unlike currently available DMD algorithms, BOP-DMD provides a stable and robust model for probabilistic, or Bayesian, forecasting with comprehensive UQ metrics. This article is part of the theme issue 'Data-driven prediction in dynamical systems'.

1106-P: Real-World Confirmation of the Nonlinear Relationship between Glucose and Glycated Hemoglobin

Glycated hemoglobin (A1C) is a key biomarker for glycemic control in diabetes and has an important relationship with glucose. The objective of this study was to evaluate the relationship of real-world laboratory results for patient samples with both glucose and A1C. 213,698 paired glucose and A1C measurements from 709 laboratories in 39 countries were collected January to October 2021. Values within the measuring interval 70 to 400 mg/dL for glucose and 4% to 13% (NGSP) for A1C were analyzed. The pairs were analyzed in twenty equal-sized groups by composite rank order. The summed absolute relative differences (SARD) were compared between best-fit nonlinear and linear models. The group mean values presented a clear nonlinear relationship, with an SARD seven times lower than the best-fit linear model. The nonlinear Michaelis constant for glucose and glucose transporter 1 (GLUT1) was 4mg/dL, which agrees with literature values. The 95% prediction intervals at the diabetes diagnosis levels of 6.5% A1C and 126 mg/dL glucose were narrower for the nonlinear model. This model-based formula allows better prediction of the relationship of glucose and A1C which can lead to improved personalized clinical decisions. Figure: Nonlinear (left) and linear (right) best-fit models, N = 213,698. Each point represents the mean of approximately n=10,680 paired measurements. Dotted lines are 95% prediction intervals. Disclosure T.Dunn: Employee; Abbott. Y.Xu: Employee; Abbott Diabetes. S.Schneider: None. S.Gawel: Employee; Abbott Diagnostics. R.Gopinath: Employee; Abbott Diagnostics. M.Berman: None. A.P.Orzechowski: Employee; Abbott.

Development of a Third Trimester Contingent Prognostic Prediction Scheme for Suspected Early-Onset Pre-Eclampsia

Introduction: Short-term prediction of pre-eclampsia (PE) using soluble FMS-like tyrosine kinase-1 (sFlt-1)/ placental growth factor (PlGF) ratio has high false-positive rate. Therefore, we developed a prognostic prediction tool that predicts early-onset PE leading to delivery within 1 week on pregnancies with an sFlt-1/PlGF ratio above 38 and compared it with an analogous model based on sFlt-1/PlGF ratio and with the 655 sFlt-1/PlGF ratio cutoff. Methods: Cohort study of 363 singleton pregnancies with clinical suspicion of PE before 34 weeks of gestation, allowing repeated assessments (522). 213 samples with an sFlt-1/PlGF ratio above 38 were assessed to construct and identify the best-fit linear mixed model. N-terminal pro-B-type natriuretic peptide (NT-proBNP), sFlt-1 MoM, PlGF MoM, and sFlt-1/PlGF ratio combined with gestational age (GA) were assessed. Results: None of the pregnancies with an sFlt-1/PlGF ratio of 38 or below developed early-onset PE (309 samples from 240 pregnancies). Conversely, 47 women of 213 assessments (22.1%) with an sFlt-1/PlGF ratio above 38 developed the assessed outcome. The selected model included sFlt-1 MoM, NT-proBNP, and GA. Differences in area under the curve were observed between the selected model and the GA + sFlt-1/PlGF model (p = 0.04). At an sFlt-1/PlGF ratio cutoff of 655, detection rate was 31.9% (15/47), while the selected model detection was 55.3% (26/47) (p = 0.008). Discussion: Considering repeated assessments, the sFlt-1/PlGF ratio of 38 or below adequately ruled out early-onset PE, leading to delivery within 1 week. However, when sFlt-1/PlGF ratio is above 38, the prediction tool derived from linear mixed model based on GA, NT-proBNP, and sFlt-1 MoM, provided a better prognosis prediction than the sFlt-1/PlGF ratio.

Kilohertz quasi-periodic oscillations in neutron-star X-ray binaries: flattening of the lag spectrum with increasing luminosity

ABSTRACT We study the energy-dependent time lags and rms fractional amplitude of the kilohertz quasi-periodic oscillations (kHz QPOs) of a group of neutron-star low-mass X-ray binaries. We find that for the lower kHz QPO both the slope of the best-fitting linear model to the time-lag spectrum and the total rms amplitude integrated over the 2–25 keV energy band decrease exponentially with the luminosity of the source. For the upper kHz QPO, the slope of the time-lag spectrum is consistent with zero, while the total rms amplitude decreases exponentially with the luminosity of the source. We show that both the slope of the time-lag spectrum and the total rms amplitude of the lower kHz QPO are linearly correlated with a slope of ∼1. Finally, we discuss the mechanism that could be responsible for the radiative properties of the kHz QPOs, with the variability originating in a Comptonizing cloud or corona that is coupled to the innermost regions of the accretion disc, close to the neutron star.

Responses to Drought Stress Levels of Strawberry Grown in Greenhouse Conditions

This experimental study was carried out using the 'Camarosa' variety strawberry plants grown in pots in greenhouse conditions. One control and two drought levels were created by bringing the existing soil water content of the pot to the field capacity (I100-control) and using its 66% (I66-mild drought stress) and 33% (I33-severe drought stress) in irrigation. The experimental design of the randomized complete blocks design was applied in four replicates with 10 pots per replicate amounting to a total of 120 pots. In order to determine the plant response to the generated stress levels, stomatal conductivity (Sc, mmol m2 s-1), total chlorophyll content (SPAD, μmol m-2 s-1), chlorophyll concentration (CC, mg g-1), leaf surface temperature (LST, °C), photosynthetic quantum yield (Qy, %), photosynthetically active radiation (PAR, W s-1), leaf water content (LWC-%), yield (g pot-1), leaf area (LA), leaf number (LN), and crop water use were measured in three plants per each replicate. 1.89, 3.62, and 5.82 L pot-1 were applied to I33, I66, and I100 as irrigation water, while 2.59, 3.92, and 5.59 L pot-1 were crop water used from them, respectively. Average strawberry yield varied between 80 and 400 g pot-1. The increased drought stress decreased Sc, SPAD, CC, Qy, PAR, LWC, LA, and LN but increased LST. All the measured variables had significant relationships with irrigation water and crop water use. Yield had a linear relationship with LST and LN and a polynomial relationship with Sc, SPAD, CC, Qy, PAR, LWC, and LA. Water and light use efficiencies were quantified and predicted through the best-fit (non-) linear models.

Abstract 15580: The Impact of Sex and Gender-related Factors on Length-of-stay Following NSTEMI: A Multicountry Analysis

Introduction: Gender refers to psycho-socio-cultural characteristics typically ascribed to men, women and gender-diverse individuals and has been shown to be associated with adverse clinical outcomes in AMI independent of sex. Substantial heterogeneity in hospital length of stay exists among patients admitted with NSTEMI. Whether sex and gender-based differences contribute to length-of-stay (LOS) among patients with NSTEMI remains unknown. Methods: To examine the relationship between sex, gender-related factors and LOS in adults hospitalized for NSTEMI, data from the GENESIS-PRAXY (n=1,210, Canada, U.S. and Switzerland), EVA (n=430, Italy) and VIRGO (n=3,572, U.S., Spain and Australia) studies of adults hospitalized for AMI were combined and analyzed. A best-fit linear regression model was selected through incremental analysis by stepwise addition of gender-related variables thought to be different in either impact or distribution between men and women. Results: Among the overall cohort (n=5,212), 2,218 participants with a diagnosis of NSTEMI were included in the final cohort (66% women, mean age 48.5 years, 67.8% U.S.). Half of the patients had a LOS of longer than 4 days (n=1,124) and were more likely to be white and have a clustering of cardiac risk factors in comparison to those with shorter LOS. No association between sex and LOS was observed in the bivariate analysis (p=0.87). In the multivariable model adjusted for sex, age, country of hospitalization, level of education, marital status, employment status, income, and social support, age (0.062 days/year, p=0.0002), being employed (-0.63 days in workers, p=0.01) and the treatment country relative to Canada (Italy=4.1 days; Spain=1.7 days; and the U.S.=-1.0 days, all p-value&lt;0.001) were significant predictors of LOS. Conclusions: Employed individuals are more likely to experience a shorter LOS following NSTEMI. Variation in LOS exists across different countries and is likely due to institutional policy, resource allocation, and differences in cultural and psychosocial influences.

A semi-analytical solution to the maximum-likelihood fit of Poisson data to a linear model using the Cash statistic

The Cash statistic, also known as the statistic, is commonly used for the analysis of low-count Poisson data, including data with null counts for certain values of the independent variable. The use of this statistic is especially attractive for low-count data that cannot be combined, or re-binned, without loss of resolution. This paper presents a new maximum-likelihood solution for the best-fit parameters of a linear model using the Poisson-based Cash statistic. The solution presented in this paper provides a new and simple method to measure the best-fit parameters of a linear model for any Poisson-based data, including data with null counts. In particular, the method enforces the requirement that the best-fit linear model be non-negative throughout the support of the independent variable. The method is summarized in a simple algorithm to fit Poisson counting data of any size and counting rate with a linear model, by-passing entirely the use of the traditional statistic.

Walking-speed estimation using a single inertial measurement unit for the older adults.

BackgroundAlthough walking speed is associated with important clinical outcomes and designated as the sixth vital sign of the elderly, few walking-speed estimation algorithms using an inertial measurement unit (IMU) have been derived and tested in the older adults, especially in the elderly with slow speed. We aimed to develop a walking-speed estimation algorithm for older adults based on an IMU.MethodsWe used data from 659 of 785 elderly enrolled from the cohort study. We measured gait using an IMU attached on the lower back while participants walked around a 28 m long round walkway thrice at comfortable paces. Best-fit linear regression models were developed using selected demographic, anthropometric, and IMU features to estimate the walking speed. The accuracy of the algorithm was verified using mean absolute error (MAE) and root mean square error (RMSE) in an independent validation set. Additionally, we verified concurrent validity with GAITRite using intraclass correlation coefficients (ICCs).ResultsThe proposed algorithm incorporates the age, sex, foot length, vertical displacement, cadence, and step-time variability obtained from an IMU sensor. It exhibited high estimation accuracy for the walking speed of the elderly and remarkable concurrent validity compared to the GAITRite (MAE = 4.70%, RMSE = 6.81 𝑐𝑚/𝑠, concurrent validity (ICC (3,1)) = 0.937). Moreover, it achieved high estimation accuracy even for slow walking by applying a slow-speed-specific regression model sequentially after estimation by a general regression model. The accuracy was higher than those obtained with models based on the human gait model with or without calibration to fit the population.ConclusionsThe developed inertial-sensor-based walking-speed estimation algorithm can accurately estimate the walking speed of older adults.

Assessing matrix quality by Raman spectroscopy helps predict fracture toughness of human cortical bone

Developing clinical tools that assess bone matrix quality could improve the assessment of a person’s fracture risk. To determine whether Raman spectroscopy (RS) has such potential, we acquired Raman spectra from human cortical bone using microscope- and fiber optic probe-based Raman systems and tested whether correlations between RS and fracture toughness properties were statistically significant. Calculated directly from intensities at wavenumbers identified by second derivative analysis, Amide I sub-peak ratio I1670/I1640, not I1670/I1690, was negatively correlated with Kinit (N = 58; R2 = 32.4%) and J-integral (R2 = 47.4%) when assessed by Raman micro-spectroscopy. Area ratios (A1670/A1690) determined from sub-band fitting did not correlate with fracture toughness. There were fewer correlations between RS and fracture toughness when spectra were acquired by probe RS. Nonetheless, the I1670/I1640 sub-peak ratio again negatively correlated with Kinit (N = 56; R2 = 25.6%) and J-integral (R2 = 39.0%). In best-fit general linear models, I1670/I1640, age, and volumetric bone mineral density explained 50.2% (microscope) and 49.4% (probe) of the variance in Kinit. I1670/I1640 and v1PO4/Amide I (microscope) or just I1670/I1640 (probe) were negative predictors of J-integral (adjusted-R2 = 54.9% or 37.9%, respectively). While Raman-derived matrix properties appear useful to the assessment of fracture resistance of bone, the acquisition strategy to resolve the Amide I band needs to be identified.

Spatial and seasonal variations of radiocesium concentrations in an algae-grazing annual fish, ayu Plecoglossus altivelis collected from Fukushima Prefecture in 2014

To elucidate spatial and seasonal variations of radiocesium (134Cs and 137Cs) concentrations in ayu Plecoglossus altivelis, amphidromous fish samples were collected both from shallow coastal waters in March 2014 and from seven rivers in Fukushima Prefecture during May–October 2014. Of the rivers, two were located within the designated evacuation zone. After standard length (SL) and body weight measurements, fish were dissected to two parts: body without head and internal organs, BD; head and internal organs, HI. Radiocesium concentrations in these parts were measured using germanium semiconductor detectors. Those in the whole body (WB) were reconstructed using their concentrations and weights. The 137Cs concentrations in BD in oceanic samples (4.3–6.7 cm SL) were below the detection limit, whereas those in riverine samples (6.4–18.8 cm SL) were 0.719–902 Bq kg−1-wet. Statistical analyses indicated a best-fit linear mixed-effects model including “SL” and “sampling site” as explanatory variables. Significant differences in 137Cs concentrations in BD among rivers were detected. Our results indicate that 137Cs concentrations in BD of riverine ayu, which showed lower but site-specifically constant values compared with those in HI or WB, are useful as a good indicator of radiocesium contamination of the respective rivers.

R&amp;D Spending, Knowledge Capital, and Agricultural Productivity Growth: A Bayesian Approach

AbstractIn this article, we employ Bayesian hierarchical modeling to better capture and communicate the uncertainties surrounding the transformation of U.S. public agricultural research and development (R&amp;D) expenditures to knowledge capital stocks as well as its contribution to the historic growth of U.S. agricultural total factor productivity. Compared to studies based on classical statistics, analytical methods grounded in Bayesian inference explicitly incorporate existing information and permit revision of our knowledge regarding the distribution of the unknown model parameters as additional information becomes available. Bayesian hierarchical modeling is particularly useful in statistically estimating the underlying parameters of the R&amp;D lag weight structure, as well as the R&amp;D knowledge stocks given observed data on agricultural productivity and R&amp;D expenditures. Our results show a significant level of uncertainty on the R&amp;D lag weight structure, indicating that published assumptions about the R&amp;D lag structure can now be tested and validated against available data. Estimating the R&amp;D lag weights and knowledge stocks also make a large difference in the uncertainties surrounding economic returns from R&amp;D investments. Indeed, our results show that the best‐fit linear model yields slightly higher mean returns to R&amp;D spending relative to the log model results and have significantly less uncertainty. This suggests that marginal returns to U.S. public agricultural research spending might have remained relatively constant despite a century of growth in expenditure. Furthermore, we find that such investments could take a longer time to bear fruit than previously realized.

Causal relationship between biodiversity of insect population and agro-management in organic and conventional apple orchard

Organic management of fruit orchards may increase biodiversity and therefore contributes to achieving an ecologically balanced and productive agroecosystem. In 2013–2015, using a standard methodology for field monitoring, our study investigated and described dynamics of selected insect indicator taxa in the soil, on orchard surface and apple trees in an organic apple orchard and a reference conventional orchard in the region of Plovdiv, Bulgaria. Aiming to determine the impact of agro-management on biodiversity, our study revealed statistically significant correlations between biodiversity (i.e., as indices of the diversity of Shannon (entropy) and Simpson (1-D)) and agro-management practices (i.e., as an agricultural intensification index (AI index)). We found that density and diversity of insect indicator taxa were high in organic soil and in the conventional soil, which was attributed to above-the-norms rainfall in 2014 and 2015 and agro-management practices such as mulching and organic fertilization. The cubic regression models showed positive correlations between the AI index and biodiversity indices of indicator taxa in organic soil (R 2 = 0.489 to 0.497) and on orchard surface (grassed inter-rows) (R 2 = 0.399 to 0.419). On organic trees, changes in population dynamics of beneficial insect taxa followed the changes of pest insect taxa and were related to food availability and climate conditions. Here, the best-fit linear regression models signified that ecological intensification through organic practices here expressed as high-AI index leads to a high diversity (i.e., high indices of Shannon and Simpson) of key beneficial insect taxa such as Coccinellidae, Chrysopidae, and Cantharidae which keeps the pest population below economic threshold levels. Farmers, therefore, should target practices leading to higher density and diversity of beneficial added by measures such as pheromone mating-disruption dispensers and selective bio-pesticides. Our study presents an example of how can biodiversity be assessed in such complex agro-ecological system as orchards are. However, we suggest re-designing the AI index to reflect important factors such as agroecological conditions (e.g., variable climate, soil fertility) and agro-management practices (e.g., time of mowing, irrigation regime, and type of pesticides and their application).

Modeling and mapping of solar radiation using geostatistical analysis methods in Iran

Because of economic and technical limitations, measuring solar energy received at ground level (R s ) isn’t possible in all parts of the country, and in only 12% of synoptic stations is this parameter measured and recorded. Thus, it should be estimated and modeled spatially based on other climatic variables using mathematical methods. In this research, many attempts have been made to introduce an air temperature-based model for Rs estimation, and then, based on the output of the mentioned models, several geostatistical methods have been tested, and finally an elegant spatial model is proposed for (Rs) zoning in Iran. In this regard, the relationships between the measured amounts of monthly solar radiation and other climatic parameters, such as a monthly average, maximum and minimum temperature, precipitation, relative humidity, and the number of sunny hours during the period 1970–2010, are examined and modeled. It was revealed that based on the linear relationship between the monthly average air temperatures and solar radiation values recorded in each of the stations, that the best-fit linear model, with R 2 = 0.822, MAE = 1.81, RMSE = 2.51%, and MAPE = 10.08, can be introduced for Rs estimation. Then, using the outputs of the proposed model, the amounts of (R s ) are estimated in another 171 meteorological stations (a total of 192 stations), and eight geostatistical methods (IDW, GPI, RBF, LPI, OK, SK, UK, and EBK) were investigated for zoning. Comparing the resulting variograms showed that in addition to proof of spatial correlation between solar radiation data, they can be applied for modeling changes in various directions. Analyzing the ratio of the nugget effect on the roof of the variograms showed that the Gaussian model with the lowest ratio (Co/Co + C = 0.883) and (R 2 = 0.972), could model the highest correlation between the data and, therefore, it was used for data interpolation. To select the best geostatistical model, R2, MAE, and RMSE were used. On this basis, it was found that the RBF method with R 2 = 0.904, MAE = 3.02, RMSE = 0.39% is the most effective. Also, the IDW method with R 2 = 0.90, MAE = 3.08, RMSE = 0.391%, compared to other methods is the most effective. In addition, for data validation, correlations between observed and estimated values of solar radiation were studied and found R 2 = 0.86.

Evaluation of Masticatory Stimulation Effect on the Maxillary Transversal Growth in Ectodermal Dysplasia Children.

AimsSevere oligodontia is one of the most important symptoms in children with hypohidrotic ectodermal dysplasia (HED). The growth of the maxilla is a key consideration in restoring their mouth. The aim of this study was to evaluate the transversal maxillary sutural growth, after passive masticatory stimulation, in HED children. We also thought to assess the efficiency and functional outcome of the proposed propriocep-tive passive expansion (PPE) prosthetic device.Materials and methodsWe studied 13 children (age 6-11 years) suffering from HED with severe oligodontia. Their maxilla was restored by a PPE device formed from two parts and joined by a passive slide system. Distance between the two parts was noted at the anterior and posterior regions at each control visit over an average of 23 months. We also conducted and filled a satisfaction questionnaire over the same period.We tested the hypothesis that the posterior expansion is greater than the anterior expansion (one-tailed Student’s t-test with p-value <0.05). Best-fit linear and quadratic models were used to explore the relationship between age, duration of observation, and the rate of growth.ResultsThe average opening of the device was 2.27 mm in the anterior region and 2.96 mm in the posterior region. The questionnaire response was positive for all children. There are no significant linear or quadratic relationships between the data at the 5% significance level. The posterior expansion is greater than the anterior expansion at the 5% significance level (p-value 0.000394).LimitationsFurther studies are mandatory to assess the reliability of our particular intervention and treatment modalities for these cases.ConclusionThe PPE device, we propose, assures function and esthetics in the long- term. It enhances stimulation by a passive way that leads to physiological growth of the palatal suture.Clinical significanceUsing this PPE device to restore the maxilla in children with HED promotes physiological growth. The passive nature of this prosthesis helps by eliminating the need for any changes or replacement over time.How to cite this articleSfeir E, Nahass MG, Mourad A. Evaluation of Masticatory Stimulation Effect on the Maxillary Transversal Growth in Ectodermal Dysplasia Children. Int J Clin Pediatr Dent 2017;10(1):55-61.