Nonhomogeneous Variance Research Articles

Influence of Head Circumference on the Accuracy of Facial Scanning: An In vitro Study

ObjectivesThis study aimed to investigate the impact of head circumference on the accuracy of three-dimensional (3D) facial scans, focusing on trueness and precision across three mannequin heads of different sizes. Material and methodsThree 3D-printed mannequin heads with circumferences of 30, 50, and 65 cm were used. Ten facial landmarks were identified to measure seven interlandmark distances and two angles. Direct anthropometric measurement, serving as the reference value, was taken using a digital vernier calliper for linear distance, and angle was calculated using the law of cosines. Each head was scanned six times using two systems: a dual-structured light facial scanner (iTom) and a stereophotogrammetry system (3dMD). Digital measurements were analysed using Meshlab and Blender for distances and angles, respectively. Trueness values were determined by comparing measurements to reference measurements, while precision values were derived from the variability among the six scans. Statistical analysis was performed using the Kruskal–Wallis test due to nonhomogeneous variances, followed by Bonferroni correction for pairwise group comparisons. ResultsFor each scanning system, overall deviations in trueness and precision significantly increased with head circumference. Five of the seven distances and one angle showed significant compromises in trueness with larger head circumferences. Most measurements exhibited significant precision decreasing due to head circumference changes, except for N-Pn and Pn-Sn. Additionally, 3dMD displayed higher overall trueness compared to iTom, with five out of seven linear measurements and one angular measurement showing better results. ConclusionHead circumference significantly affects both trueness and precision of 3D facial scans for both technologies, suggesting that facial imaging should be used with caution for larger faces. Selecting an appropriate scanning system, such as 3dMD, can help mitigate the negative effects of scanning larger objects.

Association of Body Mass Index and Central Obesity with Spinopelvic Alignment Parameters in a Chinese Population: A Prospective Study

The purpose of this study was to explore the impact of central obesity on spinal sagittal balance in adults aged 18 and older by examining correlations between waist circumference (WC) and abdominal circumference (AC) and spinopelvic alignment parameters. This prospective cohort study included 350 adults aged 18 and older. Participants underwent whole-body biplanar radiography using the EOS imaging system. Spinal and pelvic parameters were measured and correlated with body mass index, WC, and AC. Statistical analyses included one-way analysis of variance, Wilcoxon rank-sum tests for data with nonhomogeneous variances, and chi-squared tests for categorical data. Intra-rater and inter-rater reliability were assessed using intraclass correlation coefficients, with subsequent analyses to explore correlations between body measurements and spinal parameters. The study found significant correlations between increased WC and AC and changes in spinopelvic parameters. However, obesity did not uniformly influence all sagittal alignment parameters. Significant variations in spinal measurements indicate that central obesity plays a role in altering spinal stability and alignment. The findings highlight the impact of central obesity on spinal alignment and emphasize the importance of considering central obesity in clinical assessments of spinal pathologies. Further research is essential to better understand the relationship between obesity, spinal sagittal balance, and related health conditions.

Fragility Analysis for Subway Station Using Artificial Neural Network

ABSTRACT To avoid the limitations of the assumptions for the traditional probabilistic seismic demand model (PSDM), this study proposed a novel PSDM for conducting fragility analysis of subway stations based on artificial neural network (ANN). The proposed ANN-based PSDM consists of an ANN-based trend model and a probabilistic-neural-network (PNN)-based error model. A two-story and three-span subway station in Shanghai was taken as a case. The results show that compared with the traditional PSDM, the proposed ANN-based PSDM can better predict the seismic responses of structures and describes the nonhomogeneous variance of residuals of the predicted seismic responses.

Gadolinium Clearance in the First 5 Weeks After Repeated Intravenous Administration of Gadoteridol, Gadoterate Meglumine, and Gadobutrol to rats.

BackgroundStudies of gadolinium (Gd) clearance from animals in the first weeks after administration of gadolinium‐based contrast agents (GBCAs) have previously looked at solitary timepoints only. However, this does not give information on differences between GBCAs and between organs in terms of Gd elimination kinetics.PurposeTo compare Gd levels in rat cerebellum, cerebrum, skin, and blood at 1, 2, 3, and 5 weeks after repeated administration of macrocyclic GBCAs.Study TypeProspective.Animal ModelOne hundred eighty male Sprague–Dawley rats randomized to three groups (n = 60/group), received intravenous administrations of gadoteridol, gadoterate meglumine, or gadobutrol (0.6 mmol/kg for each) four times/week for 5 consecutive weeks. Rats were sacrificed after washout periods of 1, 2, 3, or 5 weeks.Field Strength/SequenceNot applicable.AssessmentCerebellum, cerebrum, skin, and blood were harvested for Gd determination by inductively coupled plasma‐mass spectrometry (15 animals/group/all timepoints).Statistical TestsAnova and Dunnett's test (data with homogeneous variances and normal distribution). Kruskal–Wallis and Wilcoxon's rank sum tests (data showing nonhomogeneous variances or a non‐normal distribution, significance levels: P < 0.05, P < 0.01, and P < 0.001).ResultsGd levels in cerebellum, cerebrum, and skin were significantly lower after gadoteridol than after gadoterate and gadobutrol at all timepoints. Mean cerebellum Gd concentrations after gadoteridol, gadoterate, and gadobutrol decreased from 0.693, 0.878, and 1.011 nmol Gd/g at 1 week to 0.144, 0.282, and 0.297 nmol Gd/g at 5 weeks after injection. Similar findings were noted for cerebrum and skin. Conversely, significantly higher Gd levels were noted in blood after gadoteridol compared to gadobutrol at 1, 2, and 3 weeks and compared to gadoterate at all timepoints.Data ConclusionGadoteridol is eliminated more rapidly from rat cerebellum, cerebrum, and skin compared to gadoterate and gadobutrol in the first 5 weeks after administration, resulting in lower levels of retained Gd in these tissues.Evidence Level1Technical EfficacyStage 5

HOW TO ESTIMATE BLACK WATTLE ABOVEGROUND BIOMASS FROM HETEROSCEDASTIC DATA?

This study developed a system of equations for predicting total aboveground and component biomass in black wattle trees. A total of 140 black wattle trees at age 10 years were measured regarding their diameter at 1.30 m height above the ground (d), total tree height (h), basic wood density (branches and stem), and biomass (stem, crown, and aboveground). We evaluated the performance of linear and nonlinear allometric models by comparing the statistics of R2adj., RRMSE%, and BIC. Nonlinear models performed better when predicting crown biomass (using only d as an independent variable), and stem and aboveground biomass (using d and h as independent variables). Adding basic density did not significantly improve biomass modeling. The residuals had non-homogeneous variance; thus, the fitted equations were weighted, with weights derived from a function containing the same independent variables of the fitted biomass function. Subsequently, we used a simultaneous set of equations to ensure that the sum of each component's estimated biomass values was equal to the total biomass values. Simultaneous fitting improved the performance of the equations by guaranteeing the components' additivity, and weighted regression allowed to stabilize error variance, ensuring the homoscedasticity of the residuals.

The Effect of Problem Centered Learning (PCL) Approach to Critical Thinking Skills of Class XI MAS Tanah Datar District

The mathematics learning outcomes of MAS students in Tanah Datar Regency are still relatively low. This is because students who enter private schools are students who do not pass the selection in public schools, besides learning is still centered on the teacher and the lack of critical thinking skills of students is one of the obstacles that affect the low student learning outcomes. One of the efforts is made to overcome this problem is to apply the PCL approach to learning. This study aims to determine the effect of the PCL approach on students' critical thinking skills, especially in line and series material. The study was conducted on two classes which were used as the experimental class and the control class. In the experimental class the treatment of learning was given by applying the PCL approach while the control class was given treatment with conventional learning. The research data was obtained from the results of the initial ability test (pre-test) given before the sequences and series subject matter was started and the final test (post-test) was given after the PCL treatment was carried out, while the data analysis was performed using non-parametric tests because the data was not normally distributed and has a non-homogeneous variance. The results of data analysis showed that there was the influence of the PCL approach to critical thinking skills of students class XI of MAS Salimpaung. The critical thinking of students with PCL approach was better than the critical thinking of students taught with conventional learning in class XI MAS Salimpaung.

Distributional regression forests for probabilistic precipitation forecasting in complex terrain

To obtain a probabilistic model for a dependent variable based on some set of explanatory variables, a distributional approach is often adopted where the parameters of the distribution are linked to regressors. In many classical models this only captures the location of the distribution but over the last decade there has been increasing interest in distributional regression approaches modeling all parameters including location, scale and shape. Notably, so-called nonhomogeneous Gaussian regression (NGR) models both mean and variance of a Gaussian response and is particularly popular in weather forecasting. Moreover, generalized additive models for location, scale and shape (GAMLSS) provide a framework where each distribution parameter is modeled separately capturing smooth linear or nonlinear effects. However, when variable selection is required and/or there are nonsmooth dependencies or interactions (especially unknown or of high-order), it is challenging to establish a good GAMLSS. A natural alternative in these situations would be the application of regression trees or random forests but, so far, no general distributional framework is available for these. Therefore, a framework for distributional regression trees and forests is proposed that blends regression trees and random forests with classical distributions from the GAMLSS framework as well as their censored or truncated counterparts. To illustrate these novel approaches in practice, they are employed to obtain probabilistic precipitation forecasts at numerous sites in a mountainous region (Tyrol, Austria) based on a large number of numerical weather prediction quantities. It is shown that the novel distributional regression forests automatically select variables and interactions, performing on par or often even better than GAMLSS specified either through prior meteorological knowledge or a computationally more demanding boosting approach.

Harmonization of chest CT scans for different doses and reconstruction methods.

To develop and validate a computed tomography (CT) harmonization technique by combining noise-stabilization and autocalibration methodologies to provide reliable densitometry measurements in heterogeneous acquisition protocols. We propose to reduce the effects of spatially variant noise such as nonuniform patterns of noise and biases. The method combines the statistical characterization of the signal-to-noise relationship in the CT image intensities, which allows us to estimate both the signal and spatially variant variance of noise, with an autocalibration technique that reduces the nonuniform biases caused by noise and reconstruction techniques. The method is firstly validated with anthropomorphic synthetic images that simulate CT acquisitions with variable scanning parameters: different dosage, nonhomogeneous variance of noise, and various reconstruction methods. We finally evaluate these effects and the ability of our method to provide consistent densitometric measurements in a cohort of clinical chest CT scans from two vendors (Siemens, n=54 subjects; and GE, n=50 subjects) acquired with several reconstruction algorithms (filtered back-projection and iterative reconstructions) with high-dose and low-dose protocols. The harmonization reduces the effect of nonhomogeneous noise without compromising the resolution of the images (25% RMSE reduction in both clinical datasets). An analysis through hierarchical linear models showed that the average biases induced by differences in dosage and reconstruction methods are also reduced up to 74.20%, enabling comparable results between high-dose and low-dose reconstructions. We also assessed the statistical similarity between acquisitions obtaining increases of up to 30% points and showing that the low-dose vs high-dose comparisons of harmonized data obtain similar and even higher similarity than the observed for high-dose vs high-dose comparisons of nonharmonized data. The proposed harmonization technique allows to compare measures of low-dose with high-dose acquisitions without using a specific reconstruction as a reference. Since the harmonization does not require a precalibration with a phantom, it can be applied to retrospective studies. This approach might be suitable for multicenter trials for which a reference reconstruction is not feasible or hard to define due to differences in vendors, models, and reconstruction techniques.

Interval Parameter Estimation of Quantile Regression Using Bca-Bootstrap Approach with Application to Open Unemployment Rate Study

Parameter estimation in the linear regression model using ordinary least square (OLS) method is less precise to analyze data containing outliers. It is because outliers can cause unstable parameter estimate. In addition, the existence of outliers causes residuals to be larger so that the residual’s variance is not constant (heteroscedasticity). One model that is able to overcome the effect of outliers is quantile regression because it can accommodate the non-homogeneous variances in modeling. In this study, the confidence interval of the parameter estimate in the quantile regression model was obtained, i.e., the Bias-Corrected and accelerated (BCa) bootstrap method. The proposed method was applied in modeling the open unemployment rate in Indonesia in 2017. The quantile value used in this study is quantile 0.05, 0.5, and 0.95 with 1500 resampling in BCa-bootstrap approach. The empirical result shows that the best quantile regression model is obtained at the value of quantile 0.95 which has a Pseudo R2 value is 60.45 percent. The model at quantile 0.95 shows that the percentage of youth, economic growth rate, and labor force participation rate have a significant effect on the open unemployment rate in Indonesia.

Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data

Recent developments in remote sensing (RS) technology have made several sources of auxiliary data available to support forest inventories. Thus, a pertinent question is how different sources of RS data should be combined with field data to make inventories cost-efficient. Hierarchical model-based estimation has been proposed as a promising way of combining: (i) wall-to-wall optical data that are only weakly correlated with forest structure; (ii) a discontinuous sample of active RS data that are more strongly correlated with structure; and (iii) a sparse sample of field data. Model predictions based on the strongly correlated RS data source are used for estimating a model linking the target quantity with weakly correlated wall-to-wall RS data. Basing the inference on the latter model, uncertainties due to both modeling steps must be accounted for to obtain reliable variance estimates of estimated population parameters, such as totals or means. Here, we generalize previously existing estimators for hierarchical model-based estimation to cases with non-homogeneous error variance and cases with correlated errors, for example due to clustered sample data. This is an important generalization to take into account data from practical surveys. We apply the new estimation framework to case studies that mimic the data that will be available from the Global Ecosystem Dynamics Investigation (GEDI) mission and compare the proposed estimation framework with alternative methods. Aboveground biomass was the variable of interest, Landsat data were available wall-to-wall, and sample RS data were obtained from an airborne LiDAR campaign that produced simulated GEDI waveforms. The results show that generalized hierarchical model-based estimation has potential to yield more precise estimates than approaches utilizing only one source of RS data, such as conventional model-based and hybrid inferential approaches.

Quantile Regression Neural Network Model For Forecasting Consumer Price Index In Indonesia

The main purpose of time series analysis is to obtain the forecasting result from an observation for future values. Quantile Regression Neural Network is a statistical method that can model data with non-homogeneous variance with artificial neural network approach that can capture nonlinear patterns in the data. Real data that allegedly have such characteristics is Consumer Price Index (CPI). CPI forecasting is important to assess price changes associated with cost of living as well as identifying periods of inflation or deflation. The purpose of this research is to compare several method of forecasting CPI in Indonesia. The data used in this study during January 2007 until April 2018 period. QRNN method will be compared with Neural Network with RMSE evaluation criteria. The result is QRNN is the best method for forecasting CPI with RMSE 0.95.

Quantile Regression Neural Network for Forecasting Inflow and Outflow in Yogyakarta

Quantile Regression Neural Network (QRNN) is a hybrid method that be developed based on quantile regression (QR) that can model data with non-homogeneous variance and neural network (NN) approach that can capture nonlinear patterns in the data. One example of real data that supposedly have such characteristics is the inflow and outflow of currency, where the inflow is the amount of money flow coming from banks and the public to Bank Indonesia (BI) while outflow is the flow of money out of BI to the banks and community. The data used in this research are inflow and outflow in Yogyakarta as many as 14 notes during January 2003 until December 2016 period. This study aims to forecast inflow and outflow in Yogyakarta using QRNN method and compare the results with individual method, i.e. ARIMAX-GARCH and NN. Based on RMSE and MdAE evaluation criteria, the results show that the best model for inflow of Rp100,000, RP20,000 Rp10,000, and Rp5,000 are ARIMAX, while for the inflow Rp50,000, Rp2,000, Rp1,000, and outflow Rp100,000, Rp50,000, Rp20,000, Rp10,000, Rp5,000, Rp2,000, and Rp1,000 are QRNN. In general, the QRNN method yields better forecasting results and can capture well the effects of calendar variations on the data.

Medication safety knowledge, attitudes and practices among community pharmacists in Lebanon

Background: The effectiveness of a national post-marketing surveillance program depends directly on the active participation of all health professionals. There is no current comprehensive and active pharmacovigilance program available in Lebanon.Objectives: To assess the knowledge, attitudes, and practices (KAP) among community pharmacists in Lebanon with respect to potential pharmacovigilance and adverse-drug-reaction reporting in Lebanon.Methods: A cross-sectional descriptive study, using a self-administered KAP questionnaire and conducted between March and July 2016, included 1857 pharmacists practicing in community settings. Statistical analysis included χ2 test for dichotomous or multinomial qualitative variables, and Wilcoxon test for quantitative variables with non-homogeneous variances or non-normal distribution.Results: The majority of responders had good knowledge concerning the concept and purpose of pharmacovigilance as well as adverse drug reactions (how to report these/the importance of reporting adverse events/the definition of an adverse event and pharmacovigilance). Concerning community pharmacists’ attitudes and practice towards pharmacovigilance, the majority described having a positive attitude towards their role in adverse drug reaction reporting and this activity was even seen as one of their core duties. The questionnaire revealed a lack of practice and training regarding pharmacovigilance. Nonetheless, the pharmacists agreed on the Order of Pharmacists in Lebanon and the Ministry of Health’s role in promoting this practice and helping them be more involved in reporting adverse drug reactions (ADRs). The pharmacists thought that they are well positioned regarding patient-safety practice in their pharmacies and the results were not statistically different between pharmacy employers and employees.Conclusion: Lebanese pharmacists have the required knowledge and positive attitude to start reporting ADRs, were aware of ADRs occurring with various medicines post-marketing, yet were currently not able to disseminate this information widely or to record it centrally, emphasizing the importance of establishing a national ADR reporting system.

Community pharmacy in Lebanon: A societal perspective.

Objective:To assess patients’ attitudes towards the community pharmacist’s role and determine their negative and positive reactions towards community pharmacists in Lebanon.Methods:A cross-sectional study, conducted between January and April 2016, was designed to assess the general public satisfaction with the services provided by the community pharmacies. It was carried out, using a proportionate random sampling of Lebanese community pharmacies from each district. Two sided statistical tests were used to compare between group percentages, Wilcoxon test for quantitative variables with non-homogeneous variances or non-normal distribution, and Student’s t-test for quantitative variables of normal distribution and homogeneous variances. The ANOVA test was used to compare between three groups or more, and Pearson correlation coefficient were used to correlate between quantitative variables.Results:a total of 565 participants completely answered the survey questions with a response rate of 94%. The bivariate analysis showed that the patient perception index was positively and significantly correlated with the patient level of expectation index, the overall pharmacy experience and the patient’s reason for visiting the pharmacy (p<0.001 for all 3 variables) but was negatively correlated with the barriers for asking questions significantly (p=0.032). On the other hand, this perception index was significantly and positively associated with the number of pharmacy visits, the age categories, the level of education and the family monthly income (p<0.05 for all variables).Conclusion:Public perception and attitude toward community pharmacist in Lebanon is poor despite highly qualified pharmacists. Aspects of pharmacy services most relevant to patients were respect, empathy, a friendly staff, listening carefully, giving quality time, responding quickly to their needs and respecting their privacy. The ministry of Health in Lebanon, along with the Lebanese Order of Pharmacists should educate the pharmacist about working on the different issues patients are complaining about in order to play a more important role in the society and become the number one trusted health care professional.

Correlation between chromosomal distribution and embryonic findings on ultrasound in early pregnancy loss after IVF-embryo transfer.

Do early pregnancy losses (EPLs) with and without embryos differ in chromosomal distributions? The chromosomal abnormality rate is significantly higher in miscarriages with embryos than without after in vitro fertilization (IVF)-embryo transfer. Chromosomal abnormalities are the main causes of EPLs, the rate of which is up to 24-30% in the IVF population. Little research has been conducted on the correlations between the chromosomal distributions of EPL and the existence of an embryo or with the postmortem embryonic pole length, and the existing results have been inconsistent. The data of 2172 women who underwent dilation and curettage (D&C) from January 2008 to December 2013 for missed abortion were analyzed retrospectively. The existence of an embryonic pole and the length of the postmortem embryonic pole of the EPL were evaluated by transvaginal sonography (TVS). Ultrasound findings were compared with karyotype results. This analysis included 2172 infertility patients who had singleton pregnancies and experienced EPLs after IVF-embryo transfer. The EPLs were divided into embryonic and anembryonic groups based on TVS diagnosis. The crown-rump length of the fetal pole (observed once) was measured twice for each fetus after confirmation of fetal death, subject to the final measurement before D&C. The karyotype analysis was performed using comparative genomic hybridization (CGH) plus fluorescence in situ hybridization technology. The chromosomal abnormality rate was significantly higher in male miscarriages with an embryo than in those without an embryo (54.14% versus 37.50%, P ≤0.001). In the anembryonic group, the abnormal karyotype rate was significantly higher in the yolk sac only than that in the empty sac group (46.11% versus 29.77%, P=0.001); in the embryonic group, the abnormal karyotype rate in miscarriages with postmortem embryonic pole length >20 mm was significantly lower than that in miscarriages with pole length <10 mm (P=0.006) and 10-20 mm (P=0.036). There were significant differences in abnormal karyotype rates among miscarriages of different developmental stages (P≤0.001). The cases with embryonic stages had the highest risk (54.89%) of an abnormal karyotype and those with fetal stages had the lowest risk (18.18%). There were significant differences in the length of postmortem embryonic poles among groups with different karyotypes (P≤0.001). In addition, trisomy 21, monosomy X and triploidy had the longest lengths of postmortem embryonic poles (16, 15.3 and 11.6 mm, respectively). Although the efficacy of a non-parametric test is less than that of a parametric test, non-parametric testing was used to compare the embryonic pole lengths in this study, owing to the non-normal distribution and non-homogeneous variances caused by limited cases of some rare chromosomal abnormalities. Another limitation was that CGH was unable to detect mosaicism. Furthermore, the results were not compared with a non-IVF population. Finally, maternal cell contamination is a major problem in studying miscarriage tissue, even using molecular techniques. Although TVS findings clearly cannot replace karyotype information, our results are important because they call attention to the fact that EPLs that occur after implantation but prior to embryo formation frequently have normal karyotypes. This finding might direct future research towards studies of DNA sequences, or of epigenetic correlations with pregnancy failure. This study was funded by the Scientific Research Foundation of Reproductive and Genetic Hospital of Citic-Xiangya. The authors have declared that no conflicts of interest exist. N/A.

Quantile-Based Bayesian Maximum Entropy Approach for Spatiotemporal Modeling of Ambient Air Quality Levels

Understanding the daily changes in ambient air quality concentrations is important to the assessing human exposure and environmental health. However, the fine temporal scales (e.g., hourly) involved in this assessment often lead to high variability in air quality concentrations. This is because of the complex short-term physical and chemical mechanisms among the pollutants. Consequently, high heterogeneity is usually present in not only the averaged pollution levels, but also the intraday variance levels of the daily observations of ambient concentration across space and time. This characteristic decreases the estimation performance of common techniques. This study proposes a novel quantile-based Bayesian maximum entropy (QBME) method to account for the nonstationary and nonhomogeneous characteristics of ambient air pollution dynamics. The QBME method characterizes the spatiotemporal dependence among the ambient air quality levels based on their location-specific quantiles and accounts for spatiotemporal variations using a local weighted smoothing technique. The epistemic framework of the QBME method can allow researchers to further consider the uncertainty of space-time observations. This study presents the spatiotemporal modeling of daily CO and PM10 concentrations across Taiwan from 1998 to 2009 using the QBME method. Results show that the QBME method can effectively improve estimation accuracy in terms of lower mean absolute errors and standard deviations over space and time, especially for pollutants with strong nonhomogeneous variances across space. In addition, the epistemic framework can allow researchers to assimilate the site-specific secondary information where the observations are absent because of the common preferential sampling issues of environmental data. The proposed QBME method provides a practical and powerful framework for the spatiotemporal modeling of ambient pollutants.

Feasibility of Predicting Ease of Peeling of Tomato Fruits by Using a Handheld Infrared Spectrometer

The potential of infrared (IR) handheld spectrometers, paired with chemometrics, to predict peelability of processing tomatoes was examined. A pilot plant scale study was carried out where tomato fruits underwent lye treatment and peeled with rotating rubber disks. The degree of peeling was regressed on the infrared spectral data collected from various regions on the fruits using partial least squares regression procedure. Peelability was best explained by spectra collected from areas adjacent to the blossom and stem ends. The regression model exhibited residuals with nonhomogeneous variance and was accurate only in predicting “very hard to peel” values, using IR frequencies associated with phenolics in the cuticular domain. The use of a handheld infrared spectrometer paired with chemometrics showed promise for predicting ease of peel of processing tomatoes. There is a need to improve spectral quality through better sample contact with the attenuated total reflectance accessory for predicting intermediate-peeling tomatoes. Practical Applications The tomato industry incurs in significant losses during the peeling stage, accounting for up to 28% of the waste generated in commercial canneries. Current technology used to assess tomato maturity (color, soluble solids, titratable acidity) has not correlated to tomato-peeling losses. The use of a handheld IR spectrometer paired with chemometrics has shown potential as a tool for predicting peelability of processing tomatoes. We have shown that infrared spectra reflect the peel's chemical state, which in turn could be related to the fruit's developmental stage.

Making logits behave

For the multivariate analysis of binary (or n-ary) dependent variables, common statistical analysis systems offer models with non-homogeneous error variance (LSN-models), and optionally transformations (“response functions”) of the relative frequencies, above all the logit function. Using the simplest possible example—two groups with a relative frequency in each—it is shown that significance tests based on transformed and non-transformed variables can yield substantially diverging results, and even an example of flagrantly inadequate behavior of logit as a response function in significance tests is presented. Dispensing with LSN models can remedy the latter problem; this recommendation is in line with other known objections against using LSN with binary dependents. Furthermore, an improved approximation for the variance of (any) response function makes tests based on transformed and non-transformed variables equivalent, so that problems of discrepant results depending on the response function used are completely removed. In the Appendix a sketch is given of a method to extend the approach to more general situations.

A mixture model approach for the analysis of small exploratory microarray experiments

The microarray is an important and powerful tool for prescreening of genes for further research. However, alternative solutions are needed to increase power in small microarray experiments. Use of traditional parametric and even non-parametric tests for such small experiments lack power and have distributional problems. A mixture model is described that is performed directly on expression differences assuming that genes in alternative treatments are expressed or not in all combinations (i) not expressed in either condition, (ii) expressed only under the first condition, (iii) expressed only under the second condition, and (iv) expressed under both conditions, giving rise to 4 possible clusters with two treatments. The approach is termed a Mean-Difference-Mixture-Model (MD-MM) method. Accuracy and power of the MD-MM was compared to other commonly used methods, using both simulations, microarray data, and quantitative real time PCR (qRT-PCR). The MD-MM was found to be generally superior to other methods in most situations. The advantage was greatest in situations where there were few replicates, poor signal to noise ratios, or non-homogeneous variances.

Biomass Equations for Tropical Tree Plantation Species in Young Stands Using Secondary Data from the Philippines

Estimation of the magnitude of sinks and sources of carbon requires reliable estimates of the biomass of forests and of individual trees. Equations for predicting tree biomass have been developed using secondary data involving destructive sampling in plantations (mostly less than 10 years of age) in several localities in the Philippines. These equations allow estimates of carbon sequestration to be made at much lower cost than would be incurred if detailed stand inventories were undertaken. The species included in the study reported here include Gmelina arborea Roxb., Paraserianthes falcataria (L.) Nielsen Swietenia macrophylla King and Dipterocarp species in Mindanao, and Leucaena leucocephala de Wit from Laguna, Antique, Cebu, Iloilo, Rizal, and Ilocos Sur. Non-linear regression was used to derive speciesspecific, site specific and generic equations between yield and diameter of the form y = αDβ. Equations were evaluated based on the correlation coefficient, standard error of estimate and residual plots. Regressions resulted in high r values (&gt;0.90). In some cases, non-homogeneous variance was encountered. The generic equation improved estimates compared with models used in previous studies.