Combination Of Predictor Variables Research Articles

Integrated decision strategies for skin sensitization hazard.

One of the top priorities of the Interagency Coordinating Committee for the Validation of Alternative Methods (ICCVAM) is the identification and evaluation of non-animal alternatives for skin sensitization testing. Although skin sensitization is a complex process, the key biological events of the process have been well characterized in an adverse outcome pathway (AOP) proposed by the Organisation for Economic Co-operation and Development (OECD). Accordingly, ICCVAM is working to develop integrated decision strategies based on the AOP using in vitro, in chemico and in silico information. Data were compiled for 120 substances tested in the murine local lymph node assay (LLNA), direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens assay. Data for six physicochemical properties, which may affect skin penetration, were also collected, and skin sensitization read-across predictions were performed using OECD QSAR Toolbox. All data were combined into a variety of potential integrated decision strategies to predict LLNA outcomes using a training set of 94 substances and an external test set of 26 substances. Fifty-four models were built using multiple combinations of machine learning approaches and predictor variables. The seven models with the highest accuracy (89-96% for the test set and 96-99% for the training set) for predicting LLNA outcomes used a support vector machine (SVM) approach with different combinations of predictor variables. The performance statistics of the SVM models were higher than any of the non-animal tests alone and higher than simple test battery approaches using these methods. These data suggest that computational approaches are promising tools to effectively integrate data sources to identify potential skin sensitizers without animal testing. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

The relationship between daily European precipitation and measures of atmospheric water vapour transport

ABSTRACTIn this study we investigate the relationship between daily European precipitation totals and different measures of atmospheric water vapour transport during both the winter and summer seasons. Using gridded gauge‐based precipitation estimates across Europe and the Modern‐Era Retrospective Analysis for Research and Applications (MERRA) reanalysis as the source of the water vapour transport, we examine what combination of predictor variables (related to wind and humidity at different levels and total column water) explains the most precipitation variability. Results show that stronger relationships occur in the winter than in the summer, and that the best linear models for explaining the daily rainfall variability contain the zonal and meridional water vapour transport components as predictors [e.g. winter precipitation in the European Alps (western Norway) has R2 values of about 0.5 (0.45)]. Precipitation regions most related to zonal and meridional moisture fluxes are also discussed. We also show that models requiring just three fields, namely the integrated water vapour, and the 850‐hPa zonal and meridional winds, are able to explain the precipitation variability in Europe well. Given the computational and initial storage requirements in calculating water vapour transport integrated over the atmospheric column (in particular for climate change projections), it would be possible to use these three fields in assessments of future water vapour fluxes over Europe, in turn allowing inferences to be made on future rainfall and flood events.

Statistical framework to simulate daily rainfall series conditional on upper-air predictor variables

We propose a statistical framework to generate synthetic rainfall time series at daily resolution, conditional on predictor variables indicative of the atmospheric circulation at the mesoscale. We do so by first introducing a dimensionless measure to assess the relative influence of upper-air variables at different pressure levels on ground-level rainfall statistics, and then simulating rainfall occurrence and amount by proper conditioning on the selected atmospheric predictors. The proposed scheme for conditional rainfall simulation operates at a daily time step (avoiding discrete approaches for identification of weather states), can incorporate any possible number and combination of predictor variables, while it is capable of reproducing rainfall seasonality directly from the variation of upper-air variables, without any type of seasonal analysis or modeling. The suggested downscaling approach is tested using atmospheric data from the ERA-Interim archive and daily rainfall measurements from western Greece. The model is found to accurately reproduce several statistics of actual rainfall time series, at both annual and seasonal levels, including wet day fractions, the alternation of wet and dry intervals, the distributions of dry and wet spell lengths, the distribution of rainfall intensities in wet days, short-range dependencies present in historical rainfall records, the distribution of yearly rainfall maxima, dependencies of rainfall statistics on the observation scale, and long-term climatic features present in historical rainfall records. The suggested approach is expected to serve as a useful tool for stochastic rainfall simulation conditional on climate model outputs at a regional level, where climate change impacts and risks are assessed.

Identifying the Factors Underlying Discontinuation of Triptans

To identify factors associated with triptan discontinuation among migraine patients. It is unclear why many migraine patients who are prescribed triptans discontinue this treatment. This study investigated correlates of triptan discontinuation with a focus on potentially modifiable factors to improve compliance. This multicenter cross-sectional survey (n = 276) was performed at US tertiary care headache clinics. Headache fellows who were members of the American Headache Society Headache Fellows Research Consortium recruited episodic and chronic migraine patients who were current triptan users (use within prior 3 months and for ≥1 year) or past triptan users (no use within 6 months; prior use within 2 years). Univariate analyses were first completed to compare current triptan users to past users for: migraine characteristics, other migraine treatments, triptan education, triptan efficacy, triptan side effects, type of prescribing provider, Migraine Disability Assessment (MIDAS) scores and Beck Depression Inventory (BDI) scores. Then, a multivariable logistic regression model was selected from all possible combinations of predictor variables to determine the factors that best correlated with triptan discontinuation. Compared with those still using triptans (n = 207), those who had discontinued use (n = 69) had higher rates of medication overuse (30 vs. 18%, P = .04) and were more likely to have ever used opioids for migraine treatment (57 vs. 38%, P = .006) as well as higher MIDAS (mean 63 vs. 37, P = .001) and BDI scores (mean 10.4 vs. 7.4, P = .009). Compared with discontinued users, current triptan users were more likely to have had their triptan prescribed by a specialist (neurologist, headache specialist, or pain specialist) (74 vs. 54%, P = .002) and were more likely to report headache resolution (53 vs. 14%, P < .001) or a reduction in pain intensity (71 vs. 28%, P < .001) most of the time from their triptan. On a 1-5 scale (1 = disagree, 5 = agree), triptan users felt they had more: control over their migraine attacks (2.9 vs. 2.1), confidence in their prescribing provider (4.5 vs. 4.0), and were more educated about triptan use (4.2 vs. 3.7) compared with triptan discontinuers (P < .001 for all comparisons). Although both current and prior users reported similar rates of side effects (48 vs. 43%, P = .44), of those who discontinued use, the main reasons were for lack of effect (44%) and side effects (29%). Our multivariable modeling revealed that the strongest correlate of triptan discontinuation was lack of efficacy (odds ratio = 17, 95% confidence interval [8.8, 33.0]). Other factors associated with discontinuation included MIDAS > 24 (2.6, [1.5, 4.6]), BDI >4 (2.5, [1.4, 4.5]), and a history of ever using opioids for migraine therapy (2.2, [1.3, 3.8]). Having a triptan prescribed by a specialist and using at least 1 other abortive medication with the triptan were associated with a decreased likelihood of triptan discontinuation (0.41, [0.2-0.7] and 0.44 [0.3, 0.8], respectively). As expected, discontinuation was most correlated with lack of efficacy, but other important factors associated with those who had discontinued use included greater migraine-related disability, depression, and the use of opioids for migraine attacks. Compared with patients who had discontinued triptans, current triptan users felt more: educated about their triptan, control over their migraine attacks, and confidence in their prescribing provider. Current triptan users had their triptan prescribed by a specialist and used other abortive medications with their triptan more often compared with patients who had discontinued triptans. Given the cross-sectional nature of this study, we cannot determine if these factors contributed to triptan discontinuation or reflect the impact of such discontinuation. Interventions that address modifiable risk factors for triptan discontinuation may decrease the likelihood of triptan discontinuation and thus improve overall migraine control. Because lack of efficacy was most strongly associated with triptan discontinuation, future research should determine why triptans are effective for some patients but not others.

Benthic communities in the deep Mediterranean Sea: exploring microbial and meiofaunal patterns in slope and basin ecosystems

Abstract. The long-held perception of the deep sea consisting of monotonous slopes and uniform oceanic basins has over the decades given way to the idea of a complex system with wide habitat heterogeneity. Under the prism of a highly diverse environment, a large dataset was used to describe and compare spatial patterns of the dominant small-size components of deep-sea benthos, metazoan meiofauna and microbes, from Mediterranean basins and slopes. A grid of 73 stations sampled at five geographical areas along the central-eastern Mediterranean Basin (central Mediterranean, northern Aegean Sea, Cretan Sea, Libyan Sea, eastern Levantine) spanning over 4 km in depth revealed a high diversity, irrespective of the benthic group or level of taxonomic analysis. A common decreasing bathymetric trend was detected for meiobenthic abundance, major taxa diversity and nematode genera richness, but no differences were found between the two habitats (basin vs slope). In contrast, microbial richness is significantly higher at the basin ecosystem and tends to increase with depth. Multivariate analyses (β- and δ-diversity and ordination analysis) complemented these results and underlined the high within-habitat variability of benthic communities. Meiofaunal communities in particular were found to change gradually and vary more towards the abyss. On the other hand, microbial communities were highly variable, even among samples of the same area, habitat and bathymetry. A significant proportion of the variation of benthic communities and their descriptors was explained by depth and proxies of food availability (sedimentary pigments and organic content), but the combination of predictor variables and the strength of the relationship varied depending on the data set used (based on type of habitat, benthic component, taxonomic level). This, along with the observed high within-habitat variability suggests that other factors, which tend to vary at local scale (hydrodynamics, substrate structure, geochemistry, food quality, etc.), may also relate to the observed benthic patterns. Overall, the results presented here suggest that differences in small-size benthos between the basin and slope habitats are neither strong nor consistent; it appears that within-habitat variability is high, differences among depth ranges are important and further investigation of possible environmental drivers of benthic patterns is needed.

Mehanizmi suočavanja sa stresom, optimizam i generalna samoefikasnost kao prediktori psihofizičkog zdravlja

Osnovni cilj istraživanja podrazumevao je proveru prediktivne moći mehanizama suočavanja sa stresom, optimizma i generalne samoefikasnosti u odnosu na ukupan skor na Skali psihofizičkog zdravlja, koji je figurisao u svojstvu kriterijumske varijable. Isti složaj prediktorskih varijabli koristili smo u pokušaju predviđanja pojedinih aspekata psihofizičkog zdravlja koji se odnose na: poremećaj fizičkog zdravlja, strah i anksioznost, depresivne reakcije, umor, te poremećaj socijalnog ponašanja. Istraživanje je sprovedeno na prigodnom uzorku od 269 studenata Univerziteta u Novom Sadu, oba pola. Obuhvaćeni su studenti osnovnih i diplomskih studija. Radi operacionalizacije spomenutih varijabli, korišćeni su sledeći instrumenti: Skala psihofizičkog zdravlja, Life Orientation Test-Revised, kojim se meri dispozicioni optimizam, Skala opšte samoefikasnosti i Brief COPE kojim su mere operacionalizovani mehanizmi suočavanja sa stresom. Analizom faktorske strukture skale Brief COPE izdvojena su tri faktora, uvrštena u set prediktorskih varijabli: Traženje socijalne podrške, Suočavanje usmereno na problem i Suočavanje izbegavanjem. Kao značajni prediktori opšteg psihofizičkog zdravlja izdvojili su se: Suočavanje izbegavanjem (β = .24, p &lt; .01), Optimizam (β = -.35, p &lt; .01) i Opšta samoefikasnost (β = -.18, p &lt; .05). Kad je reč o pojedinačnim aspektima psihofizičkog zdravlja, koji su u naknadnim analizama imali status kriterijumskih varijabli, pomenuti složaj prediktora pokazao se značajnim u predviđanju svakog pojedinačnog aspekta. Upoređujući pojedinačne doprinose prediktorskih varijabli, ustanovljeno je da se varijabilnost psihofizičkog zdravlja u svim razmatranim aspektima može objasniti razlikama u stepenu optimizma i prijemčivosti za strategiju izbegavanja.

Multivariate Screening Model for Later Word Reading Achievement: Predictive Utility of Prereading Skills and Cognitive Ability

The present study used multiple regression to determine the predictive value of Kindergarten phonemic awareness, rapid serial naming, letter knowledge, and cognitive ability for predicting first-grade word reading and fluency. Participants were 131 first-grade students from a mid-Atlantic school system. A combination of predictor variables was found to be more effective than single measures in predicting later word reading and reading fluency, with cognitive ability, phonemic awareness, and letter knowledge contributing significantly to the prediction of skill. The results underscore the need to use a multivariate battery, rather than any single measure, along with consideration of intelligence, to identify children for early intervention.

Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models

Predicting student academic performance has long been an important research topic in many academic disciplines. The present study is the first study that develops and compares four types of mathematical models to predict student academic performance in engineering dynamics - a high-enrollment, high-impact, and core course that many engineering undergraduates are required to take. The four types of mathematical models include the multiple linear regression model, the multilayer perception network model, the radial basis function network model, and the support vector machine model. The inputs (i.e., predictor variables) of the models include student's cumulative GPA, grades earned in four pre-requisite courses (statics, calculus I, calculus II, and physics), and scores on three dynamics mid-term exams (i.e., the exams given to students during the semester and before the final exam). The output of the models is students' scores on the dynamics final comprehensive exam. A total of 2907 data points were collected from 323 undergraduates in four semesters. Based on the four types of mathematical models and six different combinations of predictor variables, a total of 24 predictive mathematical models were developed from the present study. The analysis reveals that the type of mathematical model has only a slight effect on the average prediction accuracy (APA, which indicates on average how well a model predicts the final exam scores of all students in the dynamics course) and on the percentage of accurate predictions (PAP, which is calculated as the number of accurate predictions divided by the total number of predictions). The combination of predictor variables has only a slight effect on the APA, but a profound effect on the PAP. In general, the support vector machine models have the highest PAP as compared to the other three types of mathematical models. The research findings from the present study imply that if the goal of the instructor is to predict the average academic performance of his/her dynamics class as a whole, the instructor should choose the simplest mathematical model, which is the multiple linear regression model, with student's cumulative GPA as the only predictor variable. Adding more predictor variables does not help improve the average prediction accuracy of any mathematical model. However, if the goal of the instructor is to predict the academic performance of individual students, the instructor should use the support vector machine model with the first six predictor variables as the inputs of the model, because this particular predictor combination increases the percentage of accurate predictions, and most importantly, allows sufficient time for the instructor to implement subsequent educational interventions to improve student learning.

Lumbopelvic manipulation in patients with patellofemoral pain syndrome

Objectives: A recent clinical prediction rule (CPR) identified characteristics that may predict an immediate reduction in pain following lumbopelvic manipulation in patients with patellofemoral pain syndrome. The purpose of this single-arm cohort study was to replicate the proposed CPR in a different population and investigate changes in self-reported pain, hip range of motion, strength, and function immediately following lumbopelvic manipulation.Methods: Forty-four subjects (63·6% female; mean age 27·4 years) met inclusion criteria. Hip internal rotation range of motion, lower extremity strength using a handheld dynamometer, and single/triple hop tests were assessed prior to and immediately following a spinal manipulation. A global rating of change questionnaire was administered after testing and telephonically at 1 week. Paired t-tests compared pre- and post-manipulation range of motion, strength, and hop test limb symmetry indices (α = 0·05).Results: Fifty-seven percent of subjects had a successful outcome measured by the numerical pain rating scale immediately following manipulation. Twenty-five of subjects experienced a successful outcome as measured by the global rating of change questionnaire at 1 week. No single individual or combination of predictor variables predicted a positive outcome immediately following the lumbopelvic manipulation (+likelihood ratio 0·7 with three of five predictor variables present). Statistically significant differences (P<0·05) were found in hip extension and abduction strength and hip internal rotation symmetry post-manipulation, but do not appear to be clinically meaningful.Discussion: The previously identified CPR was not able to be replicated and no clinically meaningful changes in range of motion, strength, or function were apparent. Future research should focus on a comprehensive impairment-based treatment approach in patients with patellofemoral pain syndrome.

Early-Stage Entrepreneurial Activity: An Explanatory Model for Cross-Country Comparisons

The paper argues that when developing an explanatory model of the early-stage entrepreneurial activity level (measured by total index of early entrepreneurial activity - TEA) one should consider the ‘path dependency’ of the ‘institutional matrix’ of different societies. Otherwise one could wonder why some theoretical models of TEA determining factors, as provided by a lot of studies, are not statistically significant for younger market systems and entrepreneurship in transitional economies. However, comparing Global Entrepreneurship Monitor (GEM) data with the scope of official statistics provides a deeper insight into adults’ intrinsic incentives to become entrepreneurial. A statistical analysis of national TEA levels does not support the thesis that TEA levels, and structure, change under economic slowdown. Therefore, it seems logical to suggest that to interpret the TEA level it is important to examine some fundamental specific of different types of national markets rather than just the actual economic situation itself. When testing this hypothesis, the authors compared the characteristics of GEM countries with stable, high or low TEA levels. A Fisher’s linear discriminant analysis (FLDA) is used to examine whether different groups of countries can be distinguished by linear combinations of predictor variables and to determine which variables are responsible for this separation. The FLDA model explains the parabolic form of the relation between the level of economic development and TEA. A database of independent variables includes some different quantitative, ordinal and nominal variables determining the context of the national capital accumulation history. Using FLDA, we argue, one might foresee future tendencies of TEA - not only for GEM participating countries.

Polynomial multivariate least-squares regression for modeling nonlinear data applied to in-depth characterization of chromatographic resolution

Well-established, linear multivariate calibration methods such as multivariate least-squares regression (MLR), principal component regression (PCR), or partial least squares (PLS) have two limitations: (i) measured data must be linearly related to the response variables and (ii) predictor variables xn = 1, …, N cannot be coupled to each other. For evaluation of nonlinear data, however, these restrictions need to be overcome and thus polynomial multivariate least-squares regression (PMLR or “response surfaces”) has been introduced here. PMLR is based on multivariate least squares but incorporates all combinations of predictor variables up to a user-selected polynomial order (e.g., including u or v = 0). Because of the inclusion of such coupled terms and their powers, PMLR models are better adapted to model nonlinear data and can help to enhance the prediction step's accuracy and precision. PMLR has been based on MLR because it facilitates—unlike PCR or PLS—a physical and chemical interpretation of the predictors. Hence, the origins and the relevance of nonlinear and/or coupled predictors can be investigated. The details of the PMLR algorithm and its implementation are presented along with a method for model optimization utilizing gradients of response surfaces. Newly developed PMLR models up to quintic order have been applied to predict a chromatograph's peak resolution as a function of six-instrument parameters. It has been demonstrated that PMLR is better capable than MLR and PCR to describe these nonlinear and coupled instrument parameters. In addition, the novel software tool has been utilized for model optimization to determine instrument parameters, which result in the best chromatographic resolution. Copyright © 2011 John Wiley & Sons, Ltd.

Discriminant Analysis of 18F-Fluorothymidine Kinetic Parameters to Predict Survival in Patients with Recurrent High-Grade Glioma

The primary objective of this study was to investigate whether changes in 3'-deoxy-3'-[¹⁸F]fluorothymidine (¹⁸F-FLT) kinetic parameters, taken early after the start of therapy, could predict overall survival (OS) and progression-free survival (PFS) in patients with recurrent malignant glioma undergoing treatment with bevacizumab and irinotecan. High-grade recurrent brain tumors were investigated in 18 patients (8 male and 10 female), ages 26 to 76 years. Each had 3 dynamic positron emission tomography (PET) studies as follows: at baseline and after 2 and 6 weeks from the start of treatment, ¹⁸F-FLT (2.0 MBq/kg) was injected intravenously, and dynamic PET images were acquired for 1 hour. Factor analysis generated factor images from which blood and tumor uptake curves were derived. A three-compartment, two-tissue model was applied to estimate tumor ¹⁸F-FLT kinetic rate constants using a metabolite- and partial volume-corrected input function. Different combinations of predictor variables were exhaustively searched in a discriminant function to accurately classify patients into their known OS and PFS groups. A leave-one-out cross-validation technique was used to assess the generalizability of the model predictions. In this study population, changes in single parameters such as standardized uptake value or influx rate constant did not accurately classify patients into their respective OS groups (<1 and ≥ 1 year; hit ratios ≤ 78%). However, changes in a set of ¹⁸F-FLT kinetic parameters could perfectly separate these two groups of patients (hit ratio = 100%) and were also able to correctly classify patients into their respective PFS groups (<100 and ≥ 100 days; hit ratio = 88%). Discriminant analysis using changes in ¹⁸F-FLT kinetic parameters early during treatment seems to be a powerful method for evaluating the efficacy of therapeutic regimens.

Allometric estimation of total leaf area in the neotropical palm Euterpe oleracea at La Selva, Costa Rica

We estimated the magnitude of the total leaf area of the neotropical palm Euterpe oleracea and examined its allometry relative to the variation in stem height and diameter at La Selva Biological Station in Costa Rica. The allometric relationships between frond leaf area and frond length (from tip to base), and between frond leaf area and number of leaflets, were determined by natural logarithmic regressions to estimate the total area of each frond. Palm total leaf area was then estimated by adding the area of the composing fronds. We fit 14 separate regression models that related one or more of the morphological variables (number of fronds, diameter at breast height, stem height) to the total leaf area. Our results show that palm total leaf area is directly proportional to the number of fronds and palm size as reflected in stem height and diameter. Eight out of the 14 models had r2 values of >0.90 and incorporated a diverse combination of predictor variables. Simple linear regression models were more congruent with the observed values of total leaf area, whereas natural logarithmic models overestimated the value of total leaf area for large palms. Both approaches show a high degree of association among morphological characters in E. oleracea supporting the hypothesis that palms behave like unitary organisms, and are morphologically constrained by the lack of secondary meristems. To afford attaining canopy heights, woody palms need to show a high degree of phenotypic integration, shaping their growth and allometric relationships to match spatial and temporal changes in resources.

Coping capacities and rural livelihoods: Challenges to community risk management in Southern Sri Lanka

It is widely acknowledged that the impacts of environmental perturbations are partly due to the socio-economic condition of populations at risk. This was evident in the immediate aftermath of the Indian Ocean Tsunami where the poor socio-economic status of fisher communities along the southern and eastern coasts of Sri Lanka presented greater challenges for people to cope with and recover from the event. The purpose of this study is to develop a coping capacity index that takes into account social, economic, and demographic characteristics of populations in three districts in Sri Lanka. The coping capacity index is derived from a regression model, where a causal link is established between poverty, the dependent variable, and a combination of predictor variables associated with income, assets, education, gender, and ethnicity. These variables characterize the root causes of poverty and vulnerability among rural populations that influence the way in which they deal with and recover from changes in the environment. The findings of the research reveal a wide range of heterogeneity across the study area in how social, economic, and demographic factors influence coping capacities. In some areas, similar variables contribute toward improving coping capacities, and in others they undermine them. Spatial variations in the combination of variables that contribute to coping capacity were observed across districts and District Secretary’s divisions, the subdivision of a district. Temporal differences were also observed in the way in which variables contribute to coping capacity in light of policies and programs implemented between the two time periods analyzed, 2001/2002 and 2006/2007. These temporal and spatial variations highlight the importance of context, and the impact of development policies and reconstruction programs when assessing the vulnerability of people in a given location.

CATCH: a clinical decision rule for the use of computed tomography in children with minor head injury

There is controversy about which children with minor head injury need to undergo computed tomography (CT). We aimed to develop a highly sensitive clinical decision rule for the use of CT in children with minor head injury. For this multicentre cohort study, we enrolled consecutive children with blunt head trauma presenting with a score of 13-15 on the Glasgow Coma Scale and loss of consciousness, amnesia, disorientation, persistent vomiting or irritability. For each child, staff in the emergency department completed a standardized assessment form before any CT. The main outcomes were need for neurologic intervention and presence of brain injury as determined by CT. We developed a decision rule by using recursive partitioning to combine variables that were both reliable and strongly associated with the outcome measures and thus to find the best combinations of predictor variables that were highly sensitive for detecting the outcome measures with maximal specificity. Among the 3866 patients enrolled (mean age 9.2 years), 95 (2.5%) had a score of 13 on the Glasgow Coma Scale, 282 (7.3%) had a score of 14, and 3489 (90.2%) had a score of 15. CT revealed that 159 (4.1%) had a brain injury, and 24 (0.6%) underwent neurologic intervention. We derived a decision rule for CT of the head consisting of four high-risk factors (failure to reach score of 15 on the Glasgow coma scale within two hours, suspicion of open skull fracture, worsening headache and irritability) and three additional medium-risk factors (large, boggy hematoma of the scalp; signs of basal skull fracture; dangerous mechanism of injury). The high-risk factors were 100.0% sensitive (95% CI 86.2%-100.0%) for predicting the need for neurologic intervention and would require that 30.2% of patients undergo CT. The medium-risk factors resulted in 98.1% sensitivity (95% CI 94.6%-99.4%) for the prediction of brain injury by CT and would require that 52.0% of patients undergo CT. The decision rule developed in this study identifies children at two levels of risk. Once the decision rule has been prospectively validated, it has the potential to standardize and improve the use of CT for children with minor head injury.

Mathematical Modeling to Predict Surface Roughness in CNC Milling

Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness prediction before milling process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 84 samples were run in this study by using FANUC CNC Milling α-Τ14ιE. Those samples were randomly divided into two data sets- the training sets (m=60) and testing sets(m=24). ANOVA analysis showed that at least one of the population regression coefficients was not zero. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using Multiple Regression Method equation, the average percentage deviation of the testing set was 9.8% and 9.7% for training data set. This showed that the statistical model could predict the surface roughness with about 90.2% accuracy of the testing data set and 90.3% accuracy of the training data set.

Predictors of Academic Achievement for Latino Middle Schoolers

The aim of this study was to examine specific child, home, and school factors that predict academic achievement for Latino middle school students. The sample for this study (N = 173) was drawn from seven public schools located in three school districts in the northeastern United States. Hierarchical multiple regression analyses were used to predict academic achievement from a combination of predictor variables: gender, parent educational attainment, parent employment status, acculturation, parent involvement in school, and school identification. Findings indicate that school identification and parent involvement in school are protective factors that promote Latino middle grade students' academic achievement. These findings suggest that collaborative partnerships between school social workers, teachers, and Latino students and their parents are an important strategy that can optimize connections to school that result in academic achievement.

Classifying place of articulation and palatalization of fricatives based on cepstral coefficients and spectral moments.

Hidden Markov models (HMMs) with two types of acoustic features were used to classify fricatives by place of articulation and palatalization status. The data were recordings of 31 native speakers of Romanian who produced a total of 3674 fricatives. Segments from four places (labial, alveolar, postalveolar, and dorsal) were examined, each of which appeared as plain and palatalized (with a palatal secondary articulation). Both the fricatives and the preceding vowels were divided by HMM training into three acoustically uniform regions, corresponding to the three states of the HMM models. Separate sets of monophone HMMs were trained using (a) the first four spectral moments plus rms amplitude and (b) the first five Bark‐cepstral coefficients. Generally, the first and second regions/states of the fricatives were more important in classifying the segments by place, while the third state contributed more to the classification by palatalization status. The success of the classification depended on the specific combination of predictor variables (acoustic features and regions) used. Thus, the overall accuracy in classifying segments according to place or palatalization ranged from 62% to 95%. These analyses shed light on the differential distribution over time of acoustic features related to place and palatalization.

Modifications to the Patient Rule‐Induction Method that utilize non‐additive combinations of genetic and environmental effects to define partitions that predict ischemic heart disease

This article extends the Patient Rule-Induction Method (PRIM) for modeling cumulative incidence of disease developed by Dyson et al. (Genet Epidemiol 31:515-527) to include the simultaneous consideration of non-additive combinations of predictor variables, a significance test of each combination, an adjustment for multiple testing and a confidence interval for the estimate of the cumulative incidence of disease in each partition. We employ the partitioning algorithm component of the Combinatorial Partitioning Method to construct combinations of predictors, permutation testing to assess the significance of each combination, theoretical arguments for incorporating a multiple testing adjustment and bootstrap resampling to produce the confidence intervals. An illustration of this revised PRIM utilizing a sample of 2,258 European male participants from the Copenhagen City Heart Study is presented that assesses the utility of genetic variants in predicting the presence of ischemic heart disease beyond the established risk factors.

Tattling on the Teacher: A Study of Factors Influencing Peer Reporting of Teachers who Violate Standardized Testing Protocol

Allegations of teacher and administrator cheating on standardized tests appear to be rampant. One primary way that cheating is held in check occurs when peers turn in other teachers for cheating. The purpose of this study was to exam five variables as predictors of peer reporting of this particular type of unethical behavior. Three hundred thirty (N = 330) Texas school teachers responded to a survey that included a scenario depicting another teacher violating standardized testing protocol. Results indicated no differences in reporting intentions across impression management tactics (justification vs. intimidation). However, canonical correlation results indicated that a combination of predictor variables was positively and significantly related to peer-reporting intentions. A perception of participatory culture was the primary contributor to the composite. Communication confidence and policy attitude were moderate contributors.