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Concept: Linear regression


Correctly assessing a scientist’s past research impact and potential for future impact is key in recruitment decisions and other evaluation processes. While a candidate’s future impact is the main concern for these decisions, most measures only quantify the impact of previous work. Recently, it has been argued that linear regression models are capable of predicting a scientist’s future impact. By applying that future impact model to 762 careers drawn from three disciplines: physics, biology, and mathematics, we identify a number of subtle, but critical, flaws in current models. Specifically, cumulative non-decreasing measures like the h-index contain intrinsic autocorrelation, resulting in significant overestimation of their “predictive power”. Moreover, the predictive power of these models depend heavily upon scientists' career age, producing least accurate estimates for young researchers. Our results place in doubt the suitability of such models, and indicate further investigation is required before they can be used in recruiting decisions.

Concepts: Scientific method, Regression analysis, Linear regression, Prediction, Futurology, Future, Science, Forecasting


BACKGROUND: Studies have documented a positive relationship between regular breakfast consumption and cognitive outcomes in youth. However, most of these studies have emphasized specific measures of cognition rather than cognitive performance as a broad construct (e.g., IQ test scores) and have been limited to Western samples of school-age children and adolescents. This study aims to extend the literature on breakfast consumption and cognition by examining these constructs in a sample of Chinese kindergarten-age children. METHODS: This cross-sectional study consisted of a sample of 1269 children (697 boys and 572 girls) aged 6 years from the Chinese city of Jintan. Cognition was assessed with the Chinese version of the Wechsler Preschool and Primary Scale of Intelligence - Revised. Breakfast habits were assessed through parental questionnaire. Analyses of variance and linear regression models were used to analyze the association between breakfast habits and IQ. Socioeconomic and parental psychosocial variables related to intelligence were controlled for. RESULTS: Findings showed that children who regularly have breakfast on a near-daily basis had significantly higher full scale, verbal, and performance IQ test scores (all p<0.001) compared to children who "sometimes" have breakfast. This relationship persisted for VIQ (verbal IQ) and FIQ (full IQ) even after adjusting for gender, current living location, parental education, parental occupation, and primary child caregiver. CONCLUSION: Findings may reflect nutritional as well as social benefits of regular breakfast consumption on cognition, and regular breakfast consumption should be encouraged among young children.

Concepts: Linear regression, Intelligence quotient, Wechsler Adult Intelligence Scale, Wechsler Intelligence Scale for Children, Kindergarten, David Wechsler, Intelligence tests, Wechsler Preschool and Primary Scale of Intelligence


BACKGROUND: The socioeconomic gradient in obesity and overweight is amply documented. However, the contribution of different socioeconomic indicators on trends of body mass index (BMI) over time is less well known. The aim of this study was to investigate the associations of education and income with (BMI) from the late 1970s to the early 2000s. METHODS: Data were derived from nationwide cross-sectional health behaviour surveys carried out among Finns annually since 1978. This study comprises data from a 25-year period (1978-2002) that included 25 339 men and 25 330 women aged 25-64 years. BMI was based on self-reported weight and height. Education in years was obtained from the questionnaire and household income from the national tax register. In order to improve the comparability of the socioeconomic position measures, education and income were divided into gender-specific tertiles separately for each study year. Linear regression analysis was applied. RESULTS: An increase in BMI was observed among men and women in all educational and income groups. In women, education and income were inversely associated with BMI. The magnitudes of the associations fluctuated but stayed statistically significant over time. Among the Finnish men, socioeconomic differences were more complicated. Educational differences were weaker than among the women and income differences varied according to educational level. At the turn of the century, the high income men in the lowest educational group had the highest BMI whereas the income pattern in the highest educational group was the opposite. CONCLUSION: No overall change in the socio-economic differences of BMI was observed in Finland between 1978 and 2002. However, the trends of BMI diverged ini sub-groups of the studied population: the most prominent increase in BMI took place in high income men with low education and in low income men with high education. The results encourage further research on the pathways between income, education, living conditions and the increasing BMI.

Concepts: Regression analysis, Linear regression, Statistics, Obesity, Socioeconomic status, Econometrics, Mass, Body mass index


Funding has been viewed in the literature as one of the main determinants of scientific activities. Also, at an individual level, securing funding is one of the most important factors for a researcher, enabling him/her to carry out research projects. However, not everyone is successful in obtaining the necessary funds. The main objective of this work is to measure the effect of several important factors such as past productivity, scientific collaboration or career age of researchers, on the amount of funding that is allocated to them. For this purpose, the paper estimates a temporal non-linear multiple regression model. According to the results, although past productivity of researchers positively affects the funding level, our findings highlight the significant role of networking and collaboration. It was observed that being a member of large scientific teams and getting connected to productive researchers who have also a good control over the collaboration network and the flow of information can increase the chances for securing more money. In fact, our results show that in the quest for the research money it is more important how researchers build their collaboration network than what publications they produce and whether they are cited.

Concepts: Scientific method, Regression analysis, Linear regression, Econometrics, Academic publishing, Science, Research, Errors and residuals in statistics


Many spatial interpolation methods perform well for gentle terrains when producing spatially continuous surfaces based on ground point data. However, few interpolation methods perform satisfactorily for complex terrains. Our objective in the present study was to analyze the suitability of several popular interpolation methods for complex terrains and propose an optimal method. A data set of 153 soil water profiles (1 m) from the semiarid hilly gully Loess Plateau of China was used, generated under a wide range of land use types, vegetation types and topographic positions. Four spatial interpolation methods, including ordinary kriging, inverse distance weighting, linear regression and regression kriging were used for modeling, randomly partitioning the data set into 2/3 for model fit and 1/3 for independent testing. The performance of each method was assessed quantitatively in terms of mean-absolute-percentage-error, root-mean-square-error, and goodness-of-prediction statistic. The results showed that the prediction accuracy differed significantly between each method in complex terrain. The ordinary kriging and inverse distance weighted methods performed poorly due to the poor spatial autocorrelation of soil moisture at small catchment scale with complex terrain, where the environmental impact factors were discontinuous in space. The linear regression model was much more suitable to the complex terrain than the former two distance-based methods, but the predicted soil moisture changed too sharply near the boundary of the land use types and junction of the sunny (southern) and shady (northern) slopes, which was inconsistent with reality because soil moisture should change gradually in short distance due to its mobility in soil. The most optimal interpolation method in this study for the complex terrain was the hybrid regression kriging, which produced a detailed, reasonable prediction map with better accuracy and prediction effectiveness.

Concepts: Regression analysis, Linear regression, Statistics, Forecasting, Interpolation, Landform, Kriging, Multivariate interpolation


The objective of our study was to measure the impact of transportation-related noise and total environmental noise on sleep disturbance for the residents of Montreal, Canada. A telephone-based survey on noise-related sleep disturbance among 4336 persons aged 18 years and over was conducted. LNight for each study participant was estimated using a land use regression (LUR) model. Distance of the respondent’s residence to the nearest transportation noise source was also used as an indicator of noise exposure. The proportion of the population whose sleep was disturbed by outdoor environmental noise in the past 4 weeks was 12.4%. The proportion of those affected by road traffic, airplane and railway noise was 4.2%, 1.5% and 1.1%, respectively. We observed an increased prevalence in sleep disturbance for those exposed to both rail and road noise when compared for those exposed to road only. We did not observe an increased prevalence in sleep disturbance for those that were both exposed to road and planes when compared to those exposed to road or planes only. We developed regression models to assess the marginal proportion of sleep disturbance as a function of estimated LNight and distance to transportation noise sources. In our models, sleep disturbance increased with proximity to transportation noise sources (railway, airplane and road traffic) and with increasing LNight values. Our study provides a quantitative estimate of the association between total environmental noise levels estimated using an LUR model and sleep disturbance from transportation noise.

Concepts: Scientific method, Regression analysis, Linear regression, Statistics, Transport, Road, Noise pollution, Rail transport


Seafood consumption during pregnancy is thought to be beneficial for child neuropsychological development, but to our knowledge no large cohort studies with high fatty fish consumption have analyzed the association by seafood subtype. We evaluated 1,892 and 1,589 mother-child pairs at the ages of 14 months and 5 years, respectively, in a population-based Spanish birth cohort established during 2004-2008. Bayley and McCarthy scales and the Childhood Asperger Syndrome Test were used to assess neuropsychological development. Results from multivariate linear regression models were adjusted for sociodemographic characteristics and further adjusted for umbilical cord blood mercury or long-chain polyunsaturated fatty acid concentrations. Overall, consumption of seafood above the recommended limit of 340 g/week was associated with 10-g/week increments in neuropsychological scores. By subtype, in addition to lean fish, consumption of large fatty fish showed a positive association; offspring of persons within the highest quantile (>238 g/week) had an adjusted increase of 2.29 points in McCarthy general cognitive score (95% confidence interval: 0.42, 4.16). Similar findings were observed for the Childhood Asperger Syndrome Test. Beta coefficients diminished 15%-30% after adjustment for mercury or long-chain polyunsaturated fatty acid concentrations. Consumption of large fatty fish during pregnancy presents moderate child neuropsychological benefits, including improvements in cognitive functioning and some protection from autism-spectrum traits.

Concepts: Cohort study, Linear regression, Longitudinal study, Nutrition, Essential fatty acid, Umbilical cord, Cord blood, Cord blood bank


Background/Objectives:Cesarean section (CS) and antibiotic use during pregnancy may alter normal maternal-fetal microbiota exchange, thereby contributing to aberrant microbial colonization of the infant gut and increased susceptibility to obesity later in life. We hypothesized that i) maternal use of antibiotics in the second or third trimester of pregnancy and ii) CS are independently associated with higher risk of childhood obesity in the offspring.Subjects/Methods:Of the 727 mothers enrolled in the Northern Manhattan Mothers and Children Study, we analyzed the 436 mother-child dyads followed until 7 years of age with complete data. We ascertained prenatal antibiotic by a questionnaire administered late in the third trimester, and delivery mode by medical record. We derived age- and sex-specific BMI z scores using the CDC SAS Macro, and defined obesity as BMI z≥95(th) percentile. We used binary regression with robust variance and linear regression models adjusted for maternal age, ethnicity, pre-gravid BMI, maternal receipt of public assistance, birth weight, sex, breast feeding in the first year, and gestational antibiotics or delivery mode.Results:Compared to children not exposed to antibiotics during the second or third trimester, those exposed had 84% (33-154%) higher risk of obesity, after multivariable adjustment. Second or third trimester antibiotic exposure was also positively associated with BMI z scores, waist circumference, and % body fat (all P<0.05). Independent of prenatal antibiotic usage, CS was associated with 46% (8-98%) higher offspring risk of childhood obesity. Associations were similar for elective and non-elective CS.Conclusions:In our cohort, CS and exposure to antibiotics in the second or third trimester were associated with higher offspring risk of childhood obesity. Future studies that address the limitations of our study are warranted to determine if prenatal antibiotic use is associated with offspring obesity. Research is also needed to determine if alterations in neonatal gut microbiota underlie the observed associations.International Journal of Obesity accepted article preview online, 09 October 2014. doi:10.1038/ijo.2014.180.

Concepts: Linear regression, Pregnancy, Childbirth, Infant, Bacteria, Gut flora, Obesity, Obstetrics


This study utilized data from the Framingham Heart Study Offspring Cohort to examine the relationship between midlife and late-life alcohol consumption, cognitive functioning, and regional brain volumes among older adults without dementia or a history of abusing alcohol. The results from multiple linear regression models indicate that late life, but not midlife, alcohol consumption status is associated with episodic memory and hippocampal volume. Compared to late life abstainers, moderate consumers had larger hippocampal volume, and light consumers had higher episodic memory. The differences in episodic memory according to late life alcohol consumption status were no longer significant when hippocampal volume was included in the regression model. The findings from this study provide new evidence that hippocampal volume may contribute to the observed differences in episodic memory among older adults and late life alcohol consumption status.

Concepts: Regression analysis, Linear regression, Econometrics, Cognition, Neurology, Memory, Hippocampus, Episodic memory


Emerging evidence suggests that there is interplay between the frequency and circadian timing of eating and metabolic health. We examined the associations of eating frequency and timing with metabolic and inflammatory biomarkers putatively associated with breast cancer risk in women participating in the National Health and Nutrition Examination 2009-2010 Survey. Eating frequency and timing variables were calculated from 24-hour food records and included (1) proportion of calories consumed in the evening (5pm-midnight), (2) number of eating episodes per day, and (3) nighttime fasting duration. Linear regression models examined each eating frequency and timing exposure variable with C-reactive protein (CRP) concentrations and the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). Each 10 percent increase in the proportion of calories consumed in the evening was associated with a 3 percent increase in CRP. Conversely, eating one additional meal or snack per day was associated with an 8 percent reduction in CRP. There was a significant interaction between proportion of calories consumed in the evening and fasting duration with CRP (p = 0.02). A longer nighttime fasting duration was associated with an 8 percent lower CRP only among women who ate less than 30% of their total daily calories in the evening (p = 0.01). None of the eating frequency and timing variables were significantly associated with HOMA-IR. These findings suggest that eating more frequently, reducing evening energy intake, and fasting for longer nightly intervals may lower systemic inflammation and subsequently reduce breast cancer risk. Randomized trials are needed to validate these associations.

Concepts: Inflammation, Regression analysis, Linear regression, Cancer, Nutrition, Obesity, Insulin resistance, C-reactive protein