Chapter 3 Dietary Patterns and Gestational Weight Gain
This chapter is based on
Myrte J. Tielemans, Nicole S. Erler, Elisabeth T.M. Leermakers, Marion van den Broek, Vincent W.V. Jaddoe, Eric A.P. Steegers, Jessica C. Kiefte-de Jong and Oscar H. Franco. A priori and a posteriori dietary patterns during pregnancy and gestational weight gain: The Generation R Study. Nutrients, 2015; 7(11), 9383–9399. doi:10.3390/nu7115476
Abstract
Abnormal gestational weight gain (GWG) is associated with adverse pregnancy outcomes. We examined whether dietary patterns are associated with GWG in 3374 pregnant women participating in a population-based cohort study in the Netherlands. Dietary intake during pregnancy was assessed with food-frequency questionnaires. Three a posteriori-derived dietary patterns were identified using factor analysis: a “Vegetable, oil and fish”, a “Nuts, high-fibre cereals and soy”, and a “Margarine, sugar and snacks” pattern. An a priori-defined dietary pattern was based on national dietary recommendations. Weight was repeatedly measured around 13, 20 and 30 weeks of pregnancy; pre-pregnancy and maximum weight were self-reported. Normal-weight women with 1 SDS higher score on the “Nuts, high-fibre cereals and soy” pattern gained weight slightly slower, while normal-weight women with higher adherence to the “Margarine, sugar and snacks” pattern had higher weight (0.3kg; 95% CI [0.07, 0.52]) throughout pregnancy. Normal-weight women with higher adherence to the “Vegetable, oil and fish” pattern had 16 g/week (95% CI [6.5, 25.6]) higher early-pregnancy GWG. The a priori-defined pattern was not associated with GWG. In conclusion, specific dietary patterns may play a role in early pregnancy but were not consistently associated with GWG in our data.
3.1 Introduction
Abnormal maternal weight gain during pregnancy (i.e., too little or too much) has been associated with unfavourable pregnancy outcomes in both mother and child. Insufficient gestational weight gain (GWG) is associated with both preterm birth and low birthweight (Han et al. 2011), and excessive GWG increases the risk of giving birth to large-for-gestational-age infants (Kim et al. 2014). Excessive GWG is also associated with maternal pregnancy complications, including hypertensive disorders (Johnson et al. 2013; Gaillard et al. 2013) and gestational diabetes (Hedderson, Gunderson, and Ferrara 2010), which can increase the risk of the mother developing cardiometabolic diseases after pregnancy (Brown et al. 2013; Bellamy et al. 2009).
Energy intake during pregnancy is associated with GWG (Gaillard et al. 2013; Streuling et al. 2011), but literature is scarce on whether GWG could be influenced by dietary composition. Some studies have examined the influence of food groups on GWG (Stuebe, Oken, and Gillman 2009; Olafsdottir et al. 2006; Martins and Benicio 2011). These studies found no association of fruit or vegetable intake with GWG (Stuebe, Oken, and Gillman 2009; Martins and Benicio 2011) but unhealthier foods (e.g., sweets and processed foods) were associated with higher prevalence of excessive GWG (Stuebe, Oken, and Gillman 2009; Olafsdottir et al. 2006; Martins and Benicio 2011). Weight gain during pregnancy involves both maternal components (e.g., blood volume increase, fat accretion) and fetal components (e.g., weight of the fetus, amniotic fluid) (Pitkin 1976). Therefore, the effect of diet on weight gain may differ between pregnant and non-pregnant women.
Assessing overall diet in relation to GWG has several advantages over studying individual foods or nutrients. First, the intakes of different nutrients are often highly correlated, which complicates the assessment of individual nutrients (Hu 2002). Second, possible associations between nutrient intake and GWG might be affected by biological interactions between nutrients (Hu 2002). For these reasons, evaluating diet using a dietary pattern approach may improve our understanding of which patterns of diet are most beneficial during pregnancy. Moreover, this approach can facilitate future food-based dietary guidelines (World Health Organization and Food and Agriculture Organization of the United Nations 1998).
Only a few studies have focused on the relationship between dietary patterns and GWG (Uusitalo et al. 2009; Shin et al. 2014; Rifas-Shiman et al. 2009; Hillesund et al. 2014). However, no study evaluated dietary patterns and longitudinal development of weight during pregnancy. We hypothesized that specific dietary patterns may influence the development of maternal weight during pregnancy. In addition, dietary patterns are likely to differ between countries and populations (Hu 2002), hence, it is important to identify country-specific dietary patterns that may be associated with GWG.
Therefore, the purpose of our study was to determine whether a priori-defined and a posteriori-derived dietary patterns are associated with weight development during pregnancy and GWG during different phases in pregnancy in Dutch women participating in a population-based cohort.
3.2 Experimental Section
3.2.1 Study Design
This study was embedded in the Generation R Study, a population-based prospective cohort from fetal life onwards in Rotterdam, the Netherlands. Details of this study have been described previously (Kruithof et al. 2014). Briefly, pregnant women with an expected delivery date between April 2002 and January 2006, living in the urban area around Rotterdam, were approached to participate. All participants provided written informed consent. The study was conducted according to the World Medical Association Declaration of Helsinki and was approved by the Medical Ethics Committee, Erasmus Medical Center Rotterdam (the Netherlands, MEC<PHONE_NUMBER>.31).
3.2.2 Population of Analysis
For the current analysis, women of Dutch ancestry who entered the Generation R Study during pregnancy (n = 4097) were included. Women of non-Dutch ancestry were excluded from the analysis since the dietary assessment method that was used was designed to evaluate a Dutch diet. Furthermore, women with missing dietary information (n = 538) were excluded and only women with singleton live births (n = 3479) were considered. For five women weight during pregnancy was not measured and women who were underweight before pregnancy (body mass index (BMI) < 18.5 kg/m2; n = 100) were excluded, leaving 3374 women for the current analysis (Figure ).
3.2.3 Dietary Assessment
Dietary intake in early pregnancy was assessed at enrolment (median: 13.4 weeks of gestation, Q3 – Q1: 12.2 – 15.5) using a 293-item semi-quantitative food-frequency questionnaire (FFQ) that covered dietary intake over the preceding three months. The FFQ contained questions regarding foods that are frequently consumed in a traditional Dutch diet, their consumption frequency, portion size (Donders-Engelen and Heijden 2003), preparation methods, and additions to foods. The average daily intake of energy and nutrients was calculated using the Dutch food-composition table (Netherlands Nutrition Center 2006). The FFQ was designed for and validated in an elderly population (Klipstein-Grobusch et al. 1998), and has additionally been validated against three 24-h dietary recalls in 71 Dutch pregnant women who visited a midwifery in Rotterdam. The intra-class correlation coefficients for energy-adjusted macronutrients ranged between 0.48 and 0.68.
A Posteriori-Derived Dietary Patterns
We used three a posteriori-derived dietary patterns, which had been identified previously by factor analysis using the principal component method and Varimax rotation (Hu 2002; Kaiser 1958), and are described in detail elsewhere (Van den Broek et al. 2015). Briefly, the 293 individual food items from the FFQ were aggregated into 23 food groups. Subsequently, factors that had an eigenvalue of \(\geq\) 1.5 were extracted and are regarded as dietary patterns (Jolliffe 2011). The factor loadings, which describe the contribution of each food group to each of the patterns, are presented in Table 3.1. Finally, factor scores (i.e., adherence scores) for each participant and each pattern were determined by calculating the individual sum of the intake of the food groups, weighting them with their factor loadings and standardizing those weighted sums to have mean zero and standard deviation one (standard deviation score). A higher factor score indicates that a woman’s diet was closer to that dietary pattern.
Three a posteriori-derived dietary patterns were thereby identified, namely a “Vegetable, oil and fish” pattern, a “Nuts, high-fibre cereals and soy” pattern and a “Margarine, sugar and snacks” pattern, together explaining 25.8% of the variance in maternal dietary intake.
| Food Group | Vegetable, Oil and Fish | Nuts, High-Fibre Cereals and Soy | Margarine, Sugar and Snacks |
|---|---|---|---|
| potatoes and other tubers | 0.05 | -0.53 | 0.21 |
| vegetables | 0.78 | 0.17 | -0.03 |
| fruits | 0.13 | 0.37 | 0.02 |
| dairy products—high fat | 0.26 | -0.26 | 0.29 |
| dairy products—low fat | -0.15 | 0.29 | 0.16 |
| cereals—high fiber | 0.24 | 0.43 | 0.36 |
| cereals—low fiber | 0.23 | -0.16 | 0.25 |
| meat and meat products | 0.08 | -0.54 | 0.33 |
| fish and shellfish | 0.45 | 0.24 | -0.11 |
| eggs and egg products | 0.27 | 0.05 | 0.19 |
| vegetable oils | 0.74 | 0.08 | -0.12 |
| margarine and butter | -0.06 | -0.03 | 0.61 |
| sugar and confectionary and cakes | -0.11 | 0.13 | 0.56 |
| snacks | 0.05 | 0.08 | 0.4 |
| coffee and tea | 0.28 | 0.34 | 0.1 |
| sugar-containing beverages | -0.14 | -0.28 | 0.29 |
| light soft drinks | 0.13 | 0.28 | 0.02 |
| alcoholic beverages | 0.35 | 0 | -0.04 |
| condiments and sauces | 0.05 | -0.09 | 0.39 |
| soups and bouillon | 0.19 | -0.02 | 0.15 |
| nuts, seeds and olives | 0.03 | 0.64 | 0.3 |
| soy products | 0.01 | 0.39 | -0.1 |
| legumes | 0.44 | -0.02 | 0.07 |
<IP_ADDRESS> A Priori-Defined Dietary Pattern
The a priori-defined dietary pattern was based on the Dutch Healthy Diet Index (Van Lee et al. 2012). This index was developed to measure adherence to the Dutch guidelines for a healthy diet (Health Council of the Netherlands 2006) and consisted of ten components: physical activity, vegetable, fruit, dietary fibre, fish, saturated fatty acids, trans-fatty acids, consumption of acidic drinks and foods, sodium, and alcohol. We omitted the components physical activity, trans-fatty acids, and the consumption of acidic drinks and foods because this information had not been collected. Furthermore, we did not include the alcohol component as alcohol abstinence is recommended during pregnancy. The score of each component ranged between 0 and 10 points, resulting in a total score ranging from 0 to 60 points. A higher score on the Dutch Healthy Diet Index corresponds with a higher adherence to the 2006 Dutch healthy diet guidelines and thus reflects a healthier diet. Finally, to facilitate comparison between all dietary patterns, we standardized the “Dutch Healthy Diet Index” pattern to a standard deviation score.
3.2.4 Maternal Weight Gain
Information on pre-pregnancy weight was collected at enrolment using a questionnaire and was used to calculate pre-pregnancy BMI (kg/m2). Women visited our research centre three times at median gestational ages of 12.9 (Q3 – Q1: 12.1 – 14.4) weeks (first visit), 20.4 (Q3 – Q1: 19.9 –- 21.1) weeks (second visit), and 30.2 (Q3 – Q1: 29.9 – 30.8) weeks (third visit). During each visit, maternal height and weight were measured without shoes and heavy clothing. Six weeks after childbirth, women were asked to report their highest weight during pregnancy using a questionnaire, which we used as maximum weight in pregnancy.
Gestational Weight Gain during Different Phases in Pregnancy
GWG in different phases of pregnancy was calculated for three consecutive periods, namely early-pregnancy GWG (calculated as weight at the first visit minus pre-pregnancy weight, divided by follow-up duration (g/week), n = 2425), mid-pregnancy GWG (calculated as weight at the second visit minus weight the first visit, divided by follow-up duration (g/week), n = 2748), and late-pregnancy GWG (calculated as weight at the third visit minus weight at the second visit, divided by follow-up duration (g/week), n = 3158). GWG until early-third trimester was calculated as weight at the third visit minus pre-pregnancy weight, divided by follow up duration (g/week, n = 2815).
3.2.5 Covariates
Several maternal sociodemographic and lifestyle characteristics were considered as potential confounders. We obtained information from prenatal questionnaires that were sent in different trimesters regarding maternal age, educational level (Van Rossem et al. 2009), household income (\(\leq\) 2200 vs >2200 euro/month), parity (no child vs \(\geq\) 1 child), pre-pregnancy weight, pre-existing comorbidities, vomiting, smoking or alcohol consumption (both categorized as never during pregnancy, stopped when pregnancy was known, or continued throughout pregnancy), folic acid supplementation (started periconceptionally, started first 10 weeks, or no supplementation), energy intake, and stress during pregnancy (using the Global Severity Index (Derogatis and Spencer 1993)). To calculate pre-pregnancy BMI, height was measured at enrolment. Gestational age was determined based on ultrasound examination, and during the third visit an ultrasound was performed to estimate fetal weight. Information on fetal sex was obtained from delivery reports.
3.2.6 Statistical Analyses
In order to adequately estimate the relationship between a posteriori-derived dietary patterns and trajectories of gestational weight in the presence of incomplete covariates, we performed a longitudinal analysis using linear mixed modelling in the Bayesian framework. This method has been described in detail previously (Erler et al. 2016) and allows for simultaneous analysis and imputation of missing values. Briefly, by modelling the joint distribution of exposure, outcome and covariates, all available information is used to impute the missing values, and parameter estimates are obtained that take into account the added uncertainty due to the missing values.
We considered two sets of possible confounders in the analysis. Model 1 was adjusted for gestational age at the mid-point of the follow-up interval and pre-pregnancy BMI. Model 2 was further adjusted for age, educational level, household income, parity, smoking during pregnancy, alcohol consumption during pregnancy, stress during pregnancy, and fetal sex. The selection of potential confounders was based on factors found in the literature and on a change of at least 10% in effect estimate in a preliminary analysis assessing the association of dietary patterns with GWG until early-third trimester. As GWG is related to BMI (Institute of Medicine and National Research Council 2009) and preliminary analyses showed significant interaction terms for the “Vegetable, oil and fish” pattern and the “Nuts, high-fibre cereals and soy” pattern with pre-pregnancy BMI, we stratified all analyses (including the Bayesian linear mixed model) on the basis of weight status (normal-weight: BMI < 25 kg/m2 and overweight: BMI \(\geq\) 25 kg/m2).
In the Bayesian linear mixed model, all main effects from Model 2 (except gestational age at the mid-point of the follow-up interval), interaction terms between the dietary pattern variables and a linear and quadratic effect for gestational age were included in the fixed effects structure. Correlation between weight measurements within each individual was modelled by including random effects for the intercept and slope (for gestational age) into the model. For this analysis, the reported parameter estimates and 95% credible intervals were obtained by taking the mean and 2.5% and 97.5% quantiles of the posterior samples.
To analyse the association of the a priori-defined and a posteriori-derived dietary patterns with GWG during different phases in pregnancy, specifically GWG during each trimester, until early third trimester and maximal GWG, we performed multivariable linear regression analysis on imputed datasets. For these analyses, ten imputed values were drawn from the posterior samples of each of the incomplete covariates derived in the Bayesian analysis described above. Missing observations of gestational weight were not imputed. The reported results from the cross-sectional models were pooled over all ten completed datasets using Rubin’s rules (Rubin 1987).
Sensitivity Analyses
To test the stability of the results, four sensitivity analyses for the association between dietary patterns and GWG until early-third trimester were performed in Model 2. First, because energy intake may be an intermediate factor in the association of maternal diet with GWG, we further adjusted for energy intake (kcal/day). Second, we excluded women with pre-existing comorbidities (n = 182) and women with hypertensive complications in pregnancy (Coolman et al. 2010) or gestational diabetes (n = 272) since these conditions may influence both dietary intake and GWG. Third, we excluded women who reported vomiting more than once per week during the three months prior to enrolment (n = 421), since this might alter dietary intake and GWG. Moreover, we explored effect modification of the association between dietary patterns and GWG with educational level and household income.
Additionally, we evaluated whether the associations of dietary patterns with GWG would markedly change when using self-reported maximum weight during pregnancy instead of measured weight at the third visit (n = 1917). Furthermore, we explored long-term maternal weight gain and evaluated whether this long-term weight gain differed in women with inadequate, adequate or excessive GWG using Analysis of Variance (ANOVA). The cut-off values of adequate weekly GWG were 0.35 –- 0.50 kg/week for normal weight women, 0.23 –- 0.33 kg/week for overweight women, and 0.17 –- 0.27 kg/week for obese women (Institute of Medicine and National Research Council 2009). Finally, we calculated the correlation between weight at the third visit and weight 6 years after childbirth.
All statistical analyses were performed in SPSS version 21.0 (IBM Corp., Armonk, NY, USA) or R version 3.2.1 (R Foundation for Statistical Computing, Vienna, Austria) and JAGS version 3.4.0 (Plummer 2003).
3.3 Results
3.3.1 Study Population
Baseline characteristics for normal-weight women (n = 2544; 75%) and overweight women (n = 830; 25%) are presented in Table 3.2. The mean score \(\pm\) SD on the Dutch Healthy Diet Index was \(32 \pm 8\) and ranged from 8 to 59. Overall, 43% of women had excessive GWG (n = 826); excessive GWG was found in 37% of the normal-weight women (n = 557) and in 63% of the overweight women (n = 269).
| subject characteristic |
normal-weight women (n = 2544) |
overweight women (n = 830) |
|---|---|---|
| Age (years) | 31.6 (\(\pm\) 4.3) | 31.0 (\(\pm\) 4.4) |
| Educational level (1.3% NA) | ||
| Low and midlow | 307 (12.1%) | 201 (24.2%) |
| Midhigh | 1283 (50.4%) | 436 (52.5%) |
| High | 954 (37.5%) | 193 (23.3%) |
| Household income (10.3% NA) | ||
| < 2200 Euro/month | 620 (24.4%) | 266 (32.1%) |
| \(\geq\) 2200 Euro/month | 1924 (75.6%) | 564 (67.9%) |
| Parity (0.2% NA) | ||
| 0 | 1554 (61.1%) | 465 (56.0%) |
| \(\geq\) 1 | 990 (38.9%) | 365 (44.0%) |
| Pre-pregnancy BMI (kg/m2; 14.2% NA) | 21.6 [20.4, 23.0] | 27.7, [26.0, 30.5] |
| Smoking during pregnancy (7.4% NA) | ||
| Never during pregnancy | 1911 (75.1%) | 612 (73.7%) |
| Until pregnancy was known | 233 (9.2%) | 61 (7.3%) |
| Continued throughout pregnancy | 400 (15.7%) | 157 (19.0%) |
| Alcohol consumption during pregnancy (8.1% NA) | ||
| Never during pregnancy | 764 (30.0%) | 359 (43.2%) |
| Until pregnancy was known | 416 (16.4%) | 138 (16.6%) |
| Continued throughout pregnancy | 1364 (53.6%) | 334 (40.2%) |
| Stress during pregnancy (score 0–4; 12% NA) | 0.12 [0.06, 0.24] | 0.13 [0.06; 0.26] |
| Energy intake (kcal/day) | 2162 (\(\pm\) 507) | 2090 (\(\pm\) 514) |
| Dutch Healthy Diet Index (score 0–60) | 32 (\(\pm\) 8) | 30 (\(\pm\) 8) |
| Fetal sex | ||
| Male | 1287 (50.6%) | 415 (50.0%) |
| Female | 1257 (49.4%) | 415 (50.0%) |
| Gestational weight gain (kg; 43.2% NA) | 14.7 (\(\pm\) 7.3) | 12.9 (\(\pm\) 7.7) |
| Adequacy of gestational weight gain (43.2% NA) | ||
| Inadequate | 370 (24.8%) | 89 (20.9%) |
| Adequate | 565 (37.9%) | 67 (15.8%) |
| Excessive | 557 (37.3%) | 269 (63.3%) |
3.3.2 Dietary Patterns and Trajectories of Gestational Weight
Results from the Bayesian linear mixed models for normal-weight and overweight women are summarized in Table 3.3 and visualized in Figure 3.2. The figure shows the expected gestational weight over time with corresponding 95% credible intervals under six different scenarios. In each scenario the expected weight, either for normal-weight or overweight women, is shown for a -0.5 SDS and 0.5 SDS adherence to one of the a posteriori-derived dietary patterns, while the adherence to the other patterns and confounders are held constant at reference values (0 SDS for adherence to other dietary patterns, and population median and reference category for continuous and categorical confounders, respectively).
In both normal-weight and overweight women, there was no evidence of either main effects of diet or interaction with gestational age for most dietary patterns (Table 3.3). Only adherence to the “Margarine, sugar and snacks” pattern was associated with higher weight in normal-weight women (0.30; 95% CI [0.07, 0.52]) and the “Nuts, high-fibre cereals and soy” pattern was associated with slightly slower weight gain in normal-weight women (-0.01; 95% CI [-0.02, -0.00]).
| pattern | 0.22 [-0.01, 0.44] | 0.26 [-0.18, 0.72] |
| pattern | 0.15 [-0.08, 0.39] | -0.31 [-0.77, 0.14] |
| pattern | 0.30 [0.07, 0.52] | 0.32 [-0.07, 0.71] |
| gest. age | 0.13 [0.12, 0.14] | 0.10 [0.08, 0.12] |
| gest. age | 0.01 [0.01, 0.01] | 0.01 [0.01, 0.01] |
| gest. age \(\times\) pattern | -0.00 [-0.01, 0.01] | 0.01 [-0.01, 0.02] |
| gest. age \(\times\) pattern | -0.01 [-0.02, -0.00] | 0.01 [-0.00, 0.02] |
| gest. age \(\times\) pattern | 0.01 [-0.00, 0.01] | 0.00 [-0.01, 0.02] |
3.3.3 Dietary Patterns and Gestational Weight Gain in Different Phases in Pregnancy
For normal-weight women a 1 SDS higher adherence to the “Vegetable, oil and fish” pattern was associated with a 16 g/week (95% CI [6.5, 25.6]) greater early-pregnancy GWG when adjusting for lifestyle and sociodemographic variables (Table 3.4). We observed no such association in overweight women (Table 3.5). The “Nuts, high-fibre cereals and soy” pattern was associated with lower early-pregnancy GWG in Model 1 in both normal-weight and overweight women. However, after additional adjustment (Model 2) this pattern was no longer significantly associated with early-pregnancy GWG. Neither the “Margarine, sugar and snacks” pattern nor the “Dutch Healthy Diet Index” pattern was associated with early-pregnancy GWG.
| a posteriori dietary patterns | ||
| Vegetable, Oil and Fish | 14.3 [ 5.2, 23.4] | 16.0 [ 6.5, 25.6] |
| Nuts, High-Fibre Cereals and Soy | -14.5 [-23.7, -5.3] | -6.5 [-16.4, 3.5] |
| Margarine, Sugar and Snacks | 7.6 [ -1.7, 17.0] | 4.4 [ -4.9, 13.7] |
| a priori dietary pattern | ||
| Dutch Healthy Diet Index | 0.8 [ -8.3, 9.9] | -6.2 [-15.5, 3.1] |
| a posteriori dietary patterns | ||
| Vegetable, Oil and Fish | 5.8 [-17.8, 29.5] | 14.8 [-11.2, 40.7] |
| Nuts, High-Fibre Cereals and Soy | -28.8 [-52.2, -5.4] | -24.0 [-48.8, 0.7] |
| Margarine, Sugar and Snacks | 14.1 [ -7.3, 35.5] | 12.4 [ -9.0, 33.9] |
| a priori dietary pattern | ||
| Dutch Healthy Diet Index | 10.8 [-10.3, 31.9] | 2.4 [-19.5, 24.2] |
No significant associations were found for any of the dietary patterns with mid-pregnancy GWG in normal-weight or overweight women (Tables 3.6 and 3.7). Tables 3.8 and 3.9 show that in normal-weight women, only the “Nuts, high-fibre cereals and soy” pattern was inversely associated with late-pregnancy GWG in Model 1, but these results largely attenuated after adjustment for sociodemographic and lifestyle factors (Model 2). In overweight women, none of the dietary patterns was significantly associated with late-pregnancy GWG.
| a posteriori dietary patterns | ||
| Vegetable, Oil and Fish | -8.5 [-22.0, 5.0] | -7.1 [-21.4, 7.2] |
| Nuts, High-Fibre Cereals and Soy | 9.5 [ -4.1, 23.2] | 11.3 [ -3.6, 26.2] |
| Margarine, Sugar and Snacks | 3.9 [ -9.7, 17.5] | 4.2 [ -9.5, 18.0] |
| a priori dietary pattern | ||
| Dutch Healthy Diet Index | 3.2 [-10.2, 16.6] | 2.8 [-11.2, 16.7] |
| a posteriori dietary patterns | ||
| Vegetable, Oil and Fish | 35.6 [ 6.1, 65.1] | 19.1 [-13.8, 52.0] |
| Nuts, High-Fibre Cereals and Soy | 23.5 [ -5.7, 52.7] | 3.5 [-28.0, 34.9] |
| Margarine, Sugar and Snacks | 8.3 [-18.3, 34.8] | 14.5 [-12.3, 41.4] |
| a priori dietary pattern | ||
| Dutch Healthy Diet Index | -17.9 [-44.7, 8.9] | -2.2 [-30.0, 25.7] |
| a posteriori dietary patterns | ||
| Vegetable, Oil and Fish | -8.3 [-18.3, 1.6] | -3.9 [-14.4, 6.6] |
| Nuts, High-Fibre Cereals and Soy | -13.2 [-23.4, -3.0] | -4.6 [-15.6, 6.3] |
| Margarine, Sugar and Snacks | 0.3 [ -9.8, 10.5] | 0.4 [ -9.8, 10.5] |
| a priori dietary pattern | ||
| Dutch Healthy Diet Index | 3.2 [ -6.7, 13.2] | -1.5 [-11.8, 8.8] |
| a posteriori dietary patterns | ||
| Vegetable, Oil and Fish | -3.8 [-24.5, 16.9] | 7.4 [-15.5, 30.2] |
| Nuts, High-Fibre Cereals and Soy | 4.4 [-17.0, 25.8] | 9.5 [-13.4, 32.3] |
| Margarine, Sugar and Snacks | -8.5 [-28.0, 11.1] | -9.8 [-29.5, 10.0] |
| a priori dietary pattern | ||
| Dutch Healthy Diet Index | 2.1 [-17.6, 21.7] | 0.2 [-20.1, 20.5] |
3.3.4 Sensitivity Analyses
The results of the sensitivity analyses are presented in Tables 3.10 – 3.14. In line with the results from early-pregnancy GWG, normal-weight women with a 1 SDS higher adherence to the “Vegetable, oil and fish” pattern had 6.7 g/week (95% CI [0.9, 12.5]) higher GWG until the early-third trimester, whereas no association was found in overweight women. The other dietary patterns were not associated with GWG until the early-third trimester (Table 3.10).
Additional adjustment for energy intake resulted in attenuation of the effect estimate of the “Vegetable, oil and fish” pattern with GWG until early-third trimester. The results did not alter greatly after exclusion of women who vomited more than once per week (Table 3.12) or exclusion of women with pre-existing comorbidities or pregnancy complications (Table 3.13). The evaluation of maximum GWG showed results that were in line with the results of the previous analyses (Table 3.14).
The association between dietary patterns and GWG was not modified by educational level or household income.
Six years after childbirth, women had gained on average 3.4 kg (Q1, Q3: 0.4, 7.0) compared to their pre-pregnancy weight (n = 2247). The median (Q1, Q3) long-term weight gain was significantly different between the categories of GWG adequacy: women with inadequate GWG gained 2.2 kg (-0.6, 5.2), those with adequate GWG gained on average 2.6 kg (0.2, 5.2), and women with excessive GWG were 4.6 kg (1.4, 8.8) heavier (F-test 27.5, p-value < 0.001). The weight 6 years after childbirth was highly correlated with the weight at the third visit in pregnancy (R = 0.85; p-value < 0.001).
3.4 Discussion
3.4.1 Summary of the Main Findings
The results from our analysis of a population-based Dutch cohort suggest that specific a posteriori-derived dietary patterns have limited influence on early-pregnancy GWG, the prevalence of excessive GWG, and weight development in pregnancy. We neither found consistent associations of any dietary pattern with the prevalence of inadequate GWG nor was the a priori-defined dietary pattern associated with GWG.
3.4.2 Interpretation and Comparison with other Studies
The association of dietary patterns during pregnancy with GWG has been evaluated previously in a few studies (Uusitalo et al. 2009; Shin et al. 2014; Rifas-Shiman et al. 2009; Hillesund et al. 2014), but these studies did not evaluate longitudinal development of gestational weight and were conducted in different populations. Uusitalo et al. (2009) found that higher adherence to an a posteriori-derived dietary pattern characterized by high intake of sweets, fast food and snacks was associated with higher weekly GWG. In line with these results, we found that higher adherence to the unhealthy “Margarine, sugar and snacks” pattern was associated with higher prevalence of excessive GWG. Additionally, Uusitalo et al. (2009) reported that a pattern that was high in vegetables, fish and fruits was not associated with GWG. In contrast, we found that the “Vegetable, oil and fish” pattern, a relatively healthy pattern, was associated with higher GWG in early pregnancy.
In our study, the a priori-defined “Dutch Healthy Diet Index” pattern was not consistently associated with any measure of GWG. This result was in accordance with two studies showing no relationship between the a priori-defined “US healthy eating index of 2005” (HEI-2005) and the “Alternate Healthy Eating Index, slightly modified for pregnancy” (AHEI-P) with the prevalence of inadequate or excessive GWG (Shin et al. 2014; Rifas-Shiman et al. 2009). Nevertheless, a large population-based cohort study of over 66,000 participants found that high adherence to the a priori-defined “New Nordic Diet score” was associated with a 7% lower prevalence of excessive GWG in normal weight women, compared with low adherence (Hillesund et al. 2014). The inconsistency in findings regarding the associations between the a priori-defined dietary patterns may be due to different items that were included in the diet scores. The “New Nordic Diet score” contained items on meal patterns and the type of beverages consumed, among others (Hillesund et al. 2014); these items were not evaluated in our Dutch Healthy Diet Index, nor in other a priori-defined dietary patterns (Shin et al. 2014; Rifas-Shiman et al. 2009).
The association of a posteriori-derived dietary patterns with weight trajectories over pregnancy has not been evaluated previously, to our knowledge. Studying this association longitudinally has the advantage that all available weight measurements can be used while taking into account the correlation between these measurements. In addition, weekly GWG is not constant over pregnancy and differs considerably by individual (Institute of Medicine and National Research Council 2009; Carmichael, Abrams, and Selvin 1997), which complicates cross-sectional comparisons of GWG. Our longitudinal analysis showed that women with higher adherence to the “Nuts, high-fibre and soy” pattern had a more moderate increase in weight during pregnancy than did women with low adherence to this dietary pattern, although absolute differences were small.
Results from both observational and interventional studies indicated that women with higher energy intake had higher GWG compared with women who have lower energy intake (Streuling et al. 2011), results that were also found in our cohort (Gaillard et al. 2013). In our analyses, the association of the “Vegetable, oil and fish” pattern remained significantly associated with GWG after additional adjustment for energy intake. This may indicate that dietary patterns are associated with GWG beyond energy intake.
Evaluating weight gain in pregnancy is important because GWG has been associated with many adverse pregnancy and birth outcomes. Gaining excessive weight during pregnancy can have short-term consequences such as delivery complications, and giving birth to a child that is large for its gestational age (Johnson et al. 2013; Gaillard et al. 2013; Institute of Medicine and National Research Council 2009). Additionally, it has been associated with long-term health consequences including post-partum obesity of the mother (Nehring et al. 2011) due to retaining their excess fat mass, and childhood obesity (Gaillard et al. 2013). Indeed, in our population, six years after childbirth women had gained on average 3.4 kg compared to their pre-pregnancy weight.
Weight gain during pregnancy consists of several maternal and fetal components that contribute differently to GWG over time (Pitkin 1976). For example, during the first half of pregnancy, maternal fat gain is a major contributor of GWG (Clapp et al. 1988; Kopp-Hoolihan et al. 1999), and most of the fat gain that takes place during pregnancy is in that period (Forsum, Sadurskis, and Wager 1988). In our study, the higher GWG in women with high adherence to the “Vegetable, oil and fish” pattern could not be explained by fetal growth and was mainly found in early pregnancy, meaning this higher GWG is likely due to maternal components, e.g., fat mass.
Our results for normal-weight women differed from those for overweight women, particularly for the “Vegetable, oil and fish” pattern and for the “Nuts, high-fibre cereals and soy” pattern. Similarly, Hillesund et al. (2014) reported differential associations for women below and above a BMI of 25 kg/m2. These difference in findings may be explained by differences in the reporting of dietary intake (Freisling et al. 2012) or by differing contribution of the individual components of GWG for normal-weight and overweight women (Butte et al. 2003). In addition, our longitudinal analyses showed that over the whole course of pregnancy, normal-weight women with higher adherence to the “Margarine, sugar and snacks” pattern tend to be heavier than women with lower adherence.
3.4.3 Strengths and Limitations
A strength of our study is that we used a comprehensive approach to analyse the relation between diet and GWG by evaluating the associations of dietary patterns with trajectories of gestational weight and GWG during different phases in pregnancy. Another strength is the use of two distinct methods to define dietary patterns, which enabled us to evaluate the effects of dietary patterns derived by a data-driven and by a hypothesis-driven approach. Dietary patterns represent the combined effects of all foods consumed (Hu 2002), which may lead to a more powerful effect than the effects of the individual components, although it may also have led to a dilution of the effects of individual components that are associated with GWG (Willett 2012). For example, the food groups of vegetables and high-fat dairy products were strongly associated with the “Vegetable, oil and fish” dietary pattern. Yet, higher intake of fruits and vegetables has been associated with lower GWG (Olson and Strawderman 2003), whereas dairy products were associated with higher GWG (Stuebe, Oken, and Gillman 2009; Olafsdottir et al. 2006). Consequently, this may result in an overall null effect of the dietary pattern. Furthermore, imputing the missing covariate values in the Bayesian framework allowed us to use all available information in the imputation. Especially in settings with a longitudinal outcome, imputation methods that are available in standard software and, hence, are more commonly used, often fail to appropriately include the outcome into the imputation procedure which may lead to severely biased results (Erler et al. 2016). Other strengths of our study are its population-based design, the collection of numerous covariates, and that the population was restricted to women of Dutch ancestry. We excluded women with other ethnicities to minimize measurement error since the FFQ was designed to evaluate a Dutch diet. However, this restriction may have reduced the generalizability of our results to other ethnicities.
Our study also has some limitations. First, maternal weight before pregnancy, as well as maximum weight, were obtained using questionnaires, which may have resulted in a larger measurement error. Although we found no indication of systematic measurement error, random error may have resulted in loss of precision in GWG assessment. Furthermore, we were not able to calculate GWG per trimester because we did not have weight measurements at the required time points and the available data was insufficient for imputing those values. Another limitation is the lack of information on the separate components of GWG, in particular, maternal fat mass, and the lack of information on postpartum maternal weight. Future studies should collect detailed information on maternal body composition during pregnancy or measure the participants’ weight a few weeks postpartum to evaluate associations with the different components of GWG. Furthermore, we could not use information on absolute dietary intake because dietary information collected using an FFQ does not provide this information. However, FFQs have been shown to be accurate in ranking participants according to their dietary intake (Kipnis et al. 2003a). Furthermore, we assessed maternal diet only once during pregnancy and were therefore not able to account for changes in dietary intake. Nevertheless, dietary patterns and macronutrient composition may not change largely during pregnancy despite an increased energy intake (Crozier et al. 2009; Rad et al. 2011). Additionally, we found that our results did not change after excluding women who may have altered their dietary intake due to illness or vomiting. Finally, the numerous statistical analyses performed may have resulted in chance findings (type I error). However, our results for weight trajectories and early-pregnancy GWG remained statistically significant when a more stringent significance level of 1 - 0.05/4 = 0.9875 was used.
3.4.4 Conclusions and Implications
In conclusion, our results suggest that dietary composition during pregnancy may play a role in GWG in early pregnancy but has limited influence on total GWG in a population of Dutch women. The strength of the associations between dietary patterns and GWG differs for different definitions of dietary patterns and GWG. This suggests that the relationship between dietary patterns and GWG may be complex and may need further elucidation in order to facilitate the development of dietary guidelines during pregnancy and to adequately advise pregnant women on their diet.
Appendix
3.A Supplementary Materials
| a posteriori dietary patterns | ||
| Vegetable, Oil and Fish | 6.7 [ 0.9, 12.5] | 10.0 [ -5.0, 25.1] |
| Nuts, High-Fibre Cereals and Soy | -2.0 [ -8.1, 4.0] | -5.6 [-20.7, 9.4] |
| Margarine, Sugar and Snacks | 2.5 [ -3.2, 8.2] | 6.0 [ -7.2, 19.1] |
| a priori dietary pattern | ||
| Dutch Healthy Diet Index | -4.3 [ -10.0, 1.4] | 1.6 [-11.7, 14.9] |
| a posteriori dietary patterns | ||
| Vegetable, Oil and Fish | 3.4 [ -3.0, 9.8] | 8.9 [ -7.3, 25.2] |
| Nuts, High-Fibre Cereals and Soy | -4.8 [-11.2, 1.7] | -6.5 [-22.3, 9.3] |
| Margarine, Sugar and Snacks | -11.8 [-24.6, 1.0] | 1.0 [-29.8, 31.8] |
| a priori dietary pattern | ||
| Dutch Healthy Diet Index | -4.3 [ -10.0, 1.4] | 1.6 [-11.7, 14.9] |
| a posteriori dietary patterns | ||
| Vegetable, Oil and Fish | 8.0 [ 2.0, 14.0] | 9.5 [ -6.0, 24.9] |
| Nuts, High-Fibre Cereals and Soy | -1.4 [ -7.6, 4.9] | -6.0 [-21.6, 9.7] |
| Margarine, Sugar and Snacks | 1.9 [ -4.0, 7.8] | 9.5 [ -4.8, 23.8] |
| a priori dietary pattern | ||
| Dutch Healthy Diet Index | -5.7 [-11.6, 0.2] | 1.3 [-12.6, 15.1] |
| a posteriori dietary patterns | ||
| Vegetable, Oil and Fish | 4.8 [ -1.3, 10.8] | 14.2 [ -2.1, 30.5] |
| Nuts, High-Fibre Cereals and Soy | -2.3 [ -8.6, 4.0] | -7.7 [-23.8, 8.5] |
| Margarine, Sugar and Snacks | 0.5 [ -5.3, 6.4] | 5.2 [ -9.5, 19.8] |
| a priori dietary pattern | ||
| Dutch Healthy Diet Index | -5.2 [-11.0, 0.7] | -4.8 [-19.3, 9.7] |
| a posteriori dietary patterns | ||
| Vegetable, Oil and Fish | 4.4 [ -4.9, 13.8] | -9.3 [-34.0, 15.4] |
| Nuts, High-Fibre Cereals and Soy | 1.4 [ -8.3, 11.2] | 5.2 [-19.5, 29.8] |
| Margarine, Sugar and Snacks | 8.1 [ -1.2, 17.5] | 17.8 [ -6.0, 41.6] |
| a priori dietary pattern | ||
| Dutch Healthy Diet Index | -0.8 [-10.3, 8.7] | 14.4 [ -8.3, 37.2] |
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I started taking Paxil after being clinically diagnosed with Social Anxiety Disorder and panic attacks. Now, I have always been willing to try anything once, so I followed my doctors advice.
It took about 1 week for me to notice the effects of the drug. I started on 10 mg a day and increased to 20 mg in two weeks. After 2 weeks I was able to function in society without panicking and wanting to die. It was instant confidence for me which before I had only been able to reach through alcohol or E. Friends and family think it is a miracle. A few friends often get irratated and jealous of my new-found happiness and upbeat nature.
After a visit to a new doctor today, he mentioned
a few intersting things.
1. That I will need to be on Paxil for the rest of my life or until medical science develops something other than a pink pill.
2. That research shows Paxil is re-constructing DNA in certain areas of the brain.... ??? This is quite interesting
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A few side effects I have noticed...
some are fun... some are not.
1. Balance - like I am walking on a rocky boat from time to time.
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7. Have always been blessed by the gift of telepathy, it is now slowly withering away. Can no longer read minds, and this saddens me.
I am still taking Paxil and will continue for fear of falling back into my old self. I am now vey scared for what will happen if I stop taking the drug. So in fact, medical science has now trapped me financially and emotionally. I am now dependent on a little pink pill... until there is a new teal, turquoise or fuschia pill.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


This documentation follows the text conventions as described below.
External applications are indicated in italics, Helvatica, deep-red such as: Tracker.
Names of files, paths etc. are formatted in Teletype in a green such as: /boot/system/apps or Beezer.hpkg etc.
Keyboard strokes, combinations are indicated in bold, Teletype, purple such as: the SHIFT key!
Menu items, toolbar buttons preference items are formatted using Helvetica, navy such as: Actions –> Extract or toolbar: Extract etc. This means to choose the "Extract" command under the "Actions" menu or to click the "Extract" toolbar button from the toolbar.


ambient.create_nc_db¶
-
ambient.
create_nc_db
(nc_file, summary, source, sea_name, p_lat, p_lon, p_time)[source]¶ Create an empty netCDF4-classic dataset to store ambient data
Creates an netCDF dataset file in netCDF4-classic format for use in storing ambient profile data. This function creates the minimum data structure necessary to run the TAMOC simulation models. This includes temperature, salinity, and pressure as a function of depth, as well as the auxiliary metadata, such as latitude, longitude, and time, etc.
- Parameters
- nc_filefile path or string
File name (supplied as a path or string) to use to create the netCDF database
- summarystring
String containing the text for the dataset global variable summary. This should describe the TAMOC simulation project for which the dataset was created.
- sourcestring
String containing the text for the dataset global variable source. Use this variable to document the original source of the data (e.g., station BM54 recorded by R/V Brooks McCall).
- sea_namestring
String containing the NODC Sea Name. If unsure, consult the list at http://www.nodc.noaa.gov/General/NODC-Archive/seanamelist.txt
- p_latfloat
Latitude of the CTD profile location in decimal degrees from North
- p_lonfloat
Longitude of the CTD profile location in decimal degrees from East
- p_time :
Time the profile was taken in seconds since 1970-01-01 00:00:00 0:00 following a julian calendar. See Examples section for how to create this variable using Python’s datetime module and the netCDF4 modules netCDF4.date2num and netCDF4.num2date.
- Returns
- ncnetCDF4.Dataset object
An object that contains the empty database ready to input profile data
See also
netCDF4
Notes
This function creates an empty netCDF dataset that conforms to the standard format for an Orthogonal Multidimensional Array Representation of Profiles as defined by the National Oceanographic Data Center (NODC). See http://www.nodc.noaa.gov/data/formats/netcdf for more details.
NODC recommends using a netCDF4-classic data format, which does not support groups. This works well for this dataset, so we implement this data format here. The documentation for the Python netCDF4 package here https://code.google.com/p/netcdf4-python/ uses groups throughout the tutorial. The classic format is equivalent to having the single group root_grp.
Examples
>>> from datetime import datetime, timedelta >>> from netCDF4 import num2date, date2num >>> nc_file = './test/output/test.nc' >>> summary = 'Test file' >>> source = 'None' >>> sea_name = 'No Sea Name' >>> p_lat = 28.5 >>> p_lon = 270.7 >>> p_time = date2num(datetime(2010, 5, 30), units = 'seconds since 1970-01-01 00:00:00 0:00', calendar = 'julian') >>> nc = create_nc_db(nc_file, summary, source, sea_name, p_lat, p_lon, p_time) >>> print nc.variables OrderedDict([('time', <netCDF4.Variable object at 0x76f2978>), ('lat', <netCDF4.Variable object at 0x76f2a98>), ('lon', <netCDF4.Variable object at 0x76f2ae0>), ('z', <netCDF4.Variable object at 0x76f2b28>), ('temperature', <netCDF4.Variable object at 0x76f2b70>), ('salinity', <netCDF4.Variable object at 0x76f2bb8>), ('pressure', <netCDF4.Variable object at 0x76f2c00>)]) >>> print num2date(nc.variables['time'][0], units = 'seconds since 1970-01-01 00:00:00 0:00', calendar = 'julian') 2010-05-30 00:00:00 >>> z_data = np.linspace(0., 1500., 30.) >>> nc.variables['z'][:] = z_data >>> nc.variables['z'].valid_max = np.max(z_data) >>> print nc.variables['z'] float64 z(u'z',) long_name: depth below the water surface standard_name: depth units: m axis: Z positive: down valid_min: 0.0 valid_max: 1500.0 unlimited dimensions = (u'z',) current size = (30,) >>> nc.close()


I am currently a Graduate Student Pursuing Masters in Computer Science from University of Albany,New York. I have 1.5+ years of experience in fields of Full Stack, Cloud Services and DevOps. My goal is to be engaged in Software Development building high-performance, robust and scalable applications, also by not being limited to a few technology stacks. Besides technical stuff, I like to travel a lot and explore new places. Since schooling days, I have developed a keen interests in extra curricular as well as sports activities. Let's talk about football and baseball .
I have worked on Web Technologies and Programming languages since the Second Year of my Bachelors.Here are some of the skills which I developed till date. Recently, I have been working on Spring Boot and Data Science. You can look at my complete profile in my resume.
JAVA
PYTHON
Node.js
JAVASCRIPT (ES6+)
MULESOFT
Azure
MYSQL
Docker
Reduced data generation time by 30% using selenium to implement automated authorization testing in online social networks.
Generated system graph using random graph theory for managing dataset of size more than 1 million and to monitor relationship based access control in the system. This helped save approximately 20 % of developers time to gather and analyze data.
Integrated multiple applications by building Docker images to support deployment and testing for CI/CD pipelines. Containerized these applications using Docker swarm services which reduced deployment time by 15 % and improved application performance by 40%.
Designed and implemented 3 layers of the banking application which uses enterprise service bus architecture and integration layer. Developed features of banking application like loan approval process, transaction history, loan registration using springboot.
Developed server side application to interact with database using Spring Boot and Hibernate, RESTful services. Improved 20% of server performance by optimizing API calls using modular application design and light weight dependencies
Automated logs deletion process for all production databases using control-M query scheduler to write optimize query that runs in the background every 15 days which saved approximately 2 weeks worth of manual work every year and improved database response time by 20%
Developed automatic query evaluator tool for generating query. Used natural language processor to determine query sentences automatically by given keywords. Won Infy Insta Award for successfully reducing database operations cost by 60%.
Created Object-Relational (O/R) mapping using Hibernate, Data Access Object persistence layer and improved data consistency.
Constructed various Angular custom directives and developed reusable components and templates to enhance reusability in the application, Created Typescript reusable components and services to consume REST API's Component-based architecture to reduce cost of maintenance.
I am pursuing MS in Computer Science at University Of Albany,New York. I joined university in Spring 2020 and I am expected to graduate in May 2021.
I completed my Bachelors in Information Technology at DDU, Nadiad. During these four years of my undergrad, I learnt various concepts related to computational programming, web development and many more.
I compeleted 10 + 2 Education . Main subjects where Maths, Science ,Languages and Intro to Computer Science.


Automaticator shows trip data from your Automatic on a map in real time. Visualize ignition status, trip completed, and MIL (Check Engine Light) changes.


Kevin van Zonneveld
on 2009-08-04 11:29:23
@ coderjoe & Brett Zamir: awesome : )
Brett Zamir
on 2009-07-29 03:08:54
@coderjoe: Up until you mentioned it, we were using strcoll() to do this, since that is PHP's locale-specific version; however, in SVN, I just changed the behavior of strcoll() to avoid using this built-in but non-transparent JS locale-aware sort function in favor of letting strcoll()'s behavior be configurable through setlocale() (which I also just modified--LC_COLLATE to be specific). This will let people set the locale to whatever locale they wish (though we currently only have an English collating function implemented in setlocale()).
coderjoe
on 2009-07-24 23:31:23
Couldn't this use ECMA-262's String.prototype.localeCompare function?
function strcmp ( str1, str2 ) {
// Binary safe string comparison
// using ECMA-262 section <IP_ADDRESS>
// String.prototype.localeCompare
return str1.localeCompare(str2);
}
coderjoe
on 2009-07-24 23:31:02
Couldn't this use ECMA-262's String.prototype.localeCompare function?
function strcmp ( str1, str2 ) {
// Binary safe string comparison
// using ECMA-262 section <IP_ADDRESS>
// String.prototype.localeCompare
return str1.localeCompare(str2);
}
narendra
on 2009-04-08 11:10:41
just fun
Kevin van Zonneveld
on 2009-02-01 22:05:04
@ Brett Zamir: sweet!
Brett Zamir
on 2009-01-28 17:04:11
Here's a related one...
function strncmp ( str1, str2, lgth ) {
// http://kevin.vanzonneveld.net
// + original by: Waldo Malqui Silva
// + input by: Steve Hilder
// + improved by: Kevin van Zonneveld (http://kevin.vanzonneveld.net)
// + revised by: gorthaur
s1 = str1.substr(0, lgth);
s2 = str2.substr(0, lgth);
return ( ( s1 == s2 ) ? 0 : ( ( s1 > s2 ) ? 1 : -1 ) );
}
alert(strncmp('aaa', 'aab', 2)); // 0
alert(strncmp('aaa', 'aab', 3)); // -1
Kevin van Zonneveld
on 2008-06-18 22:49:20
@ gorthaur: I must admit I personally never use this function in PHP. Thanks for improving php.js.
gorthaur
on 2008-06-18 20:14:20
You gotta be kidding! This code is unbelievably silly and displays gross lack of understanding string comparison. Try these test cases
strcmp( 'waldo', 'owald' );
strcmp( 'owald', 'waldo' );
which should return +1 and -1.
This should work:
function strcmp ( str1, str2 ) {
return ( ( str1 == str2 ) ? 0 : ( ( str1 > str2 ) ? 1 : -1 ) );
}
Kevin van Zonneveld
on 2008-05-08 22:16:21
@ Steve Hilder: I must say I'm not very familiar with this specific function, but I did some reading up on it, and I think I agree that in it's current form it makes no sense. I've updated it so calculate every character in both strings, this is better right.
Steve Hilder
on 2008-05-08 17:24:41
Err... this doesn't work at all; it only evaluates the first character.
strcmp('test', 'tomato') = 0 /* incorrect */


% include ../component/business/head-util %>
<% include ../component/business/header %> <% include ../component/business/nav %> <% include ../component/business/swiper %> <% include ../component/business/equipment/equipment-intro %> <% include ../component/business/footer %> <% include ../component/business/copyright %> <% include ../component/business/script-util %>


Welcome to Cops and Robbers
Cops objective is to stop all robbers.
Robbers objective is to save prisoners.
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Decrease the amount of space between the tool window border and the text in tree views, for example, in the Project tool window.


Responsive table sample
Name
Lastname
Age
Job
Hobby
Marcelo
Tosco
34
Programmer
code
Jenifer
Lawrence
28
Actress
party
Ra
Ra
3000
God
snow ski
Live output
Resize the browser to see it in action.


QUESTION #1
What order of parameter is for error message?
QUESTION #2
What order of parameter is for runtime stack trace?
QUESTION #3
What order of parameter is for error level?
QUESTION #4
What are the different levels that can be used in ErrorHandleFunc?
QUESTION #5
Which of the following is the proper way to initialize the ErrorHandleFunc?
QUESTION #6
Suppose you have the given source code.
uadmin.Trail(uadmin.DEBUG, "Debugging error.")
Result: Debugging error.
Which of the following will print the result as shown above?
QUESTION #7
Suppose you have the given source code.
uadmin.Trail(uadmin.OK, "Password has been accessed without authorization.")
Result: Password has been accessed without authorization.
Which of the following will print the result as shown above?
QUESTION #8
Suppose you have the given source code.
uadmin.Trail(uadmin.WORKING, "Eliminating errors...")
Result: 1
Which of the following will print the result as shown above?
QUESTION #9
Suppose you have the given source code.
uadmin.Trail(uadmin.ERROR, "$#%0X!+=!!!!!!")
Result:
main.main()
/home/user/go/src/github.com/johndoe/project/main.go:19 +0xc9
Which of the following will print the result as shown above?
QUESTION #10
Suppose you have the given source code.
uadmin.Trail(uadmin.WARNING, "The database is near the limit size. Please remove some of your data.")
Result:
main.main()
/home/user/go/src/github.com/johndoe/project/main.go:19 +0xc9
Which of the following will print the result as shown above?
QUESTION #11
Suppose you have the given source code.
uadmin.All("")
Condition:
ErrorHandleFunc checks if the error level is WARNING or ERROR.
Result:
DB error in All(string). unsupported destination, should be slice or struct
Which of the following will print the result as shown above?
QUESTION #12
Suppose you have the given source code.
uadmin.Filter(&models.Animal{}, "")
Condition:
ErrorHandleFunc checks if the error level is WARNING or ERROR.
Result:
DB error in Filter(). record not found
Which of the following will print the result as shown above?
QUESTION #13
Suppose you have the given source code.
uadmin.Get(&models.Furniture{}, true)
Condition:
ErrorHandleFunc checks if the error level is WARNING or ERROR.
Result:
github.com/uadmin/uadmin.Get(0x9cb0e0, 0xc0002a8be0, 0x9ddae0, 0x11313a1, 0x0, 0x0, 0x0, 0xc000086058, 0x0)
/home/user/go/src/github.com/uadmin/uadmin/db.go:242 +0x25a
main.main()
/home/user/go/src/github.com/johndoe/project/main.go:21 +0xe6
Which of the following will print the result as shown above?
QUESTION #14
Suppose you have the given source code.
uadmin.AdminPage("id", true, 0, -1, "", "")
Condition:
ErrorHandleFunc checks if the error level is WARNING or ERROR.
Result:
DB error in AdminPage(string). unsupported destination, should be slice or struct
github.com/uadmin/uadmin.AdminPage(0xc0002d2cc0, 0x9, 0x1, 0x0, 0xffffffffffffffff, 0x9e3200, 0xb6cb10, 0x9e3200, 0xb6cb20, 0x0, ...)
/home/user/go/src/github.com/uadmin/uadmin/db.go:427 +0x686
main.main()
/home/user/go/src/github.com/johndoe/project/main.go:22 +0x11b
Which of the following will print the result as shown above?
QUESTION #15
Suppose you have the given source code.
uadmin.Count("", 15)
Condition:
ErrorHandleFunc checks if the error level is WARNING or ERROR.
Result:
github.com/uadmin/uadmin.Count(0x9e31c0, 0xb6cad0, 0x9e2300, 0xb695d0, 0x0, 0x0, 0x0, 0x0)
/home/dev1/go/src/github.com/uadmin/uadmin/db.go:439 +0x24a
main.main()
/home/dev1/go/src/github.com/rn1hd/builder/main.go:22 +0xd8
DB error in Count(string). no such table:
Which of the following will print the result as shown above?


This page was created and is controled by the creater of Marshy's site, not Baloghpets. Back to exsplor page


'3BlackChicks Review...'
THE VIRGIN SUICIDES (1999) Rated R; running time 98 minutes Genre: Drama IMDB site: http://us.imdb.com/Details?0159097 Official site: http://www.virginsuicides.com/ Written by: Sofia Coppola (based on the novel by Jeffrey Eugenides) Directed by: Sofia Coppola Cast: James Woods, Kathleen Turner, Kirsten Dunst, Josh Hartnett, Hanna Hall, Chelse Swain, A.J. Cook, Leslie Hayman, Anthony DeSimone, Jonathan Tucker (Tim Weiner), Noah Shebib (Parkie Denton), Robert Schwartzmann (Paul Baldino), Lee Kagan (David Barker), Chris Hale (Peter Sisten), Joe Dinicol (Dominic Palazzolo), Suki Kaiser (Lydia Perl, Insensitive Reporter), Dawn Greenhalgh (Mrs. Scheer), Allen Stewart-Coates (Mr. Scheer), Sherry Miller (Mrs. Buell), Jonathon Whittaker (Mr. Buell), Michelle Duquet (Mrs. Denton), Murray McRae (Mr. Denton), Paul Sybersma (Joe Larson), Susan Sybersma (Mrs. Larson), Roberta Hanley (Mrs. Weiner), Peter Snider (Trip's Dad), Gary Brennan (Donald), Charles Boyland (Curt Van Osdol), Dustin Ladd (Chip Willard), Kristin Fairlie (Amy Schraff), Melody Johnson (Julie), Sheyla Molho (Danielle), Ashley Ainsworth (Shelia Davis), Courtney Hawkrigg (Grace), Francois Klanfer (Doctor), Mackenzie Lawrenz (Jim Czeslawski), Tim Hall (Kurt Siles), Amos Crawley (John), Andrew Gillies (Principal Woodhouse), Marilyn Smith (Mrs. Woodhouse), John Deans-Buchan (John), Jaya Karsemeyer (Gloria), Leah Straatsma (Rannie), Sally Cahil (Mrs. Hedlie), Tracy Ferencz (Nurse), Scot Denton (Mr. O'Connor), Timothy Adams (Buzz 'Rope' Romano), Joe Roncetti (Kevin Head), Hayden Christensen (Jake Hill Conley), Michael Michaelessi (Parks Dept. Foreman), Megan Kennedy (Cheerleader), Sandi Stahlbrand (Meredith Thompson), Neil Girvan (Drunk Man in Pool), Anne Wessels (Woman in Chiffon in Pool), Mark Polley (Cemetery Worker #1), Kirk Gonnsen (Cemetery Worker #2), Catherine Swing (Mrs. O'Connor), Sarah Minhas (Wanda Brown), Michael Pare (Trip Fontaine, 1997), Danny DeVito (Dr. Horniker), Scott Glenn (Father Moody), Giovanni Ribisi (Narrator)
Review Copyright Rose Cooper, 2000 Review URL: http://www.3blackchicks.com/bamsvirgin.html
I grew up in 1970's Detroit, not very far from Grosse Point--the setting in both time and place for this movie--but it might as well have been set in a galaxy far, far away in the year 2525, for all intents and purposes. It's like my Grandfather Wolfe used'ta say: "Some things are universal; but some shit's just Weird."
The Story (WARNING: **spoilers contained below**): Life according to The Virgin Suicides shows that 1970's Grosse Pointe, Michigan to be a place of superficial calm and temporary beauty; the beauty would be erased by the cutting-down of numerous dead trees that lined the neighborhood, and the calm would be shattered by neighborhood gossips, scavenging reporters, and a strange family for whom the word "dysfunctional" was probably coined.
Mr. and Mrs. Lisbon (James Woods and Kathleen Turner) are completely out-of-touch with their five teenage daughters, 13-year-old Cecilia (Hanna Hall), 14-year-old Lux (Kirsten Dunst), 15-year-old Bonnie (Chelse Swain), 16-year-old Mary (A.J. Cook), and 17-year-old Therese (Leslie Hayman). The neighborhood boys [their names are listed above; hellifi could tell you which one was which] are fascinated by the girls, especially after Cecilia commits suicide--and local stud, Trip Fontaine (Josh Hartnett), takes a liking to Lux. Unfortunately, Lux's burgeoning libido, and Trip's massive brain spasm, combine for disasterous effects for the four remaining sisters.
The Upshot: In my review of AMERICAN BEAUTY earlier this year, I disagreed with The Diva's AB review, on the basis of how she saw the dysfunctions in the families that were its primary focus; but Diva could've as easily been writing on-point about the Lisbon family - and more precisely, about the disconnect I felt with damn near everything about "Virgin". That last bit is key: because where I felt that I "knew" good old Lester and Carolyn and the rest of the gang, I haven't the slightest clue _what_ the Lisbon family's deal was. And what's more: I just couldn't bring myself to care.
Maybe it was the sheer number of unexplained - no, make that _undefined_ - characters. Lux was fairly easy to pick out of a crowd; Kirsten Dunst is a force to be reckoned with, even when everything around her seems to be less-than-gelled. And the Trip Fontaine character, as played by Josh "Young Tommy Lee Jones" Hartnett, also seemed otherworldy. Looking at the movie on terms of these two characters and actors, you get a *completely* different vibe than what happens when the rest of the motley crew joins in.
Most of the other characters, and moreso, the actors who played them, seemed to be in a psychedelic haze, wondering what exactly their Motivation was supposed to be. Particularly jarring were James Woods and Kathleen Turner (who hasn't aged well at all, poor girl) as Ma and Pa Lisbon; both actors seemed worlds away from what their resumes have shown they're capable of. As for their characters, all signs pointed to their Major Problem being somehow involved (to a detrimental point) with religion. I think. Or maybe it was just the ill-effects of being in a community full of neighbors as rotten as the rapidly dying trees, who knows. Or, cares. The rest [say it in a B&W Gilliganese fashion - "...the movie star/and the rest..." - and you'll Feel me] of the characters - with possibly the exception of Chase (Anthony DeSimone) - blended into a chorus of sorts, having no real distinguishing marks to tell one from the other. The credits for the cast list ironically told me more about who was who, than anything I saw on-screen. I included the detailed cast and character list above, just for giggles; it would take a supreme act to make me want to actually include all of the numerous undefined players within the body of this review.
To her credit, though, writer/director Sophia Coppola showed some gutsiness in putting "Virgin" together. Knowing her primarily as Francis Ford Coppola's sproggen, and the worst thing about a pretty bad rendering of GODFATHER 3 (1 and 2 were masterpieces; wha'happen?), I was prepared to write this one off early on, and probably would not have gone to see it at all had it not been for favorable buzz about this project [makes me wonder just what movie those critics saw; but, Strokes For Folks]. Coppola was a lot more competent than I would've given her credit for, and though I couldn't catch her groove, I could definitely see her distinctive style shining through. Wasn't quite enough to win me over, but still, I give props where props are due.
Usually, when I see based-on-a-book movies that I don't particularly like, I have a hankerin' to read the book. Not so, this time. One thing, though: whoever decided that this film was a "dark comedy", was probably smokin' whatever Trip left behind in his roach clip. Just Say No, dude; Just Say No.
Bammer's Bottom Line: I used to sweat it when I watched an Art Fillum and just didn't see what everyone was raving about. Bump *that*; I haven't got time for the pain. Call me uncouth if you wanna - hey, I've been called worse - but I just couldn't get with this one. I like my Dysfunctional Families a little more understandable in their dysfunctions; weird for weird's sake just goes right past me. So when's American Beauty coming out on DVD, eh?

Rose "Bams" Cooper /~\ Webchick and Editor, /','\ 3BlackChicks Review /','`'\ Movie Reviews With Flava! /',',','/`, Copyright Rose Cooper, 2000 `~-._'c / EMAIL<EMAIL_ADDRESS>`\ ( http://www.3blackchicks.com/ /====\
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


S2 Fig
Viral loads in abdomen (A) and head/thorax (B) after infection of Ae. albopictus Tunisia with CHIKV, DENV, and ZIKV. Mosquitoes were infected with a blood meal at a titer of 10 7 ffu/mL and were processed to estimate the viral load in abdomen and head/thorax by titration on cells. Means are represented by horizontal bars. (PDF)
Click here for additional data file.


'Cannot deny it' - Aston Villa flop wins first cap after Champions League revival
Aston Villa latest | Pierluigi Gollini was a Roberto Di Matteo signing in 2016 - but he was overtaken at Villa Park by Sam Johnstone later in the 2016-17 season
by Josh MurrayFormer Aston Villa goalkeeper Pierluigi Gollini has made his senior international debut for Italy.
The 24-year-old - who was signed at Villa by Roberto Di Matteo in 2016 - made a late substitute appearance in his country's 3-0 win over Bosnia & Herzegovina in European Championship qualifying, replacing AC Milan stopper Gianluigi Donnarumma.
The Italians had already booked their place at next summer's finals.
“It was really a fantastic feeling and I cannot deny it reminded me of my Serie A debut,” Gollini told Rai Sport in Zenica.
“I am so happy, even to be there only for a few minutes, for this debut.”
Aston Villa latest
This is your round up of all things Villa; the one-stop shop that will keep you updated on the latest goings on at the club and beyond.
Transfers, injuries, match-days and managers, we’ve got you covered.
We’ll be bringing the very latest on the Villains throughout the week and around the clock.
Make sure you keep yourself updated with our handy daily catch up...
Must reads...
- Villa fans make Chelsea transfer demand
- 'Worried' fans react to major announcement
- Former Villan reveals Newcastle bombshell
- Morelos saga takes new twist
For all your latest AVFC news, opinion, analysis and transfer gossip, click here
You can follow our Villa reporter Ashley Preece on Twitter, and his email address is<EMAIL_ADDRESS>Gollini made 20 Championship appearances for Villa, all before Christmas of the 2016-17 campaign.
He then headed out on loan to Atalanta later that season after Sam Johnstone's first loan arrival at Villa Park, and Gollini returned to the Italian club for a season-long loan in 2017-18.
He sealed a permanent move in July 2018.


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This challenge allows you to practice the concepts and techniques learned over the past Sprint and apply them in a concrete project. This Sprint explored class components, custom hooks, and testing. In your challenge for this Sprint, you will demonstrate proficiency by creating an application that consumes data from an API using a custom hook, displays that data on the DOM, and tests your React components.
The data you will get back from the server will be the Women’s World Cup players ranked by search interest from Google Trends, June-July 2019, worldwide - https://googletrends.github.io/data/.
Read these instructions carefully. Understand exactly what is expected before starting this Sprint Challenge.
This is an individual assessment. All work must be your own. Your challenge score is a measure of your ability to work independently using the material covered through this sprint. You need to demonstrate proficiency in the concepts and objectives introduced and practiced in preceding days.
You are not allowed to collaborate during the Sprint Challenge. However, you are encouraged to follow the twenty-minute rule and seek support from your PM and Instructor in your cohort help channel on Slack. Your work reflects your proficiency with ReactJS Fundamentals and your command of the concepts and techniques in the Functional Components.
You have three hours to complete this challenge. Plan your time accordingly.
Commit your code regularly and meaningfully. This helps both you (in case you ever need to return to old code for any number of reasons and your Team Lead.
Demonstrate your understanding of this Sprint’s concepts by answering the following free-form questions. Edit the ANSWERS.md
file to include your answers after each question. Make sure to leave a blank line above and below your answer so it is clear and easy to read by your project manager.
Follow these steps to set up and work on your project:
git checkout -b <firstName-lastName>
.
yarn
to download dependencies.
yarn start
or node server.js
.
client
folder and run yarn install
to download dependencies.
client
folder run yarn start
to run the client application.
git push origin <firstName-lastName>
.
Follow these steps for completing your project:
Your finished project must include all of the following requirements:
http://localhost:5000/api/players


UPDATE: Warrant Of Arrest For Air Peace Founder Issued By US Court
The warrant authorised US Marshals Service to take Onyema into custody.
by SaharaReporters, New YorkA warrant of arrest has been issued against Allen Onyema of Air Peace for alleged money laundering and bank fraud in the United States, according to court documents seen by PREMIUM TIMES.
The warrant was signed by Justin Anand, an American magistrate of the United States District Court of the Northern District of Georgia, on November 19.
The warrant authorised US Marshals Service to take Onyema into custody.
Onyema’s whereabouts as of Saturday morning could not be confirmed but he was seen at Transcorp Hilton in Abuja on Thursday.
The US and Nigeria have a mutual legal assistance policy that allows both countries to extradite suspects facing criminal charges.
It was unclear whether the US authorities have sought an extradition of Onyema as part of efforts to execute warrant of arrest issued against him.
Onyema was indicted on Friday for allegedly laundering suspicious funds in excess of $20 million dollars in the US together with Ejiroghene Eghagha, Air Peace’s Head of Finance and Administration.
A warrant for the arrest of Eghagha was also issued by the US court.
Also, about $14m traced to Onyema’s bank accounts in the US and Canada have been frozen as federal authorities pursue a final forfeiture order.
The seizure came after extensive investigation of how the Air Peace founder allegedly conducted the fraud between 2010 and 2018.
Lawyers of both men have revealed that they would challenge the charges in court.


Why taxes on pensioners in Germany have risen up to 500 percent since 2010
by DPA/The LocalThe tax burden on pensioners has increased fivefold within ten years in some cases, according to figures released by the Finance Ministry on Wednesday.
For example, new pensioners now have to pay €430 in income tax per year on a monthly gross pension of €1500, compared with €79 in 2010.
The figures were released on Wednesday, as part of an inquiry submitted to the Federal Ministry of Finance by Die Linke.
READ ALSO: How to maximize your German pension - even if you plan to retire elsewhere
For an individual with a monthly gross pension of €1700, €294 was due at the start of pension payments in 2010. This year that figure has risen to €758.
If the monthly earnings were €2000, €679 in taxes would have been due in 2010. Now the figure has risen to €1326.
Die Linke Left Party leader Dietmar Bartsch criticized the increasing pension taxation as a "de facto pension cut".
Why is there an increase?
The sharp increase is due to a change which was introduced in 2005. Until then, a tax-free allowance of 50 percent of the pension was permitted.
Since then, the tax rate on pensions has risen by two percentage points annually.
Health and long-term care insurance contributions and some everyday expenses can also be claimed.
The tax becomes due when the total income of a pensioner exceeds the basic annual tax-free allowance (€9168 or €764 per month). The average monthly pension in 2018 was €1219.
"In the next few years, the tax burden for small and medium-sized pensions will continue to rise,” Barsch told DPA.
He called on Finance Minister Olaf Scholz and Labour Minister Hubertus Heil (both SPD) to present a plan with significantly higher tax allowances to protect small and medium-sized pensions.
In 2015, a total of €34.65 billion in income tax was paid to the state by taxpayers with pension income.
There is currently a push around Germany to raise the pension age to 69, up from 67, in light of rising lifespans.
Read more of our coverage of pensions in Germany HERE.
Vocabulary
Tax burden - (die) Steuerbelastung, (die) Steuerlast
Fivefold - um das Fünffache
due/payable - fällig
Pension contributions - (die) Rentenbeiträge
Basic tax-free allowance - (der) Grundfreibetrag
To deduct - abziehen
We're aiming to help our readers improve their German by translating vocabulary from some of our news stories. Did you find this article useful? Let us know.


[continued]
If you want to cast gold, silver, copper or brass, it is necessary that they be perfectly red, and fired on the inside when you cast, and perfectly reheated twice, if there is something inside to be burnt and cleaned.
The molds of animals that you burn must be reheated in such a manner that the animal burns up. But if it has big bones, it is frustrating to take out and ☿ often breaks delicate things because of its heaviness. One does not put ☿in molds that can open. It is often molds for flowers that do not open because they are cast in one go.
When you want to reheat your molds, fix cramp—irons on the joints, so that when they reheat they will not bend, contract or break. This is done once the cast is made.
The scrapings of the mold can also be used instead of bricks after being reheated, and also the leftover bits of the mold that have already been used. You can also pat it over important things, like works in gold or silver. You can also reheat it and blend it with water diluted with sal ammoniac, like spar from Germany, and is excellent sand for box molds for all metals.
The molds with things that need to be burnt out inside them, those ones will not open until the thing that is inside is burnt, like the molds for crayfish, crab, great—horned beetles. Do not open the molds in which you will burn something, not before the thing is burnt, it could be crayfishes, crabs, stag beetles, pictures, and pieces of wax and black sulphur, which do not come out of the mold easily.
You must not mold on brick or wood because they absorb and attract water too early, and do not allow the sand to set. The best place for the sand is on a fresh layers of clay. And yet, I have experienced that grey earth dries out the mold too quickly. Yellow earth is better.
at left middle margin
For medals, and flat things, the sand must be quite thick and moistened so that it sets quickly. And when the sand is thusly thick, you can shake and move the table where you mold lies, so that the movement makes the sand go everywhere. But when the sand is clear, as for flowers and herbs, you cannot shake it, nor can you do it when there is something attached to wax or other things which are prone to come off, such as the legs of crayfish or other similar things. And if by chance your sand is too thick, you must quickly add some water. And having put the sand in the water, examine it until it is thick on the bottom and clear on top. The clearest is thrown in immediately and then bubbles and the thickest is thrown in at the end so that it strengthens the mold.


Hi. My (nick)name is angian. You can find me on GitHub.
This is where I collect my implementations of FreeCodeCamp Backend API Projects.
Here is the list of what I have done so far:


The animate()
function provides a way to create smooth animations on the properties of an object.
Click the buttons below this

element to see how various properties can be animated.
Animate Me
The animate()
function provides a way to create smooth animations on the properties of an object.
Click the buttons below this

element to see how various properties can be animated.


| Methods' Details |
| createDataPilotDescriptor
|
insertNewByName
- Description
- creates a new data pilot table and adds it to the collection.
- Parameter aName
- the name of the data pilot table used in the collection.
- Parameter aAoutputAddress
- the top left cell of the location of the data pilot table in
the spreadsheet document.
- Parameter xDescriptor
- the descriptor containing the settings of the data pilot table.
- See also
- DataPilotDescriptor
|
|
removeByName
| void |
| removeByName( |
[in] string |
aName ); |
- Description
- deletes a data pilot table from the collection.
|
|
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