Adaptation of Mindfulness in Parenting Questionnaire (MIPQ) for parents of children aged 2-12 years

Mindful parenting intervention programs and trait can support positive parenting conditions and affect children's psychosocial development. However, the measurement of mindful parenting has not been widely developed. The Mindfulness in Parenting Questionnaire (MIPQ) scale is one approach that has been developed; it is considered to have good psychological properties and has been validated in several countries. This study aims to find evidence for the validity of the Indonesian version of the MIPQ score interpretation (MIPQ-Ind) in a population of parents of children aged 2-12 years. The total participants are 822 parents (268 fathers and 554 mothers) who live in Jakarta. Using the split sample technique and employing EFA and CFA tests, the research results show that the MIPQ-Ind has two valid factors, as indicated by the index χ2/df= 2.8, CFI= 0.9, GFI= 0.96 RMSEA= 0.06, and RMSR = 0.04. The internal structure validity is 0.913 for being in the moment with child (BMC) factor and 0.906 for the mindful discipline (MD) factor. The study shows that MIPQ-Ind can measure mindful parenting in the population of parents of children aged 2-12 years in Indonesia.

A multivariate model for successful publication of intensive care medicine randomized controlled trials in the highest impact factor journals: the SCOTI score

Background Critical care randomized controlled trials (RCTs) are often published in high-impact journals, whether general journals [the New England Journal of Medicine (NEJM), The Lancet, the Journal of the American Medical Association (JAMA)] or critical care journals [Intensive Care Medicine (ICM), the American Journal of Respiratory and Critical Care Medicine (AJRCCM), Critical Care Medicine (CCM)]. As rejection occurs in up to 97% of cases, it might be appropriate to assess pre-submission probability of being published. The objective of this study was to develop and internally validate a simplified score predicting whether an ongoing trial stands a chance of being published in high-impact general journals. Methods A cohort of critical care RCTs published between 1999 and 2018 in the three highest impact medical journals (NEJM, The Lancet, JAMA) or the three highest impact critical care journals (ICM, AJRCCM, CCM) was split into two samples (derivation cohort, validation cohort) to develop and internally validate the simplified score. Primary outcome was journal of publication assessed as high-impact general journal (NEJM, The Lancet, JAMA) or critical care journal (ICM, AJRCCM, CCM). Results A total of 968 critical care RCTs were included in the predictive cohort and split into a derivation cohort (n = 510) and a validation cohort (n = 458). In the derivation cohort, the sample size (P value < 0.001), the number of centers involved (P value = 0.01), mortality as primary outcome (P value = 0.002) or a composite item including mortality as primary outcome (P value = 0.004), and topic [ventilation (P value < 0.001) or miscellaneous (P value < 0.001)] were independent factors predictive of publication in high-impact general journals, compared to high-impact critical care journals. The SCOTI score (Sample size, Centers, Outcome, Topic, and International score) was developed with an area under the ROC curve of 0.84 (95% Confidence Interval, 0.80–0.88) in validation by split sample. Conclusions The SCOTI score, developed and validated by split sample, accurately predicts the chances of a critical care RCT being published in high-impact general journals, compared to high-impact critical care journals.

Estimation of Hemi-Abdominal-Based Free Flap Weight Using Two Computed Tomography-Derived Measurements

Background Accurate flap weight estimation is crucial for preoperative planning in microsurgical breast reconstruction; however, current flap weight estimation methods are time consuming. It was our objective to develop a parsimonious and accurate formula for the estimation of abdominal-based free flap weight. Methods Patients who underwent hemi-abdominal-based free tissue transfer for breast reconstruction at a single institution were retrospectively reviewed. Subcutaneous tissue thicknesses were measured on axial computed tomography angiograms at several predetermined points. Multivariable linear regression was used to generate the parsimonious flap weight estimation model. Split-sample validation was used to for internal validation. Results A total of 132 patients (196 flaps) were analyzed, with a mean body mass index of 31.2 ± 4.0 kg/m2 (range: 22.6–40.7). The mean intraoperative flap weight was 990 ± 344 g (range: 368–2,808). The full predictive model (R 2 = 0.68) estimated flap weight using the Eq. 91.3x + 36.4y + 6.2z – 1030.0, where x is subcutaneous tissue thickness (cm) 5 cm lateral to midline at the level of the anterior superior iliac spine (ASIS), y is distance (cm) between the skin overlying each ASIS, and z is patient weight (kg). Two-thirds split-sample validation was performed using 131 flaps to build a model and the remaining 65 flaps for validation. Upon validation, we observed a median percent error of 10.2% (interquartile range [IQR]: 4.5–18.5) and a median absolute error of 108.6 g (IQR: 45.9–170.7). Conclusion We developed and internally validated a simple and accurate formula for the preoperative estimation of hemi-abdominal-based free flap weight for breast reconstruction.

Morphological characteristics of goat breeds in Nigeria using discriminant analysis approach

Information on morphological characteristics is a prerequisite to sustainable breed classification and conservation for proper management and utilization. The present study aimed to identify morphological characteristics that best classify the Sahel, Sokoto Red and West African Dwarf goats of Nigeria. A total of 584 goats of both sexes; Sahel (N = 163), Sokoto Red (N = 171) and West African Dwarf (N = 250) were randomly sampled and used for this study. The body parameters measured included body weight (BWT), height at wither (HW), body length (BL), paunch girth (PG), heart girth (HG) and ear length (EL). Multivariate technique of discriminant analysis procedure of SPSS 20.0 statistical package was used to classify the Sahel, Sokoto Red and West African Dwarf goats into their original breed. Accuracy of the classification was checked using cross-validation (leave-one-out, jack-knife or split-sample) procedure. Results showed that the mean body weight (kg) of Sokoto Red was significantly (P<0.05) higher than those of Sahel and West African Dwarf goats (23.596, 17.117 and 14.800 respectively). Highest correlation values were recorded between body weight and height at withers (0.777) in Sahel, body weight and paunch girth (0.707) in Sokoto Red and body weight and heart girth (0.797) in West African Dwarf goats. The discriminant analysis on body weight and the linear body measurements revealed that paunch girth and height at withers were the most discriminating variables and clearly separated and classified the three goat breeds into their breeds of origin. The discriminant function obtained correctly classified 99.0% of individuals from the sample of known goat populations. The classification accuracy of the function was cross-validated using the split sample method, and indicated a 99.0% success rate (97.5%, 100.0% and 99.2% of Sahel, Sokoto Red and West African Dwarf goats respectively). It was concluded that there was a clear separation between Barcha and Atlas goats. It was concluded that discriminant tool may be used successfully in the field to classify Sahel, Sokoto Red and West African Dwarf in the field, however there is need to complement this with molecular characterization using DNA marker for better conservation and improvement programme of indigenous goat genetic resources. L'information sur les caractéristiques morphologiques est une condition préalable à la classification et à la conservation durables des races pour une gestion et une utilisation appropriées. Cet étude visait à identifier les caractéristiques morphologiques qui classent le mieux les chèvres sahéliennes, rouges de Sokoto et naines d'Afrique de l'Ouest du Nigeria. Un total de 584 chèvres des deux sexes ; Sahel (N = 163), Sokoto Red (N = 171) et West African Dwarf (N = 250) ont été échantillonnés au hasard et utilisés pour cette étude. Les paramètres corporels mesurés comprenaient le poids corporel (PC), la hauteur au garrot (HG), lalongueur du corps (BL), la circonférence de la panse (CP), la circonférence du cœur (CC) et la longueur des oreilles (LO). La technique multivariée de la procédure d'analyse discriminante du progiciel statistique SPSS 20.0 a été utilisée pour classer les chèvres sahéliennes, rouges de Sokoto et naines d'Afrique de l'Ouest dans leur race d'origine. L'exactitude de la classification a été vérifiée à l'aide d'une procédure de validation croisée (en laisser un, un couteau ou un échantillon divisé). Les résultats ont montré que le poids corporel moyen (kg) de Sokoto Red était significativement (P<0,05) supérieur à celui des chèvres naines du Sahel et d'Afrique de l'Ouest (23,596, 17,117 et 14,800 respectivement). Les valeurs de corrélation les plus élevées ont été enregistrées entre le poids corporel et la hauteur au garrot (0,777) au Sahel, le poids corporel et la circonférence de la panse (0,707) chez le Sokoto Red et le poids corporel et la circonférence cardiaque (0,797) chez les chèvres naines d'Afrique de l'Ouest. L'analyse discriminante sur le poids corporel et les mesures corporelles linéaires ont révélé que la circonférence de la panse et la hauteur au garrot étaient les variables les plus discriminantes et ont clairement séparé et classé les trois races caprines dans leurs races d'origine. La fonction discriminante obtenue a correctement classé 99,0 % des individus de l'échantillon de populations caprines connues. La précision de la classification de la fonction a été contre-validée à l'aide de la méthode de l'échantillon divisé et a indiqué un taux de réussite de 99,0 % (respectivement 97,5 %, 100,0 % et 99,2 % des chèvres sahéliennes, rouges de Sokoto et naines d'Afrique de l'Ouest). Il a été conclu qu'il y avait une nette séparation entre les chèvres Barcha et Atlas. Il a été conclu que l'outil discriminant peut être utilisé avec succès sur le terrain pour classer le Sahel, le rouge de Sokoto et le nain d'Afrique de l'Ouest sur le terrain, mais il est nécessaire de le compléter par une caractérisation moléculaire à l'aide d'un marqueur ADN pour une meilleure conservation et un programme d'amélioration de la génétique caprine indigène. Ressources.

Quantile mapping for improving precipitation extremes from regional climate models

The potential of quantile mapping (QM) as a tool for bias correction of precipitation extremes simulated by regional climate models (RCMs) is investigated in this study. We developed an extended version of QM to improve the quality of bias-corrected extreme precipitation events. The extended version aims to exploit the advantages of both non-parametric methods and extreme value theory. We evaluated QM by applying it to a small ensemble of hindcast simulations, performed with RCMs at six different locations in Europe. We examined the quality of both raw and bias-corrected simulations of precipitation extremes using the split sample and cross-validation approaches. The split-sample approach mimics the application to future climate scenarios, while the cross-validation framework is designed to analyse “new extremes”, that is, events beyond the range of calibration of QM. We demonstrate that QM generally improves the simulation of precipitation extremes, compared to raw RCM results, but still tends to present unstable behaviour at higher quantiles. This instability can be avoided by carefully imposing constraints on the estimation of the distribution of extremes. The extended version of the bias-correction method greatly improves the simulation of precipitation extremes in all cases evaluated here. In particular, extremes in the classical sense and new extremes are both improved. The proposed approach is shown to provide a better representation of the climate change signal and can thus be expected to improve extreme event response for cases such as floods in bias-corrected simulations, a development of importance in various climate change impact assessments. Our results are encouraging for the use of QM for RCM precipitation post-processing in impact studies where extremes are of relevance.

Robustness of a parsimonious subsurface drainage model at the French national scale

Drainage systems are currently implemented on agricultural plots subjected to temporary or permanent waterlogging issues. Drained plots account for 9 % of all arable soils in France. As such, the need for accurate hydrological modeling is crucial, especially in an unstable future context affected by climate change. The aim of this paper is to assess the capacity of the SIDRA-RU hydrological drainage model to represent the variability in pedoclimatic conditions within French metropolitan areas and to demonstrate the utility of this model as a long-term management tool. The model is initially calibrated using the KGE′ criterion as an objective function (OF) on a large and unique database encompassing 22 plots spread across France and classified according to three main soil textures (silty, silty–clay, and clayey). The performance of SIDRA-RU is evaluated by monitoring both the set of KGE′ calibration values and the quality of simulations on each plot with respect to high and low discharges, as well as the annual drained water balance. Next, the temporal robustness of the model is assessed by conducting, on selected plots, the split-sample test capable of satisfying the data requirements. Results show that the SIDRA-RU model accurately simulates drainage discharge, especially on silty soils. The performance on clayey soils is slightly weaker than that on silty soils yet remains acceptable. Similarly, the split-sample test indicates that SIDRA-RU is temporally robust on all three soil textures. Consequently, the SIDRA-RU model closely replicates the diversity of French drained soil and could be used for its long-term management potential.

Combining split-sample testing and hidden Markov modelling to assess the robustness of hydrological models

The impacts of climate and land-use changes make the stationary assumption in hydrology obsolete. Moreover, there is still considerable uncertainty regarding the future evolution of the Earth’s climate and the extent of the alteration of flow regimes. Climate change impact assessment in the water sector typically involves a modelling chain in which a hydrological model is needed to generate hydrologic projections from climate forcings. Considering the inherent uncertainty of the future climate, it is crucial to assess the performance of the hydrologic model over a wide range of climates and their corresponding hydrologic conditions. In this paper, numerous, contrasted, climate sequences identified by a hidden Markov model (HMM) are used in a differential split-sample testing framework to assess the robustness of a hydrologic model. The differential split-sample test based on a HMM classification is implemented on the time series of monthly river discharges in the upper Senegal River basin in West Africa, a region characterized by the presence of low-frequency climate signals. A comparison with the results obtained using classical rupture tests shows that the diversity of hydrologic sequences identified using the HMM can help with assessing the robustness of the hydrologic model.

Is It Still a Mandate If We Don’t Enforce It? The Politics of COVID-related Mask Mandates in Conservative States

Questions of whether to enforce COVID-related mask mandates are complex. While enforced mandates are more effective at controlling community spread, government imposed behavioral controls have met significant opposition in conservative states, where a political bloc on the right is skeptical that COVID presents a significant and immediate threat. The authors conduct a split sample survey in order to examine how inclusion of a fine provision attached to mask mandates affects support. The survey was conducted in Idaho (a Republican dominated state) at a time when a mask mandate was a central debate. Unsurprisingly, respondents were more supportive of a mask mandate if a fine was not included. Further investigation indicates this is primarily a result of shifting Republican attitudes, which highlights the complex political situation in conservative states as leaders consider best mechanisms for battling COVID.

Unhealthy Behaviours and Risk of Parkinson’s Disease: A Mendelian Randomisation Study

Background: Tobacco smoking and alcohol intake have been identified in observational studies as potentially protective factors against developing Parkinson’s disease (PD); the impact of body mass index (BMI) on PD risk is debated. Whether such epidemiological associations are causal remains unclear. Mendelian randomsation (MR) uses genetic variants to explore the effects of exposures on outcomes; potentially reducing bias from residual confounding and reverse causation. Objective: Using MR, we examined relationships between PD risk and three unhealthy behaviours: tobacco smoking, alcohol intake, and higher BMI. Methods: 19,924 PD cases and 2,413,087 controls were included in the analysis. We performed genome-wide association studies to identify single nucleotide polymorphisms associated with tobacco smoking, alcohol intake, and BMI. MR analysis of the relationship between each exposure and PD was undertaken using a split-sample design. Results: Ever-smoking reduced the risk of PD (OR 0.955; 95%confidence interval [CI] 0.921–0.991; p = 0.013). Higher daily alcohol intake increased the risk of PD (OR 1.125, 95%CI 1.025–1.235; p = 0.013) and a 1 kg/m2 higher BMI reduced the risk of PD (OR 0.988, 95%CI 0.979–0.997; p = 0.008). Sensitivity analyses did not suggest bias from horizontal pleiotropy or invalid instruments. Conclusion: Using split-sample MR in over 2.4 million participants, we observed a protective effect of smoking on risk of PD. In contrast to observational data, alcohol consumption appeared to increase the risk of PD. Higher BMI had a protective effect on PD, but the effect was small.

Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging

As machine learning research in the field of cardiovascular imaging continues to grow, obtaining reliable model performance estimates is critical to develop reliable baselines and compare different algorithms. While the machine learning community has generally accepted methods such as k-fold stratified cross-validation (CV) to be more rigorous than single split validation, the standard research practice in medical fields is the use of single split validation techniques. This is especially concerning given the relatively small sample sizes of datasets used for cardiovascular imaging. We aim to examine how train-test split variation impacts the stability of machine learning (ML) model performance estimates in several validation techniques on two real-world cardiovascular imaging datasets: stratified split-sample validation (70/30 and 50/50 train-test splits), tenfold stratified CV, 10 × repeated tenfold stratified CV, bootstrapping (500 × repeated), and leave one out (LOO) validation. We demonstrate that split validation methods lead to the highest range in AUC and statistically significant differences in ROC curves, unlike the other aforementioned approaches. When building predictive models on relatively small data sets as is often the case in medical imaging, split-sample validation techniques can produce instability in performance estimates with variations in range over 0.15 in the AUC values, and thus any of the alternate validation methods are recommended.