Pediatric Head CT: Automated Quantitative Analysis with Quantile Regression.

There is increasing evidence of abnormal neurodevelopmental outcomes in preterm infants with low-grade intraventricular hemorrhage (IVH). The purpose of the study was to explore whether brain microstructure and volume are associated with neuro-behavioral outcomes at 40 weeks corrected gestational age in preterm infants with low-grade IVH. MR imaging at term-equivalent age (TEA) was performed in 25 preterm infants with mild IVH (Papile grading I/II) and 40 control subjects without IVH. These subjects all had neonatal behavioral neurological assessment (NBNA) at 40 weeks’ corrected age. Microstructure and volume evaluation of the brain were performed by using diffusion kurtosis imaging (DKI) and Synthetic MRI. Correlations among microstructure parameters, volume, and developmental outcomes were explored by using Spearman's correlation. In preterm infants with low-grade IVH, the volume of brain parenchymal fraction (BPF) was reduced. In addition, mean kurtosis (MK), fractional anisotropy (FA), radial kurtosis (RK), axial kurtosis (AK) in several major brain regions were reduced, while mean diffusivity (MD) was increased (P < 0.05). BPF, RK in the cerebellum, MK in the genu of the corpus callosum, and MK in the thalamus of preterm infants with low-grade IVH were associated with lower NBNA scores (r = 0.831, 0.836, 0.728, 0.772, P < 0.05). DKI and Synthetic MRI can quantitatively evaluate the microstructure alterations and brain volumes in preterm infants with low-grade IVH, which provides clinicians with a more comprehensive and accurate neurobehavioral assessment of preterm infants with low-grade IVH.

PurposeWe aimed to validate the estimation of the brain parenchymal fraction (BPF) in patients with multiple sclerosis (MS) using synthetic magnetic resonance imaging (SyMRI) by comparison with software tools of the FMRIB Software Library (FSL). In addition to a cross-sectional method comparison, longitudinal volume changes were assessed to further elucidate the suitability of SyMRI for quantification of disease-specific changes.MethodsMRI data from 216 patients with MS and 28 control participants were included for volume estimation by SyMRI and FSL-SIENAX. Moreover, longitudinal data from 35 patients with MS were used to compare registration-based percentage brain volume changes estimated using FSL-SIENA to difference-based calculations of volume changes using SyMRI.ResultsWe observed strong correlations of estimated brain volumes between the two methods. While SyMRI overestimated grey matter and BPF compared to FSL-SIENAX, indicating a systematic bias, there was excellent agreement according to intra-class correlation coefficients for grey matter and good agreement for BPF and white matter. Bland–Altman plots suggested that the inter-method differences in BPF were smaller in patients with brain atrophy compared to those without atrophy. Longitudinal analyses revealed a tendency for higher atrophy rates for SyMRI than for SIENA, but SyMRI had a robust correlation and a good agreement with SIENA.ConclusionIn summary, BPF based on data from SyMRI and FSL-SIENAX is not directly transferable because an overestimation and higher variability of SyMRI values were observed. However, the consistency and correlations between the two methods were satisfactory, and SyMRI was suitable to quantify disease-specific atrophy in MS.

(1) Background: Programming of atherosclerosis results in vascular structure and function alterations, which may be attributed to fetal exposure to maternal tobacco smoking, alcohol consumption and several lifestyle factors in the first few years of life. This review aims to study the effects of teratogen exposure in utero on vascular dysfunction in offspring and consider mediators and pathways originating from the fetal environment. (2) Methods: Eligible studies were identified in the PubMed, Scopus and Web of Science databases. After the full-text screening, 20 articles were included in the narrative synthesis. (3) Results: The literature presents evidence supporting the detrimental effects of fetal exposure to tobacco smoking on vascular alterations in both human and animal studies. Alcohol exposure impaired endothelial dilation in animal studies, but human studies on both tobacco and alcohol exposure are still sparse. Reduction in nitric oxide (NO) bioavailability and alterations in the epigenome in infants through the upregulation of pro-oxidative and proinflammatory genes may be the common denominators. (4) Conclusion: While maternal smoking and alcohol consumption have more negative outcomes on the infant in the short term, several factors during the first few years of life may mediate the development of vascular dysfunction. Therefore, more prospective studies are needed to ascertain the long-term effects of teratogen exposure, specifically in South Africa.

The infant brain experiences explosive growth in the first few years of life. The developing topology of the functional network mirrors the emergence of complex cognitive functions. However, early development of brain topological properties in infants is still largely unclear due to the dearth of high-quality longitudinal infant functional MRI (fMRI) data. In this study, we employed advanced methods to investigate the developmental trajectories of various network features on high-resolution, longitudinal fMRI data of infants from birth to 2 years of age. The developmental trajectories of various global and nodal metrics were evaluated with linear mixed-effect modeling. We then investigated the association between these developmental trajectories and the visual reception ability, an important skill that could shape the future development of other cognitive functions. Four global metrics (shortest path length, global efficiency, local efficiency, and sigma (i.e., small-worldness)) showed significant developmental changes to facilitate more efficient information processing. Significant developmental changes were also found in the nodal characters with a prominent spatial specificity, and some brain regions showed increasing importance along the development. Most importantly, different associations between developmental trajectories in both global and nodal network characters and varied visual reception ability were revealed. This is the first longitudinal study on the early development of the brain functional connectome and its potential relationship to the individual variability of the visual abilities. These findings provide valuable knowledge for better understanding of normative and abnormal neurodevelopment in the first few years of life.

This paper focuses on the Least Square (LS) regression using the mean and Quantile (M) regression analysis using median which is based on “well-Known” parametric estimation methodologies. Data from Oregon and California highway bridges were used for the comparison of the two methods. Relationships were developed to predict the unit cost of FRP repair work and FRP cost was found to have a high degree of correlation with FRP area for both Oregon and California. It was observed that the Cost Estimating Relationships (CERs) obtained by Quantile (M) regression method had the smaller Mean Absolute Deviation (MAD) values and lower Mean Absolute Percentage Error (MAPE) values than Least Square (LS) regression. The stuudy showed that Quantile Regression is much less sensitive to outliers than Least Squares Regression.

Varying coefficient model (VCM) is extensively used in various scientific fields due to its capability of capturing the changing structure of predictors. Classical mean regression analysis is often complicated in the existence of skewed, heterogeneous and heavy-tailed data. For this purpose, this work employs the idea of model averaging and introduces a novel comprehensive approach by incorporating quantile-adaptive weights across different quantile levels to further improve both least square (LS) and quantile regression (QR) methods. The proposed procedure that adaptively takes advantage of the heterogeneous and sparse nature of input data can gain more efficiency and be well adapted to extreme event case and high-dimensional setting. Motivated by its nice properties, we develop several robust methods to reveal the dynamic close-to-truth structure for VCM and consistently uncover the zero and nonzero patterns in high-dimensional scientific discoveries. We provide a new iterative algorithm that is proven to be asymptotic consistent and can attain the optimal nonparametric convergence rate given regular conditions. These introduced procedures are highlighted with extensive simulation examples and several real data analyses to further show their stronger predictive power compared with LS, composite quantile regression (CQR) and QR methods.

Objective: We investigated the independent and joint associations of changes in estimated cardiorespiratory fitness (eCRF) and symptoms of anxiety and depression with brain volumes in individuals from the general population.Method: 751 participants (52% women, aged 50–67 years) from the Nord-Trøndelag Health Study (HUNT) MRI cohort were included. eCRF obtained from a non-exercise algorithm and symptoms of anxiety and depression were assessed twice; at HUNT2 (1995–97) and HUNT3 (2006–08). Brain MRI was performed shortly after HUNT3. Brain parenchymal fraction (BPF), bilateral hippocampal and total cortical volume were extracted from brain MRI obtained at 1.5T, using FreeSurfer and Statistical Parametric Mapping.Results: Multiple regression revealed that participants whose eCRF increased had larger BPF (β = 0.09, 95% CI 0.02, 0.16) and larger hippocampal volume (β = 0.09, 95% CI 0.03, 0.16) compared to participants whose eCRF remained low. Participants whose eCRF remained high had larger BPF (β = 0.15, 95% CI 0.07, 0.22) and larger cortical volume (β = 0.05, 95% CI 0.01, 0.09). Participants whose anxiety symptoms worsened had smaller BPF (β = −0.09, 95% CI −0.15, −0.02) and cortical volume (β = −0.05, −0.08, −0.01) than participants whose anxiety symptoms remained low. Each ml/kg/min increase in eCRF was associated with larger cortical volume among individuals with worsening of anxiety symptoms (β = 0.13, 95% CI 0.001, 0.27), and larger BPF among individuals whose depressive symptoms improved (β = 0.28, 95% CI 0.02, 0.53).Conclusion: Promoting exercise intended to improve eCRF may be an important public health initiative aimed at maintaining brain health among middle-aged individuals with and without changing psychological symptoms.

Hypertension is an important risk factor for Alzheimer's disease (AD) and cerebral small vessel disease. Angiotensin converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are common anti-hypertensive treatments, but have differential effects on cortical amyloid. The objective of this study was to evaluate associations between anti-hypertensive treatment, brain volume, and cognition, using a propensity-weighted analysis to account for confounding by indication. We identified a cohort of normal elderly adults and individuals with mild cognitive impairment (MCI) or AD (N = 886; mean age = 75.0) from the Alzheimer's Disease Neuroimaging Initiative. Primary outcomes were brain parenchymal fraction, total hippocampal volume, and white matter hyperintensity (WMH) volume. Secondary outcomes were standardized scores on neuropsychological tests. Propensity-weighted adjusted multivariate linear regression was used to estimate associations between anti-hypertensive treatment class and MRI volumes and cognition. Individuals treated with ARBs showed larger hippocampal volumes (R2 = 0.83, p = 0.05) and brain parenchymal fraction (R2 = 0.83, p = 0.01) than those treated with ACEIs. When stratified by diagnosis, this effect remained only in normal elderly adults and MCI patients, and a significant association between ARBs and lower WMH volume (R2 = 0.83, p = 0.03) emerged for AD patients only. ARBs were also associated with significantly better performance on tests of episodic and verbal memory, language, and executive function (all p < 0.05). Findings are consistent with evidence for a neuroprotective effect of treatment with ARBs for brain structure and cognition. This study has potential implications for the treatment of hypertension, particularly in elderly adults at risk of cognitive decline and AD.

This paper presents two probabilistic approaches based on bootstrap method and quantile regression (QR) method to estimate the uncertainty associated with solar photovoltaic (PV) power point forecasts. Solar PV output power forecasts are obtained using a hybrid intelligent model, which is composed of a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on radial basis function neural network (RBFNN) that is optimized by particle swarm optimization (PSO) algorithm. The point forecast capability of the proposed hybrid WT + RBFNN + PSO intelligent model is examined and compared with other hybrid models as well as individual SCM. The performance of the proposed bootstrap method in the form of probabilistic forecasts is compared with the QR method by generating different prediction intervals (PIs). Numerical tests using real data demonstrate that the point forecasts obtained from the proposed hybrid intelligent model can be effectively used to quantify PV power uncertainty. The performance of these two uncertainty quantification methods is assessed through reliability.

This study aims to construct the model for the length of hospital stay for patients with COVID-19 using quantile regression and Bayesian quantile approaches. The quantile regression models the relationship at any point of the conditional distribution of the dependent variable on several independent variables. The Bayesian quantile regression combines the concept of quantile analysis into the Bayesian approach. In the Bayesian approach, the Asymmetric Laplace Distribution (ALD) distribution is used to form the likelihood function as the basis for formulating the posterior distribution. All 688 patients with COVID-19 treated in M. Djamil Hospital and Universitas Andalas Hospital in Padang City between March-July 2020 were used in this study. This study found that the Bayesian quantile regression method results in a smaller 95% confidence interval and higher value than the quantile regression method. It is concluded that the Bayesian quantile regression method tends to yield a better model than the quantile method. Based on the Bayesian quantile regression method, it investigates that the length of hospital stay for patients with COVID-19 in West Sumatra is significantly influenced by Age, Diagnoses status, and Discharge status.

Objective: To assess various marginal effects of nutrient intakes, health behaviours and nutrition knowledge on the entire distribution of body mass index (BMI) across individuals. Design: Quantitative and distributional study. Setting: Taiwan Methods: This study applies Becker’s (1965) model of health production to construct an individual’s BMI determining function. Quantile regression method is used to analyse the entire distribution of BMI across a nationwide survey sample in Taiwan. Endogeneity associated with choice variables of nutrient intakes, health behaviours and nutrition knowledge in determining BMI is corrected using a two-stage least-squares method. Results: Empirical results indicate that energy, oleic acid and cholesterol increase BMI, while fibre, calcium and sodium reveal a reverse effect. For overweight and obese females, protein lowers BMI. For underweight and slightly to severely obese males, vitamin C reduces BMI. Jogging lowers BMI, but drinking raises non-obese individuals’ BMI. For males whose BMI is in the ideal weight to slightly overweight ranges, nutrition knowledge reduces BMI, but this effect is insignificant for females. Conclusions: Several important conclusions can be drawn. (1) Having a BMI which is too high or too low is a risk factor to health. (2) Quantile regression is a useful empirical method to evaluate crucial factors determining the entire distribution (high, medium and low) of BMI. (3) The econometric problem of endogeneity in regressors has to be considered and corrected when using a quantile regression approach. (4) BMI-relevant health education and policy can be evaluated and derived using quantile regression effects of an individual’s choices of nutrient intakes, health behaviours and nutrition knowledge.

A battery capacity estimation method based on the equivalent circuit model and quantile regression using vehicle real-world operation data

SLC4A10 is a plasma-membrane bound transporter that utilizes the Na+ gradient to drive cellular HCO3− uptake, thus mediating acid extrusion. In the mammalian brain, SLC4A10 is expressed in principal neurons and interneurons, as well as in epithelial cells of the choroid plexus, the organ regulating the production of CSF.Using next generation sequencing on samples from five unrelated families encompassing nine affected individuals, we show that biallelic SLC4A10 loss-of-function variants cause a clinically recognizable neurodevelopmental disorder in humans. The cardinal clinical features of the condition include hypotonia in infancy, delayed psychomotor development across all domains and intellectual impairment. Affected individuals commonly display traits associated with autistic spectrum disorder including anxiety, hyperactivity and stereotyped movements. In two cases isolated episodes of seizures were reported in the first few years of life, and a further affected child displayed bitemporal epileptogenic discharges on EEG without overt clinical seizures. While occipitofrontal circumference was reported to be normal at birth, progressive postnatal microcephaly evolved in 7 out of 10 affected individuals. Neuroradiological features included a relative preservation of brain volume compared to occipitofrontal circumference, characteristic narrow sometimes ‘slit-like’ lateral ventricles and corpus callosum abnormalities.Slc4a10−/− mice, deficient for SLC4A10, also display small lateral brain ventricles and mild behavioural abnormalities including delayed habituation and alterations in the two-object novel object recognition task. Collapsed brain ventricles in both Slc4a10−/− mice and affected individuals suggest an important role of SLC4A10 in the production of the CSF. However, it is notable that despite diverse roles of the CSF in the developing and adult brain, the cortex of Slc4a10−/− mice appears grossly intact.Co-staining with synaptic markers revealed that in neurons, SLC4A10 localizes to inhibitory, but not excitatory, presynapses. These findings are supported by our functional studies, which show the release of the inhibitory neurotransmitter GABA is compromised in Slc4a10−/− mice, while the release of the excitatory neurotransmitter glutamate is preserved. Manipulation of intracellular pH partially rescues GABA release.Together our studies define a novel neurodevelopmental disorder associated with biallelic pathogenic variants in SLC4A10 and highlight the importance of further analyses of the consequences of SLC4A10 loss-of-function for brain development, synaptic transmission and network properties.

BackgroundThere is a well-documented social gradient in obesity in most developed countries. Many previous studies have conventionally categorised individuals according to their body mass index (BMI), focusing on those above a certain threshold and thus ignoring a large amount of the BMI distribution. Others have used linear BMI models, relying on mean effects that may mask substantial heterogeneity in the effects of socioeconomic variables across the population.MethodIn this study, we measure the social gradient of the BMI distribution of the adult population in Spain over the past two decades (1993–2014), using unconditional quantile regressions. We use three socioeconomic variables (education, income and social class) and evaluate differences in the corresponding effects on different percentiles of the log-transformed BMI distribution. Quantile regression methods have the advantage of estimating the socioeconomic effect across the whole BMI distribution allowing for this potential heterogeneity.ResultsThe results showed a large and increasing social gradient in obesity in Spain, especially among females. There is, however, a large degree of heterogeneity in the socioeconomic effect across the BMI distribution, with patterns that vary according to the socioeconomic indicator under study. While the income and educational gradient is greater at the end of the BMI distribution, the main impact of social class is around the median BMI values. A steeper social gradient is observed with respect to educational level rather than household income or social class.ConclusionThe findings of this study emphasise the heterogeneous nature of the relationship between social factors and obesity across the BMI distribution as a whole. Quantile regression methods might provide a more suitable framework for exploring the complex socioeconomic gradient of obesity.Electronic supplementary materialThe online version of this article (doi:10.1186/s12939-016-0454-1) contains supplementary material, which is available to authorized users.

Purpose – This paper aims to investigate whether there is heterogeneity in the relationship between the bank loan interest rate and its determinants using the quantile regression method and to reconcile some conflicting findings in prior literature. Design/methodology/approach – First, the effects of 18 determinants were examined on the bank loan interest rate using the ordinary least squares method (OLS). Second, it was investigated whether the relationship between the loan rate and its determinants is heterogeneous across quantiles of loan rates using the quantile regression method. Findings – Considerable heterogeneity was found in the relationship between the loan rate and its determinants. Specifically, a determinant that is beneficial for the bank loan rate, on average, as revealed by the OLS method may become unimportant or even detrimental for firms located at extremely high or low loan rate quantiles. By revealing extreme heterogeneity in the relationship between the loan rate and some of its determinants, the authors potentially explain two conflicting findings in prior literature. Originality/value – The conventional OLS method masks the heterogeneity in the relationship between the bank loan interest rate and its determinants. Quantile regression can be used to supplement the OLS estimates to gain a more detailed and complete picture of the relationship between the dependent variable and explanatory variables.