Characteristics of heavy metal migration during pyrolysis of typical oily wastes and environmental risk assessment of pyrolysis residues.

Solid-phase residues from pyrolysis of oily wastes (OS) are widely used due to their rich pore structure and strong adsorption capacity. In this study, pyrolysis residues (OS-P) were obtained from the pyrolysis treatment of four typical OS in Karamay, Xinjiang. The results indicate that the crystalline substances in OS-P mainly were SiO2, BaSO4, and graphite. The heavy metals of OS-P were higher than that of OS in the following order: Zn > Cu > Ni > Cr > Pb > Cd. The results of the improvement of Community Bureau of Reference (BCR) sequential extraction showed that the proportion of Cu, Ni and Cr in OS1-P in the residual fraction was higher than that of the other three OS. The residual fraction of Cu, Ni, and Cr in OS1-P increased from 16.0 %, 30.0 %, and 11.0 % to 66.1 %, 81.9 %, and 89.2 %, respectively. After pyrolysis treatment, the leaching concentration of heavy metals in the residue was reduced. Referring to the requirements for heavy metal control limits (GB 4284-2018), all heavy metals in OS-P showed low risk. Their potential ecological risk indices were 4.11, 3.13, 4.87 and 5.35, respectively, indicating that the potential ecological hazards of heavy metals from OS-P were slight. There was no significant effect on the histopathological changes of kidney, lung, liver, ovary and testis of mice, showing that the rational use of OS-P in production will not produce toxic effects on target animals. Based on risk assessment and safety evaluation, the application of OS-P is controllable, safe and reliable for resource utilization.

Racial and ethnic differences in disease course Medicare expenditures for beneficiaries with dementia.

BACKGROUND: Research on racial and ethnic disparities in costs of care during the course of dementia is sparse. We analyzed Medicare expenditures for beneficiaries with dementia to identify when during the course of care costs are the highest and whether they differ by race and ethnicity. METHODS: We analyzed data from the 2000-2016 Health and Retirement Study (HRS) linked with corresponding Medicare claims to estimate total Medicare expenditures for four phases: (1) the year before a dementia diagnosis, (2) the first year following a dementia diagnosis, (3) ongoing care for dementia after the first year, and (4) the last year of life. We estimated each patient's phase-specific and disease course Medicare expenditures by using a race-specific survival model and monthly expenditures adjusted for patient characteristics. We investigated healthcare utilization by service type across races/ethnicities and phases of care. RESULTS: Adjusted mean total Medicare expenditures for non-Hispanic (NH) Black ($165,730) and Hispanic beneficiaries with dementia ($160,442) exceeded corresponding expenditures for NH Whites ($136,326). In the year preceding and immediately following initial dementia diagnosis, mean Medicare expenditures for NH Blacks ($26,337 and $20,429) exceeded expenditures for Hispanics and NH Whites ($21,399-23,176 and 17,182-18,244). The last year of life was responsible for the greatest cost contribution: $51,294 (NH Blacks), $47,469 (Hispanics), and $39,499 (NH Whites). These differences were driven by greater use of high-cost services (e.g., emergency department, inpatient and intensive care), especially during the last year of life. CONCLUSIONS: NH Black and Hispanic beneficiaries with dementia had higher disease course Medicare expenditures than NH Whites. Expenditures were highest for NH Black beneficiaries in every phase of care. Further research should address mechanisms of such disparities and identify methods to improve communication, shared decision-making, and access to appropriate services for all populations.

Kochia scoparia L., a newfound candidate halophyte, for phytoremediation of cadmium-contaminated saline soils.

In recent years, heavy metal pollution in saline soil is increasingly severe due to the rapid development of industry and agriculture. Halophytes can survive at higher concentrations of salt and heavy metal, which make them suitable candidates for the phytoremediation of heavy metals in saline soils. In the present study, the halophyte plant Kochia scoparia (L.) Schrad. seedlings were exposed to different doses of Cd (0, 5, 10, 30 mg/kg) and NaCl (0, 200, 400, 800 mM) to explore its tolerance and phytoremediation ability for Cd. There was no significant toxic effect of Cd on the K. scoparia seedlings. NaCl reduced the biomass of K. scoparia compared with the control, but did not show any visible toxic symptom. Furthermore, Cd accumulation in K. scoparia is mainly distributed in the shoot; especially when exposed to low-Cd (5 mg/kg) treatment, the accumulation of Cd in the shoots was up to 5.42-22.25 mg/kg, which was 3.18-53.4 times of that in the roots. Moreover, the contents of glutathione and oxalate in plants increased gradually with the increase of NaCl concentration. Under the treatment of 800 mM NaCl without Cd, the content of glutathione reached the highest 51.21 µg/g, and the proportion of oxalate reached the highest 28.76% under the treatment of 30 mg/kg Cd with 400 Mm NaCl. Finally, we also found the significant alterations of cadmium chemical forms in rhizosphere soil with the addition of NaCl. Overall, K. scoparia could be an efficient and valuable candidate for the phytoextraction of low-Cd (5 mg/kg)-contaminated saline soil.

Sustained intake of animal-sourced foods is associated with less stunting in young children.

The value of animal-sourced foods (ASFs) in providing key nutrients, particularly for child growth and where diets are of low quality, is understood mainly from cross-sectional assessment of current consumption. Longitudinal panel data from Nepal, Bangladesh and Uganda were used here to assess associations among previous (lagged) and contemporaneous ASF intake with linear growth of children aged 6-24 months. Lagged ASF consumption was significantly correlated with a 10% decline in stunting in Nepali children who consumed any ASF in the previous year, while current intake was associated with a 9% decline in stunting in Uganda. Previous consumption of two or more ASFs showed a stronger association, ranging from a 10% decline in stunting in Bangladesh to a 16% decline in Nepal. This novel lagged analysis emphasizes the need for regular and appropriate levels of ASF intake by young children to support healthy growth in resource-constrained settings.

Using mobile phone data helps estimate community-level food insecurity: Findings from a multi-year panel study in Nepal.

Household food insecurity remains a major policy challenge in low-income countries. Identifying accurate measures that are relatively easy to collect has long been an important priority for governments seeking to better understand and fund solutions for communities in remote settings. Conventional approaches based on surveys can be time-consuming and costly, while data derived from satellite imagery represent proxies focused on biological processes (such as rainfall and crop growth) lack granularity in terms of human behaviors. As a result, there has recently been interest in tapping into the large digital footprint offered by mobile phone usage. This paper explores empirical relationships between data relating to mobile phones (ownership and spending on service use), and food insecurity in rural Nepal. The work explores models for estimating community-level food insecurity through aggregated mobile phone variables in a proof-of-concept approach. In addition, sensitivity analyses were performed by considering the performance of the models under different settings. The results suggest that mobile phone variables on ownership and expenditure can be used to estimate food insecurity with reasonable accuracy. This suggests that such an approach can be used in and beyond Nepal as an option for collecting timely food insecurity information, either alone or in combination with conventional approaches.

Multi-tensor investigation of orbitofrontal cortex tracts affected in subcaudate tractotomy.

Subcaudate tractotomy (SCT) is a neurosurgical lesioning procedure that can reduce symptoms in medically intractable obsessive compulsive disorder (OCD). Due to the putative importance of the orbitofrontal cortex (OFC) in symptomatology, fibers that connect the OFC, SCT lesion, and either the thalamus or brainstem were investigated with two-tensor tractography using an unscented Kalman filter approach. From this dataset, fibers were warped to Montreal Neurological Institute space, and probability maps with center-of-mass analysis were subsequently generated. In comparing fibers from the same OFC region, including medial OFC (mOFC), central OFC (cOFC), and lateral OFC (lOFC), the area of divergence for fibers connected with the thalamus versus the brainstem is posterior to the anterior commissure. At the anterior commissure, fibers connected with the thalamus run dorsal to those connected with the brainstem. As OFC fibers travel through the ventral aspect of the internal capsule, lOFC fibers are dorsal to cOFC and mOFC fibers. Using neuroanatomical comparison, tracts coursing between the OFC and thalamus are likely part of the anterior thalamic radiations, while those between the OFC and brainstem likely belong to the medial forebrain bundle. These data support the involvement of the OFC in OCD and may be relevant to creating differential lesional procedures of specific tracts or to developing deep brain stimulation programming paradigms.

Cultivar variations in cadmium and lead accumulation and distribution among 30 wheat (Triticum aestivum L.) cultivars.

In recent years, heavy metal pollution in agricultural soil in China has received public concern. The concept of low-accumulation cultivars (LACs) was proposed to minimize the influx of pollutants to the human food chain. Variations in Cd and Pb accumulation, distribution, and tolerance among 30 wheat (Triticum aestivum L.) cultivars were studied in a hydroponic experiment to preliminary identify LACs of Cd or Pb for further field experiments. Of the 30 wheat cultivars tested, 27 and 26 wheat cultivars showed no effect of the Cd/Pb treatments on the shoot and root biomass, respectively. The results showed that the tested wheat cultivars had considerable tolerance to Cd and Pb toxicity. Significant (p < 0.05) differences in shoot Cd concentration were observed among the tested wheat cultivars under treatments Cd1.0 and Cd1.0Pb15, ranging from 0.91 to 6.74 and from 0.87 to 5.96, with the mean of 3.83 and 2.94 mg kg(-1) DW, respectively. Significant (p < 0.05) differences in shoot Pb concentration were also observed among the tested wheat cultivars under treatments Pb15 and Cd1.0Pb15, ranging from 22.18 to 94.03 and from 18.30 to 76.88, with the mean of 50.38 and 41.20 mg kg(-1) DW, respectively. Low accumulation and internal distribution may both affect the cultivar differences in Cd and Pb accumulation in wheat shoots. Overall, wheat cultivars LF-13, LF-16, and LF-21 had lower Cd-accumulating abilities in their shoots. Wheat cultivars LF-13, LF-23, LF-26, and LF-27 showed low Pb accumulation characteristics in their shoots. An antagonistic interaction occurred between Cd and Pb in accumulation in wheat roots and shoots, which will be further studied in field experiments.

Toward Defining the Neural Substrates of ADHD: A Controlled Structural MRI Study in Medication-Naïve Adults.

OBJECTIVE: We assessed the neural correlates of adult ADHD in treatment-naïve participants, an approach necessary for identifying neural substrates unconfounded by medication effects. METHOD: The sample consisted of 24 medication-naïve adults with Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV) diagnosed ADHD and 24 healthy controls, comparable on age, sex, handedness, reading achievement, IQ, and psychiatric comorbidity. All participants were assessed with structured diagnostic interviews. Magnetic resonance imaging (MRI)-based regional voxel-based morphometry (r-VBM) was used to assess volumetric differences in a priori defined brain regions of interest. RESULTS: VBM analysis revealed group differences in the hypothesized cortical and subcortical areas; however, only cerebellar volume reductions in ADHD retained significance (p < .05) after corrections for multiple comparisons. CONCLUSION: These results support the notion that medication-naïve ADHD as expressed in adulthood, manifests subtle brain volume reductions from normal in the cerebellum, and possibly in other syndrome-congruent gray-matter structures. Larger samples are required to confirm these findings.

Further understanding of the comorbidity between Attention-Deficit/Hyperactivity Disorder and bipolar disorder in adults: an MRI study of cortical thickness.

Although Attention-Deficit/Hyperactivity Disorder (ADHD) and Bipolar Disorder (BPD) frequently co-occur and represent a particularly morbid clinical form of both disorders, neuroimaging research addressing this comorbidity is scarce. Our aim was to evaluate cortical thickness in ADHD and BPD, testing the hypothesis that comorbid subjects (ADHD+BPD) would have neuroanatomical correlates of both disorders. Magnetic Resonance Imaging (MRI) findings were compared between 31 adults with ADHD+BPD, 18 with BPD, 26 with ADHD, and 23 healthy controls. Cortical thickness analysis of regions of interest was estimated as a function of ADHD and BPD status, using linear regression models. BPD was associated with significantly thicker cortices in 13 regions, independently of ADHD status and ADHD was associated with significantly thinner neocortical gray matter in 28 regions, independent of BPD. In the comorbid state of ADHD plus BPD, the profile of cortical abnormalities consisted of structures that are altered in both disorders individually. Results support the hypothesis that ADHD and BPD independently contribute to cortical thickness alterations of selective and distinct brain structures, and that the comorbid state represents a combinatory effect of the two. Attention to comorbidity is necessary to help clarify the heterogeneous neuroanatomy of both BPD and ADHD.

Covariance modeling of MRI brain volumes in memory circuitry in schizophrenia: Sex differences are critical.

Women have consistently demonstrated better verbal memory on tests that evaluate immediate and delayed free recall. In patients with schizophrenia, these verbal memory processes are relatively more preserved in women than men. However an understanding of the brain anatomy of the female advantage for verbal memory is still unclear. 29 females and 59 males with schizophrenia made comparable to 21 female and 27 male healthy volunteers were scanned using structural magnetic resonance imaging (sMRI) in order to assess volumes of regions across the entire brain. Sex differences within and between groups in the covariance structure of memory circuitry regions were evaluated using a novel approach to covariance analysis (the Box M Test). Brain areas of interest included the prefrontal cortex (PFC), inferior parietal lobule (iPAR), anterior cingulate gyrus (ACG), parahippocampus, and hippocampus (HIPP). Results showed significant differences in the covariance matrices of females and males with schizophrenia compared with their healthy counterparts, in particular the relationships between iPAR-PFC, iPAR-ACG, and HIPP-PFC. Sex differences in the iPAR-PFC relationship were significantly associated with sex differences in verbal memory performance. In control women, but not in men ACG volume correlated strongly with memory performance. In schizophrenia, ACG volume was reduced in females, but not in men, relative to controls. Findings suggest that the relationship between iPAR and PFC is particularly important for understanding the relative preservation of verbal memory processing in females with schizophrenia and may compensate for ACG volume reductions. These results illustrate the utility of a unique covariance structure modeling approach that yields important new knowledge for understanding the nature of schizophrenia.

Gray matter alterations in adults with attention-deficit/hyperactivity disorder identified by voxel based morphometry.

BACKGROUND: Gray and white matter volume deficits have been reported in many structural magnetic resonance imaging (MRI) studies of children with attention-deficit/hyperactivity disorder (ADHD); however, there is a paucity of structural MRI studies of adults with ADHD. This study used voxel based morphometry and applied an a priori region of interest approach based on our previous work, as well as from well-developed neuroanatomical theories of ADHD. METHODS: Seventy-four adults with DSM-IV ADHD and 54 healthy control subjects comparable on age, sex, race, handedness, IQ, reading achievement, frequency of learning disabilities, and whole brain volume had an MRI on a 1.5T Siemens scanner. A priori region of interest hypotheses focused on reduced volumes in ADHD in dorsolateral prefrontal cortex, anterior cingulate cortex, caudate, putamen, inferior parietal lobule, and cerebellum. Analyses were carried out by FSL-VBM 1.1. RESULTS: Relative to control subjects, ADHD adults had significantly smaller gray matter volumes in parts of six of these regions at p ≤ .01, whereas parts of the dorsolateral prefrontal cortex and inferior parietal lobule were significantly larger in ADHD at this threshold. However, a number of other regions were smaller and larger in ADHD (especially fronto-orbital cortex) at this threshold. Only the caudate remained significantly smaller at the family-wise error rate. CONCLUSIONS: Adults with ADHD have subtle volume reductions in the caudate and possibly other brain regions involved in attention and executive control supporting frontostriatal models of ADHD. Modest group brain volume differences are discussed in the context of the nature of the samples studied and voxel based morphometry methodology.

Predictive learning with structured (grouped) data.

Many applications of machine learning involve sparse and heterogeneous data. For example, estimation of diagnostic models using patients' data from clinical studies requires effective integration of genetic, clinical and demographic data. Typically all heterogeneous inputs are properly encoded and mapped onto a single feature vector, used for estimating a classifier. This approach, known as standard inductive learning, is used in most application studies. Recently, several new learning methodologies have emerged. For instance, when training data can be naturally separated into several groups (or structured), we can view model estimation for each group as a separate task, leading to a Multi-Task Learning framework. Similarly, a setting where the training data are structured, but the objective is to estimate a single predictive model (for all groups), leads to the Learning with Structured Data and SVM+ methodology recently proposed by Vapnik [(2006). Empirical inference science afterword of 2006. Springer]. This paper describes a biomedical application of these new data modeling approaches for modeling heterogeneous data using several medical data sets. The characteristics of group variables are analyzed. Our comparisons demonstrate the advantages and limitations of these new approaches, relative to standard inductive SVM classifiers.

Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA.

In functional magnetic resonance imaging (fMRI) analysis, although the univariate general linear model (GLM) is currently the dominant approach to brain activation detection, there is growing interest in multivariate approaches such as principal component analysis, canonical variate analysis (CVA), independent component analysis and cluster analysis, which have the potential to reveal neural networks and functional connectivity in the brain. To understand the effect of processing options on performance of multivariate model-based fMRI processing pipelines with real fMRI data, we investigated the impact of commonly used fMRI preprocessing steps and optimized the associated multivariate CVA-based, single-subject processing pipelines with the NPAIRS (nonparametric prediction, activation, influence and reproducibility resampling) performance metrics [prediction accuracy and statistical parametric image (SPI) reproducibility] on the Fiswidgets platform. We also compared the single-subject SPIs of univariate GLM with multivariate CVA-based processing pipelines from SPM, FSL.FEAT, NPAIRS.GLM and NPAIRS.CVA software packages (or modules) using a novel second-level CVA. We found that for the block-design data, (a) slice timing correction and global intensity normalization have little consistent impact on the fMRI processing pipeline, but spatial smoothing, temporal detrending or high-pass filtering, and motion correction significantly improved pipeline performance across all subjects; (b) the combined optimization of spatial smoothing, temporal detrending and CVA model parameters on average improved between-subject reproducibility; and (c) the most important pipeline choices include univariate or multivariate statistical models and spatial smoothing. This study suggests that considering options other than simply using GLM with a fixed spatial filter may be of critical importance in determining activation patterns in BOLD fMRI studies.

Evaluation and comparison of GLM- and CVA-based fMRI processing pipelines with Java-based fMRI processing pipeline evaluation system.

Activation patterns identified by fMRI processing pipelines or fMRI software packages are usually determined by the preprocessing options, parameters, and statistical models used. Previous studies that evaluated options of GLM (general linear model)--based fMRI processing pipelines are mainly based on simulated data with receiver operating characteristics (ROC) analysis, but evaluation of such fMRI processing pipelines on real fMRI data is rare. To understand the effect of processing options on performance of GLM-based fMRI processing pipelines with real fMRI data, we investigated the impact of commonly-used fMRI preprocessing steps; optimized the associated GLM-based single-subject processing pipelines; and quantitatively compared univariate GLM (in FSL.FEAT and NPAIRS.GLM) and multivariate CVA (canonical variates analysis) (in NPAIRS.CVA)-based analytic models in single-subject analysis with a recently developed fMRI processing pipeline evaluation system based on prediction accuracy (classification accuracy) and reproducibility performance metrics. For block-design data, we found that with GLM analysis (1) slice timing correction and global intensity normalization have little consistent impact on fMRI processing pipelines, spatial smoothing and high-pass filtering or temporal detrending significantly increases pipeline performance and thus are essential for robust fMRI statistical analysis; (2) combined optimization of spatial smoothing and temporal detrending improves pipeline performance; and (3) in general, the prediction performance of multivariate CVA is higher than that of the univariate GLM, while univariate GLM is more reproducible than multivariate CVA. Because of the different bias-variance trade-offs of univariate and multivariate models, it may be necessary to consider a consensus approach to obtain more accurate activation patterns in fMRI data.

A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.

Automatic segmentation of left and right cerebral hemispheres from MRI brain volumes using the graph cuts algorithm.

An automated algorithm has been developed to segment stripped (non-brain tissue excluded) T1-weighted MRI brain volumes into left and right cerebral hemispheres and cerebellum+brainstem. The algorithm, which uses the Graph Cuts technique, performs a fully automated segmentation in approximately 30 s following pre-processing. It is robust and accurate and has been tested on datasets from two scanners using different field strengths and pulse sequences. We describe the Graph Cuts algorithm and compare the results of Graph Cuts segmentations against "gold standard" manual segmentations and segmentations produced by three popular software packages used by neuroimagers: BrainVisa, CLASP, and SurfRelax.