Assessing the environmental risk and mobility of cobalt in sediment near nonferrous metal mines with risk assessment indexes and the diffusive gradients in thin films (DGT) technique.

The Jialing River is the tributary of the Yangtze River with the largest drainage area. In recent years, the Jialing River has suffered a series of environmental problems, such as discharge of industrial effluent and sand mining activities, which have severely threatened the aquatic ecosystem of the river. In the present study, we employed risk assessment indexes, sequential extraction and the diffusive gradients in thin films (DGT) technique to assess environmental risks and study the remobilization of cobalt (Co) in sediments. The potential ecological risk index and risk assessment code results demonstrated that Co may pose a low environmental and ecological risk to the local aquatic environment. However, BCR sequential extraction showed that the sum of the F1, F2 and F3 fractions of Co still accounted for over 50% of the Co in the study areas, indicating that sediments may be a source of Co release. The DGT results showed an increasing trend for DGT-labile Co in deep sediments (-8 cm to -12 cm), and the calculated flux values ranged from 0.08 to 15.54 ng cm2·day-1, indicating that Co tends to transfer across the sediment-water interface at all sampling sites. Correlation analysis showed that F1-Co, F2-Co and F3-Co are the fractions readily captured by DGT and can be used for predicting Co remobilization in sediment. Sand mining activities contribute substantially to the release of Co from the F1 and F3 fractions as a result of strong stirring of sediments and introduction of oxygen into the sediments. The reductive dissolution of iron (Fe) and manganese (Mn) hydroxides or oxides causes the release of Co and Fe/Mn in the sediment, which leads to Co release from the reducible fraction. The above work suggests that sand mining in the Jialing River should be reasonably regulated to prohibit illegal sand mining activities.

Gyral Hinges Account for the Highest Cost and the Highest Communication Capacity in a Corticocortical Network.

Prior studies reported the global structure of brain networks exhibits the "small-world" and "rich-world" attributes. However, the underlying structural and functional architecture highlighted by these graph theory findings hasn't been explicitly related to the morphology of the cortex. This could be attributed to the lower resolution of used folding patterns, such as gyro-sulcal patterns. By defining a novel gyral folding pattern, termed gyral hinge (GH), which is the conjunction of ordinary gyri from multiple directions, we found GHs possess the highest length and cost in the white matter fiber connective network, and the shortest paths in the network tend to travel through GHs in their middle part. Based on these findings, we would hypothesize GHs could reside in the centers of a network core, thereby accounting for the highest cost and the highest communication capacity in a corticocortical network. The following results further support our hypothesis: 1) GHs possess stronger functional network integration capacity. 2) Higher cost is found on the connection with GHs to hinges and GHs to GHs. 3) Moving GHs introduces higher extra network cost. Our findings and hypotheses could reveal a profound relationship among the cortical folding patterns, axonal wiring architectures, and brain functions.

The spatial and temporal evolution of provincial eco-efficiency in China based on SBM modified three-stage data envelopment analysis.

Eco-efficiency plays a significant role in expressing how efficient the economic activity consumes nature's goods and services. To accurately measure eco-efficiency, the method slack-based measure modified three-stage data envelopment analysis (DEA) is adopted to evaluate environmental conditions in China's 30 provinces from year 2004 to 2016. This study treats carbon emissions and three industrials wastes as undesirable outputs and excludes the influences from external environment and random errors when make adjustments. Based on the results, this study makes the following conclusions: Firstly, industrial structure, trade openness, and population have negative effects on eco-efficiency while technology investment, urbanization process, foreign direct investment, and fiscal decentralization have positive effects on eco-efficiency. Secondly, the eco-efficiency for most provinces after adjusted is lower than the pre-adjusted, which indicates the overestimation in eco-efficiency when using traditional approaches. Thirdly, the eco-efficiency in China showed a clear geographical step distribution, with the highest eco-efficiency in the east area, followed by the central, northwest, and southwest regions.

Temporal Dynamics Assessment of Spatial Overlap Pattern of Functional Brain Networks Reveals Novel Functional Architecture of Cerebral Cortex.

OBJECTIVE: Various studies in the brain mapping field have demonstrated that there exist multiple concurrent functional networks that are spatially overlapped and interacting with each other during specific task performance to jointly realize the total brain function. Assessing such spatial overlap patterns of functional networks (SOPFNs) based on functional magnetic resonance imaging (fMRI) has thus received increasing interest for brain function studies. However, there are still two crucial issues to be addressed. First, the SOPFNs are assessed over the entire fMRI scan assuming the temporal stationarity, while possibly time-dependent dynamics of the SOPFNs is not sufficiently explored. Second, the SOPFNs are assessed within individual subjects, while group-wise consistency of the SOPFNs is largely unknown. METHODS: To address the two issues, we propose a novel computational framework of group-wise sparse representation of whole-brain fMRI temporal segments to assess the temporal dynamic spatial patterns of SOPFNs that are consistent across different subjects. RESULTS: Experimental results based on the recently publicly released Human Connectome Project grayordinate task fMRI data demonstrate that meaningful SOPFNs exhibiting dynamic spatial patterns across different time periods are effectively and robustly identified based on the reconstructed concurrent functional networks via the proposed framework. Specifically, those SOPFNs locate significantly more on gyral regions than on sulcal regions across different time periods. CONCLUSION: These results reveal novel functional architecture of cortical gyri and sulci. SIGNIFICANCE: Moreover, these results help better understand functional dynamics mechanisms of cerebral cortex in the future.

Connectome-scale assessment of structural and functional connectivity in mild traumatic brain injury at the acute stage.

Mild traumatic brain injury (mTBI) accounts for over one million emergency visits each year in the United States. The large-scale structural and functional network connectivity changes of mTBI are still unknown. This study was designed to determine the connectome-scale brain network connectivity changes in mTBI at both structural and functional levels. 40 mTBI patients at the acute stage and 50 healthy controls were recruited. A novel approach called Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs) was applied for connectome-scale analysis of both diffusion tensor imaging and resting state functional MRI data. Among 358 networks identified on DICCCOL analysis, 41 networks were identified as structurally discrepant between patient and control groups. The involved major white matter tracts include the corpus callosum, and superior and inferior longitudinal fasciculi. Functional connectivity analysis identified 60 connectomic signatures that differentiate patients from controls with 93.75% sensitivity and 100% specificity. Analysis of functional domains showed decreased intra-network connectivity within the emotion network and among emotion-cognition interactions, and increased interactions among action-emotion and action-cognition as well as within perception networks. This work suggests that mTBI may result in changes of structural and functional connectivity on a connectome scale at the acute stage.

Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment.

Due to the difficulties in establishing correspondences between functional regions across individuals and populations, systematic elucidation of functional connectivity alterations in mild cognitive impairment (MCI) in comparison with normal controls (NC) is still a challenging problem. In this paper, we assessed the functional connectivity alterations in MCI via novel, alternative predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. First, ICA-clustering was used to construct RSNs from R-fMRI data in NC group. Second, since the RSNs in MCI are already altered and can hardly be constructed directly from R-fMRI data, structural landmarks derived from DTI data were employed as the predictive models of RSNs for MCI. Third, given that the landmarks are structurally consistent and correspondent across NC and MCI, functional connectivities in MCI were assessed based on the predicted RSNs and compared with those in NC. Experimental results demonstrated that the predictive models of RSNs based on multimodal R-fMRI and DTI data systematically and comprehensively revealed widespread functional connectivity alterations in MCI in comparison with NC.

Predictive models of resting state networks for assessment of altered functional connectivity in MCI.

Due to the difficulties in establishing accurate correspondences of brain network nodes across individual subjects, systematic elucidation of possible functional connectivity (FC) alterations in mild cognitive impairment (MCI) compared with normal controls (NC) is a challenging problem. To address this challenge, in this paper, we develop and apply novel predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and DTI data to assess large-scale FC alterations in MCI. Our rationale is that some RSNs in MCI are substantially altered and can hardly be directly compared with those in NC. Instead, structural landmarks derived from DTI data are much more consistent and correspondent across MCI/NC brains, and therefore can be employed to encode RSNs in NC and serve as the predictive models of RSNs for MCI. To derive these predictive models, RSNs in NC are constructed by group-wise ICA clustering and employed to functionally annotate corresponding structural landmarks. Afterwards, these functionally-annotated structural landmarks are predicted in MCI based on DTI data and used to assess FC alterations in MCI. Experimental results demonstrated that the predictive models of RSNs are effective and can comprehensively reveal widespread FC alterations in MCI.

[Relation between social capital factors and self-rated health among Chinese adults].

OBJECTIVE: To examine the relations between factors of social capital and self-rated health among Chinese adults. METHODS: Univariate and multivariate analyses were used, based on 33 610 respondents in cross-sectional data of Chinese Family Panel Studies implemented by Institute of Social Science Survey, Peking University. RESULTS: In the study, 47.4% of the respondents reported "good" in self-rated health. The result of univariate analysis showed that those who took part in any organizations (P<0.001) or had frequent interaction with others (P<0.001) tended to report relatively higher level on self-rated health. After controlling the physical health and demographic factors, the social participation (P<0.01), social interaction (P<0.001) and perceived social equity (P<0.001) were all the correlates of self-rated health among Chinese residents. CONCLUSION: Factors of social capital are important correlates of self-rated health in China, controlling the physical health and demographic factors. self-rated health can indicate people's social health to certain extent.

Critical assessment of high-throughput standalone methods for secondary structure prediction.

Sequence-based prediction of protein secondary structure (SS) enjoys wide-spread and increasing use for the analysis and prediction of numerous structural and functional characteristics of proteins. The lack of a recent comprehensive and large-scale comparison of the numerous prediction methods results in an often arbitrary selection of a SS predictor. To address this void, we compare and analyze 12 popular, standalone and high-throughput predictors on a large set of 1975 proteins to provide in-depth, novel and practical insights. We show that there is no universally best predictor and thus detailed comparative studies are needed to support informed selection of SS predictors for a given application. Our study shows that the three-state accuracy (Q3) and segment overlap (SOV3) of the SS prediction currently reach 82% and 81%, respectively. We demonstrate that carefully designed consensus-based predictors improve the Q3 by additional 2% and that homology modeling-based methods are significantly better by 1.5% Q3 than ab initio approaches. Our empirical analysis reveals that solvent exposed and flexible coils are predicted with a higher quality than the buried and rigid coils, while inverse is true for the strands and helices. We also show that longer helices are easier to predict, which is in contrast to longer strands that are harder to find. The current methods confuse 1-6% of strand residues with helical residues and vice versa and they perform poorly for residues in the ß- bridge and 3(10)-helix conformations. Finally, we compare predictions of the standalone implementations of four well-performing methods with their corresponding web servers.

[Determinants of self-rated health in old people].

OBJECTIVE: To describe the status of self-rated health (SRH) among the elderly and find out the relationship between SRH and other health measures such as two weeks prevalence and chronic disease prevalence. METHODS: The data used was generated from cross-sectional household health survey conducted in the year of 2009 in Shunyi district, Beijing. SPSS software was used to conduct univariate and multivariate liner regression analysis with self-rated health. RESULTS: The average score of self-rated health among the elderly is 72.49. Univariate analyses suggest that are age, sex, marital status, income level, education, employment, medical insurance type, self-perceived anxious or depression, disease state are all associated with poor SRH score. Multiple liner regression model shows that age, job, medical insurance, self-perceived anxious or depression, suffer from two-week illness and chronic disease had effects on SRH of the elderly. CONCLUSION: Physical and psychological unhealthy are independent risk factors of SRH among the elderly, disease status is the most influential predictor on SRH score. Thus, a single measurement of SRH question can be used in health status assessment of the elderly.