The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various ...neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).
Recent studies have suggested that the brain's structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and ...functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface (GUI); (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website.
The effects of heat treatments on the microstructures and corrosion behaviors of extruded Mg-2Nd-2Zn alloys in 3.5% NaCl electrolyte were investigated. It was found that the average corrosion rate of ...solid solution Mg-2Nd-2Zn alloy is 45.86 mm/y, which is much lower than that of the as-extruded and solution-aged Mg alloys. The nano-scale oxides were formed by the oxidation of solid solution atoms during the corrosion process and filled into the porous corrosion product film, which was conducive to reducing the corrosion rates of Mg alloys. Therefore, the solid solution treatment remarkably improved the corrosion rate of Mg alloys.
•Heat treatments remarkably increase the grain sizes of Mg alloys and reduce grain boundary corrosion.•The corrosion products of Mg-2Nd-2Zn alloy are mainly composed of Mg(OH)2 and Mg2Cl (OH)3·4H2O.•Cl- attacks Mg(OH)2 to form loose Mg2Cl(OH)3·4H2O, resulting in a poor corrosion resistance of Mg alloys in Cl- solution.•Nano-scale oxides of solute atomic embedded in corrosion product film improves the corrosion resistance of Mg alloys.
Addition of a small amount of Ca improves the ductility of Mg alloys. However, the mechanism underlying this effect is not well understood. In this work, tensile testing of an extruded Mg−0.47 wt% Ca ...alloy was conducted inside a scanning electron microscope. Electron backscattered diffraction-based slip trace analysis was performed to study in-grain slip activities at 1%, 2%, 4%, 8%, and 16% tensile strain. While the majority of the grains were deformed by {0001} basal slip, slip lines from {11_00} prismatic planes and {11_01} pyramidal I planes were also frequently observed, and their fractions increased with strain. Ex situ transmission electron microscopy indicated that the pyramidal I slip lines were associated with dislocations instead of <c+a> dislocations. From Schmid factor analysis, the critical resolved shear stresses of prismatic slip and pyramidal slip are approximately twice that of basal slip in this Mg–Ca alloy. The enhanced activity of non-basal slip improved the material's ductility. Our first-principles calculations found that solute Ca atoms would reduce the unstable stacking fault energy for all slip modes.
•Mechanism of ductility enhancement in Mg by Ca alloying was investigated.•Slip trace analysis showed different slip system activities at different strains.•Ca addition was found to enhance the activity of non-basal slip.•Influence of Ca on the GSFE curves of different slip systems was assessed.
Background Neuroimaging studies have shown that major depressive disorder (MDD) is accompanied by structural and functional abnormalities in specific brain regions and connections; yet, little is ...known about alterations of the topological organization of whole-brain networks in MDD patients. Methods Thirty drug-naive, first-episode MDD patients and 63 healthy control subjects underwent a resting-state functional magnetic resonance imaging scan. The whole-brain functional networks were constructed by thresholding partial correlation matrices of 90 brain regions, and their topological properties (e.g., small-world, efficiency, and nodal centrality) were analyzed using graph theory-based approaches. Nonparametric permutation tests were further used for group comparisons of topological metrics. Results Both the MDD and control groups showed small-world architecture in brain functional networks, suggesting a balance between functional segregation and integration. However, compared with control subjects, the MDD patients showed altered quantitative values in the global properties, characterized by lower path length and higher global efficiency, implying a shift toward randomization in their brain networks. The MDD patients exhibited increased nodal centralities, predominately in the caudate nucleus and default-mode regions, including the hippocampus, inferior parietal, medial frontal, and parietal regions, and reduced nodal centralities in the occipital, frontal (orbital part), and temporal regions. The altered nodal centralities in the left hippocampus and the left caudate nucleus were correlated with disease duration and severity. Conclusions These results suggest that depressive disorder is associated with disruptions in the topological organization of functional brain networks and that this disruption may contribute to disturbances in mood and cognition in MDD patients.
Morphological brain networks, in particular those at the individual level, have become an important approach for studying the human brain connectome; however, relevant methodology is far from being ...well-established in their formation, description and reproducibility. Here, we extended our previous study by constructing and characterizing single-subject morphological similarity networks from brain volume to surface space and systematically evaluated their reproducibility with respect to effects of different choices of morphological index, brain parcellation atlas and similarity measure, sample size-varying stability and test-retest reliability. Using the Human Connectome Project dataset, we found that surface-based single-subject morphological similarity networks shared common small-world organization, high parallel efficiency, modular architecture and bilaterally distributed hubs regardless of different analytical strategies. Nevertheless, quantitative values of all interregional similarities, global network measures and nodal centralities were significantly affected by choices of morphological index, brain parcellation atlas and similarity measure. Moreover, the morphological similarity networks varied along with the number of participants and approached stability until the sample size exceeded ~70. Using an independent test-retest dataset, we found fair to good, even excellent, reliability for most interregional similarities and network measures, which were also modulated by different analytical strategies, in particular choices of morphological index. Specifically, fractal dimension and sulcal depth outperformed gyrification index and cortical thickness, higher-resolution atlases outperformed lower-resolution atlases, and Jensen-Shannon divergence-based similarity outperformed Kullback-Leibler divergence-based similarity. Altogether, our findings propose surface-based single-subject morphological similarity networks as a reliable method to characterize the human brain connectome and provide methodological recommendations and guidance for future research.
The utility of CRISPR-Cas9 and TALENs for genome editing may be compromised by their off-target activity. We show that integrase-defective lentiviral vectors (IDLVs) can detect such off-target ...cleavage with a frequency as low as 1%. In the case of Cas9, we find frequent off-target sites with a one-base bulge or up to 13 mismatches between the single guide RNA (sgRNA) and its genomic target, which refines sgRNA design.
Thin-film composite reverse osmosis membranes were dried under different membrane pre-treatment procedures and evaluated at increased temperatures by gas separation tests. The obtained permeance and ...selectivity values indicated the presence of highly-permeable regions in the dry samples of the commercial membranes.
Treatment with ethanol–hexane in a solvent exchange process, as well as membrane immersion in t-butanol followed by freeze drying, increased the gas permeance by a factor of 1.8 to 9, and from 1.6 to 3.2, respectively, by comparison with room temperature and oven drying. Nevertheless, a Knudsen-diffusion transport mechanism was dominant after both pre-treatments.
The permeation temperature remarkably influenced gas selectivity and permeance, and a maximum He/N2 selectivity occurred at 150°C with considerable high permeance results, which may suggest the use of polyamide membranes as alternative materials for high-temperature separation processes. The temperature-induced changes in the polymer structure and in the transport of compounds can be explained by Knudsen and activated diffusion mechanisms throughout a highly-permeable regions and a dense polyamide matrix, respectively.
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•Polyamide thin-film composite RO membranes were evaluated by gas separation tests.•Gas permeance evidenced layer alteration after different membrane pre-treatments.•Gas transport was explained by Knudsen and activated diffusion mechanism.•The inhomogeneous polyamide layer consists of a dense and high permeable region.•Polyamide membranes show promises as high-temperature gas separation materials.
Introduction
Structural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features among regions, however, are ...not well understood. Here, we constructed individual‐level morphological brain networks and systematically examined their topological organization and long‐term test–retest reliability under different analytical schemes of spatial smoothing, brain parcellation, and network type.
Methods
This study included 57 healthy participants and all participants completed two MRI scan sessions. Individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional gray matter volume in terms of the Kullback–Leibler divergence measure. Graph‐based global and nodal network measures were then calculated, followed by the statistical comparison and intra‐class correlation analysis.
Results
The morphological brain networks were highly reproducible between sessions with significantly larger similarities for interhemispheric connections linking bilaterally homotopic regions. Further graph‐based analyses revealed that the morphological brain networks exhibited nonrandom topological organization of small‐worldness, high parallel efficiency and modular architecture regardless of the analytical choices of spatial smoothing, brain parcellation and network type. Moreover, several paralimbic and association regions were consistently revealed to be potential hubs. Nonetheless, the three studied factors particularly spatial smoothing significantly affected quantitative characterization of morphological brain networks. Further examination of long‐term reliability revealed that all the examined network topological properties showed fair to excellent reliability irrespective of the analytical strategies, but performing spatial smoothing significantly improved reliability. Interestingly, nodal centralities were positively correlated with their reliabilities, and nodal degree and efficiency outperformed nodal betweenness with respect to reliability.
Conclusions
Our findings support single‐subject morphological network analysis as a meaningful and reliable method to characterize structural organization of the human brain; this method thus opens a new avenue toward understanding the substrate of intersubject variability in behavior and function and establishing morphological network biomarkers in brain disorders.
We proposed a method to construct individual‐level morphological brain networks from structural MRI data. We demonstrated that morphological brain networks derived from this method were specifically organized, test–retest reliable and dependent on different analytic strategies of data preprocessing and network construction methods.
Owing to the advantages of high power density, fast charge/discharge rates, as well as long lifetime, micro‐supercapacitors have drawn much attention for their potential application in miniaturized ...electronics. Great progress has been made in recent years. On the one hand, many efforts have been devoted to the design and fabrication of high‐performance miniaturized supercapacitors. On the other hand, integration of micro‐supercapacitors with multiple functional materials and devices has emerged with the development of portable and wearable microelectronics. This review first discusses the recent progress of fabrication methods and strategies in the micro‐supercapacitor field. The recent reports on integration of micro‐supercapacitors with smart functions, for instance, self‐charging, self‐protection, electrochromism, self‐healing, sensing, stretchability, as well as photo‐switchimg, are summarized. The perspectives on micro‐fabrication strategies and integrated multifunctionalities of smart micro‐supercapacitors are provided at the end.
Micro‐supercapacitors for miniaturized electronics and wearable electronics have been developed rapidly in recent years. Recent advances in device design, fabrication methods, and highly integrated multifunctional devices at the material‐level and device‐level in micro‐supercapacitors are summarized in this review. The current challenges and future potentials in developing smart micro‐supercapacitors are discussed.
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