Age-Related Decline in the Variation of Dynamic Functional Connectivity: A Resting State Analysis

Normal aging is typically characterized by abnormal resting-state functional connectivity (FC), including decreasing connectivity within networks and increasing connectivity between networks, under the assumption that the FC over the scan time was stationary. In fact, the resting-state FC has been shown in recent years to vary over time even within minutes, thus showing the great potential of intrinsic interactions and organization of the brain. In this article, we assumed that the dynamic FC consisted of an intrinsic dynamic balance in the resting brain and was altered with increasing age. Two groups of individuals (N = 36, ages 20–25 for the young group; N = 32, ages 60–85 for the senior group) were recruited from the public data of the Nathan Kline Institute. Phase randomization was first used to examine the reliability of the dynamic FC. Next, the variation in the dynamic FC and the energy ratio of the dynamic FC fluctuations within a higher frequency band were calculated and further checked for differences between groups by non-parametric permutation tests. The results robustly showed modularization of the dynamic FC variation, which declined with aging; moreover, the FC variation of the inter-network connections, which mainly consisted of the frontal-parietal network-associated and occipital-associated connections, decreased. In addition, a higher energy ratio in the higher FC fluctuation frequency band was observed in the senior group, which indicated the frequency interactions in the FC fluctuations. These results highly supported the basis of abnormality and compensation in the aging brain and might provide new insights into both aging and relevant compensatory mechanisms

Theoretical Study on the Mechanism of Asymmetrical Large Deformation of Heading Roadway Facing Mining

The problem of asymmetric large deformation of surrounding rock of heading roadways is prominent due to the superposition of mining stress in the mining intersection area. Therefore, on the basis of the background of 18,106 tailentry in the Xiegou Coal Mine, this paper establishes a mechanical model of surrounding rock deformation of mining roadways under the effect of advanced abutment pressure. In the model, we deduce the theoretical calculation formula of roadway full-section deformation and discuss the influence factors of roadway surrounding rock deformation. Accordingly, the deformation mechanism of surrounding rock of mining roadways and the engineering suggestions and measures are revealed. The main results and finding are threefold. Firstly, the increase of the stress concentration factor of the coal pillar rib and the increase of the width of the failure zone are the fundamental reasons leading to the aggravation of the surrounding rock deformation on the side of the coal pillar in the heading roadway. Secondly, the deformation of the coal pillar rib increases with the increase of stress concentration factor and decreases with the increase of coal cohesion, internal friction angle, elastic modulus, and roadway rib support resistance. Additionally, the deformation of the roadway roof and floor decreases with the increase of roadway rib support resistance and is inversely proportional to the cubic power of rock beam thickness and elastic modulus. The deformation rate of the roadway roof and floor increases with the increase of vertical stress concentration factor of the coal pillar rib, and the maximum deformation position shifts to the side of the coal pillar. Therefore, increasing the strength and stiffness of the roadway surrounding rock and the supporting resistance of surrounding rock can reduce the deformation of roadway surrounding rock and the influence of advanced abutment pressure on roadway deformation. In the end, the rationality and feasibility of the theoretical analysis is verified through an engineering example. Under the influence of advanced abutment pressure, the deformation of roadway floor heave is the most severe, the asymmetrical deformation on both sides of the roadway is remarkable, and the deformation of coal pillar side is about twice that of solid coal side

Temporal and spatial variation of soil moisture of small watershed in gully catchment of the Loess Plateau of China

The temporal and spatial variation characteristics of soil moisture in typical slope and gully of Jiulongquangou small watershed were studied in the hilly and gully region of the Loess Plateau of China. The variation of soil moisture in the 0-30 cm layer on the surface of the hilly and gully region of the Loess Plateau is greater than the variation of soil moisture in each layer between 40 and 100 cm. In the study area, the model parameters such as coefficient of variation (Cv), nugget (C0), sill (C0+C), spatial degrees of freedom(C0/(C+C0)) and variable change can be used to quantitative analysis the spatial varying law. On the slope surface, the average soil water content and the coefficient of variation are negatively correlated, and can be approximated by an exponential function, while the two are positively correlated in the gully

Vanadate Bio-Detoxification Driven by Pyrrhotite with Secondary Mineral Formation

Vanadium(V) is a redox-sensitive heavy-metal contaminant whose environmental mobility is strongly influenced by pyrrhotite, a widely distributed iron sulfide mineral. However, relatively little is known about microbially mediated vanadate [V(V)] reduction characteristics driven by pyrrhotite and concomitant mineral dynamics in this process. This study demonstrated efficient V(V) bioreduction during 210 d of operation, with a lifespan about 10 times longer than abiotic control, especially in a stable period when the V(V) removal efficiency reached 44.1 ± 13.8%. Pyrrhotite oxidation coupled to V(V) reduction could be achieved by an enriched single autotroph (e.g., Thiobacillus and Thermomonas) independently. Autotrophs (e.g., Sulfurifustis) gained energy from pyrrhotite oxidation to synthesize organic intermediates, which were utilized by the heterotrophic V(V) reducing bacteria such as Anaerolinea, Bacillus, and Pseudomonas to sustain V(V) reduction. V(V) was reduced to insoluble tetravalent V, while pyrrhotite oxidation mainly produced Fe(III) and SO42–. Secondary minerals including mackinawite (FeS) and greigite (Fe3S4) were produced synchronously, resulting from further transformations of Fe(III) and SO42– by sulfate reducing bacteria (e.g., Desulfatiglans) and magnetotactic bacteria (e.g., Nitrospira). This study provides new insights into the biogeochemical behavior of V under pyrrhotite effects and reveals the previously overlooked mineralogical dynamics in V(V) reduction bioprocesses driven by Fe(II)-bearing minerals

Geo-Object-Based Vegetation Mapping via Machine Learning Methods with an Intelligent Sample Collection Scheme: A Case Study of Taibai Mountain, China

Precise vegetation maps of mountainous areas are of great significance to grasp the situation of an ecological environment and forest resources. In this paper, while multi-source geospatial data can generally be quickly obtained at present, to realize effective vegetation mapping in mountainous areas when samples are difficult to collect due to their perilous terrain and inaccessible deep forest, we propose a novel and intelligent method of sample collection for machine-learning (ML)-based vegetation mapping. First, we employ geo-objects (i.e., polygons) from topographic partitioning and constrained segmentation as basic mapping units and formalize the problem as a supervised classification process using ML algorithms. Second, a previously available vegetation map with rough-scale label information is overlaid on the geo-object-level polygons, and candidate geo-object-based samples can be identified when all the grids’ labels of vegetation types within the geo-objects are the same. Third, various kinds of geo-object-level features are extracted according to high-spatial-resolution remote sensing (HSR-RS) images and multi-source geospatial data. Some unreliable geo-object-based samples are rejected in the candidate set by comparing their features and the rules based on local expert knowledge. Finally, based on these automatically collected samples, we train the model using a random forest (RF)-based algorithm and classify all the geo-objects with labels of vegetation types. A case experiment of Taibai Mountain in China shows that the methodology has the ability to achieve good vegetation mapping results with the rapid and convenient sample collection scheme. The map with a finer geographic distribution pattern of vegetation could clearly promote the vegetation resources investigation and monitoring of the study area; thus, the methodological framework is worth popularizing in the mapping areas such as mountainous regions where the field survey sampling is difficult to implement

Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data

Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas. Optical remote sensing is effective in regions with good illumination; however, it usually fails to meet requirements for highly accurate crop classification in cloud-covered areas and rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, it has strong resistance to cloud and rain interference. In this study, we designed an improved crop planting structure mapping framework for cloudy and rainy regions by combining optical data and SAR data, and we revealed the synchronous-response relationship of these two data types. First, we extracted geo-parcels from optical images with high spatial resolution. Second, we built a recurrent neural network (RNN)-based classifier suitable for remote sensing images on the geo-parcel scale. Third, we classified crops based on the two datasets and established the network. Fourth, we analyzed the synchronous response relationships of crops based on the results of the two classification schemes. This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future

Treatment with Rhizoma Dioscoreae Extract Has Protective Effect on Osteopenia in Ovariectomized Rats

The aims of this study were to evaluate the osteoprotective effect of aqueous extract from Rhizoma Dioscoreae (RDE) on rats with ovariectomy- (OVX-) induced osteopenia. Our results show that RDE could inhibit bone loss of OVX rats after a 12-week treatment. The microarray analysis showed that 68 genes were upregulated and that 100 genes were downregulated in femurs of the RDE group rats compared to those in the OVX group. The Ingenuity Pathway Analysis (IPA) showed that several downregulated genes had the potential to code for proteins that were involved in the Wnt/β-catenin signaling pathway (Sost, Lrp6, Tcf7l2, and Alpl) and the RANKL/RANK signaling pathway (Map2k6 and Nfatc4). These results revealed that the mechanism for an antiosteopenic effect of RDE might lie in the synchronous inhibitory effects on both the bone formation and the bone resorption, which is associated with modulating the Wnt/β-catenin signaling and the RANKL/RANK signaling