Light availability, soil phosphorus and different nitrogen forms negatively affect the functional diversity of subtropical forests

Understanding the relationship between functional diversity (FD) and species diversity changes and the effects of environmental factors on FD during succession is useful to improve forest management, conservation and restoration strategies. In this study, we measured 9 environmental factors related to light availability, soil water content and nutrients, and 19 leaf functional traits related to leaf light and nutrient utilization, growth and defense, water-use efficiency, and leaf respiration strategies in the dominant species during subtropical forest succession in southern China. Logarithmic function analysis and linear mixed model were used to explore the relationships between FD and species diversity and between FD and environmental factors. The results showed that FD and species diversity were not linearly correlated during succession. The light availability (represented by leaf area index), soil phosphorus, and different nitrogen forms were negatively related to the FD, suggesting these factors were the main environmental factors affecting FD during succession in the subtropical forest. By dividing FD into components corresponding to the diversity of different plant strategies, this study improves our understanding of the roles of light availability and soil nutrients in plant community functional structure, and provides useful information for forest conservation and restoration

Tris(piperazinediium) phosphatododeca­molybo(V,VI)phosphate

The title compound, (C4H12N2)3[PMo12O40] or (H2pip)3[PMo12O40] (pip is piperazine), was prepared under hydro­thermal conditions. The asymmetric unit contains one-sixth of a mixed-valent Mo(V,VI) pseudo-Keggin-type [PMo12O40]6− anion and half a piperazinediium cation, (H2pip)2+. The discrete Keggin-type [PMo12O40]6- anion has site symmetry and the three (H2pip)2+ cations each have site symmetry at the centres of the mol­ecules. The central P atom is on special position , which is a roto-inversion position and generates the disorder of the PO4 tetra­hedron. Furthermore, six doubly bridging oxide groups are also disordered with an occupancy factor of 0.5 for each O atom. The anions and cations are linked by an extensive network of inter­molecular N—H⋯O and C—H⋯O hydrogen bonds

DMRF-UNet: A Two-Stage Deep Learning Scheme for GPR Data Inversion under Heterogeneous Soil Conditions

Traditional ground-penetrating radar (GPR) data inversion leverages iterative algorithms which suffer from high computation costs and low accuracy when applied to complex subsurface scenarios. Existing deep learning-based methods focus on the ideal homogeneous subsurface environments and ignore the interference due to clutters and noise in real-world heterogeneous environments. To address these issues, a two-stage deep neural network (DNN), called DMRF-UNet, is proposed to reconstruct the permittivity distributions of subsurface objects from GPR B-scans under heterogeneous soil conditions. In the first stage, a U-shape DNN with multi-receptive-field convolutions (MRF-UNet1) is built to remove the clutters due to inhomogeneity of the heterogeneous soil. Then the denoised B-scan from the MRF-UNet1 is combined with the noisy B-scan to be inputted to the DNN in the second stage (MRF-UNet2). The MRF-UNet2 learns the inverse mapping relationship and reconstructs the permittivity distribution of subsurface objects. To avoid information loss, an end-to-end training method combining the loss functions of two stages is introduced. A wide range of subsurface heterogeneous scenarios and B-scans are generated to evaluate the inversion performance. The test results in the numerical experiment and the real measurement show that the proposed network reconstructs the permittivities, shapes, sizes, and locations of subsurface objects with high accuracy. The comparison with existing methods demonstrates the superiority of the proposed methodology for the inversion under heterogeneous soil conditions

3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion

The reconstruction of the 3D permittivity map from ground-penetrating radar (GPR) data is of great importance for mapping subsurface environments and inspecting underground structural integrity. Traditional iterative 3D reconstruction algorithms suffer from strong non-linearity, ill-posedness, and high computational cost. To tackle these issues, a 3D deep learning scheme, called 3DInvNet, is proposed to reconstruct 3D permittivity maps from GPR C-scans. The proposed scheme leverages a prior 3D convolutional neural network with a feature attention mechanism to suppress the noise in the C-scans due to subsurface heterogeneous soil environments. Then a 3D U-shaped encoder-decoder network with multi-scale feature aggregation modules is designed to establish the optimal inverse mapping from the denoised C-scans to 3D permittivity maps. Furthermore, a three-step separate learning strategy is employed to pre-train and fine-tune the networks. The proposed scheme is applied to numerical simulation as well as real measurement data. The quantitative and qualitative results show the network capability, generalizability, and robustness in denoising GPR C-scans and reconstructing 3D permittivity maps of subsurface objects

Bryophyte diversity is related to vascular plant diversity and microhabitat under disturbance in karst caves

Plant diversity, habitat properties, and their relationships in karst caves remain poorly understood. We surveyed vascular plant and bryophyte diversities and measured the habitat characteristics in six karst caves in south China with different disturbance histories (one had been disturbed by poultry feeding, three had been disturbed by tourism, and two were undisturbed). The plant diversity differences among the six caves were analyzed using cluster analysis, and the relationships of plant diversity and microhabitat were assessed using canonical correspondence analysis. We found a total of 43 angiosperm species from 27 families, 20 lycophyte and fern species from 9 families, and 20 species of bryophytes from 13 families in the six caves. Habitat characteristics including light intensity, air relative humidity, air temperature, and soil properties varied among the caves. The plant diversity in karst caves was not rich, but the species composition was unique. The caves with high disturbance had the lowest species richness, numbers of individuals, and Shannon-Wiener diversity indices but the highest Simpson’s dominance indices. The caves with less disturbance had the highest numbers of species, numbers of individuals, and Shannon-Wiener diversity indices but the lowest Simpson’s dominance indices. The disturbed caves were often dominated by drought-tolerant, tenacious mosses (bryophytes), while the relatively undisturbed caves contained abundant liverworts (bryophytes), which were better adapted to humid environments. Plant diversity in karst caves was closely related to habitat heterogeneity, light and water status, and nutrient availability. Tourism and poultry farming were associated with the degradation of vegetation in some karst caves. Protecting and restoring bryophytes might facilitate the settlement, growth, and succession of vascular plants in karst caves. Bryophytes can be used as indicators of overall plant diversity and restoration status in karst caves

A Deep Learning-Based GPR Forward Solver for Predicting B-Scans of Subsurface Objects

The forward full-wave modeling of ground-penetrating radar (GPR) facilitates the understanding and interpretation of GPR data. Traditional forward solvers require excessive computational resources, especially when their repetitive executions are needed in signal processing and/or machine learning algorithms for GPR data inversion. To alleviate the computational burden, a deep learning-based 2D GPR forward solver is proposed to predict the GPR B-scans of subsurface objects buried in the heterogeneous soil. The proposed solver is constructed as a bimodal encoder-decoder neural network. Two encoders followed by an adaptive feature fusion module are designed to extract informative features from the subsurface permittivity and conductivity maps. The decoder subsequently constructs the B-scans from the fused feature representations. To enhance the network's generalization capability, transfer learning is employed to fine-tune the network for new scenarios vastly different from those in training set. Numerical results show that the proposed solver achieves a mean relative error of 1.28%. For predicting the B-scan of one subsurface object, the proposed solver requires 12 milliseconds, which is 22,500x less than the time required by a classical physics-based solver

Hypoxia-mimetic agents inhibit proliferation and alter the morphology of human umbilical cord-derived mesenchymal stem cells

<p>Abstract</p> <p>Background</p> <p>The therapeutic efficacy of human mesenchymal stem cells (hMSCs) for the treatment of hypoxic-ischemic diseases is closely related to level of hypoxia in the damaged tissues. To elucidate the potential therapeutic applications and limitations of hMSCs derived from human umbilical cords, the effects of hypoxia on the morphology and proliferation of hMSCs were analyzed.</p> <p>Results</p> <p>After treatment with DFO and CoCl₂, hMSCs were elongated, and adjacent cells were no longer in close contact. In addition, vacuole-like structures were observed within the cytoplasm; the rough endoplasmic reticulum expanded, and expanded ridges were observed in mitochondria. In addition, DFO and CoCl₂treatments for 48 h significantly inhibited hMSCs proliferation in a concentration-dependent manner (<it>P </it>< 0.05). This treatment also increased the number of cells in G0/G1 phase and decreased those in G2/S/M phase.</p> <p>Conclusions</p> <p>The hypoxia-mimetic agents, DFO and CoCl₂, alter umbilical cord-derived hMSCs morphology and inhibit their proliferation through influencing the cell cycle.</p

Multiclass Semantic Segmentation to Identify Anatomical Sub-Regions of Brain and Measure Neuronal Health in Parkinson's Disease

Automated segmentation of anatomical sub-regions with high precision has become a necessity to enable the quantification and characterization of cells/ tissues in histology images. Currently, a machine learning model to analyze sub-anatomical regions of the brain to analyze 2D histological images is not available. The scientists rely on manually segmenting anatomical sub-regions of the brain which is extremely time-consuming and prone to labeler-dependent bias. One of the major challenges in accomplishing such a task is the lack of high-quality annotated images that can be used to train a generic artificial intelligence model. In this study, we employed a UNet-based architecture, compared model performance with various combinations of encoders, image sizes, and sample selection techniques. Additionally, to increase the sample set we resorted to data augmentation which provided data diversity and robust learning. In this study, we trained our best fit model on approximately one thousand annotated 2D brain images stained with Nissl/ Haematoxylin and Tyrosine Hydroxylase enzyme (TH, indicator of dopaminergic neuron viability). The dataset comprises of different animal studies enabling the model to be trained on different datasets. The model effectively is able to detect two sub-regions compacta (SNCD) and reticulata (SNr) in all the images. In spite of limited training data, our best model achieves a mean intersection over union (IOU) of 79% and a mean dice coefficient of 87%. In conclusion, the UNet-based model with EffiecientNet as an encoder outperforms all other encoders, resulting in a first of its kind robust model for multiclass segmentation of sub-brain regions in 2D images