Soil amendment with biochar and manure alters wood stake decomposition and fungal community composition

Biochar and manure can be used for sustainable land management. However, little is known about how soil amendments might affect surface and belowground microbial processes and subsequent wood decomposition. In a split-split-split plot design, we amended soil with two rates of manure (whole plot; 0 and 9 Mg ha(-1)) and biochar (split plot; 0 and 10 Mg ha(-1)). Wood stakes of three species (hybrid poplar, triploid Populus tomentosa Carr.; aspen, Populus tremuloides Michx.; and pine, Pinus taeda L.) were placed in two positions (horizontally on the soil surface, and inserted vertically in the mineral soil), which served as a substrate for fungal growth. In 3 years, the decomposition rate (density loss), moisture content, and fungal community (via high-throughput sequencing methods) of stakes were evaluated. Results indicated that biochar and/or manure increased the wood stake decomposition rates, moisture content, and operational taxonomic unit abundance. However, the richness and diversity of fungi were dependent on wood stake position (surface > mineral), species (pine > the two Populus), and sample dates. This study highlights that soil amendment with biochar and/or manure can alter the fungal community, which in turn can enhance an important soil process (i.e., decomposition)

Zero frequency zonal flow excitation by energetic electron driven beta-induced Alfven eigenmode

Zero frequency zonal flow (ZFZF) excitation by trapped energetic electron driven beta-induced Alfven eigenmode (eBAE) is investigated using nonlinear gyrokinetic theory. It is found that, during the linear growth stage of eBAE, resonant energetic electrons (EEs) not only effectively drive eBAE unstable, but also contribute to the nonlinear coupling, leading to ZFZF excitation. The trapped EE contribution to ZFZF generation is dominated by EE responses to eBAE in the ideal region, and is comparable to thermal plasma contribution to Reynolds and Maxwell stresses.Comment: submitted to Plasma Physics and Controlled Fusion (2020

Potentials of neuron-specific enolase as a biomarker for gastric cancer

Purpose: To investigate the potentials of neuron-specific enolase (NSE) as a biomarker for gastric cancer (GC). Methods: Gastric cancer (GC) patients (n = 412) who underwent gastrectomy were recruited over a 3- year period for this study. Their clinicopathological data such as age, sex, histological type, depth, tumor invasion, lymph node metastasis, and distant metastasis were analyzed. The patients were followed up for four years and the outcomes were also assessed. Histological changes in biopsies and levels of expression of NSE in biopsies and serum of patients were determined using immunohistochemical staining, western blotting and enzyme-linked immunosorbent assay (ELISA), respectively. Results: Immunohistochemical staining showed that NSE was differentially expressed in the cytoplasm of GC cells. Histological changes in biopsies of patients in the overexpression group were not significantly different from those of patients in under-expression group (p > 0.05). In NSE overexpression group, the number of patients in early stage GC subgroup (n = 186, 86.10 %, T1) were significantly higher than that in advanced GC subgroup (n = 124, 62.20 % T2–T4) (p < 0.05). However, in NSE under-expression group, there were more patients in advanced GC subgroup (n = 72, 37.70 %) than in early GC subgroup (n = 30, 13.80 %) (p < 0.05). Patients in NSE overexpression group survived longer than those in NSE under-expression group (p < 0.05). The level of expression of NSE significantly decreased with increase in TNM stage (p < 0.05). There was no significant difference in serum NSE level between GC patients and healthy control (p > 0.05). The results of the correlation analysis indicated that NSE levels were positively associated with GC. Conclusion: The results obtained in this study suggest that NSE could serve as a potential biomarker for GC. Keywords: Biomarker, Gastric cancer, Neuron-specific enolase, Overexpression, TNM stagin

Low-frequency shear Alfv\'en waves at DIII-D: theoretical interpretation of experimental observations

The linear properties of the low-frequency shear Alfv\'en waves such as those associated with the beta-induced Alfv\'en eigenmodes (BAEs) and the low-frequency modes observed in reversed-magnetic-shear DIII-D discharges (W. Heidbrink, et al 2021 Nucl. Fusion 61 066031) are theoretically investigated and delineated based on the theoretical framework of the general fishbone-like dispersion relation (GFLDR). By adopting representative experimental equilibrium profiles, it is found that the low-frequency modes and BAEs are, respectively, the reactive-type and dissipative-type unstable modes with dominant Alfv\'enic polarization, thus the former being more precisely called low-frequency Alfv\'en modes (LFAMs). More specifically, due to different instability mechanisms, the maximal drive of BAEs occurs, in comparison to LFAMs, when the minimum of the safety factor ($q_{min}$) deviates from a rational number. Meanwhile, the BAE eigenfunction peaks at the radial position of the maximum energetic particle pressure gradient, resulting in a large deviation from the $q_{min}$ surface. Moreover, the ascending frequency spectrum patterns of the experimentally observed BAEs and LFAMs can be theoretically reproduced by varying $q_{min}$ and also be well interpreted based on the GFLDR. The present analysis illustrates the solid predictive capability of the GFLDR and its practical usefulness in enhancing the interpretative capability of both experimental and numerical simulation results

Preparation and characterization of polypropylene/silica composite particle with interpenetrating network via hot emulsion sol–gel approach

AbstractA novel interpenetrating structural ultrafine polypropylene-silica nanocomposite particles were synthesized by a modified sol–gel approach in the presence of the melt polypropylene emulsion. A series of samples with different polypropylene content was prepared to investigate the unique characteristics of this original nanocomposite. The thermal gravimetric analysis and differential scanning calorimetry results showed that the nanocomposites had the interpenetrating structure and good thermal stability, and the crystallization behavior of polypropylene was confined by the silica matrix. The interpenetrating structure of nanocomposites was also suggested by the nitrogen adsorption–desorption measurement results. The scanning electronic microscope and transmission electron microscopy images indicated that the nanocomposites had irregular particle morphology. The nanoparticle tracking analysis results show that the mean size of the nanocomposites was around 160nm. According to the results obtained from different measurements, a reasonable formation mechanism was proposed

Typhoon Intensity Prediction with Vision Transformer

Predicting typhoon intensity accurately across space and time is crucial for issuing timely disaster warnings and facilitating emergency response. This has vast potential for minimizing life losses and property damages as well as reducing economic and environmental impacts. Leveraging satellite imagery for scenario analysis is effective but also introduces additional challenges due to the complex relations among clouds and the highly dynamic context. Existing deep learning methods in this domain rely on convolutional neural networks (CNNs), which suffer from limited per-layer receptive fields. This limitation hinders their ability to capture long-range dependencies and global contextual knowledge during inference. In response, we introduce a novel approach, namely "Typhoon Intensity Transformer" (Tint), which leverages self-attention mechanisms with global receptive fields per layer. Tint adopts a sequence-to-sequence feature representation learning perspective. It begins by cutting a given satellite image into a sequence of patches and recursively employs self-attention operations to extract both local and global contextual relations between all patch pairs simultaneously, thereby enhancing per-patch feature representation learning. Extensive experiments on a publicly available typhoon benchmark validate the efficacy of Tint in comparison with both state-of-the-art deep learning and conventional meteorological methods. Our code is available at https://github.com/chen-huanxin/Tint.Comment: 8 pages, 2 figures, accepted by Tackling Climate Change with Machine Learning: workshop at NeurIPS 202