-harboring microorganisms facilitate mineralization of organic phosphorus (P), while their role in the regulation of soil P turnover under P-limited conditions in
plantations is poorly understood. ...The aim of the present study was to investigate the effects of stand age and season on soil P fractions and
-harboring microorganism communities in a chronosequence of Chinese
plantations including 3, 19, and 58 years. The soil P fractions (i.e., CaCl
-P, citrate-P, enzyme-P, and HCl-P) varied seasonally, with the higher values observed in the rainy season. The concentrations of the fractions were higher in old plantation (OP) soils and lower in young planation (YP) soils in both seasons. The OTU abundances were negatively correlated with total available P concentration, while were positively correlated with alkaline phosphomonoesterase (ALP) activity at 0-10 cm soil depth. The results indicate that
-harboring microorganisms have great potential to mineralize organic P under P-poor conditions and highlights those microorganisms are indicators of P bioavailability in
plantations.
The Deep Neural Networks are vulnerable to adversarial examples (Figure 1), making the DNNs-based systems collapsed by adding the inconspicuous perturbations to the images. Most of the existing works ...for adversarial attack are gradient-based and suffer from the latency efficiencies and the load on GPU memory. The generative-based adversarial attacks can get rid of this limitation, and some relative works propose the approaches based on GAN. However, suffering from the difficulty of the convergence of training a GAN, the adversarial examples have either bad attack ability or bad visual quality. In this work, we find that the discriminator could be not necessary for generative-based adversarial attack, and propose the Symmetric Saliency-based Auto-Encoder (SSAE) to generate the perturbations, which is composed of the saliency map module and the angle-norm disentanglement of the features module. The advantage of our proposed method lies in that it is not depending on discriminator, and uses the generative saliency map to pay more attention to label-relevant regions. The extensive experiments among the various tasks, datasets, and models demonstrate that the adversarial examples generated by SSAE not only make the widely-used models collapse, but also achieves good visual quality. The code is available at: https://github.com/BravoLu/SSAE.
The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1), making the DNNs-based systems collapsed byadding the inconspicuous perturbations to the images. Most of the existing works ...for adversarial attack are gradient-based and suf-fer from the latency efficiencies and the load on GPU memory. Thegenerative-based adversarial attacks can get rid of this limitation,and some relative works propose the approaches based on GAN.However, suffering from the difficulty of the convergence of train-ing a GAN, the adversarial examples have either bad attack abilityor bad visual quality. In this work, we find that the discriminatorcould be not necessary for generative-based adversarial attack, andpropose theSymmetric Saliency-based Auto-Encoder (SSAE)to generate the perturbations, which is composed of the saliencymap module and the angle-norm disentanglement of the featuresmodule. The advantage of our proposed method lies in that it is notdepending on discriminator, and uses the generative saliency map to pay more attention to label-relevant regions. The extensive exper-iments among the various tasks, datasets, and models demonstratethat the adversarial examples generated by SSAE not only make thewidely-used models collapse, but also achieves good visual quality.The code is available at https://github.com/BravoLu/SSAE.
Many paleo-karstic bauxite deposits contain elevated concentrations of Ni and Co. It has been known that Co and Ni are dominantly hosted in sulfides, but it remains unclear how Ni and Co were ...incorporated into sulfides and what kind of physiochemical conditions were involved. The Maochang bauxite deposit, as one of the large Ni- and Co-bearing paleo-karstic bauxite deposits in Guizhou Province, southwest China, was selected to tackle these questions. Detailed mineralogical and sulfur isotopic analyses using integrated EBSD, EPMA, LA-ICPMS, and LA-MC-ICPMS methods revealed three stages of sulfide mineralization: stage I with pyrite (PyI), stage II with pyrite (PyII), millerite, sphalerite, chalcopyrite and violarite, and stage III with pyrite (PyIII), chalcopyrite and galena. The fine-grained, euhedral PyI has up to 0.79 wt% Ni and up to 0.53 wt% Co. In contrast, PyII, which replaced or overgrew PyI, exhibits high but variable Ni, Co and As contents, and is further divided into three sub-types: Ni-rich PyII-a (up to 16.54 wt% Ni, 0.94 wt% Co, and 0.38 wt% As) and PyII-b (up to 3.30 wt% Ni, 1.41 wt% Co, and 0.36 wt% As), and Co-rich PyII-c (up to 0.99 wt% Ni, 7.57 wt% Co, and 1.81 wt% As). PyIII is Ni- and Co-poor. Ni increases from PyI to PyIIa and PyIIb, and then decreases to PyIIc and PyIII, whereas Co remains at similar levels for PyI, PyIIa and PyIIb, significantly increases to PyIIc, and then drops abruptly to PyIII. δ34SV-CDT values vary dramatically from negative values for PyI (−6.9 to −16.6‰) to positive values for PyII-b (+19.4 to +22.8‰), sphalerite (+17.2 to +19.7‰), and PyIII (+25.3 to +32.3‰). Based on these results, and considering that the protoliths of the bauxites were likely Ni- and Co-rich black shales according to previous studies, a four-step model is proposed to explain the enrichment of Ni and Co in the bauxites: 1) during weathering and bauxite formation on the surface, Ni and Co were leached from the black shale together with sulfate; the metals were dissolved in the solution and partly adsorbed by Fe-oxides/hydroxides under moderate to high fO2; 2) during early diagenesis, the sulfate was partially reduced to sulfide via the bacterial sulfate reduction (BSR) process and formed PyI; much of the Ni and Co, together with sulfate, remained in the solution; 3) with increasing burial, temperature increased and fO2 decreased, and the remaining sulfate (with elevated δ34SV-CDT) was reduced to sulfide via the same BSR process and formed PyII; Ni was largely consumed and incorporated into PyIIa and PyIIb, whereas Co remained in the solution until precipitation of PyIIc; 4) by the time of PyIII precipitation, both Ni and Co had been taken by PyII, leaving a Ni- and Co-poor solution and PyIII. Step 1 occurred in a relatively open system, whereas steps 2–4 likely occurred in a closed system.
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As the "roof of the world", the Tibetan Plateau (TP) is a unique geographical unit on Earth. In recent years, vegetation has gradually become a key factor reflecting the ecosystem since it is ...sensitive to ecological changes especially in arid and semi-arid areas. Based on the normalized difference vegetation index (NDVI) dataset of TP from 2000 to 2015, this study analyzed the characteristics of vegetation variation and the correlation between vegetation change and climatic factors at different time scales, based on a Mann-Kendall trend analyses, the Hurst exponent, and the Pettitt change-point test. The results showed that the vegetation fractional coverage (VFC) generally increased in the past 16 years, with 60.3% of the TP experiencing an increase, of which significant (
< 0.05) increases accounted for 28.79% and were mainly distributed in the north of the TP. Temperature had the largest response with the VFC on the seasonal scale. During the growing season, the correlation between precipitation and sunshine duration with VFC was high (
< 0.05). The change-points of the VFC were mainly distributed in the north of the TP during 2007-2009. Slope and elevation had an impact on the VFC; the areas with large vegetation change are mainly distributed in slopes <20° and elevation of 3000-5000 m. For elevation above 3000-4000 m, the response of the VFC to precipitation and temperature was the strongest. This study provided important information for ecological environment protection and ecosystem degradation on the Tibetan Plateau.
Gamma-aminobutyric acid (GABA), one of the main active components in
leaves, can be widely used to treat multiple diseases including inflammation.
In this study, the anti-inflammatory activity and ...the underlying anti-inflammatory mechanism of the GABA-enriched
leaves fermentation broth (MLFB) were investigated on lipopolysaccharide (LPS)-induced RAW 264.7 cells model. The key active components changes like total flavonoids, total polyphenols and organic acid in the fermentation broth after fermentation was also analyzed.
ELISA, RT-qPCR and Western blot results indicated that MLFB could dose-dependently inhibit the secretions and intracellular expression levels of pro-inflammatory cytokines like 1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8) and tumor necrosis factor-α (TNF-α). Furthermore, MLFB also suppressed the expressions of prostaglandin E
(PGE
) and inducible nitric oxide synthase (iNOS). Moreover, the mRNA expressions of the key molecules like Toll-like receptor 4 (TLR-4) and nuclear factor (NF)-κB in the NF-κB signaling pathway were also restrained by MLFB in a dose-dependent manner. Besides, the key active components analysis result showed that the GABA, total polyphenols, and most organic acids like pyruvic acid, lactic acid as well as acetic acid were increased obviously after fermentation. The total flavonoids content in MLFB was still remained to be 32 mg/L though a downtrend was presented after fermentation.
Our results indicated that the MLFB could effectively alleviate LPS-induced inflammatory response by inhibiting the secretions of pro-inflammatory cytokines and its underlying mechanism might be associated with the inhibition of TLR-4/NF-κB inflammatory signaling pathway activation. The anti-inflammatory activity of MLFB might related to the relative high contents of GABA as well as other active constituents such as flavonoids, phenolics and organic acids in MLFB. Our study provides the theoretical basis for applying GABA-enriched
leaves as a functional food ingredient in the precaution and treatment of chronic inflammatory diseases.
With the evolution of artificial intelligence, the explosive growth of data from sensory terminals gives rise to severe energy-efficiency bottleneck issues due to cumbersome data interactions among ...sensory, memory, and computing modules. Heterogeneous integration methods such as chiplet technology can significantly reduce unnecessary data movement; however, they fail to address the fundamental issue of the substantial time and energy overheads resulting from the physical separation of computing and sensory components. Brain-inspired in-sensor neuromorphic computing (ISNC) has plenty of room for such data-intensive applications. However, one key obstacle in developing ISNC systems is the lack of compatibility between material systems and manufacturing processes deployed in sensors and computing units. This study successfully addresses this challenge by implementing fully CMOS-compatible TiN/HfO x -based neuristor array. The developed ISNC system demonstrates several advantageous features, including multilevel analogue modulation, minimal dispersion, and no significant degradation in conductance (@125 °C). These characteristics enable stable and reproducible neuromorphic computing. Additionally, the device exhibits modulatable sensory and multi-store memory processes. Furthermore, the system achieves information recognition with a high accuracy rate of 93%, along with frequency selectivity and notable activity-dependent plasticity. This work provides a promising route to affordable and highly efficient sensory neuromorphic systems.
With the evolution of artificial intelligence, the explosive growth of data from sensory terminals gives rise to severe energy-efficiency bottleneck issues due to cumbersome data interactions among ...sensory, memory, and computing modules. Heterogeneous integration methods such as chiplet technology can significantly reduce unnecessary data movement; however, they fail to address the fundamental issue of the substantial time and energy overheads resulting from the physical separation of computing and sensory components. Brain-inspired in-sensor neuromorphic computing (ISNC) has plenty of room for such data-intensive applications. However, one key obstacle in developing ISNC systems is the lack of compatibility between material systems and manufacturing processes deployed in sensors and computing units. This study successfully addresses this challenge by implementing fully CMOS-compatible TiN/HfOx-based neuristor array. The developed ISNC system demonstrates several advantageous features, including multilevel analogue modulation, minimal dispersion, and no significant degradation in conductance (@125 °C). These characteristics enable stable and reproducible neuromorphic computing. Additionally, the device exhibits modulatable sensory and multi-store memory processes. Furthermore, the system achieves information recognition with a high accuracy rate of 93%, along with frequency selectivity and notable activity-dependent plasticity. This work provides a promising route to affordable and highly efficient sensory neuromorphic systems.
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