Calculation of Carbon Sink of Bamboo Forest in Zhejiang Province and Its Value Realization Path

The biomass method was applied to measure the carbon sequestration status of bamboo forests in Zhejiang province, and the carbon emissions of Zhejiang province from 1989 to 2018 were estimated by using the energy activity CO2 emission measurement method, and the carbon sink contribution of bamboo forests was analyzed by comparison. The results show that the total carbon sequestration in bamboo forests increased from 18,206,400 t to 34,604,000 t during the ninth national forest inventory, with a net increase of 16,397,600 t and a growth rate of 90.07%, showing an overall increasing trend, among which moso bamboo forests are the main carbon sequestration species; the carbon emissions in Zhejiang province showed a stable growth trend, but the growth rate has decreased in recent periods; the amount of carbon sequestered by bamboo forests and carbon emissions show a convergent growth trend, but the amount of carbon sequestered by bamboo forests is relatively small for the overall carbon emissions, and the contribution of carbon sequestration is small. In order to effectively contribute to the process of carbon peaking and carbon neutrality in Zhejiang province, corresponding measures should be taken to effectively play the function of bamboo forest carbon sink and realize its value

Multi-view Graph Convolutional Networks with Differentiable Node Selection

Multi-view data containing complementary and consensus information can facilitate representation learning by exploiting the intact integration of multi-view features. Because most objects in real world often have underlying connections, organizing multi-view data as heterogeneous graphs is beneficial to extracting latent information among different objects. Due to the powerful capability to gather information of neighborhood nodes, in this paper, we apply Graph Convolutional Network (GCN) to cope with heterogeneous-graph data originating from multi-view data, which is still under-explored in the field of GCN. In order to improve the quality of network topology and alleviate the interference of noises yielded by graph fusion, some methods undertake sorting operations before the graph convolution procedure. These GCN-based methods generally sort and select the most confident neighborhood nodes for each vertex, such as picking the top-k nodes according to pre-defined confidence values. Nonetheless, this is problematic due to the non-differentiable sorting operators and inflexible graph embedding learning, which may result in blocked gradient computations and undesired performance. To cope with these issues, we propose a joint framework dubbed Multi-view Graph Convolutional Network with Differentiable Node Selection (MGCN-DNS), which is constituted of an adaptive graph fusion layer, a graph learning module and a differentiable node selection schema. MGCN-DNS accepts multi-channel graph-structural data as inputs and aims to learn more robust graph fusion through a differentiable neural network. The effectiveness of the proposed method is verified by rigorous comparisons with considerable state-of-the-art approaches in terms of multi-view semi-supervised classification tasks

Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training

In recent years, graph contrastive learning (GCL) has emerged as one of the optimal solutions for various supervised tasks at the node level. However, for unsupervised and structure-related tasks such as community detection, current GCL algorithms face difficulties in acquiring the necessary community-level information, resulting in poor performance. In addition, general contrastive learning algorithms improve the performance of downstream tasks by increasing the number of negative samples, which leads to severe class collision and unfairness of community detection. To address above issues, we propose a novel Community-aware Efficient Graph Contrastive Learning Framework (CEGCL) to jointly learn community partition and node representations in an end-to-end manner. Specifically, we first design a personalized self-training (PeST) strategy for unsupervised scenarios, which enables our model to capture precise community-level personalized information in a graph. With the benefit of the PeST, we alleviate class collision and unfairness without sacrificing the overall model performance. Furthermore, the aligned graph clustering (AlGC) is employed to obtain the community partition. In this module, we align the clustering space of our downstream task with that in PeST to achieve more consistent node embeddings. Finally, we demonstrate the effectiveness of our model for community detection both theoretically and experimentally. Extensive experimental results also show that our CEGCL exhibits state-of-the-art performance on three benchmark datasets with different scales.Comment: 12 pages, 7 figure

Transforming growth factor alpha (TGFα) regulates granulosa cell tumor (GCT) cell proliferation and migration through activation of multiple pathways.

Granulosa cell tumors (GCTs) are the most common ovarian estrogen producing tumors, leading to symptoms of excessive estrogen such as endometrial hyperplasia and endometrial adenocarcinoma. These tumors have malignant potential and often recur. The etiology of GCT is unknown. TGFα is a potent mitogen for many different cells. However, its function in GCT initiation, progression and metastasis has not been determined. The present study aims to determine whether TGFα plays a role in the growth of GCT cells. KGN cells, which are derived from an invasive GCT and have many features of normal granulosa cells, were used as the cellular model. Immunohistochemistry, Western blot and RT-PCR results showed that the ErbB family of receptors is expressed in human GCT tissues and GCT cell lines. RT-PCR results also indicated that TGFα and EGF are expressed in the human granulosa cells and the GCT cell lines, suggesting that TGFα might regulate GCT cell function in an autocrine/paracrine manner. TGFα stimulated KGN cell DNA synthesis, cell proliferation, cell viability, cell cycle progression, and cell migration. TGFα rapidly activated EGFR/PI3K/Akt and mTOR pathways, as indicated by rapid phosphorylation of Akt, TSC2, Rictor, mTOR, P70S6K and S6 proteins following TGFα treatment. TGFα also rapidly activated the EGFR/MEK/ERK pathway, and P38 MAPK pathways, as indicated by the rapid phosphorylation of EGFR, MEK, ERK1/2, P38, and CREB after TGFα treatment. Whereas TGFα triggered a transient activation of Akt, it induced a sustained activation of ERK1/2 in KGN cells. Long-term treatment of KGN cells with TGFα resulted in a significant increase in cyclin D2 and a decrease in p27/Kip1, two critical regulators of granulosa cell proliferation and granulosa cell tumorigenesis. In conclusion, TGFα, via multiple signaling pathways, regulates KGN cell proliferation and migration and may play an important role in the growth and metastasis of GCTs

Enhancement of liver mitochondrial complex I and energy metabolism induced by enteritis: The key role of gut microbiota derived endotoxins

publishedVersio

Mechanistic study of visible light-driven CdS or g-C₃N₄-catalyzed C–H direct trifluoromethylation of (hetero)arenes using CF₃SO₂Na as the trifluoromethyl source

The mild and sustainable methods for C–H direct trifluoromethylation of (hetero)arenes without any base or strong oxidants are in extremely high demand. Here, we report that the photo-generated electron-hole pairs of classical semiconductors (CdS or g-C3N4) under visible light excitation are effective to drive C–H trifluoromethylation of (hetero)arenes with stable and inexpensive CF3SO2Na as the trifluoromethyl (TFM) source via radical pathway. Either CdS or g-C3N4 propagated reaction can efficiently transform CF3SO2Na to [rad]CF3 radical and further afford the desired benzotrifluoride derivatives in moderate to good yields. After visible light initiated photocatalytic process, the key elements (such as F, S and C) derived from the starting TFM source of CF3SO2Na exhibited differential chemical forms as compared to those in other oxidative reactions. The photogenerated electron was trapped by chemisorbed O2 on photocatalysts to form superoxide radical anion (O2[rad]−) which will further attack [rad]CF3 radical with the generation of inorganic product F− and CO2. This resulted in a low utilization efficiency of [rad]CF3 (&lt;50%). When nitro aromatic compounds and CF3SO2Na served as the starting materials in inert atmosphere, the photoexcited electrons can be directed to reduce the nitro group to amino group rather than being trapped by O2. Meanwhile, the photogenerated holes oxidize SO2CF3− into [rad]CF3. Both the photogenerated electrons and holes were engaged in reductive and oxidative paths, respectively. The desired product, trifluoromethylated aniline, was obtained successfully via one-pot free-radical synthesis.</p

Ultrathin MOF nanosheet assembled highly oriented microporous membrane as an interlayer for lithium-sulfur batteries

Abstract(#br)Lithium sulfur (Li-S) batteries are attracting increasing attentions as promising next-generation rechargeable batteries. However, the rapid capacity fading of sulfur cathodes caused by the shuttling of polysulfide intermediates between the cathodes and anodes restricts the application of Li-S batteries. In this work, a facile wet-chemistry method is developed for the direct synthesis of few-molecular-layer thin metal-organic framework (MOF) nanosheets without using surfactant. By assembling these ultrathin MOF nanosheets with a facile vacuum filtration method, a highly oriented and flexible MOF membrane with favorable mechanical properties is achieved for the first time. The excellent features make the as-prepared MOF nanosheets ideal to fabricate lightweight interlayer modified separators for suppressing the polysulfide shuttling of Li-S batteries. When using the MOF membrane modified separator, the Li-S batteries made from commercial carbon materials exhibits the significantly enhanced cycling stabilities. This work brings new opportunities for the synthesis and application of MOF materials

A dexamethasone prodrug reduces the renal macrophage response and provides enhanced resolution of established murine lupus nephritis

We evaluated the ability of a macromolecular prodrug of dexamethasone (P-Dex) to treat lupus nephritis in (NZB × NZW)F1 mice. We also explored the mechanism underlying the anti-inflammatory effects of this prodrug. P-Dex eliminated albuminuria in most (NZB × NZW)F1 mice. Furthermore, P-Dex reduced the incidence of severe nephritis and extended lifespan in these mice. P-Dex treatment also prevented the development of lupus-associated hypertension and vasculitis. Although P-Dex did not reduce serum levels of anti-dsDNA antibodies or glomerular immune complexes, P-Dex reduced macrophage recruitment to the kidney and attenuated tubulointerstitial injury. In contrast to what was observed with free dexamethasone, P-Dex did not induce any deterioration of bone quality. However, P-Dex did lead to reduced peripheral white blood cell counts and adrenal gland atrophy. These results suggest that P-Dex is more effective and less toxic than free dexamethasone for the treatment of lupus nephritis in (NZB × NZW)F1 mice. Furthermore, the data suggest that P-Dex may treat nephritis by attenuating the renal inflammatory response to immune complexes, leading to decreased immune cell infiltration and diminished renal inflammation and injury