Novel fungal consortium pretreatment of waste oat straw to enhance economic and efficient biohydrogen production

Bio-pretreatment using a fungal consortium to enhance the efficiency of lignocellulosic biohydrogen production was explored. A fungal consortium comprised of T. viride and P. chrysosporium as microbial inoculum was compared with untreated and single-species-inoculated samples. Fungal bio-pretreatment was carried out at atmospheric conditions with limited external energy input. The effectiveness of the pretreatment is evaluated according to its lignin removal and digestibility. Enhancement of biohydrogen production is observed through scanning electron microscopy (SEM) analysis. Fungal consortium pretreatment effectively degraded oat straw lignin (by >47% in 7 days) leading to decomposition of cell-wall structure as revealed in SEM images, increasing biohydrogen yield. The hydrogen produced from the fungal consortium pretreated straw increased by 165% 6 days later, and was more than produced from either a single fungi species of T. viride or P. chrysosponium pretreated straw (94% and 106%, respectively). No inhibitory effect on hydrogen production was observed

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Cripto-1 overexpression is involved in the tumorigenesis of nasopharyngeal carcinoma

<p>Abstract</p> <p>Background</p> <p>Human Cripto-1, a member of the EGF-CFC family, is indispensable for early embryonic development. Cripto-1 plays an important oncogenic role during tumorigenesis and is overexpressed in a wide range of epithelial carcinomas, yet little is known about Cripto-1 in nasopharyngeal carcinoma (NPC). The aim of this study was to analyze the roles of Cripto-1 in the progression and clinical characteristics in NPC clinical samples and cell lines.</p> <p>Methods</p> <p>The expression of Cripto-1 at mRNA level was detected by the reverse transcription-polymerase chain reaction (RT-PCR) and real time RT-PCR, and western blot was used to examine the protein expression. Cripto-1 expression and its clinical characteristics were investigated by performing immunohistochemical analysis on a total of 37 NPC clinical tissue samples. Lentiviral vectors were constructed to get an efficient expression of anti-Cripto-1 siRNA in CNE-2 and C666-1 cells, with invalid RNAi sequence as control. After the inhibition of the endogenous Cripto-1, the growth, cell cycle and invasion of cells were detected by MTT, FACS and Boyden chamber assay respectively. Moreover, <it>in vivo</it>, the proliferation of the tumor cells was evaluated in xenotransplant nude mice model with whole-body visualizing instrument.</p> <p>Results</p> <p>The results of real-time RT-PCR and western blot showed that the expression level of Cripto-1 was markedly higher in NPC cell lines than that in the immortalized nasopharyngeal epithelial cell at both mRNA and protein levels. RT-PCR of 17 NPC tissues showed a high expression rate in 76.5% (13/17) cases. In an immunohistochemical study, Cripto-1 was found to express in 54.1% (20/37) cases of NPC. In addition, Cripto-1 overexpression was significantly associated with N classification (<it>p </it>= 0.034), distant metastasis (<it>p </it>= 0.036), and clinical stage (<it>p = </it>0.007). Inhibition of endogenous Cripto-1 by lentivirus-mediated RNAi silencing technique suppressed NPC cell growth and invasion <it>in vitro</it>. <it>In vivo</it>, the average weight (<it>p </it>= 0.026) and volume (<it>p </it>= 0.044) of tumor in CNE-2/GFP⁺/Cripto-1^-xenotransplant mice group were significantly lower than those in the control group. The Ki67 index was obviously lower in Cripto-1 RNAi treated tumors (<it>p </it>< 0.01).</p> <p>Conclusion</p> <p>Data of this study suggest that Cripto-1 overexpression is connected with the tumorigenesis and progression of NPC, lentivector-mediated RNAi might be feasible for the inhibition of the growth and invasion of NPC.</p

Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs

To bridge the gaps between topology-aware Graph Neural Networks (GNNs) and inference-efficient Multi-Layer Perceptron (MLPs), GLNN proposes to distill knowledge from a well-trained teacher GNN into a student MLP. Despite their great progress, comparatively little work has been done to explore the reliability of different knowledge points (nodes) in GNNs, especially their roles played during distillation. In this paper, we first quantify the knowledge reliability in GNN by measuring the invariance of their information entropy to noise perturbations, from which we observe that different knowledge points (1) show different distillation speeds (temporally); (2) are differentially distributed in the graph (spatially). To achieve reliable distillation, we propose an effective approach, namely Knowledge-inspired Reliable Distillation (KRD), that models the probability of each node being an informative and reliable knowledge point, based on which we sample a set of additional reliable knowledge points as supervision for training student MLPs. Extensive experiments show that KRD improves over the vanilla MLPs by 12.62% and outperforms its corresponding teacher GNNs by 2.16% averaged over 7 datasets and 3 GNN architectures