Statistical optimization of culture medium for production of exopolysaccharide from endophytic fungus Bionectria ochroleuca and its antitumor effect in vitro

Endophytic fungi have been recognized as possible useful sources of bioactive metabolites. However, exopolysaccharide (EPS) production from endophytic fungi and its antitumor activity have been less explored. In the present study, endophtic fungus Bionectria ochroleuca M21 was exploited for the production of EPS in submerged culture. Among tested medium components, glucose, yeast extract, MgSO4 and Tween80 were found to be effective and significant on EPS production. Response surface methodology (RSM) was employed to optimize medium composition. The results showed that the significant factors were glucose, yeast extract and Tween80. The optimal medium was observed at the composition of glucose 55.7 g/L, yeast extract 6.04 g/L, MgSO4 0.25g/L and Tween80 0.1 % (v/v). Using the optimized medium, EPS production was achieve at 2.65 ± 0.16 g/L after 4 days fermentation in a 5L bioreactor. Examination of cytotoxicity showed that the EPS from B. ochroleuca M21 did not have cytotoxic activity on human liver HL-7702 cells at concentration 0.025–1.6 mg/mL. In contrast, the EPS exhibited antiproliferative activities against cell lines of liver cancer (HepG2), gastric cancer (SGC-7901) and colon cancer (HT29) in a dose- and time-dependent manner in the concentration ranges of 0.1-0.45 mg/mL

SGL-PT: A Strong Graph Learner with Graph Prompt Tuning

Recently, much exertion has been paid to design graph self-supervised methods to obtain generalized pre-trained models, and adapt pre-trained models onto downstream tasks through fine-tuning. However, there exists an inherent gap between pretext and downstream graph tasks, which insufficiently exerts the ability of pre-trained models and even leads to negative transfer. Meanwhile, prompt tuning has seen emerging success in natural language processing by aligning pre-training and fine-tuning with consistent training objectives. In this paper, we identify the challenges for graph prompt tuning: The first is the lack of a strong and universal pre-training task across sundry pre-training methods in graph domain. The second challenge lies in the difficulty of designing a consistent training objective for both pre-training and downstream tasks. To overcome above obstacles, we propose a novel framework named SGL-PT which follows the learning strategy ``Pre-train, Prompt, and Predict''. Specifically, we raise a strong and universal pre-training task coined as SGL that acquires the complementary merits of generative and contrastive self-supervised graph learning. And aiming for graph classification task, we unify pre-training and fine-tuning by designing a novel verbalizer-free prompting function, which reformulates the downstream task in a similar format as pretext task. Empirical results show that our method surpasses other baselines under unsupervised setting, and our prompt tuning method can greatly facilitate models on biological datasets over fine-tuning methods

Poly[bis­[μ-1,4-bis­(imidazol-1-yl)butane]dicyanato­cadmium(II)]

The coordination geometry of the CdII atom in the title complex, [Cd(NCO)2(C10H14N4)2]n or [Cd(NCO)2(bimb)2]n, where bimb is 1,4-bis­(imidazol-1-yl)butane, is distorted octa­hedral with the CdII atom located on an inversion center and connected to four N atoms from the imidazole units of four symmetry-related bimb ligands and two O atoms from two symmetry-related NCO− ligands. The CdII atoms are bridged by four bimb ligands, forming a two-dimensional (4,4) network

Intra-Storm Temporal Patterns of Rainfall in China Using Huff Curves

Intra-storm temporal distributions of precipitation are important for infiltration, runoff, and erosion process understanding and models. A convenient and established method for characterizing precipitation hyetographs is the use of non-dimensional Huff curves. In this study, 11,801 erosive rainfall events with 1 min resolution data collected over 30 to 40 years from 18 weather stations located across the central and eastern parts of China were analyzed to produce Huff curves. Each event was classified according to the quartile period within the event that contained the greatest fraction of rainfall. The results showed that 38.3% of events had the maximum rainfall amounts in the first quartile, followed by the second (26.8%), third (22.4%), and fourth (12.5%) quartiles. Quartile I and II events were generally characteristic of shorter duration and heavier intensity events. Quartile I events averaged 23% shorter durations than quartile IV events, whereas the mean intensity (Iavg), mean maximum 30 min intensity (I30), and mean rainfall erosivity index (EI30) were 1.71, 1.22, and 1.23 times greater, respectively, than those for quartile IV and were significant at a 5% level based on two-sample t-tests. The proportion of quartile I events was less for events of longer duration, whereas the proportions of quartile III and IV events were greater. Two-sample Kolmogorov-Smirnov tests suggested that regional Huff curves can be derived for the central and eastern parts of China. Regional Huff curves developed in this study exhibited dissimilarities in terms of the percentages of storms for different quartiles and the shapes of the curves compared to those reported for Illinois, peninsular Malaysia, and Santa Catarina in Brazil

Low major histocompatibility complex class II DQA diversity in the Giant Panda (Ailuropoda melanoleuca)

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