Bulk and amino acid isotope evidence of supplementary food sources besides euphotic production for a deep-sea coral community in the South China Sea

Deep-sea coral communities, rich in various zoobenthos, have been discovered in the South China Sea (SCS) in recent years. Yet little is known about the trophic structure of these communities. In this study, we applied bulk isotope and compound-specific isotope analysis of amino acids (CSIA-AAs) to explore feeding strategies and estimate the trophic positions (TPs) and isotopic baseline for 6 deep-sea gorgonians and 7 other zoobenthos collected from a deep-sea coral community in the SCS. Bulk carbon and nitrogen isotope values (δ13C and δ15N) suggested that the zoobenthos in the community have a variety of food sources. Amino acids δ15N results indicated that the TP is 2.3 ± 0.2 (mean ± 1σ) for the deep-sea gorgonians and varies from 2.0 ± 0.3 (sponge) to 3.5 ± 0.5 (starfish) for other zoobenthos. The δ15N values of phenylalanine revealed variable isotopic baselines ranging from +3.0 ± 0.9‰ to +11.7 ± 0.5‰, reflecting the incorporation of nitrogen from sources not limited to surface primary producers. Taken together, our data suggest that zoobenthos in the deep-sea coral community are mostly omnivorous, and their diet does not come solely from export production from the sea surface, with symbiotic bacteria as a potential important source

Experimental study and modelling of average void fraction of gas-liquid two-phase flow in a helically coiled rectangular channel

Void fraction is an important parameter in designing and simulating the relevant gas-liquid two-phase flow equipment and systems. Although numerous experimental research and modelling of void fraction in straight circular channels have been conducted over the past decades, the experimental data and prediction methods for the average void fraction in helically coiled channels are limited and needed. Especially, there is no such information in helically coiled channels with rectangular cross section. Therefore, it is essential to advance the relevant knowledge through experiments and to develop the corresponding prediction methods in helically coiled rectangular channels. This paper presents experimental results of the average void fraction and new models for the void fraction in a horizontal helically coiled rectangular channel. First, experiments were conducted with air-water two-phase flow in the horizontal helically coiled rectangular channel at a wide range of test conditions: the liquid superficial velocity ranges from 0.11 to 2 m/s and the gas superficial velocity ranges from 0.18 to 16 m/s. The average void fractions were measured with a quick-closing valve (QCV) method. The measured void fraction ranges from 0.012 to 0.927 which cover four flow regimes including unsteady pulsating, bubbly, intermittent and annular flow observed with a high speed camera. Second, comparisons of the entire measured average void fraction data to 32 void fraction models and correlations were made. It shows a low accuracy of these models and correlations in predicting the experimental data for the void fraction smaller than 0.5 while the drift flux model of Dix (Woldesemayat and Ghajar, 2007) predicts 98.3% of the entire experimental data within ±10% for the void fraction larger than 0.5. Therefore, the Dix model is recommended for the void fraction larger than 0.5. Furthermore, the observed flow regimes in the coiled channels were compared to two mechanistic flow regime maps developed for horizontal straight circular tubes. The flow regime maps do not capture all flow regimes in the present study. Finally, the effects of the limiting affecting parameters on the void fraction models are analyzed according to the physical phenomena and mechanisms. Incorporating the main affecting parameters, new void fraction models have been proposed for the void fractions in the ranges of 0 < α ≤ 0.2 and 0.2 < α ≤ 0.5 respectively according to the slip flow model. Both models predict the experimental data reasonably well. Overall, the new proposed models and the recommended model predict 92.8% of the entire void fraction data within ±30%

CD24 Expression as a Marker for Predicting Clinical Outcome in Human Gliomas

CD24 is overexpressed in glioma cells in vitro and in vivo. However, the correlation of its expression with clinicopathological parameters of gliomas and its prognostic significance in this tumor remain largely unknown. To address this problem, 151 glioma specimens and 10 nonneoplastic brain tissues were collected. Quantitative real-time PCR, immunochemistry assay, and Western blot analysis were carried out to investigate the expression of CD24. As per the results, CD24 was overexpressed in gliomas. Its expression levels in glioma tissues with higher grade (P < 0.001) and lower KPS (P < 0.001) were significantly higher than those with lower grade and higher KPS, respectively. Cox multifactor analysis showed that CD24 (P = 0.02) was an independent prognosis factor for human glioma. Our data provides convincing evidence for the first time that the overexpression of CD24 at gene and protein levels is correlated with advanced clinicopathological parameters and poor prognosis in patients with glioma

Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services

In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i.e., in different geographical locations and at different times, user click behavior will change significantly. Appropriate spatiotemporal enhancement modeling of user click behavior and large-scale sparse attributes is key to building an LBS model. Although most of existing methods have been proved to be effective, they are difficult to apply to takeaway scenarios due to insufficient modeling of spatiotemporal information. In this paper, we address this challenge by seeking to explicitly model the timing and locations of interactions and proposing a Spatiotemporal-Enhanced Network, namely StEN. In particular, StEN applies a Spatiotemporal Profile Activation module to capture common spatiotemporal preference through attribute features. A Spatiotemporal Preference Activation is further applied to model the personalized spatiotemporal preference embodied by behaviors in detail. Moreover, a Spatiotemporal-aware Target Attention mechanism is adopted to generate different parameters for target attention at different locations and times, thereby improving the personalized spatiotemporal awareness of the model.Comprehensive experiments are conducted on three large-scale industrial datasets, and the results demonstrate the state-of-the-art performance of our methods. In addition, we have also released an industrial dataset for takeaway industry to make up for the lack of public datasets in this community.Comment: accepted by CIKM workshop 202