Response of Freshwater Biofilm to pollution and ecosystem in Baiyangdian Lake of China

AbstractAn experimental study was undertaken to highlight the potential applicability of biofilms as biomonitors forming simultaneously on natural and artificial substrata in Baiyngdian Lake(China).We investigated the responses of freshwater biofilm in 8 site of Baiyngdian Lake and compared with control site (a reservoir) to assess the relative health of water. Exposure to pollution and its impact on biofilms were assessed by measuring the biomass production, Chlorophyll concentration, the algal composition, extracellular enzyme activity of bacterial communities and Polysaccharide content. This relation between the biological characters of biofilms and water quality were discussed, and the relative health of regions were demonstrated by the degree of deviation based on bioflim indicator in the following order: Fu river (S4) < Duan cun (S8) < Nan Liuzhuang (S5) < Wang jiazai (S1) < Cai putai (S7) < Zao lingzhuang (S2)< Shao Chedian (S3).. The result indicated that biofilm can provide information for pollution detection and ecological health assessment of water, and biofilm on aritificial substrata was recommended for biomonitoring in the Baiyangdian Lake

A Novel Strategy to Construct Yeast Saccharomyces cerevisiae Strains for Very High Gravity Fermentation

Very high gravity (VHG) fermentation is aimed to considerably increase both the fermentation rate and the ethanol concentration, thereby reducing capital costs and the risk of bacterial contamination. This process results in critical issues, such as adverse stress factors (ie., osmotic pressure and ethanol inhibition) and high concentrations of metabolic byproducts which are difficult to overcome by a single breeding method. In the present paper, a novel strategy that combines metabolic engineering and genome shuffling to circumvent these limitations and improve the bioethanol production performance of Saccharomyces cerevisiae strains under VHG conditions was developed. First, in strain Z5, which performed better than other widely used industrial strains, the gene GPD2 encoding glycerol 3-phosphate dehydrogenase was deleted, resulting in a mutant (Z5ΔGPD2) with a lower glycerol yield and poor ethanol productivity. Second, strain Z5ΔGPD2 was subjected to three rounds of genome shuffling to improve its VHG fermentation performance, and the best performing strain SZ3-1 was obtained. Results showed that strain SZ3-1 not only produced less glycerol, but also increased the ethanol yield by up to 8% compared with the parent strain Z5. Further analysis suggested that the improved ethanol yield in strain SZ3-1 was mainly contributed by the enhanced ethanol tolerance of the strain. The differences in ethanol tolerance between strains Z5 and SZ3-1 were closely associated with the cell membrane fatty acid compositions and intracellular trehalose concentrations. Finally, genome rearrangements in the optimized strain were confirmed by karyotype analysis. Hence, a combination of genome shuffling and metabolic engineering is an efficient approach for the rapid improvement of yeast strains for desirable industrial phenotypes

Charge Transport Properties of BO-Chelated Azadipyrromethenes

Intramolecular BO-chelated azadipyrromethenes are promising organic semiconductors. Here, we evaluated the electron and hole mobility of a series of BO-chelated azadipyrromethenes using the Space Charge Limited Current(SCLC) method. In order to determine the mobility of the material, Mott Gurney\u27s law was applied using the film thickness and slope of the J1/2 versus voltage plot in the SCLC graph. The best electron mobility observed for BO-chelated materials is 4.64—10-6 cm2V-1s-1 and the best hole mobility is 7.38—10-4 cm2V-1s-1. These results suggest that BO-chelated materials are promising p-type semiconductors for electronic applications.https://commons.case.edu/intersections-fa20/1028/thumbnail.jp

GAIN: A Gated Adaptive Feature Interaction Network for Click-Through Rate Prediction

CTR (Click-Through Rate) prediction has attracted more and more attention from academia and industry for its significant contribution to revenue. In the last decade, learning feature interactions have become a mainstream research direction, and dozens of feature interaction-based models have been proposed for the CTR prediction task. The most common approach for existing models is to enumerate all possible feature interactions or to learn higher-order feature interactions by designing complex models. However, a simple enumeration will introduce meaningless and harmful interactions, and a complex model structure will bring a higher complexity. In this work, we propose a lightweight, yet effective model called the Gated Adaptive feature Interaction Network (GAIN). We devise a novel cross module to drop meaningless feature interactions and preserve informative ones. Our cross module consists of multiple gated units, each of which can independently learn an arbitrary-order feature interaction. We combine the cross module with a deep module into GAIN and conduct comparative experiments with state-of-the-art models on two public datasets to verify its validity. Our experimental results show that GAIN can achieve a comparable or even better performance compared to its competitors. Furthermore, in order to verify the effectiveness of the feature interactions learned by GAIN, we transfer learned interactions to other models, such as Logistic Regression (LR) and Factorization Machines (FM), and find out that their performance can be significantly improved

Characteristics of surface water quality and stable isotopes in Bamen Bay watershed, Hainan Province, China.

Bamen Bay is located at the intersection of the Wenjiao River and Wenchang River in Hainan Province (China), where mangroves have been facing a threat of water quality deterioration. Therefore, it is imperative to study the characteristics of the surface water quality on a watershed scale. Water samples were collected three times from 36 monitoring sites from 2015 to 2016. It was found that nitrate was the main inorganic nitrogen form and all the surface water types were alkaline. Meanwhile, aquaculture water had high content of nitrogen, total phosphorus, chlorophyll a (Chl.a), total organic carbon (TOC), and chemical oxygen demand (COD). Significant spatial and temporal variations were found for most parameters. However, stable isotopes of δD and δ18O indicated that river water mainly originated from atmospheric precipitation and experienced strong evaporation. The water chemistry and isotopes of the Bamen Bay, mangroves, and aquaculture water were initially affected by the mixing of fresh water and seawater, followed by evaporation. The river and reservoir water chemistry were mainly controlled by water-rock interactions and cation exchange as deduced from the ionic relationships and Gibbs plots. These interactions involved the dissolution of calcite-, bicarbonate-, carbonate-, and calcium-containing minerals. Oxidized environments (river, reservoir, and Bamen Bay) were conducive for nitrification, while anaerobic conditions (mangrove and aquaculture water) were beneficial to the reduced nitrogen forms

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Web object is defined to represent any meaningful object embedded in web pages (e.g. images, music) or pointed to by hyperlinks (e.g. downloadable files). Users usually search for information of a certain ‘object’, rather than a web page containing the query terms. To facilitate web object searching and organizing, in this paper, we propose a novel approach to web object indexing, by discovering its inherent structure information with domain knowledge. In our approach, Layered LSI spaces are built for the hierarchically structured domain knowledge, in order to emphasize the specific semantics and term space in each layer of the domain knowledge. Then, the web object representation is constructed by hyperlink analysis, and further pruned to remove the noises. Finally, the structure attributes of the web object are extracted with the knowledge document that best matches the web object. Our approach also indicates a new way to use trust-worthy Deep Web knowledge to help organiz

Research Track Paper Web Object Indexing Using Domain Knowledge

A web object is defined to represent any meaningful object embedded in web pages (e.g. images, music) or pointed to by hyperlinks (e.g. downloadable files). In many cases, users would like to search for information of a certain ‘object’, rather than a web page containing the query terms. To facilitate web object searching and organizing, in this paper, we propose a novel approach to web object indexing, by discovering its inherent structure information with existed domain knowledge. In our approach, first, Layered LSI spaces are built for a better representation of the hierarchically structured domain knowledge, in order to emphasize the specific semantics and term space in each layer of the domain knowledge. Meanwhile, the web object representation is constructed by hyperlink analysis, and further pruned to remove the noises. Then an optimal matching between the web object and the domain knowledge is performed, in order to pick out the structure attributes of the web object from the knowledge. Finally, the obtained structure attributes are used to re-organize and index the web objects. Our approach also indicates a new promising way to use trust-worthy Deep Web knowledge to help organize dispersive information of Surfac

KoopmanLab: Machine learning for solving complex physics equations

Numerous physics theories are rooted in partial differential equations (PDEs). However, the increasingly intricate physics equations, especially those that lack analytic solutions or closed forms, have impeded the further development of physics. Computationally solving PDEs by classic numerical approaches suffers from the trade-off between accuracy and efficiency and is not applicable to the empirical data generated by unknown latent PDEs. To overcome this challenge, we present KoopmanLab, an efficient module of the Koopman neural operator (KNO) family, for learning PDEs without analytic solutions or closed forms. Our module consists of multiple variants of the KNO, a kind of mesh-independent neural-network-based PDE solvers developed following the dynamic system theory. The compact variants of KNO can accurately solve PDEs with small model sizes, while the large variants of KNO are more competitive in predicting highly complicated dynamic systems govern by unknown, high-dimensional, and non-linear PDEs. All variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier–Stokes equation and the Bateman–Burgers equation in fluid mechanics) and ERA5 (i.e., one of the largest high-resolution global-scale climate datasets in earth physics). These demonstrations suggest the potential of KoopmanLab to be a fundamental tool in diverse physics studies related to equations or dynamic systems

Maresin 1 Mitigates Inflammatory Response and Protects Mice from Sepsis

Sepsis, frequently caused by infection of bacteria, is considered as an uncontrollable systematic inflammation response syndrome (SIRS). Maresin 1 (Mar1) is a new proresolving mediator with potent anti-inflammatory effect in several animal models. However, its effect in sepsis is still not investigated. To address this question, we developed sepsis model in BALB/c mice by cecal ligation and puncture (CLP) with or without Mar1 treatment. Our data showed that Mar1 markedly improved survival rate and decreased the levels of proinflammatory cytokines in CLP mice such as interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and interleukin-1β (IL-1β). Furthermore, Mar1 reduced serum level of lipopolysaccharide (LPS) and enhanced the bacteria clearance in mice sepsis model. Moreover, Mar1 attenuated lung injury and decreased level of alanine transaminase (ALT), aspartate transaminase (AST), creatinine (Cre), and blood urea nitrogen (BUN) in serum in mice after CLP surgery. Treatment with Mar1 inhibited activation of nuclear factor kappa B (NF-κb) pathway. In conclusion, Mar1 exhibited protective effect in sepsis by reducing LPS, bacteria burden in serum, inhibiting inflammation response, and improving vital organ function. The possible mechanism is partly involved in inhibition of NF-κb activation

Spatio-Temporal Variation and Controlling Factors of Water Quality in Yongding River Replenished by Reclaimed Water in Beijing, North China

Reclaimed water is useful for replenishing dried up rivers in North China, although changes in water quality could be an issue. Therefore, it is essential to understand the spatio-temporal variation and the controlling factors of water quality. Samples of Yongding River water were collected seasonally, and 24 water quality parameters were analyzed in 2015. All waters were alkaline, and nitrate-nitrogen was the main form of nitrogen, while phosphorus was mostly below detection level. The water quality parameters varied in time and space. Cluster analysis showed a distinct difference between winter and the other seasons and between the natural river section and the section with reclaimed water. Based on the analysis of Gibbs plots, principal component analysis, and ionic relationships, the water chemistry was controlled by dissolution of rocks in natural river section, the quality of replenished water, the effects of dilution, and the reaction of aqueous chemistry in the reclaimed water section. The positive oxidation environment in most of the river water was conducive to the formation of nitrate-nitrogen by nitrification, and not conducive to denitrification