Key Aquatic Environmental Factors Affecting Ecosystem Health of Streams in the Dianchi Lake Watershed, China

AbstractStreams in a lake watershed are important landscape corridors which link the lake and terrestrial ecosystems. Therefore, the ecosystem health of streams is usually used to indicate aquatic biodiversity of the lake ecosystem, as well as being affected by aquatic environmental factors in response to changes in land use cover of the terrestrial ecosystem due to natural geographic characteristics of the watershed with the closure of ridge lines. This study was carried out at a shallow freshwater lake watershed in the Yunnan-Guizhou Plateau of China, the Dianchi Lake watershed (DLW). Field survey of periphytic algal and macrozoobenthic biodiversity during July and August of 2009, as well as monthly monitoring of water temperature, pH, TSS, DO, TN, TP, NH3N, NO3N, CODMn, BOD, TOC, and the heavy metals Zn (II), Cd (II), Pb (II), Cu (II), and Cr (VI) from January to December 2009 was carried out in 29 streams flowing into Dianchi lake. Multivariate statistical techniques such as factor analysis and canonical correspondence analysis were applied to analyze the structure of the aquatic community in relation to aquatic environmental factors in order to provide controlling objectives for integrated watershed management and improvement of stream rehabilitation in the DLW. The results showed that the structure of the periphytic algal and macrozoobenthic communities were dominated by pollution-tolerant genera, namely the bacillariophytes Navicula and the annelids Tubificidae respectively, and TN, NH3N and TP were key aquatic environmental factors affecting the ecosystem health of streams in the DLW

Predicting Influenza Antigenicity by Matrix Completion With Antigen and Antiserum Similarity

The rapid mutation of influenza viruses especially on the two surface proteins hemagglutinin (HA) and neuraminidase (NA) has made them capable to escape from population immunity, which has become a key challenge for influenza vaccine design. Thus, it is crucial to predict influenza antigenic evolution and identify new antigenic variants in a timely manner. However, traditional experimental methods like hemagglutination inhibition (HI) assay to select vaccine strains are time and labor-intensive, while popular computational methods are less sensitive, which presents the need for more accurate algorithms. In this study, we have proposed a novel low-rank matrix completion model MCAAS to infer antigenic distances between antigens and antisera based on partially revealed antigenic distances, virus similarity based on HA protein sequences, and vaccine similarity based on vaccine strains. The model exploits the correlations of viruses and vaccines in serological tests as well as the ability of HAs from viruses and vaccine strains in inferring influenza antigenicity. We also compared the effects of comprehensive 65 amino acids substitution matrices in predicting influenza antigenicity. As a result, we applied MCAAS into H3N2 seasonal influenza virus data. Our model achieved a 10-fold cross validation root-mean-squared error (RMSE) of 0.5982, significantly outperformed existing computational methods like antigenic cartography, AntigenMap and BMCSI. We also constructed the antigenic map and studied the association between genetic and antigenic evolution of H3N2 influenza viruses. Finally, our analyses showed that homologous structure derived amino acid substitution matrix (HSDM) is most powerful in predicting influenza antigenicity, which is consistent with previous studies

Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures

Playing critical roles as post-transcriptional regulators, microRNAs (miRNAs) are a family of short non-coding RNAs that are derived from longer transcripts called precursor miRNAs (pre-miRNAs). Experimental methods to identify pre-miRNAs are expensive and time-consuming, which presents the need for computational alternatives. In recent years, the accuracy of computational methods to predict pre-miRNAs has been increasing significantly. However, there are still several drawbacks. First, these methods usually only consider base frequencies or sequence information while ignoring the information between bases. Second, feature extraction methods based on secondary structures usually only consider the global characteristics while ignoring the mutual influence of the local structures. Third, methods integrating high-dimensional feature information is computationally inefficient. In this study, we have proposed a novel mutual information-based feature representation algorithm for pre-miRNA sequences and secondary structures, which is capable of catching the interactions between sequence bases and local features of the RNA secondary structure. In addition, the feature space is smaller than that of most popular methods, which makes our method computationally more efficient than the competitors. Finally, we applied these features to train a support vector machine model to predict pre-miRNAs and compared the results with other popular predictors. As a result, our method outperforms others based on both 5-fold cross-validation and the Jackknife test

BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network

In recent years, it has been increasingly clear that long noncoding RNAs (lncRNAs) play critical roles in many biological processes associated with human diseases. Inferring potential lncRNA-disease associations is essential to reveal the secrets behind diseases, develop novel drugs, and optimize personalized treatments. However, biological experiments to validate lncRNA-disease associations are very time-consuming and costly. Thus, it is critical to develop effective computational models. In this study, we have proposed a method called BPLLDA to predict lncRNA-disease associations based on paths of fixed lengths in a heterogeneous lncRNA-disease association network. Specifically, BPLLDA first constructs a heterogeneous lncRNA-disease network by integrating the lncRNA-disease association network, the lncRNA functional similarity network, and the disease semantic similarity network. It then infers the probability of an lncRNA-disease association based on paths connecting them and their lengths in the network. Compared to existing methods, BPLLDA has a few advantages, including not demanding negative samples and the ability to predict associations related to novel lncRNAs or novel diseases. BPLLDA was applied to a canonical lncRNA-disease association database called LncRNADisease, together with two popular methods LRLSLDA and GrwLDA. The leave-one-out cross-validation areas under the receiver operating characteristic curve of BPLLDA are 0.87117, 0.82403, and 0.78528, respectively, for predicting overall associations, associations related to novel lncRNAs, and associations related to novel diseases, higher than those of the two compared methods. In addition, cervical cancer, glioma, and non-small-cell lung cancer were selected as case studies, for which the predicted top five lncRNA-disease associations were verified by recently published literature. In summary, BPLLDA exhibits good performances in predicting novel lncRNA-disease associations and associations related to novel lncRNAs and diseases. It may contribute to the understanding of lncRNA-associated diseases like certain cancers

fault gouge graphitization as evidence of past seismic slip

One moderate- to large-magnitude earthquake (M > 6) nucleates in Earth's crust every three days n average, but the geological record of ancient fault slip at meters-per-second seismic velocities (as opposed to subseismic slow-slip creep) remains debated because of the lack of established fault-zone evidence of seismic slip. Here we show that the irreversible temperature-dependent transformation of carbonaceous material (CM, a constituent of many fault gouges) into graphite is a reliable tracer of seismic fault slip. We sheared CM-bearing fault rocks in the laboratory at just above subseismic and at seismic velocities under both water-rich and water-deficient conditions and modeled the temperature evolution with slip. By means of micro-Raman spectroscopy and focused-ion beam transmission electron microscopy, we detected graphite grains similar to those found in the principal slip zone of the A.D. 2008 Wenchuan (Mw 7.9) earthquake (southeast Tibet) only in experiments conducted at seismic velocities. The experimental evidence presented here suggests that high-temperature pulses associated with seismic slip induce graphitization of CM. Importantly, the occurrence of graphitized fault-zone CM may allow us to ascertain the seismogenic potential of faults in areas worldwide with incomplete historical earthquake catalogues

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

A new deliverability evaluation method of gas condensate wells in gas–liquid two-phase state

Gas well deliverability evaluation and analysis are challenging due to the frequent abnormalities of deliverability test data of gas condensate wells caused by seepage of oil and gas phases in the reservoirs. To this end, based upon the pseudo-single-phase seepage equation and the oil–gas two-phase seepage equation, a new deliverability evaluation method was established which is applicable to the following two cases when the flow of a gas well reaches the quasi-steady stage, i.e., the pseudo-single-phase stable point deliverability evaluation for the case when the formation pressure is above the dew pressure; the gas–liquid two-phase stable point deliverability evaluation for the case when the formation pressure is below the dew pressure. Using this established deliverability evaluation method, based on the basic parameters of the Yaha gas field, Tarim Basin, the IPR curves were first obtained of gas wells do not get this at the same production gas–oil ratio and at the formation pressure above and below the dew point pressure; then, according to the four condensate gas fields, such as Yaha, Tazhong I, Qianmiqiao and Dina 2, the absolute open flow (AOF) potentials of condensate gas wells under different gas–oil production ratios were calculated. Finally, through statistical analysis of the calculation results from typical wells, the following findings were obtained. This new deliverability evaluation method under the two states of condensate gas wells with quasi-single-phase and gas–liquid two-phase stable points can be used to avoid cases due to the oil–gas flow in a condensate gas well which has remained unresolved by the classical deliverability evaluation methods. Also, with the increase of gas–oil ratios in gas condensate wells, a variable discrepancy is gradually reduced in AOF potentials calculated respectively by the quasi-single-phase and gas–liquid two-phase stable point deliverability evaluation equations. For the condensate gas wells with high condensate content and low condensate gas production rates, the AOF potentials calculated by the gas–liquid two-phase stable point deliverability equation is more appropriate and reliable compared with that obtained by classical methods. Keywords: Gas condensate reservoir, Gas well, Gas–liquid two-phase flow, Turbulent flow equation, Quasi-single-phase, Gas–liquid two-phase, Stable point, Deliverability evaluation method, Absolute open flow potentia

Soil Moisture Retrieval Using Microwave Remote Sensing Data and a Deep Belief Network in the Naqu Region of the Tibetan Plateau

Soil moisture plays an important role in the land surface model. In this paper, a method of using VV polarization Sentinel-1 SAR and Landsat optical data to retrieve soil moisture data was proposed by combining the water cloud model (WCM) and the deep belief network (DBN). Since the simple combination of training data in the neural network cannot effectively improve the accuracy of the soil moisture inversion results, a WCM physical model was used to eliminate the effect of vegetation cover on the ground backscatter, in order to obtain the bare soil backscatter coefficient. This improved the correlation of ground soil backscatter characteristics with soil moisture. A DBN soil moisture inversion model based on the bare soil backscatter coefficients as the foundation training data combined with radar incidence angle and terrain factors obtained good inversion results. Studies in the Naqu area of the Tibetan Plateau showed that vegetation cover had a significant effect on the soil moisture, and the goodness of fit (R2) between the backscatter coefficient and soil moisture before and after the elimination of vegetation cover was 0.38 and 0.50, respectively. The correlation between the backscatter coefficient and the soil moisture was improved after eliminating the vegetation cover. The inversion results of the DBN soil moisture model were further improved through iterative parameters. The model prediction reached its highest level of accuracy when the restricted Boltzmann machine (RBM) was set to seven layers, the bias and R were 0.007 and 0.88, respectively. Ten-fold cross-validation showed that the DBN soil moisture model performed stably with different data. The prediction was further improved when the bare soil backscatter coefficient was used as the training data. The mean values of the root mean square error (RMSE), the inequality coefficient (TIC), and the mean absolute percent error (MAPE) were 0.023, 0.09, and 11.13, respectively