Unreliable quantitation of species abundance based on high-throughput sequencing data of zooplankton communities

High-throughput sequencing (HTS) is rapidly becoming a popular and robust tool to characterize biodiversity of complex communities, especially for those dominated by microscopic species such as zooplankton. The popular use of HTS-based methods has prompted a possible method of inferring relative species abundance from sequencing data. However, these methods remain largely untested in many communities as to whether sequence data can reliably quantify relative species abundance. Here we tested the relationship between species abundance and sequence abundance in zooplankton using 2 methods: (1) spiking known amounts of indicator species into existing zooplankton communities, and (2) comparing results obtained from parallel replicates for the same natural zooplankton communities. Although we detected a general trend that low-abundance species usually corresponded to low-abundance sequence reads, further statistical analyses revealed that sequencing data could not reliably quantify relative species abundance, even for the same indicator species spiked into different zooplankton communities. The distribution of sequence reads statistically varied even between parallel replicates of the same natural zooplankton communities. Our study reveals that sequence abundance may generally qualitatively reflect species abundance as the general trend between these 2 variables exists; however, extra caution is required when using HTS-based approaches to make quantitative inferences regarding zooplankton communities

Farrerol ameliorates diabetic hepatopathy in rat model of type 2 diabetes mellitus via modulation of oxidativeinflammatory stress

Purpose: To investigate the effect of farrerol on diabetic hepatopathy in a rat model of type 2 diabetes mellitus (T2DM).Methods: Adult male Wistar rats (n = 40) were randomly assigned to four groups of ten rats each: normal control, diabetic control, farrerol control and treatment groups. With the exception of normal control and farrerol control groups, the rats were fed high-fat diet (HFD) for four weeks, and thereafter injected streptozotocin (STZ) at a dose of 30 mg/kg body weight intraperitoneally (i.p.) for induction of T2DM. Rats in farrerol control and treatment groups received 50 mg/kg farrerol orally/day. Serum levels of triacylglycerol (TG), total cholesterol (TC), high-density lipoprotein  cholesterol (HDL-C) and lowdensity lipoprotein cholesterol (LDL-C) were determined. Superoxide dismutase (SOD) activity and malondialdehyde (MDA) levels were assessed in liver homogenate while mRNA and protein expressions of glucose transporter 2 (GLUT2) were assayed in liver using real-time quantitative polymerase chain reaction (qRT-PCR) and Western blotting, respectively. Expression levels of tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) were also determined using qRT-PCR.Results: Diabetes mellitus (DM) led to significant reductions in rat body weight and SOD activity, while increasing fasting blood glucose (FBG) and MDA levels (p < 0.05). However, treatment with farrerol significantly reversed the effect of DM on these parameters (p < 0.05). The mRNA expressions of TNF-α and IL-1β were significantly higher in diabetic control group than in normal control group, but were significantly reduced after farrerol treatment (p < 0.05). Treatment with farrerol also significantly reversed the effect of DM on rat lipid profile (p < 0.05). The expression of GLUT2 protein was significantly downregulated in the liver of diabetic control rats, when compared with normal control rats, but was significantly upregulated after treatment with farrerol (p < 0.05).Conclusion: The results of this study show that farrerol alleviates STZ-induced hyperglycemia and dyslipidemia via reduction in oxidative stress and inflammation, and upregulation of GLUT2 protein expression. Thus, farrerol has antidiabetic and hepatoprotective potentials for clinical use in  humans. Keywords: Diabetes mellitus, Dyslipidemia, Farrerol, Hepatopathy, High-fat die

Forecasting the BAO Measurements of the CSST galaxy and AGN Spectroscopic Surveys

The spectroscopic survey of the China Space Station Telescope (CSST) is expected to obtain a huge number of slitless spectra, including more than one hundred million galaxy spectra and millions of active galactic nuclei (AGN) spectra. By making use of these spectra, we can measure the Baryon Acoustic Oscillation (BAO) signals over large redshift ranges with excellent precisions. In this work, we predict the CSST measurements of the post-reconstruction galaxy power spectra at 0<z<1.2 and pre-reconstruction AGN power spectra at 0<z<4, and derive the BAO signals at different redshift bins by constraining the BAO scaling parameters using the Markov Chain Monte Carlo method. Our result shows that the CSST spectroscopic survey can provide accurate BAO measurements with precisions higher than 1% and 3% for the galaxy and AGN surveys, respectively. By comparing with current measurements in the same range at low redshifts, this can improve the precisions by a factor of $2\sim3$, and similar precisions can be obtained in the pessimistic case. We also investigate the constraints on the cosmological parameters using the measured BAO data by the CSST, and obtain stringent constraint results for the energy density of dark matter, Hubble constant, and equation of state of dark energy.Comment: 15 pages, 9 figures, 4 table

Cosmological Constraint Precision of the Photometric and Spectroscopic Multi-probe Surveys of China Space Station Telescope (CSST)

As one of Stage IV space-based telescopes, China Space Station Telescope (CSST) can perform photometric and spectroscopic surveys simultaneously to efficiently explore the Universe in extreme precision. In this work, we investigate several powerful CSST cosmological probes, including cosmic shear, galaxy-galaxy lensing, photometric and spectroscopic galaxy clustering, and number counts of galaxy clusters, and study the capability of these probes by forecasting the results of joint constraints on the cosmological parameters. By referring to real observational results, we generate mock data and estimate the measured errors based on CSST observational and instrumental designs. To study the systematical effects on the results, we also consider a number of systematics in CSST photometric and spectroscopic surveys, such as the intrinsic alignment, shear calibration uncertainties, photometric redshift uncertainties, galaxy bias, non-linear effects, instrumental effects, etc. The Fisher matrix method is used to derive the constraint results from individual or joint surveys on the cosmological and systematical parameters. We find that the joint constraints by including all these CSST cosmological probes can significantly improve the results from current observations by one order of magnitude at least, which gives $\Omega_m$ and $\sigma_8$ $<$1% accuracy, and $w_0$ and $w_a$ $<$5% and 20% accuracies, respectively. This indicates that the CSST photometric and spectroscopic multi-probe surveys could provide powerful tools to explore the Universe and greatly improve the studies of relevant cosmological problems.Comment: 17 pages, 12 figures, 3 tables. Accepted for publication in MNRA

Constructing Tree-based Index for Efficient and Effective Dense Retrieval

Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness, the application of DR is still limited. In contrast to statistic retrieval models that rely on highly efficient inverted index solutions, DR models build dense embeddings that are difficult to be pre-processed with most existing search indexing systems. To avoid the expensive cost of brute-force search, the Approximate Nearest Neighbor (ANN) algorithm and corresponding indexes are widely applied to speed up the inference process of DR models. Unfortunately, while ANN can improve the efficiency of DR models, it usually comes with a significant price on retrieval performance. To solve this issue, we propose JTR, which stands for Joint optimization of TRee-based index and query encoding. Specifically, we design a new unified contrastive learning loss to train tree-based index and query encoder in an end-to-end manner. The tree-based negative sampling strategy is applied to make the tree have the maximum heap property, which supports the effectiveness of beam search well. Moreover, we treat the cluster assignment as an optimization problem to update the tree-based index that allows overlapped clustering. We evaluate JTR on numerous popular retrieval benchmarks. Experimental results show that JTR achieves better retrieval performance while retaining high system efficiency compared with widely-adopted baselines. It provides a potential solution to balance efficiency and effectiveness in neural retrieval system designs.Comment: 10 pages, accepted at SIGIR 202

Structures of human gastrin-releasing peptide receptors bound to antagonist and agonist for cancer and itch therapy

Gastrin releasing peptide receptor (GRPR), a member of the bombesin (BBN) G protein-coupled receptors, is aberrantly overexpressed in several malignant tumors, including those of the breast, prostate, pancreas, lung, and central nervous system. Additionally, it also mediates non-histaminergic itch and pathological itch conditions in mice. Thus, GRPR could be an attractive target for cancer and itch therapy. Here, we report the inactive state crystal structure of human GRPR in complex with the non-peptide antagonist PD176252, as well as two active state cryo-electron microscopy (cryo-EM) structures of GRPR bound to the endogenous peptide agonist gastrin-releasing peptide and the synthetic BBN analog [D-Ph

k-Regret Minimizing Set: Efficient Algorithms and Hardness

We study the k-regret minimizing query (k-RMS), which is a useful operator for supporting multi-criteria decision-making. Given two integers k and r, a k-RMS returns r tuples from the database which minimize the k-regret ratio, defined as one minus the worst ratio between the k-th maximum utility score among all tuples in the database and the maximum utility score of the r tuples returned. A solution set contains only r tuples, enjoying the benefits of both top-k queries and skyline queries. Proposed in 2012, the query has been studied extensively in recent years. In this paper, we advance the theory and the practice of k-RMS in the following aspects. First, we develop efficient algorithms for k-RMS (and its decision version) when the dimensionality is 2. The running time of our algorithms outperforms those of previous ones. Second, we show that k-RMS is NP-hard even when the dimensionality is 3. This provides a complete characterization of the complexity of k-RMS, and answers an open question in previous studies. In addition, we present approximation algorithms for the problem when the dimensionality is 3 or larger

Research on neural network prediction method for upgrading scale of natural gas reserves

With the gradual decline of natural gas production, reserve upgrading has become one of the important issues in natural gas exploration and development. However, the traditional reserve upgrade forecasting method is often based on experience and rules, which is subjective and unreliable. Therefore, a prediction method based on neural network is proposed in this paper to improve the accuracy and reliability of reserve upgrade prediction. In order to achieve this goal, by collecting the relevant data of natural gas exploration and development in Sichuan Basin, including geological parameters, production parameters and other indicators, and processing and analyzing the data, the relevant characteristics of reserves increase are extracted. Then, a neural network model based on multi-layer perceptron (MLP) is constructed and trained and optimized using backpropagation algorithm. The results show that the prediction accuracy of the constructed neural network model can reach more than 90% and can effectively predict the reserve upgrading. Experiments show that the model has high accuracy and reliability, and is significantly better than the traditional prediction methods. The method has good stability and reliability, and is suitable for a wider range of natural gas fields

Accelerated Li⁺ Desolvation for Diffusion Booster Enabling Low‐Temperature Sulfur Redox Kinetics via Electrocatalytic Carbon‐Grazfted‐CoP Porous Nanosheets

Lithium–sulfur (Li–S) batteries are famous for their high energy density and low cost, but prevented by sluggish redox kinetics of sulfur species due to depressive Li ion diffusion kinetics, especially under low-temperature environment. Herein, a combined strategy of electrocatalysis and pore sieving effect is put forward to dissociate the Li+ solvation structure to stimulate the free Li+ diffusion, further improving sulfur redox reaction kinetics. As a protocol, an electrocatalytic porous diffusion-boosted nitrogen-doped carbon-grafted-CoP nanosheet is designed via forming the NCoP active structure to release more free Li+ to react with sulfur species, as fully investigated by electrochemical tests, theoretical simulations and in situ/ex situ characterizations. As a result, the cells with diffusion booster achieve desirable lifespan of 800 cycles at 2 C and excellent rate capability (775 mAh g−1 at 3 C). Impressively, in a condition of high mass loading or low-temperature environment, the cell with 5.7 mg cm−2 stabilizes an areal capacity of 3.2 mAh cm−2 and the charming capacity of 647 mAh g−1 is obtained under 0 °C after 80 cycles, demonstrating a promising route of providing more free Li ions toward practical high-energy Li–S batteries