An RNN Model for Generating Sentences with a Desired Word at a Desired Position

Generating sentences with a desired word is useful in many natural language processing tasks. State-of-the-art recurrent neural network (RNN)-based models mainly generate sentences in a left-to-right manner, which does not allow explicit and direct constraints on the words at arbitrary positions in a sentence. To address this issue, we propose a generative model of sentences named Coupled-RNN. We employ two RNN\u27s to generate sentences backwards and forwards respectively starting from a desired word, and inject position embeddings into the model to solve the problem of position information loss. We explore two coupling mechanisms to optimize the reconstruction loss globally. Experimental results demonstrate that Coupled-RNN can generate high quality sentences that contain a desired word at a desired position

Effects of water temperature on swimming performance of <em>Siniperca knerii</em> Garman under the Yuanshui River cascade development

We examined the swimming parameters and oxygen consumption rate at four temperature levels (15，20，25 and 30°C) of Siniperca knerii Garman (11.81 ±2.21 cm, 26.4 ± 6.28g) from Yuanshui River for 30 days to analyze the ecological adaptability of the typical fish and the conservation of fishery resources from the Dongting Lake water system. Their relationship was also analyzed, and the results showed an approximately linear increasing trend of the fish's critical swimming speed and preferred swimming speed with the change of temperature (PSiniperca knerii Garman. The changes were due to physiological and biochemical regulation with temperature and environmental changes. This experiment provided essential data support for the adaptive mechanism of sports physiology and ecology of typical fishes in the Yuanshui River

Expounding the role of tick in Africa swine fever virus transmission and seeking effective prevention measures: A review

African swine fever (ASF), a highly contagious, deadly infectious disease, has caused huge economic losses to animal husbandry with a 100% mortality rate of the most acute and acute infection, which is listed as a legally reported animal disease by the World Organization for Animal Health (OIE). African swine fever virus (ASFV) is the causative agent of ASF, which is the only member of the Asfarviridae family. Ornithodoros soft ticks play an important role in ASFV transmission by active biological or mechanical transmission or by passive transport or ingestion, particularly in Africa, Europe, and the United States. First, this review summarized recent reports on (1) tick species capable of transmitting ASFV, (2) the importance of ticks in the transmission and epidemiological cycle of ASFV, and (3) the ASFV strains of tick transmission, to provide a detailed description of tick-borne ASFV. Second, the dynamics of tick infection with ASFV and the tick-induced immune suppression were further elaborated to explain how ticks spread ASFV. Third, the development of the anti-tick vaccine was summarized, and the prospect of the anti-tick vaccine was recapitulated. Then, the marked attenuated vaccine, ASFV-G-ΔI177L, was compared with those of the anti-tick vaccine to represent potential therapeutic or strategies to combat ASF

Reversal of Cocaine-Conditioned Place Preference through Methyl Supplementation in Mice: Altering Global DNA Methylation in the Prefrontal Cortex

Analysis of global methylation in cells has revealed correlations between overall DNA methylation status and some biological states. Recent studies suggest that epigenetic regulation through DNA methylation could be responsible for neuroadaptations induced by addictive drugs. However, there is no investigation to determine global DNA methylation status following repeated exposure to addictive drugs. Using mice conditioned place preference (CPP) procedure, we measured global DNA methylation level in the nucleus accumbens (NAc) and the prefrontal cortex (PFC) associated with drug rewarding effects. We found that cocaine-, but not morphine- or food-CPP training decreased global DNA methylation in the PFC. Chronic treatment with methionine, a methyl donor, for 25 consecutive days prior to and during CPP training inhibited the establishment of cocaine, but not morphine or food CPP. We also found that both mRNA and protein level of DNMT (DNA methytransferase) 3b in the PFC were downregulated following the establishment of cocaine CPP, and the downregulation could be reversed by repeated administration of methionine. Our study indicates a crucial role of global PFC DNA hypomethylation in the rewarding effects of cocaine. Reversal of global DNA hypomethylation could significantly attenuate the rewarding effects induced by cocaine. Our results suggest that methionine may have become a potential therapeutic target to treat cocaine addiction

Draft genome sequence of the mulberry tree Morus notabilis

Human utilization of the mulberry–silkworm interaction started at least 5,000 years ago and greatly influenced world history through the Silk Road. Complementing the silkworm genome sequence, here we describe the genome of a mulberry species Morus notabilis. In the 330-Mb genome assembly, we identify 128 Mb of repetitive sequences and 29,338 genes, 60.8% of which are supported by transcriptome sequencing. Mulberry gene sequences appear to evolve ~3 times faster than other Rosales, perhaps facilitating the species’ spread worldwide. The mulberry tree is among a few eudicots but several Rosales that have not preserved genome duplications in more than 100 million years; however, a neopolyploid series found in the mulberry tree and several others suggest that new duplications may confer benefits. Five predicted mulberry miRNAs are found in the haemolymph and silk glands of the silkworm, suggesting interactions at molecular levels in the plant–herbivore relationship. The identification and analyses of mulberry genes involved in diversifying selection, resistance and protease inhibitor expressed in the laticifers will accelerate the improvement of mulberry plants

Improving Automated Essay Scoring by Prompt Prediction and Matching

Automated essay scoring aims to evaluate the quality of an essay automatically. It is one of the main educational application in the field of natural language processing. Recently, Pre-training techniques have been used to improve performance on downstream tasks, and many studies have attempted to use pre-training and then fine-tuning mechanisms in an essay scoring system. However, obtaining better features such as prompts by the pre-trained encoder is critical but not fully studied. In this paper, we create a prompt feature fusion method that is better suited for fine-tuning. Besides, we use multi-task learning by designing two auxiliary tasks, prompt prediction and prompt matching, to obtain better features. The experimental results show that both auxiliary tasks can improve model performance, and the combination of the two auxiliary tasks with the NEZHA pre-trained encoder produces the best results, with Quadratic Weighted Kappa improving 2.5% and Pearson’s Correlation Coefficient improving 2% on average across all results on the HSK dataset

Sol-Gel Derived Zno/Pvp Nanocomposite Thin Film For Superoxide Radical Sensor

Pure ZnO films and ZnO nanoparticle-dispersed polyvinylpyrrolidone (PVP) composite films are prepared on Pyrex glass substrates by the sol-gel dip-coating technique utilizing zinc acetate precursor. The thin films are extensively characterized for surface morphology, chemistry, and nanocrystallite size using various advanced analytical techniques including Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM). For the processing conditions considered, ZnO semiconductor thin films with nanocrystallite size 20-30 nm are obtained. The ZnO nanoparticle size in the PVP composite film increases with increase in ZnO content. The resistance of both the synthesized ZnO and ZnO/PVP thin films decrease significantly after exposure to solution containing superoxide anion radicals (SOR). The results thus indicate that ZnO and ZnO/PVP composite thin films can be used as biosensors for SOR and potentially for characterizing the antioxidant properties of fluids. © 2006 Elsevier B.V. All rights reserved

Modeling Macroscopic Transport Of Superoxide Radicals Through Nonoheterogeneous Biosensor Film

We develop a mathematical model to represent the transport phenomena and electrochemical reaction kinetics during amperometric measurement of superoxide radical concentration using ZnO-polymer nanocomposite sensor. This model assumes a logarithmic normal distribution for the nanoparticles immobilized in the polymer matrix and an empirical relation for the diffusion coefficient of superoxide radicals as a function of pore volume fraction. A kinetic with secondary order rate constant is used to represent the electrochemical reactions of electron transfer from the superoxide radicals to nanoparticles. The predicted results include the effect of diffusion coefficient on concentration and electrical conductivity. © 2004 Elsevier B.V. All rights reserved

Synthesis And Characterization Of Sol-Gel Derived Nanostructured Composite Of Zno/Pvp Thin Film As Biosensor

Undoped and Mn-doped ZnO films and ZnO nanoparticles dispersed polyvinylpyrrolidone (PVP) films were prepared on Pyrex glass substrate by the sol-gel dip-coating technique utilizing zinc acetate precursor. The thin film is extensively characterized for its surface morphology, chemistry, thickness, and nanocrystallite size using various advanced analytical techniques such as Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM). Under the given processing conditions, ZnO semiconductor thin film with nanocrystallite size 15-20nm is obtained and the ZnO nanoparticles of varying shapes are uniformly distributed in the PVP polymer matrix

A Machine Learning Classification Algorithm for Vocabulary Grading in Chinese Language Teaching

Vocabulary grading is of great importance in Chinese vocabulary teaching. This paper starts with an analysis of the lexical attributes that affect lexical complexity, followed by an explanation of the extraction of lexical attribute information combined with the constructed word-formation knowledge base, the construction of mapping functions corresponding to lexical attributes, and the quantitative representation of the attributes that form the basis for vocabulary grading. Based on this, a machine learning classification algorithm is creatively applied to the Chinese vocabulary grading problem. Using the comparative analysis of vocabulary grading models based on common machine learning classification algorithms, the importance measurement analysis of Chinese vocabulary attributes based on different feature selection methods is performed, and a vocabulary grading model is constructed based on the machine learning classification algorithm and feature importance selection of different feature selection algorithms. A comparison of the experimental results demonstrated that the classification model based on the support vector machine (SVM) algorithm and top six attribute groups by the importance of feature selection received the best effect. To improve vocabulary grading, a variety of feature selection algorithms were used to fuse the importance of lexical attributes on average. Then an experiment was conducted for vocabulary grading combined with the Bagging + SVM integration algorithm and top six attribute groups by the importance of feature selection. The experimental results demonstrated that the combination scheme achieved a better effect