Identification of a novel iron regulated basic helix-loop-helix protein involved in Fe homeostasis in Oryza sativa

<p>Abstract</p> <p>Background</p> <p>Iron (Fe) is the most limiting micronutrient element for crop production in alkaline soils. A number of transcription factors involved in regulating Fe uptake from soil and transport in plants have been identified. Analysis of transcriptome data from <it>Oryza sativa </it>grown under limiting Fe conditions reveals that transcript abundances of several genes encoding transcription factors are altered by Fe availability. These transcription factors are putative regulators of Fe deficiency responses.</p> <p>Results</p> <p>Transcript abundance of one nuclear located basic helix-loop-helix family transcription factor, <it>OsIRO3</it>, is up-regulated from 25- to 90-fold under Fe deficiency in both root and shoot respectively. The expression of <it>OsIRO3 </it>is specifically induced by Fe deficiency, and not by other micronutrient deficiencies. Transgenic rice plants over-expressing <it>OsIRO3 </it>were hypersensitive to Fe deficiency, indicating that the Fe deficiency response was compromised. Furthermore, the Fe concentration in shoots of transgenic rice plants over-expressing <it>OsIRO3 </it>was less than that in wild-type plants. Analysis of the transcript abundances of genes normally induced by Fe deficiency in <it>OsIRO3 </it>over-expressing plants indicated their induction was markedly suppressed.</p> <p>Conclusion</p> <p>A novel Fe regulated bHLH transcription factor (OsIRO3) that plays an important role for Fe homeostasis in rice was identified. The inhibitory effect of <it>OsIRO3 </it>over-expression on Fe deficiency response gene expression combined with hypersensitivity of <it>OsIRO3 </it>over-expression lines to low Fe suggest that OsIRO3 is a negative regulator of the Fe deficiency response in rice.</p

Dynamics of a Stage Structure Pest Control Model with Impulsive Effects at Different Fixed Time

Many existing pest control models, which control pests by releasing natural enemies, neglect the effect that natural enemies may get killed. From this point of view, we formulate a pest control model with stage structure for the pest with constant maturation time delay (through-stage time delay) and periodic releasing natural enemies and natural enemies killed at different fixed time and perform a systematic mathematical and ecological study. By using the comparison theorem and analysis method, we obtain the conditions for the global attractivity of the pest-eradication periodic solution and permanence of the system. We also present a pest management strategy in which the pest population is kept under the economic threshold level (ETL) when the pest population is uniformly permanent. We show that maturation time delay, impulsive releasing, and killing natural enemies can bring great effects on the dynamics of the system. Numerical simulations confirm our theoretical results

Active Relation Discovery: Towards General and Label-aware Open Relation Extraction

Open Relation Extraction (OpenRE) aims to discover novel relations from open domains. Previous OpenRE methods mainly suffer from two problems: (1) Insufficient capacity to discriminate between known and novel relations. When extending conventional test settings to a more general setting where test data might also come from seen classes, existing approaches have a significant performance decline. (2) Secondary labeling must be performed before practical application. Existing methods cannot label human-readable and meaningful types for novel relations, which is urgently required by the downstream tasks. To address these issues, we propose the Active Relation Discovery (ARD) framework, which utilizes relational outlier detection for discriminating known and novel relations and involves active learning for labeling novel relations. Extensive experiments on three real-world datasets show that ARD significantly outperforms previous state-of-the-art methods on both conventional and our proposed general OpenRE settings. The source code and datasets will be available for reproducibility.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A Survey of Natural Language Generation

This paper offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-to-text generation deep learning methods, as well as new applications of NLG technology. This survey aims to (a) give the latest synthesis of deep learning research on the NLG core tasks, as well as the architectures adopted in the field; (b) detail meticulously and comprehensively various NLG tasks and datasets, and draw attention to the challenges in NLG evaluation, focusing on different evaluation methods and their relationships; (c) highlight some future emphasis and relatively recent research issues that arise due to the increasing synergy between NLG and other artificial intelligence areas, such as computer vision, text and computational creativity.Comment: Accepted by ACM Computing Survey (CSUR) 202

Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels

Deep neural models have hitherto achieved significant performances on numerous classification tasks, but meanwhile require sufficient manually annotated data. Since it is extremely time-consuming and expensive to annotate adequate data for each classification task, learning an empirically effective model with generalization on small dataset has received increased attention. Existing efforts mainly focus on transferring task-relevant knowledge from other similar data to tackle the issue. These approaches have yielded remarkable improvements, yet neglecting the fact that the task-irrelevant features could bring out massive negative transfer effects. To date, no large-scale studies have been performed to investigate the impact of task-irrelevant features, let alone the utilization of this kind of features. In this paper, we firstly propose Task-Irrelevant Transfer Learning (TIRTL) to exploit task-irrelevant features, which mainly are extracted from task-irrelevant labels. Particularly, we suppress the expression of task-irrelevant information and facilitate the learning process of classification. We also provide a theoretical explanation of our method. In addition, TIRTL does not conflict with those that have previously exploited task-relevant knowledge and can be well combined to enable the simultaneous utilization of task-relevant and task-irrelevant features for the first time. In order to verify the effectiveness of our theory and method, we conduct extensive experiments on facial expression recognition and digit recognition tasks. Our source code will be also available in the future for reproducibility

Pharmacokinetic study of isoquercitrin in rat plasma after intravenous administration at three different doses

O objetivo deste estudo é desenvolver um método simples e específico de HPLC usando vitexina como padrão interno para investigar a farmacocinética do isoquercitrina (ISOQ) após três doses diferentes administradas por via intravenosa a ratos. Os parâmetros farmacocinéticos foram calculados pelas abordagens compartimental e não compartimental. Os resultados mostraram que ISOQ se encaixa no modelo de três compartimentos. Os valores de AUC aumentaram proporcionalmente na faixa de 5-10 mg·kg-1. Além disso, a meia-vida, b meia-vida, ªCL, MRT0-t and MRT0&#8594;&#8734; de ISOQ em ratos mostraram diferenças significativas entre 20 mg·kg-1 e outras doses, o que significa que ISOQ apresenta farmacocinética dose-dependente no intervalo de 5-10 mg·kg-1 e farmacocinética não linear em doses mais elevadas.The aim of this study is to develop a simple and specific HPLC method using vitexin as the internal standard to investigate the pharmacokinetics of isoquercitrin (ISOQ) after three different doses administrated intravenously to rats. The pharmacokinetic parameters were calculated by both compartmental and non-compartmental approaches. The results showed that ISOQ fitted a three-compartment open model. The values of AUC increased proportionally within the range of 5-10 mg·kg-1. Moreover, a half-life, b half-life, ªCL, MRT0-t and MRT0&#8594;&#8734; of ISOQ in rats showed significant differences between 20 mg·kg-1 and other doses, indicating that ISOQ presented dose-dependent pharmacokinetics in the range of 5-10 mg·kg-1 and non-linear pharmacokinetics at higher doses

Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking

Entity Linking (EL) is a fundamental task for Information Extraction and Knowledge Graphs. The general form of EL (i.e., end-to-end EL) aims to first find mentions in the given input document and then link the mentions to corresponding entities in a specific knowledge base. Recently, the paradigm of retriever-reader promotes the progress of end-to-end EL, benefiting from the advantages of dense entity retrieval and machine reading comprehension. However, the existing study only trains the retriever and the reader separately in a pipeline manner, which ignores the benefit that the interaction between the retriever and the reader can bring to the task. To advance the retriever-reader paradigm to perform more perfectly on end-to-end EL, we propose BEER$^2$, a Bidirectional End-to-End training framework for Retriever and Reader. Through our designed bidirectional end-to-end training, BEER$^2$ guides the retriever and the reader to learn from each other, make progress together, and ultimately improve EL performance. Extensive experiments on benchmarks of multiple domains demonstrate the effectiveness of our proposed BEER$^2$.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl