Characterization of Dof Transcription Factors and the Heat-Tolerant Function of PeDof-11 in Passion Fruit (Passiflora edulis).

Abiotic stress is the focus of passion fruit research since it harms the industry, in which high temperature is an important influencing factor. Dof transcription factors (TFs) act as essential regulators in stress conditions. TFs can protect against abiotic stress via a variety of biological processes. There is yet to be published a systematic study of the Dof (PeDof) family of passion fruit. This study discovered 13 PeDof family members by using high-quality genomes, and the members of this characterization were identified by bioinformatics. Transcriptome sequencing and qRT-PCR were used to analyze the induced expression of PeDofs under high-temperature stress during three periods, in which PeDof-11 was significantly induced with high expression. PeDof-11 was then chosen and converted into yeast, tobacco, and Arabidopsis, with the findings demonstrating that PeDof-11 could significantly respond to high-temperature stress. This research lays the groundwork for a better understanding of PeDof gene regulation under high-temperature stress.

Variable-Length Resolvability for General Sources and Channels.

We introduce the problem of variable-length (VL) source resolvability, in which a given target probability distribution is approximated by encoding a VL uniform random number, and the asymptotically minimum average length rate of the uniform random number, called the VL resolvability, is investigated. We first analyze the VL resolvability with the variational distance as an approximation measure. Next, we investigate the case under the divergence as an approximation measure. When the asymptotically exact approximation is required, it is shown that the resolvability under two kinds of approximation measures coincides. We then extend the analysis to the case of channel resolvability, where the target distribution is the output distribution via a general channel due to a fixed general source as an input. The obtained characterization of channel resolvability is fully general in the sense that, when the channel is just an identity mapping, it reduces to general formulas for source resolvability. We also analyze the second-order VL resolvability.

First- and Second-Order Hypothesis Testing for Mixed Memoryless Sources.

The first- and second-order optimum achievable exponents in the simple hypothesis testing problem are investigated. The optimum achievable exponent for type II error probability, under the constraint that the type I error probability is allowed asymptotically up to ε , is called the ε -optimum exponent. In this paper, we first give the second-order ε -optimum exponent in the case where the null hypothesis and alternative hypothesis are a mixed memoryless source and a stationary memoryless source, respectively. We next generalize this setting to the case where the alternative hypothesis is also a mixed memoryless source. Secondly, we address the first-order ε -optimum exponent in this setting. In addition, an extension of our results to the more general setting such as hypothesis testing with mixed general source and a relationship with the general compound hypothesis testing problem are also discussed.

Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition.

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K-nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction.

Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application.

In recent years, an increasing popularity of deep learning model for intelligent condition monitoring and diagnosis as well as prognostics used for mechanical systems and structures has been observed. In the previous studies, however, a major assumption accepted by default, is that the training and testing data are taking from same feature distribution. Unfortunately, this assumption is mostly invalid in real application, resulting in a certain lack of applicability for the traditional diagnosis approaches. Inspired by the idea of transfer learning that leverages the knowledge learnt from rich labeled data in source domain to facilitate diagnosing a new but similar target task, a new intelligent fault diagnosis framework, i.e., deep transfer network (DTN), which generalizes deep learning model to domain adaptation scenario, is proposed in this paper. By extending the marginal distribution adaptation (MDA) to joint distribution adaptation (JDA), the proposed framework can exploit the discrimination structures associated with the labeled data in source domain to adapt the conditional distribution of unlabeled target data, and thus guarantee a more accurate distribution matching. Extensive empirical evaluations on three fault datasets validate the applicability and practicability of DTN, while achieving many state-of-the-art transfer results in terms of diverse operating conditions, fault severities and fault types.

Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions.

Recent years have witnessed increasing popularity and development of deep learning spanning through various fields. Deep networks, and in particular convolutional neural network (CNN) have also achieved many state-of-the-art competition results in the intelligent fault diagnosis of mechanical systems. However, most of the existing studies have been performed with the assumption that the same distribution holds for both the training data and the test data, which is not in accord with situations in real diagnosis tasks. To tackle this problem, a transfer learning framework based on pre-trained CNN, which leverages the knowledge learned from the training data to facilitate diagnosing a new but similar task, is presented in this work. First, the CNN is trained on large datasets to learn the hierarchical features from the raw data. Then, the architecture and weights of the pre-trained CNN are transferred to new tasks with proper fine-tuning instead of training a network from scratch. To adapt the pre-trained CNN in a specific case, three transfer learning strategies are discussed and compared to investigate the applicability as well as the significance of feature transferability from the different levels of a deep structure. The case studies show that the proposed framework can transfer the features of the pre-trained CNN to boost the diagnosis performance on unseen machine conditions in terms of diverse working conditions and fault types.

Temporary suppression the sequestrated function of host macrophages for better nanoparticles tumor delivery.

Orchestration of nanoparticles to achieve targeting has become the mainstream for efficient delivery of antitumor drugs. However, the low delivery efficiency becomes the biggest barrier for clinical translation of cancer nanomedicines, as most of them are sequestrated in the liver where more macrophages located in are responsible for capture of systemic administrated nanoparticles. In this study, we found that the depletion of the liver macrophages could lead to a superior improvement in the nanoparticles delivery. Firstly, we developed clodronate-containing liposomes (clodrolip) to transiently suppress the phagocytic function of macrophages, the residual macrophages in liver only accounted for less than 1% when the mice were treated with clodrolip in advance. In addition, the pharmacokinetics results of treatment with paclitaxel-poly(lactic-co-glycolic acid) (PTX-PLGA) nanoparticles disclosed that the AUC of PTX in the macrophages depletion group increased 2.11-fold. These results meant that the removal of macrophages would decrease the nanoparticles accumulation in the liver and better the biodistribution and bioavailability of nanoparticles delivery systems. Moreover, treatment of mice with melanoma by the combination of clodrolip and PTX-PLGA nanoparticles resulted in an elevated anti-tumor efficacy, the tumor inhibition ratio was nearly reached to 80%. Furthermore, these combinatorial regimens have demonstrated negligible toxicity in incidence of adverse effects. In conclusion, the encouraging results from this study inspire the generation of a rational strategy to focus on microenvironmental priming for modulation of innate immunity and to improve delivery efficiency of nanoparticles.

Folic acid-functionalized drug delivery platform of resveratrol based on Pluronic 127/D-α-tocopheryl polyethylene glycol 1000 succinate mixed micelles.

A folic acid (FA)-functionalized drug vehicle platform based on Pluronic 127 (P127)/D-α-tocopheryl polyethylene glycol 1000 succinate (TPGS) mixed micelles was orchestrated for an effective delivery of the model drug resveratrol in order to address the problem of poor water solubility and rapid metabolism of resveratrol and improve its targeted accumulation at tumor site. The FA-decorated mixed micelles were prepared using thin-film hydration method and optimized by central composite design approach. The micelles were also characterized in terms of size and morphology, drug entrapment efficiency and in vitro release profile. In addition, the cytotoxicity and cell uptake of the micelles were evaluated in folate receptor-overexpressing MCF-7 cell line. In vivo pharmacokinetic and biodistribution studies were also performed. The average size of the micelles was ~20 nm with a spherical shape and high encapsulation efficiency (99.67%). The results of fluorescence microscopy confirmed the targeting capability of FA-conjugated micelles in MCF-7 cells. FA-modified micelles exhibited superior pharmacokinetics in comparison with that of solution. Further, the low accumulation of resveratrol-loaded FA micelles formulation in the heart and kidney avoided toxicity of these vital organs. It could be concluded that folate-modified P127/TPGS mixed micelles might serve as a potential delivery platform for resveratrol.