Massey Research Online - Browsing by Author "Wu J"

Browsing by Author "Wu J"

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Basic Volcanic Elements of the Arxan-Chaihe Volcanic Field, Inner Mongolia, NE China
(inTech Open: Rijeka, Croatia, 2020-10-30) Li B; Nemeth K; Palmer A; Wu J; Procter J; Liu J
The Arxan-Chaihe Volcanic Field, Inner Mongolia, NE China is a Pleistocene to Recent volcanic field still considered to be active. In this chapter we provide an update of current volcanological research conducted in the last four years to describe the volcanic architecture of the identified vents, their eruptive history and potential volcanic hazards. Here we provide an evidence-based summary of the most common volcanic eruption styles and types the field experienced in its evolution. The volcanic field is strongly controlled by older structural elements of the region. Hence most of the volcanoes of the field are fissure-controlled, fissure-aligned and erupted in Hawaiian to Strombolian-style creating lava spatter and scoria cone cone chains. One of the largest and most complex volcano of the field (Tongxin) experienced a violent phreatomagmatic explosive phase creating a maar in an intra-mountain basin, while the youngest known eruptions formed a triple vent set (Yanshan) that reached violent Strombolian phases and created an extensive ash and lapilli plains in the surrounding areas. This complex vent system also emitted voluminous lava flows that change the landscape by damming fluival networks, providing a volcanological paradise for the recently established Arxan UNESCO GLobal Geopark.
DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network
(BioMed Central Ltd, 2023-09-18) Zhang J; Liu B; Wu J; Wang Z; Li J
Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies. However, these techniques suffer from inherent limitations such as time consumption and high costs. To address these challenges, the field of bioinformatics has increasingly turned to deep learning technologies for analyzing gene sequences. Nevertheless, the pursuit of improved experimental results has led to the inclusion of numerous complex analysis function modules, resulting in models with a growing number of parameters. To overcome these limitations, it is proposed a novel approach for analyzing DNA transcription factor sequences, which is named as DeepCAC. This method leverages deep convolutional neural networks with a multi-head self-attention mechanism. By employing convolutional neural networks, it can effectively capture local hidden features in the sequences. Simultaneously, the multi-head self-attention mechanism enhances the identification of hidden features with long-distant dependencies. This approach reduces the overall number of parameters in the model while harnessing the computational power of sequence data from multi-head self-attention. Through training with labeled data, experiments demonstrate that this approach significantly improves performance while requiring fewer parameters compared to existing methods. Additionally, the effectiveness of our approach is validated in accurately predicting DNA transcription factor sequences.
DeepSIM: a novel deep learning method for graph similarity computation
(Springer-Verlag GmbH, 2024-01) Liu B; Wang Z; Zhang J; Wu J; Qu G
Abstract: Graphs are widely used to model real-life information, where graph similarity computation is one of the most significant applications, such as inferring the properties of a compound based on similarity to a known group. Definition methods (e.g., graph edit distance and maximum common subgraph) have extremely high computational cost, and the existing efficient deep learning methods suffer from the problem of inadequate feature extraction which would have a bad effect on similarity computation. In this paper, a double-branch model called DeepSIM was raised to deeply mine graph-level and node-level features to address the above problems. On the graph-level branch, a novel embedding relational reasoning network was presented to obtain interaction between pairwise inputs. Meanwhile, a new local-to-global attention mechanism is designed to improve the capability of CNN-based node-level feature extraction module on another path. In DeepSIM, double-branch outputs will be concatenated as the final feature. The experimental results demonstrate that our methods perform well on several datasets compared to the state-of-the-art deep learning models in related fields.
DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning
(BioMed Central Ltd, 2023-12) Wu J; Liu B; Zhang J; Wang Z; Li J
PURPOSE: Sequenced Protein-Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets. RESULTS: In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.
Does board diversity in industry-experience boost firm value? The role of corporate innovation
(Elsevier, 31/08/2023) Huang P; Lu Y; Wu J
Previous studies examining board diversity disproportionately focus on directors' demographic features. In this paper, we construct a sophisticated measure of board diversity based on directors' industry-experience diversity (BIED) and examine its effect on firm value. Using a sample of S&P1500 firms, we find that higher BIED leads to higher firm value. This result survives both firm fixed effects and an instrumental variable approach, at least partially suggesting a causal relationship. We argue that a more diverse board brings more perspectives, viewpoints, knowledge and information to the firm, enhances directors’ capability of advising, and thus creates higher firm value. We further find one possible underlying economic mechanism through which BIED facilitates value creation. That is, BIED creates value by promoting corporate innovation. Overall, BIED constitutes a valuable corporate governance mechanism.
Entitlement-Based Access Control for Smart Cities Using Blockchain
(MDPI (Basel, Switzerland), 2021-08-04) Sabrina F; Jang-Jaccard J; Dai H-N; Wu J; Wang H
Smart cities use the Internet of Things (IoT) devices such as connected sensors, lights, and meters to collect and analyze data to improve infrastructure, public utilities, and services. However, the true potential of smart cities cannot be leveraged without addressing many security concerns. In particular, there is a significant challenge for provisioning a reliable access control solution to share IoT data among various users across organizations. We present a novel entitlement-based blockchain-enabled access control architecture that can be used for smart cities (and for any ap-plication domains that require large-scale IoT deployments). Our proposed entitlement-based access control model is flexible as it facilitates a resource owner to safely delegate access rights to any entities beyond the trust boundary of an organization. The detailed design and implementation on Ethereum blockchain along with a qualitative evaluation of the security and access control aspects of the proposed scheme are presented in the paper. The experimental results from private Ethereum test networks demonstrate that our proposal can be easily implemented with low latency. This validates that our proposal is applicable to use in the real world IoT environments.
Inactivation of salmonella enterica serovar enteritidis on chicken eggshells using blue light
(MDPI (Basel, Switzerland), 2021-08-10) Hu X; Sun X; Luo S; Wu S; Chu Z; Zhang X; Liu Z; Wu J; Wang X; Liu C; Wang X; Santini A
Salmonella enterica serovar Enteritidis (S. Enteritidis) is a pathogen that poses a health risk. Blue light (BL), an emerging sanitization technology, was employed for the first time in the present study to inactivate S. Enteritidis on eggshell surfaces and its influence on maintaining eggshell freshness was investigated systematically. The results showed that 415 nm-BL irradiation at a dose of 360 J/cm2 reduced 5.19 log CFU/mL of S. Enteritidis in vitro. The test on eggshells inoculated with S. Enteritidis showed that a BL dose at 54.6 J/cm2 caused a 3.73 log CFU reduction per eggshell surface and the impact of BL inactivation could be sustained in post-5-week storage. The quality of the tested eggs (weight loss, yolk index, Haugh unit (HU) and albumen pH) demonstrated that BL treatments had negligible effects on the albumen pH of eggs. However, compared to the control, BL-treated eggs showed lower weight loss and higher HU after 5 weeks of storage at 25◦C and 65% humidity and yolk index in the control group could not be determined after 5 weeks of storage. Besides, the total amino acid content of the BL-treated egg was higher than the control, exhibiting an advantage of BL irradiation in maintaining the nutrient quality of whole eggs. The current study determined the efficacy of BL against S. Enteritidis on eggshell and suggested that BL could be an effective application in maintaining the freshness and quality of eggs.
The complete chloroplast genome of the first registered Paeonia Itoh hybrid cv. Hexie in China.
(Taylor and Francis Group, 2024-06-24) Duan S; Dai R; Hao M; Shrestha DK; Dijkwel PP; Gao K; Wu J; Fan B
The first registered Paeonia Itoh hybrid cv. Hexie in China is a naturally occurring intersectional hybrid of Sect. Paeonia and Sect. Moutan. In this study, we sequenced, assembled, and analyzed the complete chloroplast genome of Paeonia Itoh hybrid cv. Hexie. The result showed that the chloroplast genome of Hexie, with a typical circular tetrad structure, is 152,958 bp in length, comprising a large single copy (LSC) region of 84,613 bp, a small single copy (SSC) region of 17,051 bp, and two reverse complementary sequences (IRs) of 25,647 bp. The chloroplast genome encoded 116 genes, including 80 protein-coding genes, 32 tRNA genes, and 4 rRNA genes. Phylogenetic analysis inferred from the shared protein-coding genes showed that the Paeonia Itoh hybrid cv. Hexie had the closest phylogenetic relationship with P. suffruticosa, followed by P. ostii, indicating that P. suffruticosa was its maternal parent. This study provides a molecular resource for phylogenetic and maternal parent studies of Paeonia Itoh hybrid, contributing to a basis for Paeonia Itoh hybrid breeding strategies in the future.

Browsing by Author "Wu J"

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