Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
Browse
15 results
Search Results
Item Systematic Reanalysis of KMTNet Microlensing Events. II. Two New Planets in Giant-source Events(IOP Publishing on behalf of the American Astronomical Society., 2025-06-01) Yang H; Yee JC; Zhang J; Lee C-U; Kim D-J; Bond IA; Udalski A; Hwang K-H; Zang W; Qian Q; Gould A; Mao S; Albrow MD; Chung S-J; Han C; Jung YK; Ryu Y-H; Shin I-G; Shvartzvald Y; Cha S-M; Kim H-W; Kim S-L; Lee D-J; Lee Y; Park B-G; Pogge RW; Abe F; Bando K; Bennett DP; Bhattacharya A; Fukui A; Hamada R; Hamada S; Hamasaki N; Hirao Y; Silva SI; Itow Y; Koshimoto N; Matsubara Y; Miyazaki S; Muraki Y; Nagai T; Nunota K; Olmschenk G; Ranc C; Rattenbury NJ; Satoh Y; Sumi T; Suzuki D; Terry SK; Tristram PJ; Vandorou A; Yama H; Mróz P; Skowron J; Poleski R; Szymański MK; Soszyński I; Pietrukowicz P; Kozłowski S; Ulaczyk K; Rybicki KA; Iwanek P; Wrona MIn this work, we continue to apply the updated KMTNet tender-love care photometric pipeline to historical microlensing events. We apply the pipeline to a subsample of events from the KMTNet database, which we refer to as the giant source sample. Leveraging the improved photometric data, we conduct a systematic search for anomalies within this sample. The search successfully uncovers four new planet-like anomalies and recovers two previously known planetary signals. After detailed analysis, two of the newly discovered anomalies are confirmed as clear planets: KMT-2019-BLG-0578 and KMT-2021-BLG-0736. Their planet-to-host mass ratios are q ∼ 4 × 10−3 and q ∼ 1 × 10−4, respectively. Another event, OGLE-2018-BLG-0421 (KMT-2018-BLG-0831), remains ambiguous. Both a stellar companion and a giant planet in the lens system could potentially explain the observed anomaly. The anomaly signal of the last event, MOA-2022-BLG-038 (KMT-2022-BLG-2342), is attributed to an extra source star. Within this sample, our procedure doubles the number of confirmed planets, demonstrating a significant enhancement in the survey sensitivity.Item 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 JUnderstanding 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.Item Systematic reanalysis of KMTNet microlensing events, paper I: Updates of the photometry pipeline and a new planet candidate(Oxford University Press on behalf of the Royal Astronomical Society., 2024-02-01) Yang H; Yee JC; Hwang K-H; Qian Q; Bond IA; Gould A; Hu Z; Zhang J; Mao S; Zhu W; Albrow MD; Chung S-J; Kim S-L; Park B-G; Han C; Jung YK; Ryu Y-H; Shin I-G; Shvartzvald Y; Cha S-M; Kim D-J; Kim H-W; Lee C-U; Lee D-J; Lee Y; Pogge RW; Zang W; Abe F; Barry R; Bennett DP; Bhattacharya A; Donachie M; Fujii H; Fukui A; Hirao Y; Itow Y; Kirikawa R; Kondo I; Koshimoto N; Silva SI; Li MCA; Matsubara Y; Muraki Y; Suzuki D; Tristram PJ; Yonehara A; Ranc C; Miyazaki S; Olmschenk G; Rattenbury NJ; Satoh Y; Shoji H; Sumi T; Tanaka Y; Yamawaki TIn this work, we update and develop algorithms for KMTNet tender-love care (TLC) photometry in order to create a new, mostly automated, TLC pipeline. We then start a project to systematically apply the new TLC pipeline to the historic KMTNet microlensing events, and search for buried planetary signals. We report the discovery of such a planet candidate in the microlensing event MOA-2019-BLG-421/KMT-2019-BLG-2991. The anomalous signal can be explained by either a planet around the lens star or the orbital motion of the source star. For the planetary interpretation, despite many degenerate solutions, the planet is most likely to be a Jovian planet orbiting an M or K dwarf, which is a typical microlensing planet. The discovery proves that the project can indeed increase the sensitivity of historic events and find previously undiscovered signals.Item DeepSIM: a novel deep learning method for graph similarity computation(Springer-Verlag GmbH, 2024-01) Liu B; Wang Z; Zhang J; Wu J; Qu GAbstract: 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.Item 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 JPURPOSE: 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.Item Genomic Insights Into Clinical Shiga Toxin-Producing Escherichia coli Strains: A 15-Year Period Survey in Jönköping, Sweden(Frontiers Media S.A., 2021-02-05) Bai X; Zhang J; Hua Y; Jernberg C; Xiong Y; French N; Löfgren S; Hedenström I; Ambikan A; Mernelius S; Matussek A; González-Escalona NShiga toxin-producing Escherichia coli (STEC) are important foodborne pathogens that can cause human infections ranging from asymptomatic carriage to bloody diarrhea (BD) and fatal hemolytic uremic syndrome (HUS). However, the molecular mechanism of STEC pathogenesis is not entirely known. Here, we demonstrated a large scale of molecular epidemiology and in-depth genomic study of clinical STEC isolates utilizing clinical and epidemiological data collected in Region Jönköping County, Sweden, over a 15-year period. Out of 184 STEC isolates recovered from distinct patients, 55 were from patients with BD, and 129 were from individuals with non-bloody stools (NBS). Five individuals developed HUS. Adults were more associated with BD. Serotypes O157:H7, O26:H11, O103:H2, O121:H19, and O104:H4 were more often associated with BD. The presence of Shiga toxin-encoding gene subtypes stx 2a, stx 2a + stx 2c, and stx 1a + stx 2c was associated with BD, while stx 1 a was associated with milder disease. Multiplex virulence and accessory genes were correlated with BD; these genes encode toxins, adhesion, autotransporters, invasion, and secretion system. A number of antimicrobial resistance (AMR) genes, such as aminoglycoside, aminocoumarin, macrolide, and fluoroquinolone resistance genes, were prevalent among clinical STEC isolates. Whole-genome phylogeny revealed that O157 and non-O157 STEC isolates evolved from distinct lineages with a few exceptions. Isolates from BD showed more tendency to cluster closely. In conclusion, this study unravels molecular trait of clinical STEC strains and identifies genetic factors associated with severe clinical outcomes, which could contribute to management of STEC infections and disease progression if confirmed by further functional validation.Item The Genetic Relatedness and Antimicrobial Resistance Patterns of Mastitis-Causing Staphylococcus aureus Strains Isolated from New Zealand Dairy Cattle(MDPI (Basel, Switzerland), 2021-11-22) Greening SS; Zhang J; Midwinter AC; Wilkinson DA; McDougall S; Gates MC; French NP; Butaye PStaphylococcus aureus is one of the leading causes of bovine mastitis worldwide and is a common indication for use of antimicrobials on dairy farms. This study aims to investigate the association between on-farm antimicrobial usage and the antimicrobial resistance (AMR) profiles of mastitis-causing S. aureus. Whole-genome sequencing was performed on 57 S. aureus isolates derived from cows with either clinical or subclinical mastitis from 17 dairy herds in New Zealand. The genetic relatedness between isolates was examined using the core single nucleotide polymorphism alignment whilst AMR and virulence genes were identified in-silico. The association between gene presence-absence and sequence type (ST), antimicrobial susceptibility and dry cow therapy treatment was investigated using Scoary. Altogether, eight STs were identified with 61.4% (35/57) belonging to ST-1. Furthermore, 14 AMR-associated genes and 76 virulence-associated genes were identified, with little genetic diversity between isolates belonging to the same ST. Several genes including merR1 which is thought to play a role in ciprofloxacin-resistance were found to be significantly overrepresented in isolates sampled from herds using ampicillin/cloxacillin dry cow therapy. Overall, the presence of resistance genes remains low and current antimicrobial usage patterns do not appear to be driving AMR in S. aureus associated with bovine mastitis.Item Whole-genome sequencing and ad hoc shared genome analysis of Staphylococcus aureus isolates from a New Zealand primary school(Springer Nature Limited, 2021-10-13) Scott P; Zhang J; Anderson T; Priest PC; Chambers S; Smith H; Murdoch DR; French N; Biggs PJEpidemiological studies of communicable diseases increasingly use large whole-genome sequencing (WGS) datasets to explore the transmission of pathogens. It is important to obtain an initial overview of datasets and identify closely related isolates, but this can be challenging with large numbers of isolates and imperfect sequencing. We used an ad hoc whole-genome multi locus sequence typing method to summarise data from a longitudinal study of Staphylococcus aureus in a primary school in New Zealand. Each pair of isolates was compared and the number of genes where alleles differed between isolates was tallied to produce a matrix of "allelic differences". We plotted histograms of the number of allelic differences between isolates for: all isolate pairs; pairs of isolates from different individuals; and pairs of isolates from the same individual. 340 sequenced isolates were included, and the ad hoc shared genome contained 445 genes. There were between 0 and 420 allelic differences between isolate pairs and the majority of pairs had more than 260 allelic differences. We found many genetically closely related S. aureus isolates from single individuals and a smaller number of closely-related isolates from separate individuals. Multiple S. aureus isolates from the same individual were usually very closely related or identical over the ad hoc shared genome. Siblings carried genetically similar, but not identical isolates. An ad hoc shared genome approach to WGS analysis can accommodate imperfect sequencing of the included isolates, and can provide insights into relationships between isolates in epidemiological studies with large WGS datasets containing diverse isolates.Item Increased precipitation enhances soil respiration in a semi-arid grassland on the Loess Plateau, China(PeerJ Inc., 2021-02-02) Wang Y; Xie Y; Rapson G; Ma H; Jing L; Zhang Y; Zhang J; Li J; Zhu BBACKGROUND: Precipitation influences the vulnerability of grassland ecosystems, especially upland grasslands, and soil respiration is critical for carbon cycling in arid grassland ecosystems which typically experience more droughty conditions. METHODS: We used three precipitation treatments to understand the effect of precipitation on soil respiration of a typical arid steppe in the Loess Plateau in north-western China. Precipitation was captured and relocated to simulate precipitation rates of 50%, 100%, and 150% of ambient precipitation. RESULTS AND DISCUSSION: Soil moisture was influenced by all precipitation treatments. Shoot biomass was greater, though non-significantly, as precipitation increased. However, both increase and decrease of precipitation significantly reduced root biomass. There was a positive linear relationship between soil moisture and soil respiration in the study area during the summer (July and August), when most precipitation fell. Soil moisture, soil root biomass, pH, and fungal diversity were predictors of soil respiration based on partial least squares regression, and soil moisture was the best of these. CONCLUSION: Our study highlights the importance of increased precipitation on soil respiration in drylands. Precipitation changes can cause significant alterations in soil properties, microbial fungi, and root biomass, and any surplus or transpired moisture is fed back into the climate, thereby affecting the rate of soil respiration in the future.Item Transmission dynamics of an antimicrobial resistant Campylobacter jejuni lineage in New Zealand’s commercial poultry network(Elsevier B.V, 2021-12) Greening SS; Zhang J; Midwinter AC; Wilkinson DA; Fayaz A; Williamson DA; Anderson MJ; Gates MC; French NPUnderstanding the relative contribution of different between-farm transmission pathways is essential in guiding recommendations for mitigating disease spread. This study investigated the association between contact pathways linking poultry farms in New Zealand and the genetic relatedness of antimicrobial resistant Campylobacter jejuni Sequence Type 6964 (ST-6964), with the aim of identifying the most likely contact pathways that contributed to its rapid spread across the industry. Whole-genome sequencing was performed on 167C. jejuni ST-6964 isolates sampled from across 30 New Zealand commercial poultry enterprises. The genetic relatedness between isolates was determined using whole genome multilocus sequence typing (wgMLST). Permutational multivariate analysis of variance and distance-based linear models were used to explore the strength of the relationship between pairwise genetic associations among the C. jejuni isolates and each of several pairwise distance matrices, indicating either the geographical distance between farms or the network distance of transportation vehicles. Overall, a significant association was found between the pairwise genetic relatedness of the C. jejuni isolates and the parent company, the road distance and the network distance of transporting feed vehicles. This result suggests that the transportation of feed within the commercial poultry industry as well as other local contacts between flocks, such as the movements of personnel, may have played a significant role in the spread of C. jejuni. However, further information on the historical contact patterns between farms is needed to fully characterise the risk of these pathways and to understand how they could be targeted to reduce the spread of C. jejuni.