Browsing by Author "Zhang J"

Now showing 1 - 16 of 16
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    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.
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    DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites.
    (29/06/2022) Zhang J; Liu B; Wang Z; Lehnert K; Gahegan M
    BACKGROUND: Addressing the laborious nature of traditional biological experiments by using an efficient computational approach to analyze RNA-binding proteins (RBPs) binding sites has always been a challenging task. RBPs play a vital role in post-transcriptional control. Identification of RBPs binding sites is a key step for the anatomy of the essential mechanism of gene regulation by controlling splicing, stability, localization and translation. Traditional methods for detecting RBPs binding sites are time-consuming and computationally-intensive. Recently, the computational method has been incorporated in researches of RBPs. Nevertheless, lots of them not only rely on the sequence data of RNA but also need additional data, for example the secondary structural data of RNA, to improve the performance of prediction, which needs the pre-work to prepare the learnable representation of structural data. RESULTS: To reduce the dependency of those pre-work, in this paper, we introduce DeepPN, a deep parallel neural network that is constructed with a convolutional neural network (CNN) and graph convolutional network (GCN) for detecting RBPs binding sites. It includes a two-layer CNN and GCN in parallel to extract the hidden features, followed by a fully connected layer to make the prediction. DeepPN discriminates the RBP binding sites on learnable representation of RNA sequences, which only uses the sequence data without using other data, for example the secondary or tertiary structure data of RNA. DeepPN is evaluated on 24 datasets of RBPs binding sites with other state-of-the-art methods. The results show that the performance of DeepPN is comparable to the published methods. CONCLUSION: The experimental results show that DeepPN can effectively capture potential hidden features in RBPs and use these features for effective prediction of binding sites.
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    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.
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    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.
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    Factors influencing individual ability to follow physical distancing recommendations in Aotearoa New Zealand during the COVID-19 pandemic: a population survey
    (Taylor and Francis Group, 2021-02-14) Gray L; Rose SB; Stanley J; Zhang J; Tassell-Matamua N; Puloka V; Kvalsvig A; Wiles S; Murton SA; Johnston DM; Becker JS; MacDonald C; Baker MG
    Physical distancing (also commonly known as social distancing) is an important non-pharmaceutical strategy to minimise the risk of transmission of SARS-CoV-2 virus. A range of restrictions to promote physical distancing form a key part of the Aotearoa New Zealand (NZ) all-of-government response to the global COVID-19 pandemic. The effectiveness of physical distancing strategies is highly dependent on buy-in and the actions of individuals, households and communities. This NZ population survey was conducted to identify people’s views on the effectiveness of various strategies, and factors impacting on their capacity to follow physical distancing requirements during Alert Levels 4, 3, and 2 (April 24th–June 8th 2020). The majority of the 2407 participants were supportive of the public health measures implemented to promote physical distancing across Alert Levels. Few substantial differences were observed in relation to demographic characteristics, suggesting high overall levels of understanding and willingness to adhere to distancing requirements. Around half of the participants reported difficulties practicing physical distancing when in public. Reasons included being an essential worker and challenges related to the behaviour of others. These survey findings highlight the willingness of NZ’s population to play their part in eliminating COVID-19 transmission, and the way in which behavioural change was rapidly adopted in line with government requirements.
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    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 N
    Shiga 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.
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    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 B
    BACKGROUND: 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.
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    Monomorphic genotypes within a generalist lineage of Campylobacter jejuni show signs of global dispersion
    (Microbiology Society, 1/10/2016) Llarena AK; Zhang J; Vehkala M; Välimäki N; Hakkinen M; Hänninen M; Roasto M; Mäesaar M; Taboada E; Barker D; Garofolo G; Cammà C; Di Giannatale E; Corander J; Ross M
    The decreased costs of genome sequencing have increased the capability to apply whole-genome sequencing to epidemiological surveillance of zoonotic Campylobacter jejuni. However, knowledge of the genetic diversity of this bacteria is vital for inferring relatedness between epidemiologically linked isolates and a necessary prerequisite for correct application of this methodology. To address this issue in C. jejuni we investigated the spatial and temporal signals in the genomes of a major clonal complex and generalist lineage, ST-45 CC, by analysing the population structure and genealogy as well as applying genome-wide association analysis of 340 isolates from across Europe collected over a wide time range. The occurrence and strength of the geographical signal varied between sublineages and followed the clonal frame when present, while no evidence of a temporal signal was found. Certain sublineages of ST-45 formed discrete and genetically isolated clades containing isolates with extremely similar genomes regardless of time and location of sampling. Based on a separate data set, these monomorphic genotypes represent successful C. jejuni clones, possibly spread around the globe by rapid animal (migrating birds), food or human movement. In addition, we observed an incongruence between the genealogy of the strains and multilocus sequence typing (MLST), challenging the existing clonal complex definition and the use of whole-genome gene-by-gene hierarchical nomenclature schemes for C. jejuni.
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    Non-negative Matrix Factorization: A Survey
    (Oxford University Press on behalf of the British Computer Society, 2021-07-01) Gan J; Liu T; Li L; Zhang J
    Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to data mining and machine learning community, due to its advantages such as simple form, good interpretability and less storage space. In this paper, we give a detailed survey on existing NMF methods, including a comprehensive analysis of their design principles, characteristics and drawbacks. In addition, we also discuss various variants of NMF methods and analyse properties and applications of these variants. Finally, we evaluate the performance of nine NMF methods through numerical experiments, and the results show that NMF methods perform well in clustering tasks.
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    Prediction of seasonal population dynamics of Grapholita molesta (Busck) and Adoxophyes orana (Fischer von Röslerstamm) in peach orchards using sex pheromone trap and degree-days and its implications in pest management
    (2023-10-04) Ma A; Zhang H; Ran H; Yang X; J Hao J; Zhang J; Li H; Yu Z; Wang X; He X; Li J
    The successful management of lepidopteran moths in orchards usually depends on the precise forecast of adult activity. However, the seasonal phenology of moths varies between crop cultivars and years, making it difficult to schedule the control measures. Here, we monitored male flight activity of oriental fruit moth Grapholita molesta and summer fruit tortrix moth Adoxophyes orana by using sex pheromone traps in peach orchards of three different cultivars for three successive years. We developed a logistic multiple-peaks model to fit data and then calculated degree-days (DD) required for male activity and neonate emergency. Results show that G. molesta and A. orana males had 4–5 and 3 flight peaks per year, respectively. The seasonal phenology of G. molesta or A. orana was quite stable with an identical timing of each flight peak between cultivars in a year. The flight activity was usually higher in the second and third peaks for both moths, with a higher cumulative number of G. molesta males captured than that of A. orana. Compared to A. orana, G. molesta emerged early in spring and required lower degree-days to reach the subsequent flight peaks and for neonate emergency. Our results suggest that to decline the possibility of outbreaks of moths during the growing seasons, pheromone traps should be scheduled in April with a cumulative DD between 49.6 and 207.1 for G. molesta and in mid-May–early June with a cumulative DD between 450.4 and 866.7 for A. orana, aiming to trap the newly emerged male adults or disrupting female mating success of overwintered moths in orchards. Based on the thermal requirement for egg hatching (i.e., 79.4 DD for G. molesta and 90.0 DD for A. orana), insecticide treatments would be applied in late-April–early May and late May–early June to reduce the field population density of neonates of G. molesta and A. orana, respectively, to reduce fruit damage in orchards. Furthermore, pheromone traps set up in late July–early August (573.8–1025.2 DD) for G. molesta and in mid-September (1539.7–1788.9 DD) for A. orana may suppress overwintering populations and thus decrease pest infestation in next year.
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    Roadmap on signal processing for next generation measurement systems
    (IOP Publishing, 2022-01-01) Iakovidis DK; Ooi M; Kuang YC; Demidenko S; Shestakov A; Sinitsin V; Henry M; Sciacchitano A; Discetti S; Donati S; Norgia M; Menychtas A; Maglogiannis I; Wriessnegger SC; Chacon LAB; Dimas G; Filos D; Aletras AH; Töger J; Dong F; Ren S; Uhl A; Paziewski J; Geng J; Fioranelli F; Narayanan RM; Fernandez C; Stiller C; Malamousi K; Kamnis S; Delibasis K; Wang D; Zhang J; Gao RX
    Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.
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    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 T
    In 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.
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    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 P
    Staphylococcus 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.
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    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 NP
    Understanding 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.
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    Transverse-momentum and pseudorapidity distributions of charged hadrons in pp collisions at square root of s = 7 TeV.
    (AMER PHYSICAL SOC, 9/07/2010) Khachatryan V; Sirunyan AM; Tumasyan A; Adam W; Bergauer T; Dragicevic M; Erö J; Fabjan C; Friedl M; Frühwirth R; Ghete VM; Hammer J; Hänsel S; Hoch M; Hörmann N; Hrubec J; Jeitler M; Kasieczka G; Kiesenhofer W; Krammer M; Liko D; Mikulec I; Pernicka M; Rohringer H; Schöfbeck R; Strauss J; Taurok A; Teischinger F; Waltenberger W; Walzel G; Widl E; Wulz C-E; Mossolov V; Shumeiko N; Suarez Gonzalez J; Benucci L; Ceard L; De Wolf EA; Hashemi M; Janssen X; Maes T; Mucibello L; Ochesanu S; Roland B; Rougny R; Selvaggi M; Van Haevermaet H; Van Mechelen P; Van Remortel N; Adler V; Beauceron S; Blyweert S; D'Hondt J; Devroede O; Kalogeropoulos A; Maes J; Maes M; Tavernier S; Van Doninck W; Van Mulders P; Villella I; Chabert EC; Charaf O; Clerbaux B; De Lentdecker G; Dero V; Gay APR; Hammad GH; Marage PE; Vander Velde C; Vanlaer P; Wickens J; Costantini S; Grunewald M; Klein B; Marinov A; Ryckbosch D; Thyssen F; Tytgat M; Vanelderen L; Verwilligen P; Walsh S; Zaganidis N; Basegmez S; Bruno G; Caudron J; De Favereau De Jeneret J; Delaere C; Demin P; Favart D; Giammanco A; Grégoire G; Hollar J; Lemaitre V; Militaru O; Ovyn S; Pagano D; Pin A; Piotrzkowski K; Quertenmont L; Schul N; Beliy N; Caebergs T; Daubie E; Alves GA; Pol ME; Souza MHG; Carvalho W; Da Costa EM; De Jesus Damiao D; De Oliveira Martins C; Fonseca De Souza S; Mundim L; Oguri V; Santoro A; Silva Do Amaral SM; Sznajder A; Torres Da Silva De Araujo F; Dias FA; Dias MAF; Fernandez Perez Tomei TR; Gregores EM; Marinho F; Novaes SF; Padula SS; Darmenov N; Dimitrov L; Genchev V; Iaydjiev P; Piperov S; Stoykova S; Sultanov G; Trayanov R; Vankov I; Dyulendarova M; Hadjiiska R; Kozhuharov V; Litov L; Marinova E; Mateev M; Pavlov B; Petkov P; Bian JG; Chen GM; Chen HS; Jiang CH; Liang D; Liang S; Wang J; Wang J; Wang X; Wang Z; Yang M; Zang J; Zhang Z; Ban Y; Guo S; Hu Z; Mao Y; Qian SJ; Teng H; Zhu B; Cabrera A; Carrillo Montoya CA; Gomez Moreno B; Ocampo Rios AA; Osorio Oliveros AF; Sanabria JC; Godinovic N; Lelas D; Lelas K; Plestina R; Polic D; Puljak I; Antunovic Z; Dzelalija M; Brigljevic V; Duric S; Kadija K; Morovic S; Attikis A; Fereos R; Galanti M; Mousa J; Nicolaou C; Papadakis A; Ptochos F; Razis PA; Rykaczewski H; Tsiakkouri D; Zinonos Z; Mahmoud M; Hektor A; Kadastik M; Kannike K; Müntel M; Raidal M; Rebane L; Azzolini V; Eerola P; Czellar S; Härkönen J; Heikkinen A; Karimäki V; Kinnunen R; Klem J; Kortelainen MJ; Lampén T; Lassila-Perini K; Lehti S; Lindén T; Luukka P; Mäenpää T; Tuominen E; Tuominiemi J; Tuovinen E; Ungaro D; Wendland L; Banzuzi K; Korpela A; Tuuva T; Sillou D; Besancon M; Dejardin M; Denegri D; Descamps J; Fabbro B; Faure JL; Ferri F; Ganjour S; Gentit FX; Givernaud A; Gras P; Hamel de Monchenault G; Jarry P; Locci E; Malcles J; Marionneau M; Millischer L; Rander J; Rosowsky A; Rousseau D; Titov M; Verrecchia P; Baffioni S; Bianchini L; Bluj M; Broutin C; Busson P; Charlot C; Dobrzynski L; Elgammal S; Granier de Cassagnac R; Haguenauer M; Kalinowski A; Miné P; Paganini P; Sabes D; Sirois Y; Thiebaux C; Zabi A; Agram J-L; Besson A; Bloch D; Bodin D; Brom J-M; Cardaci M; Conte E; Drouhin F; Ferro C; Fontaine J-C; Gelé D; Goerlach U; Greder S; Juillot P; Karim M; Le Bihan A-C; Mikami Y; Speck J; Van Hove P; Fassi F; Mercier D; Baty C; Beaupere N; Bedjidian M; Bondu O; Boudoul G; Boumediene D; Brun H; Chanon N; Chierici R; Contardo D; Depasse P; El Mamouni H; Fay J; Gascon S; Ille B; Kurca T; Le Grand T; Lethuillier M; Mirabito L; Perries S; Tosi S; Tschudi Y; Verdier P; Xiao H; Roinishvili V; Anagnostou G; Edelhoff M; Feld L; Heracleous N; Hindrichs O; Jussen R; Klein K; Merz J; Mohr N; Ostapchuk A; Perieanu A; Raupach F; Sammet J; Schael S; Sprenger D; Weber H; Weber M; Wittmer B; Actis O; Ata M; Bender W; Biallass P; Erdmann M; Frangenheim J; Hebbeker T; Hinzmann A; Hoepfner K; Hof C; Kirsch M; Klimkovich T; Kreuzer P; Lanske D; Magass C; Merschmeyer M; Meyer A; Papacz P; Pieta H; Reithler H; Schmitz SA; Sonnenschein L; Sowa M; Steggemann J; 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Gershtein Y; Gray R; Halkiadakis E; Hidas D; Hits D; Lath A; Rose K; Schnetzer S; Somalwar S; Stone R; Thomas S; Cerizza G; Hollingsworth M; Spanier S; Yang ZC; York A; Asaadi J; Eusebi R; Gilmore J; Gurrola A; Kamon T; Khotilovich V; Montalvo R; Nguyen CN; Pivarski J; Safonov A; Sengupta S; Toback D; Weinberger M; Akchurin N; Bardak C; Damgov J; Jeong C; Kovitanggoon K; Lee SW; Mane P; Roh Y; Sill A; Volobouev I; Wigmans R; Yazgan E; Appelt E; Brownson E; Engh D; Florez C; Gabella W; Johns W; Kurt P; Maguire C; Melo A; Sheldon P; Velkovska J; Arenton MW; Balazs M; Buehler M; Conetti S; Cox B; Hirosky R; Ledovskoy A; Neu C; Yohay R; Gollapinni S; Gunthoti K; Harr R; Karchin PE; Mattson M; Milstène C; Sakharov A; Anderson M; Bachtis M; Bellinger JN; Carlsmith D; Dasu S; Dutta S; Efron J; Gray L; Grogg KS; Grothe M; Hall-Wilton R; Herndon M; Klabbers P; Klukas J; Lanaro A; Lazaridis C; Leonard J; Lomidze D; Loveless R; Mohapatra A; Polese G; Reeder D; Savin A; Smith WH; Swanson J; Weinberg M; CMS Collaboration
    Charged-hadron transverse-momentum and pseudorapidity distributions in proton-proton collisions at square root of s = 7  TeV are measured with the inner tracking system of the CMS detector at the LHC. The charged-hadron yield is obtained by counting the number of reconstructed hits, hit pairs, and fully reconstructed charged-particle tracks. The combination of the three methods gives a charged-particle multiplicity per unit of pseudorapidity dN(ch)/dη|(|η|<0.5) = 5.78 ± 0.01(stat) ± 0.23(syst) for non-single-diffractive events, higher than predicted by commonly used models. The relative increase in charged-particle multiplicity from square root of s = 0.9 to 7 TeV is [66.1 ± 1.0(stat) ± 4.2(syst)]%. The mean transverse momentum is measured to be 0.545 ± 0.005(stat) ± 0.015(syst)  GeV/c. The results are compared with similar measurements at lower energies.
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    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 PJ
    Epidemiological 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.