Massey Research Online - Browsing by Author "Vignes M"

Browsing by Author "Vignes M"

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A multi-objective genetic algorithm to find active modules in multiplex biological networks
(PLOS, 2021-08-30) Novoa-Del-Toro EM; Mezura-Montes E; Vignes M; Térézol M; Magdinier F; Tichit L; Baudot A; Jensen P
The identification of subnetworks of interest-or active modules-by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease.
A novel Bayesian Latent Class Model (BLCM) evaluates multiple continuous and binary tests: A case study for Brucella abortus in dairy cattle.
(Elsevier B.V., 2024-03-01) Wang Y; Vallée E; Compton C; Heuer C; Guo A; Wang Y; Zhang Z; Vignes M
Bovine brucellosis, primarily caused by Brucella abortus, severely affects both animal health and human well-being. Accurate diagnosis is crucial for designing informed control and prevention measures. Lacking a gold standard test makes it challenging to determine optimal cut-off values and evaluate the diagnostic performance of tests. In this study, we developed a novel Bayesian Latent Class Model that integrates both binary and continuous testing outcomes, incorporating additional fixed (parity) and random (farm) effects, to calibrate optimal cut-off values by maximizing Youden Index. We tested 651 serum samples collected from six dairy farms in two regions of Henan Province, China with four serological tests: Rose Bengal Test, Serum Agglutination Test, Fluorescence Polarization Assay, and Competitive Enzyme-Linked Immunosorbent Assay. Our analysis revealed that the optimal cut-off values for FPA and C-ELISA were 94.2 mP and 0.403 PI, respectively. Sensitivity estimates for the four tests ranged from 69.7% to 89.9%, while specificity estimates varied between 97.1% and 99.6%. The true prevalences in the two study regions in Henan province were 4.7% and 30.3%. Parity-specific odds ratios for positive serological status ranged from 1.2 to 2.2 for different parity groups compared to primiparous cows. This approach provides a robust framework for validating diagnostic tests for both continuous and discrete tests in the absence of a gold standard test. Our findings can enhance our ability to design targeted disease detection strategies and implement effective control measures for brucellosis in Chinese dairy farms.
Centrality statistics of symptom networks of schizophrenia: a systematic review
(Cambridge University Press, 2024-01-04) Buchwald K; Narayanan A; Siegert RJ; Vignes M; Arrowsmith K; Sandham M
The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature. More specifically, we will focus on centrality statistics. Thirty-two studies met the inclusion criteria. The results suggest that cognition, and social, and occupational functioning are central to the network of symptoms. Positive symptoms, particularly delusions were central among participants in many studies that did not include cognitive assessment. Nodes representing cognition were most central in those studies that did. Nodes representing negative symptoms were not as central as items measuring positive symptoms. Some studies that included measures of mood and affect found items or subscales measuring depression were central nodes in the networks. Cognition, and social, and occupational functioning appear to be core symptoms of schizophrenia as they are more central in the networks, compared to variables assessing positive symptoms. This seems consistent despite heterogeneity in the design of the studies.
Evaluating approaches to identifying research supporting the United Nations Sustainable Development Goals
(The MIT Press, 2024-05-01) Kashnitsky Y; Roberge G; Mu J; Kang K; Wang W; Vanderfeesten M; Rivest M; Chamezopoulos S; Jaworek R; Vignes M; Jayabalasingham B; Boonen F; James C; Doornenbal M; Labrosse I; Larivière V
The United Nations (UN) Sustainable Development Goals (SDGs) challenge the global community to build a world where no one is left behind. Recognizing that research plays a fundamental part in supporting these goals, attempts have been made to classify research publications according to their relevance in supporting each of the UN’s SDGs. In this paper, we outline the methodology that we followed when mapping research articles to SDGs and which is adopted by Times Higher Education in its Social Impact rankings. We compare our solution with other existing queries and models mapping research papers to SDGs. We also discuss various aspects in which the methodology can be improved and generalized to other types of content apart from research articles. The results presented in this paper are the outcome of the SDG Research Mapping Initiative, which was established as a partnership between the University of Southern Denmark, the Aurora European Universities Alliance (represented by Vrije Universiteit Amsterdam), the University of Auckland, and Elsevier to bring together broad expertise and share best practices on identifying research contributions to UN’s Sustainable Development Goals.
GADAG: A genetic algorithm for learning directed acyclic graphs
(2017-04-11) Champion M; Picheny V; Vignes M
Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm.
Identifying Health Status in Grazing Dairy Cows from Milk Mid-Infrared Spectroscopy by Using Machine Learning Methods
(MDPI (Basel, Switzerland), 2021-08) Contla Hernández B; Lopez-Villalobos N; Vignes M; Van Winden S
The early detection of health problems in dairy cattle is crucial to reduce economic losses. Mid-infrared (MIR) spectrometry has been used for identifying the composition of cow milk in routine tests. As such, it is a potential tool to detect diseases at an early stage. Partial least squares discriminant analysis (PLS-DA) has been widely applied to identify illness such as lameness by using MIR spectrometry data. However, this method suffers some limitations. In this study, a series of machine learning techniques-random forest, support vector machine, neural network (NN), convolutional neural network and ensemble models-were used to test the feasibility of identifying cow sickness from 1909 milk sample MIR spectra from Holstein-Friesian, Jersey and crossbreed cows under grazing conditions. PLS-DA was also performed to compare the results. The sick cow records had a time window of 21 days before and 7 days after the milk sample was analysed. NN showed a sensitivity of 61.74%, specificity of 97% and positive predicted value (PPV) of nearly 60%. Although the sensitivity of the PLS-DA was slightly higher than NN (65.6%) the specificity and PPV were lower (79.59% and 15.25%, respectively). This indicates that by using NN, it is possible to identify a health problem with a reasonable level of accuracy.
Infrared spectroscopy of serum fails to identify early biomarker changes in an equine model of traumatic osteoarthritis
(Elsevier Ltd on behalf of Osteoarthritis Research Society International (OARSI), 2022-12) Panizzi L; Vignes M; Dittmer KE; Waterland MR; Rogers CW; Sano H; McIlwraith CW; Pemberton S; Owen M; Riley CB
OBJECTIVE: to determine the accuracy of infrared (IR)-based serum biomarker profiling to differentiate horses with early inflammatory changes associated with a traumatically induced model of equine carpal osteoarthritis (OA) from controls. METHOD: unilateral carpal OA was induced in 9 of 17 healthy Thoroughbred fillies, while the remainder served as sham operated controls. Serum samples were obtained before induction of OA (Day 0) and weekly thereafter until Day 63 from both groups. Films of dried serum were created, and IR absorbance spectra acquired. Following pre-processing, partial least squares discriminant analysis (PLSDA) and principal component analysis (PCA) were used to assess group and time differences and generate predictive models for wavenumber ranges 1300-1800 cm-1 and 2600-3700 cm-1. RESULTS: the overall correct classification rate when classifying samples by group (OA or Sham) was 52.7% (s.d. = 12.8%), while it was 94.0% (s.d. = 1.4%) by sampling Day. The correct classification results by group-sampling Day combinations with pre-intervention serum (Day 0) was 50.5% (s.d. = 21.7%). CONCLUSION: with the current approach IR spectroscopic analysis could not differentiate serum of horses with induced carpal OA from that of controls. The high classification rate obtained by Day of sampling may reflect the effect of exercise on the biomarker profile. A longer study period (advanced disease) or naturally occurring disease may provide further information on the suitability of this technique in horses.
Infrared Spectroscopy of Synovial Fluid Shows Accuracy as an Early Biomarker in an Equine Model of Traumatic Osteoarthritis
(MDPI (Basel, Switzerland), 2024-03-22) Panizzi L; Vignes M; Dittmer KE; Waterland MR; Rogers CW; Sano H; McIlwraith CW; Riley CB; Kaneps AJ
Osteoarthritis is a leading cause of lameness and joint disease in horses. A simple, economical, and accurate diagnostic test is required for routine screening for OA. This study aimed to evaluate infrared (IR)-based synovial fluid biomarker profiling to detect early changes associated with a traumatically induced model of equine carpal osteoarthritis (OA). Unilateral carpal OA was induced arthroscopically in 9 of 17 healthy thoroughbred fillies; the remainder served as Sham-operated controls. The median age of both groups was 2 years. Synovial fluid (SF) was obtained before surgical induction of OA (Day 0) and weekly until Day 63. IR absorbance spectra were acquired from dried SF films. Following spectral pre-processing, predictive models using random forests were used to differentiate OA, Sham, and Control samples. The accuracy for distinguishing between OA and any other joint group was 80%. The classification accuracy by sampling day was 87%. For paired classification tasks, the accuracies by joint were 75% for OA vs. OA Control and 70% for OA vs. Sham. The accuracy for separating horses by group (OA vs. Sham) was 68%. In conclusion, SF IR spectroscopy accurately discriminates traumatically induced OA joints from controls.
Investigating the genetic components of tuber bruising in a breeding population of tetraploid potatoes
(BioMed Central Ltd, 2023-05-05) Angelin-Bonnet O; Thomson S; Vignes M; Biggs PJ; Monaghan K; Bloomer R; Wright K; Baldwin S
BACKGROUND: Tuber bruising in tetraploid potatoes (Solanum tuberosum) is a trait of economic importance, as it affects tubers' fitness for sale. Understanding the genetic components affecting tuber bruising is a key step in developing potato lines with increased resistance to bruising. As the tetraploid setting renders genetic analyses more complex, there is still much to learn about this complex phenotype. Here, we used capture sequencing data on a panel of half-sibling populations from a breeding programme to perform a genome-wide association analysis (GWAS) for tuber bruising. In addition, we collected transcriptomic data to enrich the GWAS results. However, there is currently no satisfactory method to represent both GWAS and transcriptomics analysis results in a single visualisation and to compare them with existing knowledge about the biological system under study. RESULTS: When investigating population structure, we found that the STRUCTURE algorithm yielded greater insights than discriminant analysis of principal components (DAPC). Importantly, we found that markers with the highest (though non-significant) association scores were consistent with previous findings on tuber bruising. In addition, new genomic regions were found to be associated with tuber bruising. The GWAS results were backed by the transcriptomics differential expression analysis. The differential expression notably highlighted for the first time the role of two genes involved in cellular strength and mechanical force sensing in tuber resistance to bruising. We proposed a new visualisation, the HIDECAN plot, to integrate the results from the genomics and transcriptomics analyses, along with previous knowledge about genomic regions and candidate genes associated with the trait. CONCLUSION: This study offers a unique genome-wide exploration of the genetic components of tuber bruising. The role of genetic components affecting cellular strength and resistance to physical force, as well as mechanosensing mechanisms, was highlighted for the first time in the context of tuber bruising. We showcase the usefulness of genomic data from breeding programmes in identifying genomic regions whose association with the trait of interest merit further investigation. We demonstrate how confidence in these discoveries and their biological relevance can be increased by integrating results from transcriptomics analyses. The newly proposed visualisation provides a clear framework to summarise of both genomics and transcriptomics analyses, and places them in the context of previous knowledge on the trait of interest.
Plasma and Synovial Fluid Cell-Free DNA Concentrations Following Induction of Osteoarthritis in Horses
(MDPI (Basel, Switzerland), 2023-03-14) Panizzi L; Dittmer KE; Vignes M; Doucet JS; Gedye K; Waterland MR; Rogers CW; Sano H; McIlwraith CW; Riley CB; Zucca E
Biomarkers for osteoarthritis (OA) in horses have been extensively investigated, but translation into clinical use has been limited due to cost, limited sensitivity, and practicality. Identifying novel biomarkers that overcome these limitations could facilitate early diagnosis and therapy. This study aimed to compare the concentrations of synovial fluid (SF) and plasma cell-free DNA (cfDNA) over time in control horses with those with induced carpal OA. Following an established model, unilateral carpal OA was induced in 9 of 17 healthy Thoroughbred fillies, while the remainder were sham-operated controls. Synovial fluid and plasma samples were obtained before induction of OA (Day 0) and weekly thereafter until Day 63, and cfDNA concentrations were determined using fluorometry. The SF cfDNA concentrations were significantly higher for OA joints than for sham-operated joints on Days 28 (median 1430 μg/L and 631 μg/L, respectively, p = 0.017) and 63 (median 1537 μg/L and 606 μg/L, respectively, p = 0.021). There were no significant differences in plasma cfDNA between the OA and the sham groups after induction of carpal OA. Plasma cfDNA measurement is not sufficiently sensitive for diagnostic purposes in this induced model of OA. Synovial fluid cfDNA measurement may be used as a biomarker to monitor early disease progression in horses with OA.
The African swine fever modelling challenge: Model comparison and lessons learnt
(Elsevier BV, 2022-09) Ezanno P; Picault S; Bareille S; Beaunée G; Boender GJ; Dankwa EA; Deslandes F; Donnelly CA; Hagenaars TJ; Hayes S; Jori F; Lambert S; Mancini M; Munoz F; Pleydell DRJ; Thompson RN; Vergu E; Vignes M; Vergne T
Robust epidemiological knowledge and predictive modelling tools are needed to address challenging objectives, such as: understanding epidemic drivers; forecasting epidemics; and prioritising control measures. Often, multiple modelling approaches can be used during an epidemic to support effective decision making in a timely manner. Modelling challenges contribute to understanding the pros and cons of different approaches and to fostering technical dialogue between modellers. In this paper, we present the results of the first modelling challenge in animal health - the ASF Challenge - which focused on a synthetic epidemic of African swine fever (ASF) on an island. The modelling approaches proposed by five independent international teams were compared. We assessed their ability to predict temporal and spatial epidemic expansion at the interface between domestic pigs and wild boar, and to prioritise a limited number of alternative interventions. We also compared their qualitative and quantitative spatio-temporal predictions over the first two one-month projection phases of the challenge. Top-performing models in predicting the ASF epidemic differed according to the challenge phase, host species, and in predicting spatial or temporal dynamics. Ensemble models built using all team-predictions outperformed any individual model in at least one phase. The ASF Challenge demonstrated that accounting for the interface between livestock and wildlife is key to increasing our effectiveness in controlling emerging animal diseases, and contributed to improving the readiness of the scientific community to face future ASF epidemics. Finally, we discuss the lessons learnt from model comparison to guide decision making.
Using network analysis to identify factors influencing the heath-related quality of life of parents caring for an autistic child
(Elsevier Ltd., 2024-09-01) Shepherd D; Buchwald K; Siegert RJ; Vignes M
BACKGROUND: Raising an autistic child is associated with increased parenting stress relative to raising typically developing children. Increased parenting stress is associated with lower parent wellbeing, which in turn can negatively impact child wellbeing. AIMS: The current study sought to quantify parenting stress and parent health-related quality of life (HRQOL) in the autism context, and further understand the relationship between them by employing a relatively novel statistical method, Network Analysis. METHODS AND PROCEDURES: This cross-sectional study involved 476 parents of an autistic child. Parents completed an online survey requesting information on parent and child characteristics, parent's perceptions of their autistic child's symptoms and problem behaviours, and assessed their parenting stress and HRQOL. OUTCOMES AND RESULTS: Relative to normative data, parent HRQOL was significantly lower in terms of physical health and mental wellbeing. The structure extracted by the Network Analysis indicated that child age and externalising behaviours were the main contributors to parenting stress, and that externalising behaviours, ASD core behavioural symptoms, and parenting stress predicted HRQOL. CONCLUSIONS AND IMPLICATIONS: Parental responses to child-related factors likely determine parent HRQOL. Findings are discussed in relation to the transactional model, emphasising the importance of both parent and child wellbeing.
Validation of low-cost air quality monitoring platforms using model-based control charts
(Elsevier Ltd, 2024-04-01) Boulic M; Phipps R; Wang Y; Vignes M; Adegoke NA
The SARS COVID-19 pandemic highlighted the importance of routine indoor air quality (IAQ) monitoring. Recent advances in IAQ sensors and remote logging technologies offer opportunities to use low-cost platforms to monitor indoor air. The sensor's accuracy and stability are critical for reliable monitoring and health protection. Data from our low-cost IAQ platform (SKOMOBO) was validated against a commercial platform for carbon dioxide, temperature, and relative humidity measurements to test the reliability of the low-cost instrument. The traditional statistical method to test the variability between two data sets is the coefficient of determination method. We identified that this traditional method did not detect drifts in measurements, when comparing data from two platforms, in a controlled and uncontrolled environment. In our paper, we propose two complementary methods to detect potential drifts in measurements (a modified Shewhart method and a cumulative sum control chart method). The traditional coefficient of determination method indicated strong consistency (between 0.70 and 0.99) in the measurements between SKOMOBO and the reference platforms for both tested environments. Our more sensitive methods detected 100 % data matching for the controlled environment between the SKOMOBO and the reference platform but detected some drifts for the uncontrolled environment (between 81 % and 100 % data matching). It was expected that the uncontrolled environment would create more drifts in measurements than the controlled environment. Our new statistical methods achieved two important results; namely it advanced the validation process and proved the reliability of our low-cost platform for IAQ monitoring and assurance.

Browsing by Author "Vignes M"

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