Altered developmental programming of the mouse mammary gland in female offspring following perinatal dietary exposures : a systems-biology perspective.

Mishaps in prenatal development can influence mammary gland development and, ultimately, affect susceptibility to factors that cause breast cancer. This research was based on the underlying hypothesis that maternal dietary composition during pregnancy can alter developmental (fetal) programming of the mammary gland. We used a computational systems-biology approach and Bayesian-based stochastic search variable selection algorithm (SSVS) to identify differentially expressed genes and biological themes and pathways. Postnatal growth trajectories and gene expression in the mammary gland at 10-weeks of age in female mice were investigated following different maternal diet exposures during prenatal-lactational-early-juvenile development. This correlated a decrease in expression of energy pathways with a reciprocal increase in cytokine and inflammatory-signaling pathways. These findings suggest maternal dietary fat exposure significantly influences postnatal growth trajectories, metabolic programming, and signaling networks in the mammary gland of female offspring. In addition, the adipocytokine pathway may be a sensitive trigger to dietary changes and may influence or enhance activation of an immune response, a key event in cancer development

Internet of things (IoT) based adaptive energy management system for smart homes

PhD ThesisInternet of things enhances the flexibility of measurements under different environments, the development of advanced wireless sensors and communication networks on the smart grid infrastructure would be essential for energy efficiency systems. It makes deployment of a smart home concept easy and realistic. The smart home concept allows residents to control, monitor and manage their energy consumption with minimal wastage. The scheduling of energy usage enables forecasting techniques to be essential for smart homes. This thesis presents a self-learning home management system based on machine learning techniques and energy management system for smart homes. Home energy management system, demand side management system, supply side management system, and power notification system are the major components of the proposed self-learning home management system. The proposed system has various functions including price forecasting, price clustering, power forecasting alert, power consumption alert, and smart energy theft system to enhance the capabilities of the self-learning home management system. These functions were developed and implemented through the use of computational and machine learning technologies. In order to validate the proposed system, real-time power consumption data were collected from a Singapore smart home and a realistic experimental case study was carried out. The case study had proven that the developed system performing well and increased energy awareness to the residents. This proposed system also showcases its customizable ability according to different types of environments as compared to traditional smart home models. Forecasting systems for the electricity market generation have become one of the foremost research topics in the power industry. It is essential to have a forecasting system that can accurately predict electricity generation for planning and operation in the electricity market. This thesis also proposed a novel system called multi prediction system and it is developed based on long short term memory and gated recurrent unit models. This proposed system is able to predict the electricity market generation with high accuracy. Multi Prediction System is based on four stages which include a data collecting and pre-processing module, a multi-input feature model, multi forecast model and mean absolute percentage error. The data collecting and pre-processing module preprocess the real-time data using a window method. Multi-input feature model uses single input feeding method, double input feeding method and multiple feeding method for features input to the multi forecast model. Multi forecast model integrates long short term memory and gated recurrent unit variations such as regression model, regression with time steps model, memory between batches model and stacked model to predict the future generation of electricity. The mean absolute percentage error calculation was utilized to evaluate the accuracy of the prediction. The proposed system achieved high accuracy results to demonstrate its performance

Computational Tools for the Processing and Analysis of Time-course Metabolomic Data

Modern, high-throughput techniques for the acquisition of metabolomic data, combined with an increase in computational power, have provided not only the need for, but also the means to develop and use, methods for the interpretation of large and complex datasets. This thesis investigates the methods by which pertinent information can be extracted from nontargeted metabolomic data and reviews the current state of chemometric methods. The analysis of real-world data and research questions relevant to the agri-food industry reveals several problems for which novel solutions are proposed. Three LC-MS datasets are studied: Medicago, Alopecurus and aged Beef, covering stress resistance, herbicide resistance and product misbranding. The new methods include preprocessing (batch correction, data-filtering), processing (clustering, classification) and visualisation and their use facilitated within a flexible data-to-results pipeline. The resulting software suite with a user-friendly graphical interface is presented, providing a pragmatic realisation of these methods in an easy to access workflow

Post-weaning blood transcriptomic differences between Yorkshire pigs divergently selected for residual feed intake

Meta data for RNA-seq and RT-qPCR assays. (XLSX 20 kb

Peripheral blood gene expression signature differentiates children with autism from unaffected siblings

Autism spectrum disorder (ASD) is one of the most prevalent neurodevelopmental disorders with high heritability, yet a majority of genetic contribution to pathophysiology is not known. Siblings of individuals with ASD are at increased risk for ASD and autistic traits, but the genetic contribution for simplex families is estimated to be less when compared to multiplex families. To explore the genomic (dis-) similarity between proband and unaffected sibling in simplex families, we used genome-wide gene expression profiles of blood from 20 proband-unaffected sibling pairs and 18 unrelated control individuals. The global gene expression profiles of unaffected siblings were more similar to those from probands as they shared genetic and environmental background. A total of 189 genes were significantly differentially expressed between proband-sib pairs (nominal p < 0.01) after controlling for age, sex, and family effects. Probands and siblings were distinguished into two groups by cluster analysis with these genes. Overall, unaffected siblings were equally distant from the centroid of probands and from that of unrelated controls with the differentially expressed genes. Interestingly, five of 20 siblings had gene expression profiles that were more similar to unrelated controls than to their matched probands. In summary, we found a set of genes that distinguished probands from the unaffected siblings, and a subgroup of unaffected siblings who were more similar to probands. The pathways that characterized probands compared to siblings using peripheral blood gene expression profiles were the up-regulation of ribosomal, spliceosomal, and mitochondrial pathways, and the down-regulation of neuroreceptor-ligand, immune response and calcium signaling pathways. Further integrative study with structural genetic variations such as de novo mutations, rare variants, and copy number variations would clarify whether these transcriptomic changes are structural or environmental in origin.Simons Foundatio

Integrating genome-wide co-association and gene expression to identify putative regulators and predictors of feed efficiency in pigs

International audienceAbstractBackgroundFeed efficiency (FE) has a major impact on the economic sustainability of pig production. We used a systems-based approach that integrates single nucleotide polymorphism (SNP) co-association and gene-expression data to identify candidate genes, biological pathways, and potential predictors of FE in a Duroc pig population.ResultsWe applied an association weight matrix (AWM) approach to analyse the results from genome-wide association studies (GWAS) for nine FE associated and production traits using 31K SNPs by defining residual feed intake (RFI) as the target phenotype. The resulting co-association network was formed by 829 SNPs. Additive effects of this SNP panel explained 61% of the phenotypic variance of RFI, and the resulting phenotype prediction accuracy estimated by cross-validation was 0.65 (vs. 0.20 using pedigree-based best linear unbiased prediction and 0.12 using the 31K SNPs). Sixty-eight transcription factor (TF) genes were identified in the co-association network; based on the lossless approach, the putative main regulators were COPS5, GTF2H5, RUNX1, HDAC4, ESR1, USP16, SMARCA2 and GTF2F2. Furthermore, gene expression data of the gluteus medius muscle was explored through differential expression and multivariate analyses. A list of candidate genes showing functional and/or structural associations with FE was elaborated based on results from both AWM and gene expression analyses, and included the aforementioned TF genes and other ones that have key roles in metabolism, e.g. ESRRG, RXRG, PPARGC1A, TCF7L2, LHX4, MAML2, NFATC3, NFKBIZ, TCEA1, CDCA7L, LZTFL1 or CBFB. The most enriched biological pathways in this list were associated with behaviour, immunity, nervous system, and neurotransmitters, including melatonin, glutamate receptor, and gustation pathways. Finally, an expression GWAS allowed identifying 269 SNPs associated with the candidate genes’ expression (eSNPs). Addition of these eSNPs to the AWM panel of 829 SNPs did not improve the accuracy of genomic predictions.ConclusionsCandidate genes that have a direct or indirect effect on FE-related traits belong to various biological processes that are mainly related to immunity, behaviour, energy metabolism, and the nervous system. The pituitary gland, hypothalamus and thyroid axis, and estrogen signalling play fundamental roles in the regulation of FE in pigs. The 829 selected SNPs explained 61% of the phenotypic variance of RFI, which constitutes a promising perspective for applying genetic selection on FE relying on molecular-based prediction

Group technology: amalgamation with design of organisational structures

Group technology has been studied extensively from an ‘industrial engineering’ perspective (layout, scheduling, workflow, etc.), but less often from an organisational design viewpoint. To study this implication of group technology, the approach of applied systems theory for the design of organisational structures was used as framework for analysis in three empirical cases. To increase the reliability of findings from the analysis of these three empirical cases, five more cases were drawn from archival search. Cluster analysis and product flow analysis were the methods used for forming groups of machines and employees in manufacturing cells, whereas the coding of parts was not employed to this end. Furthermore, the results indicate that the implementation of group technology generally meets shifts in performance requirements caused by competitive pressures, particularly flexibility and responsiveness, albeit the companies considered group technology only when under pressure of ‘poor’ business performance. However, group technology is not always a solution to challenges that companies experience; one empirical case shows that defunctionalisation and scheduling with virtual groups was more beneficial. Nevertheless, when the introduction of group technology is feasible, it also allows firms to consider delegating responsibility for production planning and scheduling to lower levels in the hierarchy and semi-autonomous groups as an alternative to ‘complex’ software applications (a socio-technical approach). Whereas the current study sheds light on the relationship between group technology and design of organisational structures, further research is necessary into the design of these structures and their relationship to group technology