In this article, we re-trace the history of the Swedish SF archive and reflect on how this collection of historic newsreels has been reappropriated and remixed through-out more recent media history. In particular, we focus on the work of director and film historian Gardar Sahlberg, who made extensive use of the SF archive, first in a series of documentary films, then in a number of historical TV programmes. We are interested in how historic film footage travels and circulates through time, but foremost we explore how algorithms can help identify instances of audio-visual reuse in large datasets. Hence the article discusses algorithmic ways of examining archival film reuse, introducing a method for mapping video reuse with the help of artificial intelligence or more precisely machine learning that uses so-called convo-lutional neural nets. The article presents the Video Reuse Detector (VRD), a tool that uses machine learning to identify visual similarities within a given audiovisual database such as the SF archive.
Background: Bioconversion of wood into bioproducts and biofuels is hindered by the recalcitrance of woody raw material to bioprocesses such as enzymatic saccharification. Targeted modification of the chemical composition of the feedstock can improve saccharification but this gain is often abrogated by concomitant reduction in tree growth.
Results: In this study, we report on transgenic hybrid aspen (Populus tremula × tremuloides) lines that showed potential to increase biomass production both in the greenhouse and after 5 years of growth in the field. The transgenic lines carried an overexpression construct for Populus tremula × tremuloides vesicle-associated membrane protein (VAMP)-associated protein PttVAP27-17 that was selected from a gene-mining program for novel regulators of wood formation. Analytical-scale enzymatic saccharification without any pretreatment revealed for all greenhouse-grown transgenic lines, compared to the wild type, a 20–44% increase in the glucose yield per dry weight after enzymatic saccharification, even though it was statistically significant only for one line. The glucose yield after enzymatic saccharification with a prior hydrothermal pretreatment step with sulfuric acid was not increased in the greenhouse-grown transgenic trees on a dry-weight basis, but increased by 26–50% when calculated on a whole biomass basis in comparison to the wild-type control. Tendencies to increased glucose yields by up to 24% were present on a whole tree biomass basis after acidic pretreatment and enzymatic saccharification also in the transgenic trees grown for 5 years on the field when compared to the wild-type control.
Conclusions: The results demonstrate the usefulness of gene-mining programs to identify novel genes with the potential to improve biofuel production in tree biotechnology programs. Furthermore, multi-omic analyses, including transcriptomic, proteomic and metabolomic analyses, performed here provide a toolbox for future studies on the function of VAP27 proteins in plants.
Background: Secretory Carrier-Associated Membrane Proteins (SCAMPs) are highly conserved 32–38 kDa proteins that are involved in membrane trafficking. A systems approach was taken to elucidate function of SCAMPs in wood formation of Populus trees. Phenotypic and multi-omics analyses were performed in woody tissues of transgenic Populus trees carrying an RNAi construct for Populus tremula x tremuloides SCAMP3 (PttSCAMP3;Potri.019G104000).
Results: The woody tissues of the transgenic trees displayed increased amounts of both polysaccharides and lignin oligomers, indicating increased deposition of both the carbohydrate and lignin components of the secondary cell walls. This coincided with a tendency towards increased wood density as well as significantly increased thickness of the suberized cork in the transgenic lines. Multivariate OnPLS (orthogonal projections to latent structures) modeling of five different omics datasets (the transcriptome, proteome, GC-MS metabolome, LC-MS metabolome and pyrolysis-GC/MS metabolome) collected from the secondary xylem tissues of the stem revealed systemic variation in the different variables in the transgenic lines, including changes that correlated with the changes in the secondary cell wall composition. The OnPLS model also identified a rather large number of proteins that were more abundant in the transgenic lines than in the wild type. Several of these were related to secretion and/or endocytosis as well as both primary and secondary cell wall biosynthesis.
Conclusions: Populus SCAMP proteins were shown to influence accumulation of secondary cell wall components, including polysaccharides and phenolic compounds, in the woody tissues of Populus tree stems. Our multi-omics analyses combined with the OnPLS modelling suggest that this function is mediated by changes in membrane trafficking to fine-tune the abundance of cell wall precursors and/or proteins involved in cell wall biosynthesis and transport. The data provides a multi-level source of information for future studies on the function of the SCAMP proteins in plant stem tissues.
Integration of multiomics data remains a key challenge in fulfilling the potential of comprehensive systems biology. Multiple-block orthogonal projections to latent structures (OnPLS) is a Multi projection method that simultaneously models multiple data matrices, reducing feature space without relying on a priori biological knowledge. In order to improve the interpretability of OnPLS models, the associated multi-block variable influence on orthogonal projections (MB-VIOP) method is used to identify variables with the highest contribution to the model. This study combined OnPLS and MB-VIOP with interactive visualization methods to interrogate an exemplar multiomics study, using a subset of 22 individuals from an asthma cohort. Joint data structure in six data blocks was assessed: transcriptomics; metabolomics; targeted assays for sphingolipids, oxylipins, and fatty acids; and a clinical block including lung function, immune cell differentials, and cytokines. The model identified seven components, two of which had contributions from all blocks (globally joint structure) and five that had contributions from two to five blocks (locally joint structure). Components 1 and 2 were the most informative, identifying differences between healthy controls and asthmatics and a disease sex interaction, respectively. The interactions between features selected by MB-VIOP were visualized using chord plots, yielding putative novel insights into asthma disease pathogenesis, the effects of asthma treatment, and biological roles of uncharacterized genes. For example, the gene ATP6 V1G1, which has been implicated in osteoporosis, correlated with metabolites that are dysregulated by inhaled corticoid steroids (ICS), providing insight into the mechanisms underlying bone density loss in asthma patients taking ICS. These results show the potential for OnPLS, combined with MB-VIOP variable selection and interaction visualization techniques, to generate hypotheses from multiomics studies and inform biology.
Patents are an important source of technological knowledge, but the amount of existing patents is vast and quickly growing. This makes development of tools and methodologies for quickly revealing patterns in patent collections important. In this paper, we describe how structured chemometric principles of multivariate data analysis can be applied in the context of text analysis in a novel combination with common machine learning preprocessing methodologies. We demonstrate our methodology in 2 case studies. Using principal component analysis (PCA) on a collection of 12338 patent abstracts from 25 companies in big pharma revealed sub-fields which the companies are active in. Using PCA on a smaller collection of patents retrieved by searching for a specific term proved useful to quickly understand how patent classifications relate to the search term. By using orthogonal projections to latent structures (O-PLS) on patent classification schemes, we were able to separate patents on a more detailed level than using PCA. Lastly, we performed multi-block modeling using OnPLS on bag-of-words representations of abstracts, claims, and detailed descriptions, respectively, showing that semantic variation relating to patent classification is consistent across multiple text blocks, represented as globally joint variation. We conclude that using machine learning to transform unstructured data into structured data provide a good preprocessing tool for subsequent chemometric multivariate data analysis and provides an easily interpretable and novel workflow to understand large collections of patents. We demonstrate this on collections of chemical and pharmaceutical patents.
Thanks to improvements in technology more data than ever before is generated in almost all fields of science and industry.
The data is analyzed to hopefully provide valuable information and knowledge about a product or process, such as how to improve the quality of a manufactured product.
Analysis of collected data is often performed on a single dataset or data source at a time. In this thesis, I have focused on multiblock analysis, a concept that includes multiple sources or data blocks. Analogous to how the human senses combine to let us experience the world around us, multiblock analysis integrates multiple data sources, providing a fuller examination of the product or process under study.
My thesis introduces Joint and Unique Multiblock Analysis, JUMBA, a complete analysis workflow for data integration. I describe each step of JUMBA, including data pre-treatment, model building and validation as well as model interpretation. Special focus is put on several newly developed visualizations for model validation and interpretation to make it as easy as possible to draw conclusions from the analysis.
By reading my thesis, the reader will gain a working understanding of the process of performing multiblock analysis, including solutions to common problems that are often encountered.
In the present paper, we introduce an end-to-end workflow called joint and unique multiblock analysis (JUMBA), which allows multiple sources of data to be analyzed simultaneously to better understand how they complement each other. In near-infrared (NIR) spectroscopy, calibration models between NIR spectra and responses are used to replace wet-chemistry methods, and the models tend to be instrument-specific. Calibration-transfer techniques are used for standardization of NIR-instrumentation, enabling the use of one model on several instruments. The current paper investigates both the similarities and differences among a variety of NIR instruments using JUMBA. We demonstrate JUMBA on both a previously unpublished data set in which five NIR instruments measured mushroom substrate and a publicly available data set measured on corn samples. We found that NIR spectra from different instrumentation largely shared the same underlying structures, an insight we took advantage of to perform calibration transfer. The proposed JUMBA transfer displayed excellent calibration-transfer performance across the two analyzed data sets and outperformed existing methods in terms of both prediction accuracy and stability. When applied to a multi-instrument environment, JUMBA transfer can integrate all instruments in the same model and will ensure higher consistency among them compared with existing calibration-transfer methods.
Understanding and making the most of complex data collected from multiple sources is a challenging task. Data integration is the procedure of describing the main features in multiple data blocks, and several methods for multiblock analysis have been previously developed, including OnPLS and JIVE. One of the main challenges is how to visualize and interpret the results of multiblock analyses because of the increased model complexity and sheer size of data. In this paper, we present novel visualization tools that simplify interpretation and overview of multiblock analysis. We introduce a correlation matrix plot that provides an overview of the relationships between blocks found by multiblock models. We also present a multiblock scatter plot, a metadata correlation plot, and a variation distribution plot, that simplify the interpretation of multiblock models. We demonstrate our visualizations on an industrial case study in vibration spectroscopy (NIR, UV, and Raman datasets) as well as a multiomics integration study (transcript, metabolite, and protein datasets). We conclude that our visualizations provide useful tools to harness the complexity of multiblock analysis and enable better understanding of the investigated system.
Modern profiling technologies enable obtaining large amounts of data which can be later used for comprehensive understanding of the studied system. Proper evaluation of such data is challenging, and cannot be faced by bare analysis of separate datasets. Integrated approaches are necessary, because only data integration allows finding correlation trends common for all studied data sets and revealing hidden structures not known a priori. This improves understanding and interpretation of the complex systems. Joint and Unique MultiBlock Analysis (JUMBA) is an analysis method based on the OnPLS-algorithm that decomposes a set of matrices into joint parts containing variation shared with other connected matrices and variation that is unique for each single matrix. Mapping unique variation is important from a data integration perspective, since it certainly cannot be expected that all variation co-varies. In this work we used JUMBA for integrated analysis of lipidomic, metabolomic and oxylipin datasets obtained from profiling of plasma samples from children infected with P. falciparum malaria. P. falciparum is one of the primary contributors to childhood mortality and obstetric complications in the developing world, what makes development of the new diagnostic and prognostic tools, as well as better understanding of the disease, of utmost importance. In presented work JUMBA made it possible to detect already known trends related to disease progression, but also to discover new structures in the data connected to food intake and personal differences in metabolism. By separating the variation in each data set into joint and unique, JUMBA reduced complexity of the analysis, facilitated detection of samples and variables corresponding to specific structures across multiple datasets and by doing this enabled fast interpretation of the studied system. All this makes JUMBA a perfect choice for multiblock analysis of systems biology data.
Data integration has been proven to provide valuable information. The information extracted using data integration in the form of multiblock analysis can pinpoint both common and unique trends in the different blocks. When working with small multiblock datasets the number of possible integration methods is drastically reduced. To investigate the application of multiblock analysis in cases where one has a few number of samples and a lack of statistical power, we studied a small metabolomic multiblock dataset containing six blocks (i.e., tissue types), only including common metabolites. We used a single model multiblock analysis method called the joint and unique multiblock analysis (JUMBA) and compared it to a commonly used method, concatenated principal component analysis (PCA). These methods were used to detect trends in the dataset and identify underlying factors responsible for metabolic variations. Using JUMBA, we were able to interpret the extracted components and link them to relevant biological properties. JUMBA shows how the observations are related to one another, the stability of these relationships, and to what extent each of the blocks contribute to the components. These results indicate that multiblock methods can be useful even with a small number of samples