Independent component analysis (ICA) is a matrix factorization approach where the signals captured by each individual matrix factors are optimized to become as mutually independent as possible. Initially suggested for solving source blind separation problems in various fields, ICA was shown to be successful in analyzing functional magnetic resonance imaging (fMRI) and other types of biomedical data. In the last twenty years, ICA became a part of the standard machine learning toolbox, together with other matrix factorization methods such as principal component analysis (PCA) and non-negative matrix factorization (NMF). Here, we review a number of recent works where ICA was shown to be a useful tool for unraveling the complexity of cancer biology from the analysis of different types of omics data, mainly collected for tumoral samples. Such works highlight the use of ICA in dimensionality reduction, deconvolution, data pre-processing, meta-analysis, and others applied to different data types . We particularly focus on the technical aspects of ICA application in omics studies such as using different protocols, determining the optimal number of components, assessing and improving reproducibility of the ICA results, and comparison with other popular matrix factorization techniques. We discuss the emerging ICA applications to the integrative analysis of multi-level omics datasets and introduce a conceptual view on ICA as a tool for defining functional subsystems of a complex biological system and their interactions under various conditions. Our review is accompanied by a Jupyter notebook which illustrates the discussed concepts and provides a practical tool for applying ICA to the analysis of cancer omics datasets. Cancer research is one of the most important providers of large-scale molecular profiling data, which help in understanding not only the state of human cells in disease but also shed light on the normal physiological processes measurable and detectable in various kinds of omics datasets. Determining robust and biologically meaningful ways of quantifying cellular and organismal and normal and pathological physiology using high-throughput molecular data remains a major challenge (making biology a quantitative science). Different kinds of biological processes leave characteristic traces at different levels of genome-wide measurements depending on their nature and timescales: some significantly affect transcriptomes, some rather modify DNA methylation programs or mutational spectrum, others are measurable only at the level of proteome and phosphoproteome. In order to reliably quantify some of these biological mechanisms, one will need to design multi-omics signatures spanning several levels of molecular data descriptions. On top of this, various technical factors interplay with biological ones, frequently in a way which makes it difficult to clearly distinguish both.linear mixture of signals plays a pivotal role in the modern molecular data analysis. In this framework, one assumes that a measurable elementary quantity such as expression of a single gene is a result of weighted summation of some latent, and not always directly observable, factor activities which should have associated numerical values. The nature, the number of factors and the way they are represented numerically can be known or unknown in advance. A toolbox of existing mathematical approaches provides concrete scenarios in which the additive factors can be determined and quantified, under acceptance of certain assumptions about the statistical properties of their numerical values or the weights connecting them to the measurements.Rarely does molecular data “speak for themselves”: they need to be properly pre-processed, analyzed in the light of mathematical modeling, statistical assumptions, and prior biological knowledge and, finally, should be represented at some pre-defined level of abstraction. In this sense, one of the simplest paradigms of One of the standard methods in such a toolbox is independent component analysis (ICA) having a long standing history of application to biological data, including the analysis of molecular profiles . Formally, ICA belongs to a family of methods called matrix factorizations , the mosThe first applications of ICA in biology contrasted it to PCA and standard clustering methods and found that the factors determined through ICA are easier to interpret biologically ,2. This Independent component analysis and matrix factorization approaches are standard methods in the rapidly growing arsenal of machine learning methods applied to the molecular biology and medical data. At the same time, remarkable success has recently been achieved in applying deep learning techniques in certain fields of cancer biology such as clinical imaging of various kinds ,6,7,8,9.Over the last decade, significant experience in applying ICA to different kinds of omics data for addressing various problems has been obtained, including data pre-processing, task of cell type deconvolution, and meta-analysis of multiple omics datasets c. In thihttps://github.com/sysbio-curie/ICA-in-Cancer-research-review-materials.This review is accompanied by interactive Jupyter notebook located at N, and with number of observed features, m) as an input and approximates it as a sum of products of p pairs of vectors of size N and m. The fundamental equation for all matrix factorization methods states (note that the product of ka and ks vectors gives a one-rank matrix of the same dimension as X):ka and ks such that: Independent component analysis belongs to a family of matrix factorization methods. Each of these methods takes a rectangular matrix ka and ks will be called a component throughout this review. Therefore, a component is represented by a vector ks of size m containing weights of omics variables . At the same time a component is associated to a vector ka of size N, containing contributions of the component to measured samples. We will use these notations and meaning of ka and ks vectors throughout the whole review.Each vector pair ka and ks. For example, the terms “loadings”, “activations”, “factor strength” or “sample-associated weights” have been used to denote the elements of ka vectors. The matrix composed from the ka vectors is sometimes called the “mixing matrix” and denoted as A. The elements of ks vectors have been called “weights of the component” or “signals” and the matrix composed of them (denoted as S) is sometimes called the “signal matrix”. Moreover, ks vectors themselves are frequently referred to as “components” or “factors”. In the matrix factorization literature, various terms are used to denote the elements of the vectors ks vector is frequently named a metagene [ks even when describing application of ICA to various data types. Similarly, the ka vectors are sometimes called metasamples, and we will adopt this term in the text , see In the context of transcriptomic data analysis, the metagene ; in the p metagenes. Each metagene abstractly represents a molecular program  by assigning a numerical weight to each gene of the organismal genome. The activity of metagenes in a sample is combined additively, and each metagene acts on a sample with a sample-specific strength or activity. Activities of the same metagene over all measured samples is called a metasample. A metasample is the profile of the corresponding metagene activity similarly to a gene expression profile across samples.Intuitively, a transcriptome of a biological sample is described as a combined action of X matrix is known; the ka and ks vectors are unknown. As such, the problem of matrix factorization (**) is heavily underdetermined, and additional constraints need to be introduced on ka and ks vectors in order to find its solution. First of all, it can be required that the all ka vectors would have length one. In the equation (*), only the ka vectors:  = 0, for i ≠ j and that the solution of (**) should give the same result for different orders of matrix decomposition p, i.e., ka and ks vectors computed for the order p = p’ would be the same as for the decomposition of order p’’ > p’. In this case, solving (**) is equivalent to computing the singular value decomposition (SVD) of X and gives a set of principal components. There exist several ways to introduce PCA, as reviewed in Reference [Furthermore, one can require orthogonality of the eference .ka and ks vectors would be non-negative. This constrains the problem (**) and leads to NMF. The simplest approach to solve (**) with these constraints is to repetitively apply the non-negative least squares regression method, considering ka as unknown at one iteration and ks as unknown at the next iteration, until convergence to a local minimum.Alternatively, one can require that all elements of as mutually independent as possible. More precisely, the elements of vectors ks  have to represent maximally mutually independent distributions, for different k. The perfect independence would mean that the joint probability distribution P can be factorized as ks are i.i.d. samples of the underlying probability distributions kP(ks).When computing ICA, the resulting components are required to be ka vectors and the components cannot be naturally ranked. The NMF components contain only non-negative elements, which makes the intuitive picture of the additive action of metagenes simpler to interpret, while in PCA and ICA some metagenes can cancel the action of other metagenes if they are summed up with different signs.From the different nature of the constraints follow different properties of matrix factorization algorithms . It can be shown that maximizing entropy of joint distributions of pairs of ks leads to minimizing their mutual information. One of the historically first and still popular practical algorithms for solving ICA problem is based on the general Infomax (or maximum entropy) principle . Indeed,ks distributions [It appeared also that under some assumptions, minimizing the mutual information is equivalent to maximizing the non-Gaussianity of the individual ibutions . Quantifal works ,20. ks or ka vectors is always finite in real-life applications, one needs to introduce the way to effectively approximate it from the finite samples. For this purpose, various surrogate functions  have been proposed, one the most popular of which involving the kurtosis. Empirically, kurtosis was found to be an appropriate choice of non-linearity in the analysis of transcriptomic data. Other types of non-linearity functions have been suggested; however, the appropriate choice of non-linearity for applying ICA to different kinds of omics measurements remains an open question. The two most popular ICA algorithms based on non-Gaussianity maximization are fastICA [Since the length of the  fastICA  and join fastICA . Most of fastICA ,23.A typical preprocessing step used before application of ICA algorithms is the so-called data whitening or sphering see b. Whitenka and ks vector elements. This allows combining the nice properties of non-negative mixture problem and the requirement for mutual independence of the components. A kernel version of ICA have been developed [Various flavors of ICA have been suggested and some of them were tried on omics data. Bayesian ICA with prior constraints have been suggested and tried on the metabolomics data . The prieveloped  and spareveloped , but boteveloped ). Finalleveloped ,29 and rSome flavors of ICA seems interesting to explore more in biological applications, in the view of the concept of the integration of functional subsystems see , such asBesides the choice of ICA algorithm (which is frequently fastICA), there are several choices to be made when ICA is applied to omics datasets. The first evident but non-trivial choice concerns a necessity for data log-transformation, which is especially important in the case of gene expression and protein expression data. On one hand, it is strongly desirable in the case of, for example, RNA-Seq data, since empirically they are found to be characterized by log-normal distribution. When ICA is applied to non-transformed data, the resulting components are frequently dominated by single genes or single samples , which contradicts the initial concept of linear mixture . Simple log-transformation usually fixes this issue. However, log-transformation makes the direct interpretation of the ICA model difficult, since, formally speaking, one deals with a multiplicative rather than an additive model of signal mixture. This is particularly important for the applications of ICA in the field of cell type deconvolution where the linearity assumption is explicitly made for mixing transcriptomes of different cell types (see Reference  which ciks) or metasamples (vectors ka). Technically, the first case corresponds to the application of ICA algorithm to the initial matrix X containing samples as rows and omics variables as columns, and the second case corresponds to the application of ICA to the transposed matrix X. Surprisingly, both ways of applying ICA to omics data are wide-spread, and sometimes it makes an effort to figure out in which way ICA was applied. Some studies aim at maximizing the non-Gaussianity of metagenes [Another choice in applying ICA to a matrix of omics measurements is the choice between what distribution independence (or non-Gaussianity) is maximized . One canetagenes ,33,34,35etagenes ,37. Empietagenes . This chicasso method, introduced by the creators of fastICA [Furthermore, in several studies it was found that stabilized or consensus independent components have better characteristics in terms of generalization and interpretation ,39,40,41ka and ks can be inverted simultaneously without changing the definition of the component. Some methods (such as BIODICA or DeconICA) avoid this ambiguity by assuming that the heaviest tail of the ks distribution should correspond to positive values, which usually gives satisfactory results. In Reference [ka and ks. In this case, the orientation was chosen based on the values of ka vectors. For example, in a disease study, one can require that any component would be oriented towards aggravation of the disease condition . This approach was recently used for quantifying disease comorbidity using ICA [Lastly, in some applications of ICA , it is desirable to fix the orientation of the independent components. We remind that in PCA and ICA, the signs of the elements in the vectors eference , each ICsing ICA .In several recent studies, ICA was systematically compared with the other most used matrix factorization methods such as PCA and NMF, using large collections of cancer omics measurements. In Reference , it was Three major matrix factorization approaches were systematically discussed in a recent review for their ability to discover functional subsystems or tissue-type specific signals . The maiall computed components. In the case of ICA, which geometrically only rotates the PCA axes, choosing the number of independent components can rely either on the methods for determining the number of relevant principal components or it can use some features of the independent components themselves in order to determine the optimal decomposition order.The most important parameter in the application of any matrix factorization method is the number of components to determine. This question is less crucial in the case of PCA due to the orthogonality constraint and that computing higher-order components does not affect the definition of the lower-order ones. However, this is not the case with ICA and NMF: choosing the order of decomposition affects the definition of In the first case, the effective global dimensionality of the data can be determined through the standard Kaiser rule, use of broken stick distribution, Horn’s parallel analysis or estimating the conditional number of the covariance matrix . One canHowever, the second case appears to be more consistent in applications, even being computationally more challenging. Thus, in Reference , Bayesiaks distributions are characterized by the presence of one or few weights with exceptionally large values, separated by a significant gap from the other values. Secondly, it was shown that a certain level of over-decomposition of transcriptomic datasets, i.e., choosing the number of independent components several times larger than MSTD, does not drastically change the definition of most of the components within the MSTD range. At the same time, it was observed that increasing the number of independent components over the MSTD value sometimes leads to biologically meaningful splitting of the components. For example, a component within the MSTD range which was associated with the total level of immune infiltrate in tumoral microenvironment splits into three components in higher-order decompositions which can be associated with the presence of T-cells, B-cells, and macrophages [In Reference  stabilitrophages ,42.In Reference , a rangeks vectors  to the set of most contributing genes, or Gene Set Enrichment Analysis to the whole ranking defined by ks), using large-scale collections of reference gene sets. The distribution of gene weights from ks vectors can be projected on top of genome-wide biological network reconstructions where the network edges represent different types of interactions or regulations between genes and/or proteins. This can be further used for various types of network-based analyses, leading to the determination of biological network “hotspot” areas and eliminating the need of having a reference gene set collection [ks vectors  can be projected onto genome and be a subject of peak-calling analysis, which can sometimes lead to associating a component to genomic alterations [Assigning a meaning to the extracted independent factors remains a major problem in exploiting ICA in biological research. Standard practice consists of applying various kinds of functional enrichment analyses to llection . The sk erations . ka are used to associate components to sample annotations such as clinical data . Metasamples can be also associated with some clinically relevant molecular data, such as mutations in known cancer drivers for a particular cancer type. Metasamples can be also associated with known labels for molecular tumor subtype.Metasample weights ks vector can be projected on cancer-specific biological network maps such as the Atlas of Cancer Signaling Network (ACSN) using user-friendly Google Maps-based online platforms such as NaviCell and MINERVA [In parallel to rigorous statistical testing, insightful visualizations of the results of ICA application can be of great help. For example, gene weights from  MINERVA ,52. Func MINERVA . https://github.com/LabBandSB/BIODICA), a set of tools is provided for performing hypergeometric tests of the metagenes, automated feeding of Gene Set Enrichment Analysis with ICA metagenes, projecting metagenes onto biological network maps, correlating computed metagenes with a reference database of previously annotated metagenes, associating components with categorical and numerical sample annotations, and tools for meta-analysis of ICA decompositions. There exist integrated solutions allowing the computation of ICA components for omics datasets and containing a built-in set of tools for their interpretation. For example, in the BIODICA package . The simplest idea is to select the variables  bypassing the threshold in p standard deviations, with some choice for p . A combined set of the most contributing to different ICs genes can be used to define a subset of data for further analysis.Frequently, each one-dimensional ka vector to the dates of sample preparation. Another component frequently identified in the analysis of transcriptomic data is related to GC-content, which might reflect the influence of GC-content on the RNA amplification step common for both microarray-based and sequencing-based methodologies. In Reference [Interestingly, ICA decomposition can be used to identify and disregard technical biases among omics datasets produced by different platforms. For example, in the study of 198 bladder cancers in Reference , one of eference , a smallInterestingly, ICA-based analysis sometimes can lead to identification of the factors whose origin is intermediate between technical and biological. For example, in Reference  one of tSome components identified through ICA could describe various cell populations present in the sample in addition to cells of direct interest. Typically, this was the case for the stroma-related signals in the ICA-based analysis of tumor bulk samples see . ICA canAnother frequently employed idea is to use the results of ICA decomposition in order to define a set of variables for further application of various machine learning methods. Zhang et al.  were amoRecently ICA was used to engineer features for further use in cancer-related classification tasks, using naïve Bayes classifier . In RefeAny matrix factorization method can be used for dimensionality reduction. The specifics of ICA are in that it is usually performed in an already reduced space and only defines a new coordinate basis in the principal linear manifold. Therefore, ICA itself does not reduce the data dimension more that it is done by PCA. Nevertheless, it is a frequent practice to consider the coordinate basis defined by few independent components as a subspace to further application of various data analyses. For example, this approach is used for a standard pipeline of single cell RNA-Seq data analysis . SimilarOne of the strongest motivations behind applications of ICA to omics data is in that it can help identifying functional subsystems  which are the building blocks determining response to perturbation of a tumoral cell or a whole cellular ecosystem such as tumor microenvironment (TME) composed of different cell types. The underlying principle is that genes or proteins do not react to an external stimulus individually but always integrate into a (sub-)system with more or less defined limits. Importantly, it is biologically feasible to assume the phenomenon of plurifunctionality, i.e., potential participation of an elementary entity (such as gene or protein) into several functional subsystems. ks vector (weights associated with the omics variables) or metagene. The level of activation (or inhibition) of an identified functional subsystem ks can be read in the corresponding metasample vector ka. The same is relevant for an independent component associated with a technical factor intensity.The composition of a functional subsystem is defined by a matrix factorization method in the form of the ks) to the definition of the subsystem, which can be positive or negative. However, those variables having close to zero weights can be neglected from the subsystem definition. An important characteristic of a metagene is the set of the most contributing genes (see discussion in the previous section). The most contributing genes are useful to characterize the functional subsystem and to identify if this subsystem corresponds to an existing known one. After determination of the sets of the most contributing genes per each metagene , one can check if a gene is associated with the subsystem exclusively or contributes to several ones. This analysis can be used to identify potential coupling between the subsystems and their concrete mechanisms (see further discussion). Sometimes it is convenient to distinguish two gene sets per metagene, having the largest and the smallest set of weights, from the positive and the negative sides of the ks distribution. If no explicit sparsity constraint is imposed when computing the vectors, then each omics variable  has a non-zero contribution  ,64. If aIdentification of functional subsystems (modules) from cancer omics data was first historically approached with hierarchical clustering of genes . Matrix Functional subsystems identified by ICA can reveal an important coupling of several known biological mechanisms and relate it to the biological phenotype such as cancer patient outcome. In the case of breast cancer, this phenomenon was described in Reference  through ka is the unimodal or bi- or multi-modal character of the distribution. In the case of well-defined bimodality of a metasample, one can stratify the distribution of samples into two groups, with respect to the nature of the functional subsystem identified. A typical example of this kind is the identification of the functional subsystem of proliferation in single-cell RNA-Seq data, where the corresponding metasamples frequently have two modes, corresponding to proliferative and non-proliferative cell states.One important characteristics of the weight distribution composing Functional subsystems have been systematically identified using ICA from a large cohort of transcriptomic profiles in Reference , where 2ks vectors were matched with each other through correlation in order to reveal the functional modules implicated in colon cancer tumor cells’ and the variability of tumoral microenvironment.Identification of the functional subsystems and distinguishing them from potentially technology driven factors can be strongly improved by the application of ICA analysis to multiple similar datasets independently (without merging them). In this scenario, the ICA results from several datasets were compared with each other in terms of the correlation or other suitable similarity measure among metagenes . In the In the case of a very good match between ICA-based metagene definitions from several independent datasets, one can define a consensus metagene definition from the meta-analysis (a meta-metagene). An exemplary set of such reference metagenes was built in Reference  and usedIn cancer biology, bulk omics data  represent heterogeneous samples such as peripheral blood mononuclear cell (PBMC) and tumor biopsies, in which the expression profiles of distinct cell types are mixed in each sample at a priori unknown proportions. The tumor microenvironment is composed of many different cells including a plethora of immune cells, stromal cells, and blood and lymphatic vessels . The quaTherefore, one of the major challenges for computational analysis of bulk samples is evaluating the proportions and the properties of individual cell types composing the sample, frequently called deconvolution problem in this context . In geneDeconvolution of cancer bulk transcriptomes gained a lot of popularity in the last several years due to the abundance of data sources. Several methods were proposed to estimate the abundance of immune infiltration in cancers at different levels of granularity ,68,69,70In response to this problem, reference-free  approaches propose a more data-driven way of performing the deconvolution. This group of approaches is able to discover the cell types and their markers as well as approximate profiles of those cell types (perform “complete deconvolution”). Different types of matrix factorization are suitable for solving this problem. Even though these deconvolution methods are called reference-free, known reference profiles are used to interpret and select the cell type-related components. Different possible benefits of reference-free approaches can be listed as (a) flexibility—discovering the context-dependent cell-type markers, (b) discovery of new cell types or cell types that are specific to a certain context, (c) determining deconvoluted profiles of cell types that can be used to remove the immune-related signal or to better understand the cell type features, and (d) ability to characterize biological processes (such as cell cycle activity) simultaneously with the cell types.The reference-free approaches were already applied for deconvolution of cell types in blood using semi-supervised NMF . They wehttps://github.com/UrszulaCzerwinska/DeconICA) was developed with the objective to apply ICA to the task of cell type deconvolution. It was shown that ICA is able to efficiently estimate the cell type proportions with better accuracy than leading supervised algorithms even though it can identify less cell sub-types than most of the published solutions. It suggests that ICA-based deconvolution is less prone to overfitting and enables discovery and quantification of strong and stable signals (not necessary the most abundant but rather the most specific). DeconICA was applied to a big corpus of data containing more than 100 transcriptomic datasets composed of over 28,000 samples of 40 tumor types generated by different technologies and processed independently. In addition, the ICA-derived metagenes were used as context-specific signatures in order to study the characteristics of immune cells in different tumor types. The analysis revealed a large diversity and plasticity of immune cells dependent and independent on tumor type. Some conclusions of the study can be helpful in the identification of new drug targets or biomarkers for immunotherapy of cancer. In Reference  icasso-sCell-type composition can be also computed from DNA methylation data. In the EWAS (Epigenome Wide Association Studies), the variation origination from cell types is considered as an important confounding factor that should be removed before comparing cases and controls and defining Differentially Methylated Positions (DMPs). For example, in Reference  ten toolStatistical properties of ICA seem to be very attractive to justify its application to the emerging wealth of single-cell omics data profiles. ICA can serve here to improve the data analysis regarding dimensionality reduction, removing technical biases, integrating datasets. ICA also looks promising and represents an alternative to the standard dimensionality reduction followed by clustering methodology for identifying cell types or states, suggesting a more continuous way of considering them, with a possibility of existence of intermediate or mixed cell populations.Similar to bulk RNA-seq, technical biases and batch effects are limiting factors for single-cell RNA-Seq and should be either removed or taken into account. One example of ICA application to normalize batch effect was recently reported by Dirkse et al. . The autThe ICA-based dimensionality reduction is a standard step in the most popular packages for analyzing single cell RNA-Seq data. In MONOCLE ,81, ICA In Reference , ICA wasICA served as a principal machine learning method for discovering functional subsystems involved in the response of Ewing sarcoma cells to the induction of the chimeric oncogenic transcription factor EWSR-FLI1 . In thisIn principle, ICA is the methodology able to exploit strong non-Gaussianity in the multidimensional distributions formed by single cells in the space of omics profiles. However, in order to optimally use this potential, one probably needs to identify the most suitable non-linearity functions, for each particular type of single cell measurements, and take into account the nature of the multivariate distribution of points in data space. Recently, a matrix factorization-based method ZINB-WaVE was adapted to the single cell RNA-Seq measurements, using the model of zero-inflated negative binomial distribution (ZINB) . In prinThe majority of published works on applying ICA in cancer research deals with transcriptomic data. This is connected in part to the relatively high abundance of such data type from collections of bulk tumors, and in part to the availability of bioinformatics tools helping to interpret the obtained components . Yet another aspect is that transcriptomic data are better connected so far to the clinical questions such as defining molecular subtypes of tumors. However, applying ICA should not be limited to only one level of omics profiling, and there is a lot of potential in applying it to several levels of molecular description. The multi-level datasets become increasingly available in the cancer biology. The levels of molecular description can be gene copy number profiles, binary mutation profiles, measured mutational signatures, measured total expression of genes or spliced mRNA isoforms or non-coding genes such as microRNAs, DNA methylation or histone mark modification profiling, protein or protein phosphoforms relative abundances or some other less frequently used omics types. Identification of functional subsystems can be facilitated through the use of several data types, since the adaptation process is frequently expected to span several levels. As a good example of such a multi-omics dataset, one can cite recent work on comprehensive characterization of medulloblastoma .ka vectors (metasamples). Such an approach was recently applied in Reference [Ideally, several levels of omics profiling should be collected for the same and sufficiently large set of samples. Independent components can be then computed for each data type separately and then the identified components can be compared by computing correlations between the corresponding eference  to a seteference , 77 breaAn alternative and somewhat more powerful idea consists in stacking several matrices corresponding to the different levels of omics profiling into a tensor , in order to apply the tensorial version of ICA. In this case, ICA will be able to learn and jointly optimize the signals which can involve variables from several levels of molecular description. This requires making at least two dimensions of the data common, while the third matrix dimension indicates the data type. Typically, all molecular measurements are mapped onto the genes through application of procedures that can be non-trivial . ijkX has dimensions “number of samples × number of genes × number of data types”. For example, Xi = 4, j = 5, k = 2 element in the tensor can indicate DNA methylation level of the promoter of the gene 5 in the sample 4. Xi = 4, j = 5, k = 1 could indicate expression of the same gene in the same sample. In the case of tensor factorization, the resulting components represent matrices rather than vectors having dimensions “number of genes versus number of data types” (for metagenes) and “number of data types versus number of samples” (for metasamples). The existence of correlations among different data types within the same matrix-component indicates coupling among several levels of molecular descriptions captured by tensorial ICA.The resulting three-dimensional measurement tensor Tensorial ICA was recently applied in Reference  to colonApplications of ICA to data types other than transcriptomic or to several data types simultaneously remain limited; however, first applications of this approach in cancer biology are rather promising ,92. MultFunctional subsystems identified through ICA and fixed in the form of metagenes can be studied for their statistical relationships within a dataset, among datasets of the same kind, or among datasets that are not closely related in terms of the nature of the biological samples profiled. ka vectors. This analysis confirmed previously identified comorbidity patterns based on the analysis of individual gene expression profiles  and suggested new molecular mechanisms of comorbidity between lung cancer and Alzheimer’s disease such as estrogen receptor signaling pathway or the involvement of cadherins. In the latter case, one can use ICA for studying disease–disease relationships. An example of such a relation is the phenomenon of inverse comorbidity between cancer and some other diseases, in terms of the anti-correlated activation pattern of the common functional subsystems. For example, the ICA method was exploited in Reference  in orderindependent component splitting and merging might depend on the order of ICA decomposition , on theThe theoretical principles of functional subsystems integration have been developed ,64. HoweIn recent decades, independent component analysis has become a standard tool for the analysis of tables of omics measurements in cancer biology. In certain applications, it was shown to have advantages, especially in terms of reproducibility or generalizability and biological interpretability, compared to other popular matrix factorization methods. Despite ICA being shown to be a useful tool, it seems to be under-appreciated partly due to the fact of historical reasons and partly due to the presence of existing confusions in the underlying assumptions and/or interpretation of the resulting matrix decompositions. For example, it is frequently commented that biological processes are not perfectly independent and that they are expected to be correlated in some conditions. Even though this is true, ICA can distinguish signals coupled to some extent by making the corresponding components as independent as possible. In this review, we made a comprehensive effort to mention most of the recent studies in cancer research where ICA served as the essential data analysis tool. We classified them into several common topics: data preprocessing, data dimension reduction and visualization, identification of functional subsystems and their correlations or interactions, deconvolution of cell types, data integration and meta-analysis. We also reviewed the methodological works aimed at defining the best practices of applying ICA to concrete types of omics data. Compared to the early times of applying ICA to omics datasets, today there exists a variety of implementations and improved methodologies allowing us to use the valuable idea behind ICA  in the best possible way. Certain progress has been made in clarifying such important questions such as determining the optimal number of components to retain or establishing the biological significance of the extracted components.Most of the existing applications of ICA have been done so far for transcriptomic data even if the interpretation of the components used other types of molecular data . Since recently, ICA started to be applied to other types of omics profiles, including methylation profiles and proteomics datasets. It seems interesting to determine, using ICA, independent sources of variance in newly emerging omics data types, such as systematic Chip-Seq datasets mapping the state of histone modifications or mutational signature profiles. More experience, standardization and assessment are required to use ICA in the most optimal way for the analysis of single-cell and multi-omics datasets. Moreover, in this review we did not even mention other fields of ICA application in cancer biology, including the analysis of imaging data , clinicMatrix factorization represents an alternative approach to the standard clustering methods, being more flexible in terms of taking into account gene plurifunctionality and ability for unsupervised deconvolution of factors whose activity can be correlated. It is worth noticing that some ICA algorithms (such as fastICA) are computationally performant when properly implemented and potentially able to deal with large amounts of molecular measurements. In this sense, ICA remains competitive vis-a-vis many other approaches .We believe there are interesting directions to further explore and more deeply use the concepts behind ICA in the context of cancer biology data analyses. It would be interesting to reconsider the roots of independent component analysis into artificial neural network methodology, suggesting novel scalable autoencoding-based techniques in order to solve the problem of blind source separation adapted to the nature of the biological data. Assessing the value of supervised learning of the features extracted by ICA from omics and other data types and comparing them to “hand-crafted” or convolutional neural network-based features can lead to designing performant hybrid learning approaches, as in Reference . It appeTo conclude, as a team of authors all having extensive experience in applying independent component analysis as a tool in computational cancer biology, we advocate for its wider use in making sense of the growing amount of omics data in this and other fields.


Understanding and quantifying total economic impacts of flood events is essential for flood risk management and adaptation planning. Yet, detailed estimations of joint direct and indirect flood-induced economic impacts are rare. In this study an innovative modeling procedure for the joint assessment of short-term direct and indirect economic flood impacts is introduced. The procedure is applied to 19 economic sectors in eight federal states of Germany after the flood events in 2013. The assessment of the direct economic impacts is object-based and considers uncertainties associated with the hazard, the exposed objects and their vulnerability. The direct economic impacts are then coupled to a supply-side Input-Output-Model to estimate the indirect economic impacts. The procedure provides distributions of direct and indirect economic impacts which capture the associated uncertainties. The distributions of the direct economic impacts in the federal states are plausible when compared to reported values. The ratio between indirect and direct economic impacts shows that the sectors Manufacturing, Financial and Insurance activities suffered the most from indirect economic impacts. These ratios also indicate that indirect economic impacts can be almost as high as direct economic impacts. They differ strongly between the economic sectors indicating that the application of a single factor as a proxy for the indirect impacts of all economic sectors is not appropriate. Flood events can have multiple impacts on economic sectors at all scales of an affected region. Those are not limited to direct impacts on companies, which commonly occur inside the flooded areas, but also include indirect impacts across economic sectors, which typically occur outside the flooded regions , 2, by eThe most common approach to estimate direct flood damage to buildings goes back to Grigg & Hellweg , who relThe performance of models estimating the direct economic flood impacts to companies is generally low , 22, andAnother source for inaccurate estimates of direct economic impacts is the inconsistent handling of spatial scales. Direct economic impacts can be estimated at different spatial scales , which cYet, the assessment of direct economic flood impacts on the national scale is still mostly conducted on the basis of land use . One exaIndirect economic impacts of natural hazards are mostly assessed with macroeconomic models that describe interactions between different economic sectors as well as the public sector, e.g. Input-Output (IO) models and Computable General Equilibrium (CGE) models . IO and These macroeconomic modeling techniques have also been used for assessing flood impacts. For example, Hallegatte  developeHowever, most flood impact assessments neglect indirect economic impacts , 36. SomTherefore, this study develops a novel procedure for a comprehensive quantitative assessment of economic flood impacts that (1) includes uncertainties associated with the estimation of direct impact  and (2) The study focuses on two main aspects. The first aspect is the application of seamless estimation and uncertainty quantification of direct economic flood impacts to the national level by means of newly available exposure data sets. The second aspect is the linkage between the estimation of the direct and indirect economic flood impacts on a national level, including the uncertainties associated with the estimation of the direct economic impacts.Direct economic impacts are estimated by the probabilistic, object-based approach developed by . Tree-baTwelve federal states were affected during the flood event in 2013. For this study the eight federal states along the catchments of the rivers Danube and Elbe are considered, which were most affected by extensive flooding , 44. Thihttp://www.jbarisk.com). A data base of single buildings in Germany is extracted from openstreetmap.org (OSM). Only buildings with an occupancy related to a commercial, educational, industrial, public or mixed use are counted for the number of affected companies in the federal states. Buildings related to infrastructure, government or gathering venues are excluded for the counting.The flooded area and the related water depths are taken from a water mask provided by JBA Risk Management (https://www-genesis.destatis.de/genesis/online/). Data regarding the distribution of company size classes (number of employees grouped in classes) and economic sectors separated by different federal states are taken from the German Federal Statistical Office, GENESIS Table no. 52111-0003. Net fixed assets of buildings and equipment from different economic sectors are taken from the national accounts (VGR des Bundes), GENESIS Table no. 81000-0117. Official statistics, taken from the German Federal Statistical Office, are used to derive the characteristics  of companies in different federal states. The data sets can be accessed via the GENESIS online data base  and the asset values of the companies. The vulnerability is described by a tree-based damage model.Since the individual characteristics of the affected companies are unknown, the broad range of individual specifications was reflected by sampling company characteristics from distributions derived by a water mask, official statistics and survey data. The number of directly affected companies in each federal state is determined by an overlap of the water mask and the OSM data. The affiliation of single companies to specific economic sectors is sampled from official statistics. Company characteristics, such as precautionary measures or the size of a company, are sampled from official statistics and survey data. The net fixed assets of a company are calculated by multiplying the number of employees with the net fixed asset value per employee of the corresponding economic sector . It is aFor each of the sampled company version the relative damage is estimated with a tree-based model, that is derived from the survey data collected after the 2002 and 2013 flood events . IndividPrevious studies on tree-based flood damage models usually take the mean value of observations with comparable input variables as an estimator for the relative damage , 51–53. The probability distributions for the relative damage of a single company is transferred to absolute values by multiplying the relative damage with the net fixed asset values. To obtain the direct economic impact per federal state, the absolute values of the damage distributions are summed up. Similarly, the direct economic impacts to individual economic sectors can be estimated by summing up absolute economic impact estimates for the affected companies in the corresponding sector. The latter distributions are used to shock the IO model, as illustrated in A national IO table of the economy of Germany for the year 2013 was obtained from the German Federal Statistical Office. On this basis a supply-side IO model was used to compute the indirect economic effects.The IO table, taken from the German Federal Statistical Office, can be accessed via the GENESIS online data base with the table no. 81511-0003. It consists of 72 economic sectors. For this study the table was aggregated to 19 economic sectors following NACE Rev. 2 according to the European statistical classification of economic activities in the European Community : AgroforZ of values/products between the sectors, which are producing and purchasing the products to each other. Traditionally, Input-Output models can be used to estimate the changes of the inputs xj resulting from a change in the outputs xi, e.g. final demand f. This can be estimated as follows:x, whereby (I − A)−1 is known as the so-called Leontief inverse. This model is often referred to as demand-side IO model.IO models originate from the work on economic problems of Wassily Leontief . The corxi resulting from a change of the inputs xj, e.g. the capital stock Kj by changing the “column-wise” view of the demand-side model to a “row-wise” view. introducFor a comprehensive introduction of demand-side and supply-side IO models see .Originally, the changes estimated by supply-side models were interpreted as physical output changes, which was critized as implausible by several authors , 59. A rIn this study the Ghosh-price model is used to estimate the impacts caused by a change of the capital stock resulting from a flood event on the outputs of an economy expressed as the change in prices.In general, most IO case studies do not include uncertainty analyses . AlthougDirect economic impacts of all federal states are aggregated for each economic sectors for whole Germany. Distributions of direct economic impact estimates for each of the 1000 different company versions, and therefore sector versions, are used to shock the IO model. Hence, the IO model is shocked 1000 times using 1000 direct economic impact distributions per economic sector in Dj is assumed to affect the capital stock of every economic sector Kj directly. The flood impacts on the capital stock of the companies are calculated as follows:The calculated direct economic impact per sector is used as input to the Ghosh-price model to estimate the indirect economic impacts. The direct economic flood impact v is recalculated by a summation of the labor L and the shocked capital stock Ks:In a next step the value added vs is then used to calculate the outputs xs shocked by the flood event with:The shocked value added Since these results reflect the direct as well as the indirect economic impacts, a further differentiation between these two impacts is necessary. This is done by the subtraction of the direct from the indirect economic impacts before calculating the price changes.x can be estimated as:Consequently, the price change Δxj shows the decrease of output prices caused by an decrease of the inputs. This can be interpreted as the price decrease, which affected sectors have to compensate for.In this case ΔFirst, the results of the identification of affected buildings and the estimates of the direct economic impact on companies of individual federal states as well as for the single economic sectors are presented and discussed. Second, the estimates of the indirect economic impacts and ratios between indirect and direct economic impact are shown.A total of 11382 companies affected by the flood in the eight federal states are identified by the overlap of the water mask with the OSM data . The SaxThe overall direct economic impact to companies’ buildings and equipment is estimated per federal state . The disGenerally, data for validation of estimated direct economic impact is scarce . TherefoThe validity check shows that ranges reported for each of the eight federal states lie within the distributions of estimated direct economic impact of the respective federal state. In some federal states, e.g. Bavaria and Brandenburg, the reported values are located towards the left tail of the distributions, suggesting an overestimation of the direct economic impacts. However, the derived percentages of 15 and 35% cover only the industrial and commercial sector, whereas the estimated direct economic impacts also cover service sectors such as education or arts, entertainment and recreation and partly infrastructure sectors such as electricity, gas, steam and air conditioning supply . TherefoThe method of Sieg et al.  allows nThe distributions of the direct impacts vary widely between the sectors. The variation in the range is mainly caused by different asset values  and compEconomic sectors which suffered the highest direct economic impacts are the Manufacturing, Electricity, gas, steam, and air conditioning supply, Water supply, sewerage, waste management and remediation activities, and Transportation and storage. These results seem plausible, since manufacturing companies are the most common and the asset values per employee of the other three economic sectors are high . The lowThe indirect impacts, also sometimes named second-order or macroeconomic impacts, are induced by direct impacts which affect and permeate throughout the value chain. Examples are negative feedbacks to the wider economy, such as the production losses of suppliers and customers of the companies directly affected by the flood . The disTheoretically, the estimation of the indirect economic impacts can be applied at any spatial scale as the estimation of the direct economic impacts is scale independent. However, this is limited by the availability of Input-Output tables at the anticipated spatial scale. At the country level this information is readily available e.g. via the GTAP database . These nThis study proposes a new modeling procedure for the joint assessment of direct and indirect economic flood impacts under uncertainties. For the first time an object-based estimation of direct economic flood impacts at the national level is successfully conducted. The resulting estimations are plausible compared to reported damage values. Hence, data sets containing individual buildings are suitable for the seamless estimation of direct economic flood impact at large spatial scales.Within the same modeling procedure the direct economic impacts are linked to a supply-side IO model for estimating the indirect economic impacts. The ratio between indirect and direct economic impacts reveals to which kind of impact the economic sectors are more prone to. Large differences in this ratio between the economic sectors are identified. This indicates that the application of a single factor to direct economic impacts as a proxy for the indirect impacts is inappropriate. Furthermore, direct and indirect economic flood impacts are found to be almost equal, highlighting the importance to include indirect economic impacts in flood risk assessments and management.The proposed procedure can be applied at any spatial scale, although a limiting factor is the data availability for IO models. This crucial data source is readily available at the country level allowing the application of the proposed approach at the country level in other regions as well, while an application to smaller spatial units is more challenging. The procedure’s probabilistic nature also allows its use for projections of the consequences of future flood events, since assumptions about possible future developments of e.g. the economy can be expressed by probability distributions. Therefore, uncertainties associated with the assumptions are captured.Future research should investigate the uncertainty associated with the estimation of indirect economic impacts. CGE models could be integrated into the proposed procedure to analyze the long-term flood impacts. This model class allows for a more flexible economic adjustment process than IO models, which might better reflect real world circumstances in the medium to long-term. Furthermore, validating especially indirect economic impacts is an urgent challenge.S1 FileThese files comprise values of the direct economic impacts per federal state  and valu(ZIP)Click here for additional data file.


Consensus filtering is an effective method for distributed state estimation of distributed sensor networks and the assumption of white measurement noise is widely used. However, when the measurement noise is colored, the traditional consensus filter cannot work well. In this paper, we first propose a consensus-based distributed filter for colored measurement noise by augmenting the state to include the colored measurement noise. To improve the efficiency of the filter, only local colored measurement noise is integrated into the augmented state for each local filter. Furthermore, another consensus-based distributed filter based on measurement differencing scheme is developed to eliminate the ill-conditioned computations of the augmented state approach. In addition, this method does not need to augment the state and thus has lower dimension than the augmented state filter. Simulation results demonstrate the superiority of the proposed methods. Distributed state estimation has drawn a lot of attention in recent years for its advantages. There is no need for the fusion center to gather raw measurements of all nodes in the sensor network . TherefoThere are many strategies to process distributed state estimation. One of these strategies is the sequential method . In thisThe measurement noise characteristics play an important role in state estimation problem. However, the aforementioned distributed state estimation methods assume that the measurement noise is white. This assumption is too restrictive, and applications with non-white noise frequently arise in practice such as speech signal processing, radar signal processing and signal processing of Global Navigation Satellite System (GNSS) ,28,29. TIn this paper, we focus on the distributed state estimation problem for sensor network with colored measurement noise which is the output of a discrete-time linear system with white noise. The consensus distributed filter for colored measurement noise based on state augmentation is derived first. Since the augmentation of colored measurement noise fo all sensor nodes may cause high-dimensional matrix calculation, we only augment the local colored measurement noise into the local state. Moreover, only the state and error covariance without local colored measurement noise are exchanged with neighboring nodes and achieved consensus. In the state augmentation method, the correlation matrix of the measurement noise is singular for there is no noise in the measurements. To eliminate the ill-conditioned computations of the augmented state approach and reduce the dimension of the augmentation state, the consensus-based distributed filter based on measurement differencing is proposed. Simulations are processed for sensor network with colored measurement noises. Results show that the proposed methods outperform the traditional consensus-based distributed state estimation method.The reminder of this paper is organized as follows. j can receive data from the node i. In addition, for any node i itself), i.e., This paper considers a sensor network denoted by n-dimension state vector, d-dimension measurement vector of the sensor node Consider a discrete-time linear stochastic systemThen some assumptions are given to approximate conditional distributions and dynamic of these parameters. For the process noise Assumption 1.The process noise Assumption 2.The measurement noise where Assumption 3.The process noise and the measurement noise are uncorrelatedHere we give an example figure of comparison between white noise and colored noise. The purpose of consensus filter in this paper is to obtain a consensus state of a sensor network with colored measurement noise.Consensus algorithm is the information exchange rule that ensures that the amount of concern of each node achieves consistency. We define the weighted Kullback-Leibler average (KLA) rding to 10)p¯=ap¯=ap¯(·)ℓ is the iteration index and iterations are initialized with Therefore, the solution can be obtained by exchanging the local data with the neighbors via convex combination in a iterative wayIf the PDFs are Gaussian such as Remark 1.It should be noted that the consensus is only achieved when the network is strongly connected. In the network The direct way to solve the colored measurement noise problem is augmented state approach. This augmented state approach is originally proposed in  for singWe augment the original system model for multiple sensors as follows:Since the measurement noise among each sensor node is independent, then the measurement model for each sensor node If we run consensus on the augmented state, we will not only exchange the original state but also the noise states of all sensors with neighbors. However, the communication capability of sensor network may be too limited to afford these large data. Moreover, the quantity we interested in sensor network is the original state instead of the augmented state. Thus, we suggest that the consensus is carried out only on the original state and each sensor node runs a local filter with a local augmented state. Then the system model for each local sensor node The system model  and measThen the covariance of the process noise and the covariance of the measurement noise can be computed asUsing the modified system model  and meask as follows.Suppose we have the augmented state estimate (1)Prediction of local filter: Compute the predicted augmented state estimate (2)Update of local filter: Calculate the residual and the associated covarianceCalculate the local filter gainUpdate the augmented state and the associated error covariance(3)L-step consensus iteration, the consensus on posterior information is carried out in the form n is the dimension of the original state.Consensus on the original information matrices and information vectors: For a Niπi,j=1 . The ite(4)d is the dimension of the measurement of each node.Reset the local filter:R. Instead, another filter capable of converting the received colored noise into white noise called measurement differencing is proposed for single sensor in [However, when constructing a data processing filter, the augmented state process may result in ill-conditioned calculations. The absence of noise in some measurements is a singular problem because in the Kalman filtering, the correlation matrix of the measured noise is singular, i.e., there is no ensor in . SimilarFor each sensor node, we define an auxiliary measurement signal:Substituting −1i into , we can w and the new measurement noise Thus, we obtain a new measurement equation where the measurement is i: i till time k. The error covariance of We use the following notation to denote the actual estimates for each sensor node k is defined as the expected value of the state i up to and including time In the following part, we use the formal notion The gain posed in  to exact seen in ,34,35. Tk as follows.Suppose we have the predicted state estimate (1)Update of local filter:(2)L-step consensus iteration, the consensus on posterior information is carried out byConsensus on the information matrices and information vectors: For a (3)Prediction of local filter:i comes, there is not yet sufficient information to calculate It should be noted that when the first measurement In this section, the stability and other properties of the proposed algorithm are discussed. To clearly show stability properties, the following assumptions are givenAssumption 4.The system matrix Assumption 5.The system is globally observable, i.e., the pair According to Theorem 4 in , under AThe communication cost over the sensor network is also a big concern in the development of consensus algorithms. In the traditional consensus algorithms, only system state estimates are propagated between neighboring nodes. For the augmented state algorithm, since the strategy we adopt is to make only the system state estimation consistent, only the system state estimation propagates between neighbor nodes. For the measurement differencing algorithm, the state of the system does not change, and thus only the state of the propagation system. Therefore, compared with the traditional consensus algorithm, the two algorithms proposed in this paper do not increase the communication burden of the network system.T = 1 s andA two-dimension tracking scenario is considered in this paper and the target moves in a horizontal plane. The target dynamic consists of the state deled by  accordinThe target trajectory is generated by the above model with the following true initial stateIn the simulations, initial states for filters are chosen randomly from There are 10 sensor nodes in the sensor network of which the graphical topology representation is shown in The variance of measurement noise is k is defined byN denotes the number of Monte Carlo runs, K is the simulation step of a certain Monte Carlo. The definition of ARMSE of velocity is similar to the ARMSE of position.For performance comparison, the root mean-squared error (RMSE) of position is used as performance metric. In the target tracking application, the amount we focus on is position and velocity of target. The RMSE of position at time We compare the performance of the traditional distributed consensus filter (DCF) in  which dointed by , only onThe state matrix  measuremTo evaluate performance of the three filters under different colored noises, we have The ARMSE of different measurement noise covariances are given in When a node in the sensor network fails, the sensor noise may suddenly increase. At this point, if the local filter does not sense the fault, the node may be unstable. Here we assume that the noise standard deviation of certain node suddenly increases by 20 times due to the cause of the fault. The RMSE for local filter without consensus (consensus step To evaluate performance of the three filters under white Gaussian noise, we haveThe RMSE of the estimated position obtained by 100 Monte Carlo runs are shown in To show the performance of the proposed methods with large network size, we have performed a simulation with 100 sensors see  and the This paper proposed the consensus-based distributed state estimate methods for distributed sensor network with colored measurement noise. The dynamics system model of each sensor node is modified to include the model of the colored measurement noise. Then the state is augmented to consist of the colored measurement noises. For each local filter, we only integrate the colored measurement noise of each local node into the augmented state to make the filter more effective. Moreover, the consensus is only carried out on original state without the colored measurement noise for each sensor node. To deal with the potential ill-conditioned computations and reduce the dimension of the augmented state approach, the consensus-based distributed filter based on measurement differencing scheme is proposed. The contribution of colored portion of the noise to the received signal is estimated in each sensor node, then this part is eliminated from the received signal. Simulation results show that the proposed methods outperform the traditional method, which does not take the colored measurement noise into account under the situation where the measurement noise is colored in the sensor network. Even for the white Gaussian noise, the performance of the proposed methods are not lower than the traditional method. The proposed algorithms are derived under assumption of linear system model and known statistic characteristics of colored measurement noise. In the future, we shall extend these algorithms to handling nonlinear models and unknown statistic characteristics of colored measurement noise.


A standard 12-lead electrocardiogram (ECG) is an important tool in the diagnosis of heart diseases. Here, Ag/AgCl electrodes with conductive gels are usually used in a 12-lead ECG system to access biopotentials. However, using Ag/AgCl electrodes with conductive gels might be inconvenient in a prehospital setting. In previous studies, several dry electrodes have been developed to improve this issue. However, these dry electrodes have contact with the skin directly, and they might be still unsuitable for patients with wounds. In this study, a wearable 12-lead electrocardiogram monitoring system was proposed to improve the above issue. Here, novel noncontact electrodes were also designed to access biopotentials without contact with the skin directly. Moreover, by using the mechanical design, this system allows the user to easily wear and take off the device and to adjust the locations of the noncontact electrodes. The experimental results showed that the proposed system could exactly provide a good ECG signal quality even while walking and could detect the ECG features of the patients with myocardial ischemia, installation pacemaker, and ventricular premature contraction. The standard 12-lead electrocardiogram (ECG) is an important tool to assist the diagnosis of myocardial ischemia and arrhythmia. In particular, for the diagnosis of myocardial infarction, 12-lead ECG provides important and meaningful information. The timely treatment of occluded coronary artery is critical to reduce myocardial injury and mortality. In order to save the ischemia myocardium, a 12-lead ECG of the patient must be obtained in emergency or prehospital settings. Myocardial infarction must be identified as early as possible and the patient must be taken to a hospital with a cardiac cath lab. In general, the conventional Ag/AgCl ECG electrodes with conductive gels are used to measure ECG signals. The use of conductive gels can effectively improve the conductivity of the skin electrode interface to acquire a better ECG signal quality [In order to improve the above issue of the conventional ECG electrodes, several novel dry electrodes have been proposed in previous studies. Some studies applied the technique of microelectromechanical systems (MEMS) in the development of novel dry electrodes . HoweverDifferent from the above dry electrodes which have to contact with the skin directly, the noncontact dry electrode was developed in recent years. In general, the design of dry electrodes has to minimize the impedance of the skin-electrode interface. Different from other skin-electrode models of other dry electrodes, the skin-electrode interface model of a noncontact dry electrode can be viewed as a coupling capacitance. In 2013, Lin et al. successfully applied the technique of noncontact electrode to acquire a lead I ECG signal . Based oThe electrode-skin interface models of conventional Ag/AgCl electrode and noncontact electrode are shown in i and Vo denote the ECG signal source of the human body and the output of the noncontact electrode, respectively, Cg denote the coupling capacitance formed by the skin and the metal electrode plate, Rg be the equivalent impedance of the bias pathway, and Ri and Ci denote the equivalent resistance and the equivalent capacitance of the used operational amplifier, respectively. Therefore, the transfer function of the used noncontact electrode can be expressed as followings.In the electrode-skin interface model of a noncontact electrode, the skin layer and the metal electrode can be viewed as two parallel plates, and the clothes can be viewed as an isolation layer. Therefore, the electrode-skin interface model of a noncontact electrode can be viewed as a capacitor. i could provide the larger amplitude response when Cg, Rg, and Ri are large enough. In the design of the noncontact electrode, Ri and Ci are decided by the selection of the used operational amplifier. Cg is a coupling capacitor, formed by the metal plate of the noncontact electrode and the skin. In order to increase the value of Cg, the electrode surface area has to be increased or the distance between the skin and the electrode has to be decreased.From the above formula, it showed that decreasing CThe standard 12-lead ECG system is used to collect 12 different ECG signals from different locations simultaneously to completely estimate the vector of electrocardiogram. The measurement of 12-lead ECG system can be simply classified into three parts, including three bipolar limb leads , three unipolar limb leads , and six unipolar chest leads (V1–V6). The measurement of LeadI–LeadIII uses three electrodes placed on the left arm (LA), the right arm (RA), and the left leg (LL) respectively. These unipolar limb leads can be represented as Equations (2)–(4).kth chest leads and the voltage of the kth chest electrode, respectively.The six unipolar chest leads (V1 to V6) represent the voltage difference between the chest voltage and the average voltages of LA, RA, and LL, and they can be expressed byThe system architecture and photograph of the proposed system is shown in The block diagram of the wireless 12-lead ECG acquisition module is shown in In this system, a commercial tablet was used as the platform for the host system, and a 12-lead ECG monitoring program was also designed to continuously monitor the 12-lead ECG signals. This program would first build the graphical user interface to allow the user operating and setting the program parameters. When clicking the start button, it would call the Bluetooth application programming interface (API) to search the wireless 12-lead ECG acquisition module and to make a connection with this module. After connecting with this module, the thread of DataREC would receive the 12-lead ECG signals, store them into the program buffer, real-time display them on the screen, and store the raw data into the local files.In this section, the electrical specifications of the designed noncontact electrodes were first investigated. Next, the ECG signal quality obtained by the proposed noncontact electrode was compared with that of the conventional ECG electrode with conductive gels. In this experiment, the noncontact electrodes were placed across the chest through a thin T-shirt, and their locations were close to that of these conventional ECG electrodes. In this section, the influence of motion artifact on the ECG signal quality of the proposed system was first investigated. In Several dry and noncontact electrodes have been proposed in previous studies, and their specification comparison is summarized in lectrode . The sizlectrode . The siz quality . The ccEIn previous studies, several wearable 12-lead ECG systems have also been designed, and a comparison between the proposed system and other systems is listed in rt shirt , and itsIn this study, a wearable 12-lead noncontact electrocardiogram monitoring system was proposed and successfully applied in measuring the ECG signals of patients with myocardial ischemia and arrhythmia. From the experimental results, the magnitude and phase responses of the designed noncontact electrodes were suitable for measuring ECG signal, and its referred noise was less than


Background: The cognitive function could be affected following exposure to organic solvents.The purpose of this study was to examine the cognitive performance of workers exposed to different levels of organic solvents in a painting unit of an automotive industry.Methods: This case-control study was conducted, in 2017, on 121 and 111 workers from the painting and assembly units of an automotive industry as the case and control groups,respectively. Exposure of workers to organic solvents were determined according to National Institute for Occupational Safety and Health (NIOSH) method. The cognitive performance of the studied population was evaluated by the computerized tests.Results: The obtained scores of the N-Back in 1 and 2 blocks and the simple reaction time tests(SRTTs) were significantly differed in the exposed group (p<0.05). No significant difference was observed between case and control groups in the Stroop test score (p> 0.05). Moreover, a significant relationship between the hazard quotient and the cognitive function test scores were observed except the Stroop test.Conclusion: Exposure to organic solvents affect the cognitive functions even below the Occupational Exposure Limits (OELs). Moreover, workers with high exposure levels of organic solvents had highest risk of poor memory and reaction time. These characteristics produce adverse effects on the central nervous system (CNS) in acute-high level exposure.3 Although the neurotoxic effects of solvents on the peripheral nervous system in chronic-moderate and high-level exposures have long been well documented, persistent effects of long-term and low-level exposures on the CNS have remained unclear.4 Alteration in domains of memory, attention, and other cognitive functions status as neuropsychological endpoints are linked with organic solvent exposure.5 Various studies have been carried out to investigate adverse effects of chronic low-level exposure on cognitive functions.7 In the majority of studies, the exposure assessment parameters have not been clearly considered. For example, some studies have only investigated the cognitive effects of exposure to one particular solvent. Considering the combined effects of solvent exposure is of great importance because it is well known that organic solvents are usually present in mixed form at work places.9 Therefore, applying a suitable indicator to consider effects of mixed solvent exposure is necessary.10 An exposure index (EI) has been widely used in order to estimate the occupational exposure to mixed organic solvents in previous studies. EI is calculated based on the ratio of the concentration of solvents to the threshold limit value.7 Whereas, it is well-known that the endpoints taken into account for the determination of threshold limit values are rarely based on the neurotoxic effects.10 Additionally, it is well recognized that cognitive performance is directly influenced by duration of exposure and organic solvent concentration. In the previous human studies, the different levels of exposure as the main factor in cognitive decline have rarely been considered. Furthermore, conducting animal studies that take into account different ranges of exposure to VOCs is almost difficult and needs a large budget. Based on the above-mentioned descriptions, it is required to establish a dose-response relationship between the levels of exposure to organic solvents and the cognitive function decrease.Broad range applications of volatile organic compounds (VOCs) such as degreaser and constituents of ink, paint, aerosol spray and intermediates has led to exposure of workers to different organic solvents.To the best of our knowledge, there is no study to consider the relationship between different exposure levels of mixed organic solvents and neurotoxic effects. Giving to the aforementioned issues, this study was aimed at evaluating the cognitive performance and mixed organic solvents exposure using a new approach.This case-control study was conducted on a total of 232 male participants, including 121 cases and 111 controls, in the morning shift of the painting and assembly line units in an automotive industry in Tehran, Iran in 2017. The painting unit workers had been exposed to organic solvents through different painting processes and the assembly line workers had no history of solvent exposure. In addition, the control group workers had no significant exposure to the confounding factors such as noise and heat stress. To examine the dose-effect relationship, painting unit workers were divided into 3 groups including spray painters, pre-painters and polishing workers.Background informationPersonal and occupational information was obtained using a self-designed questionnaire. Informational on work characteristic such as employment date, job history, work shift schedule, working hours per day and use of personal protective equipment was collected. All potential personal confounders including, level of education, personal habits such as alcohol and caffeine consumption, smoking, use of medication, drug abuse and also other following confounders including cerebral or head injury, hypertension, and exposure to neurotoxic solvents during leisure time were inquired about, asked and recorded from participants.In order to identify the type of solvents in the case group workers and also being sure that control group workers have no exposure to organic solvents, four samples (2 samples from each group’s workplace) were collected from the workplace air. In the next step, the collected samples were analyzed by a gas chromatography-mass spectrometry (GC-MS) . The results of the GC-MS analysis indicated that no chemical substances were detected in the workplace air of the control group (the group with no history of exposure to organic solvents). Moreover, analyzing the samples taken from workplace air of the exposed group revealed that benzene, toluene, ethylbenzene, and xylene (BTEX) are the most significant organic solvents involved with this group. In the next step, the NIOSH 1501 analytical method was used for sampling and analysis of BTEX compounds. The number of required samples for exposure assessment of exposed group to BTEX compounds was estimated to be 36. To conduct sampling, similar exposure groups (SEGs) were determined and the required samples (36 samples) were assigned to the three exposed groups including spray workers, pre-painting and polishing workers. Then, the breathing zone air of the workers was sampled on charcoal tubes (SKC 226-01) using a portable micro pump. The pump flow rate was adjusted to 50-200 mL/min. After sampling, the samples were immediately transferred to the lab. Extraction of the samples was carried out, using 1.0 mL of carbon disulfide (99.5%)  as the eluent. After extraction, the samples were analyzed through Gas Chromatography using a Flame Ionization Detector (GC-FID) .11:The EI was used to evaluate the combined effects of current exposure to organic compounds and it was calculated using the following equationC is the concentration of organic solvents, and OEL is occupational exposure limit based on international standards.Where, The average inhalation exposure over the workers’ lifetime was calculated through the average daily inhalation intake based on the annual average exposure level:I is defined as the average daily inhalation intake (μg/m3), C is the concentration of the compound in the breathing zone air of the workers (μg/m3), ET is the exposure time (h/day), EF is the exposure frequency (days/year), ED is the exposure duration (years), and AT is an average lifetime (hours).Where 12 Using to the following equation, cumulative exposure to organic solvents was calculated for each subject.In order to assess cumulative and lifetime relative risk, the hazard quotient index (HQ) was utilized.13The MRL is the minimal risk level introduced by ATDSR.Three domains of neurobehavioral functions including psychomotor speed, memory, and attention, which have been identified as the factors affected in occupational exposure to organic solvents, were measured using the computer-controlled tests. In order to eliminate the effects of any distracting factors, all the tests were administered and conducted in a quiet room using a personal computer.14 The n-back test is a continuous performance task that is commonly used to measure a part of working memory and working memory capacity. To conduct the test, a sequence of 100 numbers as stimuli was presented for each participant. The participants were requested to memorize the recently presented stimulus and decided whether it matches the one n steps back in the sequence. The load factor “N” can be adjusted to make the task more or less difficult. For example “1-N” means that participants have to remember the position of the item one turn back and “2-N” means remembering the position of the item 2 turns back, and so on.14The computerized version of the n-back test was applied to assess memory function by increasing task load. Working memory was assessed through n-back 1 and 2 tests.15Selective attention, the ability to ignore distracting visual and verbal stimuli, was measured by the computerized Stroop test. In this task, a number of 4 circles in different colors  were presented on the computer screen. The name of the color which might be conflicted with its color had been written. The participants were asked to select the right button on the keyboard according to the presented color not written color names.16Vasomotor speed was measured by a computerized simple reaction time test (SRTT). In this test, the workers were instructed and requested to react as quickly as they can recognize the 50 identical optical signals by pressing the selective button on the key board.P value < 0.050 was considered as a significant level within the 95% CI.Statistical analyzing was performed by using SPSS software (version 22). A ’s t test was used to examine significant differences between case and control groups. Analysis of variance (ANOVA) was also used to compare variables among multiple groups. Moreover, the multiple adjusted regressions were applied to find out the relationship between participant characteristics such as age, working experience, smoking habit as well as hazard quotient and cognitive test scores.The paired studentIn total, 32 subjects could not to meet inclusion criteria and were excluded from the analysis. It is worth mentioning that lack of education as the main factor contributed to decreased cognitive performance was controlled through limitation. In the other words, all participants had mid-level of education and a few with different educational status were excluded from the study. Also, the subjects with alcohol consumption were excluded from analysis. Actually, there was an uncertainty in worker’s report on alcohol usage and therefore, we were unable to establish a correct statistical analysis for the alcohol usage content and cognitive performance. Finally, data was provided for 200 workers, including 98 painting unit workers (exposed group) and 102 reference workers (control group). 17 It is worth mentioning that the calculated EI for polishing workers group was far below 1.After analyzing the samples gathered from different sections of the breathing zone of painting unit workers, the BTEX compounds were recognized as the main organic solvents according to GC-MS analysis results. The average and standard deviation of organic solvent concentration as well as the EI have been presented in 12 As mentioned before, the hazard quotient is obtained from inhalation intake divided by the minimal risk level (MRL); MRL amounts for BTEX are 0.003, 1, 0.06 and 0.05, respectively. The affecting parameters for calculation of inhalation intake are exposure level, age and work duration, which influence on the neurobehavioral functions. Using the hazard quotient, it is possible to compare the neurobehavioral status of workers with different working characteristics. As it is seen, the hazard quotient level in spray workers (painters) is higher than the others.Bivariate correlation coefficients showed a significant relationship between the hazard quotient and test scores except for Stroop test which did not change with the HQ value . As TablMoreover, multiple regression analysis did not show any significant relationship between the cognitive tests and the demographic data except for one between the duration of exposure and the SRTT (data was not shown).20 were selected as the selective solvents in the present study. In addition to the neurological characteristics, the peak level of BTEX compounds was much bigger than the other identified compounds in the GC-MS chromatogram. Therefore, BTEX compounds were selected as the main compounds for further experiments. According to the Koffi Badjagbo study, these compounds have also been reported as the main organic solvents emitted from the automotive industry.21 Two important factors affecting cognitive functions in occupational exposure to organic solvents, which have been reported in the previous study, are level and duration of exposure.22This study investigated the occupational exposure to different levels of mixed organic solvents and its effects on the cognitive functions of the workers in the painting unit of an automotive industry. To the best of our knowledge, our study was the first study which investigated the effects of exposure to mixed BTEX compounds on cognitive performance. In order to identify the types of organic solvents in the exposed group (painting unit), environmental samples were taken from the painting unit and subsequently were analyzed by GC-MS. Based on obtained results from GC-MS analysis, BTEX compounds, which neurotoxicity effects have been confirmed in previous studies,23 The possible reasons for the high level of benzene in this section may be the presence of benzene as an impurity in solvents and also the unnecessary use of thinner for cleaning the surface. Cognitive test scores revealed that spray workers (painters) had a poorer cognitive function in the domain of memory and reaction time. There has been a long history of research related to neurotoxicity of solvents in painters.24 Memory has been stated as the most prevalent domain of cognitive performance alteration in occupational exposure to mixed organic solvents.25 In this study, there was a noticeable difference between the exposed and the control group in the reaction time test. This finding is in line with most previous similar studies.26 Although in a meta-analysis study, conducted by Meyer-Baron, attention was introduced as the most frequent domain of cognition deterioration in occupational exposure to organic solvents, in our study, there were no significant differences in the Stroop test scores between the case and control groups. Several reasons might explain this inconsistency in the findings across these studies. In the present study, toluene, which is well-known to have more neurotoxic characteristics than the other studied compounds , had the lowest concentration. It is worth mentioning that in the Meyer meta-analysis, the average concentration of toluene, which was reported to be responsible for altering the attention was in the ranges of 33 to 89 ppm,10 while in the current study, the concentration of toluene was much lower than the reported value of the Meyer research. The second reason for our findings is attributed to the type of the test used for evaluating the attention status. In the present study, a computerized test was used to investigate the attention status.Although the concentration of toluene, ethylbenzene, and xylene in the spray workers group was lower than the occupational exposure level (OEL), the cumulative EI of BTEX was higher than the provided standard value. This finding emphasizes that benzene concentration has a substantial effect on increasing the combined EI value concerning spray workers. Regarding the obtained results, the spray section was known as the most polluted environment among the studied sections. This finding is in line with the Dehghani et al study.All 3 exposure groups had a greater risk of poorer memory in both of the 1 and 2 blocks of n-back compared to the reference group. Although polishing workers had a trivial exposure level to organic solvents, they got a lower memory score than the non-exposed workers. This finding indicated that occupational exposure to low levels of organic solvents can also affect memory status in long-term exposures.P value >0.05), the level of exposure was regarded as the main variable for calculating the hazard quotient value. Given all the above, it seems that exposure level plays an important role in cognitive performance alteration. Moreover, in a cohort study, exposure to high levels of organic solvents proved to have resulted in decreasing cognitive functions.22 These findings were in line with our study results. The results of the other study, which was carried out on the painters, revealed that the cumulative exposure level is directly associated with the alteration in cognitive performance.27As presented in In spite of the call to remove benzene from the paint ingredients, it still seems to be a matter in the workplace. Exposure to the low levels of the BTEX compounds, even below the OELs, has caused an increased risk of neurobehavioral changes in the domains of memory and reaction. Furthermore, increasing concentration levels and long-term exposure to organic solvents were significantly related to decrease in cognitive function scores. It is worth mentioning that hazard quotient, a quotient obtained by considering some potential factors that might affect neurobehavioral functions, showed a dose-response relationship in exposed workers in memory and especially in the reaction time tests.To conduct this research, permission was obtained from the ethical committee of Tehran University of Medical Sciences (TUMS). Informed consent was also obtained from each participant.The authors declare that they have no competing interests.This study was conducted with financial support from the Tehran University of Medical Sciences.FD collected the data and drafted the manuscript. FG and SAZ oversaw the whole study process and designed the study. MS performed statistical analysis. All authors read and approved the final manuscript.The authors would like to appreciate from Tehran University of Medical Sciences. We also appreciate the workers and HSE personal of the car manufacturing factory for their help and cooperation in this project.