Very often data we encounter in practice is a collection of matrices rather than a single matrix. These multi-block data often share some common features, due to the background in which they are measured. In this study we propose a new concept of linked blind source separation (BSS) that aims at discovering and extracting unique and physically meaningful common components from multi-block data, which also contain strong individual components. The validity and potential of the proposed method is justified by simulations.
Event-related potentials (ERP)s are electrophysiological responses that are commonly used for detecting the brain response to external stimuli. In this paper, we propose to use the sparse common component and innovations model (SCCI) to extract ERPs from multiple EEG signals recorded across closely located electrodes. This model finds the sparse representation of the common component of the signals and their innovation components with respect to pre-determined common and innovation dictionaries, where the common component refer to an event captured by adjacent electrodes such as ERPs. However, different stimuli may produce different responses and predetermining the dictionary may not always be optimal. Therefore, we introduce a structured dictionary learning method to simultaneously learn the two dictionaries from training data. The proposed method is applied to a study of error monitoring where two different types of brain responses are elicited corresponding to the decision made by the subject. The learned dictionaries can discriminate between the response types and extract the ERP corresponding to the two responses.
Legumes form symbiotic associations with either nitrogen-fixing bacteria or arbuscular mycorrhizal fungi. Formation of these two symbioses is regulated by a common set of signalling components that act downstream of recognition of rhizobia or mycorrhizae by host plants. Central to these pathways is the calcium and calmodulin-dependent protein kinase (CCaMK)-IPD3 complex which initiates nodule organogenesis following calcium oscillations in the host nucleus. However, downstream signalling events are not fully understood. Here we show that Medicago truncatula DELLA proteins, which are the central regulators of gibberellic acid signalling, positively regulate rhizobial symbiosis. Rhizobia colonization is impaired in della mutants and we provide evidence that DELLAs can promote CCaMK-IPD3 complex formation and increase the phosphorylation state of IPD3. DELLAs can also interact with NSP2-NSP1 and enhance the expression of Nod-factor-inducible genes in protoplasts. We show that DELLA is able to bridge a protein complex containing IPD3 and NSP2. Our results suggest a transcriptional framework for regulation of root nodule symbiosis.
The aim of this work is to compare a novel exploratory chemometrics method, Common Components Analysis (CCA), with Principal Components Analysis (PCA) and Independent Components Analysis (ICA). CCA consists in adapting the multi-block statistical method known as Common Components and Specific Weights Analysis (CCSWA or ComDim) by applying it to a single data matrix, with one variable per block. As an application, the three methods were applied to SPME-GC-MS volatolomic signatures of livers in an attempt to reveal volatile organic compounds (VOCs) markers of chicken exposure to different types of micropollutants. An application of CCA to the initial SPME-GC-MS data revealed a drift in the sample Scores along CC2, as a function of injection order, probably resulting from time-related evolution in the instrument. This drift was eliminated by orthogonalization of the data set with respect to CC2, and the resulting data are used as the orthogonalized data input into each of the three methods. Since the first step in CCA is to norm-scale all the variables, preliminary data scaling has no effect on the results, so that CCA was applied only to orthogonalized SPME-GC-MS data, while, PCA and ICA were applied to the “orthogonalized”, “orthogonalized and Pareto-scaled”, and “orthogonalized and autoscaled” data. The comparison showed that PCA results were highly dependent on the scaling of variables, contrary to ICA where the data scaling did not have a strong influence. Nevertheless, for both PCA and ICA the clearest separations of exposed groups were obtained after autoscaling of variables. The main part of this work was to compare the CCA results using the orthogonalized data with those obtained with PCA and ICA applied to orthogonalized and autoscaled variables. The clearest separations of exposed chicken groups were obtained by CCA. CCA Loadings also clearly identified the variables contributing most to the Common Components giving separations. The PCA Loadings did not highlight the most influencing variables for each separation, whereas the ICA Loadings highlighted the same variables as did CCA. This study shows the potential of CCA for the extraction of pertinent information from a data matrix, using a procedure based on an original optimisation criterion, to produce results that are complementary, and in some cases may be superior, to those of PCA and ICA.
We describe a scheme for rate-distortion with distributed encoding in which the sources to be compressed contain a common component. We show that this scheme is optimal in some situations and that it strictly improves upon existing schemes, which do not make full use of common components. This establishes that independent quantization followed by independent binning is not optimal for the two-encoder problem with a distortion constraint on one source. We also show that independent quantization and binning is suboptimal for the three-encoder problem in which the goal is to reproduce one of the sources losslessly. This provides a counterexample that is fundamentally different from one provided earlier by Körner and Marton. The proofs rely on the binary analogue of the entropy power inequality and the existence of a rate loss for the binary symmetric Wyner-Ziv problem.
Amyloid fibrils are fibrillar polypeptide aggregates from several degenerative human conditions, including Alzheimer's and Creutzfeldt-Jakob diseases. Analysis of amyloid fibrils derived from various human diseases (AA, ATTR, Aβ M, ALλ, and ALκ amyloidosis) shows that these are associated with a common lipid component that has a conserved chemical composition and that is specifically rich in cholesterol and sphingolipids, the major components of cellular lipid rafts. This pattern is not notably affected by the purification procedure, and no tight lipid interactions can be detected when preformed fibrils are mixed with lipids. By contrast, the early and prefibrillar aggregates formed in an AA amyloid-producing cell system interact with the raft marker ganglioside-1, and amyloid formation is impaired by addition of cholesterol-reducing agents. These data suggest the existence of common cellular mechanisms in the generation of different types of clinical amyloid deposits.
Common Components Analysis (CCA) summarizes the results of program evaluations that utilize randomized control trials and have demonstrated effectiveness in improving their intended outcome(s) into their key elements. This area of research has integrated and modified the existing CCA approach to provide a means of evaluating components of programs without a solid evidence-base, across a variety of target outcomes. This adapted CCA approach (a) captures a variety of similar program characteristics to increase the quality of the comparison within components; (b) identifies components from four primary areas (i.e., content, process, barrier reduction, and sustainability) within specific programming domains (e.g., vocation, social); and (c) proposes future directions to test the extent to which the common components are associated with changes in intended program outcomes (e.g., employment, job retention). The purpose of this paper is to discuss the feasibility of this adapted CCA approach. To illustrate the utility of this technique, researchers used CCA with two popular employment programs that target successful Veteran reintegration but have limited program evaluation – Hire Heroes USA and Hire Our Heroes. This adapted CCA could be applied to longitudinal research designs to identify all utilized programs and the most promising components of these programs as they relate to changes in outcomes.
Cross-view classification that means to classify samples from heterogeneous views is a significant yet challenging problem in computer vision. An effective solution to this problem is the multi-view subspace learning (MvSL), which intends to find a common subspace for multi-view data. Although great progress has been made, existing methods usually fail to find a suitable subspace when multi-view data lies on nonlinear manifolds, thus leading to performance deterioration. To circumvent this drawback, we propose Multi-view Common Component Discriminant Analysis (MvCCDA) to handle view discrepancy, discriminability and nonlinearity in a joint manner. Specifically, our MvCCDA incorporates supervised information and local geometric information into the common component extraction process to learn a discriminant common subspace and to discover the nonlinear structure embedded in multi-view data. Optimization and complexity analysis of MvCCDA are also presented for completeness. Our MvCCDA is competitive with the state-of-the-art MvSL based methods on four benchmark datasets, demonstrating its superiority.
Benzophenone-3 (BP-3) has been widely used in sunscreens and many other consumer products, including cosmetics. The widespread use of BP-3 has resulted in its release into the water environment, and hence its potential impact on aquatic ecosystem is of concern. To better understand the risk associated with BP-3 in aquatic ecosystems, we conducted a thorough review of available articles regarding the physicochemical properties, toxicokinetics, environmental occurrence, and toxic effects of BP-3 and its suspected metabolites. BP-3 is lipophilic, photostable, and bioaccumulative, and can be rapidly absorbed via oral and dermal routes. BP-3 is reported to be transformed into three major metabolites in vivo, i.e., benzophenone-1 (BP-1), benzophenone-8 (BP-8), and 2,3,4-trihydroxybenzophenone (THB). BP-1 has a longer biological half-life than its parent compound and exhibits greater estrogenic potency in vitro. BP-3 has been detected in water, soil, sediments, sludge, and biota. The maximum detected level in ambient freshwater and seawater is 125 ng/L and 577.5 ng/L, respectively, and in wastewater influent is 10,400 ng/L. The major sources of BP-3 are reported to be human recreational activities and wastewater treatment plant (WWTP) effluents. BP-3 and its derivatives have been also detected in fish lipid. In humans, BP-3 has been detected in urine, serum, and breast milk samples worldwide. BP-1 has also been detected in placental tissues of delivering women. While sunscreens and cosmetics are known to be major sources of exposure, the fact that BP-3 has been detected frequently among young children and men suggests other sources. An increasing number of in vitro studies have indicated the endocrine disrupting capacity of BP-3. Based on a receptor binding assay, BP-3 has shown strong anti-androgenic and weak estrogenic activities but at the same time BP-3 displays anti-estrogenic activity as well. Predicted no effect concentration (PNEC) for BP-3 was derived at 1.32 μg/L. The levels observed in ambient water are generally an order of magnitude lower than the PNEC, but in wastewater influents, hazard quotients (HQs) greater than 1 were noted. Considering limited ecotoxicological information and significant seasonal and spatial variations of BP-3 in water, further studies on environmental monitoring and potential consequences of long-term exposure in aquatic ecosystem are warranted.
The Rapid Alert System for Food and Feed (RASFF) has reported many cases of different UV curing inks components in foodstuffs during the last few years. These contaminants reach foodstuffs mainly by set-off, their principal migration mechanism from the package. Under this premise, this work has tried to characterize the process of migration of two common UV ink components: a photoinitiator (4-Methylbenzophenone) and a coinitiator (Ethyl-4-(dimethylamino) benzoate), from the most common plastic material used in food packaging low-density polyethylene (LDPE) into six different food simulants. The migration kinetics tests were performed at four different common storage temperatures, obtaining the key migration parameters for both molecules: the coefficients of diffusion and partition. The migration process was highly dependent on the storage conditions, the photoinitiator properties and the pH of the foodstuff.