Multivariate Methodology Research Articles

Emotional Intelligence as a Predictor of Prosocial Behaviors in Spanish and Colombian Older Adults Based on Path Models.

Emotional intelligence (EI), empathy, and prosocial behavior (PB) are widely studied in the early stages of life, up to adolescence. However, there have been few studies in older adults. Using a multivariate methodology, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were applied with structural equation models (SEM) in 271 older adults in Colombia, along with a Spanish sample made up of 139 adults over 60 years of age, for a total sample of 411 older adults. The results confirmed that EI, as measured with the WLEIS, scale is the best predictor of PB, with excellent adjustment criteria (GFI = 0.99; CFI = 0.98; RMSEA = 0.064; PCLOSE = 0.28; SRMR = 0.023; AIC = 57.30). It is concluded that the path model yielded a reliable predictive explanation of PB, including EI as a key variable that explains prosocial tendencies.

Técnica híbrida multicritérios para a seleção de mercados internacionais baseada no processo analítico hierárquico (AHP)

Abstract Purpose: To present a rigorous technique for merchandise companies that are internationalizing and pursuit the best options of export markets to direct their products. Originality/value: The literature suggests that companies, especially small and medium enterprises (SME), due to their lack of experience when participating in international business, tend to employ uncertainty processes that affect in decision making when exporting. For this reason, an innovative multivariable technique for international markets selection (IMS) is presented, in which factors such as trade barriers, economics, cost, logistics and culture are considered. Design/methodology/approach: Consulting different databases obtained from the internet, it was possible to analyze the factors indicated above, which, in turn, are integrated by different variables. Thus, this work used a multivariable methodology and/or technique that is complemented with the analytic hierarchy process (AHP) to assign the weights of each factor and their respective variables; all this, including a survey among experts and members of the foreign trade department of the company under study, in this case, dedicated to the production of confectionery. Findings: According to the statistical data processed with the multivariable technique and weighted with the AHP process, the results suggest that by their score the best export markets are Ireland, Netherlands and Denmark. In this sense, the empirical evidence indicates that the pointed countries are the most attractive markets for the factors considered, as well as for their general features.

A Semi-Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses.

Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs.

Sequential and Simultaneous Determination of Cd, Fe and Ni in Toothpastes Employing Slurry Sampling High-Resolution Continuum Source Graphite Furnace Atomic Absorption Spectrometry

Here is reported an analytical method for the multielemental determination of Cd, Fe and Ni in toothpaste employing slurry sampling high-resolution continuum source electrothermal atomic absorption spectrometry (HR-CS ET AAS). Using a single sample aliquot, Cd was determined at 228.802 nm, and, sequentially, Fe (232.036 nm) and Ni (232.003 nm) were simultaneously determined. Pyrolysis and atomization temperatures were 800 and 1500 °C for Cd, respectively. An atomization temperature of 2600 °C was used for Fe and Ni. A Pd and Mg mixture was used as the chemical modifier for standards and samples. The procedure for the slurry preparation was optimized using multivariate methodology with a multiple response function while employing a sample mass of about 250 mg. The optimized conditions were: 0.8 mol L−1 HNO3, 0.5% (m/v) Triton X-100 and a sonication time of 15 min. Using aqueous standards for the calibration, limits of detection and quantification were of 3.2 and 10.8 ng g−1 for Cd, 2.2 and 7.4 µg g−1 for Fe, and 0.044 and 0.148 µg g−1 for Ni. The precision estimated as relative standard deviation (n = 5) was 6.1% for Cd, 3.5% for Fe and 4.4% for Ni. The method accuracy was evaluated by standard addition and recovery tests and the recoveries varied from 93 to 114%. The method was applied for the analysis of five toothpaste samples of different brands. The concentrations ranged from 52.0 to 73.6 µg g−1 for Fe, and < 0.148 to 0.227 µg g−1 for Ni. For Cd, the concentrations were below the limit of detection.

GDP and Public Expenditure in Education, Health, and Defense. Empirical Research for Greece

Wagner Law and Keynesian approaches are the two fundamental theories of public finance. The aim of this study is to assess empirical evidence for the public spending–national income relationship at a disaggregated level for the time period 1995–2019. The sectoral public expenditures include education, health, and defense. The data employed were derived by EUROSTAT and OECD. Based on our findings, a sole relationship of the variables was validated, while the causality of the relationship provides conflict results depending on whether two-variate or multivariate methodology is employed. In the case of the multivariate framework that outperforms the two-variate approach in terms of information, the causality is directed from government expenses to the GDP level, validating the Keynesian approach in the long run as well as in the short run. On the other hand, the results validate Wagner Law based on the results of Granger causality pairwise test. A potential interpreatation for the results found is related to the measures imposed by the Memorandum, since the disproportionate cuts of the public expenses in the period of crisis have determined the evolution of national income. The scientific value of the presents study stands on the suggestion of potential effective measures aiming at the limitation of national income shrinkage in periods of severe economic crises worldwide.

Tergitol@SiO2@Fe3O4 magnetic nano-material and experimental design methodology: An effective and selective adsorbent for solid phase microextraction and flame atomic absorption spectrometric analysis of lead in different matrixes

In current paper, we synthesized a new and functional magnetic Tergitol@SiO2@Fe3O4. The synthesized magnetic nano-material was characterized in detail using transmission electron microscopy, energy dispersive spectrometer, X-ray photoelectron spectroscopy, and fourier transform infrared spectrometer. Then, the applicability of the synthesized nano-material to the separation and preconcentration of Pb(II) ions was investigated prior to flame atomic absorption spectrometric determination. In order to ensure efficient and selective extraction of Pb(II), major variables such as pH, adsorbent amount, and adsorption time were optimized by multivariate methodology based on Box-Behnken design. Under the optimized conditions, linearity, detection limit, enhancement factor, and relative standard deviation (RSD%) were 0.2–250 µg L−1, 0.07 µg L−1, 84, and 1.8%, respectively. Accuracy and precision of the optimized method were investigated by using two certified reference materials (INCT-TL-1-tea leaves and SRM-1643e Trace elements in water), and good recoveries (94.7–103.9%) with low RSDs were achieved. Finally, the optimized method was successfully applied for the determination and separation of lead in different matrixes.

Chemometric strategies for authenticating extra virgin olive oils from two geographically adjacent Catalan protected designations of origin

This paper proposes using multivariate methodology to authenticate the geographical origin of monovarietal Arbequina extra virgin olive oils from two geographically adjacent Catalan PDOs (protected designations of origin). Two spectroscopic techniques, fluorescence and FT-Raman were used to obtain multivariate data. The results obtained by principal component analysis (PCA), partial least squares–discriminant analysis (PLS–DA) and low and high-data fusion are discussed. The training and test samples were randomly chosen. To obtain greater representativeness in the results, the data set was independently divided three times. Thus, three different training and test sets were obtained and used to build independent classification models and carry out data fusion strategies. When the two fusion strategies were applied, the performance parameters (sensitivity and specificity) were better than when they were applied individually, which indicates that synergies between FT-Raman and fluorescence expand the information about the samples. It was also observed that the influence on the results of the type of data set obtained by the random division of the data set was minimized.

Multivariate optimization of a dispersive liquid-liquid microextraction method for determination of copper and manganese in coconut water by FAAS

In this study, multivariate methodologies were applied in the optimization of a dispersive liquid–liquid microextraction (DLLME) method, aiming at the determination of Cu and Mn in coconut water samples by flame atomic absorption spectrometry. Some extractors (chloroform and CCl4), dispersants (ethanol, methanol and acetonitrile) and complexing agents (5-Br-PADAP and Dithzone) were previously tested in the extraction. A mixture design was used to optimize the component proportions formed by chloroform (10%), acetonitrile (76%), and 0.020% 5-Br-PADAP solution (14%). Doehlert design optimized the variables pH, NaCl, and buffer amounts for the extraction of both metals. The following analytical characteristics, respectively for Cu and Mn, were accessed: limit of quantification (4.83 and 3.32 µg L−1), enrichment factors (11 and 8 fold), and precision (6.6 and 6.0% RSD, n = 10). Addition/recovery tests of the analytes allowed to find values in the range of 96.5–120% for Cu and 99–107% for Mn.

A two-stage Bayesian semiparametric model for novelty detection with robust prior information

Novelty detection methods aim at partitioning the test units into already observed and previously unseen patterns. However, two significant issues arise: there may be considerable interest in identifying specific structures within the novelty, and contamination in the known classes could completely blur the actual separation between manifest and new groups. Motivated by these problems, we propose a two-stage Bayesian semiparametric novelty detector, building upon prior information robustly extracted from a set of complete learning units. We devise a general-purpose multivariate methodology that we also extend to handle functional data objects. We provide insights on the model behavior by investigating the theoretical properties of the associated semiparametric prior. From the computational point of view, we propose a suitable varvec{xi }-sequence to construct an independent slice-efficient sampler that takes into account the difference between manifest and novelty components. We showcase our model performance through an extensive simulation study and applications on both multivariate and functional datasets, in which diverse and distinctive unknown patterns are discovered.

Automatic Identification of Paraffin Pixels on FTIR Images Acquired on FFPE Human Samples.

The transfer of mid-infrared spectral histopathology to the clinic will be possible provided that its application in clinical practice is simple. Rapid analysis of formalin-fixed paraffin-embedded (FFPE) tissue section is thus a prerequisite. The chemical dewaxing of these samples before image acquisition used by the majority of studies is in contradiction with this principle. Fortunately, the in silico analysis of the images acquired on FFPE samples is possible using extended multiplicative signal correction (EMSC). However, the removal of pure paraffin pixels is essential to perform a relevant classification of tissue spectra. So far, this task was possible only if using manual and subjective histogram analysis. In this article, we thus propose a new automatic and multivariate methodology based on the analysis of optimized combinations of EMSC regression coefficients by validity indices and KMeans clustering to separate paraffin and tissue pixels. The validation of our method is performed using simulated infrared spectral images by measuring the Jaccard index between our partitions and the image model, with values always over 0.90 for diverse baseline complexity and signal-to-noise ratio. These encouraging results were also validated on real images by comparing our method with classical ones and by computing the Jaccard index between our partitions and the KMeans partitions obtained on the infrared image acquired on the same samples but after chemical dewaxing, with values always over 0.84.

A quantitative multivariate methodology for unsupervised class identification in pistachio (Pistacia vera L.) plant leaves size

Aim of study: Genetic diversity of pistachio, can be evaluated by using different descriptors, as adopted in international certification systems. Mainly the descriptors are morphological traits as leaf, which represents an important organ for its sensibility to growth conditions during the expansion phase. This study adopted a rapid and quantitative non-hierarchic clustering classification (k-means), to extract size classes basing on the contemporary combination of different morphological traits (i.e., leaf stalk length, terminal leaf length, terminal leaf width and terminal leaf ratio) of a varietal collection composed by 21 pistachio cultivars.Area of study: Worldwide.Material and methods: The unsupervised non-hierarchic clustering technique was adopted to the entire samples of pistachio leaves from k=2 to k=15 for both four morphological variables (i.e., leaf stalk length, terminal leaf length, terminal leaf width and terminal leaf ratio) and three morphological variables (i.e., terminal leaf length, terminal leaf width and terminal leaf ratio).Main results: A classification model only on the three morphological variables (for results of statistical analysis in which the groups resulted to be more separated and different for all the variables), with k= 5 (five groups), was constructed using a non-linear artificial neural network approach. The percentages of bad prediction in both training and testing resulted equal to 0%. The “terminal leaf length” returned the higher impact (44.89%).Research highlights: The contemporary combination of different morphological leaf traits, allowed to create an automatic classification of size classes of great importance for cultivar identification and comparison.

Study of Performance of RZhFA‑6500 Smoothing Reactors

DC traction power supply systems have become widespread in several countries. Electricity consumption by traction power supply systems has pronounced seasonal and daily peaks. The peak nature of consumption leads to additional heating of live parts since time constants of transient thermal and electrophysical processes differ by several orders of magnitude, therefore, reducing ohmic resistance of electrical equipment, other things being equal, is an extremely important task.Within the framework of solving the problem of increasing energy efficiency of equipment of DC traction substations, smoothing reactor filter devices with an armoured radial-cylindrical magnetic core with low ohmic resistance, named RZhFA‑6500, were developed and manufactured.In 2018, two blocks of RZhFA‑6500 smoothing reactors were put into pilot operation at one of the most heavily loaded sections of DC electrified railways, providing traffic of trains weighing up to 9000 tons and traffic volume of about a hundred pairs of trains per day.To determine comparative indicators of energy efficiency, data on consumption of electric power for traction of trains for the compared periods before and after introduction of smoothing reactors of a new type were requested. The data received from JSC Russian Railways contained information on the traffic volume, type of rolling stock, and weight of the transported goods in accordance with the executed traffic schedule through section under consideration. The resulting set of data served as the basis for development of a multivariate methodology for assessing energy efficiency of reactors of a new type, as well as their contribution to efficiency of train operation over the considered comparative time interval. Since efficiency of power equipment also depends on the ambient temperature, the average integral parameter of average daily temperature was introduced into the calculation methodology, which, as it turned out, during processing initial data and construction of characteristic graphical dependencies, has a comparative difference of less than one percent for the same monitored periods.Accurate quantitative accounting of the data on consumption of electrical energy provided for traction of trains, exchange of reactive energy with the supply network, volume of transportation allowed to obtain an objective comparison of contribution of smoothing reactors of a new type to increasing efficiency of train operation and reducing consumption of electrical energy for traction of trains.

Novel approach to the selection of Psidium guajava genotypes using latent traits to bypass multicollinearity

Multicollinearity is a very common problem in studies that employ path analysis in agronomic crops, which generates unrealistic results and erroneous interpretations. This study was aimed at assessing the path analysis in data obtained from guava tree full-sib based on modelling multiple regressions applying latent variables to neutralize the effects of multicollinearity. Seven explanatory variables were measured – fruit mass (FM), fruit length (FL), fruit diameter (FD), mesocarp thickness (MT), peel thickness (PT), pulp mass (PM), total number of fruits (NTF) –, plus the main dependent variable, total yield per plant (YIELD). In accordance with the multicollinearity scenario, eleven values were tested with the addition of the constant K to the diagonal of the correlation matrix X’X. Path analysis was applied in two models: all the explanatory variables with direct effect on the dependent one and another model with multiple regression with more than one chain and the presence of latent variables. The path analysis in the multivariate methodology of structural equation modelling (SEM), which uses latent variable prediction, provided better results than the traditional and ridge path analyses.

A Web‐based Open‐source Geoinformation Tool for Regional Water Resources Assessment

To reduce the impact of droughts and increase the resilience of regional water systems, various competing demands, such as hydropower, supply, irrigation and river ecosystems, need to be reconciled. In this perspective, for a sustainable and efficient management of the water resources, planners and practitioners would benefit from using the most recent data and easily accessible analysis tools, that can make hydrological model applications easier to apply. In this paper, a web-based open-source geoinformation tool for estimating Flow Duration Curves (FDCs) in ungauged basins is presented. The FDC estimation derives from a regional statistical model, that relates basin topographic parameters, climatic characteristic and other environmental factors to FDC features via multivariate regression. The software tool has the primary function of simplifying the GIS-intensive application of the multivariate methodology, allowing computation of the necessary basin features through web services. These are developed in compliance with the OGC specifications for geospatial Web Processing Service (WPS), while scripting and model implementation are developed server-side, which ensures the user access to always updated data, and to procedures free from software client compatibility issues. An operational application is available for the North-Western Italy.

Drivers for Development in Regional Higher Education

Recently, higher education in Russia and abroad has been transformed significantly. Both universities and the state have to tackle challenging and multidirectional tasks. Analysis of theoretical research on the types of higher education institutions and the determining factors provides insights into drivers for development. Simultaneously, multivariate methodology for assessing the state of higher education allows identifying the factors that most affect its transformation. Th e stud y demonstrate s that these factors depend on the development strategy and economic model of a university as well as government programmes for the development of education and science. One of the major factors affecting the development of regional higher education is the presence of a leading university in the region. This situation is economically justified due to the strong dependence of universities on the federal budget. Moreover, a leading university creates an educational environment for academic staff and students. Their participation in scientifi c researc h an d academi c activitie s positivel y influen ce t he developme nt of regional systems. The growing gap between the leading university and other education institutions leads to monopolisation, decrease in competition, and, consequently, decline in performance. Thus, there is a need for structural changes. The particular factors affecting the development of the higher education are the indicators of academic activity and student performance. The conducted analysis suggests the intensification of the trends, indicating further differentiation of regional higher education system with a simultaneous improvement of individual universities in research and development. Additionally, it is necessary to examine the feasibility of structural adjustment within regional systems due to the growing imbalances in development.

Biocultural evidence of precise manual activities in an Early Holocene individual of the high-altitude Peruvian Andes.

Cuncaicha, a rockshelter site in the southern Peruvian Andes, has yielded archaeological evidence for human occupation at high elevation (4,480 masl) during the Terminal Pleistocene (12,500-11,200 cal BP), Early Holocene (9,500-9,000 cal BP), and later periods. One of the excavated human burials (Feature 15-06), corresponding to a middle-aged female dated to ~8,500 cal BP, exhibits skeletal osteoarthritic lesions previously proposed to reflect habitual loading and specialized crafting labor. Three small tools found in association with this burial are hypothesized to be associated with precise manual dexterity. Here, we tested this functional hypothesis through the application of a novel multivariate methodology for the three-dimensional analysis of muscle attachment surfaces (entheses). This original approach has been recently validated on both lifelong-documented anthropological samples as well as experimental studies in nonhuman laboratory samples. Additionally, we analyzed the three-dimensional entheseal shape and resulting moment arms for muscle opponens pollicis. Results show that Cuncaicha individual 15-06 shows a distinctive entheseal pattern associated with habitual precision grasping via thumb-index finger coordination, which is shared exclusively with documented long-term precision workers from recent historical collections. The separate geometric morphometric analysis revealed that the individual's opponens pollicis enthesis presents a highly projecting morphology, which was found to strongly correlate with long joint moment arms (a fundamental component of force-producing capacity), closely resembling the form of Paleolithic hunter-gatherers from diverse geo-chronological contexts of Eurasia and North Africa. Overall, our findings provide the first biocultural evidence to confirm that the lifestyle of some of the earliest Andean inhabitants relied on habitual and forceful precision grasping tasks.

Comparing basal dendrite branches in human and mouse hippocampal CA1 pyramidal neurons with Bayesian networks

Pyramidal neurons are the most common cell type in the cerebral cortex. Understanding how they differ between species is a key challenge in neuroscience. A recent study provided a unique set of human and mouse pyramidal neurons of the CA1 region of the hippocampus, and used it to compare the morphology of apical and basal dendritic branches of the two species. The study found inter-species differences in the magnitude of the morphometrics and similarities regarding their variation with respect to morphological determinants such as branch type and branch order. We use the same data set to perform additional comparisons of basal dendrites. In order to isolate the heterogeneity due to intrinsic differences between species from the heterogeneity due to differences in morphological determinants, we fit multivariate models over the morphometrics and the determinants. In particular, we use conditional linear Gaussian Bayesian networks, which provide a concise graphical representation of the independencies and correlations among the variables. We also extend the previous study by considering additional morphometrics and by formally testing whether a morphometric increases or decreases with the distance from the soma. This study introduces a multivariate methodology for inter-species comparison of morphology.

Who really values healthy food?

PurposeWhen selecting manufactured foods, customers consider several product features. Given the contemporary trends of food consumption, the purpose of this paper is to determine the influence that some demographic and psychographic key variables have on the chances of a consumer belonging to a market segment characterised by health-related food preferences.Design/methodology/approachThe food choice scale is revised to develop a multidimensional measure of the factors underlying consumer food choices. Data of 288 sampled consumers were used to validate the scale and to group consumers into four segments based on the value assigned to several food-product meta-attributes. Depending on these food choice values, the study identified four dissimilar clusters: utilitarian, protecting, toning and highly demanding.FindingsConsumers use multiple attributes when choosing food products. However, emerging segments tend to prefer health-related attributes over utilitarian or conventional attributes, such as price, flavour or accessibility. The consumers of these segments tend to be older, more health conscious and more prone to psychological health risks.Originality/valueDemographic and psychographic traits tend to drive trade-offs between health- and non-health-related attributes when considering food products. Several multivariate methodologies were innovatively coupled to characterize consumers based on their healthy food preferences and individual traits.

Contrasting storage-flux-age interactions revealed by catchment inter-comparison using a tracer-aided runoff model

Water storage dynamics modulate fluxes within catchments, control the rainfall-runoff response and regulate the velocity of water particles through mixing associated processes. Tracer-aided models are useful tools for tracking the interactions between catchment storage and fluxes, as they can capture both the celerity of the runoff response and the velocity of water particles revealed by tracer dynamics. The phase-space reconstruction of modelled systems can help in this regard; it traces the evolution of a dynamic system from a known initial state as phase trajectories in response to inputs. In this study, we compared the modelled storage-flux dynamics obtained from the application of a spatially distributed tracer-aided hydrological model (STARR) in five contrasting long-term research catchments with varying degrees of snow influence. The models were calibrated using a consistent multivariate methodology based on discharge, isotope composition and snowpack water equivalent. Analysis of extracted modelled storage dynamics gave insights into the system functioning. Large volumes of total stored water needed to be invoked at most sites to reconcile celerity and travel times to match observe discharge and isotope responses. This is because changes in dynamic storage from water balance considerations are small when compared to volume of storage necessary for observed tracer dampening. In the phase-space diagrams, the rates of storage change gave insights into the relative storage volume and seasonal catchment functioning. The storage increase was dominated by hydroclimatic inputs; thus, it presented a stochastic response. Furthermore, depending on the dominance of snow or rainfall inputs, catchments had different seasonal responses in storage dynamics. Decreases in storage were more predictable and reflected the efficiency of catchment drainage, yet at lower storages the influence of ET was also evident. Activation of flow paths due to overland and near-surface flows resulted in non-linearity of catchment functioning largely at high storage states. The storage-discharge relationships generally showed a non-linear distribution, with more scattered states during wettest condition. In turn, all the catchments exhibited an inverse storage effect, with modelled water ages decreasing with increasing storage as lateral flow paths were activated. Insights from this inter-comparison of storage-flux-age dynamics show the benefits of tracer-aided hydrological models in exploring their interactions at well-instrumented sites to better understand hydrological functioning of contrasting catchments.

Sensory Modality-Independent Activation of the Brain Network for Language.

The meaning of a sentence can be understood, whether presented in written or spoken form. Therefore, it is highly probable that brain processes supporting language comprehension are at least partly independent of sensory modality. To identify where and when in the brain language processing is independent of sensory modality, we directly compared neuromagnetic brain signals of 200 human subjects (102 males) either reading or listening to sentences. We used multiset canonical correlation analysis to align individual subject data in a way that boosts those aspects of the signal that are common to all, allowing us to capture word-by-word signal variations, consistent across subjects and at a fine temporal scale. Quantifying this consistency in activation across both reading and listening tasks revealed a mostly left-hemispheric cortical network. Areas showing consistent activity patterns included not only areas previously implicated in higher-level language processing, such as left prefrontal, superior and middle temporal areas, and anterior temporal lobe, but also parts of the control network as well as subcentral and more posterior temporal-parietal areas. Activity in this supramodal sentence-processing network starts in temporal areas and rapidly spreads to the other regions involved. The findings indicate not only the involvement of a large network of brain areas in supramodal language processing but also that the linguistic information contained in the unfolding sentences modulates brain activity in a word-specific manner across subjects.SIGNIFICANCE STATEMENT The brain can extract meaning from written and spoken messages alike. This requires activity of both brain circuits capable of processing sensory modality-specific aspects of the input signals as well as coordinated brain activity to extract modality-independent meaning from the input. Using traditional methods, it is difficult to disentangle modality-specific activation from modality-independent activation. In this work, we developed and applied a multivariate methodology that allows for a direct quantification of sensory modality-independent brain activity, revealing fast activation of a wide network of brain areas, both including and extending beyond the core network for language.