Regression and alignment for functional data and network topology.

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

Functional Data Analysis: An Introduction and Recent Developments.

Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a dataset on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands-on application, the code for these practical examples is made available through a code and data supplement and on GitHub.

Activation of the NF-κB pathway alters the phenotype of MSCs in the tracheal aspirates of preterm infants with severe BPD.

Mesenchymal stromal cells (MSCs) are released into the airways of preterm infants following lung injury. These cells display a proinflammatory phenotype and are associated with development of severe bronchopulmonary dysplasia (BPD). We aimed to characterize the functional properties of MSCs obtained from tracheal aspirates of 50 preterm infants who required invasive ventilation. Samples were separated by disease severity. The increased proliferative capacity of MSCs was associated with longer duration of mechanical ventilation and higher severity of BPD. Augmented growth depended on nuclear accumulation of NFκBp65 and was accompanied by reduced expression of cytosolic α-smooth muscle actin (α-SMA). The central role of NF-κB signaling was confirmed by inhibition of IκBα phosphorylation. The combined score of proliferative capacity, accumulation of NFκBp65, and expression of α-SMA was used to predict the development of severe BPD with an area under the curve (AUC) of 0.847. We mimicked the clinical situation in vitro, and stimulated MSCs with IL-1ß and TNF-α. Both cytokines induced similar and persistent changes as was observed in MSCs obtained from preterm infants with severe BPD. RNA interference was employed to investigate the mechanistic link between NFκBp65 accumulation and alterations in phenotype. Our data indicate that determining the phenotype of resident pulmonary MSCs represents a promising biomarker-based approach. The persistent alterations in phenotype, observed in MSCs from preterm infants with severe BPD, were induced by the pulmonary inflammatory response. NFκBp65 accumulation was identified as a central regulatory mechanism. Future preclinical and clinical studies, aimed to prevent BPD, should focus on phenotype changes in pulmonary MSCs.

Testing differentially expressed genes in dose-response studies and with ordinal phenotypes.

When testing for differentially expressed genes between more than two groups, the groups are often defined by dose levels in dose-response experiments or ordinal phenotypes, such as disease stages. We discuss the potential of a new approach that uses the levels' ordering without making any structural assumptions, such as monotonicity, by testing for zero variance components in a mixed models framework. Since the mixed effects model approach borrows strength across doses/levels, the test proposed can also be applied when the number of dose levels/phenotypes is large and/or the number of subjects per group is small. We illustrate the new test in simulation studies and on several publicly available datasets and compare it to alternative testing procedures. All tests considered are implemented in R and are publicly available. The new approach offers a very fast and powerful way to test for differentially expressed genes between ordered groups without making restrictive assumptions with respect to the true relationship between factor levels and response.

A note on modeling sparse exponential-family functional response curves.

Non-Gaussian functional data are considered and modeling through functional principal components analysis (FPCA) is discussed. The direct extension of popular FPCA techniques to the generalized case incorrectly uses a marginal mean estimate for a model that has an inherently conditional interpretation, and thus leads to biased estimates of population and subject-level effects. The methods proposed address this shortcoming by using either a two-stage or joint estimation strategy. The performance of all methods is compared numerically in simulations. An application to ambulatory heart rate monitoring is used to further illustrate the distinctions between approaches.

Longitudinal scalar-on-functions regression with application to tractography data.

We propose a class of estimation techniques for scalar-on-function regression where both outcomes and functional predictors may be observed at multiple visits. Our methods are motivated by a longitudinal brain diffusion tensor imaging tractography study. One of the study's primary goals is to evaluate the contemporaneous association between human function and brain imaging over time. The complexity of the study requires the development of methods that can simultaneously incorporate: (1) multiple functional (and scalar) regressors; (2) longitudinal outcome and predictor measurements per patient; (3) Gaussian or non-Gaussian outcomes; and (4) missing values within functional predictors. We propose two versions of a new method, longitudinal functional principal components regression (PCR). These methods extend the well-known functional PCR and allow for different effects of subject-specific trends in curves and of visit-specific deviations from that trend. The new methods are compared with existing approaches, and the most promising techniques are used for analyzing the tractography data.

Spatially regularized estimation for the analysis of dynamic contrast-enhanced magnetic resonance imaging data.

Competing compartment models of different complexities have been used for the quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging data. We present a spatial elastic net approach that allows to estimate the number of compartments for each voxel such that the model complexity is not fixed a priori. A multi-compartment approach is considered, which is translated into a restricted least square model selection problem. This is done by using a set of basis functions for a given set of candidate rate constants. The form of the basis functions is derived from a kinetic model and thus describes the contribution of a specific compartment. Using a spatial elastic net estimator, we chose a sparse set of basis functions per voxel, and hence, rate constants of compartments. The spatial penalty takes into account the voxel structure of an image and performs better than a penalty treating voxels independently. The proposed estimation method is evaluated for simulated images and applied to an in vivo dataset.

Regression and Alignment for Functional Data and Network Topology.

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of pre-processing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

Penalized optimal scaling for ordinal variables with an application to international classification of functioning core sets.

Ordinal data occur frequently in the social sciences. When applying principal component analysis (PCA), however, those data are often treated as numeric, implying linear relationships between the variables at hand; alternatively, non-linear PCA is applied where the obtained quantifications are sometimes hard to interpret. Non-linear PCA for categorical data, also called optimal scoring/scaling, constructs new variables by assigning numerical values to categories such that the proportion of variance in those new variables that is explained by a predefined number of principal components (PCs) is maximized. We propose a penalized version of non-linear PCA for ordinal variables that is a smoothed intermediate between standard PCA on category labels and non-linear PCA as used so far. The new approach is by no means limited to monotonic effects and offers both better interpretability of the non-linear transformation of the category labels and better performance on validation data than unpenalized non-linear PCA and/or standard linear PCA. In particular, an application of penalized optimal scaling to ordinal data as given with the International Classification of Functioning, Disability and Health (ICF) is provided.

Statistical inference for ordinal predictors in generalized additive models with application to Bronchopulmonary Dysplasia.

OBJECTIVE: Discrete but ordered covariates are quite common in applied statistics, and some regularized fitting procedures have been proposed for proper handling of ordinal predictors in statistical models. Motivated by a study from neonatal medicine on Bronchopulmonary Dysplasia (BPD), we show how quadratic penalties on adjacent dummy coefficients of ordinal factors proposed in the literature can be incorporated in the framework of generalized additive models, making tools for statistical inference developed there available for ordinal predictors as well. RESULTS: The approach presented allows to exploit the scale level of ordinally scaled factors in a sound statistical framework. Furthermore, several ordinal factors can be considered jointly without the need to collapse levels even if the number of observations per level is small. By doing so, results obtained earlier on the BPD data analyzed could be confirmed.

Supervised learning for analysing movement patterns in a virtual reality experiment.

The projection into a virtual character and the concomitant illusionary body ownership can lead to transformations of one's entity. Both during and after the exposure, behavioural and attitudinal changes may occur, depending on the characteristics or stereotypes associated with the embodied avatar. In the present study, we investigated the effects on physical activity when young students experience being old. After assignment (at random) to a young or an older avatar, the participants' body movements were tracked while performing upper body exercises. We propose and discuss the use of supervised learning procedures to assign these movement patterns to the underlying avatar class in order to detect behavioural differences. This approach can be seen as an alternative to classical feature-wise testing. We found that the classification accuracy was remarkably good for support vector machines with linear kernel and deep learning by convolutional neural networks, when inserting time sub-sequences extracted at random and repeatedly from the original data. For hand movements, associated decision boundaries revealed a higher level of local, vertical positions for the young avatar group, indicating increased agility in their performances. This occurrence held for both guided movements as well as achievement-orientated exercises.

Human perception of color differences using computer vision system measurements of raw pork loin.

In the food industry, product color plays an important role in influencing consumer choices. Yet, there remains little research on the human ability to perceive differences in product color; therefore, preference testing is subjective rather than based on quantitative colors. Using a de-centralized computer-aided systematic discrimination testing method, we ascertain consumers' ability to discern between systematically varied colors. As a case study, the colors represent the color variability of fresh pork as measured by a computer vision system. Our results indicate that a total color difference (ΔE) of approximately 1 is discriminable by consumers. Furthermore, we ascertain that a change in color along the b*-axis (yellowness) in CIELAB color space is most discernable, followed by the a*-axis (redness) and then the L*-axis (lightness). As developed, our web-based discrimination testing approach allows for large scale evaluation of human color perception, while these quantitative findings on meat color discrimination are of value for future research on consumer preferences of meat color and beyond.

Supervised feature selection in mass spectrometry-based proteomic profiling by blockwise boosting.

When feature selection in mass spectrometry is based on single m/z values, problems arise from the fact that variability is not only in vertical but also in horizontal direction, i.e. also slightly differing m/z values may correspond to the same feature. Hence, we propose to use the full spectra as input to a classifier, but to select small groups -- or blocks -- of adjacent m/z values, instead of single m/z values only. For that purpose we modify the LogitBoost to obtain a version of the so-called blockwise boosting procedure for classification. It is shown that blockwise boosting has high potential in predictive proteomics.

Bacterial Colonization within the First Six Weeks of Life and Pulmonary Outcome in Preterm Infants <1000 g.

Bronchopulmonary dysplasia (BPD) is a multifactorial disease mainly provoked by pre- and postnatal infections, mechanical ventilation, and oxygen toxicity. In severely affected premature infants requiring mechanical ventilation, association of bacterial colonization of the lung and BPD was recently disclosed. To analyze the impact of bacterial colonization of the upper airway and gastrointestinal tract on moderate/severe BPD, we retrospectively analyzed nasopharyngeal and anal swabs taken weekly during the first 6 weeks of life at a single center in n = 102 preterm infants <1000 g. Colonization mostly occurred between weeks 2 and 6 and displayed a high diversity requiring categorization. Analyses of deviance considering all relevant confounders revealed statistical significance solely for upper airway colonization with bacteria with pathogenic potential and moderate/severe BPD (p = 0.0043) while no link could be established to the Gram response or the gastrointestinal tract. Our data highlight that specific colonization of the upper airway poses a risk to the immature lung. These data are not surprising taking into account the tremendous impact of microbial axes on health and disease across ages. We suggest that studies on upper airway colonization using predefined categories represent a feasible approach to investigate the impact on the pulmonary outcome in ventilated and non-ventilated preterm infants.

Sustainable use of tainted boar meat: Blending is a strategy for processed products.

While forming mixtures is a widely used approach for other raw materials in food industry, it has not yet been systematically analyzed for boar tainted meat. That is why we simultaneously studied four factors relevant for the production of emulsion-type sausages: percentage boar meat (skatole concentrations up to 0.3 µg/g, androstenone up to 3.8 µg/g in melted backfat), duration of traditional smoke and concentration levels of two spices. 16 variants of Frankfurters were produced in two independent studies and evaluated by in total 211 consumers. A linear mixed effects model revealed that increased levels of boar tainted meat significantly reduced consumer acceptance which could not be compensated by increased smoke or spice levels. We propose a non-inferiority test to identify the mixture which is similarly accepted as the reference made without boar tainted meat. Up to 33% tainted boar meat is proposed, assuming a liking drop of 0.5 on a 9 point liking scale as benchmark for an inferior product.

Noise and accustomation: A pilot study of trained assessors' olfactory performance.

While recent studies suggest an influence of noise on olfactory performance, it is unclear as to what extent the influence varies between subjects who are accustomed to noise and those who are not. Two groups of panelists were selected: a University panel usually working under silent conditions and an abattoir panel usually working on the slaughter line with abattoir noise. Odor discrimination, odor identification, and odor detection thresholds were studied. Furthermore, a sensory quality control task using 40 boar samples was performed. All tests were accomplished both with and without extraneous noise recorded at an abattoir (70 dB) using headphones. Contrary to the researchers' expectations, abattoir noise hardly affected the olfactory tests nor was the quality control task impaired. Abattoir noise did not influence the perceived intensity of boar taint and the classification results of the testers, regardless of whether they were accustomed to such noise or not. The results indicate that sensory quality control can be conducted in a manufacturing environment with constant noise without diminishing the assessors' performance.

Correction: Noise and accustomation: A pilot study of trained assessors' olfactory performance.

[This corrects the article DOI: 10.1371/journal.pone.0174697.].

Consumers' perception and acceptance of boiled and fermented sausages from strongly boar tainted meat.

Characteristic off-flavours may occur in uncastrated male pigs depending on the accumulation of androstenone and skatole. Feasible processing of strongly tainted carcasses is challenging but gains in importance due to the European ban on piglet castration in 2018. This paper investigates consumers' acceptability of two sausage types: (a) emulsion-type (BOILED) and (b) smoked raw-fermented (FERM). Liking (9 point scales) and flavour perception (check-all-that-apply with both, typical and negatively connoted sensory terms) were evaluated by 120 consumers (within-subject design). Proportion of tainted boar meat (0, 50, 100%) affected overall liking of BOILED, F (2, 238)=23.22, P<.001, but not of FERM sausages, F (2, 238)=0.89, P=.414. Consumers described the flavour of BOILED-100 as strong and sweaty. In conclusion, FERM products seem promising for processing of tainted carcasses whereas formulations must be optimized for BOILED in order to eliminate perceptible off-flavours. Boar taint rejection thresholds may be higher for processed than those suggested for unprocessed meat cuts.

Boar taint detection: A comparison of three sensory protocols.

While recent studies state an important role of human sensory methods for daily routine control of so-called boar taint, the evaluation of different heating methods is still incomplete. This study investigated three common heating methods (microwave (MW), hot-water (HW), hot-iron (HI)) for boar fat evaluation. The comparison was carried out on 72 samples with a 10-person sensory panel. The heating method significantly affected the probability of a deviant rating. Compared to an assumed 'gold standard' (chemical analysis), the performance was best for HI when both sensitivity and specificity were considered. The results show the superiority of the panel result compared to individual assessors. However, the consistency of the individual sensory ratings was not significantly different between MW, HW, and HI. The three protocols showed only fair to moderate agreement. Concluding from the present results, the hot-iron method appears to be advantageous for boar taint evaluation as compared to microwave and hot-water.

Interaction of Skatole and Androstenone in the Olfactory Perception of Boar Taint.

This study analyzed odor-odor interactions of two malodorous volatile substances, androstenone and skatole, that may accumulate in fat and meat of uncastrated male (boar) pigs. Therefore, fat samples were collected from 1000+ entire male pig carcasses for sensory evaluation and quantification of boar taint compounds using gas chromatography-mass spectrometry (GC-MS). Each sample was sniffed by 10 trained assessors, resulting in 11 000+ individual ratings, which were subjected to statistical analysis. Pearson correlations of chemical traits and sensory traits (panel average) were higher for skatole [r(1029) = 0.59; p < 0.001] than for androstenone [r(1029) = 0.44; p < 0.001]. Linear terms of androstenone and skatole as well as their interaction significantly (p < 0.05) contributed to perception of deviant smell (R(2) = 0.43). Standardized regression coefficients illustrate the higher importance of skatole (ß = 0.68) than androstenone (ß = 0.39). Interindividual differences in the responses of assessors to androstenone and skatole are confirmed. A new curved approach is suggested because it better accounts for the interaction of androstenone and skatole than the "safe box" approach. On the basis of these data, sorting strategies using instrumental measurements are discussed. An automated detection based on only skatole measurements is recommended because its performance is only slightly inferior to a sorting based on both androstenone and skatole. Sorting thresholds need to be calibrated against consumer acceptance though.