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In this paper, we reconsider the merits of unfolding solutions based on loss functions involving a normalization on the variance per subject. In the literature, solutions based on Stress-2 are often diagnosed to be degenerate in the majority of cases. Here, the focus lies on two frequently occurring types of degeneracies. The first type typically locates some subject points far away from a compact cluster of the other points. In the second type of solution, the object points lie on a circle. In this paper, we argue that these degenerate solutions are well fitting and informative. To reveal the information, we introduce mixtures of plots based on the ideal point model of unfolding, the vector model, and on the signed distance model. In addition to a different representation, we provide a new iterative majorization algorithm to optimize the average squared correlation between the distances in the configuration and the transformed data per individual. It is shown that this approach is equivalent to minimizing Kruskal's Stress-2.

In this paper, we reconsider the merits of unfolding solutions based on loss functions involving a normalization on the variance per subject. In the literature, solutions based on Stress-2 are often diagnosed to be degenerate in the majority of cases. Here, the focus lies on two frequently occurring types of degeneracies. The first type typically locates some subject points far away from a compact cluster of the other points. In the second type of solution, the object points lie on a circle. In this paper, we argue that these degenerate solutions are well fitting and informative. To reveal the information, we introduce mixtures of plots based on the ideal point model of unfolding, the vector model, and on the signed distance model. In addition to a different representation, we provide a new iterative majorization algorithm to optimize the average squared correlation between the distances in the configuration and the transformed data per individual. It is shown that this approach is equivalent to minimizing Kruskal's Stress-2. [ABSTRACT FROM AUTHOR]

Abstract Salmonella Typhimurium infections in pigs often result in the development of carriers that intermittently excrete Salmonella in very low numbers. During periods of stress, for example transport to the slaughterhouse, recrudescence of Salmonella may occur, but the mechanism of this stress related recrudescence is poorly understood. Therefore, the aim of the present study was to determine the role of the stress hormone cortisol in Salmonella recrudescence by pigs. We showed that a 24 h feed withdrawal increases the intestinal Salmonella Typhimurium load in pigs, which is correlated with increased serum cortisol levels. A second in vivo trial demonstrated that stress related recrudescence of Salmonella Typhimurium in pigs can be induced by intramuscular injection of dexamethasone. Furthermore, we found that cortisol, but not epinephrine, norepinephrine and dopamine, promotes intracellular proliferation of Salmonella Typhimurium in primary porcine alveolar macrophages, but not in intestinal epithelial cells and a transformed cell line of porcine alveolar macrophages. A microarray based transcriptomic analysis revealed that cortisol did not directly affect the growth or the gene expression or Salmonella Typhimurium in a rich medium, which implies that the enhanced intracellular proliferation of the bacterium is probably caused by an indirect effect through the cell. These results highlight the role of cortisol in the recrudescence of Salmonella Typhimurium by pigs and they provide new evidence for the role of microbial endocrinology in host-pathogen interactions.

Abstract 1 Background High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account. 2 Results We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. 3 Conclusion Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform (group lasso approach) as well as structures that involve all data platforms (Elitist lasso approach). 4 Availability The additional file contains a MATLAB implementation of the sparse simultaneous component method.

Abstract Campylobacter contaminated broiler chicken meat is an important source of foodborne gastroenteritis and poses a serious health burden in industrialized countries. Broiler chickens are commonly regarded as a natural host for this zoonotic pathogen and infected birds carry a very high C. jejuni load in their gastrointestinal tract, especially the ceca. This eventually results in contaminated carcasses during processing. Current intervention methods fail to reduce the colonization of broiler chicks by C. jejuni due to an incomplete understanding on the interaction between C. jejuni and its avian host. Clearly, C. jejuni developed several survival and colonization mechanisms which are responsible for its highly adapted nature to the chicken host. But how these mechanisms interact with one another, leading to persistent, high-level cecal colonization remains largely obscure. A plethora of mutagenesis studies in the past few years resulted in the identification of several of the genes and proteins of C. jejuni involved in different aspects of the cellular response of this bacterium in the chicken gut. In this review, a thorough, up-to-date overview will be given of the survival mechanisms and colonization factors of C. jejuni identified to date. These factors may contribute to our understanding on how C. jejuni survival and colonization in chicks is mediated, as well as provide potential targets for effective subunit vaccine development.

Abstract Background In contemporary biology, complex biological processes are increasingly studied by collecting and analyzing measurements of the same entities that are collected with different analytical platforms. Such data comprise a number of data blocks that are coupled via a common mode. The goal of collecting this type of data is to discover biological mechanisms that underlie the behavior of the variables in the different data blocks. The simultaneous component analysis (SCA) family of data analysis methods is suited for this task. However, a SCA may be hampered by the data blocks being subjected to different amounts of measurement error, or noise. To unveil the true mechanisms underlying the data, it could be fruitful to take noise heterogeneity into consideration in the data analysis. Maximum likelihood based SCA (MxLSCA-P) was developed for this purpose. In a previous simulation study it outperformed normal SCA-P. This previous study, however, did not mimic in many respects typical functional genomics data sets, such as, data blocks coupled via the experimental mode, more variables than experimental units, and medium to high correlations between variables. Here, we present a new simulation study in which the usefulness of MxLSCA-P compared to ordinary SCA-P is evaluated within a typical functional genomics setting. Subsequently, the performance of the two methods is evaluated by analysis of a real life Escherichia coli metabolomics data set. Results In the simulation study, MxLSCA-P outperforms SCA-P in terms of recovery of the true underlying scores of the common mode and of the true values underlying the data entries. MxLSCA-P further performed especially better when the simulated data blocks were subject to different noise levels. In the analysis of an E. coli metabolomics data set, MxLSCA-P provided a slightly better and more consistent interpretation. Conclusion MxLSCA-P is a promising addition to the SCA family. The analysis of coupled functional genomics data blocks could benefit from its ability to take different noise levels per data block into consideration and improve the recovery of the true patterns underlying the data. Moreover, the maximum likelihood based approach underlying MxLSCA-P could be extended to custom-made solutions to specific problems encountered.

Abstract Background Data integration is currently one of the main challenges in the biomedical sciences. Often different pieces of information are gathered on the same set of entities (e.g., tissues, culture samples, biomolecules) with the different pieces stemming, for example, from different measurement techniques. This implies that more and more data appear that consist of two or more data arrays that have a shared mode. An integrative analysis of such coupled data should be based on a simultaneous analysis of all data arrays. In this respect, the family of simultaneous component methods (e.g., SUM-PCA, unrestricted PCovR, MFA, STATIS, and SCA-P) is a natural choice. Yet, different simultaneous component methods may lead to quite different results. Results We offer a structured overview of simultaneous component methods that frames them in a principal components setting such that both the common core of the methods and the specific elements with regard to which they differ are highlighted. An overview of principles is given that may guide the data analyst in choosing an appropriate simultaneous component method. Several theoretical and practical issues are illustrated with an empirical example on metabolomics data for Escherichia coli as obtained with different analytical chemical measurement methods. Conclusion Of the aspects in which the simultaneous component methods differ, pre-processing and weighting are consequential. Especially, the type of weighting of the different matrices is essential for simultaneous component analysis. These types are shown to be linked to different specifications of the idea of a fair integration of the different coupled arrays.

Abstract Background Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions based on their similarity in overall expression over profiles but also to gain information on more subtle relations between genes and conditions. Getting a clear visual overview of all these patterns in a single easy-to-grasp representation is a useful preliminary analysis step: We propose to use for this purpose an advanced exploratory method, called multidimensional unfolding. Results We present a novel algorithm for multidimensional unfolding that overcomes both general problems and problems that are specific for the analysis of gene expression data sets. Applying the algorithm to two publicly available microarray compendia illustrates its power as a tool for exploratory data analysis: The unfolding analysis of a first data set resulted in a two-dimensional representation which clearly reveals temporal regulation patterns for the genes and a meaningful structure for the time points, while the analysis of a second data set showed the algorithm's ability to go beyond a mere identification of those genes that discriminate between different patient or tissue types. Conclusion Multidimensional unfolding offers a useful tool for preliminary explorations of microarray data: By relying on an easy-to-grasp low-dimensional geometric framework, relations among genes, among conditions and between genes and conditions are simultaneously represented in an accessible way which may reveal interesting patterns in the data. An additional advantage of the method is that it can be applied to the raw data without necessitating the choice of suitable genewise transformations of the data.

Camp ylobacter jejuni is the most common cause of bacterial-mediated diarrheal disease worldwide. Because poultry and poultry products are a major source of C. jejuni infections in humans, efforts should be taken to develop strategies to decrease Camp ylobacter shedding during primary production. For this purpose, the efficacy of medium-chain fatty acids (MCFA) as feed additives to control C. jejuni colonization in broiler chickens was analyzed. First, the antimicrobial activity of the MCFA caproic, caprylic, and capric acid on C. jejuni was evaluated in vitro. Minimal inhibitory concentrations were 0.25 mM for caproic and 0.5 mM for caprylic and capric acids at pH 6.0 and 4 mM for all 3 compounds at pH 7.5. Time-kill curves revealed strong bactericidal properties of the tested compounds toward C. jejuni at pH 6.0. Concentrations as low as 4 mM caprylic and capric acids and 16 mM caproic acid killed all bacteria within 24 h. Capric acid had the highest activity, with concentrations of 4 mM killing all bacteria within the hour. Together these data show a profound bactericidal, dose-dependent activity of the tested MCFA toward C. jejuni in vitro. For this reason, the effect of these 3 MCFA on C. jejuni was evaluated in vivo. The addition of any of the acids to the feed, from 3 d before euthanization, was not capable of reducing cecal Camp ylobacter colonization in 27-d-old broilers experimentally infected with C. jejuni at 15 d of age. Using a cecal loop model, sodium caprate was not able to reduce cecal Camp ylobacter counts. When time-kill curves were conducted in the presence of chick intestinal mucus, capric acid was less active against C. jejuni. At 4 mM, all bacteria were killed only after 24 h. Thus, despite the marked bactericidal effect of MCFA in vitro, supplementing these acids to the feed does not reduce cecal Camp ylobacter colonization in broiler chickens under the applied test conditions, probably due to the protective effect of the mucus layer. [ABSTRACT FROM AUTHOR]

Introduction: The advent of biological and targeted synthetic therapies has revolutionized rheumatoid arthritis (RA) treatment. However, this has come at the price of an increased risk of infections. The aim of this study was to present an integrated overview of both serious and non-serious infections, and to identify potential predictors of infection risk in RA patients using biological or targeted synthetic drugs., Methods: We systematically reviewed available literature from PubMed and Cochrane and performed multivariate meta-analysis with meta-regression on the reported infections. Randomized controlled trials and prospective and retrospective observational studies including patient registry studies were analyzed, combined as well as separately. We excluded studies focusing on viral infections only., Results: Infections were not reported in a standardized manner. Meta-analysis showed significant heterogeneity that persisted after forming subgroups by study design and follow-up duration. Overall, the pooled proportions of patients experiencing an infection during a study were 0.30 (95% CI, 0.28-0.33) and 0.03 (95% CI, 0.028-0.035) for any kind of infections or serious infections only, respectively. We found no potential predictors that were consistent across all study subgroups., Conclusions: The high heterogeneity and the inconsistency of potential predictors between studies show that we do not yet have a complete picture of infection risk in RA patients using biological or targeted synthetic drugs. Besides, we found non-serious infections outnumbered serious infections by a factor 10:1, but only a few studies have focused on their occurrence. Future studies should apply a uniform method of infectious adverse event reporting and also focus on non-serious infections and their impact on treatment decisions and quality of life., (© 2023. The Author(s).)

The growing availability of high-dimensional data sets offers behavioral scientists an unprecedented opportunity to integrate the information hidden in the novel types of data (e.g., genetic data, social media data, and GPS tracks, etc.,) and thereby obtain a more detailed and comprehensive view towards their research questions. In the context of clustering, analyzing the large volume of variables could potentially result in an accurate estimation or a novel discovery of underlying subgroups. However, a unique challenge is that the high-dimensional data sets likely involve a significant amount of irrelevant variables. These irrelevant variables do not contribute to the separation of clusters and they may mask cluster partitions. The current paper addresses this challenge by introducing a new clustering algorithm, called Cardinality K-means or CKM, and by proposing a novel model selection strategy. CKM is able to perform simultaneous clustering and variable selection with high stability. In two simulation studies and an empirical demonstration with genetic data, CKM consistently outperformed competing methods in terms of recovering cluster partitions and identifying signaling variables. Meanwhile, our novel model selection strategy determines the number of clusters based on a subset of variables that are most likely to be signaling variables. Through a simulation study, this strategy was found to result in a more accurate estimation of the number of clusters compared to the conventional strategy that utilizes the full set of variables. Our proposed CKM algorithm, together with the novel model selection strategy, has been implemented in a freely accessible R package., (© 2022. The Author(s).)

Background: Thyroid cancer (TC) patients are understudied but appear to be at risk for poor physical and psychosocial outcomes. Knowledge of the course and determinants of these deteriorated outcomes is lacking. Furthermore, little is known about mediating biological mechanisms., Objectives: The WaTCh-study aims to; 1. Examine the course of physical and psychosocial outcomes. 2. Examine the association of demographic, environmental, clinical, physiological, and personality characteristics to those outcomes. In other words, who is at risk? 3. Reveal the association of mediating biological mechanisms (inflammation, kynurenine pathway) with poor physical and psychological outcomes. In other words, why is a person at risk?, Design and Methods: Newly diagnosed TC patients from 13 Dutch hospitals will be invited. Data collection will take place before treatment, and at 6, 12 and 24 months after diagnosis. Sociodemographic and clinical information is available from the Netherlands Cancer Registry. Patients fill-out validated questionnaires at each time-point to assess quality of life, TC-specific symptoms, physical activity, anxiety, depression, health care use, and employment. Patients are asked to donate blood three times to assess inflammation and kynurenine pathway. Optionally, at each occasion, patients can use a weighing scale with bioelectrical impedance analysis (BIA) system to assess body composition; can register food intake using an online food diary; and can wear an activity tracker to assess physical activity and sleep duration/quality. Representative Dutch normative data on the studied physical and psychosocial outcomes is already available., Impact: WaTCh will reveal the course of physical and psychosocial outcomes among TC patients over time and answers the question who is at risk for poor outcomes, and why. This knowledge can be used to provide personalized information, to improve screening, to develop and provide tailored treatment strategies and supportive care, to optimize outcomes, and ultimately increase the number of TC survivors that live in good health., (© 2023. The Author(s).)

Worldwide, approximately 22% of all individuals aged 50 years and older are currently estimated to fall somewhere on the Alzheimer's disease (AD) continuum, which can be roughly divided into preclinical AD, mild cognitive impairment (MCI), and AD dementia. While episodic memory loss (among other aspects) is typically required for a diagnosis of AD dementia, MCI is said to have occurred when cognitive impairment (including memory loss) is worse than expected for the person's age but not enough to be classified as dementia. On the other hand, preclinical AD can currently only be detected using biomarkers; clinical symptoms are not apparent using traditional neuropsychological tests. The main aim of the current paper was to explore the possibility of a test which could distinguish preclinical AD from normal aging. Recent scientific evidence suggests that the Famous Faces Test (FFT) could differentiate preclinical AD from normal aging up to 5 years before a clinical AD diagnosis. Problematic with existing FFTs is the selection of stimulus material. Faces famous in a specific country and a specific decade might not be equally famous for individuals in another country or indeed for people of different ages. The current article describes how famous faces were systematically selected and chosen for the Dutch older (60+) population using five steps. The goal was to design and develop short versions of the FFT for Dutch older adults of equivalent mean difficulty. In future work, these nine parallel versions will be necessary for (a) cross-sectional comparison as well as subsequent longitudinal assessment of cognitively normal and clinical groups and (b) creating personalized norms for the normal aged controls that could be used to compare performance within individuals with clinical diagnoses. The field needs a simple, cognitive test which can distinguish the earliest stages of the dementia continuum from normal aging., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 van den Elzen, Brehmer, Van Deun and Mark.)

We examined the neural correlates of facial attractiveness by presenting pictures of male or female faces (neutral expression) with low/intermediate/high attractiveness to 48 male or female participants while recording their electroencephalogram (EEG). Subjective attractiveness ratings were used to determine the 10% highest, 10% middlemost, and 10% lowest rated faces for each individual participant to allow for high contrast comparisons. These were then split into preferred and dispreferred gender categories. ERP components P1, N1, P2, N2, early posterior negativity (EPN), P300 and late positive potential (LPP) (up until 3000 ms post-stimulus), and the face specific N170 were analysed. A salience effect (attractive/unattractive > intermediate) in an early LPP interval (450-850 ms) and a long-lasting valence related effect (attractive > unattractive) in a late LPP interval (1000-3000 ms) were elicited by the preferred gender faces but not by the dispreferred gender faces. Multi-variate pattern analysis (MVPA)-classifications on whole-brain single-trial EEG patterns further confirmed these salience and valence effects. It is concluded that, facial attractiveness elicits neural responses that are indicative of valenced experiences, but only if these faces are considered relevant. These experiences take time to develop and last well beyond the interval that is commonly explored., Competing Interests: Conflicts of interest This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. There are no conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Being able to classify experienced emotions by identifying distinct neural responses has tremendous value in both fundamental research (e.g. positive psychology, emotion regulation theory) and in applied settings (clinical, healthcare, commercial). We aimed to decode the neural representation of the experience of two discrete emotions: sadness and disgust, devoid of differences in valence and arousal. In a passive viewing paradigm, we showed emotion evoking images from the International Affective Picture System to participants while recording their EEG. We then selected a subset of those images that were distinct in evoking either sadness or disgust (20 for each), yet were indistinguishable on normative valence and arousal. Event-related potential analysis of 69 participants showed differential responses in the N1 and EPN components and a support-vector machine classifier was able to accurately classify (58%) whole-brain EEG patterns of sadness and disgust experiences. These results support and expand on earlier findings that discrete emotions do have differential neural responses that are not caused by differences in valence or arousal., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Predicting recovery after trauma is important to provide patients a perspective on their estimated future health, to engage in shared decision making and target interventions to relevant patient groups. In the present study, several unsupervised techniques are employed to cluster patients based on longitudinal recovery profiles. Subsequently, these data-driven clusters were assessed on clinical validity by experts and used as targets in supervised machine learning models. We present a formalised analysis of the obtained clusters that incorporates evaluation of (i) statistical and machine learning metrics, (ii) clusters clinical validity with descriptive statistics and medical expertise. Clusters quality assessment revealed that clusters obtained through a Bayesian method (High Dimensional Supervised Classification and Clustering) and a Deep Gaussian Mixture model, in combination with oversampling and a Random Forest for supervised learning of the cluster assignments provided among the most clinically sensible partitioning of patients. Other methods that obtained higher classification accuracy suffered from cluster solutions with large majority classes or clinically less sensible classes. Models that used just physical or a mix of physical and psychological outcomes proved to be among the most sensible, suggesting that clustering on psychological outcomes alone yields recovery profiles that do not conform to known risk factors., (© 2022. The Author(s).)

To take cancer survivorship research to the next level, it's important to gain insight in trajectories of changing patient-reported outcomes and impaired recovery after cancer. This is needed as the number of survivors is increasing and a large proportion is confronted with changing health after treatment. Mechanistic research can facilitate the development of personalized risk-stratified follow-up care and tailored interventions to promote healthy cancer survivorship. We describe how these trajectories can be studied by taking the recently extended Dutch population-based Patient Reported Outcomes Following Initial treatment and Long term Evaluation of Survivorship (PROFILES) registry as an example. PROFILES combines longitudinal assessment of patient-reported outcomes with novel, ambulatory and objective measures (eg, activity trackers, blood draws, hair samples, online food diaries, online cognitive tests, weighing scales, online symptoms assessment), and cancer registry and pharmacy databases. Furthermore, we discuss methods to optimize the use of a multidomain data collection-like return of individual results to participants, which may improve not only patient empowerment but also long-term cohort retention. Also, advanced statistical methods are needed to handle high-dimensional longitudinal data (with missing values) and provide insight into trajectories of changing patient-reported outcomes after cancer. Our coded data can be used by academic researchers around the world. Registries like PROFILES, which go beyond boundaries of disciplines and institutions, will contribute to better predictions of who will experience changes and why. This is needed to prevent and mitigate long-term and late effects of cancer treatment and to identify new interventions to promote health., (© The Author(s) 2022. Published by Oxford University Press.)

Background: Research into the clustering of symptoms may improve the understanding of the underlying mechanisms that affect survivors' symptom burden. This study applied network analyses in a balanced sample of cancer survivors to 1) explore the clustering of symptoms and 2) assess differences in symptom clustering between cancer types, treatment regimens, and short-term and long-term survivors., Methods: This study used cross-sectional survey data, collected between 2008 and 2018, from the population-based Patient Reported Outcomes Following Initial Treatment and Long Term Evaluation of Survivorship registry, which included survivors of 7 cancer types (colorectal cancer, breast cancer, ovarian cancer, thyroid cancer, chronic lymphocytic leukemia, Hodgkin lymphoma, and non-Hodgkin lymphoma). Regularized partial correlation network analysis was used to explore and visualize the associations between self-reported symptoms (European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire) and the centrality of these symptoms in the network (ie, how strongly a symptom was connected to other symptoms) for the total sample and for subgroups separately., Results: In the total sample (n = 1330), fatigue was the most central symptom in the network with moderate direct relationships with emotional symptoms, cognitive symptoms, appetite loss, dyspnea, and pain. These relationships persisted after adjustments for sociodemographic and clinical characteristics. Connections between fatigue and emotional symptoms, appetite loss, dyspnea, and pain were consistently found across all cancer types (190 for each), treatment regimens, and short-term and long-term survivors., Conclusions: In a heterogenous sample of cancer survivors, fatigue was consistently the most central symptom in all networks. Although longitudinal data are needed to build a case for the causal nature of these symptoms, cancer survivorship rehabilitation programs could focus on fatigue to reduce the overall symptom burden., (© 2021 The Authors. Cancer published by Wiley Periodicals LLC on behalf of American Cancer Society.)

The COVID-19 pandemic affects society and may especially have an impact on mental health of vulnerable groups, such as perinatal women. This prospective cohort study of 669 participating women in the Netherlands compared perinatal symptoms of depression and stress during and before the pandemic. After a pilot in 2018, recruitment started on 7 January 2019. Up until 1 March 2020 (before the pandemic), 401 women completed questionnaires during pregnancy, of whom 250 also completed postpartum assessment. During the pandemic, 268 women filled out at least one questionnaire during pregnancy and 59 postpartum (1 March-14 May 2020). Pregnancy-specific stress increased significantly in women during the pandemic. We found no increase in depressive symptoms during pregnancy nor an increase in incidence of high levels of postpartum depressive symptoms during the pandemic. Clinicians should be aware of the potential for increased stress in pregnant women during the pandemic., (© 2021. The Author(s).)

PCA is a popular tool for exploring and summarizing multivariate data, especially those consisting of many variables. PCA, however, is often not simple to interpret, as the components are a linear combination of the variables. To address this issue, numerous methods have been proposed to sparsify the nonzero coefficients in the components, including rotation-thresholding methods and, more recently, PCA methods subject to sparsity inducing penalties or constraints. Here, we offer guidelines on how to choose among the different sparse PCA methods. Current literature misses clear guidance on the properties and performance of the different sparse PCA methods, often relying on the misconception that the equivalence of the formulations for ordinary PCA also holds for sparse PCA. To guide potential users of sparse PCA methods, we first discuss several popular sparse PCA methods in terms of where the sparseness is imposed on the loadings or on the weights, assumed model, and optimization criterion used to impose sparseness. Second, using an extensive simulation study, we assess each of these methods by means of performance measures such as squared relative error, misidentification rate, and percentage of explained variance for several data generating models and conditions for the population model. Finally, two examples using empirical data are considered., (© 2021. The Author(s).)

The current publication describes most recent so far unpublished (key) guideline and GLP compliant reproductive and developmental toxicity studies of lithium carbonate in rats, including their interpretation and conclusions in terms of human hazard assessment when compared to existing literature. Particular attention was paid to the target organs and dose response of lithium ion related effects to differentiate between a primary (pharmacokinetic/pharmacodynamic) action and secondary effects as a result of systemic and target organ toxicity. In the key two-generation reproduction toxicity (OECD TG 416) study in rats, doses of 5, 15 and 45 mg/kg bw/d (0.95, 2.9 and 8.6 mg Li + /kg bw/d) were given by oral gavage, resulting in clear NOAELs of 15 mg/kg bw/d (2.9 mg Li + /kg bw/d) for systemic parental toxicity and 45 mg/kg bw/d (8.6 mg Li + /kg bw/d) for reproductive toxicity and fetal toxicity. Target organ changes were consistently observed in liver (cytoplasmic rarefaction) and kidney (dilated tubuli). In the key developmental toxicity (OECD TG 414) study in rats, doses given by oral gavage were 10, 30 and 90 mg/kg bw/d (1.9, 5.7 and 17.1 mg Li + /kg bw/d) was investigated resulting in NO(A)ELs of 30 mg/kg bw/d (5.7 mg Li + /kg bw/d) (maternal toxicity) and 90 mg/kg bw/d (17 mg Li + /kg bw/d) (fetal toxicity and teratogenicity). The highest dose of 90 mg/kg bw/day resulted in clear signs of toxicity and peak plasma concentrations at the toxic range (>1.0 mEq lithium/L). Toxic effects of lithium carbonate were not seen in the reproductive and developmental organs. No adverse effects on sperm (total motility, progressive motility and morphology of testicular and cauda epididymal sperm) were observed in the two-generation rat reproduction toxicity study. There was also no impact on fertility indices or on litter sizes in this study, nor were there any fetal effects in the two-generation reproduction toxicity and developmental toxicity study at doses causing already systemic toxicity in the dams. Secondary effects such as decreased weight (gain) and food consumption were reported in the developmental toxicity study. The absence of any reproductive/developmental findings at dose levels causing clear systemic toxicity in the test animals in these key mammalian studies, does not suggest an immediate concern for possible human reproductive or developmental toxicity effects from exposure to lithium during drug use., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Principal covariates regression (PCovR) allows one to deal with the interpretational and technical problems associated with running ordinary regression using many predictor variables. In PCovR, the predictor variables are reduced to a limited number of components, and simultaneously, criterion variables are regressed on these components. By means of a weighting parameter, users can flexibly choose how much they want to emphasize reconstruction and prediction. However, when datasets contain many criterion variables, PCovR users face new interpretational problems, because many regression weights will be obtained and because some criteria might be unrelated to the predictors. We therefore propose PCovR2, which extends PCovR by also reducing the criteria to a few components. These criterion components are predicted based on the predictor components. The PCovR2 weighting parameter can again be flexibly used to focus on the reconstruction of the predictors and criteria, or on filtering out relevant predictor components and predictable criterion components. We compare PCovR2 to two other approaches, based on partial least squares (PLS) and principal components regression (PCR), that also reduce the criteria and are therefore called PLS2 and PCR2. By means of a simulated example, we show that PCovR2 outperforms PLS2 and PCR2 when one aims to recover all relevant predictor components and predictable criterion components. Moreover, we conduct a simulation study to evaluate how well PCovR2, PLS2 and PCR2 succeed in finding (1) all underlying components and (2) the subset of relevant predictor and predictable criterion components. Finally, we illustrate the use of PCovR2 by means of empirical data., (© 2021. The Psychonomic Society, Inc.)

Interdisciplinary research often involves analyzing data obtained from different data sources with respect to the same subjects, objects, or experimental units. For example, global positioning systems (GPS) data have been coupled with travel diary data, resulting in a better understanding of traveling behavior. The GPS data and the travel diary data are very different in nature, and, to analyze the two types of data jointly, one often uses data integration techniques, such as the regularized simultaneous component analysis (regularized SCA) method. Regularized SCA is an extension of the (sparse) principle component analysis model to the cases where at least two data blocks are jointly analyzed, which - in order to reveal the joint and unique sources of variation - heavily relies on proper selection of the set of variables (i.e., component loadings) in the components. Regularized SCA requires a proper variable selection method to either identify the optimal values for tuning parameters or stably select variables. By means of two simulation studies with various noise and sparseness levels in simulated data, we compare six variable selection methods, which are cross-validation (CV) with the "one-standard-error" rule, repeated double CV (rdCV), BIC, Bolasso with CV, stability selection, and index of sparseness (IS) - a lesser known (compared to the first five methods) but computationally efficient method. Results show that IS is the best-performing variable selection method.