Suicide Seasonality: Complex Demodulation as a Novel Approach in Epidemiologic Analysis

Seasonality of suicides is well-known and nearly ubiquitous, but recent evidence showed inconsistent patterns of decreasing or increasing seasonality in different countries. Furthermore, strength of seasonality was hypothesized to be associated with suicide prevalence. This study aimed at pointing out methodological difficulties in examining changes in suicide seasonality. METHODODOLOGY/PRINCIPAL FINDINGS: The present study examines the hypothesis of decreasing seasonality with a superior method that allows continuous modeling of seasonality. Suicides in Austria (1970-2008, N = 67,741) were analyzed with complex demodulation, a local (point-in-time specific) version of harmonic analysis. This avoids the need to arbitrarily split the time series, as is common practice in the field of suicide seasonality research, and facilitates incorporating the association with suicide prevalence. Regression models were used to assess time trends and association of amplitude and absolute suicide numbers. Results showed that strength of seasonality was associated with absolute suicide numbers, and that strength of seasonality was stable during the study period when this association was taken into account.Continuous modeling of suicide seasonality with complex demodulation avoids spurious findings that can result when time series are segmented and analyzed piecewise or when the association with suicide prevalence is disregarded

Incomplete Inhibition of Sphingosine 1-Phosphate Lyase Modulates Immune System Function yet Prevents Early Lethality and Non-Lymphoid Lesions

BACKGROUND: S1PL is an aldehyde-lyase that irreversibly cleaves sphingosine 1-phosphate (S1P) in the terminal step of sphingolipid catabolism. Because S1P modulates a wide range of physiological processes, its concentration must be tightly regulated within both intracellular and extracellular environments. METHODOLOGY: In order to better understand the function of S1PL in this regulatory pathway, we assessed the in vivo effects of different levels of S1PL activity using knockout (KO) and humanized mouse models. PRINCIPAL FINDINGS: Our analysis showed that all S1PL-deficient genetic models in this study displayed lymphopenia, with sequestration of mature T cells in the thymus and lymph nodes. In addition to the lymphoid phenotypes, S1PL KO mice (S1PL(-/-)) also developed myeloid cell hyperplasia and significant lesions in the lung, heart, urinary tract, and bone, and had a markedly reduced life span. The humanized knock-in mice harboring one allele (S1PL(H/-)) or two alleles (S1PL(H/H)) of human S1PL expressed less than 10 and 20% of normal S1PL activity, respectively. This partial restoration of S1PL activity was sufficient to fully protect both humanized mouse lines from the lethal non-lymphoid lesions that developed in S1PL(-/-) mice, but failed to restore normal T-cell development and trafficking. Detailed analysis of T-cell compartments indicated that complete absence of S1PL affected both maturation/development and egress of mature T cells from the thymus, whereas low level S1PL activity affected T-cell egress more than differentiation. SIGNIFICANCE: These findings demonstrate that lymphocyte trafficking is particularly sensitive to variations in S1PL activity and suggest that there is a window in which partial inhibition of S1PL could produce therapeutic levels of immunosuppression without causing clinically significant S1P-related lesions in non-lymphoid target organs

A joint process model of consensus and longitudinal dynamics

The Extended Condorcet Model allows us to explore interindividual consensus concerning culturally held knowledge. Also, it enables a process-level description of interindividual differences in the knowledge a person has of the consensus, their willingness to guess in the absence of knowledge, and their bias in guessing. These person-specific characteristics might be tied to one's everyday life experiences. Here, we develop a cognitive latent variable model in which dynamic process parameters from intensive longitudinal daily life data are systematically linked to parameters of the Extended Condorcet Model. We apply this joint model of consensus and longitudinal dynamics to study whether subjective beliefs on what makes people feel loved are linked to daily life experiences of love

A joint process model of consensus and longitudinal dynamics

The Extended Condorcet Model allows us to explore interindividual consensus concerning culturally held knowledge. Also, it enables a process-level description of interindividual differences in the knowledge a person has of the consensus, their willingness to guess in the absence of knowledge, and their bias in guessing. These person-specific characteristics might be tied to one's everyday life experiences. Here, we develop a cognitive latent variable model in which dynamic process parameters from intensive longitudinal daily life data are systematically linked to parameters of the Extended Condorcet Model. We apply this joint model of consensus and longitudinal dynamics to study whether subjective beliefs on what makes people feel loved are linked to daily life experiences of love

Bayesian Cultural Consensus Theory

In this article, we present a Bayesian inference framework for cultural consensus theory (CCT) models for dichotomous (True/False) response data and provide an associated, user-friendly software package along with a detailed user’s guide to carry out the inference. We believe that the time is ripe for Bayesian statistical inference to become the default choice in the field of CCT. Unfortunately, a lack of publications presenting a practical description of the Bayesian framework in the context of CCT models as well as a dearth of accessible software to apply Bayesian inference to CCT data has so far prevented this from happening. We introduce the Bayesian treatment of several CCT models, focusing on the various merits of Bayesian parameter estimation and interpretation of results, and also introduce the Bayesian Cultural Consensus Toolbox software package

Bayesian Cultural Consensus Theory

In this article, we present a Bayesian inference framework for cultural consensus theory (CCT) models for dichotomous (True/False) response data and provide an associated, user-friendly software package along with a detailed user’s guide to carry out the inference. We believe that the time is ripe for Bayesian statistical inference to become the default choice in the field of CCT. Unfortunately, a lack of publications presenting a practical description of the Bayesian framework in the context of CCT models as well as a dearth of accessible software to apply Bayesian inference to CCT data has so far prevented this from happening. We introduce the Bayesian treatment of several CCT models, focusing on the various merits of Bayesian parameter estimation and interpretation of results, and also introduce the Bayesian Cultural Consensus Toolbox software package

Improved information pooling for hierarchical cognitive models through multiple and covaried regression

International audienceCognitive process models are fit to observed data to infer how experimental manipulations modify the assumed underlying cognitive process. They are alternatives to descriptive models, which only capture differences on the observed data level, and do not make assumptions about the underlying cognitive process. Process models may require more observations than descriptive models however , and as a consequence, usually fewer conditions can be simultaneously modeled with them. Unfortunately, it is known that the predictive validity of a model may be compromised when fewer experimental conditions are jointly accounted for (e.g., overestimation of predictor effects, or their incorrect assignment). We develop a hierarchical and covaried multiple regression approach to address this problem. Specifically, we show how to map the recurrences of all conditions, participants, items, and/or traits across experimental design cells to the process model parameters. This systematic pooling of information can facilitate parameter estimation. The proposed approach is particularly relevant for multi-factor experimental designs, and for mixture models that parameterize per cell to assess predictor effects. This hierarchical framework provides the capacity to model more conditions jointly to improve parameter recovery atlow observation numbers (e.g., using only 1/6 of trials,recovering as well as standard hierarchical Bayesian meth-ods), and to directly model predictor and covariate effects onthe process parameters, without the need for post hoc anal-yses (e.g., ANOVA). An example application to real data isalso provided