Complex Associative Structures Research Articles

Analysis of Multivariate Survival Data under Semiparametric Copula Models

AbstractModelling multivariate survival data is complicated by the complex association structure among the responses. To balance model flexibility and interpretability, we propose a semiparametric copula model to modulate multivariate survival data, with the marginal distributions of the response components described by semiparametric linear transformation models. To conduct inference about the model parameters, we develop a two‐stage maximum likelihood method and a three‐stage pseudo‐likelihood estimation procedure. We investigate the impact of model misspecification on the estimation of covariate effects and identify a scenario in which consistent estimation of the marginal parameters is retained even when the copula model is misspecified. The proposed methods are justified both theoretically and empirically. An application to a real dataset is provided to demonstrate the utility of the proposed method.

Extensions of the distributed lag non-linear model (DLNM) to account for cumulative mortality

The effects of meteorological factors on health outcomes have gained popularity due to climate change, resulting in a general rise in temperature and abnormal climatic extremes. Instead of the conventional cross-sectional analysis that focuses on the association between a predictor and the single dependent variable, the distributed lag non-linear model (DLNM) has been widely adopted to examine the effect of multiple lag environmental factors health outcome. We propose several novel strategies to model mortality with the effects of distributed lag temperature measures and the delayed effect of mortality. Several attempts are derived by various statistical concepts, such as summation, autoregressive, principal component analysis, baseline adjustment, and modeling the offset in the DLNM. Five strategies are evaluated by simulation studies based on permutation techniques. The longitudinal climate and daily mortality data in Taipei, Taiwan, from 2012 to 2016 were implemented to generate the null distribution. According to simulation results, only one strategy, named MVDLNM, could yield valid type I errors, while the other four strategies demonstrated much more inflated type I errors. With a real-life application, the MVDLNM that incorporates both the current and lag mortalities revealed a more significant association than the conventional model that only fits the current mortality. The results suggest that, in public health or environmental research, not only the exposure may post a delayed effect but also the outcome of interest could provide the lag association signals. The joint modeling of the lag exposure and the delayed outcome enhances the power to discover such a complex association structure. The new approach MVDLNM models lag outcomes within 10 days and lag exposures up to 1 month and provide valid results.

A molecular insight into the dissociable regulation of associative learning and motivation by the synaptic protein neuroligin-1

BackgroundIn a changing environment, a challenge for the brain is to flexibly guide adaptive behavior towards survival. Complex behavior and the underlying neural computations emerge from the structural components of the brain across many levels: circuits, cells, and ultimately the signaling complex of proteins at synapses. In line with this logic, dynamic modification of synaptic strength or synaptic plasticity is widely considered the cellular level implementation for adaptive behavior such as learning and memory. Predominantly expressed at excitatory synapses, the postsynaptic cell-adhesion molecule neuroligin-1 (Nlgn1) forms trans-synaptic complexes with presynaptic neurexins. Extensive evidence supports that Nlgn1 is essential for NMDA receptor transmission and long-term potentiation (LTP), both of which are putative synaptic mechanisms underlying learning and memory. Here, employing a comprehensive battery of touchscreen-based cognitive assays, we asked whether impaired NMDA receptor transmission and LTP in mice lacking Nlgn1 does in fact disrupt decision-making. To this end, we addressed two key decision problems: (i) the ability to learn and exploit the associative structure of the environment and (ii) balancing the trade-off between potential rewards and costs, or positive and negative utilities of available actions.ResultsWe found that the capacity to acquire complex associative structures and adjust learned associations was intact. However, loss of Nlgn1 alters motivation leading to a reduced willingness to overcome effort cost for reward and an increased willingness to exert effort to escape an aversive situation. We suggest Nlgn1 may be important for balancing the weighting on positive and negative utilities in reward-cost trade-off.ConclusionsOur findings update canonical views of this key synaptic molecule in behavior and suggest Nlgn1 may be essential for regulating distinct cognitive processes underlying action selection. Our data demonstrate that learning and motivational computations can be dissociated within the same animal model, from a detailed behavioral dissection. Further, these results highlight the complexities in mapping synaptic mechanisms to their behavioral consequences, and the future challenge to elucidate how complex behavior emerges through different levels of neural hardware.

Effect of Phenylalanine on the Physicochemical, Structural, and Transport Characteristics of a Profiled MK-40 Sulfoacid Cation Exchange Membrane

The set of properties of a profiled MK-40 sulfoacid cation exchange membrane after 80 h of contact with aqueous solutions of phenylalanine with different concentrations is studied. A reduction associated with the intercalation of the hydrophobic amino acid ions into its micro- and macropores is observed in the total exchange capacity, moisture content, and thickness of the membrane. The drop in the moisture content of the membrane results in the condensation of its structure, so the height and radius of the elements of the surface profile shrink and the fraction and sizes of the macropores both on the surface and in the volume of the membrane fall. The drop in the diffusion permeability of the sulfoacid cation exchange membrane upon an increase in the concentration of the contacting aqueous solution of phenylalanine is associated with the collapse of pores and their reduced moisture content and number of active functional strongly acidic groups in the membrane as a result of the formation of complex associative structures of the amino acid in the external and internal solutions.

Two-stage model for multivariate longitudinal and survival data with application to nephrology research.

In many follow-up studies different types of outcomes are collected including longitudinal measurements and time-to-event outcomes. Commonly, it is of interest to study the association between them. Joint modeling approaches of a single longitudinal outcome and survival process have recently gained increasing attention from both frequentist and Bayesian perspective. However, in many studies several longitudinal biomarkers are of interest and instead of selecting one single biomarker, the relationships between all these outcomes and their association with survival needs to be investigated. Our motivating study comes from Peritoneal Dialysis Programme in Nephrology research from Nephrology Unit, CHP (Hospital de Santo António), Porto, Portugal in which the interest relies on the possible association between various biomarkers (calcium, phosphate, parathormone, and creatinine) and the patients' survival. To this aim, we propose a two-stage model-based approach for multivariate longitudinal and survival data that allowed us to study such complex association structure. The multivariate model suggested in this paper provided new insights in the area of nephrology research showing valid results in comparison with those models studying each longitudinal biomarker with survival separately.

Marginal models for the association structure of hierarchical binary responses

ABSTRACTClustered binary responses are often found in ecological studies. Data analysis may include modeling the marginal probability response. However, when the association is the main scientific focus, modeling the correlation structure between pairs of responses is the key part of the analysis. Second-order generalized estimating equations (GEE) are established in the literature. Some of them are more efficient in computational terms, especially facing large clusters. Alternating logistic regression (ALR) and orthogonalized residual (ORTH) GEE methods are presented and compared in this paper. Simulation results show a slightly superiority of ALR over ORTH. Marginal probabilities and odds ratios are also estimated and compared in a real ecological study involving a three-level hierarchical clustering. ALR and ORTH models are useful for modeling complex association structure with large cluster sizes.

Undernutrition in Benin—an analysis based on graphical models

Undernutrition is one of the most important health problems in developing countries. Examining its determinants implies the investigation of a complex association structure including a large number of potential influence variables and different types of influences. A recently developed statistical technique to cope with such situations are graphical chain models. In this paper, this approach is used to investigate the determinants of undernutrition in Benin (West Africa). Since this method also reveals indirect influences, interesting insight is gained into the association structure of all variables incorporated. The analysis identifies mother's education, socioeconomic status, and religion as three variables with particularly strong direct and indirect linkages to undernutrition.

An ALGOL-based associative language

A high level programming language for large, complex associative structures has been designed and implemented. The underlying data structure has been implemented using a hash-coding technique. The discussion includes a comparison with other work and examples of applications of the language.

Storage and retrieval of aspects of meaning in directed graph structures

An experimental system that uses LISP to make a conceptual dictionary is described. The dictionary associates with each English word the syntactic information, definitional material, and references to the contexts in which it has been used to define other words. Such relations as class inclusion, possession, and active or passive actions are used as definitional material. The resulting structure serves as a powerful vehicle for research on the logic of question answering. Examples of methods of inputting information and answering simple English questions are given. An important conclusion is that, although LISP and other list processing languages are ideally suited for producing complex associative structures, they are inadequate vehicles for language processing on any large scale—at least until they can use auxiliary memory as a continuous extension of core memory.