Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections.

The immune system has evolved to sense invading pathogens, control infection, and restore tissue integrity. Despite symptomatic variability in patients, unequivocal evidence that an individual's immune system distinguishes between different organisms and mounts an appropriate response is lacking. We here used a systematic approach to characterize responses to microbiologically well-defined infection in a total of 83 peritoneal dialysis patients on the day of presentation with acute peritonitis. A broad range of cellular and soluble parameters was determined in peritoneal effluents, covering the majority of local immune cells, inflammatory and regulatory cytokines and chemokines as well as tissue damage-related factors. Our analyses, utilizing machine-learning algorithms, demonstrate that different groups of bacteria induce qualitatively distinct local immune fingerprints, with specific biomarker signatures associated with Gram-negative and Gram-positive organisms, and with culture-negative episodes of unclear etiology. Even more, within the Gram-positive group, unique immune biomarker combinations identified streptococcal and non-streptococcal species including coagulase-negative Staphylococcus spp. These findings have diagnostic and prognostic implications by informing patient management and treatment choice at the point of care. Thus, our data establish the power of non-linear mathematical models to analyze complex biomedical datasets and highlight key pathways involved in pathogen-specific immune responses.

Building Consumer Understanding by Utilizing a Bayesian Hierarchical Structure within the Behavioral Perspective Model.

This study further develops the theoretical and empirical literature on the Behavioral Perspective Model (BPM) in three ways through an empirical analysis of the Great Britain (GB) biscuit category. First, following a literature review and a category analysis, a more complex model is constructed using the BPM structure and then testing the hypothesis uncovered. Second, the structure of the data theoretically calls for a hierarchical structure of the model, and hence, this is introduced into the BPM framework and is compared to a non-hierarchical structure of the same model. Finally, a discussion is undertaken on the advantages of a Bayesian approach to calculating parameter inference. Two models are built by utilizing vague and informed prior distributions respectively, and the results are compared. This study shows the importance of building appropriate model structures for analysis and demonstrates the advantages and challenges of utilizing a Bayesian approach. It also further demonstrates the BPM's suitability as a vehicle to better understand consumer behavior.

NEURAL Networks and Consumer Behavior: NEURAL Models, Logistic Regression, and the Behavioral Perspective Model.

This paper investigates the ability of connectionist models to explain consumer behavior, focusing on the feedforward neural network model, and explores the possibility of expanding the theoretical framework of the Behavioral Perspective Model to incorporate connectionist constructs. Numerous neural network models of varying complexity are developed to predict consumer loyalty as a crucial aspect of consumer behavior. Their performance is compared with the more traditional logistic regression model and it is found that neural networks offer consistent advantage over logistic regression in the prediction of consumer loyalty. Independently determined Utilitarian and Informational Reinforcement variables are shown to make a noticeable contribution to the explanation of consumer choice. The potential of connectionist models for predicting and explaining consumer behavior is discussed and routes for future research are suggested to investigate the predictive and explanatory capacity of connectionist models, such as neural network models, and for the integration of these into consumer behavior analysis within the theoretical framework of the Behavioral Perspective Model.