Systemic lupus erythematosus in African-American women: immune cognitive modules, autoimmune disease, and pathogenic social hierarchy

Examining elevated rates of systemic lupus erythematosus in African-American women from the perspective of the emerging theory of immune cognition suggests the disease constitutes an internalized physiological image of external patterns of psychosocial stress, a 'pathogenic social hierarchy' involving the synergism of racism and gender discrimination. The disorder represents the punctuated resetting of 'normal' immune self-image to a self-attacking 'excited' state, a process formally analogous to models of punctuated equilibrium in evolutionary theory. We speculate that this punctuated onset takes place in the context of an immunological 'cognitive module' similar to what has been proposed by evolutionary psychologists for the human mind. We discuss the broader implications of a high rate of this disorder within a marginalized population, finding it to be a leading indicator for phenomena likely to entrain powerful subgroups into a larger pattern of embedding patholog

Toward Cultural Oncology: The Evolutionary Information Dynamics of Cancer

'Racial' disparities among cancers, particularly of the breast and prostate, are something of a mystery. For the US, in the face of slavery and its sequelae, centuries of interbreeding have greatly leavened genetic differences between 'Blacks' and 'whites', but marked contrasts in disease prevalence and progression persist. 'Adjustment' for socioeconomic status and lifestyle, while statistically accounting for much of the variance in breast cancer, only begs the question of ultimate causality. Here we propose a more basic biological explanation that extends the theory of immune cognition to include elaborate tumor control mechanisms constituting the principal selection pressure acting on pathologically mutating cell clones. The interplay between them occurs in the context of an embedding, highly structured, system of culturally specific psychosocial stress which we find is able to literally write an image of itself onto disease progression. The dynamics are analogous to punctuated equilibrium in simple evolutionary proces

Robust Odorant Recognition in Biological and Artificial Olfaction

Accurate detection and identification of gases pose a number of challenges for chemical sensory systems. The stimulus space is enormous; volatile compounds vary in size, charge, functional groups, and isomerization among others. Furthermore, variability arises from intrinsic (poisoning of the sensors or degradation due to aging) and extrinsic (environmental: humidity, temperature, flow patterns) sources. Nonetheless, biological olfactory systems have been refined over time to overcome these challenges. The main objective of this work is to understand how the biological olfactory system deals with these challenges, and translate them to artificial olfaction to achieve comparable capabilities. In particular, this thesis focuses on the design and computing mechanisms that allow a relatively simple invertebrate olfactory system to robustly recognize odorants even though the sensory neurons inputs may vary due to the identified intrinsic, or extrinsic factors. In biological olfaction, signal processing in the central circuits is largely shielded from the variations in the periphery arising from the constant replacement of older olfactory sensory neurons with newer ones. Inspired by this design principle, we developed an analytical method where the operation of a temperature programmed chemiresistor is treated akin to a mathematical input/output (I/O) transform. Results show that the I/O transform is unique for each analyte-transducer combination, robust with respect to sensor aging, and is highly reproducible across sensors of equal manufacture. This enables decoupling of the signal processing algorithms from the chemical transducer, and thereby allows seamless replacement of sensor array, while the signal processing approach was kept a constant. This is a key advance necessary for achieving long-term, non-invasive chemical sensing. Next, we explored how the biological system maintains invariance while environmental conditions, particularly with respect to changes in humidity levels. At the sensory level, odor-evoked responses to odorants did not vary with changes in humidity levels, however, the spontaneous activity varied significantly. Nevertheless, in the central antennal lobe circuits, ensembles of projection neurons robustly encoded information about odorant identity and intensity irrespective of the humidity levels. Interestingly, variations in humidity levels led to variable compression of intensity information which was carried forward to behavior. Taken together, these results indicate how the influence of humidity is diminished by central neural circuits in the biological olfactory system. Finally, we explored a potential biomedical application where a robust chemical sensing approach will be immensely useful: non-invasive assay for malaria diagnosis based on exhaled breath analysis. We developed a method to screen gas chromatography/mass spectroscopy (GC/MS) traces of human breath and identified 6 compounds that have abundance changes in malaria infected patients and can potentially serve as biomarkers in exhaled breath for their diagnosis. We will conclude with a discussion of on-going efforts to develop a non-invasive solution for diagnosing malaria based on breath volatiles. In sum, this work seeks to understand the basis for robust odor recognition in biological olfaction and proposes bioinspired and statistical solutions for achieving the same abilities in artificial chemical sensing systems

Domain Generalization -- A Causal Perspective

Machine learning models rely on various assumptions to attain high accuracy. One of the preliminary assumptions of these models is the independent and identical distribution, which suggests that the train and test data are sampled from the same distribution. However, this assumption seldom holds in the real world due to distribution shifts. As a result models that rely on this assumption exhibit poor generalization capabilities. Over the recent years, dedicated efforts have been made to improve the generalization capabilities of these models collectively known as -- \textit{domain generalization methods}. The primary idea behind these methods is to identify stable features or mechanisms that remain invariant across the different distributions. Many generalization approaches employ causal theories to describe invariance since causality and invariance are inextricably intertwined. However, current surveys deal with the causality-aware domain generalization methods on a very high-level. Furthermore, we argue that it is possible to categorize the methods based on how causality is leveraged in that method and in which part of the model pipeline is it used. To this end, we categorize the causal domain generalization methods into three categories, namely, (i) Invariance via Causal Data Augmentation methods which are applied during the data pre-processing stage, (ii) Invariance via Causal representation learning methods that are utilized during the representation learning stage, and (iii) Invariance via Transferring Causal mechanisms methods that are applied during the classification stage of the pipeline. Furthermore, this survey includes in-depth insights into benchmark datasets and code repositories for domain generalization methods. We conclude the survey with insights and discussions on future directions

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 333)

This bibliography lists 122 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during January, 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Graph Signal Processing: Overview, Challenges and Applications

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE

Aerospace medicine and biology: A continuing bibliography with indexes

This bibliography lists 161 reports, articles, and other documents introduced into the NASA scientific and technical information system in November, 1987