Interactions between the neuromodulatory systems and the amygdala: exploratory survey using the Allen Mouse Brain Atlas.

Neuromodulatory systems originate in nuclei localized in the subcortical region of the brain and control fundamental behaviors by interacting with many areas of the central nervous system. An exploratory survey of the cholinergic, dopaminergic, noradrenergic, and serotonergic receptor expression energy in the amygdala, and in the neuromodulatory areas themselves was undertaken using the Allen Mouse Brain Atlas. The amygdala was chosen because of its importance in cognitive behavior and its bidirectional interaction with the neuromodulatory systems. The gene expression data of 38 neuromodulatory receptor subtypes were examined across 13 brain regions. The substantia innominata of the basal forebrain and regions of the amygdala had the highest amount of receptor expression energy for all four neuromodulatory systems examined. The ventral tegmental area also displayed high receptor expression of all four neuromodulators. In contrast, the locus coeruleus displayed low receptor expression energy overall. In general, cholinergic receptor expression was an order of magnitude greater than other neuromodulatory receptors. Since the nuclei of these neuromodulatory systems are thought to be the source of specific neurotransmitters, the projections from these nuclei to target regions may be inferred by receptor expression energy. The comprehensive analysis revealed many connectivity relations and receptor localization that had not been previously reported. The methodology presented here may be applied to other neural systems with similar characteristics, and to other animal models as these brain atlases become available

Model Cards for Model Reporting

Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related AI technology, increasing transparency into how well AI technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation

Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias

Facial analysis models are increasingly used in applications that have serious impacts on people's lives, ranging from authentication to surveillance tracking. It is therefore critical to develop techniques that can reveal unintended biases in facial classifiers to help guide the ethical use of facial analysis technology. This work proposes a framework called \textit{image counterfactual sensitivity analysis}, which we explore as a proof-of-concept in analyzing a smiling attribute classifier trained on faces of celebrities. The framework utilizes counterfactuals to examine how a classifier's prediction changes if a face characteristic slightly changes. We leverage recent advances in generative adversarial networks to build a realistic generative model of face images that affords controlled manipulation of specific image characteristics. We then introduce a set of metrics that measure the effect of manipulating a specific property on the output of the trained classifier. Empirically, we find several different factors of variation that affect the predictions of the smiling classifier. This proof-of-concept demonstrates potential ways generative models can be leveraged for fine-grained analysis of bias and fairness.Comment: Presented at CVPR 2019 Workshop on Fairness Accountability Transparency and Ethics in Computer Visio

Nomenclature and heterogeneity : consequences for the use of mesenchymal stem cells in regenerative medicine

Mesenchymal stem cells (MSCs) are in development for many clinical indications, based both on “stem” properties (tissue repair or regeneration) and on signalling repertoire (immunomodulatory and anti-inflammatory effects). Potential conflation of MSC properties with those of tissue-derived stromal cells presents difficulties in comparing study outcomes and represents a source of confusion in cell therapy development. Cultured MSCs demonstrate significant heterogeneity in clonogenicity and multi-lineage differentiation potential. However in vivo biology of MSCs includes native functions unrelated to regenerative medicine applications, so do nomenclature and heterogeneity matter? In this perspective we examine some consequences of the nomenclature debate and heterogeneity of MSCs. Regulatory expectations are considered, emphasising that product development should prioritise detailed characterisation of therapeutic cell populations for specific indications

Diagnostic techniques for inflammatory eye disease: past, present and future: a review

Investigations used to aid diagnosis and prognosticate outcomes in ocular inflammatory disorders are based on techniques that have evolved over the last two centuries have dramatically evolved with the advances in molecular biological and imaging technology. Our improved understanding of basic biological processes of infective drives of innate immunity bridging the engagement of adaptive immunity have formed techniques to tailor and develop assays, and deliver targeted treatment options. Diagnostic techniques are paramount to distinguish infective from non-infective intraocular inflammatory disease, particularly in atypical cases. The advances have enabled our ability to multiplex assay small amount of specimen quantities of intraocular samples including aqueous, vitreous or small tissue samples. Nevertheless to achieve diagnosis, techniques often require a range of assays from traditional hypersensitivity reactions and microbe specific immunoglobulin analysis to modern molecular techniques and cytokine analysis. Such approaches capitalise on the advantages of each technique, thereby improving the sensitivity and specificity of diagnoses. This review article highlights the development of laboratory diagnostic techniques for intraocular inflammatory disorders now readily available to assist in accurate identification of infective agents and appropriation of appropriate therapies as well as formulating patient stratification alongside clinical diagnoses into disease groups for clinical trials

Investigating the Interactions of Neuromodulators: A Computational Modeling, Game Theoretic, Pharmacological, Embodiment, and Neuroinformatics Perspective

Neuromodulatory systems originate in nuclei localized in the subcortical region of the brain and control fundamental behaviors by interacting with many areas of the central nervous system. Much is known about neuromodulators, but their structural and functional implications in fundamental behavior remain unclear. This dissertation set out to investigate the interaction of neuromodulators and their role in modulating behaviors by combining methodologies in computational modeling, game theory, embodiment, pharmacological manipulations, and neuroinformatics. The first study introduces a novel computational model that predicts how dopamine and serotonin shape competitive and cooperative behavior in a game theoretic environment. The second study adopted the model from the first study to gauge how humans react to adaptive agents, as well as measuring the influence of embodied agents on game play. The third study investigates functional activity of these neuromodulatory circuits by exploring the expression energy of neuromodulatory receptors using the Allen Brain Atlas. The fourth study features a web application known as the Allen Brain Atlas-Drive Visualization, which provides users with a quick and intuitive way to survey large amounts of expression energy data across multiple brain regions of interest. Finally, the last study continues exploring the interaction of dopamine and serotonin by focusing specifically on the reward circuit using the Allen Brain Atlas. The first two studies provide a more behavioral understanding of how dopamine and serotonin interacts, what that interaction might look like in the brain, and how those interactions transpire in complex situations. The remaining three studies uses a neuroinformatics approach to reveal the underlying empirical structure and function behind the interactions of dopamine, serotonin, acetylcholine and norepinephrine in brain regions responsible for the behaviors discussed in the first two studies. When combined, each study provides an additional level of understanding about neuromodulators. This is of great importance because neuroscience simply cannot be explained through one methodology. It is going to take a multifaceted effort, like the one presented in this dissertation, to obtain a deeper understanding of the complexity behind neuromodulators and their structural and functional relationship with each other