Stem cell biology and drug discovery

There are many reasons to be interested in stem cells, one of the most prominent being their potential use in finding better drugs to treat human disease. This article focuses on how this may be implemented. Recent advances in the production of reprogrammed adult cells and their regulated differentiation to disease-relevant cells are presented, and diseases that have been modeled using these methods are discussed. Remaining difficulties are highlighted, as are new therapeutic insights that have emerged

TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework for Model Monitoring

Continuous monitoring of trained ML models to determine when their predictions should and should not be trusted is essential for their safe deployment. Such a framework ought to be high-performing, explainable, post-hoc and actionable. We propose TRUST-LAPSE, a "mistrust" scoring framework for continuous model monitoring. We assess the trustworthiness of each input sample's model prediction using a sequence of latent-space embeddings. Specifically, (a) our latent-space mistrust score estimates mistrust using distance metrics (Mahalanobis distance) and similarity metrics (cosine similarity) in the latent-space and (b) our sequential mistrust score determines deviations in correlations over the sequence of past input representations in a non-parametric, sliding-window based algorithm for actionable continuous monitoring. We evaluate TRUST-LAPSE via two downstream tasks: (1) distributionally shifted input detection, and (2) data drift detection. We evaluate across diverse domains - audio and vision using public datasets and further benchmark our approach on challenging, real-world electroencephalograms (EEG) datasets for seizure detection. Our latent-space mistrust scores achieve state-of-the-art results with AUROCs of 84.1 (vision), 73.9 (audio), and 77.1 (clinical EEGs), outperforming baselines by over 10 points. We expose critical failures in popular baselines that remain insensitive to input semantic content, rendering them unfit for real-world model monitoring. We show that our sequential mistrust scores achieve high drift detection rates; over 90% of the streams show < 20% error for all domains. Through extensive qualitative and quantitative evaluations, we show that our mistrust scores are more robust and provide explainability for easy adoption into practice.Comment: Keywords: Mistrust Scores, Latent-Space, Model monitoring, Trustworthy AI, Explainable AI, Semantic-guided A

Measuring Attitudes Toward the Rights of Indigenous People: An Index of Global Citizenship

Global citizenship has emerged as a key objective of liberal education.  Because the status of indigenous persons world-wide is inextricably linked to globalization and imperialism, mainstream culture students’ attitudes toward the rights of indigenous persons can be taken as an index of global citizenship.  The items comprising the Measure of Attitudes Toward the Rights of Indigenous Persons (MATRIP) draw directly from the United Nations’ 2007 Declaration on the Rights of Indigenous Peoples.  Twenty-three statements about indigenous peoples’ rights--as explicated in the UN Declaration--were transformed into Likert-type items measuring five dimensions: Preservation of Culture, Lands & Resources, Self-Governance, Restitution, and Services and Representation.  Questionnaires were administered to 226 undergraduates. MATRIP measurement properties were tested using confirmatory factor analysis. Results indicate that a final scale consisting of 20 items adequately measures the hypothesized dimensions. Potential uses for the scale are discussed in the context of education abroad

Equilibrium Properties of Temporally Asymmetric Hebbian Plasticity

A theory of temporally asymmetric Hebb (TAH) rules which depress or potentiate synapses depending upon whether the postsynaptic cell fires before or after the presynaptic one is presented. Using the Fokker-Planck formalism, we show that the equilibrium synaptic distribution induced by such rules is highly sensitive to the manner in which bounds on the allowed range of synaptic values are imposed. In a biologically plausible multiplicative model, we find that the synapses in asynchronous networks reach a distribution that is invariant to the firing rates of either the pre- or post-synaptic cells. When these cells are temporally correlated, the synaptic strength varies smoothly with the degree and phase of synchrony between the cells.Comment: 3 figures, minor corrections of equations and tex

Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models

Multivariate signals are prevalent in various domains, such as healthcare, transportation systems, and space sciences. Modeling spatiotemporal dependencies in multivariate signals is challenging due to (1) long-range temporal dependencies and (2) complex spatial correlations between sensors. To address these challenges, we propose representing multivariate signals as graphs and introduce GraphS4mer, a general graph neural network (GNN) architecture that captures both spatial and temporal dependencies in multivariate signals. Specifically, (1) we leverage Structured State Spaces model (S4), a state-of-the-art sequence model, to capture long-term temporal dependencies and (2) we propose a graph structure learning layer in GraphS4mer to learn dynamically evolving graph structures in the data. We evaluate our proposed model on three distinct tasks and show that GraphS4mer consistently improves over existing models, including (1) seizure detection from electroencephalography signals, outperforming a previous GNN with self-supervised pretraining by 3.1 points in AUROC; (2) sleep staging from polysomnography signals, a 4.1 points improvement in macro-F1 score compared to existing sleep staging models; and (3) traffic forecasting, reducing MAE by 8.8% compared to existing GNNs and by 1.4% compared to Transformer-based models

Functional consequences of Wnt-induced dishevelled 2 phosphorylation in canonical and non-canonical Wnt signaling

This research was originally published in Journal of Biological Chemitry. González-Sancho. Functional Consequences of Wnt-Induced Dishevelled2 Phosphorylation in Canonical and Non-Canonical Signaling. Journal of Biological Chemistry . 2013. 288 9428-9437 © the American Society for Biochemistry and Molecular BiologyEl título del postprint: Functional Consequences of Wnt-Induced Dishevelled2 Phosphorylation in Canonical and Non-Canonical SignalingDishevelled (Dvl) proteins are intracellular effectors of Wnt signaling that have essential roles in both canonical and noncanonical Wnt pathways. It has long been known that Wnts stimulate Dvl phosphorylation, but relatively little is known about its functional significance. We have previously reported that both Wnt3a and Wnt5a induce Dvl2 phosphorylation that is associated with an electrophoretic mobility shift and loss of recognition by monoclonal antibody 10B5. In the present study, we mapped the 10B5 epitope to a 16-amino acid segment of human Dvl2 (residues 594–609) that contains four Ser/Thr residues. Alanine substitution of these residues (P4m) eliminated the mobility shift induced by either Wnt3a or Wnt5a. The Dvl2 P4m mutant showed a modest increase in canonical Wnt/β-catenin signaling activity relative to wild type. Consistent with this finding, Dvl2 4Pm preferentially localized to cytoplasmic puncta. In contrast to wild-type Dvl2, however, the P4m mutant was unable to rescue Wnt3a-dependent neurite outgrowth in TC-32 cells following suppression of endogenous Dvl2/3. Earlier work has implicated casein kinase 1δ/ϵ as responsible for the Dvl mobility shift, and a CK1δ in vitro kinase assay confirmed that Ser594, Thr595, and Ser597 of Dvl2 are CK1 targets. Alanine substitution of these three residues was sufficient to abrogate the Wnt-dependent mobility shift. Thus, we have identified a cluster of Ser/Thr residues in the C-terminal domain of Dvl2 that are Wnt-induced phosphorylation (WIP) sites. Our results indicate that phosphorylation at the WIP sites reduces Dvl accumulation in puncta and attenuates β-catenin signaling, whereas it enables noncanonical signaling that is required for neurite outgrowth.This work was supported, in whole or in part, by National Institutes of Health Grant R01 CA123238 (to A. M. C. B.) and Postdoctoral Fellowship F32 CA117662 (to C. L. A.). This work was also supported by a fellowship from the Ministerio de Educación, Cultura, y Deportes, of Spain (to J. M. G.-S.), by New York State Department of Health postdoctoral Fellowship NYS C021339 (to Y. T.), and by charitable donations to Strang Cancer Prevention Center. This research also was supported in part by the Intramural Research Program of the National Institutes of Health, National Cancer Institut

Application of Neuroanatomical Ontologies for Neuroimaging Data Annotation

The annotation of functional neuroimaging results for data sharing and re-use is particularly challenging, due to the diversity of terminologies of neuroanatomical structures and cortical parcellation schemes. To address this challenge, we extended the Foundational Model of Anatomy Ontology (FMA) to include cytoarchitectural, Brodmann area labels, and a morphological cortical labeling scheme (e.g., the part of Brodmann area 6 in the left precentral gyrus). This representation was also used to augment the neuroanatomical axis of RadLex, the ontology for clinical imaging. The resulting neuroanatomical ontology contains explicit relationships indicating which brain regions are “part of” which other regions, across cytoarchitectural and morphological labeling schemas. We annotated a large functional neuroimaging dataset with terms from the ontology and applied a reasoning engine to analyze this dataset in conjunction with the ontology, and achieved successful inferences from the most specific level (e.g., how many subjects showed activation in a subpart of the middle frontal gyrus) to more general (how many activations were found in areas connected via a known white matter tract?). In summary, we have produced a neuroanatomical ontology that harmonizes several different terminologies of neuroanatomical structures and cortical parcellation schemes. This neuroanatomical ontology is publicly available as a view of FMA at the Bioportal website1. The ontological encoding of anatomic knowledge can be exploited by computer reasoning engines to make inferences about neuroanatomical relationships described in imaging datasets using different terminologies. This approach could ultimately enable knowledge discovery from large, distributed fMRI studies or medical record mining