Diffusion Causal Models for Counterfactual Estimation

We consider the task of counterfactual estimation from observational imaging data given a known causal structure. In particular, quantifying the causal effect of interventions for high-dimensional data with neural networks remains an open challenge. Herein we propose Diff-SCM, a deep structural causal model that builds on recent advances of generative energy-based models. In our setting, inference is performed by iteratively sampling gradients of the marginal and conditional distributions entailed by the causal model. Counterfactual estimation is achieved by firstly inferring latent variables with deterministic forward diffusion, then intervening on a reverse diffusion process using the gradients of an anti-causal predictor w.r.t the input. Furthermore, we propose a metric for evaluating the generated counterfactuals. We find that Diff-SCM produces more realistic and minimal counterfactuals than baselines on MNIST data and can also be applied to ImageNet data. Code is available https://github.com/vios-s/Diff-SCM.Comment: Accepted at CLeaR (Causal Learning and Reasoning) 202

The ciliary machinery is repurposed for T cell immune synapse trafficking of LCK

Upon engagement of the T cell receptor with an antigen-presenting cell, LCK initiates TCR signaling by phosphorylating its activation motifs. However, the mechanism of LCK activation specifically at the immune synapse is a major question. We show that phosphorylation of the LCK activating Y394, despite modestly increasing its catalytic rate, dramatically focuses LCK localization to the immune synapse. We describe a trafficking mechanism whereby UNC119A extracts membrane-bound LCK by sequestering the hydrophobic myristoyl group, followed by release at the target membrane under the control of the ciliary ARL3/ARL13B. The UNC119A N terminus acts as a “regulatory arm” by binding the LCK kinase domain, an interaction inhibited by LCK Y394 phosphorylation, thus together with the ARL3/ARL13B machinery ensuring immune synapse focusing of active LCK. We propose that the ciliary machinery has been repurposed by T cells to generate and maintain polarized segregation of signals such as activated LCK at the immune synapse

Association of Childhood Physical and Sexual Abuse with Intimate Partner Violence, Poor General Health and Depressive Symptoms among Pregnant Women

This research was supported by an award from the National Institutes of Health (NIH), the Eunice Kennedy Shriver Institute of Child Health and Human Development (R01-HD- 059835). The NIH had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication. The authors wish to thank the dedicated staff members of Asociacion Civil Proyectos en Salud (PROESA), Peru and Instituto Materno Perinatal, Peru for their expert technical assistance with this research.Objective We examined associations of childhood physical and sexual abuse with risk of intimate partner violence (IPV). We also evaluated the extent to which childhood abuse was associated with self-reported general health status and symptoms of antepartum depression in a cohort of pregnant Peruvian women. Methods In-person interviews were conducted to collect information regarding history of childhood abuse and IPV from 1,521 women during early pregnancy. Antepartum depressive symptomatology was evaluated using the Patient Health Questionnaire-9. Multivariable logistic regression procedures were used to estimate adjusted odds ratios (aOR) and 95% confidence intervals (95%CI). Results Any childhood abuse was associated with 2.2-fold increased odds of lifetime IPV (95%CI: 1.72–2.83). Compared with women who reported no childhood abuse, those who reported both, childhood physical and sexual abuse had a 7.14-fold lifetime risk of physical and sexual IPV (95%CI: 4.15–12.26). The odds of experiencing physical and sexual abuse by an intimate partner in the past year was 3.33-fold higher among women with a history of childhood physical and sexual abuse as compared to women who were not abused as children (95%CI 1.60–6.89). Childhood abuse was associated with higher odds of self-reported poor health status during early pregnancy (aOR = 1.32, 95%CI: 1.04–1.68) and with symptoms of antepartum depression (aOR = 2.07, 95%CI: 1.58–2.71). Conclusion These data indicate that childhood sexual and physical abuse is associated with IPV, poor general health and depressive symptoms in early pregnancy. The high prevalence of childhood trauma and its enduring effects of on women’s health warrant concerted global health efforts in preventing violence.: This research was supported by an award from the National Institutes of Health (NIH), the Eunice Kennedy Shriver Institute of Child Health and Human Development (R01-HD-059835). The NIH had no further role in study design; in the collection,Revisión por pare

A Causal Ordering Prior for Unsupervised Representation Learning

Unsupervised representation learning with variational inference relies heavily on independence assumptions over latent variables. Causal representation learning (CRL), however, argues that factors of variation in a dataset are, in fact, causally related. Allowing latent variables to be correlated, as a consequence of causal relationships, is more realistic and generalisable. So far, provably identifiable methods rely on: auxiliary information, weak labels, and interventional or even counterfactual data. Inspired by causal discovery with functional causal models, we propose a fully unsupervised representation learning method that considers a data generation process with a latent additive noise model (ANM). We encourage the latent space to follow a causal ordering via loss function based on the Hessian of the latent distribution

A Functional MRI and Magneto/Electro Source Imaging Procedure for Cognitive and Pre-surgical Evaluation

AbstractAnalysis of normal/pathological brain activity using neuroimaging methods is necessary to avoid operation risks, and the outcome serves as prior information for surgical neuronavigation. We present an fMRI/MEG/EEG-based methodology for tasks demanding mainly sensorimotor and visual/cognitive responses. This consists of carefully selected/designed stimulation paradigms and statistical parametric mapping methods that demonstrate the practicability of these techniques for clinical applications. The results replicate known findings in the brain-imaging field, with the improvement that our analyses are restricted to grey matter tissue. The latter enhance computations, which is advantageous for the massive data analyses that are typical of clinical and radiological functional brain “checkup” services