Sounding Bodies: Modeling 3D Spatial Sound of Humans Using Body Pose and Audio

While 3D human body modeling has received much attention in computer vision, modeling the acoustic equivalent, i.e. modeling 3D spatial audio produced by body motion and speech, has fallen short in the community. To close this gap, we present a model that can generate accurate 3D spatial audio for full human bodies. The system consumes, as input, audio signals from headset microphones and body pose, and produces, as output, a 3D sound field surrounding the transmitter's body, from which spatial audio can be rendered at any arbitrary position in the 3D space. We collect a first-of-its-kind multimodal dataset of human bodies, recorded with multiple cameras and a spherical array of 345 microphones. In an empirical evaluation, we demonstrate that our model can produce accurate body-induced sound fields when trained with a suitable loss. Dataset and code are available online.Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Multiface: A Dataset for Neural Face Rendering

Photorealistic avatars of human faces have come a long way in recent years, yet research along this area is limited by a lack of publicly available, high-quality datasets covering both, dense multi-view camera captures, and rich facial expressions of the captured subjects. In this work, we present Multiface, a new multi-view, high-resolution human face dataset collected from 13 identities at Reality Labs Research for neural face rendering. We introduce Mugsy, a large scale multi-camera apparatus to capture high-resolution synchronized videos of a facial performance. The goal of Multiface is to close the gap in accessibility to high quality data in the academic community and to enable research in VR telepresence. Along with the release of the dataset, we conduct ablation studies on the influence of different model architectures toward the model's interpolation capacity of novel viewpoint and expressions. With a conditional VAE model serving as our baseline, we found that adding spatial bias, texture warp field, and residual connections improves performance on novel view synthesis. Our code and data is available at: https://github.com/facebookresearch/multifac

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment

A panel of lung injury biomarkers enhances the definition of primary graft dysfunction (PGD) after lung transplantation

BACKGROUND: We aimed to identify combinations of biomarkers to enhance the definition of PGD for translational research. METHODS: Biomarkers reflecting lung epithelial injury (sRAGE and SP-D), coagulation cascade (PAI-1 and Protein C), and cell adhesion (ICAM-1) were measured in the plasma of 315 subjects derived from the LTOG cohort at 6 and 24 hours after transplantation. We assessed biomarker utility in two ways: first, we tested the discrimination of grade 3 PGD within 72 hours; second, we tested the predictive utility of plasma biomarkers for 90-day mortality. RESULTS: 86/315 subjects (27%) developed PGD. 23 subjects (8%) died within 90 days of transplantation, of which 16 (70%) had PGD. Biomarkers measured at 24 hours had greater discrimination than at 6 hours. Individually, sRAGE (AUC 0.71) and PAI-1 (AUC 0.73) had the best discrimination of PGD. The combinations of sRAGE with PAI-1 (AUC 0.75), PAI-1 with ICAM-1 (AUC 0.75), and PAI-1 with SP-D (AUC 0.76) had the best discrimination. Combinations of greater than 2 biomarkers did not significantly enhance discrimination of PGD. ICAM-1 with PAI-1 (AUC 0.72) and ICAM-1 with sRAGE (AUC of 0.72) had the best prediction for 90-day mortality. The addition of ICAM-1, PAI-1, or sRAGE to the concurrent clinical PGD grade significantly improved prediction of 90-day mortality (p<0.001 each). CONCLUSIONS: Measurement of the combination of a marker of impaired fibrinolysis with an epithelial injury or cell adhesion marker had the best discrimination for PGD and prediction for early mortality, and may provide an alternative outcome useful in future research

Analysis of shared common genetic risk between amyotrophic lateral sclerosis and epilepsy

Because hyper-excitability has been shown to be a shared pathophysiological mechanism, we used the latest and largest genome-wide studies in amyotrophic lateral sclerosis (n = 36,052) and epilepsy (n = 38,349) to determine genetic overlap between these conditions. First, we showed no significant genetic correlation, also when binned on minor allele frequency. Second, we confirmed the absence of polygenic overlap using genomic risk score analysis. Finally, we did not identify pleiotropic variants in meta-analyses of the 2 diseases. Our findings indicate that amyotrophic lateral sclerosis and epilepsy do not share common genetic risk, showing that hyper-excitability in both disorders has distinct origins