GenoTools: An Open-Source Python Package for Efficient Genotype Data Quality Control and Analysis.

GenoTools, a Python package, streamlines population genetics research by integrating ancestry estimation, quality control (QC), and genome-wide association studies (GWAS) capabilities into efficient pipelines. By tracking samples, variants, and quality-specific measures throughout fully customizable pipelines, users can easily manage genetics data for large and small studies. GenoTools' "Ancestry" module renders highly accurate predictions, allowing for high-quality ancestry-specific studies, and enables custom ancestry model training and serialization, specified to the user's genotyping or sequencing platform. As the genotype processing engine that powers several large initiatives including the NIH's Center for Alzheimer's and Related Dementias (CARD) and the Global Parkinson's Genetics Program (GP2). GenoTools was used to process and analyze the UK Biobank and major Alzheimer's Disease (AD) and Parkinson's Disease (PD) datasets with over 400,000 genotypes from arrays and 5000 sequences and has led to novel discoveries in diverse populations. It has provided replicable ancestry predictions, implemented rigorous QC, and conducted genetic ancestry-specific GWAS to identify systematic errors or biases through a single command. GenoTools is a customizable tool that enables users to efficiently analyze and scale genotype data with reproducible and scalable ancestry, QC, and GWAS pipelines.

Harnessing diversity to study Alzheimer's disease: A new iPSC resource from the NIH CARD and ADNI.

The iDA Project (iPSCs to Study Diversity in Alzheimer's and Alzheimer's Disease-related Dementias) is generating 200 induced pluripotent stem cell lines from Alzheimer's Disease Neuroimaging Initiative participants. These lines are sex balanced, include common APOE genotypes, span disease stages, and are ancestrally diverse. Cell lines and characterization data will be shared openly.

Haptic perception using optoelectronic robotic flesh for embodied artificially intelligent agents.

Flesh encodes a variety of haptic information including deformation, temperature, vibration, and damage stimuli using a multisensory array of mechanoreceptors distributed on the surface of the human body. Currently, soft sensors are capable of detecting some haptic stimuli, but whole-body multimodal perception at scales similar to a human adult (surface area ~17,000 square centimeters) is still a challenge in artificially intelligent agents due to the lack of encoding. This encoding is needed to reduce the wiring required to send the vast amount of information transmitted to the processor. We created a robotic flesh that could be further developed for use in these agents. This engineered flesh is an optical, elastomeric matrix "innervated" with stretchable lightguides that encodes haptic stimuli into light: temperature into wavelength due to thermochromic dyes and forces into intensity due to mechanical deformation. By exploiting the optical properties of the constitutive materials and using machine learning, we infer spatiotemporal, haptic information from light that is read by an image sensor. We demonstrate the capabilities of our system in various assemblies to estimate temperature, contact location, normal and shear force, gestures, and damage from temporal snapshots of light coming from the entire haptic sensor with errors <5%.