Gesture Controlled Collaborative Robot Arm and Lab Kit

In this paper, a mechatronics system was designed and implemented to include the subjects of artificial intelligence, control algorithms, robot servo motor control, and human-machine interface (HMI). The goal was to create an inexpensive, multi-functional robotics lab kit to promote students’ interest in STEM fields including computing and mechtronics. Industrial robotic systems have become vastly popular in manufacturing and other industries, and the demand for individuals with related skills is rapidly increasing. Robots can complete jobs that are dangerous, dull, or dirty for humans to perform. Recently, more and more collaborative robotic systems have been developed and implemented in the industry. Collaborative robots utilize artificial intelligence to become aware of and capable of interacting with a human operator in progressively natural ways. The work created a computer vision-based collaborative robotic system that can be controlled via several different methods including a touch screen HMI, hand gestures, and hard coding via the microcontroller integrated development environment (IDE). The flexibility provided in the framework resulted in an educational lab kit with varying levels of difficulty across several topics such as C and Python programming, machine learning, HMI design, and robotics. The hardware being used in this project includes a Raspberry Pi 4, an Arduino Due, a Braccio Robotics Kit, a Raspberry Pi 4 compatible vision module, and a 5-inch touchscreen display. We anticipate this education lab kit will improve the effectiveness of student learning in the field of mechatronics

The role of inflammation in age-related disease.

The National Institutes of Health (NIH) Geroscience Interest Group (GSIG) sponsored workshop, The Role of Inflammation inAge-Related Disease, was held September 6th-7th, 2012 in Bethesda, MD. It is now recognized that a mild pro-inflammatory state is correlated with the major degenerative diseases of the elderly. The focus of the workshop was to better understand the origins and consequences of this low level chronic inflammation in order to design appropriate interventional studies aimed at improving healthspan. Four sessions explored the intrinsic, environmental exposures and immune pathways by which chronic inflammation are generated, sustained, and lead to age-associated diseases. At the conclusion of the workshop recommendations to accelerate progress toward understanding the mechanistic bases of chronic disease were identified

Pair-Instability Supernovae at the Epoch of Reionization

Pristine stars with masses between ~140 and 260 M_sun are theoretically predicted to die as pair-instability supernovae. These very massive progenitors could come from Pop III stars in the early universe. We model the light curves and spectra of pair-instability supernovae over a range of masses and envelope structures. At redshifts of reionization z >= 6, we calculate the rates and detectability of pair-instability and core collapse supernovae, and show that with the James Webb Space Telescope, it is possible to determine the contribution of Pop III and Pop II stars toward reionization by constraining the stellar initial mass function at that epoch using these supernovae. We also find the rates of Type Ia supernovae, and show that they are not rare during reionization, and can be used to probe the mass function at 4-8 M_sun. If the budget of ionizing photons was dominated by contributions from top-heavy Pop III stars, we predict that the bright end of the galaxy luminosity function will be contaminated by pair-instability supernovae.Comment: 12 pages, 11 figures. Matches MNRAS accepted versio

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy

Motivation Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia. Results We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified. Availability and implementation Datasets and scripts for reproduction of results are available through: Https://nalab.stanford.edu/multiomics-pregnancy/

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin