U of Minnesota Psychiatry/Psychology (Alik Widge, MD, PhD)

Teaming Overview and Objectives

Project Overview

• Deep bench of expertise in cognitive profiling, including assays proven as test-retest stable

• Access to large cohorts (1000s) of patients receiving brain-based interventions to optimize mental function

• Supercomputing facilities, deep learning/informatics expertise, on-campus medical device prototyping

• Success in past DARPA programs (SUBNETS, ElectRx) requiring similar phenotyping, profiling

• Need partners in: genomics, metabolomics, wearables, other non-brain quantification

• Core concept: success in specialized roles is driven by mental performance/readiness, not physical

• Design a Reliable, Repeatable, and Robust framework for tracking mental performance capacity

• Multi-domain: self-control, motivation/drive, working memory, arousal regulation, etc.

• Link mental performance to physiologic/expressomicmarkers that can be tracked in real time

Impact• Wearable or minimally implantable personal ”brain

performance monitor”, totally passive and transparent• Measure individual cognitive capability (mission readiness)

on a day-to-day, hour-to-hour timescale• Optimize not just execution, but training, use of recovery

time, overall psychological health/well-being• Envision a consumer/business-facing performance

optimization technology as a major output• Identify high-potential leadership candidates• Optimize coaching of inconsistent performers• Maximize educational/training outcomes

Peripheral Markers

Optimal Performance

Self-Control

Drive to Win

Focused Mind

CircuitsRhythms Skills

Brain Health and Capacity

Alik Widge, MD, PhD | awidge@umn.edu | 612-625-7594

David Putrino, Icahn School of Medicine at Mount Sinai, Abilities Research Center

Teaming Overview and Objectives

Project Overview

• Multiple partners inside and outside of the institution• >50 peer-reviewed publications, book chapters and

presentations. • International reputation for training some of the best

athletes in the world• Core facilities (in addition to everything medical):

• Motion capture• Genomics and microbiome• AI and data science core facility • Tech transfer capabilities

• Looking for collaborators who can assist with device fabrication and development, rapid prototyping, bioengineering

• Access to elite athletes, high performance executives and special forces soldiers

• We have a strong background in tracking movement and behavior in both human and non-human models

• Our goal is to quantify less traditional predictors of performance and monitor how they vary during different high performance conditions

• We have been investigating social, emotional and cognitive predictors of human performance in multiple settings

Impact• Our primary outcome measure will be to show that

performance prediction is enhanced by adding objective social, emotional and cognitive to our predictive model

• Enhanced ability to identify and organize performers into effective teams

• Quantify the effect of different social emotional features on daily performance

• If successful, translate findings into a platform that can be replicated

david.putrino@mountsinai.org – 617 955 4364

Louisiana State University System and CollaboratorsPennington Biomedical Research Center LSU Athletics LSU School of Kinesiology

Teaming Overview and Objectives

Project Overview

Existing team members and partners Pennington Biomedical Research Center LSU Health Sciences CenterLSU Athletics Baton Rouge General Sports Med.LSU School of Kinesiology Baton Rouge OrthopedicsNASA, USARIEM, WRAIR Our Lady of the Lake Neuroscience

Relevant experience• Highly-trained, proven research and athletics training teams with extensive military,

external, and private collaborators• $80 million in DOD funding over 30 years with > 100 peer-reviewed publications• Single and multi-center NIH clinical trial experience including MoTrPAC, LIFE, et. al.• >15 past and ongoing funded research projects that utilized elite athletes

Institutional assets• Internationally-recognized genomics, proteomics, metabolomics, immuno-metabolomics,

microbiome, bioinformatics, behavioral/cognitive, whole-body phenotyping, and bioinformatics cores

• Performance phenotyping and intervention laboratories for exercise testing and training• World-renowned recruiting and marketing core• Interventional resource management core with stellar participant retention/compliance

For which technical challenges are you seeking collaborators?• Access to “machine-learning” level data processing to comprehend expression circuits

Does your team have unique sample/cohort access?• Access to elite professional, collegiate, and high school athletes

• Elite athletes posses the genetic and phenotypic characteristics, including cognitive/behavioral aptitude, physical prowess, and resiliency, similar to special forces personnel.

• Our research team has access to elite athletes, and the research experience and facilities that will allow us to examine the genetic, physical, cognitive, and behavioral elements (expression circuits) present in high school or early collegiate athletes, implement specialized training and nutritional interventions, and validate developed prediction models for successful attainment of “professional“ status.

• For all funded proposals in TA3, the major technical challenge will be to recruit, test, and intervene on potential special forces quality individuals to validate products from TA1 and TA2.

ImpactAlter the way the special forces select and train soldiers to enhance the health and performance of warfighters.Potential applications enabled by this technology:

• Identify individuals who, if desired, can fast-track in the military to special forces.• Select treatments to promote health and performance, and select individuals to foster team

adhesion, in collegiate and professional athletics.• Offsite diagnosis and monitoring of patients with health-related concerns or who are at risk

of future disease. • Allow individuals in the private sector to track their own health related outcomes.

Unique metrics and milestones:• Multi-center collaborations with TA1/TA2 sites• Continue fruitful collaborations with analysis cites for metabolomics• Excellent participant recruitment and protocol compliance• Near real-time electronic data entry and QA

How will the team pursue transition of this technology?• We will effectively collaborate with the TA1 and TA2 teams to implement the technology in

participants including high level athletes and participants from the greater BR area. • Technologies validated in TA3 will be cross-validated in the DOD through our existing

collaborations with NASA, USARIEM, WRAIR, and others.

Jennifer Rood Jennifer.Rood@pbrc.edu Jack Marucci jmarucc@lsu.edu Neil Johannsen njohan1@lsu.edu

PI: J. Craig Venter, Venter Institute Center for Human PerformanceTeam: UCSD, Caltech, Metabolon, NSI, Beyond Limits

Team: J. Craig Venter (PI), Pamila Brar (Chief Medical Officer), Maria Giovanni, Ewen Kirkness, Todd Michael, Marcelo Freire; AI: Gary Fogel, NSI; AJ Abdallat, Mark James, Beyond Limits; MRI and Neuroscience: Jim Brewer, Anders Dale, UCSD; Implantable Devices: Axel Scherer, Azita Emami, Caltech.Experience: 25 years pioneering human genomics, sequenced first free-living organism, first human genome, first diploid genome, first microbiome, and created first synthetic cell; >10,000 human genomes analyzed; performed first integrative study of microbiome / metabolome / human genome with predictive analysis.Assets: Venter Institute Center for Human Performance - a genotype-phenotype platform to comprehensively access an individual’s physical, cognitive, physiological and behavioral traits and accurately sequence their genome; using whole body and brain MRI; behavioral, cognitive and physiological tests; metabolome and microbiome assays; large scale predictive analytics capabilities.

J. Craig Venter - jcventer@jcvi.org - 858-200-1890

Large scale whole genome sequencing

10,000 human genomesTelenti et al., PNAS 2016

Genotype-phenotype integration, predictive AI

analytics

Facial predictionLippert et al., PNAS 2017

Whole body & brain MRIHou et al., BioRxiv 2018

Advanced imaging technology

The integrated

platform will lead to direct

phenotype predictions

from the genetic code

Human microbiome predictive health

Metagenome of fatty liver diseaseLoomba et al., Cell Metab 2017

Success, Technology, Milestones, Transition:• Sequenced and de novo assembled diploid genomes to produce complete and accurate

genome data.• Extensive phenotype characterization from imaging to cognitive testing for

comprehensive and complete human representation.• Artificial intelligence (AI) including deep learning approaches to enable prediction of

phenotypes from the genome sequence.• Not-for-profit and for-profit entities to provide best in class genotype-phenotype

services; computational tools and models for prediction of human performance and disease; nano-sensing body fluids with implantable technologies for elite performers.

1 billion data points

Mohammed Eslami, Netrias

Teaming Overview and Objectives ImpactTA1 omics learning & data integration (Netrias), cross-validated

nested phenotypic assays (Nascent), bioinformatics (NYU),active & adaptive learning (PARC)

TA2 wearable biometric and continuous saliva sensing;low-power wireless electronics; sensor fusion & analytics;UX/UI design; system integration (PARC); real-time in-vivosensing (UCSB)

Relevant Experience (Netrias/Nascent/NYU)Transdisciplinary assessment for Special Forces & astronautsMachine learning and Data integration of ‘omics dataLeaders in the DARPA SD2 Program/NIH CPTAC/NASA HRP

Assets (Netrias)Active Discovery EngineTM for rapid data integration/curation

Desired partnersAdditional experience with in-vivo molecular sensing

Unique sample/cohort access: No

Deep biological structure of phenotypic assays revealed by management and integration of heterogeneous measurements from multiple layers and time scales that are concurrent, interrelated, and non-interfering.

TA1/

2TA

2

You?

TA1

Nascent S&T

Deep Phenotyping Rapid Integration and CurationAI/ML

ML-Ready Data

Physio-logical

Behavioral Cognitive

Social

Anticipated Impact Continuous cross-validation of nested phenotype measures for rapid tuning to individual differences and situations (adaptive personalized assessment); Automated data integration & curation

Unique metrics & milestones 10x reduction of uncertainty in personalized assessment for uniquely objective selection, >95% accuracy of expression circuit → phenotype prediction, 80% reduced time for data integration

ApplicationsPrecision health; medical home model; predictive diagnostics and assessment for personalized training; Train the Trainer

Technology Transition Netrias works with pharmaceutical companies to transition infrastructure & analytics to develop their drug and s/w offerings

Nested scalesof assays

Mohammed Eslami – meslami@netrias.com – 202.213.0191

TA1 Netrias: omics learning & data integration Nascent S&T: phenotypic assays NYU: bioinformaticsPARC: active & adaptive learning

TA2 PARC: wearable biometric, continuous saliva, wirelessly powered sensing low-power wireless electronics sensor fusion & analytics UX/UI design, system integration

UCSB: real-time in-vivo sensing

Technical Areas & Teaming

Project Overview

Relevant Experience (PARC)Development & deployment of wearable sensors, Data analysis from dispersed sensors & Remote RF power,Success in DARPA Sensor Tape & Explainable AI, ARPA-E MONITOR programs

Assets (PARC)Cleanroom/fabrication facilities for sensor/device manufacturingComputing assets for AI/ML

Desired partnersAdditional experience with in-vivo molecular sensingMedical device manufacturers with regulatory experience

Unique sample/cohort access: No

Anticipated impact Real-time sensing: warfighter situational & performance awarenessActive & adaptive learning: customized training programs

Unique metrics & milestones Real-time molecular detection in-vivoUnobtrusive biometric sensingBiomarker selectivity and sensitivity

ApplicationsFirst-responders, high-performing athletes & healthcare

Technology TransitionPARC’s business model focuses on commercialization of new technologies; our in-house business development team works with a wide range of commercial interests across fields

Advanced Sensing

AI/ML

Communication

UI/UX

Palo Alto Research Center – Hoda Eldardiry, Sean Doris

TA1/

2TA

2

You?

TA1

Nascent S&T

Objective PARC proposes a real-time molecular target monitoring platform to track biomarkers identified by TA1

Hoda Eldardiry, Ph.D. – (650) 812-4790 – hoda.eldardiry@parc.com; Sean Doris, Ph.D. – (650) 812-4242 – Sean.Doris@parc.com

Machine Learning

Rich Interactive Platform

Knowledge Base

Expert User

Input

Teaming Overview and Objectives

Project Overview

• Vincent Emanuele, PI○ Former visiting scientist at CDC for 6.5 years, head of data science R&D

at Wellcentive○ Experience in interdisciplinary machine learning R&D (proteomics,

genomics, physiological sensors, patient medical records)• Yiftach Eisenberg

○ Former DARPA PM/Deputy OD and AI thought leader within DARPA○ Experience in R&D related to machine learning problems in national

security• James Cheng

○ Senior machine learning researcher, former Georgia Tech Postdoc, and researcher at Wellcentive

○ Has successfully researched and commercialized ensemble learning systems on a large scale dataset of 30M patients

• Bryant Menn○ Biomedical engineer (Georgia Tech)○ 3+ years experience working as both a data engineer and data scientist

to support machine learning research

• We’d like to combine our extensive experience in machine learning research with collaborators who are strong on the lab side for TA1 and TA2

• We have experience in proteomics, genomics, physiological sensors, patient medical records, and mobile sensing

• TA1: data analysis and new algorithms surrounding phenotypic assays / physiological output

• TA2: Data visualization platforms for real-time molecular target monitoring

Impact• We go from white board to software prototype and work

on large datasets• We deliver machine learning prototype solutions that

integrate well as part of larger teams (conterization, APIs)• We have considerable commercial R&D experience and are

well positioned to transition any technology that is developed

PI: Vincent Emanuele, PhD

Vincent Emanuele – vince@embedintel.com

Kimia Ghobadi, Sauleh Siddiqui, Johns Hopkins University, Center for Systems Science and Engineering, Department of Civil Engineering

Teaming Overview and Objectives

Project Overview

• Existing Team: Kimia Ghobadi (inverse optimization for data analysis, real-time approximation algorithms), Sauleh Siddiqui (Bilevel optimization, machine learning in healthcare), Lauren Gardner (network analysis for data integration)

• Relevant experience: Over 50 publications in systems-level data analysis and optimization with applications to diverse healthcare settings including cancer treatment, congestive heart failure, and spread of infectious disease.

• Institutional assets: Computational capabilities.• We are seeking collaborators for TA1/TA2, specifically for

Pheonotypic Assays and real-time molecular target monitoring.

• Our team does not have unique sample/cohort access.

• We propose a systems-based computational framework using bottom-up modeling for integrating multilayered expression circuit analysis. The uniqueness of this project rests on integrating appropriate methodologies suited to expression circuits underlying each measured phenotype.

• We can contribute to expression circuit analysis and development of expression circuit analysis tools as part of TA1. We can also contribute to developing tools that will help validate the expression circuits in TA2.

Impact• Our framework bridges the gap between data-driven, ad

hoc approaches that tend to ignore the theoretical foundations of systems science and fundamental, science-based approaches that have limited empirical evidence to be broadly applicable.

• Applications to integrating multiscale, multilayered, biological datasets, which can be as broad as developing interventions to stop the spread of infectious disease.

• Metrics and milestones: data fitting using inverse optimization, high prediction accuracy using bilevel optimization, and significant measures of correlation between phenotypic observations and specific biomarkers.

• This framework will be transitioned for use by developing an open-source tool for integrating multilayered datasets.

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Sauleh Siddiqui – siddiqui@jhu.edu – 4105166411

Machine Learning in Complex Biological Systems

Two Six Labs spans the spectrum of:

• Machine learning for biological systemsAnomaly detection from RNA-seq.Inference from high-throughput assays.de Novo protein design using neural networks.

• Machine learning on mobile platformsNeural networks on Android for real-time detection of laboratory equipment.

• Securing embedded and mobile systemsCustom operating systems to ensure data privacy and platform security.

Prime for DARPA on related programs (SHARE, Brandeis, SD2) and multiple projects for IARPA, DHS, and others.

We offer expertise applying deep learning and transfer learning across a range of bioinformatics datasets to overcome sparsity and noise.

Neural Machine Translation

Refinement with CNN

DesignConstraint

An example applying deep learning to synthetic protein design on DARPA SD2.

Two Six Labs will bring expertise in:

• Machine learning for biological systemsOvercoming sparse gene expression data.Deep learning over heterogenous data types.

• Machine learning on mobile platformsDeploying memory and power constrained neural networks on mobile devices.

• Securing embedded and mobile systemsDeploying privacy preserving embedded systems to ensure platform security.

Researched, developed and transitioned machine learning algorithms to offline mobile devices for DTRA and deployed secure mobile devices for warfighters.

Scott.Novotney@twosixlabs.com

Erich Izdepski, BTS Software Solutions

Teaming Overview and Objectives

Project Overview

• Team: BTS, Hippocampus Analytics• Currently providing cloud-based data search, sharing and

analysis platform to DARPA• ML and Data Science/Curation expertise; data integration• Working with various sensors: fitness type devices, RF

sensors, and IoT sensors• Our team offers our BIFROST cloud platform as an asset

for data sharing and cloud analytics. No cost for the platform, just for web hosting and software engineering operations and maintenance

• Data Compression experts with patented real-time lossless data compression algorithms (another asset)

• Our platform and capabilities can help on any TA

• Our system is aimed at enhancing collaboration to improve scientific research outcomes

• Challenges on this project:• Running ML model on a smart device and give near real-time

feedback to the user• Collecting baseline/control data and using it to identify real

signals and reduce errors• Identifying and using changes in gene expression caused by

feedback from taking device recommendations• Updating and re-deploying performance enhancing ML models in

military tactical environment

ImpactCreate a model to identify what you can be really good at based on geneticsUse the feedback device to make yourself perform betterBe happier!• We know companies already applying AI to improving

human performance• Examples:

• Mental focus, sports, musician, surgeon, pilot• Unique metrics/milestones:

• Coaching via a smartphone application. Causing positive phenotype change through change in lifestyle. Positive feedback to gene expression

Erich Izdepski, CTO of BTS – erich.izdepski@bts-s2.com – 703.906.5752

Human Performance Analytic Dashboard

Teaming Overview and Objectives

Project Overview

• Extensive experience turning data into information bringing together multiple techniques that outline individual adaptations and response to stressors.

• Looking for collaborators with data to validate models and methods, across new and current populations

• Collaboration with genetic and biological systems in reference to specific human muscle, tendon, ligament and bone loading

• Large Elite Athlete Performance, Physiological, Psychological, Nutritional, Medical Data Sets

• Summarized and visualized in an easy to understand format

• Complete Analytical tool that allows for Statistical, Tactical Analytics, and individualizing the interaction of all Metrics and Subjects

• Cluster, Multivariate, Archetype and Network analyses to identify variable relationships

• Identify the individual metrics that influence injury risk and optimal performance by individual and population

Impact• COMPREHENSIVE INJURY PREDICTION MODEL!• Individualized analyses in relation to performance that

identifies a performer’s strengths, weaknesses, and response metrics

• Unit analyses allows for understanding the effectiveness of each part of the unit as well as the unit as a whole.

• Analysis tool brings the communication/intervention across multidisciplinary groups

Dean Golich– deangolich@gmail.com– 720-938-6066

Clinical Studies at West Point

Athletics• NCAA, Club, and Intramural athletics• All cadets participate in combatives sports and training• All cadets are required to complete routine physical fitness

assessmentsFaculty and Staff• A blend of military and government civilian• 75% Military (15% permanent, 85% rotating)• 25% Civilian (since 1994)Assessments and Predictors of Performance• Demographics• Academic Record• Physical Record• Leadership Development Record• Medical Records

Founded in 1802Undergraduate Degree • Bachelor of Science• Middle States Commission

on Higher Education • 13 Academic DepartmentsStudents• ~4,440 Aged 17-24• 75% men, 25% women• All graduates

commissioned into active duty military service

Natural ExperimentsACL Injury:

Concussion:

West Point Infrastructure Assets• Core molecular and cellular

instrument facility• Keller Army Community Hospital• John A. Feagin, Jr. Sports Medicine

Fellowship• Army Physical Therapy PhD Program

(Baylor University)• Athletic Team Trainers• Active Duty and Civilian Nurses and

Physicians

Lyme Disease:

J. Kenneth Wickiser, PhDLife Science Program DirectorAssociate ProfessorDepartment of Chemistry and Life ScienceWest Point

Contact Yuri Shoshan CEO – yuri@augm.com - +1-312-940-5459

Augmanity Nano, Rehovot, Israel - PI: Dr. Ido Bachelet

Teaming Overview and Objectives

Measuring Biological Aptitude – TA2

Team members: • Ido Bachelet, PhD: Expert in medical DNA nanotechnology/computation, postdoc in

engineering (MIT) and DNA nanotechnology (Harvard), 15 patents, 7 on medical DNA nanotech

• Anastasia Shapiro, MSc: Expert in design and imaging of DNA devices, completing PhD on DNA-polymer computers (Technion), 6 patents on DNA nanotech

Relevant experience: • Publications in Nature Nanotechnology, Artificial Life, Nucleic Acids Res, and more

recently submitted• Patent applications on aptamer discovery platforms, DNA sensor and actuator

designs, ribosomal RNA nanodevices• Unique expertise in imaging and analyzing DNA devices in AFM Institutional assets:• Bio-AFM with live imaging and fast scan modules, next-generation sequencing core,

multi-GPU-based bioinformatics core• Affiliate at Tel Aviv Sourasky Medical Center with GMP DNA synthesis capabilities• Partnership with 2 commercial GLP animal facilities with imaging expertiseUnique access to Israel Defense Forces for samples / cohorts

• A set of novel molecular tools for real-time, in-vivo monitoring of gene and biomarker expression

• We will apply our expertise in the building of DNA-based nanoscale devices to:

• Design and manufacture in-vivo sensors triggered by expression circuit on/off state

• Develop and apply signal transduction methodologies including magnetic resonance and other technologies for external monitoring

• Technical challenges include applying contrast agents at nanoscale and design / portability of external device

Impact

Seeking partners for TA1 and device design and manufacture

Potential applications:• Biomedical imaging• Personalized medicine• Real-time treatment monitoring• Sensitive diagnosis• Gene therapy Metrics & milestones:• Design a modular DNA-based device for sensing a range

of possible biomarkers• Verify the sensor functionality in-vitro• Patent• Signal transduction experiment in-vitro• In-vivo experiments, including:

• sensor functionality• signal transduction• Stability• PK and PD• toxicity• efficiency

100 nm

Javier L. Prieto, PhD. R&D Director

Team Overview and Objectives

Project Overview

• 12 years experience deploying 85M wearable devices with physiological sensors

• Research experts on data science, artificial intelligence and algorithm development.

• Hardware experts on large scale physiological sensor design.• Access to biometrics of 25M+ active users• Biometric data: activity, HR, RHR, HRV, caloric intake, sleep• 7.5+ Billion nights of sleep logged including sleep stages• Gaming platform able to assess cognitive performance• Implemented human and AI driven coaching programs at scale• Dedicated testing facilities and experience with large scale

validation studiesSeeking collaborators to:• Acquire/Analyze genotype data relevant to the program• Acquire/Analyze blood biomarkers and proteomics data• Acquire/Analyze microbiome data• Implement sweat sensors for wearable patches for collection

of biomarker information

• Analyze continuous biometric and physiological data on a large cohort of participants among the 25M+ Fitbit users to determine correlations between these data and their mental and physical health and performance.

• Design artificial intelligence based algorithms to assess the risk and screen for stress and depression• Build coaching programs and gaming platforms to be used to guide individuals towards their peak physical, metabolic

and mental performance.• Combine biometric data and genotype data to identify and validate markers that indicate injury risk and faster recovery.• With the help of collaborators develop and deploy new connected wearable sensors for the detection of biomarkers in

sweat to help assess hydration and exertion levels.

Impact• Turnkey deployment of findings to large cohort of

users through Fitbit products and services• Development of new “wearable patch” form factor

devices for the evaluation of sweat biomarkers• Results would provide insights on:

- how activity, dietary and sleep habits affects physical and mental performance

- which genetic markers are associated with physical performance, injury risk and physical recovery

- validated sweat biomarkers for assessment of physical exertion

- how to assess mental acuity of general and aging population

- how to screen for acute stress and depression- coaching programs for glycemic control for peak

metabolic performance

Javier Prieto – jprieto@fitbit.com – (949) 294 0101

• Translating elite microbiomes into performance probiotics

• Mining metagenomic data for roles in energy metabolism, protein metabolism, neurology, immunology

• Discovery of a performance enhancing microbe – converts lactic acid into SCFAs to promote endurance

• Scale, clinical studies (endurance, recovery, lactate metabolism), and commercialize

• Expand cohort for novel performance microbes, metabolic pathways, markers, genes….with Syn Bio applications

• Looking for collaborations in: elite cohorts, complimentary ‘omics data & analysis, biometric detection & analysis

Jonathan Scheiman, PhDChief Executive Officer

Renee Wurth, PhDData Science Nutrition

Alex Kostic, PhDChief Technology Officer