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Community RApid
-
Response to Infectious Disease Outbreaks


CRAIDO

CUBIT Working Group (CWG)

20.August.2009

Martin Dudziak

Facilitator:

TETRADYN Corporation

Richmond, VA

Charlotte, NC

Chickamauga, GA

San Antonio, TX

San Diego, CA

serving a

National Community

Resilience Network

2

1. PROLOGUE






[3]


2. CRAIDO For the Resilience Network



[5]


3. BACKGROUND





[10]


4. CRAIDO PILOT





[24]


5. NEXT STEPS






[25]


6. CONCLUSION





[26]


7. Additional Technical Background Reference


[29]


8. NOTES






[49]


9. CONTACTS






[50]


Copyright © 2009 TETRADYN Corporation, All Rights Reserved

CONTENTS

3

We propose here a solution to the requirements and desires for a robust, rapidly
-
deployable, fault
-
tolerant, and socially acceptable system that can be used by diverse communities for sustaining their
populations and their social integrity during and after a major public health disruption such as the current
H1N1 pandemic.

We propose that the CRAIDO
1

network, based upon the use of CUBIT
-
Delta
2

diagnostic
-
tracking
-
forecasting stations, employing “multiscalar and multispectral” biological sensing, PCR
3

and
immunoassay
-
based diagnostics, and extensive analytics and informatics tailored to epidemiological
outbreaks such as influenza, is precisely the optimal desired and available way to implement the
Resilience Network as it has been conceived, described, and promoted by a strong group of experts
from the medical, public health and public (homeland) security domains.

We further propose that additional and current (existing, proven, economical, field
-
ready, deployable)
devices and processes, including a comprehensive architecture and suite for situation awareness,
notification and emergency response (SANER) known collectively as NomadEyes, provides the optimal
suite of tools, functions, and humanly/socially practical processes needed for supra
-
medical, post
-
diagnostic social response and management during a major ECP (Emergent Critical Process) such as
the projected H1N1 pandemic.


What is described in this presentation is brief and somewhat of a top
-
down, birds
-
eye overview. It is
such in the interests of reaching the most individuals within the decision
-
making structure of the MPHIS,
White House Pandemic Initiative, and Resilience Network communities, in the least amount of time, and
with the least amount of confusion over terminologies and idiosyncratic details. This presentation has
also been developed in a time
-
crunch and with minimal resources. Any errors and misprints are
acknowledged by the author, Dr. M. Dudziak.


Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Prologue [1]

4

The core element in the CRAIDO is described as a node or station because it is a set of instruments and
a suite of software and a process of human and computer and social interactions that span from early
warning detection of pathogenic incidents to strain
-
specific diagnostics to situation
-
appropriate decisions
and actions that go beyond the realm of only the biomedical and therapeutic.

At the “core of the core” is the technology and the service of processing samples from humans and/or
animals for biopathogenic content, for routing and distribution of informatic results, and for analysis and
forecasting of mutation trends and behavioral vectors that can be used to isolate critical “hot spots”
where a specific community may be entering into a condition of being a progenitor source for a new and
more lethal form of a viral (or bacterial) transmissible disease.

CRAIDO, however, is also addressing the issues of “
preparedness, mitigation, crisis, consequence, and
recovery management”
4
. Specifically, it is designed to assist in not only the explicit tasks of early
warning, detection, identification, diagnosis, and “SANER” functions, but in the meeting of the five critical
objectives as stated for the Resilience Networks, namely,


Anti
-
viral distribution for moderate


severe cases;


Prepare health systems for overwhelming surge;


Develop a viable global health vaccination program;


Develop community resilience activities; and


Enhance all forms of communication.

Here in this brief and admittedly hasty first
-
draft presentation, we outline how CRAIDO will operate. It is
very important to understand that everything that is described, presented and promoted herein consists
of previously constructed, tested, and field
-
proven technology, with appropriate accreditations and in
almost all cases USA agency approvals (and where not so finalized, then several other international
approvals). All components exist and can be manufactured and delivered “to spec” for the level of
quantities indicated and expected during a major influenza pandemic.



Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Prologue [2]

5

1. A PILOT installation, to be initiated immediately, for both demonstration and direct field
-
clinical use.
This is proposed for the San Diego region. Details are addressed in the “Pilot” section of this
presentation. The Pilot will be put through tests that include “stress tests” of high usage and shortage of
supplies and staffing as well as simulated incidents of critical social infrastructure failure and acts of
sabotage and/or terrorism directed at the Resilience Network operations and operators.

2. A deployment of CRAIDO stations (nodes) for first Five Key Pilot Cities and then, in alignment with the
proposed four phase roll
-
out, to additional urban areas and other key nexus/hub regions. It is proposed
that the goal of Phase IV, namely, “
a Resilience Network platform usable by any community, institution,
or other organic social network,” can be effectively demonstrated within Phase I through the CRAIDO
architecture.


3. Philosophically, the CRAIDO deployment paradigm may appear at first glance to run counter to
established, orthodox systems thinking and project management practices. The argument is given here
that:

(I) We are already immersed irreversibly in an Emergent Critical Process the scope and proportions of
which are unknown, unpredictable, but ominous with respect to loss of life, economic disabling, social
unrest, and potentially severe social and political consequences;

(II) There is insufficient time for a traditional approach to conceptual studies, iterative review and
redesign, limited prototyping, conventional funding and budgeting, and high
-
overhead program
management with complicated bureaucratic procedures;

(III) We need to look back in history to at least two prominent examples that worked well under
extraordinary stress, uncertainty, and in one case, active physical disruption (bombardment)
-

Bletchley
Park and the Manhattan Project;

(IV) We need to further examine and learn techniques from our foe, namely, the influenza virus itself,
regarding adaptability and methods of “self
-
improvement” for overcoming communication and logistic
functions.

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

CRAIDO for the Resilience Network [1]

6

1. The Basic Station

Mobile unit built into a core PodLab (
EcOasis

PodLab; additional information in Background sections)

RT PCR laboratory with lab
-
on
-
chip diagnostic panels

Full assay resources and equipment for sample preparation and handling

Full internet connectivity and transmissivity for any required destination

Complete GPS and GIS interfacing for data and also for the Station, including real
-
time camera
observation of surrounding regions (GPSIT technology)

Backup power generation (conventional generator plus solar panel; optional fuel
-
cell system)

Backup laboratory staff accommodations for rest and comfort

Full complement of early warning environmental and patient sampling and analysis onboard and in the
region, employing first
-
level rapid antigen and bioluminescence and mid
-
level immunoassay sensor and
diagnosis capabilities

Fully automated situation awareness, notification and emergency response informatics to assist other
Resilience Network operative units in conducting vaccinations, medication distribution and monitoring,
management of hospitalization and triage functions, enabling and supporting community resilience and
safety management functions, and general critical infrastructure support and fault
-
tolerance operations
(the Stations as a network can provide a fail
-
safe backup for critical communications through the satellite
internet and optional shortwave packet radio capabilities built
-
in to each Station).

Supplement of medications, vaccines, and other supplies.

Real
-
time live webcast capability (via LCD panel displays attached to the Station) for the benefit of
people coming to the Station as patients or other types of visitors.

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

CRAIDO for the Resilience Network [2]

7

2. Integration with Incident Command System (ICS) and other networks

Each Station will be fully integrated electronically and digitally with the ICS and thereby with other
medical, security, safety, and social systems, enhancing the mission of the Resilience Network to support
“cross
-
contextual multi
-
agency communication and coordination.”

4
.

By employing a PodLab platform, the Station will be truly mobile but can also be completely stationary
and even in an indoor setting. PodLab modules can be moved in and out of the PodLab trailer/container
with relative ease. For outdoor operations, using a trailer base of approximately 8’ x 16’ workspace, a
Station can be transported virtually anywhere upon momentary demand, using a conventional pickup
truck or SUV as the hauling vehicle. Transport can also be effected by helicopter or boat (pontoon, raft).
The robustness of the key diagnostic instrumentation is such that certain tests could conceivably be
conducted even while the Station was in transit.

Data exchange to and from ICS and other networks is achieved in a seamless manner by the use of
industry
-
standard software designed for precisely such cross
-
format communications.

The interfaces of the CRAIDO Station are such that, in comparison to many other system integration and
IT integration tasks, there should be minimal challenges or delays in bridging data flow to and from the
Station with any industry
-
standard database or network application.

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

CRAIDO for the Resilience Network [3]

8

3. Integration with community groups and networks

An even cursory review of news media, social networks, and the open web reveals the state of
uncertainty, ignorance, anxiety, and in some instances militant resistance to virtually all social and public
health measures for mitigating this present pandemic. This includes resistance to vaccination,
distribution of medication, and public health or emergency social measures in general. when one adds in
the factors of a drastically reduced work force, affecting basic social services, food supplies, water
supplies, police and fire teams, etc., the recipe for severe breakdown of the Social Contract is clearly
printed before our eyes. Each CRAIDO station will and must have designed into it the capabilities and
indeed the aim to support “local, state, federal, tribal, academic, private industry, faith
-
based and other
private sector groups”
4

for overcoming the impulses and actions of social disruption and for sustaining,
at the level of individuals, families, neighborhoods, and especially public crowds, “rule of law” and basic
common moral sense and discipline.

For this reason, mobile (or stationary) CRAIDO nodes (stations) have designed into them more than only
biomedical equipment and functions. This is why something as simple as the internet
-
enabled large
display with live or pre
-
recorded social
-
education webcasts is so important for the success of the
Resilience Network. (This is just one example; there is more within CRAIDO but there is neither time nor
place to put all of this information here in this “introductory overview” presentation.

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

CRAIDO for the Resilience Network [4]

9

4. Project logistics, budgets, schedules, and disruptive factors

CUBIT, leading into CRAIDO, has been extensively planned and replanned, along with the NomadEyes
distributed data collection, analysis and communication network and other functions for SANER activity
and for public education and training. This work began, from a fundamental scientific perspective, in the
early 1990’s and included research and development at Medical College of Virginia (VCU), Silicon
Dominion, Intel, and TETRADYN leading up to the present mature state of design. There has been prior
funding and support from DOE, NSF, NIH, USMC and DARPA leading into and feeding this overall
project.

NomadEyes and CUBIT entered a more intensive and concentrated phase of research and development
beginning in 2002 and particularly ramped up starting in 2004. There is an extensive history, a deep
foundation upon prior and other R&D and product development, and a heavy emphasis upon
collaboration, teaming, and consortium
-
based development. Thus, at present there is much more done
than one may consider at first glance, because of the work methodologies employed.

As a result, there should be strikingly fewer problems to implement a CUBIT
-
CRAIDO PILOT and then a
series of extensions and expansions along the lines indicated for Phases I through IV of the Resilience
Network.

Budgets have been addressed (cf. Background slides) but also are flexible and yet to be determined
based upon what will actually go “into” a CRAIDO node, for instance. Each station, however, is clearly
within the few $100s and not a big
-
ticket item as is usually the case for such projects.

Schedules are flexible but the key point made here is that there can be sufficient people working on the
PILOT by Sept. 1, 2009 and indeed the core team is ready to begin immediately, complete with a
“Special Operations” mentality that the Job must get done Right and that failure is not ever an option.

What can disrupt the successful deployment of CUBIT
-
CRAIDO for the Resilience Network? Only the
worst virus known to man
-

bureaucracy.

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

CRAIDO for the Resilience Network [5]

10

Protect

Detect

Track

Respond

Inspection, testing, analysis

Surface Bioprotection

Staff Awareness Training

Business Process Cotinuity
Planning

Re
-
inspection

Re
-
testing

Follow
-
up on all processes

CRAIDO = CUBIT
-

Comprehensive, Multifaceted, Multifunctional

Emergency Environmental
Response Services (focus on
rapid
-
action field analytical and
supply services including onsite
mobile testing and remediation
using modular Pod (trailer
-
based) systems)

What mutation

Which similarities

What trends

What probabilities

Which vectors

Lore transmissible or less

More lethal or less

Multi
-
spectral

Rapid antigen

Immunoassay

Cehmical assay

CEBIT

Stand
-
off spectroscopy

Mapping the threats and the
threat risks and channels

RT
-
PCR
-

details on the strain,
the specifics

GIS and GPS

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Prior Background

11

CUBIT, CUBIT
-
Delta, VSRB, CRAIDO

Introduction

CUBIT

refers to an open architecture for biothreat identification, intervention and treatment protocols that incorporate
sensors, diagnostics (particularly real
-
time PCR based methods and also immunoassay first
-
level monitoring), and
informatics that includes both situation assessment and notification as well as public educational engagement and training.


CUBIT
-
Delta

is a program of clinical and research activity to be coordinated and managed by TETRADYN and
conducted within a network of collaborative investigators and institutions specializing in the diagnosis and treatment of
infectious diseases, and in particular influenza.

The program may be described in one sentence:

Rapid and more timely tracking of trends in known and projected new variations of infectious diseases that are
emerging or spreading at epidemic rates through identified populations and regions, with an aim to accelerate
knowledge, focus of attention, and provision of resources among genetic researchers, vaccine developers,
pharmacological developers, and public health agencies in order to circumvent the rise of more virulent and
infectious forms of a given disease into pandemic and lethal forms.


VSRB

= Virtual Sample Repository Bank. This refers to a derivative of CUBIT
-
Delta that is focused upon the tracking
and dissemination of information pertaining to the location, conditions, availability and dispositions of selective patient
samples of high
-
probability infectious disease agents (e.g., HxNy influenza).


CRAIDO

= Community Rapid Response for infectious Disease Outbreaks. This is the integrated communicating network
of deployed CUBIT
-
Delta field stations that is planed, step by step, for the USA and selective sites worldwide.

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Prior Background

12

The CRAIDO Project Long
-
Term Outreach Net

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Prior Background

13

CUBIT
-
Delta

Phase 1

Objective: Establish rapid field diagnostics minilab for infectious diseases and demonstrate capability for rapid cloning and

cu
stomization of
mobilizable lab version for deployment to highly diversified locations

Performance: Process patient naso
-
laryngeal samples and distribute results realtime to CDC and other clinical and research recip
ients

Focus: Respiratory viruses


influenza


H1N1
-
2009

Functions and features:

(a) Processing (classification by gene sequence matching) of samples using RT
-
PCR (VereID base instrument plus VereFlu multiplex

microarray and optional other probe chip panels) and CUBIT analytical and notification software

(b) Investigation of techniques (based upon inverse relational maps, mutual information, deformable image registration and no
nli
near
statistical pattern recognition and forecasting) to identify and localize indicators and trends in gene sequence mutations oc
cur
ring within the
sampled populations, for enhancement of epidemiological and therapeutic planning and pharmacological development.

Configuration:

Two TEU* capacity room or mobile unit (PodLab)

VereID plus probe chip panels [1]

One lab technician plus backup personnel

Nominal broadband internet connectivity (e.g., minimum 250Kb/sec. average up/down)

Protocol for receipt of samples and storage of processed probe chip panels

Minilab Locations: Vanderbilt University, School of Medicine (precise lab TBD)

[optional but preferred] Clinical hospital in Mexico City or Puebla State

Outputs: RT_PCR record stream (digital image and text data sets managed in SQL
-
compliant database and also delivered as an RSS d
ata feed)

Recipients (outputs): Participating clinical and research teams including (optimally) Vanderbilt [a], CDC [b], NYICE [c], Mt.

Si
nai [d], UT
-
Knoxville
[e], U
-
Wisconsin [f] and optionally others (note: the CUBIT
-
Delta model and program can be very open
-
ended and there is no stric
t limitation on
participation)

Technical and Lab Support: To be provided through and by TETRADYN technical staff and management


Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Prior Background

14

CUBIT
-
Delta

Phase 2

Objective: Extend deployment of CUBIT
-
Delta minilab collection, diagnostics and reporting operation in two stages to sites withi
n North America
and other continents

Performance:

(a) Expansion of the basis functions and refinement of the mapping and prediction of gene sequence mutations and trends as we
ll
as indicators of
imminent virulence amplification or diminution

(b) [optional but preferred] Expansion beyond influenza virus strains to include selected other viral and bacterial infectiou
s d
iseases, as per the
refinement and availability of multiplexed RT
-
PCR protocols and probe chip panels

Focus: Respiratory viruses


influenza


H1N1
-
2009

Functions and features:

(a) and (b) as in Phase 1

(c) Expansion of SANER functions (SANER = situation Awareness, Notification and Emergency Response) through the CUBIT informa
tic
s
architecture and software.

(d) Investigation of associative (acausal or hypothetical causal) relationships between population demographics and environme
nta
l factors
with known or projected/predicted mutations within identifiable gene sequences

(e) Investigation of population anomalies indicative of emerging trends in stronger or weaker immune system response due to p
rio
r viral
exposure and infection, prior vaccination, or other ancillary immune system or epigenetic factors within the regional popluat
ion
s sampled

Configuration (per minilab site):

Identical to Phase 1 configuration, with expected order
-
of
-
magnitude improvements including minimization of space requirements,
acceleration of sample preparation and PCR amplification/hybridization cycle, and also simplification and ease of use for lab

te
chnical staff

Minilab Locations: refer to Figure 2.1

Outputs: RT_PCR record stream (digital image and text data sets managed in SQL
-
compliant database and also delivered as an RSS d
ata feed)

Recipients (outputs): The same group as in Phase 1, with expectation of expansion in two significant areas:

(a) Additional researchers in USA

(b) Key national centers in participating nations

Technical and Lab Support: To be provided through and by TETRADYN technical staff and management


Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Prior Background

15

NOTES


[
----------------------------------------------------------------------

(currently available)

Influenza (H1, H3, H5, H9 variants, including H1N1, H3N2, H5N1, H9N2, and B; essentially all major strains to present
including now H1N1
-
2009)

----------------------------------------------------------------------

(currently available)

B. anthracis, Y. pestis, F. tularensis, and Variola virus

----------------------------------------------------------------------

(available Q3/2009)

Salmonella enterica (Typhi)

Escherichia coli O157:H7


Serratia marcescens

Staphylococcus aureus (MRSA252)

Listeria monocytogenes


Vibrio cholerae

Shigella dysenteriae


Norwalk virus



Rotavirus

Campylobacter jejuni


Clostridium botulinum


Yersinia enterica

Bacillus cereus

----------------------------------------------------------------------

(est. available late 2009)

Staphylococcal enterotoxin B

Ricin (plant toxin from the castor bean)

Botulinum toxins (Clostridium botulinum)

Mycotoxins filamentous fungi (Fusarium, Myrotecium, Cephalosporium, Trichoderma, Verticimonosporium, Stachybotrys
species)

----------------------------------------------------------------------

(est, available early 2010)

Malaria

Dengue

Chikungunya


Rift Valley

West Nile

JE


EV71


SARS


Yellow Fever

TB

Typhoid Fever

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Prior Background

16

1

2

2

2

3

3

1

2

3

3

3

3

Figure 3.1


Proposed Phase 1 CUBIT
-
Delta Nodes in North America
(many more in Phase 2)

(note that (1) includes the main and first hub and, along with the MX node, extending from Phase 1)

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Prior Background

17

3

3

3

3

2

2

3

3

3

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Figure 3.2


Proposed Phase 1 CUBIT
-
Delta Nodes Worldwide

Prior Background

18

Analytics
Intelligently

Coupled with Informatics in a
Business
-
Organic

Clinical Management Process

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Prior Background

19

Give people an EcOasis filled with earth, water, air and fire, and it becomes a
Microeconomic Reactor
, a Grameen
-
Bank
-
on
-
Growth
-
Hormone,

a Way Out and Up, an Answer that makes capital and doesn’t just burn it.

This is Sensible
-
Sustainable
-
Renewability.

This is Progress and Survival.

The economic and social development dimension, coupled with a
highly visible non
-
profit project

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Prior Background

20

Nomad Eyes CONOPS Vision

Unified Field


Lab


Field
Communications from Every Perspective

Translating Data Visualization & Analysis
into concrete “go here, don’t go there”
messages

Real
-
Time Situational Awareness: Take into
account local actions, response, priorities

Get the Right E
-
A Information to the
Right People for the Purpose of
Saving
Lives
.

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Prior Background

21

The GIS components are essential for critical interactive interpretation and response based upon
sampling and lab analysis. Missing “ingredients” can be identified, and a faster step taken to get more
sampling from the right sites. With architectures such as TETRADYN’s
Nomad Eyes
, the
communication gaps can be crossed for data gathering, verification, planning and response including
evacuation or redirection of worker and civilian populations.

Incident Detection/Prediction Example

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Prior Background

22

GAEA from TETRAD

INFORMATICS Plus

22

Very Rapid Disaster Response/Avoidance Components

Background

23

Analysis Job
(Sample) related
data, spanning
contract confirm.
to final reports

Components of the 21
st

Century Global Environ. Analysis Lab



LIMS

Tech data,
including
public/common
methods,
stds
, non
-
prop reports



LARS



ECOPORTAL



Physical Lab Tools



Information Lab Tools

Public (open)

Public but Registered
Users

Prospective and
Actual Customers

Licensed/NDA
Custmrs incl. TTG

TTG Internal &
Auditors

Webcasts,
courses,

trainings,

seminars

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Prior Background

24

1. Physical prototyping and operation within the general San Diego community (exact location TBD).

2. RT
-
PCR system focused principally upon H1N1.

3. Rapid antigen and immunoassay capabilities.

4. Mobile housing unit, with functions as described in the previous section and in supporting
documentation for the EcOasis PodLab architecture.

5. Satellite internet capabilities for video
-
quality download and upload.

6. GPS tracking network capable of manual or automated redeployment and extension throughout the
vicinity of the CRAIDO node.

7. Live testing on a schedulable 7x24 basis, for human and animal specimens.

8. Direct
-
connectivity testing with community ICS and other networks.

9. Implementation of first phase of VSRP (Virtual sample Repository Bank), an informatics and physical
sample management system for high
-
interest infectious disease samples appearing in an epidemic or
pandemic setting and of interest to researchers and clinicians studying and tracking mutation trends

10. Initial consortium team to include members of research and development, engineering, clinical, and
support staff from:

TETRADYN Corporation

GPSIT, Inc.

Vanderbilt University

San Diego State University

and others to be determined from among a core group (pool) already established and in working
relationships with one another;

with consultants and advisors from: Mt. Sinai Medical School, Duke University, Univ. of Rochester, Univ.
of North Carolina, Univ. of Tennessee at Chattanooga, HCA (Hospital Corp. of America)

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

CRAIDO Pilot

25

1. Create a rapid meeting for all key interesting and ready
-
to
-
go
-
ahead participants

2. Define Go
-
Ahead Agreement

3. Establish formal and concise CUBIT Working Group (CWG)

4. Provide seed operating budget for CWG and CRAIDO Pilot

5. Define precise Pilot location and connectivity within community organizations and networks

6. Define precise Pilot specifications and criteria for measurement and evaluation

7. Establish plan for testing operations including samplings and simulations

8.
Produce Produce Produce
-

remember,



Time is of the essence
--

and
--

We have run out of time

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Next Steps [1]

26

1

2

2

2

3

3

2

3

3

3

3

Figure 5.1


Proposed Phase 1 CUBIT
-
Delta Nodes in North America
(many more in Phase 2)

(note that (1) includes the Pilot hub)

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Next Steps [2]

2

2

2

2

3

3

3

3

3

3

3

3

3

27

3

3

3

3

2

2

3

3

3

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Figure 5.2


Proposed Phase 1 CUBIT
-
Delta Nodes Worldwide

Next Steps [3]

3

3

3

3

3

2

2

28

1.
CUBIT and the CRAIDO network are quite in sync with the objectives of the whole
resilience Network and the san Diego Resilience in particular as a first
-
instance

2. A Pilot can be implemented straight
-
away

3. There are no scientific or technological barriers

4. The Resilience Network can be deeply, richly and securely sustained by deploying a
CRAIDO
-
like solution as its Platform

5. Everything is capable of change, adjustment, modification, tailoring, to suit the general
and particular “other needs and requirements” of the Resilience network and its many
experienced designers, proponents and stakeholders

6. The solution as thus envisaged is also very economical and malleable to the needs of
localities as well as federal and private interests.

7. We are Out of Time with respect to the current Pandemic and we need to be working
without delay.

8. All partners can and should make some sacrifices for the greater good which is quite at
stake.


Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Conclusion

29

The slides in this section are intended to illustrate some of the technology base for CUBIT including the
NomadEyes network. These are for reference and “backup” discussion only and for the most part
require the author (M. Dudziak) to be present physically and engaged with the viewer(s) in order for
these slides to be sufficiently and properly understood. Nonetheless, they can provide some useful
illustrations, particularly to viewers who are accustomed to “connecting the dots” on their own…

Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Additional Tech Background Reference

30

CUBIT Systemic View

The diagram on this pager refers to a prior (prototype) architecture designed for the deployment and management of rapid
-
respons
e units in
situations of suspected outbreaks of high
-
contagion
-
risk infectious diseases.

Copyright © 2009 TETRADYN, LLC and Institute for Innovative Studies

31

CUBIT in relation to Diagnostics

Although real
-
time PCR is only part of the technology array employed in a CUBIT operations scenario, it is the dominant technolo
gy for fast,
through
-
to
-
subtyping classification of infectious agents and other genetically
-
discriminatable conditions.

Copyright © 2009 TETRADYN, LLC and Institute for Innovative Studies

32

CUBIT relation with Pod Lab (mobile mini
-
lab)

An original plan for CUBIT applications has included what is known as the PodLab, a reconfigurable trailer/container unit tha
t t
ypically contains a
PCR
-
centric analysis workstation and support technologies that are appropriate to the particular community and environmental sit
uation.


Copyright © 2009 TETRADYN, LLC and Institute for Innovative Studies

33

CUBIT relation with Pod Lab (continued)

The modularity, from physical container and shelving slots to universal electro
-
mechanical connectors, coupled with the schedule
r and planner
software, is part of what sets the PodLab apart.

Copyright © 2009 TETRADYN, LLC and Institute for Innovative Studies

Transceiver
Antenna, Dish

Polymer Solar panels

Bioprotectant surface treatment

supplies, equipment

Water bag supplies

Burn relief & medical supplies

Ceramic
water
filtration
units

Chem/Bio sensors/meters

Multi
-
fuel

generator
-
engine

High
-
performance, high
-
output
water purification system

Computing equipment

Bioprotected
eco
-
fashion
clothing &
apparel
samplers

Trailer / Container Pod

Air
-
inflated shelters

Fuel cell aux power

Modular interior

Webcasts & educational media

Site hazard monitoring

34

CUBIT
-
Delta


Genet and I
4



the mathematics and computational dimension


Copyright © 2009 TETRADYN, LLC and Institute for Innovative Study, Inc.

[text, graphics and references in preparation, 13.Jun.09



some slides from previous papers and presentations are used here]

35



Staged, hierarchical evaluation of pairs or triplets of models corresponding to
specific components of observed objects




measures of mutual agreement or discord at each stage





conventional image relational
-
space data parameters (

,

,

,

,

…)






network elements describing transform operators (

,

,

,

,

,…)

that can be
interpreted as a way to
encode

one object (or segment) into another, namely a set
S1 = {a(p1), b(q), c(p2), d(r)} representing a movement of goods or $$ hawala
-
style from person a via b and c to d can be encoded as S2 = {x(j), y(k), x(l)}
representing a benign retail transaction between companies x, y, z (which happen
to be owned and controlled by a, b, c, and d in various capacities)




sets of functions that can be applied for fast, accurate transformations
forward/backward in time representing spatial and temporal behavior of the
observed objects



This is something like an
encryption scheme

applied to geometrical objects that
follow very complex, dynamic rules (in this case: organic, metabolic)

Proposed Solution

/////////

36


Improve object (volume) registration accuracy AND utility


generate information that
can be cognitive triggers,
catalysts for analysts



Improve computational performance and accuracy


just like with imaging, because we
are not doing lots of ((((((((a) (b)) (c)) (d)…) string operations but lots of PDEs


Be able to rapidly point out the ways in which (for example) scene A (left) could relate to
scene B (right)

Requirements (Ideal)

37



Mutual information is, first of all, the only information really, because information is always
about something. So I[X;Y] is telling us about what we have in X that can tell about Y. It is
also telling us about uncertainty, insofar as I[H;Y] = H(X) + H(Y)


H(X,Y)



This leads into relative entropy or information divergence which can be thought of as a
``distance'' between the joint probability p(x,y) of and the probability q(x,y) = p(x)p(y) ,
which is the joint probability under the assumption of independence



The converse of the distance or separation is the mutual dependence or agreement between
for instance two object models. Here we can treat p(x) and p(y) in terms of some object or
artifact that may / may not be present in a portion of an image.



For any p(x) we are concerned with p(x
0
) for some object and its absence, p(x
1
) = 1
-
p(x
0
). Is
some object (feature) of interest present or not?



The feature may be a parameter set, a range of values. Color, location, diameter of a
definable marker, a fractal texture, an estimable volume, all characteristics that can be found
in both the input data (e.,g., image) and the object model, compared, and examined for
mutual agreement.

Some Foundations (A)

38

Starting with basic information H =


log s = log s


and moving to H =
-


p
i

log p
i

we have three
interpretations to entropy:

i) information given by some event that occurs

ii) uncertainty of the outcome of some event

iii) dispersion set of probabilities connected with the event


Woods et al (1992
-
93)


registration based on similar regions of similar/same image type

Hill et al (1993)


feature space, defining regions by clustering in the 2d space

Within image registration, the key task is alignment


misalignment means dispersion of clusters in
the feature space

Collignon et al (1995)


use entropy as measure of registration; measure dispersion of probability
distributions:

LOW


few, sharp
-
definition, dominant peaks

HIGH (max)


all outcomes equal probability


Goal is to minimize the joint distribution entropy


-


p(i,j) log p(i,j)


i,j


Some Foundations (B)

39

Mutual information I (A,B) = H(B) {Shannon entropy for object B }


H(B|A) {conditional entropy
based on chance that value b in B exists given that corresponding element in A has value a}


Another expression is more like joint entropy: I(A,B) = H(A) + H(B)


H(A, B)


Kullback
-
Leibler distance


measure of distance between two distributions


i p(i) log (p(i)/q(i)) where p and q are two distributions


Mutual information for A and B (images, or in our case relational objects):

I(A,B) =


p(a,b) log (p(a,b) / p(a)p(b))


a,b


This measures distance between joint distribution of some values (e.g., grey values in images) and
joint distribution in case of independence of A and B. Thus it actually serves as a measure of
dependence between A and B.

MAX


alignment

MIN
-

misalignment

Some Foundations (C)

40




Collignon, A. et al..
Automated multi
-
modality image registration based on information
theory,

in Bizais, Y., editor,
Proceedings of the Information Processing in Medical Imaging
Conference
, pages 263
--
274. Kluwer Academic Publishers.


Collignon, A., Vandermuelen, D., Suetens, P., and Marchal, G.,
3d multi
-
modality medical
image registration using feature space clustering,

in Ayache, N., editor,
Computer Vision,
Virtual Reality and Robotics in Medicine
, pages 195
--

204. Springer Verlag.


Cover, T. and Thomas, J.,
Elements of Information Theory,

John Wiley and Sons, Inc, 1991


Duda, R. and Hart, P.,
Pattern Classification and Scene Analysis,

John Wiley and Sons, 1973


Hyvärinen, Aapo,
Survey on Independent Component Analysis,
Helsinki University of
Technology,
http://www.cis.hut.fi/aapo/papers/NCS99web/NCS99web.html


Kruppa, Hannes and Schiel, Bernt,
Hierarchical combination of object models using mutual
information,
in
Proceedings of the 12th British Machine Vision Conference
, pages 103
-
112,
2001


Studholme, C., Hill, D., and Hawkes, D.,
Multiresolution voxel similarity measures for mr
-
pet registration
, in Bizais, Y., editor,
Proceedings of the Information Processing in Medical
Imaging Conference
, Kluwer Academic Publishers


Thévenaz, P., Unser, M.,
An Efficient Mutual Information Optimizer for Multiresolution
Image Registration,
in
Proceedings of the 1998 IEEE International Conference on Image
Processing (ICIP'98)
, Chicago IL, USA, October 4
-
7, 1998, vol. I, pp. 833
-
837.



Viola, P. and Wells III, W.,
Alignment by maximization of mutual information,

in
Proceedings of the 5th International Conference on Computer Vision



Wells III, W., Atsumi, H., Nakajima, S., Kikinis, R.,

Multi
-
Modal Volume Registration by Maximization of Mutual Information
,
http://splweb.bwh.harvard.edu:8000/pages/papers/swells/mia
-
html/mia.html





Foundation Lit

41

Alignment becomes a task of alignment and repositioning, attempting to
constructively FIT one complex fuzzy shape to another

Alignment = similarities of players, objects, relations, modus operandi for the
Relational Maps being studied

Misalignment = maybe nothing in common, maybe simply dissimilar OR

Major misalignment = situation worth examining for its anomalies

Particular misalignment = perhaps a concerted and deliberate operation of
deception, event
-
encryption, and really worth a special look

And then what meaning (use)?

42

NSCIP Example

Organizational Network 1

The big leap is in moving to different types of networks that are not images, not
physical volumes, but network descriptions of organizations and traffic within and
among those organizations

O2
-

one year later

O1


one year later

Organizational Network 2

43

Transforms of Interest



Temporal:
M
1
(t1)


M
1

(t2)



Displacement: M
1
(x
0
, y
0
,z
0
)


M
1
(
x
1
, y
1
,z
1
)



Orientation: Observer(x
i
, y
i
,z
i
) [M
1
(x
0
, y
0
,z
0
)]


Observer(x
i+j
, y
i+j
,z
i+j
) [
M
1
(
x
0
,
y
0
,z
0
)]



Modality (MRI, CT, PET, US): M
1


M
2




In each of these transforms there can be many other transforms occurring. It is desirable
if we can establish what are some of the rules by which they can occur, what are
the dependencies, and what then are the likely paths by which to deform and
reshape the expected distributions of parameters for a given model or sub
-
model in
order to optimize (maximize) the mutual information between the two.

44



Faces


seemingly a much easier problem, more ‘tractable’ challenges:

(a) mostly the same shape, and normalizable in geometry

(b) color, lighting, shadow can throw off one model or another



Multimodal descriptions of organizations, event
-
sequences, operative processes

(a) linking events (X now in London, Y went London to Lahore to London)

(b) sensor reports (CCTV or Nomad Eyes data)


(c) financial account anomalies among suspect accounts

(d) projected possible changes in appearance, residence, and other “what ifs”


(e) almost by definition, models M
i

and M
j

will have differences that involve qualitative
differences that cannot be easily compared



So there must be some transform functions defined to map from the attributes of M
1

to those
of M
2

that cannot be compared as if they were simple scalar or vector transforms of each
other


Challenges

45



Consider two objects, static 3D models M
1

and M
2
, generated from transformations of 2D
data sets (e.g., MRI and CT, or multiple CT). Let M
1

and M
2

be static representations of the
same anatomical object

i
, an instance of a type


object (e.g., hepatoma).



Objective: to derive a geometrical encoding network that aids in adaptive deformable
registration from M
1



M
2

and that is extensible to other models M
i

of the same anatomical
object

i
, s.t.


(a) Mutual agreement between M
1

and M
2
can be maximized (this is the classic goal; mutual
information is maximal, and there is a useful relation established between the objects)


(b) a deformation function f
D

can be applied to variations of M
1

and M
2
(M
1(k)

and M
2(k)
) that
are individually generated
-

following sets of rules governing objects of type


-

but that are
independent of each other (namely M
2(k)

and M
1(k)
)



CLAIM: such a network, if it exists or can be approximated, will potentially be useful as a
means of rapid registration between members of a larger class of models representing

i
including incomplete models M
x
that individually may not be readily classified into
agreement with any other model M
x+n

derived from similar data sets

GENET Overview

46

CUBIT
-
Delta
---

Proposed Participation Network

(Variable levels of involvement, ranging from user and reviewer of output data and research reports to more active collaborat
ion
, all levels “TBD”
and based upon ongoing and upcoming/invited discussions. This is a proposed list without implication of commitment or obligat
ion

or level of
participation)

Key Institutions for (

) on
-
site Sampling and Diagnostics and/or (

) Data Reception and Uitilization



[a] J. Crowe, J. Chappel, Yi
-
Wei Tang (Vanderbilt Univ., School of Medicine)



[b] N. Cox, M. Shaw, C. Smith et al (U. S. Center for Disease Control, Influenza Unit)


[c] J. Treanor, D. Topham (Univ. of Rochester Medical Center, NYICE)


[d] C. Basler, P. Palese (Mt. Sinai School of Medicine, Center for Research on Influenza Pathogenesis)


[e] M. Sangster (Univ. of Tennessee


Knoxville, Dept. of Microbiology)


[f] Y. Kawaoka (Univ. of Wisconsin
-
Madison, School of Veterinary Medicine)


Collaborative Critical Consultants and Reviewers

William Jarvis, MD (Jarvis & Jason; formerly CDC)

Thomas Webb, PhD, Auburn Univ.

Bruce Smith, PhD, Duke Univ.


Devra Davis, PhD (Univ. of Pittsburgh, Grad. School of Public Health)

Jean
-
Pierre Issa, MD (Univ. of Texas MD Anderson Cancer Center, Center for Cancer Epigenetics)

Andre Muelanaer, MD (Director, Carillion Medical Center Bniomedical Institute)

Kristin Omber, PhD, Los Alamos Nat’l Laboratory

Chris Poulin, Dartmouth Univ.

Donald Low, MD (Mt. Sinai Hospital, Toronto)


TETRADYN Core Team Component (North America based)

M. Dudziak, PhD (P.I.)

C. Davis, MS




C. Horn, MS



C. King

S. Catudel, MS



P. Cox, MS

V. Bogin, MD




E. Miller, PhD


M. Junghanns

M. Kon, PhD




H. Choi, PhD



Copyright © 2009 TETRADYN, LLC and Institute for Innovative Study, Inc.

47

Proposed CUBIT
-
Delta Nodes (Phase 2) and Rationale Summary (1 of 2)


Total number of nodes: 20

(from Phase 1
---

2)


(first stage, Phase 2
---

6)


(second stage, Phase 2
---

12)


First Stage Expansion Set (level 2)

(in alphabetical order)

(North America)

Houston


Very large population center, air traffic hub, international traffic hub, large/diversified multinational population,
major port

Los Angeles


Very large population center, air traffic hub, international traffic hub, large/diversified multinational populatio
n, major port

New York City

Very large population center, air traffic hub, international traffic hub, large/diversified multinational populati
on, major port

Seattle


Large population center, air traffic hub, international traffic hub, diversified population, major port, critical infra
structure


(outside N. America)

Japan


Very large population center, air traffic hub, international traffic hub, large/diversified multinational population, ma
jor
port, and already a base for VereID
-
based clinical testing

Singapore


Large population center, air traffic hub, international traffic hub, large/diversified multinational population, maj
or port,
already a base for VereID
-
based clinical testing (National University Hospital) and the design
-
center for Veredus Labs




Second Stage Expansion Set (level 3)

(see next page)



Copyright © 2009 TETRADYN, LLC and Institute for Innovative Study, Inc.

48

Proposed CUBIT
-
Delta Nodes (Phase 2) and Rationale Summary (2 of 2)


Second Stage Expansion Set (level 3)

(in alphabetical order)

(North America)

Chicago


Very large population center, air traffic hub, international traffic hub, large/diversified multinational population,
major


food processing hub

Dallas/Ft. Worth

Very large population center, air traffic hub, international traffic hub, large/diversified multinational popu
lation

Miami


Large population center, air traffic hub, international traffic hub, large/diversified multinational population, major p
ort

San Francisco

Very large regional population center, air traffic hub, international traffic hub, large/diversified multinationa
l population,


critical infrastructure

Toronto


Very large population center, air traffic hub, international traffic hub, large/diversified multinational population

Washington, DC

Large population center, air traffic hub, international traffic hub, critical infrastructure, national security


(outside N. America)

Hamburg, Germany

Large population center, international traffic hub, large/diversified multinational population, major port

Moscow, Russia

Very large population center, air traffic hub, international traffic hub, large/diversified multinational populat
ion, critical


infrastructure

Mumbai, India

Very large population center, air traffic hub, international traffic hub, large/diversified multinational populati
on, major port

Rio de Janeiro

Very large population center, air traffic hub, international traffic hub, large/diversified multinational populat
ion, major port

St. Petersburg, Russia

Large population center, international traffic hub, major port, Russia is already a base for VereID
-
based

clinical testing
and TETRADYN has a technical presence in Russia

Shanghai, China

Very large population center, air traffic hub, international traffic hub, large/diversified multinational popul
ation, major
port, China is already a base for VereID
-
based clinical testing and TETRADYN has a technical presence in China



Copyright © 2009 TETRADYN, LLC and Institute for Innovative Study, Inc.

49

1
-

CRAIDO =
Community RApid
-
Response to Infectious Disease Outbreaks


2
-

CUBIT =
Coordinative and Unified Intervention of Threats to Biosystems, aka Coordinated Unified
Biothreat Intervention and Treatment

3
-

PCR = Polymerase Chain Reaction, a common technique for nucleic acid replication and the basis for
gene
-
sequence
-
based matching and identification of specific DNA or RNA sequences.

4
-

Credits are given to the (unspecified) author of the memorandum, “North American Bio
-
Security H1N1
Leadership Summit and San Diego Resilience Network ”.



Copyright © 2009 TETRADYN Corporation, All Rights Reserved

Notes

50

Contact Information


TETRADYN Corporation

Richmond, VA

(offices & staff also in CA, TX, GA, NC)

(757) 847
-
5511 or (202) 415
-
7295

http://tetradyn.com


Principal Contacts for the Team and Consortium


Chris Aigin

caigin@tetradyn.com

(704) 363
-
1054

Kimberly Johnson

kjohnson@tetradyn.com

(423) 342
-
4338, (423) 400
-
1094

Martin Dudziak

martinjd@tetradyn.com

(202) 415
-
7295, (757) 847
-
5511

Copyright © 2009 TETRADYN Corporation, All Rights Reserved