Optimal

experiment

selection

for

parameter

estimation

in

biological

differential

equation

models

Background:

Parameter

estimation

in

biological

models

is

a

common

yet

challenging

problem.

In

this

work

we

explore

the

problem

for

gene

regulatory

networks

modeled

by

differential

equations

with

unknown

parameters,

such

as

decay

rates,

reaction

rates,

Michaelis-Menten

constants,

and

Hill

coefficients.

We

explore

the

question

to

what

extent

parameters

can

be

efficiently

estimated

by

appropriate

experimental

selection.

Results:

A

minimization

formulation

is

used

to

find

the

parameter

values

that

best

fit

the

experiment

data.

When

the

data

is

insufficient,

the

minimization

problem

often

has

many

local

minima

that

fit

the

data

reasonably

well.

We

show

that

selecting

a

new

experiment

based

on

the

local

Fisher

Information

of

one

local

minimum

generates

additional

data

that

allows

one

to

successfully

discriminate

among

the

many

local

minima.

The

parameters

can

be

estimated

to

high

accuracy

by

iteratively

performing

minimization

and

experiment

selection.

We

show

that

the

experiment

choices

are

roughly

independent

of

which

local

minima

is

used

to

calculate

the

local

Fisher

Information.

Conclusions:

We

show

that

by

an

appropriate

choice

of

experiments,

one

can,

in

principle,

efficiently

and

accurately

estimate

all

the

parameters

of

gene

regulatory

network.

In

addition,

we

demonstrate

that

appropriate

experiment

selection

can

also

allow

one

to

restrict

model

predictions

without

constraining

the

parameters

using

many

fewer

experiments.

We

suggest

that

predicting

model

behaviors

and

inferring

parameters

represent

two

different

approaches

to

model

calibration

with

different

requirements

on

data

and

experimental

cost.

In

this

paper

we

have

described

a

method

of

selecting

experiments

to

infer

unknown

model

parameters.

We

have

shown

that

when

data

is

sparse,

the

parameter

uncertainty

is

large

and

the

cost

surface

has

many

local

minima.

In

spite

of

this,

by

selecting

experiments

based

on

the

uncertainty

estimated

by

the

local

Fisher

Information,

we

are

able

to

reduce

parameter

uncertainty

and

constrain

the

set

of

reasonable

fits

to

the

data

to

lie

within

a

single

region

around

the

best

fit.

Although

this

method

will

produce

a

different

sequence

of

experiments

based

upon

at

which

minima

the

Fisher

Information

is

calculated,

we

have

seen

that

collection

of

experiments

generated

from

different

minima

is

in

fact

very

similar..

As

we

have

noted,

our

method

for

selecting

experiments

is

very

similar

to

the

greedy

method

described

by

Apgar

et

al.

Our

work

goes

beyond

previous

results,

however,

in

that

we

have

explored

the

effect

of

experiment

selection

on

the

global

cost

surface.

Since

in

most

cases

the

parameter

uncertainty

extends

well

beyond

the

harmonic

approximation

and

may

even

include

noncontiguous

patches

of

acceptable

parameters

around

local

minima,

it

is

not

obvious

that

a

selection

criterion

that

is

based

only

local

information

(i.e.

the

Fisher

Information)

will

be

effective.

Our

results

help

to

validate

the

method

of

previous

work

while

demonstrating

its

applicability

to

additional

models..

When

selecting

experiments,

it

is

important

for

them

to

be

complimentary

and

probe

independent

degrees

of

freedom

of

the

model.

Using

the

proposition

that

models

should

be

thought

of

as

a

generalized

interpolation

scheme,

we

have

understood

that

measurements

lying

"between"

observed

data

is

not

as

effective

at

reducing

parameter

uncertainty

as

measurements

that

probe

independent

degrees

of

freedom.

We

have

shown

that

we

can

qualitatively

understand

which

experiments

probe

these

degrees

of

freedom

by

inspecting

the

network

topology

of

the

model.

conceived

of

the

study

and

participated

in

its

design

and

coordination

and

helped

to

draft

the

manuscript.

Both

authors

read

and

approved

the

final

manuscript.

Note

that

the

success

of

the

Fisher

Information

in

selecting

experiments

cannot

be

attributed

to

the

parameter

uncertainty

being

well-approximated

by

the

linearized

We

list

the

sequence

of

experiments

for

estimating

the

predictions

for

several

protein

concentrations

in

model

At

each

iteration

we

reduce

the

relative

uncertainty

in

the

predictions

given

by

Eq.

which

is

reported

in

the

estimatd

error

column.

For

this

measure

of

error,

a

value

of

corresponds

to

an

accuracy

comparable

to

the

experimental

noise.

Notice

that

the

uncertainty

in

the

predictions

can

be

reduced

to

the

experimental

uncertainty

with

just

four

experiments.

Even

though

the

parameters

are

largely

unconstrained

after

four

experiments,

the

model

is

nevertheless

able

to

make

falsifiable

predictions..

residuals.

The

uncertainties

in

the

inferred

parameters

after

fitting

to

the

startup

data

are

very

large

and

extend

well

beyond

the

linear

approximation

in

which

the

Fisher

Information

is

valid.

This

can

be

seen

explicitely

in

Figure

in

which

we

show

the

cost

barrier

for

a

straight

line

path

between

two

good

fits.

As

both

endpoints

of

the

path

fit

the

data

very

well,

both

points

are

contained

within

the

confidence

region

of

the

parameters.

However,

since

the

behavior

of

the

cost

along

the

straight

line

connecting

them

is

far

from

quadratic,

nonlinearities

are

clearly

important.

The

distinct

minima

reported

in

section

Fitting

the

initial

data

and

estimating

uncertainty

should

not

be

misinterpreted

as

a

sampling

of

the

Bayesian

posterior

distribution.

Rather,

they

represent

potential

local

minima

of

the

cost

surface.

As

they

each

fit

the

data

very

well,

of

these

local

minima

can

be

ruled

out

by

initial

data

and

additional

experiments

are

necessary

to

distinguish

among

them.

Although

we

cannot

rigorously

identify

whether

these

fits

represent

local

minima

or

are

connected

by

a

flat

canyon,

by

appropriately

selecting

experiments

we

were

able

to

successfully

distinguish

among

them.

In

Figure

we

interpret

the

effect

of

the

experiment

selection

on

the

confidence

interval.

The

initial

confidence

region

is

very

large

and

encompasses

all

the

good

fits

from

the

initial

search.

As

additional

data

are

added,

the

confidence

interval

shrinks

and

the

location

of

the

minimimum

is

adjusted.

After

each

additional

experiment,

the

new

minimimum

lies

within

the

new

confidence

region..

While

the

precise

order

of

selected

experiments

varies

depending

on

the

minimum

used,

there

are

patterns

that

With

the

initial

data,

there

is

a

large

region

of

acceptable

fits

(black)

which

encompasses

all

the

good

fits

found

in

the

initial

search.

Adding

a

new

experiment

reduces

the

confidence

region

(red)

and

the

best

fit

moves

to

a

new

point

within

the

new

confidence

region.

A

second

new

experiment

reduces

it

further

(blue).

This

process

is

iterated

until

the

confidence

region

is

acceptably

small.

can

be

understood

ex

post

facto

by

inspecting

the

network

topology.

For

example,

among

the

list

of

experiments

in

Table

there

is

a

strong

preference

for

perturbations

of

gene

Inspecting

Figure

we

see

that

gene

acts

as

a

type

of

head

node

for

the

network.

It

is

not

regulated

by

any

other

gene

while

any

perturbation

of

gene

expression

should

effect

the

entire

network.

It

is

natural

to

suspect

that

experiments

perturbing

gene

will

be

the

most

influential.

The

remaining

perturbation

experiments

involved

genes

and

These

experiments

can

be

understood

by

noting

that

these

genes

form

a

negative

feedback

loop..

Similarly,

we

can

also

understand

the

choice

of

measurements.

For

example,

from

Figure

we

see

that

the

protein

produced

by

gene

does

not

regulate

any

other

genes.

Consequently,

without

a

measurement

of

the

concentration

of

protein

the

parameter

controlling

the

production

of

protein

is

completely

unconstrained.

Because

of

this,

the

parameter

uncertainty

with

the

initial

data

is

infinite

(see

Table

and

we

always

select

a

measurement

of

protein

concentrations

as

the

initial

experiment..

To

understand

the

remaining

measurements,

note

that

there

are

two

channels

through

which

signals

are

passed,

either

through

genes

or

through

genes

Typically,

measurements

are

selected

to

observe

the

effect

on

both

sequences..

The

work

of

Apgar

et

al.

has

shown

the

importance

that

experiments

be

selected

that

contain

complimentary

information.

Our

qualitative

understanding

of

the

choice

of

experiments

in

the

previous

paragraphs

reinforces

this

claim.

Recent

work

studying

the

behavior

of

large

nonlinear

models,

such

as

these,

from

an

information

geometric

viewpoint

has

suggested

that

models

can

be

interpreted

as

generalized

interpolation

schemes

We

offer

an

interpretation

of

the

experiment

choices

from

this

viewpoint..

Data

leaves

parameters

unconstrained

when

it

probes

fewer

effective

degrees

of

freedom

than

the

model

has

parameters.

Consider

Figure

where

we

give

sample

data

for

model

Although

the

time

series

corresponding

to

the

concentration

of

mRNA

has

many

data

points,

the

model

effectively

has

only

two

degrees

of

freedom

in

such

a

time

series:

the

magnitude

of

the

equilibrium

concentration

and

the

time

scale

it

takes

to

equilibrate.

Similarly,

the

time

series

of

protein

has

two

similar

degrees

of

freedom;

however,

since

protein

concentration

necessarily

echoes

its

corresponding

mRNA

concentration,

these

degrees

of

freedom

are

not

independent.

Furthermore,

by

noting

that

gene

is

promoted

by

gene

and

inhibited

by

gene

we

see

that

the

six

time

series

in

Figure

explore

many

of

the

same

degrees

of

freedom

and

are

not

really

independent

measurements.

In

the

language

of

interpolation,

the

data

for

gene

is,

in

some

sense,

"between"

the

data

from

genes

and

and

could

be

inferred

by

interpolating

between

the

two.

Quantitative

interpolation

is

precisely

the

role

of

the

model.

Indeed,

even

with

largely

unconstrained

parameters,

measurements

of

time

series

for

gene

and

would

be

sufficient

to

give

accurate

predictions

for

behavior

of

gene

The

choice

of

experiments

in

Table

can

be

understood

in

a

similar

manner

to

those

in

Table

In

this

case,

the

three

selected

experiments

are

analogous

to

the

perturbations

in

the

desired

predictions.

Indeed,

the

primary

perturbation

in

the

desired

predictions

is

a

massive

overexpression

of

gene

consequently,

an

experiment

was

selected

corresponding

to

a

more

mild

over-expression

of

gene

as

well

as

experiments

to

accurately

probe

the

feedback

loop

that

regulates

gene

The

measurement

experiments

correspond

primarily

to

the

three

time

series

to

be

predicted.

By

interpreting

the

model

as

a

generalized

interpolation

scheme,

we

can

understand

both

why

the

experiments

in

Table

were

selected,

as

well

has

how

the

model

can

make

accurate

predictions

without

enough

data

to

constrain

the

parameters.

Just

as

the

time

series

for

gene

can

be

predicted

in

Figure

since

its

behavior

is,

in

some

sense,

"between"

the

behavior

of

genes

and

the

desired

predictions

lie

"between"

the

experimental

observations

in

Table

The

fact

that

the

cost

of

inferring

parameters

is

much

larger

than

that

of

inferring

a

few

predictions

(as

measured

by

the

relative

number

of

experiments

in

Tables

and

suggests

two

differing

approaches

to

modeling

and

experimental

design.

While

knowing

the

parameters

has

the

aesthetic

appeal

of

allowing

one

to

know

all

potential

model

predictions,

if

one

is

only

interested

in

a

few

predictions,

conducting

experiments

to

infer

all

the

parameters

is

not

a

cost

effective

approach.

Furthermore,

in

most

cases

it

is

unknown

whether

the

proposed

model

represents

the

true

biological

network.

In

these

cases,

there

will

likely

be

several

competing

models

that

can

only

be

distinguished

by

their

ability

to

make

falsifiable

predictions.

The

cost

advantage

of

selecting

experiments

based

on

the

predictions

rather

than

parameter

values

is

likely

to

be

more

dramatic..

One

could

generalize

this

method

of

selecting

experiments

by

choosing

alternative

measures

of

information.

In

particular,

our

criterion

in

Eq.

is

based

on

a

linearization

of

the

residuals,

and

one

could

construct

a

more

accurate

measure

for

when

the

linear

approximation

breaks

down.

One

approach

might

be

based

on

an

MCMC

sampling

of

the

Bayesian

posterior

using

Eqs.

and

as

priors

or

some

other

appropriate

choice.

In

fact,

we

have

sampled

the

Bayesian

posterior

for

our

models,

and

find

that

the

allowed

range

of

many

parameters

is

always

dominated

by

the

prior

for

any

choice

of

weights.

These

parameters

can

fluctuate

to

infinity

or

zero

with

only

a

statistically

insignificant

Figure

Independent

model

degrees

of

freedom.

Sample

data

from

model

for

the

mRNA

and

protein

concentration

corresponding

to

gene

as

well

as

the

two

genes

which

regulate

these

concentrations

(genes

and

By

noting

that

gene

is

promoted

by

gene

and

inhibited

by

gene

it

is

clear

that

these

time

series

contain

redundant

information.

The

rise

and

subsequent

fall

in

the

mRNA

and

protein

concentrations

of

gene

can

be

predicted

from

the

time

series

of

genes

and

Measuring

all

six

time

series

would

be

less

effective

than

alternative

experiments

that

probed

independent

degrees

of

freedom.

increase

in

the

bare

cost.

It

is

therefore

a

nontrivial

problem

how

to

construct

a

measure

of

information

that

adequately

reflects

the

information

content

of

the

experiments

without

being

dominated

by

the

prior..

Fortunately,

we

have

shown

that

Eq.

often

provides

an

effective

criterion

for

selecting

experiments

even

when

the

parameter

uncertainties

extend

well

beyond

the

linear

approximation

as

they

do

in

our

case.

Indeed,

the

main

result

of

this

paper

is

that

the

Fisher

Information

is

efficient

for

selecting

experiments

under

these

conditions.

Recall

that

the

Fisher

Information

describes

the

curvature

of

the

cost

function

around

a

local

minimum.

Since,

this

curvature

describes

the

parameter

uncertainty

in

the

asymptotic

limit,

selecting

experiments

to

maximize

the

curvature

is

actually

a

reasonable

choice.

Experiments

which

minimize

Eq.

can

be

understood

as

those

that

bring

us

closest

to

the

asymptotic

regime.

We

have

seen

that

this

argument

seems

to

hold

even

when

the

Fisher

Information

is

evaluated

at

different

local

minima.

Fitting

the

initial

data

and

estimating

uncertainty

By

using

the

methods

described

above,

the

fitting

process

is

essentially

automated,

and

we

are

able

to

efficiently

explore

parameter

space

by

searching

for

local

minima

of

the

cost

from

random

initial

guesses.

With

several

repeated

runs

we

are

able

to

identify

the

best

fit

parameters.

There

is

evidence

that

the

cost

landscape

for

models

such

as

these

is

very

rugged

with

many

local

minima

We

therefore

also

search

extensively

for

local

minima..

We

explore

the

extent

to

which

our

models

have

local

minima

in

the

cost

by

searching

from

random

starting

points

chosen

uniformly

on

a

log

scale

in

the

range

corresponding

to

the

penalties

described

above.

The

algorithm

successfully

found

a

local

minimum

about

of

the

time

for

each

model.

The

failures

were

due

to

the

differential

equation

solver

being

unable

to

accurately

integrate

the

differential

equations

at

extreme

values

of

the

parameters.

With

starting

points,

our

sampling

of

the

entire

search

space

is

necessarily

not

very

dense,

a

limitation

due

to

the

high-dimensionality

of

the

search

space.

Notice

that

the

typical

number

of

points

per

parameter

axis

for

the

smallest

model

is

while

for

the

largest

model

it

is

However,

because

such

a

large

fraction

of

the

attempts

failed

due

to

extreme

parameter

values,

the

sampling

is

also

very

diffuse.

Therefore,

our

results

cannot

be

attributed

to

our

search

being

localized

to

a

small

portion

of

parameter

space.

Furthermore,

this

success

rate

could

be

increased

by

reducing

the

range

of

starting

points.

Although

the

search

is

sparse

and

diffuse,

if

our

models

had

many

local

minima,

we

anticipate

they

would

be

discovered

by

our

investigation..

Among

the

roughly

successful

attempts

of

the

geodesic

Levenberg-Marquardt

algorithm,

the

majority

(anywhere

from

to

depending

on

the

model)

correspond

to

"good"

fits

of

the

data,

i.e.

fits

within

experimental

errors.

The

remaining

"bad"

fits

correspond

to

fits

that

fail

to

fit

one

or

more

qualitative

features

of

the

data

and

had

much

larger

values

of

the

cost

function

than

the

good

fits..

Inspecting

the

parameter

values

of

the

several

"good"

fits,

we

find

that

the

parameters

vary

over

a

very

wide

range,

suggesting

that

there

are

many

local

minima

that

fit

the

data

well.

However,

the

eigenvalues

structure

of

the

Fisher

Information

evaluated

at

these

minima

suggests

that

cost

surface

has

many

narrow

canyons,

i.e.

there

are

many

very

small

eigenvalues.

Because

the

cost

surface

is

very

flat

along

the

bottom

of

these

canyons,

it

is

possible

that

these

fits

actually

correspond

to

the

same

basin

of

attraction.

Indeed,

the

cost

surface

is

sufficiently

flat

along

these

canyons

that

one

would

expect

numerical

noise,

such

as

rounding

errors

in

the

differential

equation

solver,

to

create

artificial

local

minima

along

the

bottom

of

the

canyon.

We

therefore

cannot

use

parameter

differences

as

a

criterion

for

distinguishing

distinct

local

minima..

A

better

criterion

to

identify

unique

local

minima

is

to

use

relative

differences

in

the

residual

vector,

r

i

(θ)

as

a

distinguishing

criterion.

This

is

a

natural

choice

as

it

corresponds

to

how

the

search

algorithm

checks

convergence.

The

algorithm

monitors

the

angle

between

the

unfit

residuals

and

the

surface

of

potential

fits

in

data

space,

known

as

the

Model

Manifold

When

this

angle

approaches

the

algorithm

is

near

a

local

minimum

as

illustrated

in

Figure

Furthermore,

from

the

algorithm's

tolerance

we

can

estimate

the

distance

to

the

true

minimum

in

data

space.

If

two

fits

are

nearer

than

the

distance

specified

by

the

algorithm's

tolerance,

then

we

assume

that

they

belong

to

the

same

minimum,

even

though

their

parameter

values

may

be

very

different..

Using

this

criterion,

we

identify

distinct

minima

for

model

that

were

good

fits

to

the

data.

Closer

inspection

of

the

good

fits

reveals

that

their

residual

vectors

are

separated

by

a

distance

only

slightly

larger

than

the

tolerance

specified

by

the

search

algorithm.

Furthermore,

a

direct

line

in

parameter

space

connecting

the

distinct

minima

reveals

they

are

separated

by

very

shallow

barriers

in

the

cost

function

as

we

show

in

Figure

These

observations

leave

open

the

possibility

that

these

good

fits

are

not

actually

distinct,

but

belong

to

the

same

basin

of

attraction.

However,

it

is

safe

to

conclude

that

even

if

the

good

fits

are

distinct

minima,

they

all

reside

within

some

broader

basin

and

are

separated

only

by

shallow

barriers..

Finally,

we

compare

the

orientation

of

the

unconsrained

parameter

directions

as

measured

by

the

Fisher

Figure

Convergence

criterion

for

minimization

algorithm.

The

set

of

model

predictions

in

data

space

form

a

surface

known

as

the

model

manifold.

By

comparing

the

angle

between

the

tangent

plane

to

the

Model

Manifold

and

the

vector

of

unfit

data,

we

can

monitor

convergence

to

a

local

minimum

of

the

cost.

If

α

is

approximately

a

right

angle,

then

we

are

likely

near

a

local

minimum.

Fits

separated

by

more

than

|r(θ)|

cos

α

are

likely

to

correspond

to

different

minima.

A

popular

class

of

biological

models

are

differential

equations

models

describing

the

dynamics

of

several

reactive

agents.

Models

of

this

type

often

involve

a

large

number

of

unknown

parameters

which

need

to

be

inferred

from

experimental

data,

a

process

known

as

model

calibration.

This

inference

problem

can

be

very

challenging

for

many

reasons.

Due

to

the

lack

of

prior

information

about

parameter

values,

it

is

often

necessary

to

search

a

large

region

of

a

high-dimensional

space

to

find

parameter

values

that

produce

reasonable

fits

to

the

data.

Furthermore,

the

models

are

often

highly

nonlinear

functions

of

the

unknown

parameters,

making

it

difficult

to

navigate

this

space

efficiently.

This

is

particularly

*Correspondence:

pqiu@mdanderson.org

Department

of

Bioinformatics

and

Computational

Biology,

University

of

Texas

M.D.

Anderson

Cancer

Cneter,

Houston

Texas,

USA

true

for

models

which

are

defined

as

ordinary

differential

equations.

Challenges

notwithstanding,

models

of

this

sort

have

attracted

a

lot

of

interest

in

the

systems

biology

community

and

much

effort

has

focused

on

calibrating

these

models..

Typically,

the

fitting

problem

for

nonlinear

models

is

very

ill-conditioned

with

large

uncertainties

in

the

inferred

parameters,

a

phenomenon

sometimes

known

as

sloppiness

For

one

model

it

was

observed

that

inferred

parameters

had

a

relative

uncertainty

of

several

hundreds

of

thousands

suggesting

that

inferring

parameters

accurately

might

require

unreasonable

amounts

of

data.

More

recent

work

applying

experimental

design

techniques,

however,

has

shown

that

parameters

for

the

same

model

could

be

inferred

with

just

a

few

experiments,

provided

the

experiments

probed

complimentary

degrees

of

freedom

Although

the

experiments

may

still

require

a

large

amount

of

data

to

achieve

the

desired

accuracy

they

are

nevertheless

a

dramatic

improvement

over

previous

results

and

the

present

work

is

motivated

in

large

part

by

this

approach..

More

generally,

experimental

design

has

been

used

extensively

in

guiding

modeling

of

biological

systems,

see

reference

for

a

review.

Interest

in

experimental

design

has

been

further

motivated

by

the

need

to

infer

topological

relationships

among

biological

agents

in

protein

signalling

and

gene

regulatory

networks

In

general,

the

relative

complexity

of

models

in

combination

with

the

limited

amount

of

quantitative

data

makes

optimal

experimental

design

an

ongoing

challenge

in

systems

biology

This

work

is

also

motivated

by

the

recent

Dialogue

on

Reverse-Engineering

Assessment

and

Methods

parameter

estimation

challenge

This

challenge

provided

three

models

and

required

contestants

to

"purchase"

noisy

experimental

data

on

a

limited

budget

with

the

goal

of

inferring

the

model

parameters

and

predicting

the

time

series

of

the

protein

concentrations

after

perturbation.

In

this

work

we

follow

the

challenge

closely,

attempting

to

infer

the

parameters

and

model

predictions

using

the

same

set

of

perturbation

experiments

available

in

the

challenge.

However,

the

challenge

was

a

type

of

metaoptimization

problem;

contestants

were

required

to

balance

the

costs

of

different

types

of

experiments

with

the

goal

of

estimation

accuracy.

Although

most

real-world

decisions

will

hinge

on

this

trade-off,

in

this

study

we

do

not

weigh

different

experiments

by

their

costs.

The

problem

we

address

is

therefore

separate

from,

but

related

to,

that

of

the

challenge.

The

work

described

in

this

paper

was

conducted

after

the

conclusion

of

the

challenge..

The

main

result

of

this

paper

is

that

the

Fisher

Information

can

be

used

as

an

effective

criterion

for

experiment

selection.

The

Fisher

Information

is

a

measure

of

information

content

based

on

a

local

linearization

of

the

model.

We

show

that

even

when

parameter

uncertainty

is

too

large

to

justify

the

linear

approximation,

the

Fisher

Information

is

still

an

effective

metric

for

experiment

selection.

Our

method

of

selecting

experiments

is

therefore

computationally

efficient

since

it

is

based

on

a

sensitivity

anslysis

at

a

point

estimate

of

the

parameters.

It

does

not

require,

for

example,

a

sampling

of

a

Bayesian

posterior

or

other

rigorous

methods

of

estimating

confidence

intervals

in

order

to

select

a

maximally

informative

experiment.

It

is

also

robust

to

which

parameter

values

are

used

to

calculate

the

Fisher

Information.

We

find

that

by

calculating

the

Fisher

Information

at

a

local

minimum

rather

than

the

best

fit

still

produces

reliable

experiment

choices

to

efficiently

find

the

true

parameters.

Furthermore,

our

method

can

be

generalized

to

select

experiments

that

reduce

uncertainties

in

predictions

without

a

need

for

estimating

parameters

directly.

Indeed,

we

find

that

model

predictions

can

often

be

constrained

with

considerely

less

cost

than

the

parameters.

In

real-world

scenarios

in

which

costs

must

be

balanced

against

research

goals,

we

anticipate

this

approach

to

be

useful..

In

the

current

approach,

we

assume

that

the

true

mathematical

form

of

the

model

and

the

distribution

of

experimental

noise

are

known,

while

the

model

parameters

are

unknown.

Although

such

assumptions

are

generally

not

true

in

practice,

this

problem

represents

a

step

toward

the

more

general

problem

of

model

inference.

O O O O B I O O B I I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O 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Personalized Sightseeing Tours Support Using Mobile Devices In this paper, we present PSiS (Personalized Sightseeing Tours Recommendation System) Mobile. PSiS Mobile is our proposal to a mobile recommendation and planning support system, which is designed to provide effective support during the tourist visit with context-aware information and recommendations about places of interest (POI), exploiting tourist preferences and context. We also provide a state of the art on tourism mobile device applications and describe some of the main problems encountered on the development of such applications. Finally, we present a study to discover which is the lightest and fastest way to exchange information between a server and a mobile client in order to minimize the mobile device limitations. evolution of society and expectations, a tourist that uses a system like this expects location-aware information about the destination domain including history, culture, folk, art, economics, environment and nature. Advanced tourists also expect individualized information and services taking into account their own interests and their history of activities and history of information.[2]. This adaptation can be performed using mobile devices, due it's pocket size and computational capabilities, it can help a tourist on the planning of his stay, show detailed information of a specific point of interest and recommend nearby points of interest to visit, according with his profile and environment context [3] . With this, mobile devices have gained an even more significant role regarding the support of the tourist activity, being used as electronic guides, interactive or not.Thus, the question if people would like to use computers as a replacement of the traditional books and maps as tourist guides splits the tourists into two groups: traditionalists that want to stick with classical paperwork, and experimentalists that would like to try out new technologies.Mobile terminals are embedded systems with very limited capabilities (limited display size, resolution, power consumption, processing capabilities, low memory and networking capacity) compared to a traditional computer. These limitations need to be considered because of possible technical, ergonomic or economic implications for the mobile user. So, mobile client-server applications performance is a crucial aspect that depends on many factors, such as bandwidth, connectivity, positioning capability and support for the paradigms of interaction, the user interface and security issues. It should be also considered other issues, such as the balance between the use of reliable and secure real-time information with the ability to store that information on device.The set of information that is necessary to interact with the client devices is vast. To a client computer, the volume of information is not very important, depending usually on Internet access speed, but for a mobile device that question is very different. This is different in mobile devices, because their wireless connection can be slower compared with fixed or wired data connections. Besides, they often have a measurably higher latency and it can lead to long retrieval times, especially for lengthy content. Sometimes it's preferable to download one big file, than a lot of small files. Even so, this can lead to another problem, which is in the case that the connection is very unstable and always going down, thus the large file can never be downloaded. But nowadays, many mobile carriers are quickly adding high-speed network capacity based on Wi-Fi. With this it is easier to connect to wireless hot spots with mobile phones, in an effort to deliver fast data and clear calls in areas where neither might be possible. Apart from mobile carriers, some cities are also implementing Wi-Fi connectivity throughout their territory. This provides mobile device users, especially tourists, access to the internet. With this, one of the constraints of mobile devices is partially solved. Partially solved, because it is only implemented in a few big cities and, in some areas, the price per MB (Mega Byte) is very expensive.Batteries still don't provide the desired amounts of energy without the need to be constantly recharged, and wireless communication increases the power consumption of the device. This means we require less network traffic in communications between clients and servers; to combine on less costs, less battery consumption and better performance. When a tourist goes to a new location (country, city or region) he would certainly like to have available a user-friendly tool that would help planning his stay according to his objectives, preferences, knowledge, budget and period of stay. It is well known that the task of planning where to go and what to do, in the limited amount of time available, are common problems encountered by tourists when visiting a city for the first time.. In effect, cities are large information spaces, and in order to navigate these spaces visitors often require numerous guide books and maps that provide large amounts of information. Häubl and Dellaert [1] state that this can be both a blessing and a curse. Although the amount of information allows tourists to select more appropriate points of interest, it also turns the process so complex that the tourist might not be able to assimilate all this information adequately.. Mobile devices applications can be used to provide an effective support to tourists in tour planning process. There can be distinguished two types of applications: Guide applications, which only provide information about sights and; recommendation systems that helps the tourist narrow the universe of choice, giving results according to the tourist preferences. This type of system is able to process much more information and points of interest than the tourist could possibly do. With the actual There are already systems that provide tourism recommendations and others that only act as a tourism guide, some of them can be seen on chapter 2, where we also perform a comparison between studied systems.. Subsequently in chapter 3 we will present our case study to find out which is the fastest and lightest way to exchange information between our server and our mobile client and we present our proposal for a tourism recommendation system, the PSiS Mobile, with a special concern in providing good, fast and reliable information to users. Finally on chapter 4 we will conclude and provide information about our future work. We have introduced the main concerns present in the development of a mobile application in a client-server environment, as well as a state of the art in tourism mobile recommendation applications. Until now several applications were developed, but they have important limitations and most of them do not account for the multiple restrictions of mobile devices, thus lacking a solid implementation to be used in the real world. We present a case study to find out which is the fastest and lightest way to exchange information between a server and a mobile client and we describe the PSiS Mobile architecture, and in the future, we will evolve further by exploring new technologies and possibilities. Although mobile devices have many constraints, we want to provide a good user experience, giving tourists a fast and user friendly tool including context-aware adaption, a route planning system, augmented reality and built-in social networking features, to provide the user with important and significant details about what he is seeing or is about to see. To ensure more accurate results, we made not one but four different types of tests regarding the exchange of information. The first test consisted of sending information about only one point of interest. The second test consisted in sending 250 points of interest at once. In the third test, were sent all the 461 points of interest present in the database. Finally, a more thorough test was made, consisting in the transmission of four times, all the points of interest. In this paper we will present results in the form of charts, where there is a chart for each metric, but for only one of the test scenarios, the third test. This is because the results were identical in all the test scenarios. The only exception was the fourth test, where all the methods, except HTTP with ProtocolBuffers and HTTP with SAX Parser, gave an "Out of Memory" error due to the PDA lack of memory. With this, we can see that mobile need special considerations regarding memory usage by the client application, and we must have attention to details even if they seem insignificant. As can been seen from the charts, ProtocolBuffers with HTTP was the fastest and lightest way to exchange information between the server and mobile client.. It was concluded early in this study that raw sockets were left behind due to the little advantage that they actually bring, compared to the HTTP protocol. Also, raw sockets are much more complex and hard to work with. It's like reinventing the wheel when it already exists. On the other hand, HTTP is reliable and is able to perform error handling. This is the chosen method for the implementation of PSiS Mobile.. Therefore, and starting with XML, the study showed that after all it isn't so slow to parse a XML file. This method is only slightly behind the others regarding file size, since it includes multiple tags.. Then, and as expected in this field, JSON occupies less space. It is one of its main objectives. However, it demonstrates to be slower than the best XML parser's.. It is noteworthy that DOM is definitely the slowest and most complex to work with method. The SAX ends up having a performance at all similar to ProtocolBuffers, which proved to be the lightest of all and the fastest in all tests. That is, these two are, according to our tests, the best. SAX is overtaken by Protocol Buffers when it comes to speed, thus we conclude that ProtocolBuffers is the best serialization method to be used along with the HTTP Protocol.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Scan Chain Ordering to Reduce Test Data for BIST-Aided Scan Test Using Compatible Scan Flip-Flops In this paper, the scan chain ordering method for BISTaided scan test for reducing test data and test application time is proposed. In this work, we utilize the simple LFSR without a phase shifter as PRPG and configure scan chains using the compatible set of flip-flops with considering the correlations among flip-flops in an LFSR. The method can reduce the number of inverter codes required for inverting the bits in PRPG patterns that conflict with ATPG patterns. The experimental results for some benchmark circuits are shown to present the feasibility of our test method. For testing highly integrated circuits, scan design is widely used to achieve high fault coverage for sequential circuits. Since much test data is required for scan design to set internal states of the circuit, reducing test cost such as test data volume and test application time is required. One of the techniques to reduce the test data volume is built-in self-test (BIST) that has test pattern generator such as linear feedback shift register (LFSR) inside the chip. For testing sequential logic circuit, scan-based BIST methods have been proposed. Since there exist some faults called random pattern resistant faults for which only few input patterns exist that can detect these faults, random pattern generated by LFSR can not achieve high fault coverage in practical time. Hence several enhancements of BIST design have been proposed. Some of the methods control the clock for the LFSR to hold some input vectors for reducing test data volume and scan power [1] , [2] while the conventional methods provide all of test clocks to LFSR. A BIST-aided scan test (BAST) architecture [3] , [4] is one of the techniques that has an inverter block to flip a random pattern to a deterministic ATPG pattern. The method proposed in [2] reduces the control data for the inverter block in BAST by holding the internal state of the inverter block for some test vectors. In this work, to reduce the test data in the BAST architecture, compatible sets of flip-flops are utilized to reduce the data required to flip the bit pattern of the random pattern. A compatible set of flip-flops is defined as a set of flip-flops for which same values can be assigned in any test vectors. It is utilized for configuring multiple scan chain like Illinois scan and scan tree architectures [5] - [7] . In this work, arrangements of flip-flops in BAST are determined based on the compatible sets of flip-flops. Since the flip-flops are placed considering the correlation between the patterns of flip-flops in an LFSR, PRPG can be constructed without a phase shifter in our method. In Sect. 2, a basic concept of BAST and some test data volume reduction methods on the BAST architecture are described. Section 3 shows our method that utilizes compatible sets of flip-flops to arrange scan flip-flops on scan chains for the BAST architecture. Section 4 shows the procedure of our method. The experimental results for benchmark circuits are shown in Sect. 5 and Sect. 6 concludes the paper. The test data and test time reduction method for BIST-aided scan test is proposed. To reduce the conflicts between random patterns generated by LFSR and ATPG patterns, the compatible sets of flip-flops are utilized for scan chain ordering. Along with the inverter block that is capable of keeping the inverter codes, our method can reduce the test data and test time. Since the compatible set of flip-flops can be replaced each other in scan chain, the method to find optimized arrangements for LFSR is still left as the future work.

B I I O O O O O B I I O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Coupling dense and landmark-based approaches for nonrigid registration Abstract-In this paper, we investigate the introduction of cortical constraints for non rigid intersubject brain registration. We extract sulcal patterns with the active ribbon method, presented by Le Goualher et al. (1997). An energy based registration method (Hellier et al., 2001), which will be called photometric registration method in this paper, makes it possible to incorporate the matching of cortical sulci. The local sparse similarity and the photometric similarity are, thus, expressed in a unified framework. We show the benefits of cortical constraints on a database of 18 subjects, with global and local assessment of the registration. This new registration scheme has also been evaluated on functional magnetoencephalography data. We show that the anatomically constrained registration leads to a substantial reduction of the intersubject functional variability. We have presented in that paper a hybrid registration method, that takes advantage of both a photometric registration and landmark-based registration. The approach is based on our previous work presented in [25] . We have chosen to incorporate sparse sulcal constraints, because sulci are relevant brain cortical landmarks from an anatomical and functional point of view. The sulci are extracted with the active ribbon method, presented in [30] . The energy-based framework [25] makes it possible to incorporate naturally the local sparse constraints and to express the complete registration problem in the same formalism.. On a database of 18 subjects, of which 12 major sulci have been extracted for each subject, we have demonstrated the efficiency of sparse constraints. We have designed global measures (average volume, mean error, overlapping of brain tissues), as well as local measures (distance between sulci after registration, shape comparison based on PCA). Both measures have shown the efficiency of cortical constraints.. Finally, we have conducted evaluation on functional data. The somatosensory MEG data have been acquired and mapped onto the reference subject accordingly to the result of the anatomical registration. We have shown that the functional variability is significantly reduced with the sulcal constraints. It is commonly admitted that part of the intersubject variability is encoded in the anatomical variability. The sulcal constrained registration method reduces significantly the dispersion of dipoles after registration (that is to say the intersubject functional variability). As a consequence, it is more relevant and appropriate than the photometric one.

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Neighborhood Filters and the Decreasing Rearrangement Abstract Nonlocal filters are simple and powerful techniques for image denoising. In this paper, we give new insights into the analysis of one kind of them, the Neighborhood filter, by using a classical although not commonly used transformation: the decreasing rearrangement of a function. Independently of the dimension of the image, we reformulate the Neighborhood filter and its iterative variants as an integral operator defined in a one-dimensional space. The simplicity of this formulation allows to perform a detailed analysis of its properties. Among others, we prove that the filtered image is a contrast change of the original image, an that the filtering procedure behaves asymptotically as a shock filter combined with a border diffusive term, responsible for the staircaising effect and the loss of contrast. where h is a positive constant, and. is a normalization factor, intended to allow constants to be fixed points of NF h . The Neighborhood filter (NF) is the simplest particular case of other related filters involving local terms, notably the Yaroslavsky filter [32, 33] , the SUSAN filter [28] introduced by Smith and Brady, the Bilateral filter [31] of Tomasi and Manduchi, and the Nonlocal Means filter (NLM) [6] by Buades, Coll and Morel.. These methods have been introduced in the last decades as efficient alternatives to local methods such as those expressed in terms of nonlinear diffusion partial differential equations (PDE's), among which the pioneering approaches of Perona and Malik [22] , Álvarez, Lions and Morel [2] and Rudin, Osher and Fatemi [26] are fundamental. We refer the reader to [9] for a review and comparison of these methods.. Among all these filters, the Neighborhood filter is the simplest, but yet useful, method due to its compromise between denoising quality and computational speed. Indeed, although it creates shocks and staircasing effects [8] , the computational cost is by far lower than those of other integral kernel filters or PDE's based methods.. Since, usually, a single denoising step of the nonlocal filters is not enough, an iteration is performed according to several choices of the iteration actualization, see (3) and (4) for two of such strategies.. In this context, the Neighborhood filter and its variants have been analyzed from different points of view. For instance, Barash [3] , Elad [13] , Barash et al. [4] , and Buades et al. [7] investigate the asymptotic relationship between the Yaroslavsky filter and the Perona-Malik PDE. Gilboa et al. [17] study certain applications of nonlocal operators to image processing. In [23] , Peyré establishes a relationship between the non-iterative nonlocal filtering schemes and thresholding in adapted orthogonal basis. In a more recent paper, Singer et al. [27] interpret the Neighborhood filter as a stochastic diffusion process, explaining in this way the attenuation of high frequencies in the processed images. In Galiano [16] , an application of the Neighborhood filter to time-frequency representations of one-dimensional signals is studied.. In this article, we reformulate the Neighborhood filter in terms of the decreasing rearrangement of the initial image, u, which is defined as the inverse of the distribution function q ∈ R → m u (q) = |{x ∈ Ω : u(x) > q}|, see Sect. 2 for the precise definition.. Realizing that the structure of level sets of u is invariant through the Neighborhood filter operation as well as through the decreasing rearrangement of u allows us to rewrite (1) in terms of a one-dimensional integral expression, see Theorem 1.. Although from expression (1) is readily seen that only computation on level lines is needed to perform the filtering, the alternative expression in terms of the decreasing rearrangement offers room for further analysis of the iterative scheme.. Perhaps, the most important consequence of the rearrangement is, apart from the dimensional reduction, the reinterpretation of the NF as a local algorithm. Thanks to this, we may prove some properties of the Neighborhood filter nonlinear iterative scheme, see (4) , among which -The asymptotic behavior of the NF as a shock filter of the type introduced by Álvarez et al. [1] , combined with a contrast loss effect, see Theorem 3. -The contrast change character of the NF, i.e. the existence of a continuous and increasing function g :. As mentioned above, the most salient advantage of the Neighborhood filter in its rearranged version is speed. If N denotes the image size in pixels, the complexity of other nonlocal filters such as the classical NF, the Bilateral or the NLM filters are of the order C × N , where C depends on different parameters (window sizes) used in those models. A typical value of C may be of the order 10 5 , for the NLM. However, the complexity of the rearranged version of the NF depends on the number of the initial image intensity levels, which we assume quantized in Q levels, and on some small constant related to the window size, h, resulting in a complexity of the order c × Q, being a typical value of c of the order 10 2 . Thus, the complexity of the NF is independent of the image size, once the level lines of the initial image have been identified.. However, denoising quality of the NF is, by far, poorer. The staircaising effect, always present in algorithms reducing the Total Variation of the initial image, is especially strong for the NF. In fact, after several iterations, and depending on the window size h, the NF output image concentrates most of its pixels mass on few level sets, producing a segmentation-like effect on the initial image.. A quick explanation of the difference between the NF and the Bilateral and NLM filters is that the NF does not retain any local information of the image, diffusing the intensity values just according to the mass of their corresponding level lines. Thus, a pixel belonging to a level line with large mass will retain its value even if it is isolated in a component of the image with different intensity value.. Due to this, the Neighborhood filter and morphological filters deduced from the topographic map of an image, see for instance the monograph by Caselles and Monasse [10] , such as the Grain and the Killer filters, are also different since the latter use the local geometry of level sets connected components in a fundamental manner.. However, they do share some similitudes in their level set based framework, and this could be used for combining both algorithms. For instance, the isolated small regions belonging to a level line with large mass which remain in the filtered image after the NF application could be removed by morphological procedures, such as opening and closing operators.. The segmentation-like behavior of the NF may be explained in terms of the relationship between the inflexion points of the decreasing rearrangement and the local extreme points of the image histogram, h(q) = −m u (q). Thus, since the NF behaves asymptotically as a shock filter, and shock filters accumulate mass in inflexion points, we deduce that the NF works as an histogram based segmentation algorithm.. Since for our analysis the Gaussian form of the integral kernel is not important, we consider the following generalization of the Neighborhood filter. with. For the moment, we only assume K ∈ L 1 loc (R) and K ≥ 0 to have (2) well defined, although more meaningful conditions will be stated later.. We consider the following iterative schemes, for n ∈ N (including n = 0), and u (0) = u:. 1. Iteration with fixed kernel (linear operator),. 2. Iteration with varying kernel (nonlinear operator),. Observe that, for both schemes, we have. for all n ∈ N, and therefore the iterations are well defined. The plan of the article is the following. In Sect. 2 we introduce the notion of decreasing rearrangement and establish the equivalence between (3) and (4) and its corresponding versions under this transformation. In addition, we show some examples on the relationship between an image and its relative rearrangement and histogram. In Sect. 3, we prove some qualitative properties of the nonlinear iterative scheme, among which its behavior as a shock filter. In Sect. 4, we describe the discretization of the continuous model and demonstrate with examples the denoising capabilities of the NF in comparison with the Bilateral and NLM filters, and its interpretation as a segmentation-like algorithm. In Sect. 5 we give our conclusions.. Finally, let us emphasize that the reformulation of the Neighborhood filter we propose produces the same filtered image than that obtained through the classical NF. However, there are two important advantages in our approach: (i) the filtering process is performed through a one-dimensional integral, reducing largely the algorithm complexity, and (ii) the rearranged version exposes the NF functioning to a deeper mathematical analysis. In this paper we introduced the use of the decreasing rearrangement to express nonlinear and nonlocal filters in terms of integral operators in the one-dimensional space [0, |Ω|].. We have proved properties related to the Neighborhood filter nonlinear iterative scheme. In particular, geometric properties like the invariance of level sets and the performance of the filter as a contrast change.. We have also proven a detailed qualitative behavior of the iterations as a power expansion in terms of the window size, and with coefficients which depend on up to second order derivatives of the iterations. This allowed us to distinguish two kind of effects of the filtering process: an anti-diffusive effect of shock-filter type, and a contrast loss effect.. Motivated by the possible piece-wise constant steady state of the discrete problem, we have illustrated the interpretation of the filter as a segmentation algorithm, indeed connected to other techniques involving the histogram thresholding.. The main conclusion of our work is that, for certain kind of images, among which those having concentrated their pixel mass around few intensity levels, the NF is appropriate both as a denoising and as a histogram-maxima based segmentation algorithm. The execution time of its rearranged version clearly out-performs those of other algorithms. However, for other kind of images, specially those with a relatively flat histogram, the results of the NF are poor.

B I O O B I O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Introduction: Configuring Anthropology The authors present examples of how anthropologists are presently using computers to advance ethnographic research in new directions while building on what has come before. All the methods, protocols and tools created by the authors are free, open source, and available on the internet. The contributions are the authors' attempts to address greater complexity through greater 'control' over the data and structures within which we work. These methods are suitable to a large number of problems, basic and applied, across the range of anthropology from its humanities axis to its science axis. Anthropology is what we make of it, and each author is attempting to make a little bit more of anthropology, to configure anthropology for addressing old problems in new ways, and positioning anthropology to address new problems and new opportunities to influence others through anthropology. A computer application is an expression of specific methodological and theoretical positions. Or perhaps, more generally, computer programs are a medium for the description and expression of information, relationships and structures, just as pen, paper and human language. However, unlike pen and paper, computer programs are more flexible than simple static and linear representations where the dynamic character of writing depends entirely on great powers of interpretation by the reader. Computer based media are far more explicit in representing dynamic and non-linear qualities, and while capable of replicating all other representational media humans have created, whether of sound, vision or text, can go beyond as a medium of ideas dynamically applied to these representational media. In simpler terms, computer software is a way of expressing our ideas in a much more explicit form that exposes at least some of the pragmatic dimensions of our ideas. Just as literature communicates by using language to evoke a world and the events unfolding within it, a computer program communicates by its processural transformations of an initial symbolic structure. Like literature this unfolding can be fantasy, or can provide insights on deep relationships. Unlike literature, the reader has far less control over this unfolding than the author.. As with all disciplines that focus on people and their productions, anthropology tends to be information rich and understanding poor. Anthropology has a reputation, with anthropologists at least, for borrowing heavily from other disciplines. The computing methods used by particular specialist anthropologists tend to be similar to methods in use in allied humanities, social science or science subjects.. However, there are distinctive applications of computing to support distinctive issues. Anthropology is a broad subject, with diverse sub-disciplines ranging from folklore to molecular anthropology. If a discipline is defined as a group of people who can 'converse' with each other, anthropology manages to leverage its diversity well. Using fairly restrictive standards, Borofsky (2002) reports a relatively high level of almost 10% direct substantial cross-field inclusion between anthropology's four major sub-fields across a century of articles from American Anthropologist [1] . The relationship between the sub-fields with respect to informing, shaping and evaluation of research is much higher. Where anthropology tends to be distinctive within all this assimilation is its use and evaluation of information comparatively within and between peoples and cultures, contextualized by the wide range of symbolic and material human activity and creations that anthropological specializations have investigated, documented and theorised upon.. Socio-cultural anthropology is organized around ethnographic research that is comparable (in principle) using fairly radical participant observation relative to most other disciplines practicing ethnographic methods [2] . Participant observation as a anthropological method tends to consist of an extended period of living with your research subjects. You stay in a house, hut or tent just as they do. You eat the same food, sit around the same fire, bar or tea stall and talk, get involved in personal relationships and personal obligations. You catch some of the same diseases and suffer from the cold or heat. Because of your origins as an 'outsider' and the surety that your future will not be theirs (or at least only in episodes, and you can always leave, not an option for most), you cannot be a 'real' member of the community, but you do gain a great deal of experience and grounding that would be difficult to achieve otherwise. This also introduces a complication in that the researcher is directly within the research process and at least some of the research data is 'located' within the researcher, and must be exteriorized in some form or another. Ethnographic writing continues to be the vehicle of choice for presenting the results of ethnographic research, including implicitly or explicitly the 'interior' data in the form of narrative or selection.. From this experience-rich method anthropologists possess a great deal of specific information they use to interpret what people say, what people do, and what people say they do. Although many sub-disciplines of anthropology cannot practice participant observation (e.g. archaeology and molecular anthropology), all branches of anthropology are influenced by the information collected by those who can. From this body of information, collectively called ethnography, emerge the problems that define anthropology, and thus the wider goals of all anthropologists.. If we are to identify a distinctive synergy of computer methods with anthropology it will most likely be in the collection, reporting and analysis of ethnographic data. Ethnographic research is about processes. Some of these are short term processes like greeting guests, cooking meals or collecting food. Others relate to longer term processes of social and cultural change. All that we see and experience around us is the result of processes of creation, formation, interaction, interconnection, fission and destruction.. Anthropologists have devised a number of ways to represent and analyze this most complex aspect of research, including simple procedural descriptions, narrative enactments, time-motion studies, poetry, video recordings and radical participation. All of these share a common problem in that they all require inevitable reduction of experience to different kinds of abstracted results. Each method has its advocates with respect to which aspects are best to emphasize.. The contributions in this special issue are examples [3] of how anthropologists are presently using computers to advance the discipline in new directions while building on what has come before. Although the computers skills of the authors are representative of the higher end among anthropologists, all are working on tools and methods that are accessible to any anthropologist who might want to use these. All the methods, protocols and tools created by the authors are free, open source, and available on the internet, unlike the intellectual products of using these which, due to the vagaries of publishing, are often restricted. The other aspect that all the contributions relate are the authors' attempts to address greater complexity through greater 'control' over the data and structures within which we work. There is no sense in which we propose that these methods are suitable for all problems, but they are applicable to a large number of problems, basic and applied, across the range of anthropology from its humanities axis to its science axis. Anthropology is what we make of it, and each author is attempting to make a little bit more of anthropology, to configure anthropology for addressing old problems in new ways, as well as positioning anthropology to address new problems and new opportunities to influence others through anthropology. All the authors indicate an impression that computational anthropologists have consistently reported over the four decades that anthropologists have employed computer based methods: the process of developing a program is in itself a part of the research process, not simply the construction of a tool to assist in doing the research. I was facinated by this effect in the first two simulations I produced in 1979 (Fischer, 1980; Buchler and Fischer, 1986) . In the case of Buchler and Fischer (1986) the design stage of the simulation (which was about two hours as I recall) uncovered a rare flaw in the ethnographic reporting of L. Pospisil in Kapauku Economy (1956) , and produced a convincing result to the "Kapauku Sweet Potato problem" in which a number of anthropologists had only managed very weak accounts using mathematical models. This was also the case in my research on arranged marraige in Pakistan (Fischer 1994b) , where I build a knowledge-based representation (similar to that described by Bharwani, this issue) of the decisions underlying arranging a marriage, and discovered the same patterns used by college students when self-selecting lovers. Looking back the data had been available before, but I had not made the connection. In the past I have claimed this effect as an advantage to computer-based methods, but did not find it compelling enough to make strong claims. However, the experience seems to be so pervasive amongst anthropologists who are using computer-based modeling of any variety that I am inclined to revise to a stronger claim. There is a question as to why.. I suspect that it is not simply the formalization that is responsible for these insights, but rather formalization for the purpose of instantiation. Because of the nature of computer programs, the mode of expression is always in terms of instantiation: transcending from information, structures and principles to enactment. Instantiation 'flattens' irregularities, incompatibilities and conflicts because enactment requires that something must happen, even if we don't understand why. By investigating why we uncover problems with our analysis, or simply learn that there are intrinsic conflicts that end up accounting for variation in results. Perhaps this effect is part of the appeal of interpretive anthropology, for that is all about creating instantiations.. The contributions in this issue demonstrate that, for a variety of reasons, computer-based methods have the capacity to advance anthropology. Four of the authors are early to early-mid career, and I beleive we will find more and more younger anthropologists using computers for communication, collaboration, fieldwork and analysis who will re-configure anthropology for the twenty-first century.

O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Protein Fragment Swapping: A Method for Asymmetric, Selective Site-directed Recombination Abstract. This paper presents a new approach to site-directed recombination, swapping combinations of selected discontiguous fragments from a source protein in place of corresponding fragments of a target protein. By being both asymmetric (differentiating source and target) and selective (swapping discontiguous fragments), our method focuses experimental effort on a more restricted portion of sequence space, constructing hybrids that are more likely to have the properties that are the objective of the experiment. Furthermore, since the source and target need to be structurally homologous only locally (rather than overall), our method supports swapping fragments from functionally important regions of a source into a target "scaffold"; e.g., to humanize an exogenous therapeutic protein. A protein fragment swapping plan is defined by the residue position boundaries of the fragments to be swapped; it is assessed by an average potential score over the resulting hybrid library, with singleton and pairwise terms evaluating the importance and fit of the swapped residues. While we prove that it is NP-hard to choose an optimal set of fragments under such a potential score, we develop an integer programming approach, which we call SWAGMER, that works very well in practice. We demonstrate the effectiveness of our method in two types of swapping problem: selective recombination between beta-lactamases and activity swapping between glutathione transferases. We show that the selective recombination approach generates a better plan (in terms of resulting potential score) than a traditional site-directed recombination approach. We also show that in both cases the optimized experiment is significantly better than one that would result from stochastic methods. Protein recombination constructs libraries of hybrids by recombining fragments from two or more parents, with the goal of discovering hybrids with beneficial properties such as improved thermostability, activity, or substrate specificity [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] . For example, Stemmer demonstrated the development of beta-lactamase hybrids with a 32,000-fold increase in the required minimum inhibitory concentration of the antibiotic cefotaxime [1] . In contrast with mutagenesis techniques, recombination uses amino acid combinations that already exist in wild-type proteins and thus are likely to produce viable proteins. Site-directed techniques seek to to improve the "hit rate" of good hybrids by recombining the parents at selected breakpoint positions, rather than stochastically. For example, Arnold, Mayo, and co-workers showed that selecting breakpoints so as to minimize disruption of interacting amino acid pairs yields beta-lactamase hybrids that are more likely to be stably folded and functional than random ones [4] .. Typically site-directed recombination is both exhaustive and symmetric: a combinatorial library of hybrids is constructed from fragments covering all residues (exhaustive) and taken uniformly from all parents (symmetric). However, in many applications it may be desirable to relax these requirements. A selective approach may be warranted if the parents have regions that are significantly "gappy" (insertions/deletions) in a sequence alignment or that are significantly different structurally. In such a case much experimental effort may be wasted on constructing and screening a large number of poor quality hybrids, instead of focusing on those that recombine the non-gappy and structurally analogous regions, and thus are more likely to be stably folded and functional. An asymmetric approach may be in order if the goal is to swap portions of particular functional importance from one protein into another. One such application is humanization of exogenous therapeutic proteins, where part of a foreign source is swapped into a human protein target that acts as a scaffold (and will not elicit an immune response). Antibodies have long been humanized this way, e.g., combining murine variable regions with human constant regions [14, 15] . An approach for the much more difficult task of humanizing enzymes (which lack the overtly modular nature of antibodies) was recently demonstrated [10, 11] , introducing activity from a rat glutathione transferase into a human one.. In order to enable the optimization of asymmetric, selective site-directed recombination experiments, we develop here a new approach that we call protein fragment swapping (Fig. 1 ). We distinguish a source parent and a target parent, and construct a library that swaps combinations of selected discontiguous fragments from the source to the target. By swapping from source to target, our approach is asymmetric; by swapping fragments that can be discontiguous, our approach is selective. Furthermore, fragment swapping does not require the parents to be homologous (in sequence or structure) overall, but only requires there to be corresponding regions of the source and target in which we may swap fragments. Thus it directly supports the humanization application discussed in the previous paragraph. Traditional combinatorial site-directed library construction is a special case of fragment swapping, where the swapped fragments must be contiguous. By enabling the protein engineer to define sequence regions of interest, swapping focuses the experimental effort on a smaller portion of sequence space that is believed to be more relevant. Thus it improves the chance of finding beneficial new hybrids in the resulting library.. We develop an algorithm, which we call "SWAGMER" (a word created by swapping part of "frAGMEnt" into "swAPPEr") for planning protein fragment swapping experiments. The objective is to select, from the corresponding source and target sequence regions of interest, "good" source fragments to be combinatorially swapped in for corresponding target fragments. To assess possible plans, we employ a statistical potential score analogous to those used in combinatorial recombination to help ensure stability of the resulting hybrids [4, [16] [17] [18] . The potential averages over the entire resulting hybrid library a set of singleton and pairwise terms evaluating the importance of the residues and how well they match. While the averages can be computed efficiently (i.e., without enumerating the exponential number of hybrids), we show that the inclusion of pairwise terms leads to an NP-hard optimization problem. To solve the problem in practice, we develop an integer programming approach that represents swapping assignments for the residues by binary variables, and optimizes the potential score for the resulting library. To demonstrate the effectiveness of our approach, we planned experiments for selective recombination between beta-lactamases and for activity swapping between glutathione transferases. In both cases, the optimized plans outperform all randomlygenerated plans (as would result from stochastic recombination methods). We further compared the selective plan with an optimized traditional site-directed recombination plan, and show that the swapping library has a better average potential score, increasing the probability of obtaining functional variants. We have developed a new general framework for recombination, protein fragment swapping. By swapping only selected discontiguous regions, fragment swapping can focus on functionally important regions in parent sequences, is applicable to parents with heterogeneous structures, and is flexible in the number of residues participating in recombination. Furthermore, the asymmetric role of the source and target parents enables specific construction of libraries seeking to introduce activities from one parent into the other. Our SWAGMER method provides an efficient, effective approach to optimizing fragment swapping experiments. There are three main steps ( Fig. 1) to planning a fragment swapping experiment for a given source and target. We assume here that we are given a single source protein S of length m and target protein T of length n; the approach can readily be generalized to multiple proteins.

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The paraconsistent process order control method We have already developed some kinds of paraconsistent annotated logic programs. In this paper we propose the paraconsistent process order control method based on a paraconsistent annotated logic program called before-after extended vector annotated logic program with strong negation (bf-EVALPSN) with a small example of pipeline process order verification. Bf-EVALPSN can deal with before-after relations between two processes (time intervals) in its annotations, and its reasoning system consists of two kinds of inference rules called the basic bf-inference rule and the transitive bf-inference rule. We introduce how the bf-EVALPSN-based reasoning system can be applied to the safety verification for process order. In this paper, we have introduced the process order control method based on a paraconsistent annotated logic program bf-EVALPSN, which can deal with before-after relation between processes with a small pipeline process order safety verification control. We would like to conclude this paper by describing the advantages and disadvantages of the process order control method based on bf-EVALPSN safety verification.

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