Employing

topographical

height

map

in

colonic

polyp

measurement

and

false

positive

reduction

CT

colonography

(CTC)

is

an

emerging

minimally

invasive

technique

for

screening

and

diagnosing

colon

cancers.

Computer

aided

detection

(CAD)

techniques

can

increase

sensitivity

and

reduce

false

positives.

Inspired

by

the

way

radiologists

detect

polyps

via

virtual

fly-through

in

CTC,

we

borrowed

the

idea

from

geographic

information

systems

to

employ

topographical

height

map

in

colonic

polyp

measurement

and

false

positive

reduction.

After

a

curvature

based

filtering

and

a

CT

feature

classifier,

a

height

map

is

computed

for

each

detection

using

a

ray-casting

algorithm.

We

design

a

concentric

index

to

characterize

the

concentric

pattern

in

polyp

height

map

based

on

the

fact

that

polyps

are

protrusions

from

the

colon

wall

and

round

in

shape.

The

height

map

is

optimized

through

a

multi-scale

spiral

spherical

search

to

maximize

the

concentric

index.

We

derive

several

topographic

features

from

the

map

and

compute

texture

features

based

on

wavelet

decomposition.

We

then

send

the

features

to

a

committee

of

support

vector

machines

for

classification.

We

have

trained

our

method

on

patients

polyps)

and

tested

it

on

patients

polyps).

Results

showed

that

we

can

achieve

sensitivity

at

false

positives

per

patient

and

the

height

map

features

can

reduce

false

positives

by

more

than

We

compute

the

polyp

height

and

width

measurements

and

correlate

them

with

manual

measurements.

The

Pearson

correlations

are

(p

and

(p

for

height

and

width,

respectively.Published

by

Elsevier

Ltd.

Colorectal

cancer

is

the

second

leading

cause

of

cancer

death

in

the

United

States,

estimated

to

claim

lives

in

alone

Screening

is

currently

the

most

suitable

method

for

early

detection

and

eventually

removal

of

colonic

polyps.

CT

colonography

(CTC)

is

a

minimally

invasive

technique

and

can

be

used

to

detect

both

precancerous

polyps

and

colon

cancers.

The

accuracy

of

CTC

is

reported

by

some

investigators

to

be

as

good

as

that

of

traditional

optical

colonoscopy

(OC)

One

characteristic

used

to

predict

the

malignancy

risk

of

a

polyp

is

its

size

including

the

height

and

width

Polyp

size

is

usually

determined

by

making

a

linear

measurement

during

OC.

In

this

paper,

we

propose

a

novel

technique

to

employ

topographical

height

map

in

polyp

detection

and

measurement..

Computer

aided

detection

(CAD)

of

colonic

polyps

using

CTC

have

been

under

investigation

in

the

past

decade.

Most

systems

used

features

computed

from

the

original

CT

data

set.

Summers

et

al.

extracted

features

based

on

surface

curvature.

Yao

et

al.

employed

a

deformable

model

to

segment

polyps

and

thus

obtain

quantitative

features.

Paik

et

al.

proposed

a

surface

normal

overlap

method

to

detect

round

shape

objects

such

as

colonic

polyps.

Nappi

and

Yoshida

extracted

the

directional

gradient

concentration

from

the

volume

data.

They

noticed

that

the

directional

gradient

for

polyps

concentrated

while

other

structures

did

not.

Zhao

et

al.

extended

the

scalar

curvature

to

lines

of

curvature

for

polyp

detection.

Sundaram

et

al.

further

smoothed

the

curvature

and

shape

estimation

using

a

geometry

processing

approach.

Hong

et

al.

proposed

a

pipeline

by

integrating

texture

and

shape

analysis

on

a

flattened

colon.

Bhotika

et

al.

combined

simple

geometric

shapes

such

as

spherical

and

cylindrical

structures

to

represent

and

differentiate

polyps

and

folds.

Gokturk

et

al.

proposed

a

random

orthogonal

shape

sections

method

which

combined

the

information

from

many

random

images

to

generate

reliable

signatures

of

shape.

Dijkers

et

al.

developed

a

technique

based

on

surface

patch

growing

to

segment

and

measure

polyp.

Konukoglu

et

al.

used

level

set

evolution

to

enhance

spherical

polyp

structure

as

a

pre-processing

step

to

improve

the

performance..

of

the

above

methods

made

use

of

the

knowledge

of

the

way

radiologists

detect

polyps.

Radiologists

read

CTC

studies

in

mainly

two

ways.

One

is

scan-through,

where

radiologists

scan

through

slices

and

form

pictures

in

their

brain.

Another

is

flythrough,

where

computer

graphics

techniques

are

employed

to

reconstruct

the

surface

of

the

colon

from

the

CT

data

and

the

radiologist

navigates

through

the

colon

along

the

center

line.

One

investigation

suggested

that

radiologists

can

achieve

higher

diagnostic

accuracy

in

fly-through

than

in

scan-through

This

observation

prompts

us

to

explore

ways

to

extract

features

from

colon

fly-through

which

can

be

used

to

distinguish

polyps

from

normal

colon

surface.

Li

et

al.

proposed

a

method

based

on

endoluminal

projection

images.

They

computed

the

projection

images

using

graphical

snapshot

through

an

optimized

viewpoint

and

derived

wavelet

coefficients

as

texture

features

for

classification.

In

this

paper

we

propose

an

approach

based

on

topographic

height

map,

which

is

potentially

more

robust..

Height

maps

are

commonly

used

in

geographic

information

systems

(GIS),

where

they

are

also

called

digital

elevation

models

Remote

sensing

techniques

such

as

radar

or

LIDAR

are

applied

to

determine

the

distance

from

the

sensor

to

an

object

or

surface.

Similar

idea

has

also

been

applied

in

range

imaging

where

structure

lights

coupled

with

calibrated

optical

cameras

are

employed

to

reconstruct

the

depth

features

of

a

surface.

The

distance

or

depth

is

then

converted

to

elevation

data.

The

elevation

data

can

be

used

to

recognize

structures

in

a

terrain

For

instance,

mountains

usually

have

higher

elevation

than

their

surrounding

regions.

Fig.

is

the

aerial

view

of

Mt.

St.

Helens

from

NASA's

WorldWind

The

viewer

can

easily

recognize

the

mountain

just

from

the

height

contrast

of

mountain

and

plain

by

navigating

the

landscape.

The

same

strategy

can

be

employed

in

polyp

detection.

Colonic

polyps

are

abnormal

growths

protruding

outward

from

the

colon

wall

and

are

characteristically

round

in

shape.

In

contrast,

haustral

folds

and

other

normal

colonic

structures

tend

to

be

circumferential

and

ridge-shaped.

The

main

idea

is

that

the

colon

surface

can

be

viewed

as

a

terrain

and

polyps

as

mountains

(bumps)

on

the

terrain.

The

unique

topographic

features

of

mountains

(bumps)

can

assist

us

in

distinguishing

polyps

from

the

rest

of

the

colon

surface.

Fig.

is

a

picture

from

a

fly-through

of

CTC.

We

notice

the

similarity

between

polyps

in

CTC

fly

through

and

mountains

in

terrain

navigation.

In

the

case

of

CTC,

we

can

survey

the

colon

surface

by

placing

a

camera

above

the

detections.

Using

the

ray

casting

technique,

we

compute

the

distance

from

the

camera

to

the

surface.

The

inverted

distance

can

be

converted

to

the

elevation

height

from

the

base

of

the

polyp.

The

height

data

is

then

digitized

and

stored

in

a

grid.

Due

to

the

special

characteristics

of

polyps,

their

height

map

will

present

concentric

patterns

and

other

topographic

features.

Fig.

shows

one

CT

slice

of

a

polyp,

its

endoluminal

view

and

height

map..

There

are

two

main

applications

for

the

height

map

technique:

one

is

the

CAD

system

for

polyp

detection,

another

is

the

measurement

of

polyp

size.

The

paper

is

organized

as

follows.

In

Section

we

first

describe

our

approach

to

apply

the

height

map

in

our

CAD

pipeline.

We

then

describe

the

methods

in

height

map

generation,

topographic

feature

computation,

view

direction

optimization

and

polyp

height

and

width

measurement.

In

Section

we

present

the

CAD

results

in

both

the

training

sets

and

independent

test

sets.

We

also

analyze

the

correlation

and

agreement

between

the

manual

height

and

width

measurements

and

those

derived

from

the

height

map.

Finally

we

provide

some

discussions

in

Section

Some

initial

results

from

this

paper

were

previously

published

in

two

conference

papers

In

this

paper,

we

improve

our

technique

by

introducing

virtual

flattening

techniques

and

new

topographic

features

based

on

principal

component

analysis.

We

also

conduct

much

more

thorough

validation

experiments

and

analysis

and

provide

more

discussions.

We

proposed

a

novel

technique

to

distinguish

colonic

polyps

from

normal

colon

surfaces

based

on

topographical

height

maps.

The

novelty

of

this

technique

is

that

in

addition

to

using

the

original

CT

image,

it

uses

height

maps

which

reveal

several

visual

properties

that

radiologists

perceive

to

detect

polyps.

The

technique

can

reduce

FPs

by

more

than

and

work

well

for

both

big

polyps

mm)

and

medium

sized

polyps

mm)..

The

endoluminal

projection

image

technique

proposed

by

Li

et

al.

is

close

to

our

height

map

method

in

the

sense

that

we

both

try

to

mimic

the

radiologist's

detection

process.

The

endoluminal

projection

is

more

visually

realistic

compared

to

height

map.

However,

the

endoluminal

projection

method

has

several

limitations.

Firstly,

lighting

condition

plays

an

important

role

in

the

image

formation

and

is

difficult

to

optimize.

Secondly,

excessive

and

complex

background

could

influence

the

feature

extraction

for

the

main

structures

in

the

scene

(polyps

in

this

case).

Thirdly,

texture

features

are

dependent

on

the

in-plane

rotation

and

translation

of

the

projection

plane.

The

height

map

approach

handles

these

issues.

The

height

map

generation

does

not

rely

on

any

lighting

condition.

A

height

threshold

can

be

applied

to

filter

out

most

of

the

background.

Topographic

features

based

on

the

directional

slopes

from

the

apex

to

the

base

of

a

polyp

are

invariant

to

in-plane

scale,

translation

and

rotation.

The

height

map

is

a

map.

However,

information

is

still

preserved

in

the

map

since

height

value

is

stored

at

each

pixel

grid.

This

enables

us

to

compute

features

such

as

height,

spread

and

slope.

Furthermore,

we

can

also

apply

the

wavelet

transform

to

extract

texture

features

of

the

polyp

pattern,

which

cannot

be

easily

computed

from

the

data

directly.

The

Harr

wavelet

is

currently

used

in

our

method,

which

is

not

rotation

invariant.

However

since

we

are

looking

for

concentric

texture

pattern

for

polyps,

directional

analysis

is

not

necessary..

Instead

of

using

the

height

map

classifier

as

a

post-processing

step

to

reduce

FP

detections,

we

can

combine

the

features

from

CT

data

and

height

map

features

to

form

a

hybrid

classifier.

The

benefit

of

this

scheme

is

that

it

may

improve

the

sensitivity.

However,

it

requires

height

map

evaluation

on

all

detections

before

the

second

stage

of

classification,

which

may

be

too

computationally

expensive

since

the

second

stage

classification

typically

eliminates

of

false

detections.

Furthermore,

putting

all

features

together

may

over-train

the

classifier.

The

two-stage

classifiers

in

current

system

are

trained

separately.

The

height

map

classifier

is

trained

on

the

data

from

the

first

stage

classifier

(Fig.

One

can

argue

that

the

performance

improvement

in

the

two-stage

classifiers

could

be

attributed

to

increased

classifier

complexity.

However,

since

we

validated

our

system

using

separate

test

sets

and

obtained

the

same

improvement,

this

indicated

that

the

system

was

not

overtrained..

The

height

map

is

generated

using

the

ray

casting

technique.

Both

orthogonal

and

perspective

projections

were

investigated.

We

chose

the

orthogonal

projection

since

it

is

invariant

to

the

projection

distance

and

can

preserve

polyp

size

information.

Another

advantage

is

that

only

the

projection

direction

needs

to

be

set

in

the

orthogonal

projection,

which

makes

the

optimization

much

simpler.

Orthogonal

projection

is

also

much

more

efficient

to

compute

since

we

can

precompute

the

projection

rays.

We

adopted

a

simple

virtual

flattening

algorithm

to

compensate

the

artificial

height

difference

caused

by

the

curved

colon

surface.

Unlike

the

flattening

techniques

in

Refs.

our

virtual

flattening

operation

is

limited

to

a

small

local

neighborhood

around

the

polyp.

In

Eq.

r

is

generally

much

bigger

than

b

times),

so

d

is

a

small

value.

Therefore

the

geometric

distortion

associated

with

most

flattening

algorithms

is

insignificant

in

our

method..

The

concentric

pattern

of

the

polyp

can

be

presented

only

when

the

projection

direction

is

parallel

to

the

polyp

elevation

direction.

Choosing

the

correct

projection

direction

is

essential.

The

concentric

index

based

on

the

directional

slope

is

proven

to

be

an

effective

metric.

Other

metrics

such

as

image

entropy

to

maximize

the

information

in

the

map

have

also

been

employed

in

computer

vision

in

trying

to

get

the

optimal

projection

angle

However,

in

our

case,

more

information

does

not

equal

stronger

differentiating

power.

We

search

the

entire

projection

direction

space

through

a

multi-scale

scheme.

An

optimization

algorithm

such

as

gradient

descent

and

Powell's

method

can

be

applied

to

improve

the

efficiency..

In

future

work,

more

topographic

features

such

as

profile

curvatures,

ridge

lines

and

incident

radiation

could

be

computed

and

applied

in

the

classification.

A

more

precise

concentric

index

will

also

be

investigated.

We

are

also

working

on

using

the

height

map

features

to

evaluate

polyp

conspicuity.

The

patient

population

consisted

of

adults

between

and

years

of

age

at

three

medical

institutions.

In

total,

there

were

polyps

larger

than

mm.

We

divided

the

data

into

independent

training

and

test

sets.

Table

lists

the

distribution

of

CTC

studies

and

polyps

in

the

training

and

test

sets.

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


Improved parallelism and scheduling in multi-core software routers Abstract Recent technological advances in commodity server architectures, with multiple multi-core CPUs, integrated memory controllers, high-speed interconnects, and enhanced network interface cards, provide substantial computational capacity, and thus an attractive platform for packet forwarding. However, to exploit this available capacity, we need a suitable software platform that allows effective parallel packet processing and resource management. In this paper, we at first introduce an improved forwarding architecture for software routers that enhances parallelism by exploiting hardware classification and multi-queue support, already available in recent commodity network interface cards. After evaluating the original scheduling algorithm of the widely-used Click modular router, we propose solutions for extending this scheduler for improved fairness, throughput, and more precise resource management. To illustrate the potential benefits of our proposal, we implement and evaluate a few key elements of our overall design. Finally, we discuss how our improved forwarding architecture and resource management might be applied in virtualized software routers.N. Egi ( ) · L. Mathy Recent advances in server technology promise significant speedups to applications amenable to parallelization. Router workloads appear ideally suited to exploit these advances, which has led to a renewed interest in the applicability of software routers. Software routers offer several advantages, like a familiar programming environment and ease of extensibility, which offers the potential to serve as a single platform onto which one can consolidate many network functions typically implemented by various middleboxes. Examples are the single server-based routers running open-source router software as offered by Vyatta [1] . which have even been touted as cheaper alternatives to other commercial routers. However, the limitation of software routers has always been performance. Which is why the possibility of leveraging recent server advances to further the reach of software routers has been of high interest [2] [3] [4] [5] .. The recent trajectory of server advances has been of growing the available resource capacity through increased parallelism. Multi-core CPUs increase the available CPU resources; multiple memory controllers integrated in the CPU sockets increase the available memory capacity; multiple I/O links and PCIe buses do the same for I/O and-importantly, high-speed point-to-point interconnects offer high-capacity access between components.. However, precisely because the underlying hardware is parallel, achieving high performance will rely greatly on our ability to distribute packet-processing well across the available resources (not unlike routing across a network-the more alternate routes, the more a routing algorithm has to work to make sure the network capacity is well exploited). Traditionally, this is the job of the scheduler to decide which set of tasks are assigned to which cores and when (if at all) tasks must be moved between cores.. One of the major points of software routers is that they can simultaneously support very different forms of packet processing, with different resource characteristics, on a single hardware architecture. For example, one can imagine having multiple customers-for one we are performing encryption, and for the other deep packet inspection. Since certain operations are more expensive (in terms of the server resources they consume) than others we want to make sure that each customer receives a fair access to the server resources. In general, this requires proper abstractions for flexible and efficient flow differentiation as well as resource allocation. While we also need scheduling techniques that make good use of the resources so as to achieve high performance, and can also support various scenarios of fair resource sharing and isolation.. In this paper, we observe that neither traditional forwarding architectures nor traditional schedulers are ideally suited to our task, for the following reasons: (1) Traditional forwarding architectures are not capable of taking advantage of recent server advances (e.g. multiple I/O links, hardware classification, and multi-queueing in the network interface cards), and thus are limited in exploiting the highest performance achievable. (2) Schedulers are CPU centric. This can be problematic since one of the distinguishing features of a packet-processing workload is that it stresses more than just the CPU. This becomes even more likely as the number of cores increases resulting in bottleneck potentially being any of the CPU, the memory, or the I/O, and hence being "fair" in terms of CPU may not mean an adequate solution. (3) Schedulers fail to take into account the heterogeneity in workloads. (4) Schedulers also fail to consider the basic question of whether we can accurately measure the resource consumption for different packets/flows in a dynamically changing workload. We cannot hope to allocate resources appropriately if we cannot first accurately account for their consumption.. To this aim, we approach the limitations from two directions. On the one hand, we propose an improved forwarding architecture that outperforms, in terms of throughput and latency, the traditional way of packet forwarding, while it also provides additional advantages for resource management and scheduling. We then focus on understanding the limitations of traditional schedulers and on quantifying their impact, thus identifying the requirements for an improved scheduler design.. The paper is organized into the following sections. In Sect. 2, we discuss the limitations of traditional forwarding architectures and propose an architecture that overcomes these limitations. Section 3 focuses on the resource management, while Sect. 4 describes how the proposed forwarding architecture and resource management extensions might be applied in virtualized software routers. Finally, Sect. 5 concludes the paper. The performance of modern multi-core commodity architectures clearly indicates its viability for high performance packet forwarding. However, as we demonstrated in this paper, a software router platform has to be designed in a way that provides effective parallelism and proper resource management for workloads with diverse resource characteristics.. Our proposed forwarding architecture enables full parallelization of the resources, while it also improves performance by simplifying the basic structure of the forwarding paths, made possible by hardware multi-queues.. Our results in Sect. 3 clearly indicate that we need to pay attention to the utilization of multiple resources, and not only the CPU, when allocating resources to different workloads. For predictable system behavior, we identified Principle #1, recommending to co-schedule workloads with similar characteristics within the same thread, while for improved performance co-schedule workloads with different characteristics on adjacent hyper-threads (Principle #2). Our proposed architecture is also capable of performing flow-based packet processing, while it can also be used as a forwarding plane of virtualized software routers.

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 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 B 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 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 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 B I 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 B I 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 B I 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 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 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


Modeling of Discrete/Continuous Optimization Problems: Characterization and Formulation of Disjunctions and their Relaxations Abstract. This paper addresses the relaxations in alternative models for disjunctions, big-M and convex hull model, in order to develop guidelines and insights when formulating Mixed-Integer Non-Linear Programming (MINLP), Generalized Disjunctive Programming (GDP), or hybrid models. Characterization and properties are presented for various types of disjunctions. An interesting result is presented for improper disjunctions where results in the continuous space differ from the ones in the mixed-integer space. A cutting plane method is also proposed that avoids the explicit generation of equations and variables of the convex hull. Several examples are presented throughout the paper, as well as a small process synthesis problem, which is solved with the proposed cutting plane method. Developing optimization models with discrete and continuous variables is not a trivial task. The modeler has often several alternative formulations for the same problem, and each of them can have a very different performance in the efficiency on the problem solution. In the area of Process System Engineering models commonly involve linear and nonlinear constraints and discrete choices. The traditional model that has been used in the past corresponds to a mixed-integer optimization program whose representation can be expressed in the following equation form (Grossmann and Kravanja, 1997) :. Ay ≥ a x ∈ R n , y ∈ {0,1} q where f (x), g(x) and r(x) are linear and/or nonlinear functions. In the model (PA) the discrete choices are represented with the binary variables y involving linear terms.. More recently, generalized disjunctive programming (Raman and Grossmann, 1994; Türkay and Grossmann, 1996) . The attractive feature of GDP is that it allows a symbolic/quantitative representation of discrete and continuous optimization problems.. Modeling language for GDP problem has been discussed by Vecchietti and Grossmann (2000) . An approach that combines the previous two models is a hybrid model proposed by Vecchietti and Grossmann (1999) where the discrete choices can be modeled as mixedinteger constraints and/or disjunctions. In this way we can potentially exploit the advantages of the two previous formulations by expressing part of it only in algebraic form, and the other in a symbolic/quantitative form. The hybrid formulation is as follows: where r(x) + Ly ≤ 0 is general mixed-integer constraints that can be linear/nonlinear equations/inequalities. These terms can be seen as disjunctions transformed into mixedinteger form. Ay ≥ a represents general integer equalities/inequalities transformed from former logic propositions.. An issue that is unclear is how the modeler should express the discrete choices, either as a symbolic disjunction, or in a mixed-integer form (Bockmayr and Kasper 1998) . One possible guideline for this decision is the gap between the optimal value of the continuous relaxation and the optimal integer value. Since several algorithms involve the solution of the relaxed problem, we will investigate in this paper the tightness of different relaxations for a disjunctive set: the big-M formulation (Nemhauser and Wolsey, 1988) , the Beaumont surrogate (Beaumont, 1990 ) and the convex hull relaxation (Balas, 1979; Lee and Grossmann, 2000) . The big-M formulation and the Beaumont surrogate can be regarded as "obvious" constraints. However, the convex hull relaxation of a disjunction is tighter, and can be transformed into a set of mixed-integer constraints. The advantage of the convex hull relaxation is that the tight lower bound helps to reduce the search effort in the branch and bound procedure, in both nonlinear and linear problems (for examples of significant node reductions see Lee and Grossmann, 2000; Jackson and Grossmann, 2002) . But the drawback with the convex hull formulation is that it increases the number of continuous variables and constraints of the original problem. This can potentially make a problem more expensive to solve, especially in large problems. The big-M relaxation is more convenient to use when the problem size does not increase substantially when compared to the convex hull relaxation (see Yeomans and Grossmann, 1999 , who found the big-M to be more effective). But generally the lower bound by big-M relaxation is weaker, which may require longer CPU time than the convex hull relaxation. Therefore, depending on the case, there is a trade-off between the best possible relaxation and the problem size. In order to exploit the tightness of the convex hull relaxation, but without the substantial increase of the constraints, it will be shown that cutting planes can be used that correspond to a facet of the convex hull.. In this paper we first introduce the definition and properties of a disjunctive set. We then present the different relaxations and their properties. Finally, a cutting plane method is discussed, and illustrated with several small example problems. The goal of this paper is not to perform a detailed computational study, but rather to provide insights into the modeling and solution of disjunctive problems. The purpose of this paper has been to analyze the different alternatives of modeling the discrete choices as disjunctions or as mixed-integer (0-1) inequalities, in order to provide guidelines on this decision. The resulting model can correspond to one of the three formulations: mixed-integer constraints (PA), disjunctive constraints (GDP) or hybrid (PH). For the analysis, we considered three different possible relaxations of a disjunctive set, the convex hull, the big-M relaxation and the Beaumont surrogate. The analysis was performed mainly on the first two since the big-M formulation is widely used.. Although it was proved that the convex hull relaxation yields a tighter relaxation than the traditional 0-1 big-M relaxation, there are several cases when the big-M relaxation can compete with the convex hull relaxation. As a general rule, the big-M model is competitive when good bounds can be provided for the variables, and for large problems where it is important to keep the number of equations and variables as small as possible. For convex improper disjunction both the convex hull and the big-M model give the same relaxation in the x space, but this may not be true in the x-y space as was demonstrated with examples. For proper disjunctions where the feasible regions have some intersection, the objective function plays an important role, if the minimizer of the objective function is inside the feasible region of the disjunctive set, both the big-M and the convex hull relaxation may yield the same relaxation value. Otherwise the convex hull should be generally better, but the big-M constraints with appropriate bounds can be competitive. For proper disjunctions with an empty intersection on the feasible regions (disjoint terms) the convex hull is generally better than the big-M relaxation. Although these conclusions are not general, we believe they help to provide some insight in the modeling of discrete/continuous optimization problems.. Finally, to address the problem of formulating tight models without generating the explicit equations of the convex hull, a cutting plane algorithm has been proposed. A number of examples have been presented to illustrate the various ideas in this paper as well as the cutting plane method. Proof. First consider R BM ⊆ R B . For the linear case, Beaumont (1990) proved that R BM = R B . Therefore R BM ⊆ R B holds. For the nonlinear case, we consider one disjunction for simplicity. Given a nonlinear disjunctive set,

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


A CaRBS analysis of hip replacement approaches and non-pathology This study investigates the differences in hip biomechanics for subjects following atotal hip arthroplasty (THA), through the lateral approach (LA) and posterior approach (PA), to those with no pathology (NP). The principal component analysis was performed on two kinematic and two kinetic waveforms (subject-based characteristics) from level gait to identify salient portions of the waveforms for comparison between the subject cohorts. These were classified to identify the differences between post-THA and non-pathological cohorts. The primary technique exposited in the THA analysis is classification and ranking belief simplex (CaRBS). Within the analysis, from the configuration of aCaRBS model, there is discussion on the model fit and contribution of the subject-based characteristics. Where appropriate, comparisons to the CaRBS model are made with the results from al ogistic regression (LR) analysis. In terms of model fit, using CaRBS, 24 out of 27 LA/PA subjects (88.89%) and 13 out of 16 NP subjects (81.25%) were correctly classified as exhibiting either post-THA or NP hip functional characteristics during level gait, combining to 86.05% classification accuracy, compared with 81.40% classification accuracy when using LR. Total hip arthroplasty (THA) is routinely performed to effectively relieve the pain and disability associated with hipo steoarthritis ( Ritter et al.1 995) .T hiss tudy determines whether differences in gait exist between subjectsw ith aT HA and thosew ith no pathology (NP), using kinematic and kinetic subject-based characteristics in this classification problem.. The classificationand ranking belief simplex (Beynon 2005a (Beynon , 2005b Beynon et al. 2006) , known as CaRBS, is a novel technique for analysing the data where there may be imprecision, inexactness and uncertainty in its information content (Beynon et al. 2006) . The ability of the CaRBS technique to work with the notion of uncertain reasoning is due to its mathematical rudiments beingb ased on the Dempster -Shafertheory (Dempster 1967; Shafer 1976) . It therefore intuitively lendsi tselft ot he analysiso fl arge amounts of data generated from amotionanalysisdata-set, which is both corroborating and conflicting in nature.. Despite the continueds uccess of aT HA, the hip recovered biomechanical function during levelg ait does not necessarily return to what is typicallyq uantified as normal (Madsen et al. 2004; Whatling et al. 2008 ). There are numerous surgicala pproaches to THA, each compromising different muscles and static constraints surrounding the hip. For this reason, the surgicalapproach used may affect both the biomechanics and stability of a total hip replacement. Studies have shown that lateral surgicalaccess to the hipjoint weakens the hip abductors, affecting the mechanical ability of the abductor mechanism to control the hip and pelvis in the frontal plane (Baker and Bitounis 1989; Dijk et al. 2009 ), whereasp osterior surgicalaccess compromises the joint capsule and external rotatorm uscle group, affecting the posterior and lateral stability of the hip joint (Gore et al. 1982) . It is important to establish whetherthis is an influential factor on patient recovery as there is currently no consensus on the best surgicalapproach (Gore et al. 1982) .. Two principal surgical approaches are the McFarlandOsborne direct lateral approach (LA; McFarland and Osborne1 954) andt he Moore( southern exposure) posterior approach (PA; Moore1959). In an earlier study by Whatling et al. (2008) ,the motionanalysisoflevel gait revealedt hat subjects following the PA used ag reater range of pelvic obliquity (motiono ft heir pelvis in the frontal plane), and larger frontal planep owers and moments were measured at the hip, as compared to those following the LA. Subjects following the LA were also found to have asignificantly lower range of motion (ROM) in the sagittal plane for their operative hip as comparedto their non-operative hip. As ignificant challenge to the clinical use of gait information is the successful analysis of the data (Chau 2001) . Acommon difficulty is not only the vast amount of data yielded but its variability which can be difficulttointerpret subjectively.. Theaims of this studyare to (1) demonstrate the use of the nascent CaRBS technique in assessing the gait characteristics to discern betweens ubject groups associated with THA function (LA and PA) and NP; (2) investigate the level of modelfi tu sing the CaRBS model by comparing with the results from al ogistic regression (LR) analysis, at echnique commonly used in gait-based studies (Archer et al. 2006; Chang et al. 2007 ) and (3) quantify and visually represent the contribution of each subject-based characteristic to the CaRBS analysis. This study has demonstrated the use of the CaRBS technique employedint he analysisofhip replacement. It has been used to classify the subjects who have undergone THA surgery using LA or PA as comparedtothe subjects with NP. The use of PCs to determine subject-based characteristicsand their subsequent CaRBS analysis have demonstrated that frontal planep ower, pelvic obliquity and sagittal hip rotation are important in determining the changesinhip function associated with THA surgery. This was also truewhenusing LR, although this approach does not allow avisual representationofthe contributions of the characteristics.. The findingsf ound that reduced hip flexion( C1), pelvic obliquity (C2), frontal planeh ip power( C3) and frontal plane hip moment (C4) are indicative of subjects following THA. Examining the classification of LA and PA subjects separately revealedagreaterr ange of functional ability of subjects following the PA to surgery, confirming the findingso fM adsen et al. (2004) and Whatling et al. (2008) . This study has shownthe ability of the CaRBS technique to classify the data from the motion analysisand to visualise the contributions of each variable andh ow each contributionr elates to theo riginal biomechanical waveform. Thus, this technique provides au seful biomechanical tool for the analysiso fc omplex data.. proportions( ratios) to thev alues,. Thes et of characteristicB OEs{ m j , i (·), i ¼ 1, ..., n 0 }, associated with an individual subject characteristic, can be combined using Dempster'scombination rule into a subject BOE, denoted by m j (·). This combination rule, denoted by ½ m j ; i 1 % m j ; i 2 (on two BOEs m j ; i 1 ð · Þ and m j ; i 2 ð · Þ ), is defined by ½ m j ; i 1 % m j ; i 2 ðy Þ¼ 0 ; y ¼ B ; P where F 1 and F 2 are the focal elements from the independent BOEs m j ; i 1 ð · Þ and m j ; i 2 ð · Þ ,respectively.This combination rule can then be used iteratively to combine any number of BOEs.. With the objects (subjects) known to be associated with either x or : x ,aconfigured CaRBS model can be constructed, with respect to the intended optimisation of the required segmentation (to x or : x ). The effectiveness of such a configured CaRBS model is governed by the values assigned to the incumbent control variables k i , u i , A i and B i , i ¼ 1, ... , n P (see Figure A1 ). This configuration process is defined as aconstrained optimisation problem (using standardised response values), solved here using trigonometric differential evolution (TDEFan and Lampinen 2003) , using an objective function (OB), which, from Beynon (2005b) , is defined by. where E (·)r epresents an equivalence class of subject, to either x or : x in this case (it can be shown that the value of the objective function lies within the domain 0 # OB # 1). It is noted, within the definition of the OB, that maximising a difference value such as ( m j ({x }) 2 m j ({ : x })) only indirectly affects the associated ignorance, rather than making it ad irect issue (the mass value m j ({x , : x }) is not included in the definition of the OB).. To gauge the relevance (quality) of the individual survey questions in the optimum segmentation of objects (respondents), the average characteristic BOE is introduced. As its name suggests, this BOE is simply the average of the characteristic BOEs associated with an equivalence class of objects ( E ( x )o r E ( : x )i nt his case). The general average characteristic BOE, associated with ac haracteristic v i and equivalence class of subjects E ( R ), defined am i , R (·), is given by. where R j is as ubject. Since the average characteristic BOE is a BOE, it can be represented by asimplexcoordinate in asimplex plot, hence agraphical exposition of the contribution of subjectbased characteristics. PCs were calculated to represent the salient regions of kinematic and kinetic waveformsinadiscrete form while retaining temporal information. The first PC from each waveforma ccounts for the greatest variance, which was found to exist during hip flexion, pelvic obliquity during part of the stance and all of the swing phase,frontal plane powerduring parts of the stance phase and moment during the majority of the stance phase.. When examiningt he regions of thew aveforms represented by the PCs used in the CaRBS model (Figure 2) , agreater difference is observed betweenthe LA andN Pw aveforms compared to theP Aa nd NP waveforms for C1, C3 and C4, where the LA subjects use ar educed sagittal plane hipR OM, reduced frontal planep owera nd moment. For C2, pelvic obliquity is similar for both surgical groups throughout the gait cycle, with as lightly greater difference observed in the swing phase of gait betweenthe PA and NP cohorts, compared to the LA and NP cohorts. These findingsadd strengthtothe different levels of classification accuracy exhibited by the different LA and PA subject cohorts, as showninFigure 2. This supports the findingsofWhatling et al. (2008) , where the differences in hip function betweenPAand NP cohorts were lessclearly defined as comparedtobetweenLAand NP cohorts. Madsen et al. (2004) also found that subjects following the LA exhibited ag ait pattern deviating from NP whereas several subjectsf ollowing the PA exhibited NP gait patterns.. Inspection of the constituenta verage characteristic BOEs in Figure3showst hat for the subject-based characteristics, C1, C3 and C4, the LA points are nearer the {LA, PA} vertex than the respective PA points, indicating that theses ubject-based characteristicso ffered evidence that enabled the LA subjectstobemore correctly discerned from the NP subjects than that for the PA subjects. For the case of the subject-based characteristic C2, with the PA point nearer the {LA, PA} vertex than the respective LA point, its evidence enabled the PA subjects to be mored iscerned from the NP subjects than the LA subjectsw ere. Interestingly, for C4, the short distance betweent he LA/PA and NP average characteristic BOEs initially indicates as mall range in the frontal plane hip power between the NP and LA/PA cohorts. However, when examining the average characteristic BOE for the LA and PA cohorts separately, there is al arge variability in the frontal hip moment betweenthe LA and PA cohorts. The average C4 characteristic BOE for the PA cohorti s closer to the average NP BOE but has ah igh ignorance. The average characteristic BOE for the LA cohort is further from the average NP and has lessignorance.. From the CaRBS analysis, the contribution of the subject-based characteristics is measured through the respective characteristic BOEs, see Figure 4 . The most significant factorsassociated with the post THA gait were the PC characteristicsr elated to hip flexion( C1), pelvic obliquity (C2) and frontal plane power( C3). The salient measures in the frontal plane, i.e. reduced pelvic obliquity and frontal plane power and moments, may be indicative of abductor muscle dysfunction and weakness (Baker and Bitounis 1989; Dijk et al. 2009 ) in the surgicals ubject cohort( subjects associated with LA or PA). Forf rontal plane moments( C4), the level of ignorancer eaches 1.00 for high values where no associationcan be madetoeither cohortand the majority of the contribution implies that as the frontal moment reduces the subject has undergone surgery. From Figure1 (b) , it can be seen that during the swing phase of the gait cycle, the position of the pelvis was either level or falling below the horizontal position on the unsupported side for the THA sample, indicating abductor weaknessa nd Trendelenburg gait (Hardcastle and Nade 1985) . Fourteen subjects from the surgicals ubject cohort receivedt he LA to surgery which may influence these variables since this surgicala pproach compromises the abductor mechanism.F igure1 (d) illustrates that patients following the LA approach to THA had reduced moment acting at the hip in the frontal plane comparedtoboth PA and NP. Themoment waveformfor both LA and PA lacks the doublet rough identifiablei nt he NP frontal plane moment waveforms. During the two characteristic regions of the gait cyclei nF igure1 (a) for C1, NP subjects use a greater hip flexion( and ag reater ROM) than the surgical cohorts. Ar educed sagittal plane hip ROM has been reportedfor the subjects that have undergone THA surgery by Madsen et al. (2004) and Whatling et al. (2008) . The results from CaRBS and LR analyses of the PCs are givenb elow. The contributions of each characteristic PC to the classificationsa re also detailed. The differences in non-pathological and THA hip gait biomechanics were investigated from the motion analysis data using principal component analysis( PCA),C aRBS and LR. The selection of gait characteristics and analyses is described below.

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


CheS-Mapper

for

visual

validation

of

(Q)SAR

models

Background:

Sound

statistical

validation

is

important

to

evaluate

and

compare

the

overall

performance

of

(Q)SAR

models.

However,

classical

validation

does

not

support

the

user

in

better

understanding

the

properties

of

the

model

or

the

underlying

data.

Even

though,

a

number

of

visualization

tools

for

analyzing

(Q)SAR

information

in

small

molecule

datasets

exist,

integrated

visualization

methods

that

allow

the

investigation

of

model

validation

results

are

still

lacking.We

propose

visual

validation,

as

an

approach

for

the

graphical

inspection

of

(Q)SAR

model

validation

results.

The

approach

applies

the

viewer

CheS-Mapper,

an

open-source

application

for

the

exploration

of

small

molecules

in

virtual

space.

The

present

work

describes

the

new

functionalities

in

CheS-Mapper

that

facilitate

the

analysis

of

(Q)SAR

information

and

allows

the

visual

validation

of

(Q)SAR

models.

The

tool

enables

the

comparison

of

model

predictions

to

the

actual

activity

in

feature

space.

The

approach

is

generic:

It

is

model-independent

and

can

handle

physico-chemical

and

structural

input

features

as

well

as

quantitative

and

qualitative

endpoints.

Conclusions:

Visual

validation

with

CheS-Mapper

enables

analyzing

(Q)SAR

information

in

the

data

and

indicates

how

this

information

is

employed

by

the

(Q)SAR

model.

It

reveals,

if

the

endpoint

is

modeled

too

specific

or

too

generic

and

highlights

common

properties

of

misclassified

compounds.

Moreover,

the

researcher

can

use

CheS-Mapper

to

inspect

how

the

(Q)SAR

model

predicts

activity

cliffs.

The

CheS-Mapper

software

is

freely

available

at

Graphical

abstract:

Comparing

actual

and

predicted

activity

values

with

CheS-Mapper.Data

curation:

It

is

important

to

inspect

(groups

of)

misclassified

compounds

(in

case

of

classification),

or

compounds

with

high

prediction

error

(regression).

Investigating

possible

reasons

for

the

erroneous

predictions

might

aid

in

detecting

errors

in

the

training

data,

like

mis-measured

endpoint

values.

The

researcher

might

as

well

discover

that

the

misclassifications

are

outliers

or

that

more

training

data

is

required.

Model

improvement:

Another

possible

reason

for

bad

model

performance

may

be

improper

feature

choice,

e.g.

the

available

features

can

not

be

used

to

distinguish

between

some

active

and

inactive

compounds.

Moreover,

the

selected

model

might

be

too

specific

(overfitting)

or

too

general

(underfitting).

Additionally,

visual

validation

can

show

the

effect

of

different

model

parameters.

Mechanistic

interpretation:

It

is

also

possible

to

extract

knowledge

from

groups

of

compounds

that

are

correctly

classified.

Compounds

with

similar

feature

values

and

endpoint

values

might

have

similar

modes

of

action.

Consequently,

visual

validation

can

support

the

researcher

in

deriving

a

mechanisticFigure

Comparing

actual

and

predicted

activity

values

with

CheS-Mapper.

The

CPDB

hamster

dataset

is

embedded

into

space,

based

on

physico-chemical

(PC)

descriptors

that

have

been

computed

with

CheS-Mapper

using

Open

Babel.

The

compounds

are

predicted

with

a

Neighbor

approach

employing

the

same

PC

features.

The

inner

sphere

corresponds

to

the

actual

endpoint

activity,

with

class

values

active

(red)

and

inactive

(blue).

The

outer,

flattened

spheroid

depicts

the

prediction.

We

employ

visual

validation

with

CheS-Mapper

to

analyze

real

world

datasets

that

include

experimentally

derived

activity

endpoints.

Table

provides

an

overview

of

the

subsequent

use

cases.

We

show

how

researchers

can

employ

the

new

functionalities

to

investigate

the

correlation

between

feature

values

and

activity

values.

Furthermore,

we

explore

(Q)SAR

model

prediction

and

validation

results

with

CheS-Mapper,

and

inspect

different

applicability

domain

approaches

that

exclude

different

compounds

from

a

dataset.

Please

note

that

all

datasets

and

configuration

settings

are

provided

in

Additional

file

Moreover,

an

additional

use

case

is

described

in

Additional

file

on

visually

validating

(Q)SAR

modeling

of

the

same

endpoint

with

different

sets

of

descriptors.

Visualization

of

(quantitative)

structure-activity

relationship

((Q)SAR)

information

in

chemical

datasets

is

a

very

active

field

of

research

in

cheminformatics

Many

approaches

are

being

developed

that

help

to

understand

existing

correlations

between

the

structure

of

chemical

compounds,

their

physico-chemical

properties,

and

biological

or

toxic

effects.

These

correlations

are

employed

by

(Q)SAR

models

to

predict

the

activity

of

unseen

compounds.

The

predictive

performance

of

(Q)SAR

models

can

then

be

evaluated

with

numerous

statistical

validation

techniques.

There

is

however,

to

the

best

of

the

authors'

knowledge,

no

visualization

method

yet

that

incorporates

(Q)SAR

predictions

and

validation

results.

One

reason

for

this

might

be

that

most

(Q)SAR

models

are

the

results

*Correspondence:

kramer@informatik.uni-mainz.de

Information

Systems,

Institut

für

Informatik,

Johannes

Gutenberg

-Universität

Mainz,

Staudingerweg

Mainz,

Germany

Full

list

of

author

information

is

available

at

the

end

of

the

article

of

applying

statistical

machine

learning

approaches

to

chemical

datasets

and

the

resulting

models

are

sometimes

opaque

and

it

is

commonly

not

an

easy

task

to

extract

the

reasoning

behind

a

prediction.

Some

models

induced

by

machine

learning

approaches,

however,

are

relatively

easy

to

understand,

like

decision

trees,

rule

learners

or

nearest

neighbor

models.

The

predictions

of

these

models

are

easy

to

comprehend,

as

long

as

the

number

of

features

that

are

employed

for

predictions

is

not

too

large

(e.g.

the

size

of

the

decision

tree

is

reasonably

small).

Therefore,

several

model-dependent

visualization

tools

exist

In

contrast,

many

other

models

can

rather

be

seen

as

black

boxes,

like

artificial

neural

networks

or

support

vector

machines.

In

addition,

these

complex

models

are

often

more

predictive

than

intuitive

and

simpler

models..

In

this

paper,

we

propose

a

model-independent

visual

analysis

of

validation

results

employing

the

viewer

CheS-Mapper

The

presented

approach

does

not

investigate

how

predictions

are

made

by

each

model,

but

rather

allows

the

comparison

of

actual

and

predicted

activity

values

in

the

feature

space

(see

Figure

We

call

this

approach

visual

validation.

It

should

be

regarded

as

complementary

to

the

standard

statistical

validation.

Visually

examining

(Q)SAR

model

validation

results

can

aid

in

understanding

the

model

itself

as

well

as

the

modeled

data,

and

can

furthermore

yield

the

following

benefits:.

interpretation.

Mechanistic

interpretation

and

proper

model

validation

are

requirements

of

the

OECD

guidelines

for

valid

(Q)SAR

models

To

this

end,

visual

validation

could

also

help

to

improve

the

acceptance

of

(Q)SAR

models

by

regulatory

authorities

as

alternative

testing

methods.

The

CheS-Mapper

software

is

implemented

in

Java.

It

is

provided

as

a

Java

Web

Start

application,

which

can

directly

be

started

from

a

web

browser.

Additionally,

the

program

can

also

be

downloaded

as

stand-alone

version.

CheS-Mapper

is

an

open-source

project

hosted

at

GitHub

The

code

architecture

allows

developers

to

easily

integrate

novel

algorithms,

e.g.

for

clustering,

structure

calculation,

etc.

A

range

of

Java

libraries

is

integrated

into

the

project:

the

viewer

for

molecules

Jmol

the

Chemistry

Development

Kit

(CDK

and

the

data

mining

workbench

WEKA

Extended

functions

are

provided

in

CheS-Mapper

in

case

the

free

software

tools

Open

Babel

and

R

are

installed

on

the

local

computer.

Open

Babel

is

a

C++

library

for

cheminformatics

that

can

be

used

for

additional

computing,

matching

of

SMARTS

(Smiles

Arbitrary

Target

Specification)

fragments,

and

structural

fragment

mining.

The

statistical

computing

tool

R

is

exploited

by

CheS-Mapper

for

clustering

and

embedding.

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


On the number of optimal identifying codes in a twin-free graph Let G be a simple, undirected graph with vertex set V . For v ∈ V and r ≥ 1, we denote by B G,r (v) the ball of radius r and centre v. A set C ⊆ V is said to be an r-identifying code in G if the sets B G,r (v) ∩ C , v ∈ V , are all nonempty and distinct. A graph G which admits an r-identifying code is called r-twin-free or r-identifiable, and in this case the smallest size of an r-identifying code in G is denoted by γ ID r (G). We study the number of different optimal r-identifying codes C , i.e., such that |C| = γ ID r (G), that a graph G can admit, and try to construct graphs having ''many'' such codes. We introduce basic definitions and notation for graphs (for which we refer to, e.g., [1, 5] ) and for identifying codes (see [12] and the bibliography at [13] ).. We shall denote by G = (V , E) a simple, undirected graph with vertex set V and edge set E, where an edge between x ∈ V and y ∈ V is indifferently denoted by {x, y}, {y, x}, xy or yx. The order of a graph is its number of vertices |V |.. A path P n = x 1 x 2 . . . x n is a sequence of n distinct vertices x i , 1 ≤ i ≤ n, such that x i x i+1 is an edge for i ∈ {1, 2, . . . , n−1}. The length of P n is its number of edges, n − 1.. A graph G is called connected if for any two vertices x and y, there is a path between them. It is called disconnected otherwise. In a connected graph G, we can define the distance between any two vertices x and y, denoted by d G (x, y), as the length of any shortest path between x and y, since such a path exists. This definition can be extended to disconnected graphs, using the convention that d G (x, y) = +∞ if there is no path between x and y.. For any vertex v ∈ V and integer r ≥ 1, the ball of radius r and centre v, denoted by B G,r (v) , is the set of vertices within distance r from v:. Two vertices x and y such that B G,r (x) = B G,r (y) are called (G, r)-twins; if G has no (G, r)-twins, that is, if ∀x, y ∈ V with x ̸ = y, B G,r (x) ̸ = B G,r (y), then we say that G is r-twin-free or r-identifiable. When there is no ambiguity about the graph G, we may use simply B r (v).. Whenever two vertices x and y are within distance r from each other in G, i.e., x ∈ B r (y) and y ∈ B r (x), we say that x and y r-cover each other. When three vertices x, y, z are such that x ∈ B r (z) and y ̸ ∈ B r (z), we say that z r-separates x and y in G (note that z = x is possible). A set is said to r-separate x and y in G if it contains at least one vertex which does.. A code C is simply a subset of V , and its elements are called codewords. For each vertex v ∈ V , the r-identifying set of v, with respect to C , is the set of codewords r-covering v, and is denoted by I G,C ,r (v):. We say that C is an r-dominating code in G if all the sets I G,C ,r (v), v ∈ V , are nonempty (see, e.g., [8] or [9] on the large topic of domination).. We say that C is an r-identifying code [12] if all the sets I G,C ,r (v), v ∈ V , are nonempty and distinct: in other words, every vertex is r-covered by at least one codeword, and every pair of vertices is r-separated by at least one codeword. Or:. given the (nonempty) identifying set I G,C ,r (v) of an unknown vertex v ∈ V , we can uniquely recover v (we also say that we r-identify v).. It is quite easy to observe that a graph G admits an r-identifying code if and only if G is r-twin-free; this is why r-twin-free graphs are also called r-identifiable.. When G is r-twin-free, we denote by γ ID r (G) the smallest cardinality of an r-identifying code in G. Any r-identifying code C such that |C| = γ ID r (G) is said to be optimal. The search for an optimal r-identifying code in given graphs or families of graphs is an important part of the studies devoted to identifying codes. In general, this problem is NP-hard [3] .

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 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 B I O O O O O O O O


Bioinformatics

resource

manager

an

integrated

software

environment

for

systems

biology

with

microRNA

and

cross-species

analysis

tools

Background:

MicroRNAs

(miRNAs)

are

noncoding

RNAs

that

direct

post-transcriptional

regulation

of

protein

coding

genes.

Recent

studies

have

shown

miRNAs

are

important

for

controlling

many

biological

processes,

including

nervous

system

development,

and

are

highly

conserved

across

species.

Given

their

importance,

computational

tools

are

necessary

for

analysis,

interpretation

and

integration

of

high-throughput

(HTP)

miRNA

data

in

an

increasing

number

of

model

species.

The

Bioinformatics

Resource

Manager

(BRM)

is

a

software

environment

for

data

management,

mining,

integration

and

functional

annotation

of

HTP

biological

data.

In

this

study,

we

report

recent

updates

to

BRM

for

miRNA

data

analysis

and

cross-species

comparisons

across

datasets.

Results:

BRM

has

the

capability

to

query

predicted

miRNA

targets

from

multiple

databases,

retrieve

potential

regulatory

miRNAs

for

known

genes,

integrate

experimentally

derived

miRNA

and

mRNA

datasets,

perform

ortholog

mapping

across

species,

and

retrieve

annotation

and

cross-reference

identifiers

for

an

expanded

number

of

species.Here

we

use

BRM

to

show

that

developmental

exposure

of

zebrafish

to

uM

nicotine

from

hours

post

fertilization

(hpf)

results

in

behavioral

hyperactivity

in

larval

zebrafish

and

alteration

of

putative

miRNA

gene

targets

in

whole

embryos

at

developmental

stages

that

encompass

early

neurogenesis.

We

show

typical

workflows

for

using

BRM

to

integrate

experimental

zebrafish

miRNA

and

mRNA

microarray

datasets

with

example

retrievals

for

zebrafish,

including

pathway

annotation

and

mapping

to

human

ortholog.

Functional

analysis

of

differentially

regulated

gene

targets

in

BRM

indicates

that

nicotine

exposure

disrupts

genes

involved

in

neurogenesis,

possibly

through

misregulation

of

nicotine-sensitive

miRNAs.Conclusions:

BRM

provides

the

ability

to

mine

complex

data

for

identification

of

candidate

miRNAs

or

pathways

that

drive

phenotypic

outcome

and,

therefore,

is

a

useful

hypothesis

generation

tool

for

systems

biology.

The

miRNA

workflow

in

BRM

allows

for

efficient

processing

of

multiple

miRNA

and

mRNA

datasets

in

a

single

software

environment

with

the

added

capability

to

interact

with

public

data

sources

and

visual

analytic

tools

for

HTP

data

analysis

at

a

systems

level.

BRM

is

developed

using

Java™

and

other

open-source

technologies

for

free

distribution

BRM

has

the

capability

to

query

predicted

miRNA

targets

from

multiple

databases

(TargetScan,

MicroCosm/

miRBase,

miRNA.org),

retrieve

potential

regulatory

miRNAs

for

known

genes,

integrate

experimentally

derived

miRNA

and

mRNA

datasets,

perform

ortholog

mapping

across

species,

and

retrieve

annotation

and

cross-reference

identifiers

for

an

expanded

number

of

species.

These

tools

combined

with

the

ability

of

BRM

to

directly

broadcast

to

publically

available

tools

for

additional

visualization

and

biological

enrichment

analysis

provides

a

platform

for

seamless

data

analysis.

We

provide

example

workflows

using

mRNA

and

miRNA

microarray

data

collected

after

developmental

exposure

of

zebrafish

to

nicotine

to

successfully

determine

the

differentially

expressed

mRNA

transcripts

that

are

putatively

regulated

by

misexpression

of

miRNAs

and

the

biological

consequences

of

nicotine

in

altering

miRNA

signaling

pathways

during

development.

Overall,

BRM

provides

computational

tools

with

a

simple

user

interface

necessary

for

biologists

to

perform

miRNA

data

analysis

and

crossspecies

mapping

as

part

of

an

integrated

software

environment

that

supports

data

management,

mining,

integration

and

functional

annotation

of

HTP

datasets..

updates

and

helped

to

draft

the

manuscript.

RLT

and

KMW

conceived

of

the

study,

participated

in

its

design

and

coordination

and

helped

to

draft

the

manuscript.

All

authors

read

and

approved

the

final

manuscript.

MicroRNAs

(miRNAs)

are

nucleotide

noncoding

RNAs

that

function

as

post-transcriptional

regulators

of

gene

expression.

Since

miRNAs

typically

act

on

mRNA

targets

through

sequence

complementarity

in

the

it

is

possible

to

computationally

predict

miRNA

target

genes.

Over

of

human

protein

coding

genes

are

predicted

to

be

regulated

by

miRNAs,

which

are

also

highly

conserved

across

species

and

associated

with

diverse

biological

processes

and

diseases,

including

nervous

system

development,

cardiac

function,

metabolism

and

cancer

miRNAs

also

demonstrate

cross-talk

with

epigenetics

in

that

they

are

subject

to

epigenetic

regulation

by

methylation

and

are

involved

in

establishing

methylation

patterns

Given

the

importance

of

miRNAs

in

controlling

gene

regulation,

it

is

necessary

to

develop

computational

tools

to

understand

the

biological

and

functional

consequences

of

miRNAs

in

a

growing

number

of

model

organisms..

There

are

several

public

resources

available

for

miRNA

research

including

miRNA

databases

(miRBase;

and

target

prediction

algorithms

such

as

TargetScan

PicTar

and

miRanda

Computational

tools

have

been

developed

to

query

multiple

prediction

databases

simultaneously

(miRGen

or

incorporate

experimentally

validated

targets

(TarBase;

miRGator

facilitates

functional

understanding

of

miRNAs

by

providing

disease

associations

and

gene

set

analysis

for

a

reference

database

of

curated

miRNA

and

gene

expression

datasets.

Other

tools

further

the

integration

of

experimentally

derived

human

miRNA

and

mRNA

datasets

(MMIA

MAGIA

However,

no

tools

offer

biologists

the

ability

to

integrate

heterogeneous

miRNA

and

mRNA

datasets

for

multiple

species

within

a

platform

that

also

provides

additional

functional

analysis

and

visualization

tools.

Furthermore,

computational

tools

for

non-human

research

models,

such

as

zebrafish,

are

in

high

demand

as

genome

sequencing

improves

for

these

species..

The

Bioinformatics

Resource

Manager

(BRM)

is

a

software

environment

developed

for

data

retrieval,

management,

analysis

and

visualization

of

high-throughput

(HTP)

biological

data

BRM

provides

computational

tools

for

biologists

to

merge

datasets,

cross-reference

gene

identifiers

and

add

functional

annotation

from

NCBI

UniProt

CMR

GO

or

KEGG

databases.

These

tools

simplify

mundane

analysis

tasks

of

data

reformatting

and

table

merging

that

can

be

intensive,

thereby

providing

a

platform

for

straightforward

integration

of

heterogeneous

HTP

datasets

BRM

further

facilitates

connectivity

between

data

sources

and

visual

analytic

tools

for

more

effective

data

mining

within

a

single

software

environment.

By

utilizing

the

GAGGLE

communication

framework,

BRM

can

broadcast

data

to

publically

available

tools,

such

as

R,

Cytoscape

TMeV

and

DAVID

and

then

back

to

the

BRM

project

browser

for

seamless

data

analysis

In

this

way,

BRM

provides

essential

data

management

capabilities

such

that

all

data

files

and

metadata

associated

with

those

files

are

permanently

stored

to

the

user

profile..

We

report

here

several

new

features

in

BRM

that

provide

miRNA

resources

and

cross-species

identifier

retrieval

for

Homo

sapiens

(human),

Mus

musculus

(mouse),

Rattus

norvegicus

(rat),

Danio

rerio

(zebrafish),

and

Macaca

mulatta

(macaque).

The

miRNA

workflows

within

BRM

allow

for

identification

of

conserved

miRNA

gene

targets

from

multiple

public

databases

or

of

potential

regulatory

miRNAs

for

genes

of

interest.

BRM

makes

it

easy

to

merge

predicted

target

lists

with

experimental

mRNA

lists

and

analyze

the

resulting

genes

using

clustering,

functional

enrichment

and

network

modeling

tools.

It

is

now

also

possible

to

compare

data

across

different

species

in

BRM

using

the

Ensembl

ortholog

database

and

to

cross-reference

or

annotate

identifiers

for

numerous

species,

including

those

listed

above.

These

new

features

make

BRM

a

convenient

software

program

for

both

cross-species

data

analysis

and

determining

the

functional

consequences

of

genes

from

HTP

microarray

or

RNAseq

platforms

that

are

regulated

by

miRNAs.

BRM

is

implemented

in

Java

and

is

a

client-server

based

system

using

Enterprise

Java

Beans

technology.

The

server

is

also

Java

based

and

uses

the

JBOSS

application

server.

A

PostgreSQL

database

supports

all

data

storage

for

the

system.

The

client

application

is

lightweight

in

that

all

data

manipulation,

query,

and

storage

are

on

the

server

side..

In

earlier

versions

of

BRM,

the

data

from

public

sources,

such

as

NCBI

and

KEGG,

were

accessed

on

the

fly

with

direct

connections

through

various

API's

(Shah

et

al.,

However,

due

to

restrictive

licensing

and

a

lack

of

API

support

by

many

data

source

providers,

BRM

changed

its

model

to

store

a

copy

in

its

own

server

and

update

on

a

regular

basis.

Every

six

months

the

BRM

team

updates

its

data

store

to

retrieve

any

updates

from

the

many

data

sources

it

makes

available.

This

has

the

added

benefit

of

upgrading

performance

especially

if

the

client

and

server

are

on

the

same

network.

It

also

allows

for

a

complete

stand-alone

(no

internet

required)

implementation

if

warranted..

BRM

was

designed

to

keep

data

transfer

and

clientserver

communication

at

a

minimum

to

support

performance

and

the

user

experience.

That

model

still

holds

even

while

data

volume

has

grown

exponentially.

Even

so,

there

are

limitations

placed

on

certain

types

of

queries

that

would

overload

the

system.

The

users

are

informed

a

priori

of

long

running

queries

and,

at

times,

are

only

provided

with

a

subset

of

results

(up

to

at

a

time.

BRM

has

also

changed

its

deployment

model

to

take

advantage

of

Java

Webstart

technology

which

allows

for

clients

to

be

automatically

updated

when

needed.

Each

time

a

client

application

is

started,

the

server

is

pinged

for

any

updates

in

the

code.

Updates

are

then

downloaded

immediately

behind

the

scenes.

This

model

allows

for

more

frequent

bug-fixes

and

updates

without

user

intervention

to

uninstall

and

reinstall

for

every

update.

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


Decentralized Coverage Control for Multi-Agent Systems with Nonlinear Dynamics In this paper, we study the decentralized coverage control problem for an environment using a group of autonomous mobile robots with nonholonomic kinematic and dynamic constraints. In comparison with standard coverage control procedures, we develop a combined controller for Voronoi-based coverage approach in which kinematic and dynamic constraints of the actual mobile sensing robots are incorporated into the controller design. Furthermore, a collision avoidance component is added in the kinematic controller in order to guarantee a collision free coverage of the area. The convergence of the network to the optimal sensing configuration is proven with a Lyapunov-type analysis. Numerical simulations are provided approving the effectiveness of the proposed method through several experimental scenarios. Sensor networks have a broad application in environmental sampling, ecosystem monitoring, and military surveillance. One of the fundamental problems of multi-robot mobile sensor networks is how to deploy a group of autonomous robots to spread out over an environment in order to monitor some quantity of interest over an area, which is called coverage control. Researchers have proposed various solutions to a lot of interesting sensor network coverage problems. In [1] coverage controllers are categorized in three common kinds, a Voronoi controller, which is geometric in nature, a minimum variance controller, which has a probabilistic interpretation, and a potential field controller.. In [2] , Du et. al. introduce the centroidal Voronoi tessellations as a comprehensive solution to a series of partition problems which sheds light on the sensor coverage problem, and also provide some of several centralized algorithms under deterministic and probabilistic domains. Cortes et. al. [3] propose a decentralized control law for multi-robot coverage of an area partitioned into Voronoi diagram, in the sense that continually driving the robots toward the centroids of their Voronoi cells. A recent text that presents much of this work in a cohesive fashion is [4] and an excellent overview is given in [5] . Different extensions of the framework devised in [3] have been proposed in the literature. In [6] the problem of limited-range interaction be- tween agents was addressed. In [7] the basic approach was extended to deal with the agents with limited energy. The problem of the online learning of the distribution density function, while moving toward the optimal locations, was addressed in [8] , [9] . In [1] the authors propose a cost function form for coverage problems that can be specialized to fit different distributed sensing and actuation scenarios. The cost function is shown to subsume several different kinds of existing coverage cost functions. There has been another extension to heterogeneous groups of finite size robots and non-convex environments in [10] . Standard approaches to Voronoi based coverage control assume simple integrator dynamics for the robots, yielding the ability of traversing both smooth and non-smooth trajectories for robots. They do not address the kinematic and dynamic constraints of physical nonholonomic mobile robots in developing coverage algorithms. However, most of the actual robots such as differential drive ones suffer from kinematic nonholonomic constraints confining the plausible motions of the robot. Once an appropriate feedback velocity control inputs are designed for kinematic steering system, one should take into account the specific dynamic vehicles to convert a steering system command into control inputs for the actual vehicle [11] .. Stabilization and tracking control of nonholonomic mobile robots has been a subject of intense research in the past years [12] - [14] . Many approaches have been proposed to address this issue of nonholonomic stabilization. As pointed out by Kim and Tsiotras [15] , the majority of nonholonomic control laws are based on kinematic models [16] - [18] . Stabilization of dynamic models for nonholonomic systems has also been addressed in [19] - [21] . A popular way of implementing a kinematic control law to a dynamic nonholonomic system is by backstepping the velocity control commands to acceleration input [23] . Backstepping has been used in translating kinematic controllers into equivalent dynamic ones in [11] , [24] .. In this paper, we extend the contributions in kinematic and dynamic control of single nonholonomic mobile robots to the Voronoi-based locational optimization framework introduced in [3] , and propose a decentralized control law with the aim of coverage control problem. After including a kinematic velocity controller in the coverage problem, we seek to incorporate the dynamics of the robots into the coverage controller design based on the backstepping approach of [11] . Using Lyapunov stability theory, we prove that the Copyright c 2011 The Institute of Electronics, Information and Communication Engineers control law causes the network to converge to an optimal sensing configuration. Then we propose a collision avoidance component be incorporated with the kinematic controller in order for the robots to avoid collision among them, and prove that the robots approach to their optimal configuration with bounded error.. The remainder of the paper is organized as follows. We describe problem setup along with some background on nonholonomic mobile robots and locational optimization problem in Sect. 2. In Sect. 3, we first present the proposed kinematic controller and prove its stability. Then we add a collision avoidance component so that no two robots get too close to each other. In Sect. 4 we present the proposed dynamic controller. Numerical simulation results are given in Sect. 5. Finally, we conclude the paper in Sect. 6. In this study, we first considered the decentralized coverage problem of autonomous mobile sensing robots subject to nonholonomic kinematic and dynamic constraints, and introduced an extension to the standard Voronoi-based coverage problem for single integrator agents with point dimensions. A Lyapunov based stability was used to investigate the converge of the robots to their centroidal Voronoi configuration. The proposed method is decentralized in the sense of both coverage and collision avoidance, and it has been successfully verified in numerical simulations. Future work will focus on the extension of the proposed controller for coverage in unknown environments.

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


A knowledge-based, concept-oriented view generation system for clinical data Information overload is a well-known problem for clinicians who medical records (EMRs) gradually replace paper charts, must review large amounts of data in patient records. Concept-oriented many problems are being solved. The amount of data availviews, which organize patient data around clinical concepts such as able for clinicians to review, however, has not been reduced. Depending on their goals, clinicians may only be interkey relevant data identification function is accomplished by a ruleested in certain subsets of the data, which we refer to as based traversal of the semantic network. This paper focuses on the views. It would therefore be useful to reduce the quantity design and implementation of the system; an evaluation of the system of information for clinicians to process by generating such is reported separately. ᭧

O O B I 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