A Data Fusion Methodology for Wireless Sensor Systems Abstract: An efficient DFA (data fusion algorithm) plays an important role in tracking for moving objects over WSS (wireless sensor system) deployments in order to track the objects accurately. Accuracy in object tracking is mainly dominated by the prediction for those moving targets by filtering and refining the results from wireless mobile sensors deployed in WSS environment. A DFA based on CHHN (competitive Hopfield neural network) technique for obtaining the relationship between measurements results from wireless mobile sensors and estimation of existing tracks over WSS (wireless sensor system) is proposed in this paper. Embedded within the CHNN is also a competitive learning mechanism which creatively removes the dilemma of occasional irrational solutions in traditional HNN (Hopfield neural networks). In this research, except the proposed approach is established with CHNN, the methodology of data fusion over WSS is guaranteed to converge into a stable state when performing a data association. In words, the CHNN-based DFA is combined with wireless mobile sensors in a WSS environment to demonstrate the target tracking capabilities. Computer simulation results illustrate that the new methodology of data fusion based on CHNN is not only successfully able to solve the data association problems addressed over WSS environments, but the specified simulated targets can also be tracked without large scale missing. Mobile-sensor data association tracking is generally an essential technique for WSS (wireless sensor system) surveillance systems employing one or more sensors, which may be deployed as stationary or maneuverable, together with computer subsystems. The main objective of a data association tracking algorithm is to partition sensor data into sets of observations produced by the same target; the other object to avoid the coupling effect existing between the mobile sensors for the same target. Once tracks are formed and confirmed, it not merely the number of mobile sensors that can be estimated and quantified; rather, the information gathered by the tracking algorithm can also be associated and fused. It is known that the role of multi-sensor data association in WSS environments involves acquiring, processing and combining data [1, 2] coming from different sources, including sensors and databases, into a more precise set of data. Thus, cooperative decision is made on the basis of the simultaneous input of a community of neurons. Each neuron receives information from other neurons and also conveys information to others [11, 14] . With this collective information, each neuron moves to a stable stage with the lowest value of a predefined energy function. With such a result, the operation of association between mobile sensor measurements and existing tracks can be obtained under global optimal consideration, which in turn can increase the accuracy of mobile sensor tracking systems. Furthermore, due to the embedded competitive updating scheme, the CHNN concept has the capability of removing the burden of weight setting. It has also been demonstrated that the network is guaranteed to converge into a stable and rational stage during evolution. [13] Thus, the dilemma of lapsing into irrational solutions such as those in traditional HNNs can be eliminated.. To the best of the author knowledge, the proposed mean is an innovative idea for discussion when exploring the field of WSNs. The aim of this study is to enhance system performance by means of a DFA for WSS environments, the focus being on a CHNN to obtain global matching between mobile sensor measurements and existing tracks. As illustrated in Fig.1 , moving targets tracked by sensor nodes are considered for improving the tracking deployment for mobile-sensor data association with assumed targets. Moreover, in an environment where the dense targets are spread out randomly, some targets can be very close to each other. The measurements produced by these close targets can confuse the data association computation algorithm and result in inaccurate relations hips. Consequently, the approach for solving the data association problem should be considered globally. The reminder of this report is organized as follows. In Section II the problem formulation includes the gating technique, and the basic concept of the HNN is illustrated; developing a DFA based on the CHNN scheme is presented in Section III; the maneuvering compensator algorithm is explicated in Section IV; Section V reports the simulation results for the proposed algorithm; and Section VI both concludes the report and offers recommendations for further research. An innovative DFA algorithm based on CHNN techniques for tracking multiple targets with mobile sensors over WSS environments has been proposed in this report. This tracking technique has been investigated for its advantages in choosing an optimal correlation between mobile sensor measurements and existing target tracks. Because of the application of the CHNN technique, it was discovered that the system is relieved of the burden of determining the proper weighting factors as in a traditional HNN; therefore, the network can always achieve a rational solution. Moreover, an adaptive procedure for tracking maneuvering targets is also employed in this algorithm via stochastic process obtained by the DFA. On the basis of the simulation results obtained in this study, it can be claimed that this DFA algorithm is capable of obtaining the optimal correlations between true targets and mobile sensor measurements in WSS scenarios. Finally, the approach developed in this research has demonstrated not only stable performance for tracking procedures but definitely also excellent efficiency when tracking both constant velocity and maneuvering targets.

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


A

cloud

based

architecture

capable

of

perceiving

and

predicting

multiple

vessel

behaviour

Progressively

huge

amounts

of

data,

tracking

vessels

during

their

voyages

across

the

seas,

are

becoming

available,

mostly

due

to

the

Automatic

Identification

System

(AIS)

that

vessels

of

specific

categories

are

required

to

carry.

These

datasets

provide

detailed

insights

into

the

patterns

vessels

follow,

while

safely

navigating

across

the

globe,

under

various

conditions.

In

this

paper,

we

develop

an

Artificial

Neural

Network

(ANN)

capable

of

predicting

a

vessels

future

behavior

(position,

speed

course),

based

on

events

that

occur

in

a

predictable

pattern,

across

large

map

areas.

The

main

concept

of

this

study,

is

to

determine

if

an

ANN

is

capable

of

inferring

the

unique

behavioral

patterns

that

each

vessel

follows

and

successively

use

this

as

a

means

for

predicting

multiple

vessel

behavior

into

a

future

point

in

time.

We

design,

train

and

implement

a

proof

of

concept

ANN,

as

a

cloud

based

web

application,

with

the

ability

of

overlaying

predicted

short

and

long

term

vessel

behavior

on

an

interactive

map.

Our

proposed

approach

could

potentially

assist

in

busy

port

scheduling,

vessel

route

planning,

anomaly

detection

and

increasing

overall

Maritime

Domain

Awareness.

Safety

of

navigation

can

be

defined

as

"such

conditions

of

conducting

the

ships

at

sea

which

ensure

that

ships

are

not

endangered

by

collisions,

stranding

or

storm

damage"

Nowadays,

Information

Communication

systems

are

been

widely

deployed,

in

an

attempt

to

ensure

such

conditions

or

at

least

provide

aids

to

those

navigating

dangerous

waters.

These

systems

make

use

of

radio

or

satellite

communications

to

transmit

vessel

and

environment

related

information,

such

as

a

vessel's

navigational

properties

and

traffic

conditions,

but

also

safety

and

distress

warnings.

As

of

December

the

International

Maritime

Organization

(IMO)

requires

all

vessels

over

to

carry

an

AIS

transponder

on

board,

which

transmits

their

position,

speed

and

course,

amongst

other

static

information

(such

as

vessel's

name,

dimensions

and

voyage

details).

AIS

transponders

include

a

GPS

(Global

Positioning

System)

receiver,

which

collects

position

and

movement

details,

and

a

VHF

transmitter,

which

transmits

periodically

this

information

on

two

VHF

channels

(frequencies

MHz

and

MHz)

making

this

data

available

to

the

public

domain.

Much

of

this

data

is

filtered

through

to

the

general

public

via

the

Internet.

Authorities

are

interested

in

using

this

data

to

gain

a

better

understanding

of

The

JDL

proposed

a

number

of

possible

ways

of

differentiating

among

types

of

data

fusion

processes,

named

fusion

levels,

that

were

later

improved

by

Steinberg,

Bowman

White,

"to

more

accurately

representing

logically

different

types

of

problems,

which

are

generally

(though

not

necessarily)

solved

by

different

techniques".

These

levels

were

defined

as

Level

Sub-Object:

Assessment

estimation

and

prediction

of

signal

or

object

observable

states

on

the

basis

of

pixel/signal-level

data

association

and

characterization

Level

Object

Assessment:

estimation

and

prediction

of

entity

states

on

the

basis

of

observationto-track

association,

continuous

state

estimation

(e.g.

kinematics)

and

discrete

state

estimation

(e.g.

target

type

and

ID)

Level

Situation

Assessment:

estimation

and

prediction

of

relations

among

entities,

to

include

force

structure

and

cross

force

relations,

communications

and

perceptual

influences,

physical

context)

Level

Impact

Assessment:

estimation

and

prediction

of

effects

on

situations

of

planned

or

estimated/predicted

actions

by

the

participants

Level

Process

Refinement:

adaptive

data

acquisition

and

processing

to

support

mission

objectives.

Higher-level

fusion

aims

to

enhance

situational

awareness

and

assessment

Currently

large

data

sets

of

vessel

activities

and

behaviours

are

becoming

available.

Data

of

sufficient

quantity

and

quality

is

believed

to

provide

a

fair

representation

of

the

current

state

of

reality;

if

the

data

covers

all

aspects

of

a

situation

in

a

timely

manner,

one

can

then

say

that

complete

and

continuous

situational

awareness

has

been

achieved

Most

current

approaches

to

vessel

monitoring

using

AIS

data

are

assessed

at

Level

Level

fusion

levels,

object

assessment,

ultimately

providing

an

estimation

and

prediction

of

vessel

states

on

the

basis

of

observation

to

track

association

and

continuous

state

estimation

(Vessel

ID,

kinematics

and

vessel

type).

In

the

aspect

of

improving

MDA,

it

is

now

becoming

a

necessity

that

related

Information

Systems

(IS)

progress

to

higher

levels

of

data

fusion.

This

requires

that

related

IS's

develop

the

capabilities

to

accurately

capture

contextual

information

related

to

vessel

routes,

estimation

and

prediction

of

vessel

behaviour;

thus

progressing

to

level

and

of

data

fusion..

A

pattern

is

composed

of

events

that

repeat

in

a

specific

timeframe

and

predictable

manner.

Vessel

behaviour

can

be

defined

as

the

sum

of

all

characteristics

defining

vessels

movement,

such

as

vessel

position,

course,

heading

and

speed,

observed

over

a

given

period

of

time.

Vessel

behaviour

monitored

over

time,

provides

insights

into

the

navigational

patterns

followed

by

each

vessel

on

specific

routes.

Machine

learning

techniques,

can

be

employed

to

"learn"

vessel

behaviour

and

be

used

for

predicting

vessels

behaviours.

Such

an

approach

can

assist

in

busy

port

scheduling,

collision

avoidance,

vessel

route

planning,

but

also

anomaly

detection

and

thus

increasing

overall

Maritime

Domain

Awareness..

The

development

of

global

ship

tracking

systems

opens

possibilities

of

advancing

maritime

security

far

beyond

the

simple

collision

prevention.

Anomaly

detection

has

been

identified

as

a

critical

component

in

order

to

achieve

Situation

Awareness

in

the

context

of

information

fusion

and

maritime

surveillance

Laxhammar,

Falkman

Sviestins

define

anomaly

detection

as

a

method

that

supports

the

situation

assessment

process

at

JDL

level

by

indicating

objects

and

situations

that,

in

some

sense,

deviate

from

the

expected,

known

or

"normal"

behavior

and

thus

may

be

of

interest

for

further

investigation

Generally,

the

methods

which

are

used

in

the

context

of

anomaly

detection

are

based

on

statistical/probabilistic

models

such

as

the

Gaussian

Mixture

Model

(GMM)

and

the

adaptive

Kernel

Density

Estimator

(KDE)

Bayesian

networks

but

also

neural

networks

and

hybrid

approaches

In

this

paper

we

discuss

preliminary

results

of

on-going

research

into

vessel

behaviour

prediction

and

anomaly

detection.

Examining

the

results,

we

are

able

to

claim

with

confidence,

that

the

trained

ANN

is

capable

of

inferring

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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


VaccImm:

simulating

peptide

vaccination

in

cancer

therapy

Background:

Despite

progress

in

conventional

cancer

therapies,

cancer

is

still

one

of

the

leading

causes

of

death

in

industrial

nations.

Therefore,

an

urgent

need

of

progress

in

fighting

cancer

remains.

A

promising

alternative

to

conventional

methods

is

immune

therapy.

This

relies

on

the

fact

that

low-immunogenic

tumours

can

be

eradicated

if

an

immune

response

against

them

is

induced.

Peptide

vaccination

is

carried

out

by

injecting

tumour

peptides

into

a

patient

to

trigger

a

specific

immune

response

against

the

tumour

in

its

entirety.

However,

peptide

vaccination

is

a

highly

complicated

treatment

and

currently

many

factors

like

the

optimal

number

of

epitopes

are

not

known

precisely.

Therefore,

it

is

necessary

to

evaluate

how

certain

parameters

influence

the

therapy.

Results:

We

present

the

VaccImm

Server

that

allows

users

to

simulate

peptide

vaccination

in

cancer

therapy.

It

uses

an

agent-based

model

that

simulates

peptide

vaccination

by

explicitly

modelling

the

involved

cells

(immune

system

and

cancer)

as

well

as

molecules

(antibodies,

antigens

and

semiochemicals).

As

a

new

feature,

our

model

uses

real

amino

acid

sequences

to

represent

molecular

binding

sites

of

relevant

immune

cells.

The

model

is

used

to

generate

detailed

statistics

of

the

population

sizes

and

states

of

the

single

cell

types

over

time.

This

makes

the

VaccImm

web

server

well

suited

to

examine

the

parameter

space

of

peptide

vaccination

in

silico.

VaccImm

is

publicly

available

without

registration

on

the

web

at

all

major

browsers

are

supported.

Conclusions:

The

VaccImm

Server

provides

a

convenient

way

to

analyze

properties

of

peptide

vaccination

in

cancer

therapy.

Using

the

server,

we

could

gain

interesting

insights

into

peptide

vaccination

that

reveal

the

complex

and

patient-specific

nature

of

peptide

vaccination.

We

have

presented

VaccImm,

a

user-friendly

server

to

simulate

the

effect

of

peptide

vaccination

in

cancer

therapy.

We

believe

this

tool

is

very

useful

for

analyzing

parameters

of

peptide

vaccination

in

cancer

therapy.

It

is

the

first

web

server

that

can

model

cancer

immunotherapy

based

on

cancer

epitope

sequences

and

MHC

genotypes.

All

sequence-related

parameters

can

be

selected

by

the

user,

along

with

parameters

concerning

size

and

duration

of

the

simulation.

The

output

shows

detailed

cell

population

statistics

for

all

modelled

cell

types.

Their

population

sizes

and

activation

states

can

be

studied

over

time..

As

stated

in

the

introduction,

VaccImm

can

be

very

useful

to

explore

the

parameter

space

of

peptide

vaccination..

For

instance,

we

have

performed

some

studies

analyzing

the

influence

of

several

parameters

on

the

success

of

peptide

vaccination.

The

results

presented

above

revealed

the

complex

nature

of

peptide

vaccination

as

the

influences

of

the

single

parameters

strongly

depend

upon

each

other.

The

selection

of

the

antigen

is

crucial

for

peptide

vaccination,

because

the

antigen

must

be

able

to

evoke

an

immune

response.

However,

the

quality

of

a

potential

antigen

in

turn

depends

on

the

MHC-genotype

of

the

patient.

This

is

because

the

antigens

are

processed

and

subsequently

different

MHCs

may

present

different

parts

of

the

antigens.

VaccImm

accounts

for

this

by

predicting

the

MHC

binding

peptides

based

on

the

MHC-genotype.

It

could

also

be

shown,

that

a

successful

therapy

gets

more

likely

the

more

different

peptides

are

injected,

still

bearing

the

risk

of

an

autoimmune

reaction.

One

feature

that

is

currently

not

included

in

our

model

is

mutation

of

cancer

cells.

Therefore,

the

initial

size

of

the

tumour

has

no

qualitative

effect

on

the

outcome

of

the

simulation.

If

mutation

of

tumour

cells

was

possible,

one

would

expect

a

higher

probability

for

a

cell

in

a

large

tumour

to

mutate

in

such

a

way

that

it

is

not

or

less

affected

by

the

evoked

immune

response.

Thereby

the

probability

of

a

successful

treatment

can

be

expected

to

become

smaller

with

increasing

tumour

size.

Due

to

the

modular

structure

of

our

model,

it

is

easily

possible

to

add

such

behaviour

in

a

future

version.

To

mount

an

immune

response

against

tumours

is

the

objective

of

immunotherapy

and

constitutes

a

promising

alternative

to

conventional

methods

However,

the

applicability

of

this

approach

remains

limited

so

far,

because

of

largely

undefined

treatment

parameters.

For

instance,

the

success

of

immunotherapy

depends

strongly

on

the

cancer

epitope

sequence,

the

individual

major

histocompatibility

complex

(MHC)

genotype

of

the

individual,

and

on

the

overall

immune

response

dynamics.

To

explore

this

parameter

space

systematically,

we

have

developed

an

agent-based

model

simulating

a

certain

line

of

immunotherapy

called

peptide

vaccination

Our

model

VaccImm

has

the

ability

to

take

the

MHC

genotype,

the

amino

acid

epitope

sequences

and

the

spatial

cell

dynamics

into

account..

Here,

we

present

a

server

that

allows

users

to

run

these

simulations

online.

Several

parameters,

like

the

cancer

antigens

and

the

MHC

genotype

of

the

virtual

individual,

can

be

selected

for

the

simulation.

Agent-based

models

are

well

suited

to

model

the

immune

system.

They

consist

of

independent

agents,

each

represented

with

individual

properties

and

behavioural

rules.

For

example,

every

single

cell

is

localized

within

a

given

simulated

volume,

has

a

specific

type,

clonotype

(in

case

of

lymphocytes)

and

developmental

stage.

This

allows

agent-based

models

to

simulate

the

immune

system,

in

which

the

different

cells

and

cell

types

may

be

distributed

very

inhomogenously

Back

in

Celada

and

Seiden

published

a

cellular

automaton

called

ImmSim

to

model

cellular

interactions

in

the

immune

system

In

this

model,

the

interactions

of

immune

cells

were

rather

simplistically

modelled

by

bit

string

complementarity.

Nevertheless,

this

automaton

was

able

to

reproduce

basic

immune

properties

like

clonal

expansion

of

B-cells

and

T-cells

after

stimulation

as

well

as

more

advanced

immune

system

traits

like

the

competition

between

cross

reacting

clones

Since

several

other

rule

based

models

have

been

developed

in

order

to

simulate

the

immune

system

The

original

ImmSim

has

been

upgraded

and

extended

in

the

meantime,

leading

to

different

forks

like

C-ImmSim

and

the

mice-specific

SimTriplex

C-ImmSim

is

a

refined

version

of

the

original

ImmSim

model

that

was

ported

to

ANSI

C

language.

However,

the

interaction

between

immune

cells

is

still

modelled

as

bit

strings

in

C-ImmSim,

thus

having

no

direct

translation

to

biologically

meaningful

amino

acid

sequences.

Therefore,

Rapin

et

al.

developed

an

extension

of

C-ImmSim

simulating

the

branch

of

bacterial

infection

As

immune

reactions

against

cancer

cells

follow

different

rules,

they

left

out

this

part

of

C-ImmSim..

In

a

completely

independent

approach,

we

have

created

VaccImm

-another

extension

of

C-ImmSim

-that

now

simulates

immune

reactions

against

cancer

using

amino

acid

sequences

and

knowledge-based

interaction

potentials

to

predict

cell

interaction.

The

parameters

used

in

VaccImm

were

carefully

examined

elsewhere

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


Cloud

identity

management

security

issues

solutions:

a

taxonomy

Purpose:

Cloud

computing

systems

represent

one

of

the

most

complex

computing

systems

currently

in

existence.

Current

applications

of

Cloud

involve

extensive

use

of

distributed

systems

with

varying

degree

of

connectivity

and

usage.

With

a

recent

focus

on

large-scale

proliferation

of

Cloud

computing,

identity

management

in

Cloud

based

systems

is

a

critical

issue

for

the

sustainability

of

any

Cloud-based

service.

This

area

has

also

received

considerable

attention

from

the

research

community

as

well

as

the

IT

industry.

Numerous

Cloud

Identity

Management

Systems

(IDMSs)

have

been

proposed

so

far;

however,

most

of

those

systems

are

neither

widely

accepted

nor

considered

highly

reliable

due

to

their

constraints

in

terms

of

scope,

applicability

and

security.

In

order

to

achieve

reliability

and

effectiveness

in

IDMs

for

Cloud,

further

extensive

research

needs

to

be

carried

out

to

critically

examine

Cloud

based

IDMSs

and

their

level

of

security.

Methods:

In

this

work,

we

have

holistically

analyzed

Cloud

IDMSs

to

better

understand

the

general

as

well

as

the

security

aspects

of

this

domain.

From

the

security

perspective,

we

present

a

comprehensive

list

of

attacks

that

occur

frequently

in

Cloud

based

IDMSs.

In

order

to

alleviate

those

attacks,

we

present

a

well-organized

taxonomy

tree

covering

the

most

desired

features

essential

for

any

Cloud-based

IDMSs.

Additionally,

we

have

specified

various

mechanisms

of

realization

(such

as

access

control

polices,

encryption,

self-service)

against

each

of

the

features

of

Cloud

IDMSs.

We

have

further

used

the

proposed

taxonomy

as

an

assessment

criterion

for

the

evaluation

of

Cloud

based

IDMSs.

Results:

Our

in-depth

analysis

of

various

Cloud

based

IDMSs

reveals

that

most

of

the

systems

do

not

offer

support

to

all

the

essential

features

of

Cloud

IDMS

and

the

ones

that

do,

have

their

own

certain

weaknesses.

of

the

discussed

techniques

heuristically

covers

all

the

security

features;

moreover,

they

lack

compliance

to

international

standards

which,

understandably,

undermines

their

credibility.

Conclusion:

Presented

work

will

help

Cloud

subscribers

and

providers

in

understanding

the

available

solutions

as

well

as

the

involved

risks,

allowing

them

to

make

more

knowledgeable

decisions

while

selecting

potential

Cloud

IDMSs

that

best

suits

their

functional

and

security

requirements.

In

this

paper,

we

have

presented

research

that

is

a

step

towards

the

development

of

an

assessment

criterion

that

can

be

used

by

other

researchers

and

industry

professionals

to

perform

the

evaluation

of

existing,

as

well

as

future

Cloud

based

IDMSs.

Since

the

domain

of

Cloud

IDMS

is

still

in

its

early

stages,

therefore,

requires

considerable

attention

from

the

research

community

and

IT

industry.

We

have

contributed

in

this

regard,

by

presenting

a

research

work

that

holistically

covers

the

domain

of

Cloud

IDMSs.

In

particular,

our

contribution

is

multi-dimensional:

first,

we

have

presented

a

comprehensive

list

of

security

attacks

that

involve

Cloud

identity

management

systems

and

identity

credentials

as

an

attack

tool

or

target,

secondly

we

have

identified

the

features

that

will

act

as

countermeasure

against

the

mentioned

attacks.

Thirdly,

state-of-the-art

mechanisms

against

each

feature

are

filtered

out

with

an

objective

to

maximize

their

performance,

security

and

interoperability.

As

another

positive

contribution,

we

have

explicated

the

featuremechanism

relationship

in

the

form

of

a

well-organized

taxonomy

which

is

later

used

to

analyze/evaluate

various

Cloud

based

IDMSs.

Lastly,

we

have

applied

the

previously

discussed

attacks

against

each

Cloud

IDMS

to

confirm

their

reliability

and

applicability

in

Cloud.

Our

findings

from

the

analysis

are

presented

in

the

subsequent

tables

which

prove

that

Cloud

IDMSs

invariably

have

some

pros

and

cons

in

their

architecture

and

functionality.

Furthermore,

most

of

these

Cloud

IDMSs

have

limitations

in

terms

of

their

reliability

and

applicability,

as

they

are

confined

only

to

specific

Cloud

identity

management

scenarios.

In

conclusion,

our

work

will

augment

the

research

which

leads

to

the

development

and

designing

of

a

robust

and

holistically

secure

Cloud

based

IDMS

covering

all

the

identified

features.

Presented

taxonomy

will

allow

CSCs

and

CSPs

to

make

a

knowledgeable

decision

by

selecting

an

appropriate

IDMS

that

best

satisfies

their

security

and

functional

requirements.

MAS:

has

significantly

supported

the

first

author

during

regular

meetings,

critically

revised

the

paper

for

important

intellectual

content,

provided

new

ideas,

and

gave

final

approval

of

the

version

to

be

published.

MN:

has

significantly

contributed

to

the

research

by

introducing

large-scale

perspective

for

cloud

IDMSs,

critically

revised

the

paper,

and

gave

approval

of

the

version

to

be

published.

All

authors

read

and

approved

the

final

manuscript.

Cloud

computing

has

emerged

as

a

relatively

new

and

influential

paradigm

for

managing

and

delivering

internet-based

services

and

is

considered

to

be

an

evolution

of

grid

computing,

which

itself

is

based

on

traditional

distributed

system

concepts

(Youseff

et

al.

Cloud

computing

offers

many

benefits

to

the

IT

industry

by

offering

them

unlimited

storage

and

computing

capacity.

In

addition,

Cloud

is

based

on

pay-as-you-use

model

that

allows

organizations

to

outsource

their

data

and

IT

services,

offering

on-demand

self-service,

broad

network

access

and

rapid

elasticity

at

low

cost

(Mahmood

Wang

and

Mu

Cloud,

being

a

service

oriented

computing

architecture,

is

capable

of

providing

anything-as-a-service,

including

but

not

limited

to

Software-as-a-service

(SaaS),

Platform-as-a-service

(PaaS),

Infrastructure-as-a-service

(IaaS),

Database-as-aservice

(DbaaS)

or

Identity-as-a-service

(IDaaS).

Google

(PaaS),

Amazon

(PaaS

and

IaaS)

and

Salesforce

(SaaS)

are

few

examples

of

major

Cloud

Service

Providers

(CSP)

that

offer

on-demand

and

low-cost

services/applications

to

the

Cloud

Service

Consumers

(CSC)..

Another

aspect

of

the

Cloud

systems

is

complexity.

The

problem

in

understanding

cloud

systems

stems

from

the

fact

that

it

is

simply

quite

difficult

to

model

them.

Cloud

is

a

very

dynamic

system

with

numerous

users,

devices

and

networks,

connecting

and

disconnecting

simultaneously

with

the

cloud.

This

complexity

is

to

such

an

extent

that

it

can

perhaps

be

likened

to

the

complexity

of

a

human

brain

where

neurons

connect

and

change

their

synaptic

structure

continuously

to

store

information.

However,

what

is

a

problem

here

is

the

fact

that

unlike

the

brain,

where

the

connecting

neurons

are

already

authenticated,

cloud

systems

require

extensive

authentication

as

well

as

identity

management

systems.

Still,

these

are

simply

not

enough

to

cater

for

the

ever-growing

requirements

of

novel

paradigms

such

as

the

Internet

of

Things

(IoT)

in

relation

to

its

connectivity

with

the

cloud..

Despite

the

very

attractive

features

that

Cloud

promises,

the

rate

of

migration

to

Cloud

is

rather

slow,

mainly

due

to

the

inherent

security

challenges

associated

with

the

technology.

These

challenges

include

data

privacy,

transparency,

risk

management,

compliance

and

information

security

(Fox

et

al.

Jansen

Subashini

and

Kavitha

Consequently,

the

security

of

Cloud

paradigm

has

become

a

hot

research

area,

which

is

being

explored

by

both

academic

and

industrial

research

communities.

In

this

regard,

issues

related

to

the

handling

and

management

of

sensitive

identity

credentials

have

garnered

a

lot

of

interest

amongst

the

research

communities

(Albeshri

and

Caelli

Chen

and

Zhao

Gunjan

et

al.

Storage

and

processing

of

identity

information

by

a

third

party

(CSP),

outside

the

organizational

boundary,

brings

in

loss

of

control

and

transparency

issues.

This

contributes

to

the

reluctance

of

organizations

to

move

their

critical

identity

information

to

Cloud..

Cloud

based

Identity

Management

Systems

(IDMSs)

differ

from

the

traditional

IDMSs

in

that

they

require

dynamic

governance

of

provisioning,

de-provisioning,

synchronization,

entitlement,

scalability

and

access

control

(Gopalakrishnan

Jansen

In

addition

to

this,

Cloud

IDMSs

are

required

to

have

updated

and

synchronized

identity

information

to

avoid

any

conflicts

caused

by

the

usage

of

old

user

data.

Management

of

sensitive

identity

information

in

the

Cloud

environment

raises

many

privacy

and

confidentiality

concerns.

Moreover,

security

requirements

vary

from

CSC

to

CSC;

therefore,

it

is

very

important

to

choose

the

most

suitable

identity

management

system

that

best

supports

the

CSC's

security

requirements..

An

extensive

survey

on

existing

Cloud

identity

management

related

literature

reveals

that

it

is

quite

dispersed

and

mainly

covers

only

few

specific

security

features

at

a

time;

for

instance

authentication,

authorization

and

access

right

delegation

etc.

Several

Cloud

identity

management

systems

(Ates

et

al.

Chowdhury

and

Noll

Choudhury

et

al.

Ranchal

et

al.

have

been

proposed

so

far;

however,

of

those

systems

holistically

meet

the

requirements

of

the

IT

industry,

which

necessitate

a

comprehensive

identity

management

system

for

Cloud.

Additionally,

to

be

effective

in

Cloud,

IDMSs

are

required

to

incorporate

all

the

essential

functional

and

security

features

of

this

paradigm.

Furthermore,

there

exists

no

benchmark

against

which

one

can

evaluate

existing

and

newly

proposed

Cloud

identity

management

systems.

As

a

result,

whenever

a

CSC

or

CSP

needs

to

opt

for

an

identity

management

system,

they

are

left

unclear

about

what

an

IDMS

is

offering

(in

terms

of

their

services)

and

how

their

security

or

privacy

requirements

will

be

fulfilled..

After

reviewing

many

state-of-the-art

Cloud

based

IDMSs,

we

present

a

comprehensive

list

of

attacks

that

involve

identity

either

as

an

attack

tool

or

as

a

target.

Keeping

in

view

the

potential

attacks

and

security

requirements

of

Cloud

IDMSs,

we

have

identified

attacks

and

proposed

key

features

that

should

be

a

part

of

every

Cloud

IDMS.

Presented

taxonomy

would

help

IT

professionals

and

researchers

understand

the

importance

of

these

features

that

are

worth

considering

when

implementing

or

selecting

an

IDMS

for

Cloud.

We

have

presented

those

identified

features

in

the

form

of

a

well-organized

taxonomy,

and

have

suggested

ideal

mechanisms

for

providing

said

features.

As

a

test

case,

we

have

applied

the

proposed

taxonomy

as

an

assessment

criterion

for

the

evaluation

of

existing

Cloud

IDMSs..

This

paper

presents

a

holistic

view

of

identity

management

domain:

a

brief

introduction

about

the

evolution

of

IDMSs

followed

by

identity

lifecycle

management,

categories

of

IDMSs,

list

of

attacks

that

can

be

launched

against

an

IDMS,

features

pertaining

to

the

security

of

IDMSs

in

the

form

of

well-informed

taxonomy

etc.

The

paper

is

further

organized

as

follows:

Section

'Identity

lifecycle

management'

describes

the

identity

lifecycle

management.

Further,

the

novel

concept

of

identity-as-a-service

is

presented

in

Section

'Cloud

Identity-as-a-Service

(IDaaS)'.

Evolution

of

identity

management

systems

is

discussed

in

Section

'Classification

of

identity

management

systems',

whereas

Section

'Open-source

cloud

platforms

identity

management'

presents

a

comprehensive

list

of

well-known

open-source

Cloud

computing

platforms

and

highlight

their

identity

management

services

and

properties.

Section

'Cloud

identity

management:

security

challenges'

highlights

many

open

IDM

related

security

issues

and

challenges.

Well-known

attacks

against

Cloud

IDMSs

are

presented

in

Section

'Cloud

identity

management:

known

attack

matrix'.

Taxonomy

based

on

the

proposed

security

features

is

elaborated

in

Section

'Methods'.

Section

'Results'

comprises

of

the

evaluation

of

Cloud

identity

management

systems

using

the

proposed

taxonomy.

Section

'Discussion'

highlights

current

gaps,

challenges

and

future

key-trends

related

to

Cloud

identity

management

systems.

Finally,

the

Section

'Conclusion'

highlights

the

key-points

derived

out

of

this

study.

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


Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction Abstract The comparison of eight tools applicable to ligand-binding site prediction is presented. The methods examined cover three types of approaches: the geometrical (CASTp, PASS, Pocket-Finder), the physicochemical (QSiteFinder, FOD) and the knowledge-based (ConSurf, SuMo, WebFEATURE). The accuracy of predictions was measured in reference to the catalytic residues documented in the Catalytic Site Atlas. The test was performed on a set comprising selected chains of hydrolases. The results were analysed with regard to size, polarity, secondary structure, accessible solvent area of predicted sites as well as parameters commonly used in machine learning (F-measure, MCC). The relative accuracies of predictions are presented in the ROC space, allowing determination of the optimal methods by means of the ROC convex hull. Additionally the minimum expected cost analysis was performed. Both advantages and disadvantages of the eight methods are presented. Characterization of protein chains in respect to the level of difficulty in the active site prediction is introduced. The main reasons for failures are discussed. Overall, the best performance offers SuMo followed by FOD, while PocketFinder is the best method among the geometrical approaches. Understanding of how biological systems function is the salient motivation for the research in the field of biochemistry and molecular biology. The most comprehensive approach that aims at gaining insight into molecular function and mechanism of thousands of proteins relies on structural genomics initiatives [1] [2] [3] . One of the major challenges in structural genomics is identifying the function and evaluating the functional integrity of proteins [4] . Another goal, justifying the huge investments already made in structural genomics initiatives, is the ability to predict druggability of a particular protein based solely on its 3D structure [5, 6] . Accordingly, experimental as well as computational methods for identifying and characterizing ligand-binding sites on protein targets are being intensively developed nowadays [7, 8] . Herein we focus on in silico methods, as promising tools for finding and annotating functional sites in novel structures from structural genomics.. The strategies for prediction of ligand-binding sites that have already been developed, can be roughly divided into three groups. Methods that are tailored to detect pockets and clefts on the basis of pure geometric criteria such as POCKET [9] , SurfNet [10] , APROPOS [11] , CAST [12, 13] , LIGSITE [14] or PASS [15] constitute the first group. The methods in the second group, in addition to structural data use biophysical and/or chemical properties, such as pKa [16] , electrostatic energy [17] , solvent mapping [7, 18, 19] , physical potential [20, 21] , favourable regions for van der Waals probes on the protein surface [22] or hydrophobicity deficiency [23] . The third group of methods relies on knowledge derived from biochemical data and different types of databases. Many of them search for clusters or patterns of conserved residues [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] , and therefore may be applicable to proteins that have homologues. There is also a series of tools that exploit various pattern matching approaches. They generally search for local structural similarity of a protein structure to known functional sites [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] . The main limitation of these methods is a finite set of functional sites they can identify, and therefore they are not suited to annotate new functional motifs that may be present in novel folds. Moreover, there are many methods that rely on statistical approaches [49, 50] and machine learning techniques based on neural networks [51] [52] [53] , support vector machines [54] [55] [56] or Naive Bayes classifications [57] . They exploit a wealth of knowledge included in a training set, and aim at predicting specific functional roles of residues rather than broadly defined ligand-binding sites. Apart from the methods devoted to prediction of functional sites, alternative approaches such as molecular dynamics simulations [58] or docking [59] [60] [61] were successfully employed to identify ligand-binding sites. A thorough review of strategies for ligand-binding site detection is presented elsewhere [62] .. Here we present the extensive comparison of eight methods designed for ligand-binding site prediction in order to reveal limitations that should be overcome in the future. The tools examined cover all three groups of approaches briefly described above. As representatives of the geometry-based approaches CASTp [13, 63] , PocketFinder (an implementation of LIGSITE [14] ) and PASS [15] were chosen. From the second group Q-SiteFinder [22] and FOD [23] were selected. The knowledge-based methods are represented by ConSurf [64] , SuMo [45, 46] and WebFEATURE [49, 65, 66] . Short description of each of the eight methods is presented in Table 1 . All the methods require protein's 3D structure to make predictions, but only ConSurf exploits its sequence by means of calculation of an evolutionary conservation. Moreover all of them are freely available as web services or standalone executables and exhibit relatively short time of calculations. The results returned are straightforward, mainly in the form of a list of atoms/residues predicted as binding [67] [68] [69] [70] [71] [72] . Secondly, this class of enzymes is very extensively studied and therefore much is known about their mechanisms of catalysis [73] [74] [75] [76] [77] and dynamics of its structures [78] [79] [80] . Moreover this class is structurally and functionally miscellaneous [81, 82] . The accuracy of predictions was measured in reference to the catalytic residues documented in the Catalytic Site Atlas (CSA) [83] . The choice of a reference set is dictated by its reliability, clarity and an easy access (CSA). Even though the tested methods are more suitable to detect ligand-binding sites, the evaluation based on catalytic sites is justified as active sites are located within binding sites and provided that results are taken with the awareness. The measure of accuracy was another aspect we have focused on. Therefore, to designate the best method, different measures and criteria were employed. In conclusion, the diversity of protein structures and functions requires multiple approaches in order to correctly predict their active sites. Even though the tested methods are more suitable for prediction of ligand-binding sites than active sites it was shown that they may be also valuable in the latter. The main challenge is the determination of the most suitable one for a particular protein structure. The alternative solution may be a protocol providing for many methods, not only designed for binding-site prediction. Especially useful would be calculations yielding information about quaternary structure as well as dynamic nature of proteins. Simultaneously intensive studies devoted to characterization of binding sites are necessary in order to guide improvement of in silico approaches for function prediction.. Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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


Segmentation of Whole Cells and Cell Nuclei From 3-D Optical Microscope Images Using Dynamic Programming Abstract-Communications between cells in large part drive tissue development and function, as well as disease-related processes such as tumorigenesis. Understanding the mechanistic bases of these processes necessitates quantifying specific molecules in adjacent cells or cell nuclei of intact tissue. However, a major restriction on such analyses is the lack of an efficient method that correctly segments each object (cell or nucleus) from 3-D images of an intact tissue specimen. We report a highly reliable and accurate semi-automatic algorithmic method for segmenting fluorescence-labeled cells or nuclei from 3-D tissue images. Segmentation begins with semi-automatic, 2-D object delineation in a user-selected plane, using dynamic programming (DP) to locate the border with an accumulated intensity per unit length greater that any other possible border around the same object. Then the two surfaces of the object in planes above and below the selected plane are found using an algorithm that combines DP and combinatorial searching. Following segmentation, any perceived M. Nakaya was with the Cancer and Developmental Biology Laboratory, National Cancer Institute, Frederick, MD 21702 USA. He is now with the Department of Histology and Embryology, Graduate School of Medical Science, Kanazawa University 13-1, C OMMUNICATION between cells in multicellular organisms is fundamental to the development and homeostasis of organisms and tissues within an organism. Furthermore, altered cell-to-cell communication underlies many disease-related processes, such as tumorigenesis. Attempts to understand the nature of such communications and their downstream effects on cell behavior are widespread, and are often performed using optical microscopy of samples fluorescence labeled for specific molecules of interest. However, interpretation of microscope images, which is done predominantly by qualitative visual examination, is limited. Quantitative analysis of 3-D microscopy images on the other hand provides a much richer source of information leading to much more in depth understandings of cell-cell communication processes and potentially leading to improved diagnosis and treatment of major human diseases, such as cancer. The information derived from quantitative analysis for example can serve as input for testing hypothesized mathematical models of complex cell-cell processes from which fundamental kinetic parameters of underlying molecular interactions can be determined. While recently there have been several reports of mathematical modeling of cell communication processes, they have been restricted to theoretical or idealized representations of the actual cells in the tissue [1] . We believe that this restriction is in large part due to the lack of efficient and accurate computational algorithms for segmenting (delineating) individual cells from within tissue samples. Following segmentation, it is possible to quantitatively measure features relating to cell morphology, spatial organization of cells, and the distribution of specific molecules inside and on individual cells.. Early work on cell segmentation concentrated on the automatic detection of cell nuclei in 2-D images from the standpoint of automating and improving disease diagnosis of cyto-logical and histological samples. Significant interest in the 3-D segmentation of cells emerged with the advent of confocal, fluorescence microscopy approximately 15 years ago, which enabled researchers to directly acquire 3-D images of biological samples. It was quickly realized that computational 3-D analysis was essential for the accurate, individual and contextual quantification of cells.. Concurrent with the introduction of confocal microscopy, several advanced image analysis approaches were being developed for other applications that could potentially improve the accuracy of the segmentation of cells in culture and tissue samples. Dynamic programming (DP) was one of the first of these advanced segmentation approaches, which has the particular advantage of finding the globally optimal path between two points in 2-D space based on an a priori figure-of-merit and with some modest constraints on the curvature of the path. The global search makes the method extremely robust against noise, enabling it to correctly identify paths that are significantly corrupted by noise, missing segments, and low resolution. In this sense, DP is similar to the geodesic active contour method that minimizes a path integral with an implicit border constraint [2] . DP was applied early on to optimally detect curves in 2-D noisy pictures [3] , and its first utilization in cell segmentation was for automatically determining the optimum dividing paths between clustered cell nuclei in 2-D images [4] , [5] . Since then DP has been used for a variety of other applications, including finding networks of lines in 2-D images from which neuritis could be traced and cells segmented in heart tissue [6] , [7] , tracking individual fluorescence particles in time-lapse microscopy [8] , and tracking living cells in 4-D time-lapse images [9] . Recently we have developed a semi-automated DP-based segmentation method for detecting whole cells or cell nuclei in tissue [10] , and an algorithm based on 2-D DP for segmenting 3-D electron tomogram images of HIV particles was reported by Bartesaghi et al. [11] . A 3-D version was developed for delineating surfaces of cells in 3-D images [12] , but it had not been formally proven that it finds the globally optimal solution, neither had the method been demonstrated in practical applications involving cells in tissue.. In parallel with the utilization of DP based techniques, several other advanced image analysis approaches have been adopted for cell segmentation. Gradient-curvature driven flow and level sets and related approaches became widely known in the 1990s [13] , [14] . They can identify edges of objects in significantly degraded images and have the major advantage of extending to three or more dimensions. Consequently, several applications for fluorescence microscope images based on this approach have emerged. One of the first was segmentation of whole cells and cell nuclei in tissue sections [15] . Since then the approach has been used to track the dynamics of living cells [16] , and recently Appleton and Talbot reported a 3-D segmentation technique based on finding globally minimal surfaces by continuous maximal flows [17] . Other promising approaches for segmentation of cells in microscope images include utilization of the watershed algorithm [18] - [21] , the "active snake" method [22] , statistical partitioning of images into objects [23] - [27] , the Markov chain Monte Carlo method [28] , Markov random fields [29] , [30] , and graph-cut methods [31] .. Although a variety of promising approaches for segmenting cells in fluorescence microscope images exist, there is not yet a segmentation method that is highly reliable and accurate across the wide range of 3-D cell images encountered in biomedicine, as well as practical to use. Here, we report a highly reliable and accurate semi-automatic algorithmic method for segmenting 3-D images of individual whole cells and cell nuclei (objects) from inside tissue. The 3-D images must be acquired with high-resolution optical microscopy and the surfaces of the objects must be fluorescence labeled, or in the case of nuclei their volumes can be labeled instead. When considering the design of our algorithm, we accepted the "gold-standard" for correct segmentation as being the human visual system. In order to incorporate this gold-standard into the segmentation, the procedure must either permit retrospective visual verification and interactive correction of an automatic result, or alternatively incorporate prospective guidance by the human in the segmentation procedure for each object. The retrospective approach is tedious because it requires the user to search through all segmented objects to find and correct errors. Instead, we have chosen the prospective approach as being potentially more accurate and less demanding on the user, because it enables the algorithm to direct the user to each object being segmented one at a time. A similar reasoning had been used earlier in the design of an algorithm for neurite tracing [7] Furthermore, it means that erroneous segmentations are detected and corrected earlier in the process, which if left uncorrected could precipitate erroneous segmentations of subsequent objects. Additionally, it allows the segmentation of visually unsatisfactory objects to be trivially avoided. The other major design criterion for our method was that it should work on very noisy images that could arise from using low levels of fluorescent dyes and low excitation light intensity in order to minimize damage to living cells. We chose dynamic programming as the basis of our segmentation method, since it finds the border by globally maximizing an intuitive figure-of-merit, and, therefore, is extremely insensitive to noise. This was recently demonstrated to be the case by us when segmenting 2-D images [10] and earlier by Meijering et al. for neurite tracing [7] .. The paper is organized as follows. The major part of the paper, Section II, outlines and illustrates the main steps involved in segmentation, and includes a detailed description and justification for each step. The latter part of this section describes the generation of computer simulations for assessing the performance of the segmentation approach, followed by a description of the preparation and image acquisition of cell and tissue samples used for testing the segmentation procedure. Section III presents the accuracy of the segmentation using simulated objects with noise levels significantly greater than expected in confocal microscope images and with objects closely juxtaposed to each other in order to rigorously test our method under realistic tissue-like conditions. This is followed by segmentation results from biological samples. The paper ends with a discussion of future enhancements to the segmentation approach and concludes with ideas of biological questions that could be answered following the segmentation of individual cells or cell nuclei in their tissue context. We have developed a highly robust method for segmenting whole cells, cell nuclei and other objects from 3-D optical microscopic images of biological samples, thus removing a major bottleneck preventing quantitative and contextual analysis of cells in tissue samples. DP was utilized for calculating the surface, because of its robustness to noise. A modest and acceptable level of a priori user interaction was involved in the segmentation process in order to guarantee 100% correct segmentation of each object based on visual assessment. For the vast majority of nuclei [ Fig. 4(j)-(r) ], only the minimum number of mouse clicks were needed for each nucleus. In a modified user-interface, which was written after completion of this study, the minimum number of clicks was three. In addition, user interaction readily enabled unsatisfactory objects to be excluded.. There were several minor limitations to our approach, which caused small inaccuracies in the segmented surfaces. One of these limitations was the restriction to point convex objects that was inherent in our method, because the segmented surface must have exactly one radius value for each pair of angles ( and ) in the spherically transformed space. In the Cartesian image, this translates to the requirement that any radial line from the user marked internal point must intersect the surface exactly once. In practice, however, this restriction is not severe since it still allows the detection of objects with concavities ( Figs. 3 and 4) . Inaccuracies in the segmented surface also occurred at places where the angle between the tangent to the surface and the line from the surface to the internal point approached zero. At these places, the radial distance increased by more than one unit for changes or by , defying the connectivity constraint imposed by the algorithm. However, this error could readily be reduced by increasing the number of elements assigned to and , although it comes at the cost of less "look ahead." High noise caused distortion in the surface for two reasons. First, by design the algorithm maximized the accumulated intensity (or intensity gradient) of the segmented surface, favoring voxels with high intensity due to noise to be included in the surface. This was the reason for the increase in AVSCR with increasing noise in Table I . There is no solution to this limitation because more accurate information was not available from the images except by exploiting additional a priori information about the shape of the objects. Second, high noise had significant effect at places of high curvature, where the segmented surface tended to "cut the corner" [ Fig. 3(c) ]. On the other hand, such errors can be interactively corrected. An additional source of error is the spatial resolution of the confocal microscope that is poorer in the axial versus lateral dimensions. This introduced segmentation errors, by biasing the surface inwards or outwards at locations with convex or concave curvature respectively. Nevertheless, such distortions were considered insignificant. Coarse voxel sampling in the , , or dimensions, which may occur when imaging living samples, also decreases segmentation accuracy. However, it is always possible to obtain segmentation results as good as visual judgment by using the interactive correction option.. We envision several improvements to our method to solve some of the limitations mentioned above. The restriction to point convex objects could be removed by enabling interactive marking of multiple centers resulting in the segmentation of multiple partial objects that are then merged together. The spatial resolution of the imaging system is an inherent limitation, which although can be improved by deconvolving the images prior to segmentation, imposes an upper limit on the curvature of the object surfaces as represented in the images. This upper limit could be exploited as a constraint on the segmented surface and thus further increasing the robustness of the segmentation.. Although other segmentation methods calculate an actual global optimum [17] , [31] , whereas ours closely approximates a global optimum, other errors and restrictions that are common to all methods exist. These include the situation when the calculated surface differs from visual judgment due to poor choice of cost function, when the optimum surface encloses more than one object and when objects are not point convex. It is likely that a combination of different image analysis approaches will ultimately prove more accurate and thus lead to further automation than the use of only one approach. For example, Park and Keller [33] combined four image analysis approaches: watershed algorithm, snakes, multiresolution analysis, and dynamic programming to segment 2-D images of cells. Zeng et al. [34] combined dynamic programming with anisotropic diffusion for finding 3-D sulcal ribbons in magnetic resonance images. Also a significant subset of segmentation algorithms applied to cells and nuclei are model based in order to exploit shape and texture features of the objects [26] . Thus, it is likely that a merging of DP with a model-based method will increase robustness and thus reduce user interaction. A further consideration is the close packing of cells in tissue that to a large extent determines cell and nuclear shapes. This property of tissue has not yet been taken full advantage of in image analysis and implies that algorithms designed for segmenting multiple objects simultaneously will be more robust than segmenting each object separately [16] .. We believe our 3-D segmentation method will be most useful in applications where tens to hundreds of cells per sample require segmentation, with the confidence that the segmentation of each cell chosen for segmentation is accurate based on visual judgment. Following 3-D segmentation, it is possible to analyze the structural and molecular properties of individual cells using morphological and texture features [35] . However, not only is it possible to analyze the properties of individual cells, but it is possible to quantitatively analyze the contextual relationships between cells in their true tissue environment using spatial statistical methods [36] . The combination of individual and contextual quantitative feature measurement is a rich source of information that will lead to a much deeper understanding of numerous biological processes in multicellular organisms, particularly those related to tissue development and solid tumorigenesis. Examples include understanding genetic variations from cell to cell that arise in solid tumors [37] , interactions between drug-resistant and drug-sensitive cells [38] , and cell polarity in tissue development and homeostasis [1] .. In conclusion, we have developed an accurate, practical, and robust method for segmenting fluorescence labeled cells from 3-D confocal microscope images. This method enables the quantitative analysis of the individual properties of cells and their contextual relationships inside intact tissue samples.

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


An

Informatics

benchmarking

statement

Objectives:

Benchmarking

statements

provide

a

mechanism

for

making

academic

standards

explicit

within

a

subject

area.

They

allow

comparisons

between

courses

to

be

based

on

learning

outcomes

rather

than

by

defining

a

curriculum.

No

such

statement

has

been

produced

for

informatics.

In

the

absence

of

any

established

benchmarking

statements

for

informatics

a

new

biomedical

informatics

course

at

St.

George’s

has

developed

a

first

benchmarking

statement

which

defines

the

skills

knowledge

and

understanding

a

biomedical

informatics

student

should

acquire

by

the

time

they

complete

the

course.

Methods:

Review

of

national

biomedical

science

and

computing

subject

benchmarking

statements

and

academic

educational

objectives

and

national

occupational

competencies

in

informatics.

Results:

We

have

developed

a

twenty-item

benchmarking

statement

and

this

is

available

on-line

at:

This

benchmarking

statement

includes

a

definition

and

justification

for

all

twenty

statements.

We

found

international

educational

objectives

and

national

informatics

competencies

useful

and

these

are

mapped

to

each

one.

National

subject

benchmarks

for

computing

and

biomedical

science

were

less

useful

and

have

not

been

systematically

mapped.

Conclusions:

Benchmarking

the

skills,

knowledge

and

understanding

that

a

student

should

acquire

during

their

course

of

study

may

be

more

useful

than

setting

a

standard

curriculum.

This

benchmarking

statement

is

a

first

step

towards

defining

the

learning

outcomes

and

competencies

a

student

of

this

discipline

should

acquire.

The

international

informatics

community

should

consider

moving

from

a

standard

curriculum

to

an

agreed

subject

benchmarking

statement

for

medical,

health

and

biomedical

informatics.

Benchmarking

is

am

ethodo

fm

easuring

performance

against

established

standards

of

best

practice

Historically,

benchmarkswere

made

on

survey

stones

as

areference

point

against

which

other

topographical

features

might

be

measured.

Benchmarkings

tatements

provideau

seful

mechanism

for

making

academics

tandards

explicit.

Benchmarking

statementsw

ere

adopted

first

in

NorthAmerica

in

followed

by

Australiaa

nd

theU

Kw

heret

heyh

ave

beenadopted

by

theUK-based

Quality

Assurance

Agency(

QAA)

Benchmarking

statementsh

avea

lso

beeni

mplemented

within

theE

uropeanH

igher

Educational

Area

(EHEA)

as

atool

to

enable

description

of

qualifications

as

well

as

an

enabler

of

trans-national

education(

TNE)

within

Europe

Within

informatics

benchmarkingisalso

recognisedsothat

informaticians

are

able

to

work

globally

though

as

yet

no

suchstatementhas

been

produced

for

informatics

graduates..

Benchmarking

statementss

hould

help

ensure

the

qualityo

fu

ndergraduate

informatics

courses.

Universitiesprepare

students

for

the

workplace

through

academic

study

andvocational

courses;

employers

develop

occupationalstandards

to

enable

employees

to

understand

the

competenciesthat

theyneed

to

have.Students

achieve

learning

outcomesand

acquirecompetencies

as

aresult

of

their

studies.These

termsencompass

the

acquisition

of

knowledge

andu

nderstanding,knowing

howtoact

(this

includes

skills

andp

ractical

activity)

andk

nowing

howt

ob

e(

this

includes

social,l

egal

and

professionali

ssues

as

well

as

livingw

ith

othersinasocial

context)

The

first

full-time

UK

undergraduate

biomedical

informatics

course

has

been

developed

at

St

George's,

University

of

London,

in

collaborationwith

Kingston

Universityand

Royal

Holloway,

University

of

London

.Thiscourse

canbecompleted

as

abachelor'sdegree

(BSc)after

three

years

or

as

am

aster's(

MSci)

in

science

after

four,

with

theoptionofadding

an

additional

work

placement

between

years

twoand

three.

The

three-year

BSc

course

consists

of

eightsubject

areas

delivered

through

modules;

the

eights

ubjects

are

taught

with

increasing

complexity

over

the

three

years

of

the

course

following

thep

rinciples

of

Bruner's

spiral

curriculum

Four

themes

providec

ontinuity

of

approachb

etween

the

modules.

The

course

wasd

esigned

usingt

he

International

MedicalI

nformatics

Association's

(IMIA)guidance

and,inthe

absenceof

anyinformatics

benchmarks,

links

in

partto

thec

omputinga

nd

biomedicals

cience

benchmarks

The

course

sets

outtoprovide

an

academic

grounding

in

biomedical

informatics

at

thesame

time

as

beingavocational

course

fort

hose

looking

to

acquire

skills

andc

ompetencies

for

their

future

careers.

Whilst

theacademic

model

came

from

IMIA,

there

are

no

internationallye

stablished

core

competenciesfor

informaticians.

We

therefore

adopted

theN

HS

National

Occupational

Standards

(NOS)

forhealth

informatics

and

lookedtoensurethat

studentscompletingthe

course

had

theopportunity

to

achieve

thefirst

twol

evels

of

the

NHS

Information

Management

and

Technology

(IM&T)professional

awards

In

ordert

oh

avear

ational

basist

ol

ink

academic

objectives

andthe

need

for

vocational

preparation

for

acareer

as

abiomedical

informaticsprofessionalweconstructed

abenchmarking

statement

for

informatics.

Establishedacademic

learning

outcomes

and

vocational

competencies

in

informaticsc

an

be

mapped

together

to

produce

abenchmarkings

tatement

for

informatics.

This

benchmarkstatement

provides

aconceptual

framework

for

biomedical

informaticsa

nd

the

techniques

and

competencies

that

need

to

be

acquired

by

astudent

of

this

discipline.

The

international

informaticscommunity

should

consider

moving

from

ac

urricula-based

standardisation

of

courses

to

one

based

on

a

subject

benchmark

for

our

discipline.

Thesix

sources

identifiedb

your

literature

search

were

mappedt

op

enchmark

statements.

Each

of

thesebenchmarking

statementsislinked

to

aformwhich

includedamored

etailedd

efinitiono

ft

he

statement,ajustification,and

the

most

relevantI

MIA,

EDUCTRA,

BCS

E-S,N

HS

NOS,

and

NHS

IM+T

objectiveorcompetency

linked

to

it.

The

benchmark

statements

arec

lassified

according

to

Bloom's

taxonomy

andgroupedbylevels

of

learning

The

statementsare

showninTable

can

be

viewedinmore

detail

at:U

RL:h

Within

the

on-linep

ilot

benchmarking

statement

the

IMIA

recommendationsa

re

numberedaccording

to

their

location

in

the

three

main

sections

of

the

source,

i.e.

Section

s"

Methodology

and

technology

for

the

processing

of

information

and

knowledgeinmedicine

andhealth

care".Thefirst

part

of

Section

Reasons

forthe

necessity

of

systematicallyp

rocessed

data,

information

and

knowledgeinmedicine

andhealthcare"was

the

so

on.

We

also

developed

ac

oding

system

for

the

British

ComputerSociety

Education

Steps(BCS-ES)

The

components

fromE

DUCTRA

and

NHS

NOS

are

labelledasfound

in

the

original

sourcematerial.

The

IT

EDUCTRA

is

composed

of

is

no

unit

in

the

NHSNOS

are

labelledthrough

from

Thefirst

component

of

Module

Aofthe

NHSIM+T

is

the

firstofthe

Bmodule

is

labelled

andsoon.

Thepreceding

letters

C

andDstand

forCertificateand

Diplomarespectively.

Other

featureso

ft

he

tool

We

conducted

al

iteraturer

eviewf

or

informatics

with

searches

on

biomedical

informaticsc

oursesa

nd

vocational

qualificationsu

sing

as

search

terms;

medicine-,

health-,b

iomedical-,

bioinformatics-.

We

firstcarefullyreviewedthe

biomedical

science

andc

omputing

benchmarking

statementsp

roduced

by

the

UK

Quality

Assurance

Agency(QAA)

These

statements

providedusefulelements

used

in

the

course,h

owever,i

ti

mmediately

became

clear

thattheycould

not

be

readilyutilised

to

createab

enchmarking

statement

for

informatics..

We

identifiedt

hreei

nternationallyd

eveloped

publicationswhich

define

the

academicscope

of

informatics:the

recommendationso

ft

he

InternationalM

edical

Informatics

Association

(IMIA)onEducation

in

Health

andMedical

Informatics

and

the

proceedingsofasubsequent

conference

organisedb

yits

Education

Wo

rking

Group

Secondly,

education

andt

raining

in

health

informatics(

IT

EDUCTRA)E

uropean

standards

work-setsi

ndex

Thirdly,

the

BritishC

omputer

Society's

(BCS)

Health

InformaticsForum

Education

Steps(

BCS-ES)

We

identified,

also,

nationallyr

elevanta

nd

vocationally-orientatedsources:

National

OccupationalStandardsf

or

Health

Informatics(

NHS

NOS),

National

Health

Service

vocational

awards

in

Information

Management

and

Technology(NHS

IM+T)

andthe

draft

programme

fort

he

St

George's,

Kingston

andR

oyal

The

conceptual

framework

that

givesadiscipline

itscoherence

and

identity;

about

the

intellectual

capabilityand

understanding

thatshould

be

developed,the

techniques

and

skillswhich

are

associated

withdeveloping

an

understanding

in

that

discipline;

and

the

level

of

intellectual

demand

and

challengewhich

is

appropriatetothat

discipline.

Identify

the

needfor

IT

applications

in

medicineand

healthcare

from

the

perspective

of

the

patient

and

the

healthcare

professional

and

describe

the

importance

of

maintaining

quality

assured

data

processes..

Demonstrate

competenceinthe

useofappropriate

technologies,communicationand

organisational

skillstofacilitate

learning

and

development..

Applyorganisational

techniques

to

interpret

information

and

useknowledge

services;

summarise

information

from

multiple

sources..

Deploy

skills

requiredinthe

administration

of

patients

and

their

records

and

demonstrate

awareness

of

the

principles

governing

maintenance

of

manual

and

computing

records..

Describe

the

characteristics

of

health

and

social

care

information

systems..

Define

basicterminologyand

theoreticalconcepts

of

informatics

and

computer

science

and

list

the

strengths

and

weaknessesof

e-communication

in

healthcare..

Explainconcepts

in

mathematics

and

biometry

withanemphasisonstatistics

and

presentation

of

simple

statistical

reports.

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Priority Queueing Models for Cognitive Radio Networks with Traffic Differentiation In this paper, we present a new queueing model providing the accurate average system time for packets transmitted over a cognitive radio (CR) link for multiple traffic classes with the preemptive and non-preemptive priority service disciplines. The analysis considers general packet service time, general distributions for the channel availability periods and service interruption periods, and a service-resume transmission. We further introduce and analyze two novel priority service disciplines for opportunistic spectrum access (OSA) networks which take advantage of interruptions to preempt low priority traffic at a low cost. Analytical results, in addition to simulation results to validate their accuracy, are also provided and illustrate the impact of different OSA network parameters on the average system time. We particularly show that, for the same average CR transmission link availability, the packet system time significantly increases in a semi-static network with long operating and interruption periods compared to an OSA network with fast alternating operating and interruption periods. We also present results indicating that, due to the presence of interruptions, priority queueing service disciplines provide a greater differentiated service in OSA networks than in traditional networks. The analytical tools presented in this paper are general and can be used to analyze the traffic metrics of most OSA networks carrying multiple classes of traffic with priority queueing service differentiation. Opportunistic spectrum access (OSA) is considered an important technology to address current and predicted exponential traffic growth in wireless networks [1] - [4] . Such growth is predominantly driven by multimedia traffic, such as video streaming [4] . Thus, it is expected that OSA networks will carry several traffic classes with different quality of service (QoS) requirements and importance.. The research objective of this paper is to obtain analytical tools to analyze traffic metrics, such as the packet system time, for differentiated services in opportunistic spectrum access networks. Such tools are required to evaluate the packet-level impact of OSA network parameters, novel medium access control (MAC) algorithms, channel sensing order strategies, etc. Moreover, those analytical tools can be used as a decision-making process for multimedia MAC algorithms [5] , for OSA networks employing cognitive radio (CR) nodes, which possess learning and decision-making capabilities. Queueing models is the preferred approach to derive analytical results to analyze traffic metrics [6] .. Priority service disciplines, such as the preemptive and non-preemptive service disciplines, are the most common approaches to implement service differentiation in communication networks. Furthermore, in an OSA network, the CR users must stop transmitting on an operating channel if the channel's primary user (PU) is detected or if the channel quality is unacceptable due, for example, to deep fading or interference.. In the queueing model, the operating channel is the server of the queue. To achieve our objective, we must therefore analyze queueing models with priority service disciplines in the presence of frequent queue server interruptions.. Queueing models with preemptive priority service discipline and interruptions have been previously studied [7] - [11] . Some of that work considered that interruption periods are server busy periods generated by higher priority classes of traffic. For this approach, the interruption periods are not generally distributed since they depend on the arrival rate and service rate of higher priority classes. Obtaining the interruption period distribution is therefore not always straightforward. Inversely, given an interruption period distribution, it is not easy to find the appropriate arrival and service processes whose busy period has this distribution. The other articles that studied generally distributed interruption periods only considered a single traffic class and only provided bounds.. Few have attempted to provide queueing models for opportunistic spectrum access networks. In [6] , [12] , [13] , queueing models for an OSA network with a single class of traffic were derived using a similar approach as in [7] - [11] whereby the server interruption periods for the cognitive radio users are busy periods generated by the preemptive primary traffic. This approach has several major deficiencies.. First, those models are limited to exponential operating period length. Also, as previously discussed, they can not address arbitrary interruption period lengths before the transmission can resume. This is particularly important for OSA networks since the interruption period length depends on several factors such as the MAC policy, the number of available channels, the number of competing CR users, etc.. Even for the simple case where CR users wait until the PU releases the channel, the interruption period is not necessarily distributed as the busy period of a PU Poisson traffic [14] . The approach of simply considering the interruption periods as a preemption from PU traffic is therefore not accurate and general enough to analyze OSA networks.. In [15] we addressed several of those problems in a new queueing model for a single class of CR traffic for general operating and interruption period lengths. However, this model was limited to constant service time. In [16] , an optimal threshold for the queue length to decide whether a packet should join the queue or not is derived. However, the model is again not general and can not be used to analyze traffic metrics.. To the best of our knowledge [17] is one of the few papers discussing a queueing system with multiple classes of traffic in cognitive radio networks. The authors analyze a T-preemptive scheme and, similarly to the other work on opportunistic spectrum access networks, the queueing analysis does not consider general interruption lengths and it is specific to the priority service disciplines considered.

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