Network-based

analysis

of

comorbidities

risk

during

an

infection:

SARS

and

HIV

case

studies

Background:

Infections

are

often

associated

to

comorbidity

that

increases

the

risk

of

medical

conditions

which

can

lead

to

further

morbidity

and

mortality.

SARS

is

a

threat

which

is

similar

to

MERS

virus,

but

the

comorbidity

is

the

key

aspect

to

underline

their

different

impacts.

One

UK

doctor

says

"I'd

rather

have

HIV

than

diabetes"

as

life

expectancy

among

diabetes

patients

is

lower

than

that

of

HIV.

However,

HIV

has

a

comorbidity

impact

on

the

diabetes.

Results:

We

present

a

quantitative

framework

to

compare

and

explore

comorbidity

between

diseases.

By

using

neighbourhood

based

benchmark

and

topological

methods,

we

have

built

comorbidity

relationships

network

based

on

the

OMIM

and

our

identified

significant

genes.

Then

based

on

the

gene

expression,

PPI

and

signalling

pathways

data,

we

investigate

the

comorbidity

association

of

these

infective

pathologies

with

other

diseases

(heart

failure,

kidney

disorder,

breast

cancer,

neurodegenerative

disorders,

bone

diseases,

Type

and

Type

diabetes).

Phenotypic

association

is

measured

by

calculating

both

the

Relative

Risk

as

the

quantified

measures

of

comorbidity

tendency

of

two

disease

pairs

and

the

φ-correlation

to

measure

the

robustness

of

the

comorbidity

associations.

The

differential

gene

expression

profiling

strongly

suggests

that

the

response

of

SARS

affected

patients

seems

to

be

mainly

an

innate

inflammatory

response

and

statistically

dysregulates

a

large

number

of

genes,

pathways

and

PPIs

subnetworks

in

different

pathologies

such

as

chronic

heart

failure

genes),

breast

cancer

genes)

and

bone

diseases

genes).

induces

comorbidities

relationship

with

many

other

diseases,

particularly

strong

correlation

with

the

neurological,

cancer,

metabolic

and

immunological

diseases.

Similar

comorbidities

risk

is

observed

from

the

clinical

information.

Moreover,

SARS

and

HIV

infections

dysregulate

genes

GNS,

and

genes

TFRC,

GHITM)

respectively

that

affect

the

ageing

process.

It

is

notable

that

HIV

and

SARS

similarly

dysregulated

genes

and

pathways.

Only

significantly

dysregulated

genes

are

common

between

SARS-CoV

and

MERS-CoV,

including

NFKBIA

that

is

a

key

regulator

of

immune

responsiveness

implicated

in

susceptibility

to

infectious

and

inflammatory

diseases.Our

method

presents

a

ripe

opportunity

to

use

data-driven

approaches

for

advancing

our

current

knowledge

on

disease

mechanism

and

predicting

disease

comorbidities

in

a

quantitative

way.

In

this

study,

we

have

considered

all

available

categories

of

omics

and

phenotypic

data

to

quantify

the

SARS

and

infections

centred

comorbidity

associations.

We

have

shown

that

the

phenotype

disease

network

(PDN)

has

a

heterogeneous

structure

where

some

diseases

are

highly

connected

while

others

are

hardly

connected

at

all.

Our

findings

showed

that

disease

progression

can

be

represented

and

studied

using

network

methods,

offering

the

potential

to

enhance

our

understanding

of

the

origin

and

evolution

of

human

diseases.

Detecting

comorbidity

in

a

large

population

is

of

clinical

interest

due

to

the

fact

that

it

may

reveal

new

information

useful

for

cause

of

diseases

as

well

as

for

new

treatment

strategies.

This

study

demonstrates

the

value

of

an

integrated

approach

in

revealing

disease

relationships

and

new

opportunities

for

therapeutic

applications.

So

we

can

say

that

this

kind

of

approach

will

be

helpful

for

making

evidence-based

recommendations

about

disease

comorbidities.

Moreover,

considering

environmental

factors

(such

as

physiological

stress,

diet),

ethnic

group

and

gender

discriminations

are

important

factors

in

the

comorbidity

analysis.

Our

network

approach

could

be

extended

as

a

comorbidity

map

by

integrating

diet,

exercise

and

other

factors

as

in

We

presented

and

analysed

multi-relational

disease

comorbidity

relationships

of

SARS

and

infections

with

other

diseases

or

infections

based

on

the

associations

of

genetics,

proteomics,

molecular

signalling

pathways

and

phenotypic

disorders.

The

combination

of

molecular

biology,

genetics

and

clinical

medicine

has

greatly

facilitated

understanding

of

how

different

diseases

relate

to

each

other.

Based

on

the

combined

genetics,

PPIs,

pathways

and

clinical

data,

our

disease

networks

can

disclose

potentially

novel

disease

relationships

that

have

not

been

captured

by

previous

individual

studies.

The

underlying

hypothesis

behind

this

line

of

research

is

that

once

we

catalogue

all

disease-related

genes,

PPI

complex

and

signalling

pathways,

if

we

do

not

consider

environmental

changes,

we

will

be

able

to

predict

the

susceptibility

of

each

individual

to

future

diseases

using

various

molecular

biomarkers

and

it

will

help

us

to

enter

an

era

of

predictive

medicine.

Our

results

indicate

that

such

a

combination

of

molecular

and

population-level

data

could

help

to

build

novel

hypotheses

about

disease

mechanisms.

Furthermore,

if

two

or

more

diseases

have

associated

comorbidity,

the

occurrence

of

one

of

them

in

a

patient

may

increase

the

likelihood

of

developing

the

other

diseases..

We

have

also

studied

the

differences

between

MERS-CoV

and

SARS-CoV

in

the

host

response.

This

enables

rapid

assessment

of

viral

properties

and

the

ability

to

anticipate

possible

differences

in

human

clinical

responses

to

MERS-CoV

and

SARS-CoV

and

their

impact

on

comorbidities

with

respect

to

the

general

comorbidities

conditions.

We

used

this

information

to

predict

potential

effective

drugs

against

SARS-CoV,

a

method

that

could

be

more

generally

used

to

identify

candidate

therapeutics

in

future

disease

outbreaks.

These

investigation

approach

may

also

help

to

generate

hypotheses

and

make

rapid

advancements

in

characterising

the

new

viruses..

We

also

found

that

patients'

response

of

SARS

appears

to

be

mainly

an

innate

inflammatory

response

using

NFK-BIA,

rather

than

any

specific

immune

response

against

a

viral

infection

such

as

HIV.

However,

HIV

infection

and

highly

active

antiretroviral

therapy

(HAART)

also

increase

the

immune

reconstitution

inflammatory

syndrome

(IRIS)

and

inflammation

through

the

NF-κB

pathways

Moreover

we

have

studied

before

about

the

impact

of

HIV

infection

on

bone

diseases

and

infection

(e.

g.

osteoporosis

and

osteomyelitis).

We

observed

that

genes

(e.g.

RANKL)

and

pathways

(e.g.

NF-κB)

that

are

dysregulated

by

HIV

infection

also

impact

on

the

bone

remodelling

and

bone

related

diseases.

It

is

also

recognised

that

inflammation

plays

a

role

in

cancer

aetiology,

and

various

studies

have

found

that

inflammation

may

causes

IRIS,

obesity

and

tumour-promoting

effects

Moreover,

inflammation

is

an

important

concomitant

cause

of

many

major

age-associated

pathologies

such

as

cancer,

neurodegeneration

and

diabetes

Our

study

provides

important

evidence

to

associate

diseases

with

the

ageing

process

at

the

system

level

and

helps

to

understand

more

about

the

comorbidities

of

the

complex

diseases.

The

ageing

process

itself

is

accompanied

by

a

chronic

low-grade

inflammation,

which

is

termed

"inflammageing".

The

combination

of

metabolic-driven

and

age-driven

inflammatory

pathways

plays

a

pivotal

role

in

disease

progression.

This

observation

suggests

that

inflammageing

and

meta-inflammation

can

share

stimuli

and

pathogenic

mechanisms

for

comorbidities..

We

suppose

that

what

is

happening

for

the

comorbidities

we

investigate

is

similar

to

what

found

We

have

presented

a

systematic

and

quantitative

approach

to

discover

human

disease

comorbidities

using

different

sources

of

available

mRNA

expression,

protein-protein

interactions,

signalling

pathways,

disease-gene

associations,

disease-disease

associations

and

disease-drug

associations

data.

It

has

been

shown

that

SARS

coronavirus

infects

and

replicates

in

a

wide

variety

of

host

cells

in

susceptible

animals

and

human

beings

To

understand

the

host

response

to

this

pathogen,

we

analysed

the

gene

expression

patterns

of

SARS

infected

patients,

compared

to

normal

subjects

using

oligonucleotide

microarrays

from

the

NCBI

GEO

ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1739)

We

analysed

the

microarray

gene

expression

data

of

over

genes

from

the

PBMCs

of

SARS

patients,

and

compared

with

healthy

control

samples.

We

found

that

genes

(p

fold

change)

were

differentially

expressed

as

compared

to

healthy

controls

in

which

genes

were

significantly

up

regulated

and

genes

were

significantly

down

regulated

(see

Additional

file

Table

On

the

other

hand,

monocytes

are

the

key

immune

responsive

cells

whose

function

is

adversely

impacted

by

infection

radically

alters

the

monocyte

phenotype,

which

is

reflected

in

an

induced

gene

expression

analysis.

Monocyte

gene

expression

microarray

data

were

collected

for

control

and

HIV

patients

from

the

NCBI

GEO

To

find

out

the

significant

dysregulated

genes

during

the

infection,

we

have

performed

global

gene

expression

analysis.

We

found

that

genes

(p

fold

change)

were

differentially

expressed

as

compared

to

healthy

controls

in

which

genes

were

up

regulated

and

genes

were

down

regulated

(see

Additional

file

Table

Considering

the

significantly

dysregulated

genes

of

SARS

genes)

and

genes)

infections,

and

gene-disease

associations

information,

we

have

constructed

two

gene-disease

associations

networks

(GDN),

which

are

used

to

explore

the

shared

genetic

associations

and

disease

comorbidity.

Starting

from

the

bipartite

graph

we

generated

biologically

relevant

network

projections

and

constructed

multi-relational

gene-disease

network

in

which

nodes

are

diseases

or

genes,

and

edges

indicate

association

between

gene

and

disease.

This

bipartite

graph

consists

of

two

disjoint

sets

of

nodes,

where

one

set

corresponds

to

all

known

genetic

disorders

and

the

other

set

corresponds

to

all

of

our

identified

significant

genes

for

SARS

and

infections.

The

list

of

disorders,

disease

genes,

and

associations

between

them

were

obtained

from

the

Online

Mendelian

Inheritance

in

Man

(OMIM)

a

compendium

of

human

disease

genes

and

phenotypes

(see

details

in

the

Methods

section).

We

classified

each

disorder

into

one

of

disorder

categories

based

on

the

physiological

system

affected

as

introduced

in

Goh,

Cusick,

Valle,

Childs,

Vidal,

Barabasi

et

al.

In

the

GDN,

nodes

represent

diseases

class

or

genes,

and

two

disorders

are

connected

to

each

other

if

they

share

at

least

one

gene

in

which

mutations

are

associated

with

both

diseases

groups

(Figures

and

The

number

of

interlinked

genes

between

SARS

infection

and

other

diseases

indicates

that

immunological,

hematological,

neurological,

metabolic

and

dermatological

diseases

categories

are

strongly

associated

with

the

SARS

infection

(see

Figure

and

Additional

file

Table

Few

genes

are

also

shared

between

more

than

categories

of

diseases

i.e

those

disease

groups

are

also

associated

through

at

least

that

genes.

For

an

instance,

the

gene

ATM

shared

among

SARS

infection,

cancer

and

immunological

diseases.

Therefore,

cancer

and

immunological

diseases

are

also

interrelated

through

the

gene

ATM.

Among

all

these

disease

classes

immunological

diseases

class

is

tightly

correlated

with

the

SARS

infection

due

to

the

highest

number

of

genes

genes)

shared

between

them.

On

the

other

hand,

the

number

of

associated

genes

between

HIV

infection

and

other

diseases

indicates

that

neurological,

metabolic,

cancer

and

hematological

diseases

categories

are

strongly

correlated

with

the

HIV

infection

(see

Figure

and

Additional

file

Figure

The

gene-disease

association

network

centred

on

the

SARS

infection

is

constructed

based

on

the

different

categories

of

diseases

that

are

connected

and

showed

comorbidities

with

the

SARS

infection

through

the

different

genes.

Red

colour

represents

different

categories

of

disorders

and

green

colour

represents

different

genes

that

are

common

with

the

other

categories

of

disorders.

The

size

of

a

disease

node

is

proportional

to

the

number

of

dysregulated

genes

shared

between

the

infections/disorder

groups.

A

link

is

placed

between

a

disorder

and

a

disease

gene

if

mutations

in

that

gene

lead

to

the

specific

disorder.

The

term

"comorbidity"

refers

to

the

coexistence

of

multiple

diseases

or

disorders

in

relation

to

a

primary

disease

or

disorder

in

an

individual

A

comorbidity

relationship

between

two

diseases

exists

whenever

they

appear

simultaneously

in

a

patient

more

than

chance

alone

It

represents

the

co-occurrence

of

diseases

or

presence

to

chronic

obstructive

pulmonary

disease

(COPD)

obesity

mental

disorders

immune-related

diseases

cancer

etc..

Comorbidity

can

be

attributed

to

the

disease

connections

on

the

molecular

level,

such

as

dysregulated

genes,

PPIs

(protein-protein

interactions),

and

metabolic

pathways

as

potential

causes

of

comorbidity

From

a

genetic

perspective,

a

pair

of

diseases

is

connected

because

they

have

both

been

associated

with

the

same

dysregulated

genes

whereas

from

a

proteomics

perspective

phenotypically

similar

diseases

are

related

via

biological

modules

such

as

PPIs

or

molecular

pathways

Population-based

disease

association

is

important

in

conjunction

with

molecular

and

genetic

data

to

uncover

the

molecular

origins

of

diseases

and

disease

comorbidities.

Patient

medical

records

contain

important

clarification

regarding

the

co-occurrences

of

diseases

affecting

the

same

patient

During

the

last

few

years,

several

researchers

have

been

conducted

the

disease

comorbidity

analysis

to

understand

the

origins

of

many

diseases

Goh,

Cusick,

Valle,

Childs,

Vidal,

Barabasi

et

al.

and

Feldman,

Rzhetsky,

Vitkup

et

al.

built

networks

of

gene-disease

associations

by

connecting

diseases

that

have

been

associated

with

the

same

genes

whereas

Lee,

Park,

Kay,

Christakis,

Oltvai

and

Barabási

et

al.

constructed

a

network

in

which

two

diseases

are

linked

if

metabolic

reactions

are

associated

between

them

Disease

association

studies

from

proteomic

point

of

view

have

been

studied

by

Rual,

Venkatesan,

Hao,

Hirozane-Kishikawa,

Dricot,

Li,

Berriz,

Gibbons,

Dreze,

Ayivi-Guedehoussou

et

al.

and

Stelzl,

Worm,

Lalowski,

Haenig,

Brembeck,

Goehler,

Stroedicke,

Zenkner,

Schoenherr,

Koeppen

et

al.

Rzhetsky,

Wajngurt,

Park

and

Zheng

et

al.

inferred

the

comorbidity

links

between

disorders

from

the

disease

history

of

million

patients

However,

all

of

these

efforts

have

focused

on

the

role

of

a

single

molecular

or

phenotypic

measure

to

capture

disease-disease

relationships.

In

our

work

we

have

used

disease-gene

associations,

PPIs,

molecular

pathways

and

clinical

information

to

obtain

statistically

significant

associations

and

comorbidity

risks

among

diseases..

Inflammation

is

a

hallmark

of

many

serious

human

infectious

diseases

associated

to

a

wide

variety

of

infections,

such

as

UK

doctor

Max

Pemberton

says

"I'd

rather

have

HIV

than

diabetes"

as

life

expectancy

among

diabetes

patients

is

lower

than

that

of

HIV

However,

HIV

has

a

comorbidity

impact

on

the

diabetes.

Also

the

flu

can

cause

complications,

including

bacterial

pneumonia,

or

the

worsening

of

chronic

health

problems.

Asthma

is

the

most

common

comorbidity

in

patients

hospitalized

for

swine

influenza

infection

Dengue

can

cause

myocardial

impairment,

arrhythmias

and,

occasionally,

fulminant

myocarditis

Chronic

medical

conditions,

such

as

heart

disease,

lung

disease,

diabetes,

renal

disease,

rheumatologic

disease,

dementia,

and

stroke

are

risk

factors

for

influenza

complications

Common

chronic

infections

such

as

periodontitis

or

infection

with

Helicobacter

pylori

may

also

increase

stroke

risk

Moreover,

the

severity

of

pneumonia

in

patients

coinfected

with

influenza

virus

and

bacteria

is

significantly

higher

than

in

those

infected

with

bacteria

alone.

The

incidence

of

flu

is

higher

in

children

and

younger

adults

than

in

older

individuals,

but

influenzaassociated

morbidity

and

mortality

increase

with

age,

especially

for

individuals

with

underlying

medical

conditions

such

as

chronic

cardiovascular

diseases

During

the

ageing

process

the

immune

system

becomes

compromised

and

it

causes

an

increasing

inflammation

In

particular,

chronic

inflammation

(inflammageing)

and

metabolic

function

are

strongly

affected

by

the

ageing

process

The

ageing

of

populations

is

leading

to

an

unprecedented

increase

different

diseases

like

cancer

and

fatalities.

It

is

reported

that

of

the

elderly

population

has

three

or

more

chronic

conditions

On

the

other

hand,

respiratory

viruses

are

an

emerging

threat

to

global

health

security

and

have

led

to

worldwide

epidemics

with

substantial

morbidity

and

mortality

Coronaviruses

(CoVs)

cause

respiratory

and

enteric

diseases

in

human

and

other

animals

that

induce

fatal

respiratory,

gastrointestinal

and

neurological

disease.

Severe

acute

respiratory

syndrome

(SARS)

is

an

epidemic

human

disease,

is

caused

by

a

coronavirus

(CoV),

called

SARSassociated

coronavirus

(SARS-CoV)

SARS

patients

may

present

with

a

spectrum

of

disease

severity

ranging

from

flu-like

symptoms

and

viral

pneumonia

to

acute

respiratory

distress

syndrome

and

death

Most

of

the

deaths

were

attributed

to

complications

related

to

sepsis,

ARDS

and

multiorgan

failure,

which

occurred

commonly

in

the

elderly

for

comorbidities

Age

and

comorbidity

(e.g.

diabetes

mellitus,

heart

disease)

were

consistently

found

to

be

significant

independent

predictors

of

various

adverse

outcomes

in

SARS

Children

with

SARS

have

better

prognosis

than

adults

Advanced

age

and

comorbidities

were

significantly

associated

with

increased

risk

of

SARS-CoV

related

death,

due

to

acute

respiratory

distress

syndrome

Mild

degree

of

anaemia

is

common

in

the

SARS

infected

patients

and

patients

who

have

recovered

from

SARS

show

symptoms

of

psychological

trauma

Another

novel

coronavirus

MERS-CoV,

which

is

a

new

threat

for

public

health,

has

similar

clinical

characteristics

to

SARS-CoV,

but

the

comorbidity

is

the

key

aspect

to

underline

their

different

impacts

MERS-CoV

causes

respiratory

infections

of

varying

severity

and

sometimes

fatal

infections

in

humans

including

kidney

failure

and

severe

acute

pneumonia

Despite

sharing

some

clinical

similarities

with

SARS

(eg,

fever,

cough,

incubation

period),

there

are

also

some

important

differences

such

as

the

rapid

progression

to

respiratory

failure,

which

we

have

studied

on

comorbidities

point

of

view..

Infection

with

the

human

immunodeficiency

(HIV)

and

the

resulting

acquired

immune

deficiency

syndrome

(AIDS)

affects

cellular

immune

regulation

HIV

infection

severely

impacts

on

the

immune

system

causing

phenotypic

changes

in

peripheral

cells

and

dysregulates

the

innate

immune

system

Significant

number

of

infected

patients

exhibits

osteopenia

and

osteoporosis,

leading

to

higher

incidence

to

develop

weak

and

fragile

bones

during

the

course

of

disease

HIV

has

also

been

associated

with

an

increased

risk

of

developing

both

diabetes

and

cardiovascular

disease

Infection

with

HIV

weakens

the

immune

system

and

reduces

the

body's

ability

to

fight

infections

that

may

lead

to

cancer

People

infected

with

human

immunodeficiency

virus

(HIV)

have

a

higher

risk

of

some

types

of

cancer

(Kaposi

sarcoma,

non-Hodgkin

lymphoma,

cervical

cancer,

anal,

liver,

lung

cancer,

and

Hodgkin

lymphoma)

than

uninfected

people

Many

people

infected

with

HIV

are

also

infected

with

other

viruses

that

cause

certain

cancers

HIV

infection

even

when

controlled

by

highly

active

antiretroviral

therapy

(HAART)

is

being

linked

to

chronic

inflammation

People

with

infection

appear

to

have

a

markedly

higher

rate

of

chronic

kidney

disease

than

the

general

public

It

is

because

some

of

the

risk

factors

associated

with

acquisition

are

the

same

as

those

that

lead

to

kidney

disease

because

of

the

virus

itself

and

some

therapies

(e.g.

HAART

therapy).

Antiretroviral

therapy

for

HIV

may

increase

the

risk

of

developing

metabolic

syndrome

(abdominal

obesity,

hyperglycaemia,

dyslipidaemia

and

hypertension)

and

thus

predispose

to

type

diabetes

and

cardiovascular

disease.

Many

of

the

biologic

factors

thought

to

be

causally

associated

with

inflammation

in

HIV

disease

are

also

thought

to

be

causally

associated

with

the

inflammation

of

ageing

Infections

(acute

and

chronic

conditions)

are

often

associated

to

comorbidity

that

increases

the

risk

of

medical

conditions

which

can

lead

to

further

morbidity

and

mortality.

Comorbidities

related

to

flu

have

been

recently

investigated

Comorbidities

for

tuberculosis

have

also

been

studied

recently

To

understand

the

overall

mechanism

we

have

studied

the

comorbidity

associations

of

SARS

and

HIV

infections.

Both

HIV

and

SARS

are

emerging

infectious

diseases

in

the

modern

world;

each

of

these

diseases

has

caused

global

societal

and

economic

impact

related

to

unexpected

illnesses

and

deaths

SRAS

is

a

significant

public

health

threat

and

HIV

is

a

long

term

chronic

infection.

Since

these

two

infections

are

associated

with

high

mortality

rates

and

there

are

no

clinically

approved

antiviral

treatments

or

vaccines

available

for

either

of

these

infections,

we

have

selected

these

two

infections

for

our

study.

Centred

on

the

SARS

and

infections

we

have

investigated

highly

heterogeneous

disease

comorbidity

networks

using

the

disease-gene

associations,

PPI

subnetwork,

molecular

pathways

and

clinical

information.

The

method

of

global

gene

expression

analysis

using

oligonucleotide

microarrays

has

proven

to

be

a

sensitive

method

to

develop

and

refine

the

molecular

determinants

of

human

disorders

Using

this

technology,

we

compared

the

gene

expression

profiles

of

SARS,

HIV

and

other

diseases.

To

avoid

the

problems

of

comparing

microarray

data

of

different

platforms

and

experimental

systems,

we

normalized

the

gene

expression

data

in

each

microarray

sample

(disease

state

or

control)

using

the

Z-score

transformation

(Z

ij

for

each

disease

gene

expression

matrix

using

Z

ij

where

SD

is

the

standard

deviation,

g

ij

represents

the

expression

value

of

gene

i

in

sample

j.

This

transformation

allows

for

the

direct

comparison

of

gene

expression

values

across

various

microarray

samples

and

diseases.

To

combined

more

than

one

data

series

or

experiments

for

a

given

disease,

we

employed

a

linear

regression

approach

to

obtain

a

combined

t-test

statistic

between

two

conditions.

Data

were

log

-transformed

and

we

calculated

expression

level

for

each

gene

using

a

linear

regression

model

where

Y

i

is

the

gene

expression

value

and

X

i

is

a

disease

state

(disease

or

control).

The

coefficients

β

and

β

are

the

parameters

of

this

model

and

were

estimated

by

least

squares.

The

t-test

statistic,

when

estimating

the

value

of

β

is

the

same

as

the

standard

t-test

statistic

between

disease

and

control

states..

Time

series

microarray

gene

expression

data

analysis

was

divide

into

two

steps:

pre-processing

and

identification

of

statistically

significant

points

by

t-test,

ANOVA

and

regression

analysis

to

find

differently

expressed

gene

profiles

in

different

time

points.

In

the

first

step,

we

preprocessed

the

experimental

data

using

different

statistical

methods

and

finally

followed

by

post

less

normalization,

recommended

by

the

Golden

Spike

Project

In

the

second

step,

we

have

used

a

most

suitable

method

"maSigPro"

(microarray

Significant

Profiles)

to

identify

differentially

expressed

genes

in

time-course

microarray

experiments,

which

is

a

two

step

regression

method

successfully

applied

on

more

than

one

groups

of

time-series

This

two

steps

regression

strategy

is

used

to

find

genes

with

significant

temporal

expression

changes

and

significant

differences

between

experimental

groups.

This

procedure

first

adjusts

this

global

model

by

the

leastsquared

technique

to

identify

differentially

expressed

genes

and

selects

significant

genes

applying

false

discovery

rate

control

procedures.

Then

stepwise

regression

is

applied

as

a

variable

selection

strategy

to

study

differences

between

experimental

groups

and

statistically

significant

profiles.

After

finding

differentially

gene

expression

profiles

among

the

group

of

experiments,

the

next

step

is

to

cluster

them

according

to

their

profile

similarities.

The

hierarchical

clustering

and

the

median

gene

expression

profiles

of

clusters

are

performed

according

to

the

"maSigPro"

package

in

R

Student's

unpaired

T-test

was

performed

to

identify

genes

that

were

differentially

expressed

in

patients

over

normal

samples

and

significant

genes

were

selected.

A

threshold

of

at

least

fold

change

and

a

p

value

for

the

t-tests

of

less

than

were

chosen.

In

addition,

a

two-way

ANOVA

with

Bonferroni's

post-hoc

test

was

used

to

establish

statistical

significance

between

groups

Pathways

and

functional

categories

were

considered

as

over-represented

when

Fisher's

exact

test

p

value

was

For

presenting

the

signalling

and

interaction

pathways

of

the

different

significant

genes,

we

used

cytoscape

for

data

integration

and

network

visualization

and

reactome

functional

interaction

(FI)

cytoscape

plugin

for

knowledge

base

of

human

biological

pathways

and

network

processes

For

the

gene

disease

association,

we

have

considered

the

neighbourhood

based

benchmark

and

topological

methods,

which

are

better

suited

to

our

networks

In

this

case,

topological

refers

to

methods

that

rely

only

on

the

structure

of

the

network

to

draw

conclusions.

We

construct

a

GDN

from

gene-disease

associations

where

the

node

in

the

network

can

be

either

a

disease

or

a

gene.

This

network

can

also

be

regarded

as

a

bipartite

graph.

Diseases

are

connected

when

the

diseases

share

at

least

one

significant

dysregulated

genes.

Let

a

particular

set

of

human

diseases

D

and

a

set

of

human

genes

G,

genedisease

associations

attempt

to

find

whether

gene

g

G

is

associated

with

disease

d

D.

If

G

i

and

G

j

the

sets

of

significant

up

and

down

dysregulated

genes

associated

with

diseases

i

and

j

respectively,

then

the

number

of

shared

dysregulated

genes

(n

g

ij

associated

with

both

diseases

i

and

j

is

as

follows:.

The

co-occurrence

refers

to

the

number

of

shared

genes

in

the

GDN.

The

common

neighbours

is

the

based

on

the

Jaccard

Coefficient

method,

where

the

edge

prediction

score

for

the

node

pair

is

as:.

where

E

is

the

set

of

all

edges.

The

number

of

shared

pathways

and

protein

subnetwork

that

links

between

diseases

i

and

j

are

calculated

using

the

equation

and

the

link

prediction

score

is

measured

using

the

equation

To

estimate

the

correlation

starting

from

disease

cooccurrence,

we

need

to

quantify

the

strength

of

disease

association

for

comorbidities

by

dipicting

a

distance

between

two

diseases.

For

the

analysis

of

the

phenotypic

data,

we

used

the

Relative

Risk

(RR

ij

as

the

quantified

measures

of

comorbidity

tendency

of

two

disease

pairs

and

checked

φ-correlation

(φ

ij

to

measure

the

robustness

of

the

comorbidity

associations.

The

RR

ij

is

observing

in

a

pair

of

diseases

i

and

j

affecting

the

same

patient.

When

two

diseases

co-occur

more

frequently

than

expected

by

chance,

we

will

get

RR

ij

and

φ

ij

However,

RR

ij

and

φ

ij

are

not

independent

of

each

other

and

each

carries

unique

biases

that

are

complementary

So,

we

used

both

measures

of

comorbidity

to

ensure

the

robustness

of

our

investigations.

The

RR

ij

allows

us

to

quantify

the

co-occurrence

of

disease

pairs

compared

with

the

random

expectation

which

is

calculated

as:.

where

N

is

the

total

number

of

patients

in

the

population,

P

i

is

the

incidences/prevalences

of

disease

i,

P

j

is

the

incidence

of

disease

j

and

C

ij

is

the

number

of

patients

that

have

been

diagnosed

with

both

diseases

i

and

j.

For

RR

ij

comorbidity

is

larger

than

expected

by

chance

and

for

RR

ij

comorbidity

is

smaller

than

expected

by

chance.

To

calculate

the

significance

of

the

relative

risk

RR

ij

we

used

the

Katz,

Baptista,

Azen

and

Pike

et

al.

method

to

estimate

confidence

intervals

According

to

their

estimation,

the

confidence

interval

for

the

RR

ij

between

two

diseases

i

and

j

is

calculated

by:

Lower

bounds

of

the

confidence

interval

(LB)

RR

ij

σ

ij

and

Upper

bounds

of

the

confidence

interval

(UB)

RR

ij

σ

ij

where

σ

ij

is

given

by:.

Disease

pairs

within

the

confidence

interval

are

only

considered

if

the

LB

value

is

larger

than

when

RR

ij

is

larger

than

or

if

the

UB

value

is

smaller

than

when

RR

ij

is

smaller

than

Relative

risk

measure

is

intrinsically

biased

towards

overestimation

of

relationships

between

rare

diseases

and

underestimates

the

co-morbidity

of

more

frequent

diseases

This

bias

can

be

reduced

by

introduction

of

a

φ-correlation

measure.

We

can

quantify

the

strength

of

comorbidities

by

calculating

the

correlation

coefficient

associated

with

a

pair

of

diseases

i

and

j

as:.

where

C

ij

is

the

number

of

patients

affected

by

both

diseases,

N

is

the

total

number

of

patients

in

the

studied

population,

and

P

i

and

P

j

are

the

morbidity

or

incidence

of

the

i

th

and

j

th

diseases

respectively.

The

φ-correlation

is

the

Pearson's

correlation

for

the

variables

which

only

take

or

values

For

φ

ij

comorbidity

is

larger

than

expected

by

chance

and

for

φ

ij

comorbidity

is

smaller

than

expected

by

chance.

We

can

determine

the

significance

of

φ

by

performing

a

t-test.

This

consists

of

calculating

t

according

to

the

formula:

t

φ

1−φ

where

n

is

the

number

of

observations

used

to

calculate

φ..

To

predict

the

comorbidities

considering

the

primary

or

index

disease

we

have

calculated

the

conditional

relative

risk

(conditional

RR

ij

as

follows:.

for

all

possible

disease

pairs

i

and

j,

for

the

cases

that

one

index

disease

(I)

is

present

(k

true)

or

absent

(k

false).

Then

for

each

pair

of

diseases,

we

say

that

i

and

j

are

mediated

by

that

index

disease

if

the

RR

true

ij

is

significantly

different

(higher

or

lower)

from

RR

false

ij

(p

We

have

weighted

the

edges

using

a

mutual

information

metric

which

quantifies

how

much

greater

the

edge

relationship

is

with

respect

to

co-occurrence.

The

mutual

information

weight

between

two

diseases

i

and

j

is

defined

as.

where

C

ij

is

the

observed

co-occurrence

and

P

i

and

P

j

are

the

morbidity

or

prevalence

of

the

i

th

and

j

th

diseases

respectively..

To

compare

between

SARS-CoV

and

MERS-CoV,

a

gene

set

enrichment

analysis

was

undertaken

using

GSEA

To

find

out

the

correlation

(similarities)

and

distance

(dissimilarities)

among

the

diseases

from

the

integrated

analysis

of

multidimensional

data

(gene

expression

and

protein

protein

interaction),

we

have

applied

Euclidian

distance

measurement

and

metric

multi-dimensional

scaling

(MDS)

using

majorization

MDS

is

a

set

of

methods

for

discovering

hidden

structures

in

multidimensional

data.

Based

on

a

proximity

matrix

derived

from

variables

measured

on

objects

as

input

entity,

these

distances

are

mapped

on

a

lower

dimensional

spatial

representation.

Optimization

problem

is

used

to

find

mapping

in

target

dimension

of

the

data

based

on

given

pairwise

proximity

information

while

minimize

the

objective

function.

The

particular

objective

function

(or

loss

function)

we

used

in

this

work

is

a

sum

of

squares,

commonly

called

stress.

We

used

majorization

to

minimize

stress

and

this

MDS

solving

strategy

is

known

as

SMACOF

(Scaling

by

MAjorizing

a

COmplicated

Function).

Stress

majorization

is

an

optimization

strategy

used

in

multidimensional

scaling

(MDS)

where,

for

a

set

of

nm-dimensional

data

items,

a

configuration

X

of

n

points

in

r(<<

m)-dimensional

space

is

sought

that

minimizes

the

stress

function

σ

(X)..

Here

r

is

that

means

the

(r

n)

matrix

X

lists

points

in

Euclidean

space.

We

have

applied

the

cost

function

σ

to

measures

the

squared

differences

between

ideal

(m-dimensional)

distances

and

actual

distances

in

r-dimensional

space

as

follows:.

X

of

dimension

n

p

as

the

individual's

or

judge's

configuration,

and

X

of

dimension

n

p

as

the

object's

configuration

matrix.

The

least

squares

metric

multidimensional

scaling

or

MDS

problem

is

the

minimization

of

σ

and

over

all

m

p

configurations

X.

Here

w

ij

are

given

non-negative

weights

and

d

ij

are

given

non-negative

dissimilarities.

The

d

ij

(X)

are

the

Euclidean

distances

between

rows

i

and

j

of

X.

Thus

d

ij

p

x

x

where

w

ij

is

a

weight

for

the

measurement

between

a

pair

of

points

(i,

j),

d

ij

(X)

is

the

Euclidean

distance

between

i

and

j,

and

δ

ij

is

the

ideal

distance

between

the

points

(their

separation)

in

the

m-dimensional

data

space.

Note

that

w

ij

is

used

to

specify

a

degree

of

confidence

in

the

similarity

between

points

(e.g.

can

be

specified

if

there

is

no

information

for

a

particular

pair).

A

configuration

X

which

minimizes

σ

(X)

gives

a

plot

in

which

points

that

are

close

together

correspond

to

points

that

are

also

close

together

in

the

original

m-dimensional

data

space.

Programming

scripts

are

freely

available

at

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


Sharing costs in highways: A game theoretic approach This paper introduces a new class of games, highway games, which arise from situations where there is a common resource that agents will jointly use. That resource is an ordered set of several indivisible sections, where each section has an associated fixed cost and each agent requires some consecutive sections. We present an easy formula to calculate the Shapley value, and we present an efficient procedure to calculate the nucleolus for this class of games. In this paper, we consider situations where several agents use the same public resource, where the resource consists of a finite number of ordered sections, each with its corresponding cost, and where each agent makes use of a consecutive set of these sections. A simple example that illustrates the situation is the case of a linear highway. The sections are delimited by the entry and exit points and its cost depends on the section length and on the number of vehicles that use the section. Each car or truck only needs the highway sections between his entry and exit point. Obviously, this is a simplification of a highway. In a general highway other issues of primary importance, as the congestion problem, can be considered. In our context, congestion does not appear explicitly, but it may be implicit in the cost of each section. The * The authors acknowledge Gloria Fiestras-Janeiro and two referees for their valuable comments. This work has been financed by Ministerio de Innovación y Ciencia (through project ECO2008-03484-C02-02/ECO), by Universidad del País Vasco (UPV/EHU) (through projects 9/UPV 00031. 321-15352/2003 and UFI11/51) and by Gobierno Vasco (Ayudas a Grupos de Investigación IT-293-07).. † Corresponding author. Address: Dept. of Statistics and Operations Research, Faculty of Business Administration and Tourism, Vigo University, 32004 Ourense, Spain. Tel. +34 988368765, fax +34 988368923. congestion problem of a highway (network) has been studied from a non-cooperative game point of view (Rosenthal, 1973) and, more recently, from an evolutionary game theory point of view (Sandholm, 2002) . In a classical congestion problem, the agents have several options to get from a point to another in the network and the cost of an agent depends on the route he chooses, the amount of traffic on his route, and the toll. The agents have to choose a probability distribution over their options and one tries to find an equilibrium. A linear highway model does not lend itself for this approach since agents have only one way to get from a point to another. Moreover, congestion is not really the problem for our practical application in Spain and the paying toll is a tool of the highway managers to be paid back part (or all) of its investment. From now on, for clarity of presentation, we consider a linear highway as the public resource.. We address the problem of sharing the cost of such a highway among its users. We are interested in sharing cost methods which are based on the following principles: to exactly allocate the total cost of the highway; and to be axiomatized by axioms which many consider as reasonable and fair. Although these principles are widely accepted, the results on highway cost allocation are very often controversial. Allocating a cost share involves several factors such as different classes of vehicles, traffic characteristics, congestion, environmental factors, and so on. Some of the procedures to assign costs are proportional to the road length traveled, the passenger car equivalence, the single axle load equivalence, etc. Other methods are known as incremental: vehicle classes are introduced sequentially starting from the lightest one, and each time a new class is introduced the corresponding increment is allocated to this class. In turn, the U. S. Federal Highway Administration (1997) allocates construction costs and maintenance costs separately.. Cooperative game theory has proved to be very useful for finding "fair" methods for sharing common costs. The reader is referred to e.g. Young (1985) for applications. In the particular case of highway cost allocation, game theory was successfully applied in VillarrealCavazos and García-Díaz (1985) , Makrigeorgis (1991) , and Castaño-Pardo and García-Díaz (1995) , where the cost shares were allocated to the different vehicle classes. In contrast, Dong et al. (2012) allocates these shares between all the potential users, instead of the vehicle classes.. In this paper, we propose a simple transferable utility (TU) game model called highway game for this problem. In the model we assume that an agent is completely characterized by the sections it uses, which means that all vehicles in our model are of the same class. We also assume that each section has a fixed cost, which corresponds to either initial construction costs or to periodical maintenance costs. We then analyze the behavior of two well-known solutions concepts for TU games on the class of highway games, and we apply them on realistic data. Our model is similar to that in Dong et al. (2012) . The main difference with our work is in its purpose. They deal with axiomatizations of a cost sharing method (toll pricing method), while we focus on the computation of the Shapley value (Shapley, 1953) and the nucleolus (Schmeidler, 1969) .. It turns out that the Shapley value corresponds to a very simple way of sharing costs. We show that it allocates the cost of each section equally among the agents who use it. Another classes of games where Shapley value adopts very simple formulation are airport games (Littlechild and Owen, 1973) , tree games (Megiddo, 1978; Granot et al., 2002) , convex big boss games (Muto et al., 1988) , sequencing games (Curiel et al., 1989) , infrastructure games (Fragnelli et al., 2000) , peer group games (Brânzei et al., 2002) , and weighted majority games (Algaba et al., 2003) among others. Dong et al. (2012) show that the Shapley value coincides with the toll pricing method defined there. Moreover they also axiomatize it in the class of highway toll problems.. The nucleolus is more difficult to analyze. In Luskin et al. (2001) several methods are compared, and these authors recommend the nucleolus, despite the difficulties in computing it. In Villarreal-Cavazos and García-Díaz (1985) the use of the nucleolus (called by these authors the generalized method) is also suggested for sharing costs in a practical highway situation. In addition to these practical recommendations, the nucleolus is also supported by several axiomatizations. Perhaps one of the nicest appears in Sobolev (1975) (actually it is an axiomatization for the prenucleolus). Almost all the axioms used in Sobolev's axiomatization are satisfied by the nucleolus in the class of highway games. Nevertheless, the axiom of covariance can not be applied for highway games since, if an additive game is added to a highway game, the result is not necessarily a highway game. One could think in using axioms defined for the highway model setting, as those defined by Dong et al. (2012) . Nucleolus satisfies stand alone test and dummy but it does not satisfy cost recovery, routing-proofness, additivity and toll upper bound, as they are stated in Dong et al. (2012) . It seems hard to axiomatize the nucleolus in the class of highway games. It deserves a deeper analysis and we leave it for further research. Instead, we focus on to find a computationally efficient way of calculating the nucleolus of a highway game which takes up most of the length of this paper. We prove in this paper that highway games are concave, and although theoretically computationally efficient algorithms for the nucleolus exist for the entire class of concave games (Kuipers, 1996 and Faigle et al., 2001) , these algorithms do not perform well in practice. Efficient and practical algorithms have already been developed for calculating the nucleolus of several special classes of games, e.g. for standard tree games (Megiddo, 1978; Granot et al., 1996) , assignment games (Solymosi and Raghavan, 1994) , flow games (Potters et al., 2006) , and peer games (Brânzei et al., 2005) . The algorithms described in these papers are all based on the fact that a small family of coalitions determines the nucleolus. We too prove that the nucleolus of highway games is determined by a small family of coalitions, and we exploit it in our algorithm. In our approach, we combine it with the fact that the determining family for the nucleolus must satisfy a special version of the Kohlberg criterium for concave games, a theorem proved by Arin and Iñarra (1998) . We then apply the theory of reduced games (Maschler et al., 1972) and prove that the problem reduces to one or two smaller games that are also highway games. This yields a recursive algorithm for the nucleolus, for which we derived a worst-case complexity of O(m 5 ), where m is the number of sections.. It is worth mentioning that the class of airport games is a special subclass of highway games, and that our results are a generalization of the results in that paper. On the other hand, the class of savings games derived from highway games is a subclass of the class of realization games, introduced by Koster et al. (2003) to study the allocation of public goods. The results in that paper for concavity and the Shapley value generalize our results concerning these issues. Besides, one can consider extending our model to the case of a network. This is not an easy task as it is pointed out in Çiftçi et al. (2010) . Based on a preliminary version of our paper, they extend our model to a network. They show that, for keeping the concavity property of the games, one has to restrict the shape of the network to a very particular one: weakly cyclic networks where each cycle is composed by exactly three edges (what is called a weakly triangular graph).. The paper is organized as follows. In Section 2, we introduce highway problems and their related games, and we derive the results for concavity and the Shapley value of those games. Section 3 is devoted to the nucleolus and to its algorithm. Finally, we present in Section 4 a practical case where our results can be compared with the official rates.

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


Factors influencing acceptance of technology for aging in place: A systematic review Results: Sixteen out of 2841 articles were included. Most articles investigated acceptance of technology that enhances safety or provides social interaction. The majority of data was based on qualitative research investigating factors in the pre-implementation stage. Acceptance in this stage is influenced by 27 factors, divided into six themes: concerns regarding technology (e.g., high cost, privacy implications and usability factors); expected benefits of technology (e.g., increased safety and perceived usefulness); need for technology (e.g., perceived need and subjective health status); alternatives to technology (e.g., help by family or spouse), social influence (e.g., influence of family, friends and professional caregivers); and characteristics of older adults (e.g., desire to age in place). When comparing these results * Corresponding author at: Dominee Theodor Fliednerstraat 2, 5631 BN Eindhoven, The Netherlands. Tel.: +31 683991191. to qualitative results on post-implementation acceptance, our analysis showed that some factors are persistent while new factors also emerge. Quantitative results showed that a small number of variables have a significant influence in the pre-implementation stage.Fourteen out of the sixteen included articles did not use an existing technology acceptance framework or model.Acceptance of technology in the pre-implementation stage is influenced by multiple factors. However, post-implementation research on technology acceptance by community-dwelling older adults is scarce and most of the factors in this review have not been tested by using quantitative methods. Further research is needed to determine if and how the factors in this review are interrelated, and how they relate to existing models of technology acceptance. The majority of older adults prefer to live independently for as long as they possibly can [1] [2] [3] [4] . Supporting older adults to remain in their own homes and communities is also favored by policy makers and health providers to avoid the costly option of institutional care [5] . Research shows that several interrelated factors can challenge the independence of older adults: primarily functional and cognitive impairment, chronic diseases, a diminishing social network, and a low level of physical activity [6] [7] [8] [9] . Technology might provide a solution for some of these challenges, and particularly in the last decade, much effort has been invested in the development of technology to support aging in place, such as sensorbased networks for activity monitoring, fall and wandering detection, and various e-health applications. However, older adults explicitly reserve the right to decide for themselves what they allow into their own homes [10] , and questions have been raised on the readiness of community-dwelling older adults to accept and use these technologies [11] [12] [13] . Acceptance of technologies that are electronic or digital may be more difficult for the current generation of seniors which did not grow up with these types of technologies [14] [15] [16] . In an effort to understand older adults' usage and non-usage of modern technology, researchers often turn to two technology acceptance models, stemming from the field of information systems. The search in seven databases for factors influencing the acceptance of electronic technologies that support aging in place by community-dwelling older adults generated a total of 4692 results. After the removal of duplicate results, a total of 2841 unique articles were identified (Fig. 1) . The selection process initially led to the inclusion of 15 articles [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] . The snowball method added one article [53] , bringing the total number of articles included in this review to 16.

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


Variance reduction through robust design of boundary conditions for stochastic hyperbolic systems of equations We consider a hyperbolic system with uncertainty in the boundary and initial data. Our aim is to show that different boundary conditions gives different convergence rates of the variance of the solution. This means that we can with the same knowledge of data get a more or less accurate description of the uncertainty in the solution. A variety of boundary conditions are compared and both analytical and numerical estimates of the variance of the solution is presented. As applications, we study the effect of this technique on Maxwell's equations as well as on a subsonic outflow boundary for the Euler equations. In most real-world applications based on partial differential equations, data is not perfectly known, and typically varies in a stochastic way. There are essentially two different techniques to quantify the resulting uncertainty in the solution. Non-intrusive methods [1, 2, 3, 4, 5, 6, 7] use multiple runs of existing deterministic codes for a particular statistical input. Standard quadrature techniques, often in combination with sparse grid techniques [8] can be used to obtain the statistics of interest. Intrusive methods [9, 10, 11, 12, 13, 14, 15, 16] are based on polynomial chaos expansions leading to a systems of equations for the expansion coefficients. This implies that new specific non-deterministic codes must be developed. The statistical properties are obtained by a single run for a larger system of equations. There are also examples of semi-intrusive methods [18, 17] . The different procedures are compared in [19, 20] and a review is found in [21] .. In this paper we take a step back from the technical developments mentioned above and focus on fundamental questions for the governing initial boundary value problem, and in particular on the influence of boundary conditions. Our aim is to minimize the uncertainty or variance of the solution for a given stochastic input. The variance reduction technique in this paper is closely related to well-posedness of the governing initial boundary value problem. In particular it depends on the sharpness of the energy estimate, which in turn depend on the boundary conditions.. The technique used in this paper is directly applicable to hyperbolic linear problems such as for example the Maxwell's equations, the elastic wave equations and the linearised Euler equations where the uncertainty is known and limited to the data of the problem. The theoretical derivations are for simplicity and clarity done in one space dimension and for one stochastic variable. The extension to multiple space dimensions and stochastic variables is straightforward and would add more technical details but no principal problems.. We begin by deriving general strongly well posed boundary conditions for our generic hyperbolic problem [22, 23, 54, 33] . These boundary conditions are implemented in a finite difference scheme using summation-by-parts (SBP) operators [24, 29, 30, 31] and weak boundary conditions [38, 39, 40, 41] . Once both the continuous and semi-discrete problems have sharp energy estimates, we turn to the stochastic nature of the problem.. We show how to use the previously derived estimates for the initial boundary value problem in order to bound and reduce the variance in the stochastic problem. Finally we exemplify the theoretical development by numerical calculations where the statistical moments are computed by using the nonintrusive methodology with multiple solves for different values of the stochastic variable [36, 37] . The statistical moments are calculated with quadrature formulas based on the probability density distribution [34, 35] .. The remainder of the paper proceeds as follows. In section 2 the continuous problem is defined and requirements for well-posedness on the boundary operators for homogeneous and non-homogeneous boundary data are derived. In section 3 we present the semi-discrete formulation and derive stability conditions. Section 4 presents the stochastic formulation of the problem together with estimates of the variance of the solution. We illustrate and analyze the variance for a model problem in section 5. In section 6 and we study the implications of this technique on the Maxwell's equations and on a subsonic outflow boundary conditions for the Euler equations. Finally in section 7 we summarize and draw conclusions. We have studied how the choice of boundary conditions for randomly varying initial and boundary data influence the variance of the solution for a hyperbolic system of equations. Initially, the stochastic nature of the problem was ignored and the general theory for initial boundary value problems and Table 5 : The integral of the L 1 -norm of the variance for different values of the matrices R N , R S , R E and R W for ξ ∼ N (0, 1) and ξ ∼ U(− √ 3,. .. the related stability theory was used. Energy estimates for the continuous problem and stability estimates for the numerical scheme were derived for both homogeneous and non-homogeneous boundary conditions. Next, the stochastic nature of the problem were considered and it was shown how the variance of the solution depend on the variance of the boundary data and the correlation between the characteristic variables at the boundaries. We also confirmed the close relation between the sharpness of the energy estimate and the size of the variance in the solution.. The continuous and discrete spectrum of the problem was employed to draw conclusions about the quantitative variance decay of the solution. Numerical results for a hyperbolic model problem supporting our theoretical derivations were presented for three different cases.. With perfect knowledge of the boundary data, the optimal choice in terms of variance reduction is the characteristic boundary conditions. With decaying variance on the boundary, the correlation between initial and boundary data decide which type of boundary conditions gives the lowest variance for short times. For long times, the characteristic boundary conditions are superior. With large variance on the boundary, the conclusions drawn above continue to hold, although no clear theoretical support exist for that case.. In a fluid mechanics application, we study different subsonic outflow boundary conditions with large non-decaying randomness in the data for the Euler equations. The numerical studies showed that the characteristic boundary condition is more variance reducing than two common non-characteristic boundary conditions such as specifying the pressure or the normal velocity.. In the application in electromagnetics, again we study the characteristic boundary condition together with two non-characteristic boundary conditions with large non-decaying randomness in the data. The numerical results for the Maxwell's equations are in line with the fluid mechanics application and show that the characteristic boundary condition gives a higher variance decay than the non-characteristic ones.. The general conclusions from this investigation are i) with the same knowledge of data, we can get a more or less accurate description of the uncertainty in the solution, ii) the size of the variance depend to a large extent on the boundary conditions and iii) the characteristic boundary conditions are generally a good choice.

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


Power aware page allocation One of the major challenges of post-PC computing is the need to reduce energy consumption, thereby extending the lifetime of the batteries that p ower these mobile devic es. Memory is a particularly important tar get for e orts to improve energy e ciency. Memory technolo gy is becoming available that o ers power management features such as the ability to put individual chips in any one of several di erent power modes. In this paper we explor e the interaction of page plac ement with static and dynamic har dware p olicies to exploit these emer ginghardwar efeatures. In p articular, we c onsider p age allo cation p olicies that c an be employed b y an informed operating system to complement the hardware power management strategies. We perform experiments using two complementary simulation envir onments: a tracedriven simulator with workload traces that are r epresentative of mobile computing and an execution-driven simulator with a detaile d processor/memory model and a more memoryintensive set of benchmarks (SPEC2000). Our r esults make a compelling case for a cooperative hardwar e/softwar e approach for exploiting power-aware memory, with down to as little as 45% of the Energy Delay for the best static policy and 1% to 20% of the Ener gy Delay for a traditional fullpower memory. One of the major challenges of the post-PC environment|encompassing ubiquitous mobile, embedded, and wireless devices|is the need to reduce the energy consumed in their operation, thereby extending the lifetime of the batteries that pow er them.P ow er consumption is an issue that extends well beyond the realm of battery-powered mobile devices to an y computing platform in which the production of heat or fan noise is a consideration (e.g., medical applications). Energy e ciency of computers is also desirable from the economic and environmental poin ts of view.. Sustained exponential gro wth in processor performance and memory density means that embedded processors and handheld devices can soon have performance characteristics comparable to today's w orkstations.This increased performance is usually accompanied by increased pow er consumption. Memory is a particularly important target for e orts to address the energy e ciency issue. Instructions invoking memory operations have a relativ ely high po w er cost, both within the processor and in the memory system 43]. Intel's guidelines for mobile power 19] indicate that the target for main memory should be approximately 4% of the power budget (e.g. an average 1.3W for 96MB) for year 2000 laptops. This percentage can dramatically increase in systems with low p o wer processors (e.g., Transmeta Crusoe 15]), displays 35], or without hard disks. Recent studies 10] show that memory system behavior can produce variations in energy consumption of 100% for a pocket computer with the abo vecharacteristics versus 9% for a con ventionallaptop with a high pow er processor.. Since many small devices have no secondary storage and rely on memory to retain data, there are power costs for memory even in otherwise idle systems. The amount of memory available in mobile devices is expanding with each new model to support more demanding applications (e.g., m ultimedia)while the demand for longer battery life also con tinues to grow signi cantly.. Hardware components, such as memory chips, are becoming available that o er pow er management features. In particular, we consider pow er-aw are DRAM c hips that support several di erent power modes: activ e, standby, nap and pow erdo wn in order of decreasingpow er consumption but increasing access time. Our goal in this wo r k i s t o d etermine how to exploit these emerging hardware features for the most e ective main memory pow er management.. Speci cally, w e a s k t w o basic questions: 1. How can the various pow er modes available in state-ofthe-art DRAM devices be utilized? We consider both static and dynamic hardware policies for determining the power state. Our dynamic scheme uses the time between DRAM chip accesses to determine pow er state transitions. 2. What is the e ect of code and data placement within such power-aware memory chips? Thus w e consider page allocation strategies that complement the abilit y of the hardware to adjust pow er modes. Our w ork is based on the premise that a cooperative hardware/soft w are approac h will o er expanded opportunities for energy e ciency. A primary contribution of this paper is a quantitative study that explores the interaction of virtual memory page allocation with dynamic hardware policies to orchestrate the use of power modes provided in emerging DRAM devices.. We measure the energy savings within the memory system and any additional delay in execution time resulting from these power management strategies, expressed in terms of an Energy Delay metric. Using trace-driven simulation with a simpli ed processor and memory system model we evaluate our ideas for a set of productivity applications as a workload representative of mobile laptop and handheld devices. We also use an execution-driven simulator with a more detailed processor and memory model to evaluate a set of programs from the integer SPEC2000 suite that place higher demands on the memory system than the available traces.. Our results show the following: Among static policies in which every power-aware DRAM chip in the system resides in the same base power mode between accesses, choosing the nap mode as the base achieves the lowest Energy Delay product for our workload (15% to 40% of staying in active mode). Power-aware page allocation by an informed operating system coupled with dynamic hardware policies can dramatically improve energy e ciency of memory. Power-aware allocation allows a 6% to 55% improvement i n Energy Delay over the best static hardware policy. Power-aware page allocation when used with static hardware policies can improve Energy Delay b y u p t o 30%. Dynamic hardware policies without informed OS support (i.e., using random page allocation) do not improve energy e ciency as measured by Energy Delay. In the next section, we describe the power-managed memory technology upon which this study is based and present related work. Section 3 describes the policies that determine which p o wer mode each c hip should be in. Then, in Section 4, we discuss simple page allocation strategies that exploit the power management features of the hardware. Section 5 presents our experimental methods and results are presented in Section 6. Finally, we conclude in Section 7 and describe future work. In this paper, we have built a compelling case for cooperative hardware/software policies that can exploit the power management features o ered by new PADRAM memory devices, such as the Rambus RDRAM, to dramatically improve the Energy Delay of main memory. We use tracedriven simulations of a set of personal productivity applications and execution-driven simulation of integer SPEC2000 benchmarks to evaluate static and dynamic hardware policies that determine the power states of each memory chip. We s h o w that statically assigning the nap mode as the base power mode for all memory chips in a system is a successful strategy, a c hieving an Energy Delay of 15% to 40% of static active m o d e . We s h o w t h a t p o wer aware page allocation can improve e n e r g y e c i e n c y b y up to an additional 30%.. Using power-aware page allocation in conjunction with hardware policies that dynamically adjust the power mode of each individual memory chip based on thresholds of inactivity can provide 6% to 55% improvement in Energy Delay over the best static hardware policy and o ers 99% to 80% improvement o ver a traditional full-power memory system with random page placement.. There are many opportunities left for future work with the PADRAM model of memory devices and especially with the interaction between hardware and software management. Following our belief that energy conservation should become a \ rst class" design goal for higher levels of system design, many of our plans explore ways to give the OS more explicit control over PADRAM power modes. This may e v en eventually extend into API features that allow some degree of application-level direction of memory power states. Sequential rst-touch is a simple page allocation scheme. We may consider other \page coloring" techniques and further explore the movement o f pages between chips to improve initial placements based on observed access patterns.. We note that our clustered page allocation has other power-related side-e ects. It can also be used to reduce DRAM refresh rates. By compacting physical pages into the minimum numberofinternal memory banks, we can potentially eliminate refresh for entire DRAM banks in which there are no active pages.. The threshold values in our dynamic policy are an important parameter. Unfortunately, using the same threshold value for all programs and all PADRAM chips may not produce the best results. Thus, another possible direction we are exploring is a dynamic policy that attempts to adap-tively determine the best threshold values for each c hip.. Our dynamic policies have concentrated on the transition into lower power states. Policies that support pretransitioning into higher power states, in anticipation of imminent access in a manner analogous to prefetching, may also have a role to play in improving the Energy Delay m e tric of some applications. Architectural studies have examined the impact of software structure on power consumption 6, 34, 44] 40] . However, to our knowledge, ours is the rst quantitative study to explore the interaction of page allocation with dynamic hardware policies to orchestrate the use of power modes being provided in emerging PADRAM-class memory devices.. A novel aspect of our work is the cooperative hardware/OS approach to exploit PADRAM features. Previous OS-level studies focusing on power management include work on scheduling for low p o wer processor modes 31, 32, 46] , spindown policies for disks and alternatives 1, 7, 8, 9, 16, 25, 30, 47] , and managing wireless communication 18, 24, 42] . A consortium of companies has developed a speci cation 20] that addresses the lower-level OS/device interface, providing one model for gross system-wide power states and per-component p o wer states as a basis for the development of OS-directed power management. Recent w ork with Odyssey 37, 11] demonstrates how system support for application-aware adaptation can bene t energy e ciency. Common themes that appear in these power management strategies are the identi cation / prediction of idleness in the activity patterns of a component and techniques that attempt to change those activity patterns. A particularly valuable approach i s b a s e d on the \ski rent-to-buy" problem formulation for competitive algorithms 22, 25] .. Another related area involves operating system page placement policies. Virtual memory page research originally concentrated on techniques for improving program execution time, focusing on replacement algorithms. Recent studies examined page coloring policies for selecting appropriate physical page frames to minimize cache misses 2, 23] . Other recent w ork has studied page placement aimed at improving TLB performance 41] or NUMA multiprocessor memory access 27, 28, 45, 12, 3] . Each of these problems bears some resemblance to the issues we face since they all attempt to exploit the exibility a vailable in mapping virtual to physical pages. To e v aluate energy e ciency, w e use the Energy Delay product 13]. This metric captures our goal of achieving high performance (seconds) while minimizing energy consumption (joules). Although total system energy consumption is important, it is highly dependent on speci c design choices (e.g., processor, display type, wireless network interface, etc.). Therefore, we concentrate only on PADRAM energy consumption, and ignore the energy consumed by all other system components.. To compute energy e ciency, we developed two simulators: a trace-driven simulator and a detailed executiondriven out-of-order processor simulator. One of the primary considerations that went i n to our experimental design was the choice of a workload that would seem appropriate to mobile/wireless devices. The availability o f traces from a set of popular applications used on laptops motivated the development of our trace-driven simulator. While these traces satis ed the need for a representative w orkload for the target environment, they had disadvantages for memory research: low miss rates and the constraints of trace-driven simulation (e.g., no detailed processor timing). Thus, the executiondriven simulator was developed to address the need for a more detailed processor/memory model and more memoryintensive b e n c hmarks. Rambus. While the values for a particular power state are taken from the literature, we approximate the power consumption associated with a transition between two power states as the average of the power consumed in the two states. The total energy consumption depends on the time for the transition to complete, also shown in Table 1 .

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


Local Scalar Trust Metrics with a Fuzzy Adjustment Method The interactions between people who do not know each other have been greatly increased with the on-going increase of people's cyberspace activities. In this situation, there exist potential risk factors such as the possibility of fraud, so we need a method to reduce or eliminate those risk factors. Concerning this necessity, rating systems are widely used, and many trust metrics calculated from rate values that people give to each other are proposed to help them make decisions. However, the trust metrics decrease the accuracy, and this is caused by the different rating scales and ranges of each person. So, we propose a fuzzy adjustment method to solve this problem. It is possible to catch the exact meaning of the trust value that each person selects through applying fuzzy sets, which improve the accuracy of the trust metric calculated from the trust values. We have applied our fuzzy adjustment method to the TidalTrust algorithm, a representative algorithm for calculating the local scalar trust metric, and we performed an experimental evaluation with four data sets and three evaluation methods. Interactions that are mediated by the Internet are immensely increasing due to the on-going development of the Internet. Those interactions often take place between people who do not know each other. People tend to be uncomfortable while interacting with strangers and they are interested in finding ways to relieve their discomfort. Therefore, rating systems are widely used in which users choose rate values within a given range to express their opinion of a specific topic. The range given to users can be either continuous or discrete, and a discrete range is more commonly used. Value scales have to be defined for a discrete range, and the simplest one is using binary values and values from two to ten are generally used.. However, the actual value range that each user chooses can vary. For instance, let us suppose that each user shows the reliability of their neighbors by selecting a number from one to ten from a predefined range of 1 to 10 (number ten means fully trusted). Fig. 1 demonstrates this with a weighted directed graph; the circle and arrow express the person and the direction of trust, respectively. Here, user B and user C equally give trust rates to their three neighbors. User C uses a wide range (rate 3, 6, and 9), while user B uses a narrow range (rate 7, 8, and 9). The rate value that is selected by each user is utterly subjective, so no one can raise an objection about that. However, it becomes a completely different issue when there is a specific method based on these values. For instance, user A needs a control mechanism when he allows user F or user H access to his personal data. If user A adopts an access control method based on trust values, he would consult user B and user C because he does not directly know user F and user H. User A equally gives a trust value of eight to both user B and user C. Therefore, the trust value from user A to user F is seven and the trust value from user A to user H is six. Accordingly, user A concludes that user F is more trusted than user H.. However, this is not fair because it seems that seven means low trust from the viewpoint of user B and six means average trust from the viewpoint of user C.

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