Linear B cell epitope prediction for epitope vaccine design against meningococcal disease and their computational validations through physicochemical properties Abstract Neisseria meningitidis serogroup B is predominantly known for its leading role in bacterial meningitis and septicemia worldwide. Although, polysaccharide conjugate vaccines have been developed and used successfully against many of the serogroups of N. meningitidis, such strategy has proved ineffective against group B meningococci. Here, we proposed to develop peptide epitope-based vaccine candidates from outer membrane (OM) protein contained in the outer membrane vesicles (OMV) based on our in silico analysis. In OMV, a total of 236 proteins were identified, only 15 (6.4 %) of which were predicted to be located in the outer membrane. For the preparation of specific monoclonal antibodies against pathogenic bacterial protein, identification and selection of B cell epitopes that act as a vaccine target are required. We selected 13 outer membrane proteins from OMV proteins while taking into consideration the removal of cross-reactivity. Epitopia web server was used for the prediction of B cell epitopes. Epitopes are distinguished from non-epitopes by properties such as amino acid preference on the basis of amino acid composition, secondary structure composition, and evolutionary conservation. Predicted results were subject to verification with experimental data and we performed string-based search through IEDB. Our finding shows that epitopes have general preference for charged and polar amino acids; epitopes are enriched with loop as a secondary structure element that renders them flexible and also exposes another view of antibody-antigen interaction. The interaction between an antibody and its antigen is at the heart of the humoral immune response. The detection of highly immunogenic regions within a given protein, specifically those that elicit a humoral immune response, i.e., B cell epitopes, is central to many immunodetection and immunotherapeutic applications (Irving et al. 2001) . Antibodies bind to their corresponding antigens at discrete sites known as antigenic determinants of epitopes. The precise localization of an epitope can be essential in the development of biomedical applications such as designed vaccines, diagnostic kits, and immune-therapeutics (Westwood and Hay 2001) . Predicting epitopes is fundamental to the understanding of the basis of immunological discrimination between self and non-self as well as for mechanisms of bio-recognition in general. Since proteins are one of the most abundant and diverse class of antigens, antigens of infectious agents, and allergens, much of the interest in antigen characteristics is focused on antigenic proteins (Rubinstein et al. 2008) .. B cell epitopes are classified into two different groups. The first group consists of linear or continuous epitopes. A continuous epitope comprises a single, consecutive stretch of amino acids in the protein sequence, which is specifically recognized by an antibody raised against the intact protein. The second group is formed by conformational or discontinuous epitopes. These are epitopes composed of residues separated in the protein sequence, but in spatial proximity because of the protein fold (Van Regenmortel et al. 1996) . It has been suggested that sequence-level analysis at exon and peptide level can be a valuable profile (Liou, and Huang, 2012) . A specific and isolated linear peptide, derived from the sequence of a given antigen, was able to elicit antibodies that not only bound the peptide, but also strongly cross-reacted with the native antigen. Peptides containing linear epitopes are considered to have a high potential for vaccines. In addition to the advantages of being used for vaccines; peptides are easily synthesized, purified, stored, and handled (Andersen et al. 2008) .. Neisseria meningitidis is a major cause of childhood meningitis and septicemia worldwide. Meningococcal disease is associated with a high fatality rate (approximately 10 %) and survivors may develop permanent abnormalities such as deafness, seizures, amputation, and mental retardation (Offit and Peter 2003) . A Gram-negative encapsulated bacterium Neisseria meningitides is classified into groups according to the chemical composition and immunogenic properties of capsular polysaccharides. Serogroups A, B, C, W135, and Y account for [95 % of the infections. Capsular polysaccharide or capsular polysaccharide conjugate vaccines are available against serogroup A, C, Y, and W135 strains (Jódar et al. 2002; Morley et al. 2001; Rouphael and Stephens 2012) . However, no capsule-based vaccine is available for N. meningitidis serogroup B. The immune system tolerates serogroup B capsular polysaccharide because of its similarity to human carbohydrate a(2 ? 8)N-acetyl neuraminic acid or polysialic acid, both consisting of repeated units of two to eight linked sialic acid (Finne et al. 1983 ). Therefore, our study is focused on proteins of the outer membrane for development of effective epitope vaccine against meningococcal diseases. In our previous work, we predicted and characterized the T cell epitopes for epitope vaccine design from OMV proteins of N. meningitidis serogroup B (Chandra et al. 2010 ).. An unguided experimental search for such regions is clearly laborious and resource intensive. Thus, computational approaches that are able to perform this task are desired. The humoral immune response is based on antibody-antigen interaction which leads to a solution of numerous protein-protein interfaces. We compared these interfaces with respect to physico-chemical properties at the amino acid level from amino acid composition. Aside from the physico-chemical character of the interfaces, secondary structure content was also found to be an important property of protein-protein interfaces, and has been used as one of the analysis parameters. In the present study, the outer membrane proteins of outer membrane vesicles of Neisseria meningitidis serogroup B were used for the prediction of linear B cell epitopes. Amino acid composition, physico-chemical properties, and secondary structure element preference were analyzed to identify putative patterns in the predicted epitopes. The predicted epitopes from the genome/proteome sequences of the pathogens would greatly reduce the time as well as cost and be useful for experimental planning in the development of epitope vaccines. We have predicted numerous epitopes in OMV proteins, which would be useful for the earlier identification of meningitis and septicemia. The predicted epitopes may be used for safe vaccine development against meningococcal diseases. Epitopes are enriched with charged and polar residues and depleted of hydrophobic ones. Epitopes are enriched in loops and depleted in helix to beta strands. The lack of conservation can be partially explained by the enrichment of loops in epitopes, as they are relatively tolerant to amino acid replacements. An additional explanation for the lack of epitope conservation involves self-tolerance, as the conserved regions of antigen, which are expected to have much lower potential for eliciting an immune response, may also present in the host itself. These results have important implications for the development and use of vaccines based on epitopes of OM proteins subsequent to in vitro validations. However, all the findings of this study will definitely aid in the process of vaccine design. Fig. 2 Distributions of secondary structure elements in epitopes. Major secondary structure elements such as loops, helices, and sheets have been incorporated. These elements and their frequency of distribution have been represented on the x and y axes, respectively

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


Mitochondrial Approaches for Neuroprotection A large body of evidence from post-mortem brain tissue and genetic analysis in man and biochemical and pathological studies in animal models (transgenic and toxin) of neurodegeneration suggest that mitochondrial dysfunction is a common pathological mechanism. Mitochondrial dysfunction due to oxidative stress, mitochondrial DNA deletions, pathological mutations, altered mitochondrial morphology and interaction of pathogenic proteins with mitochondria leads to neuronal demise. Therefore, therapeutic approaches targeting mitochondrial dysfunction and oxidative damage hold great promise in neurodegenerative diseases. This review discusses the potential therapeutic efficacy of creatine, coenzyme Q10, idebenone, synthetic triterpenoids, and mitochondrial targeted antioxidants (MitoQ) and peptides (SS-31) in in vitro studies and in animal models of Parkinson's disease (PD), Huntington's disease (HD), Amyotrophic Lateral Sclerosis (ALS) and Alzheimer's disease (AD). We have also reviewed the current status of clinical trials of creatine, coenzyme Q10, idebenone and MitoQ in neurodegenerative disorders. Further, we discuss newly identified therapeutic targets including PGC-1α and Sirtuins, which provide promise for future therapeutic developments in neurodegenerative disorders.

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


Multiresolutional Gaussian Mixture Model for Precise and Stable Foreground Segmentation in Transform Domain This paper describes a multiresolutional Gaussian mixture model (GMM) for precise and stable foreground segmentation. A multiple block sizes GMM and a computationally efficient fine-to-coarse strategy, which are carried out in the Walsh transform (WT) domain, are newly introduced to the GMM scheme. By using a set of variable size block-based GMMs, a precise and stable processing is realized. Our fine-to-coarse strategy comes from the WT spectral nature, which drastically reduces the computational steps. In addition, the total computation amount of the proposed approach requires only less than 10% of the original pixel-based GMM approach. Experimental results show that our approach gives stable performance in many conditions, including dark foreground objects against light, global lighting changes, and scenery in heavy snow. key words: foreground segmentation, Gaussian mixture model, fine-tocoarse strategy, Walsh transform, variable block size Foreground segmentation is a fundamental, but tough task in many computer vision applications such as video surveillance and auto-cruising. The goal of the foreground segmentation process is to detect moving objects from the current frame. Many background subtraction techniques are widely studied for this purpose in recent years [1] .. One of the best formulations is to introduce a Gaussian mixture model (GMM) into every pixel in the background. Stauffer and Grimson have proposed the GMM scheme which consists of the RGB components of a pixel [2] . This scheme is suitable for the approximation of a multimodal distribution of the background, and can be used to cope with dynamic scenes. However, the pixel-level processing has crucial drawbacks, such as a large amount of computation and that of memory capacity.. In order to overcome these problems, the employment of spatial correlation is incorporated into the GMM scheme. For the real-time processing, a hierarchical data structure is employed for the GMM scheme in [3] . This approach decreases the processing amount, compared to the GMM without using the structure. A block-based simplified GMM scheme is employed for a DSP implementation in [4] . The pixel-level foreground processing is limited within the detected blocks, based on the result of the subsequent simplified GMM scheme as coarse processing. These hierarchical approaches achieve the reduction of the calculation amount rather than the improvement of segmentation performance.. Further reduction of the computation amount is achieved by the use of a Discrete Cosine Transform (DCT) based single Gaussian model [5] . In addition to the single Gaussian model, the use of only luminance component information contributes simplification of the computational complexity. This method employs two feature parameters, the DC parameter and an AC parameter which correspond to the lowest frequency coefficient and a set of low frequency coefficients, respectively. These parameters can be considered to represent intensity and texture information. The AC feature parameter is defined by a sum of low frequency coefficients. Therefore, its distribution is assumed to be a single Gaussian, because of the Central Limit Theorem. However, the employment of the single Gaussian has caused false decision under dynamic background motions, as well as generating separated segments from a single foreground object.. In this paper, a precise, stable and computationally efficient foreground segmentation method is proposed. Our framework has two novel components: a multiple block sizes GMM and a fine-to-coarse approach, which are carried out in the Walsh transform (WT) domain. Different spatio-temporal performance is achieved by the multiple block sizes GMM. The process in a small block size mode gives precise but unstable results. On the other hand, stable but rough results are produced in a large block size mode. By combining these results, precise and stable performance is achieved. The fine-to-coarse processing is efficiently performed by introducing Walsh spectrum feature parameters. According to the WT spectral nature, a set of fine processing can be realized by an interim result for a coarse processing. Another possible approach is to employ DCT, as motion pictures are normally compressed by MPEG or H.264. However, a direct application of compressed video in the DCT or another transform domain is not possible except Intra coded Pictures (I-pictures), which do not employ the frame difference and appear once in every group of several pictures [6] . In order to realize frame-by-frame foreground segmentation, a computationally simple transform is preferable.. The rest of the paper is organized as follows. Our background models in the WT domain are described in Sect. 2. The fine-to-coarse processing and the use of multiple thresholds are explained in Sect. The precise and stable foreground segmentation algorithm has been implemented. The algorithm is based on the multiresolutional Gaussian mixture model formulation on the multiple block sizes. The Walsh Transform spectral nature realizes the computationally efficient fine-to-coarse strategy and reduces the computational steps to generate the feature parameters. Threshold decision to different block size modes is also clarified for precise and stable processing. Experimental results show the stable separation of foreground objects under several conditions, such as dark object against light, global lighting changes, uncovered region, repetitive motions of background and scenery in heavy snow.

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


BioUSeR:

a

semantic-based

tool

for

retrieving

Life

Science

web

resources

driven

by

text-rich

user

requirements

Background:

Open

metadata

registries

are

a

fundamental

tool

for

researchers

in

the

Life

Sciences

trying

to

locate

resources.

While

most

current

registries

assume

that

resources

are

annotated

with

well-structured

metadata,

evidence

shows

that

most

of

the

resource

annotations

simply

consists

of

informal

free

text.

This

reality

must

be

taken

into

account

in

order

to

develop

effective

techniques

for

resource

discovery

in

Life

Sciences.

Results:

BioUSeR

is

a

semantic-based

tool

aimed

at

retrieving

Life

Sciences

resources

described

in

free

text.

The

retrieval

process

is

driven

by

the

user

requirements,

which

consist

of

a

target

task

and

a

set

of

facets

of

interest,

both

expressed

in

free

text.

BioUSeR

is

able

to

effectively

exploit

the

available

textual

descriptions

to

find

relevant

resources

by

using

semantic-aware

techniques.

Conclusions:

BioUSeR

overcomes

the

limitations

of

the

current

registries

thanks

to:

(i)

rich

specification

of

user

information

needs,

(ii)

use

of

semantics

to

manage

textual

descriptions,

(iii)

retrieval

and

ranking

of

resources

based

on

user

requirements.

In

this

paper

we

present

BioUSeR,

a

tool

that

assists

researchers

in

Life

Sciences

in

the

discovery

of

the

most

appropriate

web

resources

for

their

well-defined

requirements.

With

BioUSeR,

users

can

easily

find

out

web

resources

that

were

previously

unknown

to

them

because

fell

out

of

the

scope

of

their

main

field

of

interest,

or

were

poorly

categorized

with

existing

tags..

BioUSeR

assists

all

kinds

of

users

from

the

requirements

specification

until

the

selection

of

the

most

appropriate

resources,

not

only

by

allowing

the

customization

of

the

queries,

but

also

by

making

the

specification

of

the

information

needs

easier

to

non-expert

users..

The

main

novelty

of

BioUSeR

with

respect

to

existing

registries

is

that

it

deals

with

text-rich

descriptions

of

the

registered

resources

apart

from

the

provided

metadata.

For

this

purpose,

BioUSeR

applies

automatic

semantic

annotation

and

information

extraction

processes.

As

a

result,

this

tool

automatically

generates

two

kinds

of

metadata:

semantic

annotations

and

facet-value

pairs.

Thus,

BioUSeR

aims

to

create

metadata

that

are

useful

to

fulfill

the

user

requirements,

which

are

usually

stated

as

free

text

descriptions

and

facet-based

requests..

Future

work

will

be

mainly

focused

on

improving

both

the

annotation

system

and

the

extraction

of

facet-values.

The

annotation

system

needs

to

be

extended

with

new

knowledge

resources

containing

specific

Bioinformatics

algorithms

and

methods

that

are

now

poorly

covered

by

the

selected

ontologies.

Also,

it

is

necessary

to

treat

ambiguous

annotations

that

can

produce

noise

in

the

retrieval

of

resources.

As

for

the

extraction

of

facets,

an

automatic

method

to

find

out

relevant

patterns

for

a

facet

should

be

designed.

In

this

way,

the

definition

of

new

facets

and

its

inclusion

into

the

tool

will

be

even

easier.

Another

interesting

issue

for

future

work

is

to

study

new

methods

for

re-ranking

retrieval

results

according

to

the

existing

relations

between

tasks.

The

main

aim

of

this

re-ranking

is

to

improve

the

compatibility

between

the

retrieved

resources

of

each

task.

Finally,

our

final

aim

is

to

build

an

unified

repository

with

existing

ones

(e.g.,

BioCatalogue,

SSWAP,

myExperiment

and

so

on),

and

integrate

it

with

Taverna

through

BioUSeR.

Most

of

the

current

registries

base

the

discovery

of

resources

on

keywords

or

concepts

coming

from

their

own

ontologies

that

describe

the

tasks

or

the

input

and

output

data

types.

There

are

approaches

that

use

string

matching

techniques

with

all

the

available

information,

and

others

that

are

based

only

on

the

metadata

of

the

resources.

The

former

assume

that

users

know

the

vocabulary

with

which

the

resources

are

described,

since

they

have

to

specify

the

correct

keywords.

The

latter

do

not

take

into

account

all

the

information

available

in

the

resource

descriptions

and

documentation

which

are

expressed

in

natural

language,

so

they

assume

that

resources

are

provided

with

useful

metadata

and,

as

we

have

mentioned

before,

this

does

not

happen

in

current

open-metadata

registries

for

Life

Sciences..

Our

approach

allows

the

user

to

specify

her

information

needs

as

rich

textual

descriptions.

While

current

registries

do

not

allow

the

user

to

combine

in

the

same

query

information

about

the

task

and

the

features

of

the

resources,

in

BioUSeR

the

user

can

provide

a

description

of

the

functional

tasks

she

needs

to

be

executed

to

achieve

a

goal

together

with

the

set

of

relevant

features

that

the

retrieved

resources

must

have.

Currently,

BioUSeR

supports

the

following

facets:

input

and

output

data

types,

the

method

and

the

disease

and

species

involved,

but

a

new

facet

can

be

easily

added

by

only

determining

its

adequate

information

extraction

patterns

and

the

involved

semantic

types.

Considering

the

features

of

the

resources,

we

bring

to

our

tool

the

well-known

advantages

of

using

facets

to

restrict

the

search

and

enhance

the

efficiency

and

precision

of

current

information

retrieval

systems

As

a

result,

BioUSeR

makes

possible

that

users

without

any

special

training

can

specify,

with

rich

textual

descriptions,

all

the

features

of

the

required

solution

for

their

information

needs.

BioUSeR

allows

this

kind

of

search

because

of

the

normalization

of

data..

Moreover,

most

of

the

resources

metadata

are

expressed

in

textual

descriptions

and

few

well-defined

metadata

are

available

on

open

registries.

As

many

Life

Science

researchers

recognize,

to

manually

describe

their

resources

by

using

standards

is

a

very

complex

task

that

usually

produces

incomplete

and

imprecise

descriptions,

being

this

the

reason

for

which

almost

always

they

prefer

to

describe

them

by

means

of

free

texts

with

nonstandard

vocabularies.

In

BioUSeR,

the

user

requirements

specification

and

all

the

available

resources

metadata

are

semantically

annotated

with

widely

accepted

Life

Science

ontologies

such

as

UMLS

and

myGrid.

Additionally,

information

extraction

techniques

are

used

to

identify

in

the

resources

metadata

relevant

information

about

the

set

of

facets

supported

by

the

system.

BioUSeR

looks

for

information

about

the

facets

in

all

the

available

metadata

of

the

resource,

and

not

only

on

specific

fields

as

most

current

registries

search

engines

do.

The

automatic

normalization

enriches

the

system

information

with

formal

knowledge

and

provides

two

main

benefits

to

the

system:

(i)

it

avoids

the

problem

of

the

use

of

specific

vocabularies

and

(ii)

it

allows

the

system

to

exploit

all

the

available

information

of

the

web

resources

independently

of

the

characteristics

of

the

metadata..

BioUSeR

retrieves

the

resources

by

the

semantic

mapping

of

the

normalized

user

requirement

specification

and

the

normalized

resources

metadata,

and

provides

the

user

with

a

ranking

of

the

retrieved

resources,

while

current

registries

only

provide

a

list.

Both

the

retrieval

and

the

ranking

of

resources

are

driven

by

the

functional

task

and

the

set

of

user-defined

facets.

Thus,

for

each

task

described

in

the

requirements

specification,

the

system

prompts

to

the

user

a

short

ranked

list

of

web

resources

that

could

be

used

to

execute

it.

Then,

the

user

can

easily

select

a

resource

for

each

task

and

to

define

a

sequence

of

resources

that

can

be

seen

as

a

workflow

specification.

In

order

to

assist

the

user

in

the

selection,

the

available

metadata

of

each

resource

can

be

visualized.

As

the

discovery

process

is

a

cyclic

process,

if

some

of

the

retrieved

resources

are

not

considered

adequate

for

the

user

requirements,

the

user

can

modify

the

initial

requirements

specification

so

that

alternative

resources

can

be

explored.

Next,

we

provide

a

brief

description

of

the

features

of

current

web

resource

registries

in

the

Life

Sciences..

Feta

is

a

faceted

retrieval

system

for

Life

Sciences

resources

in

which

the

user

queries

are

based

on

the

input

and

output

data

types,

the

method

or

type

of

an

operation,

or

a

phrase

contained

in

the

description

of

an

operation.

Feta

requires

that

resources

have

to

be

manually

annotated

with

the

my

Grid

ontology,

and

then,

searches

must

be

also

based

on

this

ontology.

In

this

work,

ranking

is

not

applied

because

they

argue

the

registry

is

very

small..

BioMoby

is

an

open-source

research

project

whose

aim

is

to

implement

a

web-resources

registry

to

facilitate

the

discovery

and

sharing

of

biological

data.

Resources

are

registered

in

MOBY

Central

by

using

a

model

that

allows

search

and

retrieval

based

on

object

and

resource

hierarchies.

Users

may

request

a

search

for

available

resources

based

on

their

input,

output,

resource

type

or

authority

by

using

keywords.

EMBRACE

Resource

Registry

is

a

Life

Sciences

web

resources

registry

with

built-in

resource

testing.

Resources

are

syntactically

annotated

using

BioXSD

The

search

is

based

on

the

string

matching

of

keywords.

This

registry

is

the

prelude

to

BioCatalogue.

BioCatalogue

is

a

Life

Sciences

registry

that

provides

a

common

interface

for

registering,

browsing

and

annotating

Life

Sciences

web

resources.

Web

resources

in

BioCatalogue

can

be

annotated

with

categories,

tags

and

descriptions.

These

annotations

are

manually

provided

by

the

resource

providers

and

the

user

community

plus

some

monitoring

and

usage

analysis

data

obtained

automatically

by

BioCatalogue

servers.

However,

at

the

moment,

most

of

these

annotations

are

expressed

as

free

text

without

following

any

controlled

vocabulary.

The

resource

discovery

is

mainly

based

on

both

keyword

search

and

filtering

mechanisms.

Filters

can

be

applied

over:

resource

type,

provider,

submitter

and

country.

To

enhance

its

accessibility

and

usability,

BioCatalogue

is

indexed

by

search

engines

such

as

Google

TM

It

also

provides

a

programmable

API

which

is

used

by

third-party

applications

such

as

Taverna

SSWAP

proposes

an

architecture,

a

protocol

and

a

platform

to

semantically

discover

and

integrate

heterogeneous

disparate

resources

on

the

web.

Unfortunately,

this

approach

heavily

relies

on

the

provided

metadata,

which

is

usually

poorly

described.

SSWAP

provides

two

types

of

searches:

keyword

search

and

resource

query

graph.

The

keyword

search

presents

the

problems

related

with

the

selection

of

keywords

and

the

lack

of

useful

metadata.

Resource

graph

query

requires

training

to

learn

how

to

build

the

graph

and

how

to

express

the

queries

with

this

format,

which

means

a

high

effort

for

those

users

not

familiar

with

these

technologies..

Magallanes

is

a

library

of

algorithms

aimed

at

discovering

bioinformatics

web

resources.

The

search

is

based

on

a

Google

TM

-like

approach,

in

which

the

user

keywords

are

matched

to

metadata

descriptions

improved

by

the

Did

you

mean...?

algorithm

which

helps

the

user

to

build

the

query.

Search

can

be

performed

on

data

type,

resource

and

resource

type

fields

and

it

is

improved

by

a

learning

process

from

users

feedback.

Moreover,

Magallanes

provides

a

way

of

composing

compatible

resources

into

workflows.

myExperiment

is

a

Life

Science

repository

whose

main

resources

are

workflows

but

other

research

objects

can

also

be

registered

in

it.

It

has

been

developed

in

the

same

project

as

BioCatalogue.

The

workflows

can

be

annotated

with

tags,

a

description,

object

type

and

other

information

about

the

provider

like

the

country,

etc.

The

search

is

based

on

keywords

and

filters

over

the

previous

fields.

myExperiment

provides

also

information

about

the

popularity

of

the

resource,

such

as

the

times

the

resource

has

been

viewed

or

downloaded,

and

a

rating

scale

that

reports

about

the

quality

of

the

resource..

Taverna

is

a

workflow

construction

environment

and

execution

engine

designed

to

support

in

silico

experiments

developed

by

the

European

Bioinformatics

Institute

(EBI)

and

University

of

Manchester.

Taverna

is

part

of

my

Grid

project

and

so

is

aligned

to

BioCatalogue

and

myExperiment.

It

is

able

to

build

complex

workflows,

to

execute

them

and

to

display

the

different

types

of

results.

The

user

selects

the

resources

with

a

keyword

search.

Taverna

contains

the

BioMoby

resources

and,

therefore,

the

input

and

output

data

types

are

well

defined.

Other

resources

can

be

imported

to

Taverna..

SADI

framework

is

a

registry

that

uses

standardcompliant

Semantic

Web

Resource

design

patterns

that

simplify

the

publication

of

resources

and

their

subsequent

discovery

in

domains

such

as

bioinformatics.

Providers

have

to

follow

SADI

conventions

to

publish

their

resources,

and

users

have

to

create

SPARQL

queries

in

order

to

discover

the

desired

resources,

which

implies

that

users

have

to

know

the

SPARQL

query

language,

which

supposes

an

extra

effort

for

example

for

biologists,

that

may

not

be

experts

in

these

technologies.

Our

approach

consists

of

three

phases

as

depicted

in

Figure

The

main

purpose

of

our

method

is

to

normalize

both

the

user

requirements

and

the

web

resources

metadata

in

order

to

compare

them

and

to

discover

the

web

resources

that

best

match

the

user's

needs.

In

this

section

we

explain

the

methods

and

techniques

applied

at

each

phase.

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


A programming language independent framework for metrics-based software evolution and analysis Abstract. Knowledge about different aspects of software quality during software evolution can be valuable information for developers and project managers. It helps to reduce the number of defects and improves the internal structure of software. However, determining software's quality and structure in heterogeneous systems is a difficult task. In this paper, a programming language independent framework for evaluating software metrics and analyzing software structure during software development and its evolution will be presented. The framework consists of the SMIILE tool for calculation of software metrics, extended with an analysis of software structure. The data are stored in a central repository via enriched Concrete Syntax Tree (eCST) for universal source code representation. The framework is demonstrated in a case study. The development of such a framework is a step forward to consistent support for software evolution by providing a change analysis and quality control. The significance of this consistency is growing today, when software projects are more complex, consisting of components developed in diverse programming languages. From the beginning of the application of software engineering, engineers have been striving to develop quality and maintainable software products. Therefore, software evolution and its quality have become an important research discipline. Early versioning systems like the Source Code Control System made it possible to record the sequential versions of software products [41] . Such software history has been important to understand what, where and when a change was applied. Beside versioning systems, software quality measures have been researched. The first published book that describe software metrics appeared in 1976 [20] but the first attempts at applying software metrics had already taken place in the late 1960s [17] . With the spread of software metrics, a need for the appropriate storage of such data grew. In 1993, Pfleeger described the importance of data collecting and determined their success in a metrics program [37] .. Various approaches have been used to analyze software evolution. The majority of them are programming language specific. Their meta-models are not general and do not enable a structural software comparison between different systems. Thus, making a comparison between the structural software evolution of two systems or of two components with the same system that are written in different programming languages (e.g. Java and C#), is not possible. A similar problem can be found in the field of software metrics. Existing approaches that define the metrics and its algorithms are programming language specific. Furthermore, the algorithms usually differ between the tools. Thus, a software metric comparison in heterogeneous systems is not accurate.. The purpose of this study is to deal with the problems surrounding programming language dependent frameworks and approaches that describe software metrics and software structure. Thus, a general framework that allows a programming language independent representation and evaluation of software artifacts has been developed. It consists of three major components. The first component of the framework is a language independent meta-model for representing a source code structure. The main purpose is to provide sufficient data for a further evolutionary analysis based on software structures (e.g. detecting structural source code changes between sequential software versions). The second one is the SMIILE (Software Metrics Independent of Input LanguagE) tool. Its main advantage is to define an universal implementation of metric algorithms (e.g. algorithm for the cyclomatic complexity) built upon the meta-model. Both components are based on the enriched Concrete Syntax Tree (eCST) that represents an internal representation of source code. The eCST is built on "universal" nodes that are common for all programming languages. However, in order to store the software artifacts and conduct a deeper analysis, an appropriate repository is needed. To fulfill this demand, a specific repository was build and integrated as the third component in the framework.. In our case study, the application of the framework will be shown. Its goal is to apply the framework (i.e. meta-models) in practice by using the SMIILE tool for defining and calculating software metric values and by using structural source code representation from different programming languages.. The contribution of this paper is the development of a programming language independent framework for metrics-based software evolution and analysis. This goal was achieved by (1) adjusting the eCST concept in order to support a language-independent source code structure representation that en-ables evolutionary analysis based on software structure and by (2) integrating the SMIILE tool with a central repository that supports eCST. This paper is organized as follows: The background, needed for understanding the study, and the motivation are described in section 2. Then, the preliminary works are introduced in section 3. The programming language independent framework for metrics and structural analysis in software evolution is briefly described in section 4. In the section 5, the framework application is presented with a case study. The validity and limitations of this research are stated in section 6. In the section 7, state-of-the-art tools and approaches for analyzing software metrics and software structure are introduced. In the last section, the conclusion and ideas for future work are provided. The programming language independent framework for analyzing software structure and metrics during software development and evolution was presented in the study. The framework consists of three modules. The first one defines the meta-model for providing sufficient data, which constitutes the basis for approaches dealing with software change detection processes. The second module uses a mechanism for evaluating software metrics. Both modules are built on the eCST for the unified representation of a source code. The last module contains an approach for collecting evolutionary software artifacts that then enables further analysis.. The integration of improved characteristics of eCST into a framework for metrics calculation and the framework for software evolution extended by software metrics and the changes repository lead to the following important benefits.. -Usage of the eCST leads to language independence. -The storing of software metrics in the software metrics repository enables a better interpretation of acquired data. -Integration with the repository additionally gives opportunities to extend it in such a way as to store data about structural software changes. -Enables the further analysis of stored data (e.g. custom metrics) and provides the opportunity to give recommendations to users about the improvement of a product and the development process or even the automation of some of the suggested improvements (e.g. automatic refactoring). The need for this analysis is examined in the related work. An advanced calculation on metric values and visualization are enabled by the software metrics repository and the rest of an intelligent analysis are planned for future work.. These features distinguish the framework from existing techniques and approaches and provide it with significant prospects in the field of software development and evolution. Furthermore, a special engine for detecting structural source code changes is already being implemented, but it is out of the scope of this paper.. The framework was successfully presented in the case study. In the first part, the software structure was analyzed from source code written in two different programming languages (i.e. C# and Java). The data extracted from the eCST into the meta-model fulfilled all described requirements. However, programming language independency of the eCST has been also shown on other case studies [39] and [40] . Therefore, the extraction is independent of an input language. In the second part, several software metrics were evaluated based on the same source code. The algorithms defined upon the universal nodes correctly calculated the values for lines of code and cyclomatic complexity and also for some other design metrics (i.e. NOC, NOA, NOM). In the last part, the results were successfully imported into the software repository for collecting and storing meta-data.. The framework and its components are still on prototype level. In future work, the framework will be tested with more programming languages and adjustments will be made if necessary. Also, support for additional algorithms for calculating software metrics will be added. Furthermore, all data will be stored in a repository with the intention of analyzing the correlation between the changes and software quality and to provide more useful information to the user or even to develop an automated quality improvement. Related research has also shown that there is no fully consistent tool support for measurement and analysis during software development and maintenance. The tools used for these purposes have some limitations (e.g. limited programming language support, weak and inconsistent usage of metrics and/or testing techniques, etc).. Large software systems are written in several programming languages. In order to ensure a high level of software quality, we have to know the condition of every part of a system. Furthermore, in order to evaluate such systems, different tools have to be used. However, these tools usually provide inconsistent values for software metrics [36] , [32] , [43] and therefore, a comparison between different parts of a system, written in different programming languages, is not applicable.. In the field of software evolution, which enforces techniques such as advising, recommending and the automating of refactoring and reengineering, solutions that are based on a common intermediate structure can be a key supporting element. This support could be based on metrics, testing and deeper static and structure analysis. The development of such support would introduce new values into the field of software engineering. For all of these reasons, a proposed universal tree could be an appropriate internal representation applicable toward all stated goals. Universality of internal structure is important for meeting consistency in all fields.. By realization of this idea a key benefit could be made from language independence of eCST and its universality and broad applicability. In the last decade, different approaches for evaluating software artifacts have been used. Therefore, this section will focus on existing tools and approaches for evaluating software metrics and software structure through the software development process. As the analisys of related work will show, the integrated approach to application of software metrics algorithms and analysis of software evolution that are independent on programming languages are not existant in usable form. The first part describes the approaches for evaluating software metrics, the second part describes the approaches for representing software structure and the third part is focused on programming language independence.. The majority of problems are related to programming language dependency. However, the last part of the analysis describes some more or less successful approaches in order to overcome this issue. To improve existing approaches, new framework that is based on internal representation of source code with improved characteristics was developed.

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


A semantic framework to protect the privacy of electronic health records with non-numerical attributes Structured patient data like Electronic Health Records (EHRs) are a valuable source for clinical research.However, the sensitive nature of such information requires some anonymisation procedure to be applied before releasing the data to third parties. Several studies have shown that the removal of identifying attributes, like the Social Security Number, is not enough to obtain an anonymous data file, since unique combinations of other attributes as for example, rare diagnoses and personalised treatments, may lead to patient's identity disclosure.To tackle this problem, Statistical Disclosure Control (SDC) methods have been proposed to mask sensitive attributes while preserving, up to a certain degree, the utility of anonymised data. Most of these methods focus on continuous-scale numerical data. Considering that part of the clinical data found in EHRs is expressed with non-numerical attributes as for example, diagnoses, symptoms, procedures, etc., their application to EHRs produces far from optimal results. In this paper, we propose a general framework to enable the accurate application of SDC methods to non-numerical clinical data, with a focus on the preservation of semantics. To do so, we exploit structured medical knowledge bases like SNOMED CT to propose semantically-grounded operators to compare, aggregate and sort non-numerical terms. Our framework has been applied to several wellknown SDC methods and evaluated using a real clinical dataset with non-numerical attributes. Results show that the exploitation of medical semantics produces anonymised datasets that better preserve the utility of EHRs. Recent advances in Electronic Health Record (EHR) technology have significantly increased the amount of clinical data electronically available. These data, consisting of medical and scientific documents and also of digitalised patient health records, are valuable resources for clinical and translational research. The analysis of the health care experience captured in clinical databases may lead to improved continuity in patient assessment, improved treatment, avoidance of adverse drugs reactions, and in ensuring that people at risk receive appropriate support services [11] [18] .. Since medical information is usually associated to individuals, privacy must be ensured when data is made available for secondary use. This is explicitly stated by the Data Protection Act 1998 and the Human Rights Act 1998, which consider clinical data as "sensitive". Nowadays, EHRs are collected and maintained by public and private institutions that made them available for clinicians and researchers. Those institutions should guarantee that health information associated to patients is only made public with patient's authorisation. Exemptions are allowed in Section 39 of the Data Protection Act for medical purposes as well as statistical or historical research [11] . Moreover, the US Health Insurance Portability and Accountability Act (HIPAA) privacy rule permits publishing personal health information for public-health purposes without patient consent, if individual's privacy is "sufficiently" guaranteed [24] . To guarantee this privacy, the HIPAA either requires that experts in statistical and/or scientific methods certify that an individual's identity is protected from exposure under reasonable expectations (Expert Determination) or requires the removal of 18 data elements, named Protected Health Information (PHI) [1] , to consider data de-identified (Safe Harbor); PHI includes names, census, geographical and financial information and biometrics, among others.. While data de-identification (by removing identifying attributes as proposed in the HIPAA privacy rules) prevents linking confidential data and patient's identity, it provides a false appearance of anonymity. Patient disclosure could still happen through statistical matching of remaining attributes.. Several studies demonstrated that it is still possible to identify a patient by combining attributes which, when considered individually, did not seem problematic [5, 11] , that is, they were not considered PHI. There have been cases of disclosure in a priori protected clinical data, such as the identification of the clinician and the patient in a late abortion case through the analysis of released tabular data [31] . The identification was possible due to the low amount of late abortions in the region in which both the patient and the clinician were located. In [18] authors developed a technique for reidentifying seemingly anonymous genomic data by analysing combinations of, a priori, nonidentifying attributes. Medical data, due to their variability and high dimensionality, is very prone to the appearance of identifying combinations of attribute values, which may enable disclosure when evaluated together.. The disclosure of patient electronic health records supposes a serious threat. Moreover, the awareness of the disclosure risks inherent to data release may lead to future reluctances and lack of trust in making data available for research. The fact that medical data does not get published or that it is excessively perturbed (e.g. encrypted or partially supressed) to minimise disclosure risk to the extent that it does not meet a level of accuracy, may produce a severe impact in its utility, hampering the benefits that can be extracted from its analysis [30] .. To avoid these problems, privacy preserving methods providing additional privacy guarantees over the basic data de-identification must be developed. These methods suppose that direct or formal identifiers (such as PHI elements) have been already removed (e.g. according to the HIPAA privacy rules), and generate some distortion (i.e., masking) on the combinations of potentially identifying values. These last attributes are considered quasi-identifiers, referring to attribute sets whose values may produce rare or even unique combinations (e.g., very specific diagnostics or personalised treatments), and which may unequivocally identify an individual. Hence, these attribute can be used by a potential attacker to disclose the identity of certain individuals. In addition to data masking, it is also equally important to assure the quality of the published data. Therefore, data distortion derived from quasi-identifiers masking should be done in a way the anonymised data retains its utility as much as possible, so that similar conclusions can be extracted from the analysis of the original and the anonymised version of the dataset.. Different techniques for masking quasi-identifiers can be envisioned according to the type of data to deal with. Medical data can be presented as unstructured textual documents or as structured patient records collecting values for a set of normalised attributes (e.g., symptoms, diagnosis and treatment observed in a visit to a certain patient). In the former case, document sanitisation methods have been developed [24] ; the latter case refers to privacy protection in structured databases [5] , which is the focus of this paper. Appropriate measures to protect the privacy of EHRs should be taken before making them available for clinical research. Previous works have shown that a basic de-identification of medical data as proposed by the HIPAA Privacy Rule is, sometimes, not enough. On the one hand, original data should be anonymised in a way that the chance of patient disclosure, even when applying statistical methods, is sufficiently reduced. On the other hand, anonymised datasets should retain as much information as possible, so that they are still useful for research tasks. SDC methods aim at balancing these two complementary dimensions, offering additional privacy guarantees with respect to the removal of direct identifiers, which represents their point of departure. However, most of these methods have been designed to deal with numerical values.. Considering the amount and importance of non-numerical data in EHRs, in this paper we presented a general framework that provides semantically-grounded comparison, aggregation and sorting operators. Exploiting a structured medical knowledge base like SNOMED-CT, and relying on the theory of semantic similarity, these operators enable a semantically-coherent interpretation of nonnumerical attributes, while also considering their distributional features. Since many SDC methods (particularly those focused on fulfilling k-anonymity) rely on these basic operators to anonymise data, as described in section 1.2, they can directly apply the proposed semantic framework to produce semantically-grounded anonymisations for non-numerical data. As shown in the evaluation performed with a real clinical dataset, the use of this framework considerably improves the degree of semantic preservation for all the considered methods, in comparison with non-semantic approaches based solely on information distribution. As a result, the meaning of data and, hence, the utility of the anonymised data from a semantic perspective is better preserved.. As future work, we plan to research on the application of our semantic framework to other privacy models and methods. Particularly it is important to note that privacy models such as k-anonymity, even though providing a more robust anonymisation in front of statistical disclosure attacks than the sole removal of identifying attributes, present some limitations. A series of attacks based on the background knowledge that a potential attacker may have about certain individuals have been identified [17] . In these cases, more robust privacy models such as differential privacy can be embraced. Differential privacy [10] ensures that released data are insensitive to any individual's data (i.e. the alteration of one input records). Hence, individual data remains uncertain for an attacker. To achieve this, an amount of noise is usually added to the output to introduce uncertainty, a process that implies a loss of information. The approach proposed in this paper could be extended to fulfil differential privacy by adding appropriate noise to, for example, the record counts of each aggregated group [25] . In this case, an equilibrium between the degree of data aggregation and the amount of noise should be carefully considered to minimise information loss.. Another line of research consists on supporting the anonymisation of less structured textual inputs such as free text. In this case, a lexico-syntactic pre-processing stage will be necessary to 1) identify nouns or noun phrases (with semantic content) within the text, and 2) to map them to their corresponding ontological concepts, so that their semantics can be interpreted. Linguistic tools including sentence/token detection, part-of-speech tagging and stemming can be considered.

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


On Irrelevance and Algorithmic Equality in Predicative Type Theory Abstract. Dependently typed programs contain an excessive amount of static terms which are necessary to please the type checker but irrelevant for computation. To separate static and dynamic code, several static analyses and type systems have been put forward. We consider Pfenning's type theory with irrelevant quantification which is compatible with a type-based notion of equality that respects η-laws. We extend Pfenning's theory to universes and large eliminations and develop its meta-theory. Subject reduction, normalization and consistency are obtained by a Kripke model over the typed equality judgement. Finally, a type-directed equality algorithm is described whose completeness is proven by a second Kripke model. We have extended Pfenning's notion of irrelevance to a type theory IITT with universes that accommodates types defined by recursion. We have constructed a Kripke model that shows soundness of IITT, yielding normalization, subject reduction and consistency, plus syntactical properties of the judgements of IITT. A second Kripke logical relation c has proven correctness of algorithmic equality and, thus, decidability of IITT.. Integrating irrelevance and data types in dependent type theory does not seem without challenges. We have succeeded to treat Pfenning's notion of irrelevance, but our proof does not scale directly to parametric function types, a stronger notion of irrelevant function types called implicit quantification by Miquel [Miq01b] .. 7 Two more type theories build on Miquel's calculus [Miq01a] , Barras and Bernardo's ICC * [BB08] and Mishra-Linger and Sheard's Erasure Pure Type Systems (EPTS) [MLS08] , but none has offered a satisfying account of large eliminations yet. Miquel's model [Miq00] features data types only as impredicative encodings. For irrelevant, parametric, and recursive functions to coexist it seems like three different function types are necessary, e. g., in the style of Pfenning's irrelevance, extensionality and intensionality. We would like to solve this puzzle in future work, not least to implement high-performance languages with dependent types.

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On Decidability Properties of One-Dimensional Cellular Automata In a recent paper Sutner proved that the first-order theory of the phase-space S A = (Q Z , −→) of a one-dimensional cellular automaton A whose configurations are elements of Q Z , for a finite set of states Q, and where −→ is the "next configuration relation", is decidable [Sut09]. He asked whether this result could be extended to a more expressive logic. We prove in this paper that this is actuallly the case. We first show that, for each one-dimensional cellular automaton A, the phase-space S A is an ω-automatic structure. Then, applying recent results of Kuske and Lohrey on ω-automatic structures, it follows that the first-order theory, extended with some counting and cardinality quantifiers, of the structure S A , is decidable. We give some examples of new decidable properties for one-dimensional cellular automata. In the case of surjective cellular automata, some more efficient algorithms can be deduced from results of [KL08] on structures of bounded degree. On the other hand we show that the case of cellular automata give new results on automatic graphs. Some properties of one-dimensional cellular automata, like injectivity or surjectivity, were shown to be decidable by Amoroso and Patt in [AP72] . These two properties are easily expressed as first-order properties of the phase-space S A = (Q Z , −→) of a cellular automaton A whose configurations are elements of Q Z , for a finite set of states Q, and where −→ is the "next configuration relation". It is then very natural to ask whether every first-order property of the structure S A is decidable. Sutner proved recently in [Sut09] that this is actually the case, i.e. that "model-checking for one-dimensional cellular automata in the first order logic L(−→) with equality is decidable". He used decidability properties of ζ-Büchi automata reading biinfinite words. On the other hand, ω-automatic structures are relational structures whose domain and relations are recognizable by Büchi automata reading infinite words. The ω-automatic structures have very nice decidability and definability properties and have been much studied in the last few years, [Hod83, BG04, KNRS07] . In particular the first-order theory of an ω-automatic structure is decidable and the class of ω-automatic structures is closed under firstorder interpretations. We show here that, for each one-dimensional cellular automaton A, the phase-space S A = (Q Z , −→) is an ω-automatic structure. Thus Sutner's result can be deduced from an earlier result of Hodgson in [Hod83] .. But we can now apply to the study of one-dimensional cellular automata some other very recent results on ω-automatic structures. Blumensath and Graëdel proved in [BG04] that the first-order theory of an ω-automatic structure, extended with the "infinity quantifier" ∃ ∞ , is decidable. Kuske and Lohrey proved in [KL08] that the first-order theory, extended with some counting and cardinality quantifiers, of an (injectively) ω-automatic structure is decidable. These quantifiers are in the form ∃ κ , for any cardinal κ, or are modulo quantifiers in the form ∃ t,k , for integers 0 ≤ t < k, whose meaning is "there exist t modulo k". This gives an answer to Sutner's question who asked whether his decidability result could be extended to a more expressive logic.. Sutner's result implies that one can decide some properties like the following ones : "there exists a 6-cycle" or "there exists exactly five fixed points". With our new results deduced from properties of ω-automatic structures, we can now decide properties like : "there exist uncountably many 3-cycles" or "there exist infinitely many 25-cycles" or "there exist t modulo k 6-cycles" or "the set of fixed points is countably infinite", and so on .. Notice that the algorithms obtained by Sutner in [Sut09] are in general non elementary. This is also the case for the algorithms obtained by Kuske and Lohrey for ω-automatic structures. However some more efficient algorithms are obtained in [KL08] in the case of ω-automatic structures of bounded degree. In the case of one-dimensional cellular automata this corresponds to the important class of surjective cellular automata. We give the complexities of algorithms obtained from [KL08] in this particular case.. We consider next the set of configurations with finite support of a given cellular automaton, equipped with the next configuration relation. It is an automatic directed graph, i.e. it is a countable structure presentable with automata reading "finite words". Then we can infer new results on automatic graphs from some results of Sutner [Sut08a] . In particular for each recursively enumerable Turing degree d there is an automatic graph in which the reachability relation has exactly the Turing degree d. This shows the richness of the class of automatic graphs, from the point of view of Turing degrees.. The paper is organized as follows. In Section 2 we recall definitions of cellular automata and of Büchi automata reading infinite words. We recall in Section 3 the notion of (injectively) ω-automatic structure. We get some new decidability properties of one-dimensional cellular automata in Section 4. We show in section 5 that the case of cellular automata give new results on automatic graphs.

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


Fine-Grain Feature Extraction from Malware's Scan Behavior Based on Spectrum Analysis Network monitoring systems that detect and analyze malicious activities as well as respond against them, are becoming increasingly important. As malwares, such as worms, viruses, and bots, can inflict significant damages on both infrastructure and end user, technologies for identifying such propagating malwares are in great demand. In the largescale darknet monitoring operation, we can see that malwares have various kinds of scan patterns that involves choosing destination IP addresses. Since many of those oscillations seemed to have a natural periodicity, as if they were signal waveforms, we considered to apply a spectrum analysis methodology so as to extract a feature of malware. With a focus on such scan patterns, this paper proposes a novel concept of malware feature extraction and a distinct analysis method named "SPectrum Analysis for Distinction and Extraction of malware features (SPADE)". Through several evaluations using real scan traffic, we show that SPADE has the significant advantage of recognizing the similarities and dissimilarities between the same and different types of malwares. Malwares are spread all over the Internet and often leads to serious security incidents that can cause significant damage to both infrastructure and end users. As countermeasures, a number of ongoing network monitoring projects are already in their operational phase [1] - [9] . Many of these projects are concentrating on the event analysis that provides statistical data, such as rapid increase in access on certain port numbers, by using network event monitoring. Particularly, it is becoming popular to monitor a dark address space (darknet), which is a set of globally announced unused IP addresses [10] , [11] .. In order to identify the root causes of network events observed on darknets, we have started the Network Incident analysis Center for Tactical Emergency Response (nicter) project, with the goal of achieving an integrated analysis of security incidents on large networks [12] - [14] . Our present focus is particularly on detecting and identifying the propagation of malwares such as worms, viruses, and bots, which can infect remote hosts through fundamental propagation steps, such as scan → exploit code → malware download, by exploiting the vulnerabilities of operating systems or server applications of the targeted hosts. Although infection methods of malwares are becoming more diversified and sophisticated in recent years (e.g., infection via web browser and USB memories), there are still vast amounts of malwares that spread by use of the network vulnerabilities of remote hosts. For instance, a malware called "Conficker" (aka "Downadup") and its variants spread to all over the world by exploiting a vulnerability of Microsoft Windows named "MS08-067", and inflicted significant damages to many enterprise networks [15] , [16] . In order to detect those malwares' propagation at an incipient stage, nicter monitors a wide region of the global Internet using distributed darknet sensors and then uses various methods to analyze the collected security events.. In darknet monitoring, it is important to categorize the observed attacking hosts, which enables us to grasp the global trend in the Internet [12] , such as rises of emerging malwares and large scale DDoS attacks. Namely, if we could identify the malware specimen infecting one of the attacking hosts in a certain cluster, we can figure out a bunch of hosts infected by the same malware at one time. Consequently, information such as a list of attacking hosts and their malware specimens can be published to other organizations at the first stage of rises of emerging malwares so that we can indirectly limit further expansion of them. On the other hand, through our large-scale darknet monitoring operation, we have learned that malware uses various kinds of scan patterns, that involves choosing destination IP addresses [17] - [20] , such as regular increment or random determination of destination IP addresses for each packet. Since these scan patterns resemble a signal waveform, we applied the discrete Fourier transform (DFT) algorithm to the feature extraction and distinction method.. In this paper, we propose a malware feature extraction and distinction method called SPectrum Analysis for Distinction and Extraction of malware features (SPADE), which analyzes the scan traffic data from a pair of attacking hosts and derives the correlation coefficient between them. The contribution of this paper is as follows.. 1. We define some requirements of a malware correlation method that can deal with the differences of network conditions among darknet sensors, and proposed the SPADE algorithm which employs the discrete Fourier transform (DFT). 2. We propose the SPADE, a novel algorithm which extracts the feature of malwares from their scan patterns. Copyright c 2010 The Institute of Electronics, Information and Communication Engineers and derives correlation between a pair of hosts. 3. We develop a simple clustering algorithm which employs the SPADE as a distance function and clusters the attacking hosts based on the similarity of scan patterns within a permissible time. 4. Through several qualitative evaluations using various scan traffic observed in the darknet monitoring, we confirm that the SPADE algorithm successfully satisfies above requirements. Moreover, by demonstrating an experimental clustering and a performance evaluation with the SPADE and the clustering algorithm, we verify the practicality and effectiveness of them for the global trend analysis.. The rest of this paper is organized as follows: Sect. 2 introduces the background of this research and related works. Section 3 discusses the requirements for the malware feature extraction algorithm. The details of the SPADE algorithm are explained in Sect. 4. Some concrete evaluations using proposed algorithm with actual darknet traffic are presented in Sect. 5. Finally, Sect. 6 presents our conclusions and future work. In this paper, focusing on the oscillations of the destination IP addresses of scan packets, we proposed a concept of malware feature extraction, and implemented and evaluated a distinct analysis method (SPADE) which applied a spectrum analysis methodology. Our contribution is to realize a fundamental technology to grasp a global trend of malware propagation only from their scan traffic data.. Specifically, we defined some requirements of a malware correlation method that deal with the differences of network conditions among darknet sensors, and proposed the SPADE algorithm which employs the discrete Fourier transform (DFT). Through some qualitative evaluations, we showed that the SPADE successfully satisfies those requirements. Additionally, the SPADE algorithm was applied to a simple clustering algorithm as a distance function. By demonstrating an experimental analysis and a performance evaluation, we verified the practicality and effectiveness of the SPADE toward the global trend analysis.. Although we applied the oscillations of the destination IP addresses of scan packets in our algorithm, we have to further consider other parameterized characteristics such as the source / destination port numbers and the interval time of packet arrival. As the future work, we will attempt to establish more multifaceted analysis techniques covering such parameters based on the SPADE algorithm. Darknet Monitoring There have been numerous studies on network monitoring and event analysis. Some of these have observed darknets to detect anomalous activities, and have provided statistical data, such as the rapid increase of accesses on certain port numbers [1] - [4] , [8] .. Some researchers [10] , [11] , [21] , [22] have monitored darknets in an entirely passive manner by examining spurious traffic arriving for non-existent hosts, using blackhole sensors, and detecting security events based on statistical algorithms. Though our work falls into this blackhole monitoring category, while most of the previous studies have detected security events based on statistical data, such as the total number of attacking hosts or packets, our work extracted the behavioral characteristics of a single attacking host in order to distinguish them. As a background of this research, we first explain the darknet monitoring and its advantage. Second, we show that malwares have various scan patterns according to our longterm and large-scale darknet monitoring, which can be used to classify the malwares. Third, we mention some related works.

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


Segmentation of the Optic Disc in 3-D OCT Scans of the Optic Nerve Head Abstract-Glaucoma is the second leading ocular disease causing blindness due to gradual damage to the optic nerve and resultant visual field loss. Segmentations of the optic disc cup and neuroretinal rim can provide important parameters for detecting and tracking this disease. The purpose of this study is to describe and evaluate a method that can automatically segment the optic disc cup and rim in spectral-domain 3-D OCT (SD-OCT) volumes. Four intraretinal surfaces were segmented using a fast multiscale 3-D graph search algorithm. After surface segmentation, the retina in each 3-D OCT scan was flattened to ensure a consistent optic nerve head shape. A set of 15 features, derived from the segmented intraretinal surfaces and voxel intensities in the SD-OCT volume, were used to train a classifier that can determine which A-scans in the OCT volume belong to the background, optic disc cup and rim. Finally, prior knowledge about the shapes of the cup and rim was incorporated into the system using a convex hull-based approach. Two glaucoma experts annotated the cup and rim area using planimetry, and the annotations of the first expert were used as the reference standard. A leave-one-subject-out experiment on 27 optic nerve head-centered OCT volumes (14 right eye scans and 13 left eye scans from 14 patients) was performed. Two different types of classification methods were compared, and experimental results showed that the best performing method had an unsigned error for the optic disc cup of Index Terms-Glaucoma, multiscale 3-D graph search, neuroretinal rim, optic disc, optic disc cup, optic nerve head (ONH), spectral-domain optical coherence tomography (OCT). From Table I reporting DSC values, in comparison between the -NN and 9--NN classifiers, the 9--NN classifier showed significantly larger DSCs for both the optic disc cup and neuroretinal rim . While optic disc cup segmentation results from the 9--NN classifier with convex hull-based fitting were not significantly different from those from the 9--NN classifier , neuroretinal rim segmentation results from the 9--NN classifier with convex hull-based fitting showed significantly larger DSCs than those from the 9--NN classifier .. Based on the unsigned border positioning errors reported in Table II , optic disc cup segmentation results from the two classifiers with convex hull-based fitting did not show a statistically significant difference . However, in neuroretinal rim segmentation results, the 9--NN classifier with convex hullbased fitting showed significantly smaller unsigned errors than the -NN classifier with convex hull-based fitting . There was no statistically significant difference between the unsigned error of the 9--NN classifier with convex hull-based fitting and the unsigned border positioning difference of the second observer for the optic disc cup and neuroretinal rim . From the signed border positioning errors reported in Table III (i.e., segmentation bias), the optic disc cup and neuroretinal rim segmentation results from the 9--NN classifier with convex hull-based fitting had significantly smaller bias for both the optic disc cup and neuroretinal rim than those from the -NN classifier with convex hull-based fitting . The bias of the 9--NN classifier with convex hull-based fitting was also significantly smaller than the (absolute) signed border positioning difference of the second observer to the reference standard ( for both the optic disc cup and neuroretinal rim).. Figs. 8 and 9 show our ONH segmentation results of the best and worst performance obtained by the 9--NN classifier with convex hull-based fitting which have the minimum and maximum sums of unsigned errors of the optic disc cup and neuroretinal rim, respectively.

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