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Comparison of field- and satellite-based vegetation cover estimation methods

Salvatore et al. BMC Medical Research Methodology  (2017) 17:34 
DOI 10.1186/s12874-017-0311-yERRATUM Open AccessErratum to: Exploring functional data
analysis and wavelet principal component
analysis on ecstasy (MDMA) wastewater
data
Stefania Salvatore1*, Jørgen G. Bramness1 and Jo Røislien1,2Erratum
After publication of the original article [1], it came to
the authors’ attention that there were errors in Fig. 3,
Fig. 4 and Additional file 1: Figure S1.
In each Figure, panels A, B and C are not correct (but
panels D, E and F are). This error was due to a mistake
in the last stages of the submission process while adjusting
the Figures’ size to fit the journal’s requirements. This
error does not impact the results, discussion and
conclusions of the paper.
The correct version of the affected Figures are
published in this erratum.
Additional file
Additional file 1: Figure S1. (A) Functional principal component
analysis using Fourier basis functions with no smoothing. (B) Functional
principal component analysis using Fourier basis functions with common
−optimal smoothing. (C) Functional principal component analysis using
Fourier basis functions with individual−optimal smoothing. (D) Functional
principal component analysis using B−spline basis functions with no
smoothing. (E) Functional principal component analysis using B−spline
basis functions with common−optimal smoothing. (F) Functional
principal component analysis using B−spline basis functions with
individual−optimal smoothing. (PDF 27 kb)
Author details
1Norwegian Centre for Addiction Research, University of Oslo, Oslo, Norway.
2Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical
Sciences, Oslo, Norway.
References
1. Salvatore S, Bramness JG, Røislien J. Exploring functional data analysis and
wavelet principal component analysis on ecstasy (MDMA) wastewater data.
BMC Med Res Methodol. 2016;16:81. doi:10.1186/s12874-016-0179-2.* Correspondence: stefania.salvatore@medisin.uio.no
1Norwegian Centre for Addiction Research, University of Oslo, Oslo, Norway
© The Author(s). 2017 Open Access This artic
International License (http://creativecommons
reproduction in any medium, provided you g
the Creative Commons license, and indicate if
(http://creativecommons.org/publicdomain/zele is distributed under the terms of the Creative Commons Attribution 4.0
.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
ive appropriate credit to the original author(s) and the source, provide a link to
changes were made. The Creative Commons Public Domain Dedication waiver
ro/1.0/) applies to the data made available in this article, unless otherwise stated.
Fig. 3 (See legend on next page.)
Salvatore et al. BMC Medical Research Methodology  (2017) 17:34 Page 2 of 5
(See figure on previous page.)
Fig. 3 Bootstrapping confidence intervals (CIs) resulting from functional principal component analysis (FPCA) on 1000 re-samples obtained by a
random sample with repetition from the original data sets. Panel a – Bootstrapping CI resulting from a FPCA using Fourier basis functions and no
smoothing parameter; Panel b – Bootstrapping CI resulting from a FPCA using Fourier basis functions and common-optimal smoothing parameter;
Panel c – Bootstrapping CI resulting from a FPCA using Fourier basis functions and individual-optimal smoothing parameter; Panel d – Bootstrapping
CI resulting from a FPCA using B-splines basis functions and no smoothing parameter; Panel e – Bootstrapping CI resulting from a FPCA
using B-splines basis functions and common-optimal smoothing parameter; Panel f – Bootstrapping CI resulting from a FPCA using
B-splines basis functions and individual-optimal smoothing parameter
Salvatore et al. BMC Medical Research Methodology  (2017) 17:34 Page 3 of 5
Fig. 4 (See legend on next page.)
Salvatore et al. BMC Medical Research Methodology  (2017) 17:34 Page 4 of 5
(See figure on previous page.)
Fig. 4 Sensitivity to missing for functional principal component analysis (FPCA) results. Panel a – Functional principal components (FPCs) resulting
from a FPCA using Fourier basis functions and no smoothing parameter for 5, 10, 15, 20 % of missing; Panel b – Functional principal components
(FPCs) resulting from a FPCA using Fourier basis functions and common-optimal smoothing parameter for 5, 10, 15, 20 % of missing; Panel c – Functional
principal components (FPCs) resulting from a FPCA using Fourier basis functions and individual-optimal smoothing parameter for 5, 10,
15, 20 % of missing; Panel d – Functional principal components (FPCs) resulting from a FPCA using B-splines basis functions and no
smoothing parameter for 5, 10, 15, 20 % of missing; Panel e – Functional principal components (FPCs) resulting from a FPCA using
B-splines basis functions and common-optimal smoothing parameter for 5, 10, 15, 20 % of missing; Panel f – Functional principal
components (FPCs) resulting from a FPCA using B-splines basis functions and individual-optimal smoothing parameter for 5, 10, 15,
20 % of missing
Salvatore et al. BMC Medical Research Methodology  (2017) 17:34 Page 5 of 5


Erratum to: Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data

Zhong-Quan Wang

Ya-Li Shi

Rou-Dan Liu

Peng Jiang

Ya-Yi Guan

Ying-Dan Chen

Jing Cui

New insights on serodiagnosis of trichinellosis during window period: early diagnostic antigens from Trichinella spiralis intestinal worms

Phillip I. Davison

Gordon H. Copp

Véronique Créach

Lorenzo Vilizzi

J. R. Britton

Application of environmental DNA analysis to inform invasive fish eradication operations

Managing risks in the Indonesian seaweed supply chain

Vicky Y Wang

Brett R Cowan

Alistair A Young

Martyn P Nash

POSTER PRESENTATION Open Access
Investigating heart failure mechanics using
personalised human biophysical models
Vicky Y Wang1*, Brett R Cowan2, Alistair A Young1,2, Martyn P Nash1,3
From 16th Annual SCMR Scientific Sessions
San Francisco, CA, USA. 31 January - 3 February 2013
Background
Heart failure (HF) is a pathological condition during which
the pump function of the heart is compromised. There are
two major forms: diastolic and systolic HF. Whilst the
majority of patients exhibit systolic HF (reduced ejection
fraction), diastolic HF (normal ejection fraction) has also
been found in almost half of HF patients. A major ques-
tion in this field is the inter-relationship between diastolic
and systolic HF. The mechanisms underlying both pathol-
ogies are still poorly understood.
Methods
In this study, personalised biomechanical models of the
human left ventricle (LV) were generated semi-automati-
cally from cardiac magnetic resonance images (MRI) using
finite element modelling techniques to investigate both
diastolic and systolic ventricular mechanics. Geometric
information of the LV throughout the cardiac cycle was
derived via semi-automatic segmentation technique called
guide-point modelling with myocardial volume preserved
throughout the cardiac cycle. After image segmentation,
the guide-point model at diastasis was used as the refer-
ence mechanics model to simulate ventricular mechanics.
Passive and contractile myocardial mechanical properties
were then tuned to best match the segmented surface data
at end-diastole and end-systole, respectively. Global and
regional indices of myocardial mechanics, including mus-
cle fibre stress and extension ratio were then quantified
and analysed.
Results
This mechanics modelling framework was applied to the
Sunnybrook Cardiac Database which consisted of normal,
hypertrophy and non-ischemic HF cases. Comparison of
the estimated passive stiffness and peak active contractile
stress between the normal and diseased cases provided
some preliminary insight into the changes in myocardial
mechanical properties during heart failure. It was found
that the passive tissue stiffness was greater for the HF and
hypertrophy patients compared to the normal subjects.
This is consistent with fibrosis that is observed during fail-
ure. On the other hand, the estimated maximum active
contractile stress did not vary significantly between the
diseased and healthy cases which may suggest that systolic
heart failure may not be associated with changes in systolic
mechanical properties.
Conclusions
This automated approach enables minimally invasive
personalised characterisation of cardiac mechanical
function in health and disease. Determining more speci-
fic variations in mechanical properties between diseased
and normal human hearts will help in the understanding
of the underlying mechanisms of HF.
Funding
This work is funded by the Health Research Council of
New Zealand.
Author details
1Auckland Bioengineering Institute, University of Auckland, Auckland, New
Zealand. 2Center for Advanced MRI, University of Auckland, Auckland, New
Zealand. 3Department of Engineering Science, University of Auckland,
Auckland, New Zealand.
Published: 30 January 2013
doi:10.1186/1532-429X-15-S1-E32
Cite this article as: Wang et al.: Investigating heart failure mechanics
using personalised human biophysical models. Journal of Cardiovascular
Magnetic Resonance 2013 15(Suppl 1):E32.
1Auckland Bioengineering Institute, University of Auckland, Auckland, New
Zealand
Full list of author information is available at the end of the article
Wang et al. Journal of Cardiovascular Magnetic
Resonance 2013, 15(Suppl 1):E32
http://www.jcmr-online.com/content/15/S1/E32
© 2013 Wang et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.


Investigating heart failure mechanics using personalised human biophysical models

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