1H MRS studies of signals from mobile lipids and from lipid metabolites: comparison of the behavior in cultured tumor cells and in spheroids
Rosi A Grande S
;
Luciani AM
;
Barone P
;
Mlynarik V
;
Viti V
;
Guidoni L
H-1 magnetic resonance studies on MCF-7 and HeLa cells were undertaken to reveal differences in lipid and lipid metabolite signals during the growth in culture. High intensity mobile lipid (ML) signals were found during the first days in culture, while afterwards the same signals declined and started increasing again at confluence and at late confluence. At the same time, signals from the lipid metabolite phosphocholine decreased in intensity while signals from glycerophosphocholine in MCF-7 and from choline in HeLa increased as cells approached confluence. Spectral parameters from actively proliferating and non-proliferating cells were used to classify cells with respect to the proliferative conditions by means of a multivariate statistical analysis. Furthermore, it was shown that polyunsaturation of mobile lipid chains was lower in the confluent group with respect to the actively proliferating cells. The examination of spectra from suspensions of MCF-7 spheroids with diameter smaller than 500 mum suggests that cells in spheroids are in condition of lipid metabolism similar to that of confluent cultured cells.
Discussion on the meeting on "statistical approaches to inverse problem"
Nason G
;
Moulines E
;
Robert C
;
Andrieu C
;
Stoffelen A
;
Paul D
;
Abramovich F
;
Aykroyd R
;
West R
;
Meng S
;
Butucea C
;
Cavalier L
;
Cressie
;
N A
;
Davy M
;
De Canditiis D
;
Pensky M
;
Golubev U
;
Hoffman R
;
KhabieZeitoune E
;
Munk A
;
Ruymgaart F
;
Olhede S
;
Tsybakov A
;
Wahba G
;
Johnstone I
;
Kerkyacharian G
;
Picard D
;
Raimondo M
;
Wolfe P
;
Godsill S
;
Ng W
;
Haario H
;
Laine M
;
Lehtinen M
;
Saksman E
;
Tamminen J
;
Cornford D
;
Csato L
;
Evans D
;
Opper
;
M
Discussion on the meeting on "statistical approaches to inverse problem"
Subspace-based MRS data quantitation of multiplets using prior knowledge
Laudadio T
;
Selen Y
;
Vanhamme L
;
Stoica P
;
Van Hecke P
;
Van Huffel S
Accurate quantitation of Magnetic Resonance Spectroscopy (MRS) signals is an essential step before converting the estimated
signal parameters, such as frequencies, damping factors, and amplitudes, into biochemical quantities (concentration, pH). Several
subspace-based parameter estimators have been developed for this task, which are efficient and accurate time-domain algorithms.
However, they suffer from a serious drawback: they allow only a limited inclusion of prior knowledge which is important for accuracy
and resolution. In this paper, a new method is presented: KNOB-SVD and its improved variant KNOB-TLS. KNOB-SVD is
a recently proposed method, based on the Singular Value Decomposition (SVD), which allows the use of more prior knowledge
about the signal parameters than previously published subspace-based methods. We compare its performance in terms of robustness
and accuracy with the performance of three commonly used methods for signal parameter estimation: HTLS, a subspace-based
method which does not allow any inclusion of prior knowledge, except for the model order; HTLSPK(Dfdeq), a subspace-based
method obtained by incorporating in HTLS the prior information that the frequency differences between doublet components are
known and the damping factors are equal; and AMARES, an interactive maximum likelihood method that allows the inclusion of a
variety of prior knowledge. Extensive simulation and in vivo studies, using 31P as well as proton MRS signals, show that the new
method outperforms HTLS and HTLSPK(Dfdeq) in robustness, accuracy, and resolution, and that it provides parameter estimates
comparable to the AMARES ones.
Total least squares
Data subspaces
Magnetic resonance spectroscopy
Biochemical prior knowledge
Singular value decomposition
The problem of obtaining relevant results in web searching has been tackled with several approaches.
Although very e0ective techniques are currently used by the most popular search engines when no a priori
knowledge on the user's desires beside the search keywords is available, in di0erent settings it is conceivable
to design search methods that operate on a thematic database of web pages that refer to a common body of
knowledge or to speci3c sets of users. We have considered such premises to design and develop a search
method that deploys data mining and optimization techniques to provide a more signi3cant and restricted set
of pages as the 3nal result of a user search. We adopt a vectorization method based on search context and
user pro&le to apply clustering techniques that are then re3ned by a specially designed genetic algorithm. In
this paper we describe the method, its implementation, the algorithms applied, and discuss some experiments
that has been run on test sets of web pages.
Search engines; Web mining; Clustering; Genetic algorithms
