3,955 research outputs found
Compressibility, laws of nature, initial conditions and complexity
We critically analyse the point of view for which laws of nature are just a
mean to compress data. Discussing some basic notions of dynamical systems and
information theory, we show that the idea that the analysis of large amount of
data by means of an algorithm of compression is equivalent to the knowledge one
can have from scientific laws, is rather naive. In particular we discuss the
subtle conceptual topic of the initial conditions of phenomena which are
generally incompressible. Starting from this point, we argue that laws of
nature represent more than a pure compression of data, and that the
availability of large amount of data, in general, is not particularly useful to
understand the behaviour of complex phenomena.Comment: 19 Pages, No figures, published on Foundation of Physic
Measurement of the Fermi Constant by FAST
An initial measurement of the lifetime of the positive muon to a precision of
16 parts per million (ppm) has been performed with the FAST detector at the
Paul Scherrer Institute. The result is tau_mu = 2.197083 (32) (15) microsec,
where the first error is statistical and the second is systematic. The muon
lifetime determines the Fermi constant, G_F = 1.166353 (9) x 10^-5 GeV^-2 (8
ppm).Comment: 15 pages, 6 figure
Nonparametric Bayesian multiple testing for longitudinal performance stratification
This paper describes a framework for flexible multiple hypothesis testing of
autoregressive time series. The modeling approach is Bayesian, though a blend
of frequentist and Bayesian reasoning is used to evaluate procedures.
Nonparametric characterizations of both the null and alternative hypotheses
will be shown to be the key robustification step necessary to ensure reasonable
Type-I error performance. The methodology is applied to part of a large
database containing up to 50 years of corporate performance statistics on
24,157 publicly traded American companies, where the primary goal of the
analysis is to flag companies whose historical performance is significantly
different from that expected due to chance.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS252 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Advanced perfusion quantification methods for dynamic PET and MRI data modelling
The functionality of tissues is guaranteed by the capillaries, which supply the microvascular
network providing a considerable surface area for exchanges between blood and tissues.
Microcirculation is affected by any pathological condition and any change in the blood supply
can be used as a biomarker for the diagnosis of lesions and the optimization of the treatment.
Nowadays, a number of techniques for the study of perfusion in vivo and in vitro are
available. Among the several imaging modalities developed for the study of microcirculation,
the analysis of the tissue kinetics of intravenously injected contrast agents or tracers is the
most widely used technique. Tissue kinetics can be studied using different modalities: the
positive enhancement of the signal in the computed tomography and in the ultrasound
dynamic contrast enhancement imaging; T1-weighted MRI or the negative enhancement of
T2* weighted MRI signal for the dynamic susceptibility contrast imaging or, finally, the
uptake of radiolabelled tracers in dynamic PET imaging. Here we will focus on the perfusion
quantification of dynamic PET and MRI data. The kinetics of the contrast agent (or the tracer)
can be analysed visually, to define qualitative criteria but, traditionally, quantitative
physiological parameters are extracted with the implementation of mathematical models.
Serial measurements of the concentration of the tracer (or of the contrast agent) in the tissue
of interest, together with the knowledge of an arterial input function, are necessary for the
calculation of blood flow or perfusion rates from the wash-in and/or wash-out kinetic rate
constants. The results depend on the acquisition conditions (type of imaging device, imaging
mode, frequency and total duration of the acquisition), the type of contrast agent or tracer
used, the data pre-processing (motion correction, attenuation correction, correction of the
signal into concentration) and the data analysis method.
As for the MRI, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a
non-invasive imaging technique that can be used to measure properties of tissue
microvasculature. It is sensitive to differences in blood volume and vascular permeability that
can be associated with tumour angiogenesis. DCE-MRI has been investigated for a range of
clinical oncologic applications (breast, prostate, cervix, liver, lung, and rectum) including
cancer detection, diagnosis, staging, and assessment of treatment response. Tumour
microvascular measurements by DCE-MRI have been found to correlate with prognostic
factors (such as tumour grade, microvessel density, and vascular endothelial growth factor
expression) and with recurrence and survival outcomes. Furthermore, DCE-MRI changes
measured during treatment have been shown to correlate with outcome, suggesting a role as
a predictive marker. The accuracy of DCE-MRI relies on the ability to model the
pharmacokinetics of an injected contrast agent using the signal intensity changes on
sequential magnetic resonance images. DCE-MRI data are usually quantified with the
application of the pharmacokinetic two-compartment Tofts model (also known as the
standard model), which represents the system with the plasma and tissue (extravascular
extracellular space) compartments and with the contrast reagent exchange rates between
them. This model assumes a negligible contribution from the vascular space and considers
the system in, what-is-known as, the fast exchange limit, assuming infinitely fast
transcytolemmal water exchange kinetics. In general, the number, as well as any assumption
about the compartments, depends on the properties of the contrast agent used (mainly
gadolinium) together with the tissue physiology or pathology studied. For this reason, the
choice of the model is crucial in the analysis of DCE-MRI data. The value of PET in clinical oncology has been demonstrated with studies in a variety of
cancers including colorectal carcinomas, lung tumours, head and neck tumours, primary and
metastatic brain tumours, breast carcinoma, lymphoma, melanoma, bone cancers, and other
soft-tissue cancers. PET studies of tumours can be performed for several reasons including
the quantification of tumour perfusion, the evaluation of tumour metabolism, the tracing of
radiolabelled cytostatic agents. In particular, the kinetic analysis of PET imaging has showed,
in the past few years, an increasing value in tumour diagnosis, as well as in tumour therapy,
through providing additional indicative parameters. Many authors have showed the benefit
of kinetic analysis of anticancer drugs after labelling with radionuclide in measuring the
specific therapeutic effect bringing to light the feasibility of applying the kinetic analysis to
the dynamic acquisition. Quantification methods can involve visual analysis together with
compartmental modelling and can be applied to a wide range of different tracers. The
increased glycolysis in the most malignancies makes 18F-FDG-PET the most common
diagnostic method used in tumour imaging. But, PET metabolic alteration in the target tissue
can depend by many other factors. For example, most types of cancer are characterized by
increased choline transport and by the overexpression of choline kinase in highly proliferating
cells in response to enhanced demand of phosphatidylcholine (prostate, breast, lung, ovarian
and colon cancers). This effect can be diagnosed with choline-based tracers as the 18Ffluoromethylcholine
(18F-FCH), or the even more stable 18F-D4-Choline. Cellular
proliferation is also imaged with 18F-fluorothymidine (FLT), which is trapped within the
cytosol after being mono phosphorylated by thymidine kinase-1 (TK1), a principal enzyme
in the salvage pathway of DNA synthesis. 18F-FLT has been found to be useful for noninvasive
assessment of the proliferation rate of several types of cancer and showed high
reproducibility and accuracy in breast and lung cancer tumours.
The aim of this thesis is the perfusion quantification of dynamic PET and MRI data of patients
with lung, brain, liver, prostate and breast lesions with the application of advanced models.
This study covers a wide range of imaging methods and applications, presenting a novel
combination of MRI-based perfusion measures with PET kinetic modelling parameters in
oncology. It assesses the applicability and stability of perfusion quantification methods,
which are not currently used in the routine clinical practice.
The main achievements of this work include: 1) the assessment of the stability of perfusion
quantification of D4-Choline and 18F-FLT dynamic PET data in lung and liver lesions,
respectively (first applications in the literature); 2) the development of a model selection in
the analysis of DCE-MRI data of primary brain tumours (first application of the extended
shutter speed model); 3) the multiparametric analysis of PET and MRI derived perfusion
measurements of primary brain tumour and breast cancer together with the integration of
immuohistochemical markers in the prediction of breast cancer subtype (analysis of data
acquired on the hybrid PET/MRI scanner).
The thesis is structured as follows:
- Chapter 1 is an introductive chapter on cancer biology. Basic concepts, including the causes
of cancer, cancer hallmarks, available cancer treatments, are described in this first chapter.
Furthermore, there are basic concepts of brain, breast, prostate and lung cancers (which are
the lesions that have been analysed in this work). - Chapter 2 is about Positron Emission Tomography. After a brief introduction on the basics
of PET imaging, together with data acquisition and reconstruction methods, the chapter
focuses on PET in the clinical settings. In particular, it shows the quantification techniques
of static and dynamic PET data and my results of the application of graphical methods,
spectral analysis and compartmental models on dynamic 18F-FDG, 18F-FLT and 18F-D4-
Choline PET data of patients with breast, lung cancer and hepatocellular carcinoma.
- Chapter 3 is about Magnetic Resonance Imaging. After a brief introduction on the basics of
MRI, the chapter focuses on the quantification of perfusion weighted MRI data. In particular,
it shows the pharmacokinetic models for the quantification of dynamic contrast enhanced
MRI data and my results of the application of the Tofts, the extended Tofts, the shutter speed
and the extended shutter speed models on a dataset of patients with brain glioma.
- Chapter 4 introduces the multiparametric imaging techniques, in particular the combined
PET/CT and the hybrid PET/MRI systems. The last part of the chapter shows the applications
of perfusion quantification techniques on a multiparametric study of breast tumour patients,
who simultaneously underwent DCE-MRI and 18F-FDG PET on a hybrid PET/MRI scanner.
Then the results of a predictive study on the same dataset of breast tumour patients integrated
with immunohistochemical markers. Furthermore, the results of a multiparametric study on
DCE-MRI and 18F-FCM brain data acquired both on a PET/CT scanner and on an MR
scanner, separately. Finally, it will show the application of kinetic analysis in a radiomic
study of patients with prostate cancer
Searching for collective behavior in a network of real neurons
Maximum entropy models are the least structured probability distributions
that exactly reproduce a chosen set of statistics measured in an interacting
network. Here we use this principle to construct probabilistic models which
describe the correlated spiking activity of populations of up to 120 neurons in
the salamander retina as it responds to natural movies. Already in groups as
small as 10 neurons, interactions between spikes can no longer be regarded as
small perturbations in an otherwise independent system; for 40 or more neurons
pairwise interactions need to be supplemented by a global interaction that
controls the distribution of synchrony in the population. Here we show that
such "K-pairwise" models--being systematic extensions of the previously used
pairwise Ising models--provide an excellent account of the data. We explore the
properties of the neural vocabulary by: 1) estimating its entropy, which
constrains the population's capacity to represent visual information; 2)
classifying activity patterns into a small set of metastable collective modes;
3) showing that the neural codeword ensembles are extremely inhomogenous; 4)
demonstrating that the state of individual neurons is highly predictable from
the rest of the population, allowing the capacity for error correction.Comment: 24 pages, 19 figure
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