Diagnostic methods in sepsis: the need of speed

OBJECTIVE: Sepsis is a common condition encountered in hospital environments. There is no effective treatment for sepsis, and it remains an important cause of death at intensive care units. This study aimed to discuss some methods that are available in clinics, and tests that have been recently developed for the diagnosis of sepsis. METHODS: A systematic review was performed through the analysis of the following descriptors: sepsis, diagnostic methods, biological markers, and cytokines. RESULTS: The deleterious effects of sepsis are caused by an imbalance between the invasiveness of the pathogen and the ability of the host to mount an effective immune response. Consequently, the host's immune surveillance fails to eliminate the pathogen, allowing it to spread. Moreover, there is a pro-inflammatory mediator release, inappropriate activation of the coagulation and complement cascades, leading to dysfunction of multiple organs and systems. The difficulty achieve total recovery of the patient is explainable. There is an increased incidence of sepsis worldwide due to factors such as aging population, larger number of surgeries, and number of microorganisms resistant to existing antibiotics. CONCLUSION: The search for new diagnostic markers associated with increased risk of sepsis development and molecules that can be correlated to certain steps of sepsis is becoming necessary. This would allow for earlier diagnosis, facilitate patient prognosis characterization, and prediction of possible evolution of each case. All other markers are regrettably constrained to research units.

Variance Reduction in Analytical Chemistry : New Numerical Methods in Chemometrics and Molecular Simulation

This thesis is based on five papers addressing variance reduction in different ways. The papers have in common that they all present new numerical methods. Paper I investigates quantitative structure-retention relationships from an image processing perspective, using an artificial neural network to preprocess three-dimensional structural descriptions of the studied steroid molecules. Paper II presents a new method for computing free energies. Free energy is the quantity that determines chemical equilibria and partition coefficients. The proposed method may be used for estimating, e.g., chromatographic retention without performing experiments. Two papers (III and IV) deal with correcting deviations from bilinearity by so-called peak alignment. Bilinearity is a theoretical assumption about the distribution of instrumental data that is often violated by measured data. Deviations from bilinearity lead to increased variance, both in the data and in inferences from the data, unless invariance to the deviations is built into the model, e.g., by the use of the method proposed in paper III and extended in paper IV. Paper V addresses a generic problem in classification; namely, how to measure the goodness of different data representations, so that the best classifier may be constructed. Variance reduction is one of the pillars on which analytical chemistry rests. This thesis considers two aspects on variance reduction: before and after experiments are performed. Before experimenting, theoretical predictions of experimental outcomes may be used to direct which experiments to perform, and how to perform them (papers I and II). After experiments are performed, the variance of inferences from the measured data are affected by the method of data analysis (papers III-V).

Heteronuclear recoupling methods in solid-state NMR

This thesis is focused on the development of heteronuclear correlation methods in solid-state NMR spectroscopy, where the spatial dependence of the dipolar coupling is exploited to obtain structural and dynamical information in solids. Quantitative results on dipolar coupling constants are extracted by means of spinning sideband analysis in the indirect dimension of the two-dimensional experiments. The principles of sideband analysis were established and are currently widely used in the group of Prof. Spiess for the special case of homonuclear 1H double-quantum spectroscopy. The generalization of these principles to the heteronuclear case is presented, with special emphasis on naturally abundant 13C-1H systems. For proton spectroscopy in the solid state, line-narrowing is of particular importance, and is here achieved by very-fast sample rotation at the magic angle (MAS), with frequencies up to 35 kHz. Therefore, the heteronuclear dipolar couplings are suppressed and have to be recoupled in order to achieve an efficient excitation of the observed multiple-quantum modes. Heteronuclear recoupling is most straightforwardly accomplished by performing the known REDOR experiment, where pi-pulses are applied every half rotor period. This experiment was modified by the insertion of an additional spectroscopic dimension, such that heteronuclear multiple-quantum experiments can be carried out, which, as shown experimentally and theoretically, closely resemble homonuclear double-quantum experiments. Variants are presented which are well-suited for the recording of high-resolution 13C-1H shift correlation and spinning-sideband spectra, by means of which spatial proximities and quantitative dipolar coupling constants, respectively, of heteronuclear spin pairs can be determined. Spectral editing of 13C spectra is shown to be feasible with these techniques. Moreover, order phenomena and dynamics in columnar mesophases with 13C in natural abundance were investigated. Two further modifications of the REDOR concept allow the correlation of 13C with quadrupolar nuclei, such as 2H. The spectroscopic handling of these nuclei is challenging in that they cover large frequency ranges, and with the new experiments it is shown how the excitation problem can be tackled or circumvented altogether, respectively. As an example, one of the techniques is used for the identification of a yet unknown motional process of the H-bonded protons in the crystalline parts of poly(vinyl alcohol).

Dynamic identification of structures: experimental assessment of modal parameteres through methods in frequency domain, in time-frequency domain and model updating

Among the experimental methods commonly used to define the behaviour of a full scale system, dynamic tests are the most complete and efficient procedures. A dynamic test is an experimental process, which would define a set of characteristic parameters of the dynamic behaviour of the system, such as natural frequencies of the structure, mode shapes and the corresponding modal damping values associated. An assessment of these modal characteristics can be used both to verify the theoretical assumptions of the project, to monitor the performance of the structural system during its operational use. The thesis is structured in the following chapters: The first introductive chapter recalls some basic notions of dynamics of structure, focusing the discussion on the problem of systems with multiply degrees of freedom (MDOF), which can represent a generic real system under study, when it is excited with harmonic force or in free vibration. The second chapter is entirely centred on to the problem of dynamic identification process of a structure, if it is subjected to an experimental test in forced vibrations. It first describes the construction of FRF through classical FFT of the recorded signal. A different method, also in the frequency domain, is subsequently introduced; it allows accurately to compute the FRF using the geometric characteristics of the ellipse that represents the direct input-output comparison. The two methods are compared and then the attention is focused on some advantages of the proposed methodology. The third chapter focuses on the study of real structures when they are subjected to experimental test, where the force is not known, like in an ambient or impact test. In this analysis we decided to use the CWT, which allows a simultaneous investigation in the time and frequency domain of a generic signal x(t). The CWT is first introduced to process free oscillations, with excellent results both in terms of frequencies, dampings and vibration modes. The application in the case of ambient vibrations defines accurate modal parameters of the system, although on the damping some important observations should be made. The fourth chapter is still on the problem of post processing data acquired after a vibration test, but this time through the application of discrete wavelet transform (DWT). In the first part the results obtained by the DWT are compared with those obtained by the application of CWT. Particular attention is given to the use of DWT as a tool for filtering the recorded signal, in fact in case of ambient vibrations the signals are often affected by the presence of a significant level of noise. The fifth chapter focuses on another important aspect of the identification process: the model updating. In this chapter, starting from the modal parameters obtained from some environmental vibration tests, performed by the University of Porto in 2008 and the University of Sheffild on the Humber Bridge in England, a FE model of the bridge is defined, in order to define what type of model is able to capture more accurately the real dynamic behaviour of the bridge. The sixth chapter outlines the necessary conclusions of the presented research. They concern the application of a method in the frequency domain in order to evaluate the modal parameters of a structure and its advantages, the advantages in applying a procedure based on the use of wavelet transforms in the process of identification in tests with unknown input and finally the problem of 3D modeling of systems with many degrees of freedom and with different types of uncertainty.

Bioinformatic methods in applied genomic research

Here I will focus on three main topics that best address and include the projects I have been working in during my three year PhD period that I have spent in different research laboratories addressing both computationally and practically important problems all related to modern molecular genomics. The first topic is the use of livestock species (pigs) as a model of obesity, a complex human dysfunction. My efforts here concern the detection and annotation of Single Nucleotide Polymorphisms. I developed a pipeline for mining human and porcine sequences. Starting from a set of human genes related with obesity the platform returns a list of annotated porcine SNPs extracted from a new set of potential obesity-genes. 565 of these SNPs were analyzed on an Illumina chip to test the involvement in obesity on a population composed by more than 500 pigs. Results will be discussed. All the computational analysis and experiments were done in collaboration with the Biocomputing group and Dr.Luca Fontanesi, respectively, under the direction of prof. Rita Casadio at the Bologna University, Italy. The second topic concerns developing a methodology, based on Factor Analysis, to simultaneously mine information from different levels of biological organization. With specific test cases we develop models of the complexity of the mRNA-miRNA molecular interaction in brain tumors measured indirectly by microarray and quantitative PCR. This work was done under the supervision of Prof. Christine Nardini, at the “CAS-MPG Partner Institute for Computational Biology” of Shangai, China (co-founded by the Max Planck Society and the Chinese Academy of Sciences jointly) The third topic concerns the development of a new method to overcome the variety of PCR technologies routinely adopted to characterize unknown flanking DNA regions of a viral integration locus of the human genome after clinical gene therapy. This new method is entirely based on next generation sequencing and it reduces the time required to detect insertion sites, decreasing the complexity of the procedure. This work was done in collaboration with the group of Dr. Manfred Schmidt at the Nationales Centrum für Tumorerkrankungen (Heidelberg, Germany) supervised by Dr. Annette Deichmann and Dr. Ali Nowrouzi. Furthermore I add as an Appendix the description of a R package for gene network reconstruction that I helped to develop for scientific usage (http://www.bioconductor.org/help/bioc-views/release/bioc/html/BUS.html).

Characterisation of supramolecular structures by novel recoupling methods in solid-state NMR

In this work, solid-state NMR methods suitable for the investigation of supramolecular systems were developed and improved. In this context, special interest was focussed on non-covalent interactions responsible for the formation of supramolecular structures, such as pi-pi interacions and hydrogen-bonds. In the first part of this work, solid-state NMR methods were presented that provide information on molecular structure and motion via the investigation of anisotropic interactions, namely quadrupole and dipole-dipole couplings, under magic-angle spinning conditions. A two-dimensional 2H double quantum experiment was developed, which is performed under off magic-angle conditions and correlates 2H isotropic chemical shifts with quasistatic DQ-filtered line shapes. From the latter, the quadrupole coupling parameters of samples deuterated at multiple sites can be extracted in a site-selective fashion. Furthermore, 7Li quadrupole parameters of lithium intercalated into TiO2 were determined by NMR experiments performed under static and MAS conditions, and could provide information on the crystal geometry. For the determination of 7Li-7Li dipole-dipole couplings, multiple-quantum NMR experiments were performed. The 1H-13C REREDOR experiment was found to be capable of determining strong proton-carbon dipole-dipole couplings with an accuracy of 500~Hz, corresponding to a determination of proton-carbon chemical-bond lengths with picometer accuracy In the second part of this work, solid-state NMR experiments were combined with quantum-chemical calculations in order to aid and optimise the interpretation of experimental results. The investigations on Calix[4]hydroquinone nanotubes have shown that this combined approach can provide information on the presence of disordered and/or mobile species in supramolecular structures. As a second example, C3-symmetric discs arranging in helical columnar stacks were investigated. In these systems, 1H chemical shifts experience large pi-shifts due to packing effects, which were found to be long-ranged. Moreover, quantum-chemical calculations revealed that helicity in these systems is induced by the propeller-like conformation of the core of the molecules.

Computational Methods in Biophysics and Medicinal Chemistry: Applications and Challenges

In this thesis I described the theory and application of several computational methods in solving medicinal chemistry and biophysical tasks. I pointed out to the valuable information which could be achieved by means of computer simulations and to the possibility to predict the outcome of traditional experiments. Nowadays, computer represents an invaluable tool for chemists. In particular, the main topics of my research consisted in the development of an automated docking protocol for the voltage-gated hERG potassium channel blockers, and the investigation of the catalytic mechanism of the human peptidyl-prolyl cis-trans isomerase Pin1.

New Methods in Organocatalysis

In the following chapters new methods in organocatalysis are described. The design of new catalysts is explored starting from the synthesis and the study of ion tagged prolines to their applications and recycle, then moving to the synthesis of new bicyclic diarylprolinol silyl ethers and their use in organocatalytic transformations. The study of new organocatalytic reaction is also investigated, in particular bifunctional thioureas are employed to catalyse the conjugate addition of nitro compounds to 3-yilidene oxindoles in sequential and domino reactions. Finally, preliminary results on photochemical organocatalytic atom transfer radical addition to alkenes are discussed in the last chapter.

Novel investigation methods in Computational Social Dynamics and Complex Systems

In this thesis the evolution of the techno-social systems analysis methods will be reported, through the explanation of the various research experience directly faced. The first case presented is a research based on data mining of a dataset of words association named Human Brain Cloud: validation will be faced and, also through a non-trivial modeling, a better understanding of language properties will be presented. Then, a real complex system experiment will be introduced: the WideNoise experiment in the context of the EveryAware european project. The project and the experiment course will be illustrated and data analysis will be displayed. Then the Experimental Tribe platform for social computation will be introduced . It has been conceived to help researchers in the implementation of web experiments, and aims also to catalyze the cumulative growth of experimental methodologies and the standardization of tools cited above. In the last part, three other research experience which already took place on the Experimental Tribe platform will be discussed in detail, from the design of the experiment to the analysis of the results and, eventually, to the modeling of the systems involved. The experiments are: CityRace, about the measurement of human traffic-facing strategies; laPENSOcosì, aiming to unveil the political opinion structure; AirProbe, implemented again in the EveryAware project framework, which consisted in monitoring air quality opinion shift of a community informed about local air pollution. At the end, the evolution of the technosocial systems investigation methods shall emerge together with the opportunities and the threats offered by this new scientific path.

Multivariate analysis methods in the search for the Higgs boson produced in association with top pairs at the ATLAS experiment at LHC.

The Large Hadron Collider, located at the CERN laboratories in Geneva, is the largest particle accelerator in the world. One of the main research fields at LHC is the study of the Higgs boson, the latest particle discovered at the ATLAS and CMS experiments. Due to the small production cross section for the Higgs boson, only a substantial statistics can offer the chance to study this particle properties. In order to perform these searches it is desirable to avoid the contamination of the signal signature by the number and variety of the background processes produced in pp collisions at LHC. Much account assumes the study of multivariate methods which, compared to the standard cut-based analysis, can enhance the signal selection of a Higgs boson produced in association with a top quark pair through a dileptonic final state (ttH channel). The statistics collected up to 2012 is not sufficient to supply a significant number of ttH events; however, the methods applied in this thesis will provide a powerful tool for the increasing statistics that will be collected during the next LHC data taking.

Helicity methods in LO and NLO QCD calculations

The goal of this thesis is the acceleration of numerical calculations of QCD observables, both at leading order and next–to–leading order in the coupling constant. In particular, the optimization of helicity and spin summation in the context of VEGAS Monte Carlo algorithms is investigated. In the literature, two such methods are mentioned but without detailed analyses. Only one of these methods can be used at next–to–leading order. This work presents a total of five different methods that replace the helicity sums with a Monte Carlo integration. This integration can be combined with the existing phase space integral, in the hope that this causes less overhead than the complete summation. For three of these methods, an extension to existing subtraction terms is developed which is required to enable next–to–leading order calculations. All methods are analyzed with respect to efficiency, accuracy, and ease of implementation before they are compared with each other. In this process, one method shows clear advantages in relation to all others.

Lattices and discrete methods in cooperative games and decisions

Questa tesi si pone l'obiettivo di presentare la teoria dei giochi, in particolare di quelli cooperativi, insieme alla teoria delle decisioni, inquadrandole formalmente in termini di matematica discreta. Si tratta di due campi dove l'indagine si origina idealmente da questioni applicative, e dove tuttavia sono sorti e sorgono problemi più tipicamente teorici che hanno interessato e interessano gli ambienti matematico e informatico. Anche se i contributi iniziali sono stati spesso formulati in ambito continuo e utilizzando strumenti tipici di teoria della misura, tuttavia oggi la scelta di modelli e metodi discreti appare la più idonea. L'idea generale è quindi quella di guardare fin da subito al complesso dei modelli e dei risultati che si intendono presentare attraverso la lente della teoria dei reticoli. Ciò consente di avere una visione globale più nitida e di riuscire agilmente ad intrecciare il discorso considerando congiuntamente la teoria dei giochi e quella delle decisioni. Quindi, dopo avere introdotto gli strumenti necessari, si considerano modelli e problemi con il fine preciso di analizzare dapprima risultati storici e solidi, proseguendo poi verso situazioni più recenti, più complesse e nelle quali i risultati raggiunti possono suscitare perplessità. Da ultimo, vengono presentate alcune questioni aperte ed associati spunti per la ricerca.

A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context

Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.