997 resultados para 2D correlation plot


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A wine fermentation has been monitored on a daily basis by 1H NMR spectroscopy. Following data pre-processing that includes synthesis of the spectra to ensure all peaks are of constant half-width, the series of spectra were examined using generalised two-dimensional correlation techniques. Synchronous and asynchronous data maps have been generated and employed to interpret the changes in the fermentation process as a function of time. The results illustrate the potential of high resolution NMR with multivariate data analysis as a tool for process monitoring and the manner in which two-dimensional correlation mapping can aid in data interpretation.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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In the present study, variable temperature FT-IR spectroscopic investigations were used to characterize the spectral changes in oleic acid during heating oleic acid in the temperature range from -30 degrees;C to 22 degrees C. In order to extract more information about the spectral variations taking place during the phase transition process, 2D correlation spectroscopy (2DCOS) was employed for the stretching (C?O) and rocking (CH2) band of oleic acid. However, the interpretation of these spectral variations in the FT-IR spectra is not straightforward, because the absorption bands are heavily overlapped and change due to two processes: recrystallization of the ?-phase and melting of the oleic acid. Furthermore, the solid phase transition from the ?- to the a-phase was also observed between -4 degrees C and -2 degrees C. Thus, for a more detailed 2DCOS analysis, we have split up the spectral data set in the subsets recorded between -30 degrees C to -16 degrees C, -16 degrees C to 10 degrees C, and 10 degrees C to 22 degrees C. In the corresponding synchronous and asynchronous 2D correlation plots, absorption bands that are characteristic of the crystalline and amorphous regions of oleic acid were separated.

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A question often posed in protein folding/unfolding studies is whether the process is fully cooperative or whether it contains sequential elements. To address this question, one needs tools capable of resolving different events. It seems that, at least in certain cases, two-dimensional (2D) IR correlation spectroscopy can provide answers to this question. To illustrate this point, we have turned to the Cro-V55C dimer of the λ Cro repressor, a protein known to undergo thermal unfolding in two discrete steps through a stable equilibrium intermediate. The secondary structure of this intermediate is compatible with that of a partially unfolded protein and involves a reorganization of the N terminus, whereas the antiparallel β-ribbon formed by the C-terminal part of each subunit remains largely intact. To establish whether the unfolding process involves sequential events, we have performed a 2D correlation analysis of IR spectra recorded over the temperature range of 20–95°C. The 2D IR correlation analysis indeed provides evidence for a sequential formation of the stable intermediate, which is created in three (closely related) steps. A first step entails the unfolding of the short N-terminal β-strand, followed by the unfolding of the α-helices in a second step, and the third step comprises the reorganization of the remaining β-sheet and of some unordered segments in the protein. The complete unfolding of the stable intermediate at higher temperatures also undergoes sequential events that ultimately end with the breaking of the H bonds between the two β-strands at the dimer interface.

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To date, biodegradable networks and particularly their kinetic chain lengths have been characterized by analysis of their degradation products in solution. We characterize the network itself by NMR analysis in the solvent-swollen state under magic angle spinning conditions. The networks were prepared by photoinitiated cross-linking of poly(dl-lactide)−dimethacrylate macromers (5 kg/mol) in the presence of an unreactive diluent. Using diffusion filtering and 2D correlation spectroscopy techniques, all network components are identified. By quantification of network-bound photoinitiator fragments, an average kinetic chain length of 9 ± 2 methacrylate units is determined. The PDLLA macromer solution was also used with a dye to prepare computer-designed structures by stereolithography. For these networks structures, the average kinetic chain length is 24 ± 4 methacrylate units. In all cases the calculated molecular weights of the polymethacrylate chains after degradation are maximally 8.8 kg/mol, which is far below the threshold for renal clearance. Upon incubation in phosphate buffered saline at 37 °C, the networks show a similar mass loss profile in time as linear high-molecular-weight PDLLA (HMW PDLLA). The mechanical properties are preserved longer for the PDLLA networks than for HMW PDLLA. The initial tensile strength of 47 ± 2 MPa does not decrease significantly for the first 15 weeks, while HMW PDLLA lost 85 ± 5% of its strength within 5 weeks. The physical properties, kinetic chain length, and degradation profile of these photo-cross-linked PDLLA networks make them most suited materials for orthopedic applications and use in (bone) tissue engineering.

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The hydrogen bonding and crystallization of a biodegradable poly(ester urethane) copolymer based on poly(L-lactide) (PLLA) as the soft segment were investigated by FTIR. On slow cooling from melt, the onset and the progress of the crystallization of the urethane hard segments were correlated to the position, width, and relative intensity of the hydrogen-bonded N-H stretching band. The interconversion between the "free" and hydrogen-bonded N-H and C=O groups in the urethane units in the process was also revealed by 2D correlation analysis of the FTIR data. The crystallization of the PLLA soft segments was monitored by the ester C=O stretching and the skeletal vibrations. It was revealed that the PLLA crystallization was restricted by the phase separation and the urethane crystallization, and at cooling rates of 10 degrees C/min or higher, the crystallization of the PLLA soft segments was prohibited.

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Our media is saturated with claims of ``facts'' made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim ``cherry-picking''? This paper proposes a Query Response Surface (QRS) based framework that models claims based on structured data as parameterized queries. A key insight is that we can learn a lot about a claim by perturbing its parameters and seeing how its conclusion changes. This framework lets us formulate and tackle practical fact-checking tasks --- reverse-engineering vague claims, and countering questionable claims --- as computational problems. Within the QRS based framework, we take one step further, and propose a problem along with efficient algorithms for finding high-quality claims of a given form from data, i.e. raising good questions, in the first place. This is achieved to using a limited number of high-valued claims to represent high-valued regions of the QRS. Besides the general purpose high-quality claim finding problem, lead-finding can be tailored towards specific claim quality measures, also defined within the QRS framework. An example of uniqueness-based lead-finding is presented for ``one-of-the-few'' claims, landing in interpretable high-quality claims, and an adjustable mechanism for ranking objects, e.g. NBA players, based on what claims can be made for them. Finally, we study the use of visualization as a powerful way of conveying results of a large number of claims. An efficient two stage sampling algorithm is proposed for generating input of 2d scatter plot with heatmap, evalutaing a limited amount of data, while preserving the two essential visual features, namely outliers and clusters. For all the problems, we present real-world examples and experiments that demonstrate the power of our model, efficiency of our algorithms, and usefulness of their results.

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Pós-graduação em Biofísica Molecular - IBILCE

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Una nuova ed originale tecnica è stata messa a punto, finalizzata alla realizzazione di una procedura per la diagnosi dell’osteoporosi, mediante l’utilizzo di scanner low field single-sided NMR. Tre differenti scanner (NMR MOLE, MOUSE PM 10 e MOUSE PM5) sono stati usati per determinare il Bone Volume-to-Total Volume ratio (BV/TV), parametro che fornisce indicazioni sulla microstruttura dell’osso. I risultati sono stati confrontati con le analisi micro-CT. Gli esperimenti sono stati condotti nel Lab. NMR del dipartimento DIFA di UNIBO e nel Lab. NMR della Victoria University di Wellington (NZ), durante un periodo di visita di cinque mesi, supportato da una borsa di studio della “Facoltà di Scienze” di UNIBO. Le analisi micro-CT sono state condotte presso il Lab. di Tecnologie Mediche dell’Istituto Ortopedico Rizzoli, Bologna. La ricerca è stata parzialmente finanziata dalla “Fondazione del Monte di Bologna e Ravenna”. La caratterizzazione dell’osso trabecolare di campioni animali e dei tessuti che lo circondano (come cartilagine e muscolo) è stata condotta tramite mappe di correlazione T1-T2 e D-T2 , dove T1 e T2 sono, rispettivamente, il tempo di rilassamento longitudinale e trasversale del nucleo 1H, e D è il coefficiente di autodiffusione molecolare. E’ stata sviluppata una sequenza di impulsi (Diffusion-Weighted T1-T2) per ottenere mappe T1-T2 pesate in diffusione. I risultati hanno consentito di mettere a punto una procedura che elimina il segnale NMR proveniente da cartilagine e muscolo, rendendo più realistico lo scenario di applicazione in-vivo. I tre diversi dispositivi NMR hanno dato risultati consistenti tra loro e con le immagini micro-CT. L’intera catena di esperimenti condotti ha mostrato che dispositivi NMR single-sided possono essere usati per valutare il BV/TV di ossa trabecolari, con il vantaggio di essere portatili, a basso costo e non invasivi, permettendo campagne di screening della popolazione a rischio osteoporosi.

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Cascading energy landscapes through funneling has been postulated as a mechanistic route for achieving the lowest energy configuration of a macromolecular system (such as proteins and polymers). In particular, understanding the molecular mechanism for the melting and crystallization of polymers is a challenging fundamental question. The structural modifications that lead to the melting of poly(ethylene glycol) (PEG) are investigated here. Specific Raman bands corresponding to different configurations of the PEG chain have been identified, and the molecular structural dynamics of PEG melting have been addressed using a combination of Raman spectroscopy, 2D Raman correlation and density functional theory (DFT) calculations. The melting dynamics of PEG have been unambiguously explained along the C-O bond rotation coordinate.

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The effect of rare-earth ion Eu3+ on hemoglobin (Hb) was studied by using two-dimensional Raman correlation spectroscopy. The results show that with the variation of Eu3+ concentrations as perturbation, the oxidation state of Hb and its spin state are both sensitive to the perturbation. Eu3+ added to Hb affects the oxidation and spin state synchronously. The four structure-sensitive bands of Hb are more accessible to the Eu3+ than other bands.

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This paper presents a kernel density correlation based nonrigid point set matching method and shows its application in statistical model based 2D/3D reconstruction of a scaled, patient-specific model from an un-calibrated x-ray radiograph. In this method, both the reference point set and the floating point set are first represented using kernel density estimates. A correlation measure between these two kernel density estimates is then optimized to find a displacement field such that the floating point set is moved to the reference point set. Regularizations based on the overall deformation energy and the motion smoothness energy are used to constraint the displacement field for a robust point set matching. Incorporating this non-rigid point set matching method into a statistical model based 2D/3D reconstruction framework, we can reconstruct a scaled, patient-specific model from noisy edge points that are extracted directly from the x-ray radiograph by an edge detector. Our experiment conducted on datasets of two patients and six cadavers demonstrates a mean reconstruction error of 1.9 mm

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In this article, the authors evaluate a merit function for 2D/3D registration called stochastic rank correlation (SRC). SRC is characterized by the fact that differences in image intensity do not influence the registration result; it therefore combines the numerical advantages of cross correlation (CC)-type merit functions with the flexibility of mutual-information-type merit functions. The basic idea is that registration is achieved on a random subset of the image, which allows for an efficient computation of Spearman's rank correlation coefficient. This measure is, by nature, invariant to monotonic intensity transforms in the images under comparison, which renders it an ideal solution for intramodal images acquired at different energy levels as encountered in intrafractional kV imaging in image-guided radiotherapy. Initial evaluation was undertaken using a 2D/3D registration reference image dataset of a cadaver spine. Even with no radiometric calibration, SRC shows a significant improvement in robustness and stability compared to CC. Pattern intensity, another merit function that was evaluated for comparison, gave rather poor results due to its limited convergence range. The time required for SRC with 5% image content compares well to the other merit functions; increasing the image content does not significantly influence the algorithm accuracy. The authors conclude that SRC is a promising measure for 2D/3D registration in IGRT and image-guided therapy in general.