Hierarchical conditional random fields for parts based models matching

A parts based model is a parametrization of an object class using a collection of landmarks following the object structure. The matching of parts based models is one of the problems where pairwise Conditional Random Fields have been successfully applied. The main reason of their effectiveness is tractable inference and learning due to the simplicity of involved graphs, usually trees. However, these models do not consider possible patterns of statistics among sets of landmarks, and thus they sufffer from using too myopic information. To overcome this limitation, we propoese a novel structure based on a hierarchical Conditional Random Fields, which we explain in the first part of this memory. We build a hierarchy of combinations of landmarks, where matching is performed taking into account the whole hierarchy. To preserve tractable inference we effectively sample the label set. We test our method on facial feature selection and human pose estimation on two challenging datasets: Buffy and MultiPIE. In the second part of this memory, we present a novel approach to multiple kernel combination that relies on stacked classification. This method can be used to evaluate the landmarks of the parts-based model approach. Our method is based on combining responses of a set of independent classifiers for each individual kernel. Unlike earlier approaches that linearly combine kernel responses, our approach uses them as inputs to another set of classifiers. We will show that we outperform state-of-the-art methods on most of the standard benchmark datasets.

Cost-Based Models of Economic Growth

In this paper we highlight the importance of the operational costs in explaining economic growth and analyze how the industrial structure affects the growth rate of the economy. If there is monopolistic competition only in an intermediate goods sector, then production growth coincides with consumption growth. Moreover, the pattern of growth depends on the particular form of the operational cost. If the monopolistically competitive sector is the final goods sector, then per capita production is constant but per capita effective consumption or welfare grows. Finally, we modify again the industrial structure of the economy and show an economy with two different growth speeds, one for production and another for effective consumption. Thus, both the operational cost and the particular structure of the sector that produces the final goods determines ultimately the pattern of growth.

Cost-Based Models of Economic Growth

In this paper we highlight the importance of the operational costs in explaining economic growth and analyze how the industrial structure affects the growth rate of the economy. If there is monopolistic competition only in an intermediate goods sector, then production growth coincides with consumption growth. Moreover, the pattern of growth depends on the particular form of the operational cost. If the monopolistically competitive sector is the final goods sector, then per capita production is constant but per capita effective consumption or welfare grows. Finally, we modify again the industrial structure of the economy and show an economy with two different growth speeds, one for production and another for effective consumption. Thus, both the operational cost and the particular structure of the sector that produces the final goods determines ultimately the pattern of growth.

Structure-Based, Rational Design of T Cell Receptors.

Adoptive cell transfer using engineered T cells is emerging as a promising treatment for metastatic melanoma. Such an approach allows one to introduce T cell receptor (TCR) modifications that, while maintaining the specificity for the targeted antigen, can enhance the binding and kinetic parameters for the interaction with peptides (p) bound to major histocompatibility complexes (MHC). Using the well-characterized 2C TCR/SIYR/H-2K(b) structure as a model system, we demonstrated that a binding free energy decomposition based on the MM-GBSA approach provides a detailed and reliable description of the TCR/pMHC interactions at the structural and thermodynamic levels. Starting from this result, we developed a new structure-based approach, to rationally design new TCR sequences, and applied it to the BC1 TCR targeting the HLA-A2 restricted NY-ESO-1157-165 cancer-testis epitope. Fifty-four percent of the designed sequence replacements exhibited improved pMHC binding as compared to the native TCR, with up to 150-fold increase in affinity, while preserving specificity. Genetically engineered CD8(+) T cells expressing these modified TCRs showed an improved functional activity compared to those expressing BC1 TCR. We measured maximum levels of activities for TCRs within the upper limit of natural affinity, K D = ∼1 - 5 μM. Beyond the affinity threshold at K D < 1 μM we observed an attenuation in cellular function, in line with the "half-life" model of T cell activation. Our computer-aided protein-engineering approach requires the 3D-structure of the TCR-pMHC complex of interest, which can be obtained from X-ray crystallography. We have also developed a homology modeling-based approach, TCRep 3D, to obtain accurate structural models of any TCR-pMHC complexes when experimental data is not available. Since the accuracy of the models depends on the prediction of the TCR orientation over pMHC, we have complemented the approach with a simplified rigid method to predict this orientation and successfully assessed it using all non-redundant TCR-pMHC crystal structures available. These methods potentially extend the use of our TCR engineering method to entire TCR repertoires for which no X-ray structure is available. We have also performed a steered molecular dynamics study of the unbinding of the TCR-pMHC complex to get a better understanding of how TCRs interact with pMHCs. This entire rational TCR design pipeline is now being used to produce rationally optimized TCRs for adoptive cell therapies of stage IV melanoma.

The potential for the use of agent-based models in ecotoxicology

This chapter introduces ABMs, their construction, and the pros and cons of their use. Although relatively new, agent-basedmodels (ABMs) have great potential for use in ecotoxicological research – their primary advantage being the realistic simulations that can be constructed and particularly their explicit handling of space and time in simulations. Examples are provided of their use in ecotoxicology primarily exemplified by different implementations of the ALMaSS system. These examples presented demonstrate how multiple stressors, landscape structure, details regarding toxicology, animal behavior, and socioeconomic effects can and should be taken into account when constructing simulations for risk assessment. Like ecological systems, in ABMs the behavior at the system level is not simply the mean of the component responses, but the sum of the often nonlinear interactions between components in the system; hence this modeling approach opens the door to implementing and testing much more realistic and holistic ecotoxicological models than are currently used.

Calibration and evaluation of individual-based models using Approximate Bayesian Computation

This paper investigates the feasibility of using approximate Bayesian computation (ABC) to calibrate and evaluate complex individual-based models (IBMs). As ABC evolves, various versions are emerging, but here we only explore the most accessible version, rejection-ABC. Rejection-ABC involves running models a large number of times, with parameters drawn randomly from their prior distributions, and then retaining the simulations closest to the observations. Although well-established in some fields, whether ABC will work with ecological IBMs is still uncertain. Rejection-ABC was applied to an existing 14-parameter earthworm energy budget IBM for which the available data consist of body mass growth and cocoon production in four experiments. ABC was able to narrow the posterior distributions of seven parameters, estimating credible intervals for each. ABC’s accepted values produced slightly better fits than literature values do. The accuracy of the analysis was assessed using cross-validation and coverage, currently the best available tests. Of the seven unnarrowed parameters, ABC revealed that three were correlated with other parameters, while the remaining four were found to be not estimable given the data available. It is often desirable to compare models to see whether all component modules are necessary. Here we used ABC model selection to compare the full model with a simplified version which removed the earthworm’s movement and much of the energy budget. We are able to show that inclusion of the energy budget is necessary for a good fit to the data. We show how our methodology can inform future modelling cycles, and briefly discuss how more advanced versions of ABC may be applicable to IBMs. We conclude that ABC has the potential to represent uncertainty in model structure, parameters and predictions, and to embed the often complex process of optimizing an IBM’s structure and parameters within an established statistical framework, thereby making the process more transparent and objective.

Structure-Based Approach for the Study of Estrogen Receptor Binding Affinity and Subtype Selectivity

Estrogens exert important physiological effects through the modulation of two human estrogen receptor (hER) subtypes, alpa (hER alpha) and beta (hER beta). Because the levels and relative proportion of hER alpha and hER beta differ significantly in different target cells, selective hER ligands could target specific tissues or pathways regulated by one receptor subtype without affecting the other. To understand the structural and chemical basis by which small molecule modulators are able to discriminate between the two subtypes, we have applied three-dimensional target-based approaches employing a series of potent hER-ligands. Comparative molecular field analysis (CoMFA) studies were applied to a data set of 81 hER modulators, for which binding affinity values were collected for both hER alpha and hER beta. Significant statistical coefficients were obtained (hER alpha, q(2) = 0.76; hER beta, q(2) = 0.70), indicating the internal consistency of the models. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. Five hER crystal structures were used in GRID/PCA investigations to generate molecular interaction fields (MIF) maps. hER alpha and hER beta were separated using one factor. The resulting 3D information was integrated with the aim of revealing the most relevant structural features involved in hER subtype selectivity. The final QSAR and GRID/PCA models and the information gathered from 3D contour maps should be useful for the design or novel hER modulators with improved selectivity.

A structure based model for liposome disruption and the role of catalytic activity in myotoxic phospholipase A(2)S

Venom phospholipase A(2)s (PLA(2)s) display a wide spectrum of pharmacological activities and, based on the wealth of biochemical and structural data currently available for PLA(2)S, mechanistic models can now be inferred to account for some of these activities. A structural model is presented for the role played by the distribution of surface electrostatic potential in the ability of myotoxic D49/K49 PLA(2)s to disrupt multilamellar vesicles containing negatively charged natural and non-hydrolyzable phospholipids. Structural evidence is provided for the ability of K49 PLA(2)s to bind phospholipid analogues and for the existence of catalytic activity in K49 PLA(2)s. The importance of the existence of catalytic activity of D49 and K49 PLA(2)s in myotoxicity is presented. (C) 2003 Elsevier Ltd. All rights reserved.

Ligand- and Structure-Based Drug Design Strategies and PPAR delta/alpha Selectivity

Peroxisome-proliferator-activated receptors are a class of nuclear receptors with three subtypes: a, ? and d. Their main function is regulating gene transcription related to lipid and carbohydrate metabolism. Currently, there are no peroxisome-proliferator-activated receptors d drugs being marketed. In this work, we studied a data set of 70 compounds with a and d activity. Three partial least square models were created, and molecular docking studies were performed to understand the main reasons for peroxisome-proliferator-activated receptors d selectivity. The obtained results showed that some molecular descriptors (log P, hydration energy, steric and polar properties) are related to the main interactions that can direct ligands to a particular peroxisome-proliferator-activated receptors subtype.

Structure-based drug design studies on a series of aldolase inhibitors

Human African trypanosomiasis, also known as sleeping sickness, is a major cause of death in Africa, and for which there are no safe and effective treatments available. The enzyme aldolase from Trypanosoma brucei is an attractive, validated target for drug development. A series of alkyl‑glycolamido and alkyl-monoglycolate derivatives was studied employing a combination of drug design approaches. Three-dimensional quantitative structure-activity relationships (3D QSAR) models were generated using the comparative molecular field analysis (CoMFA). Significant results were obtained for the best QSAR model (r2 = 0.95, non-cross-validated correlation coefficient, and q2 = 0.80, cross-validated correlation coefficient), indicating its predictive ability for untested compounds. The model was then used to predict values of the dependent variables (pKi) of an external test set,the predicted values were in good agreement with the experimental results. The integration of 3D QSAR, molecular docking and molecular dynamics simulations provided further insight into the structural basis for selective inhibition of the target enzyme.

Novel algorithms for electronic structure based molecular dynamics with linear system-size scaling

Die vorliegende Arbeit behandelt die Entwicklung und Verbesserung von linear skalierenden Algorithmen für Elektronenstruktur basierte Molekulardynamik. Molekulardynamik ist eine Methode zur Computersimulation des komplexen Zusammenspiels zwischen Atomen und Molekülen bei endlicher Temperatur. Ein entscheidender Vorteil dieser Methode ist ihre hohe Genauigkeit und Vorhersagekraft. Allerdings verhindert der Rechenaufwand, welcher grundsätzlich kubisch mit der Anzahl der Atome skaliert, die Anwendung auf große Systeme und lange Zeitskalen. Ausgehend von einem neuen Formalismus, basierend auf dem großkanonischen Potential und einer Faktorisierung der Dichtematrix, wird die Diagonalisierung der entsprechenden Hamiltonmatrix vermieden. Dieser nutzt aus, dass die Hamilton- und die Dichtematrix aufgrund von Lokalisierung dünn besetzt sind. Das reduziert den Rechenaufwand so, dass er linear mit der Systemgröße skaliert. Um seine Effizienz zu demonstrieren, wird der daraus entstehende Algorithmus auf ein System mit flüssigem Methan angewandt, das extremem Druck (etwa 100 GPa) und extremer Temperatur (2000 - 8000 K) ausgesetzt ist. In der Simulation dissoziiert Methan bei Temperaturen oberhalb von 4000 K. Die Bildung von sp²-gebundenem polymerischen Kohlenstoff wird beobachtet. Die Simulationen liefern keinen Hinweis auf die Entstehung von Diamant und wirken sich daher auf die bisherigen Planetenmodelle von Neptun und Uranus aus. Da das Umgehen der Diagonalisierung der Hamiltonmatrix die Inversion von Matrizen mit sich bringt, wird zusätzlich das Problem behandelt, eine (inverse) p-te Wurzel einer gegebenen Matrix zu berechnen. Dies resultiert in einer neuen Formel für symmetrisch positiv definite Matrizen. Sie verallgemeinert die Newton-Schulz Iteration, Altmans Formel für beschränkte und nicht singuläre Operatoren und Newtons Methode zur Berechnung von Nullstellen von Funktionen. Der Nachweis wird erbracht, dass die Konvergenzordnung immer mindestens quadratisch ist und adaptives Anpassen eines Parameters q in allen Fällen zu besseren Ergebnissen führt.

Transmission of Chlamydia trachomatis through sexual partnerships: a comparison between three individual-based models and empirical data

Chlamydia trachomatis is the most common bacterial sexually transmitted infection (STI) in many developed countries. The highest prevalence rates are found among young adults who have frequent partner change rates. Three published individual-based models have incorporated a detailed description of age-specific sexual behaviour in order to quantify the transmission of C. trachomatis in the population and to assess the impact of screening interventions. Owing to varying assumptions about sexual partnership formation and dissolution and the great uncertainty about critical parameters, such models show conflicting results about the impact of preventive interventions. Here, we perform a detailed evaluation of these models by comparing the partnership formation and dissolution dynamics with data from Natsal 2000, a population-based probability sample survey of sexual attitudes and lifestyles in Britain. The data also allow us to describe the dispersion of C. trachomatis infections as a function of sexual behaviour, using the Gini coefficient. We suggest that the Gini coefficient is a useful measure for calibrating infectious disease models that include risk structure and highlight the need to estimate this measure for other STIs.

Structure-based modeling of head -related transfer functions towards interactive customization of binaural sound systems

One of the most popular techniques for creating spatialized virtual sounds is based on the use of Head-Related Transfer Functions (HRTFs). HRTFs are signal processing models that represent the modifications undergone by the acoustic signal as it travels from a sound source to each of the listener's eardrums. These modifications are due to the interaction of the acoustic waves with the listener's torso, shoulders, head and pinnae, or outer ears. As such, HRTFs are somewhat different for each listener. For a listener to perceive synthesized 3-D sound cues correctly, the synthesized cues must be similar to the listener's own HRTFs. ^ One can measure individual HRTFs using specialized recording systems, however, these systems are prohibitively expensive and restrict the portability of the 3-D sound system. HRTF-based systems also face several computational challenges. This dissertation presents an alternative method for the synthesis of binaural spatialized sounds. The sound entering the pinna undergoes several reflective, diffractive and resonant phenomena, which determine the HRTF. Using signal processing tools, such as Prony's signal modeling method, an appropriate set of time delays and a resonant frequency were used to approximate the measured Head-Related Impulse Responses (HRIRs). Statistical analysis was used to find out empirical equations describing how the reflections and resonances are determined by the shape and size of the pinna features obtained from 3D images of 15 experimental subjects modeled in the project. These equations were used to yield “Model HRTFs” that can create elevation effects. ^ Listening tests conducted on 10 subjects show that these model HRTFs are 5% more effective than generic HRTFs when it comes to localizing sounds in the frontal plane. The number of reversals (perception of sound source above the horizontal plane when actually it is below the plane and vice versa) was also reduced by 5.7%, showing the perceptual effectiveness of this approach. The model is simple, yet versatile because it relies on easy to measure parameters to create an individualized HRTF. This low-order parameterized model also reduces the computational and storage demands, while maintaining a sufficient number of perceptually relevant spectral cues. ^

Fast Structure-Based Assignment of 15N HSQC Spectra of Selectively 15N-Labeled Paramagnetic Proteins

A novel strategy for fast NMR resonance assignment of N-15 HSQC spectra of proteins is presented. It requires the structure coordinates of the protein, a paramagnetic center, and one or more residue-selectively N-15-labeled samples. Comparison of sensitive undecoupled N-15 HSQC spectra recorded of paramagnetic and diamagnetic samples yields data for every cross-peak on pseudocontact shift, paramagnetic relaxation enhancement, cross-correlation between Curie-spin and dipole-dipole relaxation, and residual dipolar coupling. Comparison of these four different paramagnetic quantities with predictions from the three-dimensional structure simultaneously yields the resonance assignment and the anisotropy of the susceptibility tensor of the paramagnetic center. The method is demonstrated with the 30 kDa complex between the N-terminal domain of the epsilon subunit and the theta subunit of Escherichia Coll DNA polymerase III. The program PLATYPUS was developed to perform the assignment, provide a measure of reliability of the assignment, and determine the susceptibility tensor anisotropy.