Burrknots as food source for larval development of Grapholita molesta (Lepidoptera: Tortricidae) on apple trees.

Grapholita molesta (Busck) is one of the main pests of apple trees, and lives on their shoots and fruits. In southern Brazil, the insect is also found on old branches and structures similar to aerial roots, so-called burrknots. This study evaluated the development and population growth potential of G. molesta fed on burrknots, compared with apple fruit cultivar. Fuji and a corn-based artiÞcial diet. The study was carried out in the laboratory under controlled temperature (25 1C), relative humidity (7010%), and photophase (16 h). The biological parameters of the immature and adult stages were determined, and a fertility life table was constructed. Insects fed on burrknots showed a longer duration and a lower survival for the egg-to-adult period (29.3 d and 22.5%) compared with those that fed on apples (25.1 d and 30.0%) and artiÞcial diet (23.9 d and 54.8%). Insects reared on aerial roots had a lower pupal weight (10.0 mg) compared with those reared on either artiÞcial diet (13.7 mg) or apple cultivar. Fuji (12.4 mg). The fecundity and longevity of males and females did not signiÞcantly differ for the three foods. Based on the fertility life table, insects reared on burrknots had the lowest net reproductive rate (Ro), intrinsic rate of population growth (rm) and finite rate of increase, compared with insects reared on artiÞcial diet and apple fruit. Burrknots support the development of the complete cycle of G. molesta, which allows populations of this pest to increase in orchards.

Development of novel multi-substituted apatite nanophases with advanced functionalities for bone regeneration

In the field of bone substitutes is highly researched an innovative material able to fill gaps with high mechanical performances and able to stimulate cell response, permitting the complete restoration of the bone portion. In this respect, the synthesis of new bioactive materials able to mimic the compositional, morphological and mechanical features of bone is considered as the elective approach for effective tissue regeneration. Hydroxyapatite (HA) is the main component of the inorganic part of bone. Additionally ionic substitution can be performed in the apatite lattice producing different effects, depending from the selected ions. Magnesium, in substitution of calcium, and carbonate, in substitution of phosphate, extensively present in the biological bones, are able to improve properties naturally present in the apatitic phase, (i.e. biomimicry, solubility e osteoinductive properties). Other ions can be used to give new useful properties, like antiresorptive or antimicrobial properties, to the apatitic phase. This thesis focused on the development of hydroxyapatite nanophases with multiple ionic substitutions including gallium, or zinc ions, in association with magnesium and carbonate, with the purpose to provide double synergistic functionality as osteogenic and antibacterial biomaterial. Were developed bioactive materials based on Sr-substituted hydroxyapatite in the form of sintered targets. The obtained targets were treated with Pulsed Plasma Deposition (PED) resulting in the deposition of thin film coatings able to improve the roughness and wettability of PEEK, enhancing its osteointegrability. Were investigated heterogeneous gas-solid reactions, addressed to the biomorphic transformations of natural 3D porous structures into bone scaffolds with biomimetic composition and hierarchical organization, for application in load-bearing sites. The kinetics of the different reactions of the process were optimized to achieve complete and controlled phase transformation, maintaining the original 3-D morphology. Massive porous scaffolds made of ion-substituted hydroxyapatite and bone-mimicking structure were developed and tested in 3-D cell culture models.

Content-aware approach for improving biomedical image analysis: an interdisciplinary study series

Biomedicine is a highly interdisciplinary research area at the interface of sciences, anatomy, physiology, and medicine. In the last decade, biomedical studies have been greatly enhanced by the introduction of new technologies and techniques for automated quantitative imaging, thus considerably advancing the possibility to investigate biological phenomena through image analysis. However, the effectiveness of this interdisciplinary approach is bounded by the limited knowledge that a biologist and a computer scientist, by professional training, have of each other’s fields. The possible solution to make up for both these lacks lies in training biologists to make them interdisciplinary researchers able to develop dedicated image processing and analysis tools by exploiting a content-aware approach. The aim of this Thesis is to show the effectiveness of a content-aware approach to automated quantitative imaging, by its application to different biomedical studies, with the secondary desirable purpose of motivating researchers to invest in interdisciplinarity. Such content-aware approach has been applied firstly to the phenomization of tumour cell response to stress by confocal fluorescent imaging, and secondly, to the texture analysis of trabecular bone microarchitecture in micro-CT scans. Third, this approach served the characterization of new 3-D multicellular spheroids of human stem cells, and the investigation of the role of the Nogo-A protein in tooth innervation. Finally, the content-aware approach also prompted to the development of two novel methods for local image analysis and colocalization quantification. In conclusion, the content-aware approach has proved its benefit through building new approaches that have improved the quality of image analysis, strengthening the statistical significance to allow unveiling biological phenomena. Hopefully, this Thesis will contribute to inspire researchers to striving hard for pursuing interdisciplinarity.

Simulazione numerica di un getto di idrogeno sottoespanso mediante OpenFOAM

Questa tesi tratta lo studio e l’applicazione di una metodologia per la simulazione di getti gassosi sottoespansi, nello specifico getti di idrogeno in aria. La necessità di questo lavoro nasce dall’esigenza, nel mondo dei trasporti, di abbandonare i combustibili fossili derivati dal petrolio. L’idrogeno, come il metano, viene iniettato in fase gassosa, quindi occorre studiare bene i fenomeni e i meccanismi in gioco per ottimizzare la miscelazione con l’aria. La parte iniziale della tesi riguarda lo studio teorico e generale della letteratura sui getti sottoespansi, con l’obiettivo di capire cosa sono, come si formano, da cosa sono governati e quali sono i loro parametri principali. Successivamente si passa alla loro analisi numerica tramite il software CFD open source OpenFOAM. Le analisi condotte hanno permesso di valutare l’impiego di diversi approcci di modellazione al fine di trovare quello ottimale. Sono stati testati differenti risolutori (rhoCentralFoam, rhoPimpleFoam e reactingFoam), diverse configurazioni della mesh (wedge 2-D, empty 2-D e cyclic 3-D) e diversi modelli di turbolenza (k − ϵ, RNG k − ϵ e k − ω SST).

JPEG standard uniform quantization error modeling with applications to sequential and progressive operation modes

In this paper we propose a method for computing JPEG quantization matrices for a given mean square error or PSNR. Then, we employ our method to compute JPEG standard progressive operation mode definition scripts using a quantization approach. Therefore, it is no longer necessary to use a trial and error procedure to obtain a desired PSNR and/or definition script, reducing cost. Firstly, we establish a relationship between a Laplacian source and its uniform quantization error. We apply this model to the coefficients obtained in the discrete cosine transform stage of the JPEG standard. Then, an image may be compressed using the JPEG standard under a global MSE (or PSNR) constraint and a set of local constraints determined by the JPEG standard and visual criteria. Secondly, we study the JPEG standard progressive operation mode from a quantization based approach. A relationship between the measured image quality at a given stage of the coding process and a quantization matrix is found. Thus, the definition script construction problem can be reduced to a quantization problem. Simulations show that our method generates better quantization matrices than the classical method based on scaling the JPEG default quantization matrix. The estimation of PSNR has usually an error smaller than 1 dB. This figure decreases for high PSNR values. Definition scripts may be generated avoiding an excessive number of stages and removing small stages that do not contribute during the decoding process with a noticeable image quality improvement.

Modeling cumulative evidence for freedom from disease with applications to BSE surveillance trials

This investigation deals with the question of when a particular population can be considered to be disease-free. The motivation is the case of BSE where specific birth cohorts may present distinct disease-free subpopulations. The specific objective is to develop a statistical approach suitable for documenting freedom of disease, in particular, freedom from BSE in birth cohorts. The approach is based upon a geometric waiting time distribution for the occurrence of positive surveillance results and formalizes the relationship between design prevalence, cumulative sample size and statistical power. The simple geometric waiting time model is further modified to account for the diagnostic sensitivity and specificity associated with the detection of disease. This is exemplified for BSE using two different models for the diagnostic sensitivity. The model is furthermore modified in such a way that a set of different values for the design prevalence in the surveillance streams can be accommodated (prevalence heterogeneity) and a general expression for the power function is developed. For illustration, numerical results for BSE suggest that currently (data status September 2004) a birth cohort of Danish cattle born after March 1999 is free from BSE with probability (power) of 0.8746 or 0.8509, depending on the choice of a model for the diagnostic sensitivity.

RF-sputter fabrication of magnetic garnet thin films and simulation modeling for 1-D magnetic photonic crystal waveguide devices

One dimensional magnetic photonic crystals (1D-MPC) are promising structures for integrated optical isolator applications. Rare earth substituted garnet thin films with proper Faraday rotation are required to fabricate planar 1D-MPCs. In this thesis, flat-top response 1D-MPC was proposed and spectral responses and Faraday rotation were modeled. Bismuth substituted iron garnet films were fabricated by RF magnetron sputtering and structures, compositions, birefringence and magnetooptical properties were studied. Double layer structures for single mode propagation were also fabricated by sputtering for the first time. Multilayer stacks with multiple defects (phase shift) composed of Ce-YIG and GGG quarter-wave plates were simulated by the transfer matrix method. The transmission and Faraday rotation characteristics were theoretically studied. It is found that flat-top response, with 100% transmission and near 45o rotation is achievable by adjusting the inter-defect spacing, for film structures as thin as 30 to 35 μm. This is better than 3-fold reduction in length compared to the best Ce-YIG films for comparable rotations, thus allows a considerable reduction in size in manufactured optical isolators. Transmission bands as wide as 7nm were predicted, which is considerable improvement over 2 defects structure. Effect of repetition number and ratio factor on transmission and Faraday rotation ripple factors for the case of 3 and 4 defects structure has been discussed. Diffraction across the structure corresponds to a longer optical path length. Thus the use of guided optics is required to minimize the insertion losses in integrated devices. This part is discussed in chapter 2 in this thesis. Bismuth substituted iron garnet thin films were prepared by RF magnetron sputtering. We investigated or measured the deposition parameters optimization, crystallinity, surface morphologies, composition, magnetic and magnetooptical properties. A very high crystalline quality garnet film with smooth surface has been heteroepitaxially grown on (111) GGG substrate for films less than 1μm. Dual layer structures with two distinct XRD peaks (within a single sputtered film) start to develop when films exceed this thickness. The development of dual layer structure was explained by compositional gradient across film thickness, rather than strain gradient proposed by other authors. Lower DC self bias or higher substrate temperature is found to help to delay the appearance of the 2nd layer. The deposited films show in-plane magnetization, which is advantageous for waveguide devices application. Propagation losses of fabricated waveguides can be decreased by annealing in an oxygen atmosphere from 25dB/cm to 10dB/cm. The Faraday rotation at λ=1.55μm were also measured for the waveguides. FR is small (10° for a 3mm long waveguide), due to the presence of linear birefringence. This part is covered in chapter 4. We also investigated the elimination of linear birefringence by thickness tuning method for our sputtered films. We examined the compressively and tensilely strained films and analyze the photoelastic response of the sputter deposited garnet films. It has been found that the net birefringence can be eliminated under planar compressive strain conditions by sputtering. Bi-layer GGG on garnet thin film yields a reduced birefringence. Temperature control during the sputter deposition of GGG cover layer is critical and strongly influences the magnetization and birefringence level in the waveguide. High temperature deposition lowers the magnetization and increases the linear birefringence in the garnet films. Double layer single mode structures fabricated by sputtering were also studied. The double layer, which shows an in-plane magnetization, has an increased RMS roughness upon upper layer deposition. The single mode characteristic was confirmed by prism coupler measurement. This part is discussed in chapter 5.

Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction

An innovative background modeling technique that is able to accurately segment foreground regions in RGB-D imagery (RGB plus depth) has been presented in this paper. The technique is based on a Bayesian framework that efficiently fuses different sources of information to segment the foreground. In particular, the final segmentation is obtained by considering a prediction of the foreground regions, carried out by a novel Bayesian Network with a depth-based dynamic model, and, by considering two independent depth and color-based mixture of Gaussians background models. The efficient Bayesian combination of all these data reduces the noise and uncertainties introduced by the color and depth features and the corresponding models. As a result, more compact segmentations, and refined foreground object silhouettes are obtained. Experimental results with different databases suggest that the proposed technique outperforms existing state-of-the-art algorithms.

SAP R/3 - EIS : anàlisi i planificació d'un sistema EIS

Es comença parlant dels sistemes d'informació per a anar a parar al món dels ERP i, especialment, a un dels més importants a escala mundial, el SAP R/3. Després s'explica què són els DataWarehouses o magatzems de dades, com es creen i quines aplicacions tenen. Finalment, s'analitza i es planifica un cas pràctic: una empresa canvia d'ERP corporatiu per passar a SAP R/3 i, per tant, ha de canviar i adaptar el seu sistema EIS.

Molecular modeling of peptide-MHC class I complexes : applications to peptide based immunotherapy

Summary The specific CD8+ T cell immune response against tumors relies on the recognition by the T cell receptor (TCR) on cytotoxic T lymphocytes (CTL) of antigenic peptides bound to the class I major histocompatibility complex (MHC) molecule. Such tumor associated antigenic peptides are the focus of tumor immunotherapy with peptide vaccines. The strategy for obtaining an improved immune response often involves the design of modified tumor associated antigenic peptides. Such modifications aim at creating higher affinity and/or degradation resistant peptides and require precise structures of the peptide-MHC class I complex. In addition, the modified peptide must be cross-recognized by CTLs specific for the parental peptide, i.e. preserve the structure of the epitope. Detailed structural information on the modified peptide in complex with MHC is necessary for such predictions. In this thesis, the main focus is the development of theoretical in silico methods for prediction of both structure and cross-reactivity of peptide-MHC class I complexes. Applications of these methods in the context of immunotherapy are also presented. First, a theoretical method for structure prediction of peptide-MHC class I complexes is developed and validated. The approach is based on a molecular dynamics protocol to sample the conformational space of the peptide in its MHC environment. The sampled conformers are evaluated using conformational free energy calculations. The method, which is evaluated for its ability to reproduce 41 X-ray crystallographic structures of different peptide-MHC class I complexes, shows an overall prediction success of 83%. Importantly, in the clinically highly relevant subset of peptide-HLAA*0201 complexes, the prediction success is 100%. Based on these structure predictions, a theoretical approach for prediction of cross-reactivity is developed and validated. This method involves the generation of quantitative structure-activity relationships using three-dimensional molecular descriptors and a genetic neural network. The generated relationships are highly predictive as proved by high cross-validated correlation coefficients (0.78-0.79). Together, the here developed theoretical methods open the door for efficient rational design of improved peptides to be used in immunotherapy. Résumé La réponse immunitaire spécifique contre des tumeurs dépend de la reconnaissance par les récepteurs des cellules T CD8+ de peptides antigéniques présentés par les complexes majeurs d'histocompatibilité (CMH) de classe I. Ces peptides sont utilisés comme cible dans l'immunothérapie par vaccins peptidiques. Afin d'augmenter la réponse immunitaire, les peptides sont modifiés de façon à améliorer l'affinité et/ou la résistance à la dégradation. Ceci nécessite de connaître la structure tridimensionnelle des complexes peptide-CMH. De plus, les peptides modifiés doivent être reconnus par des cellules T spécifiques du peptide natif. La structure de l'épitope doit donc être préservée et des structures détaillées des complexes peptide-CMH sont nécessaires. Dans cette thèse, le thème central est le développement des méthodes computationnelles de prédiction des structures des complexes peptide-CMH classe I et de la reconnaissance croisée. Des applications de ces méthodes de prédiction à l'immunothérapie sont également présentées. Premièrement, une méthode théorique de prédiction des structures des complexes peptide-CMH classe I est développée et validée. Cette méthode est basée sur un échantillonnage de l'espace conformationnel du peptide dans le contexte du récepteur CMH classe I par dynamique moléculaire. Les conformations sont évaluées par leurs énergies libres conformationnelles. La méthode est validée par sa capacité à reproduire 41 structures des complexes peptide-CMH classe I obtenues par cristallographie aux rayons X. Le succès prédictif général est de 83%. Pour le sous-groupe HLA-A*0201 de complexes de grande importance pour l'immunothérapie, ce succès est de 100%. Deuxièmement, à partir de ces structures prédites in silico, une méthode théorique de prédiction de la reconnaissance croisée est développée et validée. Celle-ci consiste à générer des relations structure-activité quantitatives en utilisant des descripteurs moléculaires tridimensionnels et un réseau de neurones couplé à un algorithme génétique. Les relations générées montrent une capacité de prédiction remarquable avec des valeurs de coefficients de corrélation de validation croisée élevées (0.78-0.79). Les méthodes théoriques développées dans le cadre de cette thèse ouvrent la voie du design de vaccins peptidiques améliorés.

The CD4-like molecule LAG-3, biology and therapeutic applications.

IMPORTANCE OF THE FIELD: Promising immunotherapeutic agents targeting co-stimulatory pathways are currently being tested in clinical trials. One player in this array of regulatory pathways is the LAG-3/MHC class II axis. The lymphocyte activation gene-3 (LAG-3) is a negative co-stimulatory receptor that modulates T cell homeostasis, proliferation and activation. A recombinant soluble dimeric form of LAG-3 (sLAG-3-Ig, IMP321) shows adjuvant properties and enhances immunogenicity of tumor vaccines. Recent clinical trials produced encouraging results, especially when the human dimeric soluble form of LAG-3 (hLAG-3-Ig) was used in combination with chemotherapy. AREAS COVERED IN THIS REVIEW: The biological relevance of LAG-3 in vivo. Pre-clinical data demonstrating adjuvant properties, as well as the improvement of tumor immunity by sLAG-3-Ig. Recent advances in the clinical development of the therapeutic reagent IMP321, hLAG-3-Ig, for cancer treatment. WHAT THE READER WILL GAIN: This review summarizes preclinical and clinical data on the biological functions of LAG-3. TAKE HOME MESSAGE: The LAG-3 inhibitory pathway is attracting attention, in the light of recent studies demonstrating its role in T cell unresponsiveness, and Treg function after chronic antigen stimulation. As a soluble recombinant dimer, the sLAG-3-Ig protein acts as an adjuvant for therapeutic induction of T cell responses, and may be beneficial to cancer patients when used in combination therapies.