Anti-parasitic and anti-viral immune responses in insects

Insects encounter many microorganisms in nature and to survive they have developed counter measures against the invading pathogens. In Drosophila melanogaster research on insect immunity has mainly been focused on infections by bacteria and fungi. We have explored the immune response against natural infections of the parasite Octosporea muscaedomesticae and the Drosophila C virus as compared to natural infections of bacteria and fungi. By using Affymetrix Drosophila GeneChips, we were able to obtain 48 genes uniquely induced after parasitic infection. It was also clearly shown that natural infections led to different results than when injecting the pathogens. In order to search for the ultimate role of the lepidopteran protein hemolin, we used RNA interference (RNAi). We could show that injection of double stranded RNA (dsRNA) of Hemolin in pupae of Hyalophora cecropia led to embryonic malformation and lethality and that there was a sex specific difference. We continued the RNAi investigation of hemolin in another lepidopteran species, Antheraea pernyi, and discovered that hemolin was induced by dsRNA per se. A similar induction of hemolin was seen after infection with baculovirus and we therefore performed in vivo experiments on baculovirus infected pupae. We could show that a low dose of dsHemolin prolonged the period before the A. pernyi pupae showed any symptoms of infection, while a high dose led to a more rapid onset of symptoms. By performing in silico analysis of the hemolin sequence from A. pernyi in comparison with other Hemolin sequences, it was possible to select a number of sites that either by being strongly conserved or variable could be important targets for future studies of hemolin function.

L'analisi di rischio nel trasporto stradale di merci pericolose: due software a confronto

Confronto tra due software specifici per l'analisi di rischio nel trasporto stradale di merci pericolose (TRAT GIS 4.1 e QRAM 3.6) mediante applicazione a un caso di studio semplice e al caso reale di Casalecchio di Reno, comune della provincia di Bologna.

Reti socievoli: l’impatto dei social networks nella costruzione dello spazio pubblico

La tesi si è consolidata nell’analisi dell’impatto dei social networks nella costruzione dello spazio pubblico, nella sfera di osservazione che è la rete e il web2.0. Osservando che il paradigma della società civile si sia modificato. Ridefinendo immagini e immaginari e forme di autorappresentazione sui new media (Castells, 2010). Nel presupposto che lo spazio pubblico “non è mai una realtà precostituita” (Innerarity, 2008) ma si muove all’interno di reti che generano e garantiscono socievolezza. Nell’obiettivo di capire cosa è spazio pubblico. Civic engagement che si rafforza in spazi simbolici (Sassen, 2008), nodi d’incontro significativi. Ivi cittadini-consumatori avanzano corresponsabilmente le proprie istanze per la debacle nei governi.. Cultura partecipativa che prende mossa da un nuovo senso civico mediato che si esprime nelle “virtù” del consumo critico. Portando la politica sul mercato. Cultura civica autoattualizzata alla ricerca di soluzioni alle crisi degli ultimi anni. Potere di una comunicazione che riduce il mondo ad un “villaggio globale” e mettono in relazione i pubblici connessi in spazi e tempi differenti, dando origine ad azioni collettive come nel caso degli Indignados, di Occupy Wall Street o di Rai per una notte. Emerge un (ri)pensare la citizenship secondo due paradigmi (Bennett,2008): l’uno orientato al governo attraverso i partiti, modello “Dutiful Citizenship”; l’altro, modello “Self Actualizing Citizenship” per cui i pubblici attivi seguono news ed eventi, percepiscono un minor obbligo nel governo, il voto è meno significativo per (s)fiducia nei media e nei politici. Mercato e società civile si muovono per il bene comune e una nuova “felicità”. La partecipazione si costituisce in consumerismo politico all’interno di reti in cui si sviluppano azioni individuali attraverso il social networking e scelte di consumo responsabile. Partendo dall’etnografia digitale, si è definito il modello “4 C”: Conoscenza > Coadesione > Co-partecipazione > Corresposabilità (azioni collettive) > Cultura-bility.

Model reduction of stochastic groundwater flow and transport processes

This work presents a comprehensive methodology for the reduction of analytical or numerical stochastic models characterized by uncertain input parameters or boundary conditions. The technique, based on the Polynomial Chaos Expansion (PCE) theory, represents a versatile solution to solve direct or inverse problems related to propagation of uncertainty. The potentiality of the methodology is assessed investigating different applicative contexts related to groundwater flow and transport scenarios, such as global sensitivity analysis, risk analysis and model calibration. This is achieved by implementing a numerical code, developed in the MATLAB environment, presented here in its main features and tested with literature examples. The procedure has been conceived under flexibility and efficiency criteria in order to ensure its adaptability to different fields of engineering; it has been applied to different case studies related to flow and transport in porous media. Each application is associated with innovative elements such as (i) new analytical formulations describing motion and displacement of non-Newtonian fluids in porous media, (ii) application of global sensitivity analysis to a high-complexity numerical model inspired by a real case of risk of radionuclide migration in the subsurface environment, and (iii) development of a novel sensitivity-based strategy for parameter calibration and experiment design in laboratory scale tracer transport.

Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon, and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalised spline formation of the model that relates to generalised kringing of the latent traffic pollution variable and leads to a natural Bayesian Markov Chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degress of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately

Multilevel Latent Class Models with Dirichlet Mixing Distribution

Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social sciences and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this paper, we develop multilevel latent class model, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the Expectation-Maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when either the number of classes or the cluster size is large. We propose a maximum pairwise likelihood (MPL) approach via a modified EM algorithm for this case. We also show that a simple latent class analysis, combined with robust standard errors, provides another consistent, robust, but less efficient inferential procedure. Simulation studies suggest that the three methods work well in finite samples, and that the MPL estimates often enjoy comparable precision as the ML estimates. We apply our methods to the analysis of comorbid symptoms in the Obsessive Compulsive Disorder study. Our models' random effects structure has more straightforward interpretation than those of competing methods, thus should usefully augment tools available for latent class analysis of multilevel data.

Common Mistakes When Fitting Models to Data

Dr. Rossi discusses the common errors that are made when fitting statistical models to data. Focuses on the planning, data analysis, and interpretation phases of a statistical analysis, and highlights the errors that are commonly made by researchers of these phases. The implications of these commonly made errors are discussed along with a discussion of the methods that can be used to prevent these errors from occurring. A prescription for carrying out a correct statistical analysis will be discussed.

Removable partial dentures

On one side, prosthodontic reconstructions compensate for the sequelae of negative changes in the oral cavity; on the other side, they often enhance or accelerate them. As a consequence of negative changes in the oral cavity over time, treatment planning for RPDs becomes highly complex. A set of reliable criteria is necessary for decision-making and problem management It appears that the majority of published data on RPDs does not depict high effectiveness of this treatment modality. From a strict point of view of evidence-based dentistry, the level of evidence is low if not missing for RPDs. Randomized controlled trials on RPDs are difficult to design, they are not feasible for some questions due to the complexity of the material, or may remain without clinical relevance. The literature rarely gives information on the denture design, tooth selection, and management of the compromised structural integrity of teeth. So far treatment outcomes with RPDs must be considered under the aspect of bias due to the bias in indication and patient selection for RPDs. Better clinical models should be elaborated with more stringent concepts for providing RPDs. This encompasses: risk analysis and patient assessment, proper indications for maintenance or extraction of teeth, strategic placement of implants, biomechanical aspects, materials, and technology. Although there is a tendency to offer fixed prostheses to our patients, this might change again with demographic changes and with an increase in the ageing population, an increase in their reduced dentition, and low socioeconomic wealth in large parts of the world.

A comparison of the strength of biodiversity effects across multiple functions

In order to predict which ecosystem functions are most at risk from biodiversity loss, meta-analyses have generalised results from biodiversity experiments over different sites and ecosystem types. In contrast, comparing the strength of biodiversity effects across a large number of ecosystem processes measured in a single experiment permits more direct comparisons. Here, we present an analysis of 418 separate measures of 38 ecosystem processes. Overall, 45 % of processes were significantly affected by plant species richness, suggesting that, while diversity affects a large number of processes not all respond to biodiversity. We therefore compared the strength of plant diversity effects between different categories of ecosystem processes, grouping processes according to the year of measurement, their biogeochemical cycle, trophic level and compartment (above- or belowground) and according to whether they were measures of biodiversity or other ecosystem processes, biotic or abiotic and static or dynamic. Overall, and for several individual processes, we found that biodiversity effects became stronger over time. Measures of the carbon cycle were also affected more strongly by plant species richness than were the measures associated with the nitrogen cycle. Further, we found greater plant species richness effects on measures of biodiversity than on other processes. The differential effects of plant diversity on the various types of ecosystem processes indicate that future research and political effort should shift from a general debate about whether biodiversity loss impairs ecosystem functions to focussing on the specific functions of interest and ways to preserve them individually or in combination.

Quantitative morphometric analysis of lakes using GIS: rectangularity R , ellipticity E , orientation O , and the rectangularity vs. ellipticity index, REi

Quantitative measures of polygon shapes and orientation are important elements of geospatial analysis. These kinds of measures are particularly valuable in the case of lakes, where shape and orientation patterns can help identifying the geomorphological agents behind lake formation and evolution. However, the lack of built-in tools in commercial geographic information system (GIS) software packages designed for this kind of analysis has meant that many researchers often must rely on tools and workarounds that are not always accurate. Here, an easy-to-use method to measure rectangularity R, ellipticity E, and orientation O is developed. In addition, a new rectangularity vs. ellipticity index, REi, is defined. Following a step-by-step process, it is shown how these measures and index can be easily calculated using a combination of GIS built-in functions. The identification of shapes and estimation of orientations performed by this method is applied to the case study of the geometric and oriented lakes of the Llanos de Moxos, in the Bolivian Amazon, where shape and orientation have been the two most important elements studied to infer possible formation mechanisms. It is shown that, thanks to these new indexes, shape and orientation patterns are unveiled, which would have been hard to identify otherwise.

Continuum damage interactions between tension and compression in osteonal bone

Skeletal diseases such as osteoporosis impose a severe socio-economic burden to ageing societies. Decreasing mechanical competence causes a rise in bone fracture incidence and mortality especially after the age of 65 y. The mechanisms of how bone damage is accumulated under different loading modes and its impact on bone strength are unclear. We hypothesise that damage accumulated in one loading mode increases the fracture risk in another. This study aimed at identifying continuum damage interactions between tensile and compressive loading modes. We propose and identify the material constants of a novel piecewise 1D constitutive model capable of describing the mechanical response of bone in combined tensile and compressive loading histories. We performed several sets of loading–reloading experiments to compute stiffness, plastic strains, and stress-strain curves. For tensile overloading, a stiffness reduction (damage) of 60% at 0.65% accumulated plastic strain was detectable as stiffness reduction of 20% under compression. For compressive overloading, 60% damage at 0.75% plastic strain was detectable as a stiffness reduction of 50% in tension. Plastic strain at ultimate stress was the same in tension and compression. Compression showed softening and tension exponential hardening in the post-yield regime. The hardening behaviour in compression is unaffected by a previous overload in tension but the hardening behaviour in tension is affected by a previous overload in compression as tensile reloading strength is significantly reduced. This paper demonstrates how damage accumulated under one loading mode affects the mechanical behaviour in another loading mode. To explain this and to illustrate a possible implementation we proposed a theoretical model. Including such loading mode dependent damage and plasticity behaviour in finite element models will help to improve fracture risk analysis of whole bones and bone implant structures.

Proton therapy versus intensity modulated x-ray therapy in the treatment of prostate cancer: Estimating secondary cancer risks

External beam radiation therapy is used to treat nearly half of the more than 200,000 new cases of prostate cancer diagnosed in the United States each year. During a radiation therapy treatment, healthy tissues in the path of the therapeutic beam are exposed to high doses. In addition, the whole body is exposed to a low-dose bath of unwanted scatter radiation from the pelvis and leakage radiation from the treatment unit. As a result, survivors of radiation therapy for prostate cancer face an elevated risk of developing a radiogenic second cancer. Recently, proton therapy has been shown to reduce the dose delivered by the therapeutic beam to normal tissues during treatment compared to intensity modulated x-ray therapy (IMXT, the current standard of care). However, the magnitude of stray radiation doses from proton therapy, and their impact on this incidence of radiogenic second cancers, was not known. ^ The risk of a radiogenic second cancer following proton therapy for prostate cancer relative to IMXT was determined for 3 patients of large, median, and small anatomical stature. Doses delivered to healthy tissues from the therapeutic beam were obtained from treatment planning system calculations. Stray doses from IMXT were taken from the literature, while stray doses from proton therapy were simulated using a Monte Carlo model of a passive scattering treatment unit and an anthropomorphic phantom. Baseline risk models were taken from the Biological Effects of Ionizing Radiation VII report. A sensitivity analysis was conducted to characterize the uncertainty of risk calculations to uncertainties in the risk model, the relative biological effectiveness (RBE) of neutrons for carcinogenesis, and inter-patient anatomical variations. ^ The risk projections revealed that proton therapy carries a lower risk for radiogenic second cancer incidence following prostate irradiation compared to IMXT. The sensitivity analysis revealed that the results of the risk analysis depended only weakly on uncertainties in the risk model and inter-patient variations. Second cancer risks were sensitive to changes in the RBE of neutrons. However, the findings of the study were qualitatively consistent for all patient sizes and risk models considered, and for all neutron RBE values less than 100. ^

Integrating sequence information in microarray data analysis by free energy modeling

Microarray technology is a high-throughput method for genotyping and gene expression profiling. Limited sensitivity and specificity are one of the essential problems for this technology. Most of existing methods of microarray data analysis have an apparent limitation for they merely deal with the numerical part of microarray data and have made little use of gene sequence information. Because it's the gene sequences that precisely define the physical objects being measured by a microarray, it is natural to make the gene sequences an essential part of the data analysis. This dissertation focused on the development of free energy models to integrate sequence information in microarray data analysis. The models were used to characterize the mechanism of hybridization on microarrays and enhance sensitivity and specificity of microarray measurements. ^ Cross-hybridization is a major obstacle factor for the sensitivity and specificity of microarray measurements. In this dissertation, we evaluated the scope of cross-hybridization problem on short-oligo microarrays. The results showed that cross hybridization on arrays is mostly caused by oligo fragments with a run of 10 to 16 nucleotides complementary to the probes. Furthermore, a free-energy based model was proposed to quantify the amount of cross-hybridization signal on each probe. This model treats cross-hybridization as an integral effect of the interactions between a probe and various off-target oligo fragments. Using public spike-in datasets, the model showed high accuracy in predicting the cross-hybridization signals on those probes whose intended targets are absent in the sample. ^ Several prospective models were proposed to improve Positional Dependent Nearest-Neighbor (PDNN) model for better quantification of gene expression and cross-hybridization. ^ The problem addressed in this dissertation is fundamental to the microarray technology. We expect that this study will help us to understand the detailed mechanism that determines sensitivity and specificity on the microarrays. Consequently, this research will have a wide impact on how microarrays are designed and how the data are interpreted. ^