Roles of Ecdysone signaling in cell survival and epithelium morphogenesis during Drosophila melanogaster development

In Drosophila the steroid hormone ecdysone regulates a wide range of developmental and physiological responses, including reproduction, embryogenesis, postembryonic development and metamorphosis. Drosophila provides an excellent system to address some fundamental questions linked to hormone actions. In fact, the apparent relative simplicity of its hormone signaling pathways taken together with well-established genetic and genomic tools developed to this purpose, defines this insect as an ideal model system for studying the molecular mechanisms through which steroid hormones act. During my PhD research program I’ve analyzed the role of ecdysone signaling to gain insight into the molecular mechanisms through which the hormone fulfills its pleiotropic functions in two different developmental stages: the oogenesis and the imaginal wing disc morphogenesis. To this purpose, I performed a reverse genetic analysis to silence the function of two different genes involved in ecdysone signaling pathway, EcR and ecd.

Drosophila melanogaster as a model to study host-parasitoid interactions: the case of the polydnaviral protein TnBVANK1

Parasitic wasps attack a number of insect species on which they feed, either externally or internally. This requires very effective strategies for suppressing the immune response and a finely tuned interference with the host physiology that is co-opted for the developing parasitoid progeny. The wealth of physiological host alterations is mediated by virulence factors encoded by the wasp or, in some cases, by polydnaviruses (PDVs), unique viral symbionts injected into the host at oviposition along with the egg, venom and ovarian secretions. PDVs are among the most powerful immunosuppressors in nature, targeting insect defense barriers at different levels. During my PhD research program I have used Drosophila melanogaster as a model to expand the functional analysis of virulence factors encoded by PDV focusing on the molecular processes underlying the disruption of the host endocrine system. I focused my research on a member of the ankyrin (ank) gene family, an immunosuppressant found in bracovirus, which associates with the parasitic wasp Toxoneuron nigriceps. I found that ankyrin disrupts ecdysone biosynthesis by impairing the vesicular traffic of ecdysteroid precursors in the cells of the prothoracic gland and results in developmental arrest.

Silk Fibroin: a biopolymer platform for innovative pharmaceutical formulation and biomedical devices.

The protein silk fibroin (SF) from the silkworm Bombyx mori is a FDA-approved biomaterial used over centuries as sutures wire. Importantly, several evidences highlighted the potential of silk biomaterials obtained by using so-called regenerated silk fibroin (RSF) in biomedicine, tissue engineering and drug delivery. Indeed, by a water-based protocol, it is possible to obtain protein water-solution, by extraction and purification of fibroin from silk fibres. Notably, RSF can be processed in a variety of biomaterials forms used in biomedical and technological fields, displaying remarkable properties such as biocompatibility, controllable biodegradability, optical transparency, mechanical robustness. Moreover, RSF biomaterials can be doped and/or chemical functionalized with drugs, optically active molecules, growth factors and/or chemicals In this view, activities of my PhD research program were focused to standardize the process of extraction and purification of protein to get the best physical and chemical characteristics. The analysis of the chemo-physical properties of the fibroin involved both the RSF water-solution and the protein processed in film. Chemo-physical properties have been studied through: vibrational (FT-IR and Raman-FT) and optical (absorption and emission UV-VIS) spectroscopy, nuclear magnetic resonance (1H and 13C NMR), thermal analysis and thermo-gravimetric scan (DSC and TGA). In the last year of my PhD, activities were focused to study and define innovative methods of functionalization of the silk fibroin solution and films. Indeed, research program was the application of different methods of manufacturing approaches of the films of fibroin without the use of harsh treatments and organic solvents. New approaches to doping and chemical functionalization of the silk fibroin were studied. Two different methods have been identified: 1) biodoping that consists in the doping of fibroin with optically active molecules through the addition of fluorescent molecules in the standard diet used for the breeding of silkworms; 2) chemical functionalization via silylation.

Aging and memory for music: A review

People of all ages enjoy listening to music, yet most research in musical development has concentrated on infancy through childhood. Our recent research program examined various aspects of music cognition in younger (ages 18 through 30) and older adults (ages 60 through 80) with varying amounts of musical experience. The studies investigated the independent and combined influences of age and experience on a wide assortment of long and short-term memory tasks. Results showed that some musical tasks reflect the same age-related declines as seen in nonmusical tasks, and musical training does not reduce these age-related declines. In other tasks, experience differences were larger than age differences; in some cases, age differences were nonexistent. The analysis considers how aging and experience may affect different aspects of cognition, and the paper concludes by pointing out the many musical activities that even nonmusical seniors are well equipped to succeed at and enjoy.

Secondary collisions revisited: real-world crash data and relationship to crash test criteria

Objectives: Previous research conducted in the late 1980s suggested that vehicle impacts following an initial barrier collision increase severe occupant injury risk. Now over 25years old, the data are no longer representative of the currently installed barriers or the present US vehicle fleet. The purpose of this study is to provide a present-day assessment of secondary collisions and to determine if current full-scale barrier crash testing criteria provide an indication of secondary collision risk for real-world barrier crashes. Methods: To characterize secondary collisions, 1,363 (596,331 weighted) real-world barrier midsection impacts selected from 13years (1997-2009) of in-depth crash data available through the National Automotive Sampling System (NASS) / Crashworthiness Data System (CDS) were analyzed. Scene diagram and available scene photographs were used to determine roadside and barrier specific variables unavailable in NASS/CDS. Binary logistic regression models were developed for second event occurrence and resulting driver injury. To investigate current secondary collision crash test criteria, 24 full-scale crash test reports were obtained for common non-proprietary US barriers, and the risk of secondary collisions was determined using recommended evaluation criteria from National Cooperative Highway Research Program (NCHRP) Report 350. Results: Secondary collisions were found to occur in approximately two thirds of crashes where a barrier is the first object struck. Barrier lateral stiffness, post-impact vehicle trajectory, vehicle type, and pre-impact tracking conditions were found to be statistically significant contributors to secondary event occurrence. The presence of a second event was found to increase the likelihood of a serious driver injury by a factor of 7 compared to cases with no second event present. The NCHRP Report 350 exit angle criterion was found to underestimate the risk of secondary collisions in real-world barrier crashes. Conclusions: Consistent with previous research, collisions following a barrier impact are not an infrequent event and substantially increase driver injury risk. The results suggest that using exit-angle based crash test criteria alone to assess secondary collision risk is not sufficient to predict second collision occurrence for real-world barrier crashes.

["Constanze": a trinational project on avian influenza in wild birds at Lake Constance]

When highly pathogenic avian influenza H5N1 (HPAI H5N1) arrived at Lake Constance in February 2006, little was known about its ecology and epidemiology in wild birds. In order to prevent virus transmission from wild birds to poultry, the adjacent countries initiated the tri-national, interdisciplinary research program <> to investigate avian influenza infections in water birds at Lake Constance. In collaboration with government agencies scientists examined the prevalence of AI virus in the region of Lake Constance for a period of 33 months, compared the effectiveness of different surveillance methods and analysed the migration behaviour of water birds. Although virus introduction from regions as far as the Ural Mountains seemed possible based on the migration behaviour of certain species, no influenza A viruses of the highly pathogenic subtype H5N1 (HPAIV) was found. However, influenza A viruses of different low pathogenic subtypes were isolated in 2.2 % of the sampled birds (swabs). Of the different surveillance methods utilised in the program the sampling of so called sentinel birds was particularly efficient.

Estimating Patterns of CD4+ Lymphocyte Decline Using Data from a Prevalent Cohort of HIV Infected Individuals

In natural history studies of chronic disease, it is of interest to understand the evolution of key variables that measure aspects of disease progression. This is particularly true for immunological variables in persons infected with the Human Immunodeficiency Virus (HIV). The natural timescale for such studies is time since infection. However, most data available for analysis arise from prevalent cohorts, where the date of infection is unknown for most or all individuals. As a result, standard curve fitting algorithms are not immediately applicable. Here we propose two methods to circumvent this difficulty. The first uses repeated measurement data to provide information not only on the level of the variable of interest, but also on its rate of change, while the second uses an estimate of the expected time since infection. Both methods are based on the principal curves algorithm of Hastie and Stuetzle, and are applied to data from a prevalent cohort of HIV-infected homosexual men, giving estimates of the average pattern of CD4+ lymphocyte decline. These methods are applicable to natural history studies using data from prevalent cohorts where the time of disease origin is uncertain, provided certain ancillary information is available from external sources.

Estimation with Interval Censored Data in Longitudinal Studies

In biostatistical applications interest often focuses on the estimation of the distribution of a time-until-event variable T. If one observes whether or not T exceeds an observed monitoring time at a random number of monitoring times, then the data structure is called interval censored data. We extend this data structure by allowing the presence of a possibly time-dependent covariate process that is observed until end of follow up. If one only assumes that the censoring mechanism satisfies coarsening at random, then, by the curve of dimensionality, typically no regular estimators will exist. To fight the curse of dimensionality we follow the approach of Robins and Rotnitzky (1992) by modeling parameters of the censoring mechanism. We model the right-censoring mechanism by modeling the hazard of the follow up time, conditional on T and the covariate process. For the monitoring mechanism we avoid modeling the joint distribution of the monitoring times by only modeling a univariate hazard of the pooled monitoring times, conditional on the follow up time, T, and the covariates process, which can be estimated by treating the pooled sample of monitoring times as i.i.d. In particular, it is assumed that the monitoring times and the right-censoring times only depend on T through the observed covariate process. We introduce inverse probability of censoring weighted (IPCW) estimator of the distribution of T and of smooth functionals thereof which are guaranteed to be consistent and asymptotically normal if we have available correctly specified semiparametric models for the two hazards of the censoring process. Furthermore, given such correctly specified models for these hazards of the censoring process, we propose a one-step estimator which will improve on the IPCW estimator if we correctly specify a lower-dimensional working model for the conditional distribution of T, given the covariate process, that remains consistent and asymptotically normal if this latter working model is misspecified. It is shown that the one-step estimator is efficient if each subject is at most monitored once and the working model contains the truth. In general, it is shown that the one-step estimator optimally uses the surrogate information if the working model contains the truth. It is not optimal in using the interval information provided by the current status indicators at the monitoring times, but simulations in Peterson, van der Laan (1997) show that the efficiency loss is small.

The Design and Analysis of Partner Studies of HIV Transmission

Common goals in epidemiologic studies of infectious diseases include identification of the infectious agent, description of the modes of transmission and characterization of factors that influence the probability of transmission from infected to uninfected individuals. In the case of AIDS, the agent has been identified as the Human Immunodeficiency Virus (HIV), and transmission is known to occur through a variety of contact mechanisms including unprotected sexual intercourse, transfusion of infected blood products and sharing of needles in intravenous drug use. Relatively little is known about the probability of IV transmission associated with the various modes of contact, or the role that other cofactors play in promoting or suppressing transmission. Here, transmission probability refers to the probability that the virus is transmitted to a susceptible individual following exposure consisting of a series of potentially infectious contacts. The infectivity of HIV for a given route of transmission is defined to be the per contact probability of infection. Knowledge of infectivity and its relationship to other factors is important in understanding the dynamics of the AIDS epidemic and in suggesting appropriate measures to control its spread. The primary source of empirical data about infectivity comes from sexual partners of infected individuals. Partner studies consist of a series of such partnerships, usually heterosexual and monogamous, each composed of an initially infected "index case" and a partner who may or may not be infected by the time of data collection. However, because the infection times of both partners may be unknown and the history of contacts uncertain, any quantitative characterization of infectivity is extremely difficult. Thus, most statistical analyses of partner study data involve the simplifying assumption that infectivity is a constant common to all partnerships. The major objectives of this work are to describe and discuss the design and analysis of partner studies, providing a general statistical framework for investigations of infectivity and risk factors for HIV transmission. The development is largely based on three papers: Jewell and Shiboski (1990), Kim and Lagakos (1990), and Shiboski and Jewell (1992).

Efficient and Ad Hoc Estimation in the Bivariate Censoring Model

A large number of proposals for estimating the bivariate survival function under random censoring has been made. In this paper we discuss nonparametric maximum likelihood estimation and the bivariate Kaplan-Meier estimator of Dabrowska. We show how these estimators are computed, present their intuitive background and compare their practical performance under different levels of dependence and censoring, based on extensive simulation results, which leads to a practical advise.

A Note on the Asymptotic Variance of Survival Function in the Semi-Markov Model

In Malani and Neilsen (1992) we have proposed alternative estimates of survival function (for time to disease) using a simple marker that describes time to some intermediate stage in a disease process. In this paper we derive the asymptotic variance of one such proposed estimator using two different methods and compare terms of order 1/n when there is no censoring. In the absence of censoring the asymptotic variance obtained using the Greenwood type approach converges to exact variance up to terms involving 1/n. But the asymptotic variance obtained using the theory of the counting process and results from Voelkel and Crowley (1984) on semi-Markov processes has a different term of order 1/n. It is not clear to us at this point why the variance formulae using the latter approach give different results.

Markers Models in Survival Analysis and Applications to Issues Associated with AIDS

Jewell and Kalbfleisch (1992) consider the use of marker processes for applications related to estimation of the survival distribution of time to failure. Marker processes were assumed to be stochastic processes that, at a given point in time, provide information about the current hazard and consequently on the remaining time to failure. Particular attention was paid to calculations based on a simple additive model for the relationship between the hazard function at time t and the history of the marker process up until time t. Specific applications to the analysis of AIDS data included the use of markers as surrogate responses for onset of AIDS with censored data and as predictors of the time elapsed since infection in prevalent individuals. Here we review recent work on the use of marker data to tackle these kinds of problems with AIDS data. The Poisson marker process with an additive model, introduced in Jewell and Kalbfleisch (1992) may be a useful "test" example for comparison of various procedures.

Locally Efficient Estimation with Current Status Data and High Dimensional Covariates

In biostatistical applications, interest often focuses on the estimation of the distribution of time T between two consecutive events. If the initial event time is observed and the subsequent event time is only known to be larger or smaller than an observed monitoring time, then the data is described by the well known singly-censored current status model, also known as interval censored data, case I. We extend this current status model by allowing the presence of a time-dependent process, which is partly observed and allowing C to depend on T through the observed part of this time-dependent process. Because of the high dimension of the covariate process, no globally efficient estimators exist with a good practical performance at moderate sample sizes. We follow the approach of Robins and Rotnitzky (1992) by modeling the censoring variable, given the time-variable and the covariate-process, i.e., the missingness process, under the restriction that it satisfied coarsening at random. We propose a generalization of the simple current status estimator of the distribution of T and of smooth functionals of the distribution of T, which is based on an estimate of the missingness. In this estimator the covariates enter only through the estimate of the missingness process. Due to the coarsening at random assumption, the estimator has the interesting property that if we estimate the missingness process more nonparametrically, then we improve its efficiency. We show that by local estimation of an optimal model or optimal function of the covariates for the missingness process, the generalized current status estimator for smooth functionals become locally efficient; meaning it is efficient if the right model or covariate is consistently estimated and it is consistent and asymptotically normal in general. Estimation of the optimal model requires estimation of the conditional distribution of T, given the covariates. Any (prior) knowledge of this conditional distribution can be used at this stage without any risk of losing root-n consistency. We also propose locally efficient one step estimators. Finally, we show some simulation results.

Smooth Estimation of a Monotone Density

We investigate the interplay of smoothness and monotonicity assumptions when estimating a density from a sample of observations. The nonparametric maximum likelihood estimator of a decreasing density on the positive half line attains a rate of convergence at a fixed point if the density has a negative derivative. The same rate is obtained by a kernel estimator, but the limit distributions are different. If the density is both differentiable and known to be monotone, then a third estimator is obtained by isotonization of a kernel estimator. We show that this again attains the rate of convergence and compare the limit distributors of the three types of estimators. It is shown that both isotonization and smoothing lead to a more concentrated limit distribution and we study the dependence on the proportionality constant in the bandwidth. We also show that isotonization does not change the limit behavior of a kernel estimator with a larger bandwidth, in the case that the density is known to have more than one derivative.

Locally Efficient Estimation with Bivariate Right Censored Data

Estimation for bivariate right censored data is a problem that has had much study over the past 15 years. In this paper we propose a new class of estimators for the bivariate survival function based on locally efficient estimation. We introduce the locally efficient estimator for bivariate right censored data, present an asymptotic theorem, present the results of simulation studies and perform a brief data analysis illustrating the use of the locally efficient estimator.