Multiple testing in spatial epidemiology: a Bayesian approach

In this work we aim to propose a new approach for preliminary epidemiological studies on Standardized Mortality Ratios (SMR) collected in many spatial regions. A preliminary study on SMRs aims to formulate hypotheses to be investigated via individual epidemiological studies that avoid bias carried on by aggregated analyses. Starting from collecting disease counts and calculating expected disease counts by means of reference population disease rates, in each area an SMR is derived as the MLE under the Poisson assumption on each observation. Such estimators have high standard errors in small areas, i.e. where the expected count is low either because of the low population underlying the area or the rarity of the disease under study. Disease mapping models and other techniques for screening disease rates among the map aiming to detect anomalies and possible high-risk areas have been proposed in literature according to the classic and the Bayesian paradigm. Our proposal is approaching this issue by a decision-oriented method, which focus on multiple testing control, without however leaving the preliminary study perspective that an analysis on SMR indicators is asked to. We implement the control of the FDR, a quantity largely used to address multiple comparisons problems in the eld of microarray data analysis but which is not usually employed in disease mapping. Controlling the FDR means providing an estimate of the FDR for a set of rejected null hypotheses. The small areas issue arises diculties in applying traditional methods for FDR estimation, that are usually based only on the p-values knowledge (Benjamini and Hochberg, 1995; Storey, 2003). Tests evaluated by a traditional p-value provide weak power in small areas, where the expected number of disease cases is small. Moreover tests cannot be assumed as independent when spatial correlation between SMRs is expected, neither they are identical distributed when population underlying the map is heterogeneous. The Bayesian paradigm oers a way to overcome the inappropriateness of p-values based methods. Another peculiarity of the present work is to propose a hierarchical full Bayesian model for FDR estimation in testing many null hypothesis of absence of risk.We will use concepts of Bayesian models for disease mapping, referring in particular to the Besag York and Mollié model (1991) often used in practice for its exible prior assumption on the risks distribution across regions. The borrowing of strength between prior and likelihood typical of a hierarchical Bayesian model takes the advantage of evaluating a singular test (i.e. a test in a singular area) by means of all observations in the map under study, rather than just by means of the singular observation. This allows to improve the power test in small areas and addressing more appropriately the spatial correlation issue that suggests that relative risks are closer in spatially contiguous regions. The proposed model aims to estimate the FDR by means of the MCMC estimated posterior probabilities b i's of the null hypothesis (absence of risk) for each area. An estimate of the expected FDR conditional on data (\FDR) can be calculated in any set of b i's relative to areas declared at high-risk (where thenull hypothesis is rejected) by averaging the b i's themselves. The\FDR can be used to provide an easy decision rule for selecting high-risk areas, i.e. selecting as many as possible areas such that the\FDR is non-lower than a prexed value; we call them\FDR based decision (or selection) rules. The sensitivity and specicity of such rule depend on the accuracy of the FDR estimate, the over-estimation of FDR causing a loss of power and the under-estimation of FDR producing a loss of specicity. Moreover, our model has the interesting feature of still being able to provide an estimate of relative risk values as in the Besag York and Mollié model (1991). A simulation study to evaluate the model performance in FDR estimation accuracy, sensitivity and specificity of the decision rule, and goodness of estimation of relative risks, was set up. We chose a real map from which we generated several spatial scenarios whose counts of disease vary according to the spatial correlation degree, the size areas, the number of areas where the null hypothesis is true and the risk level in the latter areas. In summarizing simulation results we will always consider the FDR estimation in sets constituted by all b i's selected lower than a threshold t. We will show graphs of the\FDR and the true FDR (known by simulation) plotted against a threshold t to assess the FDR estimation. Varying the threshold we can learn which FDR values can be accurately estimated by the practitioner willing to apply the model (by the closeness between\FDR and true FDR). By plotting the calculated sensitivity and specicity (both known by simulation) vs the\FDR we can check the sensitivity and specicity of the corresponding\FDR based decision rules. For investigating the over-smoothing level of relative risk estimates we will compare box-plots of such estimates in high-risk areas (known by simulation), obtained by both our model and the classic Besag York Mollié model. All the summary tools are worked out for all simulated scenarios (in total 54 scenarios). Results show that FDR is well estimated (in the worst case we get an overestimation, hence a conservative FDR control) in small areas, low risk levels and spatially correlated risks scenarios, that are our primary aims. In such scenarios we have good estimates of the FDR for all values less or equal than 0.10. The sensitivity of\FDR based decision rules is generally low but specicity is high. In such scenario the use of\FDR = 0:05 or\FDR = 0:10 based selection rule can be suggested. In cases where the number of true alternative hypotheses (number of true high-risk areas) is small, also FDR = 0:15 values are well estimated, and \FDR = 0:15 based decision rules gains power maintaining an high specicity. On the other hand, in non-small areas and non-small risk level scenarios the FDR is under-estimated unless for very small values of it (much lower than 0.05); this resulting in a loss of specicity of a\FDR = 0:05 based decision rule. In such scenario\FDR = 0:05 or, even worse,\FDR = 0:1 based decision rules cannot be suggested because the true FDR is actually much higher. As regards the relative risk estimation, our model achieves almost the same results of the classic Besag York Molliè model. For this reason, our model is interesting for its ability to perform both the estimation of relative risk values and the FDR control, except for non-small areas and large risk level scenarios. A case of study is nally presented to show how the method can be used in epidemiology.

High-resolution stratigraphy of Central and Southern Adriatic Quaternary deposits of sub-Milankovian climate change on Mediterranean circulation

This volume is a collection of the work done in a three years-lasting PhD, focused in the analysis of Central and Southern Adriatic marine sediments, deriving from the collection of a borehole and many cores, achieved thanks to the good seismic-stratigraphic knowledge of the study area. The work was made out within European projects EC-EURODELTA (coordinated by Fabio Trincardi, ISMAR-CNR), EC-EUROSTRATAFORM (coordinated by Phil P. E. Weaver, NOC, UK), and PROMESS1 (coordinated by Serge Bernè, IFREMER, France). The analysed sedimentary successions presented highly expanded stratigraphic intervals, particularly for the last 400 kyr, 60 kyr and 6 kyr BP. These three different time-intervals resulted in a tri-partition of the PhD thesis. The study consisted of the analysis of planktic and benthic foraminifers’ assemblages (more than 560 samples analysed), as well as in preparing the material for oxygen and carbon stable isotope analyses, and interpreting and discussing the obtained dataset. The chronologic framework of the last 400 kyr was achieved for borehole PRAD1-2 (within the work-package WP6 of PROMESS1 project), collected in 186.5 m water depth. The proposed chronology derives from a multi-disciplinary approach, consisting of the integration of numerous and independent proxies, some of which analysed by other specialists within the project. The final framework based on: micropaleontology (calcareous nannofossils and foraminifers’ bioevents), climatic cyclicity (foraminifers’ assemblages), geochemistry (oxygen stable isotope, made out on planktic and benthic records), paleomagnetism, radiometric ages (14C AMS), teprhochronology, identification of sapropel-equivalent levels (Se). It’s worth to note the good consistency between the oxygen stable isotope curve obtained for borehole PRAD1-2 and other deeper Mediterranean records. The studied proxies allowed the recognition of all the isotopic intervals from MIS10 to MIS1 in PRAD1-2 record, and the base of the borehole has been ascribed to the early MIS11. Glacial and interglacial intervals identified in the Central Adriatic record have been analysed in detail for the paleo-environmental reconstruction, as well. For instance, glacial stages MIS6, MIS8 and MIS10 present peculiar foraminifers’ assemblages, composed by benthic species typical of polar regions and no longer living in the Central Adriatic nowadays. Moreover, a deepening trend in the paleo-bathymetry during glacial intervals was observed, from MIS10 (inner-shelf environment) to MIS4 (mid-shelf environment).Ten sapropel-equivalent levels have been recognised in PRAD1-2 Central Adriatic record. They showed different planktic foraminifers’ assemblages, which allowed the first distinction of events occurred during warm-climate (Se5, Se7), cold-climate (Se4, Se6 and Se8) and temperate-intermediate-climate (Se1, Se3, Se9, Se’, Se10) conditions, consistently with literature. Cold-climate sapropel equivalents are characterised by the absence of an oligotrophic phase, whereas warm-temeprate-climate sapropel equivalents present both the oligotrophic and the eutrophic phases (except for Se1). Sea floor conditions vary, according to benthic foraminifers’ assemblages, from relatively well oxygenated (Se1, Se3), to dysoxic (Se9, Se’, Se10), to highly dysoxic (Se4, Se6, Se8) to events during which benthic foraminifers are absent (Se5, Se7). These two latter levels are also characterised by the lamination of the sediment, feature never observed in literature in such shallow records. The enhanced stratification of the water column during the events Se8, Se7, Se6, Se5, Se4, and the concurring strong dilution of shallow water, pointed out by the isotope record, lead to the hypothesis of a period of intense precipitation in the Central Adriatic region, possibly due to a northward shift of the African Monsoon. Finally, the expression of Central Adriatic PRAD1-2 Se5 equivalent was compared with the same event, as registered in other Eastern Mediterranean areas. The sequence of substantially the same planktic foraminifers’ bioevents has been consistently recognised, indicating a similar evolution of the water column all over the Eastern Mediterranean; yet, the synchronism of these events cannot be demonstrated. A high resolution analysis of late Holocene (last 6000 years BP) climate change was carried out for the Adriatic area, through the recognition of planktic and benthic foraminifers’ bioevents. In particular, peaks of planktic Globigerinoides sacculifer (four during the last 5500 years BP in the most expanded core) have been interpreted, based on the ecological requirements of this species, as warm-climate, arid intervals, correspondent to periods of relative climatic optimum, such as, for instance, the Medieval Warm Period, the Roman Age, the Late Bronze Age and the Copper Age. Consequently, the minima in the abundance of this biomarker could correspond to relatively cooler and more rainy periods. These conclusions are in good agreement with the isotopic and the pollen data. The Last Occurrence (LO) of G. sacculifer has been dated in this work at an average age of 550 years BP, and it is the best bioevent approximating the base of the Little Ice Age in the Adriatic. Recent literature reports the same bioevent in the Levantine Basin, showing a rather consistent age. Therefore, the LO of G. sacculifer has the potential to be extended to all the Eastern Mediterranean. Within the Little Ice Age, benthic foraminifer V. complanata shows two distinct peaks in the shallower Adriatic cores analysed, collected hundred kilometres apart, inside the mud belt environment. Based on the ecological requirements of this species, these two peaks have been interpreted as the more intense (cold and rainy) oscillations inside the LIA. The chronologic framework of the analysed cores is robust, being based on several range-finding 14C AMS ages, on estimates of the secular variation of the magnetic field, on geochemical estimates of the activity depth of 210Pb short-lived radionuclide (for the core-top ages), and is in good agreement with tephrochronologic, pollen and foraminiferal data. The intra-holocenic climate oscillations find out in the Adriatic have been compared with those pointed out in literature from other records of the Northern Hemisphere, and the chronologic constraint seems quite good. Finally, the sedimentary successions analysed allowed the review and the update of the foraminifers’ ecobiostratigraphy available from literature for the Adriatic region, thanks to the achievement of 16 ecobiozones for the last 60 kyr BP. Some bioevents are restricted to the Central Adriatic (for instance the LO of benthic Hyalinea balthica , approximating the MIS3/MIS2 boundary), others occur all over the Adriatic basin (for instance the LO of planktic Globorotalia inflata during MIS3, individuating Dansgaard-Oeschger cycle 8 (Denekamp)).

A new snowfall detection algorithm for high latitude regions based on a combination of active and passive sensors

Precipitation retrieval over high latitudes, particularly snowfall retrieval over ice and snow, using satellite-based passive microwave spectrometers, is currently an unsolved problem. The challenge results from the large variability of microwave emissivity spectra for snow and ice surfaces, which can mimic, to some degree, the spectral characteristics of snowfall. This work focuses on the investigation of a new snowfall detection algorithm specific for high latitude regions, based on a combination of active and passive sensors able to discriminate between snowing and non snowing areas. The space-borne Cloud Profiling Radar (on CloudSat), the Advanced Microwave Sensor units A and B (on NOAA-16) and the infrared spectrometer MODIS (on AQUA) have been co-located for 365 days, from October 1st 2006 to September 30th, 2007. CloudSat products have been used as truth to calibrate and validate all the proposed algorithms. The methodological approach followed can be summarised into two different steps. In a first step, an empirical search for a threshold, aimed at discriminating the case of no snow, was performed, following Kongoli et al. [2003]. This single-channel approach has not produced appropriate results, a more statistically sound approach was attempted. Two different techniques, which allow to compute the probability above and below a Brightness Temperature (BT) threshold, have been used on the available data. The first technique is based upon a Logistic Distribution to represent the probability of Snow given the predictors. The second technique, defined Bayesian Multivariate Binary Predictor (BMBP), is a fully Bayesian technique not requiring any hypothesis on the shape of the probabilistic model (such as for instance the Logistic), which only requires the estimation of the BT thresholds. The results obtained show that both methods proposed are able to discriminate snowing and non snowing condition over the Polar regions with a probability of correct detection larger than 0.5, highlighting the importance of a multispectral approach.

Wide-scale population genomics of Atlantic bluefin tuna (Thunnus thynnus) inferred by novel high-throughput technology

My PhD project was focused on Atlantic bluefin tuna, Thunnus thynnus, a fishery resource overexploited in the last decades. For a better management of stocks, it was necessary to improve scientific knowledge of this species and to develop novel tools to avoid collapse of this important commercial resource. To do this, we used new high throughput sequencing technologies, as Next Generation Sequencing (NGS), and markers linked to expressed genes, as SNPs (Single Nucleotide Polymorphisms). In this work we applied a combined approach: transcriptomic resources were used to build cDNA libreries from mRNA isolated by muscle, and genomic resources allowed to create a reference backbone for this species lacking of reference genome. All cDNA reads, obtained from mRNA, were mapped against this genome and, employing several bioinformatics tools and different restricted parameters, we achieved a set of contigs to detect SNPs. Once a final panel of 384 SNPs was developed, following the selection criteria, it was genotyped in 960 individuals of Atlantic bluefin tuna, including all size/age classes, from larvae to adults, collected from the entire range of the species. The analysis of obtained data was aimed to evaluate the genetic diversity and the population structure of Thunnus thynnus. We detect a low but significant signal of genetic differentiation among spawning samples, that can suggest the presence of three genetically separate reproduction areas. The adult samples resulted instead genetically undifferentiated between them and from the spawning populations, indicating a presence of panmictic population of adult bluefin tuna in the Mediterranean Sea, without different meta populations.

Land use management in mountainous areas: combining ground-based and EO (Earth Observation) data to investigate the shallow landsliding susceptibility in the Duron valley (Trento, Italy).

Throughout the alpine domain, shallow landslides represent a serious geologic hazard, often causing severe damages to infrastructures, private properties, natural resources and in the most catastrophic events, threatening human lives. Landslides are a major factor of landscape evolution in mountainous and hilly regions and represent a critical issue for mountainous land management, since they cause loss of pastoral lands. In several alpine contexts, shallow landsliding distribution is strictly connected to the presence and condition of vegetation on the slopes. With the aid of high-resolution satellite images, it's possible to divide automatically the mountainous territory in land cover classes, which contribute with different magnitude to the stability of the slopes. The aim of this research is to combine EO (Earth Observation) land cover maps with ground-based measurements of the land cover properties. In order to achieve this goal, a new procedure has been developed to automatically detect grass mantle degradation patterns from satellite images. Moreover, innovative surveying techniques and instruments are tested to measure in situ the shear strength of grass mantle and the geomechanical and geotechnical properties of these alpine soils. Shallow landsliding distribution is assessed with the aid of physically based models, which use the EO-based map to distribute the resistance parameters across the landscape.