Deployment of a data analysis workflow of the ATLAS experiment on HPC systems

LHC experiments produce an enormous amount of data, estimated of the order of a few PetaBytes per year. Data management takes place using the Worldwide LHC Computing Grid (WLCG) grid infrastructure, both for storage and processing operations. However, in recent years, many more resources are available on High Performance Computing (HPC) farms, which generally have many computing nodes with a high number of processors. Large collaborations are working to use these resources in the most efficient way, compatibly with the constraints imposed by computing models (data distributed on the Grid, authentication, software dependencies, etc.). The aim of this thesis project is to develop a software framework that allows users to process a typical data analysis workflow of the ATLAS experiment on HPC systems. The developed analysis framework shall be deployed on the computing resources of the Open Physics Hub project and on the CINECA Marconi100 cluster, in view of the switch-on of the Leonardo supercomputer, foreseen in 2023.

Development of in vitro methods to test acetabular prosthetic reconstructions (messa a punto di metodi in vitro per la caratterizzazione biomeccanica di ricostruzioni acetabolari)

The revision hip arthroplasty is a surgical procedure, consisting in the reconstruction of the hip joint through the replacement of the damaged hip prosthesis. Several factors may give raise to the failure of the artificial device: aseptic loosening, infection and dislocation represent the principal causes of failure worldwide. The main effect is the raise of bone defects in the region closest to the prosthesis that weaken the bone structure for the biological fixation of the new artificial hip. For this reason bone reconstruction is necessary before the surgical revision operation. This work is born by the necessity to test the effects of bone reconstruction due to particular bone defects in the acetabulum, after the hip prosthesis revision. In order to perform biomechanical in vitro tests on hip prosthesis implanted in human pelvis or hemipelvis a practical definition of a reference frame for these kind of bone specimens is required. The aim of the current study is to create a repeatable protocol to align hemipelvic samples in the testing machine, that relies on a reference system based on anatomical landmarks on the human pelvis. In chapter 1 a general overview of the human pelvic bone is presented: anatomy, bone structure, loads and the principal devices for hip joint replacement. The purpose of chapters 2 is to identify the most common causes of the revision hip arthroplasty, analysing data from the most reliable orthopaedic registries in the world. Chapter 3 presents an overview of the most used classifications for acetabular bone defects and fractures and the most common techniques for acetabular and bone reconstruction. After a critical review of the scientific literature about reference frames for human pelvis, in chapter 4, the definition of a new reference frame is proposed. Based on this reference frame, the alignment protocol for the human hemipelvis is presented as well as the statistical analysis that confirm the good repeatability of the method.

Synthetic data generation for the assessment of antimicrobial resistance through machine learning

As a consequence of the diffusion of next generation sequencing techniques, metagenomics databases have become one of the most promising repositories of information about features and behavior of microorganisms. One of the subjects that can be studied from those data are bacteria populations. Next generation sequencing techniques allow to study the bacteria population within an environment by sampling genetic material directly from it, without the needing of culturing a similar population in vitro and observing its behavior. As a drawback, it is quite complex to extract information from those data and usually there is more than one way to do that; AMR is no exception. In this study we will discuss how the quantified AMR, which regards the genotype of the bacteria, can be related to the bacteria phenotype and its actual level of resistance against the specific substance. In order to have a quantitative information about bacteria genotype, we will evaluate the resistome from the read libraries, aligning them against CARD database. With those data, we will test various machine learning algorithms for predicting the bacteria phenotype. The samples that we exploit should resemble those that could be obtained from a natural context, but are actually produced by a read libraries simulation tool. In this way we are able to design the populations with bacteria of known genotype, so that we can relay on a secure ground truth for training and testing our algorithms.

Introducing CHIMeRA: from the development of a comprehensive biomedical database to the analysis of its sub-components.

In this thesis we discuss the expansion of an existing project, called CHIMeRA, which is a comprehensive biomedical network, and the analysis of its sub-components by using graph theory. We describe how it is structured internally, what are the existing databases from which it retrieves information and what machine learning techniques are used in order to produce new knowledge. We also introduce a new technique for graph exploration that is aimed to speed-up the network cover time under the condition that the analyzed graph is stellar; if this condition is satisfied, the improvement in the performance compared to the conventional exploration technique is extremely appealing. We show that the stellar structure is highly recurrent for sub-networks in CHIMeRA generated by queries, which made this technique even more interesting. Finally, we describe the convenience in using the CHIMeRA network for research purposes and what it could become in a very near future.