New methodologies in CT perfusion and MRI analysis to develop cancer imaging biomarkers

Quantitative imaging in oncology aims at developing imaging biomarkers for diagnosis and prediction of cancer aggressiveness and therapy response before any morphological change become visible. This Thesis exploits Computed Tomography perfusion (CTp) and multiparametric Magnetic Resonance Imaging (mpMRI) for investigating diverse cancer features on different organs. I developed a voxel-based image analysis methodology in CTp and extended its use to mpMRI, for performing precise and accurate analyses at single-voxel level. This is expected to improve reproducibility of measurements and cancer mechanisms’ comprehension and clinical interpretability. CTp has not entered the clinical routine yet, although its usefulness in the monitoring of cancer angiogenesis, due to different perfusion computing methods yielding unreproducible results. Instead, machine learning applications in mpMRI, useful to detect imaging features representative of cancer heterogeneity, are mostly limited to clinical research, because of results’ variability and difficult interpretability, which make clinicians not confident in clinical applications. In hepatic CTp, I investigated whether, and under what conditions, two widely adopted perfusion methods, Maximum Slope (MS) and Deconvolution (DV), could yield reproducible parameters. To this end, I developed signal processing methods to model the first pass kinetics and remove any numerical cause hampering the reproducibility. In mpMRI, I proposed a new approach to extract local first-order features, aiming at preserving spatial reference and making their interpretation easier. In CTp, I found out the cause of MS and DV non-reproducibility: MS and DV represent two different states of the system. Transport delays invalidate MS assumptions and, by correcting MS formulation, I have obtained the voxel-based equivalence of the two methods. In mpMRI, the developed predictive models allowed (i) detecting rectal cancers responding to neoadjuvant chemoradiation showing, at pre-therapy, sparse coarse subregions with altered density, and (ii) predicting clinically significant prostate cancers stemming from the disproportion between high- and low- diffusivity gland components.

"Making Sense of Figures": Statistics, Computing and Information Technologies in Agriculture and Biology in Britain, 1920s-1960s

Throughout the twentieth century statistical methods have increasingly become part of experimental research. In particular, statistics has made quantification processes meaningful in the soft sciences, which had traditionally relied on activities such as collecting and describing diversity rather than timing variation. The thesis explores this change in relation to agriculture and biology, focusing on analysis of variance and experimental design, the statistical methods developed by the mathematician and geneticist Ronald Aylmer Fisher during the 1920s. The role that Fisher’s methods acquired as tools of scientific research, side by side with the laboratory equipment and the field practices adopted by research workers, is here investigated bottom-up, beginning with the computing instruments and the information technologies that were the tools of the trade for statisticians. Four case studies show under several perspectives the interaction of statistics, computing and information technologies, giving on the one hand an overview of the main tools – mechanical calculators, statistical tables, punched and index cards, standardised forms, digital computers – adopted in the period, and on the other pointing out how these tools complemented each other and were instrumental for the development and dissemination of analysis of variance and experimental design. The period considered is the half-century from the early 1920s to the late 1960s, the institutions investigated are Rothamsted Experimental Station and the Galton Laboratory, and the statisticians examined are Ronald Fisher and Frank Yates.

Hybrid Methods for Resource Allocation and Scheduling Problems in Deterministic and Stochastic Environments

This work presents exact, hybrid algorithms for mixed resource Allocation and Scheduling problems; in general terms, those consist into assigning over time finite capacity resources to a set of precedence connected activities. The proposed methods have broad applicability, but are mainly motivated by applications in the field of Embedded System Design. In particular, high-performance embedded computing recently witnessed the shift from single CPU platforms with application-specific accelerators to programmable Multi Processor Systems-on-Chip (MPSoCs). Those allow higher flexibility, real time performance and low energy consumption, but the programmer must be able to effectively exploit the platform parallelism. This raises interest in the development of algorithmic techniques to be embedded in CAD tools; in particular, given a specific application and platform, the objective if to perform optimal allocation of hardware resources and to compute an execution schedule. On this regard, since embedded systems tend to run the same set of applications for their entire lifetime, off-line, exact optimization approaches are particularly appealing. Quite surprisingly, the use of exact algorithms has not been well investigated so far; this is in part motivated by the complexity of integrated allocation and scheduling, setting tough challenges for ``pure'' combinatorial methods. The use of hybrid CP/OR approaches presents the opportunity to exploit mutual advantages of different methods, while compensating for their weaknesses. In this work, we consider in first instance an Allocation and Scheduling problem over the Cell BE processor by Sony, IBM and Toshiba; we propose three different solution methods, leveraging decomposition, cut generation and heuristic guided search. Next, we face Allocation and Scheduling of so-called Conditional Task Graphs, explicitly accounting for branches with outcome not known at design time; we extend the CP scheduling framework to effectively deal with the introduced stochastic elements. Finally, we address Allocation and Scheduling with uncertain, bounded execution times, via conflict based tree search; we introduce a simple and flexible time model to take into account duration variability and provide an efficient conflict detection method. The proposed approaches achieve good results on practical size problem, thus demonstrating the use of exact approaches for system design is feasible. Furthermore, the developed techniques bring significant contributions to combinatorial optimization methods.

Patterning soft matter for cell culturing

In the search to understand the interaction between cells and their underlying substrates, life sciences are beginning to incorporate micro and nano-technology based tools to probe, measure and improve cellular behavior. In this frame, patterned surfaces provide a platform for highly defined cellular interactions and, in perspective, they offer unique advantages for artificial implants. For these reasons, functionalized materials have recently become a central topic in tissue engineering. Nanotechnology, with its rich toolbox of techniques, can be the leading actor in the materials patterning field. Laser assisted methods, conventional and un-conventional lithography and other patterning techniques, allow the fabrication of functional supports with tunable properties, either physically, or topographically and chemically. Among them, soft lithography provides an effective (and low cost) strategy for manufacturing micro and nanostructures. The main focus of this work is the use of different fabrication approaches aiming at a precise control of cell behavior, adhesion, proliferation and differentiation, through chemically and spatially designed surfaces.

Computing the structural and vibrational properties of polymorphic organic molecular crystals through van der Waals corrected density functional theory and the electronic properties of organic thin films through microelectrostatic calculations.

The present Thesis reports on the various research projects to which I have contributed during my PhD period, working with several research groups, and whose results have been communicated in a number of scientific publications. The main focus of my research activity was to learn, test, exploit and extend the recently developed vdW-DFT (van der Waals corrected Density Functional Theory) methods for computing the structural, vibrational and electronic properties of ordered molecular crystals from first principles. A secondary, and more recent, research activity has been the analysis with microelectrostatic methods of Molecular Dynamics (MD) simulations of disordered molecular systems. While only very unreliable methods based on empirical models were practically usable until a few years ago, accurate calculations of the crystal energy are now possible, thanks to very fast modern computers and to the excellent performance of the best vdW-DFT methods. Accurate energies are particularly important for describing organic molecular solids, since they often exhibit several alternative crystal structures (polymorphs), with very different packing arrangements but very small energy differences. Standard DFT methods do not describe the long-range electron correlations which give rise to the vdW interactions. Although weak, these interactions are extremely sensitive to the packing arrangement, and neglecting them used to be a problem. The calculations of reliable crystal structures and vibrational frequencies has been made possible only recently, thanks to development of some good representations of the vdW contribution to the energy (known as “vdW corrections”).

Methods for Biodata Analysis in different Omic Contexts

The world of Computational Biology and Bioinformatics presently integrates many different expertise, including computer science and electronic engineering. A major aim in Data Science is the development and tuning of specific computational approaches to interpret the complexity of Biology. Molecular biologists and medical doctors heavily rely on an interdisciplinary expert capable of understanding the biological background to apply algorithms for finding optimal solutions to their problems. With this problem-solving orientation, I was involved in two basic research fields: Cancer Genomics and Enzyme Proteomics. For this reason, what I developed and implemented can be considered a general effort to help data analysis both in Cancer Genomics and in Enzyme Proteomics, focusing on enzymes which catalyse all the biochemical reactions in cells. Specifically, as to Cancer Genomics I contributed to the characterization of intratumoral immune microenvironment in gastrointestinal stromal tumours (GISTs) correlating immune cell population levels with tumour subtypes. I was involved in the setup of strategies for the evaluation and standardization of different approaches for fusion transcript detection in sarcomas that can be applied in routine diagnostic. This was part of a coordinated effort of the Sarcoma working group of "Alleanza Contro il Cancro". As to Enzyme Proteomics, I generated a derived database collecting all the human proteins and enzymes which are known to be associated to genetic disease. I curated the data search in freely available databases such as PDB, UniProt, Humsavar, Clinvar and I was responsible of searching, updating, and handling the information content, and computing statistics. I also developed a web server, BENZ, which allows researchers to annotate an enzyme sequence with the corresponding Enzyme Commission number, the important feature fully describing the catalysed reaction. More to this, I greatly contributed to the characterization of the enzyme-genetic disease association, for a better classification of the metabolic genetic diseases.

Development of advanced methods for the simulation of the reacting mixture formation in internal combustion engines with the use of machine learning algorithms

Besides increasing the share of electric and hybrid vehicles, in order to comply with more stringent environmental protection limitations, in the mid-term the auto industry must improve the efficiency of the internal combustion engine and the well to wheel efficiency of the employed fuel. To achieve this target, a deeper knowledge of the phenomena that influence the mixture formation and the chemical reactions involving new synthetic fuel components is mandatory, but complex and time intensive to perform purely by experimentation. Therefore, numerical simulations play an important role in this development process, but their use can be effective only if they can be considered accurate enough to capture these variations. The most relevant models necessary for the simulation of the reacting mixture formation and successive chemical reactions have been investigated in the present work, with a critical approach, in order to provide instruments to define the most suitable approaches also in the industrial context, which is limited by time constraints and budget evaluations. To overcome these limitations, new methodologies have been developed to conjugate detailed and simplified modelling techniques for the phenomena involving chemical reactions and mixture formation in non-traditional conditions (e.g. water injection, biofuels etc.). Thanks to the large use of machine learning and deep learning algorithms, several applications have been revised or implemented, with the target of reducing the computing time of some traditional tasks by orders of magnitude. Finally, a complete workflow leveraging these new models has been defined and used for evaluating the effects of different surrogate formulations of the same experimental fuel on a proof-of-concept GDI engine model.

Nanomechanical characterization of soft bioelectronic interfaces via modeling-informed atomic force microscopy

The field of bioelectronics involves the use of electrodes to exchange electrical signals with biological systems for diagnostic and therapeutic purposes in biomedical devices and healthcare applications. However, the mechanical compatibility of implantable devices with the human body has been a challenge, particularly with long-term implantation into target organs. Current rigid bioelectronics can trigger inflammatory responses and cause unstable device functions due to the mechanical mismatch with the surrounding soft tissue. Recent advances in flexible and stretchable electronics have shown promise in making bioelectronic interfaces more biocompatible. To fully achieve this goal, material science and engineering of soft electronic devices must be combined with quantitative characterization and modeling tools to understand the mechanical issues at the interface between electronic technology and biological tissue. Local mechanical characterization is crucial to understand the activation of failure mechanisms and optimizing the devices. Experimental techniques for testing mechanical properties at the nanoscale are emerging, and the Atomic Force Microscope (AFM) is a good candidate for in situ local mechanical characterization of soft bioelectronic interfaces. In this work, in situ experimental techniques with solely AFM supported by interpretive models for the characterization of planar and three-dimensional devices suitable for in vivo and in vitro biomedical experimentations are reported. The combination of the proposed models and experimental techniques provides access to the local mechanical properties of soft bioelectronic interfaces. The study investigates the nanomechanics of hard thin gold films on soft polymeric substrates (Poly(dimethylsiloxane) PDMS) and 3D inkjet-printed micropillars under different deformation states. The proposed characterization methods provide a rapid and precise determination of mechanical properties, thus giving the possibility to parametrize the microfabrication steps and investigate their impact on the final device.