A machine learning based method to detect genomic imbalances exploiting X chromosome exome reads

Whole Exome Sequencing (WES) is rapidly becoming the first-tier test in clinics, both thanks to its declining costs and the development of new platforms that help clinicians in the analysis and interpretation of SNV and InDels. However, we still know very little on how CNV detection could increase WES diagnostic yield. A plethora of exome CNV callers have been published over the years, all showing good performances towards specific CNV classes and sizes, suggesting that the combination of multiple tools is needed to obtain an overall good detection performance. Here we present TrainX, a ML-based method for calling heterozygous CNVs in WES data using EXCAVATOR2 Normalized Read Counts. We select males and females’ non pseudo-autosomal chromosome X alignments to construct our dataset and train our model, make predictions on autosomes target regions and use HMM to call CNVs. We compared TrainX against a set of CNV tools differing for the detection method (GATK4 gCNV, ExomeDepth, DECoN, CNVkit and EXCAVATOR2) and found that our algorithm outperformed them in terms of stability, as we identified both deletions and duplications with good scores (0.87 and 0.82 F1-scores respectively) and for sizes reaching the minimum resolution of 2 target regions. We also evaluated the method robustness using a set of WES and SNP array data (n=251), part of the Italian cohort of Epi25 collaborative, and were able to retrieve all clinical CNVs previously identified by the SNP array. TrainX showed good accuracy in detecting heterozygous CNVs of different sizes, making it a promising tool to use in a diagnostic setting.

Real-time anomaly detection of gamma-ray bursts for the Cherenkov Telescope Array using deep learning

The Cherenkov Telescope Array (CTA) will be the next-generation ground-based observatory to study the universe in the very-high-energy domain. The observatory will rely on a Science Alert Generation (SAG) system to analyze the real-time data from the telescopes and generate science alerts. The SAG system will play a crucial role in the search and follow-up of transients from external alerts, enabling multi-wavelength and multi-messenger collaborations. It will maximize the potential for the detection of the rarest phenomena, such as gamma-ray bursts (GRBs), which are the science case for this study. This study presents an anomaly detection method based on deep learning for detecting gamma-ray burst events in real-time. The performance of the proposed method is evaluated and compared against the Li&Ma standard technique in two use cases of serendipitous discoveries and follow-up observations, using short exposure times. The method shows promising results in detecting GRBs and is flexible enough to allow real-time search for transient events on multiple time scales. The method does not assume background nor source models and doe not require a minimum number of photon counts to perform analysis, making it well-suited for real-time analysis. Future improvements involve further tests, relaxing some of the assumptions made in this study as well as post-trials correction of the detection significance. Moreover, the ability to detect other transient classes in different scenarios must be investigated for completeness. The system can be integrated within the SAG system of CTA and deployed on the onsite computing clusters. This would provide valuable insights into the method's performance in a real-world setting and be another valuable tool for discovering new transient events in real-time. Overall, this study makes a significant contribution to the field of astrophysics by demonstrating the effectiveness of deep learning-based anomaly detection techniques for real-time source detection in gamma-ray astronomy.

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.

Bioactive and toxic compounds in different foodstuffs: analytical techniques and sensory analysis

Foods that provide medical and health benefits or have a role in disease risk prevention are termed functional foods. The functionality of functional foods is derived from bioactive compounds that are extranutritional constituents present in small quantities in food. Bioactive components include a range of chemical compounds with varying structures such as carotenoids, flavonoids, plant sterols, omega-3 fatty acids (n-3), allyl and diallyl sulfides, indoles (benzopyrroles), and phenolic acids. The increasing consumer interest in natural bioactive compounds has brought about a rise in demand for these kinds of compounds and, in parallel, an increasing number of scientific studies have this type of substance as main topic. The principal aim of this PhD research project was the study of different bioactive and toxic compounds in several natural matrices. To achieve this goal, chromatographic, spectroscopic and sensorial analysis were performed. This manuscript reports the main results obtained in the six activities briefly summarized as follows: • SECTION I: the influence of conventional packaging on lipid oxidation of pasta was evaluated in egg spaghetti. • SECTION II: the effect of the storage at different temperatures of virgin olive oil was monitored by peroxide value, fatty acid activity, OSI test and sensory analysis. • SECTION III: the glucosinolate and phenolic content of 37 rocket salad accessions were evaluated, comparing Eruca sativa and Diplotaxis tenuifolia species. Sensory analysis and the influence of the phenolic and glucosinolate composition on sensory attributes of rocket salads has been also studied. • SECTION IV: ten buckwheat honeys were characterised on the basis of their pollen, physicochemical, phenolic and volatile composition. • SECTION V: the polyphenolic fraction, anthocyanins and other polar compounds, the antioxidant capacity and the anty-hyperlipemic action of the aqueous extract of Hibiscus sabdariffa were achieved. • SECTION VI: the optimization of a normal phase high pressure liquid chromatography–fluorescence detection method for the quantitation of flavanols and procyanidins in cocoa powder and chocolate samples was performed.

Studi epidemiologici su Pseudomonas syringae pv.actinidiae agente causale del cancro batterico in actinidia

Il cancro batterico dell’actinidia causato da Pseudomonas syringae pv.actinidiae (Psa) suscita grande interesse a livello globale a partire dal 2008. La malattia è comparsa in Giappone e in due anni ha avuto una diffusione epidemica in tutte le aree di coltivazione mondiale di actinidia. Gravi perdite economiche hanno attirato l’attenzione internazionale su questa problematica e grandi sforzi sono stati rivolti allo studio di questo patosistema ancora poco conosciuto. E’ emerso infatti che il patogeno può rimanere in fase latente per lunghi periodi senza causare sintomi caratteristici nelle piante infette, e che dalla comparsa dei sintomi la pianta muore nell’arco di un paio d’anni. Il monitoraggio ed il controllo della situazione è perciò di fondamentale importanza ed è ancora più importante prevenire la comparsa di nuovi focolai di infezione. A questo proposito sarebbe opportuno l’impiego di materiale vegetale di propagazione non infetto, ma in molti casi questo diventa difficile, dal momento che il materiale impiegato è generalmente quello asintomatico, non analizzato precedentemente per la presenza del patogeno. Negli ultimi anni sono state perciò messe a punto molte tecniche molecolari per l’identificazione di Psa direttamente da materiale vegetale. L’obiettivo di questo lavoro è stato quello di studiare l’epidemiologia di Psa in piante adulte infette e di verificare l’efficacia di metodi di diagnosi precoce per prevenire la malattia. A tale scopo il lavoro sperimentale è stato suddiviso in diverse fasi: i) studio della localizzazione, traslocazione e sopravvivenza di Psa nelle piante, a seguito di inoculazione in piante adulte di actinidia di ceppi marcati Psa::gfp; ii) studio della capacità di Psa di essere mantenuto in germogli di actinidia attraverso sette generazioni di micropropagazione dopo l’inoculazione delle piante madri con lo stesso ceppo marcato Psa::gfp; iii) studio ed applicazioni di un nuovo metodo di diagnosi precoce di Psa basato sull’analisi molecolare del “pianto”.

Response times in computerized adaptive testing: a method for cheating detection

In the field of educational and psychological measurement, the shift from paper-based to computerized tests has become a prominent trend in recent years. Computerized tests allow for more complex and personalized test administration procedures, like Computerized Adaptive Testing (CAT). CAT, following the Item Response Theory (IRT) models, dynamically generates tests based on test-taker responses, driven by complex statistical algorithms. Even if CAT structures are complex, they are flexible and convenient, but concerns about test security should be addressed. Frequent item administration can lead to item exposure and cheating, necessitating preventive and diagnostic measures. In this thesis a method called "CHeater identification using Interim Person fit Statistic" (CHIPS) is developed, designed to identify and limit cheaters in real-time during test administration. CHIPS utilizes response times (RTs) to calculate an Interim Person fit Statistic (IPS), allowing for on-the-fly intervention using a more secret item bank. Also, a slight modification is proposed to overcome situations with constant speed, called Modified-CHIPS (M-CHIPS). A simulation study assesses CHIPS, highlighting its effectiveness in identifying and controlling cheaters. However, it reveals limitations when cheaters possess all correct answers. The M-CHIPS overcame this limitation. Furthermore, the method has shown not to be influenced by the cheaters’ ability distribution or the level of correlation between ability and speed of test-takers. Finally, the method has demonstrated flexibility for the choice of significance level and the transition from fixed-length tests to variable-length ones. The thesis discusses potential applications, including the suitability of the method for multiple-choice tests, assumptions about RT distribution and level of item pre-knowledge. Also limitations are discussed to explore future developments such as different RT distributions, unusual honest respondent behaviors, and field testing in real-world scenarios. In summary, CHIPS and M-CHIPS offer real-time cheating detection in CAT, enhancing test security and ability estimation while not penalizing test respondents.