Computational methods for genome screening

Motivation An actual issue of great interest, both under a theoretical and an applicative perspective, is the analysis of biological sequences for disclosing the information that they encode. The development of new technologies for genome sequencing in the last years, opened new fundamental problems since huge amounts of biological data still deserve an interpretation. Indeed, the sequencing is only the first step of the genome annotation process that consists in the assignment of biological information to each sequence. Hence given the large amount of available data, in silico methods became useful and necessary in order to extract relevant information from sequences. The availability of data from Genome Projects gave rise to new strategies for tackling the basic problems of computational biology such as the determination of the tridimensional structures of proteins, their biological function and their reciprocal interactions. Results The aim of this work has been the implementation of predictive methods that allow the extraction of information on the properties of genomes and proteins starting from the nucleotide and aminoacidic sequences, by taking advantage of the information provided by the comparison of the genome sequences from different species. In the first part of the work a comprehensive large scale genome comparison of 599 organisms is described. 2,6 million of sequences coming from 551 prokaryotic and 48 eukaryotic genomes were aligned and clustered on the basis of their sequence identity. This procedure led to the identification of classes of proteins that are peculiar to the different groups of organisms. Moreover the adopted similarity threshold produced clusters that are homogeneous on the structural point of view and that can be used for structural annotation of uncharacterized sequences. The second part of the work focuses on the characterization of thermostable proteins and on the development of tools able to predict the thermostability of a protein starting from its sequence. By means of Principal Component Analysis the codon composition of a non redundant database comprising 116 prokaryotic genomes has been analyzed and it has been showed that a cross genomic approach can allow the extraction of common determinants of thermostability at the genome level, leading to an overall accuracy in discriminating thermophilic coding sequences equal to 95%. This result outperform those obtained in previous studies. Moreover, we investigated the effect of multiple mutations on protein thermostability. This issue is of great importance in the field of protein engineering, since thermostable proteins are generally more suitable than their mesostable counterparts in technological applications. A Support Vector Machine based method has been trained to predict if a set of mutations can enhance the thermostability of a given protein sequence. The developed predictor achieves 88% accuracy.

Packet Level Coding for Mobile Broadcasting

The thesis deals with channel coding theory applied to upper layers in the protocol stack of a communication link and it is the outcome of four year research activity. A specific aspect of this activity has been the continuous interaction between the natural curiosity related to the academic blue-sky research and the system oriented design deriving from the collaboration with European industry in the framework of European funded research projects. In this dissertation, the classical channel coding techniques, that are traditionally applied at physical layer, find their application at upper layers where the encoding units (symbols) are packets of bits and not just single bits, thus explaining why such upper layer coding techniques are usually referred to as packet layer coding. The rationale behind the adoption of packet layer techniques is in that physical layer channel coding is a suitable countermeasure to cope with small-scale fading, while it is less efficient against large-scale fading. This is mainly due to the limitation of the time diversity inherent in the necessity of adopting a physical layer interleaver of a reasonable size so as to avoid increasing the modem complexity and the latency of all services. Packet layer techniques, thanks to the longer codeword duration (each codeword is composed of several packets of bits), have an intrinsic longer protection against long fading events. Furthermore, being they are implemented at upper layer, Packet layer techniques have the indisputable advantages of simpler implementations (very close to software implementation) and of a selective applicability to different services, thus enabling a better matching with the service requirements (e.g. latency constraints). Packet coding technique improvement has been largely recognized in the recent communication standards as a viable and efficient coding solution: Digital Video Broadcasting standards, like DVB-H, DVB-SH, and DVB-RCS mobile, and 3GPP standards (MBMS) employ packet coding techniques working at layers higher than the physical one. In this framework, the aim of the research work has been the study of the state-of-the-art coding techniques working at upper layer, the performance evaluation of these techniques in realistic propagation scenario, and the design of new coding schemes for upper layer applications. After a review of the most important packet layer codes, i.e. Reed Solomon, LDPC and Fountain codes, in the thesis focus our attention on the performance evaluation of ideal codes (i.e. Maximum Distance Separable codes) working at UL. In particular, we analyze the performance of UL-FEC techniques in Land Mobile Satellite channels. We derive an analytical framework which is a useful tool for system design allowing to foresee the performance of the upper layer decoder. We also analyze a system in which upper layer and physical layer codes work together, and we derive the optimal splitting of redundancy when a frequency non-selective slowly varying fading channel is taken into account. The whole analysis is supported and validated through computer simulation. In the last part of the dissertation, we propose LDPC Convolutional Codes (LDPCCC) as possible coding scheme for future UL-FEC application. Since one of the main drawbacks related to the adoption of packet layer codes is the large decoding latency, we introduce a latency-constrained decoder for LDPCCC (called windowed erasure decoder). We analyze the performance of the state-of-the-art LDPCCC when our decoder is adopted. Finally, we propose a design rule which allows to trade-off performance and latency.

Model predictive flight control systems for rotorcraft UAS

Constraints are widely present in the flight control problems: actuators saturations or flight envelope limitations are only some examples of that. The ability of Model Predictive Control (MPC) of dealing with the constraints joined with the increased computational power of modern calculators makes this approach attractive also for fast dynamics systems such as agile air vehicles. This PhD thesis presents the results, achieved at the Aerospace Engineering Department of the University of Bologna in collaboration with the Dutch National Aerospace Laboratories (NLR), concerning the development of a model predictive control system for small scale rotorcraft UAS. Several different predictive architectures have been evaluated and tested by means of simulation, as a result of this analysis the most promising one has been used to implement three different control systems: a Stability and Control Augmentation System, a trajectory tracking and a path following system. The systems have been compared with a corresponding baseline controller and showed several advantages in terms of performance, stability and robustness.

Immunohistochemical and Molecular Prognostic/Predictive Markers in Neoplastic Diseases

Traditional morphological examinations are not anymore sufficient for a complete evaluation of tumoral tissue and the use of neoplastic markers is of utmost importance. Neoplastic markers can be classified in: diagnostic, prognostic and predictive markers. Three markers were analyzed. 1) Insulin-like growth factor binding protein 2 (IGFBP2) was immunohistochemically examined in prostatic tissues: 40 radical prostatectomies from hormonally untreated patients with their preoperative biopsies, 10 radical prostatectomies from patients under complete androgen ablation before surgery and 10 simple prostatectomies from patients with bladder outlet obstruction. Results were compared with α-methylacyl-CoA racemase (AMACR). IGFBP2 was expressed in the cytoplasm of untreated adenocarcinomas and, to a lesser extent, in HG-PIN; the expression was markedly lower in patients after complete androgen ablation. AMACR was similarly expressed in both adenocarcinoma and HG-PIN, the level being similar in both lesions; the expression was slightly lower in patients after complete androgen ablation. IGFBP2 may be used a diagnostic marker of prostatic adenocarcinomas. 2) Heparan surface proteoglycan immunohistochemical expression was examined in 150 oral squamous cell carcinomas. Follow up information was available in 93 patients (range: 6-34 months, mean: 19±7). After surgery, chemotherapy was performed in 8 patients and radiotherapy in 61 patients. Multivariate and univariate overall survival analyses showed that high expression of syndecan-1 (SYN-1) was associated with a poor prognosis. In patients treated with radiotherapy, such association was higher. SYN-1 is a prognostic marker in oral squamous cell carcinomas; it may also represent a predictive factor for responsiveness to radiotherapy. 3) EGFR was studied in 33 pulmonary adenocarcinomas with traditional DNA sequencing methods and with two mutation-specific antibodies. Overall, the two antibodies had 61.1% sensitivity and 100% specificity in detecting EGFR mutations. EGFR mutation-specific antibodies may represent a predictive marker to identify patients candidate to tyrosine kinase inhibitors therapy.

Predictive Ability of NGAL as a Marker of Renal Damage: evaluation of Multiple Clinical Settings

Introduction. Neutrophil Gelatinase-Associated Lipocalin (NGAL) belongs to the family of lipocalins and it is produced by several cell types, including renal tubular epithelium. In the kidney its production increases during acute damage and this is reflected by the increase in serum and urine levels. In animal studies and clinical trials, NGAL was found to be a sensitive and specific indicator of acute kidney injury (AKI). Purpose. The aim of this work was to investigate, in a prospective manner, whether urine NGAL can be used as a marker in preeclampsia, kidney transplantation, VLBI and diabetic nephropathy. Materials and methods. The study involved 44 consecutive patients who received renal transplantation; 18 women affected by preeclampsia (PE); a total of 55 infants weighing ≤1500 g and 80 patients with Type 1 diabetes. Results. A positive correlation was found between urinary NGAL and 24 hours proteinuria within the PE group. The detection of higher uNGAL values in case of severe PE, even in absence of statistical significance, confirms that these women suffer from an initial renal damage. In our population of VLBW infants, we found a positive correlation of uNGAL values at birth with differences in sCreat and eGFR values from birth to day 21, but no correlation was found between uNGAL values at birth and sCreat and eGFR at day 7. systolic an diastolic blood pressure decreased with increasing levels of uNGAL. The patients with uNGAL <25 ng/ml had significantly higher levels of systolic blood pressure compared with the patients with uNGAL >50 ng/ml ( p<0.005). Our results indicate the ability of NGAL to predict the delay in functional recovery of the graft. Conclusions. In acute renal pathology, urinary NGAL confirms to be a valuable predictive marker of the progress and status of acute injury.

Model predictive control in thermal management of multiprocessor systems-on-chip

MultiProcessor Systems-on-Chip (MPSoC) are the core of nowadays and next generation computing platforms. Their relevance in the global market continuously increase, occupying an important role both in everydaylife products (e.g. smartphones, tablets, laptops, cars) and in strategical market sectors as aviation, defense, robotics, medicine. Despite of the incredible performance improvements in the recent years processors manufacturers have had to deal with issues, commonly called “Walls”, that have hindered the processors development. After the famous “Power Wall”, that limited the maximum frequency of a single core and marked the birth of the modern multiprocessors system-on-chip, the “Thermal Wall” and the “Utilization Wall” are the actual key limiter for performance improvements. The former concerns the damaging effects of the high temperature on the chip caused by the large power densities dissipation, whereas the second refers to the impossibility of fully exploiting the computing power of the processor due to the limitations on power and temperature budgets. In this thesis we faced these challenges by developing efficient and reliable solutions able to maximize performance while limiting the maximum temperature below a fixed critical threshold and saving energy. This has been possible by exploiting the Model Predictive Controller (MPC) paradigm that solves an optimization problem subject to constraints in order to find the optimal control decisions for the future interval. A fully-distributedMPC-based thermal controller with a far lower complexity respect to a centralized one has been developed. The control feasibility and interesting properties for the simplification of the control design has been proved by studying a partial differential equation thermal model. Finally, the controller has been efficiently included in more complex control schemes able to minimize energy consumption and deal with mixed-criticalities tasks

Coding of reaching in 3-D space

Many psychophysical studies suggest that target depth and direction during reaches are processed independently, but the neurophysiological support to this view is so far limited. Here, we investigated the representation of reach depth and direction by single neurons in an area of the medial posterior parietal cortex (V6A). Single-unit activity was recorded from V6A in two Macaca fascicularis monkeys performing a fixation-to-reach task to targets at different depths and directions. We found that in a substantial percentage of V6A neurons depth and direction signals jointly influenced fixation, planning and arm movement-related activity in 3D space. While target depth and direction were equally encoded during fixation, depth tuning became stronger during arm movement planning, execution and target holding. The spatial tuning of fixation activity was often maintained across epochs, and this occurred more frequently in depth. These findings support for the first time the existence of a common neural substrate for the encoding of target depth and direction during reaching movements in the posterior parietal cortex. Present results also highlight the presence in V6A of several types of cells that process independently or jointly eye position and arm movement planning and execution signals in order to control reaches in 3D space. It is possible that depth and direction influence also the metrics of the reach action and that this effect on the reach kinematic variables can account for the spatial tuning we found in V6A neural activity. For this reason, we recorded and analyzed behavioral data when one monkey performed reaching movements in 3-D space. We evaluated how the target spatial position, in particular target depth and target direction, affected the kinematic parameters and trajectories describing the motor action properties.

The vulnerable carotid plaque. Identification of endothelial markers predictive of plaque weakness, rupture predisposition and neoangiogenesis

Background. Neoangiogenesis is crucial in plaque progression and instability. Previous data from our group demonstrated that intra-plaque neovessels show both a Nestin+/WT+ and a Nestin+/WT1- phenotype, the latter being correlated with complications and plaque instability. Aims. The aims of the present thesis are: (i) to confirm our previous results on Nestin/WT1 phenotype in a larger series of carotid atheromatous plaques, (ii) to evaluate the relationship between the Nestin+/WT1- neoangiogenesis phenotype and plaque morphology, (iii) to evaluate the relationship between the immunohistochemical and histopathological characteristics and the clinical instability of the plaques. Materials and Methods. Seventy-three patients (53 males, 20 females, mean age 71 years) were consecutively enrolled. Symptoms, brain CT scan, 14 histological variables, including intraplaque hemorrhage and diffuse calcifications, were collected. Immunohistochemistry for CD34, Nestin and WT1 was performed. RT-PCR was performed to evaluate Nestin and WT1 mRNA (including 5 healthy arteries as controls). Results. Diffusely calcified plaques (13 out of 73) were found predominantly in females (P=0.017), with a significantly lower incidence of symptoms (TIA/stroke) and brain focal lesions (P=0.019 and P=0.013 respectively) than not-calcified plaques, but with the same incidence of intraplaque complications (P=0.156). Accordingly, both calcified and not calcified plaques showed similar mean densities of positivity for CD34, Nestin and WT1. The density of Nestin and WT1 correlated with the occurrence of intra-plaque hemorrhage in all cases, while the density of CD34 correlated only in not-calcified plaques. Conclusions. We confirmed that the Nestin+/WT1- phenotype characterizes the neovessels of instable plaques, regardless the real amount of CD34-positive neoangiogenesis. The calcified plaques show the same incidence of histological complications, albeit they do not influence symptomatology and plaque vulnerability. Female patients show a much higher incidence of not-complicated or calcified plaques, receiving de facto a sort of protection compared to male patients.

Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction.

In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.

Role of Non-coding RNAs in chemotherapeutic treatments

The transcribed ultraconserved regions (T-UCRs) are a group of long non-coding RNAs involved in human carcinogenesis. The factors regulating the expression of T-UCRs and their mechanism of action in human cancers are unknown. In this work it was shown that high expression of uc.339 associates with lower survival in 204 non-small cell lung cancer (NSCLC) patients. Moreover, it was shown that uc.339 found up-regulated in archival NSCLC samples, acts as a decoy RNA for miR-339-3p, -663-3p and -95-5p. So, Cyclin E2, a direct target of three microRNAs is up-regulated, inducing cancer growth and migration. Evidence of this mechanism was provided from cell lines and primary samples confirming that TP53 directly regulates uc.339. These results support a key role for uc.339 in lung cancer.

Biomedical engineering for healthy ageing. Predictive tools for falls

Falls are common and burdensome accidents among the elderly. About one third of the population aged 65 years or more experience at least one fall each year. Fall risk assessment is believed to be beneficial for fall prevention. This thesis is about prognostic tools for falls for community-dwelling older adults. We provide an overview of the state of the art. We then take different approaches: we propose a theoretical probabilistic model to investigate some properties of prognostic tools for falls; we present a tool whose parameters were derived from data of the literature; we train and test a data-driven prognostic tool. Finally, we present some preliminary results on prediction of falls through features extracted from wearable inertial sensors. Heterogeneity in validation results are expected from theoretical considerations and are observed from empirical data. Differences in studies design hinder comparability and collaborative research. According to the multifactorial etiology of falls, assessment on multiple risk factors is needed in order to achieve good predictive accuracy.