Insights into Neural Crest Evolution

Neural crest cells are unique to vertebrates and essential to the development and evolution of the craniofacial skeleton. Using a combination of DiI cell lineage tracing, transcriptomics, and analysis of key transcription factors of the Sox Family, I examined neural crest development in the sea lamprey, Petromyzon marinus, as the most basal extant vertebrate from which it is possible to get embryos. The results have uncovered distinct cranial and trunk neural crest subpopulations along the anterior-posterior axis of the lamprey embryo, with a clear separation between the two. However, no evidence of the presence of an intermediate vagal neural crest population was uncovered. Comparing cranial neural crest genes between lamprey and chick, either by examining individual candidate genes or whole genome transcriptome analysis, reveals significant changes in the cranial neural crest gene regulatory network of lamprey compared with chick. In particular, the lamprey cranial neural crest is "missing" several gnathostome cranial crest genes. We speculate that these may underlie the evolutionary divergence of craniofacial development between jawed and jawless vertebrates. Despite the absence of vagal neural crest, DiI-labeling shows that trunk neural crest-derived cells, likely homologous to mammalian Schwann cell precursors, contribute to the lamprey enteric nervous system, potentially representing the most primitive form of neural crest cells contribution to the ENS. Finally, I characterized key members of the Sox Family (Sox B-F) due to their importance in neural crest specification in other species. In comparative studies of the SoxC genes (Sox4, Sox11, and Sox12) in both lamprey and Xenopus, I found similar expression patterns and a novel key role in early neural crest specification, suggesting a conserved role of the SoxC genes amongst vertebrates. Taken together, this work represents important progress in characterizing the early evolution of the neural crest in vertebrates and its role in the transition from jawless to jawed vertebrates.

Photovoltaic power forecast modeling with artificial neural networks

Dissertação de Mestrado, Engenharia Eletrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2016

The Role of the Transcription Factor Ets1 in Melanocyte Development

Melanocytes, pigment-producing cells, derive from the neural crest (NC), a population of pluripotent cells that arise from the dorsal aspect of the neural tube during embryogenesis. Many genes required for melanocyte development were identified using mouse pigmentation mutants. The deletion of the transcription factor Ets1 in mice results in hypopigmentation; nevertheless, the function of Ets1 in melanocyte development is unknown. The goal of the present study was to establish the temporal requirement and role of Ets1 in murine melanocyte development. In the mouse, Ets1 is widely expressed in developing organs and tissues, including the NC. In the chick cranial NC, Ets1 is required for the expression of Sox10, a transcription factor critical for the development of melanocytes, enteric ganglia, and other NC derivatives. Using a combination of immunofluorescence and cell survival assays Ets1 was found to be required between embryonic days 10 and 11, when it regulates NC cell and melanocyte precursor (melanoblast) survival. Given the requirement of Ets1 for Sox10 expression in the chick cranial NC, a potential interaction between these genes was investigated. Using genetic crosses, a synergistic genetic interaction between Ets1 and Sox10 in melanocyte development was found. Since Sox10 is essential for enteric ganglia formation, the importance of Ets1 on gut innervation was also examined. In mice, Ets1 deletion led to decreased gut innervation, which was exacerbated by Sox10 heterozygosity. At the molecular level, Ets1 was found to activate a Sox10 enhancer critical for Sox10 expression in melanoblasts. Furthermore, mutating Ets1 at a site I characterized in the spontaneous variable spotting mouse pigmentation mutant, led to a 2-fold decrease in enhancer activation. Overexpression and knockdown of Ets1 did not affect Sox10 expression; nonetheless, Ets1 knockdown led to a 6-fold upregulation of the transcription factor Sox9, a gene required for melanocyte and chondrocyte development, but which impairs melanocyte development when its expression is prolonged. Together, these results suggest that Ets1 is required early during melanocyte development for NC cell and melanoblast survival, possibly acting upstream of Sox10. The transcription factor Ets1 may also act indirectly in melanocyte fate specification by repressing Sox9 expression, and consequently cartilage fate.

Computational Models Development and Demand Response Application for Smart Grids

This paper focuses on computational models development and its applications on demand response, within smart grid scope. A prosumer model is presented and the corresponding economic dispatch problem solution is analyzed. The prosumer solar radiation production and energy consumption are forecasted by artificial neural networks. The existing demand response models are studied and a computational tool based on fuzzy clustering algorithm is developed and the results discussed. Consumer energy management applications within the InovGrid pilot project are presented. Computation systems are developed for the acquisition, monitoring, control and supervision of consumption data provided by smart meters, allowing the incorporation of consumer actions on their electrical energy management. An energy management system with integration of smart meters for energy consumers in a smart grid is developed.

Development and characterisation of a neurogenic model of brain cancer

Primary glioblastoma (GB), the most common and aggressive adult brain tumour, is refractory to conventional therapies and characterised by poor prognosis. GB displays striking cellular heterogeneity, with a sub-population, called Glioblastoma Stem Cells (GSCs), intrinsically resistant to therapy, hence the high rate of recurrence. Alterations of the tumour suppressor gene PTEN are prevalent in primary GBM, resulting in the inhibition of the polarity protein Lgl1 due to aPKC hyperactivation. Dysregulation of this molecular axis is one of the mechanisms involved in GSC maintenance. After demonstrating that the PTEN/aPKC/Lgl axis is conserved in Drosophila, I deregulated it in different cells populations of the nervous system in order to individuate the cells at the root of neurogenic brain cancers. This analysis identified the type II neuroblasts (NBs) as the most sensitive to alterations of this molecular axis. Type II NBs are a sub-population of Drosophila stem cells displaying a lineage similar to that of the mammalian neural stem cells. Following aPKC activation in these stem cells, I obtained an adult brain cancer model in Drosophila that summarises many phenotypic traits of human brain tumours. Fly tumours are indeed characterised by accumulation of highly proliferative immature cells and keep growing in the adult leading the affected animals to premature death. With the aim to understand the role of cell polarity disruption in this tumorigenic process I carried out a molecular characterisation and transcriptome analysis of brain cancers from our fly model. In summary, the model I built and partially characterised in this thesis work may help deepen our knowledge on human brain cancers by investigating many different aspects of this complicate disease.

Affordances and Language: How the Level of Object Familiarity modulates the Manipulation and Categorization of Objects across Development

According to much evidence, observing objects activates two types of information: structural properties, i.e., the visual information about the structural features of objects, and function knowledge, i.e., the conceptual information about their skilful use. Many studies so far have focused on the role played by these two kinds of information during object recognition and on their neural underpinnings. However, to the best of our knowledge no study so far has focused on the different activation of this information (structural vs. function) during object manipulation and conceptualization, depending on the age of participants and on the level of object familiarity (familiar vs. non-familiar). Therefore, the main aim of this dissertation was to investigate how actions and concepts related to familiar and non-familiar objects may vary across development. To pursue this aim, four studies were carried out. A first study led to the creation of the Familiar and Non-Familiar Stimuli Database, a set of everyday objects classified by Italian pre-schoolers, schoolers, and adults, useful to verify how object knowledge is modulated by age and frequency of use. A parallel study demonstrated that factors such as sociocultural dynamics may affect the perception of objects. Specifically, data for familiarity, naming, function, using and frequency of use of the objects used to create the Familiar And Non-Familiar Stimuli Database were collected with Dutch and Croatian children and adults. The last two studies on object interaction and language provide further evidence in support of the literature on affordances and on the link between affordances and the cognitive process of language from a developmental point of view, supporting the perspective of a situated cognition and emphasizing the crucial role of human experience.

Design, development and guidance of the Airborne’s quadrotor

The main goal of the Airborne project is to develop, at technology readiness level 8 (TRL8), a few selected robotic aerial technologies for quick localization of victims by avalanches by equipping drones with two forefront sensors used in SAR operations in case of avalanches, namely the ARVA and RECCO. This thesis focuses on the design, development, and guidance of the TRL8 quadrotor developed during the project. We present and describe the design method that allowed us to obtain an EMI shielded UAV capable of integrating both RECCO and ARVA sensors. Besides, is presented the avionics and power train design and building procedure in order to obtain a modular UAV frame that can be easily carried by rescuers and achieves all the performance benchmarks of the project. Additionally, in addition to the onboard algorithms, a multivariate regressive convolutional neural network whose goal is the localization of the ARVA signal is presented. On guidance, the automatic flight procedure is described, and the onboard waypoint generator algorithm is presented. The goal of this algorithm is the generation and execution of an automatic grid pattern without the need to know the map in advance and without the support of a control ground station (CGS). Moreover, we present an iterative trajectory planner that does not need pre-knowledge of the map and uses Bézier curves to address optimal, dynamically feasible, safe, and re-plannable trajectories. The goal is to develop a method that allows local and fast replannings in case of an obstacle pop up or if some waypoints change. This makes the novel planner suitable to be applied in SAR operations. The introduction of the final version of the quadrotor is supported by internal flight tests and field tests performed in real operative scenarios by the Club Alpino Italiano (CAI).

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.

Preharvest factors affecting quality and antioxidant levels on Abate Fétel pears: study of superficial scald with a multivariate statistical approach and neural networks

There are only a few insights concerning the influence that agronomic and management variability may have on superficial scald (SS) in pears. Abate Fétel pears were picked during three seasons (2018, 2019 and 2020) from thirty commercial orchards in the Emilia Romagna region, Italy. Using a multivariate statistical approach, high heterogeneity between farms for SS development after cold storage with regular atmosphere was demonstrated. Indeed, some factors seem to affect SS in all growing seasons: high yields, soil texture, improper irrigation and Nitrogen management, use of plant growth regulators, late harvest, precipitations, Calcium and cow manure, presence of nets, orchard age, training system and rootstock. Afterwards, we explored the spatio/temporal variability of fruit attributes in two pear orchards. Environmental and physiological spatial variables were recorded by a portable RTK GPS. High spatial variability of the SS index was observed. Through a geostatistical approach, some characteristics, including soil electrical conductivity and fruit size, have been shown to be negatively correlated with SS. Moreover, regression tree analyses were applied suggesting the presence of threshold values of antioxidant capacity, total phenolic content, and acidity against SS. High pulp firmness and IAD values before storage, denoting a more immature fruit, appeared to be correlated with low SS. Finally, a convolution neural networks (CNN) was tested to detect SS and the starch pattern index (SPI) in pears for portable device applications. Preliminary statistics showed that the model for SS had low accuracy but good precision, and the CNN for SPI denoted good performances compared to the Ctifl and Laimburg scales. The major conclusion is that Abate Fétel pears can potentially be stored in different cold rooms, according to their origin and quality features, ensuring the best fruit quality for the final consumers. These results might lead to a substantial improvement in the Italian pear industry.

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains.

Development and testing of methodologies for the control and diagnosis of innovative combustion systems

In pursuit of aligning with the European Union's ambitious target of achieving a carbon-neutral economy by 2050, researchers, vehicle manufacturers, and original equipment manufacturers have been at the forefront of exploring cutting-edge technologies for internal combustion engines. The introduction of these technologies has significantly increased the effort required to calibrate the models implemented in the engine control units. Consequently the development of tools that reduce costs and the time required during the experimental phases, has become imperative. Additionally, to comply with ever-stricter limits on 〖"CO" 〗_"2" emissions, it is crucial to develop advanced control systems that enhance traditional engine management systems in order to reduce fuel consumption. Furthermore, the introduction of new homologation cycles, such as the real driving emissions cycle, compels manufacturers to bridge the gap between engine operation in laboratory tests and real-world conditions. Within this context, this thesis showcases the performance and cost benefits achievable through the implementation of an auto-adaptive closed-loop control system, leveraging in-cylinder pressure sensors in a heavy-duty diesel engine designed for mining applications. Additionally, the thesis explores the promising prospect of real-time self-adaptive machine learning models, particularly neural networks, to develop an automatic system, using in-cylinder pressure sensors for the precise calibration of the target combustion phase and optimal spark advance in a spark-ignition engines. To facilitate the application of these combustion process feedback-based algorithms in production applications, the thesis discusses the results obtained from the development of a cost-effective sensor for indirect cylinder pressure measurement. Finally, to ensure the quality control of the proposed affordable sensor, the thesis provides a comprehensive account of the design and validation process for a piezoelectric washer test system.

Investigating perceptual multisensory impairments in Autism through a neural network: a possible neural implementation for the shift from cross-modal competition to facilitation

Resolution of multisensory deficits has been observed in teenagers with Autism Spectrum Disorders (ASD) for complex, social speech stimuli; this resolution extends to more basic multisensory processing, involving low-level stimuli. In particular, a delayed transition of multisensory integration (MSI) from a default state of competition to one of facilitation has been observed in ASD children. In other terms, the complete maturation of MSI is achieved later in ASD. In the present study a neuro-computational model is used to reproduce some patterns of behavior observed experimentally, modeling a bisensory reaction time task, in which auditory and visual stimuli are presented in random sequence alone (A or V) or together (AV). The model explains how the default competitive state can be implemented via mutual inhibition between primary sensory areas, and how the shift toward the classical multisensory facilitation, observed in adults, is the result of inhibitory cross-modal connections becoming excitatory during the development. Model results are consistent with a stronger cross-modal inhibition in ASD children, compared to normotypical (NT) ones, suggesting that the transition toward a cooperative interaction between sensory modalities takes longer to occur. Interestingly, the model also predicts the difference between unisensory switch trials (in which sensory modality switches) and unisensory repeat trials (in which sensory modality repeats). This is due to an inhibitory mechanism, characterized by a slow dynamics, driven by the preceding stimulus and inhibiting the processing of the incoming one, when of the opposite sensory modality. These findings link the cognitive framework delineated by the empirical results to a plausible neural implementation.

[the Use Of Fish On Buccal Smear To Investigate Mosaicism With A 45,x Cell Line: Study On Healthy Men And Patients With Disorders Of Sex Development].

To verify whether fluorescence in situ hybridization (FISH) of cells from the buccal epithelium could be employed to detect cryptomosaicism with a 45,X lineage in 46,XY patients. Samples of nineteen 46,XY healthy young men and five patients with disorders of sex development (DSD), four 45,X/46,XY and one 46,XY were used. FISH analysis with X and Y specific probes on interphase nuclei from blood lymphocytes and buccal epithelium were analyzed to investigate the proportion of nuclei containing only the signal of the X chromosome. The frequency of nuclei containing only the X signal in the two tissues of healthy men did not differ (p = 0.69). In all patients with DSD this frequency was significantly higher, and there was no difference between the two tissues (p = 0.38), either. Investigation of mosaicism with a 45,X cell line in patients with 46,XY DSD or sterility can be done by FISH directly using cells from the buccal epithelium.

The Influence Of The Environment On The Development Of Thyroid Tumors: A New Appraisal.

Most epidemiological studies concerning differentiated thyroid cancers (DTC) indicate an increasing incidence over the last two decades. This increase might be partially explained by the better access to health services worldwide, but clinicopathological analyses do not fully support this hypothesis, indicating that there are carcinogenetic factors behind this noticeable increasing incidence. Although we have undoubtedly understood the biology and molecular pathways underlying thyroid carcinogenesis in a better way, we have made very little progresses in identifying a risk profile for DTC, and our knowledge of risk factors is very similar to what we knew 30-40 years ago. In addition to ionizing radiation exposure, the most documented and established risk factor for DTC, we also investigated the role of other factors, including eating habits, tobacco smoking, living in a volcanic area, xenobiotics, and viruses, which could be involved in thyroid carcinogenesis, thus, contributing to the increase in DTC incidence rates observed.

Development And Shelf-life Determination Of Pasteurized, Microfiltered, Lactose Hydrolyzed Skim Milk.

The segment of the world population showing permanent or temporary lactose intolerance is quite significant. Because milk is a widely consumed food with an high nutritional value, technological alternatives have been sought to overcome this dilemma. Microfiltration combined with pasteurization can not only extend the shelf life of milk but can also maintain the sensory, functional, and nutritional properties of the product. This studied developed a pasteurized, microfiltered, lactose hydrolyzed (delactosed) skim milk (PMLHSM). Hydrolysis was performed using β-galactosidase at a concentration of 0.4mL/L and incubation for approximately 21h at 10±1°C. During these procedures, the degree of hydrolysis obtained (>90%) was accompanied by evaluation of freezing point depression, and the remaining quantity of lactose was confirmed by HPLC. Milk was processed using a microfiltration pilot unit equipped with uniform transmembrane pressure (UTP) ceramic membranes with a mean pore size of 1.4 μm and UTP of 60 kPa. The product was submitted to physicochemical, microbiological, and sensory evaluations, and its shelf life was estimated. Microfiltration reduced the aerobic mesophilic count by more than 4 log cycles. We were able to produce high-quality PMLHSM with a shelf life of 21 to 27d when stored at 5±1°C in terms of sensory analysis and proteolysis index and a shelf life of 50d in regard to total aerobic mesophile count and titratable acidity.