Fractal descriptors for quantifying brain complexity in MRI

Magnetic Resonance Imaging (MRI) is the in vivo technique most commonly employed to characterize changes in brain structures. The conventional MRI-derived morphological indices are able to capture only partial aspects of brain structural complexity. Fractal geometry and its most popular index, the fractal dimension (FD), can characterize self-similar structures including grey matter (GM) and white matter (WM). Previous literature shows the need for a definition of the so-called fractal scaling window, within which each structure manifests self-similarity. This justifies the existence of fractal properties and confirms Mandelbrot’s assertion that "fractals are not a panacea; they are not everywhere". In this work, we propose a new approach to automatically determine the fractal scaling window, computing two new fractal descriptors, i.e., the minimal and maximal fractal scales (mfs and Mfs). Our method was implemented in a software package, validated on phantoms and applied on large datasets of structural MR images. We demonstrated that the FD is a useful marker of morphological complexity changes that occurred during brain development and aging and, using ultra-high magnetic field (7T) examinations, we showed that the cerebral GM has fractal properties also below the spatial scale of 1 mm. We applied our methodology in two neurological diseases. We observed the reduction of the brain structural complexity in SCA2 patients and, using a machine learning approach, proved that the cerebral WM FD is a consistent feature in predicting cognitive decline in patients with small vessel disease and mild cognitive impairment. Finally, we showed that the FD of the WM skeletons derived from diffusion MRI provides complementary information to those obtained from the FD of the WM general structure in T1-weighted images. In conclusion, the fractal descriptors of structural brain complexity are candidate biomarkers to detect subtle morphological changes during development, aging and in neurological diseases.

A take on complexity: bio-molecules and human metabolism interaction modelling for health and nutrition with machine learning

The advent of omic data production has opened many new perspectives in the quest for modelling complexity in biophysical systems. With the capability of characterizing a complex organism through the patterns of its molecular states, observed at different levels through various omics, a new paradigm of investigation is arising. In this thesis, we investigate the links between perturbations of the human organism, described as the ensemble of crosstalk of its molecular states, and health. Machine learning plays a key role within this picture, both in omic data analysis and model building. We propose and discuss different frameworks developed by the author using machine learning for data reduction, integration, projection on latent features, pattern analysis, classification and clustering of omic data, with a focus on 1H NMR metabolomic spectral data. The aim is to link different levels of omic observations of molecular states, from nanoscale to macroscale, to study perturbations such as diseases and diet interpreted as changes in molecular patterns. The first part of this work focuses on the fingerprinting of diseases, linking cellular and systemic metabolomics with genomic to asses and predict the downstream of perturbations all the way down to the enzymatic network. The second part is a set of frameworks and models, developed with 1H NMR metabolomic at its core, to study the exposure of the human organism to diet and food intake in its full complexity, from epidemiological data analysis to molecular characterization of food structure.

Harnessing the cellular and molecular complexity of high-risk neuroblastoma to investigate novel potential treatment approaches

Neuroblastoma (NB) is the most common type of tumor in infants and the third most common cancer in children. Current clinical practices employ a variety of strategies for NB treatment, ranging from standard chemotherapy to immunotherapy. Due to a lack of knowledge about the molecular mechanisms underlying the disease's onset, aggressive phenotype, and therapeutic resistance, these approaches are ineffective in the majority of instances. MYCN amplification is one of the most well-known genetic alterations associated with high risk in NB. The following work is divided into three sections and aims to provide new insights into the biology of NB and hypothetical new treatment strategies. First, we identified RUNX1T1 as a key gene involved in MYCN-driven NB onset in a transgenic mouse model. Our results suggested that that RUNX1T1 may recruit the Co-REST complex on target genes that regulate the differentiation of NB cells and that the interaction with RCOR3 is essential. Second, we provided insights into the role of MYCN in dysregulating the CDK/RB/E2F pathway controlling the G1/S transition of the cell cycle. We found that RB is dispensable in regulating MYCN amplified NB's cell cycle, providing the rationale for using cyclin/CDK complexes inhibitors in NBs carrying MYCN amplification and relatively high levels of RB1 expression. Third, we generated an M13 bacteriophage platform to target GD2-expressing cells in NB. Here, we generated a recombinant M13 phage capable of binding GD2-expressing cells selectively (M13GD2). Our results showed that M13GD2 chemically conjugated with the photosensitizer ECB04 preserves the retargeting capability, inducing cell death even at picomolar concentrations upon light irradiation. These results provided proof of concept for M13 phage employment in targeted photodynamic therapy for NB, an exciting strategy to overcome resistance to classical immunotherapy.

Decoding Agc1 deficiency: bioinformatics approaches to unveil the functional implications of Slc25a12 on the transcriptome and epigenome

In the brain, mutations in SLC25A12 gene encoding AGC1 cause an ultra-rare genetic disease reported as a developmental and epileptic encephalopathy associated with global cerebral hypomyelination. Symptoms of the disease include diffused hypomyelination, arrested psychomotor development, severe hypotonia, seizures and are common to other neurological and developmental disorders. Amongst the biological components believed to be most affected by AGC1 deficiency are oligodendrocytes, glial cells responsible for myelination. Recent studies (Poeta et al, 2022) have also shown how altered levels of transcription factors and epigenetic modifications greatly affect proliferation and differentiation in oligodendrocyte precursor cells (OPCs). In this study we explore the transcriptomic landscape of Agc1 in two different system models: OPCs silenced for Agc1 and iPSCs from human patients differentiated to neural progenitors. Analyses range from differential expression analysis, alternative splicing, master regulator analysis. ATAC-seq results on OPCs were integrated with results from RNA-Seq to assess the activity of a TF based on the accessibility data from its putative targets, which allows to integrate RNA-Seq data to infer their role as either activators or repressors. All the findings for this model were also integrated with early data from iPSCs RNA-seq results, looking for possible commonalities between the two different system models, among which we find a downregulation in genes encoding for SREBP, a transcription factor regulating fatty acids biosynthesis, a key process for myelination which could explain the hypomyelinated state of patients. We also find that in both systems cells tend to form more neurites, likely losing their ability to differentiate, considering their progenitor state. We also report several alterations in the chromatin state of cells lacking Agc1, which confirms the hypothesis for which Agc1 is not a disease restricted only to metabolic alterations in the cells, but there is a profound shift of the regulatory state of these cells.

Decoding complex flow phenomena via Dynamic Mode Decomposition

In this thesis, the viability of the Dynamic Mode Decomposition (DMD) as a technique to analyze and model complex dynamic real-world systems is presented. This method derives, directly from data, computationally efficient reduced-order models (ROMs) which can replace too onerous or unavailable high-fidelity physics-based models. Optimizations and extensions to the standard implementation of the methodology are proposed, investigating diverse case studies related to the decoding of complex flow phenomena. The flexibility of this data-driven technique allows its application to high-fidelity fluid dynamics simulations, as well as time series of real systems observations. The resulting ROMs are tested against two tasks: (i) reduction of the storage requirements of high-fidelity simulations or observations; (ii) interpolation and extrapolation of missing data. The capabilities of DMD can also be exploited to alleviate the cost of onerous studies that require many simulations, such as uncertainty quantification analysis, especially when dealing with complex high-dimensional systems. In this context, a novel approach to address parameter variability issues when modeling systems with space and time-variant response is proposed. Specifically, DMD is merged with another model-reduction technique, namely the Polynomial Chaos Expansion, for uncertainty quantification purposes. Useful guidelines for DMD deployment result from the study, together with the demonstration of its potential to ease diagnosis and scenario analysis when complex flow processes are involved.

Characterization of ENSO changes under idealised radiative forcing in an intermediate complexity general circulation model

Intermediate-complexity general circulation models are a fundamental tool to investigate the role of internal and external variability within the general circulation of the atmosphere and ocean. The model used in this thesis is an intermediate complexity atmospheric general circulation model (SPEEDY) coupled to a state-of-the-art modelling framework for the ocean (NEMO). We assess to which extent the model allows a realistic simulation of the most prominent natural mode of variability at interannual time scales: El-Niño Southern Oscillation (ENSO). To a good approximation, the model represents the ENSO-induced Sea Surface Temperature (SST) pattern in the equatorial Pacific, despite a cold tongue-like bias. The model underestimates (overestimates) the typical ENSO spatial variability during the winter (summer) seasons. The mid-latitude response to ENSO reveals that the typical poleward stationary Rossby wave train is reasonably well represented. The spectral decomposition of ENSO features a spectrum that lacks periodicity at high frequencies and is overly periodic at interannual timescales. We then implemented an idealised transient mean state change in the SPEEDY model. A warmer climate is simulated by an alteration of the parametrized radiative fluxes that corresponds to doubled carbon dioxide absorptivity. Results indicate that the globally averaged surface air temperature increases of 0.76 K. Regionally, the induced signal on the SST field features a significant warming over the central-western Pacific and an El-Niño-like warming in the subtropics. In general, the model features a weakening of the tropical Walker circulation and a poleward expansion of the local Hadley cell. This response is also detected in a poleward rearrangement of the tropical convective rainfall pattern. The model setting that has been here implemented provides a valid theoretical support for future studies on climate sensitivity and forced modes of variability under mean state changes.

prism: rinfrangere distribuzioni di probabilità in riassunti astrattivi mediante strategie di decoding

Negli ultimi quattro anni la summarization astrattiva è stata protagonista di una evoluzione senza precedenti dettata da nuovi language model neurali, architetture transformer-based, elevati spazi dimensionali, ampi dataset e innovativi task di pre-training. In questo contesto, le strategie di decoding convertono le distribuzioni di probabilità predette da un modello in un testo artificiale, il quale viene composto in modo auto regressivo. Nonostante il loro cruciale impatto sulla qualità dei riassunti inferiti, il ruolo delle strategie di decoding è frequentemente trascurato e sottovalutato. Di fronte all'elevato numero di tecniche e iperparametri, i ricercatori necessitano di operare scelte consapevoli per ottenere risultati più affini agli obiettivi di generazione. Questa tesi propone il primo studio altamente comprensivo sull'efficacia ed efficienza delle strategie di decoding in task di short, long e multi-document abstractive summarization. Diversamente dalle pubblicazioni disponibili in letteratura, la valutazione quantitativa comprende 5 metriche automatiche, analisi temporali e carbon footprint. I risultati ottenuti dimostrano come non vi sia una strategia di decoding dominante, ma come ciascuna possieda delle caratteristiche adatte a task e dataset specifici. I contributi proposti hanno l'obiettivo di neutralizzare il gap di conoscenza attuale e stimolare lo sviluppo di nuove tecniche di decoding.

Validation of an intermediate-complexity flood risk model for climate change adaptation

In questo lavoro di tesi viene presentato e validato un modello di rischio di alluvione a complessità intermedia per scenari climatici futuri. Questo modello appartiene a quella categoria di strumenti che mirano a soddisfare le esigenze identificate dal World Climate Research Program (WRCP) per affrontare gli effetti del cambiamento climatico. L'obiettivo perseguito è quello di sviluppare, seguendo un approccio ``bottom-up" al rischio climatico regionale, strumenti che possano aiutare i decisori a realizzare l'adattamento ai cambiamenti climatici. Il modello qui presentato è interamente basato su dati open-source forniti dai servizi Copernicus. Il contributo di questo lavoro di tesi riguarda lo sviluppo di un modello, formulato da (Ruggieri et al.), per stimare i danni di eventi alluvionali fluviali per specifici i livelli di riscaldamento globale (GWL). Il modello è stato testato su tre bacini idrografici di medie dimensioni in Emilia-Romagna, Panaro, Reno e Secchia. In questo lavoro, il modello viene sottoposto a test di sensibilità rispetto a un'ipotesi enunciata nella formulazione del modello, poi vengono effettuate analisi relative all'ensemble multi-modello utilizzato per le proiezioni. Il modello viene quindi validato, confrontando i danni stimati nel clima attuale per i tre fiumi con i danni osservati e confrontando le portate simulate con quelle osservate. Infine, vengono stimati i danni associati agli eventi alluvionali in tre scenari climatici futuri caratterizzati da GWL di 1.5° C, 2.0° C e 3.0°C.

Structural Connectivity Of The Default Mode Network And Cognition In Alzheimer׳s Disease.

Disconnectivity between the Default Mode Network (DMN) nodes can cause clinical symptoms and cognitive deficits in Alzheimer׳s disease (AD). We aimed to examine the structural connectivity between DMN nodes, to verify the extent in which white matter disconnection affects cognitive performance. MRI data of 76 subjects (25 mild AD, 21 amnestic Mild Cognitive Impairment subjects and 30 controls) were acquired on a 3.0T scanner. ExploreDTI software (fractional Anisotropy threshold=0.25 and the angular threshold=60°) calculated axial, radial, and mean diffusivities, fractional anisotropy and streamline count. AD patients showed lower fractional anisotropy (P=0.01) and streamline count (P=0.029), and higher radial diffusivity (P=0.014) than controls in the cingulum. After correction for white matter atrophy, only fractional anisotropy and radial diffusivity remained significantly lower in AD compared to controls (P=0.003 and P=0.05). In the parahippocampal bundle, AD patients had lower mean and radial diffusivities (P=0.048 and P=0.013) compared to controls, from which only radial diffusivity survived for white matter adjustment (P=0.05). Regression models revealed that cognitive performance is also accounted for by white matter microstructural values. Structural connectivity within the DMN is important to the execution of high-complexity tasks, probably due to its relevant role in the integration of the network.

The Crystal-t Algorithm: A New Approach To Calculate The Sle Of Lipidic Mixtures Presenting Solid Solutions.

Lipidic mixtures present a particular phase change profile highly affected by their unique crystalline structure. However, classical solid-liquid equilibrium (SLE) thermodynamic modeling approaches, which assume the solid phase to be a pure component, sometimes fail in the correct description of the phase behavior. In addition, their inability increases with the complexity of the system. To overcome some of these problems, this study describes a new procedure to depict the SLE of fatty binary mixtures presenting solid solutions, namely the Crystal-T algorithm. Considering the non-ideality of both liquid and solid phases, this algorithm is aimed at the determination of the temperature in which the first and last crystal of the mixture melts. The evaluation is focused on experimental data measured and reported in this work for systems composed of triacylglycerols and fatty alcohols. The liquidus and solidus lines of the SLE phase diagrams were described by using excess Gibbs energy based equations, and the group contribution UNIFAC model for the calculation of the activity coefficients of both liquid and solid phases. Very low deviations of theoretical and experimental data evidenced the strength of the algorithm, contributing to the enlargement of the scope of the SLE modeling.

[principles Of Universality, Comprehensiveness And Equity In A Hearing Health Care Service].

This article analyzed whether the practices of hearing health care were consistent with the principles of universality, comprehensiveness and equity from the standpoint of professionals. It involved qualitative research conducted at a Medium Complexity Hearing Health Care Center. A social worker, three speech therapists, a physician and a psychologist constituted the study subjects. Interviews were conducted as well as observation registered in a field diary. The thematic analysis technique was used in the analysis of the material. The analysis of interviews resulted in the construction of the following themes: Universality and access to hearing health, Comprehensive Hearing Health Care and Hearing Health and Equity. The study identified issues that interfere with the quality of service and run counter to the principles of Brazilian Unified Health System. The conclusion reached was that a relatively simple investment in training and professional qualification can bring about significant changes in order to promote a more universal, comprehensive and equitable health service.

Otorhinolaryngological Findings In A Group Of Patients With Rheumatic Diseases.

Otorhinolaryngological manifestations of rheumatologic diseases represent a great challenge not only to the generalistphysician but also to the ENT doctor andrheumatologist. They often represent early manifestations of an autoimmune disorder which requires prompt and aggressive immunosuppressive treatment. Auditory, nasal, laryngeal and eye symptoms can be the first manifestation of rheumatic diseases and their proper assessment helps the doctor to identify signs of disease activity. The objective of this study is to identify the ENT manifestations in patients with rheumatic diseases in a high complexity hospital, regarding facilitating an early diagnosis and treatment. We performed clinical and complete otorhinolaryngological evaluations in patients selected from the outpatient rheumatology in a standardized manner by the use of a standardized form filling during the secondhalf of 2010. In the study group, systemic lupus erythematosus (SLE) patients had predominantly laryngeal manifestations, while patients with Sjögren's syndrome showed a higher prevalence of otologic manifestations. Changes in audiometric tests were found in 53% of Wegener's granulomatosis (WG) patients, 80% of relapsing polychondritis (RP), 33% of systemic lupus erythematosus (SLE) and 50% of Churg-Strauss syndrome (SCS). Regarding nasal alterations, these were found so prevalent in all conditions, especially Churg-Strauss syndrome. This study demonstrated that most patients treated in our hospital has the ENT signs and symptoms commonly associated in previous studies on rheumatic diseases, but further studies with a larger number of patients must be made to establish such relations.

Health-related And Socio-demographic Factors Associated With Frailty In The Elderly: A Systematic Literature Review.

Frailty is a syndrome that leads to practical harm in the lives of elders, since it is related to increased risk of dependency, falls, hospitalization, institutionalization, and death. The objective of this systematic review was to identify the socio-demographic, psycho-behavioral, health-related, nutritional, and lifestyle factors associated with frailty in the elderly. A total of 4,183 studies published from 2001 to 2013 were detected in the databases, and 182 complete articles were selected. After a comprehensive reading and application of selection criteria, 35 eligible articles remained for analysis. The main factors associated with frailty were: age, female gender, black race/color, schooling, income, cardiovascular diseases, number of comorbidities/diseases, functional incapacity, poor self-rated health, depressive symptoms, cognitive function, body mass index, smoking, and alcohol use. Knowledge of the complexity of determinants of frailty can assist the formulation of measures for prevention and early intervention, thereby contributing to better quality of life for the elderly.

Impact Of Pharmacist Interventions On Drug-related Problems And Laboratory Markers In Outpatients With Human Immunodeficiency Virus Infection.

Substantial complexity has been introduced into treatment regimens for patients with human immunodeficiency virus (HIV) infection. Many drug-related problems (DRPs) are detected in these patients, such as low adherence, therapeutic inefficacy, and safety issues. We evaluated the impact of pharmacist interventions on CD4+ T-lymphocyte count, HIV viral load, and DRPs in patients with HIV infection. In this 18-month prospective controlled study, 90 outpatients were selected by convenience sampling from the Hospital Dia-University of Campinas Teaching Hospital (Brazil). Forty-five patients comprised the pharmacist intervention group and 45 the control group; all patients had HIV infection with or without acquired immunodeficiency syndrome. Pharmaceutical appointments were conducted based on the Pharmacotherapy Workup method, although DRPs and pharmacist intervention classifications were modified for applicability to institutional service limitations and research requirements. Pharmacist interventions were performed immediately after detection of DRPs. The main outcome measures were DRPs, CD4+ T-lymphocyte count, and HIV viral load. After pharmacist intervention, DRPs decreased from 5.2 (95% confidence interval [CI] =4.1-6.2) to 4.2 (95% CI =3.3-5.1) per patient (P=0.043). A total of 122 pharmacist interventions were proposed, with an average of 2.7 interventions per patient. All the pharmacist interventions were accepted by physicians, and among patients, the interventions were well accepted during the appointments, but compliance with the interventions was not measured. A statistically significant increase in CD4+ T-lymphocyte count in the intervention group was found (260.7 cells/mm(3) [95% CI =175.8-345.6] to 312.0 cells/mm(3) [95% CI =23.5-40.6], P=0.015), which was not observed in the control group. There was no statistical difference between the groups regarding HIV viral load. This study suggests that pharmacist interventions in patients with HIV infection can cause an increase in CD4+ T-lymphocyte counts and a decrease in DRPs, demonstrating the importance of an optimal pharmaceutical care plan.

Basilar Invagination: Surgical Results.

Basilar invagination (BI) is a congenital craniocervical junction (CCJ) anomaly represented by a prolapsed spine into the skull-base that can result in severe neurological impairment. In this paper, we retrospective evaluate the surgical treatment of 26 patients surgically treated for symptomatic BI. BI was classified according to instability and neural abnormalities findings. Clinical outcome was evaluated using the Nürick grade system. A total of 26 patients were included in this paper. Their age ranged from 15 to 67 years old (mean 38). Of which, 10 patients were male (38%) and 16 (62%) were female. All patients had some degree of tonsillar herniation, with 25 patients treated with foramen magnum decompression. Nine patients required a craniocervical fixation. Six patients had undergone prior surgery and required a new surgical procedure for progression of neurological symptoms associated with new compression or instability. Most of patients with neurological symptoms secondary to brainstem compression had some improvement during the follow-up. There was mortality in this series, 1 month after surgery, associated with a late removal of the tracheal cannula. Management of BI requires can provide improvements in neurological outcomes, but requires analysis of the neural and bony anatomy of the CCJ, as well as occult instability. The complexity and heterogeneous presentation requires attention to occult instability on examination and attention to airway problems secondary to concomitant facial malformations.