Studio conformazionale di un derivato dendrimerico dell'acido L-glutammico tramite spettroscopia NMR e IR

Con questo lavoro si vuole proporre un modello conformazionale di una molecola dendrimerica tramite studi di spettroscopia NMR e FT-IR. Prima di inoltrarci nel fitto bosco di picchi spettroscopici introduciamo brevemente questa classe di composti [1a,b,c]. Le specie dendrimeriche sono dette tali in quanto strutturate da un corpo centrale (monomero di pochi u.m.a. ad esempio un amminoacido o un anello aromatico polifunzionalizzato) da cui si diramano vari bracci a loro volta ramificati. Se la molecola è molto grossa il risultato finale prende la forma di un granulo che presenta la superficie esterna ricca dei gruppi funzionali propri dei monomeri costituenti. L'utilizzo principale di questi composti comporta l'attivazione della loro superficie, ottenuta legando ai gruppi funzionali determinate molecole. È così possibile, per esempio, il veicolamento di farmaci a specifici siti attivi. Inoltre, sfruttando la formazione di legami non covalenti, alcuni dendrimeri sono utilizzati come fasi stazionarie in tecniche cromatografiche GC, HPLC, e scambio ionico. La possibilità di costruire dendrimeri con monomeri chirali ha portato queste molecole ad essere delle ottime candidate per lo studio degli effetti macroscopici della chiralità molecolare. In questo campo ci sono studi sulla chiralità del “core”, dei bracci dendrimerici o solo della superficie [2]. I gruppi funzionali superficiali possono essere di vario tipo e alcuni possono essere in grado di legarsi tra loro (ad esempio gruppi amminici e carbonilici con legami ad idrogeno). I legami possono avvenire sia tra due rami dendrimerici della stessa molecola (legami intramolecolari), sia tra gruppi funzionali di altre molecole (legami intermolecolari). Nel caso di legami ad idrogeno intermolecolari, quello che accade è l'aggregamento di due o più molecole dendrimeriche. Questo fenomeno genera lunghe catene di dendrimeri dette fibrille, le cui soluzioni assumono macroscopicamente l'aspetto di gel [3]. I gel dendrimerici ad una certa temperatura (o meglio in un intervallo di temperature) subiscono una transizione di fase, si rompono i legami ad idrogeno e le fibrille si scindono in dendrimeri liberi; questo processo non è sempre reversibile. Molto comuni sono i composti dendrimerici costruiti usando amminoacidi come monomeri, alcuni dei quali, ad esempio l'acido glutammico e la lisina, avendo più di un gruppo amminico o più di un gruppo carbossilico, sono in grado di legare, tramite legami ammidici, più molecole contemporaneamente. Questo fa si che alcuni dendrimeri vengano studiati dal punto di vista conformazionale per fare meglio luce sul tema del folding delle proteine [4]. Il dendrimero studiato in questa tesi, un trimero dell'acido glutammico, è troppo piccolo per essere considerato come un frammento significativo di una proteina; il nostro lavoro è comunque finalizzato allo studio conformazionale di questa molecola. Questo compito è stato affrontato utilizzando comuni tecniche di indagine spettroscopica NMR sia monodimensionali che bidimensionali, integrandole anche con alcuni spettri all'infrarosso che come vedremo sono stati cruciali per il fine del lavoro. I risultati verranno presentati grossomodo nell'ordine in cui sono stati registrati; i parametri sperimentali di ogni esperimento sono riportati in un capitolo a parte. In fine verrà formulata un'ipotesi conformazionale compatibile con i dati raccolti

Effect of airborne particle abrasion on microtensile bond strength of total-etch adhesives to human dentin

Aim of this study was to investigate a specific airborne particle abrasion pretreatment on dentin and its effects on microtensile bond strengths of four commercial total-etch adhesives. Midcoronal occlusal dentin of extracted human molars was used. Teeth were randomly assigned to 4 groups according to the adhesive system used: OptiBond FL (FL), OptiBond Solo Plus (SO), Prime & Bond (PB), and Riva Bond LC (RB). Specimens from each group were further divided into two subgroups: control specimens were treated with adhesive procedures; abraded specimens were pretreated with airborne particle abrasion using 50 mu m Al2O3 before adhesion. After bonding procedures, composite crowns were incrementally built up. Specimens were sectioned perpendicular to adhesive interface to producemultiple beams, which were tested under tension until failure. Data were statistically analysed. Failure mode analysis was performed. Overall comparison showed significant increase in bond strength (p < 0.001) between abraded and no-abraded specimens, independently of brand. Intrabrand comparison showed statistical increase when abraded specimens were tested compared to no-abraded ones, with the exception of PB that did not show such difference. Distribution of failure mode was relatively uniform among all subgroups. Surface treatment by airborne particle abrasion with Al2O3 particles can increase the bond strength of total-etch adhesive

Indication, from Pioneer 10/11, Galileo, and Ulysses Data, of an Apparent Anomalous, Weak, Long-Range Acceleration

Radio metric data from the Pioneer 10/11, Galileo, and Ulysses spacecraft indicate an apparent anomalous, constant, acceleration acting on the spacecraft with a magnitude $\sim 8.5\times 10^{-8}$ cm/s$^2$, directed towards the Sun. Two independent codes and physical strategies have been used to analyze the data. A number of potential causes have been ruled out. We discuss future kinematic tests and possible origins of the signal.Comment: Revtex, 4 pages and 1 figure. Minor changes for publicatio

Machine Learning for Understanding Focal Epilepsy

The study of neural dysfunctions requires strong prior knowledge on brain physiology combined with expertise on data analysis, signal processing, and machine learning. One of the unsolved issues regarding epilepsy consists in the localization of pathological brain areas causing seizures. Nowadays the analysis of neural activity conducted with this goal still relies on visual inspection by clinicians and is therefore subjected to human error, possibly leading to negative surgical outcome. In absence of any evidence from standard clinical tests, medical experts resort to invasive electrophysiological recordings, such as stereoelectroencephalography to assess the pathological areas. This data is high dimensional, it could suffer from spatial and temporal correlation, as well as be affected by high variability across the population. These aspects make the automatization attempt extremely challenging. In this context, this thesis tackles the problem of characterizing drug resistant focal epilepsy. This work proposes methods to analyze the intracranial electrophysiological recordings during the interictal state, leveraging on the presurgical assessment of the pathological areas. The first contribution of the thesis consists in the design of a support tool for the identification of epileptic zones. This method relies on the multi-decomposition of the signal and similarity metrics. We built personalized models which share common usage of features across patients. The second main contribution aims at understanding if there are particular frequency bands related to the epileptic areas and if it is worthy to focus on shorter periods of time. Here we leverage on the post-surgical outcome deriving from the Engel classification. The last contribution focuses on the characterization of short patterns of activity at specific frequencies. We argue that this effort could be helpful in the clinical routine and at the same time provides useful insight for the understanding of focal epilepsy

A reduced-order inverse distance weighting technique for the efficient mesh-motion of deformable interfaces and moving shapes in computational problems

LAUREA MAGISTRALENowadays, despite the constant technological growth of the last twenty years, Computational Fluid Dynamics (CFD) problems represent a hard challenge for scientific computing because of their large demand on computational resources. In fact, in the context of aerodynamics optimization and design, CFD applications require to simulate many different possible realizations of a system which can become prohibitive in terms of computational and memory effort. These considerations have produced an intensive development of reduced order methods to provide high-fidelity simulations via efficient and low-dimensional models. In this thesis we focus on an efficient and flexible technique, namely the Inverse Distance Weighting (IDW). This strategy can be used to solve mesh motion problems in a Fluid-Structure Interaction (FSI) framework. IDW is an interpolation strategy which computes the displacement of the grid nodes starting from the movement of some data points, called control points. The original formulation of IDW uses as control points the nodes of the grid belonging to the interface and requires assembling a matrix [N_s × N_c], where N_s is the number of values to be interpolated and N_c indicates the number of control points. Since the number of interface nodes for a typical mesh motion problem can be very large (of the order of hundreds of thousands), the system to solve assumes significantly high dimensions. In order to reduce the computational and the memory load due to these huge dimensions, we implement an ad hoc algorithm which performs a geometrical and a model order reduction of the system: the former reduction is based on an iterative procedure which selects some relevant control points among the initial ones, while the latter reduction is inspired by the Proper Orthogonal Decomposition (POD) strategy to exploit the advantages of an Offline-Online splitting between ROM construction and evaluation of the solution. The implemented algorithm is validated on some examples, from benchmark cases to more complex three dimensional configurations such as a wing or a hull. All the reduction strategies developed are implemented using an efficient C++ object oriented code and an open-source Finite Element library (libMesh), while the processing of the grids is performed by the open-source visualization software Paraview

Forecasting cryptocurrencies log-returns. A LASSO-VAR and sentiment approach

Cryptocurrencies have become a trendy topic recently, primarily due to their disruptive potential and reports of unprecedented returns. Furthermore, social media has garnered attention for its predictive capabilities in various fields, including financial markets and the economy. In this study, we exploit the predictive power of sentiment from Twitter and Reddit, alongside Google Trends indexes, to forecast log returns for 10 cryptocurrencies, namely Bitcoin, Ethereum, Tether, Binance Coin, Litecoin, Enjin Coin, Horizen, Namecoin, Peercoin and Feathercoin. We evaluate the perfor- mance of LASSO Vector Autoregression using daily data from January 2018 to January 2022. In a 30-day recursive forecast, we achieve a mean directional accuracy (MDA) rate of over 50%. Moreover, we observe a significant increase in forecast accuracy in terms of MDA when using sentiment and attention variables as predictors, but only for less capitalized cryptocurrencies. This improvement is not reflected in the RMSE. We also conduct a Granger causality test using post- double LASSO selection for high-dimensional VAR models. Our results suggest that social media sentiment does not Granger-cause cryptocurrencies returns

Targeting prolyl-isomerase Pin1 prevents mitochondrial oxidative stress and vascular dysfunction: insights in patients with diabetes

The present study demonstrates that Pin1 is a common activator of key pathways involved in diabetic vascular disease in different experimental settings including primary human endothelial cells, knockout mice, and diabetic patients. Gene silencing and genetic disruption of Pin1 prevent hyperglycaemia-induced mitochondrial oxidative stress, endothelial dysfunction, and vascular inflammation. Moreover, we have translated our findings to diabetic patients. In line with our experimental observations, Pin1 up-regulation is associated with impaired flow-mediated dilation, increased oxidative stress, and plasma levels of adhesion molecules. In perspective, these findings may provide the rationale for mechanism-based therapeutic strategies in patients with diabete

Copper Carbonate Hydroxide as Precursor of Interfacial CO in CO2 Electroreduction

Copper electrodes are especially effective in catalysis of C2 and further multi-carbon products in the CO2 reduction reaction (CO2RR) and therefore of major technological interest. The reasons for the unparalleled Cu performance in CO2RR are insufficiently understood. Here, the electrode–electrolyte interface was highlighted as a dynamic physical-chemical system and determinant of catalytic events. Exploiting the intrinsic surface-enhanced Raman effect of previously characterized Cu foam electrodes, operando Raman experiments were used to interrogate structures and molecular interactions at the electrode–electrolyte interface at subcatalytic and catalytic potentials. Formation of a copper carbonate hydroxide (CuCarHyd) was detected, which resembles the mineral malachite. Its carbonate ions could be directly converted to CO at low overpotential. These and further experiments suggested a basic mode of CO2/carbonate reduction at Cu electrodes interfaces that contrasted previous mechanistic models: the starting point in carbon reduction was not CO2 but carbonate ions bound to the metallic Cu electrode in form of CuCarHyd structures. It was hypothesized that Cu oxides residues could enhance CO2RR indirectly by supporting formation of CuCarHyd motifs. The presence of CuCarHyd patches at catalytic potentials might result from alkalization in conjunction with local electrical potential gradients, enabling the formation of metastable CuCarHyd motifs over a large range of potentials

Hey there's DALILA: a DictionAry LearnIng LibrAry

Dictionary Learning and Representation Learning are machine learning methods for decomposition, denoising and reconstruction of data with a wide range of applications such as text recognition, image processing and biological processes understanding. In this work we present DALILA, a scientific Python library for regularised dictionary learning and regularised representation learning that allows to impose prior knowledge, if available. DALILA, differently from the others available libraries for this purpose, is flexible and modular. DALILA is designed to be easily extended for custom needs. Moreover, it is compliant with the most widespread ML Python library and this allows for a straightforward usage and integration. We here present and discuss the theoretical aspects and discuss its strength points and implementation