Pubblicazioni scientifiche
Effects of In-Air Post Deposition Annealing Process on the Oxygen Vacancy Content in Sputtered GDC Thin Films Probed via Operando XAS and Raman Spectroscopy
Abstract We investigate the ionic mobility in room-temperature RF-sputtered gadolinium doped ceria (GDC) thin films grown on industrial solid oxide fuel cell substrates as a function of the air-annealing at 800 and 1000 °C. The combination of X-ray diffraction, X-ray photoelectron spectroscopy, operando X-ray absorption spectroscopy, and Raman spectroscopy allows us to study the different Ce3+/ Ce4+ ratios induced by the post growth annealing procedure, together with the Ce valence changes induced by different gas atmosphere exposure. Our results give evidence of different kinetics as a function of the annealing temperature, with the sample annealed at 800 °C showing marked changes of the Ce oxidation state when exposed to both reducing and oxidizing gas atmospheres at moderate temperature (300 °C), while the Ce valence is weakly affected for the 1000 °C annealed sample. Raman spectra measurements allow us to trace the responses of the investigated samples to different gas atmospheres on the basis of the presence of different Gd–O bond strengths inside the lattice. These findings provide insight into the microscopic origin of the best performances already observed in SOFCs with a sputtered GDC barrier layer annealed at 800 °C and are fundamental to further improve sputtered GDC thin film performance in energy devices. Autori Nunzia Coppola, Sami Ur Rehman, Giovanni Carapella, Luca Braglia, Vincenzo Vaiano, Dario Montinaro, Veronica Granata, Sandeep Kumar Chaluvadi, Pasquale Orgiani, Piero Torelli, Luigi Maritato, Carmela Aruta, Alice Galdi Rivista ACS Applied Electronic Materials Data di pubblicazione 25/09/2024 Consulta la pubblicazione
Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction
Abstract N6-Methyladenosine (m6A) is a prevalent RNA post-transcriptional modification that plays crucial roles in RNA stability, structural dynamics, and interactions with proteins. The YT521-B (YTH) family of proteins, which are notable m6A readers, functions through its highly conserved YTH domain. Recent structural investigations and molecular dynamics (MD) simulations have shed light on the mechanism of recognition of m6A by the YTHDC1 protein. Despite advancements, using MD to predict the stabilization induced by m6A on the free energy of binding between RNA and YTH proteins remains challenging due to inaccuracy of the employed force field and limited sampling. For instance, simulations often fail to sufficiently capture the hydration dynamics of the binding pocket. This study addresses these challenges through an innovative methodology that integrates metadynamics, alchemical simulations, and force-field refinement. Importantly, our research identifies hydration of the binding pocket as giving only a minor contribution to the binding free energy and emphasizes the critical importance of precisely tuning force-field parameters to experimental data. By employing a fitting strategy built on alchemical calculations, we refine the m6A partial charge parameters, thereby enabling the simultaneous reproduction of N6 methylation on both the protein binding free energy and the thermodynamic stability of nine RNA duplexes. Our findings underscore the sensitivity of binding free energies to partial charges, highlighting the necessity for thorough parametrization and validation against experimental observations across a range of structural contexts. Autori Valerio Piomponi, Miroslav Krepl, Jiri Sponer, Giovanni Bussi Rivista The Journal of Physical Chemistry B, Vol 128, Issue 37 Data di pubblicazione 06/09/2024 Consulta la pubblicazione
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels
Abstract: Time Series Classification (TSC) is essential in fields like medicine, environmental science, and finance, enabling tasks such as disease diagnosis, anomaly detection, and stock price analysis. While machine learning models like Recurrent Neural Networks and InceptionTime are successful in numerous applications, they can face scalability issues due to computational requirements. Recently, ROCKET has emerged as an efficient alternative, achieving state-of-the-art performance and simplifying training by utilizing a large number of randomly generated features from the time series data. However, many of these features are redundant or non-informative, increasing computational load and compromising generalization. Here we introduce Sequential Feature Detachment (SFD) to identify and prune non-essential features in ROCKET-based models, such as ROCKET, MiniRocket, and MultiRocket. SFD estimates feature importance using model coefficients and can handle large feature sets without complex hyperparameter tuning. Testing on the UCR archive shows that SFD can produce models with better test accuracy using only 10% of the original features. We named these pruned models Detach-ROCKET. We also present an end-to-end procedure for determining an optimal balance between the number of features and model accuracy. On the largest binary UCR dataset, Detach-ROCKET improves test accuracy by 0.6% while reducing features by 98.9%. By enabling a significant reduction in model size without sacrificing accuracy, our methodology improves computational efficiency and contributes to model interpretability. We believe that Detach-ROCKET will be a valuable tool for researchers and practitioners working with time series data, who can find a user-friendly implementation of the model at https://github.com/gon-uri/detach_rocket. Autori: Gonzalo Uribarri, Federico Barone, Alessio Ansuini, Eric Fransén Rivista: Data Mining and Knowledge Discovery Data di pubblicazione: 20/08/2024 Consulta la pubblicazione
Probing conformational dynamics of EGFR mutants via SEIRA spectroscopy: potential implications for tyrosine kinase inhibitor design
Abstract Missense mutations in EGFR’s catalytic domain alter its function, promoting cancer. SEIRA spectroscopy, supported by MD simulations, reveals structural differences in the compactness and hydration of helical motifs between active and inactive EGFR conformations models. These findings provide novel insights into the biophysical mechanisms driving EGFR activation and drug resistance, offering a robust method for studying emerging EGFR mutations and their structural impacts on TKIs efficacy. Autori Emiliano Laudadio, Federica Piccirilli, Henrick Vondracek, Giovanna Mobbili, Marta Stefania Semrau, Paola Storici, Roberta Galeazzi, Elena Romagnoli, Leonardo Sorci, Andrea Toma, Vincenzo Aglieri, Giovanni Birarda, Cristina Minnelli Rivista Physical Chemistry Chemical Physics (PCCP) Data di pubblicazione 19/08/2024 Consulta la pubblicazione
La0.2Sr0.25Ca0.45TiO3 Surface Reactivity with H2: A Combined Operando NEXAFS and Computational Study
Abstract A-site doped SrTiO3 is considered as a promising substitute for traditional anodic metals in solid oxide fuel cells (SOFCs). In this study, we present the reactivity of La0.2Sr0.25Ca0.45TiO3 (LCSTO), La0.2Sr0.7TiO3 (LSTO), and SrTiO3 (STO) toward H2 by operando ambient pressure NEXAFS spectroscopy and theoretical spectra simulation with FDMNES code. The samples were synthesized by MBE (molecular beam epitaxy), hydrothermal, and modified-Pechini routes. We found that the reducibility of the samples depends not only on their stoichiometry but also on the morphology, which is determined by the synthetic method. The results of these experiments give insight into the reducibility of Ti4+ in perovskites as well as the opportunity to further optimize the synthesis of these materials to obtain the best performance for SOFC applications. Autori F. Bassato, S. Mauri, L. Braglia, A. Yu. Petrov, E. Dobovičnik, F. Tavani, A. Tofoni, P. Ferrer, D. Grinter, G. Held, P. D’Angelo, P. Torelli Rivista The Journal of Physical Chemistry Letters Data di pubblicazione 13/08/2024 Consulta la pubblicazione
Molecular findings and virological assessment of bladder papillomavirus infection in cattle.
Abstract Bovine and ovine papillomaviruses (BPVs – OaPVs) are infectious agents that have an important role in bladder carcinogenesis of cattle. In an attempt to better understand territorial prevalence of papillomavirus genotypes and gain insights into their molecular pathway(s), a virological assessment of papillomavirus infection was performed on 52 bladder tumors in cattle using droplet digital polymerase chain reaction (ddPCR), an improved version of conventional PCR. ddPCR detected and quantified BPV DNA and mRNAs in all tumor samples, showing that these viruses play a determinant role in bovine bladder carcinogenesis. OaPV DNA and mRNA were detected and quantified in 45 bladder tumors. BPV14, BPV13, BPV2, OaPV2, OaPV1, and OaPV3 were the genotypes most closely related to bladder tumors. ddPCR quantified BPV1 and OaPV4 DNA and their transcripts less frequently. Western blot analysis revealed a significant overexpression of the phosphorylated platelet derived growth factor β receptor (PDGFβR) as well as the transcription factor E2F3, which modulate cell cycle progression in urothelial neoplasia. Furthermore, significant overexpression of calpain1, a Cys protease, was observed in bladder tumors related to BPVs alone and in BPV and OaPV coinfection. Calpain1 has been shown to play a role in producing free transcription factors of the E2F family, and molecular findings suggest that calpain family members work cooperatively to mutually regulate their protease activities in cattle bladder tumors. Altogether, these results showed territorial prevalence of BPV and OaPV genotypes and suggested that PDGFβR and the calpain system appeared to be molecular partners of both BPVs and OaPVs. Autori Francesca De Falco, Anna Cutarelli, Francesca Luisa Fedele, Cornel Catoi, Sante Roperto Rivista Veterinary Quarterly Data di pubblicazione 04/08/2024 Consulta la pubblicazione
Universality in the Structure and Dynamics of Water under Lipidic Mesophase Soft Nanoconfinement
Abstract Water under soft nanoconfinement features physical and chemical properties fundamentally different from bulk water; yet, the multitude and specificity of confining systems and geometries mask any of its potentially universal traits. Here, we advance in this quest by resorting to lipidic mesophases as an ideal nanoconfinement system, allowing inspecting the behavior of water under systematic changes in the topological and geometrical properties of the confining medium, without altering the chemical nature of the interfaces. By combining Terahertz absorption spectroscopy experiments and molecular dynamics simulations, we unveil the presence of universal laws governing the physics of nanoconfined water, recapitulating the data collected at varying levels of hydration and nanoconfinement topologies. This geometry-independent universality is evidenced by the existence of master curves characterizing both the structure and dynamics of simulated water as a function of the distance from the lipid–water interface. Based on our theoretical findings, we predict a parameter-free law describing the amount of interfacial water against the structural dimension of the system (i.e., the lattice parameter), which captures both the experimental and numerical results within the same curve, without any fitting. Our results offer insight into the fundamental physics of water under soft nanoconfinement and provide a practical tool for accurately estimating the amount of nonbulk water based on structural experimental data. Autori Eva Zunzunegui-Bru, Serena Rosa Alfarano, Patrick Zueblin, Hendrik Vondracek, Federica Piccirilli, Lisa Vaccari, Salvatore Assenza, Raffaele Mezzenga Rivista ACS Nano Data di pubblicazione 1/08/2024 Consulta la pubblicazione
Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals
Abstract Interpretability research aims to bridge the gap between empirical success and our scientific understanding of the inner workings of large language models (LLMs). However, most existing research focuses on analyzing a single mechanism, such as how models copy or recall factual knowledge. In this work, we propose a formulation of competition of mechanisms, which focuses on the interplay of multiple mechanisms instead of individual mechanisms and traces how one of them becomes dominant in the final prediction. We uncover how and where mechanisms compete within LLMs using two interpretability methods: logit inspection and attention modification. Our findings show traces of the mechanisms and their competition across various model components and reveal attention positions that effectively control the strength of certain mechanisms. Autori Francesco Ortu, Zhijing Jin, Diego Doimo, Mrinmaya Schaan, Alberto Cazzaniga, Bernhard Scholkopf Rivista Accepted at the Annual Meeting of the Association for Computational Linquistics (ACL), Arxiv preprint: 2402.11655 Data di pubblicazione 06/06/2024 Consulta la pubblicazione
Emergent representations in networks trained with the Forward-Forward algorithm
Abstract The Backpropagation algorithm has often been criticised for its lack of biological realism. In an attempt to find a more biologically plausible alternative, the recently introduced Forward-Forward algorithm replaces the forward and backward passes of Backpropagation with two forward passes. In this work, we show that the internal representations obtained by the Forward-Forward algorithm can organise into category-specific ensembles exhibiting high sparsity – composed of a low number of active units. This situation is reminiscent of what has been observed in cortical sensory areas, where neuronal ensembles are suggested to serve as the functional building blocks for perception and action. Interestingly, while this sparse pattern does not typically arise in models trained with standard Backpropagation, it can emerge in networks trained with Backpropagation on the same objective proposed for the Forward-Forward algorithm. These results suggest that the learning procedure proposed by Forward-Forward may be superior to Backpropagation in modelling learning in the cortex, even when a backward pass is used. Autori Niccolò Tosato, Lorenzo Basile, Emanuele Ballarin, Giuseppe de Alteriis, Alberto Cazzaniga, Alessio Ansuini Rivista Submitted at Advances in Neural Information Processing Systems 37 (NEURIPS 2024) Main Conference Track. Arxiv preprint: 2305.18353 Data di pubblicazione 19/06/2024 Consulta la pubblicazione
Enhancing Multi-Tip Artifact Detection in STM Images Using Fourier Transform and Vision Transformers
Abstract We address the issue of multi-tip artifacts in Scanning Tunneling Microscopy (STM) images by applying the fast Fourier transform (FFT) as a feature engineering method. We fine-tune various neural network architectures using a synthetic dataset, including Vision Transformers (ViT). The FFT-based preprocessing significantly improves the performance of ViT models compared to using only the grayscale channel. Ablation experiments highlight the optimal conditions for synthetic dataset generation. Unlike traditional methods that are challenging to implement for large datasets and used offline, our method enables on-the-fly classification at scale. Our findings demonstrate the efficacy of combining the Fourier transform with deep learning for enhanced artifact detection in STM images, contributing to more accurate analysis in material science research. Autori Tommaso Rodani, Alessio Ansuini, Alberto Cazzaniga Rivista ICML ’24 Workshop ML for Life and Material Science: From Theory to Industry Applications Data di pubblicazione 17/07/2024 Consulta la pubblicazione
Enhancing predictions of protein stability changes induced by single mutations using MSA-based language models
Abstract Protein language models offer a new perspective for addressing challenges in structural biology, while relying solely on sequence information. Recent studies have investigated their effectiveness in forecasting shifts in thermodynamic stability caused by single amino acid mutations, a task known for its complexity due to the sparse availability of data, constrained by experimental limitations. To tackle this problem, we introduce two key novelties: leveraging a protein language model that incorporates Multiple Sequence Alignments to capture evolutionary information, and using a recently released mega-scale dataset with rigorous data preprocessing to mitigate overfitting. Autori Francesca Cuturello, Marco Celoria, Alessio Ansuini, Alberto Cazzaniga Rivista Bioinformatics, 2024, 40 (7) Consulta la pubblicazione
Operando Soft X-ray Absorption of LaMn1–xCoxO3 Perovskites for CO Oxidation
Abstract We employed operando soft X-ray absorption spectroscopy (XAS) to monitor the changes in the valence states and spin properties of LaMn1–xCoxO3 catalysts subjected to a mixture of CO and O2 at ambient pressure. Guided by simulations based on charge transfer multiplet theory, we quantitatively analyze the Mn and Co 2p XAS as well as the oxygen K-edge XAS spectra during the reaction process. The Mn sites are particularly sensitive to the catalytic reaction, displaying dynamics in their oxidation state. When Co doping is introduced (x ≤ 0.5), Mn oxidizes from Mn2+ to Mn3+ and Mn4+, while Co largely maintains a valence state of Co2+. In the case of LaCoO3, we identify high-spin and low-spin Co3+ species combined with Co2+. Our investigation underscores the importance to consider the spin and valence states of catalyst materials under operando conditions. Autori Qijun Che, Mahnaz Ghiasi, Luca Braglia, Matt LJ Peerlings, Silvia Mauri, Piero Torelli, Petra de Jongh, Frank MF de Groot Rivista ACS Catalysis Data di pubblicazione 12/07/2024 Consulta la pubblicazione