Publications SISSA
URI permanente per questa collezione
Sfogliare
Immissioni recenti
1 - 5 di 14502
-
PubblicazioneThe ViSta method for stacking in the Fourier domain and its application to the dusty star-forming galaxies in the ALMA Science Archive( 2025)We present ViSta, a Visibility Stacking method to combine interferometric observations in the Fourier domain at radio to sub-millimeter wavelengths for galaxies. The goal of our method is to maximize the exploitation of available archival interferometric data. By stacking visibilities of galaxies with secure spectroscopic redshifts directly in the Fourier domain and transforming them into the rest-frame, we can enhance the stacked signal, suppress noise, and improve image reconstruction thanks to an extended coverage of the visibility domain. The ViSta method is highly flexible, allowing stacking of visibilities regardless of the array configuration or spectral setup. It is effective for both targeted sources and spurious detections offset from the phase center, whether unresolved or extended, within the field of view of the telescope. We validated the method using simulated interferometric datasets. For point-like sources, we can reconstruct the true emission with approximately 90% accuracy, obtaining similar results to classical image-plane stacking. In contrast, for faint and extended sources below the noise level, our method can provide a more accurate estimate of the signal compared to traditional image-based approaches. Finally, we applied ViSta to a sample of dusty star-forming galaxies observed with the Atacama Large Millimeter/sub-millimeter Array to detect the CO(3-2) emission line. As for the simulated case, we demonstrated that our tool performs better than image-plane stacking when the signal from individual sources is no longer easily detectable, achieving higher SNR. Finally, we outline potential future applications of this stacking approach.
-
PubblicazioneExploring Secondary Structure Predictions for RNA-Targeted Drug Discovery: Power and Challenges( 2026)RNAs are increasingly recognized as promising drug targets, as both coding and noncoding RNAs act as key regulators in disease-related biological processes. However, a significant gap persists between the number of known RNA sequences and the solved RNA structures, posing a major bottleneck for RNA-targeted drug discovery. RNA secondary structure prediction offers the potential to facilitate the identification of druggable sites in novel RNA sequences by rapidly predicting base pairing patterns. In this study, we benchmarked widely used RNA secondary structure prediction tools against a newly curated dataset of ligand-bound RNA structures. We found that most tools achieve reasonable accuracy for RNAs with short sequences and simple motifs, but their performance declines for longer RNAs and those containing pseudoknots. Notably, prediction accuracy is reduced within ligand binding sites, where noncanonical base pairs and complex secondary structure elements are prevalent yet consistently unrecognized by the tools. Consequently, RNA ligand binding sites are poorly reconstructed by secondary structure predictions. This work provides the first comprehensive assessment of RNA secondary structure prediction for ligand-bound RNAs and demonstrates the challenges for integrating these methods into RNA-targeted drug discovery pipelines.
-
PubblicazioneThe role of black hole feedback on galaxy star formation and the degeneracy with halo quenching( 2025)Aims. The interplay between the accretion of supermassive black holes (SMBHs) and the stellar mass growth of the host galaxies is still a matter of hot debate. The accretion of the central SMBHs is expected to release energy under the form of active galactic nuclei. This energy is believed to impact the star formation activity and contribute to the quenching of the host galaxies. Here, we address this key unsolved issue with our cosmological semi-empirical model DECODE (Discrete statistical sEmi-empiriCal mODEl). Methods. In DECODE, we grow galaxies with their star formation rate linked to halo accretion rate distributions via abundance matching. SMBHs are evolved following the stellar mass growth of their host galaxies by assigning an accretion rate at each redshift from the empirical Eddington ratio distributions and duty cycles. We tested the assumption that galaxies permanently quench when their central SMBHs approach the limit imposed by the observed MBH − σ★ relation, as a proxy of SMBH disruptive feedback. Results. We find that simply imposing the MBH − σ★ condition is sufficient to generate a fraction of quenched galaxies consistent with current data, including the newest ones from Euclid. In addition, our minimal data-driven model also predicts SMBH scaling relations consistent in slope and normalisation with those that have been observed, and an MBH − M★ relation weakly evolving with redshift. The model also naturally generates SMBH accretion rates peaking within 1 Gyr of their host star formation histories. Interestingly, we note that all the main predictions on galaxy quenched fractions and SMBH growth histories and scaling relations are degenerate with those expected in a halo quenching model. Conclusions. The comprehensive data-driven model presented in this work represents an invaluable tool to investigate SMBH demography across time and environments in an accurate, physically motivated manner, ideally suited to rapidly exploring the implications from large surveys, such as Euclid and Rubin-LSST.
-
PubblicazioneMachine Learning for RNA Secondary Structure Prediction: a review of current methods and challenges( 2026)Predicting the secondary structure of RNA is a core challenge in computational biology, essential for understanding molecular function and designing novel therapeutics. The field has evolved from foundational but accuracy-limited thermodynamic approaches to a new data-driven paradigm dominated by machine learning and deep learning. These models learn folding patterns directly from data, leading to significant performance gains. This review surveys the modern landscape of these methods, covering single-sequence, evolutionary-based, and hybrid models that blend machine learning with biophysics. A central theme is the field's "generalization crisis," where powerful models were found to fail on new RNA families, prompting a community-wide shift to stricter, homology-aware benchmarking. In response to the underlying challenge of data scarcity, RNA foundation models have emerged, learning from massive, unlabeled sequence corpora to improve generalization. Finally, we look ahead to the next set of major hurdles-including the accurate prediction of complex motifs like pseudoknots, scaling to kilobase-length transcripts, incorporating the chemical diversity of modified nucleotides, and shifting the prediction target from static structures to the dynamic ensembles that better capture biological function. We also highlight the need for a standardized, prospective benchmarking system to ensure unbiased validation and accelerate progress.
-
PubblicazioneA study of Quot schemes on smooth curves(SISSA, 2026-03-25)We study the geometry and topology of Quot schemes on smooth projective curves. First, we give an explicit presentation of the rational cohomology ring of the Quot scheme parametrising torsion quotients on $\PP^1$. Next, we construct a stratification of the corresponding relative Quot scheme, which recovers several known results by specialisation. We also use this stratification to prove that the integral cohomology of the Quot scheme parametrising torsion quotients is torsion-free, thereby strengthening the first result. Finally, we study the cohomology of Schur complexes associated with tautological complexes on the Quot scheme parametrising positive rank quotients on $\PP^1$, and we construct exceptional collections in its derived category.