Recent advancements in machine learning have significantly enhanced the precision and efficiency of data-driven methodologies in scientific applications. These methods have found applications in a variety of fields, including physics, medicine, and space sciences, where they help addressing complex challenges which require high-precision measurements. One such application is directional dark matter search experiments that require precise measurements of ions recoiling after their interactions with dark matter particles [1, 2]. Due to their extremely low kinetic energies, in the 1 100 keV range, recoiling ions produce tracks ranging from a few millimeters in gases at low pressure to a few hundreds of nanometers in solids [2, 3]. Taking into account that the required detector mass in practice amounts to several tons, the choice of solid materials as a sensitive medium is advantageous.

Background: Radiomics shows promise in characterizing glioblastoma, but its added value over clinical and molecular predictors has yet to be proven. This study assessed the added value of conventional radiomics (CR) and deep learning (DL) MRI radiomics for glioblastoma prognosis (<= 6 vs > 6 months survival) on a large multi-center dataset. Methods: After patient selection, our curated dataset gathers 1152 glioblastoma (WHO 2016) patients from five Swiss centers and one public source. It included clinical (age, gender), molecular (MGMT, IDH), and baseline MRI data (T1, T1 contrast, FLAIR, T2) with tumor regions. CR and DL models were developed using standard methods and evaluated on internal and external cohorts. Sub-analyses assessed models with different feature sets (imaging-only, clinical/molecular-only, combined-features) and patient subsets (S-1: all patients, S-2: with molecular data, S-3: IDH wildtype). Results: The best performance was observed in the full cohort (S-1). In external validation, the combined-feature CR model achieved an AUC of 0.75, slightly, but significantly outperforming clinical-only (0.74) and imaging-only (0.68) models. DL models showed similar trends, though without statistical significance. In S-2 and S-3, combined models did not outperform clinical-only models. Exploratory analysis of CR models for overall survival prediction suggested greater relevance of imaging data: across all subsets, combined-feature models significantly outperformed clinical-only models, though with a modest advantage of 2-4 C-index points. Conclusions: While confirming the predictive value of anatomical MRI sequences for glioblastoma prognosis, this multi-center study found standard CR and DL radiomics approaches offer minimal added value over demographic predictors such as age and gender.

Deep learning has transformed protein design, enabling accurate structure prediction, sequence optimization, and de novo protein generation. Advances in single - chain protein structure prediction via AlphaFold2, RoseTTAFold, ESM Fold, and others have achieved near - experimental accuracy, inspiring successive work extended to biomolecular complexes via AlphaFold Multimer, RoseTTAFold All - Atom, AlphaFold 3, Chai - 1, Boltz - 1 and others . Generative models such as Prot GPT 2, ProteinMPNN, and RFdiffusion have enabled sequence and backbone design beyond natural evolution - based limitations . More recently, joint sequence - structure co - design models, including ESM 3, have integrated both modalities into a unified framework, resulting in improved designability. Despite these advances, challenges still exist pertaining to modeling sequence - structure - function relationships and ensuring robust generalization beyond the regions of protein space spanned by the training data . Future advances wi ll likely focus on joint sequence - structure - function co - design frameworks that are able to model the fitness landscape more effectively than models that treat these modalities independently . Current capabilities, coupled with the dizzying rate of progress, suggest that the field will soon enable rapid, rational design of proteins with tailored structures and functions that transcend the limitations imposed by natural evolution. In this review, we discuss the current capabilities of deep learning methods for protein design, f ocusing on some of the most revolutionary and capable models with respect to their functionality and the applications that they enable, leading up to the current challenges of the field and the optimal path forward.

The present work develops ANAKIN: an Artificial iNtelligence bAsed model for (radiation induced) cell KIlliNg prediction. ANAKIN is trained and tested over 513 cell survival experiments with different types of radiation contained in the publicly available PIDE database. We show how ANAKIN accurately predicts several relevant biological endpoints over a wide broad range on ions beams and for a high number of cell--lines. We compare the prediction of ANAKIN to the only two radiobiological model for RBE prediction used in clinics, that is the Microdosimetric Kinetic Model (MKM) and the Local Effect Model (LEM version III), showing how ANAKIN has higher accuracy over the all considered biological endpoints. At last, via modern techniques of Explainable Artificial Intelligence (XAI), we show how ANAKIN predictions can be understood and explained, highlighting how ANAKIN is in fact able to reproduce relevant well-known biological patterns, such as the overkilling effect.