University of Strasbourg

Neurodevelopmental disorders (NDD) are a wide and heterogenous group of conditions due to impaired brain development, orchestrated by the crosstalk between genome and environment. Dynamic chromatin regulation during cortical development is fundamental, and chromatin remodelers are critical determinants of this process. Recently, numerous chromatin remodeling genes have been implicated in NDDs. By altering genes' epigenetic state, mutated chromatin remodelers disrupt the spatiotemporal regulation of gene expression during development, potentially leading to severe consequences. The Remodeling and Spacing Factor 1 (RSF1) gene encodes a ubiquitous nuclear protein involved in chromatin remodeling, crucial for processes such as DNA transcription, replication, and repair. In this study, we identified by gene matching (n = 7) and literature search (n = 4) eleven unrelated individuals harboring de novo or inherited from a symptomatic parent heterozygous variants in RSF1. All individuals had an NDD, whether intellectual disability, autism spectrum disorder or developmental delay. From the seven individuals with detailed clinical information, unspecific and inconsistent associated features were described, including cranio-facial morphological features, musculoskeletal, digestive, vision, tone, epilepsy and brain MRI anomalies. Our data support the hypothesis that RSF1 is important for brain development and a novel candidate gene for syndromic NDDs.

The establishment of ligand field (LF) symmetry around lanthanoid (Ln(III)) centres is of paramount importance to understand factors that govern magnetization relaxation in singlemolecule magnets (SMMs). We investigated a luminescent mononuclear Dy(III) compound [Dy(BA)₄] (pip) (1), where pip is a piperidinium cation and BA is a benzoylacetonate ligand. The stoichiometric compound exhibits zero-field SMM characteristics, quantum tunneling of magnetization (QTM) dominates magnetization relaxation. In the diluted version of the compound (1@Y), the QTM is mitigated, Raman and Orbach processes are involved in the relaxation. The effective energy barrier (Ueff = 53.61 cm-1 ; HDC = 0 Oe) estimated for magnetization relaxation is comparable with the LF splitting (ΔE = 57.2 cm-1) between the ground and first excited Kramers doublets (KDs) determined from the 4 F9/2→ 6 H15/2 Dy(III)based transition recorded at 2.4 K. By analysing the emission spectrum of the isostructural Eu(III) analogue as spectroscopic probe for site symmetry, the LF symmetry around the Dy(III) centre is assigned as C₂ᵥ. The close proximity (178 cm-1) of the triplet state of the ligand and 4 F9/2 state of the Dy(III) facilitates back energy transfer, thereby rendering 1 emissive only at cryogenic temperatures. The predictive accuracy of the complete active space spin-orbit configuration interaction (CASOCI) method is benchmarked against the experimental emission profile. Overall, we propose a strategy to assign effective LF symmetry around Dy(III), establish magneto-optical correlations, and provide a comprehensive analysis of emission process of 1.

Water confined within hydrophobic nanopores exhibits unusual thermodynamic and structural behavior that governs a wide range of nanofluidic and energy-conversion phenomena. Here, we combine high-pressure scanning calorimetry with molecular dynamics simulations in the temperature range of 298–380 K to elucidate the energetics and mechanisms of water intrusion into pure-silica LTA zeolites featuring cage-like pores. A new data-processing approach separates compression and intrusion contributions in pressure–volume curves, enabling direct quantification of temperature-dependent heat effects. Intrusion is exothermic at ambient temperature (≈−5 J g–1) and becomes nearly thermoneutral above 338 K. Simulations reveal slow intrusion kinetics, collective cage filling through hydrogen-bond-mediated chains, and pronounced stabilization at occupancies of 17–22 H2O molecules per supercell, balancing enthalpic and entropic factors. The results demonstrate that intrusion pressures correlate with accessible surface area, free pore volume, pore geometry, and connectivity rather than aperture size, thereby invalidating classical Laplace–Washburn scaling for nanopores. These findings establish microscopic design principles for tailoring wetting thermodynamics in hydrophobic nanoporous frameworks for energy storage, mechanical actuation, nanofluidic systems, and nanodevices.

AimThis systematic review and meta-analysis aimed to evaluate the fracture resistance of permanent teeth restored with polyethylene fiber-reinforced composite resin restoration.Material and methodsThe following PICOS framework used was: Population, permanent teeth requiring restorative treatment; Intervention, polyethylene fiber use; Control, composite resin restorations without fiber reinforcement, or conventional fiber posts; Outcome, fracture resistance (in Newtons); Study design, in vitro studies. A literature search was conducted independently by two reviewers up to May 18, 2025, using electronic databases (PubMed, ISI Web of Science, SciELO, Scopus, and Embase). In vitro studies examining the fracture resistance of permanent teeth restored with polyethylene fiber-reinforced resin composite restorations were included. Meta analyses were performed by comparing the standardized mean differences in the fracture resistance of teeth restored using polyethylene fibers and the teeth restored only with resin composite. Additional analysis was performed comparing the risk difference of the number of unfavorable fractures. Separate analyses were performed when fiber posts were used. A p-value of less than 0.05 was considered statistically significant.ResultsThe fracture resistance of polyethylene fiber-reinforced restorations was higher compared to non-fiber reinforced composite restorations (p < 0.001). Also, the number of unfavorable fractures were significant lower when polyethylene fibers were used (p < 0.001).ConclusionThe findings suggest that the fracture resistance of permanent teeth may be improved with the use of polyethylene fibers. However, clinical performance outcomes are necessary to validate these in vitro results.Systematic Review RegistrationThe protocol for the systematic review was developed a priori and can be accessed at: https://doi.org/10.17605/OSF.IO/5K8XB.

This article presents the heat evolved under the forced intrusion of water into hydrophobic grafted KIT-6 mesoporous silica with either alkyl or perfluorinated alkyl chains by scanning transitiometry. The experiments were conducted with two different calorimetric configurations. The first applied only a single active calorimeter cell experiencing a constant change in the rate of pressurization (dp/dt)T or a constant rate of compression (dv/dt)T. The second application is the dual active cell, where both the measuring and the reference cells experience the same changes in pressure and compression. It was found that the dual cell configuration is the best arrangement for pressure scanning calorimetry experiments, preventing the smearing of the heat effects from intrusion into the baseline heat. The average heat of high-pressure intrusion of water into hydrophobic KIT-6 was determined to be −8.5 ± 1.5 J·g–1 for alkyl grafting and −9.4 ± 0.9 J·g–1 for the perfluoroalkyl one. Perfluoroalkyl grafting was found to be the most resilient to degradation from exposure to repeated high-pressure water intrusion/extrusion cycling. Therefore, to accurately measure the heat released during the intrusion of nonwetting liquids into hydrophobic mesoporous materials, this article recommends using pressure scanning with a dual-cell differential configuration, which subtracts heat smearing effects from the signal, which otherwise remain in a single-cell configuration.

Asymmetric catalysts exert control over the reactivity and enantioselectivity of chemical reactions, often through their large size and highly flexible geometries. Current quantum chemical calculations provide a powerful means to predict enantioselectivity through exhaustive conformational sampling at the selectivity-determining transition state (TS). However, this approach still relies on a predetermined selectivity-determining step and on the assumption that selectivity is governed by the Boltzmann distribution of TS conformers. To address this issue, constructing a reaction path network that captures the kinetically accessible regions of the potential energy surface is essential. In this study, we applied a delta-learning neural network potential (ΔNNP) to an asymmetric organocatalyst belonging to the imidodiphosphorimidates (IDPi) family, comprising over 200 atoms. The ΔNNP demonstrated accuracy comparable to that of density functional theory (DFT) using GFN2-xTB as the baseline method and learning the DFT–GFN2-xTB energy differences through the neural network model. Using the ΔNNP-based single component - artificial force induced reaction (denoted by NNP/AFIR) method, we constructed a reaction path network containing 48463 paths. Kinetic simulations based on this network were then performed to predict the reaction mechanism and enantioselectivity. Within the network, neumerous paths, including asynchronous concerted and stepwise mechanisms, were found to be energetically competitive and thus to contribute to the overall kinetics. Traffic volume analysis further revealed that intermediates with negligible yields can still play important kinetic roles. Overall, the NNP/AFIR approach would provide a powerful framework not only for deepening mechanistic understanding but also for accelerating the rational design of asymmetric catalytic systems with high reactivity and enantioselectivity.

Antimicrobial peptides have emerged as a potential alternative to traditional small-molecule antimicrobials. They possess broad-spectrum efficacy and increasingly confront the challenges of bacterial resistance, especially the adaptive resistance of biofilms. However, advanced rational peptide design methods are still required to ensure optimal property profiles of such peptides, while limiting the cost of their synthesis and screening. Here, we present a computational pipeline for the rational de novo design of antimicrobial and antibiofilm peptides based on an explainable artificial intelligence (XAI) framework. The developed framework combines a Wasserstein Autoencoder (WAE) and a nonlinear dimensionality reduction method─generative topographic mapping (GTM). The WAE was used to learn the latent representation of the peptide space, while the GTM guided the generation of novel AMPs through an illustrative depiction of the latent space in the form of 2D maps. The generated peptides were subjected to screening by machine learning models, resulting in the final hit list based on their predicted activity. The efficacy of the peptides generated with the developed pipeline was experimentally verified by synthesis and testing for activity against methicillin-resistant Staphylococcus aureus (MRSA), achieving a 100% hit rate in targeting biofilms. Notably, the most potent antibiofilm peptide developed in this study demonstrated almost one order of magnitude improvement in IC50 value compared with the potent antibiofilm peptide reference "1018", used as a positive control. The developed pipeline is readily extendable for the optimization of additional peptide properties, including cytotoxicity, tendency to aggregate, and proteolytic stability, underscoring its potential utility for rational design of the peptide-based therapeutics.

We investigate the kinetics of self-assembly in solutions of polyampholytes (PAs) with identical net charge but differing charge sequences, using coarse-grained molecular dynamics simulations under moderately poor solvent conditions. Three representative sequence types─forming micelles with polyelectrolyte (PE) tails, micelles with sticky (telechelic) corona, and droplets─are studied to elucidate how charge patterning affects aggregate formation, stability, and dynamic restructuring. We identify three dominant mechanisms of mass redistribution: (1) direct binding and release of unimers, (2) merging of preassembled aggregates, and (3) bridging between cores via polyampholytic tails, which facilitates unimer exchange during inelastic collisions between larger aggregates. At low concentrations, a population of small aggregates forms that equilibrates easily through reversible association, whereas at higher concentrations, the kinetics are strongly influenced by sequence-dependent barriers and corona structures. Micelle-forming sequences exhibit kinetic trapping and minimal mass exchange, whereas droplet-forming sequences show frequent transitions and extended lifetimes of double-core aggregates. The interaction potential between aggregates is quantified and used to estimate association and dissociation times via a Fokker–Planck approach, in good agreement with simulation results. In addition, simulations of biologically relevant ProTα sequences reveal a dynamic gel-like network characterized by frequent unimer exchange. Our study highlights the crucial role of charge sequence in controlling the pathways of PA aggregation, offering insight into phase behavior in biological system and the design of functional polymeric materials.

In this study, we reported the design, synthesis, and comprehensive evaluation of a series of nitro carbazole‐based oxime photoinitiators (OPIs, OP1: oxime oxalate, OP2: oxime glyoxylate), as a series of efficient Type I photoinitiators (PIs) for the free radical photopolymerization (FRP) of trimethylolpropane triacrylate (TMPTA) and ethoxylated trimethylolpropane triacrylate (ETPTA) under blue light‐emitting diodes and sunlight irradiation. Computational molecular modeling was employed to predict the effect of OPIs structures on the photoinitiation properties. The predictions suggest that OP1 had a higher propensity for decarboxylation and therefore a better photoinitiation behavior. Compared to OP2, OP3, and commercial benchmark photoinitiator TPO, OP1 exhibits exceptional photoinitiation performance when exposed to LED@405 nm, LED@450 nm, and sunlight. OP1 undergoes decarboxylation to efficiently produce CO2 and free radicals, thereby initiating the photopolymerization reaction. Remarkably, OP1 is successfully applied in 3D printing, producing complete morphology with high‐resolution structure, showcasing its potential for advanced manufacturing applications. The photochemical mechanism of OPIs is comprehensively elucidated using the monitoring of the CO2, steady state photolysis, UV–vis absorption spectroscopy, fluorescence spectroscopy, and electron spin resonance techniques. These experimental investigations are supported by data OP1 from the molecular modelling carried out. Additionally, thermal polymerization shows that OP1 had a high thermal initiation capability, and the composites are successfully prepared together with carbon fibers. The cytotoxicity of the synthesized oxime oxalate and TPO on human umbilical vein endothelial cells (HUVECs) results in a lower cytotoxicity for the oxalate than for TPO. Therefore, OP1, which has never been reported before, can be used as a highly efficient and low cytotoxic dual photo/thermal initiator. This research not only provides theoretical and practical insights into the design and development of new efficient Type I PIs, but also opens up new perspectives for curing applications that require scalability, cost‐effectiveness, environmental sustainability, and green chemistry.