François Sicard

This study investigates the transmission of monetary policy narratives to Bitcoin prices, distinguishing the impact of ex-ante expectations from ex-post interest rate implementation. We introduce a high-frequency Monetary Policy Expectations (MPE) index, using a Large Language Model (LLM)-based classification of 118,000+ market messages to achieve a precise hawkish/dovish decomposition. Results from a framework combining Long Short-Term Memory (LSTM) networks with SHapley Additive exPlanations (SHAP) indicate that Bitcoin functions as a sensitive barometer of central bank signaling; specifically, hawkish narratives consistently trigger negative price responses independently of actual Federal Funds Rate adjustments. We demonstrate that the MPE index Granger-causes Bitcoin returns at short-to-medium horizons, establishing linear predictive causality, while the LSTM-SHAP framework reveals pronounced non-linear, macroeconomic regime-dependent interactions. These findings highlight Bitcoin's structural sensitivity to global monetary discourse, establishing LLM-derived sentiment as a potent leading macroeconomic indicator for the digital asset landscape.

This study investigates the transmission of monetary policy narratives to Bitcoin prices, distinguishing the impact of ex-ante expectations from ex-post interest rate implementation. We introduce a high-frequency Monetary Policy Expectations (MPE) index, using a Large Language Model (LLM)-based classification of 118,000+ market messages to achieve a precise hawkish/dovish decomposition. Results from a framework combining Long Short-Term Memory (LSTM) networks with SHapley Additive exPlanations (SHAP) indicate that Bitcoin functions as a sensitive barometer of central bank signaling; specifically, hawkish narratives consistently trigger negative price responses independently of actual Federal Funds Rate adjustments. We demonstrate that the MPE index Granger-causes Bitcoin returns at short-to-medium horizons, establishing linear predictive causality, while the LSTM-SHAP framework reveals pronounced non-linear, macroeconomic regime-dependent interactions. These findings highlight Bitcoin’s structural sensitivity to global monetary discourse, establishing LLM-derived sentiment as a potent leading macroeconomic indicator for the digital asset landscape.

This working paper uses the Crown Dependency of Jersey as a case study to investigate young people's attitudes towards voting. Drawing on assemblage theory as a conceptual lens, we examine how interrelated aspects of young people's voting experiences shape engagement. Assemblage mapping is used to synthesise insights from focus groups with participants aged 18 to 24, identifying diverse yet related dimensions, including voting awareness, political exposure, social influence, social media dynamics, and confidence in the act of voting. Confirmatory Factor Analysis is then used to empirically validate these dimensions, operationalised as latent categories. We examine the sequence of awareness-building, political exposure, and social mediation using structural equation modelling, revealing how different configurations of these factors influence young people's perceptions of the importance of voting and their sense of confidence. Our findings show that pathways in which political exposure precedes awareness differ markedly from those in which awareness develops without sufficient exposure or social support. We further show that social media engagement plays a key role, either encouraging or limiting participation depending on how it is positioned within the structural model. Our work highlights that low youth turnout reflects a complex disconnection influenced by both personal relationships and institutional factors, with implications for interventions designed to foster meaningful engagement among young voters.

Biohybrid nanopores combine the durability of solid-state nanopores with the precise structure and function of biological nanopores. Particular care must be taken to control how biological nanopores adapt to their surroundings once they come into contact with the solid-state nanopores. Two major challenges are to precisely control this adaptability under dynamic conditions and provide predesigned functionalities that can be manipulated for engineering applications. In this work, we report on the computational design of a distinctive class of biohybrid active membrane layers, built from the directed-insertion of an aquaporinincorporated lipid nanodisc into a model alkyl-functionalized silica pore. We show that in an aqueous environment when a pressure difference exists between the two sides of the solid-state nanopore, the preferential interactions between the hydrocarbon tail of the lipid molecules that surround the aquaporin protein and the alkyl group functionalizing the interior surface of the silica nanopore enable the insertion of the aquaporin-incorporated lipid shell into the nanopore by forcing out the water molecules. The same preferential interactions are responsible for the structural stability of the inserted aquaporin-incorporated lipid shell as well as the water sealing properties of the lipid-alkyl interface. We further show that the aquaporin protein stabilized in the alkyl-functionalized silica nanopore preserves its biological structure and function in both pure and saline water, and, remarkably, its water permeability is equal to the one measured in the biological environment. The designed biohybrid membrane could pave the way for the development of durable transformative devices for water filtration.

Technical challenges in precision medicine and environmental remediation create an increasing demand for smart materials that can select and deliver a probe load to targets with high precision. In this context, soft nanomaterials have attracted considerable attention due to their ability to simultaneously adapt their morphology and functionality to complex ambients. Two major challenges are to precisely control this adaptability under dynamic conditions and provide predesigned functionalities that can be manipulated by external stimuli. Here, we report on the computational design of a distinctive class of soft nanocarriers, built from armored nanodroplets, able to selectively encapsulate or release a probe load under specific flow conditions. First, we describe in detail the mechanisms at play in the formation of pocket-like structures in armored nanodroplets and their stability under external flow. Then we use that knowledge to test the capacity of these pockets to yield flow-assisted encapsulation or expulsion of a probe load. Finally, the rheological properties of these nanocarriers are put into perspective with those of delivery systems employed in pharmaceutical and cosmetic technology.

Synthetic anti-agglomerant molecules are surface active compounds used to prevent the growth of methane hydrates in the petroleum industry.

A variety of enhanced statistical and numerical methods are now routinely used to extract important thermodynamic and kinetic information from the vast amount of complex, high-dimensional data obtained from molecular simulations. For the characterization of kinetic properties, Markov state models, in which the long-time statistical dynamics of a system is approximated by a Markov chain on a discrete partition of configuration space, have seen widespread use in recent years. However, obtaining kinetic properties for molecular systems with high energy barriers remains challenging as often enhanced sampling techniques are required with biased simulations to observe the relevant rare events. Particularly, the calculation of diffusion coefficients remains elusive from biased molecular simulation data. Here, we propose a novel method that can calculate multidimensional position-dependent diffusion coefficients equally from either biased or unbiased simulations using the same formalism. Our method builds on Markov state model analysis and the Kramers–Moyal expansion. We demonstrate the validity of our formalism using one- and two-dimensional analytic potentials and also apply it to data from explicit solvent molecular dynamics simulations, including the water-mediated conformations of alanine dipeptide and umbrella sampling simulations of drug transport across a lipid bilayer. Importantly, the developed algorithm presents significant improvement compared to standard methods when the transport of solute across three-dimensional heterogeneous porous media is studied, for example, the prediction of membrane permeation of drug molecules.

Bacterial microcompartments (BMCs) are nanoscale proteinaceous organelles that encapsulate enzymes from the cytoplasm using an icosahedral protein shell that resembles viral capsids. Of particular interest are the carboxysomes (CBs), which sequester the CO₂-fixing enzymes ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) to enhance carbon assimilation. The carboxysome shell serves as a semi-permeable barrier for passage of metabolites in and out of the carboxysome to enhance CO₂fixation. How the protein shell directs influx and efflux of molecules in an effective manner has remained elusive. Here we use molecular dynamics and umbrella sampling calculations to determine the free-energy profiles of the metabolic substrates, bicarbonate, CO₂and ribulose bisphosphate and the product 3-phosphoglycerate associated with their transition through the major carboxysome shell protein CcmK2. We elucidate the electrostatic charge-based permeability and key amino acid residues of CcmK2 functioning in mediating molecular transit through the central pore. Conformational changes of the loops forming the central pore may also be required for transit of specific metabolites. The importance of these in-silico findings is validated experimentally by site-directed mutagenesis of the key CcmK2 residue Serine 39. This study provides insight into the mechanism that mediates molecular transport through the shells of carboxysomes, applicable to other BMCs. It also offers a predictive approach to investigate and manipulate the shell permeability, with the intent of engineering BMC-based metabolic modules for new functions in synthetic biology.