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Simulating chemical systems is highly sought after and computationally challenging, as the number of degrees of freedom increases exponentially with the size of the system. Quantum computers have been proposed as a computational means to overcome this bottleneck , thanks to their capability of representing this amount of information efficiently. Most efforts so far have been centered around determining the ground states of chemical systems. However, hardness results and the lack of theoretical guarantees for efficient heuristics for initial-state generation shed doubt on the feasibility. Here, we propose a heuristically guided approach that is based on inherently efficient routines to solve chemical simulation problems, requiring quantum circuits of size scaling polynomially in relevant system parameters. If a set of assumptions can be satisfied, our approach finds good initial states for dynamics simulation by assembling them in a scattering tree. In particular, we investigate a scattering-based state preparation approach within the context of mergo-association. We discuss a variety of quantities of chemical interest that can be measured after the quantum simulation of a process, e.g., a reaction, following its corresponding initial state preparation.
Intranasal preparations containing double-stranded RNA (dsRNA) and interferon-α2b (IFN-α2b) or IFN-γ as a part of the molecular constructs were more resistant to enzymatic degradation than as individual components. The antiviral activity of dsRNA in mouse fibroblast L929 cells infected with murine encephalomyocarditis virus (EMCV) was increased when IFN was introduced into the construct compared with that for the dsRNA substance alone. An increase in the anti-EMCV activity of the drug was observed with increasing IFN content of the construct. A dose-dependent increase in antiviral activity of IFN incorporated in the molecular constructs was shown against EMCV-infected human embryonic lung cells L-68. The data obtained confirm the prospects for the development of new drugs, the delivery system of which includes IFN and dsRNA, for intranasal administration as prophylactic and therapeutic agents against influenza and other acute respiratory viral infections.
LLMs agents are increasingly used in multi-agent settings, yet their behaviour in sustainability games remains largely unexplored. This work investigates whether lying can emerge among LLM agents in a competitive sustainability game in which agents are informed that common resources can regenerate, although regeneration does not actually occur. We develop an agent-based model of a sustainability game in which agents manage industrial, military, and ecological resources, and interact through a network. LLM agents can observe neighbours' status, declare future attacks, receive permission to lie, and access reputation information, while rule-based agents provide an interpretable behavioural baseline. The results show that neighbour information strongly changes system dynamics, increasing attacks while improving biosphere retention and coexistence. Also, the presence of future declarations reduce extinction risk without suppressing conflict. Behaviourally, deception emerges even when agents are not explicitly allowed to lie, and explicit permission mainly increases bluffing and diversion rather than direct backstabbing. Finally, the presence of reputation memory and information about the current biosphere level reduces system ecological depletion. These findings suggest that deception can arise as an emergent behaviour in LLM-agent systems and that communication between LLM-agents could support sustainability while dealing with risk.
Numerous problems in bioinformatics and computational biology can be framed as a task of learning a mapping from one state of a biological system to another relevant state or of exploring novel data points across biologically constrained spaces. However, manually deriving such mappings—for example, to transform cells in a diseased state back into a healthy state, or extrapolating from existing datasets to create new data—is often non-trivial and can require extraordinary domain expertise and resources. Fortunately, the field of generative artificial intelligence (AI) has introduced a new training paradigm referred to as (conditional) flow matching, which has emerged as a promising solution to this problem, with broad applicability in computer vision, natural language processing, and the physical and life sciences. Flow matching is a powerful and principled, data-driven framework for efficiently learning a mapping between arbitrary pairs of high-dimensional data distributions, making it well suited for addressing problems in molecular and cell biology. In this Review, we characterize the theoretical foundations of flow matching and its applications in biomolecular modelling for small molecules, proteins, DNA/RNA, and their interactions, as well as its uses in single/multi-cellular modelling for cell phenotyping and imaging, each contributing towards the development of an AI-based virtual cell. Finally, this review highlights open-source flow-matching methods and discusses future directions in flow-based generative modelling for bioinformatics and computational biology. Flow matching has emerged as a promising solution to mapping arbitrary pairs of high-dimensional data distributions, well suited to problems in molecular and cell biology. Morehead et al. review the theoretical foundations of flow-matching-based models and applications of flow matching in computational biology, and discuss its role in developing methods towards an AI-based virtual cell.
The kinetics of Ag photo-diffusion into amorphous GeS2 layer on SiO2 and Si substrates have been studied by specular neutron reflection and off-specular neutron scattering. It was found from the specular neutron reflection that the metastable Ag-rich reaction layer was formed between the Ag and GeS2 layers first by the Ag dissolution from the Ag layer, and then, subsequent Ag diffusion occurred from the Ag-rich reaction layer to the GeS2 host layer until the two layers merged into one uniform layer. The reaction process is the same as Ag/Ge20S80/ Si substrate, but it is different from GeS2/ Ag/ Si substrate. It was found from off-specular neutron scattering that the reaction interface was roughened at the early stage of the photoreaction, and that the photo-oxidized surface layer was significantly roughened by the extended light illumination after completion of Ag-diffusion.
