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Antimicrobial peptides (AMPs) play important roles in cancer, autoimmune diseases, and aging. A critical aspect of AMP functionality is their targeted interaction with pathogen membranes, which often possess altered lipid compositions. Designing AMPs with enhanced therapeutic properties relies on a nuanced understanding of these interactions, which are believed to trigger a rearrangement of these peptides from random coil to alpha-helical conformations, essential for their lytic action. Traditional supercomputing has consistently encountered difficulties in accurately modeling these structural changes, especially within membrane environments, thereby opening an opportunity for more advanced approaches. This study extends an existing quantum computing algorithm to address the complexities of antimicrobial peptide interactions at interfaces. Our approach enables the prediction of the optimal conformation of peptides located in the transition region between hydrophilic and hydrophobic phases, akin to lipid membranes. The new method has been applied to model the structure of three 10-amino-acid-long peptides, each exhibiting hydrophobic, hydrophilic, or amphipathic properties in different media and at interfaces between solvents of different polarity. Notably, our approach does not demand a higher number of qubits compared to simulations in homogeneous media, making it more feasible with current quantum computing resources. Despite existing limitations in computational power and qubit accessibility, our findings demonstrate the significant potential of quantum computing in accurately characterizing complex biomolecular processes, particularly the folding of AMPs at membrane models. This research paves the way for future advances in quantum computing to enhance the accuracy and applicability of biomolecular simulations.

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications., Comment: Field Robotics special issue: DARPA Subterranean Challenge, Advancement and Lessons Learned from the Finals

Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human-authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, to assist human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section., Comment: 11 pages, 1 figure

In this paper we present a unified Lagrangian--Hamiltonian geometric formalism to describe time-dependent contact mechanical systems, based on the one first introduced by K. Kamimura and later formalized by R. Skinner and R. Rusk. This formalism is especially interesting when dealing with systems described by singular Lagrangians, since the second-order condition is recovered from the constraint algorithm. In order to illustrate this formulation, some relevant examples are described in full detail: the Duffing equation, an ascending particle with time-dependent mass and quadratic drag, and a charged particle in a stationary electric field with a time-dependent constraint., Comment: 23 pp

We compute the contributions of dimension six two-quark operators to the non-leptonic decay width of heavy hadrons due to the flavor changing bottom-to-up-quark transition in the heavy quark expansion. Analytical expressions for the Darwin term $\rho_D$ and the spin-orbit term $\rho_{\rm LS}$ are obtained with leading order accuracy., Comment: 22 pages, 3 figures. arXiv admin note: text overlap with arXiv:2004.09485

Monitoring continental precipitation over Europe with high resolution (2 km, 15 minutes) has been possible since the operational production of the OPERA composites from the European weather radar networks. The OPERA data are the essential input to a hazard assessment tool for identifying localized rainfall-induced flash floods at European scale, and their quality determines the performance of the tool. This paper analyses the OPERA data quality during the warm seasons of 2015-2017 by comparing the estimated rainfall accumulations with the SYNOP rain gauge records over Europe. To compensate the OPERA underestimation, a simple spatially-variable bias adjustment method has been applied. The long-term comparison between the OPERA and gauge point daily rainfall accumulations at the gauge locations shows the benefit of the bias adjustment. Additionally, the daily monitoring shows gradual improvement of the OPERA data year by year. The impact of the quality of the OPERA data for effective flash flood identification is demonstrated for the case of the flash floods that occurred from 29 May to 3 June 2016 in central Europe., Comment: 22 pages, 9 figures, 1 table