Experimental Discovery Research Articles

Editorial: What does experimental pharmacology and drug discovery look like in 2035?

Editorial: What does experimental pharmacology and drug discovery look like in 2035?

Polarization conversion effect in cholesteric liquid crystal-based polarization volume gratings

Following the recent experimental discovery of a new polarization conversion phenomenon in polarization volume gratings (PVGs), in this paper, we investigate its underlying physical mechanisms in cholesteric liquid crystal (CLC) reflectors and PVGs. We examine the transition of eigenstates from circular to linear polarization as the incident angle deviates from the helical axis, which originates such an anomalous polarization conversion effect. While this transition enables PVGs to double the in-coupling efficiency and brightness uniformity in waveguide-based augmented reality (AR) displays, it also degrades the polarization selectivity and alters the transmitted polarization state in both CLC reflectors and PVGs, which in turn narrows down their spectral and angular bandwidth in the multi-layer design. These findings help not only deepen the understanding of polarization behaviors in CLC-based optical elements but also provide valuable insights to optimize their performances for emerging AR smart glasses.

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Abstract Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. The associated notebooks allow to reproduce the reported analyses and apply them to other systems.2

Efficient and Safe Induction of Diabetes in Experimental Animals: A Review on Alternative Models and Techniques

Diabetes Mellitus (DM) is a multitudinous metabolic disorder that can occur due to insufficient or inefficient levels of insulin that leads to hyperglycemia. In many conditions, diabetes can also directly or indirectly lead to other functional disorders such as dyslipidemia and hypertension making them more severe and life-threatening. It is believed that Type 1 Diabetes can be caused by to process of auto-immune destruction of beta-cells of Islet of Langerhans of the pancreas responsible for the production of insulin whereas Type 2 diabetes is because of resistance against insulin along with the futilities of beta-cells to compensate the body with the required amount of insulin. The animal models are considered an essential component in the experimental studies and drug discovery process. Animal models provide safety, effectiveness, and dose of the test substance that needs to be extrapolated to human use. There are several methods for the induction of diabetes in experimental animal models. The present review aimed to discuss and explore currently used approaches including models from streptozotocin-induced diabetes to transgenic models for reproducible and safe diabetes induction in different experimental animals (rats, mice, guinea pigs, and dogs) and sex. Additionally, some genetically modified animal models are also included and discussed in this review which will pave the way for further studies.

CompariPSSM: a PSSM-PSSM comparison tool for motif-binding determinant analysis.

Short linear motifs (SLiMs) are compact functional modules that mediate low-affinity protein-protein interactions. SLiMs direct the function of many dynamic signalling and regulatory complexes playing a central role in most biological processes of the cell. Motif-binding determinants describe the contribution of each residue in a motif-containing peptide to the affinity and specificity of binding to the motif-binding partner. Motif-binding determinants are generally defined as a motif consensus pattern or a position-specific scoring matrix (PSSM) encoding quantitative preferences. Motif-binding determinant comparison is an important motif analysis task and can be applied to motif annotation, classification, clustering, discovery and benchmarking. Currently, binding determinant comparison is generally performed by analysing consensus similarity; however, this ignores important quantitative information in both the consensus and non-consensus positions. We have created a new tool, CompariPSSM, that quantifies the similarity between motif-binding determinants using sliding window PSSM-PSSM comparison and scores PSSM similarity using a randomisation-based probabilistic framework. The tool has been benchmarked on curated data from the eukaryotic linear motif database and experimental data from proteomic peptidephage display. CompariPSSM can be used for peptide classification to validate motif classes, peptide clustering to group functionally related conserved disordered regions, and benchmarking experimental motif discovery methods. CompariPSSM is available at https://slim.icr.ac.uk/projects/comparipssm.

Enhancing Superconductor Critical Temperature Prediction: A Novel Machine Learning Approach Integrating Dopant Recognition.

Doping plays a crucial role in determining the critical temperature (Tc) of superconductors, yet accurately predicting its effects remains a significant challenge. Here, we introduce a novel doping descriptor that captures the complex influence of dopants on superconductivity. By integrating the doping descriptor with elemental and physical features within a Mixture of Experts (MoE) model, we achieve a remarkable R2 of 0.962 for Tc prediction, surpassing all published prediction models. Our approach successfully identifies optimal doping levels in the Bi2-xPbxSr2Ca2-yCuyOz system, with predictions closely aligning with experimental results. Leveraging this model, we screen compounds from the Inorganic Crystal Structure Database and employ a generative approach to explore new doped superconductors. This process reveals 40 promising candidates for high Tc superconductivity among existing and hypothetical doped materials. By explicitly accounting for doping effects, our method offers a powerful tool for guiding the experimental discovery of new superconductors, potentially accelerating progress in high-temperature superconductivity research and opening new avenues for material design.

Evidence for a Griffiths Phase to Cluster Spin Glass Transition in the La2/3Sr1/3(Mn1-3 xAl2 xTix)O3 System.

The presence of Griffiths phase to cluster spin glass transition has theoretically been predicted in both classical and quantum systems. However, its detection in a classical system has been lacking for decades, which hinders a complete understanding of the relationship between the Griffiths phase and cluster spin glass. Here, the experimental discovery of the Griffiths phase to cluster spin glass transition is reported in a classical magnetic system, diluted ferromagnets La2/3Sr1/3(Mn1-3 xAl2 xTix)O3 (0 ≤ x ≤ 0.12). The phase diagram of the system shows a transition from the Griffiths phase into a ferromagnetic state in the low disorder concentration range (0.01 < x ≤ 0.09). In the high disorder concentration range (0.09 < x ≤ 0.12), a Griffiths phase to cluster spin glass transition is identified, which nicely matches that of disordered quantum systems. Moreover, the Griffiths phase is essentially an unfrozen cluster spin glass with partially broken ergodicity is demonstrated experimentally. These findings serve as crucial experimental references for understanding the glassy phenomena in disordered magnets, facilitating future exploration of their unique properties and functionalities.

Canard explosions in turbulent thermo-fluid systems.

A sudden transition to a state of high-amplitude periodic oscillations is catastrophic in a thermo-fluid system. Conventionally, upon varying the control parameter, a sudden transition is observed as an abrupt jump in the amplitude of the fluctuations in these systems. In contrast, we present an experimental discovery of a canard explosion in a turbulent reactive flow system where we observe a continuous bifurcation with a rapid rise in the amplitude of the fluctuations within a narrow range of control parameters. The observed transition is facilitated via a state of bursting, consisting of the epochs of large amplitude periodic oscillations amidst the epochs of low-amplitude periodic oscillations. The amplitude of the bursts is higher than the amplitude of the bursts of an intermittency state in a conventional gradual transition, as reported in turbulent reactive flow systems. During the bursting state, we observe that temperature fluctuations of the exhaust gas vary at a slower time scale in correlation with the amplitude envelope of the bursts. We also present a phenomenological model for thermoacoustic systems to describe the observed canard explosion. Using the model, we explain that the large amplitude bursts occur due to the slow-fast dynamics at the bifurcation regime of the canard explosion.

Opportunities and challenges for direct electrification of chemical processes with protonic ceramic membrane reactors

Abstract The necessity of developing sustainable energy storage and process electrification technologies has built an unprecedented momentum for protonic ceramic membrane reactors (PCMRs). PCMRs are practically electrolytic cells (or even fuel cells in case of cogeneration) that extend beyond the classical approach of electrolysis towards producing a variety of value-added chemicals or fuels. The use of a ceramic electrolyte membrane to electrochemically supply or remove hydrogen offers unique advantages, such as process intensification, cogeneration of chemicals and electricity, as well as the shift of the chemical equilibrium to the desired products. During the last few years, rapid progress has not only been made in the cell components, but also for upscaling, which reveals their high potential in terms of efficiency and flexibility. Herein, we discuss recent innovations and breakthroughs in the PCMR concepts and components for different processes, while we attempt to identify challenges that may hinder their wide deployment. Closer to commercialization is the production of pressurized hydrogen from sustainable sources, i.e. biogas and ammonia, while significant advancements have been made in reversible H2O electrolysis systems. CO2/H2O co-electrolysis, hydrocarbon conversion and ammonia synthesis have been also successfully demonstrated, albeit with different obstacles related to the product selectivity and stability of the cell reactors. We conclude that future projects should target beyond the experimental discovery of materials, such as, multiscale modeling that would aid optimization of the involved surfaces, interfaces, and the operating parameters towards enhancing the viability of electrosynthesis in PCMRs.

Pentagonal Symmetry in Aperiodic Cyclic Multiply Twinned Nano- and Mesodiamonds.

This Review provides the first comprehensive overview of the structure and properties of exotic sp3-crystalline aperiodic cyclic multiply twinned nano- (10-100 nm) and meso- (up to 1 mm) diamond particles (MTPs) exhibiting pentagonal symmetry. It spans their independent experimental discoveries (1963, 1964, 1972, and 1983) and theoretical structural insights (1993) to recent advancements. The Review focuses on high-symmetry MTPs formed by the fusion of multiple cubic diamond fragments through [111] facets. The sp3 diamond lattice of individual fragments offers a vast range of MTP varieties. These particles are shown to be a special case of aperiodic crystalline solids with limited dimensions and rotational symmetry, leading to a breakdown of translational invariance. Detailed mathematical analysis of the MTPs' lattices highlights the crucial role of central cores in determining the symmetry and effective dimensions of these structures. Both structural and kinetic aspects of the formation mechanisms of pentagonal diamond particles are considered, revealing the main role of embryo seeds (low fullerenes, polyhexacyclo[5.5.1.12,6.18,12.03,11.05,9]pentadecane (C15), and polyheptacyclo[5.5.1.12,7.19,14.03,13.06,11]octadecane (C18)) in determining the MTPs' symmetry and structure. The effective dimensions of multiply twinned diamonds are shown to be limited by structural stress caused by the mismatch of perfect pentagonal and tetrahedral dihedral angles. The extraordinary mechanical and electronic properties of multiply twinned diamonds are discussed, highlighting that hexagonal interfaces between cubic diamond fragments may determine exceptional ultrahard and quantum characteristics. The MTP X-ray diffraction spectra reveal clear pentagonal patterns with single (diamond decahedrons or dodecahedrons) or ten (diamond icosahedra) central 5-fold axes. A comparative analysis of experimental structural data and simulations at different theoretical levels demonstrates a perfect correspondence of theoretical models with crystalline lattices.

Exploring Holy Basil's Bioactive Compounds for T2DM Treatment: Docking and Molecular Dynamics Simulations with Human Omentin-1.

Type 2 Diabetes Mellitus (T2DM) presents a substantial health concern on a global scale, driving the search for innovative therapeutic strategies. Phytochemicals from medicinal plants, particularly Ocimum tenuiflorum (Holy Basil), have garnered attention for their potential in T2DM management. The increased focus on plant-based treatments stems from their perceived safety profile, lower risk of adverse effects, and the diverse range of bioactive molecules they offer, which can target multiple pathways involved in T2DM. Computational techniques explored the binding interactions between O. tenuiflorum phytochemicals and Human Omentin-1, a potential T2DM target. ADMET evaluation and targeted docking identified lead compounds: Luteolin (-4.84 kcal/mol), Madecassic acid (-4.12 kcal/mol), Ursolic acid (-5.91 kcal/mol), Stenocereol (-5.59 kcal/mol), and Apigenin (-4.64 kcal/mol), to have a better binding affinity to target protein compared to the control drug, Metformin (-2.01 kcal/mol). Subsequent molecular dynamics simulations evaluated the stability of Stenocereol, Luteolin, and Metformin complexes for 200 nanoseconds, analysing RMSD, RMSF, RG, SASA, PCA, FEL, and MM-PBSA parameters. Results indicated Stenocereol's strong binding affinity with Omentin-1, suggesting its potential as a potent therapeutic agent for T2DM management. These findings lay the groundwork for further experimental validation and drug discovery endeavours.

MicroRNAs in Hepatocellular Carcinoma Pathogenesis: Insights into Mechanisms and Therapeutic Opportunities.

Hepatocellular carcinoma (HCC) presents a significant global health burden, with alarming statistics revealing its rising incidence and high mortality rates. Despite advances in medical care, HCC treatment remains challenging due to late-stage diagnosis, limited effective therapeutic options, tumor heterogeneity, and drug resistance. MicroRNAs (miRNAs) have attracted substantial attention as key regulators of HCC pathogenesis. These small non-coding RNA molecules play pivotal roles in modulating gene expression, implicated in various cellular processes relevant to cancer development. Understanding the intricate network of miRNA-mediated molecular pathways in HCC is essential for unraveling the complex mechanisms underlying hepatocarcinogenesis and developing novel therapeutic approaches. This manuscript aims to provide a comprehensive review of recent experimental and clinical discoveries regarding the complex role of miRNAs in influencing the key hallmarks of HCC, as well as their promising clinical utility as potential therapeutic targets.

Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning.

Scientific advancement is universally based on the dynamic interplay between theoretical insights, modeling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains dealing with high-dimensional object spaces, such as molecules and complex microstructures. Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop-automated experiment, is emerging as a crucial objective for accelerating scientific research. The critical aspect is to use not only theory but also on-the-fly theory updates during the experiment. Here, we introduce a method for integrating theory into the loop through Bayesian conavigation of theoretical model space and experimentation. Our approach leverages the concurrent development of surrogate models for both simulation and experimental domains at the rates determined by latencies and costs of experiments and computation, alongside the adjustment of control parameters within theoretical models to minimize epistemic uncertainty over the experimental object spaces. This methodology facilitates the creation of digital twins of material structures, encompassing both the surrogate model of behavior that includes the correlative part and the theoretical model itself. While being demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications, such as the exploration of optical properties in nanoclusters, microstructure-dependent properties in complex materials, and properties of molecular systems.

Unveiling Novel Direct Bandgap Allotropes of Germanium: A Computational Exploration

Germanium crystallizes in a cubic diamond structure, which restricts its applications in optoelectronics due to its indirect band gap. However, the slight energy difference between the direct and indirect band gaps in germanium presents a promising opportunity to engineer its structure into a direct band gap material, with the hexagonal diamond (lonsdaleite) phase being a notable example. In this study, we conducted an extensive computational search to find germanium allotropes with a direct band gap and low energy using a sensible random structure search approach informed by data-derived interatomic potentials. Among the predicted allotropes, we identified a hexagonal 8H structure with the lowest energy compared to all known germanium allotropes and only 5 meV/atom above the ground state cubic diamond structure. Compared to the cubic diamond phase, which has complete cubicity, the 8H phase consists of 3/4 cubicity and 1/4 hexagonality. This structural motif, coupled with band structure back folding in the hexagonal Brillouin zone, results in a direct band gap of 0.25 eV. Given the prior experimental discovery of 2H and 4H polytypes, the experimental synthesis of the 8H allotrope in germanium is highly feasible. Future research could explore alloying 8H germanium with silicon to optimize the bandgap energy and optical transitions, potentially achieving lifetimes comparable to those of group III-V semiconductors like GaAs.

Rate-Dependent Formation Processes in Li-O2 Batteries: Phase-Field Modeling with the Marcus Kinetics

Lithium-oxygen batteries (Li-O2) present a compelling prospect for the next generation of batteries owing to their exceptionally high theoretical energy density.[1] Yet, the performance of Li-O2 batteries is significantly limited by the current-dependent morphology of insulating oxides.[2-3] Existing mathematical models using Butler-Volmer kinetics exhibit uncertainties and inaccuracies.[4-5] In this study, we propose the integration of Marcus kinetics with the existing phase-field model to resolve a few outstanding inconsistencies between theoretical predictions and experimental discoveries. Notably, utilizing the Marcus kinetics enables a quantitative assessment of the role of solvation energy in current-dependet particle growth as opposed to film growth. Experimental results further verify the new theoretical predictions. This research offers valuable insights for the future design of Li-O2 batteries.[1] Bruce, P. G., Freunberger, S. A., Hardwick, L. J., & Tarascon, J.-M. (2012). Nucleation and growth of lithium peroxide in the Li-O2 battery. Nature Materials, 11, 19–29.[2] Read, J. (2002). Characterization of the Lithium/Oxygen Organic Electrolyte Battery. Journal of The Electrochemical Society, 149, A1190-A1195.[3] Kraytsberg, A., & Ein-Eli, Y. (2011). Review on Li–air batteries—Opportunities, limitations and perspective. Journal of Power Sources, 196, 886-893.[4] Lau, S., & Archer, L. A. (2015). Nucleation and growth of lithium peroxide in the Li-O2 battery. Nano Letters, 15(9), 6108–6114.[5] Horstmann, B., Gallant, B., Mitchell, R., Bessler, W. G., Shao-Horn, Y., & Bazant, M. Z. (2013). Rate-dependent morphology of Li2O2 growth in Li-O2 batteries. Journal of Physical Chemistry Letters, 4(24), 4227–4232.

Crystal Structure Prediction and Performance Assessment of Hydrogen Storage Materials: Insights from Computational Materials Science

Hydrogen storage materials play a pivotal role in the development of a sustainable hydrogen economy. However, the discovery and optimization of high-performance storage materials remain a significant challenge due to the complex interplay of structural, thermodynamic and kinetic factors. Computational materials science has emerged as a powerful tool to accelerate the design and development of novel hydrogen storage materials by providing atomic-level insights into the storage mechanisms and guiding experimental efforts. In this comprehensive review, we discuss the recent advances in crystal structure prediction and performance assessment of hydrogen storage materials from a computational perspective. We highlight the applications of state-of-the-art computational methods, including density functional theory (DFT), molecular dynamics (MD) simulations, and machine learning (ML) techniques, in screening, evaluating, and optimizing storage materials. Special emphasis is placed on the prediction of stable crystal structures, assessment of thermodynamic and kinetic properties, and high-throughput screening of material space. Furthermore, we discuss the importance of multiscale modeling approaches that bridge different length and time scales, providing a holistic understanding of the storage processes. The synergistic integration of computational and experimental studies is also highlighted, with a focus on experimental validation and collaborative material discovery. Finally, we present an outlook on the future directions of computationally driven materials design for hydrogen storage applications, discussing the challenges, opportunities, and strategies for accelerating the development of high-performance storage materials. This review aims to provide a comprehensive and up-to-date account of the field, stimulating further research efforts to leverage computational methods to unlock the full potential of hydrogen storage materials.

Identification of Anticancer Peptides from the Genome of Candida albicans: in Silico Screening, in Vitro and in Vivo Validations.

Anticancer peptides (ACPs) are promising future therapeutics, but their experimental discovery remains time-consuming and costly. To accelerate the discovery process, we propose a computational screening workflow to identify, filter, and prioritize peptide sequences based on predicted class probability, antitumor activity, and toxicity. The workflow was applied to identify novel ACPs with potent activity against colorectal cancer from the genome sequences of Candida albicans. As a result, four candidates were identified and validated in the HCT116 colon cancer cell line. Among them, PCa1 and PCa2 emerged as the most potent, displaying IC50 values of 3.75 and 56.06 μM, respectively, and demonstrating a 4-fold selectivity for cancer cells over normal cells. In the colon xenograft nude mice model, the administration of both peptides resulted in substantial inhibition of tumor growth without causing significant adverse effects. In conclusion, this work not only contributes a proven computational workflow for ACP discovery but also introduces two peptides, PCa1 and PCa2, as promising candidates poised for further development as targeted therapies for colon cancer. The method as a web service is available at https://app.cbbio.online/acpep/home and the source code at https://github.com/cartercheong/AcPEP_classification.git.

Weyl orbits as probe of chiral separation effect in magnetic Weyl semimetals

We consider magnetic Weyl semimetals. First of all we review relation of intrinsic anomalous Hall conductivity, band contribution to intrinsic magnetic moment, and the conductivity of chiral separation effect (CSE) to the topological invariants written in terms of the Wigner transformed Green functions (with effects of interaction and disorder taken into account). Next, we concentrate on the CSE. The corresponding bulk axial current is accompanied by the flow of the states in momentum space along the Fermi arcs. Together with the bulk CSE current this flow forms closed Weyl orbits. Their detection can be considered as experimental discovery of chiral separation effect. Previously it was proposed to detect Weyl orbits through the observation of quantum oscillations (Potter et al 2014 Nat. Commun. 5 5161). We propose the alternative way to detect existence of Weyl orbits through the observation of their contributions to Hall conductance.

Anomalous Fraunhofer-like patterns in quantum anomalous Hall Josephson junctions

The intriguing interplay between topology and superconductivity has attracted significant attention, given its potential for realizing topological superconductivity. In the quantum anomalous Hall insulators (QAHIs)-based junction, the supercurrents are carried by the chiral edge states, characterized by a 2Φ0 magnetic flux periodicity (Φ0=h/2e is the flux quantum, h the Planck constant, and e the electron charge). However, experimental observations indicate the presence of bulk carriers in QAHI samples due to magnetic dopants. In this study, we reveal a systematic transition from edge-state to bulk-state dominant supercurrents as the chemical potential varies from the bulk gap to the conduction band. This results in an evolution from a 2Φ0-periodic oscillation pattern to an asymmetric Fraunhofer pattern. Furthermore, a Fraunhoher-like pattern emerges due to the coexistence of chiral edge states and bulk states caused by magnetic domains, even when the chemical potential resides within the gap. These findings not only advance the theoretical understanding but also pave the way for the experimental discovery of the chiral Josephson effect based on QAHI doped with magnetic impurities. Published by the American Physical Society 2024

Signature decay modes of the compact doubly-heavy tetraquarks [formula omitted] and [formula omitted]

Based on the expectations that the lowest-lying doubly-bottom tetraquark Tbbu¯d¯ (JP=1+) and the bottom-charm tetraquark Tbcu¯d¯ (JP=0+) are stable against strong and electromagnetic decays, we work out a number of semileptonic and non-leptonic weak decays of these hadrons, making use of the heavy quark symmetry. In doing this, we concentrate on the exclusive decays involving also tetraquarks in the final states, i.e., transitions such as Tbbu¯d¯→Tbcu¯d¯(ℓ−νℓ,h−) and Tbcu¯d¯→Tccu¯d¯(ℓ−νℓ,h−), where h−=π−,ρ−,a1−,Ds−,Ds⁎−. So far, only the JP=1+ tetraquark Tccu¯d¯ has been discovered, which we identify with the I=0Tcc+ object, compatible with JP=1+ and having the pole mass relative to the D⁎+D0 mass threshold and decay widths δm=M(Tcc+)−(M(D⁎+)+M(D0))=−360±40−0+4 keV and Γ(Tcc+)=48−14+2 keV. Experimental discoveries of the transitions worked out here, and related ones involving doubly-heavy baryons, will quantify the diquark-antidiquark component of these tetraquarks.