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Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide
No full textUnderstanding the structure and properties of refractory oxides is critical for high temperature applications. In this work, a combined experimental and simulation approach uses an automated closed loop via an active learner, which is initialized by x-ray and neutron diffraction measurements, and sequentially improves a machine-learning model until the experimentally predetermined phase space is covered. A multiphase potential is generated for a canonical example of the archetypal refractory oxide, HfO2, by drawing a minimum number of training configurations from room temperature to the liquid state at ∼2900 °C. The method significantly reduces model development time and human effort
Disorder in a two-domain neuronal Ca<sup>2+</sup>-binding protein regulates domain stability and dynamics using ligand mimicry
No full textUnderstanding the interplay between sequence, structure and function of proteins has been complicated in recent years by the discovery of intrinsically disordered proteins (IDPs), which perform biological functions in the absence of a well-defined three-dimensional fold. Disordered protein sequences account for roughly 30% of the human proteome and in many proteins, disordered and ordered domains coexist. However, few studies have assessed how either feature affects the properties of the other. In this study, we examine the role of a disordered tail in the overall properties of the two-domain, calcium-sensing protein neuronal calcium sensor 1 (NCS-1). We show that loss of just six of the 190 residues at the flexible C-terminus is sufficient to severely affect stability, dynamics, and folding behavior of both ordered domains. We identify specific hydrophobic contacts mediated by the disordered tail that may be responsible for stabilizing the distal N-terminal domain. Moreover, sequence analyses indicate the presence of an LSL-motif in the tail that acts as a mimic of native ligands critical to the observed order–disorder communication. Removing the disordered tail leads to a shorter life-time of the ligand-bound complex likely originating from the observed destabilization. This close relationship between order and disorder may have important implications for how investigations into mixed systems are designed and opens up a novel avenue of drug targeting exploiting this type of behavior
Nanoparticles retard immune cells recruitment in vivo by inhibiting chemokine expression
No full textThe large-scale utilization of nanotechnology depends on public and consumer confidence in the safety of this new technology. Studying the interaction of nanoparticles with immune cells plays a vital role in the safety assessment of nanomedicine. Although some researches have indicated that the immune cells undergo severe interfere after phagocytosis of nanoparticles, the impact on immune system of the whole body are still unclear. Here, we use immune cells labeled transgenic zebrafish to study the mechanisms of nanoparticles on zebrafish immune cells. We demonstrate that gold nanoparticles (Au NPs) phagocytized by immune cells can reduce and retard the sensitivity of immune response, resulting nanoparticle-induced bluntness in immune cell (NIBIC). RNA-seq and functional analysis reveal that NIBIC is mainly induced by the inhibiting expression of chemokine receptor 5 (CCR5). Furthermore, PVP-modified Au NPs can eliminate NIBIC by inhibiting the cell phagocytosis. Our results highlight the potential risk for Au NPs in vivo and further the understanding of the mechanism of the interaction between Au NPs and the immune response. We should consider this factor in future material design and pay more attention to the process of using nanomedicines on immune diseases
Bacterial persisters are a stochastically formed subpopulation of low-energy cells
No full textPersisters represent a small subpopulation of non- or slow-growing bacterial cells that are tolerant to killing by antibiotics. Despite their prominent role in the recalcitrance of chronic infections to antibiotic therapy, the mechanism of their formation has remained elusive. We show that sorted cells of Escherichia coli with low levels of energy-generating enzymes are better able to survive antibiotic killing. Using microfluidics time-lapse microscopy and a fluorescent reporter for in vivo ATP measurements, we find that a subpopulation of cells with a low level of ATP survives killing by ampicillin. We propose that these low ATP cells are formed stochastically as a result of fluctuations in the abundance of energy-generating components. These findings point to a general “low energy” mechanism of persister formation
Data-efficient machine learning for molecular crystal structure prediction
No full textThe combination of modern machine learning (ML) approaches with high-quality data from quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost ratio. However, such methods are ultimately limited by the computational effort required to produce the reference data. In particular, reference calculations for periodic systems with many atoms can become prohibitively expensive for higher levels of theory. This trade-off is critical in the context of organic crystal structure prediction (CSP). Here, a data-efficient ML approach would be highly desirable, since screening a huge space of possible polymorphs in a narrow energy range requires the assessment of a large number of trial structures with high accuracy. In this contribution, we present tailored Δ-ML models that allow screening a wide range of crystal candidates while adequately describing the subtle interplay between intermolecular interactions such as H-bonding and many-body dispersion effects. This is achieved by enhancing a physics-based description of long-range interactions at the density functional tight binding (DFTB) level—for which an efficient implementation is available—with a short-range ML model trained on high-quality first-principles reference data. The presented workflow is broadly applicable to different molecular materials, without the need for a single periodic calculation at the reference level of theory. We show that this even allows the use of wavefunction methods in CSP
Erratum: Optical and electronic properties of colloidal cdse quantum rings (ACS Nano (2020) 14:11 (14740-14760) DOI: 10.1021/acsnano.0c01752)
No full textIn the originally published article, the affiliations for Violette Steinmetz, Zahra Andaji-Garmaroudi, Samuel D. Stranks, Thierry Barisien, and Raj Pandya were incorrect; the spelling of Mustafa Caglar's name was also incorrect. They have now been corrected. It was also indicated to us that there were some figures missing from the Supporting Information file; this has now been updated
Visualising food traceability systems: A novel system architecture for mapping material and information flow
No full textBackground: Traceability of food products, ingredients and associated operations are important requirements for improving food safety and consumer confidence. Food traceability systems (FTSs) often suffer from inefficiency in either material or information flow within an enterprise or between supply chain partners. Modelling of system architecture is a visualisation approach that allows multiple parties to collaborate in a system design process, identify its inefficiencies and propose improvements. However, there is little academic research on the ability to use a standard visualisation tool that supports collaborative design and considers both material and information flow for a given food traceability system. Scope & approach: The aim of this research is to propose a new visualisation approach that allows supply chain operators to collaborate effectively in the design process of FTSs capable of maintaining streamlined information flow, minimising information loss, and improving supply chain performance. Key findings & conclusion: Food traceability systems are complex, encompassing processes, material flow, information flow, techniques, infrastructure, people and control strategies. Screening of literature demonstrates that model-based system engineering (MBSE) offers a sound way for visualisation of such complex systems. However, in the food traceability literature, an MBSE-based standardised traceability system modelling approach is absent. This study makes a strong contribution to existing literature by proposing a novel, material and information flow modelling technique (MIFMT), to visualise FTS architecture. MIFMT can support common understanding and iterative implementation of effective FTSs that contextualise food supply chains at multiple levels and provides opportunity to identify points at where inefficiencies can occur so that actions can be taken to mitigate them
Achieving long-term stability of thin-film electrodes for neurostimulation<sup>☆</sup>
No full textImplantable electrodes that can reliably measure brain activity and deliver an electrical stimulus to a target tissue are increasingly employed to treat various neurological diseases and neuropsychiatric disorders. Flexible thin-film electrodes have gained attention over the past few years to minimise invasiveness and damage upon implantation. Research has previously focused on optimising the electrode's electrical and mechanical properties; however, their chronic stability must be validated to translate electrodes from a research to a clinical application. Neurostimulation electrodes, which actively inject charge, have yet to reliably demonstrate continuous functionality for ten years or more in vivo, the accepted metric for clinical viability. Long-term stability can only be achieved if the focus switches to investigating how and why such devices fail. Unfortunately, there is a field-wide reluctance to investigate device stability and failures, which hinders device optimisation. This review surveys thin-film electrode designs with a focus on adhesion between electrode layers and the interactions with the surrounding environment. A comprehensive summary of the abiotic failure modes faced by such electrodes is presented, and to encourage investigation, systematic methods for analysing their origin are recommended. Finally, approaches to reducing the likelihood of device failure are offered. Statement of significance: Neural electrodes that can deliver an electrical stimulus to a target tissue are widely used to treat various neurological diseases. Essential to the function of these electrodes is the ability to safely stimulate the target tissue for extended periods (> 10 years); however, this has not yet been clinically achieved. The key to achieving long-term stability is an increased understanding of electrode interactions with the surrounding tissue and subsequent systematic analysis of their failure modes. This review highlights the need for a change in the approach to investigating electrode failure, and in doing so summarizes the common ways in which neural electrodes fail, methods for identifying them and approaches to preventing them
Towards AI driven environmental sustainability: an application of automated logistics in container port terminals
No full textArtificial intelligence and data analytics capabilities have enabled the introduction of automation, such as robotics and Automated Guided Vehicles (AGVs), across different sectors of the production spectrum which successively has profound implications for operational efficiency and productivity. However, the environmental sustainability implications of such innovations have not been yet extensively addressed in the extant literature. This study evaluates the use of AGVs in container terminals by investigating the environmental sustainability gains that arise from the adoption of artificial intelligence and automation for shoreside operations at freight ports. Through a comprehensive literature review, we reveal this research gap across the use of artificial intelligence and decision support systems, as well as optimisation models. A real-world container terminal is used, as a case study in a simulation environment, on Europe’s fastest-growing container port (Piraeus), to quantify the environmental benefits related to routing scenarios via different types of AGVs. Our study contributes to the cross-section of operations management and artificial intelligence literature by articulating design principles to inform effective digital technology interventions at non-automated port terminals, both at operational and management levels
Rational Passivation of Sulfur Vacancy Defects in Two-Dimensional Transition Metal Dichalcogenides
No full textStructural defects vary the optoelectronic properties of monolayer transition metal dichalcogenides, leading to concerted efforts to control defect type and density via materials growth or postgrowth passivation. Here, we explore a simple chemical treatment that allows on-off switching of low-lying, defect-localized exciton states, leading to tunable emission properties. Using steady-state and ultrafast optical spectroscopy, supported by ab initio calculations, we show that passivation of sulfur vacancy defects, which act as exciton traps in monolayer MoS2 and WS2, allows for controllable and improved mobilities and an increase in photoluminescence up to 275-fold, more than twice the value achieved by other chemical treatments. Our findings suggest a route for simple and rational defect engineering strategies for tunable and switchable electronic and excitonic properties through passivation