Explore the vast range of titles available.

Contrary to simplified theoretical models, atomistic calculations presented here reveal that sufficiently large in-plane shape elongation of quantum dots can not only decrease, but even reverse the splitting of the two lowest optically active excitonic states. Such a surprising cancellation of bright-exciton splitting occurs for shape-anisotropic nanostructures with realistic elongation ratios, yet without a wetting layer, which plays here a vital role. However, this non-trivial effect due to shape-elongation is strongly diminished by alloy randomness resulting from intermixing of InAs quantum-dot material with the surrounding InP matrix. Alloying randomizes, and to some degree flattens the shape dependence of fine-structure splitting giving a practical justification for the application of simplified theories. Finally, we find that the dark-exciton spectra are rather weakly affected by alloying and are dominated by the effects of lateral elongation.

Indoor navigation poses persistent challenges due to the limitations of traditional positioning systems within buildings. In this study, we propose a novel approach to address this issue—not by continuously tracking the user’s location, but by estimating their position based on how closely their observed behavior matches the expected progression along a predefined route. This concept, while not universally applicable, is well-suited for specific indoor navigation scenarios, such as guiding couriers or delivery personnel through complex residential buildings. We explore this idea in detail in our paper. To implement this behavior-based localization, we introduce an LSTM-based method for classifying user behavior patterns, including standing, walking, and using stairs or elevators, by analyzing velocity sequences derived from smartphone sensors’ data. The developed model achieved 75% accuracy for individual activity type classification within one-second time windows, and 98.6% for full-sequence classification through majority voting. These results confirm the viability of real-time activity recognition as the foundation for a navigation system that aligns live user behavior with pre-recorded patterns, offering a cost-effective alternative to infrastructure-heavy indoor positioning systems.

Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure 1 . Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold 2 , at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective. AlphaFold is used to predict the structures of almost all of the proteins in the human proteome—the availability of high-confidence predicted structures could enable new avenues of investigation from a structural perspective.

The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients 1 . To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records 2 – 17 and using acute kidney injury—a common and potentially life-threatening condition 18 —as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests 9 . Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment. A deep learning approach that predicts the risk of acute kidney injury may help to identify patients at risk of health deterioration within a time window that enables early treatment.

Mesoporous silica materials were synthesized using inexpensive and environmentally friendly sucrose as a porogeneous agent. It was found that the presence of sucrose and the products of its chemical transformation during synthesis (e.g., furfural polymer) significantly affected the structure of the obtained porous silica. The influence of synthesis conditions (pH, temperature, time) on the textural properties of the final materials was determined. Samples obtained in an acidic medium, at pH = 1, and treated at room temperature, yielded products with a large surface area and a narrow pore size distribution in the range of 2–5 nm, while the synthesis at pH = 8 allowed for the formation of mesoporous systems with pores in the range of 14–20 nm. To generate acidity, the silicas were modified with an ammonium fluoride solution and then used as supports for iridium catalysts in a hydrogenation reaction, with toluene as a model hydrocarbon. The influence of parameters such as specific surface area, support acidity, and iridium dispersion on catalytic activity was determined. It was shown that modification with sucrose improved the porous structure, and NH4F modification generated acidity. These parameters favored better reducibility and dispersion of the active phase, resulting in higher activity of the catalysts in the studied hydrogenation reaction.

This study focuses on the catalytic properties of ruthenium catalysts supported on modified silicalite-1 (with an MFI structure). By post-synthesis modification of silicalite-1 with solutions of alkali metal compound, a novel and cost-effective method was discovered to create basic centers on the surface of silicalite-1 supports. The modification not only affected the basicity of the supports but also their porosity. The influence of the type of alkali solution (KOH or NaOH) and its concentration (0.1 M or 1.0 M) on both the basicity and porosity was investigated. The modified silicalite-1 materials were employed as supports for ruthenium catalysts (1 wt.% Ru) and evaluated for their CO2 methanation activity. The results were compared with the hydrogenation performance of ruthenium catalysts supported on unmodified silicalite-1. Characterization of the supports and catalysts was conducted using techniques such as BET, XRD, FT-IR, ICP-OES, TPR-H2, H2 chemisorption, TPD-CO2, SEM, and TEM. Remarkably, the catalytic activity of ruthenium supported on silicalite-1 treated with 1.0 M NaOH (exhibiting selectivity toward methane above 90% in a reaction temperature range of 250–450 °C) outperformed both unmodified and KOH-modified silicalite-1 supported Ru catalysts.

Lubrication is a critical aspect of the metal forming process and it is strongly influenced by the surface texture of the tool-forming surfaces. This study is focused on determining the effect of surface finish and heat treatment on wettability involving commonly used lubrication agents. Three different finishing states are evaluated (as-ground, as-polished and as-nitrided). Surface topography was measured using a focus variation microscope. Parametric evaluation was carried out according to ISO 25178, including fractal methods. The functional relations between the finish state and wettability, lubricating agent and wettability, selected surface parameters and wettability, as well as between finish state and selected surface parameters, were designated. The results showed that surface finishing treatments, particularly nitriding, influenced both surface roughness and wettability, with varying effects observed across different lubricants and droplet sizes. The findings provide valuable insights into the optimization of lubrication strategies for metal forming processes, highlighting the importance of tailored surface treatments for enhanced tool performance and longevity.

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks. We present a practical workflow describing how to use deep learning to develop continuous-risk models that can predict various adverse outcomes from structured electronic health records.

In the present study, a comprehensive evaluation of the antioxidant properties of various parts of the hemp plant, specifically its leaves and flowers, in a new feminized variety of with an admixture of was investigated. Methods such as Electron Paramagnetic Resonance (EPR) and UV-visible (UV-vis) spectroscopy were used to assess trolox equivalent antioxidant capacity (TEAC) and total polyphenol content (TPC). TEAC values of the analyzed samples ranged from 29.5 μmol TE/g DW to 150.2 μmol TE/g DW, while TPC varied between 5.4 mg GAE/g DW and 20.3 mg GAE/g DW. The findings indicate that hemp exhibits significant antioxidant properties, especially in its leaves. This is attributed to a diverse range of antioxidants, including cannabinoids, flavonoids, and phenolic compounds, which offer notable health benefits. Furthermore, the method of drying hemp has been shown to influence its antioxidant properties significantly. Research indicates that freeze-dried and air-dried hemp retains higher levels of antioxidant compounds compared to other drying methods. This suggests that selecting an appropriate drying technique is essential for preserving the bioactive compounds responsible for hemp's antioxidant activity.

Farnesyl pyrophosphate synthase (FPPS) is an enzyme of the mevalonate pathway and a well-established therapeutic target. Recent research has focused around a newly identified druggable pocket near the enzyme’s active site. Pharmacological exploitation of this pocket is deemed promising; however, its natural biological function, if any, is yet unknown. Here we report that the product of FPPS, farnesyl pyrophosphate (FPP), can bind to this pocket and lock the enzyme in an inactive state. The K d for this binding is 5–6 μM, within a catalytically relevant range. These results indicate that FPPS activity is sensitive to the product concentration. Kinetic analysis shows that the enzyme is inhibited through FPP accumulation. Having a specific physiological effector, FPPS is a bona fide allosteric enzyme. This allostery offers an exquisite mechanism for controlling prenyl pyrophosphate levels in vivo and thus contributes an additional layer of regulation to the mevalonate pathway. Farnesyl pyrophosphate (FPP) is a key building block for the synthesis of many lipids. Here the authors determine the crystal structure of farnesyl pyrophosphate synthase (FPPS) with its bound product and use kinetic measurements to show that FPP is an allosteric effector of the enzyme.