Structural Fingerprint of Crystallization in Mixed-Alkali Bioactive Glasses

Hench-type bioactive glasses such as 45S5 exhibit excellent biological and therapeutic performance, including osteogenesis, angiogenesis, bactericidal activity, and anti-inflammation properties [1, 2]. However, the pronounced devitrification tendency significantly reduces the processing window, which limits their clinical use [3]. In this study, we aim to decipher the underlying structural fingerprint correlated with the crystallization propensity of such glasses. To this end, the atomic-scale arrangements of mixed-alkali bioactive (MAB, 46.1SiO2-2.6P2O5-26.0CaO-(24.2-x)Na2O-xLi2O) glasses were determined using high energy synchrotron X-ray diffraction, reverse Monte Carlo simulation, Raman and solid-state nuclear magnetic resonance spectroscopy. The glasses were prepared by two quenching protocols with different cooling rates. The MAB glasses formed through rapid cooling (containerless aerodynamic levitation quenching) show much better stability (higher glass transition Tg and crystallization Tc temperatures) and processability (∆T = Tc-Tg) compared to the slowly cooled glasses (conventional melt quenching). Moreover, these thermal properties exhibit significant composition dependence with the Li:Na ratio. Overall, Tg shows a nonlinear negative deviation, while ∆T displays a parabolic-like tendency consistent with the mixing entropy. Variations of Tg and ∆T are intricately correlated with the hierarchical-scale network connectivity prompted by rapid cooling and mixed alkali effects, including but not limited to the flexible Si-O-P linkages that were validated in the latest simulations [4]. A physics-based structural fingerprint is then developed, where the contours of topological constraints and local configurational entropy projected on individual network-formers (Si, P) are associated with the barriers of potential nucleation. We show that the enhancement of crystalline resistance is linked to the decrease of possible nucleation sites

Structural fingerprints of transcription factor binding site regions

Fourier transforms are a powerful tool in the prediction of DNA sequence properties, such as the presence/absence of codons. We have previously compiled a database of the structural properties of all 32,896 unique DNA octamers. In this work we apply Fourier techniques to the analysis of the structural properties of human chromosomes 21 and 22 and also to three sets of transcription factor binding sites within these chromosomes. We find that, for a given structural property, the structural property power spectra of chromosomes 21 and 22 are strikingly similar. We find common peaks in their power spectra for both Sp1 and p53 transcription factor binding sites. We use the power spectra as a structural fingerprint and perform similarity searching in order to find transcription factor binding site regions. This approach provides a new strategy for searching the genome data for information. Although it is difficult to understand the relationship between specific functional properties and the set of structural parameters in our database, our structural fingerprints nevertheless provide a useful tool for searching for function information in sequence data. The power spectrum fingerprints provide a simple, fast method for comparing a set of functional sequences, in this case transcription factor binding site regions, with the sequences of whole chromosomes. On its own, the power spectrum fingerprint does not find all transcription factor binding sites in a chromosome, but the results presented here show that in combination with other approaches, this technique will improve the chances of identifying functional sequences hidden in genomic data

Self-assembly of colloidal molecules due to self-generated flow

The emergence of structure through aggregation is a fascinating topic and of both fundamental and practical interest. Here we demonstrate that self-generated solvent flow can be used to generate long-range attractions on the colloidal scale, with sub-pico Newton forces extending into the millimeter-range. We observe a rich dynamic behavior with the formation and fusion of small clusters resembling molecules, the dynamics of which is governed by an effective conservative energy that decays as $1/r$. Breaking the flow symmetry, these clusters can be made active

IMPROVING MOLECULAR FINGERPRINT SIMILARITY VIA ENHANCED FOLDING

Drug discovery depends on scientists finding similarity in molecular fingerprints to the drug target. A new way to improve the accuracy of molecular fingerprint folding is presented. The goal is to alleviate a growing challenge due to excessively long fingerprints. This improved method generates a new shorter fingerprint that is more accurate than the basic folded fingerprint. Information gathered during preprocessing is used to determine an optimal attribute order. The most commonly used blocks of bits can then be organized and used to generate a new improved fingerprint for more optimal folding. We thenapply the widely usedTanimoto similarity search algorithm to benchmark our results. We show an improvement in the final results using this method to generate an improved fingerprint when compared against other traditional folding methods

Atomic structure optimization with machine-learning enabled interpolation between chemical elements

We introduce a computational method for global optimization of structure and ordering in atomic systems. The method relies on interpolation between chemical elements, which is incorporated in a machine learning structural fingerprint. The method is based on Bayesian optimization with Gaussian processes and is applied to the global optimization of Au-Cu bulk systems, Cu-Ni surfaces with CO adsorption, and Cu-Ni clusters. The method consistently identifies low-energy structures, which are likely to be the global minima of the energy. For the investigated systems with 23-66 atoms, the number of required energy and force calculations is in the range 3-75

WGCN: Graph Convolutional Networks with Weighted Structural Features

Graph structural information such as topologies or connectivities provides valuable guidance for graph convolutional networks (GCNs) to learn nodes' representations. Existing GCN models that capture nodes' structural information weight in- and out-neighbors equally or differentiate in- and out-neighbors globally without considering nodes' local topologies. We observe that in- and out-neighbors contribute differently for nodes with different local topologies. To explore the directional structural information for different nodes, we propose a GCN model with weighted structural features, named WGCN. WGCN first captures nodes' structural fingerprints via a direction and degree aware Random Walk with Restart algorithm, where the walk is guided by both edge direction and nodes' in- and out-degrees. Then, the interactions between nodes' structural fingerprints are used as the weighted node structural features. To further capture nodes' high-order dependencies and graph geometry, WGCN embeds graphs into a latent space to obtain nodes' latent neighbors and geometrical relationships. Based on nodes' geometrical relationships in the latent space, WGCN differentiates latent, in-, and out-neighbors with an attention-based geometrical aggregation. Experiments on transductive node classification tasks show that WGCN outperforms the baseline models consistently by up to 17.07% in terms of accuracy on five benchmark datasets

Unintentional F doping of the surface of SrTiO3(001) etched in HF acid -- structure and electronic properties

We show that the HF acid etch commonly used to prepare SrTiO3(001) for heteroepitaxial growth of complex oxides results in a non-negligible level of F doping within the terminal surface layer of TiO2. Using a combination of x-ray photoelectron spectroscopy and scanned angle x-ray photoelectron diffraction, we determine that on average ~13 % of the O anions in the surface layer are replaced by F, but that F does not occupy O sites in deeper layers. Despite this perturbation to the surface, the Fermi level remains unpinned, and the surface-state density, which determines the amount of band bending, is driven by factors other than F doping. The presence of F at the STO surface is expected to result in lower electron mobilities at complex oxide heterojunctions involving STO substrates because of impurity scattering. Unintentional F doping can be substantially reduced by replacing the HF-etch step with a boil in deionized water, which in conjunction with an oxygen tube furnace anneal, leaves the surface flat and TiO2 terminated.Comment: 18 pages, 7 figure

Enhanced Raman Detection System based on a Hollow-core Fiber Probe design

This paper focus on an enhanced Raman-based detection probe and its performance evaluated. The probe employs a hollow-core fiber design to allow liquid micro-sample to be analyzed. The hollow-core fiber is used both to transmit the light signal used to excite the sample and to collect the Raman scattering signal received from the micro-sample under analysis. In order to maximize the performance of the system, various parameters have been studied experimentally, including the diameter and the height of the liquid sample in the probe. The aim has been optimizing both as a means to enhance the Raman scattering signal received from the liquid sample. As a result, a Raman-based detection probe using a reflector approach was developed and evaluated. This design enabling a greater area for interaction with the sample to be formed and to concentrate the excitation light into it. This then increases the efficiency of the light-liquid interaction and improves the collection efficiently of the forward Raman scattering light signal. With the use of this design, the detected Raman scattering signal was increased by a factor of 103~104 over what otherwise would be achieved. A key feature is that with the use of a hollow-core fiber to collect the liquid sample, only a very small volume is needed, making this well suited to practical applications where limited amounts of material are available e.g. biofluids or high value liquids. The system designed and evaluated thus provides the basis of an effective all-fiber Raman-based detection system, capable of being incorporated into portable analysis equipment for rapid detection and in-the-field use