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The development of a new materials platform capable of sustaining the functionality of proteinous sensor molecules over an extended period without being affected by biological contaminants in living systems, such as proteases, is highly demanded. In this study, our primary focus was on fabricating new core-shell fibremats using unique polymer materials, capable of functionalizing encapsulated sensor proteins while resisting the effects of proteases. The core-fibre parts of core-shell fibremats were made using a newly developed post-crosslinkable water-soluble copolymer, poly(2-hydroxypropyl methacrylamide)-co-poly(diacetone methacrylamide), and the bifunctional crosslinking agent, adipic dihydrazide, while the shell layer of the nanofibers was made of nylon 6. Upon encapsulating the lactate-sensor protein eLACCO1.1 at the core-fibre part, the fibremat exhibited a distinct concentration-dependent fluorescence response, with a dynamic range of fluorescence alteration exceeding 1000% over the lactate concentration range of 0 to 100 mM. The estimated dissociation constant from the titration data was comparable to that estimated in a buffer solution. The response remained stable even after 5 cycles and in the presence of proteases. These results indicates that our core-shell fibremat platform could serve as effective immobilizing substrates for various sensor proteins, facilitating continuous and quantitative monitoring of various low-molecular-weight metabolites and catabolites in a variety of biological samples.

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Elucidating the fouling phenomena of polymer surfaces will facilitate the molecular design of high-performance biomedical devices. Here, we investigated the remarkable antifouling properties of two acrylate materials, poly(2-methoxyethyl acrylate) (PMEA) and poly(3-methoxypropionic acid vinyl ester) (PMePVE), which have a terminal methoxy group on the side chain, via molecular dynamics simulations of binary mixtures of acrylate/methacrylate trimers with n-pentane or 2,2-dimethylpropane (neopentane), that serve as the nonpolar organic probe (organic foulants). The second virial coefficient (B2) was determined to assess the aggregation/dispersion properties in the binary mixtures. The order of the B2 values for the trimer/pentane mixtures indicated that the terminal methoxy group of the side chain plays an important role in enhancing the fouling resistance to nonpolar organic foulants. Here, we hypothesized that the antifouling properties of PMEA/PMePVE surfaces originate from the resistance. To evaluate the molecular-level accessibility of organic foulants to acrylate/methacrylate materials, we examined the radial distribution functions (RDFs) of the terminal methyl groups of neopentane around the main chains of the acrylate/methacrylate trimers. As a result, the third distinct RDF peaks are observed only for the methacrylate trimers. The peaks are attributed to the hydrophobic interactions between the methyl group of neopentane and that of the main chain of the trimer. Accordingly, the methyl group of the main chain of methacrylate materials, such as poly(2-hydroxyethyl methacrylate) and poly(2-methoxyethyl methacrylate), unfavorably induces fouling with organic foulants. In this study, we clarify that preventing hydrophobic interactions between an organic foulant and polymeric material is essential for enhancing the antifouling property. Our approach has great potential for evaluating the molecular-level affinities of organic foulant with polymer surfaces for the molecular design of excellent antifouling polymeric materials.

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Improving hydrophilicity is a key factor for enhancing the biocompatibility of polymer surfaces. Nevertheless, previous studies have reported that poly(2-methoxyethyl acrylate) (PMEA) surfaces demonstrate markedly better biocompatibility than more hydrophilic poly(2-hydroxyethyl methacrylate) (PHEMA) surfaces. In this work, the origins of the excellent biocompatibility of the PMEA surface are investigated using molecular dynamics (MD) simulations of simplified binary mixtures of acrylate/methacrylate trimers and organic solvents, with n-nonane, 1,5-pentanediol, or 1-octanol serving as the probe organic foulants. The interactions between the acrylate/methacrylate trimers and solvent molecules were evaluated by calculating the radial distribution function (RDF), with the resulting curves indicating that the 2-methoxyethyl acrylate (MEA) trimer has a lower affinity for n-nonane molecules than the 2-hydroxyethyl methacrylate (HEMA) trimer. This result agrees with the experimental consensus that the biocompatibility of PMEA surfaces is better than that of PHEMA surfaces, supporting the hypothesis that the affinity between an acrylate/methacrylate trimer and a foulant molecule in a simplified binary mixture is a significant factor in determining a surface's antifouling properties. The RDF curves obtained for the other two solvent systems exhibited behavior that further highlighted the advantages of the PMEA surfaces as biocompatible polymers. In addition, the validity of employing the second virial coefficient (B2) as a predictor of antifouling properties was explored. The order of the B2 values of different binary mixtures indicated that the MEA trimers have the lowest affinities with n-nonane molecules, which confirms that although PMEA is more hydrophobic than PHEMA, it exhibits better biocompatibility. This analysis demonstrates that the MEA's weaker miscibility with nonpolar foulants contributes to the excellent biocompatibility of PMEA. Thus, B2 is a promising criterion for assessing the miscibility between acrylate/methacrylate materials and nonpolar organic foulants, which indicates the potential for predicting the antifouling properties of acrylate/methacrylate polymer materials by evaluating the value of B2.

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We developed a novel method for measuring the dynamic surface tension of liquids using mechanically vibrated sessile droplets. Under continuous mechanical vibration, the shape of the deformed droplet was fitted by numerical analysis, taking into account the force balance at the drop surface and the momentum equation. The surface tension was determined by optimizing four parameters: the surface tension, the droplet's height, the radius of the droplet-substrate contact area, and the horizontal symmetrical position of the droplet. The accuracy and repeatability of the proposed method were confirmed using drops of distilled water as well as viscous aqueous glycerol solutions. The vibration frequency had no influence on surface tension in the case of pure liquids. However, for water-soluble surfactant solutions, the dynamic surface tension gradually increased with vibration frequency, which was particularly notable for low surfactant concentrations slightly below the critical micelle concentration. This frequency dependence resulted from the competition of two mechanisms at the drop surface: local surface deformation and surfactant transport towards the newly generated surface.

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In this work, it is shown that for the first time that, using information-entropy-based methods, one can quantitatively explore the relative impact of a wide multidimensional array of electronic and chemical bonding parameters on the structural stability of intermetallic compounds. Using an inorganic AB2 compound database as a template data platform, the evolution of design rules for crystal chemistry based on an information-theoretic partitioning classifier for a high-dimensional manifold of crystal chemistry descriptors is monitored. An application of this data-mining approach to establish chemical and structural design rules for crystal chemistry is demonstrated by showing that, when coupled with first-principles calculations, statistical inference methods can serve as a tool for significantly accelerating the prediction of unknown crystal structures.

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A Brownian dynamics simulation has been used to investigate the aggregation kinetics of bimodal colloidal mixtures with similar surface chemistries but different sizes, driven by the DLVO interaction potential. The time evolution of structural formation is examined by the mean number of neighbors under fast and slow aggregation regions. It was found that the electrolyte ionic strength affects the kinetic pattern of colloidal aggregation. Under the high electrolyte ionic strength conditions (fast aggregation), the selective aggregation of the least stable single component can take place in the early stage, while the other component is enriched in this least stable component in the later stage. With the ionic strength decreasing (towards the slow aggregation), the hybrid aggregation (selective aggregation and heteroaggregation) gradually dominates the aggregation kinetics. Also in the early stage, this evolves to the heteroaggregation of different components under lower ionic strength conditions. The volume fraction has no obvious influence on this kinetic pattern in the early stage.

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In the search for new cancer subtypes by gene expression profiling, it is essential to avoid misclassifying samples of unknown subtypes as known ones. In this paper, we evaluated the false positive error rates of several classification algorithms through a 'null test' by presenting classifiers a large collection of independent samples that do not belong to any of the tumor types in the training dataset. The benchmark dataset is available at www2.genome.rcast.u-tokyo.ac.jp/pm/. We found that k-nearest neighbor (KNN) and support vector machine (SVM) have very high false positive error rates when fewer genes (<100) are used in prediction. The error rate can be partially reduced by including more genes. On the other hand, prototype matching (PM) method has a much lower false positive error rate. Such robustness can be achieved without loss of sensitivity by introducing suitable measures of prediction confidence. We also proposed a cluster-and-select technique to select genes for classification. The nonparametric Kruskal-Wallis H test is employed to select genes differentially expressed in multiple tumor types. To reduce the redundancy, we then divided these genes into clusters with similar expression patterns and selected a given number of genes from each cluster. The reliability of the new algorithm is tested on three public datasets.

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Recognition-by-components is one of the possible strategies proposed for object recognition by the brain, but little is known about the low-level mechanism by which the parts of objects can be learned without a priori knowledge. Recent work by Lee and Seung (Nature 401 (1999) 788) shows the importance of non-negativity constraints in the building of such models. Here we propose a simple feedforward neural network that is able to learn the parts of objects by the auto-association of sensory stimuli. The network is trained to reproduce each input with only excitatory interactions. When applied to a database of facial images, the network extracts localized features that resemble intuitive notion of the parts of faces. This kind of localized, parts-based internal representation is very different from the holistic representation created by the unconstrained network, which emulates principal component analysis. Furthermore, the simple model has some ability to minimize the number of active hidden units for certain tasks and is robust when a mixture of different stimuli is presented.

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