Chung Yuan Christian University

The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we construct an unsupervised learning schema for the k-means algorithm so that it is free of initializations without parameter selection and can also simultaneously find an optimal number of clusters. That is, we propose a novel unsupervised k-means (U-k-means) clustering algorithm with automatically finding an optimal number of clusters without giving any initialization and parameter selection. The computational complexity of the proposed U-k-means clustering algorithm is also analyzed. Comparisons between the proposed U-k-means and other existing methods are made. Experimental results and comparisons actually demonstrate these good aspects of the proposed U-k-means clustering algorithm.

Environmental exposure to active pharmaceutical ingredients (APIs) can have negative effects on the health of ecosystems and humans. While numerous studies have monitored APIs in rivers, these employ different analytical methods, measure different APIs, and have ignored many of the countries of the world. This makes it difficult to quantify the scale of the problem from a global perspective. Furthermore, comparison of the existing data, generated for different studies/regions/continents, is challenging due to the vast differences between the analytical methodologies employed. Here, we present a global-scale study of API pollution in 258 of the world's rivers, representing the environmental influence of 471.4 million people across 137 geographic regions. Samples were obtained from 1,052 locations in 104 countries (representing all continents and 36 countries not previously studied for API contamination) and analyzed for 61 APIs. Highest cumulative API concentrations were observed in sub-Saharan Africa, south Asia, and South America. The most contaminated sites were in low- to middle-income countries and were associated with areas with poor wastewater and waste management infrastructure and pharmaceutical manufacturing. The most frequently detected APIs were carbamazepine, metformin, and caffeine (a compound also arising from lifestyle use), which were detected at over half of the sites monitored. Concentrations of at least one API at 25.7% of the sampling sites were greater than concentrations considered safe for aquatic organisms, or which are of concern in terms of selection for antimicrobial resistance. Therefore, pharmaceutical pollution poses a global threat to environmental and human health, as well as to delivery of the United Nations Sustainable Development Goals.

The first community competition designed to objectively compare the performance of particle tracking algorithms provides valuable practical information for both users and developers. Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

Separating molecules or ions with sub-Angstrom scale precision is important but technically challenging. Achieving such a precise separation using membranes requires Angstrom scale pores with a high level of pore size uniformity. Herein, we demonstrate that precise solute-solute separation can be achieved using polyamide membranes formed via surfactant-assembly regulated interfacial polymerization (SARIP). The dynamic, self-assembled network of surfactants facilitates faster and more homogeneous diffusion of amine monomers across the water/hexane interface during interfacial polymerization, thereby forming a polyamide active layer with more uniform sub-nanometre pores compared to those formed via conventional interfacial polymerization. The polyamide membrane formed by SARIP exhibits highly size-dependent sieving of solutes, yielding a step-wise transition from low rejection to near-perfect rejection over a solute size range smaller than half Angstrom. SARIP represents an approach for the scalable fabrication of ultra-selective membranes with uniform nanopores for precise separation of ions and small solutes.

Three diamine monomers (ethylenediamine, butylenediamine, and p -phenylenediamine) were selected for cross-linking graphene oxide (GO) to prepare composite graphene oxide-framework (GOF) membranes through filtration using a pressure-assisted self-assembly technique. The membranes were applied to separate an ethanol–water mixture by pervaporation. Unmodified GO comprised only hydrogen bonds and π–π interactions, but after cross-linking it with a diamine, attenuated total reflectance–Fourier transform infrared and X-ray photoelectron spectroscopy demonstrated that the diamine was chemically bonded both to GO and the membrane support. Moreover, GO hydrophilicity was substantially altered; water contact angle increased from 24.4° to 80.6° (from cross-linking with an aliphatic structure of diamine to cross-linking with an aromatic structure). Results of X-ray diffraction showed that d -spacing in GOF layers varied from 10.4 to 8.7 Å. For GOFs presoaked in 90 wt % ethanol–water, covalent bonds between the layer and diamine could effectively suppress stretching of d -spacing. Cross-linking with ethylenediamine produced a composite membrane that exhibited a short interlayer d -spacing and delivered an excellent pervaporation performance at 80 °C: permeation flux = 2297 g/(m 2 h); water concentration in permeate = 99.8 wt %. The membrane showed stability during a long-term operation at 30 °C for 120 h.

Synthesis of ultrasmall water-soluble fluorescent gold nanoclusters is reported. The clusters have a decent quantum yield, high colloidal stability, and can be readily conjugated with biological molecules. Specific staining of cells and nonspecific uptake by living cells is demonstrated.

Abstract The development of efficient and low energy‐consumption catalysts for CO 2 conversion is desired, yet remains a great challenge. Herein, a class of novel hollow porous carbons (HPC), featuring well dispersed dopants of nitrogen and single Zn atoms, have been fabricated, based on the templated growth of a hollow metal–organic framework precursor, followed by pyrolysis. The optimized HPC‐800 achieves efficient catalytic CO 2 cycloaddition with epoxides, under light irradiation, at ambient temperature, by taking advantage of an ultrahigh loading of (11.3 wt %) single‐atom Zn and uniform N active sites, high‐efficiency photothermal conversion as well as the hierarchical pores in the carbon shell. As far as we know, this is the first report on the integration of the photothermal effect of carbon‐based materials with single metal atoms for catalytic CO 2 fixation.

Nanoscale structural and mechanical characterization of the shell of a red abalone has been carried out. Cobble-like polygonal nanograins are basic building blocks that are used to construct individual aragonite platelets into a mother-of-pearl configuration known as nacre. The nanograin-structured aragonite platelets are not brittle in nature, but somewhat ductile. The deformability of the aragonite platelets together with the crack deflection, aragonite platelet slip, and organic adhesive interlayer contribute to the nacre's fracture toughness. Cracks formed in the outer prismatic layer of the shell do not show the crack diversion mechanism.

Modern biomedical research greatly benefits from large-scale genome-sequencing projects ranging from studies of viruses, bacteria, and yeast to multicellular organisms, like Caenorhabditis elegans. Comparative genomic studies offer a vast array of prospects for identification and functional annotation of human ortholog genes. We presented a novel comparative proteomic approach for assembling human gene contigs and assisting gene discovery. The C. elegans proteome was used as an alignment template to assist in novel human gene identification from human EST nucleotide databases. Among the available 18,452 C. elegans protein sequences, our results indicate that at least 83% (15,344 sequences) of C. elegans proteome has human homologous genes, with 7,954 records of C. elegans proteins matching known human gene transcripts. Only 11% or less of C. elegans proteome contains nematode-specific genes. We found that the remaining 7,390 sequences might lead to discoveries of novel human genes, and over 150 putative full-length human gene transcripts were assembled upon further database analyses. [The sequence data described in this paper have been submitted to the

INTRODUCTION: K-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output. CASE DESCRIPTION: Since the Euclidean distance function is the most widely used distance metric in k-NN, no study examines the classification performance of k-NN by different distance functions, especially for various medical domain problems. Therefore, the aim of this paper is to investigate whether the distance function can affect the k-NN performance over different medical datasets. Our experiments are based on three different types of medical datasets containing categorical, numerical, and mixed types of data and four different distance functions including Euclidean, cosine, Chi square, and Minkowsky are used during k-NN classification individually. DISCUSSION AND EVALUATION: The experimental results show that using the Chi square distance function is the best choice for the three different types of datasets. However, using the cosine and Euclidean (and Minkowsky) distance function perform the worst over the mixed type of datasets. CONCLUSIONS: In this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best.

Amphiphilic polymer-coating for nanoparticles: The facile one-pot synthesis of a comblike polymer can be tailored in order to vary its hydrophobic side-chains or to directly incorporate functional molecules without the need for crosslinkers. This leads to a general and robust method of phase-transfer of hydrophobic nanoparticles to aqueous solution, as well as to their modification with functional molecules. Supporting information for this article is available on the WWW under http://www.wiley-vch.de/contents/jc_2296/2008/z700654_s.pdf or from the author. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTPhysical Cues of Biomaterials Guide Stem Cell Differentiation FateAkon Higuchi*†‡§, Qing-Dong Ling§∥, Yung Chang⊥, Shih-Tien Hsu▽, and Akihiro Umezawa‡View Author Information† Department of Chemical and Materials Engineering, National Central University, Jhongli, Taoyuan 32001, Taiwan‡ Department of Reproductive Biology, National Research Institute for Child Health and Development, 2-10-1 Okura, Setagaya-ku, Tokyo 157-8535, Japan§ Cathay Medical Research Institute, Cathay General Hospital, No. 32, Ln 160, Jian-Cheng Road, Hsi-Chi City, Taipei 221, Taiwan∥ Institute of Systems Biology and Bioinformatics, National Central University, No. 300 Jhongda Rd., Jhongli, Taoyuan 32001, Taiwan⊥ Department of Chemical Engineering, R&D Center for Membrane Technology, Chung Yuan Christian University, 200 Chung-Bei Rd., Jhongli, Taoyuan 320, Taiwan▽ Taiwan Landseed Hospital, 77 Kuangtai Road, Pingjen City, Tao-Yuan County 32405, Taiwan*Tel: +866-34227151-34253. Fax: +866-3-2804271. E-mail: [email protected]Cite this: Chem. Rev. 2013, 113, 5, 3297–3328Publication Date (Web):February 7, 2013Publication History Received27 October 2012Published online7 February 2013Published inissue 8 May 2013https://pubs.acs.org/doi/10.1021/cr300426xhttps://doi.org/10.1021/cr300426xreview-articleACS PublicationsCopyright © 2013 American Chemical SocietyRequest reuse permissionsArticle Views9011Altmetric-Citations382LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Biopolymers,Cells,Differentiation,Hydrogels,Nanofibers Get e-Alerts

The noteworthy intensification in the development of nanotechnology has led to the development of various types of nanoparticles. The diverse applications of these nanoparticles make them desirable candidate for areas such as drug delivery, coasmetics, medicine, electronics, and contrast agents for magnetic resonance imaging (MRI) and so on. Iron oxide magnetic nanoparticles are a branch of nanoparticles which is specifically being considered as a contrast agent for MRI as well as targeted drug delivery vehicles, angiogenic therapy and chemotherapy as small size gives them advantage to travel intravascular or intracavity actively for drug delivery. Besides the mentioned advantages, the toxicity of the iron oxide magnetic nanoparticles is still less explored. For in vivo applications magnetic nanoparticles should be nontoxic and compatible with the body fluids. These particles tend to degrade in the body hence there is a need to understand the toxicity of the particles as whole and degraded products interacting within the body. Some nanoparticles have demonstrated toxic effects such inflammation, ulceration, and decreases in growth rate, decline in viability and triggering of neurobehavioral alterations in plants and cell lines as well as in animal models. The cause of nanoparticles' toxicity is attributed to their specific characteristics of great surface to volume ratio, chemical composition, size, and dosage, retention in body, immunogenicity, organ specific toxicity, breakdown and elimination from the body. In the current review paper, we aim to sum up the current knowledge on the toxic effects of different magnetic nanoparticles on cell lines, marine organisms and rodents. We believe that the comprehensive data can provide significant study parameters and recent developments in the field. Thereafter, collecting profound knowledge on the background of the subject matter, will contribute to drive research in this field in a new sustainable direction.

In the drive toward green and sustainable methodologies for chemicals manufacturing, biocatalysts are predicted to have much to offer in the years to come. That being said, their practical applications are often hampered by a lack of long-term operational stability, limited operating range, and a low recyclability for the enzymes utilized. Herein, we show how covalent organic frameworks (COFs) possess all the necessary requirements needed to serve as ideal host materials for enzymes. The resultant biocomposites of this study have shown the ability boost the stability and robustness of the enzyme in question, namely lipase PS, while also displaying activities far outperforming the free enzyme and biocomposites made from other types of porous materials, such as mesoporous silica and metal-organic frameworks, exemplified in the kinetic resolution of the alcohol assays performed. The ability to easily tune the pore environment of a COF using monomers bearing specific functional groups can improve its compatibility with a given enzyme. As a result, the orientation of the enzyme active site can be modulated through designed interactions between both components, thus improving the enzymatic activity of the biocomposites. Moreover, in comparison with their amorphous analogues, the well-defined COF pore channels not only make the accommodated enzymes more accessible to the reagents but also serve as stronger shields to safeguard the enzymes from deactivation, as evidenced by superior activities and tolerance to harsh environments. The amenability of COFs, along with our increasing understanding of the design rules for stabilizing enzymes in an accessible fashion, gives great promise for providing "off the shelf" biocatalysts for synthetic transformations.

Plastic pollution is a growing global emergency and it could serve as a geological indicator of the Anthropocene era. Microplastics are potentially more hazardous than macroplastics, as the former can permeate biological membranes. The toxicity of microplastic exposure on humans and aquatic organisms has been documented, but the toxicity and behavioral changes of nanoplastics (NPs) in mammals are scarce. In spite of their small size, nanoplastics have an enormous surface area, which bears the potential to bind even bigger amounts of toxic compounds in comparison to microplastics. Here, we used polystyrene nanoplastics (PS-NPs) (diameter size at ~70 nm) to investigate the neurobehavioral alterations, tissue distribution, accumulation, and specific health risk of nanoplastics in adult zebrafish. The results demonstrated that PS-NPs accumulated in gonads, intestine, liver, and brain with a tissue distribution pattern that was greatly dependent on the size and shape of the NPs particle. Importantly, an analysis of multiple behavior endpoints and different biochemical biomarkers evidenced that PS-NPs exposure induced disturbance of lipid and energy metabolism as well as oxidative stress and tissue accumulation. Pronounced behavior alterations in their locomotion activity, aggressiveness, shoal formation, and predator avoidance behavior were exhibited by the high concentration of the PS-NPs group, along with the dysregulated circadian rhythm locomotion activity after its chronic exposure. Moreover, several important neurotransmitter biomarkers for neurotoxicity investigation were significantly altered after one week of PS-NPs exposure and these significant changes may indicate the potential toxicity from PS-NPs exposure. In addition, after ~1-month incubation, the fluorescence spectroscopy results revealed the accumulation and distribution of PS-NPs across zebrafish tissues, especially in gonads, which would possibly further affect fish reproductive function. Overall, our results provided new evidence for the adverse consequences of PS-NPs-induced behavioral dysregulation and changes at the molecular level that eventually reduce the survival fitness of zebrafish in the ecosystem.

Owing to the strict restrictions imposed by the Marine Pollution Protocol and the rapid development of renewable energy, the use of solar generation and energy storage systems in ship power systems has been increasingly attracting attention. However, the improper sizing of a hybrid power generation system in a ship power system will result in a high investment cost and increased greenhouse gas emission. This paper proposes a method for determining the optimal size of the photovoltaic (PV) generation system, the diesel generator and the energy storage system in a stand-alone ship power system that minimizes the investment cost, fuel cost and the CO2 emissions. The power generation from PV modules on a ship relies on the date, local time, time zone, longitude and latitude along a navigation route and is different from the conditions of power systems on land. Thus, a method, which takes the seasonal and geographical variation of solar irradiations and temperatures along the route from Dalian in China to Aden in Yemen into account, for correcting the output of PV modules is developed in this paper. The proposed method considers five conditions along the navigation route to model the total ship load. Four cases are studied in details to demonstrate the applicability of the proposed algorithm.

A generalized version of density dependence has been introduced into the M3Y effective nucleon-nucleon $(\mathrm{NN})$ interaction that was based on the $G$-matrix elements of the Paris $\mathrm{NN}$ potential. The density dependent parameters have been chosen to reproduce the saturation binding energy and density of normal nuclear matter within a Hartree-Fock scheme, but with various values for the corresponding nuclear incompressibility $K$ ranging from 176 to 270 MeV. We use these new density dependent interactions in the folding model to calculate the real parts of $\ensuremath{\alpha}$-nucleus and nucleus-nucleus optical potentials for those systems where strongly refractive scattering patterns have been observed. These provide some information on the potentials at short distances, where there is a strong overlap of the projectile and target density distributions, and hence where the density dependence of the interaction plays an important role. We try to infer, from careful optical model (OM) analyses, the sensitivity of the scattering data to different $K$ values. Results obtained for elastic $\ensuremath{\alpha}$ scattering on targets ranging from ${}^{12}$C to ${}^{208}$Pb allow us to determine unambiguously that the $K$ value favored in this approach is within the range of 240 to 270 MeV. Similar OM analyses have also been done on measurements of the elastic scattering of ${}^{12}$C+${}^{12}$C, ${}^{16}$O+${}^{12}$C, and ${}^{16}$O+${}^{16}$O at incident energies up to 94 MeV/nucleon. These data were found to be much less sensitive over such a narrow range of $K$ values. This lack of sensitivity is due mainly to the smaller maximum overlap density which occurs for these systems, compared to that which is formed in an $\ensuremath{\alpha}$-nucleus collision. This makes the effects of density dependence less substantial. Another reason is that a small difference between two folded heavy ion potentials can often be compensated for, in part, by a small overall renormalization of one of them. This renormalization is often allowed in optical model analyses, and interpreted, for example, as accounting for a contribution from a higher-order dynamic polarization potential. In an attempt to avoid this ambiguity, some OM analyses of the extensive and accurate data for ${}^{16}$O+${}^{16}$O scattering were done using the unrenormalized folded potentials, together with the explicit addition of a correction term, expressed in terms of cubic splines. This correction term can be interpreted as representing a contribution to the real potential from the dynamic polarization potential. The results of such a ``folding+spline'' analysis suggest a tendency to favor the same $K$ value range that was found in the OM analyses of $\ensuremath{\alpha}$-nucleus scattering.

A series of nanocomposite materials that consisted of emeraldine base of polyaniline and layered montmorillonite (MMT) clay were prepared by effectively dispersing the inorganic nanolayers of MMT clay in organic polyaniline matrix via in-situ polymerization. Organic aniline monomers were first intercalated into the interlayer regions of organophilic clay hosts and followed by an one-step oxidative polymerization. The as-synthesized polyaniline−clay lamellar nanocomposite materials were characterized by infrared spectroscopy, wide-angle powder X-ray diffraction, and transmission electron microscopy. Polyaniline−clay nanocomposites (PCN) in the form of coatings with low clay loading (e.g., 0.75 wt %) on cold-rolled steel (CRS) were found much superior in corrosion protection over those of conventional polyaniline based on a series of electrochemical measurements of corrosion potential, polarization resistance, and corrosion current in 5 wt % aqueous NaCl electrolyte. The molecular weights of polyaniline extracted from PCN materials and bulk polyaniline were determined by gel permeation chromatography (GPC). Effects of the material composition on the gas barrier property, thermal stability, and mechanical strength of polyaniline along with PCN materials, in the form of both fine powder and free-standing film, were also studied by gas permeability measurements, differential scanning calorimetry, thermogravimetric analysis, and dynamic mechanical analysis.

The strong surface hydration layer of nonfouling materials plays a key role in their resistance to nonspecific protein adsorption. Poly(sulfobetaine methacrylate) (polySBMA) is an effective material that can resist nonspecific protein adsorption and cell adhesion. About eight water molecules are tightly bound with one sulfobetaine (SB) unit, and additional water molecules over 8:1 ratio mainly swell the polySBMA matrix, which is obtained through the measurement of T(2) relaxation time by low-field nuclear magnetic resonance (LF-NMR). This result was also supported by the endothermic behavior of water/polySBMA mixtures measured by differential scanning calorimetry (DSC). Furthermore, by comparing both results of polySBMA and poly(ethylene glycol) (PEG), it is found that (1) the hydrated water molecules on the SB unit are more tightly bound than on the ethylene glycol (EG) unit before saturation, and (2) the additional water molecules after forming the hydration layer in polySBMA solutions show higher freedom than those in PEG. These results might illustrate the reason for higher resistance of zwitterionic materials to nonspecific protein adsorptions compared to that of PEGs.

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.