3,901 research outputs found

    Reproductive Contributions of Foreign Wives in Taiwan: Similarities and Differences among Major Source Countries

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    In light of the entrenchment of sub-replacement fertility and the sharp increase in the stock of foreign wives in Taiwan in recent years, this research studies the reproductive contributions of Taiwan’s foreign wives from the top five source countries (China, Vietnam, Indonesia, Thailand, and the Philippines), based mainly on an application of a multinomial logit model to the micro data of the 2003 census of foreign wives. Our main findings are as follows. First, the overall fertility level of the foreign wives was probably somewhat higher than that of the native-born women and definitely lower than the replacement level. Second, among the five nationalities, those from China were much less reproductive than those from the other countries, mainly because the former were more prone to (1) having a rather old marriage age, (2) having a very large spousal age gap, (3) being separated or divorced, (4) having their current marriage being their second marriage, and (5) having a veteran as the husband. Third, among the four Southeast Asian nationalities, those from Indonesia and the Philippines were more reproductive than those from Thailand and Vietnam. This contrast was a muted reflection of the fertility difference in countries of origin. Fourth, for every nationality, marriage duration and marriage age were the most powerful explanatory factors and must be included in the model to avoid getting misleading estimated coefficients of other less powerful explanatory factors, whereas current age was a spurious factor that should not be used in the model. Fifth, in the context of marriage duration and marriage age, the explanatory factors with rather strong explanatory powers for at least one nationality included spousal age gap, marital status, remarriage status, co-residence with parent, and wife’s employment status. Sixth, the expected negative effect of wife’s educational attainment on lifetime fertility turned out to be either non-existent or modest. In particular, it had practically no effect on the probability of being childless. These findings implied that getting better educated foreign wives could increase the quality of their children with little or no reduction in the number of their children and in their probability of being childlessASEAN countries, China, international marriage, international migration, fertility, Taiwan

    Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

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    Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS

    Improving gelation efficiency and cytocompatibility of visible light polymerized thiol-norbornene hydrogels via addition of soluble tyrosine

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    Hydrogels immobilized with biomimetic peptides have been used widely for tissue engineering and drug delivery applications. Photopolymerization has been among the most commonly used techniques to fabricate peptide-immobilized hydrogels as it offers rapid and robust peptide immobilization within a crosslinked hydrogel network. Both chain-growth and step-growth photopolymerizations can be used to immobilize peptides within covalently crosslinked hydrogels. A previously developed visible light mediated step-growth thiol-norbornene gelation scheme has demonstrated efficient crosslinking of hydrogels composed of an inert poly(ethylene glycol)-norbornene (PEGNB) macromer and a small molecular weight bis-thiol linker, such as dithiothreitol (DTT). Compared with conventional visible light mediated chain-polymerizations where multiple initiator components are required, step-growth photopolymerized thiol-norbornene hydrogels are more cytocompatible for the in situ encapsulation of radical sensitive cells (e.g., pancreatic β-cells). This contribution explored visible light based crosslinking of various bis-cysteine containing peptides with macromer 8-arm PEGNB to form biomimetic hydrogels suitable for in situ cell encapsulation. It was found that the addition of soluble tyrosine during polymerization not only significantly accelerated gelation, but also improved the crosslinking efficiency of PEG-peptide hydrogels as evidenced by a decreased gel point and enhanced gel modulus. In addition, soluble tyrosine drastically enhanced the cytocompatibility of the resulting PEG-peptide hydrogels, as demonstrated by in situ encapsulation and culture of pancreatic MIN6 β-cells. This visible light based thiol-norbornene crosslinking mechanism provides an attractive gelation method for preparing cytocompatible PEG-peptide hydrogels for tissue engineering applications

    Biomimetic and enzyme-responsive dynamic hydrogels for studying cell-matrix interactions in pancreatic ductal adenocarcinoma

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    The tumor microenvironment (TME) governs all aspects of cancer progression and in vitro 3D cell culture platforms are increasingly developed to emulate the interactions between components of the stromal tissues and cancer cells. However, conventional cell culture platforms are inadequate in recapitulating the TME, which has complex compositions and dynamically changing matrix mechanics. In this study, we developed a dynamic gelatin-hyaluronic acid hybrid hydrogel system through integrating modular thiol-norbornene photopolymerization and enzyme-triggered on-demand matrix stiffening. In particular, gelatin was dually modified with norbornene and 4-hydroxyphenylacetic acid to render this bioactive protein photo-crosslinkable (through thiol-norbornene gelation) and responsive to tyrosinase-triggered on-demand stiffening (through HPA dimerization). In addition to the modified gelatin that provides basic cell adhesive motifs and protease cleavable sequences, hyaluronic acid (HA), an essential tumor matrix, was modularly and covalently incorporated into the cell-laden gel network. We systematically characterized macromer modification, gel crosslinking, as well as enzyme-triggered stiffening and degradation. We also evaluated the influence of matrix composition and dynamic stiffening on pancreatic ductal adenocarcinoma (PDAC) cell fate in 3D. We found that either HA-containing matrix or a dynamically stiffened microenvironment inhibited PDAC cell growth. Interestingly, these two factors synergistically induced cell phenotypic changes that resembled cell migration and/or invasion in 3D. Additional mRNA expression array analyses revealed changes unique to the presence of HA, to a stiffened microenvironment, or to the combination of both. Finally, we presented immunostaining and mRNA expression data to demonstrate that these irregular PDAC cell phenotypes were a result of matrix-induced epithelial-mesenchymal transition (EMT)

    Estimating Classification Accuracy for Unlabeled Datasets Based on Block Scaling

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    This paper proposes an approach called block scaling quality (BSQ) for estimating the prediction accuracy of a deep network model. The basic operation perturbs the input spectrogram by multiplying all values within a block by , where  is equal to 0 in the experiments. The ratio of perturbed spectrograms that have different prediction labels than the original spectrogram to the total number of perturbed spectrograms indicates how much of the spectrogram is crucial for the prediction. Thus, this ratio is inversely correlated with the accuracy of the dataset. The BSQ approach demonstrates satisfactory estimation accuracy in experiments when compared with various other approaches. When using only the Jamendo and FMA datasets, the estimation accuracy experiences an average error of 4.9% and 1.8%, respectively. Moreover, the BSQ approach holds advantages over some of the comparison counterparts. Overall, it presents a promising approach for estimating the accuracy of a deep network model

    Identification of hot regions in protein-protein interactions by sequential pattern mining

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    <p>Abstract</p> <p>Background</p> <p>Identification of protein interacting sites is an important task in computational molecular biology. As more and more protein sequences are deposited without available structural information, it is strongly desirable to predict protein binding regions by their sequences alone. This paper presents a pattern mining approach to tackle this problem. It is observed that a functional region of protein structures usually consists of several peptide segments linked with large wildcard regions. Thus, the proposed mining technology considers large irregular gaps when growing patterns, in order to find the residues that are simultaneously conserved but largely separated on the sequences. A derived pattern is called a cluster-like pattern since the discovered conserved residues are always grouped into several blocks, which each corresponds to a local conserved region on the protein sequence.</p> <p>Results</p> <p>The experiments conducted in this work demonstrate that the derived long patterns automatically discover the important residues that form one or several hot regions of protein-protein interactions. The methodology is evaluated by conducting experiments on the web server MAGIIC-PRO based on a well known benchmark containing 220 protein chains from 72 distinct complexes. Among the tested 218 proteins, there are 900 sequential blocks discovered, 4.25 blocks per protein chain on average. About 92% of the derived blocks are observed to be clustered in space with at least one of the other blocks, and about 66% of the blocks are found to be near the interface of protein-protein interactions. It is summarized that for about 83% of the tested proteins, at least two interacting blocks can be discovered by this approach.</p> <p>Conclusion</p> <p>This work aims to demonstrate that the important residues associated with the interface of protein-protein interactions may be automatically discovered by sequential pattern mining. The detected regions possess high conservation and thus are considered as the computational hot regions. This information would be useful to characterizing protein sequences, predicting protein function, finding potential partners, and facilitating protein docking for drug discovery.</p

    Enzyme-mediated stiffening hydrogels for probing activation of pancreatic stellate cells

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    The complex network of biochemical and biophysical cues in the pancreatic desmoplasia not only presents challenges to the fundamental understanding of tumor progression, but also hinders the development of therapeutic strategies against pancreatic cancer. Residing in the desmoplasia, pancreatic stellate cells (PSCs) are the major stromal cells affecting the growth and metastasis of pancreatic cancer cells by means of paracrine effects and extracellular matrix protein deposition. PSCs remain in a quiescent/dormant state until they are 'activated' by various environmental cues. While the mechanisms of PSC activation are increasingly being described in literature, the influence of matrix stiffness on PSC activation is largely unexplored. To test the hypothesis that matrix stiffness affects myofibroblastic activation of PSCs, we have prepared cell-laden hydrogels capable of being dynamically stiffened through an enzymatic reaction. The stiffening of the microenvironment was created by using a peptide linker with additional tyrosine residues, which were susceptible to tyrosinase-mediated crosslinking. Tyrosinase catalyzes the oxidation of tyrosine into dihydroxyphenylalanine (DOPA), DOPA quinone, and finally into DOPA dimer. The formation of DOPA dimer led to additional crosslinks and thus stiffening the cell-laden hydrogel. In addition to systematically studying the various parameters relevant to the enzymatic reaction and hydrogel stiffening, we also designed experiments to probe the influence of dynamic matrix stiffening on cell fate. Protease-sensitive peptides were used to crosslink hydrogels, whereas integrin-binding ligands (e.g., RGD motif) were immobilized in the network to afford cell-matrix interaction. PSC-laden hydrogels were placed in media containing tyrosinase for 6h to achieve in situ gel stiffening. We found that PSCs encapsulated and cultured in a stiffened matrix expressed higher levels of αSMA and hypoxia-inducible factor 1α (HIF-1α), suggestive of a myofibroblastic phenotype. This hydrogel platform offers a facile means of in situ stiffening of cell-laden matrices and should be valuable for probing cell fate process dictated by dynamic matrix stiffness. STATEMENT OF SIGNIFICANCE: Hydrogels with spatial-temporal controls over crosslinking kinetics (i.e., dynamic hydrogel) are increasingly being developed for studying mechanobiology in 3D. The general principle of designing dynamic hydrogel is to perform cell encapsulation within a hydrogel network that allows for postgelation modification in gel crosslinking density. The enzyme-mediated in situ gel stiffening is innovative because of the specificity and efficiency of enzymatic reaction. Although tyrosinase has been used for hydrogel crosslinking and in situ cell encapsulation, to the best of our knowledge tyrosinase-mediated DOPA formation has not been explored for in situ stiffening of cell-laden hydrogels. Furthermore, the current work provides a gradual matrix stiffening strategy that may more closely mimic the process of tumor development
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