Clinical features of the study patients.

<p>NOTE: Values are expressed as number (%) or mean±SD. Abbreviations: SD, standard deviation; HBV, hepatitis B virus; HCV, hepatitis C virus; HCC, hepatocellular carcinoma; MHV, middle hepatic vein; ICU, intensive care unit; MELD, Model for End-Stage Liver Disease; D-MELD, the product of donor age and MELD; SOFT, Survival Outcome Following Liver Transplantation; BAR, Balance of Risk; TRI, Transplant Risk Index.</p><p>*Only 150 cases have the information of relationship</p><p>Clinical features of the study patients.</p

Comparison of ROC curves at 1-, 3-, 6-month and 1-year.

<p>Abbreviations: ROC, receiver operating characteristic curve; MELD, Model for End-Stage Liver Disease; D-MELD, the product of donor age and MELD; SOFT, Survival Outcome Following Liver Transplantation; BAR, Balance of Risk; TRI, Transplant Risk Index.</p

Effects of the microstructure of copper through-silicon vias on their thermally induced linear elastic mechanical behavior

Through-silicon vias (TSVs) have been investigated extensively in recent years. However, the physical mechanisms behind some of the common problems associated with TSVs, such as the protrusion of Cu vias, are still unknown. In addition, since the dimensions of TSVs have been shrunk to microscopic levels, the sizes of the microstructural features of TSVs are no longer small compared to the dimensions of the vias. Therefore, the role and importance of the microstructural features of TSVs need to be studied to enable more accurate reliability predictions. This study focused on the effects the microstructural features of TSVs, i.e., the Cu grains and their [111] texture, grain size distribution, and morphology, have on the thermally induced linear elastic behavior of the vias. The results of the study indicate that stress distribution in the model that takes into account the Cu grains, whose Young's moduli and Poisson's ratios are set according to their crystallographic orientations, is more heterogeneous than that in a reference model in which the bulk properties of Cu are used. Stresses as high as 250 MPa are observed in the via of the model that takes into consideration the Cu grains, while stresses in the via of the reference model are all lower than 150 MPa. In addition, smaller Cu grains in the vias result in higher stresses; however, the variation in stress owning to changes in the grain size is within 20 MPa. The frequency of the stresses ranging from 80 MPa to 100 MPa was the highest in the stress distribution of the vias, depending on boundary conditions. The stress level in the v ias decreases with the decrease in the number of grains with the [111] texture. Finally, the stress level is lower in the model in which the grain structure is generated using a phase field model and is closer to that of the microstructures present in real materials. © 2014 The Korean Institute of Metals and Materials and Springer Science+Business Media Dordrecht

A computational method for prediction of matrix proteins in endogenous retroviruses

<div><p>Human endogenous retroviruses (HERVs) encode active retroviral proteins, which may be involved in the progression of cancer and other diseases. Matrix protein (MA), in group-specific antigen genes (<i>gag</i>) of retroviruses, is associated with the virus envelope glycoproteins in most mammalian retroviruses and may be involved in virus particle assembly, transport and budding. However, the amount of annotated MAs in ERVs is still at a low level so far. No computational method to predict the exact start and end coordinates of MAs in gags has been proposed yet. In this paper, a computational method to identify MAs in ERVs is proposed. A divide and conquer technique was designed and applied to the conventional prediction model to acquire better results when dealing with gene sequences with various lengths. Initiation sites and termination sites were predicted separately and then combined according to their intervals. Three different algorithms were applied and compared: weighted support vector machine (WSVM), weighted extreme learning machine (WELM) and random forest (RF). <i>G</i> − <i>mean</i> (geometric mean of sensitivity and specificity) values of initiation sites and termination sites under 5-fold cross validation generated by random forest models are 0.9869 and 0.9755 respectively, highest among the algorithms applied. Our prediction models combine RF & WSVM algorithms to achieve the best prediction results. 98.4% of all the collected ERV sequences with complete MAs (125 in total) could be predicted exactly correct by the models. 94,671 HERV sequences from 118 families were scanned by the model, 104 new putative MAs were predicted in human chromosomes. Distributions of the putative MAs and optimizations of model parameters were also analyzed. The usage of our predicting method was also expanded to other retroviruses and satisfying results were acquired.</p></div