On considering dual-role factor in supplier selection problem

Conventional data envelopment analysis evaluates the relative efficiency of a set of homogeneous decision making units (DMUs), where DMUs are evaluated in terms of a specified set of inputs and outputs. In some situations, however, a performance factor could serve as either an output or an input. These factors are referred to as dual-role factors. The presence of dual-role factor among performance factors gives rise to the issue of how to fairly designate the input/output status to such factor. Several studies have been conducted treating a dual-role factor in both methodological and applied nature. One approach taken to address this problem is to view the dual-role factor as being nondiscretionary and connect it to the returns to scale concepts. It is argued that the idea of classifying a factor as an input or an output within a single model cannot consider the causality relationships between inputs and outputs. In this paper we present a mixed integer linear programming approach with the aim at dealing with the dual-role factor. Model structure is developed for finding the status of a dual-role factor via solving a single model while considering the causality relationships between inputs and outputs. It is shown that the new model can designate the status of a dual-role factor with half calculations as the previous model. Both individual and aggregate points of view are suggested for deriving the most appropriate designation of the dual-role factor. A data set involving 18 supplier selections is adapted from literature review to illustrate the efficacy of the proposed models and compare the new approach with the previous ones.Web of Science82112210

Finding an initial basic feasible solution for DEA models with an application on bank industry

Nowadays, algorithms and computer programs, which are going to speed up, short time to run and less memory to occupy have special importance. Toward these ends, researchers have always regarded suitable strategies and algorithms with the least computations. Since linear programming (LP) has been introduced, interest in it spreads rapidly among scientists. To solve an LP, the simplex method has been developed and since then many researchers have contributed to the extension and progression of LP and obviously simplex method. A vast literature has been grown out of this original method in mathematical theory, new algorithms, and applied nature. Solving an LP via simplex method needs an initial basic feasible solution (IBFS), but in many situations such a solution is not readily available so artificial variables will be resorted. These artificial variables must be dropped to zero, if possible. There are two main methods that can be used to eliminate the artificial variables: two-phase method and Big-M method. Data envelopment analysis (DEA) applies individual LP for evaluating performance of decision making units, consequently, to solve these LPs an IBFS must be on hand. The main contribution of this paper is to introduce a closed form of IBFS for conventional DEA models, which helps us not to deal with artificial variables directly. We apply the proposed form to a real-data set to illustrate the applicability of the new approach. The results of this study indicate that using the closed form of IBFS can reduce at least 50 % of the whole computations.Web of Science45233632

Correction: Capsaicin Treatment Attenuates Cholangiocarcinoma Carcinogenesis.

[This corrects the article DOI: 10.1371/journal.pone.0095605.]

Capsaicin Treatment Attenuates Cholangiocarcinoma Carcinogenesis

<div><p>Capsaicin, the most abundant pungent molecule produced by pepper plants, represents an important ingredient in spicy foods consumed throughout the world. Studies have shown that capsaicin can relieve inflammation and has anti-proliferative effects on various human malignancies. Cholangiocarcinoma (CC) is a cancer disease with rising incidence. The prognosis remains dismal with little advance in treatment. The aim of the present study is to explore the anti-tumor activity of capsaicin in cultured human CC cell lines. Capsaicin effectively impaired cell proliferation, migration, invasion, epithelial to mesenchymal transition and growth of softagar colonies. Further, we show that capsaicin treatment of CC cells regulates the Hedgehog signaling pathway. <i>Conclusion:</i> Our results provide a basis for capsaicin to improve the prognosis of CCs <i>in vivo</i> and present new insights into the effectiveness and mode of action of capsaicin.</p></div

Capsaicin impairs epithelial mesenchymal transition.

<p>(A) SZ-1 and (B) TFK-1 cells were treated with control (DMSO) and capsaicin (150 µM, 200 µM) for 24 h, 48 h and 96 h. The expression of EMT markers: E-cadherin, N-cadherin and Vimentin were analyzed by Western blot. β-actin was used as a loading control. (A) SZ1 and (B) TFK1 cells showed an increase of epithelial marker E-cadherin and a dose- and time-dependent decrease of Vimentin. N-cadherin expression was nearly unchanged for both cholangiocarcinoma cell lines.</p

Capsaicin inhibits cell proliferation in human cholangiocarcinoma cell lines.

<p>The cell proliferation of (A) SZ1 and (C) TFK-1 cells was measured by cell proliferation assay. Capsaicin (150 µM, 200 µM) inhibited cell proliferation in a dose- and time-dependent manner. Light microscopic pictures (10× magnification) were taken at 96 h to show the effect of capsaicin on cell proliferation of (B) SZ1 and (D) TFK-1. Note that these results reveal the anti-proliferative effects of capsaicin on human cholangiocarcinoma cells. Data are expressed as mean ± SD of triplicates.</p

Capsaicin treatment suppresses the colony formation ability of cholangiocarcinoma cells.

<p>Soft agar assay was performed in capsaicin-treated human cholangiocarcinoma cells SZ-1 (A) and TFK-1 (B), with quantification. Compared to DMSO (control) capsaicin inhibits colony formation at a concentration starting at 150 µM. P values were calculated with ANOVA analysis of variance along with Bonferroni post test. The error bar represents standard deviation. Differences were considered as statistically significant when the P-value was less <0.05 (*), <0.005 (**) and <0.001 (***). Data are expressed as mean ± SD of triplicates.</p

Capsaicin targets Hedgehog signaling.

<p>The expression levels of Smo, Gli1 and Gli2 were analyzed by semiquantitative RT-PCR both in SZ-1 (A) and in TFK-1 cells (B) after treatment either with control (DMSO) or capsaicin (150 µM, 200 µM) for 24 h, 48 h and 96 h. A reduction of transmembrane protein Smo was seen in both cell lines after 96 h. Capsaicin down-regulates Hedgehog targets Gli1 and Gli2 in a time-dependent manner (96 h).</p