Equilibrium analysis in multi-echelon supply chain with multi-dimensional utilities of inertial players

In a supply chain, the importance of information elicitation from the supply chain players is vital to design supply chain network. The rationality and self-centredness of these players causes the information asymmetry in the supply chain and thus situation of conflict and non-participation of the players in the network design process. In such situations, it is required to analyse the supply chain players’ behaviour in order to explore potential for coordination through incentives. In this paper, a novel approach of social utility analysis is proposed to elicit the information for supply chain coordination among the supply chain players in a dyadic relationship – supplier and buyer. In principal, we consider a monopsony situation where buyer is a dominant player. With the objective of maximizing the social utility, efforts have been made to value behavioural issues in supply chain. On the other hand, the reluctance of player due to the information asymmetry is measured in the form of inertia – an offset to the supply chain profit. The suppliers’ behaviour is analysed with three distinct level of risk for two types of the product in procurement process. The useful insight from this paper is in supplier selection process where the reluctance of supplier offsets supply chain profit. The paper provides recommendations to supply chain managers for efficient decision-making ability in supplier selection process

Mining unexpected patterns using decision trees and interestingness measures: a case study of endometriosis

[[abstract]]Because clinical research is carried out in complex environments, prior domain knowledge, constraints, and expert knowledge can enhance the capabilities and performance of data mining. In this paper we propose an unexpected pattern mining model that uses decision trees to compare recovery rates of two different treatments, and to find patterns that contrast with the prior knowledge of domain users. In the proposed model we define interestingness measures to determine whether the patterns found are interesting to the domain. By applying the concept of domain-driven data mining, we repeatedly utilize decision trees and interestingness measures in a closed-loop, in-depth mining process to find unexpected and interesting patterns. We use retrospective data from transvaginal ultrasound-guided aspirations to show that the proposed model can successfully compare different treatments using a decision tree, which is a new usage of that tool. We believe that unexpected, interesting patterns may provide clinical researchers with different perspectives for future research.[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]紙本[[booktype]]電子

Transglutaminase 2 Contributes to Apoptosis Induction in Jurkat T Cells by Modulating Ca(2+) Homeostasis via Cross-Linking RAP1GDS1

BACKGROUND: Transglutaminase 2 (TG2) is a protein cross-linking enzyme known to be associated with the in vivo apoptosis program of T cells. However, its role in the T cell apoptosis program was not investigated yet. RESULTS: Here we report that timed overexpression of both the wild type (wt) and the cross-linking mutant of TG2 induced apoptosis in Jurkat T cells, the wt being more effective. Part of TG2 colocalised with mitochondria. WtTG2-induced apoptosis was characterized by enhanced mitochondrial Ca(2+) uptake. Ca(2+)-activated wtTG2 cross-linked RAP1, GTP-GDP dissociation stimulator 1, an unusual guanine exchange factor acting on various small GTPases, to induce a yet uncharacterized signaling pathway that was able to promote the Ca(2+) release from the endoplasmic reticulum via both Ins3P and ryanodine sensitive receptors leading to a consequently enhanced mitochondrial Ca(2+)uptake. CONCLUSIONS: Our data indicate that TG2 might act as a Ca(2+) sensor to amplify endoplasmic reticulum-derived Ca(2+) signals to enhance mitochondria Ca(2+) uptake. Since enhanced mitochondrial Ca(2+) levels were previously shown to sensitize mitochondria for various apoptotic signals, our data demonstrate a novel mechanism through which TG2 can contribute to the induction of apoptosis in certain cell types. Since, as compared to knock out cells, physiological levels of TG2 affected Ca(2+) signals in mouse embryonic fibroblasts similar to Jurkat cells, our data might indicate a more general role of TG2 in the regulation of mitochondrial Ca(2+) homeostasis

Stochastically Gating Ion Channels Enable Patterned Spike Firing through Activity-Dependent Modulation of Spike Probability

The transformation of synaptic input into patterns of spike output is a fundamental operation that is determined by the particular complement of ion channels that a neuron expresses. Although it is well established that individual ion channel proteins make stochastic transitions between conducting and non-conducting states, most models of synaptic integration are deterministic, and relatively little is known about the functional consequences of interactions between stochastically gating ion channels. Here, we show that a model of stellate neurons from layer II of the medial entorhinal cortex implemented with either stochastic or deterministically gating ion channels can reproduce the resting membrane properties of stellate neurons, but only the stochastic version of the model can fully account for perithreshold membrane potential fluctuations and clustered patterns of spike output that are recorded from stellate neurons during depolarized states. We demonstrate that the stochastic model implements an example of a general mechanism for patterning of neuronal output through activity-dependent changes in the probability of spike firing. Unlike deterministic mechanisms that generate spike patterns through slow changes in the state of model parameters, this general stochastic mechanism does not require retention of information beyond the duration of a single spike and its associated afterhyperpolarization. Instead, clustered patterns of spikes emerge in the stochastic model of stellate neurons as a result of a transient increase in firing probability driven by activation of HCN channels during recovery from the spike afterhyperpolarization. Using this model, we infer conditions in which stochastic ion channel gating may influence firing patterns in vivo and predict consequences of modifications of HCN channel function for in vivo firing patterns

Chlamydial Pre-Infection Protects From Subsequent Herpes Simplex Virus-2 Challenge in a Murine Vaginal Super-Infection Model

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Chlamydia trachomatis and Herpes Simplex Virus-2 (HSV-2) genital tract co-infections have been reported in humans and studied in vitro but the clinical consequences are unknown. Limited epidemiologic evidence suggests that these co-infections could be more severe than single infections of either pathogen, but the host-pathogen interactions during co-infection remain uncharacterized. To determine whether disease progression and/or pathogen shedding differs between singly-infected and super-infected animals, we developed an in vivo super-infection model in which female BALB/c mice were vaginally infected with Chlamydia muridarum (Cm) followed later by HSV-2. Pre-infection with Chlamydia 3 or 9 days prior to HSV-2 super-infection conferred significant protection from HSV-2-induced neurologic disease and significantly reduced viral recovery compared to HSV-2 singlyinfected controls. Neither protection from mortality nor reduced viral recovery were observed when mice were i) super-infected with HSV-2 on day 27 post Cm; ii) infected with UV-irradiated Cm and super-infected with HSV-2; or iii) azithromycin-treated prior to HSV-2 super-infection. Therefore, protection from HSV-2-induced disease requires active infection with viable chlamydiae and is not observed after chlamydial shedding ceases, either naturally or due to antibiotic treatment. Thus, Chlamydia-induced protection is transient and requires the continued presence of chlamydiae or their components. These data demonstrate that chlamydial pre-infection can alter progression of subsequent HSV-2 infection, with implications for HSV-2 transmission from co-infected humans

Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation

The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks