The Carboxy-Terminal Modulator Protein (CTMP) Regulates Mitochondrial Dynamics

Background: Mitochondria are central to the metabolism of cells and participate in many regulatory and signaling events. They are looked upon as dynamic tubular networks. We showed recently that the Carboxy-Terminal Modulator Protein (CTMP) is a mitochondrial protein that may be released into the cytosol under apoptotic conditions.

Are causal and intervention judgments inextricably linked? A developmental study.

The application of the formal framework of causal Bayesian Networks to children's causal learning provides the motivation to examine the link between judgments about the causal structure of a system, and the ability to make inferences about interventions on components of the system. Three experiments examined whether children are able to make correct inferences about interventions on different causal structures. The first two experiments examined whether children's causal structure and intervention judgments were consistent with one another. In Experiment 1, children aged between 4 and 8years made causal structure judgments on a three-component causal system followed by counterfactual intervention judgments. In Experiment 2, children's causal structure judgments were followed by intervention judgments phrased as future hypotheticals. In Experiment 3, we explicitly told children what the correct causal structure was and asked them to make intervention judgments. The results of the three experiments suggest that the representations that support causal structure judgments do not easily support simple judgments about interventions in children. We discuss our findings in light of strong interventionist claims that the two types of judgments should be closely linked. © 2011 Cognitive Science Society, Inc.


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Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

Semiring induced valuation algebras: Exact and approximate local computation algorithms

Local computation in join trees or acyclic hypertrees has been shown to be linked to a particular algebraic structure, called valuation algebra.There are many models of this algebraic structure ranging from probability theory to numerical analysis, relational databases and various classical and non-classical logics. It turns out that many interesting models of valuation algebras may be derived from semiring valued mappings. In this paper we study how valuation algebras are induced by semirings and how the structure of the valuation algebra is related to the algebraic structure of the semiring. In particular, c-semirings with idempotent multiplication induce idempotent valuation algebras and therefore permit particularly efficient architectures for local computation. Also important are semirings whose multiplicative semigroup is embedded in a union of groups. They induce valuation algebras with a partially defined division. For these valuation algebras, the well-known architectures for Bayesian networks apply. We also extend the general computational framework to allow derivation of bounds and approximations, for when exact computation is not feasible.

Inscribing value on business model innovations: Insights from industrial projects commercializing disruptive digital innovations

In this paper we seek to show how marketing activities inscribe value on business model innovation, representative of an act, or sequence of socially interconnecting acts. Theoretically we ask two interlinked questions: (1) how can value inscriptions contribute to business model innovations? (2) how can marketing activities support the inscription of value on business model innovations? Semi-structured in-depth interviews were conducted with the thirty-seven members from across four industrial projects commercializing disruptive digital innovations. Various individuals from a diverse range of firms are shown to cast relevant components of their agency and knowledge on business model innovations through negotiation as an ongoing social process. Value inscription is mutually constituted from the marketing activities, interactions and negotiations of multiple project members across firms and functions to counter destabilizing forces and tensions arising from the commercialization of disruptive digital innovations. This contributes to recent conceptual thinking in the industrial marketing literature, which views business models as situated within dynamic business networks and a context-led evolutionary process. A contribution is also made to debate in the marketing literature around marketing's boundary-spanning role, with marketing activities shown to span and navigate across functions and firms in supporting value inscriptions on business model innovations.

Probabilistic logic with independence

This paper investigates probabilistic logics endowed with independence relations. We review propositional probabilistic languages without and with independence. We then consider graph-theoretic representations for propositional probabilistic logic with independence; complexity is analyzed, algorithms are derived, and examples are discussed. Finally, we examine a restricted first-order probabilistic logic that generalizes relational Bayesian networks.

Incorporating PGMs into a BDI Architecture

In this paper, we present a hybrid BDI-PGM framework, in which PGMs (Probabilistic Graphical Models) are incorporated into a BDI (belief-desire-intention) architecture. This work is motivated by the need to address the scalability and noisy sensing issues in SCADA (Supervisory Control And Data Acquisition) systems. Our approach uses the incorporated PGMs to model the uncertainty reasoning and decision making processes of agents situated in a stochastic environment. In particular, we use Bayesian networks to reason about an agent’s beliefs about the environment based on its sensory observations, and select optimal plans according to the utilities of actions defined in influence diagrams. This approach takes the advantage of the scalability of the BDI architecture and the uncertainty reasoning capability of PGMs. We present a prototype of the proposed approach using a transit scenario to validate its effectiveness.

Topographical optimisation of single-storey non-domestic steel framed buildings using photovoltaic panels for zero carbon impact

The optimisation is based on a combination of neural networks and evolutionary algorithm. It has selected buildings with different midpoint configurations with zero carbon impacts. With operational energy included the structures could be offset with asymmetry.

A Hybrid Imperialist Approach in Solving University Timetabling Problems. Information Sciences. Vol. 283, 1-21

Generating timetables for an institution is a challenging and time consuming task due to different demands on the overall structure of the timetable. In this paper, a new hybrid method which is a combination of a great deluge and artificial bee colony algorithm (INMGD-ABC) is proposed to address the university timetabling problem. Artificial bee colony algorithm (ABC) is a population based method that has been introduced in recent years and has proven successful in solving various optimization problems effectively. However, as with many search based approaches, there exist weaknesses in the exploration and exploitation abilities which tend to induce slow convergence of the overall search process. Therefore, hybridization is proposed to compensate for the identified weaknesses of the ABC. Also, inspired from imperialist competitive algorithms, an assimilation policy is implemented in order to improve the global exploration ability of the ABC algorithm. In addition, Nelder–Mead simplex search method is incorporated within the great deluge algorithm (NMGD) with the aim of enhancing the exploitation ability of the hybrid method in fine-tuning the problem search region. The proposed method is tested on two differing benchmark datasets i.e. examination and course timetabling datasets. A statistical analysis t-test has been conducted and shows the performance of the proposed approach as significantly better than basic ABC algorithm. Finally, the experimental results are compared against state-of-the art methods in the literature, with results obtained that are competitive and in certain cases achieving some of the current best results to those in the literature.

Statistical Tests for Joint Analysis of Performance Measures

Recently there has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and architectural complexity). Once one has learned a model based on their devised method, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Unfortunately, the standard tests used for this purpose are not able to jointly consider performance measures. The aim of this paper is to resolve this issue by developing statistical procedures that are able to account for multiple competing measures at the same time. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameter of such models, as usually the number of studied cases is very reduced in such comparisons. Real data from a comparison among general purpose classifiers is used to show a practical application of our tests.

A novel hybrid swarm based approach for curriculum based course timetabling problem

This work applies a hybrid approach in solving the university curriculum-based course timetabling problem as presented as part of the 2nd International Timetabling Competition 2007 (ITC2007). The core of the hybrid approach is based on an artificial bee colony algorithm. Past methods have applied artificial bee colony algorithms to university timetabling problems with high degrees of success. Nevertheless, there exist inefficiencies in the associated search abilities in term of exploration and exploitation. To improve the search abilities, this work introduces a hybrid approach entitled nelder-mead great deluge artificial bee colony algorithm (NMGD-ABC) where it combined additional positive elements of particle swarm optimization and great deluge algorithm. In addition, nelder-mead local search is incorporated into the great deluge algorithm to further enhance the performance of the resulting method. The proposed method is tested on curriculum-based course timetabling as presented in the ITC2007. Experimental results reveal that the proposed method is capable of producing competitive results as compared with the other approaches described in literature

IMRT for image-guided single vocal cord irradiation

PURPOSE: We have been developing an image-guided single vocal cord irradiation technique to treat patients with stage T1a glottic carcinoma. In the present study, we compared the dose coverage to the affected vocal cord and the dose delivered to the organs at risk using conventional, intensity-modulated radiotherapy (IMRT) coplanar, and IMRT non-coplanar techniques.

METHODS AND MATERIALS: For 10 patients, conventional treatment plans using two laterally opposed wedged 6-MV photon beams were calculated in XiO (Elekta-CMS treatment planning system). An in-house IMRT/beam angle optimization algorithm was used to obtain the coplanar and non-coplanar optimized beam angles. Using these angles, the IMRT plans were generated in Monaco (IMRT treatment planning system, Elekta-CMS) with the implemented Monte Carlo dose calculation algorithm. The organs at risk included the contralateral vocal cord, arytenoids, swallowing muscles, carotid arteries, and spinal cord. The prescription dose was 66 Gy in 33 fractions.

RESULTS: For the conventional plans and coplanar and non-coplanar IMRT plans, the population-averaged mean dose ± standard deviation to the planning target volume was 67 ± 1 Gy. The contralateral vocal cord dose was reduced from 66 ± 1 Gy in the conventional plans to 39 ± 8 Gy and 36 ± 6 Gy in the coplanar and non-coplanar IMRT plans, respectively. IMRT consistently reduced the doses to the other organs at risk.

CONCLUSIONS: Single vocal cord irradiation with IMRT resulted in good target coverage and provided significant sparing of the critical structures. This has the potential to improve the quality-of-life outcomes after RT and maintain the same local control rates.