Modeling Structural Selection in Disaggregated Event Data

Growing availability of disaggregated data, such as data on activity of subnational groups (e.g. protest campaigns, insurgents, terrorist groups, political parties or movements), has raised new types of theoretical and statistical challenges. In particular, rather than random, the observability and availability of disaggregated data are often a function of specific structural processes—an issue we refer to as structural selection. For example, domestic terrorist attacks or protester violence are conditional on the formation of domestic terrorist groups or protester movements in the first place. As a result, analytical inferences derived from subnational or other types of disaggregated data may suffer from structural selection bias, which is a type of sample selection bias. We propose a simple and elegant statistical approach to ameliorate such bias and demonstrate the advantages of this approach using a Monte Carlo example. We further illustrate the importance of accounting for structural processes by replicating three prominent empirical studies of government–opposition behavior and find that structural selection affects many of the inferences drawn from the observable data

Supplier selection using a hybrid model for 3C industry

Supplier selection is a good strategy for firms that can reduce operating costs and improve competitiveness for computer, communication and consumer electronics (3C) industry. The major aim of this research is to build a systematic approach for establishing a supplier selection model, and then prioritize improvement criteria in order to best supply chain management. The study proposed a hybrid approach by using the interpretive structural modeling (ISM) method to deal with the interrelationship among criteria, and the analytic network process (ANP) method is employed to recognize the criteria of supplier selection and evaluate with respect to environmental competency for the case of Taiwan's 3C industry. The study shows that the proposed model could be an effective and efficient decision-making tool that can be easily extended to other contexts. Especially, it has provided decision-makers and researchers with better understanding of the differences in supplier selection activity needs and specific management interventions by examining these criteria

Neuronal plasticity: cell-based strategy for target identification and validation

While growing neurites are relatively plastic during development, their plasticity levels drop rapidly as neurons mature and become integrated into neuronal networks. As a consequence, the central nervous system ability to reorganize itself in response to injury or disease is insufficient. One of the main limitations for the design of therapeutic strategies to enhance neurite sprouting following neurological diseases is our poor understanding of the mechanisms underlying neurite structural plasticity. 

To overcome this limitation, we have implemented a strategy to identify, characterize and validate the most therapeutically relevant drug targets to modulate neuronal plasticity. This strategy is based on the hypothesis that the molecular regulation of the neurite shaft (controlling its ability to sprout) shares similarities with other cell systems. In an initial selection step (1), candidate targets are identified from the literature based on predetermined criteria (e.g. involvement in cell migration and growth cone collapse). (2) Compounds acting on those targets are tested in a neuronal cell-based assay for their effects on neurite sprouting as well as for early detection of undesired effects. (3) The selected targets are further analyzed during a verification step where their subcellular expression and activity is assessed. The regulation of the activity of the potential targets by branching factors is also determined at this point. (4) Once the biological relevance has been established, the signalling pathway in which the potential targets operates within neurons is mapped using activity reporters to uncover additional potential targets. 

This candidate approach, combined with the use of primary neurons, is designed to increase the probability of identifying suitable targets at the same time that it minimizes costs and time to validation. By using this strategy, we have been able to identify a set of proteins controlling neuronal sprouting together with an unsuspected mechanism of regulation of structural plasticity in neurons

Drugs acting on central nervous system (CNS) targets as leads for non-CNS targets

Out-of-the-box approaches are currently needed to replenish the souring pipelines of pharmaceutical companies across the globe. Here a theme is presented – the use of central nervous system (CNS) drugs as leads for non-CNS targets. The approach is related to the use of existing drugs for new indications. Suitable chemical modifications of the CNS drugs abolish their CNS penetration. These novel analogs may then be screened for activity against non-CNS targets. Careful selection of the appropriate structural modifications remains the key to success

Bayesian networks for constrained location choice modeling using structural restrictions and model averaging

In this work, we propose a Bayesian network approach by using structural restrictions and a model averaging algorithm for modeling the location choice of discretionary activities. In a first stage, we delimit individuals’ location choice which is set by generating an ellipse that uses empirical detour factors and a home-work axis. The choice set is further refined by an individual’s space-time constraints in order to identify the constrained destination choice set. We use structural restrictions and a model averaging method to learn the network structure of the Bayesian network in order to predict the heuristics of individuals’ location selection. The empirical study shows the proposed method can effectively obtain Bayesian networks with a consistent dependency structure. The empirical study suggests activity schedule factors significantly influence location choice decisions

Investigation of Electrocatalysts Produced by a Novel Thermal Spray Deposition Method

Common methods to produce supported catalysts include impregnation, precipitation, and thermal spray techniques. Supported electrocatalysts produced by a novel method for thermal spray deposition were investigated with respect to their structural properties, elemental composition, and electrochemical performance. This was done using electron microscopy, X-ray photoelectron spectroscopy, and cyclic voltammetry. Various shapes and sizes of catalyst particles were found. The materials exhibit different activity towards oxidation and reduction of Fe. The results show that this preparation method enables the selection of particle coverage as well as size and shape of the catalyst material. Due to the great variability of support and catalyst materials accessible with this technique, this approach is a useful extension to other preparation methods for electrocatalysts

Identification of differential gene expression in in vitro FSH treated pig granulosa cells using suppression subtractive hybridization

FSH, which binds to specific receptors on granulosa cells in mammals, plays a key role in folliculogenesis. Its biological activity involves stimulation of intercellular communication and upregulation of steroidogenesis, but the entire spectrum of the genes regulated by FSH has yet to be fully characterized. In order to find new regulated transcripts, however rare, we have used a Suppression Subtractive Hybridization approach (SSH) on pig granulosa cells in primary culture treated or not with FSH. Two SSH libraries were generated and 76 clones were sequenced after selection by differential screening. Sixty four different sequences were identified, including 3 novel sequences. Experiments demonstrated the presence of 25 regulated transcripts. A gene ontology analysis of these 25 genes revealed (1) catalytic; (2) transport; (3) signal transducer; (4) binding; (5) anti-oxidant and (6) structural activities. These findings may deepen our understanding of FSH's effects. Particularly, they suggest that FSH is involved in the modulation of peroxidase activity and remodelling of chromatin

The Evaluation Of Molecular Similarity And Molecular Diversity Methods Using Biological Activity Data

This paper reviews the techniques available for quantifying the effectiveness of methods for molecule similarity and molecular diversity, focusing in particular on similarity searching and on compound selection procedures. The evaluation criteria considered are based on biological activity data, both qualitative and quantitative, with rather different criteria needing to be used depending on the type of data available

Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed