Recognition and analysis of cell nuclear phases for high-content screening based on morphological features

Automated analysis of molecular images has increasingly become an important research in computational life science. In this paper some new and efficient algorithms for recognizing and analyzing cell phases of high-content screening are presented. The conceptual frameworks are based on the morphological features of cell nuclei. The useful preprocessing includes: smooth following and linearization; extraction of morphological structural points; shape recognition based morphological structure; issue of touching cells for cell separation and reconstruction. Furthermore, the novel detecting and analyzing strategies of feed-forward and feed-back of cell phases are proposed based on gray feature, cell shape, geometrical features and difference information of corresponding neighbor frames. Experiment results tested the efficiency of the new method.     

Optimal assembly plan generation: a simplifying approach

The main difficulty in the overall process of optimal assembly plan generation is the great number of different ways to assemble a product (typically thousands of solutions). This problem confines the application of most existing automated planning methods to products composed of only a limited number of components. The presented method of assembly plan generation belongs to the approach called “disassembly” and is founded on a new representation of the assembly process, with introduction of a new concept, the equivalence of binary trees. This representation allows to generate the minimal list of all non-redundant (really different) assembly plans. Plan generation is directed by assembly operation constraints and plan-level performance criteria. The method was tested for various assembly applications and compared to other generation approaches. Results show a great reduction in the combinatorial explosion of the number of plans. Therefore, this simplifying approach of assembly sequence modeling allows to handle more complex products with a large number of parts.     

SVM for English semantic classification in parallel environment

Semantic analysis is very important and very helpful for many researches and many applications for a long time. SVM is a famous algorithm which is used in the researches and applications in many different fields. In this study, we propose a new model using a SVM algorithm with Hadoop Map (M)/Reduce (R) for English document-level emotional classification in the Cloudera parallel network environment. Cloudera is also a distributed system. Our English testing data set has 25,000 English documents, including 12,500 English positive reviews and 12,500 English negative reviews. Our English training data set has 90,000 English sentences, including 45,000 English positive sentences and 45,000 English negative sentences. Our new model is tested on the English testing data set and we achieve 63.7% accuracy of sentiment classification on this English testing data set.     

A decision tree using ID3 algorithm for English semantic analysis

Natural language processing has been studied for many years, and it has been applied to many researches and commercial applications. A new model is proposed in this paper, and is used in the English document-level emotional classification. In this survey, we proposed a new model by using an ID3 algorithm of a decision tree to classify semantics (positive, negative, and neutral) for the English documents. The semantic classification of our model is based on many rules which are generated by applying the ID3 algorithm to 115,000 English sentences of our English training data set. We test our new model on the English testing data set including 25,000 English documents, and achieve 63.6% accuracy of sentiment classification results.     

Dynamic behaviour of a magnetically actuated floating liquid marble

This paper reports the dynamic behaviour of a magnetically actuated floating liquid marble by analysing the oscillation of the marble. A liquid marble is a liquid droplet coated with hydrophobic powder. Magnetite particles inside the marble make it magnetic. The marble floats on a carrier liquid that contains aqueous glycerol of various concentrations. A permanent magnet located under the carrier liquid drives the floating marble with the initial velocity. Stopping the magnet abruptly causes the marble to oscillate around its final position for a few seconds. The oscillation was recorded and analysed using customised image processing and evaluation software. The damped harmonic motion model was then applied to the data and tested. Subsequently, critical parameters of the system such as the initial displacement, friction correction factor, the apparent frequency and the spring constant were determined and discussed. The simple experimental set-up and convenient theoretical approach allow us to characterise the marble motion under the influence of a magnet with good accuracy.     

Fuzzy posterior-probabilistic fusion

The paradigm of the permanence of updating ratios, which is a well-proven concept in experimental engineering approximation, has recently been utilized to construct a probabilistic fusion approach for combining knowledge from multiple sources. This ratio-based probabilistic fusion, however, assumes the equal contribution of attributes of diverse evidences. This paper introduces a new framework of a fuzzy probabilistic data fusion using the principles of the permanence of ratios paradigm, and the theories of fuzzy measures and fuzzy integrals. The fuzzy sub-fusion of the proposed approach allows an effective model for incorporating evidence importance and interaction.     

Stability radius of implicit dynamic equations with constant coefficients on time scales

This paper deals with the stability radii of implicit dynamic equations on time scales when the structured perturbations act on both the coefficient of derivative and the right-hand side. Formulas of the stability radii are derived as a unification and generalization of some previous results. A special case where the real stability radius and the complex stability radius are equal is studied. Examples are derived to illustrate results.     

A spreadsheet approach to facilitate visualization of uncertainty in information

Information uncertainty is inherent in many problems and is often subtle and complicated to understand. Although visualization is a powerful means for exploring and understanding information, information uncertainty visualization is ad hoc and not widespread. This paper identifies two main barriers to the uptake of information uncertainty visualization: firstly, the difficulty of modeling and propagating the uncertainty information; and secondly, the difficulty of mapping uncertainty to visual elements. To overcome these barriers, we extend the spreadsheet paradigm to encapsulate uncertainty details within cells. This creates an inherent awareness of the uncertainty associated with each variable. The spreadsheet can hide the uncertainty details, enabling the user to think simply in terms of variables. Furthermore, the system can aid with automated propagation of uncertainty information, since it is intrinsically aware of the uncertainty. The system also enables mapping the encapsulated uncertainty to visual elements via the formula language and a visualization sheet. Support for such low-level visual mapping provides flexibility to explore new techniques for information uncertainty visualization.     

Molecular recognition in the sphingosine 1-phosphate receptor family

Computational modeling and its application in ligand screening and ligand receptor interaction studies play important roles in structure-based drug design. A series of sphingosine 1-phosphate (S1P) receptor ligands with varying potencies and receptor selectivities were docked into homology models of the S1P(1-5) receptors. These studies provided molecular insights into pharmacological trends both across the receptor family as well as at single receptors. This study identifies ligand recognition features that generalize across the S1P receptor family, features unique to the S1P(4) and S1P(5) receptors, and suggests significant structural differences of the S1P(2) receptor. Docking results reveal a previously unknown sulfur-aromatic interaction between the S1P(4) C5.44 sulfur atom and the phenyl ring of benzimidazole as well as pi-pi interaction between F3.33 of S1P(1,4,5) and aromatic ligands. The findings not only confirm the importance of a cation-pi interaction between W4.64 and the ammonium of S1P at S1P(4) but also predict the same interaction at S1P(5). S1P receptor models are validated for pharmacophore development including database mining and new ligand discovery and serve as tools for ligand optimization to improve potency and selectivity.