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Abstract Background The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens. Results Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms. Conclusion We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens.

Using radio signal strength (RSS) in sensor networks localization is an attractive method since it is a cost-efficient method to provide range indication. In this paper, we present a two-tier distributed approach for RF-based indoor location determination. Our approach, namely, INEMO, provides positioning accuracy of room granularity and office cube granularity. A target can first give a room granularity request and the background anchor nodes cooperate to accomplish the positioning process. Anchors in the same room can give cube granularity if the target requires further accuracy. Fixed anchor nodes keep monitoring status of nearby anchors and local reference matching is used to support room separation. Furthermore, we utilize the RSS difference to infer the positioning confidence. The simulation results demonstrate the efficiency of the proposed RF-based indoor location determination.

Abstract While a typical way for diabetes therapy is discrete insulin infusion based on long-time interval measurement, in this paper, we design a closed-loop control system for continuous drug infusion to improve the traditional discrete methods and make diabetes therapy automatic in practice. By exploring the accumulative function of drug to insulin, a continuous injection model is proposed. Based on this model, proportional-integral-derivative (PID) and fuzzy logic controllers are designed to tackle a control problem of the resulting highly nonlinear plant. Even with serious disturbance of glucose, such as nutrition absorption at meal time, the proposed scheme can perform well in simulation experiments.

Abstract Biomedical sensors, called invivo sensors, are implanted in human bodies, and cause some harmful effects on surrounding body tissues. Particularly, temperature rise of the invivo sensors is dangerous for surrounding tissues, and a high temperature may damage them from a long term monitoring. In this paper, we propose a thermal-aware routing algorithm, called least total-route-temperature (LTRT) protocol, in which nodes temperatures are converted into graph weights, and minimum temperature routes are obtained. Furthermore, we provide an extensive simulation evaluation for comparing several other related schemes. Simulation results show the advantages of the proposed scheme.

It is well known that for a digital filter of order n the number of nontrivial parameters in the classical optimal state-space realizations is proportional to [n.sup.2]. In this paper, a new structure is proposed for digital filter implementation. This structure possesses 5n -- 1 nontrivial parameters and is shown to be equivalent to the discrete-time counterpart of the well-known orthonormal ladder filter in [13]. A new property of this ladder filter is revealed. The performance of the proposed structure is analyzed. Expressions for evaluating the sensitivity measure and the roundoff noise gain are derived. A numerical example is presented to illustrate the design procedure. Simulations show that the performance of the proposed structure is almost the same as that of the fully parameterized optimal realizations. Index Terms--Realizations, roundoff noise, sensitivity, structures.