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Digital microfluidic biochips (DMFBs) is an attractive platform for immunoassays, point-of-care clinical diagnostics due to its flexible application and low fabrication cost and further for the development of instruments. Due to the DMFBs have been applied for these safe-critical field, those procedures require high output precision, so the reliability of the chips are extremely important. On-line testing methods are required to ensure robust DMFB operation and high confidence in the outcome of experiments, so a robust testing method is necessary. Many testing methods have been proposed, but most of them are simple functional testing or off-line testing. These testing methods are increasingly unable to meet the requirements of current chip testing. The online test means that there are testing droplet and bioassays running on the DMFB simultaneously. This paper introduces a heuristic method to optimize the testing path. The study utilizes the improved particle swarm optimization to solve the problem. The simulation results showed the proposed method is convergent and could be applied in various operations to reduce about 20% searching time, compared with the improved ant colony algorithm.

3D object reconstruction from a single image is a highly ill-posed problem, requiring strong prior knowledge of 3D shapes. Deep learning methods are popular for this task. Especially, most works utilized 3D deconvolution to generate 3D shapes. However, the resolution of results is limited by the high resource consumption of 3D deconvolution. In this paper, we propose SliceNet, sequentially generating 2D slices of 3D shapes with shared 2D deconvolution parameters. To capture relations between slices, the RNN is also introduced. Our model has three main advantages: First, the introduction of RNN allows the CNN to focus more on local geometry details,improving the results’ fine-grained plausibility. Second, replacing 3D deconvolution with 2D deconvolution reducs much consumption of memory, enabling higher resolution of final results. Third, an slice-aware attention mechanism is designed to provide dynamic information for each slice’s generation, which helps modeling the difference between multiple slices, making the learning process easier. Experiments on both synthesized data and real data illustrate the effectiveness of our method.

Digital micro-fluidic biochips (DMFBs) are revolutionizing laboratory procedures for point-of-care clinical diagnostics, environmental monitoring, and protein analysis. Those procedures require high precision of the output for every operation so it need to ensure the chip reliability and the chip lifetime. Because the electrodes on the chip may be reused at different experimental stages, a degraded electrode may be reused for many times. So the lifetime of the chips is closely related to the total actuating time of an electrode. Thus, the electrode total actuating time needs to be considered carefully in an efficient DMFBs design process. This paper proposed an improved whale optimization algorithm (IWOA), which can reduce excessive use of an electrode and reuse electrodes in average manner to optimal the longest lifetime of DMFBs during the design process. Firstly, the position mass of individual whales was improved to solve the problem that the data, calculated by the WOA, cannot be directly used to represent the sequence of operations. Secondly, the inertial weight was added to enhance the local search ability of WOA. The simulation experimental results showed that this algorithm was able to solve lifetime optimization problems. The maximum electrode time used was reduced by 10% to 20%. The performance of efficiency and convergence of algorithm were very good.

With the rapid increase in the demand for indoor positioning, such as warehouse logistics, emergency search and rescue, indoor positioning technology has become a hotspot of current research. However, due to the complex indoor environment, obvious multipath effects, and high cost of positioning equipment, indoor positioning systems based on infrared and ultrasonic technologies have not been able to be widely promoted. In response to this problem, this paper proposes indoor positioning solution using Bluetooth technology. In this system scheme, the ibeacon base station is composed of a low-power Bluetooth 4.0 module. A certain number of Bluetooth base stations are evenly deployed in the rooms that need to be located. Each Bluetooth base station can automatically search for the broadcast of the ibeacon beacon, and obtain the location-dependent Universally Unique Identifier (UUID ) number and Received Signal Strength Indicator (RSSI) value. Then on the labview platform, the location of the point to be measured is completed through a three-sided positioning algorithm. The experimental results show that the positioning accuracy of the system scheme is within 1m, which can meet the requirements of most indoor positioning.

This paper presents a novel portable chemical oxygen demand (COD) detection method and instrument design based on the microfluidic chip technology. The oxidation reaction and temperature detection are integrated in a microfluidic chip. The principle is that there is heat released during the oxidation of organic compounds and the heat is proportional to the amount of organic compounds contained in water sample. The heat variation can be converted to voltage signal which has linear relationship with COD value. In this research, the NaClO solution is employed as oxidizer and the glucose solution is served as analyte. Three different geometric microfluidic chips are designed in order to choose the most suitable microfluidic chip scheme, the oxidizer concentration and injection rates. The flows characteristics, mixing behavior and heat release of fluids within the microfluidic chip are simulated and investigated by COMSOL multi-physics simulation software. The results show that the most suitable NaClO concentration, injection rates and microfluidic chip scheme are 28mol/L, lmm/s and Y sharp with block. A portable COD detection instrument based on the microfluidic chip technology is developed. There is a simple experiment for proving the feasibility of the instrument, the experiment results show that the experiment device is work well.

Clustering is an effective approach for computing analysis of gene expression data. Various of clustering algorithms have been developed to give reasonable interpretations of biological data and discover biological meaningful patterns of cellular functions. Based on the manifold structure of gene expression data analyzed under the framework of geometric representation, a novel clustering approach is presented to reveal the nonlinear expression patterns. The novel clustering approach can be divided into the following computing steps. The first step is to construct a neighborhood graph for gene expression points through which the approximate geodesic distances between each two points can be obtained. Then, instead of Euclidean distance, approximate geodesic distance is exploited to reveal the similarity between gene profiles. Finally, via defining the geodesic distance between a cluster and a gene expression point, new clusters can be generated after essential iterative processes. Application of the approach to the yeast cell-cycle dataset validates its rationality and efficiency.

Community structure is one of the important topological characteristics of many complex networks. Detecting communities from networks has been intensively investigated in recent years. In most previous methods for community detection, the overlapping property of communities, which exists common in many real-world networks, is ignored. By combining commute-time kernel based distance measure and fuzzy affinity propagation, we present a new community detection algorithm CDKFAP for overlapping communities. Based on a new proposed index measures the fuzziness of nodes, the algorithm can rank and extract overlapping nodes of communities. The applications to computer-generated networks and real-world networks demonstrate the effectiveness of our algorithm.

To understand the gene function and complex regulatory mechanisms, it is expected to be an effective way by analyzing gene expression data using various computing approaches. However, intense nonlinearity of the expression data limits the application of traditional linear classifying and clustering methods. This paper proposed a novel geometric framework for the analysis and visualization of these expression data. Expression manifold and sample manifold are defined separately by using the conception and theory of manifold in modern differential geometry. Based on this new angle of view, algorithms of manifold learning are used to embed high-dimension expression data into a low-dimension Euclidean coordinate system. The embedding process reserves the intrinsic geometry structure locally, thus new features and function patterns can be characterized in an intuitional way. Our method is applied to analyze and interpret the cell-cycle data set of Saccharomyces cerevisiae. The results show that the geometric framework is useful for the analysis of gene expression data.

Advances of high-throughput experimental techniques have generated large amounts of gene expression data. Biologically meaningful dynamic structures are expected to be hidden in these data, which are important for understanding the mechanism of cellular activities and identifying the function of genes. In this paper, a novel scheme is presented to reveal the dynamics of gene expression. State-Space Model(SSM) is developed based on the modern control theory. Based on the decomposition of functional modules, an effective strategy is designed to determine the SSM’s parameters. Via SSM, gene expression dynamics can be well revealed. To validate our approach and demonstrate its application, numerical experiments are designed by using cell cycle-regulated gene data sets of Yeast Saccharomyces cerevisiae.