For wide-range deployment of wireless sensing nodes in power and size-constrained Internet of Things applications, self-sustaining wireless sensors without batteries are needed. The energy supply for these ubiquitous sensing devices should harness environmental energy sources to avoid frequent battery replacement and pollution. This paper presents a new soil energy harvesting system with a power management integrated circuit (IC) for wide-range maximum power point tracking (MPPT). The output power of a soil energy cell varies widely according to the soil moisture and soil contents. To extract the maximum amount of energy from the soil, high-efficiency power management with adaptive dc-dc conversion ratios and an MPPT mechanism is essential. The proposed system is implemented with a high-efficiency dc-dc converter IC in a 0.18-μm CMOS process. The design achieves a peak tracking efficiency of 90.5% in the throughput power range from 56 μW to 1 mW at an output voltage of 3 V and system efficiency exceeding 65% over an input resistance range of the soil energy source; the peak system efficiency is 73.9%. A demonstration was undertaken of the soil energy harvesting system lighting an LED array without any chemical batteries.

Digital microfluidic biochip (DMFB) is a tiny device that can carry out a rich set of bioassays without the need of bulky equipment. However, designing a good general-purpose DMFB architecture is still considered a big challenge today. NP-hard synthesis problems make on-line synthesis virtually impossible on exiting array-based architectures. In this paper, we first elaborate on the major concerns in a DMFB design flow, from the aspects of both synthesis and physical design. We then propose a versatile ring-based architecture VERBA and its corresponding fast one-pass synthesis flow. Experimental results show that VERBA incorporated with the proposed synthesis flow is a better solution than existing architectures and synthesis algorithms especially for real-time cyber-physical systems.

Sample preparation plays an essential role in most biochemical reactions. Raw reactants are diluted to solutions with desirable concentration values in this process. Since the reactants, like infant's blood, DNA evidence collected from crime scenes, or costly reagents, are extremely valuable, their usage should be minimized whenever possible. In this paper, we propose a two-phased reactant minimization algorithm (REMIA), for sample preparation on digital microfluidic biochips. In the former phase, REMIA builds a reactant-minimized interpolated dilution tree with specific leaf nodes for a target concentration. Two approaches are developed for tree construction; one is based on integer linear programming (ILP) and the other is heuristic. The ILP one guarantees to produce an optimal dilution tree with minimal reactant consumption, whereas the heuristic one ensures runtime efficiency. Then, REMIA constructs a forest consisting of exponential dilution trees to produce those aforementioned specific leaf nodes with minimal reactant consumption in the latter phase. Experimental results show that REMIA achieves a reduction of reactant usage by 32%-52% as compared with three existing state-of-the-art sample preparation approaches. Besides, REMIA can be easily extended to solve the sample preparation problem with multiple target concentrations, and the extended version also effectively lowers the reactant consumption further.

Sample preparation is one of the essential processes for most on-chip biochemical applications. During this process, raw reactants are diluted to specific concentration values. Current sample preparation algorithms are generally created for digital microfluidic biochips with the (1:1) mixing model. For other biochip architectures supporting multiple mixing models, such as flow-based microfluidic biochips, there is still no dedicated solution yet. Hence, in this paper, we propose the first sample preparation method dedicated to microfluidic biochips with various mixing models, named tree pruning and grafting (TPG) algorithm. It starts with a dilution tree created by regarding the (1:1) mixing model only, and then applies TPG through a bottom-up dynamic programming strategy to obtain a solution with minimal reactant consumption. Experimental results show that our algorithm can save reactant amount by up to 69% against the well-known bit-scanning method on a biochip with a four-segment mixer. Even compared with the state-of-the-art reactant minimization algorithm, it still achieves a reactant reduction of 37%. Therefore, it is convincing that the TPG algorithm is a promising sample preparation solution for biochip architectures that support various mixing models.

Sample preparation is an essential processing step in most biochemical applications. Various reactants are mixed together to produce a solution with the target concentration. Since reactants generally take a notable part of the cost in a bioassay, their usage should be minimized whenever possible. In this paper, we propose an algorithm, CoDOS, to prepare the target solution with many reactants using common dilution operation sharing on digital microfluidic biochips (DMFBs). CoDOS first represents the given target concentration as a recipe matrix, and then identifies rectangles in the matrix, where each rectangle indicates an opportunity of dilution operation sharing for reactant minimization. Experimental results demonstrate that CoDOS can achieve up to 27% of reactant saving as compared with the bit-scanning method in single-target sample preparation. Moreover, even if CoDOS is not developed for multi-target sample preparation, it still outperforms the recent state-of-the-art algorithm, RSMA. Hence, it is convincing that CoDOS is a better alternative for many-reactant sample preparation.

Sample preparation is one of essential processes in biochemical reactions. Raw reactants are diluted in this process to achieve given target concentrations. A bioassay may require several different target concentrations of a reactant. Both the dilution operation count and the reactant usage can be minimized if multiple target concentrations are considered simultaneously during sample preparation. Hence, in this paper, we propose a multitarget sample preparation algorithm that extensively exploits the ideas of waste recycling and intermediate droplet sharing to reduce both reactant usage and waste amount for digital microfluidic biochips. Experimental results show that our waste recycling algorithm can reduce the waste and operation count by 48% and 37%, respectively, as compared to an existing state-of-the-art multitarget sample preparation method if the number of target concentrations is ten. The reduction can be up to 97% and 73% when the number of target concentrations goes even higher.

Multiclass classification is an important technique to many complex biomedicine problems. Genetic algorithms (GA) are proven to be effective to select features prior to multiclass classification by support vector machines (SVM). However, their use is computation intensive. Based on SOA (Service Oriented Architecture) design principles, this paper proposes a cloud computing framework that exploits the inherent parallelism of GA-SVM classification to speed up the work. The performance evaluations on an mRNA benchmark cancer dataset have shown the effectiveness and efficiency of the framework. With a user-friendly web interface, the framework provides researchers an easy way to investigate the unrevealed secrets in the fast-growing repository of biomedical data.

Sample preparation is an indispensable process to biochemical reactions. Original reactants are usually diluted to the solutions with desirable concentrations. Since the reactants, like infant's blood, DNA evidence collected from a crime scene, or costly reagents, are extremely valuable, the usage of reactant must be minimized in the sample preparation process. In this paper, we propose the first reactant minimization approach, REMIA, during sample preparation on digital microfluidic biochips (DMFBs). Given a target concentration, REMIA constructs a skewed mixing tree to guide the sample preparation process for reactant minimization. Experimental results demonstrate that REMIA can save about 31%~52% of reactant usage on average compared with three existing sample preparation methods. Besides, REMIA can be extended to tackle the sample preparation problem with multiple target concentrations, and the extended version also successfully decreases the reactant usage further.

BACKGROUND: The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. RESULTS: Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO http://ehco.iis.sinica.edu.tw, to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. CONCLUSION: This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment.

In the past several years, the analysis of alternative splicing and its application to identify the origins of disease has been gaining momentum. Therefore, we developed a value added transcriptome database (Avatar). We mapped EST (expressed sequence tag) and mRNA sequences onto whole human genomic sequence. We identified 9,937 alternative splicing relationships genes through an analysis of large-scale ESTs. Besides, we provided alternative splicing information for six organisms: Homo sapiens, Mus musculus, Rattus norvegiens, Caenorhabditis elegans, Drosophila melanogaster and Arabidopsis thaliana.