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Presents the recipients of Outstanding EMBS Paper awards for 2013. [ABSTRACT FROM AUTHOR]

The meaning of the word style depends on its context. While actions have already been quite studied for a while, style in human body motion is a growing topic of interest. In the context of animation, style is crucial as it brings realism and expressiveness to the motion of a character. Even though it is undoubtedly a key element in motions, its definition and the use of the word style in itself, among research works, lack consensus. Achieving realistic motions is tedious. It requires either a large motion capture dataset or the considerable work of artist animators. The lack of consistent style data is thus a challenge. Stylistic motion generation is quite studied in order to overcome this issue. This paper focuses on the study of style in human body motion from 3D human body skeletal data. It establishes a taxonomy of definitions of style, describes the data that have been used up until now, introduces key notions about motion capture data as well as machine learning, and presents approaches about style recognition, person identification through their style and motion style generation. [ABSTRACT FROM AUTHOR]

This is a Special Issue containing selected papers presented at the 5th Euro-Asian Pulsed Power Conference (EAPPC) in Kumamoto, Japan, held on September 8-12, 2014. The present Special Issue continues the tradition of chronicling the latest advances in the domain of pulsed-power science and technology. [ABSTRACT FROM PUBLISHER]

This paper presents a microforce-sensing mobile microrobot ($\mu $ FSMM) for use in automated micromanipulation tasks. The design consists of a planar vision-based microforce sensor end-effector, while the microrobot body is made of chemically etched nickel that is driven by an exterior magnetic field. With a known stiffness, the manipulation forces can be determined from observing the deformation of the end-effector through a camera attached to an optical microscope. After analyzing and calibrating the stiffness of a micromachined prototype, the mobility and in situ force-sensing capabilities are verified through real-time, closed loop, force controlled manipulation tests with automated path planning and navigation. The calibrated stiffness of the microforce sensor end-effectors fabricated is on the order of 10−3 N/m. The online (real time) force-sensing resolution is approximately $1.5~\mu \text{N}$. The sensing range is 0–20 $\mu \text{N}$ along the two planar directions. In automated micromanipulation experiments with a microcomponent, the $\mu $ FSMM utilizes real-time force control to apply a prescribed force of $6~\mu \text{N}$ to a desired location on a fixed microobject. Similarly, in another automated micromanipulation experiment, the $\mu $ FSMM demonstrates the use of real-time force control to limit the manipulation forces experienced by the microobject to remain below a threshold of 12 $\mu \text{N}$. Note to Practitioners—This paper was motivated by recent interest single-cell biological micromanipulation tasks that seek to understand the role of environmental forces on the mechanics of cell development (mechanobiology) and the biological mechanisms that control such behavior (mechanotransduction). In addition, tissue engineering applications require the safe micromanipulation of single cells to desired locations in the workspace for growing tissue scaffolds. The $\mu $ FSMM presented here can be easily inserted into existing biological testbeds to use for these aforementioned applications. The designed magnetic coil system is compatible with standard inverted optical microscopes, while a digital camera for real-time image processing is already standard in these testbeds. The developed software interface can be used to prescribe automated microforce controlled manipulations of single cells and tissues in the workspace to carry out these aforementioned tasks. This paper is also suitable for carrying out general automated micromanipulation and microassembly tasks with advanced manufacturing applications. [ABSTRACT FROM AUTHOR]

This paper introduces \mu-NET, a microfluidic LAN that supports the exchange of both digital information and biochemical information carried by droplets moving across molecular processors in a microfluidic chip. The \mu-NET can be used to support molecular biology applications like DNA, RNA, and protein biosynthesis. The \mu-NET is the first realization of a microfluidic networking paradigm that controls movements of droplets in microfluidic chips by exploiting hydrodynamic phenomena only and builds on recent solutions to achieve communications in the microfluidic domain. The \mu-NET integrates techniques to represent addressing information, as well as switching and medium access control solutions. In fact, in \mu-NET, the address of the molecular processor where a droplet should be sent to is encoded into the distance between droplets; switching is executed to steer the droplets inside the microfluidic device; medium access control is applied to avoid collisions between droplets that may result in their fusion and, thus, loss of the biochemical information. In this paper, the design of \mu-NET is presented in detail, and simulation results validating \mu-NET operations are shown. [ABSTRACT FROM PUBLISHER]

Bioinformatics research continues to advance at an increasing scale with the help of techniques such as next-generation sequencing and the availability of tool support to automate bioinformatics processes. With this growth, a large amount of biological data gets accumulated at an unprecedented rate, demanding high-performance and high-throughput computing technologies for processing such datasets. Use of hardware accelerators, such as graphics processing units (GPUs) and distributed computing, accelerates the processing of big data in high-performance computing environments. They enable higher degrees of parallelism to be achieved, thereby increasing the throughput. In this paper, we introduce BioWorkflow, an interactive workflow management system to automate the bioinformatics analyses with the capability of scheduling parallel tasks with the use of GPU-accelerated and distributed computing. This paper describes a case study carried out to evaluate the performance of a complex workflow with branching executed by BioWorkflow. The results indicate the gains of $\times 2.89$ magnitude by utilizing GPUs and gains in speed by average $\times 2.832$ magnitude (over $n = 5$ scenarios) by parallel execution of graph nodes during multiple sequence alignment calculations. Combined speed-ups are achieved $\times 1.71$ times for complex workflows. This confirms the expected higher speed-ups when having parallelism through GPU-acceleration and concurrent execution of workflow nodes than the mainstream sequential workflow execution. The tool also provides a comprehensive user interface with better interactivity for managing complex workflows; a system usability scale score of 82.9 is confirmed high usability for the system. [ABSTRACT FROM AUTHOR]

The halogen volatile organic compound (VOC) is a common industrial pollutant, mainly including trichloroethylene, dichloromethane, trichloroacetic acid, chloro-fluoro-carbons, carbon tetrachloride, trihalomethans, chlorodifuoromethanes, hydrofluorocarbons, and some other fluor alkaline, ether, and amine. The halogen VOCs have reproductive toxicity, immunotoxicity, and neurotoxicity. The traditional methods for treating them include masking method, dilution method, chemical oxidation method, incineration method, UV oxidation method, photocatalytic oxidation method, absorption method, and biological method. In this paper, advantages and disadvantages of traditional methods have been studied and analyzed. A new plasma technology has been put forward, which has the advantage of the rapid reaction to ambient temperature, wide applications, system compactness, simplicity of operation and short residual time, and the plasma technology is especially appropriate for the decomposition of VOCs. At present, as for the control of VOCs, the plasma technology has drawn much attention as an energy-saving technology. In this paper, the equipment for the decomposition of halogen VOCs has been analyzed, including the plasma rectors, power characteristics, operation conditions, and the decomposition mechanisms of C2HCl3, CH2Cl2, C2H3Cl3, CCl4, CHCl3, CF4, C2F6, and SF6 have been investigated and concluded. Finally, it is concluded that the combination of the plasma technology with traditional methods would be main direction of study in the future. [ABSTRACT FROM AUTHOR]

Significance: The performance of traditional approaches to decoding movement intent from electromyograms (EMGs) and other biological signals commonly degrade over time. Furthermore, conventional algorithms for training neural network based decoders may not perform well outside the domain of the state transitions observed during training. The work presented in this paper mitigates both these problems, resulting in an approach that has the potential to substantially improve the quality of life of the people with limb loss. Objective: This paper presents and evaluates the performance of four decoding methods for volitional movement intent from intramuscular EMG signals. Methods: The decoders are trained using the dataset aggregation (DAgger) algorithm, in which the training dataset is augmented during each training iteration based on the decoded estimates from previous iterations. Four competing decoding methods, namely polynomial Kalman filters (KFs), multilayer perceptron (MLP) networks, convolutional neural networks (CNN), and long short-term memory (LSTM) networks, were developed. The performances of the four decoding methods were evaluated using EMG datasets recorded from two human volunteers with transradial amputation. Short-term analyses, in which the training and cross-validation data came from the same dataset, and long-term analyses, in which the training and testing were done in different datasets, were performed. Results: Short-term analyses of the decoders demonstrated that CNN and MLP decoders performed significantly better than KF and LSTM decoders, showing an improvement of up to 60% in the normalized mean-square decoding error in cross-validation tests. Long-term analyses indicated that the CNN, MLP, and LSTM decoders performed significantly better than a KF-based decoder at most analyzed cases of temporal separations (0–150 days) between the acquisition of the training and testing datasets. Conclusion: The short-term and long-term performances of MLP- and CNN-based decoders trained with DAgger demonstrated their potential to provide more accurate and naturalistic control of prosthetic hands than alternate approaches. [ABSTRACT FROM AUTHOR]

With respect to harmonic generation measurements in isotropic materials, the amplitude of the third-harmonic wave generally depends on both $\beta ^{2}$ (the square of the second-order nonlinear parameter $\beta $) and $\gamma $ (the third-order nonlinear parameter). Therefore, some care should be taken when measuring these parameters using the third-harmonic amplitude. The purpose of this paper is to investigate detailed theoretical and measurement techniques for the accurate determination of $\beta ^{2}$ which dominates the third-harmonic amplitude in most biological fluids and crystalline solids. The theory deals with harmonic generation in materials with cubic nonlinearity and defines the formula for measuring $\beta ^{2}$ with corrections for diffraction and attenuation. These corrections are derived from the Westervelt equation and play an important role in the measurement of nonlinear parameters. The third-harmonic amplitude that varies with the distance in water is obtained for the measurement of $\beta ^{2}$. $\beta $ is also measured from the second-harmonic amplitude for comparison. We also confirm the required input voltage to stably generate the third harmonic and discuss the effects of diffraction and attenuation correction on the $\beta ^{2}$ determination. The measured $\beta ^{2}$ in the propagation range of 0.05–0.2 m agrees well with the square of the measured $\beta $ , revealing that the third-harmonic amplitude is closely related to $\beta ^{2}$ , not $\gamma $. This paper covers comprehensive theories, experimentation, and analysis related to the measurement of third-harmonic generation in isotropic media and can be immediately applied to crystalline solids. [ABSTRACT FROM AUTHOR]

The use of protons and heavy ions for the treatment of tumors is increasing since it provides a better relative biological effectiveness (RBE) than traditional radiotherapy. Accurate knowledge of the energy deposition at submicrometric scales is paramount for RBE characterization. This paper shows the latest version of the silicon cylindrical microdosimeter array developed by the Instituto de Microelectrónica de Barcelona, Centro Nacional de Microelectrónica (IMB-CNM, Spain). The detector consists of a matrix of $11 \times 11$ cylindrical sensitive unit cells with individual readout etched within the silicon substrate available in different diameters and pitches between detectors. The detector employed in this paper had a diameter of $15~\mu \text{m}$ , a pitch of $200~\mu \text{m}$ , and a thickness of $5.5~\mu \text{m}$. The detectors were tested in the clinical facilities of Fondazione Centro Nazionale di Adronterapia Oncologica (CNAO) (Pavia, Italy) employing a 12C pencil beam at a therapeutic beam fluence rate. Microdosimetric spectra of lineal energy were measured in different depths of polymethyl methacrylate (PMMA) up to the Bragg peak. Results were then compared with Monte Carlo simulations using the FLUKA particle transport code, showing an excellent agreement between experimental and simulated microdosimetric distributions even at the high fluence rates associated with clinical beams. [ABSTRACT FROM AUTHOR]

Detection of gene-modules is one of the fundamental tasks for the integral analysis of network architecture. In this paper, we propose a novel algorithm using an integrated approach comprising statistical method and normalized mutual information-based hypograph mining for discovering the multiview co-similarity gene modules contained in multiview datasets. For this purpose, we first identify the statistically significant genes corresponding to each data profile and subsequently obtain the union set consisting of all these statistically significant genes. For each data profile, we then propose a new similarity score called as integrated normalized mutual information to obtain the similarity scores across all possible pairs of genes belonging to the union set by employing the results of gene clustering obtained through applying normalized mutual information-based graph clustering on the corresponding data profile. Moreover, we propose a new information theoretic measure called as multiview normalized mutual information to integrate all the similarity scores of a given gene-pair obtained across all the data profiles. For the experiment, we utilize one of the recently used multiview dataset named TCGA acute myeloid leukemia dataset comprising five different categories of data profiles. Furthermore, the co-similarity strengths of all the multiview gene modules obtained using the proposed method (PM) are reported. Finally, we provide a comparative study between the proposed and other existing methods for demonstrating the superiority of the PM over others. Code is available in http://ieeexplore.ieee.org. [ABSTRACT FROM AUTHOR]

The estimation of a covariance matrix from an insufficient amount of data is one of the most common problems in fields as diverse as multivariate statistics, wireless communications, signal processing, biology, learning theory, and finance. In a joint work of Marzetta, Tucci, and Simon, a new approach to handle singular covariance matrices was suggested. The main idea was to use dimensionality reduction in conjunction with an average over the Stiefel manifold. In this paper, we continue with this research and we consider some new approaches to handle this problem. One of the methods is called the mean conjugate estimator under Ewens measure and uses a randomization of the sample covariance matrix over all the permutation matrices with respect to the Ewens measure. The techniques used to attack this problem are broad and run from random matrix theory to combinatorics. [ABSTRACT FROM AUTHOR]

Graph networks can model data observed across different levels of biological systems that span from population graphs (with patients as network nodes) to molecular graphs that involve omics data. Graph-based approaches have shed light on decoding biological processes modulated by complex interactions. This paper systematically reviews graph-based analysis methods of Graph Signal Processing (GSP), Graph Neural Networks (GNNs) and graph topology inference, and their applications to biological data. This work focuses on the algorithms of graph-based approaches and the constructions of graph-based frameworks that are adapted to a broad range of biological data. We cover the Graph Fourier Transform and the graph filter developed in GSP, which provides tools to investigate biological signals in the graph domain that can potentially benefit from the underlying graph structures. We also review the node, graph, and interaction oriented applications of GNNs with inductive and transductive learning manners for various biological targets. As a key component of graph analysis, we provide a review of graph topology inference methods that incorporate assumptions for specific biological objectives. Finally, we discuss the biological application of graph analysis methods within this exhaustive literature collection, potentially providing insights for future research in biological sciences. [ABSTRACT FROM AUTHOR]

Photo aesthetic quality evaluation is a challenging task in artificial intelligence systems. In this paper, we propose a biologically inspired aesthetic descriptor that mimicks humans sequentially perceiving visually/semantically salientref refid="fnote1"/ id="fnote1" asterisk="no"paraIn general, visually salient regions are perceived by low-level visual features, such as the high contrast between the foreground and the background objects; while semantically salient regions are perceived by high-level visual features such as human faces.pararegions in a photo. In particular, a weakly supervised learning paradigm is developed to project the local image descriptors into a low-dimensional semantic space. Then, each graphlet can be described by multiple types of visual features, both in low-level and in high-level. Since humans usually perceive only a few salient regions in a photo, a sparsity-constrained graphlet ranking algorithm is proposed that seamlessly integrates both the low-level and the high-level visual cues. Top-ranked graphlets are those visually/semantically prominent local aesthetic descriptors in a photo. They are sequentially linked into a path that simulates humans actively viewing process. Finally, we learn a probabilistic aesthetic measure based on such actively viewing paths (AVPs) from the training photos. Experimental results show that: 1) the AVPs are 87.65% consistent with real human gaze shifting paths, as verified by the eye-tracking data and 2) our aesthetic measure outperforms many of its competitors. [ABSTRACT FROM AUTHOR]

Event sequences are central to the analysis of data in domains that range from biology and health, to logfile analysis and people's everyday behavior. Many visualization tools have been created for such data, but people are error-prone when asked to judge the similarity of event sequences with basic presentation methods. This article describes an experiment that investigates whether local and global alignment techniques improve people's performance when judging sequence similarity. Participants were divided into three groups (basic versus local versus global alignment), and each participant judged the similarity of 180 sets of pseudo-randomly generated sequences. Each set comprised a target, a correct choice and a wrong choice. After training, the global alignment group was more accurate than the local alignment group (98 versus 93 percent correct), with the basic group getting 95 percent correct. Participants’ response times were primarily affected by the number of event types, the similarity of sequences (measured by the Levenshtein distance) and the edit types (nine combinations of deletion, insertion and substitution). In summary, global alignment is superior and people's performance could be further improved by choosing alignment parameters that explicitly penalize sequence mismatches. [ABSTRACT FROM AUTHOR]

Modern information and communication technologies provide digital traces of human interactive activities, which offer novel avenues to map and analyze temporal features of human interaction networks. This paper explores mesoscopic patterns of human interactive activities from six real-world interaction networks with temporal-topological isomorphic subgraphs, i.e., temporal motifs. We discover two dominant mutual motifs, “Star,” “Ordered-chain,” and one dominant directed motif, “Ping-Pong,” which characterize the interactive patterns of “Leader,” “Queue,” and “Feedback,” respectively. Moreover,temporal dynamics shows that bursts are universal in human mesoscopic patterns, and the evolution of three dominant temporal motifs indicates the existence of characteristic time. Finally, we analyze temporal robustness and generalization to verify that 3-event temporal motifs are a simple yet powerful tool to capture the mesoscopic patterns of human interactive activities. [ABSTRACT FROM AUTHOR]

Computational tools are needed to make data-driven disaster mitigation planning accessible to planners and policymakers without the need for programming or geographic information systems expertise. To address this problem, we have created modules to facilitate quantitative analyses pertinent to a variety of different disaster scenarios. These modules, which comprise the REsponse PLan ANalyzer framework, may be used to create tools for specific disaster scenarios that allow planners to harness large amounts of disparate data and execute computational models through a point-and-click interface. Bio-E, a user-friendly tool built using this framework, was designed to develop and analyze the feasibility of ad hoc clinics for treating populations following a biological emergency event. In this paper, the design and implementation of the RE-PLAN framework are described, and the functionality of the modules used in the Bio-E biological emergency mitigation tools are demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, a dielectrically modulated symmetrical double gate, having dual gate material, Tunnel Field-Effect transistor with Buried strained Si1-xGex source structure, has been investigated as a biosensor. This structure is proposed for the very first time to electrically detect the biological molecules at very low power consumption. In the proposed biosensor structure, the top thin Si channel of TFET is overlapped with the Si1-xGex source. This increases the tunneling area, due to which ON current of the biosensor also increases. To detect the biomolecules a nanogap cavity has been created over 1nm gate oxide. Also to decrease the short channel effects, dual-gate material with different metal work functions is used on both the symmetrical double gates. By varying the small bandgap material (Ge) mole fraction in the SiGe and after inserting different biological molecules (of the different dielectric) in a cavity, the variation in transfer characteristic, $\text{I}_{ \mathrm{\scriptscriptstyle ON}}/\text{I}_{ \mathrm{\scriptscriptstyle OFF}}$ current ratio, SS along with their sensitivity is studied. Also, to signify the presence of biomolecules in the cavity, the $\text{g}_{m}/\text{I}_{d}$ ratio as a sensing metric is studied under the sub-threshold region. Along with the fully filled biomolecules cavity, the partially filled cavity and the effect of a steric hindrance have also investigated in this paper with various non-uniform step patterns of biomolecules in the cavity. Because, in a more practical situation, the steric hindrance effect doesn’t allow the cavity to be entirely filled. Also, this paper addresses the optimization of drain current sensitivity, by different cavity length with different source overlapping cavity. [ABSTRACT FROM AUTHOR]

Considering the limitation of conventional rheometry measurements and the relevance of the study of food viscosity in the study of dysphagia, we commenced developing a soft-bodied esophageal swallowing robot (ESR). Using experimental data, this paper aims to discover the differential equations (DEs) of the ESR that governs the peristaltic deformation in the esophageal conduit at the given time-varying pressure. The deformation data of the conduit are collected from a quarter version of the ESR due to the inaccessibility to the esophageal occlusion. The three-dimensional displacements of the markers placed on the interior surface of the conduit are measured using a Vicon optical motion capturing system. The dimension of the dataset is first reduced using the principal orthogonal decomposition (POD) method, which is then used to discover the ESR's DEs by the sparse identification of nonlinear dynamics (SINDy). The marker displacements are considered as the ESR's states. The ESR's states are reduced from 27 to 2 by initially applying power spectral density (PSD), and then the POD. The identified states essential to the DEs capture 95 $ \%$ of the total variance of the deformation dataset. Finally, the conduit deformation simulated from the DEs are validated by the experimental measurements in the full version of the ESR. [ABSTRACT FROM AUTHOR]

Partial correlation (PC) or conditional mutual information (CMI) is widely used in detecting direct dependencies between the observed variables in biological networks by eliminating indirect correlations/associations, but it fails whenever there are some strong correlations in a network. In this paper, we theoretically develop a multiscale association analysis to overcome this flaw. We propose a new measure, partial association (PA), based on the multiscale conditional mutual information. We show that linear PA and nonlinear PA have clear advantages over PC and CMI from both theoretical and computational aspects. Both simulated models and real omics datasets demonstrate that PA is superior to PC and CMI in terms of accuracy, and is a powerful tool to identify the direct associations or reconstruct molecular networks based on the observed data. Survival and functional analyses of the hub genes in the gene networks reconstructed from TCGA data for different cancers also validated the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Ontologies are formal representations of concepts and complex relationships among them. They have been widely used to capture comprehensive domain knowledge in areas such as biology and medicine, where large and complex ontologies can contain hundreds of thousands of concepts. Especially due to the large size of ontologies, visualisation is useful for authoring, exploring and understanding their underlying data. Existing ontology visualisation tools generally focus on the hierarchical structure, giving much less emphasis to non-hierarchical associations. In this paper we present OntoPlot, a novel visualisation specifically designed to facilitate the exploration of all concept associations whilst still showing an ontology's large hierarchical structure. This hybrid visualisation combines icicle plots, visual compression techniques and interactivity, improving space-efficiency and reducing visual structural complexity. We conducted a user study with domain experts to evaluate the usability of OntoPlot, comparing it with the de facto ontology editor Protégé. The results confirm that OntoPlot attains our design goals for association-related tasks and is strongly favoured by domain experts. [ABSTRACT FROM AUTHOR]

In this paper, we present a graphene field effect transistor (FET) fabricated on a flexible Kapton substrate using 3-D inkjet printing for use as a biosensor to detect infectious organisms. Inkjet printing process of graphene film is described with sheet resistance as low as 110 $\Omega $ /sq. To suppress background noises, the biosensors are based on intensity changes of the ac signal as a function of the biological agents’ concentration. Using the foodborne pathogen, Norovirus, as a proof-of-concept disease target, the value of S12 (i.e., the voltage gain from source to drain) at 10 GHz generates a linear response from 0.07 to 3.70 dB when the concentration of Norovirus protein increases from 0.1 to 100 \mu \text{g} /ml. While further studies are needed to improve surface functionalization and sensitivity, the current study establishes a linear response over three orders of magnitude indicating that the flexible graphene FET sensor has a wide dynamic range for detection of biological targets that could ultimately be applied for detection of a variety of disease-causing pathogens. [ABSTRACT FROM PUBLISHER]

We examine the problem of uncovering communities in complex real-world networks whose elements and their respective associations manifest as streams of data. Community detection is applied in emerging computational environments and concerns critical applications in diverse areas including social computing, web analysis, IoT and biology. Despite the already expended related research efforts, the task of revealing the community structure of massive and rapidly-evolving networks remains very challenging. More specifically, there is an emerging need for online approaches that ingest graph data as a stream. In this paper, we propose a streaming-graph community-detection algorithm that expands seed-sets of nodes to communities. We consider an online setting and process a stream of edges while aiming to form communities on-the-fly using partial knowledge of the graph structure. We use space-efficient structures to maintain very limited information regarding the nodes of the graph and the sought communities, so as to effectively process large scale networks. In addition to our novel streaming approach, we develop a technique that increases the accuracy of our algorithm considerably and additionally propose a new clustering algorithm that allows for automatically deriving the size of the communities we seek to detect. Using ground-truth communities for a wide range of large real-word and synthetic networks, our experimental evaluation shows that our approach does achieve accuracy comparable, and oftentimes better, to the state-of-the-art non-streaming community detection algorithms. More importantly, we attain significant improvements in both execution time and memory requirements. [ABSTRACT FROM AUTHOR]

Deterministic asynchronous Boolean networks play a crucial role in modeling and analysis of gene regulatory networks. In this paper, we focus on a typical type of deterministic asynchronous Boolean networks called deterministic generalized asynchronous random Boolean networks (DGARBNs). We first formulate the extended state transition graph, which captures the whole dynamics of a DGARBN and paves potential ways to analyze this DGARBN. We then propose two SMT-based methods for attractor detection and optimal control of DGARBNs. These methods are implemented in a JAVA tool called DABoolNet. Two experiments are designed to highlight the scalability of the proposed methods. We also formally state and prove several relations between DGARBNs and other models including deterministic asynchronous models, block-sequential Boolean networks, generalized asynchronous random Boolean networks, and mixed-context random Boolean networks. Several case studies are presented to show the applications of our methods. [ABSTRACT FROM AUTHOR]

Analysis of gene similarity not only can provide information on the understanding of the biological roles and functions of a gene, but may also reveal the relationships among various genes. In this paper, we introduce a novel idea of mining similar aspects from a gene information network, i.e., for a given gene pair, we want to know in which aspects (meta paths) they are most similar from the perspective of the gene information network. We defined a similarity metric based on the set of meta paths connecting the query genes in the gene information network and used the rank of similarity of a gene pair in a meta path set to measure the similarity significance in that aspect. A minimal set of gene meta paths where the query gene pair ranks the highest is a similar aspect, and the similar aspect of a query gene pair is far from trivial. We proposed a novel method, SCENARIO, to investigate minimal similar aspects. Our empirical study on the gene information network, constructed from six public gene-related databases, verified that our proposed method is effective, efficient, and useful. [ABSTRACT FROM AUTHOR]

Motivation: In bioinformatics, complex cellular modeling and behavior simulation to identify significant molecular interactions is considered a relevant problem. Traditional methods model such complex systems using single and binary network. However, this model is inadequate to represent biological networks as different sets of interactions can simultaneously take place for different interaction constraints (such as transcription regulation and protein interaction). Furthermore, biological systems may exhibit varying interaction topologies even for the same interaction type under different developmental stages or stress conditions. Therefore, models which consider biological systems as solitary interactions are inaccurate as they fail to capture the complex behavior of cellular interactions within organisms. Identification and counting of recurrent motifs within a network is one of the fundamental problems in biological network analysis. Existing methods for motif counting on single network topologies are inadequate to capture patterns of molecular interactions that have significant changes in biological expression when identified across different organisms that are similar, or even time-varying networks within the same organism. That is, they fail to identify recurrent interactions as they consider a single snapshot of a network among a set of multiple networks. Therefore, we need methods geared towards studying multiple network topologies and the pattern conservation among them. Contributions: In this paper, we consider the problem of counting the number of instances of a user supplied motif topology in a given multilayer network. We model interactions among a set of entities (e.g., genes)describing various conditions or temporal variation as multilayer networks. Thus a separate network as each layer shows the connectivity of the nodes under a unique network state. Existing motif counting and identification methods are limited to single network topologies, and thus cannot be directly applied on multilayer networks. We apply our model and algorithm to study frequent patterns in cellular networks that are common in varying cellular states under different stress conditions, where the cellular network topology under each stress condition describes a unique network layer. Results: We develop a methodology and corresponding algorithm based on the proposed model for motif counting in multilayer networks. We performed experiments on both real and synthetic datasets. We modeled the synthetic datasets under a wide spectrum of parameters, such as network size, density, motif frequency. Results on synthetic datasets demonstrate that our algorithm finds motif embeddings with very high accuracy compared to existing state-of-the-art methods such as G-tries, ESU (FANMODE)and mfinder. Furthermore, we observe that our method runs from several times to several orders of magnitude faster than existing methods. For experiments on real dataset, we consider Escherichia coli (E. coli)transcription regulatory network under different experimental conditions. We observe that the genes selected by our method conserves functional characteristics under various stress conditions with very low false discovery rates. Moreover, the method is scalable to real networks in terms of both network size and number of layers. [ABSTRACT FROM AUTHOR]

This paper reports a vision-based automated microfluidic system for morphological measurement and size-based sorting of the nematode worm C. elegans. Exceeding the capabilities of conventional worm sorting microfluidic devices purely relying on passive sorting mechanisms, our system is capable of accurate measurement of the worm length/width and active sorting of worms with the desired sizes from a mixture of worms with different body sizes. This function is realized based on the combination of real-time, vision-based worm detection and sizing algorithms and automated on-chip worm manipulation. A double-layer microfluidic device with computer-controlled pneumatic valves is developed for sequential loading, trapping, vision-based sizing, and sorting of single worms. To keep the system operation robust, vision-based algorithms on detecting multi-worm loading and worm sizing failure have also been developed. We conducted sorting experiments on 319 worms and achieved an average sorting speed of 10.4 worms per minute (5.8 s/worm) with an operation success rate of 90.3%. This system will facilitate the worm biology studies where body size measurement and size-based sorting of many worms are needed. [ABSTRACT FROM AUTHOR]

The emerging contactless cell membrane permeabilization methodology (magnetoporation) based on high pulsed magnetic field (PMF) is highly dependent on the applied pulse parameters. Due to the lack of magnetic experimental infrastructure, the parametric analysis of PMF-induced effects, scatter, and consolidation of research in this field is limited. Therefore, this paper presents a flexible high dB/dt magnetic field pulser, which is easy to reproduce. The generator is applicable in the area of contactless biological cell membrane permeabilization or study of the biological effects of PMF. The pulse forming circuit is based on the high-power ignitron switch (20 kV, up to 100 kA), which is driven by an array of silicon control rectifiers. The generator is capable of forming repetitive pulses with sub-microsecond rise time, which ensures induced electric field in the range of several tens to hundreds of V/cm. The generator is compact, programmable and includes an emergency discharge circuit for safety reasons. The setup may find direct application as a platform for basic magnetoporation experiments. [ABSTRACT FROM AUTHOR]

Networks have been a general tool for representing, analyzing, and modeling relational data arising in several domains. One of the most important aspect of network analysis is community detection or network clustering. Until recently, the major focus have been on discovering community structure in single (i.e., monoplex) networks. However, with the advent of relational data with multiple modalities, multiplex networks, i.e., networks composed of multiple layers representing different aspects of relations, have emerged. Consequently, community detection in multiplex network, i.e., detecting clusters of nodes shared by all layers, has become a new challenge. In this paper, we propose Network Fusion for Composite Community Extraction (NF-CCE), a new class of algorithms, based on four different non-negative matrix factorization models, capable of extracting composite communities in multiplex networks. Each algorithm works in two steps: first, it finds a non-negative, low-dimensional feature representation of each network layer; then, it fuses the feature representation of layers into a common non-negative, low-dimensional feature representation via collective factorization. The composite clusters are extracted from the common feature representation. We demonstrate the superior performance of our algorithms over the state-of-the-art methods on various types of multiplex networks, including biological, social, economic, citation, phone communication, and brain multiplex networks. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the optimized parameters of bent part coupling silicon oxynitride micro-racetrack optical resonators coupled to a straight waveguide, for an efficient design of label-free biosensor devices. A systematic engineering of waveguide-resonator characteristics for optimum geometry and field-overlap with analytes is proposed. Different parameters of system, such as coupling, intrinsic, and total quality factors of $Q_{\kappa }$ , $Q_{i}$ , and $Q_{t}$ , sensitivity ($S$) and figure of merit, or intrinsic limit of detection (ILOD), are examined with interest of taking into account the dispersion effect in calculations. To the best of our knowledge, considering dispersion effect in calculations has been proposed for the first time in bent part coupling racetrack resonator-based biosensors. We have shown the effective role of dispersion on the best optimized parameters of the biosensors. These investigations result in high amounts of $S$ (435 nm/RIU) and $Q_{t}$ (≥45000), simultaneously for the proposed biosensors. The devices have been optimized for operation at a wavelength of 850 nm. The biosensing performance of our biosensor is compared with lately reported theoretical and experimental investigations. Based on the ILOD calculations, the performance of our sensor structure is improved by a factor of 0.10 compared with a resonator-based biosensor, reported lately. [ABSTRACT FROM AUTHOR]

Heterogeneous networks are networks consisting of different types of objects and links. They can be found in several fields, ranging from the Internet to social sciences, biology, epidemiology, geography, finance, and many others. In the literature, several methods have been proposed for the analysis of network data, but they usually focus on homogeneous networks, where all the objects are of the same type, and links among them describe a single type of relationship. More recently, the complexity of real scenarios has impelled researchers to design methods for the analysis of heterogeneous networks, especially focused on classification and clustering tasks. However, they often make assumptions on the structure of the network that are too restrictive or do not fully exploit different forms of network correlation and autocorrelation. Moreover, when nodes which are the main subject of the classification task are linked to several nodes of the network having missing values, standard methods can lead to either building incomplete classification models or to discarding possibly relevant dependencies (correlation or autocorrelation). In this paper, we propose an ensemble learning approach for multi-type classification. We adopt the system Mr-SBC, which is originally able to analyze heterogeneous networks of arbitrary structure, within an ensemble learning approach. The ensemble allows us to improve the classification accuracy of Mr-SBC by exploiting i) the possible presence of correlation and autocorrelation phenomena, and ii) the classification of instances (which contain missing values) of other node types in the network. As a beneficial side effect, we have also that the models are more stable in terms of standard deviation of the accuracy, over different samples used for training. Experiments performed on real-world datasets show that the proposed method is able to significantly outperform the standard implementation of Mr-SBC. Moreover, it gives Mr-SBC the advantage of outperforming four other well-known algorithms for the classification of data organized in a network. [ABSTRACT FROM AUTHOR]

MiRNA-disease association prediction plays an important role in identifying human disease-related miRNAs. This approach is helpful not only to formulate individualized diagnosis schemes, but also to understand the pathogenesis of diseases. Many studies have focused on enhancing the prediction performance using explicit side information, such as miRNA functional similarity and disease semantic similarity. The existing approaches, however, often ignore the higher-order implicit proximity among miRNAs and diseases. To this end, in this paper, we first propose a novel approach HOP_MDA (Higher-Order Proximity based MiRNA and Disease Association Prediction) for predicting potential association between miRNA and disease. Both explicit interaction information and implicit higher-order proximity information between miRNA and disease are encoded with different order proximity matrices which are weightily combined into a parameterized prediction matrix. A supervised learning approach based on the known miRNAs-disease associations is proposed to determine the optimal weight parameters. The prediction matrix is then used to achieve effective prediction. Additionally, a higher-order proximity approximation technique (HOPA_MDA) is presented to make more efficient predictions. 5-fold cross validation is used to evaluate the performance of our proposed method. The average AUC values of HOPA_MDA for two real datasets are 0.921+/-0.002 and 0.944+/-0.0015, respectively. Our method can also predict potential miRNAs specific to new diseases with no known related miRNAs. [ABSTRACT FROM AUTHOR]

Low-temperature, atmospheric pressure plasmas (LTPs) produce reactive oxygen and nitrogen species [O, O2−, O2 ( ^1\Delta ), hydroxyl (OH), H2O2, NO, NO2 $\ldots $ ]. In biological and medical applications, the concentrations and fluxes of these species play a crucial role in the biological outcomes. Many of these species are produced in the gaseous phase and at the gas-liquid interface when LTP is applied to biological media. In the medium, the plasma-produced oxygen reactive species and nitrogen reactive species generate long-lived species, such as hydrogen peroxide (H2O2), nitrites (NO2−), nitrates (NO3−), and organic peroxides (RO2). In particular, hydrogen peroxide is known to cause various oxidizing reactions in biological cells. One of the pathways to the creation of hydrogen peroxide is the reaction between OH radicals. Therefore, the measurement of OH concentration is of great importance. In this paper, we report on the measurements of OH in the gas-liquid interface using laser-induced fluorescence and measurements of H2O2 concentration in biological media. In addition, the effects of plasma activated media on cancer cells are briefly discussed. [ABSTRACT FROM AUTHOR]

Authentication in a vehicular ad-hoc network (VANET) requires not only secure and efficient authentication with privacy preservation but applicable flexibility to handle complicated transportation circumstances as well. In this paper, we proposed a Two-Factor LIghtweight Privacy-preserving authentication scheme (2FLIP) to enhance the security of VANET communication. 2FLIP employs the decentralized certificate authority (CA) and the biological-password-based two-factor authentication (2FA) to achieve the goals. Based on the decentralized CA, 2FLIP only requires several extremely lightweight hashing processes and a fast message-authentication-code operation for message signing and verification between vehicles. Compared with previous schemes, 2FLIP significantly reduces computation cost by 100–1000 times and decreases communication overhead by 55.24%–77.52%. Furthermore, any certificate revocation list (CRL)-related overhead on vehicles is avoided. 2FLIP makes the scheme resilient to denial-of-service attack in both computation and memory, which is caused by either deliberate invading behaviors or jammed traffic scenes. The proposed scheme provides strong privacy preservation that the adversaries can never succeed in tracing any vehicles, even with all RSUs compromised. Moreover, it achieves strong nonrepudiation that any biological anonym driver could be conditionally traced, even if he is not the only driver of the vehicle. Extensive simulations reveal that 2FLIP is feasible and has an outstanding performance of nearly 0-ms network delay and 0% packet-loss ratio, which are particularly appropriate for real-time emergency reporting applications. [ABSTRACT FROM PUBLISHER]

High-sensitive, wide-measurement range, and small-sized load sensor was developed using AT-cut quartz crystal resonator (QCR). The quartz crystal generates a charge, which is proportional to the external force. Since it has high sensitivity and excellent temperature stability, it has been used for various sensors. In particular, QCR has superior characteristic for static load sensing in nature. However, QCR is fragile and easily broken by the stress concentration. Moreover, a retention mechanism is required to transmit the load efficiently, and we have to fix the QCR firmly while avoiding off axis force. Miniaturization of the retention mechanism is quite difficult to develop, since fabrication and assembly process is complicated. We have proposed a miniaturized sensor element using microfabrication. The QCR load sensor had enormously wide range of force sensing over 10^4 . However, output of previous sensor changes easily by parasitic capacitance change around QCR. The objective of this paper is to improve the resolution of load measurement and stability of sensor output for detection of biological signals. We fabricated QCR sensor whose sensitivity is 973 Hz/N. We succeeded in detection of multiple biological signals (breath, heartbeat, and posture) using proposed QCR load sensor with high stability. [ABSTRACT FROM PUBLISHER]

Many signals are modeled as a superposition of exponential functions in spectroscopy of chemistry, biology, and medical imaging. This paper studies the problem of recovering exponential signals from a random subset of samples. We exploit the Vandermonde structure of the Hankel matrix formed by the exponential signal and formulate signal recovery as Hankel matrix completion with Vandermonde factorization (HVaF). A numerical algorithm is developed to solve the proposed model and its sequence convergence is analyzed theoretically. Experiments on synthetic data demonstrate that HVaF succeeds over a wider regime than the state-of-the-art nuclear-norm-minimization-based Hankel matrix completion method, while it has a less restriction on frequency separation than the state-of-the-art atomic norm minimization and fast iterative hard thresholding methods. The effectiveness of HVaF is further validated on biological magnetic resonance spectroscopy data. [ABSTRACT FROM AUTHOR]

Power electronics for the utility interface of distributed energy resource (DER) offers many benefits in the electric grid operation through flexibility of the DER controls. Energy from various generation sources including renewables can be efficiently delivered to the consumers with high power quality and reliability. However, mixed source microgrids comprising both inverter- and synchronous generator-based DERs are susceptible to collapse, particularly when the system operates in the islanded mode. This paper presents robustness studies on microgrids from a qualitative point of view, borrowing features from ecological systems that are highly robust. The interrelationships between ecological species translate into the dynamic behavior of DERs, affected by their sizes and locations within the grid. The advantage of such qualitative analysis over traditional quantitative viewpoint is it explicitly identifies the nature of interactions and interconnections between the subsystems as beneficial or detrimental to stability. From the robustness studies, the best possible microgrid configurations are determined for IEEE 33-bus system having a mix of inverter-based DERs and synchronous generator-based DERs. Furthermore, all possible microgrid cluster formations are studied for finding the most robust microgrid islanding strategies. [ABSTRACT FROM AUTHOR]

Motifs in complex biological, technological, and social networks, or in other types of networks are connected to patterns that occur at significantly higher frequency compared to similar random networks. Finding motifs helps scientists to know more about networks’ structure and function, and this goal cannot be achieved without efficient algorithms. Existing methods for counting network motifs are extremely costly in CPU time and memory consumption. In addition, they restrict to the larger motifs. In this paper, a new algorithm called FraMo is presented based on ‘fractal theory’. This method consists of three phases: at first, a complex network is converted to a multifractal network. Then, using maximum likelihood estimation, distribution parameters is estimated for the multifractal network, and at last the approximate number of network motifs is calculated. Experimental results on several benchmark datasets show that our algorithm can efficiently approximate the number of motifs in any size in undirected networks and compare its performance favorably with similar existing algorithms in terms of CPU time and memory usage. [ABSTRACT FROM PUBLISHER]

Tubulin is a promising target for designing anti-cancer drugs. Identification of hotspots in multifunctional Tubulin protein provides insights for new drug discovery. Although machine learning techniques have shown significant results in prediction, they fail to identify the hotspots corresponding to a particular biological function. This paper presents a signal processing technique combining resonant recognition model (RRM) and Stockwell Transform (ST) for the identification of hotspots corresponding to a particular functionality. The characteristic frequency (CF) representing a specific biological function is determined using the RRM. Then the spectrum of the protein sequence is computed using ST. The CF is filtered from the ST spectrum using a time-frequency mask. The energy peaks in the filtered sequence represent the hotspots. The hotspots predicted by the proposed method are compared with the experimentally detected binding residues of Tubulin stabilizing drug Taxol and destabilizing drug Colchicine present in the Tubulin protein. Out of the 53 experimentally identified hotspots, 60% are predicted by the proposed method whereas around 20% are predicted by existing machine learning based methods. Additionally, the proposed method predicts some new hot spots, which may be investigated. [ABSTRACT FROM AUTHOR]

Agriculture in Japan is approaching a crisis for two reasons: a declining self-sufficiency rate and a decreasing number of agriculture workers. To solve these problems, plant factories have been attracting attention recently. Plant factories are facilities that aid the steady production of high-quality vegetables year round by artificially controlling the cultivation environment, allowing growers to drastically decrease production time. Despite the many advantages of plant factories, a main impediment is the reduction of initial and running costs. This paper utilizes leaf lettuce (Early impulse), which is a typical item cultivated in plant factories, as a target to improve productive efficiency of plant factories due to its relatively high price. Various pulsed electric fields (PEFs) were applied to roots of the lettuce to increase lettuce growth rate. The experimental results show PEF intensity from 0.2 to 1.0 kV/cm is positive for growth stimulation. Conversely, those over 1.0 kV/cm resulted in growth inhibition. Furthermore, roots of the samples which exhibited increased leaf weight grew more robustly than those of decreased leaf weight. In addition, analysis results showed that there was no significant difference when liquid fertilizer was applied prior to or after application of PEF. The results suggest that PEF does not affect the composition change of liquid fertilizer but directly influences the growth of leaf lettuce. [ABSTRACT FROM PUBLISHER]

This special issue focuses on elucidating computational neuroscience: an interdisciplinary field of scientific research in which one of the primary goals is to understand how electronic activity in brain cells and networks enables biological intelligence. The objective is to provide a selection of papers that expose and review research efforts in aspects of computational neuroscience that demonstrate its rapidly growing intersection with electrical, electronic and computer engineering, and the prospects for interaction in the near and long-term future. [ABSTRACT FROM PUBLISHER]

As advances in integrated circuit (IC) fabrication technology reduce feature sizes to dimensions on the order of nanometers, IC designers are facing many of the problems that evolution has had to overcome in order to perform meaningful and accurate computations in biological neural circuits. In this paper, we explore the current state of IC technology including the many new and exciting opportunities “beyond CMOS.” We review the role of noise in both biological and engineered systems and discuss how “stochastic facilitation” can be used to perform useful and precise computation. We explore nondeterministic methodologies for computation in hardware and introduce the concept of stochastic electronics (SE); a new way to design circuits and increase performance in highly noisy and mismatched fabrication environments. This approach is illustrated with several circuit examples whose results demonstrate its exciting potential. [ABSTRACT FROM PUBLISHER]

This paper explores the significance of persistence length in modeling and simulation of nanoscale networks. Persistence length, used by chemists and biologists, pertains to a wide range of high-aspect ratio, small-scale materials, and has a significant impact on communication at the nanoscale. For example, it applies to carbon nanotubes, microtubules, DNA, and nanowires as well as many other potential nanoscale network structures. Consider the similarities between microtubules in living cells and carbon nanotubes (CNTs). Both microtubules and carbon nanotubes have similar geometric structures and share similar properties; both are capable of transporting information at the nanoscale. Microtubules and carbon nanotubes can also self-organize to create random graph structures, which can be used as communication networks. At the same time, networks of CNTs may be used for molecular-level transport in the human body supporting treatment of diseases. This paper examines fundamental properties of network topologies created by filamentous structures and their relationship to the performance of nanoscale communication networks. This behavior depends strongly on the alignment of bond segments and filaments, which in turn depends on the persistence length of the tubes. We use graph spectral analysis for analyzing a simulated generic network of filamentous structures. A network graph is extracted from the layout of such a structure and graph properties of the resultant graph are examined. The paper presents the results of the simulation with tubes of different persistence lengths and makes the case for including persistence length as a parameter in standards for nanoscale communications. Our simulation results show that at different persistence lengths the network structure and the resultant properties of the graph structure that impact nanoscale communications can be predicted and analyzed. Such standard parameters help in calibrating an analytical model to a physical network allowing for synchronization between analytical and experimental results. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the target controllability of two-layer multiplex networks, which is an outstanding challenge faced in various real-world applications. We focus on a fundamental issue regarding how to allocate a minimum number of control sources to guarantee the controllability of each given target subset in each layer, where the external control sources are limited to interact with only one layer. It is shown that this issue is essentially a path cover problem, which is to locate a set of directed paths denoted as P and cycles denoted as C to cover the target sets under the constraint that the nodes in the second layer cannot be the starting node of any element in P, and the number of elements in P attains its minimum. In addition, the formulated path cover problem can be further converted into a maximum network flow problem, which can be efficiently solved by an algorithm called maximum flow-based target path-cover (MFTP). We rigorously prove that MFTP provides the minimum number of control sources for guaranteeing the target controllability of two-layer multiplex networks. It is anticipated that this paper would serve wide applications in target control of real-life networks. [ABSTRACT FROM AUTHOR]

Visualization has been deemed a useful technique by researchers and practitioners, alike, leaving a trail of arguments behind that reason why visualization works. In addition, examples of misleading usages of visualizations in information communication have occasionally been pointed out. Thus, to contribute to the fundamental understanding of our discipline, we require a comprehensive collection of arguments on “why visualize?” (or “why not?”), untangling the rationale behind positive and negative viewpoints. In this paper, we report a theoretical study to understand the underlying reasons of various arguments; their relationships (e.g., built-on, and conflict); and their respective dependencies on tasks, users, and data. We curated an argumentative network based on a collection of arguments from various fields, including information visualization, cognitive science, psychology, statistics, philosophy, and others. Our work proposes several categorizations for the arguments, and makes their relations explicit. We contribute the first comprehensive and systematic theoretical study of the arguments on visualization. Thereby, we provide a roadmap towards building a foundation for visualization theory and empirical research as well as for practical application in the critique and design of visualizations. In addition, we provide our argumentation network and argument collection online at https://whyvis.dbvis.de , supported by an interactive visualization. [ABSTRACT FROM AUTHOR]