ALBERT

Description: : As sequencing becomes cheaper and more widely available, there is a greater need to quickly and effectively analyze large-scale genomic data. While the functionality of AVIA v1.0, whose implementation was based on ANNOVAR, was comparable with other annotation web servers, AVIA v2.0 represents an enhanced web-based server that extends genomic annotations to cell-specific transcripts and protein-level functional annotations. With AVIA’s improved interface, users can better visualize their data, perform comprehensive searches and categorize both coding and non-coding variants. Availability and implementation : AVIA is freely available through the web at http://avia.abcc.ncifcrf.gov . Contact : Hue.Vuong@fnlcr.nih.gov Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : As new methods for multivariate analysis of genome wide association studies become available, it is important to be able to combine results from different cohorts in a meta-analysis. The R package MultiMeta provides an implementation of the inverse-variance-based method for meta-analysis, generalized to an n -dimensional setting. Availability and implementation: The R package MultiMeta can be downloaded from CRAN. Contact: dragana.vuckovic@burlo.trieste.it ; vi1@sanger.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: An ultrasonic technique, invariant to temperature changes, for a density measurement of different liquids under in situ extreme conditions is presented. The influence of geometry and material parameters of the measurement system (transducer, waveguide, matching layer) on measurement accuracy and reliability is analyzed theoretically along with experimental results. The proposed method is based on measurement of the amplitude of the ultrasonic wave, reflected from the interface of the solid/liquid medium under investigation. In order to enhance sensitivity, the use of a quarter wavelength acoustic matching layer is proposed. Therefore, the sensitivity of the measurement system increases significantly. Density measurements quite often must be performed in extreme conditions at high temperature (up to 220 °C) and high pressure. In this case, metal waveguides between piezoelectric transducer and the measured liquid are used in order to protect the conventional transducer from the influence of high temperature and to avoid depolarization. The presented ultrasonic density measurement technique is suitable for density measurement in different materials, including liquids and polymer melts in extreme conditions. A new calibration algorithm was proposed. The metrological evaluation of the measurement method was performed. The expanded measurement uncertainty Uρ = 7.4 × 10−3 g/cm3 (1%).

Description: In recent years, IoT (Internet of Things) technologies have seen great advances, particularly, the IPv6 Routing Protocol for Low-power and Lossy Networks (RPL), which provides a powerful and flexible routing framework that can be applied in a variety of application scenarios. In this context, as an important role of IoT, Wireless Sensor Networks (WSNs) can utilize RPL to design efficient routing protocols for a specific application to increase the ubiquity of networks with resource-constrained WSN nodes that are low-cost and easy to deploy. In this article, our work starts with the description of Agricultural Low-power and Lossy Networks (A-LLNs) complying with the LLN framework, and to clarify the requirements of this application-oriented routing solution. After a brief review of existing optimization techniques for RPL, our contribution is dedicated to a Scalable Context-Aware Objective Function (SCAOF) that can adapt RPL to the environmental monitoring of A-LLNs, through combining energy-aware, reliability-aware, robustness-aware and resource-aware contexts according to the composite routing metrics approach. The correct behavior of this enhanced RPL version (RPAL) was verified by performance evaluations on both simulation and field tests. The obtained experimental results confirm that SCAOF can deliver the desired advantages on network lifetime extension, and high reliability and efficiency in different simulation scenarios and hardware testbeds.

Description: The aim of this randomized clinical trial was to evaluate whether a recently described multi-sensor approach called BIONOTE® is accurate enough to verify the efficacy of treatment of patients with halitosis. A treatment with Lactobacillus brevis (CD2)–containing lozenges, compared with placebo was tested. The BIONOTE® was compared with traditional techniques used to detect halitosis: OralChroma™ and two calibrated odor judges enrolled for the organoleptic assessments. Twenty patients (10 treated and 10 placebo), suffering from active phase halitosis were included in the study. Treatment consisted of Lactobacillus brevis (CD2)—containing lozenges or placebo, 4 tablets/day for 14 days. t0 was before the beginning of the study; t1 was day 7 and t2 was day 14. The effectiveness of treatment was assessed through: (1) Rosenberg score; (2) Winkel tongue coating index (WTCI) anterior and posterior; (2) OralChroma™; (3) the new developed multi-sensor approach, called BIONOTE® (test technique). Only the WTCI anterior revealed statistically significant changes between t0 and t2 data (p = 0.014) in the treated group. Except for the WTCI anterior, all diagnostic methods revealed the lack of effectiveness for halitosis of a 14-days treatment with Lactobacillus brevis (CD2)–containing lozenges. The BIONOTE® multisensor system seems accurate in addition to OralChroma™ to assess the initial condition of halitosis and its mitigation during treatment.

Description: We examine a distributed detection problem in a wireless sensor network, where sensor nodes collaborate to detect a Gaussian signal with an unknown change of power, i.e., a scale parameter. Due to power/bandwidth constraints, we consider the case where each sensor quantizes its observation into a binary digit. The binary data are then transmitted through error-prone wireless links to a fusion center, where a generalized likelihood ratio test (GLRT) detector is employed to perform a global decision. We study the design of a binary quantizer based on an asymptotic analysis of the GLRT. Interestingly, the quantization threshold of the quantizer is independent of the unknown scale parameter. Numerical results are included to illustrate the performance of the proposed quantizer and GLRT in binary symmetric channels (BSCs).

Description: This paper presents a multipurpose and low cost sensor for the simultaneous monitoring of temperature and ullage of wine in barrels in two of the most important stages of winemaking, that being fermentation and maturation. The distributed sensor subsystem is imbedded within the bung of the barrel and runs on battery for a period of at least 12 months and costs around $27 AUD for all parts. In addition, software was designed which allows for the remote transmission and easy visual interpretation of the data for the winemaker. Early warning signals can be sent when the temperature or ullage deviates from a winemakers expectations so remedial action can be taken, such as when topping is required or the movement of the barrels to a cooler cellar location. Such knowledge of a wine’s properties or storage conditions allows for a more precise control of the final wine quality.

Description: More and more hybrid electric vehicles are driven since they offer such advantages as energy savings and better active safety performance. Hybrid vehicles have two or more power driving systems and frequently switch working condition, so controlling stability is very important. In this work, a two-stage Kalman algorithm method is used to fuse data in hybrid vehicle stability testing. First, the RT3102 navigation system and Dewetron system are introduced. Second, a modeling of data fusion is proposed based on the Kalman filter. Then, this modeling is simulated and tested on a sample vehicle, using Carsim and Simulink software to test the results. The results showed the merits of this modeling.

Description: Diffuse Correlation Spectroscopy (DCS) is a well-established optical technique that has been used for non-invasive measurement of blood flow in tissues. Instrumentation for DCS includes a correlation device that computes the temporal intensity autocorrelation of a coherent laser source after it has undergone diffuse scattering through a turbid medium. Typically, the signal acquisition and its autocorrelation are performed by a correlation board. These boards have dedicated hardware to acquire and compute intensity autocorrelations of rapidly varying input signal and usually are quite expensive. Here we show that a Raspberry Pi minicomputer can acquire and store a rapidly varying time-signal with high fidelity. We show that this signal collected by a Raspberry Pi device can be processed numerically to yield intensity autocorrelations well suited for DCS applications. DCS measurements made using the Raspberry Pi device were compared to those acquired using a commercial hardware autocorrelation board to investigate the stability, performance, and accuracy of the data acquired in controlled experiments. This paper represents a first step toward lowering the instrumentation cost of a DCS system and may offer the potential to make DCS become more widely used in biomedical applications.

Description: Wind tunnels are a key experimental tool for the analysis of airflow parameters in many fields of application. Despite their great potential impact on agricultural research, few contributions have dealt with the development of automatic control systems for wind tunnels in the field of greenhouse technology. The objective of this paper is to present an automatic control system that provides precision and speed of measurement, as well as efficient data processing in low-speed wind tunnel experiments for greenhouse engineering applications. The system is based on an algorithm that identifies the system model and calculates the optimum PI controller. The validation of the system was performed on a cellulose evaporative cooling pad and on insect-proof screens to assess its response to perturbations. The control system provided an accuracy of 〈0.06 m·s‾1 for airflow speed and 〈0.50 Pa for pressure drop, thus permitting the reproducibility and standardization of the tests. The proposed control system also incorporates a fully-integrated software unit that manages the tests in terms of airflow speed and pressure drop set points.

Description: A new application of utilizing ultra-wideband (UWB) technology to sense wind turbine blade deflections is introduced in this paper for wind energy cost reduction. The lower UWB band of 3.1–5.3 GHz is applied. On each blade, there will be one UWB blade deflection sensing system, which consists of two UWB antennas at the blade root and one UWB antenna at the blade tip. The detailed topology and challenges of this deflection sensing system are addressed. Due to the complexity of the problem, this paper will first realize the on-blade UWB radio link in the simplest case, where the tip antenna is situated outside (and on the surface of) a blade tip. To investigate this case, full-blade time-domain measurements are designed and conducted under different deflections. The detailed measurement setups and results are provided. If the root and tip antenna locations are properly selected, the first pulse is always of sufficient quality for accurate estimations under different deflections. The measured results reveal that the blade tip-root distance and blade deflection can be accurately estimated in the complicated and lossy wireless channels around a wind turbine blade. Some future research topics on this application are listed finally.

Description: A method for measuring shaft diameters is presented using structured light vision measurement. After calibrating a model of the structured light measurement, a virtual plane is established perpendicular to the measured shaft axis and the image of the light stripe on the shaft is projected to the virtual plane. On the virtual plane, the center of the measured shaft is determined by fitting the projected image under the geometrical constraints of the light stripe, and the shaft diameter is measured by the determined center and the projected image. Experiments evaluated the measuring accuracy of the method and the effects of some factors on the measurement are analyzed.

Description: Reliable data transmission over lossy communication link is expensive due to overheads for error protection. For signals that have inherent sparse structures, compressive sensing (CS) is applied to facilitate efficient sparse signal transmissions over lossy communication links without data compression or error protection. The natural packet loss in the lossy link is modeled as a random sampling process of the transmitted data, and the original signal will be reconstructed from the lossy transmission results using the CS-based reconstruction method at the receiving end. The impacts of packet lengths on transmission efficiency under different channel conditions have been discussed, and interleaving is incorporated to mitigate the impact of burst data loss. Extensive simulations and experiments have been conducted and compared to the traditional automatic repeat request (ARQ) interpolation technique, and very favorable results have been observed in terms of both accuracy of the reconstructed signals and the transmission energy consumption. Furthermore, the packet length effect provides useful insights for using compressed sensing for efficient sparse signal transmission via lossy links.

Description: This paper proposes a resonant electric cradle design with infant cries recognition, employing an Arduino UNO as the core processor. For most commercially available electric cradles, the drive motor is closely combined with the bearing on the top, resulting in a lot of energy consumption. In this proposal, a ball bearing design was adopted and the driving force is under the cradle to increase the distance from the object to fulcrum and torque. The sensors are designed to detect the oscillation state, and then the force is driven at the critical time to achieve the maximum output response while saving energy according to the principle of resonance. As for the driving forces, the winding power and motors are carefully placed under the cradle. The sensors, including the three-axis accelerometer and infrared sensor, are tested and applied under swinging amplitude control. In addition, infant cry recognition technology was incorporated in the design to further develop its functionality, which is a rare feature in this kind of hardware. The proposed nonlinear operator of fundamental frequency ( ) analysis is able to identify different types of infant cries. In conclusion, this paper proposes an energy-saving electric cradle with infant cries recognition and the experimental results demonstrate the effectiveness of this approach.

Description: Many people find that their sleep is restricted or disturbed by social obligations, including work. Sleep phase delays can affect an individual’s circadian rhythms on the following day and cause daytime sleepiness and/or poor performance. In this study, to examine weekly variations in sleep patterns, we analyzed sleep data for seven-day periods (from Sunday to Saturday) that had been collected from 2914 subjects (aged 20–79 years) over a total of 24,899 subject-weeks using contactless biomotion sensors. On the weekend, the subjects’ mean sleep midpoint, bedtime, and wake-up time were delayed by 40, 26 and 53 min, respectively, compared with those seen on weekdays. In addition, on weekdays, the mean difference between the maximum and median sleep midpoint ranged from 35 to 47 min among the subjects in their 20 s–70 s. The weekend delay and weekday variation in the subjects’ sleep patterns tended to decrease with age. This study detected sleep pattern disturbances on both weekdays and weekends. The serial changes in weekday bedtimes detected in this study suggest that sleep habits are influenced by changes in the temporal patterns of social activities/duties. We need further study the advantages of getting extra sleep and the disadvantages of sleep pattern disturbances in daily lifestyle.

Description: Currently deep learning has made great breakthroughs in visual and speech processing, mainly because it draws lessons from the hierarchical mode that brain deals with images and speech. In the field of NLP, a topic model is one of the important ways for modeling documents. Topic models are built on a generative model that clearly does not match the way humans write. In this paper, we propose Event Model, which is unsupervised and based on the language processing mechanism of neurolinguistics, to model documents. In Event Model, documents are descriptions of concrete or abstract events seen, heard, or sensed by people and words are objects in the events. Event Model has two stages: word learning and dimensionality reduction. Word learning is to learn semantics of words based on deep learning. Dimensionality reduction is the process that representing a document as a low dimensional vector by a linear mode that is completely different from topic models. Event Model achieves state-of-the-art results on document retrieval tasks.

Description: Atomic force microscope with applicable types of operation in a liquid environment is widely used to scan the contours of biological specimens. The contact mode of operation allows a tip to touch a specimen directly but sometimes it damages the specimen; thus, a tapping mode of operation may replace the contact mode. The tapping mode triggers the cantilever of the microscope approximately at resonance frequencies, and so the tip periodically knocks the specimen. It is well known that the cantilever induces extra liquid pressure that leads to drift in the resonance frequency. Studies have noted that the heights of protein surfaces measured via the tapping mode of an atomic force microscope are ~25% smaller than those measured by other methods. This discrepancy may be attributable to the induced superficial hydrodynamic pressure, which is worth investigating. In this paper, we introduce a semi-analytical method to analyze the pressure distribution of various tip geometries. According to our analysis, the maximum hydrodynamic pressure on the specimen caused by a cone-shaped tip is ~0.5 Pa, which can, for example, pre-deform a cell by several nanometers in compression before the tip taps it. Moreover, the pressure calculated on the surface of the specimen is 20 times larger than the pressure without considering the tip effect; these results have not been motioned in other papers. Dominating factors, such as surface heights of protein surface, mechanical stiffness of protein increasing with loading velocity, and radius of tip affecting the local pressure of specimen, are also addressed in this study.

Description: This study explores the potential of Synthetic Aperture Radar (SAR) to aid Unmanned Aerial Vehicle (UAV) navigation when Inertial Navigation System (INS) measurements are not accurate enough to eliminate drifts from a planned trajectory. This problem can affect medium-altitude long-endurance (MALE) UAV class, which permits heavy and wide payloads (as required by SAR) and flights for thousands of kilometres accumulating large drifts. The basic idea is to infer position and attitude of an aerial platform by inspecting both amplitude and phase of SAR images acquired onboard. For the amplitude-based approach, the system navigation corrections are obtained by matching the actual coordinates of ground landmarks with those automatically extracted from the SAR image. When the use of SAR amplitude is unfeasible, the phase content can be exploited through SAR interferometry by using a reference Digital Terrain Model (DTM). A feasibility analysis was carried out to derive system requirements by exploring both radiometric and geometric parameters of the acquisition setting. We showed that MALE UAV, specific commercial navigation sensors and SAR systems, typical landmark position accuracy and classes, and available DTMs lead to estimated UAV coordinates with errors bounded within ±12 m, thus making feasible the proposed SAR-based backup system.

Description: This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images.

Description: The combination of optical trapping with Raman spectroscopy provides a powerful method for the study, characterization, and identification of biological micro-particles. In essence, optical trapping helps to overcome the limitation imposed by the relative inefficiency of the Raman scattering process. This allows Raman spectroscopy to be applied to individual biological particles in air and in liquid, providing the potential for particle identification with high specificity, longitudinal studies of changes in particle composition, and characterization of the heterogeneity of individual particles in a population. In this review, we introduce the techniques used to integrate Raman spectroscopy with optical trapping in order to study individual biological particles in liquid and air. We then provide an overview of some of the most promising applications of this technique, highlighting the unique types of measurements enabled by the combination of Raman spectroscopy with optical trapping. Finally, we present a brief discussion of future research directions in the field.

Description: In this paper, we propose a novel extrinsic calibration method for camera networks using a sphere as the calibration object. First of all, we propose an easy and accurate method to estimate the 3D positions of the sphere center w.r.t. the local camera coordinate system. Then, we propose to use orthogonal procrustes analysis to pairwise estimate the initial camera relative extrinsic parameters based on the aforementioned estimation of 3D positions. Finally, an optimization routine is applied to jointly refine the extrinsic parameters for all cameras. Compared to existing sphere-based 3D position estimators which need to trace and analyse the outline of the sphere projection in the image, the proposed method requires only very simple image processing: estimating the area and the center of mass of the sphere projection. Our results demonstrate that we can get a more accurate estimate of the extrinsic parameters compared to other sphere-based methods. While existing state-of-the-art calibration methods use point like features and epipolar geometry, the proposed method uses the sphere-based 3D position estimate. This results in simpler computations and a more flexible and accurate calibration method. Experimental results show that the proposed approach is accurate, robust, flexible and easy to use.

Description: This paper presents preliminary polarization measurements and systematic-error characterization of the Thirty Gigahertz Instrument receiver developed for the QUIJOTE experiment. The instrument has been designed to measure the polarization of Cosmic Microwave Background radiation from the sky, obtaining the Q, U, and I Stokes parameters of the incoming signal simultaneously. Two kinds of linearly polarized input signals have been used as excitations in the polarimeter measurement tests in the laboratory; these show consistent results in terms of the Stokes parameters obtained. A measurement-based systematic-error characterization technique has been used in order to determine the possible sources of instrumental errors and to assist in the polarimeter calibration process.

Description: In this paper, we propose a distributed data-gathering scheme using an autonomous underwater vehicle (AUV) working as a mobile sink to gather data from a randomly distributed underwater sensor network where sensor nodes are clustered around several cluster headers. Unlike conventional data-gathering schemes where the AUV visits either every node or every cluster header, the proposed scheme allows the AUV to visit some selected nodes named path-nodes in a way that reduces the overall transmission power of the sensor nodes. Monte Carlo simulations are performed to investigate the performance of the proposed scheme compared with several preexisting techniques employing the AUV in terms of total amount of energy consumption, standard deviation of each node’s energy consumption, latency to gather data at a sink, and controlling overhead. Simulation results show that the proposed scheme not only reduces the total energy consumption but also distributes the energy consumption more uniformly over the network, thereby increasing the lifetime of the network.

Description: Motivation: Stem cell differentiation is largely guided by master transcriptional regulators, but it also depends on the expression of other types of genes, such as cell cycle genes, signaling genes, metabolic genes, trafficking genes, etc. Traditional approaches to understanding gene expression patterns across multiple conditions, such as principal components analysis or K-means clustering, can group cell types based on gene expression, but they do so without knowledge of the differentiation hierarchy. Hierarchical clustering can organize cell types into a tree, but in general this tree is different from the differentiation hierarchy itself. Methods: Given the differentiation hierarchy and gene expression data at each node, we construct a weighted Euclidean distance metric such that the minimum spanning tree with respect to that metric is precisely the given differentiation hierarchy. We provide a set of linear constraints that are provably sufficient for the desired construction and a linear programming approach to identify sparse sets of weights, effectively identifying genes that are most relevant for discriminating different parts of the tree. Results: We apply our method to microarray gene expression data describing 38 cell types in the hematopoiesis hierarchy, constructing a weighted Euclidean metric that uses just 175 genes. However, we find that there are many alternative sets of weights that satisfy the linear constraints. Thus, in the style of random-forest training, we also construct metrics based on random subsets of the genes and compare them to the metric of 175 genes. We then report on the selected genes and their biological functions. Our approach offers a new way to identify genes that may have important roles in stem cell differentiation. Contact: tperkins@ohri.ca Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Principal component analysis (PCA) is a basic tool often used in bioinformatics for visualization and dimension reduction. However, it is known that PCA may not consistently estimate the true direction of maximal variability in high-dimensional, low sample size settings, which are typical for molecular data. Assuming that the underlying signal is sparse, i.e. that only a fraction of features contribute to a principal component (PC), this estimation consistency can be retained. Most existing sparse PCA methods use L1-penalization, i.e. the lasso , to perform feature selection. But, the lasso is known to lack variable selection consistency in high dimensions and therefore a subsequent interpretation of selected features can give misleading results. Results: We present S4VDPCA, a sparse PCA method that incorporates a subsampling approach, namely stability selection. S4VDPCA can consistently select the truly relevant variables contributing to a sparse PC while also consistently estimate the direction of maximal variability. The performance of the S4VDPCA is assessed in a simulation study and compared to other PCA approaches, as well as to a hypothetical oracle PCA that ‘knows’ the truly relevant features in advance and thus finds optimal, unbiased sparse PCs. S4VDPCA is computationally efficient and performs best in simulations regarding parameter estimation consistency and feature selection consistency. Furthermore, S4VDPCA is applied to a publicly available gene expression data set of medulloblastoma brain tumors. Features contributing to the first two estimated sparse PCs represent genes significantly over-represented in pathways typically deregulated between molecular subgroups of medulloblastoma. Availability and implementation: Software is available at https://github.com/mwsill/s4vdpca . Contact: m.sill@dkfz.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Glycans play critical roles in many biological processes, and their structural diversity is key for specific protein-glycan recognition. Comparative structural studies of biological molecules provide useful insight into their biological relationships. However, most computational tools are designed for protein structure, and despite their importance, there is no currently available tool for comparing glycan structures in a sequence order- and size-independent manner. Results: A novel method, GS-align, is developed for glycan structure alignment and similarity measurement. GS-align generates possible alignments between two glycan structures through iterative maximum clique search and fragment superposition. The optimal alignment is then determined by the maximum structural similarity score, GS-score, which is size-independent. Benchmark tests against the Protein Data Bank (PDB) N -linked glycan library and PDB homologous/non-homologous N -glycoprotein sets indicate that GS-align is a robust computational tool to align glycan structures and quantify their structural similarity. GS-align is also applied to template-based glycan structure prediction and monosaccharide substitution matrix generation to illustrate its utility. Availability and implementation: http://www.glycanstructure.org/gsalign . Contact: wonpil@ku.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Impedance-based technologies are advancing methods for measuring proliferation of adherent cell cultures non-invasively and in real time. The analysis of the resulting data has so far been hampered by inappropriate computational methods and the lack of systematic data to evaluate the characteristics of the assay. Results: We used a commercially available system for impedance-based growth measurement (xCELLigence) and compared the reported cell index with data from microscopy. We found that the measured signal correlates linearly with the cell number throughout the time of an experiment with sufficient accuracy in subconfluent cell cultures. The resulting growth curves for various colon cancer cells could be well described with the empirical Richards growth model, which allows for extracting quantitative parameters (such as characteristic cycle times). We found that frequently used readouts like the cell index at a specific time or the area under the growth curve cannot be used to faithfully characterize growth inhibition. We propose to calculate the average growth rate of selected time intervals to accurately estimate time-dependent IC50 values of drugs from growth curves. Contact: nils.bluethgen@charite.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Capacitive-type transduction is now widely used in MEMS microphones. However, its sensitivity decreases with reducing size, due to decreasing air gap capacitance. In the present study, we proposed and developed the Electret Gate of Field Effect Transistor (ElGoFET) transduction based on an electret and FET (field-effect-transistor) as a novel mechanism of MEMS microphone transduction. The ElGoFET transduction has the advantage that the sensitivity is dependent on the ratio of capacitance components in the transduction structure. Hence, ElGoFET transduction has high sensitivity even with a smaller air gap capacitance, due to a miniaturization of the transducer. A FET with a floating-gate electrode embedded on a membrane was designed and fabricated and an electret was fabricated by ion implantation with Ga+ ions. During the assembly process between the FET and the electret, the operating point of the FET was characterized using the static response of the FET induced by the electric field due to the trapped positive charge at the electret. Additionally, we evaluated the microphone performance of the ElGoFET by measuring the acoustic response in air using a semi-anechoic room. The results confirmed that the proposed transduction mechanism has potential for microphone applications.

Description: Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings.

Description: Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work.

Description: Motivation: The storage and transmission of high-throughput sequencing data consumes significant resources. As our capacity to produce such data continues to increase, this burden will only grow. One approach to reduce storage and transmission requirements is to compress this sequencing data. Results: We present a novel technique to boost the compression of sequencing that is based on the concept of bucketing similar reads so that they appear nearby in the file. We demonstrate that, by adopting a data-dependent bucketing scheme and employing a number of encoding ideas, we can achieve substantially better compression ratios than existing de novo sequence compression tools, including other bucketing and reordering schemes. Our method, Mince, achieves up to a 45% reduction in file sizes (28% on average) compared with existing state-of-the-art de novo compression schemes. Availability and implementation : Mince is written in C++11, is open source and has been made available under the GPLv3 license. It is available at http://www.cs.cmu.edu/~ckingsf/software/mince . Contact: carlk@cs.cmu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : Current methods for motif discovery from chromatin immunoprecipitation followed by sequencing (ChIP-seq) data often identify non-targeted transcription factor (TF) motifs, and are even further limited when peak sequences are similar due to common ancestry rather than common binding factors. The latter aspect particularly affects a large number of proteins from the Cys 2 His 2 zinc finger (C2H2-ZF) class of TFs, as their binding sites are often dominated by endogenous retroelements that have highly similar sequences. Here, we present recognition code-assisted discovery of regulatory elements (RCADE) for motif discovery from C2H2-ZF ChIP-seq data. RCADE combines predictions from a DNA recognition code of C2H2-ZFs with ChIP-seq data to identify models that represent the genuine DNA binding preferences of C2H2-ZF proteins. We show that RCADE is able to identify generalizable binding models even from peaks that are exclusively located within the repeat regions of the genome, where state-of-the-art motif finding approaches largely fail. Availability and implementation: RCADE is available as a webserver and also for download at http://rcade.ccbr.utoronto.ca/ . Supplementary information: Supplementary data are available at Bioinformatics online. Contact: t.hughes@utoronto.ca

Description: Motivation: Phylogenetic estimates from published studies can be archived using general platforms like Dryad (Vision, 2010) or TreeBASE (Sanderson et al. , 1994). Such services fulfill a crucial role in ensuring transparency and reproducibility in phylogenetic research. However, digital tree data files often require some editing (e.g. rerooting) to improve the accuracy and reusability of the phylogenetic statements. Furthermore, establishing the mapping between tip labels used in a tree and taxa in a single common taxonomy dramatically improves the ability of other researchers to reuse phylogenetic estimates. As the process of curating a published phylogenetic estimate is not error-free, retaining a full record of the provenance of edits to a tree is crucial for openness, allowing editors to receive credit for their work and making errors introduced during curation easier to correct. Results : Here, we report the development of software infrastructure to support the open curation of phylogenetic data by the community of biologists. The backend of the system provides an interface for the standard database operations of creating, reading, updating and deleting records by making commits to a git repository. The record of the history of edits to a tree is preserved by git’s version control features. Hosting this data store on GitHub ( http://github.com/ ) provides open access to the data store using tools familiar to many developers. We have deployed a server running the ‘phylesystem-api’, which wraps the interactions with git and GitHub. The Open Tree of Life project has also developed and deployed a JavaScript application that uses the phylesystem-api and other web services to enable input and curation of published phylogenetic statements. Availability and implementation : Source code for the web service layer is available at https://github.com/OpenTreeOfLife/phylesystem-api . The data store can be cloned from: https://github.com/OpenTreeOfLife/phylesystem . A web application that uses the phylesystem web services is deployed at http://tree.opentreeoflife.org/curator . Code for that tool is available from https://github.com/OpenTreeOfLife/opentree . Contact : mtholder@gmail.com

Description: Motivation: Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) detects genome-wide DNA–protein interactions and chromatin modifications, returning enriched regions (ERs), usually associated with a significance score. Moderately significant interactions can correspond to true, weak interactions, or to false positives; replicates of a ChIP-seq experiment can provide co-localised evidence to decide between the two cases. We designed a general methodological framework to rigorously combine the evidence of ERs in ChIP-seq replicates, with the option to set a significance threshold on the repeated evidence and a minimum number of samples bearing this evidence. Results : We applied our method to Myc transcription factor ChIP-seq datasets in K562 cells available in the ENCODE project. Using replicates, we could extend up to 3 times the ER number with respect to single-sample analysis with equivalent significance threshold. We validated the ‘rescued’ ERs by checking for the overlap with open chromatin regions and for the enrichment of the motif that Myc binds with strongest affinity; we compared our results with alternative methods (IDR and jMOSAiCS), obtaining more validated peaks than the former and less peaks than latter, but with a better validation. Availability and implementation : An implementation of the proposed method and its source code under GPLv3 license are freely available at http://www.bioinformatics.deib.polimi.it/MSPC/ and http://mspc.codeplex.com/ , respectively. Contact : marco.morelli@iit.it Supplementary information: Supplementary Material are available at Bioinformatics online.

Description: : We announce the release of kSNP3.0, a program for SNP identification and phylogenetic analysis without genome alignment or the requirement for reference genomes. kSNP3.0 is a significantly improved version of kSNP v2. Availability and implementation : kSNP3.0 is implemented as a package of stand-alone executables for Linux and Mac OS X under the open-source BSD license. The executable packages, source code and a full User Guide are freely available at https://sourceforge.net/projects/ksnp/files/ Contact: barryghall@gmail.com

Description: Motivation: We have created an R package named phylogeo that provides a set of geographic utilities for sequencing-based microbial ecology studies. Although the geographic location of samples is an important aspect of environmental microbiology, none of the major software packages used in processing microbiome data include utilities that allow users to map and explore the spatial dimension of their data. phylogeo solves this problem by providing a set of plotting and mapping functions that can be used to visualize the geographic distribution of samples, to look at the relatedness of microbiomes using ecological distance, and to map the geographic distribution of particular sequences. By extending the popular phyloseq package and using the same data structures and command formats, phylogeo allows users to easily map and explore the geographic dimensions of their data from the R programming language. Availability and Implementation: phylogeo is documented and freely available http://zachcp.github.io/phylogeo Contact : zcharlop@rockefeller.edu

Description: : Gener is a development module for programming chemical controllers based on DNA strand displacement. Gener is developed with the aim of providing a simple interface that minimizes the opportunities for programming errors: Gener allows the user to test the computations of the DNA programs based on a simple two-domain strand displacement algebra, the minimal available so far. The tool allows the user to perform stepwise computations with respect to the rules of the algebra as well as exhaustive search of the computation space with different options for exploration and visualization. Gener can be used in combination with existing tools, and in particular, its programs can be exported to Microsoft Research’s DSD tool as well as to LaTeX. Availability and implementation : Gener is available for download at the Cosbi website at http://www.cosbi.eu/research/prototypes/gener as a windows executable that can be run on Mac OS X and Linux by using Mono. Contact : ozan@cosbi.eu

Description: Motivation: Molecular dynamics simulations provide atomic insight into the physicochemical characteristics of lipid membranes and hence, a wide range of force field families capable of modelling various lipid types have been developed in recent years. To model membranes in a biologically realistic lipid composition, simulation systems containing multiple different lipids must be assembled. Results: We present a new web service called MemGen that is capable of setting up simulation systems of heterogenous lipid membranes. MemGen is not restricted to certain lipid force fields or lipid types, but instead builds membranes from uploaded structure files which may contain any kind of amphiphilic molecule. MemGen works with any all-atom or united-atom lipid representation. Availability and implementation: MemGen is freely available without registration at http://memgen.uni-goettingen.de . Contact: jhub@gwdg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs). To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node’s role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network’s lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively.

Description: We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network.

Description: The development of public transit systems that are accessible and safe for everyone, including people with special needs, is an objective that is justified from the civic and economic points of view. Unfortunately, public transit services are conceived for people who do not have reduced physical or cognitive abilities. In this paper, we present an intelligent public transit system by highway with the goal of facilitating access and improving the safety of public transit for persons with special needs. The system is deployed using components that are commonly available in transport infrastructure, e.g., sensors, mobile communications systems, and positioning systems. In addition, the system can operate in non-urban transport contexts, e.g., isolated rural areas, where the availability of basic infrastructure, such as electricity and communications infrastructures, is not always guaranteed. To construct the system, the principles and techniques of Ubiquitous Computing and Ambient Intelligence have been employed. To illustrate the utility of the system, two cases of services rendered by the system are described: the first case involves a surveillance system to guarantee accessibility at bus stops; the second case involves a route assistant for blind people.

Description: An accurate performance analysis on the MDL criterion for source enumeration in array processing is presented in this paper. The enumeration results of MDL can be predicted precisely by the proposed procedure via the statistical analysis of the sample eigenvalues, whose distributive properties are investigated with the consideration of their interactions. A novel approach is also developed for the performance evaluation when the source number is underestimated by a number greater than one, which is denoted as “multiple-missed detection”, and the probability of a specific underestimated source number can be estimated by ratio distribution analysis. Simulation results are included to demonstrate the superiority of the presented method over available results and confirm the ability of the proposed approach to perform multiple-missed detection analysis.

Description: As society has developed, the number of vehicles has increased and road conditions have become complicated, increasing the risk of crashes. Therefore, a service that provides safe vehicle control and various types of information to the driver is urgently needed. In this study, we designed and implemented a real-time traffic information system and a smart camera device for smart driver assistance systems. We selected a commercial device for the smart driver assistance systems, and applied a computer vision algorithm to perform image recognition. For application to the dynamic region of interest, dynamic frame skip methods were implemented to perform parallel processing in order to enable real-time operation. In addition, we designed and implemented a model to estimate congestion by analyzing traffic information. The performance of the proposed method was evaluated using images of a real road environment. We found that the processing time improved by 15.4 times when all the proposed methods were applied in the application. Further, we found experimentally that there was little or no change in the recognition accuracy when the proposed method was applied. Using the traffic congestion estimation model, we also found that the average error rate of the proposed model was 5.3%.

Description: Foot pathologies can negatively influence foot function, consequently impairing gait during daily activity, and severely impacting an individual’s quality of life. These pathologies are often painful and correspond with high or abnormal plantar pressure, which can result in asymmetry in the pressure distribution between the two feet. There is currently no general consensus on the presence of asymmetry in able-bodied gait, and plantar pressure analysis during gait is in dire need of a standardized method to quantify asymmetry. This paper investigates the use of plantar pressure asymmetry for pathological gait diagnosis. The results of this study involving plantar pressure analysis in fifty one participants (31 healthy and 20 with foot pathologies) support the presence of plantar pressure asymmetry in normal gait. A higher level of asymmetry was detected at the majority of the regions in the feet of the pathological population, including statistically significant differences in the plantar pressure asymmetry in two regions of the foot, metatarsophalangeal joint 3 (MPJ3) and the lateral heel. Quantification of plantar pressure asymmetry may prove to be useful for the identification and diagnosis of various foot pathologies.

Description: In this paper, we present the development of an infrastructure-less indoor location system (ILS), which relies on the use of a microphone, a magnetometer and a light sensor of a smartphone, all three of which are essentially passive sensors, relying on signals available practically in any building in the world, no matter how developed the region is. In our work, we merge the information from those sensors to estimate the user’s location in an indoor environment. A multivariate model is applied to find the user’s location, and we evaluate the quality of the resulting model in terms of sensitivity and specificity. Our experiments were carried out in an office environment during summer and winter, to take into account changes in light patterns, as well as changes in the Earth’s magnetic field irregularities. The experimental results clearly show the benefits of using the information fusion of multiple sensors when contrasted with the use of a single source of information.

Description: A three-step iterative method with fifth-order convergence as a new modification of Newton’s method was presented. This method is for finding multiple roots of nonlinear equation with unknown multiplicity m whose multiplicity m is the highest multiplicity. Its order of convergence is analyzed and proved. Results for some numerical examples show the efficiency of the new method.

Description: The realization of the Internet of Things greatly depends on the information communication among physical terminal devices and informationalized platforms, such as smart sensors, embedded systems and intelligent networks. Playing an important role in information acquisition, sensors for stereo capture have gained extensive attention in various fields. In this paper, we concentrate on promoting such sensors in an intelligent system with self-assessment capability to deal with the distortion and impairment in long-distance shooting applications. The core design is the establishment of the objective evaluation criteria that can reliably predict shooting quality with different camera configurations. Two types of stereo capture systems—toed-in camera configuration and parallel camera configuration—are taken into consideration respectively. The experimental results show that the proposed evaluation criteria can effectively predict the visual perception of stereo capture quality for long-distance shooting.

Description: Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.

Description: A novel sensing device for fire detection in domestic environments is presented. The fire detector uses a combination of several sensors that not only detect smoke, but discriminate between different types of smoke. This feature avoids false alarms and warns of different situations. Power consumption is optimized both in terms of hardware and software, providing a high degree of autonomy of almost five years. Data gathered from the device are transmitted through a wireless communication to a base station. The low cost and compact design provides wide application prospects.

Description: Lipid multilayer gratings are recently invented nanomechanical sensor elements that are capable of transducing molecular binding to fluid lipid multilayers into optical signals in a label free manner due to shape changes in the lipid nanostructures. Here, we show that nanointaglio is suitable for the integration of chemically different lipid multilayer gratings into a sensor array capable of distinguishing vapors by means of an optical nose. Sensor arrays composed of six different lipid formulations are integrated onto a surface and their optical response to three different vapors (water, ethanol and acetone) in air as well as pH under water is monitored as a function of time. Principal component analysis of the array response results in distinct clustering indicating the suitability of the arrays for distinguishing these analytes. Importantly, the nanointaglio process used here is capable of producing lipid gratings out of different materials with sufficiently uniform heights for the fabrication of an optical nose.

Description: Broadband Power Line Communications (PLC) have taken advantage of the research advances in multi-carrier modulations to mitigate frequency selective fading, and their adoption opens up a myriad of applications in the field of sensory and automation systems, multimedia connectivity or smart spaces. Nonetheless, the use of these multi-carrier modulations, such as Wavelet-OFDM, requires a highly accurate symbol timing estimation for reliably recovering of transmitted data. Furthermore, the PLC channel presents some particularities that prevent the direct use of previous synchronization algorithms proposed in wireless communication systems. Therefore more research effort should be involved in the design and implementation of novel and robust synchronization algorithms for PLC, thus enabling real-time synchronization. This paper proposes a symbol timing estimator for broadband PLC based on cross-correlation with multilevel complementary sequences or Zadoff-Chu sequences and its efficient implementation in a FPGA; the obtained results show a 90% of success rate in symbol timing estimation for a certain PLC channel model and a reduced resource consumption for its implementation in a Xilinx Kyntex FPGA.

Description: In the smart grid, measurement devices may be compromised by adversaries, and their operations could be disrupted by attacks. A number of schemes to efficiently and accurately detect these compromised devices remotely have been proposed. Nonetheless, most of the existing schemes detecting compromised devices depend on the incremental response time in the attestation process, which are sensitive to data transmission delay and lead to high computation and network overhead. To address the issue, in this paper, we propose a low-cost remote memory attestation scheme (LRMA), which can efficiently and accurately detect compromised smart meters considering real-time network delay and achieve low computation and network overhead. In LRMA, the impact of real-time network delay on detecting compromised nodes can be eliminated via investigating the time differences reported from relay nodes. Furthermore, the attestation frequency in LRMA is dynamically adjusted with the compromised probability of each node, and then, the total number of attestations could be reduced while low computation and network overhead can be achieved. Through a combination of extensive theoretical analysis and evaluations, our data demonstrate that our proposed scheme can achieve better detection capacity and lower computation and network overhead in comparison to existing schemes.

Description: It is known that cardiac and respiratory rhythms in electrocardiograms (ECGs) are highly nonlinear and non-stationary. As a result, most traditional time-domain algorithms are inadequate for characterizing the complex dynamics of the ECG. This paper proposes a new ECG sensor card and a statistical-based ECG algorithm, with the aid of a reduced binary pattern (RBP), with the aim of achieving faster ECG human identity recognition with high accuracy. The proposed algorithm has one advantage that previous ECG algorithms lack—the waveform complex information and de-noising preprocessing can be bypassed; therefore, it is more suitable for non-stationary ECG signals. Experimental results tested on two public ECG databases (MIT-BIH) from MIT University confirm that the proposed scheme is feasible with excellent accuracy, low complexity, and speedy processing. To be more specific, the advanced RBP algorithm achieves high accuracy in human identity recognition and is executed at least nine times faster than previous algorithms. Moreover, based on the test results from a long-term ECG database, the evolving RBP algorithm also demonstrates superior capability in handling long-term and non-stationary ECG signals.

Description: Motivation: In practice, identifying and interpreting the functional impacts of the regulatory relationships between micro-RNA and messenger-RNA is non-trivial. The sheer scale of possible micro-RNA and messenger-RNA interactions can make the interpretation of results difficult. Results: We propose a supervised framework, pMim, built upon concepts of significance combination, for jointly ranking regulatory micro-RNA and their potential functional impacts with respect to a condition of interest. Here, pMim directly tests if a micro-RNA is differentially expressed and if its predicted targets, which lie in a common biological pathway, have changed in the opposite direction. We leverage the information within existing micro-RNA target and pathway databases to stabilize the estimation and annotation of micro-RNA regulation making our approach suitable for datasets with small sample sizes. In addition to outputting meaningful and interpretable results, we demonstrate in a variety of datasets that the micro-RNA identified by pMim, in comparison to simpler existing approaches, are also more concordant with what is described in the literature. Availability and implementation: This framework is implemented as an R function, pMim , in the package sydSeq available from http://www.ellispatrick.com/r-packages . Contact: jean.yang@sydney.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Cellular mRNA levels originate from the combined action of multiple regulatory processes, which can be recapitulated by the rates of pre-mRNA synthesis, pre-mRNA processing and mRNA degradation. Recent experimental and computational advances set the basis to study these intertwined levels of regulation. Nevertheless, software for the comprehensive quantification of RNA dynamics is still lacking. Results: INSPEcT is an R package for the integrative analysis of RNA- and 4sU-seq data to study the dynamics of transcriptional regulation. INSPEcT provides gene-level quantification of these rates, and a modeling framework to identify which of these regulatory processes are most likely to explain the observed mRNA and pre-mRNA concentrations. Software performance is tested on a synthetic dataset, instrumental to guide the choice of the modeling parameters and the experimental design. Availability and implementation: INSPEcT is submitted to Bioconductor and is currently available as Supplementary Additional File S1 . Contact: mattia.pelizzola@iit.it Supplementary Information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Experimentally determined gene regulatory networks can be enriched by computational inference from high-throughput expression profiles. However, the prediction of regulatory interactions is severely impaired by indirect and spurious effects, particularly for eukaryotes. Recently, published methods report improved predictions by exploiting the a priori known targets of a regulator (its local topology) in addition to expression profiles. Results: We find that methods exploiting known targets show an unexpectedly high rate of false discoveries. This leads to inflated performance estimates and the prediction of an excessive number of new interactions for regulators with many known targets. These issues are hidden from common evaluation and cross-validation setups, which is due to Simpson’s paradox. We suggest a confidence score recalibration method (CoRe) that reduces the false discovery rate and enables a reliable performance estimation. Conclusions: CoRe considerably improves the results of network inference methods that exploit known targets. Predictions then display the biological process specificity of regulators more correctly and enable the inference of accurate genome-wide regulatory networks in eukaryotes. For yeast, we propose a network with more than 22 000 confident interactions. We point out that machine learning approaches outside of the area of network inference may be affected as well. Availability and implementation: Results, executable code and networks are available via our website http://www.bio.ifi.lmu.de/forschung/CoRe . Contact: robert.kueffner@helmholtz-muenchen.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Stoichiometric and constraint-based methods of computational strain design have become an important tool for rational metabolic engineering. One of those relies on the concept of constrained minimal cut sets (cMCSs). However, as most other techniques, cMCSs may consider only reaction (or gene) knockouts to achieve a desired phenotype. Results : We generalize the cMCSs approach to constrained regulatory MCSs (cRegMCSs), where up/downregulation of reaction rates can be combined along with reaction deletions. We show that flux up/downregulations can virtually be treated as cuts allowing their direct integration into the algorithmic framework of cMCSs. Because of vastly enlarged search spaces in genome-scale networks, we developed strategies to (optionally) preselect suitable candidates for flux regulation and novel algorithmic techniques to further enhance efficiency and speed of cMCSs calculation. We illustrate the cRegMCSs approach by a simple example network and apply it then by identifying strain designs for ethanol production in a genome-scale metabolic model of Escherichia coli. The results clearly show that cRegMCSs combining reaction deletions and flux regulations provide a much larger number of suitable strain designs, many of which are significantly smaller relative to cMCSs involving only knockouts. Furthermore, with cRegMCSs, one may also enable the fine tuning of desired behaviours in a narrower range. The new cRegMCSs approach may thus accelerate the implementation of model-based strain designs for the bio-based production of fuels and chemicals. Availability and implementation: MATLAB code and the examples can be downloaded at http://www.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html . Contact : krishna.mahadevan@utoronto.ca or klamt@mpi-magdeburg.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Lipids are a large and diverse group of biological molecules with roles in membrane formation, energy storage and signaling. Cellular lipidomes may contain tens of thousands of structures, a staggering degree of complexity whose significance is not yet fully understood. High-throughput mass spectrometry-based platforms provide a means to study this complexity, but the interpretation of lipidomic data and its integration with prior knowledge of lipid biology suffers from a lack of appropriate tools to manage the data and extract knowledge from it. Results: To facilitate the description and exploration of lipidomic data and its integration with prior biological knowledge, we have developed a knowledge resource for lipids and their biology—SwissLipids. SwissLipids provides curated knowledge of lipid structures and metabolism which is used to generate an in silico library of feasible lipid structures. These are arranged in a hierarchical classification that links mass spectrometry analytical outputs to all possible lipid structures, metabolic reactions and enzymes. SwissLipids provides a reference namespace for lipidomic data publication, data exploration and hypothesis generation. The current version of SwissLipids includes over 244 000 known and theoretically possible lipid structures, over 800 proteins, and curated links to published knowledge from over 620 peer-reviewed publications. We are continually updating the SwissLipids hierarchy with new lipid categories and new expert curated knowledge. Availability: SwissLipids is freely available at http://www.swisslipids.org/ . Contact: alan.bridge@isb-sib.ch Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Both the quantitative real-time polymerase chain reaction (qPCR) and quantitative isothermal amplification (qIA) are standard methods for nucleic acid quantification. Numerous real-time read-out technologies have been developed. Despite the continuous interest in amplification-based techniques, there are only few tools for pre-processing of amplification data. However, a transparent tool for precise control of raw data is indispensable in several scenarios, for example, during the development of new instruments. Results: chipPCR is an R package for the pre-processing and quality analysis of raw data of amplification curves. The package takes advantage of R ’s S 4 object model and offers an extensible environment. chipPCR contains tools for raw data exploration: normalization, baselining, imputation of missing values, a powerful wrapper for amplification curve smoothing and a function to detect the start and end of an amplification curve. The capabilities of the software are enhanced by the implementation of algorithms unavailable in R , such as a 5-point stencil for derivative interpolation. Simulation tools, statistical tests, plots for data quality management, amplification efficiency/quantification cycle calculation, and datasets from qPCR and qIA experiments are part of the package. Core functionalities are integrated in GUIs (web-based and standalone shiny applications), thus streamlining analysis and report generation. Availability and implementation: http://cran.r-project.org/web/packages/chipPCR . Source code: https://github.com/michbur/chipPCR . Contact : stefan.roediger@b-tu.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: : A key to understanding RNA function is to uncover its complex 3D structure. Experimental methods used for determining RNA 3D structures are technologically challenging and laborious, which makes the development of computational prediction methods of substantial interest. Previously, we developed the iFoldRNA server that allows accurate prediction of short (〈50 nt) tertiary RNA structures starting from primary sequences. Here, we present a new version of the iFoldRNA server that permits the prediction of tertiary structure of RNAs as long as a few hundred nucleotides. This substantial increase in the server capacity is achieved by utilization of experimental information such as base-pairing and hydroxyl-radical probing. We demonstrate a significant benefit provided by integration of experimental data and computational methods. Availability and implementation: http://ifoldrna.dokhlab.org Contact: dokh@unc.eu

Description: : The ms-data-core-api is a free, open-source library for developing computational proteomics tools and pipelines. The Application Programming Interface, written in Java, enables rapid tool creation by providing a robust, pluggable programming interface and common data model. The data model is based on controlled vocabularies/ontologies and captures the whole range of data types included in common proteomics experimental workflows, going from spectra to peptide/protein identifications to quantitative results. The library contains readers for three of the most used Proteomics Standards Initiative standard file formats: mzML, mzIdentML, and mzTab. In addition to mzML, it also supports other common mass spectra data formats: dta, ms2, mgf, pkl, apl (text-based), mzXML and mzData (XML-based). Also, it can be used to read PRIDE XML, the original format used by the PRIDE database, one of the world-leading proteomics resources. Finally, we present a set of algorithms and tools whose implementation illustrates the simplicity of developing applications using the library. Availability and implementation: The software is freely available at https://github.com/PRIDE-Utilities/ms-data-core-api . Supplementary information: Supplementary data are available at Bioinformatics online Contact: juan@ebi.ac.uk

Description: : Scanning probe microscopy (SPM) is already a relevant tool in biological research at the nanoscale. We present ‘Flatten plus’, a recent and helpful implementation in the well-known WSxM free software package. ‘Flatten plus’ allows reducing low-frequency noise in SPM images in a semi-automated way preventing the appearance of typical artifacts associated with such filters. Availability and implementation: WSxM is a free software implemented in C++ supported on MS Windows, but it can also be run under Mac or Linux using emulators such as Wine or Parallels. WSxM can be downloaded from http://www.wsxmsolutions.com/ . Contact: ignacio.horcas@wsxmsolutions.com

Description: : Despite the plethora of methods available for the functional analysis of omics data, obtaining comprehensive-yet detailed understanding of the results remains challenging. This is mainly due to the lack of publicly available tools for the visualization of this type of information. Here we present an R package called GOplot, based on ggplot2, for enhanced graphical representation. Our package takes the output of any general enrichment analysis and generates plots at different levels of detail: from a general overview to identify the most enriched categories (bar plot, bubble plot) to a more detailed view displaying different types of information for molecules in a given set of categories (circle plot, chord plot, cluster plot). The package provides a deeper insight into omics data and allows scientists to generate insightful plots with only a few lines of code to easily communicate the findings. Availability and Implementation: The R package GOplot is available via CRAN-The Comprehensive R Archive Network: http://cran.r-project.org/web/packages/GOplot . The shiny web application of the Venn diagram can be found at: https://wwalter.shinyapps.io/Venn/ . A detailed manual of the package with sample figures can be found at https://wencke.github.io/ Contact: fscabo@cnic.es or mricote@cnic.es

Description: Motivation: Horizontal transfer of transposable (HTT) elements among eukaryotes was discovered in the mid-1980s. As then, 〉300 new cases have been described. New findings about HTT are revealing the evolutionary impact of this phenomenon on host genomes. In order to provide an up to date, interactive and expandable database for such events, we developed the HTT-DB database. Results: HTT-DB allows easy access to most of HTT cases reported along with rich information about each case. Moreover, it allows the user to generate tables and graphs based on searches using Transposable elements and/or host species classification and export them in several formats. Availability and implementation: This database is freely available on the web at http://lpa.saogabriel.unipampa.edu.br:8080/httdatabase . HTT-DB was developed based on Java and MySQL with all major browsers supported. Tools and software packages used are free for personal or non-profit projects. Contact: bdotto82@gmail.com or gabriel.wallau@gmail.com

Description: Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

Description: Monitoring ocean waves plays a crucial role in, for example, coastal environmental and protection studies. Traditional methods for measuring ocean waves are based on ultrasonic sensors and accelerometers. However, the Global Positioning System (GPS) has been introduced recently and has the advantage of being smaller, less expensive, and not requiring calibration in comparison with the traditional methods. Therefore, for accurately measuring ocean waves using GPS, further research on the separation of the wave signals from the vertical GPS-mounted carrier displacements is still necessary. In order to contribute to this topic, we present a novel method that combines complementary ensemble empirical mode decomposition (CEEMD) with a wavelet threshold denoising model (i.e., CEEMD-Wavelet). This method seeks to extract wave signals with less residual noise and without losing useful information. Compared with the wave parameters derived from the moving average skill, high pass filter and wave gauge, the results show that the accuracy of the wave parameters for the proposed method was improved with errors of about 2 cm and 0.2 s for mean wave height and mean period, respectively, verifying the validity of the proposed method.

Description: Here, we provide an overview of the content of the Special Issue on “Human-computer interaction in smart environments”. The aim of this Special Issue is to highlight technologies and solutions encompassing the use of mass-market sensors in current and emerging applications for interacting with Smart Environments. Selected papers address this topic by analyzing different interaction modalities, including hand/body gestures, face recognition, gaze/eye tracking, biosignal analysis, speech and activity recognition, and related issues.

Description: The adsorption of the proteins CD13, mucin and bovine serum albumin on VLGXE-Au and YNGRT-Au interfaces was monitored by electrochemical impedance spectroscopy in the presence of [Fe(CN)6]3−/4−. The hydrophobicity of the Au surface was tailored using specific peptides, blocking agents and diluents. The combination of blocking agents (ethanolamine or n-butylamine) and diluents (hexanethiol or 2-mercaptoethanol) was used to prepare various peptide-modified Au surfaces. Protein adsorption onto the peptide-Au surfaces modified with the combination of n-butylamine and hexanethiol produced a dramatic decrease in the charge transfer resistance, Rct, for all three proteins. In contrast, polar peptide-surfaces induced a minimal change in Rct for all three proteins. Furthermore, an increase in Rct was observed with CD13 (an aminopeptidase overexpressed in certain cancers) in comparison to the other proteins when the VLGXE-Au surface was modified with n-butylamine as a blocking agent. The electrochemical data indicated that protein adsorption may be modulated by tailoring the peptide sequence on Au surfaces and that blocking agents and diluents play a key role in promoting or preventing protein adsorption. The peptide-Au platform may also be used for targeting cancer biomarkers with designer peptides.

Description: Securing communications in wireless sensor networks is increasingly important as the diversity of applications increases. However, even today, it is equally important for the measures employed to be energy efficient. For this reason, this publication analyzes the suitability of various cryptographic primitives for use in WSNs according to various criteria and, finally, describes a modular, PKI-based framework for confidential, authenticated, secure communications in which most suitable primitives can be employed. Due to the limited capabilities of common WSN motes, criteria for the selection of primitives are security, power efficiency and memory requirements. The implementation of the framework and the singular components have been tested and benchmarked in our testbed of IRISmotes.

Description: Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters’ dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively.

Description: To solve the problem of coverage performance assessment, this study proposes an evaluation method based on the trajectory of the target, which is applicable to universal sensor networks, including both heterogeneous and homogeneous sensor networks. Different from the traditional Voronoi algorithm, the proposed Improved Coverage Force Division (ICFD) plans a coverage force division map whichscales the qualitative coverage performancebasedon both covering intensities andlocations of the nodes. Furthermore, the Trajectory-based Evaluating Schedule (TES) is responsible for solving the quantitative coverage evaluationproblem by measuringthe resulting trajectories’ Balance Values (BVs). A model of weak-point ranking conjoined in consideration of coverage force and distance can guide future deployment to compensate coverage. Comparative trials using the greedy algorithm, Voronoi algorithm, and the proposed TES verify that TES achieves the approximate results for two-stage and multistage heterogeneous sensor networks with acceptable difference and lower complexity, and it is superior to the Voronoi algorithm in homogeneous sensor networks interms of breaking the four-point circle block.

Description: Pyroelectric harvesters convert time-dependent temperature variations into electric current. The appropriate geometry of the pyroelectric cells, coupled with the optimal period of temperature fluctuations, is key to driving the optimal load resistance, which enhances the performance of pyroelectric harvesters. The induced charge increases when the thickness of the pyroelectric cells decreases. Moreover, the induced charge is extremely reduced for the thinner pyroelectric cell when not used for the optimal period. The maximum harvested power is achieved when a 100 μm-thick PZT (Lead zirconate titanate) cell is used to drive the optimal load resistance of about 40 MΩ. Moreover, the harvested power is greatly reduced when the working resistance diverges even slightly from the optimal load resistance. The stored voltage generated from the 75 μm-thick PZT cell is less than that from the 400 μm-thick PZT cell for a period longer than 64 s. Although the thinner PZT cell is advantageous in that it enhances the efficiency of the pyroelectric harvester, the much thinner 75 μm-thick PZT cell and the divergence from the optimal period further diminish the performance of the pyroelectric cell. Therefore, the designers of pyroelectric harvesters need to consider the coupling effect between the geometry of the pyroelectric cells and the optimal period of temperature fluctuations to drive the optimal load resistance.

Description: Continuous respiratory monitoring is an important tool for clinical monitoring. Associated with the development of biomedical technology, it has become more and more important, especially in the measuring of gas flow and CO2 concentration, which can reflect the status of the patient. In this paper, a new type of biomedical device is presented, which uses low-power sensors with a piezoresistive silicon differential pressure sensor to measure gas flow and with a pyroelectric sensor to measure CO2 concentration simultaneously. For the portability of the biomedical device, the sensors and low-power measurement circuits are integrated together, and the airway tube also needs to be miniaturized. Circuits are designed to ensure the stability of the power source and to filter out the existing noise. Modulation technology is used to eliminate the fluctuations at the trough of the waveform of the CO2 concentration signal. Statistical analysis with the coefficient of variation was performed to find out the optimal driving voltage of the pressure transducer. Through targeted experiments, the biomedical device showed a high accuracy, with a measuring precision of 0.23 mmHg, and it worked continuously and stably, thus realizing the real-time monitoring of the status of patients.

Description: In this work, we report a new design for an electrostatically actuated microgripper with a post-assembly self-locking mechanism. The microgripper arms are driven by rotary comb actuators, enabling the microgripper to grip objects of any size from 0 to 100 μm. The post-assembly mechanism is driven by elastic deformation energy and static electricity to produce self-locking and releasing actions. The mechanism enables the microgripper arms to grip for long periods without continuously applying the external driving signal, which significantly reduces the effects and damage to the gripped objects caused by these external driving signals. The microgripper was fabricated using a Silicon-On-Insulator (SOI) wafer with a 30 μm structural layer. Test results show that this gripper achieves a displacement of 100 μm with a driving voltage of 33 V, and a metal wire with a diameter of about 1.6 mil is successfully gripped to demonstrate the feasibility of this post-assembly self-locking mechanism.

Description: Ag-modified In2O3/ZnO bundles with micro/nano porous structures have been designed and synthesized with by hydrothermal method continuing with dehydration process. Each bundle consists of nanoparticles, where nanogaps of 10–30 nm are present between the nanoparticles, leading to a porous structure. This porous structure brings high surface area and fast gas diffusion, enhancing the gas sensitivity. Consequently, the HCHO gas-sensing performance of the Ag-modified In2O3/ZnO bundles have been tested, with the formaldehyde-detection limit of 100 ppb (parts per billion) and the response and recover times as short as 6 s and 3 s, respectively, at 300 °C and the detection limit of 100 ppb, response time of 12 s and recover times of 6 s at 100 °C. The HCHO sensing detect limitation matches the health standard limitation on the concentration of formaldehyde for indoor air. Moreover, the strategy to synthesize the nanobundles is just two-step heating and easy to scale up. Therefore, the Ag-modified In2O3/ZnO bundles are ready for industrialization and practical applications.

Description: Control of oxygen over cell cultures in vitro is a topic of considerable interest, as chronic and cyclic hypoxia can alter cell behaviour. Both static and transient hypoxic levels have been found to affect tumour cell behaviour; it is potentially valuable to include these effects in early, in vitro stages of drug screening. A barrier to their inclusion is that rates of transient hypoxia can be a few cycles/hour, which is difficult to reproduce in traditional in vitro cell culture environments due to long diffusion distances from control gases to the cells. We use a gas-permeable three-layer microfluidic device to achieve spatial and temporal oxygen control with biologically-relevant switching times. We measure the oxygen profiles with integrated, ratiometric optical oxygen sensors, demonstrate sensor and system stability over multi-day experiments, and characterize a pre-bleaching process to improve sensor stability. We show, with both finite-element modelling and experimental data, excellent control over the oxygen levels by the device, independent of fluid flow rate and oxygenation for the operating flow regime. We measure equilibration times of approximately 10 min, generate complex, time-varying oxygen profiles, and study the effects of oxygenated media flow rates on the measured oxygen levels. This device could form a useful tool for future long-term studies of cell behaviour under hypoxia.

Description: With the increasing climatic extremes, the frequency and severity of urban flood events have intensified worldwide. In this study, image-based automated monitoring of flood formation and analyses of water level fluctuation were proposed as value-added intelligent sensing applications to turn a passive monitoring camera into a visual sensor. Combined with the proposed visual sensing method, traditional hydrological monitoring cameras have the ability to sense and analyze the local situation of flood events. This can solve the current problem that image-based flood monitoring heavily relies on continuous manned monitoring. Conventional sensing networks can only offer one-dimensional physical parameters measured by gauge sensors, whereas visual sensors can acquire dynamic image information of monitored sites and provide disaster prevention agencies with actual field information for decision-making to relieve flood hazards. The visual sensing method established in this study provides spatiotemporal information that can be used for automated remote analysis for monitoring urban floods. This paper focuses on the determination of flood formation based on image-processing techniques. The experimental results suggest that the visual sensing approach may be a reliable way for determining the water fluctuation and measuring its elevation and flood intrusion with respect to real-world coordinates. The performance of the proposed method has been confirmed; it has the capability to monitor and analyze the flood status, and therefore, it can serve as an active flood warning system.

Description: Internet of Things (IoT) is driving innovation in an ever-growing set of application domains such as intelligent processing for autonomous robots. For an autonomous robot, one grand challenge is how to sense its surrounding environment effectively. The Simultaneous Localization and Mapping with RGB-D Kinect camera sensor on robot, called RGB-D SLAM, has been developed for this purpose but some technical challenges must be addressed. Firstly, the efficiency of the algorithm cannot satisfy real-time requirements; secondly, the accuracy of the algorithm is unacceptable. In order to address these challenges, this paper proposes a set of novel improvement methods as follows. Firstly, the ORiented Brief (ORB) method is used in feature detection and descriptor extraction. Secondly, a bidirectional Fast Library for Approximate Nearest Neighbors (FLANN) k-Nearest Neighbor (KNN) algorithm is applied to feature match. Then, the improved RANdom SAmple Consensus (RANSAC) estimation method is adopted in the motion transformation. In the meantime, high precision General Iterative Closest Points (GICP) is utilized to register a point cloud in the motion transformation optimization. To improve the accuracy of SLAM, the reduced dynamic covariance scaling (DCS) algorithm is formulated as a global optimization problem under the G2O framework. The effectiveness of the improved algorithm has been verified by testing on standard data and comparing with the ground truth obtained on Freiburg University’s datasets. The Dr Robot X80 equipped with a Kinect camera is also applied in a building corridor to verify the correctness of the improved RGB-D SLAM algorithm. With the above experiments, it can be seen that the proposed algorithm achieves higher processing speed and better accuracy.

Description: Standard upper-limb motor function impairment assessments, such as the Fugl-Meyer Assessment (FMA), are a critical aspect of rehabilitation after neurological disorders. These assessments typically take a long time (about 30 min for the FMA) for a clinician to perform on a patient, which is a severe burden in a clinical environment. In this paper, we propose a framework for automating upper-limb motor assessments that uses low-cost sensors to collect movement data. The sensor data is then processed through a machine learning algorithm to determine a score for a patient’s upper-limb functionality. To demonstrate the feasibility of the proposed approach, we implemented a system based on the proposed framework that can automate most of the FMA. Our experiment shows that the system provides similar FMA scores to clinician scores, and reduces the time spent evaluating each patient by 82%. Moreover, the proposed framework can be used to implement customized tests or tests specified in other existing standard assessment methods.

Description: Cupric oxide (CuO) thin films are promising materials in gas sensor applications. The CuO-based gas sensors behaved as p-type semiconductors and can be used as part of an e-nose or smart sensor array for breath analysis. The authors present the investigation results on M-doped CuO-based (M = Ag, Au, Cr, Pd, Pt, Sb, Si) sensors working at various temperatures upon exposure to a low concentration of C3H8, which can be found in exhaled human breath, and it can be considered as a one of the biomarkers of several diseases. The films have been deposited in magnetron sputtering technology on low temperature cofired ceramics substrates. The results of the gas sensors’ response are also presented and discussed. The Cr:CuO-based structure, annealed at 400 °C for 4 h in air, showed the highest sensor response, of the order of 2.7 at an operation temperature of 250 °C. The response and recovery time(s) were 10 s and 24 s, respectively. The results show that the addition of M-dopants in the cupric oxide films effectively act as catalysts in propane sensors and improve the gas sensing properties. The films’ phase composition, microstructure and surface topography have been assessed by the X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) methods.

Description: We present a short review of recent progress in the field of optical gas sensors based on porous silicon (PSi) and PSi composites, which are separate from PSi optochemical and biological sensors for a liquid medium. Different periodical and nonperiodical PSi photonic structures (bares, modified by functional groups or infiltrated with sensory polymers) are described for gas sensing with an emphasis on the device specificity, sensitivity and stability to the environment. Special attention is paid to multiparametric sensing and sensor array platforms as effective trends for the improvement of analyte classification and quantification. Mechanisms of gas physical and chemical sorption inside PSi mesopores and pores of PSi functional composites are discussed.

Description: The damage detection of a wind turbine blade enables better operation of the turbines, and provides an early alert to the destroyed events of the blade in order to avoid catastrophic losses. A new non-baseline damage detection method based on the Fiber Bragg grating (FBG) in a wind turbine blade is developed in this paper. Firstly, the Chi-square distribution is proven to be an effective damage-sensitive feature which is adopted as the individual information source for the local decision. In order to obtain the global and optimal decision for the damage detection, the feature information fusion (FIF) method is proposed to fuse and optimize information in above individual information sources, and the damage is detected accurately through of the global decision. Then a 13.2 m wind turbine blade with the distributed strain sensor system is adopted to describe the feasibility of the proposed method, and the strain energy method (SEM) is used to describe the advantage of the proposed method. Finally results show that the proposed method can deliver encouraging results of the damage detection in the wind turbine blade.

Description: Background: Normative values are lacking for daily quantity of infant leg movements. This is critical for understanding the relationship between the quantity of leg movements and onset of independent walking, and will begin to inform early therapy intervention for infants at risk for developmental delay. Methods: We used wearable inertial movement sensors to record full-day leg movement activity from 12 infants with typical development, ages 1–12 months. Each infant was tested three times across 5 months, and followed until the onset of independent walking. We developed and validated an algorithm to identify infant-produced leg movements. Results: Infants moved their legs tens of thousands of times per day. There was a significant effect of leg movement quantity on walking onset. Infants who moved their legs more walked later than infants who moved their legs less, even when adjusting for age, developmental level or percentile length. We will need a much larger sample to adequately capture and describe the effect of movement experience on developmental rate. Our algorithm defines a leg movement in a specific way (each pause or change in direction is counted as a new movement), and further assessment of movement characteristics are necessary before we can fully understand and interpret our finding that infants who moved their legs more walked later than infants who moved their legs less. Conclusions: We have shown that typically-developing infants produce thousands of leg movements in a typical day, and that this can be accurately captured in the home environment using wearable sensors. In our small sample we can identify there is an effect of leg movement quantity on walking onset, however we cannot fully explain it.

Description: Heavy occlusions in cluttered scenes impose significant challenges to many computer vision applications. Recent light field imaging systems provide new see-through capabilities through synthetic aperture imaging (SAI) to overcome the occlusion problem. Existing synthetic aperture imaging methods, however, emulate focusing at a specific depth layer, but are incapable of producing an all-in-focus see-through image. Alternative in-painting algorithms can generate visually-plausible results, but cannot guarantee the correctness of the results. In this paper, we present a novel depth-free all-in-focus SAI technique based on light field visibility analysis. Specifically, we partition the scene into multiple visibility layers to directly deal with layer-wise occlusion and apply an optimization framework to propagate the visibility information between multiple layers. On each layer, visibility and optimal focus depth estimation is formulated as a multiple-label energy minimization problem. The layer-wise energy integrates all of the visibility masks from its previous layers, multi-view intensity consistency and depth smoothness constraint together. We compare our method with state-of-the-art solutions, and extensive experimental results demonstrate the effectiveness and superiority of our approach.

Description: Orientation estimation using low cost sensors is an important task for Micro Aerial Vehicles (MAVs) in order to obtain a good feedback for the attitude controller. The challenges come from the low accuracy and noisy data of the MicroElectroMechanical System (MEMS) technology, which is the basis of modern, miniaturized inertial sensors. In this article, we describe a novel approach to obtain an estimation of the orientation in quaternion form from the observations of gravity and magnetic field. Our approach provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. We separate the problems of finding the “tilt” quaternion and the heading quaternion in two sub-parts of our system. This procedure is the key for avoiding the impact of the magnetic disturbances on the roll and pitch components of the orientation when the sensor is surrounded by unwanted magnetic flux. We demonstrate the validity of our method first analytically and then empirically using simulated data. We propose a novel complementary filter for MAVs that fuses together gyroscope data with accelerometer and magnetic field readings. The correction part of the filter is based on the method described above and works for both IMU (Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity) sensors. We evaluate the effectiveness of the filter and show that it significantly outperforms other common methods, using publicly available datasets with ground-truth data recorded during a real flight experiment of a micro quadrotor helicopter.

Description: Vibrating micro- and nanomechanical mass sensors are capable of quantitatively determining attached mass from only the first three (two) measured cantilever (suspended) resonant frequencies. However, in aqueous solutions that are relevant to most biological systems, the mass determination is challenging because the quality factor (Q-factor) due to fluid damping decreases and, as a result, usually just the fundamental resonant frequencies can be correctly identified. Moreover, for higher modes the resonance coupling, noise, and internal damping have been proven to strongly affect the measured resonances and, correspondingly, the accuracy of estimated masses. In this work, a technique capable of determining the mass for the cantilever and also the position of nanobeads attached on the vibrating micro-/nanomechanical beam under intentionally applied axial tensile force from the measured fundamental flexural resonant frequencies is proposed. The axial force can be created and controlled through an external electrostatic or magnetostatic field. Practicality of the proposed technique is confirmed on the suspended multi-walled carbon nanotube and the rectangular silicon cantilever-based mass sensors. We show that typically achievable force resolution has a negligibly small impact on the accuracy of mass measurement.

Description: In modern industrial geodesy, high demands are placed on the final accuracy, with expectations currently falling below 1 mm. The measurement methodology and surveying instruments used have to be adjusted to meet these stringent requirements, especially the total stations as the most often used instruments. A standard deviation of the measured distance is the accuracy parameter, commonly between 1 and 2 mm. This parameter is often discussed in conjunction with the determination of the real accuracy of measurements at very short distances (5–50 m) because it is generally known that this accuracy cannot be increased by simply repeating the measurement because a considerable part of the error is systematic. This article describes the detailed testing of electronic distance meters to determine the absolute size of their systematic errors, their stability over time, their repeatability and the real accuracy of their distance measurement. Twenty instruments (total stations) have been tested, and more than 60,000 distances in total were measured to determine the accuracy and precision parameters of the distance meters. Based on the experiments’ results, calibration procedures were designed, including a special correction function for each instrument, whose usage reduces the standard deviation of the measurement of distance by at least 50%.

Description: Motivation : The majority of variation identified by genome wide association studies falls in non-coding genomic regions and is hypothesized to impact regulatory elements that modulate gene expression. Here we present a statistically rigorous software tool GREGOR (Genomic Regulatory Elements and Gwas Overlap algoRithm) for evaluating enrichment of any set of genetic variants with any set of regulatory features. Using variants from five phenotypes, we describe a data-driven approach to determine the tissue and cell types most relevant to a trait of interest and to identify the subset of regulatory features likely impacted by these variants. Last, we experimentally evaluate six predicted functional variants at six lipid-associated loci and demonstrate significant evidence for allele-specific impact on expression levels. GREGOR systematically evaluates enrichment of genetic variation with the vast collection of regulatory data available to explore novel biological mechanisms of disease and guide us toward the functional variant at trait-associated loci. Availability and implementation : GREGOR, including source code, documentation, examples, and executables, is available at http://genome.sph.umich.edu/wiki/GREGOR . Contact : cristen@umich.edu Supplementary information : Supplementary data are available at Bioinformatics online.

Description: Motivation: Genome and transcriptome analyses can be used to explore cancers comprehensively, and it is increasingly common to have multiple omics data measured from each individual. Furthermore, there are rich functional data such as predicted impact of mutations on protein coding and gene/protein networks. However, integration of the complex information across the different omics and functional data is still challenging. Clinical validation, particularly based on patient outcomes such as survival, is important for assessing the relevance of the integrated information and for comparing different procedures. Results: An analysis pipeline is built for integrating genomic and transcriptomic alterations from whole-exome and RNA sequence data and functional data from protein function prediction and gene interaction networks. The method accumulates evidence for the functional implications of mutated potential driver genes found within and across patients. A driver-gene score (DGscore) is developed to capture the cumulative effect of such genes. To contribute to the score, a gene has to be frequently mutated, with high or moderate mutational impact at protein level, exhibiting an extreme expression and functionally linked to many differentially expressed neighbors in the functional gene network. The pipeline is applied to 60 matched tumor and normal samples of the same patient from The Cancer Genome Atlas breast-cancer project. In clinical validation, patients with high DGscores have worse survival than those with low scores ( P = 0.001). Furthermore, the DGscore outperforms the established expression-based signatures MammaPrint and PAM50 in predicting patient survival. In conclusion, integration of mutation, expression and functional data allows identification of clinically relevant potential driver genes in cancer. Availability and implementation: The documented pipeline including annotated sample scripts can be found in http://fafner.meb.ki.se/biostatwiki/driver-genes/ . Contact: yudi.pawitan@ki.se Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: With improvements in next-generation sequencing technologies and reductions in price, ordered RNA-seq experiments are becoming common. Of primary interest in these experiments is identifying genes that are changing over time or space, for example, and then characterizing the specific expression changes. A number of robust statistical methods are available to identify genes showing differential expression among multiple conditions, but most assume conditions are exchangeable and thereby sacrifice power and precision when applied to ordered data. Results: We propose an empirical Bayes mixture modeling approach called EBSeq-HMM. In EBSeq-HMM, an auto-regressive hidden Markov model is implemented to accommodate dependence in gene expression across ordered conditions. As demonstrated in simulation and case studies, the output proves useful in identifying differentially expressed genes and in specifying gene-specific expression paths. EBSeq-HMM may also be used for inference regarding isoform expression. Availability and implementation: An R package containing examples and sample datasets is available at Bioconductor. Contact: kendzior@biostat.wisc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Sequence discovery tools play a central role in several fields of computational biology. In the framework of Transcription Factor binding studies, most of the existing motif finding algorithms are computationally demanding, and they may not be able to support the increasingly large datasets produced by modern high-throughput sequencing technologies. Results: We present FastMotif, a new motif discovery algorithm that is built on a recent machine learning technique referred to as Method of Moments. Based on spectral decompositions, our method is robust to model misspecifications and is not prone to locally optimal solutions. We obtain an algorithm that is extremely fast and designed for the analysis of big sequencing data. On HT-Selex data, FastMotif extracts motif profiles that match those computed by various state-of-the-art algorithms, but one order of magnitude faster. We provide a theoretical and numerical analysis of the algorithm’s robustness and discuss its sensitivity with respect to the free parameters. Availability and implementation: The Matlab code of FastMotif is available from http://lcsb-portal.uni.lu/bioinformatics . Contact: vlassis@adobe.com Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Next-generation high-throughput sequencing has become a state-of-the-art technique in genome assembly. Scaffolding is one of the main stages of the assembly pipeline. During this stage, contigs assembled from the paired-end reads are merged into bigger chains called scaffolds. Because of a high level of statistical noise, chimeric reads, and genome repeats the problem of scaffolding is a challenging task. Current scaffolding software packages widely vary in their quality and are highly dependent on the read data quality and genome complexity. There are no clear winners and multiple opportunities for further improvements of the tools still exist. Results: This article presents an efficient scaffolding algorithm ScaffMatch that is able to handle reads with both short (〈600 bp) and long (〉35 000 bp) insert sizes producing high-quality scaffolds. We evaluate our scaffolding tool with the F score and other metrics (N50, corrected N50) on eight datasets comparing it with the most available packages. Our experiments show that ScaffMatch is the tool of preference for the most datasets. Availability and implementation: The source code is available at http://alan.cs.gsu.edu/NGS/?q=content/scaffmatch . Contact: mandric@cs.gsu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Identifying protein subchloroplast localization in chloroplast organelle is very helpful for understanding the function of chloroplast proteins. There have existed a few computational prediction methods for protein subchloroplast localization. However, these existing works have ignored proteins with multiple subchloroplast locations when constructing prediction models, so that they can predict only one of all subchloroplast locations of this kind of multilabel proteins. Results: To address this problem, through utilizing label-specific features and label correlations simultaneously, a novel multilabel classifier was developed for predicting protein subchloroplast location(s) with both single and multiple location sites. As an initial study, the overall accuracy of our proposed algorithm reaches 55.52%, which is quite high to be able to become a promising tool for further studies. Availability and implementation: An online web server for our proposed algorithm named MultiP-SChlo was developed, which are freely accessible at http://biomed.zzuli.edu.cn/bioinfo/multip-schlo/ . Contact: pandaxiaoxi@gmail.com or gzli@tongji.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Loops in proteins are often involved in biochemical functions. Their irregularity and flexibility make experimental structure determination and computational modeling challenging. Most current loop modeling methods focus on modeling single loops. In protein structure prediction, multiple loops often need to be modeled simultaneously. As interactions among loops in spatial proximity can be rather complex, sampling the conformations of multiple interacting loops is a challenging task. Results: In this study, we report a new method called m ulti-loop Di stance-guided S equential chain- Gro wth Monte Carlo ( M -D i SG ro ) for prediction of the conformations of multiple interacting loops in proteins. Our method achieves an average RMSD of 1.93 Å for lowest energy conformations of 36 pairs of interacting protein loops with the total length ranging from 12 to 24 residues. We further constructed a data set containing proteins with 2, 3 and 4 interacting loops. For the most challenging target proteins with four loops, the average RMSD of the lowest energy conformations is 2.35 Å. Our method is also tested for predicting multiple loops in β-barrel membrane proteins. For outer-membrane protein G, the lowest energy conformation has a RMSD of 2.62 Å for the three extracellular interacting loops with a total length of 34 residues (12, 12 and 10 residues in each loop). Availability and implementation : The software is freely available at: tanto.bioe.uic.edu/m-DiSGro. Contact: jinfeng@stat.fsu.edu or jliang@uic.edu Supplementary information : Supplementary data are available at Bioinformatics online.

Description: Motivation: Network comparison is a computationally intractable problem with important applications in systems biology and other domains. A key challenge is to properly quantify similarity between wiring patterns of two networks in an alignment-free fashion. Also, alignment-based methods exist that aim to identify an actual node mapping between networks and as such serve a different purpose. Various alignment-free methods that use different global network properties (e.g. degree distribution) have been proposed. Methods based on small local subgraphs called graphlets perform the best in the alignment-free network comparison task, due to high level of topological detail that graphlets can capture. Among different graphlet-based methods, Graphlet Correlation Distance (GCD) was shown to be the most accurate for comparing networks. Recently, a new graphlet-based method called NetDis was proposed, which was claimed to be superior. We argue against this, as the performance of NetDis was not properly evaluated to position it correctly among the other alignment-free methods. Results : We evaluate the performance of available alignment-free network comparison methods, including GCD and NetDis. We do this by measuring accuracy of each method (in a systematic precision-recall framework) in terms of how well the method can group (cluster) topologically similar networks. By testing this on both synthetic and real-world networks from different domains, we show that GCD remains the most accurate, noise-tolerant and computationally efficient alignment-free method. That is, we show that NetDis does not outperform the other methods, as originally claimed, while it is also computationally more expensive. Furthermore, since NetDis is dependent on the choice of a network null model (unlike the other graphlet-based methods), we show that its performance is highly sensitive to the choice of this parameter. Finally, we find that its performance is not independent on network sizes and densities, as originally claimed. Contact : natasha@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: The functional impact of small molecules is increasingly being assessed in different eukaryotic species through large-scale phenotypic screening initiatives. Identifying the targets of these molecules is crucial to mechanistically understand their function and uncover new therapeutically relevant modes of action. However, despite extensive work carried out in model organisms and human, it is still unclear to what extent one can use information obtained in one species to make predictions in other species. Results: Here, for the first time, we explore and validate at a large scale the use of protein homology relationships to predict the targets of small molecules across different species. Our results show that exploiting target homology can significantly improve the predictions, especially for molecules experimentally tested in other species. Interestingly, when considering separately orthology and paralogy relationships, we observe that mapping small molecule interactions among orthologs improves prediction accuracy, while including paralogs does not improve and even sometimes worsens the prediction accuracy. Overall, our results provide a novel approach to integrate chemical screening results across multiple species and highlight the promises and remaining challenges of using protein homology for small molecule target identification. Availability and implementation: Homology-based predictions can be tested on our website http://www.swisstargetprediction.ch . Contact: david.gfeller@unil.ch or vincent.zoete@isb-sib.ch . Supplementary information: Supplementary data are available at Bioinformatics online.

Description: In energy crops for biomass production a proper plant structure is important to optimize wood yields. A precise crop characterization in early stages may contribute to the choice of proper cropping techniques. This study assesses the potential of the Microsoft Kinect for Windows v.1 sensor to determine the best viewing angle of the sensor to estimate the plant biomass based on poplar seedling geometry. Kinect Fusion algorithms were used to generate a 3D point cloud from the depth video stream. The sensor was mounted in different positions facing the tree in order to obtain depth (RGB-D) images from different angles. Individuals of two different ages, e.g., one month and one year old, were scanned. Four different viewing angles were compared: top view (0°), 45° downwards view, front view (90°) and ground upwards view (−45°). The ground-truth used to validate the sensor readings consisted of a destructive sampling in which the height, leaf area and biomass (dry weight basis) were measured in each individual plant. The depth image models agreed well with 45°, 90° and −45° measurements in one-year poplar trees. Good correlations (0.88 to 0.92) between dry biomass and the area measured with the Kinect were found. In addition, plant height was accurately estimated with a few centimeters error. The comparison between different viewing angles revealed that top views showed poorer results due to the fact the top leaves occluded the rest of the tree. However, the other views led to good results. Conversely, small poplars showed better correlations with actual parameters from the top view (0°). Therefore, although the Microsoft Kinect for Windows v.1 sensor provides good opportunities for biomass estimation, the viewing angle must be chosen taking into account the developmental stage of the crop and the desired parameters. The results of this study indicate that Kinect is a promising tool for a rapid canopy characterization, i.e., for estimating crop biomass production, with several important advantages: low cost, low power needs and a high frame rate (frames per second) when dynamic measurements are required.

Description: The selfish behaviors of nodes (or selfish nodes) cause packet loss, network congestion or even void regions in real-time wireless sensor networks, which greatly decrease the network performance. Previous methods have focused on detecting selfish nodes or avoiding selfish behavior, but little attention has been paid to regulating selfish behavior. In this paper, a Game Theory-based Real-time & Fault-tolerant (GTRF) routing protocol is proposed. GTRF is composed of two stages. In the first stage, a game theory model named VA is developed to regulate nodes’ behaviors and meanwhile balance energy cost. In the second stage, a jumping transmission method is adopted, which ensures that real-time packets can be successfully delivered to the sink before a specific deadline. We prove that GTRF theoretically meets real-time requirements with low energy cost. Finally, extensive simulations are conducted to demonstrate the performance of our scheme. Simulation results show that GTRF not only balances the energy cost of the network, but also prolongs network lifetime.

Description: The unique optoelectronic properties of quantum dots (QDs) give them significant advantages over traditional organic dyes, not only as fluorescent labels for bioimaging, but also as emissive sensing probes. QD sensors that function via manipulation of fluorescent resonance energy transfer (FRET) are of special interest due to the multiple response mechanisms that may be utilized, which in turn imparts enhanced flexibility in their design. They may also function as ratiometric, or “color-changing” probes. In this review, we describe the fundamentals of FRET and provide examples of QD-FRET sensors as grouped by their response mechanisms such as link cleavage and structural rearrangement. An overview of early works, recent advances, and various models of QD-FRET sensors for the measurement of pH and oxygen, as well as the presence of metal ions and proteins such as enzymes, are also provided.

Description: A reported 30% of people worldwide have abnormal lung sounds, including crackles, rhonchi, and wheezes. To date, the traditional stethoscope remains the most popular tool used by physicians to diagnose such abnormal lung sounds, however, many problems arise with the use of a stethoscope, including the effects of environmental noise, the inability to record and store lung sounds for follow-up or tracking, and the physician’s subjective diagnostic experience. This study has developed a digital stethoscope to help physicians overcome these problems when diagnosing abnormal lung sounds. In this digital system, mel-frequency cepstral coefficients (MFCCs) were used to extract the features of lung sounds, and then the K-means algorithm was used for feature clustering, to reduce the amount of data for computation. Finally, the K-nearest neighbor method was used to classify the lung sounds. The proposed system can also be used for home care: if the percentage of abnormal lung sound frames is > 30% of the whole test signal, the system can automatically warn the user to visit a physician for diagnosis. We also used bend sensors together with an amplification circuit, Bluetooth, and a microcontroller to implement a respiration detector. The respiratory signal extracted by the bend sensors can be transmitted to the computer via Bluetooth to calculate the respiratory cycle, for real-time assessment. If an abnormal status is detected, the device will warn the user automatically. Experimental results indicated that the error in respiratory cycles between measured and actual values was only 6.8%, illustrating the potential of our detector for home care applications.