Stand-alone Platform Research Articles

A Data Management Method for Remote and Long-Term Seafloor Observation System

The deep seafloor is closest to the Earth’s interior and there are complicated interactions in the physical and biological processes of the seafloor. Oceanographic parameters such as temperature, salinity, and ocean current can provide support for climate prediction. Therefore, the long-term observation of the deep seafloor is critical to the study of global climate change and the biodiversity in complex environments. For the requirements of long-term observation and remote data transmission in the deep sea of the South China Sea, a seafloor observation prototype system is designed in this paper as a stand-alone observation platform. We then proposed a date management method based on Controller Area Network to ensure the data quality. Distributed data management can be performed through system layering, and strict data transmission standards are established between each layer. At the same time, aiming at the difficulty of seafloor video data transmission in real-time, a new solution based on satellite communication technology, object detection technology, and high-efficiency compression coding technology of video images is proposed to provide support for seafloor biodiversity research. The observation data from the experiment are given, and these results show that the system can meet the requirements of long-term observation and remote real-time data transmission, and the reliability of the system is verified.

Direction of arrival estimation using deep neural network for hearing aid applications using smartphone.

Deep neural network (DNN) techniques are gaining popularity due to performance boost in many applications. In this work we propose a DNN-based method for finding the direction of arrival (DOA) of speech source for hearing study improvement and hearing aid applications using popular smartphone with no external components as a cost-effective stand-alone platform. We consider the DOA estimation as a classification problem and use the magnitude and phase of speech signal as a feature set for DNN training stage and obtaining appropriate model. The model is trained and derived using real speech and real noisy speech data recorded on smartphone in different noisy environments under low signal to noise ratios (SNRs). The DNN-based DOA method with the pre-trained model is implemented and run on Android smartphone in real time. The performance of proposed method is evaluated objectively and subjectively in the both training and unseen environments. The test results are presented showing the superior performance of proposed method over conventional methods.

On-chip stool liquefaction via acoustofluidics.

Microfluidic-based portable devices for stool analysis are important for detecting established biomarkers for gastrointestinal disorders and understanding the relationship between gut microbiota imbalances and various health conditions, ranging from digestive disorders to neurodegenerative diseases. However, the challenge of processing stool samples in microfluidic devices hinders the development of a standalone platform. Here, we present the first microfluidic chip that can liquefy stool samples via acoustic streaming. With an acoustic transducer actively generating strong micro-vortex streaming, stool samples and buffers in microchannel can be homogenized at a flow rate up to 30 μL min-1. After homogenization, an array of 100 μm wide micropillars can further purify stool samples by filtering out large debris. A favorable biocompatibility was also demonstrated for our acoustofluidic-based stool liquefaction chip by examining bacteria morphology and viability. Moreover, stool samples with different consistencies were liquefied. Our acoustofluidic chip offers a miniaturized, robust, and biocompatible solution for stool sample preparation in a microfluidic environment and can be potentially integrated with stool analysis units for designing portable stool diagnostics platforms.

A Combined Free-Flow Electrophoresis and DIGE Approach to Compare Proteins in Complex Biological Samples.

Free-flow electrophoresis has been applied in numerous studies as a protein separation technique due to its multiple advantages such as fast and efficient sample recovery, high resolving power, high reproducibility and wide applicability to protein classes. As a stand-alone platform, however, its utility in comparative proteomic analysis is limited as protein samples must be run sequentially rather than simultaneously which introduces inherent variability when attempting to perform quantitative analysis. Here we describe an approach combining fluorescent CyDye technology (DIGE) with free-flow electrophoresis to simultaneously separate and identify differentially expressed proteins in a model cell system.

Hybrid electrochemical sensor platform for capsaicin determination using coarsely stepped cyclic squarewave voltammetry

A small, standalone electrochemical hybrid sensor platform, combining flexible electronics and screen-printed electrodes, is demonstrated in the determination of capsaicin through adsorptive stripping voltammetry. The sensing scheme was simplified to be compatible with a low-cost device. The simplification involved eliminating the need for additional modification of the electrode and employing a coarsely stepped squarewave voltammetry, a technique which is applicable with less sophisticated instrumentation. This architecture was found to be suitable for concentrations up to at least 5000 µM with a detection limit of 1.98 µM. The screen-printed carbon graphite electrodes were made reusable through an ethanol rinsing protocol. The effect of ethanol/buffer volumetric ratio in the test sample was shown to greatly influence the analytical data, and a fixed 10% (v/v) was chosen as a compromise between signal-to-noise ratio and not exceeding the solubility limit of the desired upper range.

A Low-Cost, Stand-Alone Sensory Platform for Monitoring Extreme Solar Overirradiance Events.

In this paper, we present a low-cost, stand-alone sensory platform developed for in situ monitoring of environmental parameters, for use in the Amazon region in the north of Brazil. The mission of the platform is to perform monitoring and identification of overirradiance (solar irradiance > 1000 W/m2) and extreme overirradiance events (solar irradiance > 1300 W/m2) using a photovoltaic based irradiance sensor. The sensory platform was built using the ESP8266 microcontroller, an open embedded computer capable of Wi-Fi communication using the IEEE 802.11 standard, and small photovoltaic modules, air temperature, atmospheric pressure, voltage, and current sensors, enabling the development of a low-cost system (€70/R$350.00 BRL). Calibration and tests were conducted at the Federal University of Pará (UFPA), Belém campus, Pará, where the platform measured an extreme overirradiance of 1321 W/m2 at a low-latitude (1 °S) and low altitude (7 m above sea level).

Metabolomics and Biomarker Discovery in Traumatic Brain Injury.

Traumatic brain injury (TBI) is one of the leading causes of disability and mortality worldwide. The TBI pathogenesis can induce broad pathophysiological consequences and clinical outcomes attributed to the complexity of the brain. Thus, the diagnosis and prognosis are important issues for the management of mild, moderate, and severe forms of TBI. Metabolomics of readily accessible biofluids is a promising tool for establishing more useful and reliable biomarkers of TBI than using clinical findings alone. Metabolites are an integral part of all biochemical and pathophysiological pathways. Metabolomic processes respond to the internal and external stimuli resulting in an alteration of metabolite concentrations. Current high-throughput and highly sensitive analytical tools are capable of detecting and quantifying small concentrations of metabolites, allowing one to measure metabolite alterations after a pathological event when compared to a normal state or a different pathological process. Further, these metabolic biomarkers could be used for the assessment of injury severity, discovery of mechanisms of injury, and defining structural damage in the brain in TBI. Metabolic biomarkers can also be used for the prediction of outcome, monitoring treatment response, in the assessment of or prognosis of post-injury recovery, and potentially in the use of neuroplasticity procedures. Metabolomics can also enhance our understanding of the pathophysiological mechanisms of TBI, both in primary and secondary injury. Thus, this review presents the promising application of metabolomics for the assessment of TBI as a stand-alone platform or in association with proteomics in the clinical setting.

A User-Friendly Application to Automate CT Renal Stone Measurement.

CT is the gold standard for visualizing renal and ureteral calculi. CT three-dimensional reformatting allows for automatic, accurate, and reliable measurement of stone size, volume, density, and location. In this study, we aimed to develop and test a software platform capable of calculating a battery of clinically important urinary stone parameters at the point-of-care (POC). The syngo Calcium Scoring (Siemens Corporation) algorithm was modified to identify calcium-based stones using an attenuation threshold (250 HU) within a region of interest. Information automatically obtained after reconstruction included voxel sum and calculated volume, maximum diameter, largest diameter in the x, y, and z planes, cumulative diameter, distribution of attenuation in HU, and position relative to the skin for calculation of the skin-to-stone distance (SSD). This algorithm was packaged into a stand-alone application (MATLAB 9.1). From April 2017 to May 2017, all patients undergoing a noncontrast CT of the abdomen or the abdomen and pelvis at the Johns Hopkins Hospital were eligible for inclusion in this validation cohort. A total of 55 index renal stones were included. The mean volume calculated by voxel sum was 216.53 mm3 (standard deviation [SD] ±616.19, range 1.50-4060.13). The mean volume calculated using the Ackermann's formula and for a sphere was 232.96 mm3 (SD ± 702.65, range 1.24-4074.04) and 1214.63 mm3 (SD ± 4233.41, range 1.77-25,246.40), respectively. The mean largest diameter in any one direction was 6.95 mm (SD ± 7.31, range 1.50-36.40). The maximum density of the stones ranged from 164 to 1725 HU. The mean SSD at the shortest possible point was 14.19 cm (SD ± 6.13, range 6.67-31.28). We developed a stand-alone platform with a simple easy-to-use interface, which will allow any user the ability to calculate a battery of clinically important urinary stone parameters from CT imaging at the POC. This program is now freely available online.

Compact standalone platform for neural recording with real-time spike sorting and data logging

Objective. Longitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective brain–machine interfaces (BMIs). These recordings generate enormous amounts of data for transmission and storage, and typically require offline processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of: (1) reducing the data bandwidth by circa 2 to 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission in future versions); (2) producing real-time, low-latency, spike sorted data; and (3) long term untethered operation. Approach. We have developed a headstage that operates in two phases. In the short training phase a computer is attached and classic spike sorting is performed to generate templates. In the second phase the system is untethered and performs template matching to create an event driven spike output that is logged to a micro-SD card. To enable validation the system is capable of logging the high bandwidth raw neural signal data as well as the spike sorted data. Main results. The system can successfully record 32 channels of raw neural signal data and/or spike sorted events for well over 24 h at a time and is robust to power dropouts during battery changes as well as SD card replacement. A 24 h initial recording in a non-human primate M1 showed consistent spike shapes with the expected changes in neural activity during awake behaviour and sleep cycles. Significance. The presented platform allows neural activity to be unobtrusively monitored and processed in real-time in freely behaving untethered animals—revealing insights that are not attainable through scheduled recording sessions. This system achieves the lowest power per channel to date and provides a robust, low-latency, low-bandwidth and verifiable output suitable for BMIs, closed loop neuromodulation, wireless transmission and long term data logging.

Micromechanical Rate Integrating Gyroscope With Angle-Dependent Bias Compensation Using a Self-Precession Method

Toward the objective of direct angle measurement using a rate integrating gyroscope (RIG) without a minimum rate threshold and with performance limited only by electrical and mechanical thermal noise, in this paper, we present the implementation of a generalized electronic feedback method for the compensation of MEMS gyroscope damping asymmetry (anisodamping) and stiffness asymmetry (anisoelasticity) on a stand-alone digital signal processing platform. Using the new method, the precession angle-dependent bias error and minimum rate threshold, two issues identified by Lynch for a MEMS RIG [1] due to anisodamping are overcome. To minimize angledependent bias, we augment the electronic feedback force of the amplitude regulator with a non-unity gain output distribution matrix selected to correct for anisodamping. Using this method, we have decreased the angle-dependent bias error by a factor of 12, resulting in a minimum rate threshold of ~3 °/s. To further improve RIG performance, an electronically induced self-precession rate is incorporated and successfully demonstrated to lower the rate threshold.

Towards High Accuracy De Novo Nanopore Sequencing

Nanopore DNA sequencing is a next generation technology providing long read lengths and direct detection of epigenetic base modifications. The high error rate for single-read de novo sequencing is the main obstacle to realizing nanopore sequencing as a stand-alone sequencing platform. We address the primary sources of base calling errors by using a DNA helicase with desirable properties and applying a time-varying voltage to jointly control DNA motion through the pore, and report an improved de novo sequencing accuracy for nanopore experiments.

Decoupling Network Design for Inner Current Loops of Stand-Alone Brushless Doubly Fed Induction Generation Power System

Brushless doubly fed induction generator (BDFIG) is of high reliability due to its brushless structure. However, brushless structure also results in the complicated d-q vector model, in which the d and q channels are seriously coupled. Traditionally many feedforward (FF) terms were added during control for decoupling purpose. However the FF method features several drawbacks: 1) it requires extra sensors; 2) the rotor position information needs to be known for d-q transformation; and 3) the decoupling effect highly depends on the parameter accuracy. To overcome these problems, this letter proposes a decoupling method based on the decoupling network (DN). With DN method, the BDFIG and the load are first modeled as a dual-input-dual-output system, and then the DN is designed. From control point of view, the DN is in series with the BDFIG and the load, and thus, form a new control plant with highly decoupling feature. As a result, the controllers in d and q channels can be easily designed. The proposed DN method requires neither extra sensors nor rotor position information, and the robustness is enhanced since parameter variations of BDFIG and load can be fully considered during DN design. In this letter, such a DN method is presented and applied on the inner current loops of a stand-alone BDFIG system to obtain the good decoupling feature in the overall operational range, and the design results are verified by the experiments from a stand-alone BDFIG system platform.

A versatile optical microscope for time-dependent single-molecule and single-particle spectroscopy.

This work reports the design and implementation of a multi-function optical microscope for time-dependent spectroscopy on single molecules and single nanoparticles. It integrates the now-routine single-object measurements into one standalone platform so that no reconfiguration is needed when switching between different types of sample or spectroscopy modes. The illumination modes include evanescent field through total internal reflection, dark-field illumination, and epi-excitation onto a diffraction-limited spot suitable for confocal detection. The detection modes include spectrally resolved line imaging, wide-field imaging with dual-color capability, and two-color single-element photon-counting detection. The switch between different spectroscopy and data acquisition modes is fully automated and executed through computer programming. The capability of this microscope is demonstrated through selected proof-of-principle experiments.

NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis

We present a standalone, scalable and high-throughput software platform for PET image reconstruction and analysis. We focus on high fidelity modelling of the acquisition processes to provide high accuracy and precision quantitative imaging, especially for large axial field of view scanners. All the core routines are implemented using parallel computing available from within the Python package NiftyPET, enabling easy access, manipulation and visualisation of data at any processing stage. The pipeline of the platform starts from MR and raw PET input data and is divided into the following processing stages: (1) list-mode data processing; (2) accurate attenuation coefficient map generation; (3) detector normalisation; (4) exact forward and back projection between sinogram and image space; (5) estimation of reduced-variance random events; (6) high accuracy fully 3D estimation of scatter events; (7) voxel-based partial volume correction; (8) region- and voxel-level image analysis. We demonstrate the advantages of this platform using an amyloid brain scan where all the processing is executed from a single and uniform computational environment in Python. The high accuracy acquisition modelling is achieved through span-1 (no axial compression) ray tracing for true, random and scatter events. Furthermore, the platform offers uncertainty estimation of any image derived statistic to facilitate robust tracking of subtle physiological changes in longitudinal studies. The platform also supports the development of new reconstruction and analysis algorithms through restricting the axial field of view to any set of rings covering a region of interest and thus performing fully 3D reconstruction and corrections using real data significantly faster. All the software is available as open source with the accompanying wiki-page and test data.

A Wireless Acoustic Array System for Binaural Loudness Evaluation in Cities

Networks of acoustic sensors are being deployed in smart cities to continuously monitor noise levels. In this paper, a novel acoustic sensor device is designed for binaural loudness evaluation, in a standalone platform. The audio is acquired from an array of microphones and a binaural signal is synthesized by a direction-of-arrival algorithm and a head-related transfer function. Hardware setup and software algorithms are presented and the results are discussed. Finally, the tests conducted in an early deployment show the feasibility of using the device to carry out large temporal and spatial sampling for the evaluation of binaural loudness.

Metabolite profiling of yam (Dioscorea spp.) accessions for use in crop improvement programmes

IntroductionNinety-seven percent of yam (Dioscorea spp.) production takes place in low income food deficit countries (LIFDCs) and the crop provides 200 calories a day to approximately 300 million people. Therefore, yams are vital for food security. Yams have high-yield potential and high market value potential yet current breeding of yam is hindered by a lack of genomic information and genetic resources. New tools are needed to modernise breeding strategies and unlock the potential of yam to improve livelihood in LIFDCs.ObjectivesMetabolomic screening has been undertaken on a diverse panel of Dioscorea accessions to assess the utility of the approach for advancing breeding strategies in this understudied crop.MethodsPolar and lipophilic extracts from tubers of accessions from the global yam breeding program have been comprehensively profiled via gas chromatography-mass spectrometry.ResultsA visual pathway representation of the measured yam tuber metabolome has been delivered as a resource for biochemical evaluation of yam germplasm. Over 200 compounds were routinely measured in tubers, providing a major advance for the chemo-typing of this crop. Core biochemical redundancy concealed trends that were only elucidated following detailed mining of global metabolomics data. Combined analysis on leaf and tuber material identified a subset of metabolites which allow accurate species classification and highlighted the potential of predicting tuber composition from leaf profiles. Metabolic variation was accession-specific and often localised to compound classes, which will aid trait-targeting for metabolite markers.ConclusionsMetabolomics provides a standalone platform with potential to deliver near-future crop gains for yam. The approach compliments the genetic advancements currently underway and integration with other ‘–omics’ studies will deliver a significant advancement to yam breeding strategies.

The eBioKit, a stand-alone educational platform for bioinformatics.

Bioinformatics skills have become essential for many research areas; however, the availability of qualified researchers is usually lower than the demand and training to increase the number of able bioinformaticians is an important task for the bioinformatics community. When conducting training or hands-on tutorials, the lack of control over the analysis tools and repositories often results in undesirable situations during training, as unavailable online tools or version conflicts may delay, complicate, or even prevent the successful completion of a training event. The eBioKit is a stand-alone educational platform that hosts numerous tools and databases for bioinformatics research and allows training to take place in a controlled environment. A key advantage of the eBioKit over other existing teaching solutions is that all the required software and databases are locally installed on the system, significantly reducing the dependence on the internet. Furthermore, the architecture of the eBioKit has demonstrated itself to be an excellent balance between portability and performance, not only making the eBioKit an exceptional educational tool but also providing small research groups with a platform to incorporate bioinformatics analysis in their research. As a result, the eBioKit has formed an integral part of training and research performed by a wide variety of universities and organizations such as the Pan African Bioinformatics Network (H3ABioNet) as part of the initiative Human Heredity and Health in Africa (H3Africa), the Southern Africa Network for Biosciences (SAnBio) initiative, the Biosciences eastern and central Africa (BecA) hub, and the International Glossina Genome Initiative.

CIPHER: a flexible and extensive workflow platform for integrative next-generation sequencing data analysis and genomic regulatory element prediction

BackgroundNext-generation sequencing (NGS) approaches are commonly used to identify key regulatory networks that drive transcriptional programs. Although these technologies are frequently used in biological studies, NGS data analysis remains a challenging, time-consuming, and often irreproducible process. Therefore, there is a need for a comprehensive and flexible workflow platform that can accelerate data processing and analysis so more time can be spent on functional studies.ResultsWe have developed an integrative, stand-alone workflow platform, named CIPHER, for the systematic analysis of several commonly used NGS datasets including ChIP-seq, RNA-seq, MNase-seq, DNase-seq, GRO-seq, and ATAC-seq data. CIPHER implements various open source software packages, in-house scripts, and Docker containers to analyze and process single-ended and pair-ended datasets. CIPHER’s pipelines conduct extensive quality and contamination control checks, as well as comprehensive downstream analysis. A typical CIPHER workflow includes: (1) raw sequence evaluation, (2) read trimming and adapter removal, (3) read mapping and quality filtering, (4) visualization track generation, and (5) extensive quality control assessment. Furthermore, CIPHER conducts downstream analysis such as: narrow and broad peak calling, peak annotation, and motif identification for ChIP-seq, differential gene expression analysis for RNA-seq, nucleosome positioning for MNase-seq, DNase hypersensitive site mapping, site annotation and motif identification for DNase-seq, analysis of nascent transcription from Global-Run On (GRO-seq) data, and characterization of chromatin accessibility from ATAC-seq datasets. In addition, CIPHER contains an “analysis” mode that completes complex bioinformatics tasks such as enhancer discovery and provides functions to integrate various datasets together.ConclusionsUsing public and simulated data, we demonstrate that CIPHER is an efficient and comprehensive workflow platform that can analyze several NGS datasets commonly used in genome biology studies. Additionally, CIPHER’s integrative “analysis” mode allows researchers to elicit important biological information from the combined dataset analysis.

Geolokit: An interactive tool for visualising and exploring geoscientific data in Google Earth

Virtual globes have been developed to showcase different types of data combining a digital elevation model and basemaps of high resolution satellite imagery. Hence, they became a standard to share spatial data and information, although they suffer from a lack of toolboxes dedicated to the formatting of large geoscientific dataset. From this perspective, we developed Geolokit: a free and lightweight software that allows geoscientists – and every scientist working with spatial data – to import their data (e.g., sample collections, structural geology, cross-sections, field pictures, georeferenced maps), to handle and to transcribe them to Keyhole Markup Language (KML) files. KML files are then automatically opened in the Google Earth virtual globe and the spatial data accessed and shared. Geolokit comes with a large number of dedicated tools that can process and display: (i) multi-points data, (ii) scattered data interpolations, (iii) structural geology features in 2D and 3D, (iv) rose diagrams, stereonets and dip-plunge polar histograms, (v) cross-sections and oriented rasters, (vi) georeferenced field pictures, (vii) georeferenced maps and projected gridding.Therefore, together with Geolokit, Google Earth becomes not only a powerful georeferenced data viewer but also a stand-alone work platform. The toolbox (available online at http://www.geolokit.org) is written in Python, a high-level, cross-platform programming language and is accessible through a graphical user interface, designed to run in parallel with Google Earth, through a workflow that requires no additional third party software. Geolokit features are demonstrated in this paper using typical datasets gathered from two case studies illustrating its applicability at multiple scales of investigation: a petro-structural investigation of the Ile d’Yeu orthogneissic unit (Western France) and data collection of the Mariana oceanic subduction zone (Western Pacific).

Assessing Self-Regulation of Learning Dimensions in a Stand-alone MOOC Platform

A capacity for self-regulated learning (SRL) has long been recognised as an important factor in successful studies. Although educational researchers have started to investigate the concept of SRL in the context of online education, very little is yet known about SRL in relation to massive open online courses (MOOCs) or of appropriate strategies to foster SRL skills in MOOC learners. Self-regulation is particularly important in a MOOC-based study, which demands effective independent learning, and where widely acknowledged high dropout rates are observed. This study reports an investigation and assessment of the concept of SRL using a novel MOOC platform (eLDa) by providing study options (either via a self-directed learning or instructor-led learning) using a novel learning tool. In view of this, the research presents general description of self-regulated learning and explored the various existing dimensions used to expose the learners SRL skills. Drawing comparison of the online tool, the results and findings of the data were analysed. The study dis¬cusses how the various dimensions contributed to the knowledge representation of the self-regulated learning abilities shown by the learners. We present how these SRL dimensions captured using the measuring instrument contributes to our growing understanding of the distinctive features of the individual learner’s self-regulated learning. MOOCs success required a high performance of self-regulated learning abilities which at the moment very little has shown these degree of supporting SRL skills. This paper presents preliminary evaluation of a novel e-learning tool known, as ‘eLDa’ developed to implement this investi¬gation of self-regulation of learning. The research applied a modified online self-regulated learning questionnaire (OSLQ) as the instrument to measure the SRL skills. The modified questionnaire known as MOOC OSLQ (MOSLQ) was developed with a 19-item scale questions that exposes the six SRL dimensions used in this study.