AbstractPavement condition monitoring is required to identify pavements in need of maintenance or rehabilitation. Early identification of reduction in pavement's structural resistance and improving the structural resistance by minor repairs can lead to significantly lower maintenance costs for transportation agencies. In this study, a cost‐effective wireless sensor that can be embedded in the road to measure the transient vibrations due to different applied loads was tested to determine its effectiveness in terms of pavement displacement measurements. Test results show that the vibration sensor, combined with the algorithms, can be embedded in new or existing pavements and used as an accurate wireless displacement sensor. The low cost of the sensor system allows the use of these sensors at high densities for monitoring the performance of an entire road network. Outputs from the developed system can be directly used to evaluate the condition and performance of pavement structure (increasing displacement over time indicating increasing pavement damage). In addition, displacement data from the system can be used to backcalculate pavement layer stiffnesses, which can be used to predict long‐term performance of the pavement structure. Reduction in pavement layer stiffness over time can be used to determine long‐term damage accumulation.

Many modern crop varieties contain patented biotechnology traits, and an increasing number of these crops have multiple (stacked) traits. Fast and accurate determination of transgene levels is advantageous for a variety of use cases across the food, feed and fuel value chain. With the growing number of new transgenic crops, any technology used to quantify them should have robust assays that are simple to design and optimize, thereby facilitating the addition of new traits to an assay. Here we describe a PCR-based method that is simple to design, starts from whole seeds, and can be run to end-point in less than 5 minutes. Subsequent relative quantification (trait vs. non-trait) using capillary electrophoresis performed in 5% increments across the 0–100% range showed a mean absolute error of 1.9% (s.d. = 1.1%). We also show that the PCR assay can be coupled to non-optical solid-state nanopore sensors to give seed-to-trait quantification results with a mean absolute error of 2.3% (s.d. = 1.6%). In concert, the fast PCR and nanopore sensing stages demonstrated here can be fully integrated to produce seed-to-trait quantification results in less than 10 minutes, with high accuracy across the full dynamic range.

Solid-state nanopores are a single-molecule technique that can provide access to biomolecular information that is otherwise masked by ensemble averaging. A promising application uses pores and barcoding chemistries to map molecular motifs along single DNA molecules. Despite recent research breakthroughs, however, it remains challenging to overcome molecular noise to fully exploit single-molecule data. Here, an active control technique termed "flossing" that uses a dual nanopore device is presented to trap a proteintagged DNA molecule and up to 100's of back-and-forth electrical scans of the molecule are performed in a few seconds. The protein motifs bound to 48.5 kb λ-DNA are used as detectable features for active triggering of the bidirectional control. Molecular noise is suppressed by averaging the multiscan data to produce averaged intertag distance estimates that are comparable to their known values. Since nanopore feature-mapping applications require DNA linearization when passing through the pore, a key advantage of flossing is that trans-pore linearization is increased to >98% by the second scan, compared to 35% for single nanopore passage of the same set of molecules. In concert with barcoding methods, the dual-pore flossing technique could enable genome mapping and structural variation applications, or mapping loci of epigenetic relevance.

AbstractSolid-state nanopores are a single-molecule technique that can provide access to biomolecular information that is otherwise masked by ensemble averaging. A promising application uses pores and barcoding chemistries to map molecular motifs along single DNA molecules. Despite recent research breakthroughs, however, it remains challenging to overcome molecular noise to fully exploit single molecule data. Here we present an active control technique termed “flossing” that uses a dual nanopore device to trap a protein-tagged DNA molecule and perform up to 100’s of back-and-forth electrical scans of the molecule in a few seconds. The protein motifs bound to 48 kb λDNA are used as detectable features for active triggering of the bidirectional control. Molecular noise is suppressed by averaging the multi-scan data to produce averaged inter-tag distance estimates that are comparable to their known values. Since nanopore feature-mapping applications require DNA linearization when passing through the pore, a key advantage of flossing is that trans-pore linearization is increased to >98% by the second scan, compared to 35% for single nanopore passage of the same set of molecules. In concert with barcoding methods, the dual-pore flossing technique could enable genome mapping and structural variation applications, or mapping loci of epigenetic relevance.

Methods for reducing and directly controlling the speed of DNA through a nanopore are needed to enhance sensing performance for direct strand sequencing and detection/mapping of sequence-specific features. A method is created for reducing and controlling the speed of DNA that uses two independently controllable nanopores operated with an active control logic. The pores are positioned sufficiently close to permit cocapture of a single DNA by both pores. Once cocapture occurs, control logic turns on constant competing voltages at the pores leading to a "tug-of-war" whereby opposing forces are applied to regions of the molecules threading through the pores. These forces exert both conformational and speed control over the cocaptured molecule, removing folds and reducing the translocation rate. When the voltages are tuned so that the electrophoretic force applied to both pores comes into balance, the life time of the tug-of-war state is limited purely by diffusive sliding of the DNA between the pores. A tug-of-war state is produced on 76.8% of molecules that are captured with a maximum two-order of magnitude increase in average pore translocation time relative to the average time for single-pore translocation. Moreover, the translocation slow-down is quantified as a function of voltage tuning and it is shown that the slow-down is well described by a first passage analysis for a 1D subdiffusive process. The ionic current of each nanopore provides an independent sensor that synchronously measures a different region of the same molecule, enabling sequential detection of physical labels, such as monostreptavidin tags. With advances in devices and control logic, future dual-pore applications include genome mapping and enzyme-free sequencing.

Methods for reducing and directly controlling the speed of DNA through a nanopore are needed to enhance sensing performance for direct strand sequencing and detection/mapping of sequence-specific features. We have created a method for reducing and controlling the speed of DNA that uses two independently controllable nanopores operated with an active control logic. The pores are positioned sufficiently close to permit co-capture of a single DNA by both pores. Once co-capture occurs, control logic turns on constant competing voltages at the pores leading to a `tug-of-war' whereby the molecule is pulled from both ends by opposing forces. These forces exert both conformational and speed control over the co-captured molecule, removing folds and reducing the translocation rate. When the voltages are tuned so that the electrophoretic force applied to both ends of the molecule comes into balance, the life-time of the tug-of-war state is limited purely by diffusive sliding of the DNA between the pores. We are able to produce a tug-of-war state on 76.8% of molecules that are captured with a maximum two-order of magnitude increase in average pore translocation time relative to the average time for single-pore translocation. Moreover, we quantify the translocation slow-down as a function of voltage tuning and show that the slow-down is well described by a first passage analysis for a one-dimensional sub-diffusive process. The ionic current of each nanopore provides an independent sensor that synchronously measures a different region of the same molecule, enabling sequential detection of physical labels, such as mono-streptavidin tags. With advances in devices and control logic, future dual-pore applications include genome mapping and enzyme-free sequencing.

Objective: The photoplethysmography (PPG) signal, commonly used in the healthcare settings, is easily affected by movement artefact leading to errors in the extracted heart rate and SpO2 estimates. This study aims to develop an online artefact detection system based on adaptive (dynamic) template matching, suitable for continuous PPG monitoring during daily living activities or in the intensive care units (ICUs). Approach: Several master templates are initially generated by applying principal component analysis to data obtained from the PhysioNet MIMIC II database. The master template is then updated with each incoming clean PPG pulse. The correlation coefficient is used to classify the PPG pulse into either good or bad quality categories. The performance of our algorithm was evaluated using data obtained from two different sources: (i) our own data collected from 19 healthy subjects using the wearable Sotera Visi Mobile system (Sotera Wireless Inc.) as they performed various movement types; and (ii) ICU data provided by the PhysioNet MIMIC II database. The developed algorithm was evaluated against a manually annotated ‘gold standard’ (GS). Main results: Our algorithm achieved an overall accuracy of 91.5% ± 2.9%, with a sensitivity of 94.1% ± 2.7% and a specificity of 89.7% ± 5.1%, when tested on our own data. When applying the algorithm to data from the PhysioNet MIMIC II database, it achieved an accuracy of 98.0%, with a sensitivity and specificity of 99.0% and 96.1%, respectively. Significance: The proposed method is simple and robust against individual variations in the PPG characteristics, thus making it suitable for a diverse range of datasets. Integration of the proposed artefact detection technique into remote monitoring devices could enhance reliability of the PPG-derived physiological parameters.

Proposing a new solution implies an underlying prediction that the design will succeed in its direct purpose and will minimize unintended negative consequences. These consequences could happen immediately or slowly through a cascade of changes as the system adapts to the new work patterns that the solution requires. Human Factors Engineering (HFE) has a long and storied history of being consulted after an implementation goes badly, but HFE practitioners are often not able to meaningfully impact the design of these solutions before implementation. Our panel will share exemplar cases in which HFE was not initially involved, but ultimately contributed to effective solutions. The purpose of sharing these stories is three-fold: (1) to provide examples of successful healthcare interventions, (2) to provide evidence and guidance for practitioners who are trying to influence their organizations earlier in the design process, and (3) to spur our community to share our stories more widely.

This paper presents sparse Bayesian learning (SBL)-based schemes for approximately sparse channel estimation in an orthogonal space-time block coded (OSTBC) multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) wireless system. The parameterized prior-based SBL framework is employed to present a pilot scheme for an ill-posed OSTBC MIMO-OFDM channel estimation scenario. Maximum likelihood symbol detection (MLSD) has been incorporated in the expectation-maximization framework for SBL-based channel estimation. This has led to the development of a novel scheme for joint approximately sparse channel estimation and symbol detection. The proposed scheme performs SBL-based channel estimation in the E-step followed by a modified ML decision metric-based symbol detection in the M-step. Bayesian Cramer–Rao bounds are obtained for the genie minimum mean-squared error estimators corresponding to the SBL schemes. Closed-form bit error probability expressions are derived for the MLSD in the presence of SBL-based channel estimation errors. Simulation results are presented towards the end to validate the theoretical bounds and illustrate the performance of the proposed techniques.