Real-time kinematic-based detection of foot-strike during walking

Detection of foot-strike events is an integral part of gait analysis, as it allows the temporal registration of gait cycles. At the same time, it is necessary to register gait phases in real-time for applications such as wearable assistive devices and gait biofeedback that work synchronously with the human gait. Although many algorithms have been proposed for detecting foot-strikes with either wearable (e.g. Inertial Measurement Units (IMUs)) or non-wearable (e.g. force plates) sensors, there is a great need for real-time algorithms that rely only on recording the kinematics of the leg motion. This work proposes a novel and efficient kinematic algorithm, called the Foot VErtical & Sagittal Position Algorithm (F-VESPA), which has several advantages over existing methods. First, it accurately estimates foot-strike events using kinematic data without requiring access to future data points, hence achieving reduced latency during real-time implementation. Moreover, it does not require tuning of the utilized parameters, rendering it robust to different subjects and treadmill speeds. The algorithm is tested in a large set of subjects across various treadmill speeds, and it is shown to outperform even offline implementations of existing prominent kinematic algorithms. Using a 150 Hz data collection system, the F-VESPA achieved a median of 33 ms for the total true errors in detecting foot-strike. The F-VESPA is a highly responsive kinematic algorithm that can detect foot-strike events in real-time, with high accuracy, robustness and reduced latency, enabling real-time temporal registration of gait cycles.

Highly Accelerated Real-Time Free-Breathing Cine CMR for Patients With a Cardiac Implantable Electronic Device

RATIONALE AND OBJECTIVES

To develop a 16-fold accelerated real-time, free-breathing cine cardiovascular magnetic resonance (CMR) pulse sequence with compressed sensing reconstruction and test whether it is capable of producing clinically acceptable summed visual scores (SVS) and accurate left ventricular ejection fraction (LVEF) in patients with a cardiac implantable electronic device (CIED).

MATERIALS AND METHODS

A 16-fold accelerated real-time cine CMR pulse sequence was developed using gradient echo readout, Cartesian k-space sampling, and compressed sensing. We scanned 13 CIED patients (mean age = 59 years; 9/4 males/females) using clinical standard, breath-hold cine and real-time, free-breathing cine. Two clinical readers performed a visual assessment of image quality in four categories (conspicuity of endocardial wall at end diastole, temporal fidelity of wall motion, any artifact level on the heart, noise) using a five-point Likert scale (1: worst; 3: clinically acceptable; 5: best). SVS was calculated as the sum of 4 individual scores, where 12 was defined as clinical acceptable. The Wilcoxon signed-rank test was performed to compare SVS, and the Bland-Altman analysis was conducted to evaluate the agreement of LVEF.

RESULTS

Median scan time was 3.7 times shorter for real-time (3.5 heartbeats per slice) than clinical standard (13 heartbeats per slice, excluding nonscanning time between successive breath-hold acquisitions). Median SVS was not significantly different between clinical standard (15.0) and real-time (14.5). The mean difference in LVEF was -2% (4.7% of mean), and the limits of agreement was 5.8% (13.5% of mean).

CONCLUSION

This study demonstrates that the proposed real-time cine method produces clinically acceptable SVS and relatively accurate LVEF in CIED patients.

The Brain Electrophysiological recording & STimulation (BEST) toolbox

Non-invasive brain stimulation (NIBS) experiments involve many recurring procedures that are not sufficiently standardized in the community. Given the diversity in experimental design and experience of the investigators, automated but yet flexible data collection and analysis tools are needed to increase objectivity, reliability, and reproducibility of NIBS experiments. The Brain Electrophysiological recording and STimulation (BEST) Toolbox is a MATLAB-based, open-source software with graphical user interface that allows users to design, run, and share freely configurable multi-protocol, multi-session NIBS studies, including transcranial magnetic, electric, and ultrasound stimulation (TMS, tES, TUS). Interfacing with a variety of recording and stimulation devices, the BEST toolbox analyzes EMG and EEG data, and configures stimulation parameters on-the-fly to facilitate closed-loop protocols and real-time applications. Its functionality is continuously expanded and includes e.g., TMS motor hotspot search, threshold estimation, motor evoked potential (MEP) and TMS-evoked EEG potential (TEP) measurements, dose-response curves, paired-pulse and dual-coil TMS, rTMS interventions.

Dynamic MRI of swallowing: real-time volumetric imaging at 12 frames per second at 3 T

Objective

Dysphagia or difficulty in swallowing is a potentially hazardous clinical problem that needs regular monitoring. Real-time 2D MRI of swallowing is a promising radiation-free alternative to the current clinical standard: videofluoroscopy. However, aspiration may be missed if it occurs outside this single imaged slice. We therefore aimed to image swallowing in 3D real time at 12 frames per second (fps).

Materials and methods

At 3 T, three 3D real-time MRI acquisition approaches were compared to the 2D acquisition: an aligned stack-of-stars (SOS), and a rotated SOS with a golden-angle increment and with a tiny golden-angle increment. The optimal 3D acquisition was determined by computer simulations and phantom scans. Subsequently, five healthy volunteers were scanned and swallowing parameters were measured.

Results

Although the rotated SOS approaches resulted in better image quality in simulations, in practice, the aligned SOS performed best due to the limited number of slices. The four swallowing phases could be distinguished in 3D real-time MRI, even though the spatial blurring was stronger than in 2D. The swallowing parameters were similar between 2 and 3D.

Conclusion

At a spatial resolution of 2-by-2-by-6 mm with seven slices, swallowing can be imaged in 3D real time at a frame rate of 12 fps.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10334-021-00973-6.

SquiggleNet: real-time, direct classification of nanopore signals

We present SquiggleNet, the first deep-learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than DNA passes through the pore, allowing real-time classification and read ejection. Using 1 s of sequencing data, the classifier achieves significantly higher accuracy than base calling followed by sequence alignment. Our approach is also faster and requires an order of magnitude less memory than alignment-based approaches. SquiggleNet distinguished human from bacterial DNA with over 90% accuracy, generalized to unseen bacterial species in a human respiratory meta genome sample, and accurately classified sequences containing human long interspersed repeat elements.

Real-time phase and amplitude estimation of neurophysiological signals exploiting a non-resonant oscillator

A recent advancement in the field of neuromodulation is to adapt stimulation parameters according to pre-specified biomarkers tracked in real-time. These markers comprise short and transient signal features, such as bursts of elevated band power. To capture these features, instantaneous measures of phase and/or amplitude are employed, which inform stimulation adjustment with high temporal specificity. For adaptive neuromodulation it is therefore necessary to precisely estimate a signal's phase and amplitude with minimum delay and in a causal way, i.e. without depending on future parts of the signal. Here we demonstrate a method that utilizes oscillation theory to estimate phase and amplitude in real-time and compare it to a recently proposed causal modification of the Hilbert transform. By simulating real-time processing of human LFP data, we show that our approach almost perfectly tracks offline phase and amplitude with minimum delay and is computationally highly efficient.

Solving musculoskeletal biomechanics with machine learning

Deep learning is a relatively new computational technique for the description of the musculoskeletal dynamics. The experimental relationships of muscle geometry in different postures are the high-dimensional spatial transformations that can be approximated by relatively simple functions, which opens the opportunity for machine learning (ML) applications. In this study, we challenged general ML algorithms with the problem of approximating the posture-dependent moment arm and muscle length relationships of the human arm and hand muscles. We used two types of algorithms, light gradient boosting machine (LGB) and fully connected artificial neural network (ANN) solving the wrapping kinematics of 33 muscles spanning up to six degrees of freedom (DOF) each for the arm and hand model with 18 DOFs. The input-output training and testing datasets, where joint angles were the input and the muscle length and moment arms were the output, were generated by our previous phenomenological model based on the autogenerated polynomial structures. Both models achieved a similar level of errors: ANN model errors were 0.08 ± 0.05% for muscle lengths and 0.53 ± 0.29% for moment arms, and LGB model made similar errors-0.18 ± 0.06% and 0.13 ± 0.07%, respectively. LGB model reached the training goal with only 103 samples, while ANN required 106 samples; however, LGB models were about 39 times slower than ANN models in the evaluation. The sufficient performance of developed models demonstrates the future applicability of ML for musculoskeletal transformations in a variety of applications, such as in advanced powered prosthetics.

FPGA implementation of a real-time digital pulse processing analysis for radiation detectors

A new universal, flexible firmware has been implemented on field -programmable gate array (FPGA) for gamma-ray spectroscopy. The firmware of the FPGA runs on a digitizer that we developed ourselves. The present paper describes the detailed architecture of the firmware, including the trapezoidal shaper, peak detection, pulse height analyzer, and pile-up rejection. Gamma-ray spectroscopy measurements are made using a NaI(Tl) detector, CdZnTe detector, and HPGe detector.

Real-time pharmacokinetics via online analysis of exhaled breath

The advantages that on-line breath analysis has shown in different fields have already made it stand as an interesting tool for pharmacokinetic studies. This review summarizes recent progress in the field, diving into the different analytical methods and the different advantages and hurdles encountered. We conclude that there is a wealth of limitations in the application of this technique, and key aspects like standardization are still outstanding. Nevertheless, this is an experimental field that has not yet been fully explored; and the advantages it offers for animal welfare, decrease in the amount of drug needed in experimental studies, and complementary insights to current pharmacological studies, warrant further exploration. Further studies are needed to overcome current limitations and incorporate this technique into the toolbox of pharmacological studies, both at an industrial and academic level.

Construction of DCM-based NIR fluorescent probe for visualization detection of H2S in solution and nanofibrous film

Hydrogen sulfide (H₂S) played crucial roles in biological processes and daily life, and the abnormal level of H₂S was associated with many physiological processes. In this paper, we designed and developed a dicyanomethylene-4H-chromene (DCM)-based near-infrared (NIR) fluorescent probe DCM-NO guided by theoretical calculation. The probe displayed excellent selectivity towards H₂S with a fast response time (3 min) and low detection limit (fluorescence 25.3 nM/absorption 6.61 nM) in Hela cells and real water samples. Furthermore, the probe-doped solid sensing materials (test strips and nanofibrous films) exhibited specific visualization of H₂S under ambient light or hand-held UV lamp, providing great potential for on-site and real-time application in environmental and biological systems.

The feasibility of an approximate irregular field dose distribution simulation program applied to a respiratory motion compensation system

PURPOSE

This study optimized our previously proposed simulation program for the approximate irregular field dose distribution (SPAD) and applied it to a respiratory motion compensation system (RMCS) and respiratory motion simulation system (RMSS). The main purpose was to rapidly analyze the two-dimensional dose distribution and evaluate the compensation effect of the RMCS during radiotherapy.

METHODS

This study modified the SPAD to improve the rapid analysis of the dose distribution. In the experimental setup, four different respiratory signal patterns were input to the RMSS for actuation, and an ultrasound image tracking algorithm was used to capture the real-time respiratory displacement, which was input to the RMCS for actuation. A linear accelerator simultaneously irradiated the EBT3 film. The gamma passing rate was used to verify the dose similarity between the EBT3 film and the SPAD, and conformity index (CI) and compensation rate (CR) were used to quantify the compensation effect.

RESULTS

The Gamma passing rates were 70.48-81.39% (2%/2mm) and 88.23-96.23% (5%/3mm) for various collimator opening patterns. However, the passing rates of the SPAD and EBT3 film ranged from 61.85% to 99.85% at each treatment time point. Under the four different respiratory signal patterns, CR ranged between 21% and 75%. After compensation, the CI for 85%, 90%, and 95% isodose constraints were 0.78, 0.57, and 0.12, respectively.

CONCLUSIONS

This study has demonstrated that the dose change during each stage of the treatment process can be analyzed rapidly using the improved SPAD. After compensation, applying the RMCS can reduce the treatment errors caused by respiratory movements.

Deep learning-based bird eye view social distancing monitoring using surveillance video for curbing the COVID-19 spread

The escalating transmission intensity of COVID-19 pandemic is straining the healthcare systems worldwide. Due to the unavailability of effective pharmaceutical treatment and vaccines, monitoring social distancing is the only viable tool to strive against asymptomatic transmission. Pertaining to the need of monitoring the social distancing at populated areas, a novel bird eye view computer vision-based framework implementing deep learning and utilizing surveillance video is proposed. This proposed method employs YOLO v3 object detection model and uses key point regressor to detect the key feature points. Additionally, as the massive crowd is detected, the bounding boxes on objects are received, and red boxes are also visible if social distancing is violated. When empirically tested over real-time data, proposed method is established to be efficacious than the existing approaches in terms of inference time and frame rate.

A smartphone-integrated optosensing platform based on red-emission carbon dots for real-time detection of pyrethroids

This report described the development of an optosensing platform based on red-emission carbon dots (RCDs) integrated with a smartphone application that, together, can detect pyrethroids in real time. Based on the high stability and selectivity of molecular imprinting technology, RCDs-based optosensing imprinted polymers was obtained by using a one-pot inverse microemulsion surface imprinting method. Lambda-cyhalothrin (LC), which is a pyrethroid pesticide, can interact with the widely distributed -NH₂ groups on the surface of the RCD-based optosensing nanomaterials to achieve fixed-point adsorption. The quantitative detection of pyrethroids in a wide concentration range (1-120 μg/L) could be achieved, and the limit of detection (LOD) was 0.89 μg/L. Furthermore, a portable UV light box combined with a smartphone was used to convert the change in fluorescence of the RCDs-based optosensing nanomaterials into specific values upon adding pyrethroids, and the LOD by using smartphone was 6.66 μg/L. The developed platform has numerous advantages, including low cost, simple operation, high sensitivity, and good specificity, among others, and it achieves on-site visualization and rapid detection.

Drug diffusivities in nanofibrillar cellulose hydrogel by combined time-resolved Raman and fluorescence spectroscopy

Hydrogels, natural and synthetic origin, are actively studied for their use for implants and payload carriers. These biomaterials for delivery systems have enormous potential in basic biomedical research, drug development, and long-term delivery of biologics. Nanofibrillated cellulose (NFC) hydrogels, both natural and anionic (ANFC) ones, allow drug loading for immediate and controlled release via the slow drug dissolution of solid drug crystals into hydrogel and its subsequent release. This property makes NFC originated hydrogels an interesting non-toxic and non-human origin material as drug reservoir for long-term controlled release formulation or implant for patient care. A compelling tool for studying NFC hydrogels is Raman spectroscopy, which enables to resolve the chemical structures of different molecules in a high-water content like hydrogels, since Raman spectroscopy is insensitive to water molecules. That offers real time investigation of label-free drugs and their release in high-water-content media. Despite the huge potential of Raman spectroscopy in bio-pharmaceutical applications, the strong fluorescence background of many drug samples masking the faint Raman signal has restricted the widespread use of it. In this study we used a Raman spectrometer capable of suppressing the unpleasant fluorescence background by combining a pulsed laser and time-resolved complementary metal-oxide-semiconductor (CMOS) single-photon avalanche diode (SPAD) line sensor for the label-free investigation of Metronidazole and Vitamin C diffusivities in ANFC. The results show the possibility to modulate the ANFC-based implants and drug delivery systems, when the release rate needs to be set to a desired value. More importantly, the now developed label free real-time method is universal and can be adapted to any hydrogel/drug combination for producing reliable drug diffusion coefficient data in complex and heterogeneous systems, where traditional sampling-based methods are cumbersome to use. The wide temporal range of the time-resolved CMOS SPAD sensors makes it possible to capture also the fluorescence decay of samples, giving rise to a combined time-resolved Raman and fluorescence spectroscopy, which provides additional information on the chemical, functional and structural changes in samples.

A live-cell assay for the real-time assessment of extracellular ATP levels

Extracellular ATP (eATP) is a potent damage associated molecular pattern (DAMP) molecule known to exert profound effects on the innate and adaptive immune responses. As such, it has become an important biomarker for studying means to pro-actively modulate inflammatory processes. Unfortunately, traditional methodologies employed for measuring eATP require cumbersome supernatant sampling, onerous time courses, or unnecessary duplication of effort. Here we describe a new reagent that is tolerable to test cells in extended exposures and enables a fully homogeneous assay method for real-time determinations of extracellular ATP levels. The reagent is introduced into assay plates containing cells at the time of stimulus introduction. The real-time feature of the format allows for sensitive, continuous accounting of eATP levels in the test model over at least 24 h. This work details our efforts to create and characterize this new reagent and to validate utility by demonstrating its use with multiple cell lines and chemically diverse eATP induction stimuli.

The effects of multi-echo fMRI combination and rapid T2*-mapping on offline and real-time BOLD sensitivity

A variety of strategies are used to combine multi-echo functional magnetic resonance imaging (fMRI) data, yet recent literature lacks a systematic comparison of the available options. Here we compare six different approaches derived from multi-echo data and evaluate their influences on BOLD sensitivity for offline and in particular real-time use cases: a single-echo time series (based on Echo 2), the real-time T₂*-mapped time series (T₂*FIT) and four combined time series (T₂*-weighted, tSNR-weighted, TE-weighted, and a new combination scheme termed T₂*FIT-weighted). We compare the influences of these six multi-echo derived time series on BOLD sensitivity using a healthy participant dataset (N = 28) with four task-based fMRI runs and two resting state runs. We show that the T₂*FIT-weighted combination yields the largest increase in temporal signal-to-noise ratio across task and resting state runs. We demonstrate additionally for all tasks that the T₂*FIT time series consistently yields the largest offline effect size measures and real-time region-of-interest based functional contrasts and temporal contrast-to-noise ratios. These improvements show the promising utility of multi-echo fMRI for studies employing real-time paradigms, while further work is advised to mitigate the decreased tSNR of the T₂*FIT time series. We recommend the use and continued exploration of T₂*FIT for offline task-based and real-time region-based fMRI analysis. Supporting information includes: a data repository (https://dataverse.nl/dataverse/rt-me-fmri), an interactive web-based application to explore the data (https://rt-me-fmri.herokuapp.com/), and further materials and code for reproducibility (https://github.com/jsheunis/rt-me-fMRI).

Technical considerations for positioning and placement of a transperineal ultrasound probe during prostate radiotherapy

This technical evaluation aims to provide practice 'how to' guidelines for radiation therapists (RTs) when positioning a transperineal ultrasound (TPUS) probe during prostate radiotherapy. Recommendations and practical tips will be provided for the best practice in TPUS-guided workflow to obtain optimal ultrasound images for accurate interpretation and registration of the prostate gland. This will assist the RTs in making consistent and accurate clinical decision in an ultrasound-guided radiotherapy workflow for prostate treatment. The implementation process and the associated successes and challenges will also be described to assist institutions who may be investigating the potential of implementing this system.

Using crowd-sourced data for real-time monitoring of food prices during the COVID-19 pandemic: Insights from a pilot project in northern Nigeria

The COVID-19 pandemic and related lockdown measures have disrupted food supply chains globally and caused threats to food security, especially in Sub-Saharan Africa. Yet detailed, localized, and timely data on food security threats are rarely available to guide targeted policy interventions. Based on real-time evidence from a pilot project in northern Nigeria, where food insecurity is severe, we illustrate how a digital crowdsourcing platform can provide validated real-time, high frequency, and spatially rich information on the evolution of commodity prices. Daily georeferenced price data of major food commodities were submitted by active volunteer citizens through a mobile phone data collection app and filtered through a stepwise quality control algorithm. We analyzed a total of 23,961 spatially distributed datapoints, contributed by 236 active volunteers, on the price of four commodities (local rice, Thailand rice, white maize and yellow maize) to assess the magnitude of price change over eleven weeks (week 20 to week 30) during and after the first COVID-related lockdown (year 2020), relative to the preceding year (2019). Results show that the retail price of maize (yellow and white) and rice (local and Thailand rice) increased on average by respectively 26% and 44% during this COVID-related period, compared to prices reported in the same period in 2019. GPS-tracked data showed that mobility and market access of active volunteers were reduced, travel-distance to market being 54% less in 2020 compared to 2019, and illustrates potential limitations on consumers who often seek lower pricing by accessing broader markets. Combining the price data with a spatial richness index grid derived from UN-FAO, this study shows the viability of a contactless data crowdsourcing system, backed by an automated quality control process, as a decision-support tool for rapid assessment of price-induced food insecurity risks, and to target interventions (e.g. COVID relief support) at the right time and location(s).

Real-time point-of-care total protein measurement with a miniaturized optoelectronic biosensor and fast fluorescence-based assay

Measurement of total protein in urine is key to monitoring kidney health in diabetes. However, most total protein assays are performed using large, expensive laboratory chemistry analyzers that are not amenable to point-of-care analysis or home monitoring and cannot provide real-time readouts. We developed a miniaturized optoelectronic biosensor using a vertical cavity surface-emitting laser (VCSEL), coupled with a fast protein assay based on protein-induced fluorescence enhancement (PIFE), that can dynamically measure protein concentrations in protein-spiked buffer, serum, and urine in seconds with excellent sensitivity (urine LOD = 0.023 g/L, LOQ = 0.075 g/L) and over a broad range of physiologically relevant concentrations. Comparison with gold standard clinical assays and standard fluorimetry tools showed that the sensor can accurately and reliably quantitate total protein in clinical urine samples from patients with diabetes. Our VCSEL biosensor is amenable to integration with miniaturized electronics, which could afford a portable, low-cost, easy-to-use device for sensitive, accurate, and real-time total protein measurements from small biofluid volumes.

Real‑time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm

Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep Learning (DL) methods fail to cover these aspects since training and fine-tuning the model's parameters consume much time. In this approach, the first phase comes to train a deep CNN working as a feature extractor, and the second phase comes to use Extreme Learning Machines (ELMs) for real-time detection. The main drawback of ELMs is to meet the need of a large number of hidden-layer nodes to gain a reliable and accurate detector in applying image processing since the detective performance remarkably depends on the setting of initial weights and biases. Therefore, this paper uses Chimp Optimization Algorithm (ChOA) to improve results and increase the reliability of the network while maintaining real-time capability. The designed detector is to be benchmarked on the COVID-Xray-5k and COVIDetectioNet datasets, and the results are verified by comparing it with the classic DCNN, Genetic Algorithm optimized ELM (GA-ELM), Cuckoo Search optimized ELM (CS-ELM), and Whale Optimization Algorithm optimized ELM (WOA-ELM). The proposed approach outperforms other comparative benchmarks with 98.25 % and 99.11 % as ultimate accuracy on the COVID-Xray-5k and COVIDetectioNet datasets, respectively, and it led relative error to reduce as the amount of 1.75 % and 1.01 % as compared to a convolutional CNN. More importantly, the time needed for training deep ChOA-ELM is only 0.9474 milliseconds, and the overall testing time for 3100 images is 2.937 s.

Value of Ultrasound-Guided Biopsy in Evaluating Internal Mammary Lymph Node Metastases in Breast Cancer

OBJECTIVES

This retrospective study aimed to assess the value of a real-time, ultrasound-guided biopsy in evaluating internal mammary lymph nodes (IMLNs) in breast cancer.

METHODS

Patients who were diagnosed with breast cancer and underwent real-time, ultrasound-guided core-needle biopsy (CNB) or fine-needle aspiration (FNA) in suspected IMLN metastasis were retrospectively analyzed. Patient information and ultrasonographic images were reviewed and correlated with pathology results.

RESULTS

Of the 164 IMLNs that were subjected to CNB, 131 were positive for metastasis by histopathologic confirmation, 8 were negative, and 25 were insufficient. By FNA, 84 IMLNs were regarded as positive for metastasis, 4 were negative, and 4 were insufficient. In total, there were 215 (83.98%) metastatic IMLNs, 12 benign IMLNs, and 29 unconfirmed by histopathology. There were statistically significant differences in the success of puncture sampling and detection of IMLN metastasis between the CNB and FNA groups (P < .05). There were no significant complications reported after FNA or CNB, including bleeding, nerve injury, infection, pneumothorax, or hemothorax.

CONCLUSIONS

Our study showed that ultrasonography accurately detected nodes that were likely to be malignant IMLNs, and that real-time, ultrasound-guided CNB and FNA are accurate and valuable techniques for the determination of status in breast cancer patients. Moreover, performing ultrasound-guided CNB and FNA on suspicious IMLN metastasis does not have additional severe complications.

Rapid and highly sensitive pathogen detection by real-time DNA monitoring using a nanogap impedimetric sensor with recombinase polymerase amplification

Fast detection of pathogens is important for protecting our health and society. Herein, we present a high-performance nanogap impedimetric sensor for monitoring nucleic acid amplification in real time using isothermal recombinase polymerase amplification (RPA) for rapid pathogen detection. The nanogap electrode chip has two pairs of opposing gold electrodes with a 100 nm gap and was fixed to a PCB. Then, the nanogap impedimetric sensor was immersed in RPA reaction solution for the detection of E. coli O157:H7, and target DNA amplification was evaluated through bulk solution impedance changes using impedance spectroscopy every minute during RPA. In addition, target gene amplification in the sample solution during RPA was confirmed with a 2% DNA agarose gel. Our nanogap impedimetric sensor can detect down to a single copy of the eae A gene in gDNA extracted from E. coli O157:H7 as well as a single cell of pathogenic E. coli O157:H7 strain within 5 min during direct RPA, which was performed with the pathogen itself and without the extraction and purification of target gDNA. The miniaturized nanogap impedimetric sensor has potential as a cost-effective point-of-care device for fast and accurate portable pathogen detection via real-time nucleic acid analysis.

Real-time FPGA-based implementation of the AKAZE algorithm with nonlinear scale space generation using image partitioning

The first step in a scale invariant image matching system is scale space generation. Nonlinear scale space generation algorithms such as AKAZE, reduce noise and distortion in different scales while retaining the borders and key-points of the image. An FPGA-based hardware architecture for AKAZE nonlinear scale space generation is proposed to speed up this algorithm for real-time applications. The three contributions of this work are (1) mapping the two passes of the AKAZE algorithm onto a hardware architecture that realizes parallel processing of multiple sections, (2) multi-scale line buffers which can be used for different scales, and (3) a time-sharing mechanism in the memory management unit to process multiple sections of the image in parallel. We propose a time-sharing mechanism for memory management to prevent artifacts as a result of separating the process of image partitioning. We also use approximations in the algorithm to make hardware implementation more efficient while maintaining the repeatability of the detection. A frame rate of 304 frames per second for a 1280 × 768 image resolution is achieved which is favorably faster in comparison with other work.

Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi

Background:

Drowsiness condition is one of the significant factors often encountered when an accident occurs. We aimed to detect a method to prevent accidents caused by drowsiness and lost a focused driver.

Methods:

The image processing technique has been capable of detecting the characteristic of drowsiness and lost focus driver in real-time using Raspberry Pi. Video samples were processed using the Haar Cascade Classifier method to identify areas of the face, eyes, and mouth so that drowsy conditions. The methods can be determined based on the bject detected.

Results:

Two parameters were determined, the lost focused and drowsiness driver. The highest accuracy value for driver lost focused detection was 88.00%, while the highest accuracy value for drowsiness driver detection was 90.40%.

Conclusion:

In general, a system developed with image processing methods has been able to monitor the drowsiness and lost focused drivers with high accuracy. This system still needs improvements to increase performance.

Strategies for enzymological studies and measurements of biological molecules with the cytolysin A nanopore

Pore-forming toxins are used in a variety of biotechnological applications. Typically, individual membrane proteins are reconstituted in artificial lipid bilayers where they form water-filled nanoscale apertures (nanopores). When a voltage is applied, the ionic current passing through a nanopore can be used for example to sequence biopolymers, identify molecules, or to study chemical or enzymatic reactions at the single-molecule level. Here we present strategies for studying individual enzymes and measuring molecules, also in highly complex biological samples such as blood.