Dynamic landscape of multi-elements in PM2.5 revealed by real-time analysis

Metal components in fine particulate matter (PM_2.5) are closely associated with many adverse health outcomes. Dynamic changes of metals in PM_2.5 are critical for risk assessment due to their temporal variations. Herein, an online method for real-time determination of multi-elements (As, Cd, Cs, Cu, Fe, Mg, Mn, Pb, Rb, Sn, Tl, and V) in PM_2.5 was established by directly introducing air samples into inductively coupled plasma mass spectrometry (ICPMS). Meanwhile, a quantified method using metal standard aerosols (Cr, Mo, and W) and high time resolution for 3.3 min online measurement was developed and validated. The limits of detection were in the range of 0.001-6.30 ng/m³ for different metals. Subsequently, the real-time contents of multi-elements in PM_2.5 for 12 h over 33 days were measured at different air qualities. Temporal variations of crustal elements like Fe, Mg are similar to PM_2.5, whereas toxic elements (Pb, As and Cd) have upward trends at dusk. This denoted the association with various emission sources and different exposure concentrations of metals. In addition to the acquisition of real-time information, online analysis of multi-elements in PM_2.5 is beneficial for atmospheric monitoring and provides critical insights into the different exposure risks of metals in PM_2.5 at varying times.

Pure shift amide detection in conventional and TROSY-type experiments of 13C,15N-labeled proteins

Large coupling networks in uniformly ¹³C,¹⁵N-labeled biomolecules induce broad multiplets that even in flexible proteins are frequently not recognized as such. The reason is that given multiplets typically consist of a large number of individual resonances that result in a single broad line, in which individual components are no longer resolved. We here introduce a real-time pure shift acquisition scheme for the detection of amide protons which is based on ¹³C-BIRD^r,X. As a result the full homo- and heteronuclear coupling network can be suppressed at low power leading to real singlets at substantially improved resolution and uncompromised sensitivity. The method is tested on a small globular and an intrinsically disordered protein (IDP) where the average spectral resolution is increased by a factor of ~ 2 and higher. Equally important, the approach works without saturation of water magnetization for solvent suppression and exchanging amide protons are not affected by saturation transfer.

NanoRTax, a real-time pipeline for taxonomic and diversity analysis of nanopore 16S rRNA amplicon sequencing data

Background

The study of microbial communities and their applications have been leveraged by advances in sequencing techniques and bioinformatics tools. The Oxford Nanopore Technologies long-read sequencing by nanopores provides a portable and cost-efficient platform for sequencing assays. While this opens the possibility of sequencing applications outside specialized environments and real-time analysis of data, complementing the existing efficient library preparation protocols with streamlined bioinformatic workflows is required.

Results

Here we present NanoRTax, a Nextflow pipeline for nanopore 16S rRNA gene amplicon data that features state-of-the-art taxonomic classification tools and real-time capability. The pipeline is paired with a web-based visual interface to enable user-friendly inspections of the experiment in progress. NanoRTax workflow and a simulated real-time analysis were used to validate the prediction of adult Intensive Care Unit patient mortality based on full-length 16S rRNA sequencing data from respiratory microbiome samples.

Conclusions

This constitutes a proof-of-concept simulation study of how real-time bioinformatic workflows could be used to shorten the turnaround times in critical care settings and provides an instrument for future research on early-response strategies for sepsis.

Real-time online point-of-view filming education for teaching clinical skills to medical students

PURPOSE

This study aimed to measure the educational satisfaction with and effectiveness of real-time online point-of-view filming (POVF) clinical skills education in medical students.

METHODS

Medical students participated in a 120-minute clinical skills education session. The session consisted of emergency procedures, wound management, and vascular access. The authors provided real-time online POVF using a smartphone. A questionnaire survey was issued to the students after the class, and their satisfaction with education, educational environment, and effectiveness were analyzed.

RESULTS

Responses about satisfaction with POVF education were very positive in all grades. However, approximately half of the students were satisfied with the smoothness of listening to a lecture and the video quality. More than half of the students responded positively to the question about educational effectiveness.

CONCLUSION

In these times of non-classroom teaching brought on by the coronavirus disease 2019 (COVID-19) pandemic, POVF clinical skills education is likely to be a very useful educational tool. If disadvantages such as insufficient feedback or environmental problems can be addressed, it could serve as an alternative method of clinical skills education even after the COVID-19 pandemic.

Real-time allelic assays of SARS-CoV-2 variants to enhance sewage surveillance

To effectively control the ongoing outbreaks of fast-spreading SARS-CoV-2 variants, there is an urgent need to add rapid variant detection and discrimination methods to the existing sewage surveillance systems established worldwide. We designed eight assays based on allele-specific RT-qPCR for real-time allelic discrimination of eight SARS-CoV-2 variants (Alpha, Beta, Gamma, Delta, Omicron, Lambda, Mu, and Kappa) in sewage. In silico analysis of the designed assays for identifying SARS-CoV-2 variants using more than four million SARS-CoV-2 variant sequences yielded ∼100% specificity and >90% sensitivity. All assays could sensitively discriminate and quantify target variants at levels as low as 10 viral RNA copy/µL with minimal cross-reactivity to the corresponding nontarget genotypes, even for sewage samples containing mixtures of SARS-CoV-2 variants with differential abundances. Integration of this method into the routine sewage surveillance in Hong Kong successfully identified the Beta variant in a community sewage. Complete concordance was observed between the results of viral whole-genome sequencing and those of our novel assays in sewage samples that contained exclusively the Delta variant discharged by a clinically diagnosed COVID-19 patient living in a quarantine hotel. Our assays in this method also provided real-time discrimination of the newly emerging Omicron variant in sewage two days prior to clinical test results in another quarantine hotel in Hong Kong. These novel allelic discrimination assays offer a rapid, sensitive, and specific way for detecting multiple SARS-CoV-2 variants in sewage and can be directly integrated into the existing sewage surveillance systems.

High-resolution CINE imaging of active guided knee motion using continuously acquired golden-angle radial MRI and rotary sensor information

To explore and extend on dynamic imaging of joint motion, an MRI-safe device guiding knee motion with an attached rotary encoder was used in MRI measurements of multiple knee flexion-extension cycles using radial gradient echo imaging with the golden-angle as azimuthal angle increment. Reproducibility of knee motion was investigated. Real-time and CINE mode anatomical images were reconstructed for different knee flexion angles by synchronizing the encoder information with the MRI data, and performing flexion angle selective gating across multiple motion cycles. When investigating the influence of the rotation angle window width on reconstructed CINE images, it was found that angle windows between 0.5° and 3° exhibited acceptable image sharpness without suffering from significant motion-induced blurring. Furthermore, due to flexible retrospective image reconstruction afforded by the radial golden-angle imaging, the number of motion cycles included in the reconstruction could be retrospectively reduced to investigate the corresponding influence of acquisition time on image quality. Finally, motion reproducibility between motion cycles and accuracy of the flexion angle selective gating were sufficient to acquire whole-knee 3D dynamic imaging with a retrospectively gated 3D cone UTE sequence.

Smart IoT and Machine Learning-based Framework for Water Quality Assessment and Device Component Monitoring

Water is the most important natural element present on earth for humans, yet the availability of pure water is becoming scarce and decreasing. An increase in population and rise in temperatures are two major factors contributing to the water crisis worldwide. Desalinated, brackish water from the sea, lake, estuary, or underground aquifers is treated to maximize freshwater availability for human consumption. However, mismanagement of water storage, distribution, or quality leads to serious threats to human health and ecosystems. Sensors, embedded and smart devices in water plants require proactive monitoring for optimal performance. Traditional quality and device management require huge investments in time, manual efforts, labour, and resources. This research presents an IoT-based real-time framework to perform water quality management, monitor, and alert for taking actions based on contamination and toxic parameter levels, device and application performance as the first part of the proposed work. Machine learning models analyze water quality trends and device monitoring and management architecture. The results display that the proposed method manages water monitoring and accessing water parameters efficiently than other works.

Making waves: Time for chemical surface water quality monitoring to catch up with its technical potential

A comprehensive real-time evaluation of the chemical status of surface water bodies is still utopian, but in our opinion, it is time to use the momentum delivered by recent advanced technical, infrastructural, and societal developments to get significantly closer. Procedures like inline and online analysis (in situ or in a bypass) with close to real-time analysis and data provision are already available in several industrial sectors. In contrast, atline and offline analysis involving manual sampling and time-decoupled analysis in the laboratory is still common practice in aqueous environmental monitoring. Automated tools for data analysis, verification, and evaluation are changing significantly, becoming more powerful with increasing degrees of automation and the introduction of self-learning systems. In addition, the amount of available data will most likely in near future be increased by societal awareness for water quality and by citizen science. In this analysis, we highlight the significant potential of surface water monitoring techniques, showcase "lighthouse" projects from different sectors, and pin-point gaps we must overcome to strike a path to the future of chemical monitoring of inland surface waters.

Real-time visual analytics for in-home medical rehabilitation of stroke patient-systematic review

This paper is focused on real-time visual analytics for home-based rehabilitation dedicated for brain stroke survivors. This research is at the intersection of three main domains: visual analytics for time-oriented data and dynamic visual analytics with specific focus on data analytics for rehabilitation systems. This study has emphasized the analysis of the most important research works in these domains. The studies included in this review are published between January 2008 and December 2020 that met eligibility criteria. From 243 papers retrieved from research including the Google Scholar database and manual research, 69 papers were finally included. This paper presents a classification of the reviewed research based on key features required by the visual analytics for real-time monitoring of patients. The findings suggested that real-time monitoring visual analytics for biodata captured during the rehabilitation sessions was not sufficiently addressed by previous research. To provide real-time monitoring visual analytics of biodata, the concept of a unified framework that combines the processing of batch and stream data in a distributed architecture is proposed. The system is currently under development; its validation will be carried out by an experimental study and the evaluation of the system performance.

A Neuromorphic Brain Interface based on RRAM Crossbar Arrays for High Throughput Real-time Spike Sorting

Real-time spike sorting and processing are crucial for closed-loop brain-machine interfaces and neural prosthetics. Recent developments in high-density multi-electrode arrays with hundreds of electrodes have enabled simultaneous recordings of spikes from a large number of neurons. However, the high channel count imposes stringent demands on real-time spike sorting hardware regarding data transmission bandwidth and computation complexity. Thus, it is necessary to develop a specialized real-time hardware that can sort neural spikes on the fly with high throughputs while consuming minimal power. Here, we present a real-time, low latency spike sorting processor that utilizes high-density CuO_x resistive crossbars to implement in-memory spike sorting in a massively parallel manner. We developed a fabrication process which is compatible with CMOS BEOL integration. We extensively characterized switching characteristics and statistical variations of the CuO_x memory devices. In order to implement spike sorting with crossbar arrays, we developed a template matching-based spike sorting algorithm that can be directly mapped onto RRAM crossbars. By using synthetic and in vivo recordings of extracellular spikes, we experimentally demonstrated energy efficient spike sorting with high accuracy. Our neuromorphic interface offers substantial improvements in area (~1000× less area), power (~200× less power), and latency (4.8μs latency for sorting 100 channels) for real-time spike sorting compared to other hardware implementations based on FPGAs and microcontrollers.

Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level

BACKGROUND

Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources.

METHODS

We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the weighted interval score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known.

RESULTS

All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons.

CONCLUSIONS

Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.

Evaluation of the real-time fluorescence loop-mediated isothermal amplification assay for the detection of Ureaplasma urealyticum

Ureaplasma urealyticum (UU) is commonly present in human reproductive tract, which frequently leads to genital tract infection. Hence, there is an urgent need to develop a rapid detection method for UU. In our study, a real-time fluorescence loop-mediated isothermal amplification (LAMP) assay was developed and evaluated for the detection of UU. Two primers were specifically designed based on the highly conserved regions of ureaseB genes. The reaction was carried out for 60 min in a constant temperature system using Bst DNA polymerase, and the process was monitored by real-time fluorescence signal, while polymerase chain reaction (PCR) was performed simultaneously. In real-time fluorescence LAMP reaction system, positive result was only obtained for UU among 9 bacterial strains, with detection sensitivity of 42 pg/μL (4.2 × 10⁵ CFU/mL), and all 16 clinical samples of UU could be detected. In conclusion, real-time fluorescence LAMP is a simple, sensitive, specific and effective method compared with conventional PCR, which shows great promise in the rapid detection of UU.

Recent advances in the biosensors application for the detection of bacteria and viruses in wastewater

The presence of disease-causing pathogens in wastewater can provide an excellent diagnostic tool for infectious diseases. Biosensors are far superior to conventional methods used for regular infection screening and surveillance testing. They are rapid, sensitive, inexpensive portable and carry no risk of exposure in their detection schemes. In this context, this review summarizes the most recently developed biosensors for the detection of bacteria and viruses in wastewater. The review also provides information on the new detection methods aimed at screening for SARS-CoV-2, which has now caused more than 4 million deaths. In addition, the review highlights the potential behind on-line and real-time detection of pathogens in wastewater pipelines. Most of the biosensors reported were not targeted to wastewater samples due to the complexity of the matrix. However, this review highlights on the performance factors of recently developed biosensors and discusses the importance of nanotechnology in amplifying the output signals, which in turn increases the accuracy and reliability of biosensors. Current research on the applicability of biosensors in wastewater promises a dramatic change to the conventional approach in the field of medical screening.

Real-time and simultaneous assay of monophenolase and diphenolase activity in tyrosinase catalyzed cascade reactions by combination of three-way calibration and excitation-emission matrix fluorescence

A real-time assay for multiple enzyme activities in cascade reactions is required for research on metabolism and bioengineering. Tyrosinase has the bifunctional activity of monophenolase and diphenolase. A combined strategy of three-way calibration with excitation-emission matrix (EEM) fluorescence was developed for real-time and simultaneous determination of monophenolase and diphenolase activity with tyrosine as a substrate. Mathematical separation and second-order advantage were utilized to solve spectral overlapping and uncalibrated interferents during complex dynamic enzymatic processes. Kinetic evolution profiles of EEM were monitored to stack a fusion three-way data array together with static samples. Using a parallel factor analysis (PARAFAC) algorithm, pseudo-univariate calibration curves with limits of detection (LODs) of 3.00 μM and 0.85 μM were established to simultaneously and real-time measure tyrosine and DOPA. Progress curves for tyrosine consumption by monophenolase and DOPA consumption by diphenolase were obtained using the law of mass conservation to calculate the initial velocity. The LODs for monophenolase and diphenolase were 0.0232 U⋅mL^-1 and 0.0316 U⋅mL^-1. The method achieved real-time and simultaneous assays of multiple enzyme activities in cascade reactions. It showed potential application in the metabolic pathway and biochemical industry.

MultiFRET: A Detailed Protocol for High-Throughput Multiplexed Ratiometric FRET

The newly generated software plugin MultiFRET allows for real-time measurements of multiplexed fluorescent biosensors in a near high-throughput fashion. Here we describe a detailed protocol for setup and use of this software for any purpose requiring instant feedback during fluorescence measurement experiments. We further describe its non-primary features including beam splitter misalignment correction, custom calculations through input of simple equations typed in a .txt format, customizable Excel output, and offline bulk analysis of image stacks. Finally, we supply a usage example of a cAMP measurement in cultured rat neonatal cardiomyocytes.

Tracking the emergence of disparities in the subnational spread of COVID-19 in Brazil using an online application for real-time data visualisation: A longitudinal analysis

BACKGROUND

Brazil is one of the countries worst affected by the COVID-19 pandemic with over 20 million cases and 557,000 deaths reported by August 2021. Comparison of real-time local COVID-19 data between areas is essential for understanding transmission, measuring the effects of interventions, and predicting the course of the epidemic, but are often challenging due to different population sizes and structures.

METHODS

We describe the development of a new app for the real-time visualisation of COVID-19 data in Brazil at the municipality level. In the CLIC-Brazil app, daily updates of case and death data are downloaded, age standardised and used to estimate the effective reproduction number (Rt ). We show how such platforms can perform real-time regression analyses to identify factors associated with the rate of initial spread and early reproduction number. We also use survival methods to predict the likelihood of occurrence of a new peak of COVID-19 incidence.

FINDINGS

After an initial introduction in São Paulo and Rio de Janeiro states in early March 2020, the epidemic spread to northern states and then to highly populated coastal regions and the Central-West. Municipalities with higher metrics of social development experienced earlier arrival of COVID-19 (decrease of 11·1 days [95% CI:8.9,13.2] in the time to arrival for each 10% increase in the social development index). Differences in the initial epidemic intensity (mean Rt ) were largely driven by geographic location and the date of local onset.

INTERPRETATION

This study demonstrates that platforms that monitor, standardise and analyse the epidemiological data at a local level can give useful real-time insights into outbreak dynamics that can be used to better adapt responses to the current and future pandemics.

FUNDING

This project was supported by a Medical Research Council UK (MRC-UK) -São Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0).

An Ultracompact Real-Time Fluorescence Loop-Mediated Isothermal Amplification (LAMP) Analyzer

Low-cost access to the highly sensitive and specific detection of the pathogen in the field is a crucial attribute for the next generation point-of-care (POC) platforms. In this work, we developed a real-time fluorescence nucleic acid testing device with automated and scalable sample preparation capability for field malaria diagnosis. The palm-sized battery-powered analyzer equipped with a disposable microfluidic reagent compact disc described in the companion Chap. 16 which facilitates four isothermal nucleic acid tests in parallel from raw blood samples to answer. The platform has a user-friendly interface such as touchscreen LCD and smartphone data connectivity for on-site and remote healthcare delivery, respectively. The chapter mainly focuses on describing integration procedures of the real-time fluorescence LAMP analyzer and the validation of its subsystems. The device cost is significantly reduced compared to the commercial benchtop real-time machine and other existing POC platforms. As a platform technology, self-sustainable, portable, low-cost, and easy-to-use analyzer design should create a new paradigm of molecular diagnosis toward a variety of infectious diseases at the point of need.

A real-time recombinase polymerase amplification assay for fast and accurate detection of Ditylenchus destructor

Ditylenchus destructor is a plant-parasitic nematode that seriously infests sweet potato crop in China. Thus, fast and accurate detection of D. destructor in soil and plant tissue samples is of great significance. In this study, a real-time recombinase polymerase amplification (RPA) assay was developed for the rapid and accurate detection of D. destructor in various samples. The RPA assay could be easily operated and detected as low as 1/500 individual J4 nematode DNA per reaction in 20 min at 39 °C with high specificity. The assay meets the requirements of rapid detection prior to port quarantine as well as on-site real-time detection and can be applied to detect the parasite in soil and plant samples. The modified gDNA extraction method for a single nematode established in this study significantly reduced the time of detection and improved the applicability of the real-time RPA assay for on-site detection in different environments. The real-time RPA assay to detect D. destructor will be useful for epidemiological investigations in the field as well as for quarantine processes in the sweet potato and potato trade.

A sandwich-based evanescent wave fluorescent biosensor for simple, real-time exosome detection†

Exosomes are regarded as a promising biomarker for the noninvasive diagnosis and treatment of diseases. The value of exosomes for medical research has promoted the search for a fast, efficient, and sensitive detection method. This study reported a sandwich-based evanescent wave fluorescent biosensor (S-EWFB) for exosome detection. A two-step strategy was implemented to take advantages of the simple binding of fluorescent probes with exosomes via the hydrophobic interaction between the cholesteryl and phospholipid bilayer membrane, as well as real-time detection on an evanescent wave liquid-solid interface based on CD63 aptamer-specific capture to form an exosome@fluorescence probe/aptamer sandwich structure. The one-to-many connection between exosomes and signal molecules and the aptamer-modified evanescent wave optical fiber detection platform reduced the detection limit of exosomes to 7.66 particles/mL, with a linear range of 47.5-4.75 × 10⁶ particles/mL. The entire detection process was simple, rapid, and real-time and lasted about 1 h while requiring no separation and purification. Additionally, this platform showed excellent surface regeneration capability and exhibited good performance during the analysis of tumor and non-tumor-derived exosomes.

The development of real-time digital PCR technology using an improved data classification method

For digital polymerase chain reaction (PCR), data classification is always a crucial task. The dynamic real-time amplification process information of each partition is always ignored in typical digital PCR analysis, which can easily lead to inaccurate outcomes. In this work, an integrated device that offers real-time chip-based digital PCR analysis was established. In addition, an enhanced process-based classification model (PAM) was built and trained. And then the device and the analytical model were employed in classification tasks for different concentrations of Epstein-Barr Virus (EBV) plasmid quantification assays. The results indicated that the real-time analysis device achieved a linearity of 0.97, the classification method was able to distinguish the false-positive curves, and the recognition error of positive wells was decreased by 64.4% compared with typical static analysis techniques when low concentrations of samples were tested. With these advantages, it is supposed that the real-time digital PCR analysis apparatus and the improved classification method can be employed to enhance the performance of digital PCR technology.

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.

Can news help measure economic sentiment? An application in COVID-19 times

We construct a new newspaper-based sentiment indicator for Spain that allows to monitor economic activity in real-time. As opposed to survey-based confidence indicators that are released at the end of the month, our indicator can be constructed on a daily basis. We compare our index with the popular Economic Sentiment Indicator of the European Commission and show that ours performs significantly better in nowcasting the Spanish GDP. Moreover, it proves to be helpful to predict the current COVID-19 recession from an earlier date.

A high-precision thermometry microfluidic chip for real-time monitoring of the physiological process of live tumour cells

Temperature changes in cells are generally accompanied by physiological processes. Cellular temperature measurements can provide important information to fully understand cellular mechanisms. However, temperature measurements with conventional methods, such as fluorescent polymeric thermometers and thermocouples, have limitations of low sensitivity or cell state disturbance. We developed a microfluidic chip integrating a high-precision platinum (Pt) thermo-sensor that can culture cells and monitor the cellular temperature in situ. During detection, a constant temperature system with a stability of 0.015 °C was applied. The temperature coefficient of resistance of the Pt thermo-sensor was 2090 ppm/°C, giving a temperature resolution of the sensor of less than 0.008 °C. This microchip showed a good linear correlation between the temperature and resistance of the Pt sensor at 20-40 °C (R² = 0.999). Lung and liver cancer cells on the microchip grew normally and continuously. The maximum temperature fluctuation of H1975 (0.924 °C) was larger than that of HepG2 (0.250 °C). However, the temperature of adherent HepG2 cells changed over time, showing susceptibility to the environment most of the time compared to H1975. Moreover, the temperature increment of non-cancerous cells, such as hepatic stellate cells, was monitored in response to the stimulus of paraformaldehyde, showing the process of cell death. Therefore, this thermometric microchip integrated with cell culture could be a non-disposable and label-free tool for monitoring cellular temperature applied to the study of physiology and pathology.

Magnetic Array Assisted Triboelectric Nanogenerator Sensor for Real-Time Gesture Interaction

In human-machine interaction, robotic hands are useful in many scenarios. To operate robotic hands via gestures instead of handles will greatly improve the convenience and intuition of human-machine interaction. Here, we present a magnetic array assisted sliding triboelectric sensor for achieving a real-time gesture interaction between a human hand and robotic hand. With a finger's traction movement of flexion or extension, the sensor can induce positive/negative pulse signals. Through counting the pulses in unit time, the degree, speed, and direction of finger motion can be judged in real-time. The magnetic array plays an important role in generating the quantifiable pulses. The designed two parts of magnetic array can transform sliding motion into contact-separation and constrain the sliding pathway, respectively, thus improve the durability, low speed signal amplitude, and stability of the system. This direct quantization approach and optimization of wearable gesture sensor provide a new strategy for achieving a natural, intuitive, and real-time human-robotic interaction.

Real-time electronic patient evaluation of lymphedema symptoms, referral, and satisfaction: a cross-sectional study

BACKGROUND

Lymphedema is a progressive and chronic illness. Early detection and treatment often lead to better clinical outcomes and improvement of patients' quality of life. Lymphedema symptoms can assist in detecting lymphedema. However, the use of patient-reported symptom evaluation is still limited in clinical practice. To address this gap in clinical practice, a metropolitan cancer center implemented an electronic patient evaluation of lymphedema symptoms (EPE-LE) to enable patients' real-time symptom report during patients' routine clinical visit while waiting to see their doctors in a waiting room. The purpose of this clinical project was to evaluate the usefulness of EPE-LE during patients' routine clinical visit.

METHODS

A cross-sectional design was used. Participants were outpatient post-surgical breast cancer patients and clinicians who were involved in the EPE-LE implementation at a metropolitan cancer center of US. Data were collected during the three-month EPE-LE implementation, including patients' report of lymphedema symptoms, patient and clinician satisfaction, and referral to lymphedema specialists. Descriptive statistics were used for data analysis.

RESULTS

During the three-month implementation, a total of 334 patients utilized the EPE-LE to report their lymphedema symptoms and 24 referrals to lymphedema specialists. Nearly all of the patients found that the EPE-LE was easy to use (91%) and that they were satisfied with the EPE-LE for reporting lymphedema symptoms (89%). The majority (70%) of patients reported that the EPE-LE helped them to learn about symptoms related to lymphedema and encouraged them to monitor their symptoms. All clinicians (100%) agreed that the use of the EPE-LE improved their lymphedema symptom assessment in post-surgical breast cancer patients; 75% reported that the EPE-LE increased their communication with patients related to lymphedema symptoms, 75% agreed they would recommend the EPE-LE for use at other cancer centers, and 75% reported that the information retrieved from the EPE-LE was helpful in evaluation of lymphedema.

CONCLUSIONS

The use of EPE-LE enhanced patients' real-time report of lymphedema symptoms, improved patient education on lymphedema symptoms, and helped clinicians for evaluation of lymphedema. The use of EPE-LE is an example how to implement evidence-based research into clinical practice that provides benefits for both patients and clinicians.

A Real-Time, Bioluminescent Apoptosis Assay

This chapter describes a real-time, bioluminescent apoptosis assay technique, which circumvents the well-documented "timing condundrum" encountered when employing traditional apoptosis detection chemistries after exposures with inducers of unknown potential. The assay continuously reports the translocation of phosphatidylserine (PS) from the inner membrane leaflet of a cell to the exofacial surface during apoptosis. This homogenous, no-wash, plate-based assay is made possible by two different annexin V fusion proteins, which contain complementing NanoBiT™ luciferase enzyme subunits, a time-released luciferase substrate, and a fluorescent membrane integrity reagent. During apoptosis, luminescence signal is proportional to PS exposure and fluorescence intensity correlated with the degree of secondary necrosis. Altogether, the measures provide exquisite kinetic resolution of dose- and agent-dependent apoptotic responses, from early through late phases. At exposure termination, other compatible reagents can be applied to measure additional orthogonal correlates of cell health.