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Background: Real-time digital mental health interventions, primarily enabled by smartphone technology offer continuous, personalised support, that adapts in response to the changing needs of individuals. Despite being prominently explored in populations with psychiatric disorders, there remains a notable gap in the systematic analysis of demographic characteristics, as well as the foundational design decisions or rules that underpin the personalisation of these interventions., Objectives: (a) Identifying the prevalent design decisions to enable personalisation within real-time digital mental health interventions, (b) the influence of these design decisions on the clinical outcomes of the interventions, and (c) the demographic characteristics of populations with psychiatric disorders targeted by real-time digital health interventions., Methods: Following PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines, a systematic literature review was conducted of peer-reviewed literature focusing on real-time digital interventions in populations with clinically diagnosed psychiatric disorders. We undertook a narrative synthesis to derive the demographics and personalisation design decisions of the interventions and conducted a pooled meta-analysis to evaluate clinical outcomes., Results: Interventions predominantly targeted female and Caucasian demographics, yielding modest clinical improvements. Our analysis identified nine critical personalisation design decisions concerning measurement, intervention, and interactions with health professional with varying influence on clinical outcomes., Conclusion: Understanding the complex nuances of design decisions that shape real-time digital health interventions, as well as identifying which patient demographics benefit most, is fundamental for their effective clinical impact and safe use., Prospero Registration: PROSPERO CRD42020161663., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Ultrasound imaging with flexible transducers requires the knowledge of shape geometry for effective beamforming, which such geometry is variable and often unknown. The conventional iteration-based shape estimation methods estimate transducer shape with high computational expense. Although deep-learning-based methods are introduced to reduce computation time, their low shape estimation accuracy limits the practical applications. In this paper, we propose a novel deep-learning-based approach, called FlexSANet, for shape estimation in ultrasound imaging with flexible transducers, which rapidly achieves precise shape estimation and then reconstructs high-quality images. First, in-phase/quadrature (I/Q) data are demodulated from raw radio frequency (RF) data to provide comprehensive guidance for the estimation task. A sparse processing mechanism is employed to extract crucial channel signals, resulting in sparse I/Q data and reducing the estimation time. Then, a spatial-aware shape estimation network establishes a one-shot mapping between the sparse I/Q data and the flexible probe shape. Finally, the ultrasound image is reconstructed using the delay-and-sum (DAS) beamformer with estimated shape. Massive comparisons on simulation datasets and in vivo datasets demonstrate the superiority of the proposed shape estimation method in rapidly and accurately estimating the transducer shape, leading to real-time and high-quality imaging. The mean absolute error of element position in shape estimation is below 1/8 wavelengths for simulation and in vivo experiments, indicating minimal element position error. The structural similarity between the ultrasound images reconstructed with real and estimated shapes is above 0.84 for simulation experiments and 0.80 for in vivo experiments, demonstrating superior image quality. More significantly, its estimation time on CPU of only 0.12 s promises clinical application potential of flexible ultrasound transducers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: This study aims to facilitate the translation of innovative closed-loop deep brain stimulation (DBS) strategies from theory to practice by establishing a research platform. The platform addresses the challenges of real-time stimulation artifact removal, low-latency feedback stimulation, and rapid translation from animal to clinical experiments., Materials and Methods: The platform comprises hardware for neural sensing and stimulation, a closed-loop software framework for real-time data streaming and computation, and an algorithm library for implementing closed-loop DBS strategies. The platform integrates hardware for both animal and clinical research. The closed-loop software framework handles the entire closed-loop stimulation, including data streaming, stimulation artifact removal, preprocessing, a closed-loop stimulation strategy, and stimulation control. It provides a unified programming interface for both C/C++ and Python, enabling secondary development to integrate new closed-loop stimulation strategies. Additionally, the platform includes an algorithm library with signal processing and machine learning methods to facilitate the development of new closed-loop DBS strategies., Results: The platform can achieve low-latency feedback stimulation control with response times of 6.23 ± 0.85 ms and 6.95 ± 1.11 ms for animal and clinical experiments, respectively. It effectively removed stimulation artifacts and demonstrated flexibility in implementing new closed-loop DBS algorithms. The platform has integrated several typical closed-loop protocols, including threshold-adaptive DBS, amplitude-modulation DBS, dual-threshold DBS and neural state-dependent DBS., Conclusions: This work provides a research tool for rapidly deploying innovative closed-loop strategies for translational research in both animal and clinical studies. The platform's capabilities in real-time data processing and low-latency control represent a significant advancement in translational DBS research, with potential implications for the development of more effective therapeutic interventions., Competing Interests: Conflict of Interest The authors reported no conflict of interest., (Copyright © 2024 International Neuromodulation Society. Published by Elsevier Inc. All rights reserved.)

The critical micelle concentration (CMC) is considered one of the important parameters for evaluating and comparing surfactants. We have developed a novel method for detecting the CMC of nonionic surfactants, based on the environmentally responsive characteristics of cyanine dyes probes. Notably, this method exhibits high sensitivity, visual real-time monitoring capabilities, and robust anti-interference properties. The method remains effective even under alterations in pH (2-12), metal ion concentrations (0-150 mM of K + , Na + , Ca 2+ ) and salinity levels (0-20 g/L). Furthermore, the method has been successfully applied to the analysis of real water such as lake water, water flooding, and oil-field sewage containing polymers, demonstrating its good applicability. This marks the first instance where we establish the application of detecting the CMC of nonionic surfactants using cyanine dye probes, offering a fresh perspective for developing highly sensitive, visual, real-time, and interference-resistant CMC detection methodologies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Contemporary 0.55T MRI is promising for fetal MRI, due to the larger bore, reduced safety concerns, lower acoustic noise, and improved fast imaging capability. In this work, we explore improved fetal cardiac MRI (CMR) without relying on any synchronizing devices, prospective, or retrospective gating., Purpose: To determine the feasibility of real-time MRI evaluation of fetal cardiac function as well as cardiac and great vessel anatomies by using spiral balanced steady-state free precession (bSSFP) at 0.55T., Methods: A real-time spiral bSSFP pulse sequence for fetal CMR was implemented and optimized on a 0.55T whole-body MRI. Fetal CMR was prospectively performed between May 2022 and August 2023. The protocol included: 1) real-time images at standard cardiac views, for 10-20seconds/view and 40 to 43.6 ms/frame and 2) 4-9 stacks of slices at standard cardiac views that each cover the whole heart, with 15-30 slices/stack, and 2-5seconds/slice, at 320 to 349 ms/frame. Images were evaluated by a fetal cardiologist. Quantitative measurements of cardiothoracic area ratio and cardiac axis were compared with previous reports. Diagnostic accuracy was compared against postnatal echocardiographic findings., Results: Twenty-nine participants were enrolled for 32 CMR exams, with mean maternal age 33.6±5.8 year (range 22-44 year) and mean gestational age 32.8±3.9 weeks (range 23-38 weeks). The proposed sequence enabled evaluation of the fetal heart in <30minutes in all cases (average 22minutes). Real-time MRI allowed easy adjustment of scan plan, automatic whole-heart volumetric sweeping, and flexible choice of reconstruction temporal resolution. For key cardiac anatomic features 60% were delineated well. Mean cardiothoracic area ratio and cardiac axis were 0.27±0.04 and 45.8±7.8. Diagnostic agreement with postnatal echocardiographic findings was 84%., Conclusion: A spiral real-time bSSFP pulse sequence at 0.55T can provide both low framerate and high framerate fetal heart images without relying on maternal breath-hold, specialized gating devices, or cardiac gating. The low framerate images offer high diagnostic quality structural evaluations of the fetal heart, while the high framerate images capture fetal heart motion and may enable functional assessments., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Krishna Nayak, Ye Tian receive research support provided by Siemens Healthineers. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Blood cultures are essential for diagnosing bloodstream infections, but current phenotypic tests for antimicrobial resistance (AMR) provide limited information. Oxford Nanopore Technologies introduces nanopore sequencing with adaptive sampling, capable of real-time host genome depletion, yet its application directly from blood cultures remains unexplored. This study aimed to identify pathogens and predict AMR using nanopore sequencing., Methods: In this cross-sectional genomic study, 458 positive blood cultures from bloodstream infection patients in central Taiwan were analyzed. Parallel experiments involved routine microbiologic tests and nanopore sequencing with a 15-h run. A bioinformatic pipeline was proposed to analyze the real-time sequencing reads. Subsequently, a comparative analysis was performed to evaluate the performance of species identification and AMR prediction., Results: The pipeline identified 76 species, with 88 Escherichia coli, 74 Klebsiella pneumoniae, 43 Staphylococcus aureus, and 9 Candida samples. Novel species were also discovered. Notably, precise species identification was achieved not only for monomicrobial infections but also for polymicrobial infections, which was detected in 23 samples and further confirmed by full-length 16S rRNA amplicon sequencing. Using a modified ResFinder database, AMR predictions showed a categorical agreement rate exceeding 90% (3799/4195) for monomicrobial infections, with minimal very major errors observed for K. pneumoniae (2/186, 1.1%) and S. aureus (1/90, 1.1%)., Conclusions: Nanopore sequencing with adaptive sampling can directly analyze positive blood cultures, facilitating pathogen detection, AMR prediction, and outbreak investigation. Integrating nanopore sequencing into clinical practices signifies a revolutionary advancement in managing bloodstream infections, offering an effective antimicrobial stewardship strategy, and improving patient outcomes., Competing Interests: Declarations. Ethics approval and consent to participate: This study was approved by the Institutional Review Board at Taichung Veterans General Hospital (CE22004B). All participants provided written informed consent prior to participation. The research conformed to the principles of the Helsinki Declaration. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests., (© 2024. The Author(s).)

Efficient recycling strategies are crucial for mitigating the adverse environmental impacts of escalating construction and demolition waste (CDW). While automated identification via deep-learning is a promising direction, localizing CDW recyclables is uniquely challenging due to significant clutter and compositional complexity. Recognizing that accurate and fast localization is a strong prerequisite for swift robotic action, this study provides a comprehensive assessment of state-of-the-art (s.o.t.a) real-time instance segmentation of recyclables from complex CDW streams. Unlike previous studies that simplify the real-world conditions, this study curates and employs a high-quality CDW instance segmentation dataset tailored to capture often-ignored domain intricacies such as deformation, contamination, intricate class distinctions, scale variations, and high levels of clutter, ensuring practical applicability. The results show that even advanced networks struggle with complexity of real CDW streams due to high clutter, with segmentation accuracy not surpassing 50%. To address this, the study proposes integrating patch-based inferencing techniques, which help the model focus on cluttered regions more effectively, boosting overall performance by a notable 12.9%. Additionally, to enhance the zero-shot capabilities of s.o.t.a prompt-based real-time segmentation for identifying CDW recyclables, a simple yet effective domain-transfer framework is proposed, substantially increasing number of correct mask predictions by 68%. This study serves as a practical reference for applying deep-learning tools in automated environmental management tasks such as waste sorting and suggests the best architectural framework for practical use., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: To date, most intradialytic hypotension (IDH) studies have proposed technologies to comprehensively predict the occurrence of IDH using the patient's baseline information and ultrafiltration (UF) information, but it is difficult to apply the technologies while identifying the patient's condition in real time., Methods: In this study, we propose an IDH indicator that uses heart rate (HR) change information to identify the patient's condition in real time and visually shows whether IDH has occurred. The data used were collected from 40 dialysis patients in a clinical trial conducted in the Artificial Kidney Unit at Yeungnam University Medical Center, Korea, from 18 July to 29 November 2023., Results: The IDH indicator infers changes in the patient's blood pressure during dialysis by analyzing the upper and lower maximum HRs based on the real-time average HR. Medical staff can respond to IDH in real time by looking at the IDH indicator, which visually expresses changes in the patient's HR. In addition, we propose a multilayer perceptron structure that inputs upper and lower maximum HR information based on the average HR for the time interval accumulated in real time. In learning using 40 min of data up to 5 min before IDH occurred, models using two and five layers showed excellent performance, with accuracy of 88.6% and 85.2%, respectively., Conclusions: By combining IDH visual indicators and the multi-layer perceptron method, medical staff can effectively respond to IDH in real time.

Advanced computational models and simulations to unravel the complexities of brain function have known a growing interest in recent years in the field of neurosciences, driven by significant technological progress in computing platforms. Multicompartment models, which capture the detailed morphological and functional properties of neural circuits, represent a significant advancement in this area providing more biological coherence than single compartment modeling. These models serve as a cornerstone for exploring the neural basis of sensory processing, learning paradigms, adaptive behaviors, and neurological disorders. Yet, the high complexity of these models presents a challenge for their real-time implementation, which is essential for exploring alternative therapies for neurological disorders such as electroceutics that rely on biohybrid interaction. Here, we present an accessible, user-friendly, and real-time emulator for multicompartment Hodgkin-Huxley neurons on SoC FPGA. Our system enables real-time emulation of multicompartment neurons while emphasizing cost-efficiency, flexibility, and ease of use. We showcase an implementation utilizing a technology that remains underrepresented in the current literature for this specific application. We anticipate that our system will contribute to the enhancement of computation platforms by presenting an alternative architecture for multicompartment computation. Additionally, it constitutes a step toward developing neuromorphic-based neuroprostheses for bioelectrical therapeutics through an embedded real-time platform running at a similar timescale to biological networks., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Beaubois, Cheslet, Ikeuchi, Branchereau and Levi.)

Bioplastics are a sustainable and environmental-friendly alternative to the conventional petroleum-based plastics, namely due to their source (biobased) and due to their biodegradability or both. Polyhydroxyalkanoates (PHA) stand out among the bioplastics group by being intracellular biobased, biodegradable and biocompatible polymers. PHA production has been highly investigated during the last decades. However, to date, PHA production has been monitored through offline and time-consuming tools, involving hazardous solvents, not allowing a timely control of the bioprocesses, which often results in a loss of process productivity and hinders its implementation at full scale. Therefore, two-dimensional (2D) fluorescence spectroscopy was assessed for monitoring the PHA content at real-time, as it is a non-destructive, solvent-free and non-invasive technique. The complex information of the biological broth was captured within fluorescence excitation-emission matrices (EEMs), which were deconvoluted through projection to latent structures (PLS) modelling to estimate PHA production by an enriched PHA microbial culture, using fermented brewer's spent grain as feedstock. A good correlation for PHA prediction was achieved, with an average error of ca. 4.0% g PHA /g TS for new predictions. This work demonstrates the great potential of using 2D fluorescence spectroscopy to assess the intracellular PHA content without requiring staining agents. Moreover, it unlocks the possibility of an online and real-time monitoring of the biopolymer production processes, which will contribute towards the improvement of the PHA process productivity and, consequently, its implementation at full scale., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Spinal cord injury (SCI) is a serious neurological injury that is currently extremely difficult to cure clinically. SCI involves numerous pathophysiological processes, and microRNAs (miRNAs) play an important role in these processes. Meanwhile, miRNAs have received a lot of attention for their role in other diseases as well. Therefore, the detection of disease-related miRNAs is important for the study of disease development, treatment, and prognosis. With the rapid development of molecular biology, the traditional detection methods of miRNA can no longer meet the needs of experiments. Electrochemical detection methods are widely used because of their excellent detection performance. Here, we designed an electrochemical sensor prepared using borosilicate glass microneedle electrodes for real-time monitoring of miR-21-5p expression in vivo after SCI. The sensor showed a good linear relationship between the oxidation peak current value and the concentration of miR-21-5p in the concentration range 0-2 fM ( Y = 12.025 X + 90.396, R 2 = 0.98). The limit of detection (LOD) of the sensor was 0.3667 fM. The experimental results showed that the borosilicate glass microneedle electrochemical sensor achieved fast, accurate, highly sensitive, highly specific, highly stable, and reproducible monitoring of miR-21-5p. More importantly, the electrochemical sensor has a better clinical translation prospect, which is important for the research of clinical diseases.

In the cigarette manufacturing industry, machine vision and artificial intelligence algorithms have been employed to improve production efficiency by detecting product defects. However, achieving both high accuracy and real-time defect detection for cigarettes with complex patterns remains a challenge. To address these issues, this study proposes a model based on RESNET18, combined with a feature enhancement algorithm, to improve detection accuracy. Additionally, a method is designed to deploy the model on a field-programmable gate array (FPGA) with high parallel processing capabilities to achieve high-speed detection. Experimental results demonstrate that the proposed detection model achieves a detection accuracy of 95.88% on a cigarette filter defect dataset with an end-to-end detection speed of only 9.38 ms.

This paper aims to propose a faster and more accurate network for human spatiotemporal action localization tasks. Like the YOWO model, we also use convolutional neural networks (CNNs) for feature extraction, but our model differs from YOWO in three significant ways: firstly, we don't use the feature fusion strategy, we only use spatial features extracted by 2D CNNs for action localization and spatiotemporal features extracted by 3D CNNs for action recognition; secondly, we make an improvement to the 2D CNNs network by introducing a coordinate attention mechanism and utilize the CIoU loss instead of the coordinate offset loss for bounding box regression; thirdly, we provide a more lightweight and faster spatiotemporal action localization architecture, which reduces the number of parameters by 21.76 million and achieves a speed of 39 fps on 16-frame input clips compared to the YOWO model. We test our model's performance on three public datasets: UCF-Sports, JHMDB-21 and UCF101-24. Compared with the YOWO model, we improve frame-mAP (@IoU 0.5) by 17.09% and 7.15% on the UCF-Sports and JHMDB-21 datasets, and for video-mAP, we improve by 2.7%, 8.7% and 14.4% at IoU thresholds of 0.2, 0.5 and 0.75 on the JHMDB-21 dataset., (© 2024. The Author(s).)

Background: Poor physical fitness, stress, and fatigue are factors impacting military readiness, national security, and economic burden for the United States Department of Defense. Improved accuracy of wearable biosensors and remote field biologic sample collection strategies could make critical contributions to understanding how physical readiness and occupational stressors result in on-the-job and environment-related injury, sleep impairments, diagnosis of mental health disorders, and reductions in performance in war-fighters., Objective: This study aimed to evaluate the feasibility and acceptability of intensive biomarker and biometric data collection to understand physiological and psychological stress in Army Reserved Officer Training Corps cadets before, during, and after a 96-hour field training exercise (FTX)., Methods: A prospective pilot study evaluated the feasibility and acceptability of multimodal field data collection using passive drool saliva sampling, sweat sensors, accelerometry, actigraphy, and photoplethysmography. In addition, physical fitness (Army Combat Fitness Test), self-reported injury, and psychological resilience (Brief Resilience Scale) were measured., Results: A total of 22 cadets were included. Two were lost to follow-up due to injury during FTX, for a retention rate of 91%. Assessments of performance and psychological resilience were completed for all remaining participants, resulting in 100% testing adherence. All participants provided saliva samples before the FTX, with 98% adherence at the second time point and 91% at the third. For sweat, data collection was not possible. Average daily wear time for photoplethysmography devices was good to excellent, meeting a 70% threshold with data collected for ≥80% of person-days at all time points. Of the participants who completed the FTX and 12 completed a post-FTX acceptability survey for a response rate of 60%. Overall, participant acceptance was high (≥80%) for all metrics and devices., Conclusions: This study demonstrates that wearable biosensors and remote field biologic sample collection strategies during a military FTX have the potential to be used in higher stakes tactical environments in the future for some, but not all, of the strategies. Overall, real-time biometric and biomarker sampling is feasible and acceptable during field-based training and provides insights and strategies for future interventions on military cadet and active-duty readiness, environmental stress, and recovery., (©Rachele Pojednic, Amy Welch, Margaret Thornton, Meghan Garvey, Tara Grogan, Walter Roberts, Garrett Ash. Originally published in JMIR Formative Research (https://formative.jmir.org), 18.10.2024.)

Background: Dynamic real-time detection of dendritic cell (DC) maturation is pivotal for accurately predicting immune system activation, assessing vaccine efficacy, and determining the effectiveness of immunotherapy. The heterogeneity of cells underscores the significance of assessing the maturation status of each individual cell, while achieving real-time monitoring of DC maturation at the single-cell level poses significant challenges. Surface-enhanced Raman spectroscopy (SERS) holds great potential for providing specific fingerprinting information of DCs to detect biochemical alterations and evaluate their maturation status. Methods: We developed Au@CpG@PEG nanoparticle as a self-reporting nanovaccine for DC activation and maturation state assessment, utilizing a label-free SERS strategy. Fingerprint vibrational spectra of the biological components in different states of DCs were collected and analyzed using deep learning Convolutional Neural Networks (CNN) algorithms, aiding in the rapid and efficient identification of DC maturation. Results: This approach enables dynamic real-time detection of DC maturation, maintaining accuracy levels above 98.92%. Conclusion: By employing molecular profiling, we revealed that the signal ratio of tryptophan-to-carbohydrate holds potential as a prospective marker for distinguishing the maturation status of DCs., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Background: Electrical stimulation of the spinal cord may improve rewiring of the affected pathways. Immediate modulation of stimulation parameters, and its effects of it on kinematics and electromyographic variables is unclear., Methods: This study piloted the safety and feasibility of the Reynolds Innovative Spinal Electrical Stimulation (RISES) technology with a focus on its novel closed-loop setting. This personalized, task-specific non-invasive stimulation system enables real-time stimulation parameter modulation and supports multi-data acquisition and storage. Four SCI participants underwent a clinical trial coupled with activity-based training. Primary safety outcome measures included adverse events (AEs) and skin integrity; secondary measures were vital signs, pain, and fatigue assessed at the pre, mid, and post-stimulation sessions. The trial included open-loop and closed-loop blocks of transcutaneous spinal cord stimulation (tSCS)., Results: Results showed no serious adverse events, with skin integrity unaffected. Vital signs and pain showed no significant differences across session timepoints. Fatigue levels differed significantly with post-session > mid-session > pre-session. Comparisons between open-loop and closed-loop blocks showed no significant differences in setup time, vital signs, pain, or fatigue. Average stimulation duration per task was significantly longer for open-loop (467.6 sec) than Closed-loop (410.8 sec)., Conclusions: RISES, demonstrated safety and feasibility. Further work will focus on clinical efficacy., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Objective. To develop a 2D MR acceleration method utilizing principal component analysis (PCA) in a hybrid fashion for rapid real-time applications. Approach. Retrospective testing was performed on 10 lung, 10 liver and 10 prostate 3T MRI data sets for image quality and target contourability. Sampling of k-space is performed by acquiring central (low-frequency) data in every frame while the high-frequency data is incoherently undersampled such that all of k-space is acquired in a pre-determined number of frames. Firstly, principal components (PCs) representative of intra-frame correlations between central and outer k-space data are used to estimate unsampled data in the frame of interest. Then to add further stability, PCs representative of time-domain fluctuations within a reconstruction window of the most recent frames are fit to outer k-space data (including above estimations) to obtain final estimates in the frame of interest. Accelerated reconstructions between 3x and 8x were tested for image quality and contourability along with the optimal number of PCs for fitting. Main results. It was found that at higher acceleration rates, image quality did not deteriorate significantly. Similarly, it was found that the images were of sufficient quality to contour a target using auto-contouring software at all tested acceleration rates and sites. SSIM values were found to be ⩾0.91 at all accelerations tested. Similarly dice coefficients at the different sites were found to be ⩾0.89 even at 8x accelerations which is on par with or better than intra-observer variation. Significance. This method appears to produce improved image quality and contourability compared to previous PCA methods while also allowing a greater number of PCs to be used in reconstruction. The method can be run using a simple single-channel coil and does not require significant computing power to meet real-time interventional standards (reconstruction times ∼60 ms/frame on Intel i5 CPU)., (Creative Commons Attribution license.)

Flow sensing exhibits significant potential for monitoring, controlling, and optimizing processes in industries, resource management, and environmental protection. However, achieving wireless real-time and omnidirectional sensing of gas/liquid flow on a simple, self-contained device without external power support has remained a formidable challenge. In this study, a compact-sized, fully self-powered wireless sensing flowmeter (CSWF) is introduced with a small size diameter of down to less than 50 mm, which can transmit real-time and omnidirectional wireless signals, as driven by a rotating triboelectric nanogenerator (R-TENG). The R-TENG triggers the breakdown discharge of a gas discharge tube (GDT), which enables flow rate wireless sensing through emitted electromagnetic waves. Importantly, the performance of the CSWF is not affected by the R-TENG's varied output, while the transmission distance is greater than 10 m. Real-time wireless remote monitoring of wind speed and water flow rate is successfully demonstrated. This research introduces an approach to achieve a wireless, self-powered environmental monitoring system with a diverse range of potential applications, including prolonged meteorological observations, marine environment monitoring, early warning systems for natural disasters, and remote ecosystem monitoring., (© 2024 Wiley‐VCH GmbH.)

Purpose: Clinical needle insertion into tissue, commonly assisted by 2D ultrasound imaging for real-time navigation, faces the challenge of precise needle and probe alignment to reduce out-of-plane movement. Recent studies investigate 3D ultrasound imaging together with deep learning to overcome this problem, focusing on acquiring high-resolution images to create optimal conditions for needle tip detection. However, high-resolution also requires a lot of time for image acquisition and processing, which limits the real-time capability. Therefore, we aim to maximize the US volume rate with the trade-off of low image resolution. We propose a deep learning approach to directly extract the 3D needle tip position from sparsely sampled US volumes., Methods: We design an experimental setup with a robot inserting a needle into water and chicken liver tissue. In contrast to manual annotation, we assess the needle tip position from the known robot pose. During insertion, we acquire a large data set of low-resolution volumes using a 16  ×  16 element matrix transducer with a volume rate of 4 Hz. We compare the performance of our deep learning approach with conventional needle segmentation., Results: Our experiments in water and liver show that deep learning outperforms the conventional approach while achieving sub-millimeter accuracy. We achieve mean position errors of 0.54 mm in water and 1.54 mm in liver for deep learning., Conclusion: Our study underlines the strengths of deep learning to predict the 3D needle positions from low-resolution ultrasound volumes. This is an important milestone for real-time needle navigation, simplifying the alignment of needle and ultrasound probe and enabling a 3D motion analysis., (© 2024. The Author(s).)

Rapid-response in situ fluorogenic reactions in aqueous solution are important for designing sensitive and stable sensing platforms. Herein, a wavelength-changeable and rapid-response (within 5 s) fluorescence sensing platform for monitoring tyrosinase (TYR) activity is constructed. The developed assay is based on TYR catalyzing the hydroxylation of mono-phenol to o-diphenol and MnO 4 - -triggered fluorogenic between dopamine (DA) and phenol derivatives in aqueous solution. The fluorescence wavelength can be changeable from 470 to 550 nm with strong fluorescence according to different phenol derivatives. Our proposed sensor not only exhibits a good recovery for TYR in high serum concentration (20 %), but also has been successfully applied to the screening of TYR inhibitors modeled on kojic acid. Furthermore, a paper-based wavelength-changeable fluorescence sensor was developed for on-site detection of TYR activity in potatoes with high recovery, which is consistent with our previously reported method. Consequently, the proposed sensing system has broad prospects in the practical application of TYR-associated food monitoring and clinical diagnosis., Competing Interests: Declaration of competing interest There are no conflicts of interest., (Copyright © 2024. Published by Elsevier B.V.)

In reinforcement learning, the Markov Decision Process (MDP) framework typically operates under a blocking paradigm, assuming a static environment during the agent's decision-making and stationary agent behavior while the environment executes its actions. This static model often proves inadequate for real-time tasks, as it lacks the flexibility to handle concurrent changes in both the agent's decision-making process and the environment's dynamic responses. Contemporary solutions, such as linear interpolation or state space augmentation, attempt to address the asynchronous nature of delayed states and actions in real-time environments. However, these methods frequently require precise delay measurements and may fail to fully capture the complexities of delay dynamics. However, these methods frequently require precise delay measurements and may fail to fully capture the complexities of delay dynamics. To address these challenges, we introduce a minimal information set that encapsulates concurrent information during agent-environment interactions, serving as the foundation of our real-time decision-making framework. The traditional blocking-mode MDP is then reformulated as a Minimal Information State Markov Decision Process (MISMDP), aligning more closely with the demands of real-time environments. Within this MISMDP framework, we propose the "Minimal information set for Real-time tasks using Actor-Critic" (MRAC), a general approach for addressing delay issues in real-time tasks, supported by a rigorous theoretical analysis of Q-function convergence. Extensive experiments across both discrete and continuous action space environments demonstrate that MRAC outperforms state-of-the-art algorithms, delivering superior performance and generalization in managing delays within real-time tasks., Competing Interests: Declaration of competing interest All authors disclosed no relevant relationships., (Copyright © 2024. Published by Elsevier Ltd.)

Plant signaling molecules can be divided into plant messenger signaling molecules (such as calcium ions, hydrogen peroxide, Nitric oxide) and plant hormone signaling molecules (such as auxin (mainly indole-3-acetic acid or IAA), salicylic acid, abscisic acid, cytokinin, jasmonic acid or methyl jasmonate, gibberellins, brassinosteroids, strigolactone, and ethylene), which play crucial roles in regulating plant growth and development, and response to the environment. Due to the important roles of the plant signaling molecules in the plants, many methods were developed to detect them. The development of in-situ and real-time detection of plant signaling molecules and field-deployable sensors will be a key breakthrough for botanical research and agricultural technology. Electrochemical methods provide convenient methods for in-situ and real-time detection of plant signaling molecules in plants because of their easy operation, high sensitivity, and high selectivity. This article comprehensively reviews the research on electrochemical detection of plant signaling molecules reported in the past decade, which summarizes the various types electrodes of electrochemical sensors and the applications of multiple nanomaterials to enhance electrode detection selectivity and sensitivity. This review also provides examples to introduce the current research trends in electrochemical detection, and highlights the applicability and innovation of electrochemical sensors such as miniaturization, non-invasive, long-term stability, integration, automation, and intelligence in the future. In all, the electrochemical sensors can realize in-situ, real-time and intelligent acquisition of dynamic changes in plant signaling molecules in plants, which is of great significance for promoting basic research in botany and the development of intelligent agriculture., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

ECG signals and sleep monitoring parameters complement each other and can be used for qualitative diagnosis of sleep apnea syndrome and cardio-related diseases. However, due to the limitations of the instrument volume and the detection environment, it is often challenging to integrate these two functions in practical applications. In this paper, a 12-lead dynamic electrocardiograph integrated with sleep monitoring is designed. The system's volume is reduced by combining the integrated ECG simulation front end with a miniature sensor. The system achieves the extraction, conditioning, and calculation of 12-lead ECG signals and sleep-related parameters and writes the data to a memory card in real time, which offers convenience for users and doctors in the diagnostic process.

Solar energy plays an important role in renewable energy generation, with the advantages of low pollution, easy installation, and relatively easy access. However, photovoltaic (PV) modules are susceptible to cause localized shading from external factors such as leaves in the canopy, surrounding buildings, etc., which would affect power generation efficiency and even pose safety risks. Existing methods cannot perform well in real-time conditions. This paper proposes a real-time shading monitoring method for the PV module based on computer vision. The gamma transform and histogram matching were adopted to enhance key features and adjust the global gamut strength distribution in the image of the PV module; then the gray-level slicing method finished the segmentation to detect the shadow from the video. All processing can be realized in the real-time monitor camera and the detection results can be displayed on the HMI in PC with high efficiency and low cost. According to tests in the practical complex environment, the method can have enough detection performance and high real-time performance with an accuracy of 0.98, and the F0.5 and F2 values are 0.87 and 0.85, respectively. The metrics of the proposed method are higher than those of the existing Canny detection method, the Random Forest detection method, and the CNN detection method. In addition, the average time required by the proposed method to process a frame is 0.721 s. In addition, the average time required by the method to process an image frame is 0.721 s, which has good real-time performance., (© 2024. The Author(s).)

To a certain extent, theranostic nanoplatforms promote tumor treatment efficiency. However, timely monitoring of the critical stages and signal sustainability of the entire process is challenging. In this study, multi-chambered core/shell magnetic nanoparticles (MC-MNPs) as drug and imaging agent multi-loaded nanocarriers with a synergistic release function are reported. Supraparticles with stable chambers are formed by the supercooling self-assembly of several core/shell magnetic nanoparticles composed of amphiphilic copolymers as the core and hydrophilic magnetic iron oxide nanoparticles as the shell. Desalinized doxorubicin and coumarin 6 are stored in different cavities of nanocarriers, and chitosan is used as an outer encapsulation layer. Based on their construction properties, MC-MNPs can exhibit gradient-degraded and steady-released controllability in the tumor environment. Furthermore, real-time accumulation situations and full-time diagnostic signals of nanocarriers are thoroughly demonstrated using fluorescence imaging and T 2 -weighted magnetic resonance imaging before and after magnetic hyperthermia in targeted tumors under an alternating magnetic field. Thus, MC-MNPs as theranostic nanocarriers exhibit great potential for the timely monitoring and full-time guidance of tumor treatment., (© 2024 Wiley‐VCH GmbH.)

Background: Real-time shear wave elastography (SWE) is a non-invasive imaging technique used to measure tissue stiffness by generating and tracking shear waves in real time. This advanced ultrasound-based method provides quantitative information regarding tissue elasticity, offering valuable insights into the mechanical properties of biological tissues. However, the application of real-time SWE in the musculoskeletal system and sports medicine has not been extensively studied., Aim: To explore the practical value of real-time SWE for assessing Achilles tendon hardness in older adults., Methods: A total of 60 participants were enrolled in the present study, and differences in the elastic moduli of the bilateral Achilles tendons were compared among the following categories: (1) Age: 55-60, 60-65, and 65-70-years-old; (2) Sex: Male and female; (3) Laterality: Left and right sides; (4) Tendon state: Relaxed and tense state; and (5) Tendon segment: Proximal, middle, and distal., Results: There were no significant differences in the elastic moduli of the bilateral Achilles tendons when comparing by age or sex ( P > 0.05). There were, however, significant differences when comparing by tendon side, state, or segment ( P < 0.05)., Conclusion: Real-time SWE plays a significant role compared to other examination methods in the evaluation of Achilles tendon hardness in older adults., Competing Interests: Conflict-of-interest statement: There are no conflicts of interest to report., (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)

Artificial intelligence has been employed to simulate and optimize the performance of membrane capacitive deionization (MCDI), an emerging ion separation process. However, a real-time control for optimal MCDI operation has not been investigated yet. In this study, we aimed to develop a reinforcement learning (RL)-based control model and investigate the model to find an energy-efficient MCDI operation strategy. To fulfill the objectives, we established three long-short term memory models to predict applied voltage, outflow pH, and outflow electrical conductivity. Also, four RL agents were trained to minimize outflow concentration and energy consumption simultaneously. Consequently, actor-critic (A2C) and proximal policy optimization (PPO2) achieved the ion separation goal (<0.8 mS/cm) as they determined the electrical current and pump speed to be low. Particularly, A2C kept the parameters consistent in charging MCDI, which caused lower energy consumption (0.0128 kWh/m 3 ) than PPO2 (0.0363 kWh/m 3 ). To understand the decision-making process of A2C, the Shapley additive explanation based on the decision tree model estimated the influence of input parameters on the control parameters. The results of this study demonstrate the feasibility of RL-based controls in MCDI operations. Thus, we expect that the RL-based control model can improve further and enhance the efficiency of water treatment technologies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

The detection of ammonia levels in blood is critical for diagnosing and monitoring various medical conditions, including liver dysfunction and metabolic disorders. However, traditional diagnostic methods are slow and cumbersome, often involving multiple contact-based steps such as ammonia separation in alkali conditions followed by distillation or microdiffusion, leading to delays in diagnosis and treatment. Herein, we developed a colorimetric assay capable of rapid detection of ammonia in whole blood or plasma samples, utilizing 2,2,6,6-tetramethylpiperidine 1-oxyl (TEMPO)-oxidized cellulose nanocrystals (TCNC) coupled with gold nanoparticles (AuNPs). The basis of our assay relies on either (i) the interaction between the carboxylate group ( - COO) of TEMPO and ammonium ions or (ii) the manipulation of AuNPs surface plasmon resonance (SPR) through the formation of Au(NH 3 ) 4 3+ , which displaces a redox mediator, resazurin, resulting in observable multicolor displays at various concentrations of ammonia. The colorimetric assay exhibits a wide linear detection range for dissolved NH 4 + (0.1-37 μM) with a low limit of detection (LOD) of 0.1 μM. Additionally, it effectively measures NH 3(g) concentrations in the range of 0.5-144 μM. The fabricated electrochemical nose (E-nose) device demonstrates excellent analytical performance for plasma ammonia sensing (0.05-256 μM). Experimental results demonstrate a linear detection range suitable for clinical applications, with excellent correlation to standard laboratory methods, offering a practical solution for point-of-care (PoC) testing. We anticipate that this approach can be applied broadly to improve patient monitoring and treatment by providing immediate and accurate ammonia measurements in a clinical setting.

The transmembrane nitrate reductase (Nar) is the first enzyme in the dissimilatory alternate anaerobic nitrate respiratory chain in denitrifying bacteria. To date, there has been no real-time method to determine its specific activity embedded in its native membrane; here, we describe such a new method, which is useful with the inside-out membranes of Paracoccus denitrificans and other denitrifying bacteria. This new method takes advantage of the native coupling of the endogenous NADH dehydrogenase or Complex I with the reduction of nitrate by Nar through the quinone pool of the inner membranes of P. denitrificans . This is achieved under previously reached anaerobic conditions. Inner controls confirming the specific Nar activity determined by this new method were made by the total inhibition of the Nar enzyme by sodium azide and cyanide, well-known Nar inhibitors. The estimation of the Michaelis-Menten affinity of Nar for NO 3 - of 70.4 μM, similar to previously determined values. This new Nar-JJ assay is a suitable, low-cost, and reproducible method to determine in real-time the endogenous Nar activity not only in K m of 70.4 μM, similar to previously determined values. This new Nar-JJ assay is a suitable, low-cost, and reproducible method to determine in real-time the endogenous Nar activity not only in P. denitrificans , but in other denitrifying bacteria such as Brucella canis , and potentially in other entero-pathogenic bacteria.

The use of fluorescent carbon dots (CDs) as highly precise biolabeling probes has been widespread in the fields of live cell imaging and protein labeling due to their small size and excellent photoluminescence ability to accurately target specific molecules with surface chemical properties. However, there was a lack of research on the interaction between CDs and labeled molecules. In this work, we presented a novel investigation strategy, the fluorescence microscopy-surface plasmon resonance (FM-SPR) system, which combined the use of fluorescence microscopy and wavelength modulation surface plasmon resonance to study the interaction between CDs and labeled molecules in real-time. Using this system, simultaneously recorded the SPR signals and the fluorescence images on the surface of the FM-SPR sensor chip. We observed the dynamic curve and fluorescence images of the interaction between green emissive nitrogen-doped carbon dots (N-CDs) and silk fibroin (SF) in real-time. The kinetic parameters, the quantitative analysis, and the investigation of the binding could be achieved. The results showed a strong linear relationship between the change in SPR signals and the concentration of N-CDs, with a linear coefficient of 0.99913. The linear detection range was 2.5 μg/mL-100 μg/mL, and the real lowest detection limit reached 0.5 μg/mL. Additionally, the green fluorescence points in the imaging region on the FM-SPR sensor chip increased with the concentration of N-CDs, which was consistent with the change in SPR signals. Using this system we also acquired the association rate and dissociation rate of N-CDs to SF which were 2.65 × 10 -5 /s and 1.52 × 10 -5 /s, respectively. This demonstrated the effectiveness of our method in quantitatively analyzing SF labeled with N-CDs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

This paper introduces software patterns (registration, acquire-release, and cache awareness) and data structures (Petri net, finite state machine, and protocol flag array) to support the coordinated execution of software activities (also called "components" or "agents"). Moreover, it presents and tests an implementation for Petri nets that supports real-time execution in shared memory for deployment inside one individual robot and separates event firing and handling, enabling distributed deployment between multiple robots. Experimental validation of the introduced patterns and data structures is performed within the context of activities for task execution, control and perception, and decision making for an application on coordinated navigation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Artigas, Rodrigues, Vanderseypen and Bruyninckx.)

Objective: The metabolism of different cells within the same microenvironment can differ and dictate physiological or pathological adaptions. Current single-cell analysis methods of metabolism are not label-free., Methods: The study introduces a label-free, live-cell analysis method assessing endogenous fluorescence of NAD(P)H and FAD in surface-stained cells by flow cytometry., Results: OxPhos inhibition, mitochondrial uncoupling, glucose exposure, genetic inactivation of glucose uptake and mitochondrial respiration alter the optical redox ratios of FAD and NAD(P)H as measured by flow cytometry. Those alterations correlate strongly with measurements obtained by extracellular flux analysis. Consequently, metabolically distinct live B-cell populations can be resolved, showing that human memory B-cells from peripheral blood exhibit a higher glycolytic flexibility than naïve B cells. Moreover, the comparison of blood-derived B- and T-lymphocytes from healthy donors and rheumatoid arthritis patients unleashes rheumatoid arthritis-associated metabolic traits in human naïve and memory B-lymphocytes., Conclusions: Taken together, these data show that the optical redox ratio can depict metabolic differences in distinct cell populations by flow cytometry., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier GmbH.. All rights reserved.)

Purpose: Develop a true real-time implementation of MR signature matching (MRSIGMA) for free-breathing 3D MRI with sub-200 ms latency on the Elekta Unity 1.5T MR-Linac., Methods: MRSIGMA was implemented on an external computer with a network connection to the MR-Linac. Stack-of-stars with partial k z sampling was used to accelerate data acquisition and ReconSocket was employed for simultaneous data transmission. Movienet network computed the 4D MRI motion dictionary and correlation analysis was used for signature matching. A programmable 4D MRI phantom was utilized to evaluate MRSIGMA with respect to a ground-truth translational motion reference. In vivo validation was performed on patients with pancreatic cancer, where 15 patients were employed to train Movienet and 7 patients to test the real-time implementation of MRSIGMA. Dice coefficients between real-time MRSIGMA and a retrospectively computed 4D reference were used to evaluate motion tracking performance., Results: Motion dictionary was computed in under 5 s. Signature acquisition and matching presented 173 ms latency on the phantom and 193 ms on patients. MRSIGMA presented a mean error of 1.3-1.6 mm for all phantom experiments, which was below the 2 mm acquisition resolution along the motion direction. The Dice coefficient over time between MRSIGMA and reference contours was 0.88 ± 0.02 (GTV), 0.87 ± 0.02(duodenum-stomach), and 0.78 ± 0.02(small bowel), demonstrating high motion tracking performance for both tumor and organs at risk., Conclusion: The real-time implementation of MRSIGMA enabled true real-time free-breathing 3D MRI with sub-200 ms imaging latency on a clinical MR-Linac system, which can be used for treatment monitoring, adaptive radiotherapy and dose accumulation mapping in tumors affected by respiratory motion., (© 2024 International Society for Magnetic Resonance in Medicine.)

Digital twin represents the core technology to realize the dynamic monitoring of complex industrial systems. However, the human body, as the most complex system in the physical world, digital twin is rarely applied in it. In this study, we successfully demonstrated a digital twin in the human biomedical application by proposing a dynamic monitoring system of the upper limb force. In this system, the real upper limb drives the motion of the virtual one in real-time and dynamically updates the force. Meanwhile, the virtual upper limb feeds back the monitoring-results of the force to the controller of the real upper limb via immersive virtual reality interaction. Experimental results of the typical motions of the upper limb revealed that the proposed system functioned interactively in real-time in a non-invasive manner, while ensuring the accurate solving of the muscle force. In conclusion, our digital twin-driven system is of great importance for rehabilitation medicine, biomechanical scientific research and physical training, promoting the application of the digital twin in the human biomedical field.

This paper presents an implementation of an architecture based on open-source solutions using ELK Stack - Elasticsearch, Logstash, and Kibana - for real-time data analysis and visualizations in the Medical Data Integration Center, University Hospital Cologne, Germany. The architecture addresses challenges in handling diverse data sources, ensuring standardized access, and facilitating seamless analysis in real-time, ultimately enhancing the precision, speed, and quality of monitoring processes within the medical informatics domain.

Since plastic waste has become a worldwide pollution problem, studying the ability of marine microorganisms to degrade plastic waste is important. However, conventional methods are unable to in situ real-time study the ability of microorganisms to biodegrade plastics. In recent years, Raman spectroscopy has been widely used in the characterization of plastics as well as in the study of biological metabolism due to its low cost, rapidity, label-free, non-destructive, and water-independent features, which provides us with new ideas to address the above limitations. Here, we have established a method to study the degradation ability of microorganisms on plastics using confocal Raman imaging. Alternaria alternata FB1, a recently reported polyethylene (PE) degrading marine fungus, is used as a model to perform a long-term (up to 274 days) in situ real-time nondestructive inspection of its degradation process. We can prove the degradation of PE plastics from the following two aspects, visualization and analysis of the degradation process based on depth imaging and quantification of the degradation rate by crystallinity calculations. The findings also reveal unprecedented degradation details. The method is important for realizing high-throughput screening of microorganisms with potential to degrade plastics and studying the degradation process of plastics in the future., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective. To treat neurological and psychiatric diseases with deep brain stimulation (DBS), a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects. Approach. In this study we develop SAFE-OPT, a Bayesian optimization algorithm designed to learn subject-specific safety constraints to avoid potentially harmful stimulation settings during optimization. We prototype and validate SAFE-OPT using a rodent multielectrode stimulation paradigm which causes subject-specific performance deficits in a spatial memory task. We first use data from an initial cohort of subjects to build a simulation where we design the best SAFE-OPT configuration for safe and accurate searching in silico. Main results. We then deploy both SAFE-OPT and conventional Bayesian optimization without safety constraints in new subjects in vivo , showing that SAFE-OPT can find an optimally high stimulation amplitude that does not harm task performance with comparable sample efficiency to Bayesian optimization and without selecting amplitude values that exceed the subject's safety threshold. Significance. The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of DBS., (Creative Commons Attribution license.)

Most real-time semantic segmentation networks use shallow architectures to achieve fast inference speeds. This approach, however, limits a network's receptive field. Concurrently, feature information extraction is restricted to a single scale, which reduces the network's ability to generalize and maintain robustness. Furthermore, loss of image spatial details negatively impacts segmentation accuracy. To address these limitations, this paper proposes a Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network (BMSeNet). First, to address the limitation of singular semantic feature scales, a Multiscale Context Pyramid Pooling Module (MSCPPM) is introduced. By leveraging various pooling operations, this module efficiently enlarges the receptive field and better aggregates multiscale contextual information. Moreover, a Spatial Detail Enhancement Module (SDEM) is designed, to effectively compensate for lost spatial detail information and significantly enhance the perception of spatial details. Finally, a Bilateral Attention Fusion Module (BAFM) is proposed. This module leverages pixel positional correlations to guide the network in assigning appropriate weights to the features extracted from the two branches, effectively merging the feature information of both branches. Extensive experiments were conducted on the Cityscapes and CamVid datasets. Experimental results show that the proposed BMSeNet achieves a good balance between inference speed and segmentation accuracy, outperforming some state-of-the-art real-time semantic segmentation methods.

Monitoring of the introduction of unapproved genetically modified (GM) events is required because the approval status of a GM event may differ from country to country. The on-site methods such as loop-mediated isothermal amplification (LAMP) offer a technological answer for the rapid GM detection beyond the laboratories. Real-time LAMP assays detecting one GM target were reported earlier. To increase the efficiency of the assay, a multiplex real-time LAMP simultaneously targeting Figwort Mosaic Virus promoter ( P-FMV ) that constructs region between the Cauliflower Mosaic Virus 35S promoter and cry1Ac gene ( p35S-cry1Ac ) and neomycin phosphotransferase II ( nptII ) marker gene was developed. The assay could detect as low as 0.1% for each GM target within 45 min. To the best of our knowledge, multiplexing in real-time LAMP using the Genie II system with applicability in GM detection has been reported herein for the first time. The developed method provides rapid, on-site, and real-time GM detection in seeds and food products.

An inspection in tablet appearance integrity before bottling is regarded as a routine task in a pharmaceutical factory. Although some methods such as automated optical instrument, video or artificial intelligence (AI) are currently available in industry, it usually pays for a complex computational process as well as high cost. Based on the symmetry of tablet appearance in reality, this study develops a biaxial scanning slope symmetry algorithm to realize a dynamic real-time tablet defect detection with a simple arithmetic operation. First, the tablet is discretely scanned using image sensor in two axes, i.e. X and Y directions, simultaneously. Second, the analogy output signals generated from the sensor during the scanning process is discretely digitized and stored in an array. Third, the coordinate of center point in the series data array is identified from every line scanning. Fourth, every section slope between two nearby center points from the first to last lines is formulated and calculated sequentially. Finally, the square mean error (SME) is used to evaluate the shape defect situation according to all accumulated errors from every slope variation. The experimental results verify that the proposed algorithm can achieve both fast and accurate detection performance., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2024. Published by Elsevier Inc.)

Objectives: This study aimed to systematically review the additional value of providing real-time postural feedback during balance and mobility training in older people., Methods: PubMed, Embase, CINAHL, and Web-of-Science were searched from inception to August 2023. Studies comparing the effectiveness of feedback-based versus non-feedback-based postural balance or mobility training on balance or mobility outcomes were selected. Similar outcomes were pooled in meta-analyses using a random-effect model. The quality of evidence for available outcomes was rated by Grading of Recommendations Assessment, Development and Evaluation (GRADE)., Results: Eight studies were identified with 203 subjects. Two studies showed that providing postural feedback immediately improved stability in static balance and gait. For the post-training effect, however, no significant change was found in trunk movement during single-leg standing (i.e., pitch angle, MD=0.65, 95 %CI=-0.77 to 2.07, low-quality; roll angle, MD=0.96, 95 %CI=-0.87 to 2.80, moderate-quality), in the Mini-BESTest (MD=1.88, 95 %CI=-0.05 to 3.80, moderate-quality), and in balance confidence (MD=0.29, 95 %CI=-3.43 to 4.2, moderate-quality). A worsened functional reach distance was associated with providing feedback during balance training (MD=-3.26, 95 %CI=-6.31 to -0.21, high-quality). Meta-analyses on mobility outcomes were mostly insignificant, except for the trunk-roll angle of walking (MD=0.87, 95 %CI=0.05 to 1.70, low-quality) and trunk-pitch angle of walking with head-turning (MD=1.87, 95 %CI=0.95 to 2.79, moderate-quality)., Conclusion: Adding real-time postural feedback to balance and mobility training might immediately improve stability in balance and mobility in older people. However, mixed results were reported for its post-training effect., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

While the molten salt-catalyzed chemical vapor deposition (CVD) technique is recognized for its effectiveness in producing large-area transition metal chalcogenides, understanding their growth mechanisms involving alkali metals remains a challenge. Here, we investigate the kinetics and mechanism of sodium-catalyzed molybdenum disulfide (MoS 2 ) growth and etching through image analysis conducted using an integrated CVD microscope. Sodium droplets, agglomerated via the thermal decomposition of the sodium cholate dispersant, catalyze the precipitation of supersaturated MoS 2 laminates and induce growth despite fragmentation during this process. Triangular MoS 2 crystals display a distinct self-exhausting exponential behavior and slow growth of thermodynamically favorable crystallographic faces, exhibiting a sulfur-dominant pressure. The growth and etching processes are facilitated by the scooting of sodium droplets along grain edges, displaying comparable rates. Leveraging these kinetics makes it possible to engineer atypical MoS 2 shapes. This combined microscope not only enhances the understanding of growth mechanisms but also contributes to the facile development of next-generation nanomaterials.

Purpose: MUC16 is a commonly employed biomarker to identify and predict ovarian cancer (OC). Precise measurement of MUC16 levels is essential for the accurate diagnosis, prediction, and management of OC. This research seeks to introduce a new surface plasmon resonance (SPR) biosensor design that utilizes aptamer-based technology to enable the sensitive and real-time detection of MUC16., Methods: In this study, the sensor chip was immobilized with an anti-MUC16 aptamer (Ap) by utilizing 11-mercaptoundecanoic acid (MUA) as a linker to attach the amine-terminated Ap to the chip using EDC/NHS chemistry., Results: The results indicated that the newly created aptasensor had a detection limit of 0.03 U/mL for MUC16 concentration, with a linear range of 0.09 to 0.27 U/mL. The findings demonstrate good precision and accuracy (<15%) for each MUC16 concentration, with recoveries ranging from 93% to 96%. Additionally, the aptasensor exhibited high selectivity, good repeatability, stability, and applicability in real human serum samples, indicating its potential as a valuable tool for the diagnosis and treatment of OC., Conclusion: According to the outcomes, the designed aptasensor exhibited acceptable specificity to detect the CA125 antigen and could be utilized for the serum detection of target antigen by SPR method., Competing Interests: The authors affirm that there are no competing interests or conflicts of interest related to this article., (©2024 The Authors.)

Objective: This study aimed to realise 3-D super-resolution ultrasound imaging transcutaneously with a row-column array which has far fewer independent electronic channels and a wider field of view than typical fully addressed 2-D matrix arrays. The in vivo image quality of the row-column array is generally poor, particularly when imaging non-invasively. This study aimed to develop a suite of image formation and post-processing methods to improve image quality and demonstrate the feasibility of ultrasound localisation microscopy using a row-column array, transcutaneously on a rabbit model and in a human., Methods: To achieve this, a processing pipeline was developed which included a new type of rolling window image reconstruction, which integrated a row-column array specific coherence-based beamforming technique with acoustic sub-aperture processing. This and other processing steps reduced the 'secondary' lobe artefacts, and noise and increased the effective frame rate, thereby enabling ultrasound localisation images to be produced., Results: Using an in vitro cross tube, it was found that the procedure reduced the percentage of 'false' locations from ∼26% to ∼15% compared to orthogonal plane wave compounding. Additionally, it was found that the noise could be reduced by ∼7 dB and the effective frame rate was increased to over 4000 fps. In vivo, ultrasound localisation microscopy was used to produce images non-invasively of a rabbit kidney and a human thyroid., Conclusion: It has been demonstrated that the proposed methods using a row-column array can produce large field of view super-resolution microvascular images in vivo and in a human non-invasively., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Lumbar puncture is performed to evaluate for multiple neurologic conditions, including meningitis and subarachnoid hemorrhage. However, success rates with the landmark-based technique are limited. Ultrasound is most commonly used for pre-marking without dynamic guidance, which presents several limitations, including absence of real-time guidance and lack of reliability if any patient movement occurs after skin marking. We describe a novel, ultrasound-guided paramedian approach which was successfully performed in the Emergency Department setting for lumbar puncture. Physicians should consider this technique as an alternate model using real-time guidance to reduce needle passes in those with difficult anatomy., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Inc. All rights reserved.)

This report introduces a simple method to visualize the captured thrombus in real-time during suction thrombectomy using "contrast-in-stasis technique". It enables visualization of the thrombus captured by a suction catheter as it is being retrieved through the tortuous course of the carotid artery eventually into the guiding catheter. It also offers visual identification of important findings such as fragmentation of thrombus into pieces or loss of thrombus during retrieval, and, therefore, helps clinicians to make further critical decisions during the procedure.

This article reviews the revolutionary impact of emerging technologies and artificial intelligence (AI) in reshaping modern healthcare systems, with a particular focus on the implementation of mobile diagnostic clinics. It presents an insightful analysis of the current healthcare challenges, including the shortage of healthcare workers, financial constraints, and the limitations of traditional clinics in continual patient monitoring. The concept of "Mobile Diagnostic Clinics" is introduced as a transformative approach where healthcare delivery is made accessible through the incorporation of advanced technologies. This approach is a response to the impending shortfall of medical professionals and the financial and operational burdens conventional clinics face. The proposed mobile diagnostic clinics utilize digital health tools and AI to provide a wide range of services, from everyday screenings to diagnosis and continual monitoring, facilitating remote and personalized care. The article delves into the potential of nanotechnology in diagnostics, AI's role in enhancing predictive analytics, diagnostic accuracy, and the customization of care. Furthermore, the article discusses the importance of continual, noninvasive monitoring technologies for early disease detection and the role of clinical decision support systems (CDSSs) in personalizing treatment guidance. It also addresses the challenges and ethical concerns of implementing these advanced technologies, including data privacy, integration with existing healthcare infrastructure, and the need for transparent and bias-free AI systems.

Partially automated robotic systems, such as camera holders, represent a pivotal step towards enhancing efficiency and precision in surgical procedures. Therefore, this paper introduces an approach for real-time tool localization in laparoscopy surgery using convolutional neural networks. The proposed model, based on two Hourglass modules in series, can localize up to two surgical tools simultaneously. This study utilized three datasets: the ITAP dataset, alongside two publicly available datasets, namely Atlas Dione and EndoVis Challenge. Three variations of the Hourglass-based models were proposed, with the best model achieving high accuracy (92.86%) and frame rates (27.64 FPS), suitable for integration into robotic systems. An evaluation on an independent test set yielded slightly lower accuracy, indicating limited generalizability. The model was further analyzed using the Grad-CAM technique to gain insights into its functionality. Overall, this work presents a promising solution for automating aspects of laparoscopic surgery, potentially enhancing surgical efficiency by reducing the need for manual endoscope manipulation.

Muscle ultrasound has been shown to be a valid and safe imaging modality to assess muscle wasting in critically ill patients in the intensive care unit (ICU). This typically involves manual delineation to measure the rectus femoris cross-sectional area (RFCSA), which is a subjective, time-consuming, and laborious task that requires significant expertise. We aimed to develop and evaluate an AI tool that performs automated recognition and measurement of RFCSA to support non-expert operators in measurement of the RFCSA using muscle ultrasound. Twenty patients were recruited between Feb 2023 and July 2023 and were randomized sequentially to operators using AI (n = 10) or non-AI (n = 10). Muscle loss during ICU stay was similar for both methods: 26 ± 15% for AI and 23 ± 11% for the non-AI, respectively (p = 0.13). In total 59 ultrasound examinations were carried out (30 without AI and 29 with AI). When assisted by our AI tool, the operators showed less variability between measurements with higher intraclass correlation coefficients (ICCs 0.999 95% CI 0.998-0.999 vs. 0.982 95% CI 0.962-0.993) and lower Bland Altman limits of agreement (± 1.9% vs. ± 6.6%) compared to not using the AI tool. The time spent on scans reduced significantly from a median of 19.6 min (IQR 16.9-21.7) to 9.4 min (IQR 7.2-11.7) compared to when using the AI tool (p < 0.001). AI-assisted muscle ultrasound removes the need for manual tracing, increases reproducibility and saves time. This system may aid monitoring muscle size in ICU patients assisting rehabilitation programmes., (© 2024. The Author(s).)

Isothermal microcalorimetry (IMC) is a potent analytical method for the real-time assessment of microbial metabolic activity, which serves as an indicator of microbial viability. This approach is highly relevant to the fields of probiotics and Live Biotherapeutic Products (LBPs), offering insights into microbial viability and growth kinetics. One important characteristic of IMC is its ability to measure microbial metabolic activity separately from cellular enumeration. This is particularly useful in situations where continuous tracking of bacterial activity is challenging. The focus on metabolic activity significantly benefits both probiotic research and industrial microbiology applications. IMC's versatility in handling different media matrices allows for the implementation of viability assessments under conditions that mirror those found in various industrial environments or biological models. In our study, we provide a proof of concept for the application of IMC in determining viability and growth dynamics and their correlation with bacterial count in probiotic organisms. Our findings reinforce the potential of IMC as a key method for process enhancement and accurate strain characterization within the probiotic sector. This supports the broader objective of refining the systematic approach and methods used during the development process, thereby providing detailed insights into probiotics and LBPs., Competing Interests: CM, SA, and MP were employed by Probiotical Research s.r.l. MS, MV, KK, and KB were employed by Symcel AB., (Copyright © 2024 Morazzoni, Sirel, Allesina, Veses Garcia, Kragh, Pane and Beilharz.)