Real Time Research Articles

Ex ovo omnia - Why don't we know more about egg quality via imaging?

Determining egg quality is the foremost challenge in assisted reproductive technology (ART). Although extensive advances have been made in multiple areas of ART over the last forty years, oocyte quality assessment tools have not much evolved beyond standard morphological observation. The oocyte not only delivers half of the nuclear genetic material and all of the mitochondrial DNA to an embryo but also provides complete developmental support during embryonic growth. Oocyte mitochondrial numbers far exceed those of any somatic cell, yet little work has been done to evaluate the mitochondrial bioenergetics of an oocyte. Current standard oocyte assessment in in vitro fertilization (IVF) centers include the observation of oocytes and their surrounding cell complex (cumulus cells) via stereomicroscope or inverted microscope, which is largely primitive. Additional oocyte assessments include polar body grading and polarized light meiotic spindle imaging. However, the evidence regarding the aforementioned methods of oocyte quality assessment and IVF outcomes is contradictory and non-reproducible. High resolution microscopy techniques have also been implemented in animal and human models with promising outcomes. The current era of oocyte imaging continues to evolve with discoveries in artificial intelligence models of oocyte morphology selection albeit at a slow rate. In this review, past, current, and future oocyte imaging techniques will be examined with the goal of drawing attention to the gap which limits our ability to assess oocytes in real time. The implications of improved oocyte imaging techniques on patients undergoing IVF will be discussed as well as the need to develop point of care oocyte assessment testing in IVF labs.

Online monitoring of epithelial barrier kinetics and cell detachment during cisplatin-induced toxicity of renal proximal tubule cells.

Real-time and non-invasive assessment of tissue health is crucial for maximizing the potential of microphysiological systems (MPS) for drug-induced nephrotoxicity screening. Although impedance has been widely considered as a measure of the barrier function, it has not been incorporated to detect cell detachment in MPS with top and bottom microfluidic channels separated by a porous membrane. During cell delamination from the porous membrane, the resistance between both channels decreases, while capacitance increases, allowing the detection of such detachment. Previously reported concepts have solely attributed the decrease in the resistance to the distortion of the barrier function, ignoring the resistance and capacitance changes due to cell detachment. Here, we report a two-channel MPS with integrated indium tin oxide (ITO) electrodes capable of measuring impedance in real time. The trans-epithelial electrical resistance (TEER) and tissue reactance (capacitance) were extracted from the impedance profiles. We attributed the anomalous initial increase observed in TEER, upon cisplatin administration, to the distortion of tight junctions. Cell detachment was captured by sudden jumps in capacitance. TEER profiles illuminated the effects of cisplatin and cimetidine treatments in a dose-dependent and polarity-dependent manner. The correspondence between TEER and barrier function was validated for a continuous tissue using the capacitance profiles. These results demonstrate that capacitance can be used as a real-time and non-invasive indicator of confluence and will support the accuracy of the drug-induced cytotoxicity assessed by TEER profiles in the two-channel MPS for the barrier function of a cell monolayer.

Capturing the situated, dynamic nature of EMI-lecture listening comprehension in real time

Capturing the situated, dynamic nature of EMI-lecture listening comprehension in real time

Visualizing Dynamic Single Atom Catalysis.

Many industrial chemical processes, including for producing fuels, foods, pharmaceuticals, chemicals and environmental controls, employ heterogeneous solid state catalysts at elevated temperatures in gas or liquid environments. Dynamic reactions at the atomic level play a critical role in catalyst stability and functionality. In-situ visualization and analysis of atomic-scale processes in real time under controlled reaction environments can provide important insights into practical frameworks to improve catalytic processes and materials. This review focuses on innovative real time in-situ EM methods, including recent progress in analytical in-situ environmental (scanning) transmission EM (ESTEM and ETEM) with single atom resolution for visualizing dynamic single atom catalysis under controlled flowing gas reaction environments. ESTEM studies of single atom dynamics of reactions, and of sintering deactivation, contribute to a better-informed understanding of the yield and stability of catalyst operations. Advances in in-situ technologies, including gas and liquid sample holders, nano-tomography and higher voltages, as well as challenges and opportunities in tracking reacting atoms, are highlighted. The findings show that the understanding and application of fundamental processes in catalysis can be improved, with valuable economic, environmental, and societal benefits. This article is protected by copyright. All rights reserved.

In Situ Synthesis of Liquid Metal Conductive Fibers toward Smart Cloth.

To meet the diverse needs of humans, smart cloth has become a potential research hotspot to replace traditional cloth. However, it is challenging to manufacture a flexible fabric with multiple functions. Here, we introduce a smart cloth based on liquid metal (LM) conductive fibers. Ga2O3 nanoparticles are obtained through ultrasonic pretreatment. Furthermore, a coordination bond is formed between thiol groups on the surface of protein fibers and Ga2O3 through a scraping method, allowing Ga2O3 particles to be grafted onto the surface of protein fibers in situ. Finally, LM conductive fibers are encapsulated using a photocuring adhesive. In addition, a wearable smart cloth integrated with multiple sensors has been developed based on LM conductive fibers. Users can not only monitor their movement trajectory and the surrounding environment in real time but also have their data supervised by family members through a client, achieving remote and continuous monitoring. The development of this wearable smart cloth provides strong support for future wearable, flexible electronic devices.

Wide range of possible trajectories of North Atlantic climate in a warming world.

Decadal variability in the North Atlantic Ocean impacts regional and global climate, yet changes in internal decadal variability under anthropogenic radiative forcing remain largely unexplored. Here we use the Community Earth System Model 2 Large Ensemble under historical and the Shared Socioeconomic Pathway 3-7.0 future radiative forcing scenarios and show that the ensemble spread in northern North Atlantic sea surface temperature (SST) more than doubles during the mid-twenty-first century, highlighting an exceptionally wide range of possible climate states. Furthermore, there are strikingly distinct trajectories in these SSTs, arising from differences in the North Atlantic deep convection among ensemble members starting by 2030. We propose that these are stochastically triggered and subsequently amplified by positive feedbacks involving coupled ocean-atmosphere-sea ice interactions. Freshwater forcing associated with global warming seems necessary for activating these feedbacks, accentuating the impact of external forcing on internal variability. Further investigation on seven additional large ensembles affirms the robustness of our findings. By monitoring these mechanisms in real time and extending dynamical model predictions after positive feedbacks activate, we may achieve skillful long-lead North Atlantic decadal predictions that are effective for multiple decades.

Path planning for robotic fish based on improved RRT* algorithm and dynamic window approach

Purpose The purpose of this paper is to address two major challenges faced by robotic fish when operating in underwater environments: insufficient path planning capabilities and difficulties in avoiding dynamic obstacles. To achieve this, a method is proposed that combines the Improved Rapid Randomized Tree Star (IRRT*) with the dynamic window approach (DWA). Design/methodology/approach The RRT-connect algorithm is used to determine an initial feasible path quickly. The quality of sampling points is then improved by dividing the regions and selecting each region’s probability based on its fitness value. The fitness function and roulette wheel method are introduced for region selection. Subtarget points of the DWA algorithm are extracted from the IRRT* algorithm to achieve real-time dynamic path planning. Findings In various maps, the iteration count for the IRRT* algorithm decreased by 61%, 35% and 51% respectively, compared to the RRT* algorithm, whereas the iteration time was reduced by 75%, 34% and 57%, respectively. In addition, the IRRT*-DWA algorithm can successfully navigate through multiple dynamic obstacles, and the average time, path length, etc. do not change much when parameters change, and the stability is high. Originality/value A novel IRRT*-DWA algorithm is proposed, which, by refining the sampling strategy and updating sub-target points in real time, not only addresses the limitations of existing algorithms in terms of path planning efficiency in complex environments but also enhances their capability to avoid dynamic obstacles. Ultimately, experimental results indicate a high level of similarity between the actual and ideal paths.

An in vitro assessment of teething gels' effects on human gingival mesenchymal stem cells.

The aim of this study is to examine the cytotoxic effects of dental gels with different contents, which are frequently used during teething, on gingival mesenchymal stem cells(G-MSCs). The teething gels used in this study were Dentinox, Gengigel, Osanite, and Jack and Jill. The human gingival mesenchimal stem cells(hG-MSCs) were incubated with these teething gel solutions (0.1%, 50% and 80% concentrations). Reproductive behavior of G-MSCs was monitored in real time for 72h using the xCELLigence real-time cell analyzer (RTCA) system. Two-way repeated Anova test and post hoc Bonferroni test were used to evaluate the effect of concentration and dental gel on 0-hour and 72-hour viability. Significance was evaluated at p < 0.05 level. Teething gels prepared at 50% concentration are added to the G-MSC culture, the "cell index" value of G-MSCs to which Dentinox brand gel is added is significantly lower than all other groups (p = 0.05). There is a statistically significant difference between the concentrations in terms of cell index values at the 72nd hour compared to the 0th hour (p = 0.001). The local anesthetic dental gels used in children have a more negative effect on cell viability as concentration increases.

Real time continuous glucose monitoring in high-risk people with insulin-requiring type 2 diabetes: A randomised controlled trial.

To investigate the impact of real-time continuous glucose monitoring (rtCGM) on glycaemia in a predominantly indigenous (Māori) population of adults with insulin-requiring type 2 diabetes (T2D) in New Zealand. Twelve-week, multicentre randomised controlled trial (RCT) of adults with T2D using ≥0.2 units/kg/day of insulin and elevated glycated haemoglobin (HbA1c) ≥64 mmol/mol (8.0%). Following a 2-week blinded CGM run-in phase, participants were randomised to rtCGM or control (self-monitoring blood glucose [SMBG]). The primary outcome was time in the target glucose range (3.9-10 mmol/L; TIR) during weeks 10-12, with data collected by blinded rtCGM in the control group. Sixty-seven participants entered the RCT phase (54% Māori, 57% female), median age 53 (range 16-70 years), HbA1c 85 (IQR 74, 94) mmol/mol (9.9 [IQR 8.9, 10.8]%), body mass index (36.7 ± 7.7 kg/m2). Mean (±SD) TIR increased from 37 (24)% to 53 (24)% [Δ 13%; 95% CI 4.2 to 22; P = 0.007] in the rtCGM group but did not change in the SMBG group [45 (21)% to 45 (25)%, Δ 2.5%, 95% CI -6.1 to 11, P = 0.84]. Baseline-adjusted between-group difference in TIR was 10.4% [95% CI -0.9 to 21.7; P = 0.070]. Mean HbA1c (±SD) decreased in both groups from 85 (18) mmol/mol (10.0 [1.7]%) to 64 (16) mmol/mol (8.0 [1.4]%) in the rtCGM arm and from 81 (12) mmol/mol (9.6 [1.1]%) to 65 (13) mmol/mol (8.1 [1.2]%) in the SMBG arm (P < 0.001 for both). There were no severe hypoglycaemic or ketoacidosis events in either group. Real-time CGM use in a supportive treat-to-target model of care likely improves glycaemia in a population with insulin-treated T2D and elevated HbA1c.

Real-Time Use of a Dynamic Model To Measure the Impact of Public Health Interventions on Measles Outbreak Size and Duration - Chicago, Illinois, 2024.

Measles is a highly infectious, vaccine-preventable disease that can cause severe illness, hospitalization, and death. A measles outbreak associated with a migrant shelter in Chicago occurred during February-April 2024, in which a total of 57 confirmed cases were identified, including 52 among shelter residents, three among staff members, and two among community members with a known link to the shelter. CDC simulated a measles outbreak among shelter residents using a dynamic disease model, updated in real time as additional cases were identified, to produce outbreak forecasts and assess the impact of public health interventions. As of April 8, the model forecasted a median final outbreak size of 58 cases (IQR=56-60 cases); model fit and prediction range improved as more case data became available. Counterfactual analysis of different intervention scenarios demonstrated the importance of early deployment of public health interventions in Chicago, with a 69% chance of an outbreak of 100 or more cases had there been no mass vaccination or active case-finding compared with only a 1% chance when those interventions were deployed. This analysis highlights the value of using real-time, dynamic models to aid public health response, set expectations about outbreak size and duration, and quantify the impact of interventions. The model shows that prompt mass vaccination and active case-finding likely substantially reduced the chance of a large (100 or more cases) outbreak in Chicago.

Coupling of surface plasmon resonance and mass spectrometry for molecular interaction studies in drug discovery.

Various analytical technologies have been developed for the study of target-ligand interactions. The combination of these technologies gives pivotal information on the binding mechanism, kinetics, affinity, residence time, and changes in molecular structures. Mass spectrometry (MS) offers structural information, enabling the identification and quantification of target-ligand interactions. Surface plasmon resonance (SPR) provides kinetic information on target-ligand interaction in real time. The coupling of MS and SPR complements each other in the studies of target-ligand interactions. Over the last two decades, the capabilities and added values of SPR-MS have been reported. This review summarizes and highlights the benefits, applications, and potential for further research of the SPR-MS approach.

Electronic Skin for Health Monitoring Systems: Properties, Functions and Applications.

Electronic skin (e-skin), a skin-like wearable electronic device, holds great promise in the fields of telemedicine and personalized healthcare because of its good flexibility, biocompatibility, skin conformability, and sensing performance. E-skin can monitor various health indicators of the human body in real time and over the long term, including physical indicators (exercise, respiration, blood pressure, etc.) and chemical indicators (saliva, sweat, urine, etc.). In recent years, the development of various materials, analysis, and manufacturing technologies has promoted significant development of e-skin, laying the foundation for the application of next-generation wearable medical technologies and devices. Herein, we discuss the properties required for e-skin health monitoring devices to achieve long-term and precise monitoring and summarize several detectable indicators in the health monitoring field. Subsequently, the applications of integrated e-skin health monitoring systems are reviewed. Finally, current challenges and future development directions in this field are discussed. This review is expected to generate great interest and inspiration for the development and improvement of e-skin and health monitoring systems. This article is protected by copyright. All rights reserved.

Extracorporeal closed-loop respiratory regulation for patients with respiratory difficulty using a soft bionic robot.

Respiratory regulation is critical for patients with respiratory dysfunction. Clinically used ventilators can lead to long-term dependence and injury. Extracorporeal assistance approaches such as iron-lung devices provide a noninvasive alternative, however, artificial actuator counterparts have not achieved marvelous biomimetic ventilation as human respiratory muscles. Here, we propose a bionic soft exoskeleton robot that can achieve extracorporeal closed-loop respiratory regulation by emulating natural human breath. For inspiration, a soft vacuum chamber is actuated to produce negative thoracic pressure and thus expand lung volume by pulling the rib cage up and outward through use of external negative pressure. For expiration, a soft origami array under positive pressure pushes the abdominal muscles inward and the diaphragm upward. To achieve in vitro measurement of respiratory profile, we describe a wireless respiratory monitoring device to measure respiratory profiles with high accuracy, validated by quantitative comparisons with spirometer as gold-standard reference. By constructing a human-robot coupled respiratory mechanical model, a model-based proportional controller is designed for continuous tracking of the target respiratory profile. In experiments with ten healthy participants and ten patients with respiratory difficulty, the robot can adjust its assistive forces in real time and drive human-robot coupling respiratory system to track the target profile. The biomimetic robot can achieve extracorporeal closed-loop respiratory regulation for a diverse population. The soft robot has important potential to assist respiration for people with respiratory difficulty, whether in a hospital or a home setting.

Flexible Sensor for Real-Time Monitoring of Motion Artifacts in Magnetic Resonance Imaging.

In recent years, magnetic resonance imaging has been widely used in the medical field. During the scan, if the human body moves, then there will be motion artifacts on the scan image, which will interfere with the diagnosis and only be found after the end of the scan sequence, resulting in a waste of manpower and resources. However, there is a lack of technology that halts scanning once motion artifacts arise. Here, we designed a real-time monitoring sensor (RMS) to dynamically perceive the movement of the human body and to pause in time when the movement exceeds a certain amplitude. The sensor has an array structure that can accurately sense the position of the human body in real time. The selection of the RMS ensures that there is no additional interference with the scanning results. Based on this design, the RMS can achieve the monitoring function of motion artifact generation.

ICRH operations during the JET tritium and DTE2 campaigns

The JET-ILW pure tritium and deuterium–tritium (DTE2) experimental campaigns took place in 2021–2022. Tritium (T) and deuterium–tritium (D–T) operations present challenges not encountered in present day tokamaks (Horton et al 2016 Fusion Eng. Des. 109–111 925–36). This contribution focuses on ion cyclotron resonance heating (ICRH) operations in tritium and deuterium–tritium plasmas, starting with a summary of the program of improvements to the ICRH system which spanned a few years prior to these experiments. Procedures were implemented to address specific constraints from tritium and deuterium–tritium operations (tritium safety and reduced access to the RF generator area) and increase the system reliability and power availability during plasma pulses. Operation of the upgraded real time RF power control system that maximises the launched power while taking into account limitations from the system or antenna coupling is described. We also report on the result from dedicated pulses performed to assess the potential harmful impact of the 2nd harmonic tritium resonance in the plasma, close to the inner wall, when using the standard central hydrogen minority ICRH scheme. During DTE2, the ITER-like antenna was not used because water leaked from an in-vessel capacitor into the vessel on day-2 of the experimental campaign. The lessons learnt from this incident are highlighted. Finally, the ICRH plant adjustments required to safely perform ion cyclotron wall cleaning discharges are described.

An Improved Fire Detection Approach Based On Yolo-v8 for Smart Cities

Systems for detecting fires are essential for preventing property damage and saving lives. defending people and property. Conventional techniques frequently depend on sensor-based strategies, which have limitations in intricate settings. In order to improve accuracy and efficiency, this study suggests an intelligent fire detection system that makes use of machine learning and computer vision techniques. The technology analyzes video streams in real time using deep learning algorithms to identify fire incidents based on visual patterns and attributes. Future research on fire detection systems will benefit from the information this study will provide for smoker and fire detection issues in both indoor and outdoor situations. The improved fire detection technique for smart cities that is based on the YOLOv8 algorithm is the smart fire detection system (SFDS), which uses deep learning to identify fire-specific properties in real-time. The SFDS strategy may be more cost-effective, reduce false alarms, and improve fire detection accuracy when compared to traditional methods. It can also be extended to find other intriguing aspects of smart cities, such as gas leakage or flooding. The proposed smart city framework consists of four primary levels: the application layer (i), cloud layer (iii), fog layer (ii), and internet of things layer (iv). The recommended technique uses fog, cloud computing, and the Internet of Things layer to collect and understand data in real time. This reduces the chance of damage to persons or property and enables faster reaction times. The SFDS demonstrated state- of-the-art performance in terms of precision and recall, with a high precision rate of 97.1% across all classes. Among the potential applications are intelligent security systems, forest fire monitoring, and public space fire safety management.

Advancing passive BCIs: a feasibility study of two temporal derivative features and effect size-based feature selection in continuous online EEG-based machine error detection

The emerging integration of Brain-Computer Interfaces (BCIs) in human-robot collaboration holds promise for dynamic adaptive interaction. The use of electroencephalogram (EEG)-measured error-related potentials (ErrPs) for online error detection in assistive devices offers a practical method for improving the reliability of such devices. However, continuous online error detection faces challenges such as developing efficient and lightweight classification techniques for quick predictions, reducing false alarms from artifacts, and dealing with the non-stationarity of EEG signals. Further research is essential to address the complexities of continuous classification in online sessions. With this study, we demonstrated a comprehensive approach for continuous online EEG-based machine error detection, which emerged as the winner of a competition at the 32nd International Joint Conference on Artificial Intelligence. The competition consisted of two stages: an offline stage for model development using pre-recorded, labeled EEG data, and an online stage 3 months after the offline stage, where these models were tested live on continuously streamed EEG data to detect errors in orthosis movements in real time. Our approach incorporates two temporal-derivative features with an effect size-based feature selection technique for model training, together with a lightweight noise filtering method for online sessions without recalibration of the model. The model trained in the offline stage not only resulted in a high average cross-validation accuracy of 89.9% across all participants, but also demonstrated remarkable performance during the online session 3 months after the initial data collection without further calibration, maintaining a low overall false alarm rate of 1.7% and swift response capabilities. Our research makes two significant contributions to the field. Firstly, it demonstrates the feasibility of integrating two temporal derivative features with an effect size-based feature selection strategy, particularly in online EEG-based BCIs. Secondly, our work introduces an innovative approach designed for continuous online error prediction, which includes a straightforward noise rejection technique to reduce false alarms. This study serves as a feasibility investigation into a methodology for seamless error detection that promises to transform practical applications in the domain of neuroadaptive technology and human-robot interaction.

Workout Classification Using a Convolutional Neural Network in Ensemble Learning

To meet the increased demand for home workouts owing to the COVID-19 pandemic, this study proposes a new approach to real-time exercise posture classification based on the convolutional neural network (CNN) in an ensemble learning system. By utilizing MediaPipe, the proposed system extracts the joint coordinates and angles of the human body, which the CNN uses to learn the complex patterns of various exercises. Additionally, this new approach enhances classification performance by combining predictions from multiple image frames using an ensemble learning method. Infinity AI’s Fitness Basic Dataset is employed for validation, and the experiments demonstrate high accuracy in classifying exercises such as arm raises, squats, and overhead presses. The proposed model demonstrated its ability to effectively classify exercise postures in real time, achieving high rates in accuracy (92.12%), precision (91.62%), recall (91.64%), and F1 score (91.58%). This indicates its potential application in personalized fitness recommendations and physical therapy services, showcasing the possibility for beneficial use in these fields.

Automating Fault Test Cases Generation and Execution for Automotive Safety Validation via NLP and HIL Simulation

The complexity and the criticality of automotive electronic implanted systems are steadily advancing and that is especially the case for automotive software development. ISO 26262 describes requirements for the development process to confirm the safety of such complex systems. Among these requirements, fault injection is a reliable technique to assess the effectiveness of safety mechanisms and verify the correct implementation of the safety requirements. However, the method of injecting the fault in the system under test in many cases is still manual and depends on an expert, requiring a high level of knowledge of the system. In complex systems, it consumes time, is difficult to execute, and takes effort, because the testers limit the fault injection experiments and inject the minimum number of possible test cases. Fault injection enables testers to identify and address potential issues with a system under test before they become actual problems. In the automotive industry, failures can have serious hazards. In these systems, it is essential to ensure that the system can operate safely even in the presence of faults. We propose an approach using natural language processing (NLP) technologies to automatically derive the fault test cases from the functional safety requirements (FSRs) and execute them automatically by hardware-in-the-loop (HIL) in real time according to the black-box concept and the ISO 26262 standard. The approach demonstrates effectiveness in automatically identifying fault injection locations and conditions, simplifying the testing process, and providing a scalable solution for various safety-critical systems.

Detail-preserving Joint Image Upsampling

Image operators can be instrumental to computational imaging and photography. However, many of them are computationally intensive. In this paper, we propose an effective yet efficient joint upsampling method to accelerate various image operators. We show that edge-preserving filtering can be facilitated with a downsampling-and-upsampling process. Moreover, when the extent of smoothing is mild, the process is detail-preserving, i.e., the fine details lost in the low-resolution (LR) images can be accurately restored in the high-resolution (HR) images. Given an HR input and an LR output of an operator, we downsample the HR input and calculate its affinities to the HR input. By applying the affinities to the LR output, we promote its resolution. Due to the strong detail-preserving property, the HR output derived in the previous step may exhibit aliasing artifacts around the salient edges. We further refine it based on the linear relations in a small neighborhood to rid the artifacts. Experiments on various image operators show that our method achieves superior quality over the state-of-the-art joint upsampling methods. Furthermore, the running time of our method is linear to the number of pixels. Our naive implementation derives 1080P images in real time (24 fps) on an NVIDIA GTX 3070 GPU.