Synchronization of the circadian clock to the environment tracked in real time.

The circadian system of the cyanobacterium Synechococcus elongatus PCC 7942 relies on a three-protein nanomachine (KaiA, KaiB, and KaiC) that undergoes an oscillatory phosphorylation cycle with a period of ~24 h. This core oscillator can be reconstituted in vitro and is used to study the molecular mechanisms of circadian timekeeping and entrainment. Previous studies showed that two key metabolic changes that occur in cells during the transition into darkness, changes in the ATP/ADP ratio and redox status of the quinone pool, are cues that entrain the circadian clock. By changing the ATP/ADP ratio or adding oxidized quinone, one can shift the phase of the phosphorylation cycle of the core oscillator in vitro. However, the in vitro oscillator cannot explain gene expression patterns because the simple mixture lacks the output components that connect the clock to genes. Recently, a high-throughput in vitro system termed the in vitro clock (IVC) that contains both the core oscillator and the output components was developed. Here, we used IVC reactions and performed massively parallel experiments to study entrainment, the synchronization of the clock with the environment, in the presence of output components. Our results indicate that the IVC better explains the in vivo clock-resetting phenotypes of wild-type and mutant strains and that the output components are deeply engaged with the core oscillator, affecting the way input signals entrain the core pacemaker. These findings blur the line between input and output pathways and support our previous demonstration that key output components are fundamental parts of the clock.

Detecting changes in population trends in infection surveillance using community SARS-CoV-2 prevalence as an exemplar.

Detecting and quantifying changes in growth rates of infectious diseases is vital to informing public health strategy and can inform policymakers' rationale for implementing or continuing interventions aimed at reducing impact. Substantial changes in SARS-CoV-2 prevalence with emergence of variants provides opportunity to investigate different methods to do this. We included PCR results from all participants in the UK's COVID-19 Infection Survey between August 2020-June 2022. Change-points for growth rates were identified using iterative sequential regression (ISR) and second derivatives of generalised additive models (GAMs). Consistency between methods and timeliness of detection were compared. Of 8,799,079 visits, 147,278 (1.7%) were PCR-positive. Change-points associated with emergence of major variants were estimated to occur a median 4 days earlier (IQR 0-8) in GAMs versus ISR. When estimating recent change-points using successive data periods, four change-points (4/96) identified by GAMs were not found when adding later data or by ISR. Change-points were detected 3-5 weeks after they occurred in both methods but could be detected earlier within specific subgroups. Change-points in growth rates of SARS-CoV-2 can be detected in near real-time using ISR and second derivatives of GAMs. To increase certainty about changes in epidemic trajectories both methods could be run in parallel.

Illuminating Bacterial Contamination in Water Sources: The Power of Fluorescence-Based Methods.

Bacterial contamination of water sources is a significant public health concern, and therefore, it is important to have accurate and efficient methods for monitoring bacterial concentration in water samples. Fluorescence-based methods, such as SYTO 9 and PI staining, have emerged as a promising approach for real-time bacterial quantification. In this review, we discuss the advantages of fluorescence-based methods over other bacterial quantification methods, including the plate count method and the most probable number (MPN) method. We also examine the utility of fluorescence arrays and linear regression models in improving the accuracy and reliability of fluorescence-based methods. Overall, fluorescence-based methods offer a faster, more sensitive, and more specific option for real-time bacterial quantification in water samples.

Real-time monitoring of cellular superoxide anion release in THP-1 cells using a catalytically amplified superoxide dismutase-based microbiosensor.

Reactive oxygen species (ROS) including the superoxide anion (O2•-) are typically studied in cell cultures using fluorescent dyes, which provide only discrete single-point measurements. These methods lack the capabilities for assessing O2•- kinetics and release in a quantitative manner over long monitoring times. Herein, we present the fabrication and application of an electrochemical biosensor that enables real-time continuous monitoring of O2•- release in cell cultures for extended periods (> 8 h) using an O2•- specific microelectrode. To achieve the sensitivity and selectivity requirements for cellular sensing, we developed a biohybrid system consisting of superoxide dismutase (SOD) and Ti3C2Tx MXenes, deposited on a gold microwire electrode (AuME) as O2•- specific materials with catalytic amplification through the synergistic action of the enzyme and the biomimetic MXenes-based structure. The biosensor demonstrated a sensitivity of 18.35 nA/µM with a linear range from 147 to 930 nM in a cell culture medium. To demonstrate its robustness and practicality, we applied the biosensor to monitor O2•- levels in human leukemia monocytic THP-1 cells upon stimulation with lipopolysaccharide (LPS). Using this strategy, we successfully monitored LPS-induced O2•- in THP-1 cells, as well as the quenching effect induced by the ROS scavenger N-acetyl-L-cysteine (NAC). The biosensor is generally useful for exploring the role of oxidative stress and longitudinally monitoring O2•- release in cell cultures, enabling studies of biochemical processes and associated oxidative stress mechanisms in cellular and other biological environments.

Real-Time Monitoring of Particle Size in Emulsion Polymerization: Simultaneous Turbidity and Photon Density Wave Spectroscopy.

Particle size evolution in seeded semibatch emulsion polymerization is monitored by two real-time monitoring techniques: online turbidity spectroscopy (TUS) and inline photon density wave spectroscopy (PDWS). An automatic dilution system that withdraws a sample from the reactor and upon dilution transfers to the measurement cell is used for the online TUS analysis. A PDWS probe is immersed in the reactor and collects inline the scattered light directly from the reacting latex. The particle sizes retrieved from TUS and PDWS are compared to offline dynamic light scattering (DLS) values. The particle size obtained by TUS is close to the intensity-average particle size obtained offline by DLS, while the particle size obtained by PDWS lies closer to the number-average particle size from DLS.

High-temperature PTT/CDT coordination nanoplatform realizing exacerbated hypoxia for enhancing hypoxia-activated chemotherapy to overcome tumor drug resistance.

BACKGROUND: Hypoxia-activated prodrugs present new opportunities for safe and effective tumor drug resistance therapy due to their high selectivity for hypoxic cells. However, the uneven distribution of oxygen in solid tumor and insufficient hypoxia in the tumor microenvironment greatly limit its therapeutic efficacy. RESULTS: In this paper, a novel AQ4N-Mn(II)@PDA coordination nanoplatform was designed and functionalized with GMBP1 to target drug-resistant tumor cells. Its excellent photothermal conversion efficiency could achieve local high-temperature photothermal therapy in tumors, which could not only effectively exacerbate tumor hypoxia and thus improve the efficacy of hypoxia-activated chemotherapy of AQ4N but also significantly accelerate Mn2+-mediated Fenton-like activity to enhance chemodynamic therapy. Moreover, real-time monitoring of blood oxygen saturation through photoacoustic imaging could reflect the hypoxic status of tumors during treatment. Furthermore, synergistic treatment effectively inhibited tumor growth and improved the survival rate of mice bearing orthotopic drug-resistant tumors. CONCLUSIONS: This study not only provided a new idea for PTT combined with hypoxia-activated chemotherapy and CDT for drug-resistant tumors but also explored a vital theory for real-time monitoring of hypoxia during treatment.

Dynamic monitoring soft tissue healing via visualized Gd-crosslinked double network MRI microspheres.

By integrating magnetic resonance-visible components with scaffold materials, hydrogel microspheres (HMs) become visible under magnetic resonance imaging(MRI), allowing for non-invasive, continuous, and dynamic monitoring of the distribution, degradation, and relationship of the HMs with local tissues. However, when these visualization components are physically blended into the HMs, it reduces their relaxation rate and specificity under MRI, weakening the efficacy of real-time dynamic monitoring. To achieve MRI-guided in vivo monitoring of HMs with tissue repair functionality, we utilized airflow control and photo-crosslinking methods to prepare alginate-gelatin-based dual-network hydrogel microspheres (G-AlgMA HMs) using gadolinium ions (Gd (III)), a paramagnetic MRI contrast agent, as the crosslinker. When the network of G-AlgMA HMs degrades, the cleavage of covalent bonds causes the release of Gd (III), continuously altering the arrangement and movement characteristics of surrounding water molecules. This change in local transverse and longitudinal relaxation times results in variations in MRI signal values, thus enabling MRI-guided in vivo monitoring of the HMs. Additionally, in vivo data show that the degradation and release of polypeptide (K2 (SL)6 K2 (KK)) from G-AlgMA HMs promote local vascular regeneration and soft tissue repair. Overall, G-AlgMA HMs enable non-invasive, dynamic in vivo monitoring of biomaterial degradation and tissue regeneration through MRI, which is significant for understanding material degradation mechanisms, evaluating biocompatibility, and optimizing material design.

Development of an online hydrogen fuel quality analyzer with gas chromatography-pulsed discharge helium ionization detector for applying hydrogen infrastructures.

Hydrogen fuel, which is essential for the hydrogen economy, including hydrogen cell vehicles, must be of high quality for optimal hydrogen cell use. Currently, hydrogen fuel quality control is mainly done by offline analysis with periodic sampling. However, with the anticipated surge in hydrogen charging stations, there's a pressing need for cost-effective, high-throughput online analysis systems. Additionally, the miniaturization of these analytical instruments for field application is also a challenge. In this study, we present a compact, real-time hydrogen fuel analyzer based on gas chromatography with a pulsed discharge helium ionization detector. Its dual-column system efficiently analyzes major impurities in hydrogen fuel in less than 30 min. Indicator species (CO, CO2, CH4, O2, N2, and additional hydrogen sulfide [H2S]) are determined by examining hydrogen production and supply processes. The analyzer's measurement capability is consistent with µmol/mol-level analysis, providing valuable real-time information for hydrogen infrastructure managers. Additionally, it can analyze H2S, a crucial marker of sulfur compounds acting as catalytic poisons in fuel cells. This real-time analyzer offers efficient, informed decision-making support for hydrogen infrastructure managers, enhancing the overall reliability of hydrogen fuel in fuel-cell electric vehicles.

Positively Charged Organosilanes Covalently Linked to the Silica Network as Modulating Tools for the Salinity Correction of pH Values Obtained with Colorimetric Sensor Arrays (CSAs).

Seven increasing levels of water salinity from 0.029 to 0.600 M (as NaCl) were used to investigate the dependence of pH measurement, performed using colorimetric sensor arrays (CSAs), on ionic strength. The CSAs were arrays of sensing spots prepared in the form of sol-gel-embedding Bromothymol Blue (BB) and Bromocresol Green (BCG) in a porous nitrocellulose support. The support was impregnated over the entire thickness (≈100 µm), allowing for the signal (Hue) acquisition on the opposite side to the contact with the sample solution. Three CSAs were prepared, M1, M2, and M3. M1 contained a free cationic surfactant, hexadecyltrimethylammonium p-toluenesulfonate (CTApTs), for modulating the pKa of the indicators. In M2, the surfactant dimethyloctadecyl[3-(trimethoxysilyl)propyl]ammonium chloride (DTSACl) was covalently bonded to the sol-gel. M3 was prepared like M2 but using a larger amount of ethanol as the solvent for the synthesis. The modulation of the CTApTs or the DTSACl concentration enabled the tuning of the pKa. In general, the pKa modulation ability decreased with the increase in salinity. The presence of a surfactant covalently linked to the backbone partially reduced the competitiveness of the anionic species, improving the results. Nevertheless, the salt effect was still present, and a correction algorithm was required. Between pH 5.00 and 12.00, this correction could be made automatically by using spots taken as references to produce sensors independent of salinity. As the salt effect is virtually absent above 0.160 M, M2 and M3 can be used for future applications in seawater.

Augmented Reality-Based Real-Time Visualization for Structural Modal Identification.

In the era of aging civil infrastructure and growing concerns about rapid structural deterioration due to climate change, the demand for real-time structural health monitoring (SHM) techniques has been predominant worldwide. Traditional SHM methods face challenges, including delays in processing acquired data from large structures, time-intensive dense instrumentation, and visualization of real-time structural information. To address these issues, this paper develops a novel real-time visualization method using Augmented Reality (AR) to enhance vibration-based onsite structural inspections. The proposed approach presents a visualization system designed for real-time fieldwork, enabling detailed multi-sensor analyses within the immersive environment of AR. Leveraging the remote connectivity of the AR device, real-time communication is established with an external database and Python library through a web server, expanding the analytical capabilities of data acquisition, and data processing, such as modal identification, and the resulting visualization of SHM information. The proposed system allows live visualization of time-domain, frequency-domain, and system identification information through AR. This paper provides an overview of the proposed technology and presents the results of a lab-scale experimental model. It is concluded that the proposed approach yields accurate processing of real-time data and visualization of system identification information by highlighting its potential to enhance efficiency and safety in SHM by integrating AR technology with real-world fieldwork.

Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal.

Ultrasonic diagnostics is the earliest way to predict industrial faults. Usually, a contact microphone is employed for detection, but the recording will be contaminated with noise. In this paper, a dataset that contains 10 main faults of pipelines and motors is analyzed from which 30 different features in the time and frequency domains are extracted. Afterward, for dimensionality reduction, principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are performed. In the subsequent phase, recursive feature elimination (RFE) is employed as a strategic method to analyze and select the most relevant features for the classifiers. Next, predictive models consisting of k-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM) are employed. Then, in order to solve the classification problem, a stacking classifier based on a meta-classifier which combines multiple classification models is introduced. Furthermore, the k-fold cross-validation technique is employed to assess the effectiveness of the model in handling new data for the evaluation of experimental results in ultrasonic fault detection. With the proposed method, the accuracy is around 5% higher over five cross folds with the least amount of variation. The timing evaluation of the meta model on the 64 MHz Cortex M4 microcontroller unit (MCU) revealed an execution time of 11 ms, indicating it could be a promising solution for real-time monitoring.

Development, Validation, and Comparison of a Novel Nociception/Anti-Nociception Monitor against Two Commercial Monitors in General Anesthesia.

In this paper, we present the development and the validation of a novel index of nociception/anti-nociception (N/AN) based on skin impedance measurement in time and frequency domain with our prototype AnspecPro device. The primary objective of the study was to compare the Anspec-PRO device with two other commercial devices (Medasense, Medstorm). This comparison was designed to be conducted under the same conditions for the three devices. This was carried out during total intravenous anesthesia (TIVA) by investigating its outcomes related to noxious stimulus. In a carefully designed clinical protocol during general anesthesia from induction until emergence, we extract data for estimating individualized causal dynamic models between drug infusion and their monitored effect variables. Specifically, these are Propofol hypnotic drug to Bispectral index of hypnosis level and Remifentanil opioid drug to each of the three aforementioned devices. When compared, statistical analysis of the regions before and during the standardized stimulus shows consistent difference between regions for all devices and for all indices. These results suggest that the proposed methodology for data extraction and processing for AnspecPro delivers the same information as the two commercial devices.

Biosensor-Enhanced Organ-on-a-Chip Models for Investigating Glioblastoma Tumor Microenvironment Dynamics.

Glioblastoma, an aggressive primary brain tumor, poses a significant challenge owing to its dynamic and intricate tumor microenvironment. This review investigates the innovative integration of biosensor-enhanced organ-on-a-chip (OOC) models as a novel strategy for an in-depth exploration of glioblastoma tumor microenvironment dynamics. In recent years, the transformative approach of incorporating biosensors into OOC platforms has enabled real-time monitoring and analysis of cellular behaviors within a controlled microenvironment. Conventional in vitro and in vivo models exhibit inherent limitations in accurately replicating the complex nature of glioblastoma progression. This review addresses the existing research gap by pioneering the integration of biosensor-enhanced OOC models, providing a comprehensive platform for investigating glioblastoma tumor microenvironment dynamics. The applications of this combined approach in studying glioblastoma dynamics are critically scrutinized, emphasizing its potential to bridge the gap between simplistic models and the intricate in vivo conditions. Furthermore, the article discusses the implications of biosensor-enhanced OOC models in elucidating the dynamic features of the tumor microenvironment, encompassing cell migration, proliferation, and interactions. By furnishing real-time insights, these models significantly contribute to unraveling the complex biology of glioblastoma, thereby influencing the development of more accurate diagnostic and therapeutic strategies.

Analysis of the Scenarios of Use of an Innovative Technology for the Fast and Nondestructive Characterization of Viscoelastic Materials in the Tires Field.

The properties of tires related to their viscoelastic behavior have a significant impact in the field of vehicle dynamics. They affect the performance and safety of a vehicle based on how they change when the tire performs in variable thermal conditions, interacts with various kinds of road surfaces, and accumulates mileage over time. To analyze and understand such properties of viscoelastic materials, destructive tests like dynamic mechanical analysis (DMA) are used, which make the tire unusable after the test; these are usually carried out on specimens cut from the zone of interest. The development of an innovative testing methodology connected to a hardware device called VESevo allows the characterization of the viscoelastic properties of tire compounds belonging to tread or other parts in a fast and nondestructive way. This new device provides valuable information about the evolution of the tire's viscoelastic properties, allowing it to monitor them throughout the whole lifecycle. In the paper, an overview of the possible sensitivities that can be investigated thanks to the VESevo is provided: The tread viscoelasticity was characterized and monitored for several tire tread compounds, over tire mileage, over tread thermal curing cycles, and as an index of the tread quality and uniformity in production. Preliminary results were collected and are presented. In the final paragraph, further recent applications developed from the tire field, which are not directly related, are reported.

Deep Learning-Based Prognostics and Health Management Model for Pilot-Operated Cryogenic Safety Valves.

This paper highlights the significance of safety and reliability in modern industries, particularly in sectors like petroleum and LNG, where safety valves play a critical role in ensuring system safety under extreme conditions. To enhance the reliability of these valves, this study aims to develop a deep learning-based prognostics and health management (PHM) model. Past empirical methods have limitations, driving the need for data-driven prediction models. The proposed model monitors safety valve performance, detects anomalies in real time, and prevents accidents caused by system failures. The research focuses on collecting sensor data, analyzing trends for lifespan prediction and normal operation, and integrating data for anomaly detection. This study compares related research and existing models, presents detailed results, and discusses future research directions. Ultimately, this research contributes to the safe operation and anomaly detection of pilot-operated cryogenic safety valves in industrial settings.

A Robust End-to-End IoT System for Supporting Workers in Mining Industries.

The adoption of the Internet of Things (IoT) in the mining industry can dramatically enhance the safety of workers while simultaneously decreasing monitoring costs. By implementing an IoT solution consisting of a number of interconnected smart devices and sensors, mining industries can improve response times during emergencies and also reduce the number of accidents, resulting in an overall improvement of the social image of mines. Thus, in this paper, a robust end-to-end IoT system for supporting workers in harsh environments such as in mining industries is presented. The full IoT solution includes both edge devices worn by the workers in the field and a remote cloud IoT platform, which is responsible for storing and efficiently sharing the gathered data in accordance with regulations, ethics, and GDPR rules. Extended experiments conducted to validate the IoT components both in the laboratory and in the field proved the effectiveness of the proposed solution in monitoring the real-time status of workers in mines.

SEISMONOISY: A Quasi-Real-Time Seismic Noise Network Monitoring System.

This paper introduces SEISMONOISY, an application designed for monitoring the spatiotemporal characteristic and variability of the seismic noise of an entire seismic network with a quasi-real-time monitoring approach. Actually, we have applied the developed system to monitor 12 seismic networks distributed throughout the Italian territory. These networks include the Rete Sismica Nazionale (RSN) as well as other regional networks with smaller coverage areas. Our noise monitoring system uses the methods of Spectral Power Density (PSD) and Probability Density Function (PDF) applied to 12 h long seismic traces in a 24 h cycle for each station, enabling the extrapolation of noise characteristics at seismic stations after a Seismic Noise Level Index (SNLI), which takes into account the global seismic noise model, is derived. The SNLI value can be used for different applications, including network performance evaluation, the identification of operational problems, site selection for new installations, and for scientific research applications (e.g., volcano monitoring, identification of active seismic sequences, etc.). Additionally, it aids in studying the main noise sources across different frequency bands and changes in the characteristics of background seismic noise over time.

Real-Time Monitoring on the Chinese Giant Salamander Using RPA-LFD.

The Chinese giant salamander (Andrias davidianus), listed as an endangered species under "secondary protection" in China, faces significant threats due to ecological deterioration and the expansion of human activity. Extensive field investigations are crucial to ascertain the current status in the wild and to implement effective habitat protection measures to safeguard this species and support its population development. Traditional survey methods often fall short due to the elusive nature of the A. davidianus, presenting challenges that are time-consuming and generally ineffective. To overcome these obstacles, this study developed a real-time monitoring method that uses environmental DNA (eDNA) coupled with recombinase polymerase amplification and lateral flow strip (RPA-LFD). We designed five sets of species-specific primers and probes based on mitochondrial genome sequence alignments of A. davidianus and its close relatives. Our results indicated that four of these primer/probe sets accurately identified A. davidianus, distinguishing it from other tested caudata species using both extracted DNA samples and water samples from a tank housing an individual. This method enables the specific detection of A. davidianus genomic DNA at concentrations as low as 0.1 ng/mL within 50 min, without requiring extensive laboratory equipment. Applied in a field survey across four sites in Huangshan City, Anhui Province, where A. davidianus is known to be distributed, the method successfully detected the species at three of the four sites. The development of these primer/probe sets offers a practical tool for field surveying and monitoring, facilitating efforts in population recovery and resource conservation for A. davidianus.

A Comprehensive Exploration of Caspase Detection Methods: From Classical Approaches to Cutting-Edge Innovations.

The activation of caspases is a crucial event and an indicator of programmed cell death, also known as apoptosis. These enzymes play a central role in cancer biology and are considered one promising target for current and future advancements in therapeutic interventions. Traditional methods of measuring caspase activity such as antibody-based methods provide fundamental insights into their biological functions, and are considered essential tools in the fields of cell and cancer biology, pharmacology and toxicology, and drug discovery. However, traditional methods, though extensively used, are now recognized as having various shortcomings. In addition, these methods fall short of providing solutions to and matching the needs of the rapid and expansive progress achieved in studying caspases. For these reasons, there has been a continuous improvement in detection methods for caspases and the network of pathways involved in their activation and downstream signaling. Over the past decade, newer methods based on cutting-edge state-of-the-art technologies have been introduced to the biomedical community. These methods enable both the temporal and spatial monitoring of the activity of caspases and their downstream substrates, and with enhanced accuracy and precision. These include fluorescent-labeled inhibitors (FLIs) for live imaging, single-cell live imaging, fluorescence resonance energy transfer (FRET) sensors, and activatable multifunctional probes for in vivo imaging. Recently, the recruitment of mass spectrometry (MS) techniques in the investigation of these enzymes expanded the repertoire of tools available for the identification and quantification of caspase substrates, cleavage products, and post-translational modifications in addition to unveiling the complex regulatory networks implicated. Collectively, these methods are enabling researchers to unravel much of the complex cellular processes involved in apoptosis, and are helping generate a clearer and comprehensive understanding of caspase-mediated proteolysis during apoptosis. Herein, we provide a comprehensive review of various assays and detection methods as they have evolved over the years, so to encourage further exploration of these enzymes, which should have direct implications for the advancement of therapeutics for cancer and other diseases.

Review of the Real-Time Monitoring Technologies for Lithium Dendrites in Lithium-Ion Batteries.

Lithium-ion batteries (LIBs) have the advantage of high energy density, which has attracted the wide attention of researchers. Nevertheless, the growth of lithium dendrites on the anode surface causes short life and poor safety, which limits their application. Therefore, it is necessary to deeply understand the growth mechanism of lithium dendrites. Here, the growth mechanism of lithium dendrites is briefly summarized, and the real-time monitoring technologies of lithium dendrite growth in recent years are reviewed. The real-time monitoring technologies summarized here include in situ X-ray, in situ Raman, in situ resonance, in situ microscopy, in situ neutrons, and sensors, and their representative studies are summarized. This paper is expected to provide some guidance for the research of lithium dendrites, so as to promote the development of LIBs.