Detection Of Activity Research Articles

Synergistic enhancement of polycyclic aromatic hydrocarbon degradation by Arthrobacter sp. SZ-3 and Pseudomonas putida B6-2 under high Tween80 concentration: mechanisms and efficiency.

This study investigates the advantages of combined microbial degradation of polycyclic aromatic hydrocarbons (PAHs) in reducing the inhibitory effects of high-concentration eluents commonly used in soil washing. A microbial synergistic strategy was proposed using Arthrobacter sp. SZ-3 and Pseudomonas putida B6-2 as the key bacteria in the presence of Tween 80. The results show that in systems with Tween 80, the SZ-3 strain exhibits a strong capacity to degrade three types of PAH compounds, while the B6-2 strain follows multiple degradation pathways. Mixed bacteria achieved degradation rates 60.70% higher than single bacteria at varying concentrations of Tween 80. Additionally, the average growth rates of mixed bacteria increased by 1.17-1.37 times, aligning with the changes in the functional group. Protein activity detection within each degradation system corresponded with growth quantity and the cyclic variation characteristics of ETS enzyme activity. Notably, the ETS activity of mixed bacteria was 150% higher than that of single bacteria. At a Tween 80 concentration of 500 mg/L, the degradation rates of PAHs (Phe, Flu, Pyr) by mixed bacteria were significantly higher than those by single bacteria. The catechol 1,2-dioxygenase activity of mixed bacteria was 2.30 times higher than that of single bacteria. While Tween 80 did not alter the PAH degradation pathways, it significantly influenced the accumulation amount and duration of the characteristic intermediate product. This provides a reference for the remediation of recalcitrant pollutants under conditions involving high-concentration surfactants.

Retraction Note: Simulation of optical image detection based on language activity detection algorithm in piano network teaching system

Retraction Note: Simulation of optical image detection based on language activity detection algorithm in piano network teaching system

Multi-Class Imbalanced Data Handling with Concept Drift in Fog Computing: A Taxonomy, Review, and Future Directions

A network of actual physical objects or “IoT components” linked to the internet and equipped with sensors, electronics, software, and network connectivity is known as the Internet of Things (IoT). This ability of the IoT components to gather and share data is made possible by this network connectivity. Many IoT devices are currently operating, which generate a lot of data. When these IoT devices started collecting data, the cloud was the only place to analyze, filter, pre-process, and aggregate it. However, when it comes to IoT, the cloud has restrictions regarding latency and a more centralized method of distributing programs. A new form of computing called Fog computing has been proposed to address the shortcomings of current cloud computing. In an IoT context, sensors regularly communicate signal information, and edge devices process the data obtained from these sensors using Fog computing. The sensors’ internal or external problems, security breaches, or the integration of heterogeneous equipment contribute to the imbalanced data, i.e., comparatively speaking, one class has more instances than the other. As a result of this data, the pattern extraction is imbalanced . Recent attempts have concentrated heavily on binary-class imbalanced concerns with exactly two classes. However, the classification of multi-class imbalanced data is an issue that needs to be fixed in Fog computing, even if it is widespread in other fields, including text categorization, human activity detection, and medical diagnosis. The study intends to deal with this problem. It presents a systematic, thorough, and in-depth comparative analysis of several binary-class and multi-class imbalanced data handling strategies for batch and streaming data in IoT networks and Fog computing. There are five major objectives in this study. First, reviewing the Fog computing concept. Second, outlining the optimization metric used in Fog computing. Third, focusing on binary and multi-class batch data handling for IoT networks and Fog computing. Fourth, reviewing and comparing the current imbalanced data handling methodologies for multi-class data streams. Fifth, explaining how to cope with the concept drift, including novel and recurring classes, targeted optimization measures, and evaluation tools. Finally, the best performance metrics and tools for concept drift, binary-class (batch and stream) data, and multi-class (batch and stream) data are highlighted.

Cu2O/Cu-MOF/Fe-MIL-88B with peroxidase activity for intelligent detection of p-aminophenol and barbituric acid and virtual reality display for detection mechanism

The bimetallic organic framework nanoenzyme Cu2O/Cu-MOF/Fe-MIL-88B is synthesized by the solvothermal method. The characterizations of transition electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and cyclic voltammetry prove that the amounts of oxygen vacancies are existed on the surface of Cu2O/Cu-MOF/Fe-MIL-88B and stronger electron transfer is the key factor for free radical •OH and O2·- production, thus exhibiting excellent peroxidase activity of Cu2O/Cu-MOF/Fe-MIL-88B. On the basis of the excellent peroxidase activity, versatile colourimetric and intelligent detection platforms for p-aminophenol and barbituric acid are successfully constructed on site and timely. The linear ranges of p-aminophenol and barbituric acid are 25–75 μM and 5–45 μM, and the lowest limits of detection (LOD) are 0.51 μM and 0.46 μM, severally. Moreover, the mechanisms for detecting p-aminophenol and barbituric acid were displayed by virtual reality technology. Therefore, the colourimetric and intelligent sensor of p-aminophenol and barbituric acid based on the peroxidase of Cu2O/Cu-MOF/Fe-MIL-88B have a hopeful prospect in the field of biomedicine.

Integrated noise suppression techniques for enhancing voice activity detection in degraded environments

Integrated noise suppression techniques for enhancing voice activity detection in degraded environments

An integrated active case detection and management of skin NTDs in yaws endemic health districts in Cameroon, Côte d'Ivoire and Ghana.

Integrated approaches to mapping skin Neglected Tropical Diseases (NTDs) may be cost-effective way to guide decisions on resource mobilization. Pilot studies have been carried out, but large-scale data covering multiple countries endemic for skin NTDs are lacking. Within the LAMP4YAWS project, we collected integrated data on the burden of multiple skin NTDs. From March 2021 to March 2023, integrated case searches for yaws alongside other skin conditions were performed in endemic health districts of yaws in Cameroon, Côte d'Ivoire, and Ghana. Integrated activities included training, social mobilization and active case detection. Initial screening involved a brief clinical examination of participants to determine if any skin conditions were suspected. Cases of skin NTDs were then referred to a health facility for appropriate management. Overall 61,080 individuals screened, 11,387 (18.6%) had skin lesions. The majority of individuals (>90%) examined were children aged 15 years old and under. The proportion of serologically confirmed yaws cases was 8.6% (18/210) in Cameroon, 6.8% (84/1232) in Côte d'Ivoire, and 26.8% (440/1643) in Ghana. Other skin conditions based on clinical examination included: scabies, Buruli ulcer, leprosy, lymphatic filariasis (lymphoedema and hydrocele), tungiasis, and fungal infections. The most common conditions were scabies and superficial fungal infections. In Cameroon, scabies and superficial fungal infections accounted for 5.1% (214/4204) and 88.7% (3730/4204) respectively, 25.2% (1285/5095) and 50.4% (2567/5095) in Côte d'Ivoire. In Ghana, 20% (419/2090) of individuals had scabies but superficial fungal infections were not routinely recorded and were reported in only 1.3% (28/2090). Other skin NTDs were less common across all three countries. This study confirms that integrated screening allows simultaneous detection of multiple skin NTDs, maximising use of scarce resources.

Enhanced neural activity detection with microelectrode arrays modified by drug-loaded calcium alginate/chitosan hydrogel

Microelectrode arrays (MEAs) are pivotal brain-machine interface devices that facilitate in situ and real-time detection of neurophysiological signals and neurotransmitter data within the brain. These capabilities are essential for understanding neural system functions, treating brain disorders, and developing advanced brain-machine interfaces. To enhance the performance of MEAs, this study developed a crosslinked hydrogel coating of calcium alginate (CA) and chitosan (CS) loaded with the anti-inflammatory drug dexamethasone sodium phosphate (DSP). By modifying the MEAs with this hydrogel and various conductive nanomaterials, including platinum nanoparticles (PtNPs) and poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS), the electrical properties and biocompatibility of the electrodes were optimized. The hydrogel coating matches the mechanical properties of brain tissue more effectively and, by actively releasing anti-inflammatory drugs, significantly reduces post-implantation tissue inflammation, extends the electrodes' lifespan, and enhances the quality of neural activity detection. Additionally, this modification ensures high sensitivity and specificity in the detection of dopamine (DA), displaying high-quality dual-mode neural activity during in vivo testing and revealing significant functional differences between neuron types under various physiological states (anesthetized and awake). Overall, this study showcases the significant application value of bioactive hydrogels as excellent nanobiointerfaces and drug delivery carriers for long-term neural monitoring. This approach has the potential to enhance the functionality and acceptance of brain-machine interface devices in medical practice and has profound implications for future neuroscience research and the development of strategies for treating neurological diseases.

Dual-stream Noise and Speech Information Perception based Speech Enhancement

In real-world scenarios, dynamic ambient noise often degrades speech quality, highlighting the need for advanced speech enhancement techniques. Traditional methods, which rely on static embeddings as auxiliary features, struggle to address the complexities of varying noise conditions. To overcome this, we propose a Dual-stream Noise and Speech Information Perception (DNSIP) approach that dynamically detects and processes both noise and speech through innovative information extraction and suppression mechanisms. Initially, non-speech segments predominantly contain environmental noise, while speech segments carry information about the intended speaker. To handle this dynamic nature, real-time voice activity detection (VAD) is employed to accurately differentiate between speech and noise components. Building on VAD estimates, we propose an innovative information extraction framework that selectively extracts relevant noise and speech features from the noisy input, establishing a dual-stream network for concurrent noise and speech learning. To account for the temporal and spectral variability of noise and speech, a frequency-sequence attention mechanism is integrated, enhancing the model’s ability to learn contextual and spectral dependencies. Additionally, an information suppression module is introduced to minimize cross-stream interference by attenuating noise within the speech stream and suppressing speech content within the noise stream. The derived noise and speech spectrograms are then utilized to formulate a minimum mean square error log-spectral amplitude (MMSE-LSA) estimator for robust speech enhancement. Experimental evaluations on the WSJ0 and VCTK+DEMAND datasets demonstrate that our DNSIP approach surpasses existing state-of-the-art methods, underscoring its efficacy in challenging acoustic environments.

A LysR-type regulator influencing swimming motility, galactose utilization, and virulence in Ralstonia solanacearum GMI1000

BackgroundLysR-type transcriptional regulators (LTTRs) are one of the largest families of regulators in prokaryotic organism, which help the bacterium adapt to diverse conditions by controlling a wide array of regulons, encompassing genes responsible for nitrogen and carbon fixation, oxidative stress response, bacterial virulence, and the breakdown of diverse compounds. Ralstonia solanacearum strain GMI1000 possesses 80 LTTR genes, yet the precise roles and functional contributions of only three of these LTTRs have been conclusively established among the total. In this work, our group reveal a novel LTTR member LysR7 (RS_RS02375) that exerts multiple regulatory roles in motility, carbon metabolism and virulence.ResultsIn this investigation, an in-frame deletion mutant ΔlysR7 and a complemented strain CΔlysR7 were prepared. The mutant ΔlysR7 had increased swimming motility on semi-soft medium and showed a reduced replication rate in nutrient-rich medium and in planta. Moreover, ΔlysR7 was unable to grow on nutrient-limited medium, supplemented with galactose as a single carbon resource. RT-qPCR analysis and GUS activity detection indicated that the expression of lysR7 was induced in the presence of galactose. The mutant ΔlysR7 caused weaker wilt disease on either Solanum lycopersicum or Capsicum annuum plants compared to both wild type GMI1000 and CΔlysR7. Transcriptome analysis revealed that 12 upregulated and 8 downregulated differentially expressed genes (DEGs) in ΔlysR7 were restored in CΔlysR7 relative to wild type. In particular, the expression of hrpG, a key gene responsible for type III secretion system, was downregulated. KEGG analysis revealed that, except for lysR7 gene, the 19 DEGs were most enriched in microbial metabolism in diverse environments and metabolic pathways.ConclusionsThe data indicate that LysR7 regulates multiple processes in association with motility, galactose metabolism and virulence in R. solanacearum. The study offers valuable evidence to understand comprehensive regulatory mechanisms mediated by LTTR family members in R. solanacearum.Graphical

Synergistic surface ligand modification of Ni-Pt bimetallic nanozymes: Enhanced catalytic activity and versatile detection of penicillin

Monitoring and control of penicillin (PCN) in the environment are critically important for reducing the emergence and dissemination of antibiotic-resistant bacteria, ensuring food safety, and safeguarding human health. In this study, we introduce a novel and facile strategy for modulating the catalytic activity of bimetallic nanozymes through surface ligand engineering. Experimental activity assays and theoretical model calculations confirm that Ni-Pt nanozymes coated with polyallylamine hydrochloride (PAM) exhibit significantly enhanced peroxidase (POD)-like activity compared to their counterparts coated with conventional polyvinyl pyrrolidone (PVP) and polystyrene sulfonate (PSS). Leveraging the potent reactivity between PCN and the hydroxyl radicals (∙OH) generated by Ni-Pt@PAM catalytic system, we have developed a colorimetric sensor that directly detects PCN, demonstrating a linear response range from 0.5 to 10 μM and detection limit as low as 13.5 nM. Additionally, a fluorescent sensing platform utilizing self-synthesized fluorophore Rho-Si based on inner filter effect (IFE), has been successfully established. These two sensing platforms, capable of generating both colorimetric and fluorescent signals, enable sensitive, rapid, and straightforward detection of PCN in water samples. This work not only offers an innovative approach for tuning the POD-like activity of Ni-Pt bimetallic nanozymes but also presents a promising avenue for their application in PCN monitoring.

A-318 Validation of umbilical cord blood as an alternate sample type for quantitative in vitro detection of glucose-6-phosphate dehydrogenase in neonates

Abstract Background Detection of glucose-6-phosphate dehydrogenase (G6PD) deficiency provides important information for neonatal clinical management. G6PD newborn screening is recommended using umbilical cord blood specimens in vulnerable patient populations.1 Also, cord blood is used by many clinicians instead of peripheral blood for neonatal screening of G6PD.2 Use of cord blood is a modification to the FDA-approved Pointe Scientific G6PD kinetic-340 nm assay. The aim of this study was to validate cord blood as an alternate specimen type for the Pointe Scientific G6PD assay. Methods Hemoglobin (Hgb) results are from Sysmex XN 9100™ analyzers that use SLS methodology with photometric detection. G6PD results are from Roche cobas® c502 analyzers with Pointe Scientific kinetic method and spectrophotometric detection of activity, expressed as U/g Hgb. Neonatal blood specimens were obtained as paired samples from heel sticks and umbilical cord collected in EDTA lavender top tubes. Accuracy acceptance criteria was defined as Deming regression slope between 0.90 and 1.10, r-value of >0.90 and bias ±15% and mixing study recovery of ±10%. Precision (intra-assay and inter-assay), reportable range and linearity, carryover, and stability were established using cord blood samples or control materials sourced from Thermo Fisher Scientific or Sysmex. Reference intervals3, analytical sensitivity, specificity, and clinical validity were transferred to cord blood due to comparability with whole blood FDA-approved method. EP Evaluator software by Data Innovations® was used for calculations. Results Direct comparison of G6PD results (U/g Hgb) from 125 paired samples yielded a 0.9025 correlation coefficient, 1.024 Deming slope, and -0.25% bias. Mixing and recovery of admixed samples gave % recoveries of 102, 103, 101, 102, 100, and 100 for G6PD (U/g Hgb), and 102, 102, 101, 104, 99, and 105 for Hgb (g/dL). Intra-assay precision results were 2.4, 0.6, 0.8, 0.5, 0.4 % CV for G6PD and Hgb, respectively. Inter-assay precision results were 3.1, 3.3, 0.7, 0.7, 0.5 % CV for G6PD and Hgb, respectively. Reportable range and linearity evaluation resulted in a maximum deviation from target recovery of 4%, and a slope of 0.979 for a range of 50 - 2740 U/L, and reportable range for Hgb was confirmed as 0.1 - 26.0 g/dL using controls (1% maximum deviation and 0.991 slope). Carryover was -5.8 (mean concentration) for G6PD and 0% for Hgb after testing 21 replicates of pooled high and low samples in a defined order. Testing 3 samples in 3 replicates at each defined time interval defined stability as 1 week refrigerated or 72 hours room temperature for G6PD, and 3 days refrigerated or 1 day room temperature for Hgb. Conclusions Regression statistics and mixing-recovery studies demonstrated accuracy for detecting G6PD (U/g Hgb) and Hgb (g/dL) from EDTA cord blood. Additional performance characteristics were transferrable for this specimen type from the FDA-approved method for whole blood. Umbilical cord blood is an acceptable alternate specimen type for quantitative detection of G6PD (U/g Hgb) using Pointe Scientific with Sysmex XN-9100™ and Roche cobas® c502 instrumentation.

Experimental study and techno-economic evaluation of an active fault detection kit in the prospect of future zero energy building installations

This article presents an active fault detection kit, applicable to low voltage single-phase electrical installations, in the prospect of the Zero Energy Building concept, integrating on-site renewable energy generation and energy storage. This simple, flexible, self-powered and compact kit is capable of detecting faults, such as power theft (meter tampering), unintentional islanding and neutral conductor loss. Its operation is based on harmonic voltage injection, in series with the electrical installation, through a low-power H-bridge inverter and a current transformer, along with the corresponding harmonic current measurement, to estimate the impedance and effectively detect faulty conditions; the fast and robust Goertzel algorithm is utilized. Moreover, it features IoT communication capabilities, employing the ESP32 microcontroller, to exchange data and information with the installation meters. The functionality and effective fault detection of the proposed device are validated, through experimental tests on a custom-developed hardware prototype; IoT connectivity and data uploading are experimentally tested and verified, too. Finally, a sustainability assessment study is performed, using the Life Cycle Cost Analysis tool.

Biomarkers for systemic lupus erythematosus: A scoping review.

In recent years, newly discovered potential biomarkers have great research potential in the diagnosis, disease activity prediction, and treatment of systemic lupus erythematosus (SLE). In this study, a scoping review of potential biomarkers for SLE over several years has identified the extent to which studies on biomarkers for SLE have been conducted, the specificity, sensitivity, and diagnostic value of potential biomarkers of SLE, the research potential of these biomarkers in disease diagnosis, and activity detection is discussed. In PubMed and Google Scholar databases, "SLE," "biomarkers," "predictor," "autoimmune diseases," "lupus nephritis," "neuropsychiatric SLE," "diagnosis," "monitoring," and "disease activity" were used as keywords to systematically search for SLE molecular biomarkers published from 2020 to 2024. Analyze and summarize the literature that can guide the article. Recent findings suggest that some potential biomarkers may have clinical application prospects. However, to date, many of these biomarkers have not been subjected to repeated clinical validation. And no single biomarker has sufficient sensitivity and specificity for SLE. It is not scientific to choose only one or several biomarkers to judge the complex disease of SLE. It may be a good direction to carry out a meta-analysis of various biomarkers to find SLE biomarkers suitable for clinical use, or to evaluate SLE by combining multiple biomarkers through mathematical models. At the same time, advanced computational methods are needed to analyze large data sets and discover new biomarkers, and strive to find biomarkers that are sensitive and specific enough to SLE and can be used in clinical practice, rather than only staying in experimental research and data analysis.

DNA polymerase mediated CRISPR/Cas12a trans-cleavage for dual-mode quantification of uracil DNA glycosylase activity

Since an unusual expression of uracil-DNA glycosylase (UDG) is often associated with the pathogenesis of numerous disorders, the detection of UDG activity is regarded as a promising application in disease diagnosis. Here, we develop a DNA polymerase-mediated CRISPR/Cas12a trans cleavage strategy, which can achieve dual-mode determination of UDG activity. By introducing a hairpin DNA probe containing a single uracil base, the probe undergoes specific cleavage and elongation under the existence of UDG only, thus activating the trans cleavage of ssDNA regardless of its length and sequence. To accommodate different detection modes, the ssDNA was further modified by fluorophore-quencher pairs or designed for connecting magnetic microparticles (MMPs) and polystyrene microparticles (PMPs). Finally, the UDG activity is quantified by fluorescence signal and microparticle accumulation length on a microfluidic chip visible to the naked eye. This strategy provides a detectable minimum UDG concentration of 0.00047 U/mL for fluorescent mode and 0.0048 U/mL for microfluidic mode. Furthermore, we performed the UDG inhibition test and detected UDG activity in cell lysates, suggesting its potential for inhibitor screening and detection of UDG in biological samples.

Comprehensive analysis of feature‐algorithm interactions for fall detection across age groups via machine learning

AbstractFall detection in daily activities hinges on both feature selection and algorithm choice. This study delves into their intricate interplay using the Sisfall dataset, testing 10 machine learning algorithms on 26 features encompassing diverse falls and age groups. Individual feature analysis yields key insights. RFC with the autocorrelation feature outperformed the other classifiers, achieving 97.94% accuracy and 97.51% sensitivity (surpassing F3‐SVM at 96.18% and F17‐LightGBM at 95.79%). The F3‐SVM exhibited exceptional specificity (98.72%) for distinguishing daily activities. Time‐series features employed by SVM achieved a peak accuracy of 98.60% on unseen data, exceeding motion, basic statistical, and frequency domain features. Feature combinations further excel: the Quintuple approach, fusing top‐performing features, reaches 98.69% accuracy, 98.28% sensitivity, and 99.08% specificity with the ETC, demonstrating notable sensitivity owing to its adaptability. This study underscores the crucial interplay of features and algorithms, with the Quintuple‐ETC approach emerging as the most effective. Rigorous hyperparameter tuning strengthens its performance in real‐world fall‐detection applications. Furthermore, the study investigates algorithm transferability, training models on young participants' data and applying them to the elderly—a significant challenge in machine learning. This highlights the importance of understanding the data transfer between age groups in healthcare, aging management, and medical diagnostics.

Intrusion Detection System Application with Machine Learning

Information security holds paramount importance for organizations and users alike, safeguarding against unauthorized access to sensitive data. Daily usage of the internet amplifies the importance of security measures and the detection of malicious activities. Cyber-attacks, as these malicious activities are commonly known, are continually evolving with advancements in hardware, software, and complex network algorithms. Intrusion Detection Systems play a crucial role in shielding data and information from cyberattacks. The rapid progression in machine learning and deep learning, two popular methodologies in data mining, has found applications in various fields, including security. This study focuses on the use of machine learning and deep learning methods to design an intelligent intrusion detection system. For the development of this smart intrusion detection system, two well-established datasets, NSL-KDD and Kyoto 2006+, were employed. Machine learning methods were implemented utilizing the classification algorithms available in the WEKA data mining tool. The results obtained from these classification algorithms were compared with the deep learning model designed within the scope of the study. Consequently, a detailed analysis of machine learning and deep learning methods on the NSL-KDD and Kyoto 2006+ datasets for an intelligent intrusion detection system was conducted, and suggestions were proposed for further research endeavors.

Detection of protease and lipase activity in Pseudomonas fluorescens isolated from milk and surfaces as milk deteriorating indicators

Detection of protease and lipase activity in Pseudomonas fluorescens isolated from milk and surfaces as milk deteriorating indicators

Development and Application of Reversible and Irreversible Covalent Probes for Human and Mouse Cathepsin-K Activity Detection, Revealing Nuclear Activity.

Cathepsin-K (CTSK) is an osteoclast-secreted cysteine protease that efficiently cleaves extracellular matrices and promotes bone homeostasis and remodeling, making it an excellent therapeutic target. Detection of CTSK activity in complex biological samples using tailored tools such as activity-based probes (ABPs) will aid tremendously in drug development. Here, potent and selective CTSK probes are designed and created, comparing irreversible and reversible covalent ABPs with improved recognition components and electrophiles. The newly developed CTSK ABPs precisely detect active CTSK in mouse and human cells and tissues, from diseased and healthy states such as inflamed tooth implants, osteoclasts, and lung samples, indicating changes in CTSK's activity in the pathological samples. These probes are used to study how acidic pH stimulates mature CTSK activation, specifically, its transition from pro-form to mature form. Furthermore, this study reveals for the first time, why intact cells and cell lysate exhibit diverse CTSK activity while having equal levels of mature CTSK enzyme. Interestingly, these tools enabled the discovery of active CTSK in human osteoclast nuclei and in the nucleoli. Altogether, these novel probes are excellent research tools and can be applied in vivo to examine CTSK activity and inhibition in diverse diseases without immunogenicity hazards.

MnO4−-triggered wavelength-changeable and rapid-response fluorescence sensor for paper-based on-site sensing of tyrosinase activity in potato

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 MnO4−-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.

Chinese ink-coated elastic PU/PET fibers as multifunctional flexible wearable sensors

Textiles as conductive substrates are widely used in sensing applications due to their good elasticity, high stretchability and recoverable deformation. Here, the mask rope (polyurethane/polyester blended fibers) was selected as flexible substrate to load of conductive Chinese ink using surface swelling technique in N, N-dimethylformamide. The presence of carbon nanoparticles in the ink imparts electrical conductivity to the elastic fiber while maintaining the mechanical properties and extensibility of the fiber. This simple and low-cost method allows excellent interfacial adhesion between the conductivity ink layer and the elastic fibers, and the carbon particles do not fall off even when the modified fiber was treated by sonication in water. The ink coated elastic fiber as strain sensor shows excellent stability and sensitive responsiveness after 500 stretch-release cycles. The response and recovery times during the sensitivity experiment of the flexible fiber sensor are 133.79 and 134.76 ms. The sensor exhibits physiological motion detection and quality monitoring capabilities, which can accurately monitor human movements such as walking, wrist movement, throat swallowing and articulation. The sensor also has a good Joule heating effect, and the temperature of the conductive fiber can increase 88.9°C in the 60s under 15V voltage. This work develops a simple and efficient, low-cost, mass-producible flexible sensor with potential for physiological activity, temperature, and humidity detection.