Detection Of Activity Research Articles

An ultrasensitive electrochemical biosensor based on logic transcription circuit-activated DNAzyme amplifier for flap endonuclease 1 activity detection

The accurate detection of flap endonuclease 1 (FEN1) activity is crucial for early cancer diagnosis and therapy. Herein, we have established a “target-substrate double-enhanced” DNA electrochemical biosensor by integrating a cleavage-ligation logic transcription circuit-activated DNAzyme amplifier with the transition-metal carbides-gold nanoparticles (MXene-Au) composites for the accurate detection of FEN1 activity. The FEN1 can be translated into a stable transcription hairpin DNA (TH), incorporating the catalytic strand of the DNAzyme via the logic transcription circuit. Then, the TH hybridizes with the substrate strand of DNAzyme and the DNAzyme amplifier can be activated, thus producing a significant quantity of single-stranded DNA (sDNA) and achieving the efficient amplification of the FEN1. Finally, the sDNA loaded with electroactive substances is immobilized on the capture DNA-anchored MXene-Au modified electrode, significantly enhancing the electrochemical signal intensity for highly sensitive FEN1 activity detection. The excellent selectivity and sensitivity of the proposed electrochemical biosensor can facilitate the accurate detection of FEN1 activity in biological samples and aid in screening FEN1-releated drugs. Additionally, three-input concatenated logic gates (AND-AND, INHIBIT-AND, and AND-OR) are constructed for FEN1 activity detection. Particularly, the AND-AND gate achieves logic detection of FEN1 activity in complex samples. Consequently, this bidirectionally enhanced electrochemical biosensor offers a novel analytical method for FEN1 activity detection, showing substantial promise for early diagnosis and drug discovery of FEN1-related disease.

Adequacy of sigmoidoscopy as compared to colonoscopy for assessment of disease activity in patients of ulcerative colitis: a prospective study.

Patients of ulcerative colitis (UC) on follow-up are routinely evaluated by sigmoidoscopy. There is no prospective literature to support this practice. We assessed agreement between sigmoidoscopy and colonoscopy prospectively in patients with disease extent beyond the sigmoid colon. We conducted a prospective observational study at a tertiary care institute for agreement between sigmoidoscopy and colonoscopy. We assessed endoscopic activity using the Mayo Endoscopic Score (MES) and Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and histological activity using the Nancy Index (NI), Robarts Histopathology Index (RHI), and Simplified Geboes Score (SGS). Sigmoidoscopy showed a strong agreement with colonoscopy for MES and UCEIS with a kappa (K) of 0.96 and 0.94 respectively. The misclassification rate for MES and UCEIS was 3% and 5% respectively. Sigmoidoscopy showed perfect agreement (K = 1.00) with colonoscopy for assessment of the presence of endoscopic activity in the colon using MES ??1 as activity criteria and strong agreement (K = 0.93) using MES > 1 as activity criteria. Sigmoidoscopy showed strong agreement with colonoscopy for assessment of the presence of endoscopic activity using UCEIS (K = 0.92). Strong agreement was observed between sigmoidoscopy and colonoscopy using NI (K = 0.86), RHI (K = 1.00), and SGS (K = 0.92) for the detection of histological activity. The misclassification rate for the detection of histological activity was 2%, 0%, and 1% for NI, RHI, and SGS respectively. Sigmoidoscopy showed strong agreement with colonoscopy for endoscopic and histologic disease activity. Sigmoidoscopy is adequate for assessment of disease activity in patients with UC during follow-up evaluation.

Mature beyond their years: young children who escape detection of parasitemia despite living in settings of intense malaria transmission.

Despite having the highest risk of progressing to severe disease due to lack of acquired immunity, the youngest children living in areas of highly intense malaria transmission have long been observed to be infected at lower rates than older children. Whether this observation is due to reduced exposure to infectious mosquito bites from behavioral and biological factors, maternally transferred immunity, genetic factors, or enhanced innate immunity in the young child has intrigued malaria researchers for over half a century. Recent evidence suggests that maternally transferred immunity may be limited to early infancy and that the young child's own immune system may contribute to control of malarial symptoms early in life and prior to the development of more effective adaptive immunity. Prospective studies of active and passive detection of Plasmodium falciparum blood-stage infections have identified young children (<5 years old) who remain uninfected through a defined surveillance period despite living in settings of highly intense malaria transmission. Yet, little is known about the potential immunological basis for this 'aparasitemic' phenotype. In this review, we summarize the observational evidence for this phenotype in field studies and examine potential reasons why these children escape detection of parasitemia, covering factors that are either extrinsic or intrinsic to their developing immune system. We discuss the challenges of distinguishing malaria protection from lack of malaria exposure in field studies. We also identify gaps in our knowledge regarding cellular immunity in the youngest age group and propose directions that researchers may take to address these gaps.

Development of Ln(III) Derivatives as 19F Parashift Probes.

19F parashift probes with paramagnetically shifted reporter nuclei provide attractive platforms to develop molecular imaging probes. These probes enable ratiometric detection of molecular disease markers using a direct detection technique. Here, we describe a series of trivalent lanthanide (Ln(III)) complexes that are structural analogues of the clinically approved MR contrast agent (CA) ProHance to obtain LnL 19F parashift probes. We evaluated trans-gadolinium paramagnetic lanthanides compared to diamagnetic YL for 19F chemical shift and relaxation rate enhancement. The paramagnetic contribution to chemical shift (δPCS) for paramagnetic LnL exhibited either shifts to lower frequency (δPCS < 0 for TbL, DyL, and HoL) or shifts to higher frequency (δPCS > 0 for ErL, TmL, and YbL) compared to YL 19F spectroscopic signal. Zero-echo time pulse sequences achieved 56-fold sensitivity enhancement for DyL over YL, while developing probe-specific pulse sequences with fast delay times and acquisition times achieved 0.6-fold enhancement in limit of detection for DyL. DyL provides an attractive platform to develop 19F parashift probes for ratiometric detection of enzymatic activity.

Correlation-Aided Joint Activity Detection and Channel Estimation for Multidevice Collaborative Massive Access

Correlation-Aided Joint Activity Detection and Channel Estimation for Multidevice Collaborative Massive Access

Unveiling the retinal secrets of neuromyelitis optica spectrum disorder

BackgroundVascular alterations are now recognized as important contributors to the pathophysiology of neuromyelitis optica spectrum disorder (NMOSD). This study aimed to use optical coherence tomography (OCT) and OCT-angiography (OCTA) to assess alterations in the retinal structure and microvasculature in patients with NMOSD, so we can understand pathophysiology of NMOSD, implicating this on disease activity, visual outcome and management on the future.MethodsA case–control study was conducted on 40 NMOSD patients with (NMOSD + ON) and without (NMOSD – ON) history of optic neuritis and 36 healthy subjects. The following data were assessed in NMOSD patients: clinical history, EDSS, and visual function testing. Both groups underwent spectral domain (SD)-OCT and OCTA.ResultsIn this study, NMOSD + ON patients had a statistically significant reduction in all SD-OCT parameters compared to healthy control. Regarding OCTA, there was a significant reduction in radial peripapillary capillary density (RPCD) in NMOSD + ON (P-value < 0.001) and some sectors of NMOSD–ON compared to healthy control. NMOSD + ON patients had significant differences in RPCD compared to those without (P-value < 0.001).ConclusionsHere we show that the advance of this study is that retinal microvascular alterations have been noticed in NMOSD–ON eyes, indicating that subclinical primary retinal vasculopathy and disease activity may occur in NMOSD before onset of ON and retinal atrophy. This may have implications on early detection of disease activity, early interference in management and prognostic tool to visual outcome in following the patients.

Influence of wildfire emissions to carbon dioxide (CO2) observed at the Mt. Cimone station (Italy, 2165 m asl): A multi-year investigation

This work aimed at investigating on a multi-year framework (2015–2021) the contributions of wildfire emissions to atmospheric CO2 observed at the ICOS atmospheric class-2 station Mt. Cimone (CMN, 2165 m a.s.l. - Italy). Based on the analysis of a case study occurred in March 2020, a methodology providing indications about the possible presence of wildfire plumes from different European source regions was implemented. The methodology used observed CO at CMN, active fire detection by MODIS and air mass back-trajectories. An alternative detection method based on the use of a reanalysis dataset (CAMS) providing information about the amount of PM10 tagged to wildfires was also used.The results suggested that CMN could be affected by wildfire plumes for a fraction of time ranging from 1% to 10% (as a function of the adopted methodology setting). Over the studied period, we found a potentially important contribution of plumes from eastern Europe during October–April, while during May–September there was a prevalence from the Mediterranean sectors.Looking at the possible impact of these events to the observed CO2, we detected a notable increase of CO2 residuals with respect to periods not affected by fire perturbations during October–April (from +1.8 to +3.9 ppm, on average). We did not find evident impacts during the summer months, possibly due to a contribution by biospheric uptake during air mass transport to CMN (at least for a fraction of selected events).We discussed the sensitivity of results as a function of the selection methodology settings, suggesting that the strictest set-up based on the detection of large CO excesses could trace “major” events. A medium level of agreement was found when comparing the results of our selection methodology with CAMS reanalysis (fraction of PM10 emitted by wildfires).Even if it is still preliminary, our study indicated that the observations from CMN can represent, if supported by adequate diagnostic tools, a powerful dataset to evaluate the impact of wildfires to the atmospheric CO2 variability.

UHPLC-PDA-Q-TOF-MS-α-amylase-FLD activity detection system and molecular docking.

Traditional and some scientific literature document the antidiabetic effects of the Ziziphi Spinosae Semen (ZSS). However, the bioactive compounds of ZSS responsible for the antidiabetic effects are not well known. This study aimed to investigate the material basis of the antidiabetic effects of ZSS by inhibiting α-amylase. An online analysis platform was established and optimized using an ultra-performance liquid chromatography-photo-diode array-quadrupole-time-of-flight-mass spectrometry-α-amylase-fluorescence detector (UHPLC-PDA-Q-TOF-MS-α-amylase-FLD) system to screen α-amylase inhibitors in ZSS rapidly. The inhibitory effect of these compounds was confirmed by molecular docking screening. and the molecular interactions between α-amylase and active compounds were evaluated, which strongly supported the experimental results. Seventy-eight compounds were identified in the ZSS extract, eleven of which were screened to have significant α-amylase binding activity. This study demonstrated the feasibility of using an established platform to screen for effective components in ZSS, providing a practical method for the rapid screening of potential antidiabetic active ingredients in traditional Chinese medicine.

Wavoice: An mmWave-Assisted Noise-Resistant Speech Recognition System

As automatic speech recognition evolves, deployment of the voice user interface (VUI) has boomingly expanded. Especially since the COVID-19 pandemic, the VUI has gained more attention in online communication owing to its non-contact property. However, the VUI struggles to be applied in public scenes due to the degradation of received audio signals caused by various ambient noises. In this article, we propose Wavoice , the first noise-resistant multi-modal speech recognition system that fuses two distinct voices sensing modalities (i.e., millimeter-wave signals and audio signals from a microphone) together. One key contribution is to model the inherent correlation between millimeter-wave and audio signals. Based on it, Wavoice facilitates the real-time noise-resistant voice activity detection and user targeting from multiple speakers. Additionally, we elaborate on two novel modules for multi-modal fusion embedded into the neural network, leading to accurate speech recognition. Extensive experiments prove the effectiveness of Wavoice under adverse conditions—that is, the character recognition error rate below 1% in a range of 7 m. In terms of robustness and accuracy, Wavoice considerably outperforms existing audio-only speech recognition methods with lower character error and word error rates.

Polarization-Based Reflection Suppression Method and Its Application to Target Detection

Active illumination light becomes strongly reflective interference light after specular reflection. It causes saturation in some areas of the image during target detection, resulting in the inability to recognize detailed target feature information. This greatly limits the application of active illumination detection. Based on the Mueller matrix analysis of the difference in polarization characteristics between the background specular reflected light and the target reflected light, we propose a reflection suppression method based on orthogonal polarization imaging. The method employs a polarization modulation strategy in a bidirectional manner between the light source and the detector. First, the polarization information difference is amplified by active polarized illumination between the background specular reflected light and the target reflected light. Then, the target recovery is achieved by suppressing the background specular reflected light through the polarized orthogonal imaging method. Meanwhile, this method can also be used for moving target detection. The experimental results show that the reflection suppression method of orthogonal polarization imaging can effectively suppress the interference of specular reflection on the target image. Additionally, it can reduce the problems of missed and false detection that occurs in moving target detection and improve the active illumination detection effect.

Detection of alkaline phosphatase activity with a CsPbBr3/Y6 heterojunction-based photoelectrochemical sensor.

An ultra-sensitive photoelectrochemical (PEC) sensor based on perovskite composite was developed for the determination of alkaline phosphatase (ALP) in human serum. In contrast toCsPbBr3 or Y6 that generated anodic current, the heterojunction of CsPbBr3/Y6 promoted photocarriers to separate and generated cathodic photocurrent. Ascorbic acid (AA) was produced by ALP hydrolyzing L-ascorbic acid 2-phosphate trisodium salt (AAP), which can combine with the holes on the photoelectrode surface, accelerating the transmission of photogenerated carriers, leading to enhanced photocurrent intensity. Thus, the enhancement of PEC current was linked to ALP activity. The PEC sensor exhibits good sensitivity for detection of ALP owing to the unique photoelectric properties of the CsPbBr3/Y6 heterojunction. The detection limit of the sensor was 0.012 U·L-1 with a linear dynamic range of 0.02-2000 U·L-1. Therefore, this PEC sensing platform shows great potential for the development of different PEC sensors.

One‐step synthesis of a spongy flexible substrate for surface enhanced Raman spectroscopy comprising a silver nanoparticles‐loaded poly(amidoamine) dendrimer crosslinked with glutaraldehyde

AbstractA composite comprising a silver nanoparticles‐loaded poly(amidoamine) (PAMAM) dendrimer crosslinked with glutaraldehyde (PAMAM/AgNPs) is synthesized using a one‐step method under vortex at room temperature. The structure of glutaraldehyde‐crosslinked PAMAM dendrimer is characterized using FTIR and high‐resolution magic angle spinning–carbon 13 nuclear magnetic resonance (HRMAS 13C NMR) spectroscopy. The crosslinking structure ensures the uniformity and stability of the Ag nanoparticles, and the flexibility of the PAMAM/AgNPs composite. X‐ray diffraction (XRD) analysis and energy dispersive spectroscopy (EDS) confirm the formation of Ag nanoparticles in the PAMAM/AgNPs composite. Scanning electron microscopy (SEM) and three‐dimensional (3D) micro‐Raman images reveal the coarsely porous 3D micro‐structure of the PAMAM/AgNPs composite. This unique micro‐3D architecture provides the PAMAM/AgNPs composite with spongy flexibility and a swab‐like nature, which is advantageous for the rapid and effective absorption of liquid samples on the surfaces of objects. Surface enhanced Raman spectroscopy (SERS) indicates that the detection limits of both rhodamine 6G (R6G) and thiram are 1.0 × 10−5 mol L−1, and the SERS enhancement factor of R6G is determined to be 1.35 × 104. The spongy flexible PAMAM/AgNPs SERS substrate therefore has potential for use in Raman active compound detection.

Deep Learning-Integrated Crime Detection System Using Convolutional Neural Networks

The work delivered here provides a new deep learning-based approach for crime detection, using Convolutional Neural Networks (CNNs) as a tool. Given the abundance of surveillance data which is becoming more available, the provision of automated systems to the law enforcement agencies ought to help in the identification of crimes seems to be the urgent need of the hour. We aim by this study to build a CNN model that could identify scenes in video simplifying them as criminal or non-criminal. The model is trained on the large dataset of the video clips which are labeled and it can promise the accuracy and efficiency attracted by this model. We test the proposed system on various baseline datasets and then its performance compared with state-of-the-art methods. The experimental results thereby show the effectiveness of our method in the detection of criminal activities, which highlights its potentiality for real world implementation in preventive measures and crime enforcement.

Explainable AI for Abnormal Human Activity Detection in Surveillance Videos: A Review

This review paper examines the integration of Explainable AI (XAI) techniques into abnormal human activity detection from surveillance videos, emphasizing their significance in enhancing transparency, accountability, and trustworthiness in AI-powered surveillance systems. Through an exploration of XAI methods such as LIME, SHAP, and attention mechanisms, we discuss how these techniques provide insights into the decision-making process of AI models, enabling stakeholders to understand and interpret model predictions. Furthermore, we highlight the advantages of using XAI for debugging errors, identifying biases, and increasing trust among human operators in various security contexts. Additionally, we discuss ongoing research directions in XAI for abnormal human activity detection, including the development of advanced explanation techniques and the incorporation of user feedback into the explanation process. By fostering collaboration between humans and AI, XAI holds the potential to enhance the effectiveness and responsible deployment of surveillance systems, contributing to safer and more secure environments for all stakeholders.

Active Chat Monitoring and Suspicious Chat Detection Over Internet

In the digital age, the proliferation of online communication platforms has facilitated unprecedented levels of connectivity and interaction. However, this interconnectedness has also introduced new challenges, particularly in ensuring the safety and security of users in online environments. One critical area of concern is the monitoring and detection of suspicious activities within chat platforms, where malicious actors may engage in harmful behaviors such as cyberbullying, harassment, or illicit activities. This research paper focuses on the development and implementation of active chat monitoring techniques for the detection of suspicious behavior over the internet. By leveraging advancements in natural language processing (NLP), machine learning, and data analytics, this study aims to explore effective methodologies for real-time monitoring and analysis of chat conversations to identify potentially harmful content. The paper will delve into various approaches for detecting suspicious patterns, including keyword analysis, sentiment analysis, and anomaly detection algorithms. Additionally, considerations for privacy, ethics, and legal implications surrounding chat monitoring will be discussed. Ultimately, this research endeavors to contribute to the enhancement of online safety measures by providing insights into the design and implementation of proactive monitoring systems capable of identifying and mitigating risks in virtual communication spaces. Keywords— Active Chat Monitoring, cybersecurity, user experience, digital era, security, user-controlled, effectiveness, evolving threat, Chat content analysis, Threat intelligence.

Turbidimetric bioassays: A solution to antimicrobial activity detection in asymptomatic bacteriuria isolates against uropathogenic Escherichia coli.

Traditional bacteriocin screening methods often face limitations due to diffusion-related challenges in agar matrices, which can prevent the peptides from reaching their target organism. Turbidimetric techniques offer a solution to these issues, eliminating diffusion-related problems and providing an initial quantification of bacteriocin efficacy in producer organisms. This study involved screening the cell-free supernatant (CFS) from eight uncharacterized asymptomatic bacteriuria (ABU) isolates and Escherichia coli 83972 for antimicrobial activity against clinical uropathogenic E. coli (UPEC) strains using turbidimetric growth methods. ABU isolates exhibiting activity against five or more UPEC strains were further characterized (PUTS 37, PUTS 58, PUTS 59, S-07-4, and SK-106-1). The inhibition of the CFS by proteinase K suggested that the antimicrobial activity was proteinaceous in nature, potentially bacteriocins. The activity of E. coli PUTS 58 and SK-106-1 was enhanced in an artificial urine medium, with both inhibiting all eight UPECs. A putative microcin H47 operon was identified in E. coli SK-106-1, along with a previously identified microcin V and colicin E7 in E. coli PUTS 37 and PUTS 58, respectively. These findings indicate that ABU bacteriocin-producers could serve as viable prophylactics and therapeutics in the face of increasing antibiotic resistance among uropathogens.

New G-Triplex DNA Dramatically Activates the Fluorescence of Thioflavin T and Acts as a Turn-On Fluorescent Sensor for Uracil-DNA Glycosylase Activity Detection.

G-triplexes are G-rich oligonucleotides composed of three G-tracts and have absorbed much attention due to their potential biological functions and attractive performance in biosensing. Through the optimization of loop compositions, DNA lengths, and 5'-flanking bases of G-rich sequences, a new stable G-triplex sequence with 14 bases (G3-F15) was discovered to dramatically activate the fluorescence of Thioflavin T (ThT), a water-soluble fluorogenic dye. The fluorescence enhancement of ThT after binding with G3-F15 reached 3200 times, which was the strongest one by far among all of the G-rich sequences. The conformations of G3-F15 and G3-F15/ThT were studied by circular dichroism. The thermal stability measurements indicated that G3-F15 was a highly stable G-triplex structure. The conformations of G3-F15 and G3-F15/ThT in the presence of different metal cations were studied thoroughly by fluorescent spectroscopy, circular dichroism, and nuclear magnetic resonance. Furthermore, using the G3-F15/ThT complex as a fluorescent probe, a robust and simple turn-on fluorescent sensor for uracil-DNA glycosylase activity was developed. This study proposes a new systematic strategy to explore new functional G-rich sequences and their ligands, which will promote their applications in diagnosis, therapy, and biosensing.

Design and synthesis of Lanthanide(III) complex based luminescence probes for time-resolved luminescence detection of β-galactosidase

β-Galactosidase (β-Gal), confirmed to provide energy and participate in the galactosyl transfer reaction, is primarily over-expressed in ovarian cancer cells. Herein, detecting endogenous β-Gal is essential for the diagnosis and monitoring of primary ovarian cancer cells. In this study, we proposed two structurally related cationic Lanthanide(III) triamide complexes for highly sensitive and rapid detection of β-Gal. Moreover, LDEu02 showed a low detection limit (2.3 × 10−3 U/mL) and high specificity with high affinity for β-Gal, which had great potential to the detection of the β-Gal activity by using time-resolved fluorescence techniques.

Improved sand cat swarm optimization with deep learning based enhanced malicious activity recognition for cybersecurity

The main concept of a smart city is to join manual items with electronics, software, sensors, and network connectivity for data contact via Internet of Things (IoT) gadgets. IoT improves production and efficiency by cleverly utilizing remote management, but security as well as privacy risk, rises. Cyber threats are progressing every day, affecting inadequate actions of safety and privacy. In a smart city, the exposed action of an individual or business can keep the whole city in danger. Owing to the trust of numerous modules of smart cities on data and communication techniques, cybersecurity tasks like information leakage and mischievous cyber-attacks. Malicious Activity Detection in the area of cybersecurity for smart cities is dominant in protecting the consistent organization of urban atmospheres. Leveraging innovative machine learning (ML) and deep learning (DL) approaches, mainly anomaly detection methods permits the identification of abnormal patterns and probable threats within the massive streams of data produced by smart city methods. So, this paper designs a Fusion of Optimization Algorithms and DL for Enhanced Malicious Activity Recognition (FOADL-EMAR) technique for cybersecurity in smart cities. The purpose of the FOADL-EMAR technique is to recognize the presence of malicious activities using optimal DL techniques in the environment of a smart city. To accomplish this, the FOADL-EMAR technique uses a Z-score data normalization model to measure the input dataset into a useful format. In addition, the FOADL-EMAR technique uses an improved sand cat swarm optimization (ISCSO) algorithm that can be applied to select an optimal subset of features. An ensemble of three DL techniques is used namely long short-term memory (LSTM), deep neural networks (DNNs), and extreme learning machine (ELM) for malicious activity detection. The Harris Hawks Optimization (HHO) approach has been applied to improve the detection results of the ensemble models. The simulation analysis of the FOADL-EMAR methodology occurs utilizing a benchmark database. The experimentation values demonstrate the improved detection outcomes of the FOADL-EMAR algorithm over other existing methods in terms of different measures.

Damage effect evaluation of CCD irradiated by laser based on multi-source information fusion

This study introduces an advanced approach for assessing the damage state of charge-coupled devices (CCDs) caused by laser interactions, leveraging a multi-source and multi-feature information fusion technique. We established an experimental system that simulates laser damage on CCDs and collects diverse data types including echo information from active laser detection based on the ‘cat's eye’ effect, plasma flash data, and surface image characteristics of the CCD. A probabilistic neural network (PNN) was utilized to integrate these data sources effectively. Our analysis demonstrated that using multiple features from single sources significantly improves the accuracy of the damage assessment compared to single-feature evaluations. The error rates using dual features from each information type were 10.65% for cat's eye echo, 7.3% for plasma flash, and 7.17% for surface image analysis. By combining all three information sources and six features, we successfully reduced the error rate to 0.85%, with the evaluation time under 60 milliseconds. These findings confirm that our multi-source, multi-feature fusion method is highly effective for the online and real-time evaluation of CCD damage, offering significant improvements in the operational reliability and safety of devices in high-energy environments.