Detection Scheme Research Articles

The “noise” of suicide attempts – relating LGBTQ+-specific vs. non-specific factors

Abstract Background Despite the numerous research on the determinants and risk factors of suicide attempts, an understanding of suicide attempts as a process that holistically captures the perspectives of the actors involved is still lacking. Our study, therefore, aimed to better understand, from different perspectives, how (process) and why (subjective meaning, patterns of interpretation) (a) suicide attempt(s) among LGBTQ+ youth happen and to relate LGBTQ+-specific vs. non-specific factors. Methods From 2021 to 2024, we interviewed LGBTQ+ youth in Switzerland who had attempted suicide between the ages of 14 and 25. As part of a multi-perspective approach, we also interviewed people from their social environment where possible. Data were collected and analysed using Grounded Theory Methodology, including theoretical sampling. The final sample consisted of 41 people: 7 bisexual/lesbian cis women, 4 bisexual/gay cis men, 15 trans/non-binary people of different sexual orientations, 3 heterosexual cis people, and 12 people from their social environment. Results Sexual orientation and gender identity (SOGI) emerged as either background or foreground “music” of the suicidal processes. In some LGBTQ+ youth, SOGI were key factors influencing their suicidality, e.g. experiencing a major lack of acceptance and barriers to accessing gender-affirming transition (foreground music type). In others, suicidality was mainly related to other aspects than SOGI, especially fundamental ones such as unsafe familial circumstances or sexualised violence, which were accentuated by SOGI-related factors e.g. feeling a lack of belonging (background music type). Further, a mixed type was identified, with SOGI and non-SOGI-related factors being equally important for suicide attempt(s). Conclusions Our study implies the need to adapt health promotion, suicide prevention, and early detection and intervention strategies according to the identified types of suicidal processes.

Stacking Enabled Ensemble Learning Based Intrusion Detection Scheme (SELIDS) for IoV

Stacking Enabled Ensemble Learning Based Intrusion Detection Scheme (SELIDS) for IoV

Pilot spoofing attack detection and channel estimation for secure massive MIMO

AbstractPilot spoofing attack (PSA) is an active eavesdropping attack in massive multiple‐input multiple‐output systems, where the eavesdroppers transmit the same pilot sequence as the legitimate user does to the base station to confuse the normal channel estimation during the uplink channel training phase. With the contaminated channel estimations, more information will be leaked to eavesdroppers in the downlink transmission phase. However, it is a challenging issue to detect the PSA attack due to the similarity of the received signals and the variations of the wireless channels. Here, a new PSA detection scheme by using the difference of two different estimators is presented, that is, the least square estimator and the minimum mean square error estimator, without requiring any priori of eavesdroppers. Following that, a new data‐aided channel estimation scheme is proposed to eliminate the PSA effect. Simulation results demonstrate that the proposed PSA detection scheme outperforms the conventional energy detector, and more accurate legitimate channel estimation and higher sum secrecy rates can be obtained with the proposed scheme.

Multicolor wavefront sensing using Talbot effect for high-order harmonic generation

High-order harmonic generation (HHG) using femtosecond lasers is an efficient and cost-effective method for producing attosecond and femtosecond light in the extreme ultraviolet and soft x-ray regions. This technique generates high-brightness harmonic beams with stable spectra, intensity, and wavefronts, making it a valuable tool for applications such as nanoimaging, material metrology, and studies of ultrafast dynamics. Accurate wavefront measurement of HHG beams is essential for analyzing complex incident fields, understanding atomic responses, and enhancing applications such as attosecond-driven ultrafast processes. However, quantifying the wavefronts of each harmonic component remains a challenge. Here, we present a multicolor wavefront sensing for HHG beams using the Talbot effect, validated theoretically and experimentally. Our method reconstructs individual high-order harmonic wavefronts with a spectral resolution of 4.8 eV, capturing multiple harmonic wavelengths in a single scan along the propagation direction, without the need for additional spectrometers or complex calibrations. This approach surpasses conventional Talbot effect wavefront sensing, which typically relies on quasimonochromatic waves or wavelength-averaging. Additionally, we introduce a high-accuracy centroid detection strategy that reduces tracking errors to the thousandths of a pixel while maintaining high computational efficiency, paving the way for broader applications in imaging and metrology. Published by the American Physical Society 2024

Simple strategy for dual-responsive ratio electrochemical-colorimetric detection of nitrite in food and environment.

A dual-responsive ratio electrochemical-colorimetric method for nitrite (NO2-) is established based on the combination of nanoenzyme (Mn3O4) catalysis with diazotization reactions. The Mn3O4 can oxidize colorless 3,3',5,5'-tetramethylbenzidine (TMB) into blue TMBox. The NO2- induces the diazotization reaction of TMBox, leading to a decrease of the signal at 652nm and the generation of a new signal from diazotized TMBox at 445nm. Furthermore, the presence of NO2- reduces the electrochemical oxidation signal of TMB and simultaneously provides its electrochemical signal. Compared withtraditional single-mode detection, dual-mode detection offers higher sensitivity, lower detection limits, and better interference resistance. The inherent advantages of this method make it feasible to detect NO2- in real samples, offering broad prospects for applications in food safety and environmental monitoring.

Follow-up strategy for early detection of delayed pseudoaneurysms in patients with blunt traumatic spleen injury: A single-center retrospective study

Follow-up strategy for early detection of delayed pseudoaneurysms in patients with blunt traumatic spleen injury: A single-center retrospective study

Exploring Topological Information Beyond Persistent Homology to Detect Geospatial Objects

Accurate detection of geospatial objects, particularly landslides, is a critical challenge in geospatial data analysis due to the complex nature of the data and the significant consequences of these events. This paper introduces an innovative topological knowledge-based (Topological KB) method that leverages the integration of topological, geometrical, and contextual information to enhance the precision of landslide detection. Topology, a fundamental branch of mathematics, explores the properties of space that are preserved under continuous transformations and focuses on the qualitative aspects of space, studying features like connectivity and exitance of loops/holes. We employed persistent homology (PH) to derive candidate polygons and applied three distinct strategies for landslide detection: without any filters, with geometrical and contextual filters, and a combination of topological with geometrical and contextual filters. Our method was rigorously tested across five different study areas. The experimental results revealed that geometrical and contextual filters significantly improved detection accuracy, with the highest F1 scores achieved when employing these filters on candidate polygons derived from PH. Contrary to our initial hypothesis, the addition of topological information to the detection process did not yield a notable increase in accuracy, suggesting that the initial topological features extracted through PH suffices for accurate landslide characterization. This study advances the field of geospatial object detection by demonstrating the effectiveness of combining geometrical and contextual information and provides a robust framework for accurately mapping landslide susceptibility.

(MIS)measuring cognitive load and arousal in deception: A multitrait–multimethod analysis

AbstractPurposeCognitive load and arousal are cornerstones of many deception detection strategies and theories; in turn, their effective measurement is critical. However, fundamental criteria for establishing the quality and accuracy of measures have largely been overlooked. In this study, we examined the reliability and construct validity of common cognitive load and arousal measures.MethodWe obtained three independent secondary datasets in which participants (N = 238) had lied or told the truth about witnessing a suspicious event. Using a multitrait–multimethod analysis, we assessed three measures of their cognitive load and arousal: participants' self‐reports, trained coders' observations, and objective behaviours.ResultsAlthough all measures were reliable, they achieved differing levels of validation. Specifically, measures of cognitive load showed evidence of convergent validity, but not discriminant validity. There was no empirical support for the construct validity of arousal measures.ConclusionsThese findings suggest that inconsistencies in the diagnosticity of cues to deception and theory support may be attributable to the measures employed. Researchers may not be assessing constructs of interest, particularly in the case of arousal.

Sleep quality and associated factors in older outpatients with hypertension in Vietnam: A cross-sectional study

Background & Aim: Sleep plays a crucial role in controlling blood pressure. Older adults commonly suffer from poor sleep quality which results in several negative health outcomes, such as cognitive impairment, depression, and increased risk of falling. However, limited research exists on sleep quality in older Vietnamese adults. This study aimed to investigate sleep quality and its associated factors in older adults with hypertension in Central Vietnam. Methods & Materials: A cross-sectional study was conducted at a tertiary hospital in Central Vietnam with 235 outpatients who were aged 60 years or older, diagnosed with hypertension, and without cognitive impairment. The Pittsburgh Sleep Quality Index was utilized to assess the participants' sleep quality. Multiple binary logistic regression was computed to identify factors associated with sleep quality with a P value less than 0.05. Results: The mean sleep quality score was 8.2 (±3.9), with 71.9% of participants reporting poor sleep quality. Participants with a high school education or less were more likely to report poor sleep quality than those with higher education (OR = 1.91; 95% CI: 1.05 - 3.46; p= 0.034). Additionally, the presence of musculoskeletal disease increased the risk of poor sleep quality (OR= 2.26; 95% CI: 1.04 - 4.87; p= 0.038). Conclusion: The majority of participants suffered from poor sleep quality, which was strongly associated with educational level and the presence of musculoskeletal diseases. Strategies for early detection, prevention, and treatment of poor sleep quality are essential for improving health outcomes in this population.

Anomaly detection scheme for lung CT images using vector quantized variational auto-encoder with support vector data description.

This study aims to develop an anomaly-detection scheme for lesions in CT images. Our database consists of lung CT images obtained from 1500 examinees. It includes 1200 normal and 300 abnormal cases. In this study, SVDD (Support Vector Data Description) mapping the normal latent variables into a hypersphere as small as possible on the latent space is introduced to VQ-VAE (Vector Quantized-Variational Auto-Encoder). VQ-VAE with SVDD is constructed from two encoders, two decoders, and an embedding space. The first encoder compresses the input image into the latent-variable map, whereas the second encoder maps the normal latent variables into a hypersphere as small as possible. The first decoder then up-samples the mapped latent variables into a latent-variable map with the original size. The second decoder finally reconstructs the input image from the latent-variable map replaced by the embedding representations. The data of each examinee is classified as abnormal or normal based on the anomaly score defined as the combination of the difference between the input image and the reconstructed image and the distance between the latent variables and the center of the hypersphere. The area under the ROC curve for VQ-VAE with SVDD was 0.76, showing an improvement when compared with the conventional VAE (0.63, p < .001). VQ-VAE with SVDD developed in this study can yield higher anomaly-detection accuracy than the conventional VAE. The proposed method is expected to be useful for identifying examinees with lesions and reducing interpretation time in CT screening.

A comparative study of extraction free detection of HBV DNA using sodium dodecyl sulfate, N-lauroylsarcosine sodium salt, and sodium dodecyl benzene sulfonate

This study aimed to develop an extraction-free method for quantitative and qualitative detection of HBV DNA compared to traditional nucleic acid extraction. Paired serum and dried blood spot (DBS) samples from 67 HBsAg-positive and 67 HBsAg-negative individuals were included. Two samples with known HBV DNA titers (~ 109 copies/mL) were examined by extraction-free detection using three surfactants (0.2 to 1% of Sodium dodecyl sulfate:SDS, N-Lauroylsarcosine sodium salt:NL, Sodium dodecyl benzene sulfonate:SDBS), two stabilizing agents (0.1 or 0.01% 2-Mercaptoethanol:2ME and 3.5 or 7% Bovine Serum Albumin:BSA) and two Taq polymerases (Fast Advanced and Prime Direct Probe). HBV DNA in all 67 HBsAg-positive and 67 HBsAg-negative serum and DBS samples was directly quantified by Rt-PCR using 0.4% SDS or 0.4% NL with Fast Advance or Prime Direct Probe Taq. Extraction-free amplification was also performed. Detection limits were varied by different surfactants and Taq. SDS combined with Fast Advanced Taq showed lower detection limits, while SDS with Prime Direct Probe Taq outperformed NL or SDBS-based detection. Adding 2ME to SDS improved detection limit with Prime Direct Probe Taq but not significantly compared to SDS alone. BSA did not significantly enhance detection limits but provided insights into sample stability. The senitivity and specificity of 0.4% SDS and NL in combination with either Fast advanced or Prime Direct Probe Taq polymerase in serum samples were > 90% and 100% resepctively, while it was > 80% and 100% respectively in DBS samples. Extraction-free HBV DNA amplification provided 100% identity with original genomes. Our study suggests that SDS, NL or SDBS-based extraction-free HBV DNA detection strategies using Prime Direct Probe Taq have potential to simplify and accelerate HBV DNA detection with high sensitivity and specificity in both serum and DBS samples, with implications for resource-limited settings and clinical applications. Utilizing surfactants with 2ME is optional, and further research and validation are necessary to broaden its application in real-world diagnostics.

Nanobody-Induced Aggregation of Gold Nanoparticles: A Mix-and-Read Strategy for the Rapid Detection of Cronobacter sakazakii.

Protein-nanoparticle interactions play a crucial role in both biomedical applications and the biosafety assessment of nanomaterials. Here, we found that nanobodies can induce citrate-capped gold nanoparticles (AuNPs) to aggregate into large clusters. Subsequently, we explored the mechanism behind this aggregation and proposed the "gold nucleation mechanism" to explain this phenomenon. Building on this observation, we developed a one-step label-free colorimetric method based on nanobody-induced AuNP aggregation. When nanobodies bind to target bacteria, spatial hindrance occurs, preventing further AuNPs aggregation. This alteration in surface plasmon resonance properties results in visible color changes. As an example, we present a simple and sensitive "mix-and-read" chromogenic immunosensor for Cronobacter sakazakii (C. sakazakii). The experiment can be completed within 20 min, with a visual detection limit of 103 CFU/mL and a quantitative detection limit of 136 CFU/mL. Importantly, our method exhibits no cross-reactivity with other bacterial species. This strategy harnesses the excellent properties of nanobodies and the optical characteristics of AuNPs for direct and rapid detection of foodborne pathogen.

Pump-free SERS microfluidic chip based on an identification-competition strategy for ultrasensitive and efficient simultaneous detection of liver cancer-related microRNAs

In this study, we present a pump-free SERS microfluidic chip capable of detecting liver cancer-related miR-21 and miR-155 concurrently with ultra-sensitivity and high efficiency. We employed a Fe3O4@cDNA-AuNPs@Raman reporter@H composite structure and a recognition competition strategy. When the target miRNAs (miR-21 and miR-155) are present in the test liquid, they specifically compete with the nucleic acid complementary strand(H) of Fe3O4@cDNA-AuNPs@Raman reporter@H, causing AuNPs to competitively detach from the surface of Fe3O4, resulting in a decrease in the SERS signal. Consequently, this pump-free SERS microfluidic chip enables the detection of the target miRNAs more rapidly and accurately in complex environments. This method offers an approach for the simultaneous and efficient detection of miRNAs and holds promising applications in the early diagnosis of liver cancer.

Development and Initial Evaluation of a Digital Phenotype Collection System for Adolescents: Proof-of-Concept Study.

The growing concern on adolescent mental health calls for proactive early detection and intervention strategies. There is a recognition of the link between digital phenotypes and mental health, drawing attention to their potential use. However, the process of collecting digital phenotype data presents challenges despite its promising prospects. This study aims to develop and validate system concepts for collecting adolescent digital phenotypes that effectively manage inherent challenges in the process. In a formative investigation (N=34), we observed adolescent self-recording behaviors and conducted interviews to develop design goals. These goals were then translated into system concepts, which included planners resembling interfaces, simplified data input with tags, visual reports on behaviors and moods, and supportive ecological momentary assessment (EMA) prompts. A proof-of-concept study was conducted over 2 weeks (n=16), using tools that simulated the concepts to record daily activities and complete EMA surveys. The effectiveness of the system was evaluated through semistructured interviews, supplemented by an analysis of the frequency of records and responses. The interview findings revealed overall satisfaction with the system concepts, emphasizing strong support for self-recording. Participants consistently maintained daily records throughout the study period, with no missing data. They particularly valued the recording procedures that aligned well with their self-recording goal of time management, facilitated by the interface design and simplified recording procedures. Visualizations during recording and subsequent report viewing further enhanced engagement by identifying missing data and encouraging deeper self-reflection. The average EMA compliance reached 72%, attributed to a design that faithfully reflected adolescents' lives, with surveys scheduled at convenient times and supportive messages tailored to their daily routines. The high compliance rates observed and positive feedback from participants underscore the potential of our approach in addressing the challenges of collecting digital phenotypes among adolescents. Integrating observations of adolescents' recording behavior into the design process proved to be beneficial for developing an effective and highly compliant digital phenotype collection system.

Recent Advances in Wash-Free Detection Methods of Extracellular Vesicles: A Mini Review.

Extracellular vesicles (EVs) are emerging biomarkers in liquid biopsy that have gained increasing attention in disease diagnosis and prognosis monitoring. Most reported detection methods require the isolation of EVs from complex body liquids, often involving multiple washing steps to remove excess reagents and eliminate background interference. Nonetheless, these methods not only cause the loss of EVs but also result in poor repeatability and prolonged detection duration. The focus on wash-free detection methods is increasing due to the specific ability to avoid the removal of surplus reagents and, in some cases, even the isolation and purification of EVs. Viewing from different methodological perspectives, this review summarizes the recent advances in wash-free detection of EVs, containing aggregation induction, proximity sensing, allosteric probes, phase separation, Roman spectroscopy, field-effect transistor and microcantilever. The pros and cons of each detection strategy are impartially evaluated and this review concludes the prospects for future developments in this field.

Student Learning Performance Evaluation: Mitigating the Challenges of Generative AI Chatbot Misuse in Student Assessments

Since the launch of ChatGPT, a growing number of generative artificial intelligence (AI) chatbots have entered the market. Although chatbots have the potential to help students learn, misusing them to complete assessments raises questions about the authenticity of the work and puts students at risk of academic misconduct. Given the crucial role of assessments in evaluating students’ learning performance, uncertainties about the authenticity of the work call into question the extent to which students have achieved the intended learning outcomes. This study conducted a thematic analysis to provide an overview of the challenges that chatbot misuse may pose to student learning performance evaluation, followed by the various mitigation strategies to overcome these challenges. This study searched the Education Resources Information Centre (ERIC) database for peer-reviewed articles published in scholarly journals after 30 November 2022 (the launch date of ChatGPT, as this study focuses on generative AI rather than other types of AI) and until 30 April 2024. The thematic analysis of 17 articles identified five major themes (and respective sub-themes) in the discussions of these articles, i.e., reasons students use chatbots for assessments, challenges that chatbots may pose to student learning performance evaluation, mitigation strategies, detection strategies, and counter-detection strategies. As chatbots become more prevalent and powerful, the study's findings provide education stakeholders with insightful information on the implications of students misusing chatbots for assessments and how this affects their learning performance evaluation.

A dual-mode strategy for early detection of sugarcane pokkah boeng disease pathogen: A portable sensing device based on Cross-N DNA framework and MoS2@GDY

Sucrose, a common sugar primarily derived from sugarcane, is a crucial national strategic resource. However, its yield is significantly affected by various serious diseases, with pokkah boeng disease being one of the most damaging. Therefore, developing a sensitive method for the accurate detection of the pokkah boeng disease pathogen is crucial for ensuring the safety of sugar. This work presents a portable dual-modal detection device, assisted by a smartphone, which is based on MoS2@GDY, Mn3O4@Au nanomenzyme, cross-N DNA framework and Exo III exonuclease-assisted CHA signal amplification technology. The cross-N DNA framework provides many binding sites and is not restricted by AuNPs scattering positions, enhancing the signal output strength of the sensor. Additionally, the detection system incorporates a high-power-density capacitor to further amplify the electrochemical detection signal, increasing sensitivity by 9.1 times. Moreover, the use of electrochemical and colorimetric dual-mode detection effectively avoids mutual interference, reducing the likelihood of false positives from a single signal. Under optimized conditions, the proposed method has a linear range of 0.0001–10,000 pM, and with a detection limit of 6.1 aM (S/N=3). This high-sensitivity, high-reliability portable sensing method shows significant potential for the early detection and real-time on-site monitoring of the pokkah boeng disease pathogen.

Metal‐Organic Frameworks (MOFs) for Glucose Sensing: Advancing Non‐Invasive Detection Strategies in Diabetes Management

Effective glucose monitoring is critical for managing diabetes and preventing its associated complications. While commercial glucose monitoring devices predominantly rely on blood samples, emerging research focuses on detecting glucose in alternative biofluids, harnessing advanced nanomaterials. Among these, Metal‐Organic Frameworks (MOFs), composed of metal ions and organic ligands, have garnered significant attention due to their unique properties, including tunable porosity, high surface area, and abundant active sites conducive to glucose interaction. MOFs present a versatile platform for glucose sensing, offering potential in both enzymatic and non‐enzymatic detection methods. This review delves into the recent advancements in MOFs‐based electrochemical glucose sensors, providing a comprehensive analysis of various MOFs and their composites as electrode materials. The discussion highlights the structural attributes, functionalization strategies, and electrochemical performance of MOFs in glucose sensing, emphasizing their role in the development of next‐generation, non‐invasive glucose monitoring technologies. The review provides a comprehensive overview on the application of MOFs and MOFs‐based composites in both enzymatic and non‐enzymatic electrochemical‐based glucose sensing and highlights the synthesis, mechanism, functionalization and development in the detection strategy of MOFs in glucose sensing.

Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review

The increasing incidence of and resulting deaths associated with malignant skin tumors are a public health problem that can be minimized if detection strategies are improved. Currently, diagnosis is heavily based on physicians’ judgment and experience, which can occasionally lead to the worsening of the lesion or needless biopsies. Several non-invasive imaging modalities, e.g., confocal scanning laser microscopy or multiphoton laser scanning microscopy, have been explored for skin cancer assessment, which have been aligned with different artificial intelligence (AI) strategies to assist in the diagnostic task, based on several image features, thus making the process more reliable and faster. This systematic review concerns the implementation of AI methods for skin tumor classification with different imaging modalities, following the PRISMA guidelines. In total, 206 records were retrieved and qualitatively analyzed. Diagnostic potential was found for several techniques, particularly for dermoscopy images, with strategies yielding classification results close to perfection. Learning approaches based on support vector machines and artificial neural networks seem to be preferred, with a recent focus on convolutional neural networks. Still, detailed descriptions of training/testing conditions are lacking in some reports, hampering reproduction. The use of AI methods in skin cancer diagnosis is an expanding field, with future work aiming to construct optimal learning approaches and strategies. Ultimately, early detection could be optimized, improving patient outcomes, even in areas where healthcare is scarce.

Impedance Spectroscopy for Bacterial Cell Monitoring, Analysis, and Antibiotic Susceptibility Testing.

Conventional approaches for bacterial cell analysis are hindered by lengthy processing times and tedious protocols that rely on gene amplification and cell culture. Impedance spectroscopy has emerged as a promising tool for efficient real-time bacterial monitoring, owing to its simple, label-free nature and cost-effectiveness. However, its limited practical applications in real-world scenarios pose a significant challenge. In this review, we provide a comprehensive study of impedance spectroscopy and its practical utilization in bacterial system measurements. We begin by outlining the fundamentals of impedance theory and modeling, specific to bacterial systems. We then offer insights into various strategies for bacterial cell detection and discuss the role of impedance spectroscopy in antimicrobial susceptibility testing (AST) and single-cell analysis. Additionally, we explore key aspects of impedance system design, including the influence of electrodes, media, and cell enrichment techniques on the sensitivity, specificity, detection speed, concentration accuracy, and cost-effectiveness of current impedance biosensors. By combining different biosensor design parameters, impedance theory, and detection principles, we propose that impedance applications can be expanded to point-of-care diagnostics, enhancing their practical utility. This Perspective focuses exclusively on ideally polarizable (fully capacitive) electrodes, excluding any consideration of charge transfer resulting from Faradaic reactions.