Context Of Surveillance Research Articles

Anxiety, depression and fear of cancer recurrence in uveal melanoma survivors and ophthalmologist/oncologist communication during survivorship in France - protocol of a prospective observational mixed-method study.

Quality of life (QoL) in patients undergoing surveillance for uveal melanoma (UM) can be affected by psychological sequelae. Fear of cancer recurrence (FCR) may be acute especially when prognostication indicates an increased risk of metastatic recurrence. Communication with an ophthalmologist or oncologist can then play a key role in impacting QoL. In this prospective study co-designed with patient's partners and using a mixed-method approach, 250 patients at high versus low risk of metastatic recurrence are recruited in a national UM reference centre in France. At T1, after the 6-months post-treatment surveillance visit, dyads of clinicians and eligible patients complete a questionnaire to assess their respective experience of the communication during that consultation. Patients also complete questionnaires assessing their health literacy, information preference, and satisfaction with the information received (EORTC QLQ-INFO25), genomic testing knowledge, genomic test result receipt, satisfaction with medical care (EORTC PATSAT-C33), perceived recurrence risk, anxiety and depression (HADS), fear of cancer recurrence (FCRI) and quality of life (EORTC QLQ-C30 and QLQ-OPT30). At 12-months post-treatment (T2), patients complete again the HADS, FCRI, EORTC QLQ-C30 and QLQ-OPT30. Multilevel analyses will assess the effect of satisfaction with the information received on FCR and QoL accounting for the clinicians' and patients' characteristics. In-depth interviews planned sequentially with ≈25 patients will deepen understanding of patients' care experience. As information on prognosis based on medical parameters becomes widely integrated into oncology practice, this study will highlight UM survivors' information expectations and satisfaction with communication, and its effect on FCR and QoL. Culturally adapted recommendations for doctor-patient communication will be provided for contexts of oncology surveillance involving poor prognosis in cases of recurrence. NCT06073548 (October 4, 2023).

Abstract A008: Identification of EYA2 as a promising biomarker for DICER1-related tumor predisposition

Abstract Background: DICER1-related tumor predisposition (DRTP), also known as DICER1 syndrome, is a genetic condition associated with an increased risk of developing various tumors, including pleuropulmonary blastoma, thyroid gland neoplasia, ovarian tumors, and cystic nephroma. Currently, the diagnosis and surveillance of DRTP primarily relies on genetic testing for DICER1 mutations. There is a pressing need for surrogate diagnostic biomarkers to improve early detection and monitoring. Objective: To identify and validate EYA2 as a potential biomarker for DICER1-related tumor predisposition. Methods: Murine mesenchymal stromal cell lines were developed to model the consequences of DICER1 mutations, including the DICER1E1705K mutation found in DRTP. Both bulk RNA sequencing (RNAseq) and microRNA sequencing (miRNAseq) were employed to identify differentially expressed mRNAs in these cell lines. The RNA and protein levels of Eya2 were measured by qRT-PCR and Western blot, respectively. De-repression of Eya2 mRNA by miRNA was evaluated by dual luciferase assay to link overexpression with the DICER1 defect of DRTP. Then, immunohistochemistry staining of EYA2 was performed on a panel of DRTP tumors. Results: We found that more than 2000 mRNAs were derepressed in mesenchymal stromal cells expressing DICER1E1705K as compared to DICER1WT. Among these, Eya2 emerged as a top candidate. We observed elevated Eya2 mRNA and protein levels in DICER1E1705K -expressing mesenchymal stromal cells. Utilizing a reporter assay, we also observed de-repression of the 3’ UTR of Eya2 upon expression of DICER1E1705K as compared to DICER1WT. Strong nuclear EYA2 staining was observed in a subset of DRTP tumor samples. Conclusion: Elevated EYA2 expression in tumor tissues from DRTP patients supports its potential use in both diagnostic and surveillance contexts. Further studies are warranted to validate EYA2 as a clinical biomarker and explore its utility in improving early detection and monitoring strategies for individuals at risk of DRTP. Citation Format: Mona Wu, Shengmei Zhou, Felix Kommoss, Yemin Wang, James F Amatruda. Identification of EYA2 as a promising biomarker for DICER1-related tumor predisposition [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr A008.

Unraveling the impact of genome assembly on bacterial typing: a one health perspective

BackgroundIn the context of pathogen surveillance, it is crucial to ensure interoperability and harmonized data. Several surveillance systems are designed to compare bacteria and identify outbreak clusters based on core genome MultiLocus Sequence Typing (cgMLST). Among the different approaches available to generate bacterial cgMLST, our research used an assembly-based approach (chewBBACA tool).MethodsSimulations of short-read sequencing were conducted for 5 genomes of 27 pathogens of interest in animal, plant, and human health to evaluate the repeatability and reproducibility of cgMLST. Various quality parameters, such as read quality and depth of sequencing were applied, and several read simulations and genome assemblies were repeated using three tools: SPAdes, Unicycler and Shovill. In vitro sequencing were also used to evaluate assembly impact on cgMLST results, for six bacterial species: Bacillus thuringiensis, Listeria monocytogenes, Salmonella enterica, Staphylococcus aureus, Vibrio parahaemolyticus and Xylella fastidiosa.ResultsThe results highlighted variability in cgMLST, which not only related to the assembly tools, but also induced by the intrinsic composition of the genomes themselves. This variability observed in simulated sequencing was further validated with real data for six of the bacterial pathogens studied.ConclusionThis highlights that the intrinsic genome composition affects assembly and resulting cgMLST profiles, and that variability in bioinformatics tools can induce a bias in cgMLST profiles. In conclusion, we propose that the completeness of cgMLST schemes should be considered when clustering strains.

The impact of information privacy concerns on information systems use behaviors in non-volitional surveillance contexts: A moderated mediation approach

The impact of information privacy concerns on information systems use behaviors in non-volitional surveillance contexts: A moderated mediation approach

Re‐hearing parents as risks to children: Institutional listening practices in a California child welfare court

AbstractBuilding on scholarship on the politics of listening and the listening subject, this article proposes re‐hearing as a listening practice where empowered actors assert that various qualities of personhood are hearable or perceivable in relation to what marginalized persons say, do, or are otherwise associated with. Although there is an expectation that in US courts lay actors can have a hearing and a voice before the law, I identify how practices of re‐hearing shaped how parents and their narratives were heard (or left unheard) in a California child welfare court. My ethnographic research examined the listening and entextualization practices of judges, attorneys, and social workers involved in child welfare case management in California. I found that re‐hearing practices co‐constructed the continued marginalization of lay actors within contexts of state surveillance by attributing suspicion to parents and their silences in ways that exceeded and constructed evidence collected against them. Ultimately, I argue that child welfare courts and professional actors within them collectively comprise a listening institution that normalizes re‐hearing low‐income and racialized parents through frameworks of deficiency and risk.

Sell your soul for a pot of gold: Privacy paradox in surveillance capitalism

The current study aims to examine the underlying psychological processes involved in privacy protection behaviors among tourists in retail environments using surveillance technologies. Protection Motivation Theory (PMT) and Privacy Calculus Theory (PCT) were employed to understand threat and coping appraisals, benefits (control vs monetary), and their relationships with protective behavioral intentions and actual behaviors. Data was collected from 554 respondents who had visited a retail store while traveling in the past 6 months using an online questionnaire. Three structural equation models, full mediation, partial mediation, and moderated mediation were tested and compared. The partial mediation model exhibited the best fit. Perceived severity and susceptibility increased protective behavioral intentions, while self-efficacy decreased intentions. Control compensation positively influenced intentions only. Behavioral intentions partially mediated relationships between independent variables and actual protective behaviors. No significant moderation effects were found. Integrating PMT and PCT addresses a significant gap in examining tourists’ privacy-protective behaviors in retail surveillance contexts. Additionally, it clarifies the effectiveness of control and monetary benefits in mitigating privacy concerns.

Acceptance and self-protection in government, commercial, and interpersonal surveillance contexts: An exploratory study

Digital surveillance is pervasive in cyberspace, with various parties continuously monitoring online activities. The ways in which internet users perceive and respond to such surveillance across overlapping contexts warrants deeper exploration. This study delves into the acceptance of digital surveillance by internet users and their subsequent self-protective actions against it in three distinct contexts: government, commercial, and interpersonal surveillance. Survey responses collected from 356 internet users in the U.S. showed that acceptance levels for surveillance varied between institutional and interpersonal contexts. However, the degree of self-protection remained consistent across all three contexts. Privacy concerns, algorithm awareness, and perceived privacy control played nuanced roles to both surveillance acceptance and self-protection measures in each context. Interestingly, political orientation emerged as a significant moderating factor on surveillance acceptance. Conservative-leaning participants were less accepting of surveillance overall, especially government surveillance. For conservatives, higher privacy concerns meant less acceptance of both government and corporate surveillance. Liberals’ acceptance levels remained fairly consistent and were less affected by privacy concerns. These findings underscore the significance of contextual differences in privacy and surveillance research and provide implications for refining the existing theoretical frameworks.

Differential diagnosis on measles and rubella discarded cases highlights a sharp increase in parvovirus B19 infections in Milan, Northern Italy, in the first months of 2024.

In line with European trends, since 2023 Lombardy (Northern Italy) is experiencing a resurgence of measles and an increased number of reported cases of fever and rash. Measles discarded cases observed in our region within the context of measles and rubella surveillance from the first few months of 2024 (N = 30) were investigated for parvovirus B19 (B19V) and other rash-associated viruses. Thirteen cases tested positive for B19V DNA, representing a significant increase from previous years (on average 3 cases per year, p < 0.001) and ~40% of all B19V DNA-positive patients we detected since 2017. In 2024, B19V DNA-positive subjects spanned all ages, and the virus was predominant among adolescents and adults (84.6%). Two B19V infected patients were hospitalised, and likely cross-reacting anti-measles virus IgM were found in both. Our data align with the recent reports from the ECDC and various European countries, which are experiencing a surge in B19V infections, and underline the importance of comprehensive measles and rubella surveillance systems that can adapt to changing epidemiological trends.

Eyes everywhere: the influence of digital surveillance on employee innovation performance in China

ABSTRACT Digital surveillance is widespread in the workplace, but research on its impact on employee behaviour is still limited. This study aims to investigate employee innovation performance in the digital surveillance context. Through a two-stage survey design, data from 266 employees were collected. The findings reveal that employee job autonomy mediates the relationship between digital surveillance and employee innovation performance. In addition, this relationship is moderated by employee participation in management, when employees have a lower perception of privacy invasion, the higher their participation in management, the greater their innovation performance. The results of this study offer valuable insights into managing digital human resource practices in digital era.

Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure

Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.

DODGE: automated point source bacterial outbreak detection using cumulative long term genomic surveillance.

The reliable and timely recognition of outbreaks is a key component of public health surveillance for foodborne diseases. Whole genome sequencing (WGS) offers high resolution typing of foodborne bacterial pathogens and facilitates the accurate detection of outbreaks. This detection relies on grouping WGS data into clusters at an appropriate genetic threshold. However, methods and tools for selecting and adjusting such thresholds according to the required resolution of surveillance and epidemiological context are lacking. Here we present DODGE (Dynamic Outbreak Detection for Genomic Epidemiology), an algorithm to dynamically select and compare these genetic thresholds. DODGE can analyse expanding datasets over time and clusters that are predicted to correspond to outbreaks (or "investigation clusters") can be named with established genomic nomenclature systems to facilitate integrated analysis across jurisdictions. DODGE was tested in two real-world Salmonella genomic surveillance datasets of different duration, 2 months from Australia and 9 years from the United Kingdom. In both cases only a minority of isolates were identified as investigation clusters. Two known outbreaks in the United Kingdom dataset were detected by DODGE and were recognized at an earlier timepoint than the outbreaks were reported. These findings demonstrated the potential of the DODGE approach to improve the effectiveness and timeliness of genomic surveillance for foodborne diseases and the effectiveness of the algorithm developed. DODGE is freely available at https://github.com/LanLab/dodge and can easily be installed using Conda.

YOLO Models for Security and Surveillance Applications

Abstract: Since 2015, the YOLO (You Only Look Once) series has evolved to YOLO-v8, prioritizing real-time processing and high accuracy for security and surveillance applications. Architectural enhancements in each iteration, culminating in YOLOv9, cater to rapid detection, precision, and adaptability to resource-constrained edge devices. This study examines YOLO’s evolution, emphasizing its relevance to security and surveillance contexts. Notable improvements in architecture, coupled with practical deployments for defect detection, underscore YOLO’s alignment with stringent security and surveillance requirements.

Genotyping of Leptospira spp. in wild rats leads to first time detection of L. kirshneri serovar Mozdok in Serbia.

This study aimed to investigate the prevalence and molecular characterization of Leptospira species in Belgrade, Serbia, an area where this disease is underexplored. Specifically, the study sought to employ molecular and multilocus sequence typing analyses to fill the gap in understanding the diversity and distribution of Leptospira species within the region. A comprehensive molecular analysis was conducted on kidney samples obtained from Norway rats (Rattus norvegicus) in the urban environment. The study utilized molecular diagnostic techniques including real-time PCR targeting the lipL32 gene and performing sequence-based typing schemes utilizing adk, icdA, lipL32, lipL41, rrs2, and secY genes. These methodologies were applied to ascertain the presence and characterize different Leptospira species and serovars, respectively. The findings revealed the presence of two Leptospira species and three separate serovars in the Belgrade area. This study identified the presence of L. kirschneri serovar Mozdok in Serbia for the first time, a significant discovery previously undocumented in the region. This pioneering investigation sheds light on the molecular diversity and prevalence of Leptospira species in Serbia. The study underscores the importance of employing molecular typing methods to gain insights into the epidemiology and characterization of Leptospira species. These findings significantly contribute to both local and global perspectives on leptospirosis epidemiology, providing vital insights for the development of effective control strategies and interventions. In our recent study, we explored the presence and performed molecular typing of the Leptospira species, the bacteria responsible for leptospirosis, in wild rats in Serbia. This was the first time such a study was conducted in the region. Leptospirosis is a serious disease that affects both animals and humans, often transmitted through contact with water contaminated by infected animals. Our focus was on understanding which types of Leptospira were present in these animals. Excitingly, we discovered a particular strain of Leptospira, known as L. kirshneri serovar Mozdok, for the first time in Serbia. This finding is significant because it sheds light on the presence and spread of different Leptospira serovars in Serbia. It also raises awareness about the potential health risks associated with this serovar, which was previously unknown in the area. Our work fits into a broader context of disease surveillance and public health. By identifying the types of Leptospira present in a specific region, we can better understand the risks to public health and take steps to prevent and control the spread of leptospirosis. This discovery is not just important for scientists studying infectious diseases; it has real implications for public health officials, veterinarians, and anyone concerned with preventing and treating leptospirosis. Our findings highlight the need for ongoing monitoring of Leptospira in wildlife and synanthropic fauna, to protect both animal and human health.

Seeing like police: Surveillant practices and scopic skills in Recife, Brazil

AbstractDrawing on public–private circuits for urban surveillance in Recife, Brazil, this article unpacks how camera operators and ordinary residents are sensorially attuned to what constitutes a threat to urban order, from the perspective of state security governance. Through the notion of sensory enskilment, the piece delves into how surveillant civilians learn to distinguish such threats in digitally mediated urban sightings. By attending to the development of scopic skills in everyday surveillance contexts, I shed light on how security events become visible and legible across communities of practice, contributing to the formation of policing subjectivities and the maintenance of broader regimes of vigilance. The analysis draws on qualitative research on two sets of sites that cultivate civilians' visual skills around urban security: video‐surveillance control rooms managed by the security secretariats of Recife and Pernambuco, and WhatsApp groups dedicated to the policing of specific territories. This article seeks to deepen recent debates on how vertical and lateral forms of surveillance shape political communities and sociospatial divides.

Mobile Handoff with 6LoWPAN Neighbour Discovery Auxiliary Communication

The importance of the topic under investigation stems from the burgeoning prominence of the Internet of Things (IoT) paradigm and the consequent imperative to devise and institute an efficacious methodology for neighbour detection that facilitates supplementary connections to mobile nodes. This research primarily aims to elucidate a mobile handover technique aligning with the neighbour discovery paradigm inherent in 6LoWPAN. The methodological framework employed herein amalgamates a systematic analysis of the foundational tenets governing 6LoWPAN communication standards over the IPv6 protocol in the context of IEEE 802.15.4aa personal wireless networks, supplemented with empirical evaluation of message propagation dynamics among discrete nodes of a designated personal wireless network. The findings of this investigation underscore the pre-eminence of the 6LoWPAN networking standard as a prime vehicle for bolstering the dependable communication of IoT&amp;#39;s remote entities, contingent upon the utilization of the IPv6 communication protocol. This iteration of the Internet protocol is superior, particularly in surveillance contexts, given its expansive address capacity (128 bits) juxtaposed against its antecedent, the IPv4 protocol (32 bits). The inherent adaptability of this network connection augments the quality of mobile handover, especially when one considers the real-world application of 6LoWPAN&amp;#39;s neighbour discovery auxiliary communication standard. The tangible implications of these findings beckon their potential utility in satiating the demands of IoT system users and actualizing the network collaboration ethos intrinsic to the 6LoWPAN communication standard.&amp;nbsp;

Development of a reverse transcriptase digital droplet polymerase chain reaction-based approach for SARS-CoV-2 variant surveillance in wastewater.

An urgent need for effective surveillance strategies arose due to the global emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although vaccines and antivirals are available, concerns persist about the evolution of new variants with potentially increased infectivity, transmissibility, and immune evasion. Therefore, variant monitoring is crucial for public health decision-making. Wastewater-based surveillance has proven to be an effective tool to monitor SARS-CoV-2 variants within populations. Specific SARS-CoV-2 variants are detected and quantified in wastewater in this study using a reverse transcriptase digital droplet polymerase chain reaction (RT-ddPCR) approach. The 11 designed assays were first validated in silico using a substantial dataset of high-quality SARS-CoV-2 genomes to ensure comprehensive variant coverage. The assessment of the sensitivity and specificity with reference material showed the capability of the developed assays to reliably identify target mutations while minimizing false positives and false negatives. The applicability of the assays was evaluated using wastewater samples from a wastewater treatment plant in Ghent, Belgium. The quantification of the specific mutations linked to the variants of concern present in these samples was calculated using these assays based on the detection of single mutations, which confirms their use for real-world variant surveillance. In conclusion, this study provides an adaptable protocol to monitor SARS-CoV-2 variants in wastewater with high sensitivity and specificity. Its potential for broader application in other viral surveillance contexts highlights its added value for rapid response to emerging infectious diseases. PRACTITIONER POINTS: Robust RT-ddPCR methodology for specific SARS-CoV-2 variants of concern detection in wastewater. Rigorous validation that demonstrates high sensitivity and specificity. Demonstration of real-world applicability using wastewater samples. Valuable tool for rapid response to emerging infectious diseases.

Smart Attendance System using Face Recognition

Face recognition offers a wide range of valuable applications in social media, security, and surveillance contexts. The software used for building facial recognition algorithms is Python and OpenCV. "Attendance using Face Recognition" is a method for tracking and managing attendance that makes use of facial recognition technology. By seamlessly integrating the 'Face Recognition' module, a native Python feature, and the OpenCV library, our system excels in accuracy and dependability. The system then stores attendance records in a database and provides real-time reports. In this article, we demonstrate how to create a face recognition system in Python utilizing the built-in "Face Recognition" module and the OpenCV library. Our results show that our system achieves high accuracy and robustness while being efficient and scalable, catering to a wide spectrum of educational institutions, organizations, and enterprises.

Enhancing Facial Recognition in Surveillance Systems through Embedded Super-resolution

This document details the implementation of a sub-pixel convolutional neural network designed to enhance the resolution of face images. The model uses a series of filters to progressively increase the number of pixels, estimating the necessary information for new pixels from the original image and training derived from 22000 synthetic images produced by adversarial neural networks. Within the context of surveillance and related applications, the trained convolutional network exhibits beneficial characteristics. For instance, it can be deployed within a device to achieve higher-resolution images than those the physical camera can produce. This research underscores the feasibility of such a device through the implementation and evaluation of the network on the NVIDIA Jetson TX2 embedded system. The findings demonstrate the model's practicality for real-time surveillance applications and its ability to produce superior-quality images compared to several interpolation methods, as determined by an exhaustive testing process measuring various attributes of the generated images.

Enhancing applications of surveillance through multimedia data mining

Over recent years, multimedia data has become a cornerstone for insightful data analysis, yielding vital information crucial for informed decision-making processes. This diverse data format encompasses audio, video, images, and text, offering a wealth of valuable knowledge. Advancements in multimedia acquisition, storage, and processing technologies have significantly enhanced analytical capabilities, overcoming challenges posed by semi-structured and unstructured data formats. Various entities including corporations, governmental bodies, and academic institutions are keenly interested in harnessing insights from the vast reservoirs of multimedia data generated across diverse sources. Consequently, researchers have delved into data mining methodologies, uncovering effective strategies for extracting insights from multimedia datasets. This study aims to probe the conceptual and practical dimensions of multimedia data mining within surveillance contexts, elucidating its transformative impact on diverse sectors by facilitating efficient data collection, analysis, and dissemination processes. Moreover, it underscores the significance of incorporating relevant cryptography methods to bolster the system’s integrity and completeness.

Transforming Shiga toxin-producing Escherichia coli surveillance through whole genome sequencing in food safety practices.

Shiga toxin-producing Escherichia coli (STEC) is a gastrointestinal pathogen causing foodborne outbreaks. Whole Genome Sequencing (WGS) in STEC surveillance holds promise in outbreak prevention and confinement, in broadening STEC epidemiology and in contributing to risk assessment and source attribution. However, despite international recommendations, WGS is often restricted to assist outbreak investigation and is not yet fully implemented in food safety surveillance across all European countries, in contrast to for example in the United States. In this study, WGS was retrospectively applied to isolates collected within the context of Belgian food safety surveillance and combined with data from clinical isolates to evaluate its benefits. A cross-sector WGS-based collection of 754 strains from 1998 to 2020 was analyzed. We confirmed that WGS in food safety surveillance allows accurate detection of genomic relationships between human cases and strains isolated from food samples, including those dispersed over time and geographical locations. Identifying these links can reveal new insights into outbreaks and direct epidemiological investigations to facilitate outbreak management. Complete WGS-based isolate characterization enabled expanding epidemiological insights related to circulating serotypes, virulence genes and antimicrobial resistance across different reservoirs. Moreover, associations between virulence genes and severe disease were determined by incorporating human metadata into the data analysis. Gaps in the surveillance system were identified and suggestions for optimization related to sample centralization, harmonizing isolation methods, and expanding sampling strategies were formulated. This study contributes to developing a representative WGS-based collection of circulating STEC strains and by illustrating its benefits, it aims to incite policymakers to support WGS uptake in food safety surveillance.