Search Results (443)

Background: HPV infections with high-risk subtypes are a risk factor for developing cervical and anal cancer. Despite HPV vaccination, the incidence of cervical and anal cancer is increasing. There has been substantial research regarding the benefits of screening men who have sex with men (MSM) and those diagnosed with HIV for anal HPV and dysplasia, but little data exists for women diagnosed with cervical cancer. Methods: We constructed a Markov model in Python 3.13 to simulate the outcomes and financial impact of screening women newly diagnosed with cervical cancer for anal HPV and dysplasia. Two matrices were used to represent the screened group and the unscreened group. In the screening group, all women received initial anal HPV screening and high-resolution anoscopy with biopsy. If biopsy results confirmed HSIL, each would receive treatment with electrocautery. The screening group would also undergo annual surveillance and follow-up treatment as necessary. In the unscreened group, women did not receive screening or treatment, and the disease process was allowed to progress naturally. Results: The initial cohort consisted of 5555 women diagnosed with cervical cancer and concurrent anal HPV. The incidence of anal cancer in the screening group was 271 vs. 375 in the unscreened group after three years, 642 vs. 1236 after ten years, and 863 vs. 2039 after twenty years. Moreover, anal cancer deaths were 1236 in the screening group vs. 9041 in the unscreened group after 10 years and 31,118 vs. 51,553 after twenty years. The screened group saved 330.1 million dollars after ten years and 1.33 billion dollars after twenty years when compared to the unscreened group. Over the life of the study, the screened group would also accrue 102,000 discounted QALYs when compared to the unscreened group. Conclusions: Our model strongly suggests that screening women diagnosed with cervical cancer for anal HPV and treating anal dysplasia leads to less anal cancer, less deaths from anal cancer, less economic impact on the healthcare system, and more QALYs for patients. Full article

Submarine cable systems, serving as the critical backbone of global communications, face evolving resilience threats. This work proposes a novel sensing attack that utilizes ultra-narrow-linewidth lasers to surveil these infrastructures. First, the Narrowband Jamming Attack (NJA) is introduced as an evolution of conventional physical-layer jamming. NJA is divided into three categories according to the spectral position, and the non-overlapping class represents the proposed sensing attack. Its operational principles and the key parameters determining its efficacy are analyzed, along with its deployment strategy in submarine cable systems. Finally, the sensing capability is validated via OptiSystem simulations. Results demonstrate successful localization of vibrations within the 50–200 Hz range on a 1 km fiber, achieving a spatial resolution of 1 m, and confirm the influence of vibration parameters on sensing performance. This work reveals that the proposed sensing attack has the potential to covertly monitor environmental data, thereby posing a threat to information security in submarine cable systems. Full article

Drug-resistant Mycobacterium tuberculosis remains a significant global public health threat. While whole-genome sequencing (WGS) holds immense promise for understanding transmission dynamics and drug resistance mechanisms, its integration into routine surveillance remains limited. Additionally, insights from WGS are increasingly contributing to vaccine discovery by identifying novel antigenic targets and understanding pathogen evolution. The COVID-19 pandemic catalyzed an unprecedented expansion of genomic capacity in many low- and middle-income countries (LMICs), with public health institutions acquiring next-generation sequencing (NGS) platforms and developing local expertise in real-time pathogen surveillance. This hard-won capacity now represents a transformative opportunity to accelerate TB control enabling rapid detection of drug-resistant strains and high-resolution mapping of transmission networks that are critical for timely, targeted interventions. Furthermore, the integration of machine learning with genomic and clinical data offers a powerful avenue to improve the prediction of drug resistance and to tailor patient-specific TB management strategies. This article examines the practical challenges, emerging opportunities, and policy considerations necessary to embed genomic epidemiology within national TB control programs, particularly in high-burden, resource-constrained settings. Full article

Over the past decade, researchers have advanced traffic monitoring using surveillance cameras, unmanned aerial vehicles (UAVs), loop detectors, LiDAR, microwave sensors, and sensor fusion. These technologies effectively detect and track vehicles, enabling robust safety assessments. However, pedestrian detection remains challenging due to diverse motion patterns, varying clothing colors, occlusions, and positional differences. This study introduces an innovative approach that integrates multiple surveillance cameras at signalized intersections, regardless of their types or resolutions. Two distinct convolutional neural network (CNN)-based detection algorithms accurately track road users across multiple views. The resulting trajectories undergo analysis, smoothing, and integration, enabling detailed traffic scene reconstruction and precise identification of vehicle–pedestrian conflicts. The proposed framework achieved 97.73% detection precision and an average intersection over union (IoU) of 0.912 for pedestrians, compared to 68.36% and 0.743 with a single camera. For vehicles, it achieved 98.2% detection precision and an average IoU of 0.955, versus 58.78% and 0.516 with a single camera. These findings highlight significant improvements in detecting and analyzing traffic conflicts, enhancing the identification and mitigation of potential hazards. Full article

The Flaviviruses Dengue virus (DENV), West Nile virus (WNV), Zika virus (ZIKV), and Yellow Fever virus (YFV), are mosquito-borne viruses that represent a persistent challenge to global health due to the emergence and re-emergence of outbreaks of significant magnitudes. Their positive-sense RNA genome, about 11,000 nucleotides long, encodes structural and nonstructural proteins. These viruses evolve rapidly through mutations and genetic recombination, which can lead to more virulent and transmissible strains. Although whole-genome sequencing is ideal for studying their evolution and geographic spread, its cost is a limitation. We investigated the genetic variability of DENV, ZIKV, WNV, and YFV to identify genomic regions that accurately reflect the phylogeny of the complete coding sequence and evaluated the utility of these regions in reconstructing the geographic dispersal patterns of viral genotypes and lineages. Publicly available sequences from GenBank were examined to assess variability, reconstruct phylogenies, and identify the most informative genomic regions. Once representative regions were identified, they were used to infer the global phylogeographic structure of each virus. The virus depicted distinct variation patterns, but conserved regions of high and low variability were common to all. Highly variable regions of ~2700 nt offered greater resolution in phylogenetic trees, improving the definition of internal branches and statistical support for nodes. In some cases, combined multiple highly variable regions enhanced phylogenetic accuracy. Phylogeographic reconstruction consistently grouped sequences by genotype and geographic origin, with temporal structuring revealing evolutionarily distinct clusters that diverged over decades. These findings highlight the value of targeting genomic regions for phylogenetic and phylogeographic analysis, providing an efficient alternative for genomic surveillance. Full article

Hybrid Aerial Underwater Vehicle (HAUV) is a new type of unmanned system that can operate both in air and water, and complete underwater and air operations tasks by carrying corresponding sensors. Owing to this dual-medium operational capability, HAUVs hold significant promise for coordinated air–sea surveillance and monitoring efforts. Optical methods enable high-resolution sampling across both spatial and temporal scales, offering enhanced contextual information for the interpretation of discrete observational data. In order to evaluate the feasibility of ocean optical profiling systems based on HAUVs, this paper reviews the design features of current HAUV models and summarizes advanced techniques that support their cross-medium mobility. Subsequently, we summarized the types of commercial optical instruments commonly used for underwater observation and compared the field deployment methods. By analyzing the underwater motion performance of HAUVs and the requirements for optical observation platforms, we believe that multi-rotor HAUVs can provide new observation methods for future underwater optical acquisition due to their smooth entry and exit characteristics and the ability to maintain a controlled orientation during underwater operation. Finally, the paper explores prospective applications and outlines key obstacles to be overcome in the advancement of amphibious platforms for ocean optical profiling. Full article

During the COVID-19 pandemic, a significant increase in the spread of healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) was observed. Acinetobacter baumannii, particularly carbapenem-resistant strains, poses a serious threat in intensive care units (ICUs). This study aimed to genetically characterize A. baumannii isolates from the ICU of an infectious diseases hospital repurposed for COVID-19 patient treatment. Whole-genome sequencing (WGS) was performed on 56 A. baumannii isolates from patients and environmental surfaces using the Illumina MiSeq platform. Bioinformatic analysis included multi-locus sequence typing (MLST), core-genome MLST (cgMLST), phylogenetic analysis, and in silico detection of antimicrobial resistance genes. Three sequence types (STs) were identified: ST2 (35.7%), ST78 (30.4%), and ST19 (3.5%); while 30.4% of the isolates were non-typeable. Phylogenetic analysis revealed clustering of ST2 with isolates from East Africa, ST78 with European isolates, and ST19 with isolates from Germany and Spain. Resistance genes to eight classes of antimicrobials were detected. All isolates were resistant to aminoglycosides and β-lactams. The blaOXA-23 carbapenemase gene was present in all ST2 isolates. cgMLST analysis (cgST-1746) showed significant heterogeneity among ST2 isolates (24–583 allele differences), indicating microevolution within the hospital. A novel synonymous SNP (T2220G) in the rpoB gene was identified. Environmental sampling highlighted the role of contaminated personal protective equipment (PPE) in transmission, with 47.0% of ST2 and 64.3% of ST78 isolates found on PPE. The study underscores the high resolution of WGS and cgMLST for epidemiological surveillance and confirms the critical role of infection control measures in preventing the spread of multidrug-resistant A. baumannii. Full article

Multi-camera systems (MCSs) are pivotal in aviation surveillance and autonomous navigation due to their wide coverage and high-resolution sensing. However, challenges such as complex setup, time-consuming data acquisition, and costly testing hinder research progress. To address these, we introduce MCS-Sim, a photo-realistic MCSsimulator for UAV visual perception research. MCS-Sim integrates vision sensor configurations, vehicle dynamics, and dynamic scenes, enabling rapid virtual prototyping and multi-task dataset generation. It supports dense flow estimation, 3D reconstruction, visual simultaneous localization and mapping, object detection, and tracking. With a hardware-in-loop interface, MCS-Sim facilitates closed-loop simulation for system validation. Experiments demonstrate its effectiveness in synthetic dataset generation, visual perception algorithm testing, and closed-loop simulation. Here we show that MCS-Sim significantly advances multi-camera UAV visual perception research, offering a versatile platform for future innovations. Full article

Bemisia tabaci MED is one of the most invasive and destructive agricultural pests worldwide, posing a serious threat to crop production and biosecurity. Understanding its spatiotemporal population dynamics and genetic structure is critical for early detection, effective surveillance, and sustainable management. Previous studies have shown that B. tabaci MED in China has a high genetic structure and an unstable genetic composition. The annual genetic dynamics of the B. tabaci MED population have not been investigated throughout the outbreak phase that began in 2008. Here, we report the use of 2b-RAD sequencing to estimate the spatial and temporal genetic structure of B. tabaci MED in Shandong Province over several years. We examined 198 individuals from five sites over four years (2008, 2013, 2015, and 2017). Although populations showed generally low within-population diversity (Shannon I ≤ 0.407) and a high gene flow, clear temporal differentiation emerged between the early invasion phase (2008) and later outbreak years (2013–2017). Furthermore, specific populations, notably 2017 Liaocheng and Zaozhuang, retained distinct genetic signatures compared with other regions, suggesting localized founder effects or adaptation. Our study underscores the importance of integrating high-resolution genomic tools into invasive pest surveillance programs, and understanding this heterogeneity is critical for targeted surveillance, quarantine prioritization, and sustainable management strategies. Full article

Plasmid-mediated dissemination of antimicrobial resistance (AMR) and virulence genes plays a critical role in enhancing the adaptive potential of Campylobacter spp. While Campylobacter plasmids of concern are commonly classified as pTet, pVir, pCC42 or a large plasmid encoding a T6SS (pT6SS), existing classification systems often lack the resolution to capture intra-group diversity. Here we demonstrate a plasmid typing approach with enhanced discriminatory power that categorizes these major plasmid groups into discrete subgroups and strengthens risk-assessment investigations. Pangenomic analysis of 424 Campylobacter plasmid sequences revealed 30 distinct plasmid groups. The four major groups above accounted for 74.3% of the dataset. Within these major groups, 177 plasmid type-specific loci were used to define 16 subgroups. pTet plasmids were subdivided into 5 subgroups, with subgroup 3 enriched in C. coli. pVir plasmids formed 3 subgroups, with only subgroup 3 harboring the tet(O) genes. The 5 pCC42 subgroups displayed Campylobacter species specificity while the 3 pT6SS subgroups encoded distinct AMR profiles. This high-resolution typing approach provides a unified and scalable method to characterize Campylobacter plasmid diversity and identifies genetic markers critical for pathogen surveillance, source attribution and mitigation strategies employed to safeguard human and animal health. Full article

In veterinary hospitals, the risk of C. difficile nosocomial acquired infections remains largely unknown, and only a few studies surveyed the environmental prevalence of C. difficile in these facilities. The aim of this study was to determine the prevalence of C. difficile in the Ontario Veterinary College large animal hospital environment and to characterize the recovered isolates. Methods. The environment of the large animal clinic of a university veterinary hospital was tested for the presence of C. difficile. Samples were collected from 157 surface sites and cultured using selective enriched broth and selective agar media. Multiplex PCR method for the detection of C. difficile toxin A (tcdA), toxin B (tcdB) binary toxin (cdtA⁄cdtB) genes; high-resolution capillary gel-based electrophoresis PCR-Ribotyping; multilocus sequence typing (MLST) and antimicrobial resistance predictions from sequenced genome were performed. Results. Thirteen isolates were recovered from 157 (8.3%) of multiple sampled sites of the main hospital. Ten distinct ribotypes, of which 7 were positive for toxin genes A and B, and all were negative for binary toxin genes. The two most common PCR ribotypes were 014 and 010. Isolates belong to the MLST Clade 1 and were further divided into 5 different sequence types. A high prevalence of AMR genes was observed in some isolates. Conclusions. C. difficile is present in different areas of the large animal hospital environment, particularly areas of high traffic and surfaces difficult to clean. Active surveillance and biosecurity measures should be in place to maintain a low environmental contamination and prevent nosocomial infections. Full article

Small-vessel detection in Synthetic Aperture Radar (SAR) imagery constitutes a critical capability for maritime surveillance systems. However, prevailing methodologies such as sea-clutter statistical models and deep learning-based detectors face three fundamental limitations: weak target scattering signatures, complex sea clutter interference, and computational inefficiency. These challenges create inherent trade-offs between noise suppression and feature preservation while hindering high-resolution representation learning. To address these constraints, we propose the Multi-cue Efficient Maritime detector (MCEM), an anchor-free framework integrating three synergistic components: a Feature Extraction Module (FEM) with scale-adaptive convolutions for enhanced signature representation; a Feature Fusion Module (F²M) decoupling target-background ambiguities; and a Detection Head Module (DHM) optimizing accuracy-efficiency balance. Comprehensive evaluations demonstrate MCEM’s state-of-the-art performance: achieving 45.1% $A{P}_S$ on HRSID (+2.3pp over YOLOv8) and 77.7% $A{P}_L$ on SSDD (+13.9pp over same baseline), the world’s most challenging high-clutter SAR datasets. The framework enables robust maritime surveillance in complex oceanic conditions, particularly excelling in small target detection amidst high clutter. Full article

Wild mushroom poisoning is an emerging global food safety issue, especially in subtropical regions like southwestern China, where incidents are geographically clustered. Current detection methods are often time-consuming and overlook region-specific toxins. We developed a rapid, sensitive, and accurate method for the simultaneous detection of ten characteristic mushroom toxins prevalent in Guizhou, China. The method combines graphite multi-walled carbon nanotubes (G-MWCNTs) for sample preparation with Orbitrap high-resolution mass spectrometry (HRMS). Wild mushroom samples were extracted via ultrasonic-assisted methanol–water extraction, purified using G-MWCNTs, and separated on a Hypersil GOLD C18 column (100 mm × 2.1 mm, 1.9 μm). Gradient elution was performed with 0.1% formic acid + 0.01% ammonia and acetonitrile; quantification used the external standard method. The method achieved LODs of 0.005–0.2 mg/kg and LOQs of 0.015–0.6 mg/kg, with RSDs below 18.11% and excellent linearity (R² = 0.9936–0.9989). Among 45 wild mushroom samples, toxin levels ranged from 0.032 to 445.10 mg/kg, with a detection rate of 22.22%, suggesting notable poisoning risk. This method reduces pretreatment time while ensuring high analytical performance, offering a reliable tool for rapid toxin screening and supporting regional surveillance of wild mushroom poisoning. Full article

Wastewater-based epidemiology (WBE) is increasingly recognized as a valuable tool for monitoring disease cycles, including pandemics like COVID-19. Unlike pandemics, epidemics exhibit distinct dynamics, spread patterns, multiple origin points, and varying levels of population immunity. This study evaluates the applicability of WBE for epidemic monitoring and emergency preparedness by analyzing SARS-CoV-2 presence in Jaipur’s wastewater over one year post-second pandemic wave, covering a minor surge (third) and a mild resurgence (fourth) of COVID-19. A total of 1050 samples from different city localities were analyzed using a combination of two concentration methods (the direct method and the Polyethylene Glycol (PEG) method) and two detection kits (qualitative and quantitative). WBE effectively detected both minor and major epidemic outbreak cycles of SARS-CoV-2. A total of 62.91% samples out of all untreated samples tested, were found to be positive with viral genome; however, the positivity rate of any particular day did not exceed 25% even during the peaks. Notably, short-distance transportation under ambient conditions had no significant impact (p > 0.05) on detection, and the combination of the direct method with quantitative kits provided the highest sensitivity. Based on these findings, a cost-effective bi-phase surveillance model is proposed for year-round epidemic monitoring. This model suggests routine use of the faster, cheaper direct method, switching to the PEG concentration method during rising viral loads for enhanced resolution. Such an approach ensures sustainable, resource-efficient surveillance, particularly benefiting low- and middle-income countries facing financial constraints. Full article

Infrared small target detection is a critical task in remote sensing applications, such as aerial reconnaissance, maritime surveillance, and early-warning systems. However, due to the inherent characteristics of remote sensing imagery, such as complex backgrounds, low contrast, and limited spatial resolution-detecting small-scale, dim infrared targets remains highly challenging. To address these issues, we propose MFAFNet, a novel Multi-Feature Attention Fusion Network tailored for infrared remote sensing scenarios. The network comprises three key modules: a Feature Interactive Fusion Module (FIFM), a Patch Attention Block (PAB), and an Asymmetric Contextual Fusion Module (ACFM). FIFM enhances target saliency by integrating the original infrared image with two locally enhanced feature maps capturing different receptive field scales. PAB exploits global contextual relationships by computing inter-pixel correlations across multi-scale patches, thus improving detection robustness in cluttered remote scenes. ACFM further refines feature representation by combining shallow spatial details with deep semantic cues, alleviating semantic gaps across feature hierarchies. Experimental results on two public remote sensing datasets, SIRST-Aug and IRSTD-1k, demonstrate that MFAFNet achieves excellent performance, with mean IoU values of 0.7465 and 0.6701, respectively, confirming its effectiveness and generalizability in infrared remote sensing image analysis. Full article