Double Deletion of EP402R and EP153R in the Attenuated Lv17/WB/Rie1 African Swine Fever Virus (ASFV) Enhances Safety, Provides DIVA Compatibility, and Confers Complete Protection Against a Genotype II Virulent Strain.

African swine fever virus (ASFV) is a devastating disease affecting domestic and wild suids and causing significant economic losses in the global pig industry. Attenuated modified live virus (MLV) vaccines are the most promising approaches for vaccine development. This study aimed to evaluate the safety and efficacy of four recombinant ASFV genotype II strains, derived from the non-hemadsorbing (non-HAD) attenuated isolate Lv17/WB/Rie1, through the single or simultaneous deletion of virulence-associated genes.

Seroprevalence of Antibodies to Filoviruses with Outbreak Potential in Sub-Saharan Africa: A Systematic Review to Inform Vaccine Development and Deployment.

: Orthoebolaviruses and orthomarburgviruses are filoviruses that can cause viral hemorrhagic fever and significant morbidity and mortality in humans. The evaluation and deployment of vaccines to prevent and control Ebola and Marburg outbreaks must be informed by an understanding of the transmission and natural history of the causative infections, but little is known about the burden of asymptomatic infection or undiagnosed disease. This systematic review of the published literature examined the seroprevalence of antibodies to orthoebolaviruses and orthomarburgviruses in sub-Saharan Africa.

Longitudinal Immunological Analysis of Portuguese Healthcare Workers Across the COVID-19 Pandemic Reveals Differences in the Humoral Immune Response to Vaccines.

A vaccination programme against severe acute respiratory syndrome coronavirus 2 was initiated in Portugal in December 2020. In this study, we report the findings of a prospective cohort study implemented with the objective of monitoring antibody production in response to COVID-19 vaccination. The humoral immune response to vaccination was followed up using blood samples collected from 191 healthcare workers.

Antiscience, Vaccine Hesitancy, and Pandemic Responses: Highlights from the Asia Pacific Summit on Infectious Diseases and Immunization.

The recent resurgence of mpox highlights the urgent need for rethinking vaccination strategies globally, underscored by the painful memories of past public health crises where delayed responses and inequitable vaccine distribution exacerbated the spread of infectious diseases. The inaugural APIC-ADVA Asia Pacific Summit on Infectious Diseases and Immunization, themed "Vaccination for All: Access, Confidence and Equity (ACE)", was held in Singapore from 31 October to 1 November 2023 in an attempt to present best practices and hard-won insights from battling COVID-19 and other pandemics in the Asia-Pacific region. This summit was co-convened by the Asia-Pacific Immunization Coalition (APIC) and Asia Dengue Voice and Action (ADVA).

Approaches to Enhance the Potency of Vaccines in Chickens.

Outbreaks of avian pathogens such as Newcastle disease virus, avian influenza virus, and salmonella have a major impact on economies and food security worldwide. Some pathogens also pose a significant zoonotic potential, especially avian influenza viruses. Vaccination plays a key role in controlling many poultry diseases, and there are many vaccines licenced in the United Kingdom for diseases of poultry caused by viruses, bacteria, and parasites.

Vaccination Communication Strategies and Uptake in Africa: A Systematic Review.

: African countries experience high rates of infectious diseases that are mostly preventable by vaccination. Despite the risks of infections and other adverse outcomes, vaccination coverage in the African region remains significantly low. Poor vaccination knowledge is a contributory factor, and effective communication is crucial to bridging the vaccination uptake gap.

Leveraging Data from a Provincial Electronic Immunization Registry to Analyze Immunization Coverage, Timeliness, and Defaulters Among 8.8 Million Children from the 2018 to 2023 Birth Cohorts in Sindh Province, Pakistan.

Background/objectives: Full immunization coverage in Pakistan remains suboptimal at 66%. An in-depth assessment is needed to understand the long-term trends in immunization and identify the extent of defaulters and associated risk factors of them being left uncovered by the immunization system.

Methods: We conducted a 5-year analysis using the Government's Provincial Electronic Immunization Registry data for the 2018-2023 birth cohorts in Sindh province.

Monitoring the Risk of Type-2 Circulating Vaccine-Derived Poliovirus Emergence During Roll-Out of Type-2 Novel Oral Polio Vaccine.

Although wild poliovirus type 2 has been eradicated, the prolonged transmission of the live- attenuated virus contained in the type-2 oral polio vaccine (OPV2) in under-immunized populations has led to the emergence of circulating vaccine-derived poliovirus type 2 (cVDPV2). The novel OPV2 (nOPV2) was designed to be more genetically stable and reduce the chance of cVDPV2 emergence while retaining comparable immunogenicity to the Sabin monovalent OPV2 (mOPV2). This study aimed to estimate the relative reduction in the emergence risk due to the use of nOPV2 instead of mOPV2.

Turning Cancer Immunotherapy to the Emerging Immune Checkpoint TIGIT: Will This Break Through the Limitations of the Legacy Approach?

The discovery of immune checkpoints (ICs) has pushed cancer treatment into the next era. As an emerging immune checkpoint, the TIGIT/CD155 axis inhibits the cytotoxicity of T and NK cells through multiple pathways. Immune checkpoint inhibitors (ICIs) targeting TIGIT are hopefully expected to address the issue of unresponsiveness to anti-PD-(L)1 monoclonal antibodies (mAbs) by combination therapy.

Geospatial Variation in Vaccination Coverage and Zero-Dose Prevalence at the District, Ward and Health Facility Levels Before and After a Measles Vaccination Campaign in Nigeria.

Many measles endemic countries with suboptimal coverage levels still rely on vaccination campaigns to fill immunity gaps and boost control efforts. Depending on local epidemiological patterns, national or targeted campaigns are implemented, following which post-campaign coverage surveys (PCCSs) are conducted to evaluate their performance, particularly in terms of reaching previously unvaccinated children. Due to limited resources, PCCS surveys are designed to be representative at coarse spatial scales, often masking important heterogeneities in coverage that could enhance the identification of areas of poor performance for follow-up via routine immunization strategies.

Correction: Oluwasanmi et al. Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction. 2023, , 3836.

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Correction: Almadhor et al. AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery. 2021, , 3830.

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Application of Machine Learning to Predict CO Emissions in Light-Duty Vehicles.

Climate change caused by greenhouse gas (GHG) emissions is an escalating global issue, with the transportation sector being a significant contributor, accounting for approximately a quarter of all energy-related GHG emissions. In the transportation sector, vehicle emissions testing is a key part of ensuring compliance with environmental regulations. The Vehicle Certification Agency (VCA) of the UK plays a pivotal role in certifying vehicles for compliance with emissions and safety standards.

PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph.

Poaching poses a significant threat to wildlife and their habitats, necessitating advanced tools for its prediction and prevention. Existing tools for poaching prediction face challenges such as inconsistent poaching data, spatiotemporal complexity, and translating predictions into actionable insights for conservation efforts. This paper presents PoachNet, a novel predictive system that integrates deep learning with Semantic Web reasoning to infer poaching likelihood.

The Relationship Between Daily Activity Level, Posture Distribution, Stepping Patterns, and Cadence in the BCS70 Cohort.

This study investigated the relationship between stepping-defined daily activity levels, time spent in different postures, and the patterns and intensities of stepping behaviour. Using a thigh-mounted triaxial accelerometer, physical activity data from 3547 participants with seven days of valid data were analysed. We classified days based on step count and quantified posture and stepping behaviour, distinguishing between indoor, community, and recreation stepping.

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data.

Camera traps offer enormous new opportunities in ecological studies, but current automated image analysis methods often lack the contextual richness needed to support impactful conservation outcomes. Integrating vision-language models into these workflows could address this gap by providing enhanced contextual understanding and enabling advanced queries across temporal and spatial dimensions. Here, we present an integrated approach that combines deep learning-based vision and language models to improve ecological reporting using data from camera traps.

Extracting Optimal Number of Features for Machine Learning Models in Multilayer IoT Attacks.

The rapid integration of Internet of Things (IoT) systems in various sectors has escalated security risks due to sophisticated multilayer attacks that compromise multiple security layers and lead to significant data loss, personal information theft, financial losses etc. Existing research on multilayer IoT attacks exhibits gaps in real-world applicability, due to reliance on outdated datasets with a limited focus on adaptive, dynamic approaches to address multilayer vulnerabilities. Additionally, the complete reliance on automated processes without integrating human expertise in feature selection and weighting processes may affect the reliability of detection models.

A Framework for Optimizing Deep Learning-Based Lane Detection and Steering for Autonomous Driving.

Improving the ability of autonomous vehicles to accurately identify and follow lanes in various contexts is crucial. This project aims to provide a novel framework for optimizing a self-driving vehicle that can detect lanes and steer accordingly. A virtual sandbox environment was developed in Unity3D that provides a semi-automated procedural road and driving generation framework for a variety of road scenarios.

Modelling, Analysis and Validation of Hydraulic Self-Adaptive Bearings for Elevated Floating Bridges.

Conventional floating bridge systems used during emergency repairs, such as during wartime or after natural disasters, typically rely on passive rubber bearings or semi-active control systems. These methods often limit traffic speed, stability, and safety under dynamic conditions, including varying vehicle loads and fluctuating water levels. To address these challenges, this study proposes a novel Hydraulic Self-Adaptive Bearing System (HABS).

Error Separation Method for Geometric Distribution Error Modeling of Precision Machining Surfaces Based on K-Space Spectrum.

The geometric error distributed on components' contact surfaces is a critical factor affecting assembly accuracy and precision instrument stability. Effective error separation methods can improve model accuracy, thereby aiding in performance prediction and process optimization. Here, an error separation method for geometric distribution error modeling for precision machining surfaces based on the K-space spectrum is proposed.

MR Elastography Using the Gravitational Transducer.

MR elastography is a non-invasive imaging technique that provides quantitative maps of tissue biomechanical properties, i.e., elasticity and viscosity.

A Time-Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen.

Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and poor classification performance. In contrast, ML-based methods, particularly deep learning approaches like convolutional neural networks (CNNs), automatically extract relevant features from raw data, improving the accuracy and adaptability of the damage identification process.

Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring.

Monitoring animal populations is crucial for assessing the health of ecosystems. Traditional methods, which require extensive fieldwork, are increasingly being supplemented by time-lapse camera-trap imagery combined with an automatic analysis of the image data. The latter usually involves some object detector aimed at detecting relevant targets (commonly animals) in each image, followed by some postprocessing to gather activity and population data.

Temporal Logical Attention Network for Log-Based Anomaly Detection in Distributed Systems.

Detecting anomalies in distributed systems through log analysis remains challenging due to the complex temporal dependencies between log events, the diverse manifestation of system states, and the intricate causal relationships across distributed components. This paper introduces a TLAN (Temporal Logical Attention Network), a novel deep learning framework that integrates temporal sequence modeling with logical dependency analysis for robust anomaly detection in distributed system logs. Our approach makes three key contributions: (1) a temporal logical attention mechanism that explicitly models both time-series patterns and logical dependencies between log events across distributed components, (2) a multi-scale feature extraction module that captures system behaviors at different temporal granularities while preserving causal relationships, and (3) an adaptive threshold strategy that dynamically adjusts detection sensitivity based on system load and component interactions.

Wearable System Applications in Performance Analysis of RaceRunning Athletes with Disabilities.

RaceRunning is a sport for disabled people and successful performance depends on reducing the amount of time spent travelling a specific distance. Performance analysis in RaceRunning athletes is based on traditional methods such as recording race time, distances travelled and frequency (sets and reps) that are not sufficient for monitoring training loads. The aims of this study were to monitor training loads in typical training sessions and evaluate technical adaptations in RaceRunning performance by acquiring sensor metrics.