A comparison of current analytical methods for predicting soil-structure interaction due to tunnelling

Current procedures for the assessment of buildings response to tunnelling take into account the effect of soil-structure interaction through the definition of the building stiffness relative to the soil stiffness. Limitations of these procedures are uncertainties in the evaluation of structural parameters and inconsistent results between different methods. In this paper, three existing formulations of the Relative Stiffness Method (RSM) have been critically evaluated by analysing the governing factors in the building stiffness calculation and their effect on the structural damage assessment. The results of a sensitivity study on building height, eccentricity, opening ratio, tunnel depth, soil and masonry stiffness, and trough width parameter quantified the effect of these factors on the considered RSMs. The application of different RSMs to a real masonry building adjacent to the Jubilee Line tunnel excavation underlined the significant effect of window openings, façade stiffness and neutral axis position on the building stiffness calculation and deformation prediction. These results highlight the need for a consistent and robust damage assessment procedure.</p

A comparison of current analytical methods for predicting soil-structure interaction due to tunnelling

Current procedures for the assessment of buildings response to tunnelling take into account the effect of soil-structure interaction through the definition of the building stiffness relative to the soil stiffness. Limitations of these procedures are uncertainties in the evaluation of structural parameters and inconsistent results between different methods. In this paper, three existing formulations of the Relative Stiffness Method (RSM) have been critically evaluated by analysing the governing factors in the building stiffness calculation and their effect on the structural damage assessment. The results of a sensitivity study on building height, eccentricity, opening ratio, tunnel depth, soil and masonry stiffness, and trough width parameter quantified the effect of these factors on the considered RSMs. The application of different RSMs to a real masonry building adjacent to the Jubilee Line tunnel excavation underlined the significant effect of window openings, façade stiffness and neutral axis position on the building stiffness calculation and deformation prediction. These results highlight the need for a consistent and robust damage assessment procedure.</p

Evolution of an Eurasian Avian-like Influenza Virus in Naïve and Vaccinated Pigs

Influenza viruses are characterized by an ability to cross species boundaries and evade host immunity, sometimes with devastating consequences. The 2009 pandemic of H1N1 influenza A virus highlights the importance of pigs in influenza emergence, particularly as intermediate hosts by which avian viruses adapt to mammals before emerging in humans. Although segment reassortment has commonly been associated with influenza emergence, an expanded host-range is also likely to be associated with the accumulation of specific beneficial point mutations. To better understand the mechanisms that shape the genetic diversity of avian-like viruses in pigs, we studied the evolutionary dynamics of an Eurasian Avian-like swine influenza virus (EA-SIV) in naïve and vaccinated pigs linked by natural transmission. We analyzed multiple clones of the hemagglutinin 1 (HA1) gene derived from consecutive daily viral populations. Strikingly, we observed both transient and fixed changes in the consensus sequence along the transmission chain. Hence, the mutational spectrum of intra-host EA-SIV populations is highly dynamic and allele fixation can occur with extreme rapidity. In addition, mutations that could potentially alter host-range and antigenicity were transmitted between animals and mixed infections were commonplace, even in vaccinated pigs. Finally, we repeatedly detected distinct stop codons in virus samples from co-housed pigs, suggesting that they persisted within hosts and were transmitted among them. This implies that mutations that reduce viral fitness in one host, but which could lead to fitness benefits in a novel host, can circulate at low frequencies

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Qualitative Assessment of Off-Gassing of Compounds from Field-Contaminated Firefighter Jackets with Varied Air Exposure Time Intervals Using Headspace GC-MS

Firefighters are exposed to a complex mix of volatile and semi-volatile compounds from burning construction materials, consumer products, and other elements during fire suppression and rescue. These compounds can be absorbed onto the gear worn by firefighters and, depending on their volatility, can be released from the gear under different conditions. Few studies have focused on the off-gassing of toxic compounds from firefighters’ gear, particularly in terms of qualitative analysis methods. This study introduces a novel qualitative analysis method using headspace gas chromatography–mass spectrometry (HS-GC-MS) to assess off-gassing from field-contaminated jackets at regular intervals. Our findings show that certain compounds, such as acetic acid and di-ethyl-hexyl-phthalate (DEHP), remained present even after the gear were allowed to air out for 48 h. The persistent off-gassing of chemicals, even under ambient conditions, raises concerns about potential hazards that could pose risks for personnel in the vicinity of contaminated gear, including inside fire stations. The implications of these findings extend beyond fire stations and may have significant public health implications for firefighters who are repeatedly exposed to these compounds over time

Bench-Scale and Full-Scale Level Evaluation of the Effect of Parameters on Cleaning Efficacy of the Firefighters’ PPE

The National Fire Protection Association (NFPA) 1851 document provides guidelines for firefighters on the care and maintenance of their PPE, including decontamination practices. Firefighters are exposed to various toxic chemicals during fire suppression activities, making effective decontamination crucial for their safety. This study evaluated the efficacy of different washing parameters, including temperature, time, and surfactants, on cleaning outer-shell material contaminated with nine targeted compounds from three different functional groups: phenols, polycyclic aromatic hydrocarbons (PAHs), and phthalates. The study was conducted on both bench-scale and full-scale levels, with contaminated swatches washed in a water shaker bath in the bench-scale evaluation and full-sized washer extractors used in the full-scale evaluation. The results showed that bench-scale washing demonstrated similar trends in contaminant removal to full-scale washing. Importantly, the study highlighted the complexity of removing fireground contaminants from the personal protective ensemble (PPE). The findings of this study have practical implications for the firefighting industry as they provide insight into the effectiveness of different washing parameters for PPE decontamination. Future studies could explore the impact of repeated washing on PPE and investigate the potential for developing more efficient decontamination strategies. Ultimately, the study underscores the importance of ongoing efforts to ensure the safety of firefighters, who face significant occupational hazards

Comparative Analysis of the Liquid CO₂ Washing with Conventional Wash on Firefighters’ Personal Protective Equipment (PPE)

Firefighters are exposed to several potentially carcinogenic fireground contaminants. The current NFPA 1851 washing procedures are less effective in cleaning due to the limited intensity of the washing conditions that are used. The 2020 edition of NFPA 1851 has added limited specialized cleaning for higher efficacy. The liquid carbon dioxide (CO2) laundering technique has gained popularity in recent years due to its availability to remove contaminants and its eco-friendliness. The primary aim of this study is to address the firefighter questions regarding the efficacy of cleaning with liquid CO2 and to compare it with the conventional washing technique. The unused turnout jackets were contaminated with a mixture of fireground contaminants. These turnout jackets were cleaned with conventional NFPA 1851-appoved aqueous washing and a commercially available liquid CO2 method. Post-cleaning samples were analyzed for contamination using pressurized solvent extraction and GC-MS. The liquid CO2 technique demonstrated considerable improvement in washing efficiency compared to the conventional washing