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ABSTRACTAntimicrobial resistance (AMR) in pathogenic bacteria poses a significant threat to public health, yet there is still a need for development in the tools to deeply understand AMR genes based on genetic or structural information. In this study, we present an interactive web database named Blanket Overarching Antimicrobial-Resistance gene Database with Structural information (BOARDS, sbml.unist.ac.kr), a database that comprehensively includes 3,943 reported AMR gene information for 1,997 extended spectrum beta-lactamase (ESBL) and 1,946 other genes as well as a total of 27,395 predicted protein structures. These structures, which include both wild-type AMR genes and their mutants, were derived from 80,094 publicly available whole-genome sequences. In addition, we developed the rapid analysis and detection tool of antimicrobial-resistance (RADAR), a one-stop analysis pipeline to detect AMR genes across whole-genome sequencing (WGSs). By integrating BOARDS and RADAR, the AMR prevalence landscape for eight multi-drug resistant pathogens was reconstructed, leading to unexpected findings such as the pre-existence of the MCR genes before their official reports. Enzymatic structure prediction-based analysis revealed that the occurrence of mutations found in some ESBL genes was found to be closely related to the binding affinities with their antibiotic substrates. Overall, BOARDS can play a significant role in performing in-depth analysis on AMR.IMPORTANCEWhile the increasing antibiotic resistance (AMR) in pathogen has been a burden on public health, effective tools for deep understanding of AMR based on genetic or structural information remain limited. In this study, a blanket overarching antimicrobial-resistance gene database with structure information (BOARDS)—a web-based database that comprehensively collected AMR gene data with predictive protein structural information was constructed. Additionally, we report the development of a RADAR pipeline that can analyze whole-genome sequences as well. BOARDS, which includes sequence and structural information, has shown the historical landscape and prevalence of the AMR genes and can provide insight into single-nucleotide polymorphism effects on antibiotic degrading enzymes within protein structures.

Background: Head and neck squamous cell cancer (HNSCC) has a poor prognosis, with approximately 25–30% of patients transitioning into the palliative phase at some point. The length of this phase is relatively short, with a median duration of five months. Patients in this stage often have increased prognostic information needs. Unfortunately, predicting individual life expectancy in this phase is particularly challenging, as physicians and patients tend to overestimate survival. To address this issue, we developed the prognostic model OncologIQ Palliative based on user preferences. In this study, we now aim to assess the clinical impact of utilizing this model during counselling. Methods: This study will employ both quantitative and qualitative approaches. The primary outcome is decisional conflict and satisfaction with the decision-making process after counselling without (cohort 1) and with (cohort 2) OncologIQ Palliative. Therefore, a prospective sequential cohort study will be conducted. Secondary outcomes include the amount of palliative treatment, overall survival rates, and quality of life. These measurements will be collected after the intervention. Additionally, patients' perspectives on the decision-making process and proactive care planning, including end-of-life discussions, will be explored through interviews. Discussion: By offering more personalized prognostic information for HNSCC patients in the palliative phase, we anticipate a shift towards more patient-centred counselling. This approach can facilitate enhanced end-of-life discussions and better proactive care planning. Patients may experience reduced decisional conflict, feel better prepared for what's coming, and find assistance in their decision-making process. This could potentially lead to fewer palliative treatments. Overall, these aspects can contribute to a better quality of life and quality of care for HNSCC patients in the last phase of their lives. Trial Registration: This study was registered November 18, 2024, on ClinicalTrials.gov: NCT06699316. [ABSTRACT FROM AUTHOR]

The value of Building Information Modelling (BIM) is widely discussed within all construction stages including the data-driven culture across building processes and sustainable impact in the long term. Yet, there is a need to explore the opportunities of BIM in improving construction materials management (CMM) as a core function of supply chain management. Due to the dearth of studies on BIM potential in improving CMM within the sustainability context, the authors examine the effectiveness and efficiency of BIM-enabled materials management, via three data streams: a literature review, an online survey, and interviews with subject matter experts in the field. This study aims to explore the drivers of BIM-enabled sustainable construction materials management. This is the preliminary study designed to test the initial hypotheses based on an online questionnaire application to derive tacit knowledge from industry and academic experts, followed by short interviews with respondents. Grounded in the comprehensive literature review, 24 indicators were defined for survey purposes. Preliminarily, 206 experts from 10 countries responded to the survey. The results show significant differences in the ranking of the indicators among the five factors. In developing countries, among two groups, industry sample awareness scales demonstrated lower understanding than among academic experts. Another finding relates to the significant agreement in scaling the importance of opportunities among industrial and academic sector experts. The research adds knowledge to deepen the understanding of opportunities of BIM-enabled materials management as a part of building project sustainable performance for industry policy and decision-makers. It also brings attention to the lack of sustainability awareness amongst industry experts in developing countries. Although materials constitute a solid part of any construction project cost, there is still a collaboration gap among designers, materials management, and, more broadly, supply chain management experts.

Context. Recent works use a linear birth metallicity gradient to estimate the evolution of the [Fe/H] profile in the Galactic disk over time, and infer stellar birth radii (Rbirth) from [Fe/H] and age measurements. These estimates rely on the evolution of [Fe/H] at the Galactic center ([Fe/H](0, τ)) and the birth metallicity gradient (∇[Fe/H](τ)) over time – quantities that are unknown and inferred under key assumptions. Aims. In this work, we use the sample of Milky Way and Andromeda analogues from the TNG50 simulation to investigate the ability to recover [Fe/H](R, τ) in a variety of galaxies. Methods. Using stellar disk particles, we tested the assumptions required in estimating Rbirth, [Fe/H](0, τ), and ∇[Fe/H](τ) using recently proposed methods to understand when they are valid. Results. We show that ∇[Fe/H](τ) can be recovered in most galaxies to within 26% from the range in [Fe/H] across age, with better accuracy for more massive and stronger barred galaxies. We also find that the true central metallicity is unrepresentative of the genuine disk [Fe/H] profile; thus we propose to use a projected central metallicity instead. About half of the galaxies in our sample do not have a continuously enriching projected central metallicity, with a dilution in [Fe/H] correlating with mergers. Most importantly, galaxy-specific [Fe/H](R, τ) can be constrained and confirmed by requiring the Rbirth distributions of mono-age, solar neighborhood populations to follow inside-out formation. Conclusions. We conclude that examining trends with Rbirth is valid for the Milky Way disk and similarly structured galaxies, where we expect Rbirth can be recovered to within 20% assuming today's measurement uncertainties in TNG50. [ABSTRACT FROM AUTHOR]

Three different percentages of carbon nanotubes (CNTs) (0.1%, 0.5%, and 1% by mass of asphalt cement) were used to modify conventional asphalt cement (60/70) in this study. Mechanical properties of modified asphalt cement and mixture were evaluated. Penetration grade, kinematic viscosity, softening point and dynamic shear rheometer test were measured to evaluate physical properties of modified asphalt cement. The results exhibited that modifying asphalt cement with CNTs decreased its penetration and increased its kinematic viscosity and softening point. Rutting parameter increased with CNTs at the given temperature for both unaged and RTFOT-aged samples. Marshall stability tests, low temperature cracking tests, indirect tensile tests and wheel tracking tests were conducted to assess the mechanical performance of modified hot asphalt mixture. The Marshall stability increased with CNTs but no significant difference at 0.5 and 1.0 wt%, while Marshall flow decreased with CNTs. The results of wheel tracking test showed that rut depth decreased by 45% upon adding 0.5% CNTs by weight of asphalt cement; also, this percentage of CNTs endowed improvement in low temperature cracking and indirect tensile strength of the asphalt concrete. This study underlines that adding CNTs into asphalt cement enhances the performance of asphalt concrete pavement in both hot and cold weather, which in turn prolongs the pavement’s service life and saves maintenance expenses.

Understanding the link between the energy-efficiency of buildings and climatic conditions can improve the design of energy-efficient housing. Due to global climate change and growing requirements for building energy-efficiency, the number of publications on climate zoning for buildings has grown over the last 20 years. This review attempted to give the reader an up-to-date assessment of the scientific literature in the field of climate mapping for buildings on a global and national scale, filling in the gaps of previous works and focusing on details that were not presented before. There were 105 scientific sources examined. The most dominant climate zoning variables were thoroughly analyzed. A clear categorization of climate zoning methods with specific criteria was shown. The most used methods were evaluated, emphasizing their similarities and differences, as well as their essential components and advantages. The main literature review was supported with bibliometric and bibliographic analysis. The existence of many climate zoning methods can be an indicator of the lack of agreement on the most effective strategy. A tendency has been established for the popularization among scientists of methods based on machine learning and building energy simulations, which are relatively easy to use and have proven to be the most reliable climate zoning methods. A transformation is emerging by shifting from a climate-based to a building performance-based climate zoning approach.

The latest industrial revolution 4 enabled significant performance improvement through technological advancements. Simultaneously, the industry is setting high-level expectations for changing business practices toward long-term benefits in all three sustainability dimensions. The concept of sustainability embraces all production and operation processes in the Architecture, Engineering, and Construction (AEC) industry. This study systematically explores the literature on sustainability with Enterprise Resource Planning (ERP) and Building Information Modelling (BIM) technologies in the AEC industry and the sustainability vision for their integration. The different types of ERP and BIM implementations have similarities in addressing the broad scope of functionalities. The emergence and proliferation of ERP and BIM have brought crucial changes to the business environment. Further evolution to cloud-based operations is transforming companies from technology-oriented practices to data-centric decision-making smart infrastructures. The narrative literature review investigates the sustainability insights and ideas in ERP and BIM solutions, presenting state of the art on systems integration topics. The relevant literature was retrieved to achieve the research objectives which were qualitatively analyzed to generate the basis for further research.

Photoinduced processes governed by light activated TiO2 have been studied in many ways. One of the most active areas is the development of TiO2 photocatalysis technologies on their application for reducing environmental impacts. The immobilization of TiO2 on solid support, such as cementitious materials, greatly enhances its use in practical applications. In this review, a wide range of applications for achieving eco-efficient building using cementitious composite materials containing TiO2 photocatalyst was presented. The basic mechanism of photocatalysis, such as electron excitation, charge transfer process, reactive oxygen species (ROS) generation, and its role to oxidize the pollutant and microorganisms were extensively discussed. Unlike self-cleaning and air purification systems, the study on the antibacterial function of a cement-based surface containing TiO2 is very limited. In photocatalytic cementitious materials, the key element affecting the photocatalytic performance is the accessible active surface area. However, microstructure of cementitious materials changes with age due to hydration and surface carbonation. Hence, surface area reduction and mass transfer limitation become the main drawbacks of incorporating TiO2 in cementitious materials. This review, therefore, provides the state of the art in photocatalytic cement-based composite materials and identifies the areas in which future improvement is needed.

Changes in climatic conditions are expected globally resulting in a higher rainfall intensity and longer duration of rainfall. The increase in the rainwater infiltration into the soil contributes to many geotechnical issues, such as excessive settlement, retaining wall failure and rainfall-induced slope failures. These geotechnical problems could be mitigated by the improvement of the problematic soil with the incorporation of the unsaturated soil mechanic principles. Dual-porosity soils or soils with bimodal water retention curve (WRC) are able to retain more water during prolonged drying and they would be able to drain out water faster during intense rainfall to maintain the slope stability. The objective of this study is to investigate the characteristics of the unsaturated shear strength of soil with bimodal WRC. In addition, the new mathematical equation is proposed to estimate the unsaturated shear strength of soils with a bimodal WRC. The results of the study indicated that the nonlinearity of the unsaturated shear strength is a function of the shape of bimodal WRC limited by the first and second air-entry value (AEV) of dual-porosity soils. The proposed equation agreed well with the experimental data of the unsaturated shear strength for dual-porosity soil.

Research on the digital built environment in the context of identifying and minimizing clashes is a critical area to investigate owing to the significant influence of clashes on the construction process. This research aims to identify the current state of building information modeling (BIM) enabled clash detection in the architecture, engineering, and construction and operation (AECO) industry of the Republic of Kazakhstan. It further identifies the main reasons for clash occurrence, methods, and practices used to decrease the workload on BIM project teams. It also recognizes the existence of BIM departments (their type and functionalities). It aims to answer whether the transition from clash detection to clash avoidance is possible in a country where BIM technology has just penetrated. This research conducted a literature review and review of current regulatory documents regarding BIM technology, interviews with industry professionals, and a survey among construction companies and design organizations. Although the AECO industry of Kazakhstan has just entered the BIM environment, this research has shown that BIM-enabled clash detection and resolution processes are trying to evolve in parallel. It was concluded that the transition is difficult in a country where document assistance with clash detection and resolution is not well developed.

We use numerical simulations to model Gaia  DR3 data with the aim of constraining the Milky Way (MW) bar and spiral structure parameters. We show that both the morphology and the velocity field in MW-like galactic disc models are strong functions of time, changing dramatically over a few tens of Myr. This suggests that by finding a good match to the observed radial velocity field, vR (x, y), we can constrain the bar-spiral orientation. Incorporating uncertainties into our models is necessary to match the data; most importantly, a heliocentric distance uncertainty above 10–15 per cent distorts the bar's shape and vR quadrupole pattern morphology, and decreases its apparent angle with respect to the Sun-Galactocentric line. An excellent match to the Gaia   DR3 vR (x, y) field is found for a simulation with a bar length Rb ≈ 3.6 kpc. We argue that the data are consistent with an MW bar as short as ∼3 kpc, for moderate strength inner disc spiral structure (A 2/ A 0 ≈ 0.25) or, alternatively, with a bar length up to ∼5.2 kpc, provided that spiral arms are quite weak (A 2/ A 0 ≈ 0.1), and is most likely in the process of disconnecting from a spiral arm. We demonstrate that the bar angle and distance uncertainty can similarly affect the match between our models and the data – a smaller bar angle (20° instead of 30°) requires smaller distance uncertainty (20 per cent instead of 30 per cent) to explain the observations. Fourier components of the face-on density distribution of our models suggest that the MW does not have strong m = 1 and/or m  = 3 spirals near the solar radius. [ABSTRACT FROM AUTHOR]

Background: Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response. This study aims to provide interpretable and actionable insights, particularly for clinicians. We developed and evaluated two regression tree predictive models for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry. We performed a retrospective analysis of observational routinely collected data. Methods: Two regression tree models were developed for admission and 24 h after admission. The complexity of the trees was managed via cross validation to prevent overfitting. The predictive ability of the model was assessed via bootstrapping using the Area under the Receiver-Operating-Characteristic curve, Brier score and calibration curves. The tree models were assessed on the stability of their probabilities and predictive ability, on the selected variables, and compared to a full-fledged logistic regression model that uses variable selection and variable transformations using splines. Participants included COVID-19 patients from all ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry, who were admitted at the ICU between February 27, 2020, and November 23, 2021. From the NICE registry, we included concerned demographic data, minimum and maximum values of physiological data in the first 24 h of ICU admission and diagnoses (reason for admission as well as comorbidities) for model development. The main outcome measure was in-hospital mortality. We additionally analysed the Length-of-Stay (LoS) per patient subgroup per survival status. Results: A total of 13,369 confirmed COVID-19 patients from 70 ICUs were included (with mortality rate of 28%). The optimism-corrected AUROC of the admission tree (with seven paths) was 0.72 (95% CI: 0.71–0.74) and of the 24 h tree (with 11 paths) was 0.74 (0.74–0.77). Both regression trees yielded good calibration and variable selection for both trees was stable. Patient subgroups comprising the tree paths had comparable survival probabilities as the full-fledged logistic regression model, survival probabilities were stable over six COVID-19 surges, and subgroups were shown to have added predictive value over the individual patient variables. Conclusions: We developed and evaluated regression trees, which operate at par with a carefully crafted logistic regression model. The trees consist of homogenous subgroups of patients that are described by simple interpretable constraints on patient characteristics thereby facilitating shared decision-making. [ABSTRACT FROM AUTHOR]

The value of Building Information Modelling (BIM) is widely discussed within all construction stages including the data-driven culture across building processes and sustainable impact in the long term. Yet, there is a need to explore the opportunities of BIM in improving construction materials management (CMM) as a core function of supply chain management. Due to the dearth of studies on BIM potential in improving CMM within the sustainability context, the authors examine the effectiveness and efficiency of BIM-enabled materials management, via three data streams: a literature review, an online survey, and interviews with subject matter experts in the field. This study aims to explore the drivers of BIM-enabled sustainable construction materials management. This is the preliminary study designed to test the initial hypotheses based on an online questionnaire application to derive tacit knowledge from industry and academic experts, followed by short interviews with respondents. Grounded in the comprehensive literature review, 24 indicators were defined for survey purposes. Preliminarily, 206 experts from 10 countries responded to the survey. The results show significant differences in the ranking of the indicators among the five factors. In developing countries, among two groups, industry sample awareness scales demonstrated lower understanding than among academic experts. Another finding relates to the significant agreement in scaling the importance of opportunities among industrial and academic sector experts. The research adds knowledge to deepen the understanding of opportunities of BIM-enabled materials management as a part of building project sustainable performance for industry policy and decision-makers. It also brings attention to the lack of sustainability awareness amongst industry experts in developing countries. Although materials constitute a solid part of any construction project cost, there is still a collaboration gap among designers, materials management, and, more broadly, supply chain management experts. [ABSTRACT FROM AUTHOR]