bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving the life cycle greenhouse gas emissions of transport infrastructure assets – a case study in Brussels, Belgium
Authors: Ataee Soroosh , Stephan A 
Published: December 2022

Abstract
Infrastructure transportation assets represent a significant share of environmental effects associated with built stocks. Although there is a growing body of knowledge on the life cycle assessment of buildings and material flow analysis of building stocks, infrastructure assets have received less attention. Existing life cycle assessment studies tend to rely on process analysis which is known to underestimate embodied environmental flows. The aim of this paper is to introduce a bottom-up parametric model for infrastructure transport assets, which can support a detailed characterization of the stock in a consistent and transparent manner. It relies on comprehensive hybrid exchange data for the quantification. This paper establishes the approach and equations used in the proposed model. The model consists of four main steps, namely: developing parametric representations of transport infrastructure assets, quantifying their material stock and replacement-related flows, quantifying life cycle embodied environmental flows and visualizing the results. One pilot case study of a road (including pedestrian sidewalks and bike lanes) and light railway, is used to demonstrate the model. Results assessing this pilot case study over 60 years are presented, including the material stock and replacement flows, as well as life cycle embodied greenhouse gas emissions (LCEGHG). Together these results provide important insights into material/infrastructure asset hotspots assets (e.g. steel lighting posts). Emissions-intensive material products and assets have a notable impact on results of LCEGHG, and they dominate both Initial greenhouse gas emissions (IEGHG) and recurrent greenhouse gas emissions (REGHG). However, they contribute marginally to the total mass. Another important observation is the significance of REGHG representing ~30% of LCEGHG. Interestingly, steel, asphalt and concrete are the main contributors to both REGHG and IEGHG. Future research consists of automating the proposed model and packaging it as a python-based Rhinoceros Grasshopper plugin before conducting testing and verification.
First steps in the development of standardised processes for life cycle assessments of geotechnical works
Authors: von der Tann L , Størdal I F , Ritter S , Feizi S 
Published: December 2022

Abstract
Despite geotechnical works contributing considerably to the environmental impact of buildings and infrastructure, the application of life cycle assessments (LCAs) in geotechnical engineering still needs to be developed and matured. This paper presents a scenario analysis of an excavation in a typical Norwegian geology. For three excavation depths, different design solutions were derived varying the length of the supporting wall and the amount of soil stabilisation within the excavation. The cradle-to-site impacts of the different solutions were then evaluated through a LCA. Global warming and acidification potentials were compared for the different design choices in parallel with an estimate of the respective solution’s costs and different functional units were considered. The study shows that, for excavations in the chosen setting, most emissions are caused at product stage and the environmental impact related to the excavated volume or to the additional floor space created underground increases with excavation depth. It emphasises that different impact categories need to be considered to get a full picture of environmental impact. Simple to use LCA tools can provide a direct comparison of different potential solutions. Shifting the focus from minimising cost to minimising environmental impact will likely lead to different design decisions for geotechnical works.
Towards understanding disruption in the market for professional services in construction
Authors: Tafti A D , Engström D , Sandberg M 
Published: December 2022

Abstract
The challenges faced by professional service companies in construction are increasing simultaneously as enablers such as digitalization and industrialization are developing. Currently, there are indications of two main diverging market trends: Productification and Servicification. This paper hypothesises that these two trends can be classified as Asset-dominant logic. In construction, the digital development, is currently regularly decoupled from the business model development. In support of business model innovation, this paper’s aim is to how we can understand and articulate the development of the markets for professional services in construction by describing concepts for understanding and analysing design work in the context of asset-dominant logic, as opposed to the current dominating Resource-dominated logic. The result indicates that productificiation, includes industrialization (product-based approach) by considering applying disruptive technologies, with the aid of a platform and in an ecosystem, making it possible to create value for the customer by taking service-dominant logic into account. Finally, the paper concludes with an attempt to demonstrate the mechanism of market development by considering the asset-dominant logic as a backdrop to the business model innovations that asset-dominant consulting will start to undertake in the coming months and years.
Business models for rolling out Positive Energy Buildings
Authors: Ala-Juusela M , Tuerk A 
Published: December 2022

Abstract
Positive Energy Buildings (PEBs) present a promising solution for the battle against the climate change. A PEB is a building that produces more energy with renewables than it uses. A good variety of technologies exist for realizing the PEB, and integrated concepts are emerging. Well-designed business models are recognized as a crucial element needed for a wider roll-out of PEBs, but they are largely missing. This article will look at potential business models for PEBs and their contribution to the different PEB elements, while also pointing out some aspects that need to be considered when setting up the business model. A selection of interesting or emerging business models are described, concentrating on the typical customer segments, typical value propositions, potential life-cycle phases, benefits and challenges.
Clinical characteristics and outcomes using dexmedetomidine in nonintubated patients: A poison center observational study
Authors: Kershner Emily , Tobarran Natasha , Chambers Andrew , Jenkins Zach , Wills Brandon K , Cumpston Kirk L 
Published: December 2022

AbstractPurposeDexmedetomidine is a central α2 agonist commonly used on intubated patients. It is increasingly being used off-label in nonintubated agitated patients. We sought to determine the overall clinical course, adverse effects, and need for subsequent mechanical ventilation in toxicology patients after treatment with dexmedetomidine.MethodsThis was a retrospective cohort study conducted by chart review of electronic records from the Virginia Poison Control Center from January 1, 2019 to February 4, 2022. Inclusion criteria consisted of all poison center cases where dexmedetomidine was used. The primary outcome was the presence or absence of clinical improvement following dexmedetomidine use. Secondary outcomes included adverse effects, subsequent intubation, or death.ResultsDuring this study period, there were 220 cases in which dexmedetomidine was used to treat agitation. After exclusions, 70 cases were analyzed. The categories included antimuscarinic (n = 19), polysubstance (n = 16), sedative withdrawal (n = 10), unknown agitation (n = 7), sympathomimetic (n = 5), baclofen withdrawal (n = 3), unknown ingestion (n = 3), sedative/hypnotic (n = 2), antipsychotic (n = 2), hallucinogenic (n = 2), and opioid withdrawal (n = 1). Clinical improvement occurred in 62 of 70 patients (89%). There were no deaths. A total of 4 patients were intubated after starting dexmedetomidine, 2 for refractory agitation and 2 for hypoxia after aspiration.ConclusionGlobal clinical improvement was observed in the agitated toxicology patients administered dexmedetomidine. There was one case of intubation secondary to oversedation. Dexmedetomidine could be a useful adjunctive treatment for agitated toxicologic patients but should be studied further before routinely used.
Endometrial hyperplasia with loss of APC in a novel population of Lyz2-expressing mouse endometrial epithelial cells
Authors: Kitchen-Goosen Susan M , Schumacher Heather , Good Julie , Patterson Amanda L , Boguslawski Elissa A , West Richard A , Williams Bart O , Hostetter Galen , Agnew Dalen W , Teixeira Jose M , Alberts Arthur S 
Published: December 2022

Abstract
Loss of heterozygosity and promoter hypermethylation of APC is frequently observed in human endometrial cancer, which is the most common gynecological cancer in the USA, but its carcinogenic driver status in the endometrial epithelium has not been confirmed. We have identified a novel population of progenitor endometrial epithelial cells (EECs) in mice that express lysozyme M (LysM) and give rise to approximately 15% of all EECs in adult mice. LysM is a glycoside hydrolase that is encoded by Lyz2 and functions to protect cells from bacteria as part of the innate immune system. Its expression has been shown in a subset of hematopoietic stem cells and in specialized lung and small intestinal epithelial cells. Conditional deletion of Apc in LysM + EECs results in significantly more epithelial cells compared to wild-type mice. At 5 months of age, the ApccKO mice have enlarged uterine horns with pathology that is consistent with endometrial hyperplasia with cystic endometrial glands, non-villous luminal papillae and nuclear atypia. Nuclear accumulation of β-catenin and ERα, both of which are known to induce endometrial hyperplasia, was observed in the EECs of the ApccKO mice. These results confirm that loss of APC in EECs can result in a phenotype similar to endometrial hyperplasia.
Learning to be ethical: the role of ethical capability in community development education
Authors: Agisilaou Vandra Harris , Harris Howard 
Published: December 2022

AbstractIn the complex practices of development, ethical decisions are continually demanded of practitioners. This paper addresses the process of teaching ethics, within a framework of applied ethics and with an emphasis on the development of an understanding of ethics-in-practice in current and future practitioners. Building on recent work in the area of ethics in development and humanitarian practice, it addresses approaches to teaching professional ethics in community development and other fields, and debates about the value of teaching ethics. In particular, this article discusses how students develop what we call ‘ethical capability’—the cognitive and emotional resources needed to negotiate the contradictions and dilemmas of everyday practice, and to apply ethical decision-making models. Analysing a current post-graduate applied development ethics course and experience of similar courses in undergraduate business, we investigate how these can prepare students for the messy reality of community development practice. With very few courses teaching ethics in development, we propose that it is important to enhance ethical capability in community development students and practitioners, and that doing so supports their work and wellbeing.
Hemolytic parasites affect survival in migrating red-tailed hawks
Authors: Briggs Christopher W , Dudus Kris A , Ely Teresa E , Kwasnoski Laura A , Downs Cynthia J 
Published: December 2022

AbstractMigrating birds face a myriad of hazards, including higher exposure to parasites and numerous competing energy demands. It follows that migration may act as a selective filter and limit population growth. Understanding how individual-level physiological condition and disease status scale up to population dynamics through differential survival of individuals is necessary to identify threats and management interventions for migratory populations, many of which face increasing conservation challenges. However, linking individual physiological condition, parasite infection status and survival can be difficult. We examined the relationship among two measures of physiological condition [scaled-mass index and heterophil/leukocyte (H/L) ratio], hematozoa (i.e. hemoparasites) presence and abundance, and constitutive immunity in 353 autumn migrating red-tailed hawks (Buteo jamaicensis calurus) from 2004 to 2018. Hematazoa (i.e. Haemoproteus and Leucocytozoon) were in the blood smears from 139 red-tailed hawks (39.4%). H/L ratio decreased with scaled-mass index. Adults had a significantly higher H/L ratio than juveniles. Our two measures of immune defences, hemolytic-complement activity and bacteria-killing ability, were highly positively correlated. Our most notable finding was a negative relationship between Haemoproteus parasitemia and survival (i.e. documented individual mortality), indicating that haemosporidian parasites influence survival during a challenging life stage. The effect of haemosporidian parasites on individuals is often debated, and we provide evidence that parasitemia can affect individual survival. In contrast, we did not find evidence of trade-offs between survival and immune defences.
Clinical validation of an artificial intelligence algorithm offering cross-platform detection of atrial fibrillation using smart device electrocardiograms
Authors: Mannhart D , Lefebvre B , Gardella C , Henry C , Serban T , Knecht S , Kuehne M , Sticherling C , Badertscher P 
Published: December 2022

Abstract

Introduction
Multiple smart devices capable of “screening” for atrial fibrillation (AF) based on single-lead electrocardiogram (SL ECG) are presently available. Manufacturers' algorithm capabilities and accuracy for the automated detection of AF vary. Reliable artificial intelligence (AI) algorithms would be valuable to assist physicians with managing the large amount of data. We aimed to assess the clinical value of applying a smart device agnostic AI-based algorithm for the detection of AF from five different smart devices (four smartwatches, one handheld device) and compared the results to the cardiologist-interpreted 12-lead ECG in a real world cohort of patients.

Methods
This is a prospective, observational study enrolling patients presenting to a cardiology service at a tertiary referral center. Patients were prescribed a 12-lead ECG, followed by five consecutive smart device recordings from five different manufacturers. SL ECGs were exported as PDF files from the devices and analyzed by a deep neural network (DNN) based platform which allows automated AI assisted cardiac rhythm interpretation.

Results
We prospectively enrolled 157 patients (32% female, median age 66 years). AF was present in 48 patients (31%) at time of recording, as documented by the 12-lead ECG. Accuracy for the detection of AF by the DNN-based algorithm was 96.6% for the Apple Watch 6, 95.2% for the AliveCor Kardia Mobile, 96.0% for the Fitbit Sense, 95.7% for the Samsung Galaxy Watch 3 and 93.8% for the Withings Scanwatch, respectively (Figure 1, left). While diagnostic accuracy of the DNN-based algorithm was similar compared to each manufacturer's individual algorithm, the proportion of SL ECGs with a conclusive diagnosis was significantly higher for all smart devices when using the DNN-based algorithm, p&lt;0.001 (Figure 1, right). As complementary analysis, we assessed sensitivity and specificity detection capabilities in both algorithms (Figure 2).

Conclusion
In this clinical validation, a DNN-based algorithm reported significantly more conclusive diagnoses for each smart device compared to the manufacturers' algorithms, whilst showing similarly high accuracy in the detection of AF compared to the cardiologist-interpreted standard 12-lead ECG. Given further validation, SL ECG assisted rhythm interpretation through a cross-platform AI-algorithm presents a promising clinical value for AF detection and offers a possible solution for managing the data surge for smart device-acquired ECGs.

Funding Acknowledgement
Type of funding sources: None.
Deep learning-based electrocardiogram analysis detecting paroxysmal atrial fibrillation during sinus rhythm in patients with cryptogenic stroke: validation study using implantable cardiac monitoring
Authors: Jeon K H , Kwon J M , Lee M S , Cho Y J , Oh I Y , Lee J H 
Published: December 2022

Abstract

Background
Atrial fibrillation (AF) is the most cause of cardioembolic source causing cryptogenic stroke. In these, anticoagulation therapy could reduce recurrence of stroke. However, paroxysmal AF would not be detected even by 24 hours Holter monitoring. Deep learning-based electrocardiogram (ECG) analysis models were recently developed to detect AF during sinus rhythm.

Purpose
We aimed to develop a deep learning algorithm (DLA) to detect AF during sinus rhythm and validate the model in patients with cryptogenic stroke who underwent implantable cardiac monitoring (ICM) to diagnose paroxysmal AF.

Methods
This cohort study involved three hospitals (A, B, and C). We developed a DLA to detect AF using sinus rhythm 10 s 12-lead ECG. We included adult patients aged ≥18 years from hospital A and B. We used development data from AF adult patients who had at least one atrial fibrillation rhythm in the study period (Jan 2016 to Dec 2021) and non-AF patients who had no reference to AF in the ECG and electronic medical record. DLA was based on convolutional neural network (CNN) using 10 s 12-lead. For external validation, the ECGs from 217 patients (hospital C) with cryptogenic stroke who underwent ICM were analyzed by using the DLA for validating the accuracy in the real-world clinical situations.

Results
We included 10,605 AF adult patients and 50,522 non-AF patients as development data. During the internal validation, the area under the curve (AUC) of the final DLA based on CNN was 0.793 (95% Confidence interval 0.778–0.807). In external validation data from cryptogenic stroke patients, the mean ICM duration was 15.1 months, and AF &gt;5 mins was detected in 32 patients (14.5%). The diagnostic accuracy of DLA was 0.793 to detect AF during sinus rhythm, and AUC was 0.824. The sensitivity, specificity, positive predictive value, and negative predictive value of the model were 0.844, 0.784, 0.403, and 0.967, respectively, which outperformed other conventional predictive methods based on clinical factors, such as CHARGE-AF, C2hest, and HATCH.

Conclusions
In this study, DLA accurately detected paroxysmal AF using 12-leads normal sinus rhythm ECG in patients with cryptogenic stroke and outperformed the conventional models. The DLA could be used as a screening tool to identify the cause of stroke in the future.

Funding Acknowledgement
Type of funding sources: None.
Cardiovascular disease risk prediction via machine learning using mental health data
Authors: Dorraki M , Liao Z , Abbott D , Psaltis P J , Baker E , Bidargaddi N , Van Den Hengel A , Narula J , Verjans J W 
Published: December 2022

Abstract

Background
Robust and accurate risk prediction models are much needed in cardiovascular disease. It is well-known that mental health is associated with the risk of developing cardiovascular disease. It is unknown whether mental health markers can enhance existing risk prediction models for cardiovascular disease.

Purpose
The main purpose of this study was to assess capability of mental health factors along with traditional risk factors to be used in cardiovascular predictive machine learning models, and to develop a combined machine learning approach using both traditional risk and psychological factors in 375,145 participants of the UK Biobank.

Methods
A comprehensive Pearson correlation analysis is carried out on UK Biobank data. Subsequently, an ensemble model containing decision tree, random forest, XGBoost, support vector machine (SVM), and deep neural network (DNN) classification approaches was built to predict cardiovascular diseases (CVD) in UK Biobank participants. The model was first trained using traditional cardiovascular risk factors, and subsequently trained using a combination of cardiovascular risk and psychological factors.

Results
The correlation analysis revealed that there is a correlation between CVD and mental health factors suggesting the potential of mental health application for machine learning models. Our ensemble machine learning model was able to predict CVD with an accuracy of 73.49% using CVD risk factors alone. However, by combining psychological factors with CVD risk factors in the training data, an improved accuracy of 95.70% was achieved. The accuracy and robustness of ensemble machine learning model outperformed any of five constituent learning algorithms alone.

Conclusions
Our results suggest that mental health assessment data along with traditional risk factors provides a powerful, safe and affordable machine learning model enrichment that can be used for state-of-the-art prediction of CVD.

Funding Acknowledgement
Type of funding sources: None.
PMHnet-alpha: development and validation of a neural network based discrete-time survival model for mortality prediction in ischemic heart disease
Authors: Holm P , Haue A D , Westergaard D , Banasik K , Koeber L , Brunak S , Bundgaard H 
Published: December 2022

Abstract

Background
Current risk prediction models in ischemic heart disease (IHD) use a small set of well-known risk factors, have limited predictive capabilities, and are largely the same as they were twenty years ago.
We developed and externally validated PMHnet-alpha, a neural-network based survival model for risk-stratification in ischemic heart disease that leverages the multitude of clinical features available in modern electronical health records.

Methods
We included 39,746 IHD patients from the regional Heart Registry that had been subjected to a coronary angiography between 2006 and 2017 with confirmed coronary artery disease.
Clinical data was extracted from the Danish National Patient Registry, and electronic health records.
595 different features, consisting of diagnosis codes, procedure codes, biochemical test results, and clinical measurements were used as model inputs.
Prior to model development, patients were randomly divided into a training set (n=34,746) and a tesing set (n=5,000).
The testing set was not used for model development.
Model performance was evaluated at six months, one years, three-, and five years of follow-up using time-dependent ROC curve analysis and Harrels' C-index.
Lastly, we also assessed the calibration of the model.
We benchmarked the performance of PMHnet-alpha against the GRACE Risk Score 2.0, which is widely considered to the best-performing model in current clinical use.
We explored the importance of individual features using SHAP values on the trained models.

Findings
PMHnet-alpha had very high model discrimination on the testing data with time-dependent AUCs of 0.88 (95% CI 0.86–0.90) at six months, 0.88 (95% CI 0.86–0.90) at one year, 0.84 (95% CI 0.82–0.86) at three years, and 0.82 (95% CI 0.80–0.84) at five years.
The discrimination of the benchmark model GRACE2.0 on the same data was considerably lower, 0.77 (95% CI 0.73–0.80) at six months, 0.77 (95% CI 0.74–0.80) at one year, and 0.73 (95% CI 0.70–0.75) at three years.
PMHnet-alpha is undergoing external validation in other nordic countries.
We identified that on-average, age, coronary pathology and smoking status were the most impactful features.

Interpretation
Here we present a significant improvement of the state of the art in cardiac risk prediction.
PMHnet-alpha supports better and optimized use of available healthcare data, signified by the vast improvement compared to GRACE2.0.
This also signifies an important paradigm shift in which data-driven strategies are necessary to transform the increasing amount of data generated in the modern healthcare system into evidence-based clinical decision making.

Funding Acknowledgement
Type of funding sources: Foundation. Main funding source(s): The Novo Nordisk Foundation, NordForsk