bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fast in the pandemic, durable after droughts, inequal during economic downturn. A 20 year multi-dimensional retrospective analysis of water demand change in Southern California
Authors: Gross Marie-Philine , Ajami Newsha K , Cominola Andrea 
Published: September 2023

Abstract
Determining changes in water consumption behavior due to recurring external stressors (e.g. droughts and socio-economic fluctuations) and previously unseen shocks like the COVID-19 pandemic can support future demand-side management (DSM) programs and identifying vulnerable social groups. Yet, most studies analyzing external drivers of water consumption change are noncomparative as they examine only one stressor at the time, mainly droughts. Here we develop a multi-dimensional retrospective analysis (MDRA) to quantify residential water consumption changes in Southern California over the last two decades under compound hydroclimatic, economic, and pandemic stressors. We then uncover what socio-economic groups relate to predominant behavioral changes under each stressor. We observe a long-lasting decreasing water consumption trend for more than 76% of the analyzed households. Socio-economically vulnerable households were more likely to reduce water consumption during the economic and pandemic stressors. Differently, affluent neighborhoods tended to increase water consumption during or after droughts and the pandemic. Our MDRA supports utilities and policy makers to discern the impacts of different stressors on water demand and helps to identify social and economic sensitivities across a service area to develop adaptive DSMs and assistance programs.
Physical mechanisms for the dominant summertime high-latitude atmospheric teleconnection pattern and the related Northern Eurasian climates
Authors: Kim Jin-Yong , Seo Kyong-Hwan 
Published: September 2023

Abstract
Summertime atmospheric teleconnection patterns over Eurasia have a significant influence on regional weather and climate. Despite extensive studies on the subtropical patterns, the high-latitude counterpart has received relatively less attention. This study proposes physical mechanisms for the formation and maintenance of the dominant high-latitude teleconnection pattern. The formation of the pattern is associated with variability in synoptic-scale eddy activity due to the meridional gradient of sea surface temperature anomalies in the vicinity of the Gulf Stream, causing a meridional shift of the central axis of storm track at the exit of Atlantic jet. The resultant convergence of transient vorticity fluxes to the west of the British Isles induces low-frequency cyclonic circulation anomalies and continued propagation of Rossby waves downstream along northern Eurasia. Once these circulation anomalies are formed, the subsequent latent heat-related diabatic anomalies over the northern Eurasian landmass act as another source of Rossby waves to maintain the teleconnection pattern. Regional temperature and precipitation variability is closely linked to the wave pattern along a route through northern Eurasia, and even precipitation over the East Asian summer monsoon region is influenced by the teleconnection pattern.
Evaluating the Accuracy of UAV and TLS for 3D Indoor Modelling in Large-Scale Building Environments
Authors: Zakiyon Ahmad Mirza Afiq Ahmad , Idris Ahmad Norhisyam , Hezri Razali M. , Ghani Muhammad Nasri Abdul , Syafuan Wan Mohamed 
Published: September 2023

Abstract
The accuracy of 3D indoor reconstructed models is critical in various applications such as indoor navigation, virtual reality (VR), and building information modelling (BIM). This research study aims to evaluate the accuracy of Unmanned Aerial Vehicles (UAV) and Terrestrial Laser Scanning (TLS) for 3D indoor modelling in large-scale building environments. To achieve this, several evaluations were made towards the number of point clouds, estimated costs and accuracy of the 3D indoor reconstructed model generated from dense point clouds acquired by Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanner (TLS). A small indoor classroom was selected for this study approximately 100m2. In UAV data acquisition, three (3) flight missions were set up at the front, left and right views. Meanwhile, five (5) scanning stations were placed on-site for the TLS method. Due to various different flight mission views in the UAV dataset, the number of point clouds was quite higher compared to the TLS method. However, a better-quality visualization of the TLS model has been obtained as opposed to the UAV 3D model. For the required time to generate a 3D model, it showed that UAV processing time was more consuming lots of time than the TLS method, especially when georeferencing the overlapping photographs. In terms of accuracy, the RMSE value from TLS was better than UAV at 0.003m compared to UAV at 0.021m. Overall, this study provides insights into the accuracy and suitability of UAV and TLS for 3D indoor modelling in large-scale building environments. The results can inform decision-making processes in various industries such as architecture, engineering, and construction, where accurate and reliable 3D models are crucial for design, planning, and management purposes.
Mapping Oil Palm Plantations Using WorldView-2 Satellite Imagery and Machine Learning Algorithms
Authors: Marzuki F A A , Shafri H Z M , Ang Y , Shaharum N S N , Lee Y P , Bakar S A , Abidin H , Lim H S , Abdullah R 
Published: September 2023

Abstract
Currently, remote sensing has been used extensively in the agriculture industry for oil palm monitoring due to their large plantation area. Oil palm monitoring can be done by performing land cover classification using various classification methods and machine learning algorithms. This study was conducted to perform oil palm mapping using WorldView-2 satellite imagery and classify land cover features using machine learning algorithms such as Random Forest (RF) and Linear Support Vector Classifier (LSVC). A total of 58609 sampling points were classified into six classes which are water, built-up, bare soil, forest, mature oil palm (≥9 years) and young oil palm (3-8 years). The training and testing samples were split using 3-fold cross-validation. 67% of the total sampling points were used for training samples whereas the other 33% were used for testing samples. The methods used to validate the data in this study is by using spectral reflectance and Google Earth Pro. Accuracy assessment was conducted after obtaining the classification output such as Overall Accuracy (OA), Kappa Accuracy (KA), Precision, Recall and F1-score. As a result, the oil palm mapping using RF has a higher accuracy than LSVC which is 72.49% for OA and 62.98% for KA. The p-value obtained from the McNemar’s test conducted in this study is 0.683 (>0.05) which concludes that the predictive performance of the two models are equal.
The impact of computing infrastructure on carbon emissions: an empirical study based on china national supercomputing center
Authors: Yang Haodong , Wang Gaofeng 
Published: September 2023

Abstract
Digitalization is viewed as a potential solution to environmental sustainability issues. However, existing researches suggest that the environmental impact of digital technology is uncertain. This study focuses on the National Supercomputing Center (NSC) in China, a large-scale computing infrastructure, and expands the correlation between digital infrastructure and green development from a computing perspective. Based on the synthetic control method, we select non-supercomputing cities as the control group and assign appropriate weighting. Through the fitting of a synthetic control group (refer to as the synthetic city) with similar characteristics, the analysis is conducted to compare carbon emissions (CO2 emission) between NSC city and the synthetic city. The empirical results show that the NSC may worsen regional CO2 emissions, and this result still holds true after a series of robustness tests. Mechanism examinations show that the NSC does not exhibit significant composition effect (energy structure improvement) and technology effect (green technology innovation), while scale effect (increase in energy demand and consumption) dominate the NSC’s carbon emissions. Based on these findings, we consider that in addition to improving the energy efficiency of supercomputing centers, the adoption of cleaner renewable energy and the promotion of knowledge spillovers are crucial for achieving a green transformation for computing infrastructure.
Evaluation of different machine learning algorithms for LULC classification in heterogeneous landscape by using remote sensing and GIS techniques
Authors: Pokhariya Hemant Singh , Singh D P , Prakash Rishi 
Published: September 2023

Abstract
Remote sensing land cover classification plays a crucial role in detecting changes, urbanization planning, mapping and monitoring land cover on earth surface. It is very challenging to get accurate result in remote sensing data because different classifiers are very much area dependent. Different classifiers such as decision tree (DT), K-nearest neighbor (KNN), artificial neural network (ANN), support vector machine (SVM), boosted decision tree (BDT), random forest (RF), classification and regression tree (CART) and maximum likelihood classifiers (MLC), have different accuracies for the same classes. Several studies have utilized remote sensing and GIS tools to investigate changes in land use and land cover (LULC) using different classifiers. Seasonal rivers which should be classified as water bodies are mostly classified as urban area with the conventional land cover classification schemes because spectral reflectance of these river bodies is similar to urban area due to stones present in their river bed. It is very difficult to distinguish between these river beds (which are mostly found in various districts of Uttarakhand, India) and urban area in remote sensing images. In this paper, we present a new method to distinguish these river beds with the urban area and to separate other classes easily. First of all different spectral indices such as NDVI, NDBI, EVI and MNDWI are extracted from a high resolution Sentinel-2 MSI image then these indices are integrated with Sentinel-2 MSI image for classification of different land cover classes by using four different machine learning classifiers such as RF, SVM, DT and CART. The obtained results confirm the performance strength of the suggested method as there is much improvement in accuracy.
mStrain: strain-level identification of Yersinia pestis using metagenomic data
Authors: Qian Xiuwei , Wu Yarong , Zuo Xiujuan , Peng Xin , Guo Yan , Yang Ruifu , Zhang Xianglilan , Cui Yujun 
Published: September 2023

Abstract

Motivation
High-resolution target pathogen detection using metagenomic sequencing data represents a major challenge due to the low concentration of target pathogens in samples. We introduced mStrain, a novel Yesinia pestis strain/lineage-level identification tool that utilizes metagenomic data. mStrain successfully identified Y. pestis at the strain/lineage level by extracting sufficient information regarding single-nucleotide polymorphisms (SNPs), which can therefore be an effective tool for identification and source tracking of Y. pestis based on metagenomic data during a plague outbreak.

Definition

Strain-level identification
Assigning the reads in the metagenomic sequencing data to an exactly known or most closely representative Y. pestis strain.

Lineage-level identification
Assigning the reads in the metagenomic sequencing data to a specific lineage on the phylogenetic tree.

canoSNPs
The unique and typical SNPs present in all representative strains.

Ancestor/derived state
An SNP is defined as the ancestor state when consistent with the allele of Yersinia pseudotuberculosis strain IP32953; otherwise, the SNP is defined as the derived state.

Availability and implementation
The code for running mStrain, the test dataset, and instructions for running the code can be found at the following GitHub repository: https://github.com/xwqian1123/mStrain.
Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures
Authors: Al-Masri Carmen , Trozzi Francesco , Lin Shu-Hang , Tran Oanh , Sahni Navriti , Patek Marcel , Cichonska Anna , Ravikumar Balaguru , Rahman Rayees 
Published: September 2023

Abstract

Summary
Protein kinases are a family of signaling proteins, crucial for maintaining cellular homeostasis. When dysregulated, kinases drive the pathogenesis of several diseases, and are thus one of the largest target categories for drug discovery. Kinase activity is tightly controlled by switching through several active and inactive conformations in their catalytic domain. Kinase inhibitors have been designed to engage kinases in specific conformational states, where each conformation presents a unique physico-chemical environment for therapeutic intervention. Thus, modeling kinases across conformations can enable the design of novel and optimally selective kinase drugs. Due to the recent success of AlphaFold2 in accurately predicting the 3D structure of proteins based on sequence, we investigated the conformational landscape of protein kinases as modeled by AlphaFold2. We observed that AlphaFold2 is able to model several kinase conformations across the kinome, however, certain conformations are only observed in specific kinase families. Furthermore, we show that the per residue predicted local distance difference test can capture information describing structural flexibility of kinases. Finally, we evaluated the docking performance of AlphaFold2 kinase structures for enriching known ligands. Taken together, we see an opportunity to leverage AlphaFold2 models for structure-based drug discovery against kinases across several pharmacologically relevant conformational states.

Availability and implementation
All code used in the analysis is freely available at https://github.com/Harmonic-Discovery/AF2-kinase-conformational-landscape.
Advancements in computational modelling of biological systems: seventh annual SysMod meeting
Authors: Puniya Bhanwar Lal , Dräger Andreas 
Published: September 2023

Abstract

Summary
The Computational Modelling of Systems Biology (SysMod) Community of Special Interest (COSI) convenes annually at the Intelligent Systems for Molecular Biology (ISMB) conference to facilitate knowledge dissemination and exchange of research findings on systems modelling from interdisciplinary domains. The SysMod meeting 2022 was held in a hybrid mode in Madison, Wisconsin, spanning a 1-day duration centred on modelling techniques, applications, and single-cell technology implementations. The meeting showcased innovative approaches to modelling biological systems using cell-specific and multiscale modelling, multiomics data integration, and novel tools to develop systems models using single-cell and multiomics technology. The meeting also recognized outstanding research by awarding the three best posters. This report summarizes the key highlights and outcomes of the meeting.

Availability and implementation
All resources and further information are freely accessible at https://sysmod.info.
FrameD: framework for DNA-based data storage design, verification, and validation
Authors: Volkel Kevin D , Lin Kevin N , Hook Paul W , Timp Winston , Keung Albert J , Tuck James M 
Published: September 2023

Abstract

Motivation
DNA-based data storage is a quickly growing field that hopes to harness the massive theoretical information density of DNA molecules to produce a competitive next-generation storage medium suitable for archival data. In recent years, many DNA-based storage system designs have been proposed. Given that no common infrastructure exists for simulating these storage systems, comparing many different designs along with many different error models is increasingly difficult. To address this challenge, we introduce FrameD, a simulation infrastructure for DNA storage systems that leverages the underlying modularity of DNA storage system designs to provide a framework to express different designs while being able to reuse common components.

Results
We demonstrate the utility of FrameD and the need for a common simulation platform using a case study. Our case study compares designs that utilize strand copies differently, some that align strand copies using multiple sequence alignment algorithms and others that do not. We found that the choice to include multiple sequence alignment in the pipeline is dependent on the error rate and the type of errors being injected and is not always beneficial. In addition to supporting a wide range of designs, FrameD provides the user with transparent parallelism to deal with a large number of reads from sequencing and the need for many fault injection iterations. We believe that FrameD fills a void in the tools publicly available to the DNA storage community by providing a modular and extensible framework with support for massive parallelism. As a result, it will help accelerate the design process of future DNA-based storage systems.

Availability and implementation
The source code for FrameD along with the data generated during the demonstration of FrameD is available in a public Github repository at https://github.com/dna-storage/framed, (https://dx.doi.org/10.5281/zenodo.7757762).
Multidisciplinary cardiogenic shock team approach improves the long-term outcomes of patients suffering from refractory cardiogenic shock treated with short-term mechanical circulatory support
Authors: Hérion François-Xavier , Beurton Antoine , Oddos Claire , Nubret Karine , Aguerreche Clément , Quessard Astrid , Faure Maxime , Gerbaud Edouard , Pernot Mathieu , Imbault Julien , Ouattara Alexandre 
Published: September 2023

Abstract

Aims
Short-term mechanical circulatory support (STMCS) may be used as an intentional escalation strategy to treat refractory cardiogenic shock (rCS). However, with growing technical possibilities, making the right choice at the right time can be challenging. We established a shock team in January 2013 comprising a cardiac anaesthetist–intensivist, an interventional cardiologist, and a cardiac surgeon. Since then, a diagnosis of rCS has triggered a multidisciplinary team meeting based on a common algorithm. This study aimed to compare the decision-making process for STMCS for rCS before (2007–2013) and after (2013–2019) the creation of the shock team.

Methods and results
This before-and-after cohort study was conducted over a 156-month period. Post-cardiotomy rCS were excluded. The primary outcome was a 1-year survival rate. In total, 250 consecutive adult patients were included in the analysis (84 in the control group and 166 in the shock team group). At baseline, the CardShock score was not different between the two groups (5[3–5] vs. 5[4–6], P = 0.323). The 1-year survival rate was significantly higher in the shock team group compared with the control group (59% vs. 45%, P = 0.043). After a Cox regression analysis, the shock team intervention was independently associated with a significantly improved 1-year survival rate (HR: 0.592, 95% CI: 0.398–0.880, P = 0.010).

Conclusion
A multidisciplinary shock team-based decision for STMCS device implantation in rCS is associated with better 1-year survival rates.
Quantification of myocardial extracellular volume without blood sampling
Authors: Chen Wensu , Faragli Alessandro , Goetze Collin , Zieschang Victoria , Weiss Karl Jakob , Hashemi Djawid , Beyer Rebecca , Hafermann Lorena , Stawowy Philipp , Kelle Sebastian , Doeblin Patrick 
Published: September 2023

Aims
Cardiac magnetic resonance (CMR) T1 relaxation time mapping is an established technique primarily used to identify diffuse interstitial fibrosis and oedema. The myocardial extracellular volume (ECV) can be calculated from pre- and post-contrast T1 relaxation times and is a reproducible parametric index of the proportion of volume occupied by non-cardiomyocyte components in myocardial tissue. The conventional calculation of the ECV requires blood sampling to measure the haematocrit (HCT). Given the high variability of the HCT, the blood collection is recommended within 24 h of the CMR scan, limiting its applicability and posing a barrier to the clinical routine use of ECV measurements. In recent years, several research groups have proposed a method to determine the ECV by CMR without blood sampling. This is based on the inverse relationship between the T1 relaxation rate (R1) of blood and the HCT. Consequently, a ‘synthetic’ HCT could be estimated from the native blood R1, avoiding blood sampling.

Methods and results
We performed a review and meta-analysis of published studies on synthetic ECV, as well as a secondary analysis of previously published data to examine the effect of the chosen regression modell on bias. While, overall, a good correlation and little bias between synthetic and conventional ECV were found in these studies, questions regarding its accuracy remain.

Conclusion
Synthetic HCT and ECV can provide a ‘non-invasive’ quantitative measurement of the myocardium’s extracellular space when timely HCT measurements are not available and large alterations in ECV are expected, such as in cardiac amyloidosis. Due to the dependency of T1 relaxation times on the local setup, calculation of local formulas using linear regression is recommended, which can be easily performed using available data.
Quality of life and societal costs in patients with dilated cardiomyopathy
Authors: Wiethoff Isabell , Sikking Maurits , Evers Silvia , Gabrio Andrea , Henkens Michiel , Michels Michelle , Verdonschot Job , Heymans Stephane , Hiligsmann Mickaël 
Published: September 2023

Abstract

Aims
Dilated cardiomyopathy (DCM) is a major cause of heart failure impairing patient wellbeing and imposing a substantial economic burden on society, but respective data are missing. This study aims to measure the quality of life (QoL) and societal costs of DCM patients.

Methods and results
A cross-sectional evaluation of QoL and societal costs of DCM patients was performed through the 5-level EuroQol and the Medical Consumption Questionnaire and Productivity Cost Questionnaire, respectively. QoL was translated into numerical values (i.e. utilities). Costs were measured from a Dutch societal perspective. Final costs were extrapolated to 1 year, reported in 2022 Euros, and compared between DCM severity according to NYHA classes. A total of 550 DCM patients from the Maastricht cardiomyopathy registry were included. Mean age was 61 years, and 34% were women. Overall utility was slightly lower for DCM patients than the population mean (0.840 vs. 0.869, P = 0.225). Among EQ-5D dimensions, DCM patients scored lowest in ‘usual activities’. Total societal DCM costs were €14 843 per patient per year. Cost drivers were productivity losses (€7037) and medical costs (€4621). Patients with more symptomatic DCM (i.e. NYHA class III or IV) had significantly higher average DCM costs per year compared to less symptomatic DCM (€31 099 vs. €11 446, P &lt; 0.001) and significantly lower utilities (0.631 vs. 0.883, P &lt; 0.001).

Conclusion
DCM is associated with high societal costs and reduced QoL, in particular with high DCM severity.