bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biventricular Interaction During Acute Left Ventricular Ischemia in Mice: A Combined In-Vivo and In-Silico Approach
Authors: Colebank M. J. , Taylor R. , Hacker T. A. , Chesler N. C. 
Published: July 2023

AbstractComputational models provide an efficient paradigm for integrating and linking multiple spatial and temporal scales. However, these models are difficult to parameterize and match to experimental data. Recent advances in both data collection and model analyses have helped overcome this limitation. Here, we combine a multiscale, biventricular interaction model with mouse data before and after left ventricular (LV) ischemia. Sensitivity analyses are used to identify the most influential parameters on pressure and volume predictions. The subset of influential model parameters are calibrated to biventricular pressure–volume loop data (n = 3) at baseline. Each mouse underwent left anterior descending coronary artery ligation, during which changes in fractional shortening and RV pressure–volume dynamics were recorded. Using the calibrated model, we simulate acute LV ischemia and contrast outputs at baseline and in simulated ischemia. Our baseline simulations align with the LV and RV data, and our predictions during ischemia complement recorded RV data and prior studies on LV function during myocardial infarction. We show that a model with both biventricular mechanical interaction and systems-level cardiovascular dynamics can quantitatively reproduce in-vivo data and qualitatively match prior findings from animal studies on LV ischemia.
Adsorption properties of biochars obtained by KOH activation
Authors: Jedynak Katarzyna , Charmas Barbara 
Published: July 2023

AbstractIn this study four kinds of biochars were prepared from the KOH modified biomass. As the carbon precursors there was used the sawdust from the following trees: oak, hornbeam, apple and cherry. The physicochemical properties of the materials were characterized by the N2 adsorption, scanning electron microscopy, thermal analysis (TG, DTG and DTA), infrared spectroscopy, and the Boehm’s titration method. Moreover, pHpzc (the point of zero charge) was determined. The adsorption capacity and the temperature-programmed desorption of ammonia were also studied. The obtained activated biochars were characterized by the large specific surface area (672 to 912 m2/g) and the total pore volume (0.30 to 0.4 cm3/g) as well as the well-developed microporous structure (85–97%). These observations were also confirmed by the SEM analysis. The maximum NH3 adsorption capacity of the activated biochar was determined to be 3.05 mmol/g. These results prove that the sawdust of various origins is appropriate to prepare a cost-effective, environmentally friendly biochar.
Modelling a chain of command in the incident command system using sequential characteristic function games
Authors: Krausburg Tabajara , Bordini Rafael H. , Dix Jürgen 
Published: July 2023

AbstractDisaster response is a major challenge given the social and economic impact on the communities affected by disaster incidents. We investigate how coalition formation can be used for the problem of forming a hierarchy of resources (e.g., personnel responding to the incident). As a case study, we consider the roaring river flood scenario and model the Incident Command System (ICS) framework—providing guidelines on cooperatively responding to disaster incidents. Our approach is based on sequential characteristic-function games induced by size-based valuation structures. We show that this approach can deliver a hierarchy as required by the Operations Section of the ICS and provides a promising way to generate practical solutions for some realistic disaster scenarios.
Image quality enhancement of 4D light field microscopy via reference impge propagation-based one-shot learning
Authors: Kwon Ki Hoon , Erdenebat Munkh-Uchral , Kim Nam , Kwon Ki-Chul , Kim Min Young 
Published: July 2023

AbstractFour-dimensional (4D) light-field (LF) microscopes can acquire 3D information about target objects using a microlens array (MLA). However, the resolution and quality of sub-images in the LF images are reduced because of the spatial multiplexing of rays by the element lenses of the MLA. To overcome these limitations, this study proposes an LF one-shot learning technique that can convert LF sub-images into high-quality images similar to the 2D images of conventional optical microscopes obtained without any external training datasets for image enhancement. The proposed convolutional neural network model was trained using only one training dataset comprising a high-resolution reference image captured without an MLA as the ground truth. Further, its input was the central view of the LF image. After LF one-shot learning, the trained model should be able to convert well the other LF sub-images of various directional views that were not used in the main training process. Therefore, novel learning techniques were designed for LF one-shot learning. These novel techniques include an autoencoder-based model initialization method, a feature map-based learning algorithm to prevent the overfitting of the model, and cut loss to prevent saturation. The experimental results verified that the proposed technique effectively enhances the LF image quality and resolution using a reference image. Moreover, this method enhances the resolution by up to 13 times, decreases the noise amplification effect, and restores the lost details of microscopic objects. The proposed technique is stable and yields superior experimental results compared with those of the existing resolution-enhancing methods.
Subtypes of high-grade breast ductal carcinoma in situ (DCIS): incidence and potential clinical impact
Authors: Schandiz Hossein , Park Daehoon , Kaiser Yan Liu , Lyngra Marianne , Talleraas Inger Solvang , Geisler Jürgen , Sauer Torill 
Published: July 2023

Abstract
Objective
The purpose of this study was to investigate and classify the molecular subtypes of high-grade ductal carcinoma in situ (DCIS) and identify possible high-risk subtypes. The heterogenicity of DCIS with variable clinical and histopathological presentations has been recognized. Nevertheless, only histopathological grading and diameter are currently implemented in clinical decision-making following the diagnosis of DCIS. The molecular subtypes of DCIS and their IHC surrogate markers have not been defined in conventional treatment guidelines and recommendations. We applied the definitions of molecular subtypes according to the IHC surrogate markers defined for IBC and subclassified high-grade DCIS, accordingly.

Methods
Histopathological specimens were collected, revised, and regraded from 494 patients diagnosed with DCIS between 1996 and 2018. Other in situ and papillary lesions observed in breast biopsies were excluded from this study. 357 high-grade DCIS cases were submitted to IHC analysis. The markers investigated were ER, PR, HER2, and Ki67.

Results
45 cases were classified as grade 1, 19 as grade 2, and 430 as grade 3. Sixty patients with high-grade DCIS had an additional invasive component in the surgical specimen. Thirty-three patients were diagnosed with recurrent DCIS or invasive cancer (minimum one year after their primary DCIS diagnosis). The proportions of luminal A and luminal B HER2-negative subtypes varied depending on whether 2011 or 2013 St. Gallen Consensus Conference guidelines were adopted. Luminal A was the most prevalent subtype, according to both classifications. The luminal B HER2-positive subtype was found in 22.1% of cases, HER2-enriched subtype in 21.8%, and TPN subtype in 5.6%. There were strong indications that HER2-enriched subtype was significantly more frequent among DCIS with invasive component (p = 0.0169).

Conclusions
High-grade DCIS exhibits all the molecular subtypes previously identified in IBC, but with a somewhat different distribution in our cohort. HER2-enriched subtype is substantially related to the presence of an invasive component in DCIS; consequently, it is regarded as a high-risk entity.
Simple method for obtaining regenerated cellulose nanoparticles from delignified coffee parchment, and their use in fabricating blended films
Authors: Campuzano Francisco , Escobar Diana Marcela , Torres L Ana María 
Published: July 2023

AbstractCoffee parchment is one of the residues generated in coffee processing and has been poorly explored for value-added applications. It has been reported that coffee parchment has a high cellulose content (40–49%). Cellulose has been studied in many fields and specifically regenerated cellulose has been explored for many applications. Then, in this work a simple method for obtaining regenerated cellulose nanoparticles (RCNPs) from delignified coffee parchment, was determined. A complete physicochemical characterization of RCNPs obtained is presented, FTIR showed high cellulose purity, and XRD analysis showed high crystallinity of cellulose II. TEM images revealed that nanoparticles have spherical morphology. TGA showed good thermal stability and DLS allowed to determine the stability of suspension and the Z-average of the particles. The application of RCNPs on film formation was also evaluated when blended with PVA and Glycerol showing good mechanical properties with potential application on food packing.
Migration as adaptation to freshwater and inland hydroclimatic changes? A meta-review of existing evidence
Authors: Caretta Martina Angela , Fanghella Valeria , Rittelmeyer Pam , Srinivasan Jaishri , Panday Prajjwal K. , Parajuli Jagadish , Priya Ritu , Reddy E. B. Uday Bhaskar , Seigerman Cydney Kate , Mukherji Aditi 
Published: July 2023

AbstractDue to its potential geo-political and environmental implications, climate migration is an increasing concern to the international community. However, while there is considerable attention devoted to migration in response to sea-level rise, there is a limited understanding of human mobility due to freshwater and inland hydroclimatic changes. Hence, the aim of this paper is to examine the existing evidence on migration as an adaptation strategy due to freshwater and inland hydroclimatic changes. A meta-review of papers published between 2014 and 2019 yielded 67 publications, the majority of which focus on a handful of countries in the Global South. Droughts, floods, extreme heat, and changes in seasonal precipitation patterns were singled out as the most common hazards triggering migration. Importantly, most of the papers discuss mobility as part of a portfolio of responses. Motivations to migrate at the household level range from survival to searching for better economic opportunities. The outcomes of migration are mixed — spanning from higher incomes to difficulties in finding employment after moving and struggles with a higher cost of living. While remittances can be beneficial, migration does not always have a positive outcome for those who are left behind. Furthermore, this meta-review shows that migration, even when desired, is not an option for some of the most vulnerable households. These multifaceted results suggest that, while climate mobility is certainly happening due to freshwater and inland hydroclimatic changes, studies reviewing it are limited and substantial gaps remain in terms of geographical coverage, implementation assessments, and outcomes evaluation. We argue that these gaps need to be filled to inform climate and migration policies that increasingly need to be intertwined rather than shaped in isolation from each other.
Multi-criteria analysis for rapid vulnerability assessment of marine species to climate change
Authors: Champion Curtis , Lawson James R. , Pardoe Joanna , Cruz Derrick O. , Fowler Ashley M. , Jaine Fabrice , Schilling Hayden T. , Coleman Melinda A. 
Published: July 2023

AbstractThe accelerating pace of climate-induced stress to global ecosystems threatens the sustainable management and conservation of biodiversity. To effectively respond, researchers and managers require rapid vulnerability assessment tools that can be readily implemented using diverse and existing knowledge sources. Here we demonstrate the application of multi-criteria analysis (MCA) for this purpose using a group of coastal-pelagic fishes from south-eastern Australia as a case-study. We show that MCA has the capacity to formally structure diverse knowledge sources, ranging from peer-reviewed information (which informed 29.2% of criteria among models) to expert knowledge (which informed 22.6% of criteria among models), to quantify the sensitivity of species to biophysical conditions. By integrating MCA models with spatial climate data over historical and future periods, we demonstrate the application of MCA for rapidly assessing the vulnerability of marine species to climate change. Spatial analyses revealed an apparent trend among case-study species towards increasing or stable vulnerability to projected climate change throughout the northern (i.e. equatorward) extent of the study domain and the emergence of climate refugia throughout southern (i.e. poleward) regions. Results from projections using the MCA method were consistent with past analyses of the redistribution of suitable habitat for coastal-pelagic fishes off eastern Australia under climate change. By demonstrating the value of MCA for rapidly assessing the vulnerability of marine species to climate change, we highlight the opportunity to develop user-friendly software infrastructures integrated with marine climate projection data to support the interdisciplinary application of this method.
Systematic literature review as a digital collaborative research-like learning activity: a case study
Authors: Ryan Anne Wally , Kolås Line , Nilsen Anders Grov , Almås Aslaug Grov 
Published: July 2023

AbstractWithin the higher education sector, the principle of student-active and research-based education are established in strategy documents and action plans, but at the same time there is an ongoing debate about what is meant by research-based learning and how it can be applied in practical teaching contexts. The aim of this empirical study is to explore inclusion of research elements in higher education. The study introduces the concept of using systematic literature review (SLR) and digital collaboration as a learning method, and addresses how to succeed with digital collaborative systematic literature review as a research-like learning activity in higher education. An exploratory multiple case design is used, with participatory observation technique and thematic analysis. A practical contribution of this study is an example of how SLR is well-suited to do collective research-like learning activities. The main contribution is that the higher education teacher needs integrated knowledge, including research competence in addition to the traditional link between professional, didactic and technological competence. A model for research-like learning is proposed, which illustrates the need for research knowledge in relation to the technological pedagogical content knowledge.
Time series clustering using trend, seasonal and autoregressive components to identify maximum temperature patterns in the Iberian Peninsula
Authors: Palacios Gutiérrez Arnobio , Valencia Delfa Jose Luis , Villeta López María 
Published: July 2023

AbstractTime series (TS) clustering is a crucial area of data mining that can be used to identify interesting patterns. This study introduces a novel approach to obtain clusters of TS by representing them with feature vectors that define the trend, seasonality and noise components of each series in order to identify areas of the Iberian Peninsula (IP) that follow the same pattern of change in regards to maximum temperature during 1931–2009. This representation allows for dimensionality reduction, and is obtained based on singular spectrum analysis decomposition in a sequential manner, which is a well-developed methodology of TS analysis and forecasting with applications ranging from the decomposition and filtering of nonparametric TS to parameter estimation and forecasting. In this approach, the trend, seasonality and residual components of each TS corresponding to a specific area in the Iberian region are extracted using the proposed SSA methodology. Afterwards, the feature vectors of the TS are obtained by modelling the extracted components and estimating their parameters. Finally, a clustering algorithm is applied to group the TS into clusters, which are defined according to the centroids. This methodology is applied to a climate database with reasonable results that align with the defined characteristics, enabling a spatial exploration of the IP. The results identified three differentiated zones that can be used to describe how the maximum temperature varied: in the northern and central zones, an increase in temperature was noted over time, whereas in the southern zone, a slight decrease was noted. Moreover, different seasonal variations were observed across the zones.
A geochemical and mineralogical characterization of soils associated with podoconiosis
Authors: Cooper Jamey N. , Nick Kevin E. 
Published: July 2023

AbstractPodoconiosis is a disease that causes swelling and disfiguration of the lower legs found in several developing countries where shoes are not regularly worn. The current model for the etiology of the disease proposes that mineralogical agents enter the lymph system through the skin leading to inflammation that causes swelling of the feet and legs. We collected 125 soil samples from 21 towns associated with podoconiosis, 8 towns unassociated with Podoconiosis as controls, and 3 towns of unknown status. Data collected for each soil sample included color, particle size, mineralogy, and geochemistry to distinguish unique components within the podoconiosis-associated soils. Our results indicate podoconiosis-associated soils are more highly weathered than non-podoconiosis associated soils. The enrichment of kaolinite and gibbsite suggests that these minerals, their surface chemistry, and trace elements associated with them should be prioritized in future podoconiosis research. In addition, we found that color may be a valuable tool to identify soils at greater risk for inducing podoconiosis.
The role of remote sensing in tropical grassland nutrient estimation: a review
Authors: Arogoundade Adeola M. , Mutanga Onisimo , Odindi John , Naicker Rowan 
Published: July 2023

AbstractThe carbon (C) and nitrogen (N) ratio is a key indicator of nutrient utilization and limitations in rangelands. To understand the distribution of herbivores and grazing patterns, information on grass quality and quantity is important. In heterogeneous environments, remote sensing offers a timely, economical, and effective method for assessing foliar biochemical ratios at varying spatial and temporal scales. Hence, this study provides a synopsis of the advancement in remote sensing technology, limitations, and emerging opportunities in mapping the C:N ratio in rangelands. Specifically, the paper focuses on multispectral and hyperspectral sensors and investigates their properties, absorption features, empirical and physical methods, and algorithms in predicting the C:N ratio in grasslands. Literature shows that the determination of the C:N ratio in grasslands is not in line with developments in remote sensing technologies. Thus, the use of advanced and freely available sensors with improved spectral and spatial properties such as Sentinel 2 and Landsat 8/9 with sophisticated algorithms may provide new opportunities to estimate C:N ratio in grasslands at regional scales, especially in developing countries. Spectral bands in the near-infrared, shortwave infrared, red, and red edge were identified to predict the C:N ratio in plants. New indices developed from recent multispectral satellite imagery, for example, Sentinel 2 aided by cutting-edge algorithms, can improve the estimation of foliar biochemical ratios. Therefore, this study recommends that future research should adopt new satellite technologies with recent development in machine learning algorithms for improved mapping of the C:N ratio in grasslands.
Towards a taxonomy of Roxygen documentation in R packages
Authors: Vidoni Melina , Codabux Zadia 
Published: July 2023

AbstractSoftware documentation is often neglected, impacting maintenance and reuse and leading to technical issues. In particular, when working with scientific software, such issues in the documentation pose a risk to producing reliable scientific results as they may cause improper or incorrect use of the software. R is a popular programming language for scientific software with a prolific package-based ecosystem, where users contribute packages (i.e., libraries). R packages are intended to be reused, and their users rely extensively on the available documentation. Thus, understanding what information developers provide in their packages’ documentation (generally, through a system known as Roxygen, based on Javadoc) is essential to contribute to it. This study mined 379 GitHub repositories of R packages and analysed a sample to develop a taxonomy of natural language descriptions used in Roxygen documentation. This was done through hybrid card sorting, which included two experienced R developers. The resulting taxonomy covers parameters, returns, and descriptions, providing a baseline for further studies. Our taxonomy is the first of its kind for R. Based on previous studies in pure object-oriented languages, our taxonomy could be extensible to other dynamically-typed languages used in scientific programming.
Enhancement of Cloud-native applications with Autonomic Features
Authors: Kosińska Joanna , Zieliński Krzysztof 
Published: July 2023

AbstractThe Autonomic Computing paradigm reduces complexity in installing, configuring, optimizing, and maintaining heterogeneous systems. Despite first discussing it a long ago, it is still a top research challenge, especially in the context of other technologies. It is necessary to provide autonomic features to the Cloud-native execution environment to meet the rapidly changing demands without human support and continuous improvement of their capabilities. The present work attempts to answer how to explore autonomic features in Cloud-native environments. As a solution, we propose using the AMoCNA framework. It is rooted in Autonomic Computing. The success factors for the AMoCNA implementation are its execution controllers. They drive the management actions proceeding in a Cloud-native execution environment. A similar concept already exists in Kubernetes, so we compare both execution mechanisms. This research presents guidelines for including autonomic features in Cloud-native environments. The integration of Cloud-native Applications with AMoCNA leads to facilitating autonomic management. To show the potential of our concept, we evaluated it. The developed executor performs cluster autoscaling and ensures autonomic management in the infrastructure layer. The experiment also proved the importance of observations. The knowledge gained in this process is a good authority of information about past and current state of Cloud-native Applications. Combining this knowledge with defined executors provides an effective means of achieving the autonomic nature of Cloud-native applications.