bluuebunny/crossref_metadata_2025_split huggingface dataset:
Material Legacies and Environmental Constraints Underlie Fire Resilience of a Dominant Boreal Forest Type
Authors: Day Nicola J. , Johnstone Jill F. , Reid Kirsten A. , Cumming Steven G. , Mack Michelle C. , Turetsky Merritt R. , Walker Xanthe J. , Baltzer Jennifer L. 
Published: June 2022

AbstractResilience of plant communities to disturbance is supported by multiple mechanisms, including ecological legacies affecting propagule availability, species’ environmental tolerances, and biotic interactions. Understanding the relative importance of these mechanisms for plant community resilience supports predictions of where and how resilience will be altered with disturbance. We tested mechanisms underlying resilience of forests dominated by black spruce (Picea mariana) to fire disturbance across a heterogeneous forest landscape in the Northwest Territories, Canada. We combined surveys of naturally regenerating seedlings at 219 burned plots with experimental manipulations of ecological legacies via seed addition of four tree species and vertebrate exclosures to limit granivory and herbivory at 30 plots varying in moisture and fire severity. Black spruce recovery was greatest where it dominated pre-fire, at wet sites with deep residual soil organic layers, and fire conditions of low soil or canopy combustion and longer return intervals. Experimental addition of seed indicated all species were seed-limited, emphasizing the importance of propagule legacies. Black spruce and birch (Betula papyrifera) recruitment were enhanced with vertebrate exclusion. Our combination of observational and experimental studies demonstrates black spruce is vulnerable to effects of increased fire activity that erode ecological legacies. Moreover, black spruce relies on wet areas with deep soil organic layers where other species are less competitive. However, other species can colonize these areas if enough seed is available or soil moisture is altered by climate change. Testing mechanisms underlying species’ resilience to disturbance aids predictions of where vegetation will transform with effects of climate change.
Performance of artificial intelligence for biventricular cardiovascular magnetic resonance volumetric analysis in the clinical setting
Authors: Hatipoglu Suzan , Mohiaddin Raad H. , Gatehouse Peter , Alpendurada Francisco , Baksi A. John , Izgi Cemil , Prasad Sanjay K. , Pennell Dudley J. , Krupickova Sylvia 
Published: June 2022

AbstractCardiovascular magnetic resonance (CMR) derived ventricular volumes and function guide clinical decision-making for various cardiac pathologies. We aimed to evaluate the efficiency and clinical applicability of a commercially available artificial intelligence (AI) method for performing biventricular volumetric analysis. Three-hundred CMR studies (100 with normal CMR findings, 50 dilated cardiomyopathy, 50 hypertrophic cardiomyopathy, 50 ischaemic heart disease and 50 congenital or valvular heart disease) were randomly selected from database. Manual biventricular volumetric analysis (CMRtools) results were derived from clinical reports and automated volumetric analyses were performed using short axis volumetry AI function of CircleCVI42 v5.12 software. For 20 studies, a combined method of manually adjusted AI contours was tested and all three methods were timed. Clinicians` confidence in AI method was assessed using an online survey. Although agreement was better for left ventricle than right ventricle, AI analysis results were comparable to manual method. Manual adjustment of AI contours further improved agreement: within subject coefficient of variation decreased from 5.0% to 4.5% for left ventricular ejection fraction (EF) and from 9.9% to 7.1% for right ventricular EF. Twenty manual analyses were performed in 250 min 12 s whereas same task took 5 min 48 s using AI method. Clinicians were open to adopt AI but concerns about accuracy and validity were raised. The AI method provides clinically valid outcomes and saves significant time. To address concerns raised by survey participants and overcome shortcomings of the automated myocardial segmentation, visual assessment of contours and performing manual corrections where necessary appears to be a practical approach.
From polyps to pixels: understanding coral reef resilience to local and global change across scales
Authors: Donovan Mary K. , Alves Catherine , Burns John , Drury Crawford , Meier Ouida W. , Ritson-Williams Raphael , Cunning Ross , Dunn Robert P. , Goodbody-Gringley Gretchen , Henderson Leslie M. , Knapp Ingrid S. S. , Levy Joshua , Logan Cheryl A. , Mudge Laura , Sullivan Chris , Gates Ruth D. , Asner Gregory P. 
Published: June 2022

Abstract
Context
Coral reef resilience is the product of multiple interacting processes that occur across various interacting scales. This complexity presents challenges for identifying solutions to the ongoing worldwide decline of coral reef ecosystems that are threatened by both local and global human stressors.

Objectives
We highlight how coral reef resilience is studied at spatial, temporal, and functional scales, and explore emerging technologies that are bringing new insights to our understanding of reef resilience. We then provide a framework for integrating insights across scales by using new and existing technological and analytical tools. We also discuss the implications of scale on both the ecological processes that lead to declines of reefs, and how we study those mechanisms.

Methods
To illustrate, we present a case study from Kāneʻohe Bay, Hawaiʻi, USA, linking remotely sensed hyperspectral imagery to within-colony symbiont communities that show differential responses to stress.

Results
In doing so, we transform the scale at which we can study coral resilience from a few individuals to entire ecosystems.

Conclusions
Together, these perspectives guide best practices for designing management solutions that scale from individuals to ecosystems by integrating multiple levels of biological organization from cellular processes to global patterns of coral degradation and resilience.
Anticipatory Grief in Dementia: An Ethnographic Study of Loss and Connection
Authors: Lemos Dekker Natashe 
Published: June 2022

AbstractIn this article, I address the experiences of family members of people with dementia, as they expressed the sensation of gradually losing the person with dementia. Based on ethnographic fieldwork in nursing homes in the Netherlands, and contributing to the anthropology of grief, I explore the co-existence of experiences of anticipatory grief and manifestations of care to maintain meaningful relations. I show how my interlocutors adapted to changing circumstances as the disease progressed, and in so doing found new ways to relate, as well as prepared for future losses and the expected end of life. I argue that anticipatory grief is temporal and relational, encompassing both present and future losses, and involving a continuous negotiation between the loss and the continuing relationship. I underscore the entanglement of loss and connection, showing how both exist parallel to, and may emerge from one another, and demonstrating how an anthropological approach to anticipatory grief can reveal the nuanced and equivocal character of experiences of illness and at the end of life.
Cholinergic Senescence in the Ts65Dn Mouse Model for Down Syndrome
Authors: Kirstein Martina , Cambrils Alba , Segarra Ana , Melero Ana , Varea Emilio 
Published: June 2022

AbstractDown syndrome (DS) induces a variable phenotype including intellectual disabilities and early development of Alzheimer’s disease (AD). Moreover, individuals with DS display accelerated aging that affects diverse organs, among them the brain. The Ts65Dn mouse is the most widely used model to study DS. Progressive loss of cholinergic neurons is one of the hallmarks of AD present in DS and in the Ts65Dn model. In this study, we quantify the number of cholinergic neurons in control and Ts65Dn mice, observing a general reduction in their number with age but in particular, a greater loss in old Ts65Dn mice. Increased expression of the m1 muscarinic receptor in the hippocampus counteracts this loss. Cholinergic neurons in the Ts65Dn mice display overexpression of the early expression gene c-fos and an increase in the expression of β-galactosidase, a marker of senescence. A possible mechanism for senescence induction could be phosphorylation of the transcription factor FOXO1 and its retention in the cytoplasm, which we are able to confirm in the Ts65Dn model. In our study, using Ts65Dn mice, we observe increased cholinergic activity, which induces a process of early senescence that culminates in the loss of these neurons.
Measures of state capacity: so similar, yet so different
Authors: Vaccaro Andrea 
Published: June 2022

AbstractThis study provides a systematic comparative analysis of seven common cross-national measures of state capacity by focusing on three measurement issues: convergent validity, interchangeability, and case-specific disagreement. The author finds that the convergent validity of the measures is high, but the interchangeability of the measures is low. This means that even highly correlated measures of state capacity can lead to completely different statistical inferences. The cause of this puzzling finding lies in strikingly large disagreements on some of the country scores. The author shows that these disagreements depend on two factors: differences in underlying components and the level of state capacity. Considering the findings of this study, users of measures of state capacity must not assume that any highly correlated indicator is appropriate. They should instead look at what the indicators actually measure and ensure that a given definition of state capacity matches the chosen indicator.
Economic analysis of smallholder dairy cattle enterprises in Senegal
Authors: Malenje Evaristo Mukunda , Missohou Ayao , Tebug Stanly Fon , König Emelie Zonabend , Jung’a Joseph Owino , Bett Rawlynce Cheruiyot , Marshall Karen 
Published: June 2022

AbstractSmallholder dairy production in Senegal is important to both livelihoods and food and nutrition security. Here, we examine the economic performance of smallholder dairy cattle enterprises in Senegal, using data from longitudinal monitoring of 113 households. The mean (and standard deviation) of the net returns (NR) per cow per annum (pcpa) was 21.7 (202.9) USD, whilst the NR per household herd per annum (phpa) was 106.1 (1740.3) USD. Only about half (52.2%) of the dairy cattle enterprise had a positive NR. The most significant income components were milk sale followed by animal sale, whilst the most significant cost components were animal feed followed by animal purchase. When households were grouped by ranking on NRpcpa, an interesting trend was observed: whilst the mean NRpcpa showed a fairly linear increase from the lowest to highest NR groups, income and cost did not. Income and costs were both higher for the lowest and highest NR groups, in comparison to the intermediate NR groups. The mean NRs of households grouped by the main breed type they kept were not significantly different from each other, due to large variances within the breed groups. However, the mean total income and costs were significantly higher for households mainly keeping improved dairy breeds (Bos taurus or Zebu × Bos taurus crosses) in comparison to those keeping indigenous Zebu or Zebu by Guzerat crosses. This study highlights the highly variable (and often low) profitability of smallholder dairy cattle enterprise in Senegal. Further actions to address this are strongly recommended.
Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy
Authors: Berbar Mohamed A. 
Published: June 2022

AbstractIntroductionReliable computer diagnosis of diabetic retinopathy (DR) is needed to rescue many with diabetes who may be under threat of blindness. This research aims to detect the presence of diabetic retinopathy in fundus images and grade the disease severity without lesion segmentation.MethodsTo ensure that the fundus images are in a standard state of brightness, a series of preprocessing steps have been applied to the green channel image using histogram matching and a median filter. Then, contrast-limited adaptive histogram equalisation is performed, followed by the unsharp filter. The preprocessed image is divided into small blocks, and then each block is processed to extract uniform local binary patterns (LBPs) features. The extracted features are encoded, and the feature size is reduced to 3.5 percent of its original size. Classifiers like Support Vector Machine (SVM) and a proposed CNN model were used to classify retinal fundus images. The classification is abnormal or normal and to grade the severity of DR.ResultsOur feature extraction method was tested on a binary classifier and resulted in an accuracy of 98.37% and 98.84% on the Messidor2 and EyePACS databases, respectively. The proposed system could grade DR severity into three grades (0: no DR, 1: mild DR, and 5: moderate, severe NPDR, and PDR). It obtains an F1-score of 0.9617 and an accuracy of 95.37% on the EyePACS database, and an F1-score of 0.9860 and an accuracy of 97.57% on the Messidor2 database. The resultant values are dependent on the selection of (neighbours, radius) pairs during the extraction of LBP features.ConclusionsThis study’s results proved that the preprocessing steps are significant and had a great effect on highlighting image features. The novel method of stacking and encoding the LBP values in the feature vector greatly affects results when using SVM or CNN for classification. The proposed system outperforms the state of the artwork. The proposed CNN model performs better than SVM.
Maximizing the mechanical performance of Ti3AlC2-based MAX phases with aid of machine learning
Authors: Duan Xingjun , Fang Zhi , Yang Tao , Guo Chunyu , Han Zhongkang , Sarker Debalaya , Hou Xinmei , Wang Enhui 
Published: June 2022

AbstractMechanical properties consisting of the bulk modulus, shear modulus, Young’s modulus, Poisson’s ratio, etc., are key factors in determining the practical applications of MAX phases. These mechanical properties are mainly dependent on the strength of M-X and M-A bonds. In this study, a novel strategy based on the crystal graph convolution neural network (CGCNN) model has been successfully employed to tune these mechanical properties of Ti3AlC2-based MAX phases via the A-site substitution (Ti3(Al1−xAx)C2). The structure—property correlation between the A-site substitution and mechanical properties of Ti3(Al1−xAx)C2 is established. The results show that the thermodynamic stability of Ti3(Al1−xAx)C2 is enhanced with substitutions A = Ga, Si, Sn, Ge, Te, As, or Sb. The stiffness of Ti3AlC2 increases with the substitution concentration of Si or As increasing, and the higher thermal shock resistance is closely associated with the substitution of Sn or Te. In addition, the plasticity of Ti3AlC2 can be greatly improved when As, Sn, or Ge is used as a substitution. The findings and understandings demonstrated herein can provide universal guidance for the individual synthesis of high-performance MAX phases for various applications.
On stochastic control for time changed Lévy dynamics
Authors: di Nunno Giulia 
Published: June 2022

AbstractControlled stochastic differential equations driven by time changed Lévy noises do not enjoy the Markov property in general, but can be treated in the framework of general martingales. From the modelling point of view, time changed noises constitute a feasible way to include time dependencies at noise level and still keep a reasonably simple structure. Furthermore, they are easy to simulate, with the result that time change Lévy dynamics attract attention in various fields of application. In this work we survey an approach to stochastic control via maximum principle for time changed Lévy dynamics. We emphasise the role and use of different information flows in tackling the various control problems. We show how these techniques can be extended to include Volterra type dynamics and the control of forward–backward systems of equations. Our techniques make use of the stochastic non-anticipating (NA) derivative in a general martingale framework.
Laboratory animal strain mobilities: handling with care for animal sentience and biosecurity
Authors: Peres Sara , Roe Emma 
Published: June 2022

AbstractThe global distribution of laboratory mouse strains is valued for ensuring the continuity, validity and accessibility of model organisms. Mouse strains are therefore assumed mobile and able to travel. We draw on the concept of ‘animal mobilities’ (Hodgetts and Lorimer 2019) to explain how attending to laboratory mice as living animal, commodity and scientific tool is shaping how they are transported through contemporary scientific infrastructures and communities. Our paper is framed around exploring how animalstrainstravel, rather than animals, as we show that it is only through understanding strain mobility that we can explain how and why live animal movement can be replaced by germinal products. The research is based on qualitative fieldwork in 2018 and 2019 that included 2 weeks ethnography and interviews with key informants involved in the movement of laboratory animals. The empirical analysis discusses practices that relate to managing biosecurity and animal welfare concerns when moving laboratory animal strains. In closing we reflect more broadly on the contemporary ‘ethico-onto-epistemological’ (Barad, 2014) entanglement that shapes who or what travels to support laboratory science data-making practices, and the intensity of care ‘tinkering’ practices (Mol and Law 2010) that facilitate the movement. We explain how a laboratory animal strain exceeds its value solely as a mobile and thus exchangeable commodity, illustrated in how values that relate to animal sentience and infection-risk supports its material transformation. Consequently, it is becoming increasingly common for non-sentient germinal products – embryos and gametes - to replace live sentient animals when being moved.
Environmental Justice “Light”? Transnational Tort Litigation in the Corporate Anthropocene
Authors: Bertram Daniel 
Published: June 2022

AbstractCorporations are notoriously powerful actors in the current configuration of our globalized economy. Their activities play a key role in shaping a new age of ecological precarity—the Anthropocene. Much of this environmental damage occurs in cross-border settings, hampering victims’ access to legal remedies due to widespread corporate impunity and institutional hurdles in host states. Several transnational lawsuits have recently tested the willingness of European home state judiciaries to adjudicate the extraterritorial conduct of domestic corporations. To contribute to a more nuanced understanding of this novel phenomenon, this article analyzes three legal sagas from a comparative perspective: Vedanta v. Lungowe (England & Wales), Dooh v. Shell (The Netherlands) and Lliuya v. RWE (Germany). It argues that transnational tort suits remain a problematic vehicle for the attainment of procedural and substantial environmental justice. The inherent limitations of tort law, extra-legal hurdles to transnational litigation, and the socio-cultural contingency of legal institutions severely circumscribe the space for legal contestations of the corporate Anthropocene.
Transatlantic Divergencies in the Regulation of Uncertain Risks: Co-Production, Normative Frames and Ideal Evidence-Based and Socially Acceptable Risk Approaches
Authors: Leonelli Giulia Claudia 
Published: June 2022

AbstractThis article analyses the US and the EU systems of risk regulation through the lens of two ideal models: evidence-based and socially acceptable risk approaches. The examination is conducted against the backdrop of these ideal regulatory paradigms, which broadly inform US and EU risk governance. The article employs an analysis of transatlantic divergencies in the regulation of pesticides and agricultural biotechnologies to illustrate that neither approach can lay claim to neutrality and objectivity; non-scientific normative frames are always at stake in the field of risk regulation. Through these case studies, the article thus challenges the narrative that transatlantic divergencies result from a focus on “risks” or “hazards,” “science” or “politics.” The US and the EU systems reflect different approaches to scientific uncertainty, the pursuit of different levels of protection, and consideration of different non-scientific factors. They also have very different implications. The conclusive section of the article sketches out some final considerations on the strategic vision of the Biden administration. Environmental and public health protection are high on President Biden’s agenda; further, public interest litigation is thriving in the US. Will this be sufficient to break regulatory path dependency and lay the foundations for a paradigm shift in US risk governance?
Creating and Comparing Dictionary, Word Embedding, and Transformer-Based Models to Measure Discrete Emotions in German Political Text
Authors: Widmann Tobias , Wich Maximilian 
Published: June 2022

AbstractPrevious research on emotional language relied heavily on off-the-shelf sentiment dictionaries that focus on negative and positive tone. These dictionaries are often tailored to nonpolitical domains and use bag-of-words approaches which come with a series of disadvantages. This paper creates, validates, and compares the performance of (1) a novel emotional dictionary specifically for political text, (2) locally trained word embedding models combined with simple neural network classifiers, and (3) transformer-based models which overcome limitations of the dictionary approach. All tools can measure emotional appeals associated with eight discrete emotions. The different approaches are validated on different sets of crowd-coded sentences. Encouragingly, the results highlight the strengths of novel transformer-based models, which come with easily available pretrained language models. Furthermore, all customized approaches outperform widely used off-the-shelf dictionaries in measuring emotional language in German political discourse.
Species detection framework using automated recording units: a case study of the Critically Endangered Jerdon's courser
Authors: Arvind Chiti , Joshi Viral , Charif Russell , Jeganathan Panchapakesan , Robin V. V. 
Published: June 2022

AbstractWith the advent of automated recording units, bioacoustic monitoring has become a popular tool for the collection of long-term data across extensive landscapes. Such methods involve two main components: hardware for audio data acquisition and software for analysis. In the acoustic monitoring of threatened species, a species-specific framework is often essential. Jerdon's courser Rhinoptilus bitorquatus is a Critically Endangered nocturnal bird endemic to a small region of the Eastern Ghats of India, last reported in 2008. Here we describe a reproducible and scalable acoustic detection framework for the species, comparing several commonly available hardware and detection methods and using existing software. We tested this protocol by collecting 24,349 h of data during 5 months. We analysed the data with two commercially available sound analysis programmes, following an analysis pipeline created for this species. Although we did not detect vocalizations of Jerdon's courser, this study provides a framework using a combination of hardware and software for future research that other conservation practitioners can implement. Vocal mimicry can aid or confound in detection and we highlight the potential role of mimicry in the detection of such threatened species. This species-specific acoustic detection framework can be scaled and tailored to monitor other species.