bluuebunny/crossref_metadata_2025_split huggingface dataset:
Are single global warming potential impact assessments adequate for carbon footprints of agri-food systems?
Authors: McAuliffe Graham A , Lynch John , Cain Michelle , Buckingham Sarah , Rees Robert M , Collins Adrian L , Allen Myles , Pierrehumbert Raymond , Lee Michael R F , Takahashi Taro 
Published: June 2023

Abstract
The vast majority of agri-food climate-based sustainability analyses use global warming potential (GWP100) as an impact assessment, usually in isolation; however, in recent years, discussions have criticised the ‘across-the-board’ application of GWP100 in Life Cycle Assessments (LCAs), particularly of food systems which generate large amounts of methane (CH4) and considered whether reporting additional and/or alternative metrics may be more applicable to certain circumstances or research questions (e.g. Global Temperature Change Potential (GTP)). This paper reports a largescale sensitivity analysis using a pasture-based beef production system (a high producer of CH4 emissions) as an exemplar to compare various climatatic impact assessments: CO2-equivalents using GWP100 and GTP100, and ‘CO2-warming-equivalents’ using ‘GWP Star’, or GWP*. The inventory for this system was compiled using data from the UK Research and Innovation National Capability, the North Wyke Farm Platform, in Devon, SW England. LCAs can have an important bearing on: (i) policymakers’ decisions; (ii) farmer management decisions; (iii) consumers’ purchasing habits; and (iv) wider perceptions of whether certain activities can be considered ‘sustainable’ or not; it is, therefore, the responsibility of LCA practitioners and scientists to ensure that subjective decisions are tested as robustly as possible through appropriate sensitivity and uncertainty analyses. We demonstrate herein that the choice of climate impact assessment has dramatic effects on interpretation, with GWP100 and GTP100 producing substantially different results due to their different treatments of CH4 in the context of carbon dioxide (CO2) equivalents. Given its dynamic nature and previously proven strong correspondence with climate models, out of the three assessments covered, GWP* provides the most complete coverage of the temporal evolution of temperature change for different greenhouse gas emissions. We extend previous discussions on the limitations of static emission metrics and encourage LCA practitioners to consider due care and attention where additional information or dynamic approaches may prove superior, scientifically speaking, particularly in cases of decision support.
Spatial-temporal pattern of tuberculosis mortality in China and its relationship with long-term PM2.5 exposure based on a causal inference approach
Authors: Wu Gonghua , Wang Shenghao , Jiang Zini , Hopke Philip K , Rich David Q , Chen Liang , Lin Shao , Zhang Kai , Romeiko Xiaobo Xue , Qu Yanji , Deng Xinlei , Lin Ziqiang , Xiao Jianpeng , Zhang Wangjian , Hao Yuantao 
Published: June 2023

Abstract
Evidence on the spatial-temporal distribution of tuberculosis (TB) mortality across China and its relationship with long-term particulate matter (PM2.5) exposure is limited. We aimed to address significant gaps in our understanding of the spatial-temporal clustering patterns of TB mortality in China and provide evidence for its causal links with long-term PM2.5 exposure. Annual pulmonary TB mortality, PM2.5 concentrations, and socioeconomic factors for provinces in mainland China between 2004 and 2017 were obtained. Turning points in the temporal trend and spatial clustering patterns of pulmonary TB mortality were identified. A difference-in-differences causal inference approach was applied to estimate the long-term effect of PM2.5 exposure on the mortality. The average annual percent change of pulmonary TB mortality in China was −2.5% (95% CI: −5.6%, 0.7%), with an 11.1% annual increase in the Northwest since 2012 (P= 0.029). The hot and cold spots, determined by the local Moran’s I index, were all located in northern China, where Xinjiang in the Northwest had the highest mortality across the study period. We found a significant association between long-term PM2.5 exposure and pulmonary TB mortality, with percent increase risk of mortality (IR%) being 0.74% (95 CI%, 0.04%, 1.45%) for 1 µg/m3 increase in PM2.5 concentration. This association varied across multiple socioeconomic groups, with the highest IR% in provinces with lower level of latitude (IR% = 0.83%, 95% CI: 0.01%, 1.65%), lower quartile of gross domestic product (IR% = 1.01%, 95% CI: 0.23%, 1.80%) or higher proportion (⩾14%) of people >65 years of age (IR% = 1.24%, 95% CI: 0.44%, 2.04%). Comprehensive sensitivity analyses showed a robust adverse effect of long-term PM2.5 exposure on pulmonary TB mortality. Attention needs to be paid to the rising trend of pulmonary TB mortality in Northwest China. Our study provides the stable evidence to date of the causal association between long-term PM2.5 exposure and the risk of death from pulmonary TB, especially in low-altitude, underdeveloped, and aged provinces.
Assessment of the NASA carbon monitoring system wet carbon stakeholder community: data needs, gaps, and opportunities
Authors: Brown Molly E , Mitchell Catherine , Halabisky Meghan , Gustafson Benjamin , Gomes Helga do Rosario , Goes Joaquim I , Zhang Xuesong , Campbell Anthony D , Poulter Benjamin 
Published: June 2023

Abstract
Wet carbon (WC) ecosystems are a critical part of the carbon cycle, yet they are underrepresented in many policy and science communities due to the relative under-investment in stakeholder and boundary organizations. WC systems include the hydrosphere and carbon cycling systems that operate in wetlands, oceans, rivers, streams, lakes, ponds, and permafrost. In this article, we provide evidence from a desk review of WC stakeholders, that includes individuals, groups or organizations that are affected by climate change, and utilize carbon data. These stakeholders are involved in decision-making processes in WC ecosystems, and can be private companies, non-governmental organizations, government agencies ranging in scope from local to federal, parastatals, international organizations, and more. In this paper, we identify and describe the links and interests of WC stakeholders and analyze the gaps between scientific understanding and information needs. A continued focus on WC systems could lead to increased stakeholder engagement and methodological and scientific progress. Our study revealed that stakeholder interest in WC systems was not primarily determined by its role in the carbon cycle, but rather by its significance for local policy, economics, or ecology. To bridge the gap between stakeholders and available WC data, we need improved communication of data availability and uncertainty, capacity building, engagement between stakeholder groups, and data continuity. Enhanced stakeholder engagement across various systems will facilitate greater utilization of carbon monitoring data derived from remote sensing; thereby creating more informed stakeholders as well as more effective decision-making processes.
Three-dimensional neuroimmune co-culture system for modeling Parkinson’s disease microenvironments in vitro
Authors: Rueda-Gensini Laura , Serna Julian A , Rubio Daniela , Orozco Juan Camilo , Bolaños Natalia I , Cruz Juan C , Muñoz-Camargo Carolina 
Published: June 2023

Abstract
Parkinson’s disease (PD) is a complex and multifaceted neurodegenerative disorder that results from multiple environmental factors and multicellular interactions. Although several PD neuropathologies have been identified and described, the thorough understanding of PD pathophysiology and research has been largely limited by the absence of reliable in vitro models that truly recapitulate PD microenvironments. Here, we propose a neuroimmune co-culture system that models PD neuropathologies by combining relevant multicellular interactions with environments that mimic the brain. This system is composed of: (i) 3D bioprinted cultures of mature human dopaminergic (DA) neurons grown on extracellular matrix (ECM)-derived scaffolds doped with electroconductive nanostructures, and (ii) a direct co-culture of human astrocytes and differentiated monocytes that models neuroinflammatory responses. When co-cultured in a transwell format, these two compartments recreate relevant multicellular environments that model PD pathologies after exposure to the neurotoxin A53T α-synuclein. With immunofluorescent staining and gene expression analyses, we show that functional and mature DA 3D networks are generated within our ECM-derived scaffolds with superior performance to standard 2D cultures. Moreover, by analyzing cytokine secretion, cell surface markers, and gene expression, we define a human monocyte differentiation scheme that allows the appearance of both monocyte-derived macrophages and dendritic cell phenotypes, as well as their optimal co-culture ratios with human astrocytes to recreate synergistic neuroinflammatory responses. We show that the combined response of both compartments to A53T α-synuclein stimulates the formation of intracellular α-synuclein aggregates, induces progressive mitochondrial dysfunction and reactive oxygen species production, downregulates the expression of synaptic, DA, and mitophagy-related genes, and promotes the initiation of apoptotic processes within the DA networks. Most importantly, these intracellular pathologies were comparable or superior to those generated with a rotenone-stimulated 2D control that represents the current standard for in vitro PD models and showed increased resilience towards these neurotoxic insults, allowing the study of disease progression over longer time periods than current models. Taken together, these results position the proposed model as a superior alternative to current 2D models for generating PD-related pathologies in vitro.
Hierarchical graph transformer with contrastive learning for protein function prediction
Authors: Gu Zhonghui , Luo Xiao , Chen Jiaxiao , Deng Minghua , Lai Luhua 
Published: June 2023

Abstract

Motivation
In recent years, high-throughput sequencing technologies have made large-scale protein sequences accessible. However, their functional annotations usually rely on low-throughput and pricey experimental studies. Computational prediction models offer a promising alternative to accelerate this process. Graph neural networks have shown significant progress in protein research, but capturing long-distance structural correlations and identifying key residues in protein graphs remains challenging.

Results
In the present study, we propose a novel deep learning model named Hierarchical graph transformEr with contrAstive Learning (HEAL) for protein function prediction. The core feature of HEAL is its ability to capture structural semantics using a hierarchical graph Transformer, which introduces a range of super-nodes mimicking functional motifs to interact with nodes in the protein graph. These semantic-aware super-node embeddings are then aggregated with varying emphasis to produce a graph representation. To optimize the network, we utilized graph contrastive learning as a regularization technique to maximize the similarity between different views of the graph representation. Evaluation of the PDBch test set shows that HEAL-PDB, trained on fewer data, achieves comparable performance to the recent state-of-the-art methods, such as DeepFRI. Moreover, HEAL, with the added benefit of unresolved protein structures predicted by AlphaFold2, outperforms DeepFRI by a significant margin on Fmax, AUPR, and Smin metrics on PDBch test set. Additionally, when there are no experimentally resolved structures available for the proteins of interest, HEAL can still achieve better performance on AFch test set than DeepFRI and DeepGOPlus by taking advantage of AlphaFold2 predicted structures. Finally, HEAL is capable of finding functional sites through class activation mapping.

Availability and implementation
Implementations of our HEAL can be found at https://github.com/ZhonghuiGu/HEAL.
MDTips: a multimodal-data-based drug–target interaction prediction system fusing knowledge, gene expression profile, and structural data
Authors: Xia Xiaoqiong , Zhu Chaoyu , Zhong Fan , Liu Lei 
Published: June 2023

Abstract

Motivation
Screening new drug–target interactions (DTIs) by traditional experimental methods is costly and time-consuming. Recent advances in knowledge graphs, chemical linear notations, and genomic data enable researchers to develop computational-based-DTI models, which play a pivotal role in drug repurposing and discovery. However, there still needs to develop a multimodal fusion DTI model that integrates available heterogeneous data into a unified framework.

Results
We developed MDTips, a multimodal-data-based DTI prediction system, by fusing the knowledge graphs, gene expression profiles, and structural information of drugs/targets. MDTips yielded accurate and robust performance on DTI predictions. We found that multimodal fusion learning can fully consider the importance of each modality and incorporate information from multiple aspects, thus improving model performance. Extensive experimental results demonstrate that deep learning-based encoders (i.e. Attentive FP and Transformer) outperform traditional chemical descriptors/fingerprints, and MDTips outperforms other state-of-the-art prediction models. MDTips is designed to predict the input drugs’ candidate targets, side effects, and indications with all available modalities. Via MDTips, we reverse-screened candidate targets of 6766 drugs, which can be used for drug repurposing and discovery.

Availability and implementation
https://github.com/XiaoqiongXia/MDTips and https://doi.org/10.5281/zenodo.7560544.
Do skip-N2 metastases significantly impact overall survival and disease-free interval in N2 non-small-cell lung cancer patients? A multicentre analysis
Authors: Schlachtenberger Georg , Schallenberg Simon , Doerr Fabian , Menghesha Hruy , Gaisendrees Christopher , Amorin Andres , Grathwohl Corinna , Büttner Reinhard , Quaas Alexander , Horst David , Klauschen Frederick , Frost Nikolaj , Rueckert Jens-C , Neudecker Jens , Höpker Katja , Wahlers Thorsten , Hekmat Khosro , Heldwein Matthias B 
Published: June 2023

Abstract

OBJECTIVES
Skip-N2 metastasis (N0N2), thus N2 metastasis in the absence of N1 metastasis, occurs in ∼20–30% of non-small-cell lung cancer patients. N0N2 patients have a better prognosis than continuous-N2 metastasis (N1N2) patients following surgery. However, this effect remains controversial. Therefore, we conducted a multicentre study to compare the long-term survival and disease-free interval (DFI) of N1N2- and N0N2 patients.

METHODS
One- and 3-year survival rates were measured. Kaplan–Meier curves and a Cox proportional hazards model assessed survival and were used to identify prognostic factors for overall survival. In addition, we performed propensity score matching (PSM) to rule out confounding factors. All patients received adjuvant chemoradiation therapy according to European guidelines.

RESULTS
Between January 2010 and December 2020, 218 stage IIIA/B N2 patients were included in our analysis. The Cox regression analysis revealed that N1N2 significantly influenced the overall survival rate. Before PSM, N1N2 patients showed significantly more metastatic lymph nodes (P &lt; 0.001) and significantly larger tumours (P = 0.05). After PSM, baseline characteristics did not differ between groups. Before and after PSM, N0N2 patients showed significantly better 1- (P = 0.01; P = 0.009) and 3-year (P &lt; 0.001) survival rates than N1N2 patients. Furthermore, N0N2 patients showed significantly longer DFI than N1N2 patients before and after PSM (P &lt; 000.1).

CONCLUSIONS
Prior and after PSM analysis, N0N2 patients were confirmed to have better survival and DFI than N1N2 patients. Our results demonstrate that stage IIIA/B N2 patients are heterogeneous and would benefit from a more precise subdivision and differential treatment.
Digital innovations for sustainable and resilient agricultural systems
Authors: Finger Robert 
Published: June 2023

Abstract
Digitalisation is rapidly transforming the agri-food sector. This paper investigates emerging opportunities, challenges and policy options. We show that digital innovations can contribute to more sustainable and resilient agricultural systems. For example, digital innovations enable increased productivity, reduced environmental footprints and higher resilience of farms. However, these optimistic outcomes of increasing digitalisation of the agricultural sector will not emerge on their own, but this development comes with several challenges, costs and risks, e.g. in economic, social and ethical dimensions. We provide policy recommendations to explore opportunities and avoid risks. Moreover, we discuss implications for future research in agricultural economics.
Bayesian analysis of the Substrate Ablation vs. Antiarrhythmic Drug Therapy for Symptomatic Ventricular Tachycardia trial
Authors: Ávila Pablo , Berruezo Antonio , Jiménez-Candil Javier , Tercedor Luis , Calvo David , Arribas Fernando , Fernández-Portales Javier , Merino José Luis , Hernández-Madrid Antonio , Fernández-Avilés Francisco , Arenal Ángel 
Published: June 2023

Abstract

Background and aims
Bayesian analyses can provide additional insights into the results of clinical trials, aiding in the decision-making process. We analysed the Substrate Ablation vs. Antiarrhythmic Drug Therapy for Symptomatic Ventricular Tachycardia (SURVIVE-VT) trial using Bayesian survival models.

Methods and results
The SURVIVE-VT trial randomized patients with ischaemic cardiomyopathy and monomorphic ventricular tachycardia (VT) to catheter ablation or antiarrhythmic drugs (AAD) as a first-line strategy. The primary outcome was a composite of cardiovascular death, appropriate implantable cardioverter–defibrillator shocks, unplanned heart failure hospitalizations, or severe treatment-related complications. We used informative, skeptical, and non-informative priors with different probabilities of large effects to compute the posterior distributions using Markov Chain Monte Carlo methods. We calculated the probabilities of hazard ratios (HR) being &lt;1, &lt;0.9, and &lt;0.75, as well as 2-year survival estimates. Of the 144 randomized patients, 71 underwent catheter ablation and 73 received AAD. Regardless of the prior, catheter ablation had a &gt;98% probability of reducing the primary outcome (HR &lt; 1) and a &gt;96% probability of achieving a reduction of &gt;10% (HR &lt; 0.9). The probability of a &gt;25% (HR &lt; 0.75) reduction of treatment-related complications was &gt;90%. Catheter ablation had a high probability (&gt;93%) of reducing incessant/slow undetected VT/electric storm, unplanned hospitalizations for ventricular arrhythmias, and overall cardiovascular admissions &gt; 25%, with absolute differences of 15.2%, 21.2%, and 20.2%, respectively.

Conclusion
In patients with ischaemic cardiomyopathy and VT, catheter ablation as a first-line therapy resulted in a high probability of reducing several clinical outcomes compared to AAD. Our study highlights the value of Bayesian analysis in clinical trials and its potential for guiding treatment decisions.

Trial Registration
ClinicalTrials.gov identifier: NCT03734562
Feasibility of melt segregation from a crystal mush in response to the 2011–2012 eruption at Cordón Caulle, Chile
Authors: Phelps Patrick R , Gonnermann Helge M , Winslow Heather , Ruprecht Philipp , Pritchard Matthew E , Delgado Francisco , Liao Yang 
Published: June 2023

SUMMARY
The 2011–2012 eruption at Cordón Caulle in Chile produced crystal-poor rhyolitic magma with crystal-rich mafic enclaves whose interstitial glass is of identical composition to the host rhyolite. Eruptible rhyolites are thought to be genetically associated with crystal-rich magma mushes, and the enclaves within the Cordón Caulle rhyolite support the existence of a magma mush from which the erupted magma was derived. Moreover, towards the end of the 2011–2012 eruption, subsidence gave way to inflation that has on average been continuous through at least 2020. We hypothesize that magma segregation from a crystal mush could be the source of the observed inflation. Conceptually, magma withdrawal from a crystal-poor rhyolite reservoir caused its depressurization, which could have led to upward flow of interstitial melt within an underlying crystal mush, causing a new batch of magma to segregate and partially recharge the crystal-poor rhyolite body. Because the compressibility of the crystalline matrix of the mush is expected to be lower than that of the interstitial melt, which likely contains some fraction of volatile bubbles, this redistribution of melt would result in a net increase in volume of the system and in the observed inflation. We use numerical modelling of subsurface magma flow and storage to show under which conditions such a scenario is supported by geodetic and petrologic observations.
The Genomics in Emerging Marine Systems Checklist for Clear and Reproducible Genomics in Emerging, Marine Systems
Authors: Varney Rebecca 
Published: June 2023

Synopsis
Genome sequencing becomes more accessible and powerful every year, but there is a lack of consensus on what information should be provided in publications that include genomic data. The result is a flood of sequencing data without a framework to evaluate its quality and completeness, hindering reproducibility. In non-model taxa in marine systems, a lack of detail in methods sections often hinders future researchers from adopting improved techniques, leaving them to repeat costly protocols and take up computational (wall) time with programs that are already known to fail. Here, I present a set of guidelines tailored for marine taxa (emerging model organisms) to promote consistency between publications, increase transparency of sequencing projects, and preserve the value of sequence data as sequencing technologies advance. Included is a checklist to (1) guide authors toward including more detailed information in their manuscripts, (2) expand data availability, and (3) assist reviewers to thoroughly vet methods and results of future ‘omic publications. This set of guidelines will support the usefulness of ‘omic data in future analyses by providing a framework to document and evaluate these data, leading to transparent and reproducible genomics research on emerging marine systems.
Transitional Justice in Post-terror Contexts: The Norwegian 22 July Memorial and the Ambiguity of Litigation
Authors: Sandvik Kristin Bergtora 
Published: June 2023

ABSTRACT∞
This article explores litigation as a practice of memorialization through a qualitative study of the legal mobilization to halt and re-site the Norwegian national memorial for the 22 July 2011 terror attack. In arguing that adversarial legal processes initiated by grassroots actors can represent a form of commemorative work with an ambiguous societal status, the article contributes to the emergent literature on transitional justice practices in established democracies and in post-terror contexts. In the Norwegian case, the law became an arena for contested negotiations over the hierarchy of victims. The litigation process also shaped how the fallout of 22 July is understood. Furthermore, this mobilization engendered frictions regarding the potential of future 22 July commemorations to perpetuate trauma. Litigation may be understood as a rupture with the national narrative of law as an accountability tool, because of the stigma and unresolved and painful contestation around democratic citizenship that it produces.