bluuebunny/crossref_metadata_2025_split huggingface dataset:
Combination of Decadal Predictions and Climate Projections in Time: Challenges and Potential Solutions
Authors: Befort D. J. , Brunner L. , Borchert L. F. , O’Reilly C. H. , Mignot J. , Ballinger A. P. , Hegerl G. C. , Murphy J. M. , Weisheimer A. 
Published: June 2022

AbstractThis study presents an approach to provide seamless climate information by concatenating decadal climate predictions and climate projections in time. Results for near‐surface air temperature over 29 regions indicate that such an approach has potential to provide meaningful information but can also introduce significant inconsistencies. Inconsistencies are often most pronounced for relatively extreme quantiles of the CMIP6 multi‐model ensemble distribution, whereas they are generally smaller and mostly insignificant for quantiles close to the median. The regions most affected are the North Atlantic, Greenland and Northern Europe. Two potential ways to reduce inconsistencies are discussed, including a simple calibration method and a weighting approach based on model performance. Calibration generally reduces inconsistencies but does not eliminate all of them. The impact of model weighting is minor, which is found to be linked to the small size of the decadal climate prediction ensemble, which in turn limits the applicability of that method.
Snow Nitrate Isotopes in Central Antarctica Record the Prolonged Period of Stratospheric Ozone Depletion From ∼1960 to 2000
Authors: Shi Guitao , Hu Ye , Ma Hongmei , Jiang Su , Chen Zhenlou , Hu Zhengyi , An Chunlei , Sun Bo , Hastings Meredith G. 
Published: June 2022

AbstractInterpretation of NO3− variability recorded in ice cores remains challenging as it can be lost from snow. Here, we present 60‐year records of NO3− and its isotopic composition (δ15N, δ18O, and Δ17O) in snow in central Antarctica, Dome A. In the upper ∼90 cm snowpack, variations in concentration and isotopic composition of NO3− are dominated by photolytic loss, and δ18O and Δ17O of NO3− are associated with the recycling of NOx to NO3− in the condensed phase driven by photolysis. In the deeper snowpack (∼1960–2000), we observe prolonged trends in concentration and isotopic composition of NO3−, which are best explained as enhanced snow NO3− photolysis due to long‐term decreasing total column ozone (TCO). That is, the prolonged period of trends in NO3− and its isotopes in extremely low snow accumulation sites such as Dome A relay information on variations in TCO and consequently surface solar ultraviolet radiation over time.
Annual Mean Arctic Amplification 1970–2020: Observed and Simulated by CMIP6 Climate Models
Authors: Chylek Petr , Folland Chris , Klett James D. , Wang Muyin , Hengartner Nick , Lesins Glen , Dubey Manvendra K. 
Published: June 2022

AbstractWhile the annual mean Arctic Amplification (AA) index varied between two and three during the 1970–2000 period, it reached values exceeding four during the first two decades of the 21st century. The AA did not change in a continuous fashion but rather in two sharp increases around 1986 and 1999. During those steps the mean global surface air temperature trend remained almost constant, while the Arctic trend increased. Although the “best” CMIP6 models reproduce the increasing trend of the AA in 1980s they do not capture the sharply increasing trend of the AA after 1999 including its rapid step‐like increase. We propose that the first sharp AA increase around 1986 is due to external forcing, while the second step close to 1999 is due to internal climate variability, which models cannot reproduce in the observed time.
Gleissberg Cycle Dependence of Inner Zone Proton Flux
Authors: Bregou Emily J. , Hudson Mary K. , Kress Brian T. , Qin Murong , Selesnick Richard S. 
Published: June 2022

AbstractInner zone proton flux from 1980 to mid‐2021 is examined using NOAA POES satellite data, indicating a long‐term increase corresponding to a one hundred year minimum in solar activity consistent with the Centennial Gleissberg Cycle. Variation of inner belt protons is correlated with decreasing F10.7 maxima over the 40‐year period, serving as proxy for solar EUV input to Earth's atmosphere. Extending an earlier study (Qin et al., 2014; https://doi.org/10.1002/2014JA020300) of >70 MeV protons from 1980 – 2021 using the South Atlantic Anomaly (SAA) peak flux, and at fixed L = 1.3, a comparison is made between the >35, >70 and >140 MeV energy channels on POES. All three energies show an increase in proton flux over the period 1998 – 2021 using a single spacecraft. The observed flux increase is correlated with decreasing F10.7 over the longer 40‐year time interval, as with the ∼11‐year solar cycle. A phase lag during Solar Cycle 24 (January 2010 – June 2021) between the F10.7 minimum and proton flux maximum was determined to be ∼500 days, the same at all energies studied. A model calculation of the inner zone proton flux is found to generally confirm the long‐term trend examined both in absolute magnitude and phase lag. It is concluded that this long‐term trend is a manifestation of the concurrent Gleissberg cycle minimum and accompanying decrease in solar EUV. Reduced EUV at solar maximum (F10.7 proxy) reduces proton loss to the atmosphere following solar maximum, thus explaining the long‐term flux increase observed.
Chemokine receptors differentially expressed by race category and molecular subtype in the breast cancer TCGA cohort
Authors: Vazquez Elissa D. , Fang Xiangyi , Levesque Lauren A. , Huynh Mike , Venegas Citlali , Lu Nhien , Salazar Nicole 
Published: June 2022

AbstractRacial disparities in mortality due to metastasis remain significant among breast cancer patients. Chemokine receptors contribute to breast tumors and metastatic outcome. We explored for significant differences in chemokine receptor expression in breast tumors from Black, Asian, and White patients in The Cancer Genome Atlas. We show that despite sharing the same molecular subtype, expression of the chemokine receptors ACKR1, CCR3, CCR6, CCRL1, CCRL2, CXCR1, CXCR2, CXCR4, CXCR6, and CXC3CR1 was significantly different depending on racial group. For patients with triple negative breast cancer, CCR3 was higher in Black versus White and CCRL2 was higher in Asian versus White. In luminal A tumors, ACKR1 was lower in Asian versus White, CCR3 was higher in Black versus White, and CCR6 and CXC3CR1 were lower in Black versus White. In luminal B tumors, CCRL2 was lower in Black versus White, CXCR1 and CXC3CR1 were lower in Asian versus White, and CXCR2 was lower in Black and Asian versus White. In HER2 enriched tumors, CCR3 was higher in Black versus White and CXCR4 lower in Asian versus White. CCR3, CCR6, and CXCR6 associated with worse patient survival. These findings can inform improved treatment strategies to decrease racial disparities in breast cancer burden.
Slow localized patterns in singularly perturbed two-component reaction–diffusion equations
Authors: Doelman Arjen 
Published: June 2022

Abstract
Localized patterns in singularly perturbed reaction–diffusion equations typically consist of slow parts, in which the associated solution follows an orbit on a slow manifold in a reduced spatial dynamical system, alternated by fast excursions, in which the solution jumps from one slow manifold to another, or back to the original slow manifold. In this paper we consider the existence and stability of stationary and travelling localized patterns that do not exhibit such jumps, i.e. that are completely embedded in a slow manifold of the singularly perturbed spatial dynamical system. These ‘slow patterns’ have rarely been considered in the literature, for two reasons: (i) in the classical Gray–Scott/Gierer–Meinhardt type models that dominate the literature, the flow on the slow manifold is typically linear and thus cannot exhibit homoclinic pulse or heteroclinic front solutions; (ii) the slow manifolds occurring in the literature are typically ‘vertical’, i.e. given by u ≡ u
0, where u is the fast variable, so that the stability problem is determined by a simple (decoupled) scalar equation. The present research concerns a general system of singularly perturbed reaction–diffusion equations and is motivated by several explicit ecosystem models that do give rise to non-vertical normally hyperbolic slow manifolds on which the flow may exhibit both homoclinic and heteroclinic orbits that correspond to either stationary or travelling localized slow patterns. The associated spectral stability problems are at leading order given by a nonlinear, but scalar, eigenvalue problem with Sturm–Liouville characteristics and we establish that homoclinic pulse patterns are typically unstable, while heteroclinic fronts can either be stable or unstable. However, we also show that homoclinic pulse patterns that are asymptotically close to a heteroclinic cycle may be stable. This result is obtained by explicitly determining the leading order approximations of four critical asymptotically small eigenvalues. By this analysis, that involves several orders of magnitude in the small parameter, we also obtain full control over the nature of the bifurcations—saddle-node, Hopf, global, etc—that determine the existence and stability of the (stationary and/or travelling) heteroclinic fronts and/or homoclinic pulses. Finally, we show that heteroclinic orbits may correspond to stable (slow) interfaces in two-dimensional space, while the homoclinic pulses must be unstable as localized stripes, even when they are stable in one space dimension.
Single-cell sequencing: expansion, integration and translation
Authors: Dai Xiaofeng , Cai Linhan , He Fule 
Published: June 2022

AbstractWith the rapid advancement in sequencing technologies, the concept of omics has revolutionized our understanding of cellular behaviors. Conventional omics investigation approaches measure the averaged behaviors of multiple cells, which may easily hide signals represented by a small-cell cohort, urging for the development of techniques with enhanced resolution. Single-cell RNA sequencing, investigating cell transcriptomics at the resolution of a single cell, has been rapidly expanded to investigate other omics such as genomics, proteomics and metabolomics since its invention. The requirement for comprehensive understanding of complex cellular behavior has led to the integration of multi-omics and single-cell sequencing data with other layers of information such as spatial data and the CRISPR screening technique towards gained knowledge or innovative functionalities. The development of single-cell sequencing in both dimensions has rendered it a unique field that offers us a versatile toolbox to delineate complex diseases, including cancers.
BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria
Authors: Bonidia Robson P , Santos Anderson P Avila , de Almeida Breno L S , Stadler Peter F , da Rocha Ulisses N , Sanches Danilo S , de Carvalho André C P L F 
Published: June 2022

Abstract
Recent technological advances have led to an exponential expansion of biological sequence data and extraction of meaningful information through Machine Learning (ML) algorithms. This knowledge has improved the understanding of mechanisms related to several fatal diseases, e.g. Cancer and coronavirus disease 2019, helping to develop innovative solutions, such as CRISPR-based gene editing, coronavirus vaccine and precision medicine. These advances benefit our society and economy, directly impacting people’s lives in various areas, such as health care, drug discovery, forensic analysis and food processing. Nevertheless, ML-based approaches to biological data require representative, quantitative and informative features. Many ML algorithms can handle only numerical data, and therefore sequences need to be translated into a numerical feature vector. This process, known as feature extraction, is a fundamental step for developing high-quality ML-based models in bioinformatics, by allowing the feature engineering stage, with design and selection of suitable features. Feature engineering, ML algorithm selection and hyperparameter tuning are often manual and time-consuming processes, requiring extensive domain knowledge. To deal with this problem, we present a new package: BioAutoML. BioAutoML automatically runs an end-to-end ML pipeline, extracting numerical and informative features from biological sequence databases, using the MathFeature package, and automating the feature selection, ML algorithm(s) recommendation and tuning of the selected algorithm(s) hyperparameters, using Automated ML (AutoML). BioAutoML has two components, divided into four modules: (1) automated feature engineering (feature extraction and selection modules) and (2) Metalearning (algorithm recommendation and hyper-parameter tuning modules). We experimentally evaluate BioAutoML in two different scenarios: (i) prediction of the three main classes of noncoding RNAs (ncRNAs) and (ii) prediction of the eight categories of ncRNAs in bacteria, including housekeeping and regulatory types. To assess BioAutoML predictive performance, it is experimentally compared with two other AutoML tools (RECIPE and TPOT). According to the experimental results, BioAutoML can accelerate new studies, reducing the cost of feature engineering processing and either keeping or improving predictive performance. BioAutoML is freely available at https://github.com/Bonidia/BioAutoML.
A comprehensive benchmarking of WGS-based deletion structural variant callers
Authors: Sarwal Varuni , Niehus Sebastian , Ayyala Ram , Kim Minyoung , Sarkar Aditya , Chang Sei , Lu Angela , Rajkumar Neha , Darfci-Maher Nicholas , Littman Russell , Chhugani Karishma , Soylev Arda , Comarova Zoia , Wesel Emily , Castellanos Jacqueline , Chikka Rahul , Distler Margaret G , Eskin Eleazar , Flint Jonathan , Mangul Serghei 
Published: June 2022

AbstractAdvances in whole-genome sequencing (WGS) promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from WGS data presents a substantial number of challenges and a plethora of SV detection methods have been developed. Currently, evidence that investigators can use to select appropriate SV detection tools is lacking. In this article, we have evaluated the performance of SV detection tools on mouse and human WGS data using a comprehensive polymerase chain reaction-confirmed gold standard set of SVs and the genome-in-a-bottle variant set, respectively. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of the SV detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance as the SV detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV detection tools varied widely in their performance, with several methods providing a good balance between sensitivity and precision. Additionally, we have determined the SV callers best suited for low- and ultralow-pass sequencing data as well as for different deletion length categories.
Cell–cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies
Authors: Peng Lihong , Wang Feixiang , Wang Zhao , Tan Jingwei , Huang Li , Tian Xiongfei , Liu Guangyi , Zhou Liqian 
Published: June 2022

Abstract
Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell–cell communication can be inferred through ligand–receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand–receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell–cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell–cell communication estimation tools for tumor-targeted therapy.
A comprehensive comparison on cell-type composition inference for spatial transcriptomics data
Authors: Chen Jiawen , Liu Weifang , Luo Tianyou , Yu Zhentao , Jiang Minzhi , Wen Jia , Gupta Gaorav P , Giusti Paola , Zhu Hongtu , Yang Yuchen , Li Yun 
Published: June 2022

Abstract
Spatial transcriptomics (ST) technologies allow researchers to examine transcriptional profiles along with maintained positional information. Such spatially resolved transcriptional characterization of intact tissue samples provides an integrated view of gene expression in its natural spatial and functional context. However, high-throughput sequencing-based ST technologies cannot yet reach single cell resolution. Thus, similar to bulk RNA-seq data, gene expression data at ST spot-level reflect transcriptional profiles of multiple cells and entail the inference of cell-type composition within each ST spot for valid and powerful subsequent analyses. Realizing the critical importance of cell-type decomposition, multiple groups have developed ST deconvolution methods. The aim of this work is to review state-of-the-art methods for ST deconvolution, comparing their strengths and weaknesses. In particular, we construct ST spots from single-cell level ST data to assess the performance of 10 methods, with either ideal reference or non-ideal reference. Furthermore, we examine the performance of these methods on spot- and bead-level ST data by comparing estimated cell-type proportions to carefully matched single-cell ST data. In comparing the performance on various tissues and technological platforms, we concluded that RCTD and stereoscope achieve more robust and accurate inferences.
Risk stratification through allergy history: single-centre experience of specialized COVID-19 vaccine clinic
Authors: Lyons Daniel , Murray Cliodhna , Hannigan Siobhan , Sui Jacklyn , Alamin Salma , Conlon Niall , Keogan Mary , Khalib Khairin , Fitzpatrick Chris , O’B Hourihane Jonathan , Carey Michael , Edgar J David M 
Published: June 2022

Abstract
Anaphylaxis is a rare side-effect of COVID-19 vaccines. To (a) provide direct advice and reassurance to certain persons with a history of anaphylaxis/complex allergy, in addition to that available in national guidelines, and (b) to provide a medically supervised vaccination, a specialist regional vaccine allergy clinic was established. The main objective was to determine if risk stratification through history can lead to safe COVID-19 vaccination for maximum population coverage. A focused history was taken to establish contraindications to giving COVID-19 vaccines. People who reported a high-risk allergy history were given a vaccine not containing the excipient thought to have directly caused previous anaphylaxis. All vaccines were monitored for 30 min after administration. A total of 206 people were vaccinated between 6 July 2021 and 31 August 2021; Comirnaty (Pfizer-BioNTech) (n = 34), and Janssen (n = 172). In total, 78% were women. Ninety-two people (45%) reported a high-risk allergy history. There were no cases of anaphylaxis. Three people developed urticaria and one of these also developed transient tachycardia. One vaccinee developed a pseudoseizure. Two of 208 people (&lt;1%) referred during this time declined vaccination based on personal preference, despite the assessment of low clinical risk. In our experience, all vaccines with high-risk allergy histories were administered Pfizer BioNTech or Janssen Covid-19 vaccines uneventfully following screening based on allergy-focussed history. Our data support that drug allergy is not associated with a higher risk of vaccine-related anaphylaxis but may act to guide the administration of alternate vaccines to people with polyethylene glycol/polysorbate 80/trometamol allergies or anaphylaxis after the first dose.
Immune cell–antibody interactions in health and disease
Authors: Karagiannis Sophia N , Arnold James N 
Published: June 2022

Summary
The human immune system safeguards against pathogens through a multitude of cellular and molecular signals, involving different components of the innate and adaptive response. Contrastingly, autoimmune diseases, allergic conditions, and cancer evoke different aspects of these otherwise protective processes. Understanding the immunological hallmarks for each pathological setting is essential for improving prevention, diagnosis, prognosis, and treatment. The activatory states of immune effector cells, especially in relation to their direct or indirect interactions with antibodies, are important determinants of an efficient, protective response that results in target clearance and improved clinical outcomes. Dysregulation of effector cells and their functions alongside alternatively activated humoral immune responses may contribute to several chronic diseases including allergic inflammation, autoimmune disorders and cancer. This Review Series brings to the forefront several key activation and regulatory features of immune effector cells in different diseases including cancer, infection allergy, and autoimmunity. Specific attention is drawn on how antibodies can impact effector cell states, and their pro-inflammatory and immune protective functions. Articles in this Series discuss different effector cells and antibody isotypes in infection, inflammation, tolerance and cancer immune surveillance, covering basic and translational mechanisms, clinical and epidemiological insights into these immune responses. Understanding the critical attributes of immune cells, especially those needed to effectively engage antibodies, will undoubtedly help better exploit their potential for disease management and therapy.
Mapping frontoinsular cortex from diffusion microstructure
Authors: Cabeen Ryan P , Toga Arthur W , Allman John M 
Published: June 2022

AbstractWe developed a novel method for mapping the location, surface area, thickness, and volume of frontoinsular cortex (FI) using structural and diffusion magnetic resonance imaging. FI lies in the ventral part of anterior insular cortex and is characterized by its distinctive population von Economo neurons (VENs). Functional neuroimaging studies have revealed its involvement in affective processing, and histopathology has implicated VEN loss in behavioral-variant frontotemporal dementia and chronic alcoholism; however, structural neuroimaging of FI has been relatively limited. We delineated FI by jointly modeling cortical surface geometry and its coincident diffusion microstructure parameters. We found that neurite orientation dispersion in cortical gray matter can be used to map FI in specific individuals, and the derived measures reflect a range of behavioral factors in young adults from the Human Connectome Project (N=1052). FI volume was larger in the left hemisphere than the right (31%), and the percentage volume of FI was larger in women than men (15.3%). FI volume was associated with measures of decision-making (delay discounting, substance abuse), emotion (negative intrusive thinking and perception of hostility), and social behavior (theory of mind and working memory for faces). The common denominator is that larger FI size is related to greater self-control and social awareness.