bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-modality machine learning predicting Parkinson’s disease
Authors: Makarious Mary B. , Leonard Hampton L. , Vitale Dan , Iwaki Hirotaka , Sargent Lana , Dadu Anant , Violich Ivo , Hutchins Elizabeth , Saffo David , Bandres-Ciga Sara , Kim Jonggeol Jeff , Song Yeajin , Maleknia Melina , Bookman Matt , Nojopranoto Willy , Campbell Roy H. , Hashemi Sayed Hadi , Botia Juan A. , Carter John F. , Craig David W. , Van Keuren-Jensen Kendall , Morris Huw R. , Hardy John A. , Blauwendraat Cornelis , Singleton Andrew B. , Faghri Faraz , Nalls Mike A. 
Published: April 2022

AbstractPersonalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson’s disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug–gene interactions. We performed automated ML on multimodal data from the Parkinson’s progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson’s Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.
Viruses affect picocyanobacterial abundance and biogeography in the North Pacific Ocean
Authors: Carlson Michael. C. G. , Ribalet François , Maidanik Ilia , Durham Bryndan P. , Hulata Yotam , Ferrón Sara , Weissenbach Julia , Shamir Nitzan , Goldin Svetlana , Baran Nava , Cael B. B. , Karl David M. , White Angelicque E. , Armbrust E. Virginia , Lindell Debbie 
Published: April 2022

AbstractThe photosynthetic picocyanobacteria Prochlorococcus and Synechococcus are models for dissecting how ecological niches are defined by environmental conditions, but how interactions with bacteriophages affect picocyanobacterial biogeography in open ocean biomes has rarely been assessed. We applied single-virus and single-cell infection approaches to quantify cyanophage abundance and infected picocyanobacteria in 87 surface water samples from five transects that traversed approximately 2,200 km in the North Pacific Ocean on three cruises, with a duration of 2–4 weeks, between 2015 and 2017. We detected a 550-km-wide hotspot of cyanophages and virus-infected picocyanobacteria in the transition zone between the North Pacific Subtropical and Subpolar gyres that was present in each transect. Notably, the hotspot occurred at a consistent temperature and displayed distinct cyanophage-lineage composition on all transects. On two of these transects, the levels of infection in the hotspot were estimated to be sufficient to substantially limit the geographical range of Prochlorococcus. Coincident with the detection of high levels of virally infected picocyanobacteria, we measured an increase of 10–100-fold in the Synechococcus populations in samples that are usually dominated by Prochlorococcus. We developed a multiple regression model of cyanophages, temperature and chlorophyll concentrations that inferred that the hotspot extended across the North Pacific Ocean, creating a biological boundary between gyres, with the potential to release organic matter comparable to that of the sevenfold-larger North Pacific Subtropical Gyre. Our results highlight the probable impact of viruses on large-scale phytoplankton biogeography and biogeochemistry in distinct regions of the oceans.
A dataset of non-pharmaceutical interventions on SARS-CoV-2 in Europe
Authors: Altman George , Ahuja Janvi , Monrad Joshua Teperowski , Dhaliwal Gurpreet , Rogers-Smith Charlie , Leech Gavin , Snodin Benedict , Sandbrink Jonas B. , Finnveden Lukas , Norman Alexander John , Oehm Sebastian B. , Sandkühler Julia Fabienne , Kulveit Jan , Flaxman Seth , Gal Yarin , Mishra Swapnil , Bhatt Samir , Sharma Mrinank , Mindermann Sören , Brauner Jan Markus 
Published: April 2022

AbstractDuring the second half of 2020, many European governments responded to the resurging transmission of SARS-CoV-2 with wide-ranging non-pharmaceutical interventions (NPIs). These efforts were often highly targeted at the regional level and included fine-grained NPIs. This paper describes a new dataset designed for the accurate recording of NPIs in Europe’s second wave to allow precise modelling of NPI effectiveness. The dataset includes interventions from 114 regions in 7 European countries during the period from the 1st August 2020 to the 9th January 2021. The paper includes NPI definitions tailored to the second wave following an exploratory data collection. Each entry has been extensively validated by semi-independent double entry, comparison with existing datasets, and, when necessary, discussion with local epidemiologists. The dataset has considerable potential for use in disentangling the effectiveness of NPIs and comparing the impact of interventions across different phases of the pandemic.
A spatially-explicit harmonized global dataset of critical infrastructure
Authors: Nirandjan Sadhana , Koks Elco E. , Ward Philip J. , Aerts Jeroen C. J. H. 
Published: April 2022

AbstractCritical infrastructure (CI) is fundamental for the functioning of a society and forms the backbone for socio-economic development. Natural and human-made threats, however, pose a major risk to CI. Therefore, geospatial data on the location of CI are fundamental for in-depth risk analyses, which are required to inform policy decisions aiming to reduce risk. We present a first-of-its-kind globally harmonized spatial dataset for the representation of CI. In this study, we: (1) collect and harmonize detailed geospatial data of the world’s main CI systems into a single geospatial database; and (2) develop the Critical Infrastructure Spatial Index (CISI) to express the global spatial intensity of CI. The CISI aggregates high-resolution geospatial OpenStreetMap (OSM) data of 39 CI types that are categorized under seven overarching CI systems. The detailed geospatial data are rasterized into a harmonized and consistent dataset with a resolution of 0.10 × 0.10 and 0.25 × 0.25 degrees. The dataset can be applied to explore the current landscape of CI, identify CI hotspots, and as exposure input for large-scale risk assessments.
A biologging database of juvenile white sharks from the northeast Pacific
Authors: O’Sullivan John , Lowe Christopher G. , Sosa-Nishizaki Oscar , Jorgensen Salvador J. , Anderson James M. , Farrugia Thomas J. , García-Rodríguez Emiliano , Lyons Kady , McKinzie Megan K. , Oñate-González Erick C. , Weng Kevin , White Connor F. , Winkler Chuck , Van Houtan Kyle S. 
Published: April 2022

AbstractSpecies occurrence records are vital data streams in marine conservation with a wide range of important applications. From 2001–2020, the Monterey Bay Aquarium led an international research collaboration to understand the life cycle, ecology, and behavior of white sharks (Carcharodon carcharias) in the southern California Current. The collaboration was devoted to tagging juveniles with animal-borne sensors, also known as biologging. Here we report the full data records from 59 pop-up archival (PAT) and 20 smart position and temperature transmitting (SPOT) tags that variously recorded pressure, temperature, and light-level data, and computed depth and geolocations for 63 individuals. Whether transmitted or from recovered devices, raw data files from successful deployments (n = 70) were auto-ingested from the manufacturer into the United States (US) Animal Telemetry Network’s (ATN) Data Assembly Center (DAC). There they have attributed a full suite of metadata, visualized within their public-facing data portal, compiled for permanent archive under the DataONE Research Workspace member node, and are accessible for download from the ATN data portal.
A hierarchical inventory of the world’s mountains for global comparative mountain science
Authors: Snethlage Mark A. , Geschke Jonas , Ranipeta Ajay , Jetz Walter , Yoccoz Nigel G. , Körner Christian , Spehn Eva M. , Fischer Markus , Urbach Davnah 
Published: April 2022

AbstractA standardized delineation of the world’s mountains has many applications in research, education, and the science-policy interface. Here we provide a new inventory of 8616 mountain ranges developed under the auspices of the Global Mountain Biodiversity Assessment (GMBA). Building on an earlier compilation, the presented geospatial database uses a further advanced and generalized mountain definition and a semi-automated method to enable globally standardized, transparent delineations of mountain ranges worldwide. The inventory is presented on EarthEnv at various hierarchical levels and allows users to select their preferred level of regional aggregation from continents to small subranges according to their needs and the scale of their analyses. The clearly defined, globally consistent and hierarchical nature of the presented mountain inventory offers a standardized resource for referencing and addressing mountains across basic and applied natural as well as social sciences and a range of other uses in science communication and education.
Trait rumination and social anxiety separately influence stress-induced rumination and hemodynamic responses
Authors: Laicher Hendrik , Int-Veen Isabell , Torka Florian , Kroczek Agnes , Bihlmaier Isabel , Storchak Helena , Velten-Schurian Kerstin , Dresler Thomas , Täglich Ramona , Fallgatter Andreas J. , Ehlis Ann-Christine , Rosenbaum David 
Published: April 2022

AbstractWe aimed to investigate stress-reactive rumination in response to social stress and its association with social anxiety and trait rumination. From previous investigations we know that people with a certain vulnerability to rumination show increased stress-reactive rumination. However, up to date the possible influence of social anxiety on this relationship is still unclear. Therefore, we reanalyzed the data of two of our previous studies assessing healthy low and high trait ruminators and depressed patients performing the Trier Social Stress Test (TSST). We measured cortical oxygenation using functional Near-Infrared Spectroscopy (fNIRS) as well as different behavioral outcome measures (subjective stress levels, negative affect, state rumination). On a behavioral level, we found an influence of both, social anxiety and trait rumination, on state rumination, even when correcting for the other factor, respectively, implying two potentially independent factors of influence. On a neural level, we observed reduced activation in brain regions of the cognitive control network (CCN) for higher social anxiety and trait rumination, which might be a result of reduced cognitive and attentional control. Results indicate a specific role of social anxiety, at least on a behavioral level, and therefore implicate a crucial factor to be considered in the treatment of depression.
Longitudinal relationship of liver injury with inflammation biomarkers in COVID-19 hospitalized patients using a joint modeling approach
Authors: Diaz-Louzao Carla , Barrera-Lopez Lucia , Lopez-Rodriguez Maria , Casar Clara , Vazquez-Agra Nestor , Pernas-Pardavila Hadrian , Marques-Afonso Ana , Vidal-Vazquez Martin , Montoya Jonathan G. , Andrade Ariadna H. , Fernandez-Castro Ivan , Varela Pablo , Gonzalez-Quintela Arturo , Otero Esteban , Gude Francisco , Cadarso-Suarez Carmen , Tome Santiago 
Published: April 2022

AbstractThe mechanisms underlying liver disease in patients with COVID-19 are not entirely known. The aim is to investigate, by means of novel statistical techniques, the changes over time in the relationship between inflammation markers and liver damage markers in relation to survival in COVID-19. The study included 221 consecutive patients admitted to the hospital during the first COVID-19 wave in Spain. Generalized additive mixed models were used to investigate the influence of time and inflammation markers on liver damage markers in relation to survival. Joint modeling regression was used to evaluate the temporal correlations between inflammation markers (serum C-reactive protein [CRP], interleukin-6, plasma D-dimer, and blood lymphocyte count) and liver damage markers, after adjusting for age, sex, and therapy. The patients who died showed a significant elevation in serum aspartate transaminase (AST) and alkaline phosphatase levels over time. Conversely, a decrease in serum AST levels was observed in the survivors, who showed a negative correlation between inflammation markers and liver damage markers (CRP with serum AST, alanine transaminase [ALT], and gamma-glutamyl transferase [GGT]; and D-dimer with AST and ALT) after a week of hospitalization. Conversely, most correlations were positive in the patients who died, except lymphocyte count, which was negatively correlated with AST, GGT, and alkaline phosphatase. These correlations were attenuated with age. The patients who died during COVID-19 infection displayed a significant elevation of liver damage markers, which is correlated with inflammation markers over time. These results are consistent with the role of systemic inflammation in liver damage during COVID-19.
Returning long-range PM2.5 transport into the leeward of East Asia in 2021 after Chinese economic recovery from the COVID-19 pandemic
Authors: Itahashi Syuichi , Yamamura Yuki , Wang Zhe , Uno Itsushi 
Published: April 2022

AbstractChanges in the aerosol composition of sulfate (SO42−) and nitrate (NO3−) from 2012 to 2019 have been captured as a paradigm shift in the region downwind of China. Specifically, SO42− dramatically decreased and NO3− dramatically increased over downwind locations such as western Japan due to the faster reduction of SO2 emissions than NOx emissions and the almost constant trend of NH3 emissions from China. Emissions from China sharply decreased during COVID-19 lockdowns in February–March 2020, after which China’s economic situation seemed to recover going into 2021. Given this substantial change in Chinese emissions, it is necessary to clarify the impact of long-range PM2.5 transport into the leeward of East Asia. In this study, ground-based aerosol compositions observed at three sites in western Japan were analysed. The concentrations of PM2.5, SO42− and NO3− decreased in 2020 (during COVID-19) compared with 2018–2019 (before COVID-19). In 2021 (after COVID-19), PM2.5 and NO3− increased and SO42− was unchanged. This suggests the returning long-range PM2.5 transport in 2021. From numerical simulations, the status of Chinese emissions during COVID-19 did not explain this returning impact in 2021. This study shows that the status of Chinese emissions in 2021 recovered to that before COVID-19.
Determination and dietary intake risk assessment of 35 pesticide residues in cowpea (Vigna unguiculata [L.] Walp) from Hainan province, China
Authors: Zhang Qun , Ma Chen , Duan Yun , Wu Xiaopeng , Lv Daizhu , Luo Jinhui 
Published: April 2022

AbstractThe presence of pesticide residues in cowpea raises serious health concerns. In this study, a novel, sensitive, high-performance method was developed to simultaneously analyze the residues of 35 pesticides in cowpea samples from growing areas in the Hainan province of China, from November 2018 to June 2021. The method employs modified QuEChERS sample pretreatment coupled with gas chromatography-tandem mass spectrometry. The limits of quantification of the 35 pesticides in the cowpea matrix ranged from 1.0 to 8.0 μg/kg. Twenty-seven of the 35 pesticides were detected, twelve of which are banned for use on legumes in China. Residues for ten pesticides in 17.1% of the samples exceeded their MRLs, with the highest exceedance of 380% observed in difenoconazole. Moreover, 80.8% of the samples contained one or more pesticide residues, with the most frequently detected pesticide being chlorfenapyr with a detection rate of 46.3%. In addition, the pesticide triazophos was detected through different years and regions. Notably, the chronic dietary exposure risk (%ADI) of the detected pesticides, evaluated from the national estimated acceptable daily intake, was lower than 100% in Chinese people of different age groups.
A general framework to link theory and empirics in opinion formation models
Authors: Kozitsin Ivan V. 
Published: April 2022

AbstractWe introduce a minimal opinion formation model that is quite flexible and can reproduce a wide variety of the existing micro-influence assumptions and models. The model can be easily calibrated on real data, upon which it imposes only a few requirements. From this perspective, our model can be considered as a bridge, connecting theoretical studies on opinion formation models and empirical research on social dynamics. We investigate the model analytically by using mean-field approximation and numerically via Monte Carlo simulations. Our analysis is exemplified by recently reported empirical data drawn from an online social network. We demonstrate that the model calibrated on these data may reproduce fragmented and polarizing social systems. Furthermore, we manage to generate an artificial society that features properties quantitatively and qualitatively similar to those observed empirically at the macro scale. This ability became possible after we had advanced the model with two important communication features: selectivity and personalization algorithms.
Alteration in molecular structure of alkali activated slag with various water to binder ratios under accelerated carbonation
Authors: Nguyen Thi Nhan , Phung Quoc Tri , Yu Ziyou , Frederickx Lander , Jacques Diederik , Sakellariou Dimitrios , Dauzeres Alexandre , Elsen Jan , Pontikes Yiannis 
Published: April 2022

AbstractCarbonation of alkali activated materials is one of the main deteriorations affecting their durability. However, current understanding of the structural alteration of these materials exposed to an environment inducing carbonation at the nano/micro scale remains limited. This study examined the evolution of phase assemblages of alkali activated slag mortars subjected to accelerated carbonation (1% CO2, 60% relative humidity, up to 28 day carbonation) using XRD, FTIR and 29Si, 27Al, and 23Na MAS NMR. Samples with three water to binder (w/b) ratios (0.35, 0.45, and 0.55) were investigated. The results show that the phase assemblages mainly consisted of C-A-S-H, a disordered remnant aluminosilicate binder, and a minor hydrotalcite as a secondary product. Upon carbonation, calcium carbonate is mainly formed as the vaterite polymorph, while no sodium carbonate is found after carbonation as commonly reported. Sodium acts primarily as a charge balancing ion without producing sodium carbonate as a final carbonation product in the 28-day carbonated materials. The C-A-S-H structure becomes more cross-linked due to the decalcification of this phase as evidenced by the appearance of Q4 groups, which replace the Q1 and Q2 groups as observed in the 29Si MAS NMR spectra, and the dominance of Al(IV) in 27Al MAS NMR. Especially, unlike cementitious materials, the influence of w/b ratio on the crystalline phase formation and structure of C-A-S-H in the alkali activated mortars before and after carbonation is limited.
Body fat compartment determination by encoder–decoder convolutional neural network: application to amyotrophic lateral sclerosis
Authors: Vernikouskaya Ina , Müller Hans-Peter , Felbel Dominik , Roselli Francesco , Ludolph Albert C. , Kassubek Jan , Rasche Volker 
Published: April 2022

AbstractThe objective of this study was to automate the discrimination and quantification of human abdominal body fat compartments into subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) from T1-weighted MRI using encoder-decoder convolutional neural networks (CNN) and to apply the algorithm to a diseased patient sample, i.e., patients with amyotrophic lateral sclerosis (ALS). One-hundred-and-fifty-five participants (74 patients with ALS and 81 healthy controls) were split in training (50%), validation (6%), and test (44%) data. SAT and VAT volumes were determined by a novel automated CNN-based algorithm of U-Net like architecture in comparison with an established protocol with semi-automatic assessment as the reference. The dice coefficients between the CNN predicted masks and the reference segmentation were 0.87 ± 0.04 for SAT and 0.64 ± 0.17 for VAT in the control group and 0.87 ± 0.08 for SAT and 0.68 ± 0.15 for VAT in the ALS group. The significantly increased VAT/SAT ratio in the ALS group in comparison to controls confirmed the previous results. In summary, the CNN approach using CNN of U-Net architecture for automated segmentation of abdominal adipose tissue substantially facilitates data processing and offers the opportunity to automatically discriminate abdominal SAT and VAT compartments. Within the research field of neurodegenerative disorders with body composition alterations like ALS, the unbiased analysis of body fat components might pave the way for these parameters as a potential biological marker or a secondary read-out for clinical trials.