bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spatiotemporal modelling of soil moisture in an Atlantic forest through machine learning algorithms
Authors: de Oliveira Vinicius Augusto , Rodrigues André Ferreira , Morais Marco Antônio Vieira , Terra Marcela de Castro Nunes Santos , Guo Li , de Mello Carlos Rogério 
Published: April 2021

AbstractUnderstanding the spatiotemporal behaviour of soil moisture in tropical forests is fundamental because it mediates processes such as infiltration, groundwater recharge, runoff and evapotranspiration. This study aims to model the spatiotemporal dynamics of soil moisture in an Atlantic forest remnant (AFR) through four machine learning algorithms, as these dynamics represent an important knowledge gap under tropical conditions. Random forest (RF), support vector machine, average neural network and weighted k‐nearest neighbour were studied. The abilities of the models were evaluated by means of root mean square error, mean absolute error, coefficient of determination (R2) and Nash‐Sutcliffe efficiency (NS) for two calibration approaches: (a) chronological and (b) randomized. The models were further compared with a multilinear regression (MLR). The study period spans from September 2012 to November 2019 and relies on variables representing the weather, geographical location, forest structure, soil physics and morphology. RF was the best algorithm for modelling the spatiotemporal dynamics of the soil moisture with an NS of 0.77 and R2 of 0.51 in the randomized approach. This finding highlights the ability of RF to generalize a dataset with contrasting weather conditions. Kriging maps highlighted the suitability of RF to track the spatial distribution of soil moisture in the AFR. Throughfall (TF), potential evapotranspiration (ETo), longitude (Long), diameter at breast height (DBH) and species diversity (H) were the most important variables controlling soil moisture. MLR performed poorly in modelling the spatiotemporal dynamics of soil moisture due to the highly nonlinear condition of this process.Highlights
Modelling soil moisture in an Atlantic forest through machine learning.
Machine learning algorithms are powerful tools to address the spatiotemporal dynamics of soil moisture.
Climate, position and forest variables drive the spatiotemporal pattern of soil moisture.
Random forest is the best algorithm to simulate soil moisture dynamics.
Current and projected impacts of the parasite Tetracapsuloides bryosalmonae (causative to proliferative kidney disease) on Central European salmonid populations under predicted climate change
Authors: Ros Albert , Baer Jan , Basen Timo , Chucholl Christoph , Schneider Ernst , Teschner Ruben , Brinker Alexander 
Published: April 2021

Abstract

Proliferative kidney disease (PKD) caused by the myxozoan parasite Tetracapsuloides bryosalmonae is an emerging salmonid disease implicated in recent declines in salmonid populations. Laboratory experiments have shown that the clinical symptoms of PKD exacerbate with increasing temperature. However, empirical evidence for a relationship between climate change and PKD driven declines in wild salmonid stocks is scarce.

The current study uses both empirical data and ecological niche modelling to extrapolate future changes in temperature and precipitation on the spread of PKD in relation to changes in suitability of rivers for its primary bryozoan and secondary salmonid hosts.

A 20‐year dataset on brown trout density using standardised method shows a decline of more than 50% in the population of the river Wutach, south‐western Germany at 536–755 m elevation. The decline coincides with higher prevalence of PKD. This decline was temperature related and driven by reduced local survival of parr to yearlings. Kidney hyperplasia was highest at low elevations and correlated negatively with trout density.

Niche models based on state‐wide data show strong overlap in the areas suitable for T. bryosalmonae and its primary bryozoan hosts, and a strong link with temperature. Projections based on moderate to high emission‐case climate change scenarios predict a 50%–82% decrease in the area suitable for salmonids within this century, linked to PKD.

The empirical data identify temperature‐mediated PKD as the underlying cause of the population decline. This relationship was corroborated by the niche modelling results. The highest losses are expected in projected salmonid suitable areas where salmonid habitats currently overlap with areas suitable for T. bryosalmonae.

With ongoing climate change, both current and future populations are at severe risk to T. bryosalmonae and its associated disease PKD. Conservation efforts of salmonid stocks are dependent on climate mitigation and measures to help salmonids adapt to the disease.
Variations of carbon allocation and turnover time across tropical forests
Authors: Yang Hui , Ciais Philippe , Wang Yilong , Huang Yuanyuan , Wigneron Jean‐Pierre , Bastos Ana , Chave Jérôme , Chang Jinfeng , E. Doughty Christopher , Fan Lei , Goll Daniel , Joetzjer Emilie , Li Wei , Lucas Richard , Quegan Shaun , Le Toan Thuy , Yu Kailiang 
Published: April 2021

AbstractAimThe carbon sink in tropical forests is a highly uncertain component of the global carbon budget. An understanding of the processes controlling this sink requires better quantification of carbon allocation, stocks and turnover times.LocationTropical forests.Time period2010–2017.Major taxa studiedTropical forest ecosystem.MethodsWe develop a novel data assimilation system using satellite‐based annual above‐ground biomass derived from L‐band vegetation optical depth with 25 km × 25 km grid spacing, together with leaf area, to constrain the 25 km × 25 km carbon allocation patterns of net primary productivity (NPP) into the wood, leaf and root pools, and their turnover times. Our average data‐driven estimates of these variables are broadly consistent with independent ground‐based estimates of NPP allocation and wood turnover from forest inventory plots.ResultsIn tropical forest, on average, the NPP allocation into wood (0.30 ± 0.04) is significantly higher than that into leaves (0.24 ± 0.07). From the wet to dry tropics, forest NPP allocation into both wood and leaves declines slightly. The turnover times of forest leaf pools exhibit little spatial variation, whereas the turnover times of wood pools in Africa (median and interquartile range: years) are slightly longer than those in South America ( years) and Southeast Asia ( years). Our datasets reveal emergent trade‐offs across climatic and vegetation gradients between growth and life span/turnover for both wood and leaves. The spatial gradients of NPP allocation to wood/leaves are associated with canopy height, adjusted by climate condition and nutrient acquisition. The spatial gradients of wood and leaf turnover times are influenced mainly by climate and leaf characteristics.Main conclusionsOur data‐driven estimates of carbon allocation and turnover times provide a basis for more detailed exploration of these mechanisms in field studies. This highlights that improved model representation of carbon allocation and turnover is necessary for more accurate prediction of future carbon dynamics.
Environmental heterogeneity explains contrasting plant species richness between the South African Cape and southwestern Australia
Authors: van Mazijk Ruan , Cramer Michael D. , Verboom G. Anthony 
Published: April 2021

AbstractAimGiven the importance of environmental heterogeneity as a driver of species richness through its effects on species diversification and coexistence, we asked whether the dramatic difference in species richness per unit area between two similar Mediterranean‐type biodiversity hotspots is explained by differences in environmental heterogeneity.LocationThe Greater Cape Floristic Region, South Africa (GCFR) and Southwest Australian Floristic Region (SWAFR).TaxonVascular plants (tracheophytes).MethodsComparable, geospatially explicit environmental and species occurrence data were obtained for both regions and used to generate environmental heterogeneity and species richness raster layers. Heterogeneity in multiple environmental variables and species richness per unit area were compared between the two regions at a range of spatial scales. At each scale, richness was also regressed against these individual axes and against a major axis of heterogeneity, derived by principal component analysis (PCA).ResultsThe GCFR is generally more environmentally heterogeneous and species‐rich than the SWAFR. Species richness per unit area is significantly related to the major axis of heterogeneity across both regions, the latter describing c. 38%–50% of overall heterogeneity, the slope of this relationship differing between the two regions only at the finest spatial scale. Multivariate regressions, and regressions against the first axes of the PCAs (PC1), revealed variations in the dependence of species richness on environmental heterogeneity between the two regions.Main conclusionsNotwithstanding some region‐specific effects, we present evidence of a common positive relationship between floristic richness and environmental heterogeneity across the GCFR and SWAFR. This is dependent on spatial scale, being strongest at the coarsest level of sampling. The generally greater richness per unit area of the GCFR compared to the SWAFR is thus explained by the former's generally greater environmental heterogeneity and is concordant with its greater levels of floristic turnover.
Alteration of gut microbiota with a broad‐spectrum antibiotic does not impair maternal care in the European earwig
Authors: Van Meyel Sophie , Devers Séverine , Dupont Simon , Dedeine Franck , Meunier Joël 
Published: April 2021

AbstractThe microbes residing within the gut of an animal host often increase their own fitness by modifying their host's physiological, reproductive and behavioural functions. Whereas recent studies suggest that they may also shape host sociality and therefore have critical effects on animal social evolution, the impact of the gut microbiota on maternal care remains unexplored. This is surprising, as this behaviour is widespread among animals, often determines the fitness of both juveniles and parents, and is essential in the evolution of complex animal societies. Here, we tested whether life‐long alterations of the gut microbiota with rifampicin—a broad‐spectrum antibiotic—impair pre‐ and post‐hatching maternal care in the European earwig. Our results first confirm that rifampicin altered the mothers’ gut microbial communities and indicate that the composition of the gut microbiota differs before and after egg care. Contrary to our predictions, however, the rifampicin‐induced alterations of the gut microbiota did not modify pre‐ or post‐hatching care. Independent of maternal care, rifampicin increased the females’ faeces production and resulted in lighter eggs and juveniles. By contrast, rifampicin altered none of the other 21 physiological, reproductive and longevity traits measured over the 300 days of a female's lifetime. Overall, these findings reveal that altering the gut microbiota with a large spectrum antibiotic such as rifampicin does not necessarily affect host sociality. They also emphasize that not all animals have evolved a co‐dependence with their microbiota and call for caution when generalizing the central role of gut microbes in host biology.
Applying artificial intelligence in the microbiome for gastrointestinal diseases: A review
Authors: Zeng Tao , Yu Xiangtian , Chen Zhangran 
Published: April 2021

AbstractFor a long time, gut bacteria have been recognized for their important roles in the occurrence and progression of gastrointestinal diseases like colorectal cancer, and the ever‐increasing amounts of microbiome data combined with other high‐quality clinical and imaging datasets are leading the study of gastrointestinal diseases into an era of biomedical big data. The “omics” technologies used for microbiome analysis continuously evolve, and the machine learning or artificial intelligence technologies are key to extract the relevant information from microbiome data. This review intends to provide a focused summary of recent research and applications of microbiome big data and to discuss the use of artificial intelligence to combat gastrointestinal diseases.
Right‐to‐left shunt detection using transforaminal insonation of the basilar artery
Authors: Chhabra Nikita , Kumar Gyanendra , Fruin Jennifer , Dumitrascu Oana M. 
Published: April 2021

ABSTRACTBACKGROUND AND PURPOSEInvestigation for patent foramen ovale (PFO) is warranted in patients with cryptogenic stroke (CS), as PFO closure is recommended in select CS patients for secondary stroke prevention. Transcranial Doppler (TCD) is noninvasive and has high sensitivity for PFO screening. However, 10% of the population has insufficient temporal bone windows to perform standard TCD monitoring of the middle cerebral arteries (MCAs). Prior reports showed similar diagnostic accuracy between the basilar artery and MCAs insonation. Our objective was to assess the accuracy of transforaminal insonation of the basilar artery (TIBA) in diagnosis of right‐to‐left shunt (RLS) in patients with inadequate temporal windows.METHODSWe performed a retrospective, single‐center, observational study of TIBA in patients with CS and inadequate temporal windows. We compared the PFO screening accuracy using TIBA versus echocardiogram, either transthoracic (TTE) or transesophageal echocardiogram (TEE).RESULTSSixteen females and four males were included (mean age 63.2). All patients underwent TTE, 10 (50%) underwent gold‐standard TEE. Nine patients had positive PFO with both TCD and echocardiogram. Two patients with negative echocardiogram had TCD‐positive shunt. Three patients had PFO present on echocardiogram, whereas TCD was negative. Six patients had absent shunt with both TCD and echocardiogram. The sensitivity and specificity of TIBA for RLS detection were 75%, positive predictive value 82%, and negative predictive value 67%.CONCLUSIONPFO screening accuracy using TIBA was 75%. Prospective evaluation of CS patients with TIBA and comparison to the gold‐standard TEE should be performed to further guide clinical practice.
Identification of prognostic marker genes in head and neck squamous cell carcinoma: A study based on The Cancer Genome Atlas database and experimental validation
Authors: Zhong Qilong , Zhou Li , Zhu Dan 
Published: April 2021

BackgroundEarly detection and prognostic prediction are crucial in improving the survival of patients with head and neck squamous cell carcinoma (HNSCC). Therefore, we provided potential molecular markers in this study for early diagnosis and prognosis of this cancer based on The Cancer Genome Atlas (TCGA) database analysis and experimental validations.MethodsDifferentially expressed genes (DEGs) between HNSCC tumor and normal samples were identified by TCGA database–based analyses. Univariate and multivariate Cox regression analyses were applied, respectively, to identify survival‐related DEGs and independent prognostic factors in HNSCC. Further, RT‐qPCR was employed to verify expression of DEGs in cancer and adjacent tissues from HNSCC patients recruited in our hospital, in which we also clarified the correlation between candidate genes and clinicopathological characteristics and prognosis of HNSCC patients.ResultsTCGA data analyses yielded 59 DEGs. Cox analyses identified 13 candidate genes closely related to prognosis of HNSCC patients and established a five‐gene signature comprising AC103702.2, LINC00941, RPL29, FOXL2, and CCL11. This five‐gene signature could classify patients into high‐ and low‐risk groups. The survival rate of the high‐risk group was significantly lower than that of the low‐risk group. Clinical tissue experiments further confirmed that AC103702.2, LINC00941, CCL11, and RPL29P19 genes were inversely associated with the prognosis of HNSCC patients, while CCL11 gene was positively associated. We also found that high‐risk HNSCC patients presented a higher incidence of lymph node metastasis.ConclusionFive prognostic marker genes (AC103702.2, LINC00941, CCL11, RPL29P19, and FOXL2) as a gene cluster may serve as prognostic marker genes in HNSCC.
Low potential of circulating interleukin 1 receptor antagonist as a prediction marker for squamous cell carcinoma of the head and neck
Authors: Boldrup Linda , Coates Philip , Gu Xiaolian , Wang Lixiao , Fåhraeus Robin , Wilms Torben , Sgaramella Nicola , Nylander Karin 
Published: April 2021

AbstractBackgroundCirculating markers are attractive molecules for prognosis and management of cancer that allow sequential monitoring of patients during and after treatment. Based on previous protein profiling data, circulating interleukin 1 receptor antagonist (IL‐1Ra) was evaluated as a potential diagnostic and prognostic marker for squamous cell carcinomas of the head and neck (SCCHN). In this study, we aimed at confirming the clinical relevance of plasma IL‐1Ra in SCCHN and exploring its potential as a prediction marker for SCCHN.MethodsPlasma from 87 patients with SCCHN, control plasma from 28 healthy individuals and pre‐diagnostic plasma from 44 patients with squamous cell carcinoma of the oral tongue (SCCOT) and 88 matched controls were analysed with IL‐1Ra electrochemiluminescence immunoassays from mesoscale diagnostics.ResultsPlasma IL‐1Ra was found to be up‐regulated in patients with oral tongue, gingiva and base of tongue tumours compared to healthy individuals (p < 0.01). IL‐1Ra levels positively correlated with tumour size (p < 0.01) and body mass index (p = 0.013). Comparing pre‐diagnostic plasma to the matched controls, similar IL1‐Ra levels were seen (p = 0.05).ConclusionThe anti‐inflammatory cytokine IL‐1Ra could be a diagnostic marker for SCCHN, whereas its potential as a cancer prediction marker was not supported by our data.
Spectral arc length as a method to quantify pharyngeal high‐resolution manometric curve smoothness
Authors: Scholp Austin J. , Hoffman Matthew R. , Rosen Sarah P. , Abdelhalim Suzan M. , Jones Corinne A. , Jiang Jack J. , McCulloch Timothy M. 
Published: April 2021

AbstractBackgroundPharyngeal high‐resolution manometry (HRM) has emerged over the last decade as a valuable assessment tool for oropharyngeal dysphagia. Data analysis thus far has focused primarily on measures of pressure and duration within key anatomic regions. We apply spectral arc length (SPARC), a dimensionless metric for quantifying smoothness felt to indirectly reflect neuromuscular coordination, as a new method of describing manometric curves. We then use it to distinguish swallows from healthy subjects and those with dysphagia related to stroke.MethodsPreviously collected pharyngeal HRM data from eight subjects with history of stroke and eight age‐ and sex‐matched controls were reviewed. Receiver operating characteristic (ROC) analysis was used to optimize SPARC inputs. SPARC was then computed for the velopharynx, tongue base, hypopharynx, and upper esophageal sphincter (UES), and the values were compared between the two subject groups.ResultsOptimized parameter settings yielded an ROC curve with area under the curve (AUC) of 0.953. Mean SPARC values differed between control and stroke subjects for the velopharynx (t = 3.25, p = 0.0058), tongue base (t = 4.77, p = 0.0003), and hypopharynx (t = 2.87, p = 0.0124). Values were similar for the UES (t = 0.43, p = 0.671).ConclusionsIn this preliminary study, SPARC analysis was applied to distinguish control from post‐stroke subjects. Considering alternative methods of analyzing pharyngeal HRM data may provide additional insight into the pathophysiology of dysphagia beyond what can be gleaned from measures of pressure and duration alone.
Haplotype variations of major flowering time genes in quinoa unveil their role in the adaptation to different environmental conditions
Authors: Patiranage Dilan S.R. , Asare Edward , Maldonado‐Taipe Nathaly , Rey Elodie , Emrani Nazgol , Tester Mark , Jung Christian 
Published: April 2021

AbstractResponse to photoperiod is of major importance in crop production. It defines the adaptation of plants to local environments. Quinoa is a short‐day plant which had been domesticated in the Andeans regions. We wanted to understand the adaptation to long‐day conditions by studying orthologues of two major flowering time regulators of Arabidopsis, FLOWERING LOCUS T (FT) and CONSTANS (CO) in quinoa accessions with contrasting photoperiod response. By searching the quinoa reference genome sequence, we identified 24 FT and six CO homologs. CqFT genes displayed remarkably different expression patterns between long‐ and short‐day conditions, whereas the influence of the photoperiod on CqCOL expressions was moderate. Cultivation of 276 quinoa accessions under short‐ and long‐day conditions revealed great differences in photoperiod sensitivity. After sequencing their genomes, we identified large sequence variations in 12 flowering time genes. We found non‐random distribution of haplotypes across accessions from different geographical origins, highlighting the role of CqFT and CqCOL genes in the adaptation to different day‐length conditions. We identified five haplotypes causing early flowering under long days. This study provides assets for quinoa breeding because superior haplotypes can be assembled in a predictive breeding approach to produce well‐adapted early flowering lines under long‐day photoperiods.
When Zero May Not Be Zero: A Cautionary Note on the Use of Inter-Rater Reliability in Evaluating Grant Peer Review
Authors: Erosheva Elena A. , Martinková Patrícia , Lee Carole J. 
Published: April 2021

Abstract
Considerable attention has focused on studying reviewer agreement via inter-rater reliability (IRR) as a way to assess the quality of the peer review process. Inspired by a recent study that reported an IRR of zero in the mock peer review of top-quality grant proposals, we use real data from a complete range of submissions to the National Institutes of Health and to the American Institute of Biological Sciences to bring awareness to two important issues with using IRR for assessing peer review quality. First, we demonstrate that estimating local IRR from subsets of restricted-quality proposals will likely result in zero estimates under many scenarios. In both data sets, we find that zero local IRR estimates are more likely when subsets of top-quality proposals rather than bottom-quality proposals are considered. However, zero estimates from range-restricted data should not be interpreted as indicating arbitrariness in peer review. On the contrary, despite different scoring scales used by the two agencies, when complete ranges of proposals are considered, IRR estimates are above 0.6 which indicates good reviewer agreement. Furthermore, we demonstrate that, with a small number of reviewers per proposal, zero estimates of IRR are possible even when the true value is not zero.