cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10412950
title: CyTOF dataset and Luminex data for paper "Primary prophylaxis with mTOR inhibitor enhances T-cell effector function and prevents heart transplant rejection during talimogene laherparepvec therapy of squamous cell carcinoma"

description: Contains the raw CyTOF FCS files and Luminex excel file for the paper "Primary prophylaxis with mTOR inhibitor enhances T-cell effector function and prevents heart transplant rejection during talimogene laherparepvec therapy of squamous cell carcinoma"
DOI: 10.5281/zenodo.10470569
title: GRAND-SLAM analysis comparing SLAM- and TUC-seq datasets and SLAM-seq in fixed cells for grandRescue

description: These are two processed datasets comparing 4sU induced dropout in SLAM-seq versus TUC-seq and SLAM-seq in fixed cells compared to normal SLAM-seq.

The zip files contain the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The *.tsv.gz files are the GRAND-SLAM output tables.

 

To generate the GRAND-SLAM output yourself, first prepare the mouse genome. Then run the following command with the respective cit-files, prefixes (*.cit) and genome:

gedi -e Slam -trim5p 15 -reads *.cit -genomic m.ens102 -prefix grandslam_t15/* -plot  -D -modelall

To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.

You can also start from scratch (i.e., the json file):

    Prepare the mouse genome and their rRNA sequence    Run: gedi -e Pipeline -r parallel -j *.json rnaseq_mapping.sh report.sh grandslam.sh

Software versions:

    gedi toolkit 1.0.5    GRAND-SLAM 2.0.7    STAR version 2.7.10b
DOI: 10.5281/zenodo.10470570
title: GRAND-SLAM analysis comparing SLAM- and TUC-seq datasets and SLAM-seq in fixed cells for grandRescue

description: These are two processed datasets comparing 4sU induced dropout in SLAM-seq versus TUC-seq and SLAM-seq in fixed cells compared to normal SLAM-seq.

The zip files contain the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The *.tsv.gz files are the GRAND-SLAM output tables.

 

To generate the GRAND-SLAM output yourself, first prepare the mouse genome. Then run the following command with the respective cit-files, prefixes (*.cit) and genome:

gedi -e Slam -trim5p 15 -reads *.cit -genomic m.ens102 -prefix grandslam_t15/* -plot  -D -modelall

To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.

You can also start from scratch (i.e., the json file):

    Prepare the mouse genome and their rRNA sequence    Run: gedi -e Pipeline -r parallel -j *.json rnaseq_mapping.sh report.sh grandslam.sh

Software versions:

    gedi toolkit 1.0.5    GRAND-SLAM 2.0.7    STAR version 2.7.10b
DOI: 10.25716/gude.06tp-x7pd
title: Theoretical Data: Growth of self-integrated atomic quantum wires and junctions of a Mott semiconductor

description: Continued advances in quantum technologies rely on producing nanometer-scale wires. Although several state-of-the-art nanolithographic technologies and bottom-up synthesis processes have been used to engineer these wires, critical challenges remain in growing uniform atomic-scale crystalline wires and constructing their network structures. Here, we discover a simple method to fabricate atomic-scale wires with various arrangements, including stripes, X-junctions, Y-junctions, and nanorings. Single-crystalline atomic-scale wires of a Mott insulator, whose bandgap is comparable to those of wide-gap semiconductors, are spontaneously grown on graphite substrates by pulsed-laser deposition. These wires are one unit cell thick and have an exact width of two and four unit cells (1.4 and 2.8 nm) and lengths up to a few micrometers. We show that the nonequilibrium reaction-diffusion processes may play an essential role in atomic pattern formation. Our findings offer a previously unknown perspective on the nonequilibrium self-organization phenomena on an atomic scale, paving a unique way for the quantum architecture of nano-network.
description: This research data includes the DFT (density functional theory) files obtained by FPLO and VASP: detailed README file is included.
DOI: 10.25829/mfdw-kw26
title: Community level leaf C:N ratio in Mydiv in 2021

description: The leaf quality was proposed as a potential mediator to understand the mechanisms underlying the effects of tree species richness and mycorrhizal types on the multitrophic ecosystem functions. The leaf C and N concentrations of three trees for each species in every plot were measured in July 2021 in the MyDiv experiment. The averaging leaf C:N ratio of all species was used as the proxy of leaf quality in every plot. This data was used in the study by Yi et al. (2023) published in the Journal of Ecology
DOI: 10.34740/kaggle/dsv/7358510
title: Persimmon
DOI: 10.25829/m4cw-rx75
title: R code for the MyDiv project on multitrophic ecosystem functions in 2021

description: The R code was used to perform all statistical analyses for the Mydiv project on multitrophic ecosystem functions in 2021, and all these analyses were performed in the software R, version 4.0.2. The analyses covered in this R script include (1) effect size calculation (lnRR) and two-way ANOVA, (2) calculation of correlation matrices using the function of 'pearson' (package of 'corrplot'), (3) linear mixed-effects models (‘lmer’ from the package of lme4), and (4) structural equation models (SEMs) (package of piecewiseSEM).
DOI: 10.25829/h6m1-rw77
title: Synthesis data for the MyDiv project on multitrophic ecosystem functions in 2021

description: This dataset is a synthesis dataset that served to test the hypothesis regarding how mycorrhizal types and tree species richness co-determine the multitrophic ecosystem functions. The first part of this dataset showed the basic information of the MyDiv experiment, i.e. plot names, mycorrhizal type treatment, species richness, and block number. The second part illustrates the multitrophic ecosystem functions, including three rows for foliage damage (total foliage damage, chewing damage, and hole feeding damage), three rows for predation rate (total predation, bird predation, and insect predation), and three rows for soil fauna feeding activity (the activity for total soil depth, upper soil layer, and lower soil layer). This data was used in the study by Yi et al. (2023) published in the Journal of Ecology
DOI: 10.25829/58ef-hn10
title: Effective number of layers (ENL) from MyDiv plots in 2021
DOI: 10.25829/6qmg-qd35
title: Community level total stand volume Mydiv 2020

description: We quantified tree wood volume (2020) in each plot as a proxy of aboveground tree biomass that was selected as potential mediator to understand the mechanisms underlying the effects of tree species richness and mycorrhizal types on the multitrophic ecosystem functions. This data was used in Yi et al. The Journal of Ecology 2023.
DOI: 10.25829/kx7a-be66
title: Herbivores and pathogens foliar damage from old and new (dBEF) monoculture plots in 2020

description: Plant monocultures that are cultivated over a longer period of time lead to considerable losses in productivity. This phenomenon, known as ‘yield decline’, is often caused by the accumulation of above- and below-ground plant antagonists. The effectiveness of plant defences against antagonists could help to explain the differences in yield decline between species. We would like to investigate the plant damage caused by herbivores and pathogens. 

The dataset includes the proportion of leaf area damaged by invertebrate herbivores and pathogens measured in the old and new (dBEF) monoculture in 2020.
DOI: 10.5281/zenodo.10471513
title: Development of LRRK2 designed ankyrin-repeat proteins

description: Leucine-rich repeat kinase 2 (LRRK2) is a large multidomain protein containing two catalytic domains, a kinase and a GTPase, as well protein interactions domains, including a WD40 domain. The association of increased LRRK2 kinase activity with both the familial and sporadic forms of Parkinson’s disease (PD) has led to intense interest in determining its cellular function. However, small-molecule probes that can bind to LRRK2 and report on or affect its activity are needed. Here, we identified a series of high-affinity LRRK2-binding designed ankyrin-repeat proteins (DARPins). One of these DARPins (E11) bound to the LRRK2 WD40 domain with high affinity. LRRK2 bound to DARPin E11 showed improved behavior on cryo-EM grids, resulting in higher resolution LRRK2 structures. DARPin E11 did not affect the catalytic activity of a truncated form of LRRK2 in vitro but decreased the phosphorylation of Rab8A, a LRRK2 substrate, in cells. We also found that DARPin E11 disrupts the formation of microtubule-associated LRRK2 filaments in cells, which are known to require WD40-based dimerization. Thus, DARPin E11 is a new tool to explore the function and dysfunction of LRRK2 and guide the development of LRRK2 kinase inhibitors that target the WD40 domain instead of the kinase itself.
DOI: 10.5281/zenodo.10471514
title: Development of LRRK2 designed ankyrin-repeat proteins

description: Leucine-rich repeat kinase 2 (LRRK2) is a large multidomain protein containing two catalytic domains, a kinase and a GTPase, as well protein interactions domains, including a WD40 domain. The association of increased LRRK2 kinase activity with both the familial and sporadic forms of Parkinson’s disease (PD) has led to intense interest in determining its cellular function. However, small-molecule probes that can bind to LRRK2 and report on or affect its activity are needed. Here, we identified a series of high-affinity LRRK2-binding designed ankyrin-repeat proteins (DARPins). One of these DARPins (E11) bound to the LRRK2 WD40 domain with high affinity. LRRK2 bound to DARPin E11 showed improved behavior on cryo-EM grids, resulting in higher resolution LRRK2 structures. DARPin E11 did not affect the catalytic activity of a truncated form of LRRK2 in vitro but decreased the phosphorylation of Rab8A, a LRRK2 substrate, in cells. We also found that DARPin E11 disrupts the formation of microtubule-associated LRRK2 filaments in cells, which are known to require WD40-based dimerization. Thus, DARPin E11 is a new tool to explore the function and dysfunction of LRRK2 and guide the development of LRRK2 kinase inhibitors that target the WD40 domain instead of the kinase itself.
DOI: 10.6084/m9.figshare.24578547
title: Bayesian Lesion Estimation with a Structured Spike-and-Slab Prior

description: Neural demyelination and brain damage accumulated in white matter appear as hyperintense areas on T2-weighted MRI scans in the form of lesions. Modeling binary images at the population level, where each voxel represents the existence of a lesion, plays an important role in understanding aging and inflammatory diseases. We propose a scalable hierarchical Bayesian spatial model, called BLESS, capable of handling binary responses by placing continuous spike-and-slab mixture priors on spatially varying parameters and enforcing spatial dependency on the parameter dictating the amount of sparsity within the probability of inclusion. The use of mean-field variational inference with dynamic posterior exploration, which is an annealing-like strategy that improves optimization, allows our method to scale to large sample sizes. Our method also accounts for underestimation of posterior variance due to variational inference by providing an approximate posterior sampling approach based on Bayesian bootstrap ideas and spike-and-slab priors with random shrinkage targets. Besides accurate uncertainty quantification, this approach is capable of producing novel cluster size based imaging statistics, such as credible intervals of cluster size, and measures of reliability of cluster occurrence. Lastly, we validate our results via simulation studies and an application to the UK Biobank, a large-scale lesion mapping study with a sample size of 40,000 subjects. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24578547.v1
title: Bayesian Lesion Estimation with a Structured Spike-and-Slab Prior

description: Neural demyelination and brain damage accumulated in white matter appear as hyperintense areas on T2-weighted MRI scans in the form of lesions. Modeling binary images at the population level, where each voxel represents the existence of a lesion, plays an important role in understanding aging and inflammatory diseases. We propose a scalable hierarchical Bayesian spatial model, called BLESS, capable of handling binary responses by placing continuous spike-and-slab mixture priors on spatially varying parameters and enforcing spatial dependency on the parameter dictating the amount of sparsity within the probability of inclusion. The use of mean-field variational inference with dynamic posterior exploration, which is an annealing-like strategy that improves optimization, allows our method to scale to large sample sizes. Our method also accounts for underestimation of posterior variance due to variational inference by providing an approximate posterior sampling approach based on Bayesian bootstrap ideas and spike-and-slab priors with random shrinkage targets. Besides accurate uncertainty quantification, this approach is capable of producing novel cluster size based imaging statistics, such as credible intervals of cluster size, and measures of reliability of cluster occurrence. Lastly, we validate our results via simulation studies and an application to the UK Biobank, a large-scale lesion mapping study with a sample size of 40,000 subjects. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24077747
title: A genomic estimated breeding value-assisted reduction method of single nucleotide polymorphism sets: a novel approach for determining the cutoff thresholds in genome-wide association studies and best linear unbiased prediction

description: Traditionally, the <i>p</i>-value is the criterion for the cutoff threshold to determine significant markers in genome-wide association studies (GWASs). Choosing the best subset of markers for the best linear unbiased prediction (BLUP) for improved prediction ability (PA) has become an interesting issue. However, when dealing with many traits having the same marker information, the <i>p</i>-values’ themselves cannot be used as an obvious solution for having a confidence in GWAS and BLUP. We thus suggest a genomic estimated breeding value-assisted reduction method of the single nucleotide polymorphism (SNP) set (GARS) to address these difficulties. GARS is a BLUP-based SNP set decision presentation. The samples were Landrace pigs and the traits used were back fat thickness (BF) and daily weight gain (DWG). The prediction abilities (PAs) for BF and DWG for the entire SNP set were 0.8 and 0.8, respectively. By using the correlation between genomic estimated breeding values (GEBVs) and phenotypic values, selecting the cutoff threshold in GWAS and the best SNP subsets in BLUP was plausible as defined by GARS method. 6,000 SNPs in BF and 4,000 SNPs in DWG were considered as adequate thresholds. Gene Ontology (GO) analysis using the GARS results of the BF indicated neuron projection development as the notable GO term, whereas for the DWG, the main GO terms were nervous system development and cell adhesion.
DOI: 10.6084/m9.figshare.24077747.v1
title: A genomic estimated breeding value-assisted reduction method of single nucleotide polymorphism sets: a novel approach for determining the cutoff thresholds in genome-wide association studies and best linear unbiased prediction

description: Traditionally, the <i>p</i>-value is the criterion for the cutoff threshold to determine significant markers in genome-wide association studies (GWASs). Choosing the best subset of markers for the best linear unbiased prediction (BLUP) for improved prediction ability (PA) has become an interesting issue. However, when dealing with many traits having the same marker information, the <i>p</i>-values’ themselves cannot be used as an obvious solution for having a confidence in GWAS and BLUP. We thus suggest a genomic estimated breeding value-assisted reduction method of the single nucleotide polymorphism (SNP) set (GARS) to address these difficulties. GARS is a BLUP-based SNP set decision presentation. The samples were Landrace pigs and the traits used were back fat thickness (BF) and daily weight gain (DWG). The prediction abilities (PAs) for BF and DWG for the entire SNP set were 0.8 and 0.8, respectively. By using the correlation between genomic estimated breeding values (GEBVs) and phenotypic values, selecting the cutoff threshold in GWAS and the best SNP subsets in BLUP was plausible as defined by GARS method. 6,000 SNPs in BF and 4,000 SNPs in DWG were considered as adequate thresholds. Gene Ontology (GO) analysis using the GARS results of the BF indicated neuron projection development as the notable GO term, whereas for the DWG, the main GO terms were nervous system development and cell adhesion.
DOI: 10.6084/m9.figshare.24260477
title: Compensatory enhancement of paternal care in maternally neglected mice family

description: Parental care strategies, ranging from biparental to uniparental, evolve based on factors affecting sexual conflict over care. Plasticity in how parents respond to reduction in each other’s care effort is thus proposed to be important in the evolution of parental care behaviors. Models predict that ‘obligate’ biparental care is stable when a parent responds to reduced partner effort with ‘partial’ compensation, trading-off current and future reproduction. A meta-analysis of experimental studies on biparental birds also revealed partial compensation, supporting coevolution of parental care type and plasticity pattern. However, few studies have addressed this issue across different taxa and different parental care types. In laboratory mice, a female-biased ‘facultative’ biparental species, fathers paired with a competent mother rarely provide care. We show that, when mated with a pup-neglecting mutant mother, fathers increased care effort to ‘fully’ compensate for the lost maternal care in both pup survival rate and total care amount. Pup retrieval latency was significantly shorter, and neural activity in relevant brain regions twice as high, suggesting enhanced motivation. This study with mice not only opens a road to explore the neural correlates of paternal plasticity but will also help understand how behavioral plasticity contributes to adaptive evolution of parental care behaviors.
DOI: 10.6084/m9.figshare.24260477.v1
title: Compensatory enhancement of paternal care in maternally neglected mice family

description: Parental care strategies, ranging from biparental to uniparental, evolve based on factors affecting sexual conflict over care. Plasticity in how parents respond to reduction in each other’s care effort is thus proposed to be important in the evolution of parental care behaviors. Models predict that ‘obligate’ biparental care is stable when a parent responds to reduced partner effort with ‘partial’ compensation, trading-off current and future reproduction. A meta-analysis of experimental studies on biparental birds also revealed partial compensation, supporting coevolution of parental care type and plasticity pattern. However, few studies have addressed this issue across different taxa and different parental care types. In laboratory mice, a female-biased ‘facultative’ biparental species, fathers paired with a competent mother rarely provide care. We show that, when mated with a pup-neglecting mutant mother, fathers increased care effort to ‘fully’ compensate for the lost maternal care in both pup survival rate and total care amount. Pup retrieval latency was significantly shorter, and neural activity in relevant brain regions twice as high, suggesting enhanced motivation. This study with mice not only opens a road to explore the neural correlates of paternal plasticity but will also help understand how behavioral plasticity contributes to adaptive evolution of parental care behaviors.
DOI: 10.6084/m9.figshare.22557350
title: <i>NAA10</i> Hypomethylation is associated with particulate matter exposure and worse prognosis for patients with non-small cell lung cancer

description: Airborne particulate matter (PM) is a major health hazard worldwide and is a key factor in lung cancer, which remains the most common type of malignancy and the leading cause of cancer-related deaths. DNA methylation is a critical mechanism underlying the detrimental effects of PM, however, the molecular link between PM exposure and lung cancer remains to be elucidated. N-α-acetyltransferase 10 (NAA10) is involved in the cell cycle, migration, apoptosis, differentiation, and proliferation. In order to investigate the role of NAA10 in PM-induced pathogenesis processes leading to lung cancer, we determined the expression and methylation of <i>NAA10</i> in normal human bronchial epithelial (NHBE) cells treated with PM<sub>10</sub>, PM<sub>10</sub>-polycyclic aromatic hydrocarbons (PAH), and PM<sub>2.5</sub> and evaluated the prognostic value of the <i>NAA10</i> methylation status in lung cancer patients. Exposure to all PM types significantly increased the expression of <i>NAA10</i> mRNA and decreased the methylation of the <i>NAA10</i> promoter in NHBE cells compared with the mock-treated control. <i>NAA10</i> hypomethylation was observed in 9.3% (13/140) of lung cancer tissue samples and correlated with <i>NAA10</i> transcriptional upregulation. Univariate and multivariate analyses revealed that <i>NAA10</i> hypomethylation was associated with decreased survival of patients with lung cancer. Therefore, these results suggest that PM-induced hypomethylation of the <i>NAA10</i> may play an important role in the pathogenesis of lung cancer and may be used as a potential prognostic biomarker for lung cancer progression. Further studies with large numbers of patients are warranted to confirm our findings.
DOI: 10.6084/m9.figshare.22557350.v1
title: <i>NAA10</i> Hypomethylation is associated with particulate matter exposure and worse prognosis for patients with non-small cell lung cancer

description: Airborne particulate matter (PM) is a major health hazard worldwide and is a key factor in lung cancer, which remains the most common type of malignancy and the leading cause of cancer-related deaths. DNA methylation is a critical mechanism underlying the detrimental effects of PM, however, the molecular link between PM exposure and lung cancer remains to be elucidated. N-α-acetyltransferase 10 (NAA10) is involved in the cell cycle, migration, apoptosis, differentiation, and proliferation. In order to investigate the role of NAA10 in PM-induced pathogenesis processes leading to lung cancer, we determined the expression and methylation of <i>NAA10</i> in normal human bronchial epithelial (NHBE) cells treated with PM<sub>10</sub>, PM<sub>10</sub>-polycyclic aromatic hydrocarbons (PAH), and PM<sub>2.5</sub> and evaluated the prognostic value of the <i>NAA10</i> methylation status in lung cancer patients. Exposure to all PM types significantly increased the expression of <i>NAA10</i> mRNA and decreased the methylation of the <i>NAA10</i> promoter in NHBE cells compared with the mock-treated control. <i>NAA10</i> hypomethylation was observed in 9.3% (13/140) of lung cancer tissue samples and correlated with <i>NAA10</i> transcriptional upregulation. Univariate and multivariate analyses revealed that <i>NAA10</i> hypomethylation was associated with decreased survival of patients with lung cancer. Therefore, these results suggest that PM-induced hypomethylation of the <i>NAA10</i> may play an important role in the pathogenesis of lung cancer and may be used as a potential prognostic biomarker for lung cancer progression. Further studies with large numbers of patients are warranted to confirm our findings.