cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7359623
title: ARCH-COMP 2022 Category Report: Falsification with Ubounded Resources

description: This report presents the results from the 2022 friendly competition in the ARCH workshop for the falsification of temporal logic specifications over Cyber-Physical Systems. We briefly describe the competition settings, which have been inherited and adapted from the previous years, give background on the participating teams and tools, and discuss the selected benchmarks. In this year’s competition, in addition to the result validation introduced in the previous year, we change the experimental settings for a better account of the difficulty of benchmarks and for a better comparability between the tools. There are three files. The data submitted by the participants is stored in the <strong>results </strong>archive. The validator infrastructure is stored in <strong>validation</strong> and the produced validation report is stored in <strong>report</strong>. To re-run the validation on Linux with a somewhat recent MATLAB installation, unpack the first two archives in the same directory. Make sure that the <strong>models</strong> folder from https://gitlab.com/goranf/ARCH-COMP is available at relative path <strong>../../models/</strong> which you can check by: ls ../../models<br> ls validation/falstar.sh<br> <br> To run validation, proceed as follows cd validation<br> ./falstar-config.sh # once<br> ./falstar.sh validate-$TOOL.cfg<br> <br> where $TOOL is the name of one of the tools such that the corresponding configuration file exists. If there are open issues, please contact Gidon Ernst &lt;gidon.ernst@lmu.de&gt;
DOI: 10.5282/ubm/data.341
title: Lateral root primordia development of wild type and pip mutants of Arabidopsis thaliana

description: The experiment was performed on Arabidopsis thaliana wild type (Col) and aquaporin single mutants, pip2;1-2 and pip2;8-2, labeled with a marker for plasma membrane (UBQ10pro::PIP1;4-3xGFP) and nuclei of LRP cells (GATA23pro::H2B:3xmCHERRY) in a culture medium with ½ Murashige-Skoog and 0.8% Phytoagar. The plants were grown vertically for four days, and the gravitropic stimulus (pgi) was applied by rotating the plates at 180 °. At 17 hours pgi, the seedlings were transferred to a light-sheet microscope, and the LRP development was recorded every 30 min from 18 h to 39 h pgi. Images were processed, drift corrected, and cropped using the BigDataProcessor (Tischer et al. 2019) in Fiji (Schindelin et al. 2012). Max Projection was applied after imaging processing.
DOI: 10.34943/e2739ecb-12ca-4b39-8029-22b4ffd88649
title: China Creek Video Camera Deployed 2022-11-22

description: The Axis Q6074-E 60Hz PTZ Dome Network Camera (S/N ACCC8EACA584) was deployed on 2022-11-22 at China Creek. China Creek is located in the Alberni Inlet, which is a long, narrow inlet on Vancouver Island. This device is a Video Camera. Video cameras record video of characteristics of the surrounding environments and can be deployed on fixed and mobile platforms. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.25502/fe6w-by10/d
title: Enhancing Resilient and Adaptive Agricultural Livelihoods In Uganda—Scaling of Successful Technologies - Baseline Dataset.

description: The Activity aims to improve smallholder farmers’ livelihoods through increased incomes resulting from increased yields, better organization, and coordination of farmers, improved post-harvest handling, and improved business models. The scaling will encompass the targeting of a broader geographical coverage, a more significant number of beneficiaries adopting and utilizing the successful technologies, tools, approaches, and lessons, and in engaging with more private and public stakeholders, thereby enhancing the resilience of agricultural livelihoods. The activity covers 18 districts in six regions of Uganda, which were selected based on the USAID GFSS ZOI.These include districts in Central Uganda (Luweero and Nakaseke); Northern Uganda (Gulu, Oyam, Lira, and Apac); Eastern Uganda (Mbale, Manafwa, Sironko, and Iganga); Karamoja (Abim); Southwestern Uganda (Rakai, Isingiro Ntungamo, and Bushenyi); and Western Uganda (Kabarole, Kamwenge, and Bundibugyo).
DOI: 10.6084/m9.figshare.21621384
title: Prokka annotations for Sphingopyxis sp. PET50

description: Complete genome sequence available from https://www.ncbi.nlm.nih.gov/nuccore/CP104207.1/. Annotations made with Prokka (1.14.6).
DOI: 10.6084/m9.figshare.21621384.v1
title: Prokka annotations for Sphingopyxis sp. PET50

description: Complete genome sequence available from https://www.ncbi.nlm.nih.gov/nuccore/CP104207.1/. Annotations made with Prokka (1.14.6).
DOI: 10.5281/zenodo.7360293
title: Dataset: Tailoring the Magnetic and Structural Properties of Manganese/Zinc Doped Iron Oxide Nanoparticles through Microwaves-Assisted Polyol Synthesis

description: Data set
DOI: 10.5281/zenodo.7360294
title: Dataset: Tailoring the Magnetic and Structural Properties of Manganese/Zinc Doped Iron Oxide Nanoparticles through Microwaves-Assisted Polyol Synthesis

description: Data set
DOI: 10.5281/zenodo.7352393
title: Addressing global hotspots of climate-related crop production losses

description: Meeting future food demand will require transformations toward sustainable and resilient food systems that simultaneously increase production, minimize environmental impacts, and adapt to climate change. With fluctuations in temperature and precipitation exercising a growing influence on production stability across the planet, a detailed understanding of where cropping patterns are vulnerable to climatic stresses is a missing yet critical step for developing solutions that enhance the climate resilience of crop production. Here we address this urgent need by combining gridded climate data, spatially-explicit agricultural statistics, and process-based crop modeling to quantify global patterns of rainfed and irrigated crop climate sensitivity (measured as the percent reduction in median yield under extreme climate conditions) and climate-associated production losses for 17 major crops, accounting for 75% of global primary production. This climate sensitivity metric is ideally suited for identifying locations where each crop tends to be subject to high climate variability and where crop production may be susceptible to high climate-related production losses. We estimate -10.1% and -6.8% losses in global rainfed and irrigated production (respectively) under historically observed extreme climate conditions - enough calories to feed 2.1 billion people - and find hotspots of climate sensitivity in the central US, eastern Brazil, the Mediterranean basin, and South Asia, among other regions. We then focus on monsoon cereals (rice, maize, millet, sorghum) to illustrate how sustainable irrigation expansion and targeted crop switching could reduce climate sensitivity, finding that 62% of production losses could be avoided while increasing overall production by 14%. Our new scalable and universal approach to measuring the climate sensitivity of crops enables the assessment of where climate-related production losses tend to be largest and where mitigating actions and investments can be proactively targeted to better ensure the stability and increased supply of global crop production.
DOI: 10.5281/zenodo.7352392
title: Addressing global hotspots of climate-related crop production losses

description: Meeting future food demand will require transformations toward sustainable and resilient food systems that simultaneously increase production, minimize environmental impacts, and adapt to climate change. With fluctuations in temperature and precipitation exercising a growing influence on production stability across the planet, a detailed understanding of where cropping patterns are vulnerable to climatic stresses is a missing yet critical step for developing solutions that enhance the climate resilience of crop production. Here we address this urgent need by combining gridded climate data, spatially-explicit agricultural statistics, and process-based crop modeling to quantify global patterns of rainfed and irrigated crop climate sensitivity (measured as the percent reduction in median yield under extreme climate conditions) and climate-associated production losses for 17 major crops, accounting for 75% of global primary production. This climate sensitivity metric is ideally suited for identifying locations where each crop tends to be subject to high climate variability and where crop production may be susceptible to high climate-related production losses. We estimate -10.1% and -6.8% losses in global rainfed and irrigated production (respectively) under historically observed extreme climate conditions - enough calories to feed 2.1 billion people - and find hotspots of climate sensitivity in the central US, eastern Brazil, the Mediterranean basin, and South Asia, among other regions. We then focus on monsoon cereals (rice, maize, millet, sorghum) to illustrate how sustainable irrigation expansion and targeted crop switching could reduce climate sensitivity, finding that 62% of production losses could be avoided while increasing overall production by 14%. Our new scalable and universal approach to measuring the climate sensitivity of crops enables the assessment of where climate-related production losses tend to be largest and where mitigating actions and investments can be proactively targeted to better ensure the stability and increased supply of global crop production.
DOI: 10.6084/m9.figshare.19229871.v1
title: Supplementary_Table_1_Cohort_Data_Food_and_nutrients_intake.xlsx

description: Supplementary Table 1. A. Clinical and anamnestic information of the study cohorts. B. Intake of foods, drinks, and dietary nutrients as estimated by the EPIC questionnaries for each study group. The log2FC of intake between the study groups is reported as well the result of the statistical analysis (Wilcoxon Rank-Sum test and Kruskall-Wallis test). C. Results of the Spearman correlation analysis between the estimated nutrient intakes and the levels of miRNAs and species identified as differentially abundant among the study groups.
DOI: 10.6084/m9.figshare.19229874
title: Supplementary_Table_2_Dataset_Information.xlsx

description: Supplementary Table 2. A. Statistics of quality control and alignment of the sRNA-Seq reads. B. Statistics of the preprocessing and alignment of shotgun metagenomic sequencing reads.
DOI: 10.6084/m9.figshare.19229874.v1
title: Supplementary_Table_2_Dataset_Information.xlsx

description: Supplementary Table 2. A. Statistics of quality control and alignment of the sRNA-Seq reads. B. Statistics of the preprocessing and alignment of shotgun metagenomic sequencing reads.
DOI: 10.6084/m9.figshare.19229874.v2
title: Supplementary_Table_2_Dataset_Information.xlsx

description: Supplementary Table 2. A. Statistics of quality control and alignment of the sRNA-Seq reads. B. Statistics of the preprocessing and alignment of shotgun metagenomic sequencing reads.
DOI: 10.6084/m9.figshare.19229877
title: Supplementary_Table_3_DEmiRNA_analysis.xlsx

description: Supplementary Table 3. A. Result of the stool miRNA differential expression analysis with DESeq2. B. Analysis of DEmiRNAs expression changes considering an independent group of healthy individuals. H, healthy from this study; H2, healthy omnivorous from Tarallo et al., 2021. C. Results of the Spearman correlation analysis between DEmiRNA stool levels and the years of GFD or the subjects age. D. Analysis of DEmiRNAs expression changes considering a cohort of untreated pediatric CD-TG+ patients matched with healthy controls.
DOI: 10.6084/m9.figshare.19229877.v2
title: Supplementary_Table_3_DEmiRNA_analysis.xlsx

description: Supplementary Table 3. A. Result of the stool miRNA differential expression analysis with DESeq2. B. Analysis of DEmiRNAs expression changes considering an independent group of healthy individuals. H, healthy from this study; H2, healthy omnivorous from Tarallo et al., 2021. C. Results of the Spearman correlation analysis between DEmiRNA stool levels and the years of GFD or the subjects age. D. Analysis of DEmiRNAs expression changes considering a cohort of untreated pediatric CD-TG+ patients matched with healthy controls.
DOI: 10.6084/m9.figshare.19229877.v1
title: Supplementary_Table_3_DEmiRNA_analysis.xlsx

description: Supplementary Table 3. A. Result of the stool miRNA differential expression analysis with DESeq2. B. Analysis of DEmiRNAs expression changes considering an independent group of healthy individuals. H, healthy from this study; H2, healthy omnivorous from Tarallo et al., 2021. C. Results of the Spearman correlation analysis between DEmiRNA stool levels and the years of GFD or the subjects age. D. Analysis of DEmiRNAs expression changes considering a cohort of untreated pediatric CD-TG+ patients matched with healthy controls.
DOI: 10.6084/m9.figshare.19229883
title: Supplementary_Table_4_Enrichment_analysis.xlsx

description: Supplementary Table 4. A. Results of the functional enrichment analysis of DEmiRNA target genes. For the Gene Ontology Biological Processes terms, the results from the semantic similarity are also reported. B. Validated miRNA-target interactions used for the enrichment analysis. C. List of miRNA-target interactions supported by the highest number of evidence.
DOI: 10.6084/m9.figshare.19229883.v2
title: Supplementary_Table_4_Enrichment_analysis.xlsx

description: Supplementary Table 4. A. Results of the functional enrichment analysis of DEmiRNA target genes. For the Gene Ontology Biological Processes terms, the results from the semantic similarity are also reported. B. Validated miRNA-target interactions used for the enrichment analysis. C. List of miRNA-target interactions supported by the highest number of evidence.
DOI: 10.6084/m9.figshare.19229883.v1
title: Supplementary_Table_4_Enrichment_analysis.xlsx

description: Supplementary Table 4. A. Results of the functional enrichment analysis of DEmiRNA target genes. For the Gene Ontology Biological Processes terms, the results from the semantic similarity are also reported. B. Validated miRNA-target interactions used for the enrichment analysis. C. List of miRNA-target interactions supported by the highest number of evidence.
DOI: 10.6084/m9.figshare.19229880
title: Supplementary_Table_5_DEsncRNAs.xlsx

description: Supplementary Table 5. A. Result of the DESeq2 differential expression analysis of sncRNA stool levels.B. Analysis of DEmiRNAs expression changes considering an independent group of healthy individuals. H, healthy from this study; H2, healthy omnivorous from Tarallo et al., 2021.
DOI: 10.6084/m9.figshare.19229880.v2
title: Supplementary_Table_5_DEsncRNAs.xlsx

description: Supplementary Table 5. A. Result of the DESeq2 differential expression analysis of sncRNA stool levels.B. Analysis of DEmiRNAs expression changes considering an independent group of healthy individuals. H, healthy from this study; H2, healthy omnivorous from Tarallo et al., 2021.
DOI: 10.6084/m9.figshare.19229880.v1
title: Supplementary_Table_5_DEsncRNAs.xlsx

description: Supplementary Table 5. A. Result of the DESeq2 differential expression analysis of sncRNA stool levels.B. Analysis of DEmiRNAs expression changes considering an independent group of healthy individuals. H, healthy from this study; H2, healthy omnivorous from Tarallo et al., 2021.
DOI: 10.6084/m9.figshare.19229892
title: Supplementary_Table_6_Metagenomic_data_analysis.xlsx

description: Supplementary Table 6. A) Alpha diversity values computed for each sample. B). Description and differential analysis of the abundances of microbial phyla. C-E) Results of the SIAMACAT analysis for C) tCD vs Healthy, D) tCD-TG+ vs Healthy, and E) tCD-TG+ vs tCD. F) Evaluation of the abundances of species identified as significantly different by SIAMCAT considering an independent cohort of healthy controls and data of paediatric CD-TG+ patients. G) Results of the correlation analysis between the levels of microbial species and signalling pathways identified as significantly differentially prevalent among the study groups. H) Results of the differential pathway analysis performed among the study groups. I) Result of the correlation analysis between microbial abundances and the stool DEmiRNA levels.
DOI: 10.6084/m9.figshare.19229892.v2
title: Supplementary_Table_6_Metagenomic_data_analysis.xlsx

description: Supplementary Table 6. A) Alpha diversity values computed for each sample. B). Description and differential analysis of the abundances of microbial phyla. C-E) Results of the SIAMACAT analysis for C) tCD vs Healthy, D) tCD-TG+ vs Healthy, and E) tCD-TG+ vs tCD. F) Evaluation of the abundances of species identified as significantly different by SIAMCAT considering an independent cohort of healthy controls and data of paediatric CD-TG+ patients. G) Results of the correlation analysis between the levels of microbial species and signalling pathways identified as significantly differentially prevalent among the study groups. H) Results of the differential pathway analysis performed among the study groups. I) Result of the correlation analysis between microbial abundances and the stool DEmiRNA levels.
DOI: 10.6084/m9.figshare.19229892.v1
title: Supplementary_Table_6_Metagenomic_data_analysis.xlsx

description: Supplementary Table 6. A) Alpha diversity values computed for each sample. B). Description and differential analysis of the abundances of microbial phyla. C-E) Results of the SIAMACAT analysis for C) tCD vs Healthy, D) tCD-TG+ vs Healthy, and E) tCD-TG+ vs tCD. F) Evaluation of the abundances of species identified as significantly different by SIAMCAT considering an independent cohort of healthy controls and data of paediatric CD-TG+ patients. G) Results of the correlation analysis between the levels of microbial species and signalling pathways identified as significantly differentially prevalent among the study groups. H) Results of the differential pathway analysis performed among the study groups. I) Result of the correlation analysis between microbial abundances and the stool DEmiRNA levels.
DOI: 10.6084/m9.figshare.19229889
title: Supplementary_Table_7_Classification_analysis.xlsx

description: Supplementary Table 7. A. Results of the Weka classification analysis using all features identified by DIABLO or only the subset contributing more to the classification. The analysis was performed considering the healthy control of this study or the healthy omnivorous subjects from Tarallo et al., 2021 (Healthy2). B. The list of features contributing more in classifying the different study groups.
DOI: 10.6084/m9.figshare.19229889.v1
title: Supplementary_Table_7_Classification_analysis.xlsx

description: Supplementary Table 7. A. Results of the Weka classification analysis using all features identified by DIABLO or only the subset contributing more to the classification. The analysis was performed considering the healthy control of this study or the healthy omnivorous subjects from Tarallo et al., 2021 (Healthy2). B. The list of features contributing more in classifying the different study groups.
DOI: 10.6084/m9.figshare.13489997
title: Pharmacological inactivation of CDK2 inhibits MYC/BCL-XL-driven leukemia in vivo through induction of cellular senescence

description: Deregulated expression of the MYC oncogene is a frequent event during tumorigenesis and generally correlates with aggressive disease and poor prognosis. While MYC is a potent inducer of apoptosis, it often suppresses cellular senescence, which together with apoptosis is an important barrier against tumor development. For this latter function, MYC is dependent on cyclin-dependent kinase 2 (CDK2). Here, we utilized a MYC/BCL-X<sub>L</sub>-driven mouse model of acute myeloblastic leukemia (AML) to investigate whether pharmacological inhibition of CDK2 can inhibit MYC-driven tumorigenesis through induction of senescence. Purified mouse hematopoietic stem cells transduced with MYC and BCL-X<sub>L</sub> were transplanted into lethally irradiated mice, leading to the development of massive leukemia and subsequent death 15–17 days after transplantation. Upon disease onset, mice were treated with the selective CDK2 inhibitor CVT2584 or vehicle either by daily intraperitoneal injections or continuous delivery via mini-pumps. CVT2584 treatment delayed disease onset and moderately but significantly improved survival of mice. Flow cytometry revealed a significant decrease in tumor load in the spleen, liver and bone marrow of CVT2584-treated compared to vehicle-treated mice. This was correlated with induced senescence evidenced by reduced cell proliferation, increased senescence-associated β-galactosidase activity and heterochromatin foci, expression of p19<sup>ARF</sup> and p21<sup>CIP1</sup>, and reduced phosphorylation (activation) of pRb, while very few apoptotic cells were observed. In addition, phosphorylation of MYC at Ser-62 was decreased. In summary, inhibition of CDK2 delayed MYC/BCL-X<sub>L</sub>-driven AML linked to senescence induction. Our results suggest that CDK2 is a promising target for pro-senescence cancer therapy, in particular for MYC-driven tumors, including leukemia.
DOI: 10.6084/m9.figshare.13489997.v1
title: Pharmacological inactivation of CDK2 inhibits MYC/BCL-XL-driven leukemia in vivo through induction of cellular senescence

description: Deregulated expression of the MYC oncogene is a frequent event during tumorigenesis and generally correlates with aggressive disease and poor prognosis. While MYC is a potent inducer of apoptosis, it often suppresses cellular senescence, which together with apoptosis is an important barrier against tumor development. For this latter function, MYC is dependent on cyclin-dependent kinase 2 (CDK2). Here, we utilized a MYC/BCL-X<sub>L</sub>-driven mouse model of acute myeloblastic leukemia (AML) to investigate whether pharmacological inhibition of CDK2 can inhibit MYC-driven tumorigenesis through induction of senescence. Purified mouse hematopoietic stem cells transduced with MYC and BCL-X<sub>L</sub> were transplanted into lethally irradiated mice, leading to the development of massive leukemia and subsequent death 15–17 days after transplantation. Upon disease onset, mice were treated with the selective CDK2 inhibitor CVT2584 or vehicle either by daily intraperitoneal injections or continuous delivery via mini-pumps. CVT2584 treatment delayed disease onset and moderately but significantly improved survival of mice. Flow cytometry revealed a significant decrease in tumor load in the spleen, liver and bone marrow of CVT2584-treated compared to vehicle-treated mice. This was correlated with induced senescence evidenced by reduced cell proliferation, increased senescence-associated β-galactosidase activity and heterochromatin foci, expression of p19<sup>ARF</sup> and p21<sup>CIP1</sup>, and reduced phosphorylation (activation) of pRb, while very few apoptotic cells were observed. In addition, phosphorylation of MYC at Ser-62 was decreased. In summary, inhibition of CDK2 delayed MYC/BCL-X<sub>L</sub>-driven AML linked to senescence induction. Our results suggest that CDK2 is a promising target for pro-senescence cancer therapy, in particular for MYC-driven tumors, including leukemia.