cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.6k8yxa
title: Occurrence Download

description: A dataset containing 2198 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nekemias cantoniensis (Hook. & Arn.) J.Wen & Z.L.Nie"
 ]
}

 The dataset includes 2198 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063786-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tdpukz
title: Occurrence Download

description: A dataset containing 116643 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cornus mas L."
 ]
}

 The dataset includes 116643 records from 966 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063744-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.486agh
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nervilia falcata (King & Pantl.) Schltr."
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063787-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p6tenq
title: Occurrence Download

description: A dataset containing 64547 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ophioglossum vulgatum L."
 ]
}

 The dataset includes 64547 records from 1025 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063747-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.22625999.v1
title: Additional file 4 of Golgi-apparatus genes related signature for predicting the progression-free interval of patients with papillary thyroid carcinoma

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.16864960
title: Additional file 5 of Deep learning for cancer type classification and driver gene identification

description: Additional file 5. Table S5. Top genes of the 985 pathogenetic germline transcripts
DOI: 10.6084/m9.figshare.22625654
title: Additional file 1 of Gonadal transcriptomes reveal sex-biased expression genes associated with sex determination and differentiation in red-tail catfish (Hemibagrus wyckioides)

description: Additional file 1: Table S1. The list of DEGs identified in M10dph-vs-F10dph.
DOI: 10.6084/m9.figshare.25188503
title: Supplementary Material for: A Rare Case of High-Grade Spindle Cell Sarcoma of the Breast: A Case Report

description: <b><i>Introduction:</i></b> Spindle cell sarcomas are rare breast lesions which are difficult to diagnose due to resemblance with other breast lesions. Histopathological examination and immunohistochemical staining are essential for diagnosis. <b><i>Case Presentation:</i></b> We present a rare case of a 15-year-old female presenting with high-grade rapidly progressive spindle cell sarcoma of the breast, differentiated as phyllodes tumor, with axillary lymph node involvement. Her lesion, on the left breast, measured 16.9 × 10.1 × 13.7 cm. Histology revealed malignant neoplasm arranged in sheets and individual neoplastic cells with an epithelioid to spindled morphology with scant cytoplasm and irregular nuclear membranes. Immunohistochemistry showed weakly positive focal CD-99, and negative WT-1, Myogenin, Desmin, p63, Cytokeratin, Synaptophysin, and CD-34 markers. She was successfully managed with modified radical mastectomy and discharged with regular follow-up advised. <b><i>Conclusion:</i></b> Spindle cell sarcomas have a very aggressive course and prompt diagnosis, and management is mandatory for better patient outcomes. Modified radical mastectomy is the mainstay of treatment.
DOI: 10.5281/zenodo.10628989
title: Polyphenol and L-ascorbic acid content in tomato as influenced by high lycopene genotypes and organic farming at different environments

description: Martí, R., Leiva-Brondo, M., Lahoz, I., Campillo, C., Cebolla-Cornejo, J. and Roselló, S., 2018. Polyphenol and L-ascorbic acid content in tomato as influenced by high lycopene genotypes and organic farming at different environments. Food chemistry, 239, pp.148-156.


Link to publication:
https://www.sciencedirect.com/science/article/pii/S0308814617310853


Link to repository:
https://riunet.upv.es/handle/10251/149544
DOI: 10.5281/zenodo.10628990
title: Polyphenol and L-ascorbic acid content in tomato as influenced by high lycopene genotypes and organic farming at different environments

description: Martí, R., Leiva-Brondo, M., Lahoz, I., Campillo, C., Cebolla-Cornejo, J. and Roselló, S., 2018. Polyphenol and L-ascorbic acid content in tomato as influenced by high lycopene genotypes and organic farming at different environments. Food chemistry, 239, pp.148-156.


Link to publication:
https://www.sciencedirect.com/science/article/pii/S0308814617310853


Link to repository:
https://riunet.upv.es/handle/10251/149544
DOI: 10.5281/zenodo.10635392
title: Polyphenol and L-ascorbic acid content in tomato as influenced by high lycopene genotypes and organic farming at different environments

description: Martí, R., Leiva-Brondo, M., Lahoz, I., Campillo, C., Cebolla-Cornejo, J. and Roselló, S., 2018. Polyphenol and L-ascorbic acid content in tomato as influenced by high lycopene genotypes and organic farming at different environments. Food chemistry, 239, pp.148-156.


Link to publication:
https://www.sciencedirect.com/science/article/pii/S0308814617310853


Link to repository:
https://riunet.upv.es/handle/10251/149544
DOI: 10.6084/m9.figshare.22625654.v1
title: Additional file 1 of Gonadal transcriptomes reveal sex-biased expression genes associated with sex determination and differentiation in red-tail catfish (Hemibagrus wyckioides)

description: Additional file 1: Table S1. The list of DEGs identified in M10dph-vs-F10dph.
DOI: 10.6084/m9.figshare.25188506.v1
title: Supplementary Material for: Delineating the Disease Boundaries: Homozygous <i>CDC14A</i> Variants Underlying Nonsyndromic Hearing Loss and Hearing Impairment Infertile Male Syndrome

description: <b><i>Background:</i></b> Hereditary hearing loss is a genetically heterogeneous neurosensory disorder that affects many people. Deafness and infertility can coexist in some cases, creating the hearing impairment infertile male syndrome. There are several known molecular mechanisms that can cause deafness either on its own or in conjunction with infertility. <b><i>Methods and Results:</i></b> Here, we represent two consanguineous families (A, B), both families had clinical evidence of deafness, and family B also had infertility, so we referred to them as having nonsyndromic hearing loss (NSHL) and hearing impairment infertile male syndrome (HIIMS), respectively. These families’ genetic makeup was examined using an Affymetrix GeneChip 250K Nsp array followed by Sanger sequencing. In family A, we identified a novel homozygous stop gain variant [NM_003672.4; c.1000C&gt;T; p.(Gln334*)] and a homozygous missense variant [NM_003672.4; c.684C&gt;A; p.(Asn228Lys)] in family B in <i>CDC14A</i> gene (MIM#603504). In animal models, the <i>CDC14A</i> gene causes both hearing loss and infertility; in addition, it also causes NSHL and HIIMS in humans. <b><i>Conclusions:</i></b> Our study on the <i>CDC14A</i> gene has identified two novel variants, crucial for delineating disease boundaries. Variants in exon 10 and upstream cause HIIMS, and those in exon 11 and downstream are linked exclusively to hearing impairment. This precision enhances diagnostics and offers potential for targeted interventions, marking a significant advancement in understanding the genetic basis of these conditions.
DOI: 10.25549/usctheses-ouc1416302
title: Microsoft Word - Corrected Draft2.doc
DOI: 10.25549/usctheses-ouc1397229
title: Exercise
DOI: 10.25549/usctheses-ouc1415513
title: salvador-duran-june15
DOI: 10.25549/usctheses-ouc1414519
title: Microsoft Word - Thesis.docx
DOI: 10.25549/usctheses-ouc1408001
title: Microsoft Word - Kalim_Final_Edited_Dissertation_8-3.1.11.doc
DOI: 10.25549/usctheses-ouc1410280
title: Microsoft Word - DissertationJune31.docx
DOI: 10.25549/usctheses-ouc1398432
title: Microsoft Word - dissertation_shuyapan_revised
DOI: 10.25549/usctheses-ouc1408187
title: Microsoft Word - $ASQ103248_supp_undefined_559BEF38-B8A1-11E0-A10A-3F4DD352ABB1.doc
DOI: 10.25549/usctheses-ouc1410374
title: Microsoft Word - Dissertation Tansuwan 0801_2011.docx
DOI: 10.25549/usctheses-ouc1398533
title: Dissertation Proposal
DOI: 10.25549/usctheses-ouc1408363
title: Microsoft Word - ATT00041.doc
DOI: 10.25549/usctheses-ouc1398700
title: Microsoft Word - NEWSDOCS ? thesis - format corrections - JUL17
DOI: 10.25549/usctheses-ouc1414594
title: Microsoft Word - Anil_Muhammed_-_Final_Dissertation_-_02-23-12
DOI: 10.25549/usctheses-ouc1398746
title: Microsoft Word - $ASQ113905_supp_undefined_834F9CCE-E7FC-11E0-A829-C4119E1A67F9.doc
DOI: 10.25549/usctheses-ouc1410544
title: Microsoft Word - Andrea McColl - 2nd Year Project - MA version - v6.docx
DOI: 10.25549/usctheses-ouc1415629
title: Master's Thesis-Ying Wang
DOI: 10.25549/usctheses-ouc1408440
title: Microsoft Word - labrecque_neal_wood_harrington_FINAL-MA formatted
DOI: 10.6084/m9.figshare.25188968
title: inpainting_coronal_rmnet_flair_weights.h5

description: inpainting flair weights
DOI: 10.6084/m9.figshare.25188968.v1
title: inpainting_coronal_rmnet_flair_weights.h5

description: inpainting flair weights
DOI: 10.25549/usctheses-ouc1414683
title: Popularity in Adolescent Peer Groups as a Predictor of Later Community Violence Exposure
DOI: 10.25549/usctheses-ouc1398993
title: Microsoft Word - DTucker_Thesis_ATA_V8.docx
DOI: 10.25549/usctheses-ouc1412895
title: Microsoft Word - thesis draft v16
DOI: 10.25549/usctheses-ouc1414757
title: Gabriel thesis final
DOI: 10.25549/usctheses-ouc1415809
title: Microsoft Word - 7-25-11 Thesis DeHaven _w emb fts_.doc
DOI: 10.25549/usctheses-ouc1410912
title: Thesis Final Draft V4
DOI: 10.25549/usctheses-ouc1415945
title: The Luxury Of Disintegration FINAL
DOI: 10.25549/usctheses-ouc1413299
title: Microsoft Word - vernon.docx
DOI: 10.25549/usctheses-ouc1400033
title: Depression and anxiety disorders are associated with excess disability
DOI: 10.25549/usctheses-ouc1400134
title: Utilizing the Diversity Scorecard toAssess the Transfer Readiness
DOI: 10.25549/usctheses-ouc1411252
title: Microsoft Word - $ASQ114028_supp_undefined_08DDE1C4-F37A-11E0-B76D-D447F0E6BF1D.doc
DOI: 10.25549/usctheses-ouc1414987
title: THESISFINALHeadings
DOI: 10.6084/m9.figshare.11105513
title: Bioinformatics analysis on differentially expressed genes of alveolar macrophage in IPF

description: <b>Objective:</b> This study aimed to explore the differentially expressed genes (DEGs) of pulmonary macrophages in human idiopathic pulmonary fibrosis (IPF) by bioinformatics, and elaborate on IPF on the gene level. <b>Methods:</b> The gene expression profile GSE49072 was downloaded from the gene expression omnibus (GEO) database. Genes of alveolar macrophages between normal volunteers and patients diagnosed as IPF were analyzed by GEO2R tools. Gene ontology (GO) and pathway enrichment analyses of genes were performed in the database for annotation, visualization and integrated discovery (DAVID) database, followed by functional annotation and protein-protein interaction (PPI) network construction in String website. Finally, the results were analyzed in a comprehensive way. <b>Results:</b> A total of 551 DEGs, including 205 down-regulated and 346 up-regulated were identified. The expression of 209875_s_at (secreted phosphoprotein 1, SPP1) and 214146_s_at (pro-platelet basic protein, PPBP) genes are the most significant in upregulated genes. DEGs in the MAPK（mitogen-activated protein kinase） signaling pathway and chemokine signaling pathway play important roles in the development of IPF. <b>Conclusions:</b> The up-regulation of genes such as SPP1 and PPBP affect the secretion of alveolar macrophages, thereby speeding up the process of fibrosis.
DOI: 10.6084/m9.figshare.11105513.v1
title: Bioinformatics analysis on differentially expressed genes of alveolar macrophage in IPF

description: <b>Objective:</b> This study aimed to explore the differentially expressed genes (DEGs) of pulmonary macrophages in human idiopathic pulmonary fibrosis (IPF) by bioinformatics, and elaborate on IPF on the gene level. <b>Methods:</b> The gene expression profile GSE49072 was downloaded from the gene expression omnibus (GEO) database. Genes of alveolar macrophages between normal volunteers and patients diagnosed as IPF were analyzed by GEO2R tools. Gene ontology (GO) and pathway enrichment analyses of genes were performed in the database for annotation, visualization and integrated discovery (DAVID) database, followed by functional annotation and protein-protein interaction (PPI) network construction in String website. Finally, the results were analyzed in a comprehensive way. <b>Results:</b> A total of 551 DEGs, including 205 down-regulated and 346 up-regulated were identified. The expression of 209875_s_at (secreted phosphoprotein 1, SPP1) and 214146_s_at (pro-platelet basic protein, PPBP) genes are the most significant in upregulated genes. DEGs in the MAPK（mitogen-activated protein kinase） signaling pathway and chemokine signaling pathway play important roles in the development of IPF. <b>Conclusions:</b> The up-regulation of genes such as SPP1 and PPBP affect the secretion of alveolar macrophages, thereby speeding up the process of fibrosis.
DOI: 10.25549/usctheses-ouc1400486
title: Method
DOI: 10.25549/usctheses-ouc1405396
title: 1
DOI: 10.6084/m9.figshare.16864948
title: Additional file 1 of Deep learning for cancer type classification and driver gene identification

description: Additional file 1. Table S1. 719 known oncogenes used in our pilot study
DOI: 10.6084/m9.figshare.16864957.v1
title: Additional file 4 of Deep learning for cancer type classification and driver gene identification

description: Additional file 4. Table S4. Top genes in the 985 pathogenetic cancer transcripts
DOI: 10.6084/m9.figshare.7435559.v1
title: TGF-β secreted by tumor-associated macrophages promotes proliferation and invasion of colorectal cancer via miR-34a-VEGF axis

description: Tumor-associated macrophages (TAMs) were reported to be involved in colorectal cancer (CRC) progression. However, its biological role and underlying mechanism in CRC remained to be elucidated. In this study, the expressions of the macrophage marker CD68 and transforming growth factor β1 (TGF-β1) in CRC tumor tissues and adjacent tissues were detected by immunohistochemistry. The expression levels of miR-34a, TGF-β1 and vascular endothelial growth factor (VEGF) in CRC tumor tissues and peripheral blood macrophages were measured by quantitative real-time PCR (qRT-PCR) and western blot. TGF-β1 levels in culture supernatant were detected by ELISA. The cell proliferation and invasion of human CRC cell lines CL187 and HCT116 were determined by MTT assay and Transwell assay, respectively. The results showed that the expression of miR-34a was downregulated whereas TGF-β1 and VEGF were upregulated in CRC tumor tissues and peripheral blood macrophages. TGF-β1 secreted by TAMs promoted the proliferation and invasion of CRC cells. TGF-β1-mediated miR-34a downregulation contributed to the proliferation and invasion of CRC cells via upregulating VEGF. MiR-34a in vivo exerted anti-tumor effect in CRC via inhibiting VEGF expression. In conclusion, TGF-β1 secreted by TAMs promoted CRC proliferation and invasion through regulating miR-34a/VEGF axis.
DOI: 10.6084/m9.figshare.16864951
title: Additional file 2 of Deep learning for cancer type classification and driver gene identification

description: Additional file 2. Table S2. 985 transcripts corresponding to the 719 genes
DOI: 10.6084/m9.figshare.16864960.v1
title: Additional file 5 of Deep learning for cancer type classification and driver gene identification

description: Additional file 5. Table S5. Top genes of the 985 pathogenetic germline transcripts
DOI: 10.6084/m9.figshare.21583473
title: Genome-wide characterization of circRNA expression profile in overexpression of <i>RIP2</i> chicken macrophages associated with avian pathogenic <i>E.coli</i> infection

description: Avian pathogenic <i>E. coli</i> (APEC) can cause localized and systemic diseases in poultry, threatening human health via meat or egg contamination and resulting in considerable economic losses to the poultry industry globally. Increasing evidence shows circRNAs were widely involved in various biological processes. However, the role of circRNAs in the host response against APEC infection, especially correlated with the regulation of <i>RIP2</i>, remains unclear. Herein, the RNAseq technology was used to identify the circRNA expression profiles in the overexpression of <i>RIP2</i> macrophages with or without APEC infection. A total of 256 and 287 differentially expressed (DE) circRNAs were identified in the overexpression of <i>RIP2</i> group (oeRIP2) vs. the wild-type group (WT) and oeRIP2 + APEC vs. APEC, respectively, whose parental genes were involved in MAPK signalling pathway, Wnt signalling pathway, focal adhesion, tight junction, and VEGF signalling pathways. Specifically, the key circRNAs, such as 5:814443-825127, 10:18922360-18928461, 2:8746306-8750639, and 2:124177751-124184063 might play a critical role in APEC infection and the regulation of <i>RIP2</i>. As a whole, these findings will facilitate understanding the molecular mechanism underlying circRNAs, especially related to the regulation of the <i>RIP2</i> gene. Meanwhile, the study may offer new ideas to improve host immune and inflammatory response against APEC infection.
DOI: 10.6084/m9.figshare.16864852
title: Additional file 3 of Methodological quality for systematic reviews of adverse events with surgical interventions: a cross-sectional survey

description: Additional file 3. Original data.
DOI: 10.6084/m9.figshare.16864966
title: Additional file 7 of Deep learning for cancer type classification and driver gene identification

description: Additional file 7. Table S7. Top genes of the 1970 pathogenetic germline transcripts
DOI: 10.6084/m9.figshare.21583473.v1
title: Genome-wide characterization of circRNA expression profile in overexpression of <i>RIP2</i> chicken macrophages associated with avian pathogenic <i>E.coli</i> infection

description: Avian pathogenic <i>E. coli</i> (APEC) can cause localized and systemic diseases in poultry, threatening human health via meat or egg contamination and resulting in considerable economic losses to the poultry industry globally. Increasing evidence shows circRNAs were widely involved in various biological processes. However, the role of circRNAs in the host response against APEC infection, especially correlated with the regulation of <i>RIP2</i>, remains unclear. Herein, the RNAseq technology was used to identify the circRNA expression profiles in the overexpression of <i>RIP2</i> macrophages with or without APEC infection. A total of 256 and 287 differentially expressed (DE) circRNAs were identified in the overexpression of <i>RIP2</i> group (oeRIP2) vs. the wild-type group (WT) and oeRIP2 + APEC vs. APEC, respectively, whose parental genes were involved in MAPK signalling pathway, Wnt signalling pathway, focal adhesion, tight junction, and VEGF signalling pathways. Specifically, the key circRNAs, such as 5:814443-825127, 10:18922360-18928461, 2:8746306-8750639, and 2:124177751-124184063 might play a critical role in APEC infection and the regulation of <i>RIP2</i>. As a whole, these findings will facilitate understanding the molecular mechanism underlying circRNAs, especially related to the regulation of the <i>RIP2</i> gene. Meanwhile, the study may offer new ideas to improve host immune and inflammatory response against APEC infection.
DOI: 10.6084/m9.figshare.16864963
title: Additional file 6 of Deep learning for cancer type classification and driver gene identification

description: Additional file 6. Table S6. Top genes of the 1970 pathogenetic cancer transcripts
DOI: 10.25549/usctheses-ouc1405651
title: danielle-allphin-june28
DOI: 10.5281/zenodo.7981127
title: SWECO25: Hydrologic (hydro)

description: The hydrologic category (SWECO25 v1.0.0) contains the "gwn07", "morph", and "swisstopo" datasets. 

The gwn07 dataset provides information on the distance to the hydrological network (rivers and lakes). After rasterizing and resampling the source data (Swisstopo, 2007) to the SWECO25 grid, we generated distance statistics layers for the 9 (and all together) Strahler River order and 3 (and all together) lake size classes that were available. This dataset includes a total of 14 layers. Final values were rounded and multiplied by 100. 

The morph dataset describes the ecomorphology of the Swiss rivers and streams. After rasterizing and resampling the source data (FOEN, 2009) to the SWECO25 grid, we generated individual layers for the 5 ecomorphological classes that were available (natural/near-natural, little disturbed, heavily disturbed, unnatural/artificial, and culverted). For each class, we provided the binary maps (0 or 1) and computed 13 focal statistics layers by applying a cell-level function calculating the average percentage cover value for a given class in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 71 layers. Final values were rounded and multiplied by 100.

The swisstopo dataset describes the steepness of the watercourses. After rasterizing and resampling the source data (Kaelin and Altermatt, 2016) to the SWECO25 grid, we generated two main layers, for the maximum and mean values. For each, we provided the output map and 13 focal statistics layers obtained by applying a cell-level function calculating the average value in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 28 layers.  Final values were rounded and multiplied by 100.

The detailed list of layers available is provided in SWECO25_datalayers_details_hydro.csv and includes information on the category, dataset, variable name (long), variable name (short), period, sub-period, start year, end year, attribute, radii, unit, and path.

References: Swiss Federal Office of Topography [swisstopo]. Hydrographic network VECTOR25 GWN07. (Wabern, Switzerland, 2007).

Federal Office for the Environment [FOEN]. Swiss watercourse structure and morphology. (Bern, Switzerland, 2009)

Kaelin, K. & Altermatt, F. Landscape-level predictions of diversity in river networks reveal opposing patterns for different groups of macroinvertebrates. Aquatic Ecology 50, 283-295 (2016) 
description: We gratefully acknowledge financial support through the Action Plan of the Swiss Biodiversity Strategy by the Federal Office for the Environment (FOEN) for financing the ValPar.CH and SwissCatchment projects. All the institutions and contributors who made the input data freely available are gratefully acknowledged.
DOI: 10.6084/m9.figshare.16864948.v1
title: Additional file 1 of Deep learning for cancer type classification and driver gene identification

description: Additional file 1. Table S1. 719 known oncogenes used in our pilot study
DOI: 10.6084/m9.figshare.16864954
title: Additional file 3 of Deep learning for cancer type classification and driver gene identification

description: Additional file 3. Table S3. 985 additional transcripts selected for analyses using multimodal logistic regression
DOI: 10.6084/m9.figshare.16864954.v1
title: Additional file 3 of Deep learning for cancer type classification and driver gene identification

description: Additional file 3. Table S3. 985 additional transcripts selected for analyses using multimodal logistic regression
DOI: 10.25549/usctheses-ouc1411620
title: brin_thesis_07202011