cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48597/d5cs-8brz
title: Measurement of Inorganics in air and particle phase at Cabo de Creus

description: Measurement of Inorganics in air and particle phase at Cabo de Creus
DOI: 10.48597/dhtc-te4k
title: Measurement of Inorganics in air and particle phase at Zarra

description: Measurement of Inorganics in air and particle phase at Zarra
DOI: 10.48597/dsnx-rwah
title: Measurement of Inorganics in air and particle phase at Zarra

description: Measurement of Inorganics in air and particle phase at Zarra
DOI: 10.48597/e6yq-jwa6
title: Measurement of Sulphate_total at Cabo de Creus

description: Measurement of Sulphate_total at Cabo de Creus
DOI: 10.48597/egdf-he99
title: Measurement of Inorganics in air and particle phase at Barcarrota

description: Measurement of Inorganics in air and particle phase at Barcarrota
DOI: 10.48597/erwd-mv6q
title: Measurement of Inorganics in air and particle phase at Cabo de Creus

description: Measurement of Inorganics in air and particle phase at Cabo de Creus
DOI: 10.48597/fbbf-4j2g
title: Measurement of Inorganics in air and particle phase at Barcarrota

description: Measurement of Inorganics in air and particle phase at Barcarrota
DOI: 10.48597/g57m-qdq4
title: Measurement of Inorganics in air and particle phase at Zarra

description: Measurement of Inorganics in air and particle phase at Zarra
DOI: 10.48597/guy8-pvm6
title: Measurement of Sulphate_total at Cabo de Creus

description: Measurement of Sulphate_total at Cabo de Creus
DOI: 10.48597/h47m-e3gj
title: Measurement of Inorganics in air and particle phase at Cabo de Creus

description: Measurement of Inorganics in air and particle phase at Cabo de Creus
DOI: 10.48597/hk8f-c48b
title: Measurement of Inorganics in air and particle phase at Barcarrota

description: Measurement of Inorganics in air and particle phase at Barcarrota
DOI: 10.48597/hu9e-kp7y
title: Measurement of Inorganics in air and particle phase at Zarra

description: Measurement of Inorganics in air and particle phase at Zarra
DOI: 10.48597/jzxq-jzsj
title: Measurement of Inorganics in air and particle phase at Zarra

description: Measurement of Inorganics in air and particle phase at Zarra
DOI: 10.48597/m7wa-mapr
title: Measurement of Inorganics in air and particle phase at Niembro

description: Measurement of Inorganics in air and particle phase at Niembro
DOI: 10.48597/mgzm-sqxc
title: Measurement of Inorganics in air and particle phase at Cabo de Creus

description: Measurement of Inorganics in air and particle phase at Cabo de Creus
DOI: 10.48597/njgc-3mjw
title: Measurement of Sum_ammonia_and_ammonium at Cabo de Creus

description: Measurement of Sum_ammonia_and_ammonium at Cabo de Creus
DOI: 10.48597/nqux-z2bf
title: Measurement of Inorganics in air and particle phase at Cabo de Creus

description: Measurement of Inorganics in air and particle phase at Cabo de Creus
DOI: 10.48597/nwku-mt25
title: Measurement of Inorganics in air and particle phase at Cabo de Creus

description: Measurement of Inorganics in air and particle phase at Cabo de Creus
DOI: 10.48597/nwnr-dmsu
title: Measurement of Sum_nitric_acid_and_nitrate at Campisabalos

description: Measurement of Sum_nitric_acid_and_nitrate at Campisabalos
DOI: 10.48597/prxa-t3kw
title: Measurement of Inorganics in air and particle phase at Campisabalos

description: Measurement of Inorganics in air and particle phase at Campisabalos
DOI: 10.48597/qveh-zb95
title: Measurement of Inorganics in air and particle phase at Zarra

description: Measurement of Inorganics in air and particle phase at Zarra
DOI: 10.48597/rpfn-p8fk
title: Measurement of Inorganics in air and particle phase at Campisabalos

description: Measurement of Inorganics in air and particle phase at Campisabalos
DOI: 10.48597/s364-qa4q
title: Measurement of Inorganics in air and particle phase at Cabo de Creus

description: Measurement of Inorganics in air and particle phase at Cabo de Creus
DOI: 10.48597/sfkp-68z7
title: Measurement of Sulphate_total at Zarra

description: Measurement of Sulphate_total at Zarra
DOI: 10.6084/m9.figshare.9923618
title: Quality by design (QbD) based development and validation of bioanalytical RP-HPLC method for dapagliflozin: Forced degradation and preclinical pharmacokinetic study

description: A rapid, sensitive, and reliable quality by design (QbD) based bioanalytical HPLC method was developed and validated for quantification of dapagliflozin (DFG) in rat plasma. The method was optimized using three levels and three-factor Box–Behnken statistical design (BBD). The mobile phase composition (acetonitrile % as <i>A</i>), flow rate (mL/min as <i>B</i>) and UV-wavelength (nm as <i>C</i>) selected as independent variable whereas area (AU as <i>Y</i><sub>1</sub>), retention time (min. as <i>Y</i><sub>2</sub>) and tailing factor (% as <i>Y</i><sub>3</sub>) selected as dependent variables. The developed method validated for linearity, sensitivity (LOD & LOQ), precision, accuracy, extraction recovery, matrix effect, and stability. Further the developed bioanalytical used to assess the pharmacokinetic study on a rat model. The developed method showed excellent linearity between 10 and 1200 ng/mL with <i>r</i><sup>2</sup> = 0.997, LOD (2.15 ng/mL) and LOQ (6.52 ng/mL). The validation results of the tested parameters were found within the acceptable limit. Further, DFG loaded S-SNEDDS showed the droplet size (75.34 ± 5.34 nm), PDI (0.426) and zeta-potential (−25.6 mV). The pharmacokinetic study results revealed the <i>C</i><sub>max</sub> of S-SNEDDS was 1.42 fold higher than DFG dispersion. The significantly high AUC value observed with S-SNEDDS indicated increased bioavailability of DFG as compared to DFG dispersion. From the study, it was concluded that QbD approach based bioanalytical method is suitable for the <i>in-vitro</i> and <i>in-vivo</i> estimation of DFG from bulk as well as developed formulations.
DOI: 10.6084/m9.figshare.9923618.v1
title: Quality by design (QbD) based development and validation of bioanalytical RP-HPLC method for dapagliflozin: Forced degradation and preclinical pharmacokinetic study

description: A rapid, sensitive, and reliable quality by design (QbD) based bioanalytical HPLC method was developed and validated for quantification of dapagliflozin (DFG) in rat plasma. The method was optimized using three levels and three-factor Box–Behnken statistical design (BBD). The mobile phase composition (acetonitrile % as <i>A</i>), flow rate (mL/min as <i>B</i>) and UV-wavelength (nm as <i>C</i>) selected as independent variable whereas area (AU as <i>Y</i><sub>1</sub>), retention time (min. as <i>Y</i><sub>2</sub>) and tailing factor (% as <i>Y</i><sub>3</sub>) selected as dependent variables. The developed method validated for linearity, sensitivity (LOD & LOQ), precision, accuracy, extraction recovery, matrix effect, and stability. Further the developed bioanalytical used to assess the pharmacokinetic study on a rat model. The developed method showed excellent linearity between 10 and 1200 ng/mL with <i>r</i><sup>2</sup> = 0.997, LOD (2.15 ng/mL) and LOQ (6.52 ng/mL). The validation results of the tested parameters were found within the acceptable limit. Further, DFG loaded S-SNEDDS showed the droplet size (75.34 ± 5.34 nm), PDI (0.426) and zeta-potential (−25.6 mV). The pharmacokinetic study results revealed the <i>C</i><sub>max</sub> of S-SNEDDS was 1.42 fold higher than DFG dispersion. The significantly high AUC value observed with S-SNEDDS indicated increased bioavailability of DFG as compared to DFG dispersion. From the study, it was concluded that QbD approach based bioanalytical method is suitable for the <i>in-vitro</i> and <i>in-vivo</i> estimation of DFG from bulk as well as developed formulations.
DOI: 10.6084/m9.figshare.24603692
title: Additional file 2 of FGFR1 variants contributed to families with tooth agenesis

description: Additional file 2. Table S2. The phenotypes with FGFR1 related disorders.
DOI: 10.6084/m9.figshare.24603692.v1
title: Additional file 2 of FGFR1 variants contributed to families with tooth agenesis

description: Additional file 2. Table S2. The phenotypes with FGFR1 related disorders.
DOI: 10.6084/m9.figshare.24603699
title: Additional file 2 of Nr4a1 marks a distinctive ILC2 activation subset in the mouse inflammatory lung

description: Additional file 2: Table S1. The names of 20, 98, 257, and 243 signature genes in C1, C2, C3, and C4 respectively, which matched Fig. 1h.
DOI: 10.6084/m9.figshare.24603699.v1
title: Additional file 2 of Nr4a1 marks a distinctive ILC2 activation subset in the mouse inflammatory lung

description: Additional file 2: Table S1. The names of 20, 98, 257, and 243 signature genes in C1, C2, C3, and C4 respectively, which matched Fig. 1h.
DOI: 10.6084/m9.figshare.24603702
title: Additional file 3 of Nr4a1 marks a distinctive ILC2 activation subset in the mouse inflammatory lung

description: Additional file 3: Table S2. All the signature gene sets for activation scoring (Fig. 2b), wound healing scoring (Fig. 2f), proliferation scoring (Fig. 2h), memory scoring (Fig. 2i), tissue-resident scoring (Fig. 3b), Trm scoring (Fig. 3d), Tcm/Tem scoring (Fig. 3e).
DOI: 10.6084/m9.figshare.24603702.v1
title: Additional file 3 of Nr4a1 marks a distinctive ILC2 activation subset in the mouse inflammatory lung

description: Additional file 3: Table S2. All the signature gene sets for activation scoring (Fig. 2b), wound healing scoring (Fig. 2f), proliferation scoring (Fig. 2h), memory scoring (Fig. 2i), tissue-resident scoring (Fig. 3b), Trm scoring (Fig. 3d), Tcm/Tem scoring (Fig. 3e).
DOI: 10.6084/m9.figshare.12020802
title: Model Checking for Hidden Markov Models

description: Residual analysis is a useful tool for checking lack of fit and for providing insight into model improvement. However, literature on residual analysis and the goodness of fit for hidden Markov models (HMMs) is limited. As HMMs with complex structures are increasingly used to accommodate different types of data, there is a need for further tools to check the validity of models applied to real world data. We review model checking methods for HMMs and develop new methods motivated by a particular case study involving a two-dimensional HMM developed for time series with many null events. We propose new residual analysis and stochastic reconstruction methods, which are adapted from model checking techniques for point process models. We apply the new methods to the case study model and discuss their adequacy. We find that there is not one “best” test for diagnostics but that our new methods have some advantages over previously developed tools. The importance of multiple tests for complex HMMs is highlighted and we use the results of our model checking to provide suggestions for possible improvements to the case study model. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20933491
title: Additional file 4 of Non-invasive preimplantation genetic testing for conventional IVF blastocysts

description: Additional file 4: Table S8. The CNV results and other information forms of 122 ICSI samples.
DOI: 10.6084/m9.figshare.20933491.v1
title: Additional file 4 of Non-invasive preimplantation genetic testing for conventional IVF blastocysts

description: Additional file 4: Table S8. The CNV results and other information forms of 122 ICSI samples.
DOI: 10.6084/m9.figshare.3761562.v2868
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.20933523.v1
title: Additional file 3 of The O-glycosylating enzyme GALNT2 acts as an oncogenic driver in non-small cell lung cancer

description: Additional file 3: Table S7. RNA-seq after GALNT2 knockdown in A549 cells.
DOI: 10.6084/m9.figshare.8874941
title: Additional file 1: of Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling

description: Table S1. Gene Set Enrichment Analysis and Significant Pathways. The first tab includes all statistically significant associations between radiomic features and genetic pathways included in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. All these associations are identified through Gene Set Enrichment Analysis (GSEA). In the second tab, pathways are grouped by major categories and listed with the total number of radiomic associations. (XLS 129 kb)
DOI: 10.6084/m9.figshare.19360801
title: Hsa_circ_0017639 regulates cisplatin resistance and tumor growth via acting as a miR-1296-5p molecular sponge and modulating sine oculis homeobox 1 expression in non-small cell lung cancer

description: Cisplatin (DDP)-induced chemoresistance is an important reason for the failure of non-small cell lung cancer (NSCLC) treatment. Circular RNAs (circRNAs) participate in the chemoresistance of diverse cancers. However, the function of hsa_circ_0017639 (circ_0017639) in the DDP resistance of NSCLC is unclear. Forty-one NSCLC samples (21 DDP-resistant samples and 20 DDP-sensitive samples) were utilized in the research. The relative expression levels of some genes were determined by real-time quantitative polymerase chain reaction (RT-qPCR). 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide (MTT) assay for half-maximal inhibitory concentration (IC<sub>50</sub>) value of DDP and cell viability, colony formation and 5-ethynyl-2’-deoxyuridine (EDU) assays for cell proliferation, flow cytometry assay for cell apoptosis, transwell assay for cell invasion and wound-healing assay for cell migration were performed. The regulation mechanism of circ_0017639 was demonstrated by a dual-luciferase reporter assay. We observed higher levels of circ_0017639 in DDP-resistant NSCLC samples and cells. Functionally, circ_0017639 silencing decreased tumor growth and elevated DDP sensitivity <i>in vivo</i> and induced apoptosis, repressed proliferation, invasion, and migration of DDP-resistant NSCLC cells <i>in vitro</i>. Mechanically, circ_0017639 modulated sine oculis homeobox 1 (SIX1) expression via sponging microRNA (miR)-1296-5p. Also, miR-1296-5p inhibitor restored circ_0017639 knockdown-mediated impacts on cell DDP resistance in DDP-resistant NSCLCs. Furthermore, SIX1 overexpression counteracted the inhibiting impact of miR-1296-5p upregulation on DDP resistance and malignant phenotypes of DDP-resistant NSCLC cells. In conclusion, circ_0017639 conferred DDP resistance and promoted tumor growth via elevating SIX1 expression through sequestering miR-1296-5p in NSCLC, providing a new mechanism for understanding the chemoresistance and progression of NSCLC.
DOI: 10.6084/m9.figshare.19360801.v1
title: Hsa_circ_0017639 regulates cisplatin resistance and tumor growth via acting as a miR-1296-5p molecular sponge and modulating sine oculis homeobox 1 expression in non-small cell lung cancer

description: Cisplatin (DDP)-induced chemoresistance is an important reason for the failure of non-small cell lung cancer (NSCLC) treatment. Circular RNAs (circRNAs) participate in the chemoresistance of diverse cancers. However, the function of hsa_circ_0017639 (circ_0017639) in the DDP resistance of NSCLC is unclear. Forty-one NSCLC samples (21 DDP-resistant samples and 20 DDP-sensitive samples) were utilized in the research. The relative expression levels of some genes were determined by real-time quantitative polymerase chain reaction (RT-qPCR). 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide (MTT) assay for half-maximal inhibitory concentration (IC<sub>50</sub>) value of DDP and cell viability, colony formation and 5-ethynyl-2’-deoxyuridine (EDU) assays for cell proliferation, flow cytometry assay for cell apoptosis, transwell assay for cell invasion and wound-healing assay for cell migration were performed. The regulation mechanism of circ_0017639 was demonstrated by a dual-luciferase reporter assay. We observed higher levels of circ_0017639 in DDP-resistant NSCLC samples and cells. Functionally, circ_0017639 silencing decreased tumor growth and elevated DDP sensitivity <i>in vivo</i> and induced apoptosis, repressed proliferation, invasion, and migration of DDP-resistant NSCLC cells <i>in vitro</i>. Mechanically, circ_0017639 modulated sine oculis homeobox 1 (SIX1) expression via sponging microRNA (miR)-1296-5p. Also, miR-1296-5p inhibitor restored circ_0017639 knockdown-mediated impacts on cell DDP resistance in DDP-resistant NSCLCs. Furthermore, SIX1 overexpression counteracted the inhibiting impact of miR-1296-5p upregulation on DDP resistance and malignant phenotypes of DDP-resistant NSCLC cells. In conclusion, circ_0017639 conferred DDP resistance and promoted tumor growth via elevating SIX1 expression through sequestering miR-1296-5p in NSCLC, providing a new mechanism for understanding the chemoresistance and progression of NSCLC.
DOI: 10.6084/m9.figshare.19360802
title: MicorRNA-195 links long non-coding RNA SEMA3B antisense RNA 1 (head to head) and cyclin D1 to regulate the proliferation of glioblastoma cells

description: Long non-coding RNA (lncRNA) SEMA3B antisense RNA 1 (head to head) (SEMA3B-AS1) is a recently identified tumor suppressor in gastric cancer. However, its role in glioblastoma (GBM) is unclear. This study was conducted to explore the role of SEMA3B-AS1 in GBM. In this study, the expression of SEMA3B-AS1, cyclin D1 and miR-195 were determined by RT-qPCR. Gene interactions were evaluated by dual-luciferase assay and overexpression experiments. BrdU assay was performed to monitor cell proliferation. We observed downregulation of SEMA3B-AS1 in GBM. The expression of SEMA3B-AS1 was inversely correlated with the expression of cyclin D1 but positively correlated with the expression of miR-195. In GBM cells, overexpression of SEMA3B-AS1 and miR-195 caused reduced expression levels of cyclin D1. MiR-195 inhibitor reduced the effects of overexpression of SEMA3B-AS1 on the expression of cyclin D1. Moreover, overexpression of SEMA3B-AS1 increased the expression levels of miR-195. Cell proliferation data showed that, SEMA3B-AS1 and miR-195 decreased cell proliferation, while overexpression of cyclin D1 increased GBM cell proliferation. In addition, miR-195 inhibitor inhibited the role of overexpression of SEMA3B-AS1 in cancer cell proliferation. Moreover, miR-195 interacted with cyclin D1, but not SEMA3B-AS1. Furthermore, SEMA3B-AS1 decreased the methylation of the promoter region of miR-195. Therefore, we concluded that miR-195 links lncRNA SEMA3B-AS1 and cyclin D1 to regulate the proliferation of GBM cells.
DOI: 10.6084/m9.figshare.19360802.v1
title: MicorRNA-195 links long non-coding RNA SEMA3B antisense RNA 1 (head to head) and cyclin D1 to regulate the proliferation of glioblastoma cells

description: Long non-coding RNA (lncRNA) SEMA3B antisense RNA 1 (head to head) (SEMA3B-AS1) is a recently identified tumor suppressor in gastric cancer. However, its role in glioblastoma (GBM) is unclear. This study was conducted to explore the role of SEMA3B-AS1 in GBM. In this study, the expression of SEMA3B-AS1, cyclin D1 and miR-195 were determined by RT-qPCR. Gene interactions were evaluated by dual-luciferase assay and overexpression experiments. BrdU assay was performed to monitor cell proliferation. We observed downregulation of SEMA3B-AS1 in GBM. The expression of SEMA3B-AS1 was inversely correlated with the expression of cyclin D1 but positively correlated with the expression of miR-195. In GBM cells, overexpression of SEMA3B-AS1 and miR-195 caused reduced expression levels of cyclin D1. MiR-195 inhibitor reduced the effects of overexpression of SEMA3B-AS1 on the expression of cyclin D1. Moreover, overexpression of SEMA3B-AS1 increased the expression levels of miR-195. Cell proliferation data showed that, SEMA3B-AS1 and miR-195 decreased cell proliferation, while overexpression of cyclin D1 increased GBM cell proliferation. In addition, miR-195 inhibitor inhibited the role of overexpression of SEMA3B-AS1 in cancer cell proliferation. Moreover, miR-195 interacted with cyclin D1, but not SEMA3B-AS1. Furthermore, SEMA3B-AS1 decreased the methylation of the promoter region of miR-195. Therefore, we concluded that miR-195 links lncRNA SEMA3B-AS1 and cyclin D1 to regulate the proliferation of GBM cells.
DOI: 10.6084/m9.figshare.12020802.v2
title: Model Checking for Hidden Markov Models

description: Residual analysis is a useful tool for checking lack of fit and for providing insight into model improvement. However, literature on residual analysis and the goodness of fit for hidden Markov models (HMMs) is limited. As HMMs with complex structures are increasingly used to accommodate different types of data, there is a need for further tools to check the validity of models applied to real world data. We review model checking methods for HMMs and develop new methods motivated by a particular case study involving a two-dimensional HMM developed for time series with many null events. We propose new residual analysis and stochastic reconstruction methods, which are adapted from model checking techniques for point process models. We apply the new methods to the case study model and discuss their adequacy. We find that there is not one “best” test for diagnostics but that our new methods have some advantages over previously developed tools. The importance of multiple tests for complex HMMs is highlighted and we use the results of our model checking to provide suggestions for possible improvements to the case study model. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12020802.v1
title: Model checking for hidden Markov models

description: Residual analysis is a useful tool for checking lack of fit and for providing insight into model improvement. However, literature on residual analysis and the goodness of fit for hidden Markov models (HMMs) is limited. As HMMs with complex structures are increasingly used to accommodate different types of data, there is a need for further tools to check the validity of models applied to real world data. We review model checking methods for HMMs and develop new methods motivated by a particular case study involving a 2-dimensional HMM developed for time series with many null events. We propose new residual analysis and stochastic reconstruction methods, which are adapted from model checking techniques for point process models. We apply the new methods to the case study model, and discuss their adequacy. We find that there is not one “best” test for diagnostics but that our new methods have some advantages over previously developed tools. The importance of multiple tests for complex HMMs is highlighted and we use the results of our model checking to provide suggestions for possible improvements to the case study model.
DOI: 10.6069/xqva-pt28
title: Relative complement deficiency predicts development of islet autoimmunity and type 1 diabetes

description: Type 1 diabetes (T1D) is a chronic condition caused by autoimmune destruction of the insulin-producing pancreatic β-cells. While it is known that gene-environment interactions play a key role in triggering the autoimmune process leading to T1D, the pathogenic mechanism leading to the appearance of islet autoantibodies - biomarkers of autoimmunity – is poorly understood. Here we show that disruption of the complement system precedes the detection of islet autoantibodies and persists through diagnosis. Our results suggest that children who exhibit islet autoimmunity and progress to clinical T1D are complement deficient relative to those who do not progress within a similar timeframe. Thus, the complement pathway, an understudied mechanistic and therapeutic target in T1D, merits increased attention for use as protein biomarkers of prediction and potentially prevention of T1D.