cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8015960.v1
title: Panel Data Analysis via Mechanistic Models

description: Panel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a distinct unit. Mechanistic modeling involves writing down scientifically motivated equations describing the collection of dynamic systems giving rise to the observations on each unit. A defining characteristic of panel systems is that the dynamic interaction between units should be negligible. Panel models therefore consist of a collection of independent stochastic processes, generally linked through shared parameters while also having unit-specific parameters. To give the scientist flexibility in model specification, we are motivated to develop a framework for inference on panel data permitting the consideration of arbitrary nonlinear, partially observed panel models. We build on iterated filtering techniques that provide likelihood-based inference on nonlinear partially observed Markov process models for time series data. Our methodology depends on the latent Markov process only through simulation; this plug-and-play property ensures applicability to a large class of models. We demonstrate our methodology on a toy example and two epidemiological case studies. We address inferential and computational issues arising due to the combination of model complexity and dataset size. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8015903.v1
title: Análisis de Web SEO

description: Resumen de la comparación de dos páginas web para la asignatura Búsqueda Avanzada de la información (UC3M)
DOI: 10.6084/m9.figshare.8015936.v2
title: Impact of different genetic backgrounds on the evolution of elevated antibiotic resistance in the blaADC-7

description: The same blaADC-7 in two different bacterial species yielded different MICs to a range of drugs after evolution in cefepime. Additionally, the 3rd E. coli evolutionary replicate evolved a novel point mutation in blaADC-7: S286R.
DOI: 10.6084/m9.figshare.8015852.v1
title: Proteasome dysfunction induces excessive proteome instability and loss of mitostasis that can be mitigated by enhancing mitochondrial fusion or autophagy

description: The ubiquitin-proteasome pathway (UPP) is central to proteostasis network (PN) functionality and proteome quality control. Yet, the functional implication of the UPP in tissue homeodynamics at the whole organism level and its potential cross-talk with other proteostatic or mitostatic modules are not well understood. We show here that knock down (KD) of proteasome subunits in <i>Drosophila</i> flies, induced, for most subunits, developmental lethality. Ubiquitous or tissue specific proteasome dysfunction triggered systemic proteome instability and activation of PN modules, including macroautophagy/autophagy, molecular chaperones and the antioxidant cncC (the fly ortholog of NFE2L2/Nrf2) pathway. Also, proteasome KD increased genomic instability, altered metabolic pathways and severely disrupted mitochondrial functionality, triggering a cncC-dependent upregulation of mitostatic genes and enhanced rates of mitophagy. Whereas, overexpression of key regulators of antioxidant responses (e.g., cncC or foxo) could not suppress the deleterious effects of proteasome dysfunction; these were alleviated in both larvae and adult flies by modulating mitochondrial dynamics towards increased fusion or by enhancing autophagy. Our findings reveal the extensive functional wiring of genomic, proteostatic and mitostatic modules in higher metazoans. Also, they support the notion that age-related increase of proteotoxic stress due to decreased UPP activity deregulates all aspects of cellular functionality being thus a driving force for most age-related diseases. <b>Abbreviations:</b> ALP: autophagy-lysosome pathway; ARE: antioxidant response element; Atg8a: autophagy-related 8a; ATPsynβ: ATP synthase, β subunit; C-L: caspase-like proteasomal activity; cncC: cap-n-collar isoform-C; CT-L: chymotrypsin-like proteasomal activity; Drp1: dynamin related protein 1; ER: endoplasmic reticulum; foxo: forkhead box, sub-group O; GLU: glucose; GFP: green fluorescent protein; GLY: glycogen; Hsf: heat shock factor; Hsp: Heat shock protein; Keap1: kelch-like ECH-associated protein 1; Marf: mitochondrial assembly regulatory factor; NFE2L2/Nrf2: nuclear factor, erythroid 2 like 2; Opa1: optic atrophy 1; PN: proteostasis network; RNAi: RNA interference; ROS: reactive oxygen species; ref(2)P: refractory to sigma P; SQSTM1: sequestosome 1; SdhA: succinate dehydrogenase, subunit A; T-L: trypsin-like proteasomal activity; TREH: trehalose; UAS: upstream activation sequence; Ub: ubiquitin; UPR: unfolded protein response; UPP: ubiquitin-proteasome pathway.
DOI: 10.6084/m9.figshare.8015786
title: Tumor necrosis correlates with PD-L1 and PD-1 expression in lung adenocarcinoma

description: <b>Background:</b> Predictive biomarkers for immunotherapy in lung cancer are intensively investigated; however, correlations between PD-L1/PD-1 expressions and clinical features or histopathological tumor characteristics determined on hematoxylin and eosin stained sections have not extensively been studied. <b>Material and methods:</b> We determined PD-L1 expression of tumor cells (TC) and immune cells (IC), and PD-1 expression of IC by immunohistochemistry in 268 lung adenocarcinoma (LADC) patients, and correlated the data with smoking, COPD, tumor grade, necrosis, lepidic growth pattern, vascular invasion, density of stromal IC, and <i>EGFR/KRAS</i> status of the tumors. <b>Results:</b> There was a positive correlation between PD-L1 expression of TC and IC, as well as PD-L1 and PD-1 expression of IC. Tumor necrosis was associated with higher PD-L1 expression of TC and PD-1 expression of IC. A negative correlation was observed between lepidic growth pattern and PD-L1 expression of TC and PD-L1/PD-1 expression of IC. <i>EGFR</i> mutation seemed to negatively correlate with PD-1 expression of IC, but this tendency could not be verified when applying corrections for multiple comparisons. No significant effect of the <i>KRAS</i> mutation on any of the studied variables could be established. <b>Conclusion:</b> Here we first demonstrate that the presence of necrosis correlates with higher PD-L1 expression of TC and PD-1 expression of IC in LADC. Further studies are required to determine the predictive value of this observation in LADC patients receiving immunotherapy.
DOI: 10.6084/m9.figshare.8015786.v1
title: Tumor necrosis correlates with PD-L1 and PD-1 expression in lung adenocarcinoma

description: <b>Background:</b> Predictive biomarkers for immunotherapy in lung cancer are intensively investigated; however, correlations between PD-L1/PD-1 expressions and clinical features or histopathological tumor characteristics determined on hematoxylin and eosin stained sections have not extensively been studied. <b>Material and methods:</b> We determined PD-L1 expression of tumor cells (TC) and immune cells (IC), and PD-1 expression of IC by immunohistochemistry in 268 lung adenocarcinoma (LADC) patients, and correlated the data with smoking, COPD, tumor grade, necrosis, lepidic growth pattern, vascular invasion, density of stromal IC, and <i>EGFR/KRAS</i> status of the tumors. <b>Results:</b> There was a positive correlation between PD-L1 expression of TC and IC, as well as PD-L1 and PD-1 expression of IC. Tumor necrosis was associated with higher PD-L1 expression of TC and PD-1 expression of IC. A negative correlation was observed between lepidic growth pattern and PD-L1 expression of TC and PD-L1/PD-1 expression of IC. <i>EGFR</i> mutation seemed to negatively correlate with PD-1 expression of IC, but this tendency could not be verified when applying corrections for multiple comparisons. No significant effect of the <i>KRAS</i> mutation on any of the studied variables could be established. <b>Conclusion:</b> Here we first demonstrate that the presence of necrosis correlates with higher PD-L1 expression of TC and PD-1 expression of IC in LADC. Further studies are required to determine the predictive value of this observation in LADC patients receiving immunotherapy.
DOI: 10.6084/m9.figshare.8015726
title: Geographic cline and climatic effects on cone characteristics of natural populations of <i>Pinus densiflora</i> throughout the Japanese archipelago

description: Major conifer species with a widespread geographic distribution are growing under diverse climatic conditions. For such species, populations in regions with low resource availability, where producing additional cones is costly, presumably have more individuals that produce larger, highly viable cones with a higher resource-use efficiency. Geographic variations in cone characteristics (cone and seed size, seed production and productivity per cone) were examined for 28 <i>Pinus densiflora</i> natural populations (628 trees) across the species’ distribution in Japan. The degree of phenotypic differentiation across populations (<i>P</i><sub>ST</sub>) varied among the ten characteristics investigated. The average <i>P</i><sub>ST</sub> value (0.139) was higher than the previously reported neutral genetic variation of the species (<i>F</i><sub>ST</sub> = 0.013). The more northeastern populations produced cones of larger size (in length, width and mass), had higher seed production (number of filled seeds, seed mass per cone) and had higher seed productivity (indicators for seed production per cone development cost). A generalized linear mixed model showed that geographic variation in cone size and seed productivity per cone of populations was correlated negatively with annual solar radiation and mean temperature. The larger cone size and higher seed productivity per cone observed in northeastern populations support the hypothesis of higher resource-use efficiency, and could result from a genetic or plastic response under lower temperature and less radiation.
DOI: 10.6084/m9.figshare.8015723
title: FraGen-GUI

description: FraGen-GUI is a graphical user interface program for FraGen, which is a real-space structure determination program for inorganic extended frameworks developed by Li et al (J. Appl. Cryst. 2012, 45, 855; DOI: 10.1107/s002188981201878x). FraGen-GUI provides an input panel and an output panel to help users prepare input files for FraGen and interpret output files generated by FraGen. On the input panel, there are four tabs, each of which contains different types of keywords or parameters for FraGen. Users can input their instructions and save them as a text file. The output panel consists of the main display window, the toolbar, the structure list, the tool boxes and the status window. The structure models generated by FraGen are displayed automatically in main display window. Users can perform a series of operations on these models. More importantly, FraGen-GUI can be used to remove duplicated models generated by FraGen, to add bridging atoms missing in FraGen simulations, and to simulate XRD patterns for comparison with experimental results. With the aid of FraGen-GUI, the efficiency of real-space structure determination via program FraGen will be significantly enhanced.<br>FraGen-GUI is a 64-bit Windows application developed with Python 3.6, Matplotlib 2.1.0 plotting library, and VTK 7.1.1 visualization toolkit. It has been tested on 64-bit Windows 7 and 10.
DOI: 10.6084/m9.figshare.8015726.v1
title: Geographic cline and climatic effects on cone characteristics of natural populations of <i>Pinus densiflora</i> throughout the Japanese archipelago

description: Major conifer species with a widespread geographic distribution are growing under diverse climatic conditions. For such species, populations in regions with low resource availability, where producing additional cones is costly, presumably have more individuals that produce larger, highly viable cones with a higher resource-use efficiency. Geographic variations in cone characteristics (cone and seed size, seed production and productivity per cone) were examined for 28 <i>Pinus densiflora</i> natural populations (628 trees) across the species’ distribution in Japan. The degree of phenotypic differentiation across populations (<i>P</i><sub>ST</sub>) varied among the ten characteristics investigated. The average <i>P</i><sub>ST</sub> value (0.139) was higher than the previously reported neutral genetic variation of the species (<i>F</i><sub>ST</sub> = 0.013). The more northeastern populations produced cones of larger size (in length, width and mass), had higher seed production (number of filled seeds, seed mass per cone) and had higher seed productivity (indicators for seed production per cone development cost). A generalized linear mixed model showed that geographic variation in cone size and seed productivity per cone of populations was correlated negatively with annual solar radiation and mean temperature. The larger cone size and higher seed productivity per cone observed in northeastern populations support the hypothesis of higher resource-use efficiency, and could result from a genetic or plastic response under lower temperature and less radiation.
DOI: 10.6084/m9.figshare.8015723.v1
title: FraGen-GUI

description: FraGen-GUI is a graphical user interface program for FraGen, which is a real-space structure determination program for inorganic extended frameworks developed by Li et al (J. Appl. Cryst. 2012, 45, 855; DOI: 10.1107/s002188981201878x). FraGen-GUI provides an input panel and an output panel to help users prepare input files for FraGen and interpret output files generated by FraGen. On the input panel, there are four tabs, each of which contains different types of keywords or parameters for FraGen. Users can input their instructions and save them as a text file. The output panel consists of the main display window, the toolbar, the structure list, the tool boxes and the status window. The structure models generated by FraGen are displayed automatically in main display window. Users can perform a series of operations on these models. More importantly, FraGen-GUI can be used to remove duplicated models generated by FraGen, to add bridging atoms missing in FraGen simulations, and to simulate XRD patterns for comparison with experimental results. With the aid of FraGen-GUI, the efficiency of real-space structure determination via program FraGen will be significantly enhanced.<br>FraGen-GUI is a 64-bit Windows application developed with Python 3.6, Matplotlib 2.1.0 plotting library, and VTK 7.1.1 visualization toolkit. It has been tested on 64-bit Windows 7 and 10.
DOI: 10.6084/m9.figshare.8015723.v2
title: FraGen-GUI

description: FraGen-GUI is a graphical user interface program for FraGen, which is a real-space structure determination program for inorganic extended frameworks developed by Li et al (J. Appl. Cryst. 2012, 45, 855; DOI: 10.1107/s002188981201878x). FraGen-GUI provides an input panel and an output panel to help users prepare input files for FraGen and interpret output files generated by FraGen. On the input panel, there are four tabs, each of which contains different types of keywords or parameters for FraGen. Users can input their instructions and save them as a text file. The output panel consists of the main display window, the toolbar, the structure list, the tool boxes and the status window. The structure models generated by FraGen are displayed automatically in main display window. Users can perform a series of operations on these models. More importantly, FraGen-GUI can be used to remove duplicated models generated by FraGen, to add bridging atoms missing in FraGen simulations, and to simulate XRD patterns for comparison with experimental results. With the aid of FraGen-GUI, the efficiency of real-space structure determination via program FraGen will be significantly enhanced.<br>FraGen-GUI is a 64-bit Windows application developed with Python 3.6, Matplotlib 2.1.0 plotting library, and VTK 7.1.1 visualization toolkit. It has been tested on 64-bit Windows 7 and 10.
DOI: 10.6084/m9.figshare.8015639
title: CCDB_3993.jpg

description: Wide field brain mosaic of a coronal section of brain from a transgenic mouse engineered to overexpress human alpha synuclein immunolabeled for alpha synuclein (Green) and counterstained with Hoescht 33342 (red) to reveal cell nuclei. The Hoescht staining introduced some non-specific background (red haze).
DOI: 10.6084/m9.figshare.8015639.v1
title: CCDB_3993.jpg

description: Wide field brain mosaic of a coronal section of brain from a transgenic mouse engineered to overexpress human alpha synuclein immunolabeled for alpha synuclein (Green) and counterstained with Hoescht 33342 (red) to reveal cell nuclei. The Hoescht staining introduced some non-specific background (red haze).
DOI: 10.6084/m9.figshare.8015723.v3
title: FraGen-GUI

description: FraGen-GUI is a graphical user interface program for FraGen, which is a real-space structure determination program for inorganic extended frameworks developed by Li et al (J. Appl. Cryst. 2012, 45, 855; DOI: 10.1107/s002188981201878x). FraGen-GUI provides an input panel and an output panel to help users prepare input files for FraGen and interpret output files generated by FraGen. On the input panel, there are four tabs, each of which contains different types of keywords or parameters for FraGen. Users can input their instructions and save them as a text file. The output panel consists of the main display window, the toolbar, the structure list, the tool boxes and the status window. The structure models generated by FraGen are displayed automatically in main display window. Users can perform a series of operations on these models. More importantly, FraGen-GUI can be used to remove duplicated models generated by FraGen, to add bridging atoms missing in FraGen simulations, and to simulate XRD patterns for comparison with experimental results. With the aid of FraGen-GUI, the efficiency of real-space structure determination via program FraGen will be significantly enhanced.<br>FraGen-GUI is a 64-bit Windows application developed with Python 3.6, Matplotlib 2.1.0 plotting library, and VTK 7.1.1 visualization toolkit. It has been tested on 64-bit Windows 7 and 10.
DOI: 10.6084/m9.figshare.8015723.v4
title: FraGen-GUI

description: FraGen-GUI is a graphical user interface program for FraGen, which is a real-space structure determination program for inorganic extended frameworks developed by Li et al (J. Appl. Cryst. 2012, 45, 855; DOI: 10.1107/s002188981201878x). FraGen-GUI provides an input panel and an output panel to help users prepare input files for FraGen and interpret output files generated by FraGen. On the input panel, there are four tabs, each of which contains different types of keywords or parameters for FraGen. Users can input their instructions and save them as a text file. The output panel consists of the main display window, the toolbar, the structure list, the tool boxes and the status window. The structure models generated by FraGen are displayed automatically in main display window. Users can perform a series of operations on these models. More importantly, FraGen-GUI can be used to remove duplicated models generated by FraGen, to add bridging atoms missing in FraGen simulations, and to simulate XRD patterns for comparison with experimental results. With the aid of FraGen-GUI, the efficiency of real-space structure determination via program FraGen will be significantly enhanced.<br>FraGen-GUI is a 64-bit Windows application developed with Python 3.6, Matplotlib 2.1.0 plotting library, and VTK 7.1.1 visualization toolkit. It has been tested on 64-bit Windows 7 and 10.
DOI: 10.6084/m9.figshare.8015723.v6
title: FraGen-GUI

description: FraGen-GUI is a graphical user interface program for FraGen, which is a real-space structure determination program for inorganic extended frameworks developed by Li et al (J. Appl. Cryst. 2012, 45, 855; DOI: 10.1107/s002188981201878x). FraGen-GUI provides an input panel and an output panel to help users prepare input files for FraGen and interpret output files generated by FraGen. On the input panel, there are four tabs, each of which contains different types of keywords or parameters for FraGen. Users can input their instructions and save them as a text file. The output panel consists of the main display window, the toolbar, the structure list, the tool boxes and the status window. The structure models generated by FraGen are displayed automatically in main display window. Users can perform a series of operations on these models. More importantly, FraGen-GUI can be used to remove duplicated models generated by FraGen, to add bridging atoms missing in FraGen simulations, and to simulate XRD patterns for comparison with experimental results. With the aid of FraGen-GUI, the efficiency of real-space structure determination via program FraGen will be significantly enhanced.<br>FraGen-GUI is a 64-bit Windows application developed with Python 3.6, Matplotlib 2.1.0 plotting library, and VTK 7.1.1 visualization toolkit. It has been tested on 64-bit Windows 7 and 10.
DOI: 10.6084/m9.figshare.8015723.v5
title: FraGen-GUI

description: FraGen-GUI is a graphical user interface program for FraGen, which is a real-space structure determination program for inorganic extended frameworks developed by Li et al (J. Appl. Cryst. 2012, 45, 855; DOI: 10.1107/s002188981201878x). FraGen-GUI provides an input panel and an output panel to help users prepare input files for FraGen and interpret output files generated by FraGen. On the input panel, there are four tabs, each of which contains different types of keywords or parameters for FraGen. Users can input their instructions and save them as a text file. The output panel consists of the main display window, the toolbar, the structure list, the tool boxes and the status window. The structure models generated by FraGen are displayed automatically in main display window. Users can perform a series of operations on these models. More importantly, FraGen-GUI can be used to remove duplicated models generated by FraGen, to add bridging atoms missing in FraGen simulations, and to simulate XRD patterns for comparison with experimental results. With the aid of FraGen-GUI, the efficiency of real-space structure determination via program FraGen will be significantly enhanced.<br>FraGen-GUI is a 64-bit Windows application developed with Python 3.6, Matplotlib 2.1.0 plotting library, and VTK 7.1.1 visualization toolkit. It has been tested on 64-bit Windows 7 and 10.
DOI: 10.6084/m9.figshare.8015489
title: Uptake of adenine by purine permeases of <i>Coffea canephora</i>

description: Purine permeases (PUPs) mediate the proton-coupled uptake of nucleotide bases and their derivatives into cytosol. PUPs facilitate uptake of adenine, cytokinins and nicotine. Caffeine, a purine alkaloid derived from xanthosine, occurs in only a few eudicot species, including coffee, cacao, and tea. Although caffeine is not an endogenous metabolite in Arabidopsis and rice, AtPUP1 and OsPUP7 were suggested to transport caffeine. In this study, we identified 15 PUPs in the genome of <i>Coffea canephora</i>. Direct uptake measurements in yeast demonstrated that CcPUP1 and CcPUP5 facilitate adenine – but not caffeine – transport. Adenine uptake was pH-dependent, with increased activity at pH 3 and 4, and inhibited by nigericin, a potassium–proton ionophore, suggesting that CcPUP1 and CcPUP5 function as proton-symporters. Furthermore, adenine uptake was not competitively inhibited by an excess amount of caffeine, which implies that PUPs of <i>C. canephora</i> have evolved to become caffeine-insensitive to promote efficient uptake of adenine into cytosol. CcPUP1 & CcPUP5 distinguish adenine from caffeine. The adenine uptake activity of AtPUP1 is inhibited by caffeine.