Word2Vec embedding model trained on Czech legislation (from April 2020) corpus using gensim implementation with the following parameters in addition to default settings: vector dimension = \(400\), window size = \(10\), word minimum count = \(10\), sample = \(10^{-5}\). This work was supported by the Czech Science Foundation (GAČR),grant number 19-01641S.
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Additional file 3. Data S3 Detailed information of selected variants from discovery phase.
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Supplementary Material 23
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Thesis reference: STEINER, Ludvík. DOI Assignment Practice of Czech Scientific Journal Publishers [online]. Praha, 2021[cit. 2021-05-07]. Bachelor thesis. Charles University. Faculty of Arts. Institute of Information Studies and Librarianship. This dataset contains data about scholarly journals published in the Czech Republic and about DOIs, which these journals assign. It was used to explore common patterns of DOIs and for assessment of quality of the associated metadata. It also contains data about editiorial systems of the journals and the DOI registration workflow. The thesis and this dataset are in Czech language only.
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Collection simulations of small pure bilayers (max 128 phospholipids) with cholesterol in gromacs using the charmm36 force field. The list of systems describing their particular simulation conditions can be found below: DPPC_128_CHL1_32_310K For further information read the Readme file provided for each simulation.
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Abstract Background Programmed cell death 1 (PD-1) belongs to immune checkpoint proteins ensuring negative regulation of the immune response. In non-small cell lung cancer (NSCLC), the sensitivity to treatment with anti-PD-1 therapeutics, and its efficacy, mostly correlated with the increase of tumor infiltrating PD-1+ lymphocytes. Due to solid tumor heterogeneity of PD-1+ populations, novel low molecular weight anti-PD-1 high-affinity diagnostic probes can increase the reliability of expression profiling of PD-1+ tumor infiltrating lymphocytes (TILs) in tumor tissue biopsies and in vivo mapping efficiency using immune-PET imaging. Methods We designed a 13 kDa β-sheet Myomedin scaffold combinatorial library by randomization of 12 mutable residues, and in combination with ribosome display, we identified anti-PD-1 Myomedin variants (MBA ligands) that specifically bound to human and murine PD-1-transfected HEK293T cells and human SUP-T1 cells spontaneously overexpressing cell surface PD-1. Results Binding affinity to cell-surface expressed human and murine PD-1 on transfected HEK293T cells was measured by fluorescence with LigandTracer and resulted in the selection of most promising variants MBA066 (hPD-1 KD = 6.9 nM; mPD-1 KD = 40.5 nM), MBA197 (hPD-1 KD = 29.7 nM; mPD-1 KD = 21.4 nM) and MBA414 (hPD-1 KD = 8.6 nM; mPD-1 KD = 2.4 nM). The potential of MBA proteins for imaging of PD-1+ populations in vivo was demonstrated using deferoxamine-conjugated MBA labeled with 68Galium isotope. Radiochemical purity of 68Ga-MBA proteins reached values 94.7–99.3% and in vitro stability in human serum after 120 min was in the range 94.6–98.2%. The distribution of 68Ga-MBA proteins in mice was monitored using whole-body positron emission tomography combined with computerized tomography (PET/CT) imaging up to 90 min post-injection and post mortem examined in 12 mouse organs. The specificity of MBA proteins was proven by co-staining frozen sections of human tonsils and NSCLC tissue biopsies with anti-PD-1 antibody, and demonstrated their potential for mapping PD-1+ populations in solid tumors. Conclusions Using directed evolution, we developed a unique set of small binding proteins that can improve PD-1 diagnostics in vitro as well as in vivo using PET/CT imaging. Graphical Abstract
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doi: 10.5061/dryad.tg0mt
The role of chromosome changes in speciation remains a debated topic, although demographic conditions associated with divergence should promote their appearance. We tested a potential relationship between chromosome changes and speciation by studying two Lake Whitefish (Coregonus clupeaformis) lineages that recently colonized postglacial lakes following allopatry. A dwarf limnetic species evolved repeatedly from the normal benthic species, becoming reproductively isolated. Lake Whitefish hybrids experience mitotic and meiotic instability, which may result from structurally divergent chromosomes. Motivated by this observation, we test the hypothesis that chromosome organization differs between Lake Whitefish species pairs using cytogenetics. While chromosome and fundamental numbers are conserved between the species (2n = 80, NF = 98), we observe extensive polymorphism of subtle karyotype traits. We describe intrachromosomal differences associated with heterochromatin and repetitive DNA, and test for parallelism among three sympatric species pairs. Multivariate analyses support the hypothesis that differentiation at the level of subchromosomal markers mostly appeared during allopatry. Yet we find no evidence for parallelism between species pairs among lakes, consistent with colonization effect or postcolonization differentiation. The reported intrachromosomal polymorphisms do not appear to play a central role in driving adaptive divergence between normal and dwarf Lake Whitefish. We discuss how chromosomal differentiation in the Lake Whitefish system may contribute to the destabilization of mitotic and meiotic chromosome segregation in hybrids, as documented previously. The chromosome structures detected here are still difficult to sequence and assemble, demonstrating the value of cytogenetics as a complementary approach to understand the genomic bases of speciation. Macrogen_sequence_filesFile produced during the sequencing of the PCR products used for FISH of 5S and 28S rDNA. File names contain "5S" or "28S" depending on what product they refer to.C-BandCMA3GiemsaFISH_rDNAFISH_rDNA
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The BeechCOSTe52 includes phenotypic trait measurements from individual trees measured in an international network of provenance tests compiled by the COST Action E52 (2006 – 2010). It comprises 39 trial sites and 217 provenances covering the distribution of European beech (Fagus sylvatica L.). The BeechCOSTe52 database provides individual tree phenotypic measurements of height, diameter at breast height, basal diameter, mortality, spring and autumn leaf phenology. The following files are included: Fsylvatica.csv: individual tree measurements by trial and provenance of origin. Trials_coords.csv: geographical coordinates of the trials Prov_coords.cvs: geographical coordinates of the provenances BeechCOSTe52_metadata_descriptor.docx: description of the fields of Fsylvatica.csv, Trials_coords.csv and Prov_coords.cvs databases. Subset_by_trial.R: R script to subset the original database (Fsylvatica.csv) by trial Merge_trial_prov-coords_individualmeasurements.R: R script to merge the coordinates of trials and provenances (Trials_coords.csv and Prov_coords.cvs) with the phenotypic measurements (Fsylvatica.csv). Plot_Trial-Prov_locations.R: R script to map trial and provenance locations. Hplot.R: R script to map tree height variation across provenances and trials. Fagus_sylvatica_EUFORGEN.X: Shape files containing the distribution of Fagus sylvatica from EUFORGEN (http://www.euforgen.org)
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We estimated survival and per capita production of young, as well as emigration and immigration, from 1997 to 2017 in Ross's goose Anser rossii and lesser snow goose Anser caerulescens caerulescens, which are sympatric species of migratory birds that nest in the central Canadian Arctic at one of the largest breeding colonies in North America. We formed age-structured integrated population models for each species that jointly analyzed live and dead encounter data as well as breeding adult population size and fecundity data to understand drivers of population dynamics. We compared the demography between species because both species increased during the 1990s and early 2000s yet thereafter snow geese declined, while Ross's geese continued to increase, then stabilized and similarly declined. Declines in Ross's and snow goose populations were caused by reduced per capita production of young, and juvenile survival, as well as increased adult and juvenile emigration. Stronger declines in juvenile survival in snow geese explain their earlier population decline compared to Ross's geese. Despite the divergence in population trends in Ross's and snow geese, we found strong synchrony in demographic rates which suggested substantial emigration from this colony and similar responses to environmental conditions. We provide a novel m-array implementation specific to a multi-state Burnham model which greatly improved computational efficiency and convergence of posterior estimates. Please refer to ReadMe file.
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Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Because of the shortage of adequate expertise, there has long been an interest in developing automated systems for this task. Previous attempts have been based on laborious and complex handcrafted extraction of image features, but in recent years it has been shown that sophisticated convolutional neural networks (CNNs) can learn to extract relevant features automatically, without human intervention. Unfortunately, reaching expert-level accuracy in CNN identifications requires substantial computational power and huge training datasets, which are often not available for taxonomic tasks. This can be addressed using feature transfer: a CNN that has been pretrained on a generic image classification task is exposed to the taxonomic images of interest, and information about its perception of those images is used in training a simpler, dedicated identification system. Here, we develop an effective method of CNN feature transfer, which achieves expert-level accuracy in taxonomic identification of insects with training sets of 100 images or less per category. Specifically, we extract rich representations of intermediate to high-level image features from the CNN architecture VGG16 pretrained on the ImageNet dataset. This information is fed into a linear support vector machine classifier, which is trained on the target problem. We tested the performance of our approach on two types of challenging taxonomic tasks: (1) identifying insects to higher groups when they are likely to belong to subgroups that have not been seen previously; and (2) identifying visually similar species that are difficult to separate even for experts. For the first task, our approach reaches > 92 % accuracy on one dataset (884 face images of 11 families of Diptera, all specimens representing unique species), and > 96 % accuracy on another (2936 dorsal habitus images of 14 families of Coleoptera, over 90 % of specimens belonging to unique species). For the second task, our approach outperforms a leading taxonomic expert on one dataset (339 images of three species of the Coleoptera genus Oxythyrea; 97 % accuracy), and both humans and traditional automated identification systems on another dataset (3845 images of nine species of Plecoptera larvae; 98.6 % accuracy). Reanalyzing several biological image identification tasks studied in the recent literature, we show that our approach is broadly applicable and provides significant improvements over previous methods, whether based on dedicated CNNs, CNN feature transfer, or more traditional techniques. Thus, our method, which is easy to apply, can be highly successful in developing automated taxonomic identification systems even when training datasets are small and computational budgets limited. valan2018_SUPPLEMENT.tarThis directory contains: * metadata to obtain images from the three novel datasets we designed for our study * notebooks with thorough evaluation of off-the-shelf approach for image classification based on a feature extraction with a single feed forward pass trough pretrained VGG16 * script to run on your own dataset with what we found to be optimal settings. You can also access it here https://github.com/valanm/off-the-shelf-insect-identificationSupplementary_Figure1Impact of concatenating globally max pooled (MAX) and globally average pooled (AVG) features on identification accuracy for datasets D1, D2, D3 and D4. We used input images of size 416x416 and features are extracted after 4th convolutional block (c4). Concatenation of MAX and AVG features resulted in accuracy somewhere between the global average pooling (performed the best in all cases) and global max pooling (performed the worst in all cases).valan2018_latex
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Word2Vec embedding model trained on Czech legislation (from April 2020) corpus using gensim implementation with the following parameters in addition to default settings: vector dimension = \(400\), window size = \(10\), word minimum count = \(10\), sample = \(10^{-5}\). This work was supported by the Czech Science Foundation (GAČR),grant number 19-01641S.
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Additional file 3. Data S3 Detailed information of selected variants from discovery phase.
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Supplementary Material 23
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Thesis reference: STEINER, Ludvík. DOI Assignment Practice of Czech Scientific Journal Publishers [online]. Praha, 2021[cit. 2021-05-07]. Bachelor thesis. Charles University. Faculty of Arts. Institute of Information Studies and Librarianship. This dataset contains data about scholarly journals published in the Czech Republic and about DOIs, which these journals assign. It was used to explore common patterns of DOIs and for assessment of quality of the associated metadata. It also contains data about editiorial systems of the journals and the DOI registration workflow. The thesis and this dataset are in Czech language only.
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Collection simulations of small pure bilayers (max 128 phospholipids) with cholesterol in gromacs using the charmm36 force field. The list of systems describing their particular simulation conditions can be found below: DPPC_128_CHL1_32_310K For further information read the Readme file provided for each simulation.
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Abstract Background Programmed cell death 1 (PD-1) belongs to immune checkpoint proteins ensuring negative regulation of the immune response. In non-small cell lung cancer (NSCLC), the sensitivity to treatment with anti-PD-1 therapeutics, and its efficacy, mostly correlated with the increase of tumor infiltrating PD-1+ lymphocytes. Due to solid tumor heterogeneity of PD-1+ populations, novel low molecular weight anti-PD-1 high-affinity diagnostic probes can increase the reliability of expression profiling of PD-1+ tumor infiltrating lymphocytes (TILs) in tumor tissue biopsies and in vivo mapping efficiency using immune-PET imaging. Methods We designed a 13 kDa β-sheet Myomedin scaffold combinatorial library by randomization of 12 mutable residues, and in combination with ribosome display, we identified anti-PD-1 Myomedin variants (MBA ligands) that specifically bound to human and murine PD-1-transfected HEK293T cells and human SUP-T1 cells spontaneously overexpressing cell surface PD-1. Results Binding affinity to cell-surface expressed human and murine PD-1 on transfected HEK293T cells was measured by fluorescence with LigandTracer and resulted in the selection of most promising variants MBA066 (hPD-1 KD = 6.9 nM; mPD-1 KD = 40.5 nM), MBA197 (hPD-1 KD = 29.7 nM; mPD-1 KD = 21.4 nM) and MBA414 (hPD-1 KD = 8.6 nM; mPD-1 KD = 2.4 nM). The potential of MBA proteins for imaging of PD-1+ populations in vivo was demonstrated using deferoxamine-conjugated MBA labeled with 68Galium isotope. Radiochemical purity of 68Ga-MBA proteins reached values 94.7–99.3% and in vitro stability in human serum after 120 min was in the range 94.6–98.2%. The distribution of 68Ga-MBA proteins in mice was monitored using whole-body positron emission tomography combined with computerized tomography (PET/CT) imaging up to 90 min post-injection and post mortem examined in 12 mouse organs. The specificity of MBA proteins was proven by co-staining frozen sections of human tonsils and NSCLC tissue biopsies with anti-PD-1 antibody, and demonstrated their potential for mapping PD-1+ populations in solid tumors. Conclusions Using directed evolution, we developed a unique set of small binding proteins that can improve PD-1 diagnostics in vitro as well as in vivo using PET/CT imaging. Graphical Abstract
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doi: 10.5061/dryad.tg0mt
The role of chromosome changes in speciation remains a debated topic, although demographic conditions associated with divergence should promote their appearance. We tested a potential relationship between chromosome changes and speciation by studying two Lake Whitefish (Coregonus clupeaformis) lineages that recently colonized postglacial lakes following allopatry. A dwarf limnetic species evolved repeatedly from the normal benthic species, becoming reproductively isolated. Lake Whitefish hybrids experience mitotic and meiotic instability, which may result from structurally divergent chromosomes. Motivated by this observation, we test the hypothesis that chromosome organization differs between Lake Whitefish species pairs using cytogenetics. While chromosome and fundamental numbers are conserved between the species (2n = 80, NF = 98), we observe extensive polymorphism of subtle karyotype traits. We describe intrachromosomal differences associated with heterochromatin and repetitive DNA, and test for parallelism among three sympatric species pairs. Multivariate analyses support the hypothesis that differentiation at the level of subchromosomal markers mostly appeared during allopatry. Yet we find no evidence for parallelism between species pairs among lakes, consistent with colonization effect or postcolonization differentiation. The reported intrachromosomal polymorphisms do not appear to play a central role in driving adaptive divergence between normal and dwarf Lake Whitefish. We discuss how chromosomal differentiation in the Lake Whitefish system may contribute to the destabilization of mitotic and meiotic chromosome segregation in hybrids, as documented previously. The chromosome structures detected here are still difficult to sequence and assemble, demonstrating the value of cytogenetics as a complementary approach to understand the genomic bases of speciation. Macrogen_sequence_filesFile produced during the sequencing of the PCR products used for FISH of 5S and 28S rDNA. File names contain "5S" or "28S" depending on what product they refer to.C-BandCMA3GiemsaFISH_rDNAFISH_rDNA
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The BeechCOSTe52 includes phenotypic trait measurements from individual trees measured in an international network of provenance tests compiled by the COST Action E52 (2006 – 2010). It comprises 39 trial sites and 217 provenances covering the distribution of European beech (Fagus sylvatica L.). The BeechCOSTe52 database provides individual tree phenotypic measurements of height, diameter at breast height, basal diameter, mortality, spring and autumn leaf phenology. The following files are included: Fsylvatica.csv: individual tree measurements by trial and provenance of origin. Trials_coords.csv: geographical coordinates of the trials Prov_coords.cvs: geographical coordinates of the provenances BeechCOSTe52_metadata_descriptor.docx: description of the fields of Fsylvatica.csv, Trials_coords.csv and Prov_coords.cvs databases. Subset_by_trial.R: R script to subset the original database (Fsylvatica.csv) by trial Merge_trial_prov-coords_individualmeasurements.R: R script to merge the coordinates of trials and provenances (Trials_coords.csv and Prov_coords.cvs) with the phenotypic measurements (Fsylvatica.csv). Plot_Trial-Prov_locations.R: R script to map trial and provenance locations. Hplot.R: R script to map tree height variation across provenances and trials. Fagus_sylvatica_EUFORGEN.X: Shape files containing the distribution of Fagus sylvatica from EUFORGEN (http://www.euforgen.org)
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We estimated survival and per capita production of young, as well as emigration and immigration, from 1997 to 2017 in Ross's goose Anser rossii and lesser snow goose Anser caerulescens caerulescens, which are sympatric species of migratory birds that nest in the central Canadian Arctic at one of the largest breeding colonies in North America. We formed age-structured integrated population models for each species that jointly analyzed live and dead encounter data as well as breeding adult population size and fecundity data to understand drivers of population dynamics. We compared the demography between species because both species increased during the 1990s and early 2000s yet thereafter snow geese declined, while Ross's geese continued to increase, then stabilized and similarly declined. Declines in Ross's and snow goose populations were caused by reduced per capita production of young, and juvenile survival, as well as increased adult and juvenile emigration. Stronger declines in juvenile survival in snow geese explain their earlier population decline compared to Ross's geese. Despite the divergence in population trends in Ross's and snow geese, we found strong synchrony in demographic rates which suggested substantial emigration from this colony and similar responses to environmental conditions. We provide a novel m-array implementation specific to a multi-state Burnham model which greatly improved computational efficiency and convergence of posterior estimates. Please refer to ReadMe file.
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Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Because of the shortage of adequate expertise, there has long been an interest in developing automated systems for this task. Previous attempts have been based on laborious and complex handcrafted extraction of image features, but in recent years it has been shown that sophisticated convolutional neural networks (CNNs) can learn to extract relevant features automatically, without human intervention. Unfortunately, reaching expert-level accuracy in CNN identifications requires substantial computational power and huge training datasets, which are often not available for taxonomic tasks. This can be addressed using feature transfer: a CNN that has been pretrained on a generic image classification task is exposed to the taxonomic images of interest, and information about its perception of those images is used in training a simpler, dedicated identification system. Here, we develop an effective method of CNN feature transfer, which achieves expert-level accuracy in taxonomic identification of insects with training sets of 100 images or less per category. Specifically, we extract rich representations of intermediate to high-level image features from the CNN architecture VGG16 pretrained on the ImageNet dataset. This information is fed into a linear support vector machine classifier, which is trained on the target problem. We tested the performance of our approach on two types of challenging taxonomic tasks: (1) identifying insects to higher groups when they are likely to belong to subgroups that have not been seen previously; and (2) identifying visually similar species that are difficult to separate even for experts. For the first task, our approach reaches > 92 % accuracy on one dataset (884 face images of 11 families of Diptera, all specimens representing unique species), and > 96 % accuracy on another (2936 dorsal habitus images of 14 families of Coleoptera, over 90 % of specimens belonging to unique species). For the second task, our approach outperforms a leading taxonomic expert on one dataset (339 images of three species of the Coleoptera genus Oxythyrea; 97 % accuracy), and both humans and traditional automated identification systems on another dataset (3845 images of nine species of Plecoptera larvae; 98.6 % accuracy). Reanalyzing several biological image identification tasks studied in the recent literature, we show that our approach is broadly applicable and provides significant improvements over previous methods, whether based on dedicated CNNs, CNN feature transfer, or more traditional techniques. Thus, our method, which is easy to apply, can be highly successful in developing automated taxonomic identification systems even when training datasets are small and computational budgets limited. valan2018_SUPPLEMENT.tarThis directory contains: * metadata to obtain images from the three novel datasets we designed for our study * notebooks with thorough evaluation of off-the-shelf approach for image classification based on a feature extraction with a single feed forward pass trough pretrained VGG16 * script to run on your own dataset with what we found to be optimal settings. You can also access it here https://github.com/valanm/off-the-shelf-insect-identificationSupplementary_Figure1Impact of concatenating globally max pooled (MAX) and globally average pooled (AVG) features on identification accuracy for datasets D1, D2, D3 and D4. We used input images of size 416x416 and features are extracted after 4th convolutional block (c4). Concatenation of MAX and AVG features resulted in accuracy somewhere between the global average pooling (performed the best in all cases) and global max pooling (performed the worst in all cases).valan2018_latex
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views | 37 | |
downloads | 9 |
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