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opencc-zeroDec 2015View details →

Data from: An evaluation of transcriptome-based exon capture for frog phylogenomics across multiple scales of divergence (Class: Amphibia, Order: Anura)

Custom sequence capture experiments are becoming an efficient approach for gathering large sets of orthologous markers in nonmodel organisms. Transcriptome-based exon capture utilizes transcript sequences to design capture probes, typically using a reference genome to identify intron–exon boundaries to exclude shorter exons (<200 bp). Here, we test directly using transcript sequences for probe design, which are often composed of multiple exons of varying lengths. Using 1260 orthologous transcripts, we conducted sequence captures across multiple phylogenetic scales for frogs, including outgroups ~100 Myr divergent from the ingroup. We recovered a large phylogenomic data set consisting of sequence alignments for 1047 of the 1260 transcriptome-based loci (~561 000 bp) and a large quantity of highly variable regions flanking the exons in transcripts (~70 000 bp), the latter improving substantially by only including ingroup species (~797 000 bp). We recovered both shorter (<100 bp) and longer exons (>200 bp), with no major reduction in coverage towards the ends of exons. We observed significant differences in the performance of blocking oligos for target enrichment and nontarget depletion during captures, and differences in PCR duplication rates resulting from the number of individuals pooled for capture reactions. We explicitly tested the effects of phylogenetic distance on capture sensitivity, specificity, and missing data, and provide a baseline estimate of expectations for these metrics based on a priori knowledge of nuclear pairwise differences among samples. We provide recommendations for transcriptome-based exon capture design based on our results, cost estimates and offer multiple pipelines for data assembly and analysis.

opencc-zeroDec 2014View details →

Data from: Assessing the evolutionary history of the class Synurophyceae (Heterokonta) using molecular, morphometric, and paleobiological approaches

Premise of the study: Heterokont algae of the class Synurophyceae, characterized by distinctive siliceous scales that cover the surface of the cell, are ecologically important in inland waters, yet their evolutionary history remains enigmatic. We explore phylogenetic relationships within this group of algae relative to geologic time, with a focus on evolution of siliceous components. Methods: We combined an expansive five-gene and time-calibrated molecular phylogeny of synurophyte algae with an extensive array of fossil specimens from the middle Eocene to infer evolutionary trends within the group. Key results: The group originated in the Jurassic approximately 157 million years ago (Ma), with the keystone genera Mallomonas and Synura diverging during the Early Cretaceous at 130 Ma. Mallomonas further splits into two major subclades, signaling the evolution of the V-rib believed to aid in the spacing and organization of scales on the cell covering. Synura also diverges into two primary subclades, separating taxa with forward-projecting spines on the scale from those with a keel positioned on the scale proper. Approximately one third of the fossil species are extinct, whereas the remaining taxa are linked to modern congeners. Conclusions: The taxonomy of synurophytes, which relies extensively on the morphology of the siliceous components, is largely congruent with molecular analyses. Scales of extinct synurophytes were significantly larger than those of modern taxa and may have played a role in their demise. In contrast, many fossil species linked to modern lineages were smaller in the middle Eocene, possibly reflecting growth in the greenhouse climatic state that characterized this geologic interval.

opencc-zeroDec 2015View details →

Data from: Lineage tracing of human B cells reveals the in vivo landscape of human antibody class switching

Antibody class switching is a feature of the adaptive immune system which enables diversification of the effector properties of antibodies. Even though class switching is essential for mounting a protective response to pathogens, the in vivo patterns and lineage characteristics of antibody class switching have remained uncharacterized in living humans. Here we comprehensively measured the landscape of antibody class switching in human adult twins using antibody repertoire sequencing. The map identifies how antibodies of every class are created and delineates a two-tiered hierarchy of class switch pathways. Using somatic hypermutations as a molecular clock, we discovered that closely related B cells often switch to the same class, but lose coherence as somatic mutations accumulate. Such correlations between closely related cells exist when purified B cells class switch in vitro, suggesting that class switch recombination is directed toward specific isotypes by a cell-autonomous imprinted state.

opencc-zeroDec 2014View details →

Data from: TAPBPR alters MHC class I peptide presentation by functioning as a peptide exchange catalyst

Our understanding of the antigen presentation pathway has recently been enhanced with the identification that the tapasin-related protein TAPBPR is a second MHC I-specific chaperone. We sought to determine whether, like tapasin, TAPBPR can also influence MHC I peptide selection by functioning as a peptide exchange catalyst. We show that TAPBPR can catalyse the dissociation of peptides from peptide-MHC I complexes, enhance the loading of peptide-receptive MHC I molecules, and discriminate between peptides based on affinity in vitro. In cells, the depletion of TAPBPR increased the diversity of peptides presented on MHC I molecules, suggesting that TAPBPR is involved in restricting peptide presentation. Our results suggest TAPBPR binds to MHC I in a peptide-receptive state and, like tapasin, works to enhance peptide optimisation. It is now clear there are two MHC class I specific peptide editors, tapasin and TAPBPR, intimately involved in controlling peptide presentation to the immune system.

opencc-zeroDec 2016View details →

Data from: TAPBPR bridges UDP-glucose:glycoprotein glucosyltransferase 1 onto MHC class I to provide quality control in the antigen presentation pathway

Recently we revealed that TAPBPR is a peptide exchange catalyst important for optimal peptide selection by MHC class I molecules. Here we asked if any other co-factors associate with TAPBPR which would explain its effect on peptide selection. We identify an interaction between TAPBPR and UDP-glucose:glycoprotein glucosyltransferase 1 (UGT1), a folding sensor in the calnexin/calreticulin quality control cycle known to regenerate the Glc1Man9GlcNAc2 moiety on glycoproteins. Our results suggest the formation of a multimeric complex, dependent on a conserved cysteine at position 94 in TAPBPR, in which TAPBPR promotes the association of UGT1 with peptide-receptive class I molecules. We reveal that the interaction between TAPBPR and UGT1 facilities the reglucosylation of the glycan on class I, promoting their recognition by calreticulin. Our results suggest that in addition to being a peptide-editor, TAPBPR improves peptide optimisation by promoting peptide-receptive MHC class I molecules to associate with the peptide-loading complex.

opencc-zeroDec 2014View details →

Data from: Susceptibility of amphibians to chytridiomycosis is associated with MHC class II conformation

The pathogenic chytrid fungus Batrachochytrium dendrobatidis (Bd) can cause precipitous population declines in its amphibian hosts. Responses of individuals to infection vary greatly with the capacity of their immune system to respond to the pathogen. We used a combination of comparative and experimental approaches to identify major histocompatibility complex class II (MHC-II) alleles encoding molecules that foster the survival of Bd-infected amphibians. We found that Bd-resistant amphibians across four continents share common amino acids in three binding pockets of the MHC-II antigen-binding groove. Moreover, strong signals of selection acting on these specific sites were evident among all species co-existing with the pathogen. In the laboratory, we experimentally inoculated Australian tree frogs with Bd to test how each binding pocket conformation influences disease resistance. Only the conformation of MHC-II pocket 9 of surviving subjects matched those of Bd-resistant species. This MHC-II conformation thus may determine amphibian resistance to Bd, although other MHC-II binding pockets also may contribute to resistance. Rescuing amphibian biodiversity will depend on our understanding of amphibian immune defence mechanisms against Bd. The identification of adaptive genetic markers for Bd resistance represents an important step forward towards that goal.

opencc-zeroDec 2015View details →

Data from: State-space reduction and equivalence class sampling for a molecular self-assembly model

Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving 'target information' from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of a Markov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.

opencc-by-4.0Aug 2021View details →

PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v2.1.0 - September 2021)

PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)Build Type: Class-Inverse Relations-OWLBuild Date: September 01, 2021 <h3>Important Build Information</h3>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>). 🚨 AVAILABLE FILES 🚨 <ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/September-01%2C-2021">here</a>.</li></ul>

opencc-by-4.0Apr 2021View details →

PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWLNETS (v2.1.0 - June 2021)

PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)Build Type: Class-Inverse Relations-OWLNETSBuild Date: June 01, 2021 <h3>Important Build Information</h3>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>). 🚨 AVAILABLE FILES 🚨 <ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/June-01%2C-2021">here</a>.</li></ul>

opencc-by-4.0Apr 2021View details →

PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.1.0 - June 2021)

PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)Build Type: Class-Standard Relations-OWLBuild Date: June 01, 2021 <h3>Important Build Information</h3>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>). 🚨 AVAILABLE FILES 🚨 <ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/June-01%2C-2021">here</a>.</li></ul>

RECOMMENDATIONS FOR TESTING THE WRITING SKILLS OF PRIMARY CLASS STUDENTS

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THE NECESSITY OF PSYCHOLINGUISTICS IN THE DEVELOPMENT OF LANGUAGE SKILLS OF THE PUPILS IN GERMAN LANGUAGE CLASSES

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THE EFFECTIVENESS OF ROLE PLAYING IN CLASSES

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USE OF A FRAME MODEL IN LITERATURE CLASSES IN A HIGHER PEDAGOGICAL EDUCATIONAL INSTITUTION

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USE OF A FRAME MODEL IN LITERATURE CLASSES IN A HIGHER PEDAGOGICAL EDUCATIONAL INSTITUTION

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METHODOLOGY FOR PREPARING PRIMARY CLASS STUDENTS FOR THE PIRLS INTERNATIONAL ASSESSMENT CONTROL

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METHODOLOGICAL ASPECTS OF COMPOSITION ACTIVITY LESSON LESSONS IN PRIMARY CLASSES

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THE ESSENCE OF USING INTERACTIVE METHODS IN ELEMENTARY MATHEMATICS CLASSES

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Cost Analysis Results of the Generation Circuits of the Entanglement Classes of Graph States with up to 7 Qubits

This dataset encompasses a comprehensive analysis of entanglement classes of graph states. We have explored all locally equivalent graphs, along with every isomorphism, for each of the 45 entanglement classes associated with graph states featuring 7 qubits or fewer. The results, detailing various parameters of the quantum circuits generated for all distinct isomorphisms within each graph and local equivalence class, are stored in  CSV files.