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1,944 results for “Class I”
Effect of orthopedic treatment for Class III malocclusion on upper airways: a systematic review and meta-analysis
<p>Data for all analyses run in the review.</p>
Super high frequency events: a new class of events recorded by the InSight seismometers on Mars (derived data)
<p>This repository contains derived data presented and described in the article “<em>Super high frequency events: a new class of events recorded by the InSight seismometers on Mars</em>” by Nikolaj L. Dahmen, John F. Clinton, Savas Ceylan, Martin van Driel, Domenico Giardini, Amir Khan, Simon C. Stähler, Maren Böse, Constantinos Charalambous, Anna Horleston, Taichi Kawamura, Guenolé Orhand-Mainsant, John-Robert Scholz, Fabian Euchner, William T. Pike, Renee C. Weber, Philippe Lognonné and William B. Banerdt, submitted to <em>JGR Planets (2020).</em></p> <p>Information on the provided data is given in the README file</p>
Orthopedic treatment for Class II malocclusion with functional appliances and its effect on upper airways: a systematic review with meta-analysis
<p>Dataset for all analyses in the paper.</p>
SENSEI D4.3 Annex A: Classes of Energy Efficiency Measures
<p>This document contains Appendix A: The devising of classes for Energy Efficiency Measures for Deliverable 4.3 in the SENSEI project.</p>
Data from: Contrasting evolutionary histories of MHC class I and class II loci in grouse - effects of selection and gene conversion
Genes of the major histocompatibility complex (MHC) encode receptor molecules that are responsible for recognition of intra- and extra-cellular pathogens (class I and class II genes, respectively) in vertebrates. Given the different roles of class I and II MHC genes, one might expect the strength of selection to differ between these two classes. Different selective pressures may also promote different rates of gene conversion at each class. Despite these predictions, surprisingly few studies have looked at differences between class I and II genes in terms of both selection and gene conversion. Here, we investigated the molecular evolution of MHC class I and II genes in five closely related species of prairie grouse (Centrocercus and Tympanuchus) that possess one class I and two class II loci. We found striking differences in the strength of balancing selection acting on MHC class I versus class II genes. More than half of the putative antigen-binding sites (ABS) of class II were under positive or episodic diversifying selection, compared with only 10% at class I. We also found that gene conversion played a stronger role in shaping the evolution of MHC class II than class I. Overall, the combination of strong positive (balancing) selection and frequent gene conversion has maintained higher diversity of MHC class II than class I in prairie grouse. This is one of the first studies clearly demonstrating that macroevolutionary mechanisms can act differently on genes involved in the immune response against intra- and extra-cellular pathogens.
Data from: Mixture modeling of transcript abundance classes in natural populations
BACKGROUND: Populations diverge in genotype and phenotype under the influence of such evolutionary processes as genetic drift, mutation accumulation, and natural selection. Because genotype maps onto phenotype by way of transcription, it is of interest to evaluate how these evolutionary factors influence the structure of variation at the level of transcription. Here, we explore the distributions of cis-acting and trans-acting factors and their relative contributions to expression of transcripts that exhibit two or more classes of abundance among individuals within populations. RESULTS: Expression profiling using cDNA microarrays was conducted in Drosophila melanogaster adult female heads for 58 nearly isogenic lines from a North Carolina population and 50 from a California population. Using a mixture modeling approach, transcripts were identified that exhibit more than one mode of transcript abundance across the samples. Power studies indicate that sample sizes of 50 individuals will generally be sufficient to detect divergent transcript abundance classes. The distribution of transcript abundance classes is skewed toward low frequency minor classes, which is reminiscent of the typical skew in genotype frequencies. Similar results are observed in reported data on gene expression in human lymphoblast cell lines, in which analysis of association with linked polymorphisms implies that cis-acting single nucleotide polymorphisms make only a modest contribution to bimodal distributions of transcript abundance. CONCLUSION: Population surveys of gene expression may complement genetical genomics as a general approach to quantifying sources of transcriptional variation. Differential expression of transcripts among individuals is due to a complex interplay of cis-acting and trans-acting factors.
Data from: MHC class I diversity predicts non-random mating in Chinese alligators (Alligator sinensis)
The major histocompatibility complex (MHC) plays numerous important roles in kin recognition, pathogen resistance, and mate selection. Research in fish, birds, and mammals have suggested that individuals optimize MHC diversity, and therefore offspring fitness, when choosing mates. In reptiles, however, it is unclear whether female mate choice is based on genome-wide genetic characteristics such as microsatellites DNA loci, on particular functional trait loci (e.g., MHC) or on both, and MHC effects on mate choice remain relatively under-studied. Herein, we used 13 microsatellite loci and two MHC class I loci to investigate female mate choice of Chinese alligators (Alligator sinensis) in the semi-natural condition. We also determined correlations between the MHC genotype of breeding males and male reproductive success. We found that MHC-heterozygous males harbour a greater reproductive success, which possibly is the reason that those males were more preferred by the females than MHC-homozygous males. Furthermore, MHC class I amino-acid distance and functional distance of true mating pairs were higher compared with that of randomly sampled pairs. Analysis of microsatellites revealed that despite mate choice, individuals did not completely avoid inbreeding. These findings are the first evidence of MHC-associated mate choice in Chinese alligators, and they suggest that females may adopt different mating strategies after assessing MHC characteristics of potential mates.
THE ROLE OF INFORMATION TECHNOLOGY IN TEACHING STUDENTS IN THE CLASSES OF GEOMETRY
Open the record for dataset details and reuse information.
Machine Learning class final project datasets
<p>This is the zip file for the machine learning course final project. it has training and testing data sets.</p>
Simple Steam Engine 2 (GWR 5700 Class)
Back again with another steam engine. This is MUCH better than my least endeavor in steam modelling with the GWR 4300 class. This features a respectably-made crew cabin and couplings at the back. WOW! The bare minimum! (Still no tinder tho, but I'm working on that lol). Have fun with this one. This isn't Duck, by the way. This is a modified version of the GWR 5700 that's presumably more powerful than the standard light goods and shunting version. Duck is just a shunter in the show from what I can tell and his design is pretty faithful to the pannier-tank archetype. Very proud of this model, and although it's not the best thing in the world, it's still a respectable engine. More to come from my series of steam, but I'll be taking a break from trains to bring you infrastructure: tracks suited for my models, as well as actual tinders for them with fitting couplings. And probably some warehouses and likely after a redux of my 4300, which sorely needs it (have you seen that crew cabin? Disgraceful topology.) Source: Objaverse 1.0 / Sketchfab
Pre-test and post-test scores on Grammar and Self Directed Learning in Vietnamese French class
<p>The results from 5-scale Likert survey for sefl-directed learning and grammar test before and after introducing ChatGPT as a learning instrument in a French class in University of Transport and Communications, Hanoi, Vietnam. </p> <p><strong>SURVEY FORM 01 - QUESTIONS</strong></p> <p>1<span> </span>I am satisfied with my university life overall</p> <p>2<span> </span>I am satisfied with my chosen major.</p> <p>3<span> </span>I am interested in the French courses.</p> <p>4<span> </span>I feel confident and in control when using digital technologies.</p> <p>5<span> </span>I frequently engage with ChatGPT for various tasks.</p> <p>6<span> </span>I know what I need to learn. </p> <p>7<span> </span>Regardless of the result or effectiveness of my learning, I still like learning. </p> <p>8<span> </span>I strongly hope to constantly improve and excel in my learning. </p> <p>9<span> </span>My successes and failures inspire me to continue learning. </p> <p>10<span> </span>I enjoy finding answers to questions.</p> <p>11<span> </span>I will not give up learning because I face some difficulties. </p> <p>12<span> </span>I can proactively establish my learning goals. </p> <p>13<span> </span>I know what learning strategies are appropriate for me in reaching my learning goals. </p> <p>14<span> </span>I set the priorities of my learning.</p> <p>15<span> </span>In the classroom or on my own, I am able to follow my own plan of learning.</p> <p>16<span> </span>I am good at arranging and controlling my learning time.</p> <p>17<span> </span>I know how to find resources for my learning.</p> <p>18<span> </span>I can connect new knowledge with my own personal experiences.</p> <p>19<span> </span>I understand the strengths and weakness of my learning.</p> <p>20<span> </span>I can monitor my learning progress.</p> <p>21<span> </span>I can evaluate on my own learning outcomes.</p> <p>22<span> </span>My interaction with others helps me plan for further learning.</p> <p>23<span> </span>I would like to learn the language and culture of those whom I frequently interact with.</p> <p>24<span> </span>I am able to express messages effectively in oral presentations.</p> <p>25<span> </span>I am able to communicate messages effectively in writing.</p> <p><strong>SURVEY FORM 02 - QUESTIONS</strong></p> <p>1<span> </span>In the past five weeks, I have frequently used ChatGPT for learning French grammar.</p> <p>2<span> </span>I find my experience with ChatGPT better compared to traditional grammar learning methods such as textbooks, lectures, or other educational materials.</p> <p>3<span> </span>I am satisfied with the effectiveness of using ChatGPT in learning French grammar.</p> <p>4<span> </span>I feel motivated when using ChatGPT for learning French grammar.</p> <p>5<span> </span>ChatGPT helps me to be more proactive in my study plan.</p> <p>6<span> </span>I am willing to continue using ChatGPT for learning French grammar.</p>
Figs. 11–14. 11. Dolium melanostomum Jay, 1839 in Catalog Of Recent Type Specimens In The Division Of Invertebrate Zoology, American Museum Of Natural History. V. Mollusca, Part 2 (Class Gastropoda [Exclusive Opisthobranchia And Pulmonata With Supplements To Gastropoda [Opisthobranchia], And Bivalvia
Figs. 11–14. 11. Dolium melanostomum Jay, 1839 (holotype—AMNH 56115), X 0.45. 12.
SOME ASPECTS OF PREPARING PRIMARY CLASS TEACHERS TO GUIDE STUDENTS TO NATIONAL PROFESSIONS
<p><em><span>The article discusses the methodology of developing the skills of future primary school teachers in national trades and the technologies of guiding primary school students to national trades.</span></em></p>
Data from: Using the generalized index of dissimilarity to detect gene-gene interactions in multi-class phenotypes
To find genetic association between complex diseases and phenotypic traits, one important procedure is conducting a joint analysis. Multifactor dimensionality reduction (MDR) is an efficient method of examining the interactions between genes in genetic association studies. It commonly assumes a dichotomous classification of the binary phenotypes. Its usual approach to determining the genomic association is to construct a confusion matrix to estimate a classification error, where a binary risk status is determined and assigned to each genotypic multifactor class. While multi-class phenotypes are commonly observed, the current MDR approach does not handle these phenotypes appropriately because the thresholds for the risk statuses may not be clear. In this study, we suggest a new method for estimating gene-gene interactions for multi-class phenotypes. Our approach adopts the index of dissimilarity (IDS) as an evaluation measure. This is analytically equivalent to the common association measure of balanced accuracy (BA) for the binary traits, while it is not required to determine the risk status for the estimation. Moreover, it is easily expandable to the generalized index of dissimilarity (GIDS), which has an explicit form that can handle any number of categories. The performance of the proposed method was compared with those of other approaches via simulation studies in which fifteen genetic models were generated with three class outcomes. A consistently better performance was observed using the proposed method. The effect of a varying number of categories was examined. The proposed method was also illustrated using real genome-wide association studies (GWAS) data from the Korean Association Resource (KARE) project.
Data from: Resting zone of the growth plate harbors a unique class of skeletal stem cells
Skeletal stem cells regulate bone growth and homeostasis by generating diverse cell types including chondrocytes, osteoblasts and marrow stromal cells. The emerging model postulates a distinct type of skeletal stem cells closely associated with the growth plate1-4, a special cartilaginous tissue playing critical roles in bone elongation5. The resting zone maintains the growth plate by expressing parathyroid hormone-related protein (PTHrP) that interacts with Indian hedgehog (Ihh) released from the hypertrophic zone6-10, while providing a source of other chondrocytes11. However, the identity of skeletal stem cells and how they are maintained in the growth plate are unknown. Here we show that skeletal stem cells are formed among PTHrP+ chondrocytes within the resting zone of the postnatal growth plate. PTHrP+ chondrocytes expressed a panel of markers for skeletal stem/progenitor cells and uniquely possessed the properties as skeletal stem cells in cultured conditions. Cell lineage analysis revealed that PTHrP+ resting chondrocytes continued to form columnar chondrocytes long term, which underwent hypertrophy and became osteoblasts and marrow stromal cells beneath the growth plate. Transit-amplifying chondrocytes in the proliferating zone, which was concertedly maintained by a forward signal from undifferentiated cells (PTHrP) and a reverse signal from hypertrophic cells (Ihh), provided instructive cues to maintain cell fates of PTHrP+ resting chondrocytes. Our findings unravel a unique somatic stem cell type that is initially unipotent and acquires multipotency at the post-mitotic stage, underscoring the malleable nature of the skeletal cell lineage. This system provides a model in which functionally dedicated stem cells and their niche are specified postnatally and maintained throughout tissue growth by a tight feedback regulation system.
ES-SPA, FR-FRA in Atlas of European millipedes 3: Order Chordeumatida (Class Diplopoda)
ES-SPA, FR-FRA
Fish length (TL) vs. otolith length (OL) measurements of selected eleotrids and galaxiids from New Zealand; Size classes of fish otolith specimens from early Miocene Manuherikia section, Otago
<p>Supplement data 1 and 2 to: <strong>The “Gulliver” fish fauna of an early Miocene freshwater system of New Zealand; new insights from otoliths from the Bannockburn Formation</strong>, to be published in the New Zealand Journal of Geology (NZJG).</p>
Subgroup zeta functions of free nilpotent groups of class two
<p>Results of our project calculating subgroup zeta functions of free nilpotent groups of class two.</p>
Graph-pMHC: Graph Neural Network Approach to MHC Class II Peptide Presentation and Antibody Immunogenicity
<p>Antigen presentation on MHC Class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer, and address the peptide-MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIpan-4.0 (+20.17% absolute average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data, and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our model increases ROC AUC by 2.57% compared to just filtering peptides by hits in OASis alone for predicting antibody drug immunogenicity.<br><br>NOTE!!</p> <p>It's been brought to my attention that I accidentally shuffled the graph-pmhc and netmhciipan predictions on the antibody immunogenicity dataset (AB_df_w_preds), zenodo is not allowing me to add a new version. Besides these prediction columns the data is good, so the ada labels for the antibodies is fine. The graph-pmhc and netmhciipan predictions can be derived from AB_df_all_preds_w_preds with code like this:</p> <p>df.groupby('Antibody').apply(lambda x: sum((x['Peptide Num OAS Subjects']<23)&(x[column]>0))).values</p> <p>Where df is AB_df_all_preds_w_preds loaded in pandas, and column is the prediction column (graph-pmhc or netmhciipan) of interest. Sorry about the error!!</p>
Data for: Linkage mapping of root shape traits associated with market class in two biparental carrot populations
<p>This repository contains essential data to support the findings presented in the forthcoming publication titled "Linkage Mapping of Root Shape Traits Associated with Market Class in Two Biparental Carrot Populations." It includes VCF files for two distinct carrot biparental populations, as well as R code for filtering, constructing linkage maps, and conducting QTL analysis. Furthermore, the repository hosts phenotypic data gathered from these two biparental populations during the years 2020 and 2021.</p><p>Both carrot genetic maps, one for each population, have been made available alongside their respective phenotypic data.</p><p><br>The provided R code contains absolute working directory paths that may not function as intended on your system. The primary purpose of sharing this code is to offer readers insight into the techniques employed in this study. You may need to adapt the directory paths to suit your specific setup.</p><p><br>To assist readers in understanding the logical sequence of steps involved in linkage mapping, the R code scripts have been sequentially numbered from 0 to 10.</p><p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.