Hemileuca maia
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1006484 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11051163.fastq.gz | Fastqsanger.gz | |||
| SRR11051164.fastq.gz | Fastqsanger.gz | |||
| SRR11051165.fastq.gz | Fastqsanger.gz | |||
| SRR11051166.fastq.gz | Fastqsanger.gz | |||
| SRR11051167.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 11 |
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Project description:Hemileuca maia Genome sequencing and assembly
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Project description:This paper describes the development of a multidimensional self-report measure of interoceptive body awareness. The systematic mixed-methods process involved reviewing the current literature, specifying a multidimensional conceptual framework, evaluating prior instruments, developing items, and analyzing focus group responses to scale items by instructors and patients of body awareness-enhancing therapies. Following refinement by cognitive testing, items were field-tested in students and instructors of mind-body approaches. Final item selection was achieved by submitting the field test data to an iterative process using multiple validation methods, including exploratory cluster and confirmatory factor analyses, comparison between known groups, and correlations with established measures of related constructs. The resulting 32-item multidimensional instrument assesses eight concepts. The psychometric properties of these final scales suggest that the Multidimensional Assessment of Interoceptive Awareness (MAIA) may serve as a starting point for research and further collaborative refinement.
| S-EPMC3486814 | biostudies-literature
Project description:Interoception, the process by which the nervous system senses, interprets, and integrates signals originating from within the body, has become major research topic for mental health and in particular for mind-body interventions. Interoceptive awareness here is defined as the conscious level of interoception with its multiple dimensions potentially accessible to self-report. The Multidimensional Assessment of Interoceptive Awareness (MAIA) is an 8-scale state-trait questionnaire with 32 items to measure multiple dimensions of interoception by self-report and was published in November 2012. Its numerous applications in English and other languages revealed low internal consistency reliability for two of its scales. This study's objective was to improve these scales and the psychometrics of the MAIA by adding three new items to each of the two scales and evaluate these in a new sample. Data were collected within a larger project that took place as part of the Live Science residency programme at the Science Museum London, UK, where visitors to the museum (N = 1,090) completed the MAIA and the six additional items. Based on exploratory factor analysis in one-half of the adult participants and Cronbach alphas, we discarded one and included five of the six additional items into a Version 2 of the MAIA and conducted confirmatory factor analysis in the other half of the participants. The 8-factor model of the resulting 37-item MAIA-2 was confirmed with appropriate fit indices (RMSEA = 0.055 [95% CI 0.052-0.058]; SRMR = 0.064) and improved internal consistency reliability. The MAIA-2 is public domain and available (www.osher.ucsf.edu/maia) for interoception research and the evaluation of clinical mind-body interventions.
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Project description:Purpose:Macular Integrity Assessment (MAIA) microperimetry is used widely in clinical trials and routine practice to assess paracentral scotoma. Current interpretation of MAIA is based on an assumed uniform 25 decibel (dB) cutoff for normal function irrespective of subject age and retinal location. We examined this convention by establishing an age- and loci-specific reference in healthy eyes and comparing this to the <25 dB cutoff. Methods:Retrospective MAIA results from healthy eyes were analyzed for prevalence of loci with <25 dB. At each locus, a new reference cutoff was derived from quantile regression of sensitivity against age at the 2.5th percentile. Two clinical cases of serial MAIA testing were analyzed using the new approach and compared to the <25 dB cutoff. Results:Fifty-four and 56 age-matched (range: 16-75 years) healthy eyes underwent small (37 loci) and large (68 loci) grid testing, respectively. Retinal sensitivity <25 dB was found in 5% of the small grid (1998 data points) and 10% of the large grid (3808 data points). These were found predominantly in older subjects and at the central point or in the perifoveal region. Quantile regression at each individual locus showed age-related decline with a median gradient of 0.6 dB/decade. Conclusions:We caution against using <25 dB cutoff in MAIA interpretation and advocate an age- and loci-specific cutoff criterion. Translational Relevance:Our study suggests that MAIA interpretation is influenced by the criterion used for defining abnormal pointwise measurement.
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Project description:Data Management: Afranio Maia da Silva - afranio.silva@usp.br
2021-04-14 | MSV000087195 | MassIVE
Project description:Digital imaging has become one of the most important techniques in environmental monitoring and exploration. In the case of the marine environment, mobile platforms such as autonomous underwater vehicles (AUVs) are now equipped with high-resolution cameras to capture huge collections of images from the seabed. However, the timely evaluation of all these images presents a bottleneck problem as tens of thousands or more images can be collected during a single dive. This makes computational support for marine image analysis essential. Computer-aided analysis of environmental images (and marine images in particular) with machine learning algorithms is promising, but challenging and different to other imaging domains because training data and class labels cannot be collected as efficiently and comprehensively as in other areas. In this paper, we present Machine learning Assisted Image Annotation (MAIA), a new image annotation method for environmental monitoring and exploration that overcomes the obstacle of missing training data. The method uses a combination of autoencoder networks and Mask Region-based Convolutional Neural Network (Mask R-CNN), which allows human observers to annotate large image collections much faster than before. We evaluated the method with three marine image datasets featuring different types of background, imaging equipment and object classes. Using MAIA, we were able to annotate objects of interest with an average recall of 84.1% more than twice as fast as compared to "traditional" annotation methods, which are purely based on software-supported direct visual inspection and manual annotation. The speed gain increases proportionally with the size of a dataset. The MAIA approach represents a substantial improvement on the path to greater efficiency in the annotation of large benthic image collections.
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