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Automated Analysis of Digitized Letter Fluency Data.

ABSTRACT: The letter-guided naming fluency task is a measure of an individual's executive function and working memory. This study employed a novel, automated, quantifiable, and reproducible method to investigate how language characteristics of words produced during a fluency task are related to fluency performance, inter-word response time (RT), and over task duration using digitized F-letter-guided fluency recordings produced by 76 young healthy participants. Our automated algorithm counted the number of correct responses from the transcripts of the F-letter fluency data, and individual words were rated for concreteness, ambiguity, frequency, familiarity, and age of acquisition (AoA). Using a forced aligner, the transcripts were automatically aligned with the corresponding audio recordings. We measured inter-word RT, word duration, and word start time from the forced alignments. Articulation rate was also computed. Phonetic and semantic distances between two consecutive F-letter words were measured. We found that total F-letter score was significantly correlated with the mean values of word frequency, familiarity, AoA, word duration, phonetic similarity, and articulation rate; total score was also correlated with an individual's standard deviation of AoA, familiarity, and phonetic similarity. RT was negatively correlated with frequency and ambiguity of F-letter words and was positively correlated with AoA, number of phonemes, and phonetic and semantic distances. Lastly, the frequency, ambiguity, AoA, number of phonemes, and semantic distance of words produced significantly changed over time during the task. The method employed in this paper demonstrates the successful implementation of our automated language processing pipelines in a standardized neuropsychological task. This novel approach captures subtle and rich language characteristics during test performance that enhance informativeness and cannot be extracted manually without massive effort. This work will serve as the reference for letter-guided category fluency production similarly acquired in neurodegenerative patients.

SUBMITTER: Cho S

PROVIDER: S-EPMC8359864 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Recent changes in institutional cyberinfrastructure and collections data storage methods have dramatically improved accessibility of specimen-based data through the use of digital databases and data aggregators. This analysis of digitized fish collections in the U.S. demonstrates how information from data aggregators, in this case iDigBio, can be extracted and analyzed. Data from U.S. institutional fish collections in iDigBio were explored through a strictly programmatic approach using the ridigbio package and fishfindR web application. iDigBio facilitates the aggregation of collections data on a purely voluntary fashion that requires collection staff to consent to sharing of their data. Not all collections are sharing their data with iDigBio, but the data harvested from 38 of the 143 known fish collections in the U.S. that are in iDigBio account for the majority of fish specimens housed in U.S. collections. In the 22 years since publication of the last survey providing information on these 38 collections, 1,219,168 specimen records (lots), 15,225,744 specimens, 3,192 primary types, and 32,868 records of secondary types have been added. This is an increase of 65.1% in the number of cataloged records and an increase of 56.1% in the number of specimens. In addition to providing specimen-based data for research, education, and various outreach activities, data that are accessible via data aggregators can be used to develop accurate, up-to-date reports of information on institutional collections. Such reports present collections data in an organized and accessible fashion and can guide targeted efforts by collections personnel to meet discipline-specific needs and make data more transparent to downstream users. Data from this survey will be updated and published regularly in a dynamic web application that will aid collections staff in communicating collections value while simultaneously giving stakeholders a way to explore collections holdings as they relate to the institutions in which they are housed. It is through this resource that collections will be able to leverage their data against those of similar collections to aid in the procurement of financial and institutional support.

Project description:BACKGROUNDResearch involving microbial ecosystems has drawn increasing attention in recent years. Studying microbe-microbe, host-microbe, and environment-microbe interactions are essential for the understanding of microbial ecosystems. Currently, metaproteomics provide qualitative and quantitative information of proteins, providing insights into the functional changes of microbial communities. However, computational analysis of large-scale data generated in metaproteomic studies remains a challenge. Conventional proteomic software have difficulties dealing with the extreme complexity and species diversity present in microbiome samples leading to lower rates of peptide and protein identification. To address this issue, we previously developed the MetaPro-IQ approach for highly efficient microbial protein/peptide identification and quantification.RESULTHere, we developed an integrated software platform, named MetaLab, providing a complete and automated, user-friendly pipeline for fast microbial protein identification, quantification, as well as taxonomic profiling, directly from mass spectrometry raw data. Spectral clustering adopted in the pre-processing step dramatically improved the speed of peptide identification from database searches. Quantitative information of identified peptides was used for estimating the relative abundance of taxa at all phylogenetic ranks. Taxonomy result files exported by MetaLab are fully compatible with widely used metagenomics tools. Herein, the potential of MetaLab is evaluated by reanalyzing a metaproteomic dataset from mouse gut microbiome samples.CONCLUSIONMetaLab is a fully automatic software platform enabling an integrated data-processing pipeline for metaproteomics. The function of sample-specific database generation can be very advantageous for searching peptides against huge protein databases. It provides a seamless connection between peptide determination and taxonomic profiling; therefore, the peptide abundance is readily used for measuring the microbial variations. MetaLab is designed as a versatile, efficient, and easy-to-use tool which can greatly simplify the procedure of metaproteomic data analysis for researchers in microbiome studies.

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Automated Analysis of Digitized Letter Fluency Data.

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