Correction: Talk2Me: Automated linguistic data collection for personal assessment.
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ABSTRACT:
SUBMITTER: PLOS ONE Staff
PROVIDER: S-EPMC6490913 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Language is one the earliest capacities affected by cognitive change. To monitor that change longitudinally, we have developed a web portal for remote linguistic data acquisition, called Talk2Me, consisting of a variety of tasks. In order to facilitate research in different aspects of language, we provide baselines including the relations between different scoring functions within and across tasks. These data can be used to augment studies that require a normative model; for example, we provide baseline classification results in identifying dementia. These data are released publicly along with a comprehensive open-source package for extracting approximately two thousand lexico-syntactic, acoustic, and semantic features. This package can be applied arbitrarily to studies that include linguistic data. To our knowledge, this is the most comprehensive publicly available software for extracting linguistic features. The software includes scoring functions for different tasks.
| S-EPMC6436678 | biostudies-literature
Project description:The various group and category memberships that we hold are at the heart of who we are. They have been shown to affect our thoughts, emotions, behavior, and social relations in a variety of social contexts, and have more recently been linked to our mental and physical well-being. Questions remain, however, over the dynamics between different group memberships and the ways in which we cognitively and emotionally acquire these. In particular, current assessment methods are missing that can be applied to naturally occurring data, such as online interactions, to better understand the dynamics and impact of group memberships in naturalistic settings. To provide researchers with a method for assessing specific group memberships of interest, we have developed ASIA (Automated Social Identity Assessment), an analytical protocol that uses linguistic style indicators in text to infer which group membership is salient in a given moment, accompanied by an in-depth open-source Jupyter Notebook tutorial ( https://github.com/Identity-lab/Tutorial-on-salient-social-Identity-detection-model ). Here, we first discuss the challenges in the study of salient group memberships, and how ASIA can address some of these. We then demonstrate how our analytical protocol can be used to create a method for assessing which of two specific group memberships-parents and feminists-is salient using online forum data, and how the quality (validity) of the measurement and its interpretation can be tested using two further corpora as well as an experimental study. We conclude by discussing future developments in the field.
| S-EPMC8367904 | biostudies-literature
Project description:Rapid advances in mobile data-transfer technologies offer new possibilities in the use of cell phones to conduct assessments of a person's natural environment in real time. This paper describes features of a new Internet-based, cell phone-optimized assessment technique (ICAT), which consists of a retrospective baseline assessment combined with text messages sent to the participants' personal cell phones providing a hyperlink to an Internet-stored cell phone-optimized questionnaire. Two participation conditions were used to test variations in response burden. Retention rates, completion rates, and response times in different subgroups were tested by means of χ² tests, Cox regression, and logistic regression. Among the 237 initial participants, we observed a retention rate of 90.3% from the baseline assessment to the cell-phone part, and 80.4% repeated participation in the 30 daily assessments. Each day, 40-70% of the questionnaires were returned, a fourth in less than 3 minutes. Qualitative interviews underscored the ease of use of ICAT. This technique appears to be an innovative, convenient, and cost-effective way of collecting data on situational characteristics while minimizing recall bias. Because of its flexibility, ICAT can be applied in various disciplines, whether as part of small pilot studies or large-scale, crosscultural, and multisite research projects.
| S-EPMC3839619 | biostudies-other
Project description:BackgroundThe constant evolving and development of next-generation sequencing techniques lead to high throughput data composed of datasets that include a large number of biological samples. Although a large number of samples are usually experimentally processed by batches, scientific publications are often elusive about this information, which can greatly impact the quality of the samples and confound further statistical analyzes. Because dedicated bioinformatics methods developed to detect unwanted sources of variance in the data can wrongly detect real biological signals, such methods could benefit from using a quality-aware approach.ResultsWe recently developed statistical guidelines and a machine learning tool to automatically evaluate the quality of a next-generation-sequencing sample. We leveraged this quality assessment to detect and correct batch effects in 12 publicly available RNA-seq datasets with available batch information. We were able to distinguish batches by our quality score and used it to correct for some batch effects in sample clustering. Overall, the correction was evaluated as comparable to or better than the reference method that uses a priori knowledge of the batches (in 10 and 1 datasets of 12, respectively; total = 92%). When coupled to outlier removal, the correction was more often evaluated as better than the reference (comparable or better in 5 and 6 datasets of 12, respectively; total = 92%).ConclusionsIn this work, we show the capabilities of our software to detect batches in public RNA-seq datasets from differences in the predicted quality of their samples. We also use these insights to correct the batch effect and observe the relation of sample quality and batch effect. These observations reinforce our expectation that while batch effects do correlate with differences in quality, batch effects also arise from other artifacts and are more suitably corrected statistically in well-designed experiments.
| S-EPMC9284682 | biostudies-literature
Project description:Automated home cage monitoring represents a key technology to collect animal activity information directly from the home cage. The availability of 24/7 cage data enables extensive and quantitative assessment of mouse behavior and activity over long periods of time than possible otherwise. When home cage monitoring is performed directly at the home cage rack, it is possible to leverage additional advantages, including, e.g., partial (or total) reduction of animal handling, no need for setting up external data collection system as well as not requiring dedicated labs and personnel to perform tests. In this work we introduce a home cage-home rack monitoring system that is capable of continuously detecting spontaneous animal activity occurring in the home cage directly from the home cage rack. The proposed system is based on an electrical capacitance sensing technology that enables non-intrusive and continuous home cage monitoring. We then present a few animal activity metrics that are validated via comparison against a video camera-based tracking system. The results show that the proposed home-cage monitoring system can provide animal activity metrics that are comparable to the ones derived via a conventional video tracking system, with the advantage of system scalability, limited amount of both data generated and computational capabilities required to derive metrics.
| S-EPMC6451168 | biostudies-literature
Project description:ImportanceWomen are underrepresented at higher ranks in academic medicine. However, the factors contributing to this disparity have not been fully elucidated. Implicit bias and unconscious mental attitudes toward a person or group may be factors. Although academic medical centers use physician trainee evaluations of faculty to inform promotion decisions, little is known about gender bias in these evaluations. To date, no studies have examined narrative evaluations of medical faculty by physician trainees for differences based on gender.ObjectiveTo characterize gender-associated linguistic differences in narrative evaluations of medical faculty written by physician trainees.Design, setting, and participantsThis retrospective cohort study included all faculty teaching evaluations completed for the department of medicine faculty by medical students, residents, and fellows at a large academic center in Pennsylvania from July 1, 2015, through June 30, 2016. Data analysis was performed from June 1, 2018, through July 31, 2018.Main outcomes and measuresWord use in faculty evaluations was quantified using automated text mining by converting free-text comments into unique 1- and 2-word phrases. Mixed-effects logistic regression analysis was performed to assess associations of faculty gender with frequencies of specific words and phrases present in a physician trainee evaluation.ResultsA total of 7326 unique evaluations were collected for 521 faculty (325 men [62.4%] and 196 women [37.6%]). The individual words art (odds ratio [OR], 7.78; 95% CI, 1.01-59.89), trials (OR, 4.43; 95% CI, 1.34-14.69), master (OR, 4.24; 95% CI, 1.69-10.63), and humor (OR, 2.32; 95% CI, 1.44-3.73) were significantly associated with evaluations of male faculty, whereas the words empathetic (OR, 4.34; 95% CI, 1.56-12.07), delight (OR, 4.26; 95% CI, 1.35-13.40), and warm (OR, 3.45; 95% CI, 1.83-6.49) were significantly associated with evaluations of female faculty. Two-word phrases associated with male faculty evaluations included run rounds (OR, 7.78; 95% CI, 1.01-59.84), big picture (OR, 7.15; 95% CI, 1.68-30.42), and master clinician (OR, 4.02; 95% CI, 1.21-13.36), whereas evaluations of female faculty were more likely to be associated with model physician (OR, 7.75; 95% CI, 1.70-35.39), just right (OR, 6.97; 95% CI, 1.51-32.30), and attention (to) detail (OR, 4.26; 95% CI, 1.36-13.40).Conclusions and relevanceThe data showed quantifiable linguistic differences between free-text comments about male and female faculty in physician trainee evaluations. Further evaluation of these differences, particularly in association with ongoing gender disparities in faculty promotion and retention, may be warranted.
| S-EPMC6512459 | biostudies-literature
Project description:Recent studies have identified genetic markers associated with risk for certain sports-related injuries and performance-related conditions, with the hope that these markers could be used by individual athletes to personalize their training and diet regimens. We found that we could greatly expand the knowledge base of sports genetic information by using published data originally found in health and disease studies. For example, the results from large genome-wide association studies for low bone mineral density in elderly women can be re-purposed for low bone mineral density in young endurance athletes. In total, we found 124 single-nucleotide polymorphisms associated with: anterior cruciate ligament tear, Achilles tendon injury, low bone mineral density and stress fracture, osteoarthritis, vitamin/mineral deficiencies, and sickle cell trait. Of these single nucleotide polymorphisms, 91% have not previously been used in sports genetics. We conducted a pilot program on fourteen triathletes using this expanded knowledge base of genetic variants associated with sports injury. These athletes were genotyped and educated about how their individual genetic make-up affected their personal risk profile during an hour-long personal consultation. Overall, participants were favorable of the program, found it informative, and most acted upon their genetic results. This pilot program shows that recent genetic research provides valuable information to help reduce sports injuries and to optimize nutrition. There are many genetic studies for health and disease that can be mined to provide useful information to athletes about their individual risk for relevant injuries.
| S-EPMC4412532 | biostudies-literature
Project description:Implementation of a computer program package for automated collection and processing of rotation electron diffraction (RED) data is described. The software package contains two computer programs: RED data collection and RED data processing. The RED data collection program controls the transmission electron microscope and the camera. Electron beam tilts at a fine step (0.05-0.20°) are combined with goniometer tilts at a coarse step (2.0-3.0°) around a common tilt axis, which allows a fine relative tilt to be achieved between the electron beam and the crystal in a large tilt range. An electron diffraction (ED) frame is collected at each combination of beam tilt and goniometer tilt. The RED data processing program processes three-dimensional ED data generated by the RED data collection program or by other approaches. It includes shift correction of the ED frames, peak hunting for diffraction spots in individual ED frames and identification of these diffraction spots as reflections in three dimensions. Unit-cell parameters are determined from the positions of reflections in three-dimensional reciprocal space. All reflections are indexed, and finally a list with hkl indices and intensities is output. The data processing program also includes a visualizer to view and analyse three-dimensional reciprocal lattices reconstructed from the ED frames. Details of the implementation are described. Data collection and data processing with the software RED are demonstrated using a calcined zeolite sample, silicalite-1. The structure of the calcined silicalite-1, with 72 unique atoms, could be solved from the RED data by routine direct methods.
| S-EPMC3831301 | biostudies-literature
Project description:BackgroundThe American Society for Clinical Oncology recently launched the minimal common oncology data elements project to facilitate cancer data interoperability. However, clinical data are often unrecorded in an organized way, and converting them into a structured format can be time-consuming. Clinical Data Warehouse (CDW) is a database that consolidates data from different clinical sources. However, the clinical data extracted from this database include not only structured data but also natural language generated during clinical practice. Therefore, applying these data to a clinical study is challenging because they are unstructured, and unformatted to allow essential content to be found. This study determined how best to organize a huge amount of clinical data to evaluate the upper aerodigestive tract cancers' clinical features and outcomes, including cancer of the head and neck, esophagus, lung, thymus, and mesothelioma.MethodsThe Real-time autOmatically updated data warehOuse in healThcare (ROOT) uses six main regions to describe the journey of cancer patients. This study, developed an algorithm optimized for each disease category using natural language processing of unstructured data and data capture of structured data. Data from patients diagnosed at the Samsung Medical Center from 2008-2020 were used.ResultsComprehensive clinical data for 67,617 patients across six tumor types: 28,954 with non-small-cell lung cancer, 2,540 with small-cell lung cancer, 30,035 with head and neck cancer, 4,950 with esophageal cancer, 966 with thymic cancer, and 172 with mesothelioma were collected. Additionally, the results of a longitudinal molecular study, including epidermal growth factor receptor (EGFR) mutations, anaplastic lymphoma kinase (ALK) tests, and next-generation sequencing (NGS), were included. Scattered information was integrated and automatically built up to match the cohort, allowing users to capture the most updated test results and treatment outcomes.ConclusionsThis landmark study documented the successful construction of a real-time updating system for medical big data, based on the CDW program.
| S-EPMC8577969 | biostudies-literature
Project description:BackgroundHealth care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy.ObjectiveThe aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems.MethodsWe describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States.ResultsThe majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present.ConclusionsSyndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.
| S-EPMC6054708 | biostudies-literature