555 research outputs found
Test Dataset for 3D semantic image segmentation of the various organs from CT and MR scans
No full text<p>These test cases are for the <a href="https://github.com/MIC-DKFZ/nnUNet/releases/tag/v1.7.1">nnUnet v1</a> models trained on the following datasets:<br><br></p>
<table>
<tbody>
<tr>
<td>Dataset </td>
<td>Task</td>
<td>Model Details on Zenodo</td>
</tr>
<tr>
<td> <a href="../record/6802614">TotalSegmentator</a> and <a href="../record/5903672">FLARE21</a> datasets</td>
<td>Segment Liver from CT scans</td>
<td>https://zenodo.org/record/8274976</td>
</tr>
<tr>
<td><a href="https://kits-challenge.org/kits23/">KiTS23</a> datasets and a subset of the<a href="https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=5800386#5800386566e265abf95408aa64c4917f0cbe5d9"> TCGA-KIRC </a>dataset</td>
<td>Segment Kidney, Cyst, and Tumors from CT Scans</td>
<td>https://zenodo.org/records/8277846</td>
</tr>
<tr>
<td><a href="http://ji%20yuanfeng.%20(2022).%20amos%20a%20large-scale%20abdominal%20multi-organ%20benchmark%20for%20versatile%20medical%20image%20segmentation%20[data%20set].%20zenodo.%20https">AMOS</a> and <a href="http://macdonald,%20jacob%20a.,%20zhu,%20zhe,%20konkel,%20brandon,%20mazurowski,%20maciej,%20wiggins,%20walter,%20&%20bashir,%20mustafa.%20(2020).%20duke%20liver%20dataset%20(mri)%20v2%20(2.0.0)%20[data%20set].%20zenodo.%20https//doi.org/10.5281/zenodo.7774566">DUKE Liver</a> datasets</td>
<td>Segment Liver from the MR scans</td>
<td>https://zenodo.org/record/8290124</td>
</tr>
<tr>
<td>Data from m <a href="../record/6624726">pi-cai</a></td>
<td>Segment Prostate region from MR scans</td>
<td>https://zenodo.org/record/8290093</td>
</tr>
</tbody>
</table>
Test Dataset for 3D semantic image segmentation of the various organs from CT and MR scans
No full text<p>These test cases are for the <a href="https://github.com/MIC-DKFZ/nnUNet/releases/tag/v1.7.1">nnUnet v1</a> models trained on the following datasets:<br><br></p>
<table>
<tbody>
<tr>
<td>Dataset </td>
<td>Task</td>
<td>Model Details on Zenodo</td>
</tr>
<tr>
<td> <a href="../record/6802614">TotalSegmentator</a> and <a href="../record/5903672">FLARE21</a> datasets</td>
<td>Segment Liver from CT scans</td>
<td>https://zenodo.org/record/8274976</td>
</tr>
<tr>
<td><a href="https://kits-challenge.org/kits23/">KiTS23</a> datasets and a subset of the<a href="https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=5800386#5800386566e265abf95408aa64c4917f0cbe5d9"> TCGA-KIRC </a>dataset</td>
<td>Segment Kidney, Cyst, and Tumors from CT Scans</td>
<td>https://zenodo.org/records/8277846</td>
</tr>
<tr>
<td><a href="http://ji%20yuanfeng.%20(2022).%20amos%20a%20large-scale%20abdominal%20multi-organ%20benchmark%20for%20versatile%20medical%20image%20segmentation%20[data%20set].%20zenodo.%20https">AMOS</a> and <a href="http://macdonald,%20jacob%20a.,%20zhu,%20zhe,%20konkel,%20brandon,%20mazurowski,%20maciej,%20wiggins,%20walter,%20&%20bashir,%20mustafa.%20(2020).%20duke%20liver%20dataset%20(mri)%20v2%20(2.0.0)%20[data%20set].%20zenodo.%20https//doi.org/10.5281/zenodo.7774566">DUKE Liver</a> datasets</td>
<td>Segment Liver from the MR scans</td>
<td>https://zenodo.org/record/8290124</td>
</tr>
<tr>
<td>Data from m <a href="../record/6624726">pi-cai</a></td>
<td>Segment Prostate region from MR scans</td>
<td>https://zenodo.org/record/8290093</td>
</tr>
</tbody>
</table>
AI and Inclusive Education: Enhancing Equity, Engagement and Excellence
No full textInclusive education advocates for equal learning opportunities for all students, irrespective of their abilities or backgrounds. The invention of artificial intelligence and modern technologies opens new avenues for improving each child’s learning experience-regardless of their skills and backgrounds-by promoting inclusive education through offering personalized learning experiences and supporting diverse learning needs, thus fostering a completely inclusive learning environment. By applying AI technologies, educators can design more effective, inclusive, and engaging curricula, differentiating their requirements with the diversified needs of today’s learners toward more equitable and innovative educational learning for each type of learner. This research views the present applications of AI in inclusive education as (a) assistive technologies as per the need of every student, (b) personalized learning, and (c) inclusive assessment tools (IATs) and intelligent tutoring systems (ITSs). This research looks into the following benefits of AI in inclusive education: (a) enhanced accessibility, (b) data-driven decision-making, (b) improved engagement, (d) early intervention, and (e) continuous enhancement. Some of the challenges and ethical issues that AI in inclusive education raises are data privacy, data security, teacher training and support, costs, and access. This study also sheds light on the future direction: for instance, R&D in AI for inclusive education, policy and regulations, collaboration, and partnerships of different stakeholders. The findings and implications of this study will help educators, practitioners, and policymakers to make decisions for the effective use of AI and new technologies in enhancing every student’s learning experience. © 2026 selection and editorial matter, K.M. Soni, Nitasha Hasteer, Aditi Bhardwaj, Rahul Sindhwani, and J., Paulo Davim; individual chapters, the contributors
The story of the Soni Ventorum Wind Quintet
No full textThesis (D. Mus. Arts)--University of Washington, 2000The Soni Ventorum Wind Quintet has been the wind quintet-in-residence at the University of Washington School of Music since 1968. Officially founded in 1962, when its members were on the faculty of the Conservatory of Music of Puerto Rico, the group has had a long and stable history. Through their concerts, tours, and recordings, the Soni Ventorum Wind Quintet has established an international reputation. Over the years, many distinguished composers have written works especially for the Soni Ventorum, thus expanding the repertoire of the wind quintet.This study traces the history of the Soni Ventorum Wind Quintet mainly through interviews with the quintet members themselves. This history includes antecedent quintets in which members of the Soni Ventorum Wind Quintet participated (namely, a student quintet at the Curtis Institute, The American Wind Ensemble of Vienna, and the U.S. Seventh Army Symphony Wind Quintet). It covers the founding of the Soni Ventorum Wind Quintet in 1962 at the Conservatory of Music in Puerto Rico through their tenure from 1968 through the present as the wind quintet-in-residence at the University of Washington in Seattle. It gives an account of the establishment of the Soni Ventorum's recording career, their approach to sound and ensemble, their many tours, participation in festivals and competitions, and personnel. The study details the Soni Ventorum's collaborations with colleagues at the University of Washington School of Music, especially the many composers who wrote pieces for the group. One chapter covers ensemble pieces that have been written for the members of the Son! Ventorum Wind Quintet, while another presents wind quintet and quartet arrangements that were prepared by the quintet members themselves. The final chapter provides biographies of the members of the Soni Ventorum Wind Quintet.The Introduction to the study is a brief history of wind quintets. The study concludes with detailed appendices cataloguing the Soni Ventorum Wind Quintet's repertoire, concerts, residencies, tours and a complete discography.At the time of this writing, the author is aware of no other work detailing the history of an established wind quintet
Breath rate variability: A novel measure to study the meditation effects
No full textContext: Reliable quantitative measure of meditation is still elusive. Although electroencephalogram (EEG) and heart rate variability (HRV) are known as quantitative measures of meditation, effects of meditation on EEG and HRV may well take long time as these measures are involuntarily controlled. Effect of mediation on respiration is well known; however, quantitative measures of respiration during meditation have not been studied. Aims: Breath rate variability (BRV) as an alternate measure of meditation even over a short duration is proposed. The main objective of this study is to test the hypothesis that BRV is a simple measure that differentiates between meditators and nonmeditators. Settings and Design: This was a nonrandomized, controlled trial. Volunteers meditate in their natural habitat during signal acquisition. Subjects and Methods: We used Photo-Plythysmo-Gram (PPG) signal acquisition system from BIO-PAC and recorded video of chest and abdomen movement due to respiration during a short meditation (15 min) session for 12 individuals (all males) meditating in a relaxed sitting posture. Seven of the 12 individuals had substantial experience in meditation, while others are controls without any experience in meditation. Respiratory signal from PPG signal was derived and matched with that of the video respiratory signal. This derived respiratory signal is used for calculating BRV parameters in time, frequency, nonlinear, and time-frequency domain. Statistical Analysis Used: First, breath-to-breath interval (BBI) was calculated from the respiration signal, then time domain parameters such as standard deviation of BBI (SDBB), root mean square value of SDBB (RMSSD), and standard deviation of SDBB (SDSD) were calculated. We performed spectral analysis to calculate frequency domain parameters (power spectral density [PSD], power of each band, peak frequency of each band, and normalized frequency) using Burg, Welch, and Lomb–Scargle (LS) method. We calculated nonlinear parameters (sample entropy, approximate entropy, Poincare plot, and Renyi entropy). We calculated time frequency parameters (global PSD, low frequency-high frequency [LF-HF] ratio, and LF-HF power) by Burg LS and wavelet method. Results: The results show that the mediated individuals have high value of SDSD (+24%), SDBB (+29%), and RMSSD (+26%). Frequency domain analysis shows substantial increment in LFHF power (+73%) and LFHF ratio (+33%). Nonlinear parameters such as SD1 and SD2 were also more (>20%) for meditated persons. Conclusions: As compared to HRV, BRV can provide short-term effect on anatomic nervous system meditation, while HRV shows long-term effects. Improved autonomic function is one of the long-term effects of meditation in which an increase in parasympathetic activity and decrease in sympathetic dominance are observed. In future works, BRV could also be used for measuring stress
PULSE-SMART: Pulse-Based Arrhythmia Discrimination Using a Novel Smartphone Application
No full textCo-author Apurv Soni is a medical student in the MD/PhD Program at UMass Medical School.BACKGROUND: Atrial fibrillation (AF) is a common and dangerous rhythm abnormality. Smartphones are increasingly used for mobile health applications by older patients at risk for AF and may be useful for AF screening. OBJECTIVES: To test whether an enhanced smartphone app for AF detection can discriminate between sinus rhythm (SR), AF, premature atrial contractions (PACs), and premature ventricular contractions (PVCs). METHODS: We analyzed two hundred and nineteen 2-minute pulse recordings from 121 participants with AF (n = 98), PACs (n = 15), or PVCs (n = 15) using an iPhone 4S. We obtained pulsatile time series recordings in 91 participants after successful cardioversion to sinus rhythm from preexisting AF. The PULSE-SMART app conducted pulse analysis using 3 methods (Root Mean Square of Successive RR Differences; Shannon Entropy; Poincare plot). We examined the sensitivity, specificity, and predictive accuracy of the app for AF, PAC, and PVC discrimination from sinus rhythm using the 12-lead EKG or 3-lead telemetry as the gold standard. We also administered a brief usability questionnaire to a subgroup (n = 65) of app users. RESULTS: The smartphone-based app demonstrated excellent sensitivity (0.970), specificity (0.935), and accuracy (0.951) for real-time identification of an irregular pulse during AF. The app also showed good accuracy for PAC (0.955) and PVC discrimination (0.960). The vast majority of surveyed app users (83%) reported that it was "useful" and "not complex" to use. CONCLUSION: A smartphone app can accurately discriminate pulse recordings during AF from sinus rhythm, PACs, and PVCs.MD/Ph
Association of common mental disorder symptoms with health and healthcare factors among women in rural western India: results of a cross-sectional survey
No full textFirst author Apurv Soni is a medical student in the MD/PhD Program at UMass Medical School.OBJECTIVES: Information about common mental disorders (CMD) is needed to guide policy and clinical interventions in low-income and middle-income countries. This study's purpose was to characterise the association of CMD symptoms with 3 inter-related health and healthcare factors among women from rural western India based on a representative, cross-sectional survey. SETTING: Surveys were conducted in the waiting area of various outpatient clinics at a tertiary care hospital and in 16 rural villages in the Anand district of Gujarat, India. PARTICIPANTS: 700 Gujarati-speaking women between the ages of 18-45 years who resided in the Anand district of Gujarat, India, were recruited in a quasi-randomised manner. PRIMARY AND SECONDARY OUTCOMES MEASURES: CMD symptoms, ascertained using WHO's Self-Reporting Questionnaire-20 (SRQ-20), were associated with self-reported (1) number of healthcare visits in the prior year; (2) health status and (3) portion of yearly income expended on healthcare. RESULTS: Data from 658 participants were used in this analysis; 19 surveys were excluded due to incompleteness, 18 surveys were excluded because the participants were visiting hospitalised patients and 5 surveys were classified as outliers. Overall, 155 (22·8%) participants screened positive for CMD symptoms (SRQ-20 score ≥8) with most (81.9%) not previously diagnosed despite contact with healthcare provider in the prior year. On adjusted analyses, screening positive for CMD symptoms was associated with worse category in self-reported health status (cumulative OR=9.39; 95% CI 5·97 to 14·76), higher portion of household income expended on healthcare (cumulative OR=2·31; 95% CL 1·52 to 3.52) and increased healthcare visits in the prior year (incidence rate ratio=1·24; 95% CI 1·07 to 1·44). CONCLUSIONS: The high prevalence of potential CMD among women in rural India that is unrecognised and associated with adverse health and financial indicators highlights the individual and public health burden of CMD.MD/Ph
Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?
No full textPre-trained language models consider the context of neighboring words and
documents but lack any author context of the human generating the text.
However, language depends on the author's states, traits, social, situational,
and environmental attributes, collectively referred to as human context (Soni
et al., 2024). Human-centered natural language processing requires
incorporating human context into language models. Currently, two methods exist:
pre-training with 1) group-wise attributes (e.g., over-45-year-olds) or 2)
individual traits. Group attributes are simple but coarse -- not all
45-year-olds write the same way -- while individual traits allow for more
personalized representations, but require more complex modeling and data. It is
unclear which approach benefits what tasks. We compare pre-training models with
human context via 1) group attributes, 2) individual users, and 3) a combined
approach on five user- and document-level tasks. Our results show that there is
no best approach, but that human-centered language modeling holds avenues for
different methods
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