345 research outputs found
sj-docx-1-eeg-10.1177_15500594221138292 - Supplemental material for Is There a Difference in EEG Characteristics in Acute, Chronic, and Experimentally Induced Musculoskeletal Pain States? a Systematic Review
No full textSupplemental material, sj-docx-1-eeg-10.1177_15500594221138292 for Is There a Difference in EEG Characteristics in Acute, Chronic, and Experimentally Induced Musculoskeletal Pain States? a Systematic Review by Jerin Mathew, Tyson Michael Perez, Divya Bharatkumar Adhia, Dirk De Ridder and Ramakrishnan Mani in Clinical EEG and Neuroscience</p
Scientometric Portrait of Nobel Laureate Venkatraman Ramakrishnan
No full textThe study presents an analysis of 165 research papers by Nobel Laureate Venkatraman Ramakrishnan published during 1977 to 2019 in the diverse field of science such as Biochemistry, Genetics and Molecular Biology, Medicine, Chemistry, Neuroscience, Immunology and Microbiology, Physics and Astronomy, Engineering and Materials Science. The highest number of publications contributed during the 2nd and 4th decade with 49 (29.70%) papers each. His paper entitles “Structure of the 30s ribosomal subunit” got maximum 1560 citations. Kelley, A. C. Was the most collaborative author and Europe was the most dominant continent collaborating with 132 papers whereas the United States was the top collaborated country with 100 (60.61%) papers. In the context of authorship pattern Triple authored papers were dominated with 34 (20.61%) papers. Among the most funding Sponsored body Agouron Institute topped the list with 23 (13.94%) papers, on the other hand, The Medical Research Council Laboratory of Molecular Biology, United Kingdom was the most contributed affiliation with 91 (55.15%) papers. In term of most preferred source and the most preferred subject of publications were Science with 23 (13.94%) and Biochemistry, Genetics and Molecular Biology with 97 (58.79%) papers respectively
Proceedings of the 28th Conference of Cement Microscopy
No full textBruce W. Berdenier (with Dawadi, S., Ramakrishnan, V,) is a contributing author, “Surface Morphology of Reactive Powder Concrete Containing Arsenic , pp. 205-226.
The annual meeting of the International Cement Microscopy Association consistently contributes new and innovative research on a variety of cement and concrete topics. The 2006 event featured 18 papers by international experts from universities, research institutes, and other organizations on topics including petrographic analysis, x-ray diffraction, sulfate investigation, fly ash analysis, and more. Published by ICMA
A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information
No full textAbstract
Resection and whole brain radiotherapy (WBRT) are standard treatments for brain metastases (BM) but are associated with cognitive side effects. Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. We used a streamlined approach to database-building through a PACS-integrated segmentation workflow
Enhancing clinical decision-making: An externally validated machine learning model for predicting isocitrate dehydrogenase mutation in gliomas using radiomics from presurgical magnetic resonance imaging
No full textAbstract Background Glioma, the most prevalent primary brain tumor, poses challenges in prognosis, particularly in the high-grade subclass, despite advanced treatments. The recent shift in tumor classification underscores the crucial role of isocitrate dehydrogenase (IDH) mutation status in the clinical care of glioma patients. However, conventional methods for determining IDH status, including biopsy, have limitations. Exploring the use of machine learning (ML) on magnetic resonance imaging to predict IDH mutation status shows promise but encounters challenges in generalizability and translation into clinical practice because most studies either use single institution or homogeneous datasets for model training and validation. Our study aims to bridge this gap by using multi-institution data for model validation. Methods This retrospective study utilizes data from large, annotated datasets for internal (377 cases from Yale New Haven Hospitals) and external validation (207 cases from facilities outside Yale New Haven Health). The 6-step research process includes image acquisition, semi-automated tumor segmentation, feature extraction, model building with feature selection, internal validation, and external validation. An extreme gradient boosting ML model predicted the IDH mutation status, confirmed by immunohistochemistry. Results The ML model demonstrated high performance, with an Area under the Curve (AUC), Accuracy, Sensitivity, and Specificity in internal validation of 0.862, 0.865, 0.885, and 0.713, and external validation of 0.835, 0.851, 0.850, and 0.847. Conclusions The ML model, built on a heterogeneous dataset, provided robust results in external validation for the prediction task, emphasizing its potential clinical utility. Future research should explore expanding its applicability and validation in diverse global healthcare settings
Nonlinear stochastic dynamics of a nanomechanical resonator coupled to a single electron transistor
No full textNanoelectromechanical systems (NEMS) comprise nanometer to micrometer scale mechanical oscillators coupled to electronic devices of comparable dimensions. NEMS have great potential for sensor applications as well as for exploring fundamental physics. The dynamics of a nanomechanical resonator coupled to a single electron transistor (SET) is considered in the Duffing regime using a master equation approach and a Langevin approach. In the first approach, the master equations are derived and solved using a finite element method as well as a moment approximation method for both the single-well and the (inverted) double-well Duffing potentials. It is observed that the SET damps the resonator motion much more effectively in the single-well Duffing case in comparison with the linear case. In the double-well case we observe the existence of a limit cycle wherein the SET and the resonator exist in a state of dynamic equilibrium. This is followed by the onset of instability in the numerical solutions. The results from the master equation approach are used in a numerical fitting procedure to characterize the damping term in the averaged equations of motion of the system. It is observed that a linear damping term provides the best fit in all cases except for the strongly nonlinear regime. Based on this result, a Langevin equation is written down from which a Fokker-Planck equation is derived for the system. The Fokker-Planck equation is solved analytically, in closed form, for the steady state. In the time dependent case, the equation is solved using a finite element method and the results are shown to be in qualitative agreement with those obtained using the master equation approach. Therefore it is established that the SET-resonator system attains a steady state much more rapidly in the single-well Duffing regime. Finally, the steady state analytical solution to the Fokker-Planck equation is utilized to show that the steady state effective temperature is lower in the presence of the single-well Duffing nonlinearity.Ph.D.Includes bibliographical references (p. 144-147)
Exploring image recognition: applying convoluted neural networks and learning to recognize safe cyclists
No full text"Today, there is a need to focus on the mobility revolution that is currently taking place. With the advent of more intelligent data gathering, there is also a growing need for using existing technology and infrastructure to achieve this goal, without incorporating expensive, complicated systems. As single-occupancy give way to shared mobility solutions, combined with regular mass transit and pedestrian-aware street infrastructure (traffic lights, crosswalks etc.), there is a large ""networked mobility system'' that has the potential to be tapped. Moreover, autonomous cars will be here soon, to add to the mix.
With statistics showing an increase in bicyclist related crashes over the last decade and an increase in bicycle-borne road users, there is a necessity for cities and autonomous vehicles to build bicycle safety into their adaptation to the ""driverless future"". This paper is an exploration into the use of a Convolutional Neural Network (CNN) based Machine Learning (ML) algorithm to identify bicycle-borne road users, who wear helmets.
We use a pre-made CNN framework-YOLO (You Only Look Once), and built around it further. After a brief proof-of-concept test on a publicly available dataset (including extraction, parsing and detection), the algorithm was modified. Some important features were added, such as identifying license plates, faces and encrypting them. Further, there is also a detailed account of using the ML capabilities that the framework is built with, and training it to identify bicycle-borne road users wearing a helmet."Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-08-01The student, Ramakrishnan Narayanan, accepted the attached license on 2017-07-21 at 13:17.The student, Ramakrishnan Narayanan, submitted this Thesis for approval on 2017-07-21 at 13:34.This Thesis was approved for publication on 2017-07-21 at 13:47.DSpace SAF Submission Ingestion Package generated from Vireo submission #11580 on 2018-03-02 at 13:03:11Made available in DSpace on 2018-03-02T20:02:40Z (GMT). No. of bitstreams: 2
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Erratum: Correction to: Work-Related Stressors Among Maternal, Infant, and Early Childhood Home Visiting (MIECHV) Home Visitors: A Qualitative Study (Maternal and child health journal (2018) 22 Suppl 1 (62-69))
No full textThe article "Work-Related Stressors Among Maternal, Infant, and Early Childhood Home Visiting (MIECHV) Home Visitors: A Qualitative Study", written by Paige J. Alitz, Shana Geary, Pamela C. Birriel, Takudzwa Sayi, Rema Ramakrishnan, Omotola Balogun, Alison Salloum and Jennifer T. Marshall, was originally published electronically on the publisher's internet portal (currently SpringerLink) on 31 May 2018 without open access. With the author(s)' decision to opt for Open Choice the copyright of the article changed on 25 July 2018 t
Influence of tandem axle on pavement responses and weight limit equivalency
No full textThe student, Aravind Ramakrishnan, submitted this Thesis for approval on 2020-12-08 at 12:33.This Thesis was approved for publication on 2020-12-08 at 17:03.DSpace SAF Submission Ingestion Package generated from Vireo submission #16088 on 2021-03-04 at 16:20:44Currently, pavements are designed using layered elastic theory (LET), which makes it easy to obtain the responses for a given loading configuration. However, LET minimizes the influence of axle loading configurations on pavements. Axle load limits for single (20 kips) and tandem axle (34 kips) are intended to be set so that the damage they produce is the same. In other words, axles at their corresponding weight limits are considered equivalent. Because pavement layers are more complicated than a linear elastic material, LET tends to underpredict loading responses and, hence, the damage. Therefore, to understand the effect of loading configuration on asphalt concrete (AC) behavior, Actual tandem loading and flexible pavement structure were modeled using finite element. The influence a 4.5-ft spaced tandem axle on flexible pavement responses and strain recovery were qualitatively assessed. The results showed that the effect of a tandem axle was highly pronounced for vertical strain on the subgrade, followed by that on the granular base, and then finally the transverse strain at bottom of the AC. Such responses could be approximately 1.5 to 1.75 times that of the single axle model. Stress-pulse analysis suggested that a tandem axle could be simulated accurately in laboratory tests. Although stress-pulse magnitude and shape (when no overlap is observed) are known to be independent of speed, loading-pulse duration can be calculated to identify the rest period. Similarly, domain analysis suggested that damage potential was affected by temperature and speed, which should be considered in platoon designs. Transfer functions from Mechanistic Empirical Pavement Design Guide (MEPDG) were used to compute pavement damage. Tandem axle (34 kips) and single axle (20 kips) were found to be inequivalent, confirming that the damage of the tandem axle was higher than single axle. For the given specific case, tandem axle weight of 30 kips was found to be equivalent to single axle (20 kips). This equivalency might be material, speed, tandem spacing, and structure dependent. Given the national goods movement, national weight limits should be applied after establishing an equivalency factor that consider pavement damage mechanistically.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-12-01The student, Aravind Ramakrishnan, accepted the attached license on 2020-12-08 at 12:17.Made available in DSpace on 2021-03-05T21:42:51Z (GMT). No. of bitstreams: 2
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Burnout and Attrition: Looking from the Perspective of Psychological Safety in Surgical Education
No full textThesis (Master's)--University of Washington, 2025Objective: Nearly 70% of surgical trainees suffer from burnout, 24-40% have thoughts of attrition, and 20% leave surgery residency. One approach to examine the environment in which burnout and attrition are prevalent is through assessment of a person’s psychological safety (PS). Cumulative stress of an environment with low PS could be hypothesized to contribute to burnout and attrition. The operating room (OR) is an ideal place to study PS given its complex, higher stress environment. Design:We conducted a mixed-methods survey examining 1) how PS in the OR relates to burnout and thoughts of attrition, considering duty hour violations and satisfaction with mentorship, and 2) how OR educator behaviors impact PS. Quantitative measures included validated PS and burnout scales, assessment of mentorship, thoughts of attrition, duty hour violations, and microaggressions. Thematic analysis was performed on open-ended questions about OR educator behaviors either promoting or hindering PS. Setting:Three general surgery programs across a variety of hospital settings. Participants: Categorical general surgery residents at all training levels. 35 responses were included for analysis, 37% response rate.
Results:
Increased PS was associated with increased satisfaction with mentorship, decreased duty hour violations, and decreased thoughts of attrition. Increased satisfaction with mentorship was associated with decreased burnout and decreased thoughts of attrition. 71% of residents reported that having positive experiences in the OR have a significant impact on how they view their educational experience. Four behavioral themes were found to be associated with PS: investment in the resident, encouraging a growth mindset, open communication, and creating a collaborative environment.
Conclusions:
Psychological safety in the OR appears to be associated with satisfaction with mentorship and thoughts of attrition, supporting our conceptual model describing the relationship between these factors. Understanding behavioral facilitators and decreasing barriers to PS in the OR can help institutions implement interventions for improving burnout and attrition in surgical education
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