scholarly journals Assessment of protein–protein interfaces in cryo-EM derived assemblies

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sony Malhotra ◽  
Agnel Praveen Joseph ◽  
Jeyan Thiyagalingam ◽  
Maya Topf
Keyword(s):  
Machine Learning ◽  
Crystal Structures ◽  
Model Validation ◽  
Assessment Tool ◽  
Atomistic Model ◽  
Protein Interface ◽  
Macromolecular Assemblies ◽  
Assessment Scores ◽  
Density Score ◽  
Protein Interfaces

AbstractStructures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we comprehensively assessed protein–protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed Protein Interface-score (PI-score), a density-independent machine learning-based metric, trained using the features of protein–protein interfaces in crystal structures. We evaluated 5873 interfaces in 1053 PDB-deposited cryo-EM models (including SARS-CoV-2 complexes), as well as the models submitted to CASP13 cryo-EM targets and the EM model challenge. We further inspected the interfaces associated with low-scores and found that some of those, especially in intermediate-to-low resolution (worse than 4 Å) structures, were not captured by density-based assessment scores. A combined score incorporating PI-score and fit-to-density score showed discriminatory power, allowing our method to provide a powerful complementary assessment tool for the ever-increasing number of complexes solved by cryo-EM.

scholarly journals Assessment of protein-protein interfaces in cryo-EM derived assemblies

2020 ◽  
Author(s):  
Sony Malhotra ◽  
Agnel Joseph ◽  
Jeyan Thiyagalingam ◽  
Maya Topf
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Crystal Structures ◽  
Model Validation ◽  
Assessment Tool ◽  
Atomistic Model ◽  
Low Resolution ◽  
Macromolecular Assemblies ◽  
Assessment Scores ◽  
Protein Interfaces

Abstract Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for the map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we have assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed PI-score, a density-independent machine learning-based metric, trained using protein-protein interfaces’ features in high-resolution crystal structures. Using PI-score, we were able to identify errors at interfaces in the PDB-deposited cryo-EM structures (including SARS-CoV-2 complexes) and in the models submitted for cryo-EM targets in CASP13 and the EM model challenge. Some of the identified errors, especially at medium-to-low resolution structures, were not captured by density-based assessment scores. Our method can therefore provide a powerful complementary assessment tool for the increasing number of complexes solved by cryo-EM.

scholarly journals Assessment of protein-protein interfaces in cryo-EM derived assemblies

2020 ◽  
Author(s):  
Sony Malhotra ◽  
Agnel Praveen Joseph ◽  
Jeyan Thiyagalingam ◽  
Maya Topf
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Crystal Structures ◽  
Model Validation ◽  
Assessment Tool ◽  
Atomistic Model ◽  
Low Resolution ◽  
Macromolecular Assemblies ◽  
Assessment Scores ◽  
Protein Interfaces

AbstractStructures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for the map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we have assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed PI-score, a density-independent machine learning-based metric, trained using protein-protein interfaces’ features in high-resolution crystal structures. Using PI-score, we were able to identify errors at interfaces in the PDB-deposited cryo-EM structures (including SARS-CoV-2 complexes) and in the models submitted for cryo-EM targets in CASP13 and the EM model challenge. Some of the identified errors, especially at medium-to-low resolution structures, were not captured by density-based assessment scores. Our method can therefore provide a powerful complementary assessment tool for the increasing number of complexes solved by cryo-EM.

scholarly journals Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6459
Author(s):  
Swagata Das ◽  
Wataru Sakoda ◽  
Priyanka Ramasamy ◽  
Ramin Tadayon ◽  
Antonio Vega Ramirez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lower Limb ◽  
Assessment Tool ◽  
Activation Function ◽  
Skill Assessment ◽  
Locomotive Syndrome ◽  
Japanese Orthopedic Association ◽  
Assessment Scores ◽  
Short Test ◽  
Artificial Neural Network Ann

Early and self-identification of locomotive degradation facilitates us with awareness and motivation to prevent further deterioration. We propose the usage of nine squat and four one-leg standing exercise features as input parameters to Machine Learning (ML) classifiers in order to perform lower limb skill assessment. The significance of this approach is that it does not demand manpower and infrastructure, unlike traditional methods. We base the output layer of the classifiers on the Short Test Battery Locomotive Syndrome (STBLS) test used to detect Locomotive Syndrome (LS) approved by the Japanese Orthopedic Association (JOA). We obtained three assessment scores by using this test, namely sit-stand, 2-stride, and Geriatric Locomotive Function Scale (GLFS-25). We tested two ML methods, namely an Artificial Neural Network (ANN) comprised of two hidden layers with six nodes per layer configured with Rectified-Linear-Unit (ReLU) activation function and a Random Forest (RF) regressor with number of estimators varied from 5 to 100. We could predict the stand-up and 2-stride scores of the STBLS test with correlation of 0.59 and 0.76 between the real and predicted data, respectively, by using the ANN. The best accuracies (R-squared values) obtained through the RF regressor were 0.86, 0.79, and 0.73 for stand-up, 2-stride, and GLFS-25 scores, respectively.

scholarly journals 136 Reflections on the foundational scientific knowledge of undergraduate animal science students: a three-year study

2019 ◽  
Vol 97 (Supplement_1) ◽  
pp. 79-79
Author(s):  
Lauren R Thomas ◽  
Jeremy G Powell ◽  
Elizabeth B Kegley ◽  
Kathleen Jogan
Keyword(s):  
Student Performance ◽  
Scientific Knowledge ◽  
Assessment Tool ◽  
Undergraduate Teaching ◽  
Experimental Unit ◽  
Student Knowledge ◽  
Animal Science ◽  
Muscle Foods ◽  
Assessment Scores ◽  
Percentage Points

Abstract In 2015, the University of Arkansas Department of Animal Science developed a strategy for assessing student-learning outcomes within its undergraduate teaching program. The first recognized outcome states that students will demonstrate foundational scientific knowledge in the general animal science disciplines of physiology, genetics, nutrition, muscle foods, and production animal management. Subsequently, a 58-item assessment tool was developed for direct assessment of student knowledge—focusing primarily on freshmen and senior students. Over the past 3 academic calendar years, 381 students (196 freshmen, 48 sophomores, 19 juniors, 113 seniors, 5 graduates) were assessed, either during an introduction to animal science course or by appointment with outgoing seniors majoring in animal science. Scores were categorized using demographic data collected at the beginning of the assessment tool. Comparison categories included academic class, major, and general student background (rural or urban). Data analysis were performed using the Glimmix procedure of SAS, with student serving as the experimental unit and significance set at P ≤ 0.05. Generally speaking, animal science majors performed better (P < 0.01) than students from other majors, and students with a rural background performed better (P < 0.01) than their urban-backgrounded peers. Overall, senior assessment scores averaged 23-percentage points greater (P < 0.01) than freshmen assessment scores, and the average scores for freshmen and seniors were 43% and 66% respectively. In regards to student performance within each discipline, there was an average improvement of 24 percentage points between freshmen and seniors in all of the measured disciplines except for muscle foods, which only saw a 10-percentage point improvement between the two classes. While the overall improvement in scores is indicative of increased student knowledge, the department would like to see greater improvement in all discipline scores for seniors majoring in animal science.

scholarly journals Generalisability through local validation: overcoming barriers due to data disparity in healthcare

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
William Greig Mitchell ◽  
Edward Christopher Dee ◽  
Leo Anthony Celi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Global Health ◽  
Model Validation ◽  
Health Inequity ◽  
Diverse Populations ◽  
Single Centre ◽  
Model Accuracy ◽  
South Korean ◽  
Deep Learning Model

AbstractCho et al. report deep learning model accuracy for tilted myopic disc detection in a South Korean population. Here we explore the importance of generalisability of machine learning (ML) in healthcare, and we emphasise that recurrent underrepresentation of data-poor regions may inadvertently perpetuate global health inequity.Creating meaningful ML systems is contingent on understanding how, when, and why different ML models work in different settings. While we echo the need for the diversification of ML datasets, such a worthy effort would take time and does not obviate uses of presently available datasets if conclusions are validated and re-calibrated for different groups prior to implementation.The importance of external ML model validation on diverse populations should be highlighted where possible – especially for models built with single-centre data.

Agreement and Reliability Analysis of Machine Learning Scaling in the Assessment of Acute Proximal Weakness by Experts and Non-Experts: Clinical Feasibility Study (Preprint)

2021 ◽  
Author(s):  
Eunjeong Park ◽  
Kijeong Lee ◽  
Taehwa Han ◽  
Hyo Suk Nam
Keyword(s):  
Machine Learning ◽  
Reliability Analysis ◽  
Gold Standard ◽  
Assessment Tool ◽  
Expert Knowledge ◽  
Hybrid Approach ◽  
Assessment Model ◽  
Lower Limbs ◽  
Neurological Deficits ◽  
Percent Agreement

BACKGROUND Assessing the symptoms of proximal weakness caused by neurological deficits requires expert knowledge and experienced neurologists. Recent advances in artificial intelligence and the Internet of Things have resulted in the development of automated systems that emulate physicians’ assessments. OBJECTIVE This study provides an agreement and reliability analysis of using an automated scoring system to evaluate proximal weakness by experts and non-experts. METHODS We collected 144 observations from acute stroke patients in a neurological intensive care unit to measure the symptom of proximal weakness of upper and lower limbs. A neurologist performed a gold standard assessment and two medical students performed identical tests as non-expert assessments for manual and machine learning-based scaling of Medical Research Council (MRC) proximal scores. The system collects signals from sensors attached on patients’ limbs and trains a machine learning assessment model using the hybrid approach of data-level and algorithm-level methods for the ordinal and imbalanced classification in multiple classes. For the agreement analysis, we investigated the percent agreement of MRC proximal scores and Bland-Altman plots of kinematic features between the expert- and non-expert scaling. In the reliability analysis, we analysed the intra-class correlation coefficients (ICCs) of kinematic features and Krippendorff’s alpha of the three observers’ scaling. RESULTS The mean percent agreement between the gold standard and the non-expert scaling was 0.542 for manual scaling and 0.708 for IoT-assisted machine learning scaling, with 30.63% enhancement. The ICCs of kinematic features measured using sensors ranged from 0.742 to 0.850, whereas the Krippendorff’s alpha of manual scaling for the three observers was 0.275. The Krippendorff’s alpha of machine learning scaling increased to 0.445, with 61.82% improvement. CONCLUSIONS Automated scaling using sensors and machine learning provided higher inter-rater agreement and reliability in assessing acute proximal weakness. The enhanced assessment supported by the proposed system can be utilized as a reliable assessment tool for non-experts in various emergent environments.

scholarly journals Validation and correction of Zn–Cys x His y complexes

2016 ◽  
Vol 72 (10) ◽  
pp. 1110-1118 ◽  
Author(s):  
Wouter G. Touw ◽  
Bart van Beusekom ◽  
Jochem M. G. Evers ◽  
Gert Vriend ◽  
Robbie P. Joosten
Keyword(s):  
Crystal Structures ◽  
Protein Data Bank ◽  
Model Validation ◽  
Data Bank ◽  
Zinc Complexes ◽  
Zinc Ions ◽  
Tetrahedral Complex ◽  
Context Sensitive

Many crystal structures in the Protein Data Bank contain zinc ions in a geometrically distorted tetrahedral complex with four Cys and/or His ligands. A method is presented to automatically validate and correct these zinc complexes. Analysis of the corrected zinc complexes shows that the average Zn–Cys distances and Cys–Zn–Cys angles are a function of the number of cysteines and histidines involved. The observed trends can be used to develop more context-sensitive targets for model validation and refinement.

scholarly journals Long-term streamflow forecasting for the Cascade Reservoir System of Han River using SWAT with CFS output

2018 ◽  
Vol 50 (2) ◽  
pp. 655-671
Author(s):  
Tian Liu ◽  
Yuanfang Chen ◽  
Binquan Li ◽  
Yiming Hu ◽  
Hui Qiu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Assessment Tool ◽  
Flow Simulation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Distribution Models ◽  
Han River ◽  
Reservoir System

Abstract Due to the large uncertainties of long-term precipitation prediction and reservoir operation, it is difficult to forecast long-term streamflow for large basins with cascade reservoirs. In this paper, a framework coupling the original Climate Forecasting System (CFS) precipitation with the Soil and Water Assessment Tool (SWAT) was proposed to forecast the nine-month streamflow for the Cascade Reservoir System of Han River (CRSHR) including Shiquan, Ankang and Danjiangkou reservoirs. First, CFS precipitation was tested against the observation and post-processed through two machine learning algorithms, random forest and support vector regression. Results showed the correlation coefficients between the monthly areal CFS precipitation (post-processed) and observation were 0.91–0.96, confirming that CFS precipitation post-processing using machine learning was not affected by the extended forecast period. Additionally, two precipitation spatio-temporal distribution models, original CFS and similar historical observation, were adopted to disaggregate the processed monthly areal CFS precipitation to daily subbasin-scale precipitation. Based on the reservoir restoring flow, the regional SWAT was calibrated for CRSHR. The Nash–Sutcliffe efficiencies for three reservoirs flow simulation were 0.86, 0.88 and 0.84, respectively, meeting the accuracy requirement. The experimental forecast showed that for three reservoirs, long-term streamflow forecast with similar historical observed distribution was more accurate than that with original CFS.

scholarly journals Preoperative Practice Paired With Instructor Feedback May Not Improve Obstetrics-Gynecology Residents' Operative Performance

2017 ◽  
Vol 9 (2) ◽  
pp. 190-194 ◽  
Author(s):  
Jamie Kroft ◽  
Michael Ordon ◽  
Leslie Po ◽  
Nora Zwingerman ◽  
Katie Waters ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Controlled Trial ◽  
Intraclass Correlation ◽  
Assessment Scores ◽  
Surgical Trainees ◽  
Significant Difference ◽  
Instructor Feedback ◽  
Operative Performance ◽  
Optimal Approach ◽  
Laparoscopic Salpingectomy

ABSTRACT Background There is evidence that preoperative practice prior to surgery can improve trainee performance, but the optimal approach has not been studied. Objective We sought to determine if preoperative practice by surgical trainees paired with instructor feedback improved surgical technique, compared to preoperative practice or feedback alone. Methods We conducted a randomized controlled trial of obstetrics-gynecology trainees, stratified on a simulator-assessed surgical skill. Participants were randomized to preoperative practice on a simulator with instructor feedback (PPF), preoperative practice alone (PP), or feedback alone (F). Trainees then completed a laparoscopic salpingectomy, and the operative performance was evaluated using an assessment tool. Results A total of 18 residents were randomized and completed the study, 6 in each arm. The mean baseline score on the simulator was comparable in each group (67% for PPF, 68% for PP, and 70% for F). While the median score on the assessment tool for laparoscopic salpingectomy in the PPF group was the highest, there was no statistically significant difference in assessment scores for the PPF group (32.75; range, 15–36) compared to the PP group (14.5; range, 10–34) and the F group (21.25; range, 10.5–32). The interrater correlation between the video reviewers was 0.87 (95% confidence interval 0.70–0.95) using the intraclass correlation coefficient. Conclusions This study suggests that a surgical preoperative practice with instructor feedback may not improve operative technique compared to either preoperative practice or feedback alone.

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