scholarly journals Protein Surface Characterization Using an Invariant Descriptor

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Zainab Abu Deeb ◽  
Donald A. Adjeroh ◽  
Bing-Hua Jiang
Keyword(s):  
Surface Characterization ◽  
Large Scale ◽  
Area Under The Curve ◽  
Protein Families ◽  
Protein Surface ◽  
Local Descriptor ◽  
Protein Surfaces ◽  
Related Family ◽  
Distance Distributions ◽  
Comparative Results

Aim. To develop a new invariant descriptor for the characterization of protein surfaces, suitable for various analysis tasks, such as protein functional classification, and search and retrieval of protein surfaces over a large database.Methods. We start with a local descriptor of selected circular patches on the protein surface. The descriptor records the distance distribution between the central residue and the residues within the patch, keeping track of the number of particular pairwise residue cooccurrences in the patch. A global descriptor for the entire protein surface is then constructed by combining information from the local descriptors. Our method is novel in its focus on residue-specific distance distributions, and the use of residue-distance co-occurrences as the basis for the proposed protein surface descriptors.Results. Results are presented for protein classification and for retrieval for three protein families. For the three families, we obtained an area under the curve for precision and recall ranging from 0.6494 (without residue co-occurrences) to 0.6683 (with residue co-occurrences). Large-scale screening using two other protein families placed related family members at the top of the rank, with a number of uncharacterized proteins also retrieved. Comparative results with other proposed methods are included.

scholarly journals Assisting scalable diagnosis automatically via CT images in the combat against COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bohan Liu ◽  
Pan Liu ◽  
Lutao Dai ◽  
Yanlin Yang ◽  
Peng Xie ◽  
...  
Keyword(s):  
Large Scale ◽  
Area Under The Curve ◽  
Independent Set ◽  
Reference Method ◽  
Ct Images ◽  
Loss Of Life ◽  
Case Identification ◽  
Reverse Transcription Pcr ◽  
Using Data

AbstractThe pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

scholarly journals Analysis of blood-based gene expression in idiopathic Parkinson disease

Neurology ◽  
2017 ◽  
Vol 89 (16) ◽  
pp. 1676-1683 ◽  
Author(s):  
Ron Shamir ◽  
Christine Klein ◽  
David Amar ◽  
Eva-Juliane Vollstedt ◽  
Michael Bonin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson Disease ◽  
Gene Networks ◽  
Large Scale ◽  
Expression Profiles ◽  
Area Under The Curve ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
Gene Profiles ◽  
Independent Test

Objective:To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples).Methods:Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks.Results:A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E–6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E–4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1, ATP5A1, and VDAC3.Conclusions:We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.

scholarly journals Sensitive Blood-Based Detection of Asbestos-Associated Diseases Using Cysteine-Rich Angiogenic Inducer 61 as Circulating Protein Biomarker

2020 ◽  
Author(s):  
Kai Bartkowiak ◽  
Swaantje Casjens ◽  
Antje Andreas ◽  
Lucija Ačkar ◽  
Simon A Joosse ◽  
...  
Keyword(s):  
Large Scale ◽  
Mononuclear Cells ◽  
Area Under The Curve ◽  
Immunofluorescent Staining ◽  
Youden Index ◽  
Enzyme Linked Immunosorbent Assay ◽  
Characteristic Analysis ◽  
Clinical Sensitivity ◽  
Associated Diseases ◽  
Healthy Control

Abstract Background Detection of asbestos-associated diseases like asbestosis or mesothelioma is still challenging. We sought to improve the diagnosis of benign asbestos-associated disease (BAAD) by detection of the protein cysteine-rich angiogenic inducer 61 (Cyr61) in human plasma. Methods Plasma Cyr61 was quantified using an enzyme-linked immunosorbent assay. Plasma samples from males diagnosed with BAAD, but without a malignant disease (n = 101), and malignant mesothelioma (n = 21; 15 males, 6 females), as well as nonasbestos-exposed healthy control participants (n = 150; 58 males, 92 females) were analyzed. Clinical sensitivity and specificity of Cyr61 were determined by receiver operating characteristic analysis. Results The median plasma Cyr61 concentration for healthy control participants was 0.27 ng/mL. Cytoplasmic Cyr61 in peripheral blood mononuclear cells from healthy control participants was evenly distributed, as detected by immunofluorescent staining. The increase in plasma Cyr61 concentrations in the BAAD study group was statistically significant compared to the healthy control participants (P < 0.0001). For the detection of BAAD vs male healthy control participants, clinical sensitivity was 88% and clinical specificity 95% with an area under the curve of 0.924 at maximal Youden Index. For a predefined clinical specificity of 100%, the clinical sensitivity was 76%. For male mesothelioma patients vs male healthy control participants, the clinical sensitivity at maximal Youden Index was 95% with a clinical specificity of 100% (area under the curve, 0.997) and for a predefined clinical specificity of 100%, the clinical sensitivity was 93%. Conclusions In our study, plasma Cyr61 protein concentrations showed to be a new biomarker for asbestos-associated diseases like BAAD and mesothelioma in men, which deserves further investigation in large-scale cohort studies.

scholarly journals Development of the RMT20, a composite screener to identify common mental disorders

BJPsych Open ◽  
2020 ◽  
Vol 6 (3) ◽  
Author(s):  
Philip J. Batterham ◽  
Matthew Sunderland ◽  
Natacha Carragher ◽  
Alison L. Calear
Keyword(s):  
Psychological Distress ◽  
Anxiety Disorder ◽  
Mental Disorders ◽  
Large Scale ◽  
Common Mental Disorders ◽  
Area Under The Curve ◽  
Post Traumatic Stress ◽  
Cross Sectional ◽  
Epidemiological Surveys ◽  
General Psychological Distress

Background There are few very brief measures that accurately identify multiple common mental disorders. Aims The aim of this study was to develop and assess the psychometric properties of a new composite measure to screen for five common mental disorders. Method Two cross-sectional psychometric surveys were used to develop (n = 3175) and validate (n = 3620) the new measure, the Rapid Measurement Toolkit-20 (RMT20) against diagnostic criteria. The RMT20 was tested against a DSM-5 clinical checklist for major depression, generalised anxiety disorder, panic disorder, social anxiety disorder and post-traumatic stress disorder, with comparison with two measures of general psychological distress: the Kessler-10 and Distress Questionnaire-5. Results The area under the curve for the RMT20 was significantly greater than for the distress measures, ranging from 0.86 to 0.92 across the five disorders. Sensitivity and specificity at prescribed cut-points were excellent, with sensitivity ranging from 0.85 to 0.93 and specificity ranging from 0.73 to 0.83 across the five disorders. Conclusions The RMT20 outperformed two established scales assessing general psychological distress, is free to use and has low respondent burden. The measure is well-suited to clinical screening, internet-based screening and large-scale epidemiological surveys.

LARGE-SCALE TESTING OF BIBLIOME INFORMATICS USING PFAM PROTEIN FAMILIES

2005 ◽  
Author(s):  
ANA G. MAGUITMAN ◽  
ANDREAS RECHTSTEINER ◽  
KARIN VERSPOOR ◽  
CHARLIE E. STRAUSS ◽  
LUIS M. ROCHA
Keyword(s):  
Large Scale ◽  
Protein Families ◽  
Large Scale Testing

scholarly journals Clustering of Parameter Sensitivities: Examples from a Helicopter Airframe Model Updating Exercise

2009 ◽  
Vol 16 (1) ◽  
pp. 75-87 ◽  
Author(s):  
H. Shahverdi ◽  
C. Mares ◽  
W. Wang ◽  
J.E. Mottershead
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Large Scale ◽  
Structural Changes ◽  
Model Updating ◽  
Mass Density ◽  
Main Tool ◽  
Element Model ◽  
Sensitivity Matrix ◽  
Comparative Results

The need for high fidelity models in the aerospace industry has become ever more important as increasingly stringent requirements on noise and vibration levels, reliability, maintenance costs etc. come into effect. In this paper, the results of a finite element model updating exercise on a Westland Lynx XZ649 helicopter are presented. For large and complex structures, such as a helicopter airframe, the finite element model represents the main tool for obtaining accurate models which could predict the sensitivities of responses to structural changes and optimisation of the vibration levels. In this study, the eigenvalue sensitivities with respect to Young's modulus and mass density are used in a detailed parameterisation of the structure. A new methodology is developed using an unsupervised learning technique based on similarity clustering of the columns of the sensitivity matrix. An assessment of model updating strategies is given and comparative results for the correction of vibration modes are discussed in detail. The role of the clustering technique in updating large-scale models is emphasised.

scholarly journals Gully Erosion Susceptibility Mapping in Highly Complex Terrain Using Machine Learning Models

2021 ◽  
Vol 10 (10) ◽  
pp. 680
Author(s):  
Annan Yang ◽  
Chunmei Wang ◽  
Guowei Pang ◽  
Yongqing Long ◽  
Lei Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Complex Terrain ◽  
Large Scale ◽  
Area Under The Curve ◽  
Gully Erosion ◽  
Susceptibility Mapping ◽  
Weight Of Evidence ◽  
Gradient Boosting ◽  
Machine Learning Classification ◽  
Extreme Gradient Boosting

Gully erosion is the most severe type of water erosion and is a major land degradation process. Gully erosion susceptibility mapping (GESM)’s efficiency and interpretability remains a challenge, especially in complex terrain areas. In this study, a WoE-MLC model was used to solve the above problem, which combines machine learning classification algorithms and the statistical weight of evidence (WoE) model in the Loess Plateau. The three machine learning (ML) algorithms utilized in this research were random forest (RF), gradient boosted decision trees (GBDT), and extreme gradient boosting (XGBoost). The results showed that: (1) GESM were well predicted by combining both machine learning regression models and WoE-MLC models, with the area under the curve (AUC) values both greater than 0.92, and the latter was more computationally efficient and interpretable; (2) The XGBoost algorithm was more efficient in GESM than the other two algorithms, with the strongest generalization ability and best performance in avoiding overfitting (averaged AUC = 0.947), followed by the RF algorithm (averaged AUC = 0.944), and GBDT algorithm (averaged AUC = 0.938); and (3) slope gradient, land use, and altitude were the main factors for GESM. This study may provide a possible method for gully erosion susceptibility mapping at large scale.

scholarly journals Efficacy of Monocyte Distribution Width in the Early Diagnosis of Sepsis: A Diagnostic Meta-Analysis

2021 ◽  
Author(s):  
Jiahao Chen ◽  
Qiang Guo
Keyword(s):  
Early Diagnosis ◽  
Large Scale ◽  
Meta Analysis ◽  
Delayed Diagnosis ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Inexpensive Method ◽  
Diagnostic Efficacy ◽  
Distribution Width ◽  
Non Invasive

Abstract Background: Delayed diagnosis of sepsis urgently requires a fast, convenient, and inexpensive method to improve the early diagnosis of sepsis. Increasing evidence showed that monocyte distribution width (MDW) could be used as a non-invasive biomarker with high sensitivity and specificity for the early diagnosis of sepsis. However, the accuracy and reliability of its diagnosis are still controversial in different studies. Method: A meta-analysis of all available studies regarding the association between MDW and the diagnosis of sepsis was performed to systematically evaluate the diagnostic efficacy of MDW in the prediction of sepsis. Results: The estimated results of all eight studies are as follows: sensitivity, 0.84 (95% CI 0.77, 0.90); specificity, 0.68 (95% CI 0.54, 0.80); PLR, 2.7 (95% CI 1.8, 4.1); NLR, 0.23 (95% CI 0.15, 0.35); DOR is 12 (95% CI 5, 25). The corresponding overall area under the curve is 0.85 (95% CI 0.82, 0.88). Conclusion: In conclusion, this meta-analysis demonstrates that MDW has high accuracy in distinguishing patients with sepsis from healthy controls for early diagnosis of sepsis. However, large-scale prospective studies and joint diagnosis with other indicators are urgently required to confirm our findings and their utilization for routine clinical diagnosis in the future.

scholarly journals Assisting Scalable Diagnosis Automatically via CT Images in the Combat against COVID-19

2020 ◽  
Author(s):  
Bohan Liu ◽  
Pan Liu ◽  
Lutao Dai ◽  
Yanlin Yang ◽  
Peng Xie ◽  
...  
Keyword(s):  
Large Scale ◽  
Area Under The Curve ◽  
Independent Set ◽  
Reference Method ◽  
Ct Images ◽  
Accuracy Rate ◽  
Loss Of Life ◽  
Case Identification ◽  
Reverse Transcription Pcr

The pandemic of coronavirus Disease 2019 (COVID-19) caused enormous loss of life globally. 1-3 Case identification is critical. The reference method is using real-time reverse transcription PCR (rRT-PCR) assays, with limitations that may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that application of deep learning (DL) to the 3D CT images could help identify COVID-19 infections. Using the data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 patients. COVIDNet achieved an accuracy rate of 94.3% and an area under the curve (AUC) of 0.98. Application of DL to CT images may improve both the efficiency and capacity of case detection and long-term surveillance.

scholarly journals Value of modified Burns Wean Assessment Program scores in the respiratory intensive care unit: An Egyptian study

2020 ◽  
Vol 15 ◽  
Author(s):  
Nermeen A. Abdelaleem ◽  
Sherif A.A. Mohamed ◽  
Azza S. Abd ElHafeez ◽  
Hassan A. Bayoumi
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Large Scale ◽  
Area Under The Curve ◽  
Chronic Obstructive ◽  
Multicenter Studies ◽  
Respiratory Disorders ◽  
Assessment Program ◽  
Rapid Shallow Breathing Index ◽  
Respiratory Intensive Care Unit

Background: There is no consensus on the most useful predictive indicator for weaning patients from mechanical ventilation (MV). We aimed to evaluate the utility of the modified Burns Wean Assessment Program (m-BWAP) in predicting the weaning success in patients with respiratory disorders admitted to the respiratory intensive care unit (RICU).Methods: Patients with respiratory failure requiring MV for longer than 48 hours were included. They were weaned by pressure support ventilation and spontaneous breathing trails. Patients were divided into successful and unsuccessful weaning groups according to their outcomes.Results: A total of 91 patients were enrolled. The majority had chronic obstructive pulmonary diseases (COPD); 40%, overlap syndrome (24%), and obesity hypoventilation syndrome (OHS): 15%. The successful group had significantly higher m-BWAP scores than that in the unsuccessful group (median 65; range 35 to 80 vs median 45; range 30 to 65; p=0.000), with area under the curve (AUC) of 0.854; 95% CI 0.766 to 0.919), p<0.001. At cut-off value of ≥55, the sensitivity and specificity of m-BWAP to predict successful weaning were 73.77% and 84.85%, respectively. The AUC for m-BWAP was significantly higher than that for rapid shallow breathing index (RSBI).Conclusion: We conclude that m-BWAP scores represent a good predictor of weaning success among patient with chronic respiratory disorders in the RICU. The m-BWAP checklist has many factors that are closely related to the weaning outcomes of patients with chronic respiratory disorders. Further, large-scale, multicenter studies are warrented.

Sign in / Sign up

Export Citation Format

Share Document