Validation of the CoGEF Method as a Predictive Tool for Polymer Mechanochemistry

2020 ◽  
Vol 142 (38) ◽  
pp. 16364-16381 ◽  
Author(s):  
Isabel M. Klein ◽  
Corey C. Husic ◽  
Dávid P. Kovács ◽  
Nicolas J. Choquette ◽  
Maxwell J. Robb
Keyword(s):  
Predictive Tool ◽  
Polymer Mechanochemistry

Molecular phylogenetics as a predictive tool in plant-based drug discovery in the genus Euphorbia L.

Planta Medica ◽  
2016 ◽  
Vol 81 (S 01) ◽  
pp. S1-S381 ◽  
Author(s):  
M Ernst ◽  
CH Saslis-Lagoudakis ◽  
OM Grace ◽  
N Nilsson ◽  
H Toft Simonsen ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Molecular Phylogenetics ◽  
Predictive Tool

Predictive tool for frictional performance of piston ring-pack/liner conjunction

2019 ◽  
Vol 13 (3) ◽  
pp. 5513-5527
Author(s):  
J. W. Tee ◽  
S. H. Hamdan ◽  
W. W. F. Chong
Keyword(s):  
Film Thickness ◽  
Mathematical Models ◽  
Piston Ring ◽  
Published Data ◽  
Predictive Tool ◽  
Frictional Losses ◽  
Fundamental Understanding ◽  
Minimum Film Thickness ◽  
Piston Ring Pack ◽  
Ring Pack

Fundamental understanding of piston ring-pack lubrication is essential in reducing engine friction. This is because a substantial portion of engine frictional losses come from piston-ring assembly. Hence, this study investigates the tribological impact of different piston ring profiles towards engine in-cylinder friction. Mathematical models are derived from Reynolds equation by using Reynolds’ boundary conditions to generate the contact pressure distribution along the complete piston ring-pack/liner conjunction. The predicted minimum film thickness is then used to predict the friction generated between the piston ring-pack and the engine cylinder liner. The engine in-cylinder friction is predicted using Greenwood and Williamson’s rough surface contact model. The model considers both the boundary friction and the viscous friction components. These mathematical models are integrated to simulate the total engine in-cylinder friction originating from the studied piston ring-pack for a complete engine cycle. The predicted minimum film thickness and frictional properties from the current models are shown to correlate reasonably with the published data. Hence, the proposed mathematical approach prepares a simplistic platform in predicting frictional losses of piston ring-pack/liner conjunction, allowing for an improved fundamental understanding of the parasitic losses in an internal combustion engine.

TUMOUR PATHOLOGY PREDICTS MICROSATELLITE INSTABILITY IN A POPULATION-BASED SERIES OF COLORECTAL CANCER CASES

2008 ◽  
Vol 31 (4) ◽  
pp. 12
Author(s):  
A J Hyde ◽  
D Fontaine ◽  
R C Green ◽  
M Simms ◽  
P S Parfrey ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Lynch Syndrome ◽  
Microsatellite Instability ◽  
Population Based ◽  
Pathological Features ◽  
Predictive Tool ◽  
Entire Cohort ◽  
Dna Mismatch ◽  
Bethesda Guidelines ◽  
Revised Bethesda Guidelines

Background: Lynch Syndrome is an autosomal dominant trait that accounts forapproximately 3% of all cases of colorectal cancer (CRC). It is caused by mutations in DNA mismatch repair (MMR) genes, most commonly MLH1 or MSH2. These MMR defects cause high levels of microsatellite instability (MSI-H) in the tumours. MSI testing of all CRCs to identify potential Lynch Syndrome cases is not practical, so the Bethesda Guidelines, which use clinical and pathological features, were created to identify those tumours most likely to be MSI-H^1. In 2007 Jenkins et. al. created MsPath, a tool based on the pathological features described in the rarely used 3^rd Bethesda criterion, to improve prediction of MSI-H tumours among CRC cases diagnosed before age 60 years^2. Methods: We collected a population-based cohort of 716 CRC cases diagnosed before age 75 years in Newfoundland. For each of these cases we collected family history, performed MSI analysis, and scored a number of pathological features for the purpose of evaluating the accuracy of the Bethesda Criteria and MsPath at predicting MSI-H tumours. Results: Our work validates the MsPath tool in the Newfoundland population for the same age group used to create the tool. We found it identified MSI-H cases with a sensitivity of 95% and specificity of 35% in our population of CRCcases diagnosed before age 60 years (n=290). We also tested this tool on our older population of CRCcases, diagnosed at ages 60 to 74 years (n=426). We found it to be at least as predictive in this population,with a sensitivity of 95% and a specificity of 42%. We then used our entire cohort (N=716) to compare MsPath with the other Bethesda criteria.Bethesda criteria 1, 2, 4 and 5 together predicted MSI-H cases with a sensitivity of 67% and a specificity of 51%. MsPath was better at identifying these cases, with a sensitivity of 95% and a specificity of 39%. Conclusions: We conclude that MsPath can be extended to include patients diagnosed with CRC before age 75 years. As well, we have found that MsPath is a better predictive tool than the Revised Bethesda Guidelines for identifying MSI-H cases within a population-based setting of colorectal cancer. References: 1. Umar, A. et. al. J Natl Cancer Inst 2004;96:261-8 2.Jenkins, M.A. et. al. Gastroenterology 2007;133:48-56

Modelling heavy metal mobilisation in solid waste deposits - a predictive tool for environmental risk assessment

1999 ◽  
Vol 39 (10-11) ◽  
pp. 193-196
Author(s):  
J. Petersen ◽  
J. G. Petrie
Keyword(s):  
Heavy Metal ◽  
Solid Waste ◽  
Environmental Risk ◽  
Mechanistic Model ◽  
Small Sample ◽  
Waste Material ◽  
Metal Species ◽  
Model Parameters ◽  
Assessment Methodology ◽  
Predictive Tool

The release of heavy metal species from deposits of solid waste materials originating from minerals processing operations poses a serious environmental risk should such species migrate beyond the boundaries of the deposit into the surrounding environment. Legislation increasingly places the liability for wastes with the operators of the process that generates them. The costs for long-term monitoring and clean-up following a potential critical leakage have to be factored in the overall project plan from the outset. Thus assessment of the potential for a particular waste material to generate a harmful leachate is directly relevant for estimating the environmental risk associated with the planned disposal operation. A rigorous mechanistic model is proposed, which allows prediction of the time-dependent generation of a leachate from a solid mineral waste deposit. Model parameters are obtained from a suitably designed laboratory waste assessment methodology on a relatively small sample of the prospective waste material. The parameters are not specific to the laboratory environment in which they were obtained but are valid also for full-scale heap modelling. In this way the model, combined with the assessment methodology, becomes a powerful tool for meaningful assessment of the risks associated with solid waste disposal strategies.

scholarly journals A data-driven approach to violin making

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sebastian Gonzalez ◽  
Davide Salvi ◽  
Daniel Baeza ◽  
Fabio Antonacci ◽  
Augusto Sarti
Keyword(s):  
Statistical Learning ◽  
Geometric Parameters ◽  
Data Driven ◽  
Vibrational Properties ◽  
Learning Tools ◽  
Predictive Tool ◽  
Complete Control ◽  
Data Driven Approach ◽  
To Come ◽  
Violin Making

AbstractOf all the characteristics of a violin, those that concern its shape are probably the most important ones, as the violin maker has complete control over them. Contemporary violin making, however, is still based more on tradition than understanding, and a definitive scientific study of the specific relations that exist between shape and vibrational properties is yet to come and sorely missed. In this article, using standard statistical learning tools, we show that the modal frequencies of violin tops can, in fact, be predicted from geometric parameters, and that artificial intelligence can be successfully applied to traditional violin making. We also study how modal frequencies vary with the thicknesses of the plate (a process often referred to as plate tuning) and discuss the complexity of this dependency. Finally, we propose a predictive tool for plate tuning, which takes into account material and geometric parameters.

scholarly journals Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system

2021 ◽  
Author(s):  
Faeze Salahshour ◽  
Mohammad-Mehdi Mehrabinejad ◽  
Mohssen Nassiri Toosi ◽  
Masoumeh Gity ◽  
Hossein Ghanaati ◽  
...  
Keyword(s):  
Scoring System ◽  
Chest Ct ◽  
Predictive Tool ◽  
Quantitative Scoring ◽  
Ct Features

scholarly journals Circulating let-7e-5p, miR-106a-5p, miR-28-3p, and miR-542-5p as a Promising microRNA Signature for the Detection of Colorectal Cancer

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1493
Author(s):  
Camila Meirelles S. Silva ◽  
Mateus C. Barros-Filho ◽  
Deysi Viviana T. Wong ◽  
Julia Bette H. Mello ◽  
Livia Maria S. Nobre ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Unmet Need ◽  
Area Under The Curve ◽  
Discrimination Performance ◽  
Colorectal Carcinogenesis ◽  
The Cancer Genome Atlas ◽  
Colon Perforation ◽  
Data Series ◽  
Predictive Tool ◽  
Incidence And Mortality

Colorectal cancer (CRC) is a disease with high incidence and mortality. Colonoscopy is a gold standard among tests used for CRC traceability. However, serious complications, such as colon perforation, may occur. Non-invasive diagnostic procedures are an unmet need. We aimed to identify a plasma microRNA (miRNA) signature for CRC detection. Plasma samples were obtained from subjects (n = 109) at different stages of colorectal carcinogenesis. The patients were stratified into a non-cancer (27 healthy volunteers, 17 patients with hyperplastic polyps, 24 with adenomas), and a cancer group (20 CRC and 21 metastatic CRC). miRNAs (381) were screened by TaqMan Low-Density Array. A classifier based on four differentially expressed miRNAs (miR-28-3p, let-7e-5p, miR-106a-5p, and miR-542-5p) was able to discriminate cancer versus non-cancer cases. The overexpression of these miRNAs was confirmed by RT-qPCR, and a cross-study validation step was implemented using eight data series retrieved from Gene Expression Omnibus (GEO). In addition, another external data validation using CRC surgical specimens from The Cancer Genome Atlas (TCGA) was carried out. The predictive model’s performance in the validation set was 76.5% accuracy, 59.4% sensitivity, and 86.8% specificity (area under the curve, AUC = 0.716). The employment of our model in the independent publicly available datasets confirmed a good discrimination performance in five of eight datasets (median AUC = 0.823). Applying this algorithm to the TCGA cohort, we found 99.5% accuracy, 99.7% sensitivity, and 90.9% specificity (AUC = 0.998) when the model was applied to solid colorectal tissues. Overall, we suggest a novel signature of four circulating miRNAs, i.e., miR-28-3p, let-7e-5p, miR-106a-5p, and miR-542-5p, as a predictive tool for the detection of CRC.

scholarly journals The hemocyte counts as a potential biomarker for predicting disease progression in COVID-19: a retrospective study

2020 ◽  
Vol 58 (7) ◽  
pp. 1106-1115 ◽  
Author(s):  
Yufen Zheng ◽  
Ying Zhang ◽  
Hongbo Chi ◽  
Shiyong Chen ◽  
Minfei Peng ◽  
...  
Keyword(s):  
Risk Factors ◽  
Platelet Count ◽  
Disease Progression ◽  
Neutrophil Count ◽  
Calibration Curve ◽  
Lymphocyte Count ◽  
Severe Disease ◽  
Clinical Impact ◽  
Predictive Tool ◽  
Potential Biomarker

AbstractObjectivesIn December 2019, there was an outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China, and since then, the disease has been increasingly spread throughout the world. Unfortunately, the information about early prediction factors for disease progression is relatively limited. Therefore, there is an urgent need to investigate the risk factors of developing severe disease. The objective of the study was to reveal the risk factors of developing severe disease by comparing the differences in the hemocyte count and dynamic profiles in patients with severe and non-severe COVID-19.MethodsIn this retrospectively analyzed cohort, 141 confirmed COVID-19 patients were enrolled in Taizhou Public Health Medical Center, Taizhou Hospital, Zhejiang Province, China, from January 17, 2020 to February 26, 2020. Clinical characteristics and hemocyte counts of severe and non-severe COVID patients were collected. The differences in the hemocyte counts and dynamic profiles in patients with severe and non-severe COVID-19 were compared. Multivariate Cox regression analysis was performed to identify potential biomarkers for predicting disease progression. A concordance index (C-index), calibration curve, decision curve and the clinical impact curve were calculated to assess the predictive accuracy.ResultsThe data showed that the white blood cell count, neutrophil count and platelet count were normal on the day of hospital admission in most COVID-19 patients (87.9%, 85.1% and 88.7%, respectively). A total of 82.8% of severe patients had lymphopenia after the onset of symptoms, and as the disease progressed, there was marked lymphopenia. Multivariate Cox analysis showed that the neutrophil count (hazard ratio [HR] = 4.441, 95% CI = 1.954–10.090, p = 0.000), lymphocyte count (HR = 0.255, 95% CI = 0.097–0.669, p = 0.006) and platelet count (HR = 0.244, 95% CI = 0.111–0.537, p = 0.000) were independent risk factors for disease progression. The C-index (0.821 [95% CI, 0.746–0.896]), calibration curve, decision curve and the clinical impact curve showed that the nomogram can be used to predict the disease progression in COVID-19 patients accurately. In addition, the data involving the neutrophil count, lymphocyte count and platelet count (NLP score) have something to do with improving risk stratification and management of COVID-19 patients.ConclusionsWe designed a clinically predictive tool which is easy to use for assessing the progression risk of COVID-19, and the NLP score could be used to facilitate patient stratification management.

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