A Plan to Develop Predictive Capability for Equatorial Scintillation Storms.

Developing a Predictive Capability for Bioluminescence Signatures

10.21236/ada531012 ◽

2009 ◽

Author(s):

Michael I. Latz ◽

Grant Deane ◽

M. D. Stokes ◽

Mark Hyman

Keyword(s):

Predictive Capability

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Utilizing Twitter Data Analysis and Deep Learning to Identify Drug Use (Preprint)

10.2196/preprints.14681 ◽

2019 ◽

Author(s):

Joseph Tassone ◽

Peizhi Yan ◽

Mackenzie Simpson ◽

Chetan Mendhe ◽

Vijay Mago ◽

...

Keyword(s):

Social Media ◽

Logistic Regression ◽

Deep Learning ◽

Decision Tree ◽

Semantic Meaning ◽

Predictive Capability ◽

Logistic Regression Models ◽

Twitter Data ◽

Data Points ◽

Positive Classification

BACKGROUND The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. OBJECTIVE Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. METHODS Twitter social media tweets and attribute data were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset 3,696,150 rows. The predictive classification power of multiple methods was compared including regression, decision trees, and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. RESULTS The logistic regression and decision tree models utilized 12,142 data points for training and 1041 data points for testing. The results calculated from the logistic regression models respectively displayed an accuracy of 54.56% and 57.44%, and an AUC of 0.58. While an improvement, the decision tree concluded with an accuracy of 63.40% and an AUC of 0.68. All these values implied a low predictive capability with little to no discrimination. Conversely, the CNN-based classifiers presented a heavy improvement, between the two models tested. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. CONCLUSIONS Predictive analysis without a CNN is limited and possibly fruitless. Attribute-based models presented little predictive capability and were not suitable for analyzing this type of data. The semantic meaning of the tweets needed to be utilized, giving the CNN-based classifier an advantage over other solutions. Additionally, commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased scores, improving the predictive capability. CLINICALTRIAL None

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Comparison of pretherapeutic osseous tumor volume and standard hematology for prediction of hematotoxicity after PSMA-targeted radioligand therapy

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-021-05412-1 ◽

2021 ◽

Author(s):

Liam Widjaja ◽

Rudolf A. Werner ◽

Tobias L. Ross ◽

Frank M. Bengel ◽

Thorsten Derlin

Keyword(s):

Multivariate Analysis ◽

Tumor Volume ◽

Predictive Performance ◽

Castration Resistant Prostate Cancer ◽

Operating Characteristics ◽

Predictive Capability ◽

Radioligand Therapy ◽

Castration Resistant ◽

Pet Ct ◽

Late Stages

Abstract Purpose Hematotoxicity is a potentially dose-limiting adverse event in patients with metastasized castration-resistant prostate cancer (mCRPC) undergoing prostate-specific membrane antigen (PSMA)-directed radioligand therapy (RLT). We aimed to identify clinical or PSMA-targeted imaging-derived parameters to predict hematological adverse events at early and late stages in the treatment course. Methods In 67 patients with mCRPC scheduled for 177Lu-PSMA-617 RLT, pretherapeutic osseous tumor volume (TV) from 68Ga-PSMA-11 PET/CT and laboratory values were assessed. We then tested the predictive capability of these parameters for early and late hematotoxicity (according to CTCAE vers. 5.0) after one cycle of RLT and in a subgroup of 32/67 (47.8%) patients after four cycles of RLT. Results After one cycle, 10/67 (14.9%) patients developed leukocytopenia (lymphocytopenia, 39/67 [58.2%]; thrombocytopenia, 17/67 [25.4%]). A cut-off of 5.6 × 103/mm3 for baseline leukocytes was defined by receiver operating characteristics (ROC) and separated between patients with and without leukocytopenia (P < 0.001). Baseline leukocyte count emerged as a stronger predictive factor in multivariate analysis (hazard ratio [HR], 33.94, P = 0.001) relative to osseous TV (HR, 14.24, P = 0.01). After four cycles, 4/32 (12.5%) developed leukocytopenia and the pretherapeutic leukocyte cut-off (HR, 9.97, P = 0.082) tended to predict leukocytopenia better than TV (HR, 8.37, P = 0.109). In addition, a cut-off of 1.33 × 103/mm3 for baseline lymphocytes separated between patients with and without lymphocytopenia (P < 0.001), which was corroborated in multivariate analysis (HR, 21.39, P < 0.001 vs. TV, HR, 4.57, P = 0.03). After four cycles, 19/32 (59.4%) developed lymphocytopenia and the pretherapeutic cut-off for lymphocytes (HR, 46.76, P = 0.007) also demonstrated superior predictive performance for late lymphocytopenia (TV, HR, 5.15, P = 0.167). Moreover, a cut-off of 206 × 103/mm3 for baseline platelets separated between patients with and without thrombocytopenia (P < 0.001) and also demonstrated superior predictive capability in multivariate analysis (HR, 115.02, P < 0.001 vs.TV, HR, 12.75, P = 0.025). After four cycles, 9/32 (28.1%) developed thrombocytopenia and the pretherapeutic cut-off for platelets (HR, 5.44, P = 0.048) was also superior for the occurrence of late thrombocytopenia (TV, HR, 1.44, P = 0.7). Conclusions Pretherapeutic leukocyte, lymphocyte, and platelet levels themselves are strong predictors for early and late hematotoxicity under PSMA-directed RLT, and are better suited than PET-based osseous TV for this purpose.

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Free Energy of Metals from Quasi-Harmonic Models of Thermal Disorder

Metals ◽

10.3390/met11020195 ◽

2021 ◽

Vol 11 (2) ◽

pp. 195

Author(s):

Pavel A. Korzhavyi ◽

Jing Zhang

Keyword(s):

Free Energy ◽

First Principles ◽

Density Functional ◽

Helmholtz Free Energy ◽

Complex Materials ◽

Predictive Capability ◽

Temperature Dependent ◽

Disordered Alloys ◽

Thermal Disorder ◽

Structure Calculations

A simple modeling method to extend first-principles electronic structure calculations to finite temperatures is presented. The method is applicable to crystalline solids exhibiting complex thermal disorder and employs quasi-harmonic models to represent the vibrational and magnetic free energy contributions. The main outcome is the Helmholtz free energy, calculated as a function of volume and temperature, from which the other related thermophysical properties (such as temperature-dependent lattice and elastic constants) can be derived. Our test calculations for Fe, Ni, Ti, and W metals in the paramagnetic state at temperatures of up to 1600 K show that the predictive capability of the quasi-harmonic modeling approach is mainly limited by the electron density functional approximation used and, in the second place, by the neglect of higher-order anharmonic effects. The developed methodology is equally applicable to disordered alloys and ordered compounds and can therefore be useful in modeling realistically complex materials.

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A process-based method for predicting lateral erosion rates

Natural Hazards ◽

10.1007/s11069-021-04587-y ◽

2021 ◽

Author(s):

Myron van Damme

Keyword(s):

Soil Mechanics ◽

High Surface ◽

Shear Stresses ◽

Empirical Equations ◽

Predictive Capability ◽

Surface Shear ◽

Erosion Rates ◽

Material Texture ◽

Empirical Coefficients ◽

The Impact

AbstractAn accurate means of predicting erosion rates is essential to improve the predictive capability of breach models. During breach growth, erosion rates are often determined with empirical equations. The predictive capability of empirical equations is governed by the range for which they have been validated and the accuracy with which empirical coefficients can be established. Most empirical equations thereby do not account for the impact of material texture, moisture content, and compaction energy on the erosion rates. The method presented in this paper acknowledges the impact of these parameters by accounting for the process of dilation during erosion. The paper shows how, given high surface shear stresses, the erosion rate can be quantified by applying the principles of soil mechanics. Key is thereby to identify that stress balance situation for which the dilatency induced inflow gives a maximum averaged shear resistance. The effectiveness of the model in predicting erosion rates is indicated by means of three validation test cases. A sensitivity analysis of the method is also provided to show that the predictions lie within the range of inaccuracy of the input parameters.

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Kinetic and Microhydrodynamic Modeling of Fenofibrate Nanosuspension Production in a Wet Stirred Media Mill

Pharmaceutics ◽

10.3390/pharmaceutics13071055 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1055

Author(s):

Gulenay Guner ◽

Dogacan Yilmaz ◽

Ecevit Bilgili

Keyword(s):

Rate Constant ◽

Kinetic Models ◽

Multiple Linear Regression Model ◽

Empirical Correlation ◽

P Value ◽

Order Kinetic ◽

Stirrer Speed ◽

Predictive Capability ◽

Breakage Rate ◽

The Impact

This study examined the impact of stirrer speed and bead material loading on fenofibrate particle breakage during wet stirred media milling (WSMM) via three kinetic models and a microhydrodynamic model. Evolution of median particle size was tracked via laser diffraction during WSMM operating at 3000–4000 rpm with 35–50% (v/v) concentration of polystyrene or zirconia beads. Additional experiments were performed at the center points of the above conditions, as well as outside the range of these conditions, in order to test the predictive capability of the models. First-order, nth-order, and warped-time kinetic models were fitted to the data. Main effects plots helped to visualize the influence of the milling variables on the breakage kinetics and microhydrodynamic parameters. A subset selection algorithm was used along with a multiple linear regression model (MLRM) to delineate how the breakage rate constant k was affected by the microhydrodynamic parameters. As a comparison, a purely empirical correlation for k was also developed in terms of the process/bead parameters. The nth-order model was found to be the best model to describe the temporal evolution; nearly second-order kinetics (n ≅ 2) was observed. When the process was operated at a higher stirrer speed and/or higher loading with zirconia beads as opposed to polystyrene beads, the breakage occurred faster. A statistically significant (p-value ≤ 0.01) MLRM of three microhydrodynamic parameters explained the variation in the breakage rate constant best (R2 ≥ 0.99). Not only do the models and the nth-order kinetic–microhydrodynamic correlation enable deeper process understanding toward developing a WSMM process with reduced cycle time, but they also provide good predictive capability, while outperforming the purely empirical correlation.

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Modelling the elastic mechanical properties of bioactive glass-derived scaffolds

Biomedical glasses ◽

10.1515/bglass-2020-0005 ◽

2020 ◽

Vol 6 (1) ◽

pp. 50-56

Author(s):

Francesco Baino ◽

Elisa Fiume

Keyword(s):

Elastic Properties ◽

Poisson’S Ratio ◽

Mechanical Performance ◽

Solid Material ◽

Poisson's Ratio ◽

Predictive Capability ◽

Shear Moduli ◽

Wide Range ◽

Constitutive Parameters ◽

The Relationship

AbstractPorosity is known to play a pivotal role in dictating the functional properties of biomedical scaffolds, with special reference to mechanical performance. While compressive strength is relatively easy to be experimentally assessed even for brittle ceramic and glass foams, elastic properties are much more difficult to be reliably estimated. Therefore, describing and, hence, predicting the relationship between porosity and elastic properties based only on the constitutive parameters of the solid material is still a challenge. In this work, we quantitatively compare the predictive capability of a set of different models in describing, over a wide range of porosity, the elastic modulus (7 models), shear modulus (3 models) and Poisson’s ratio (7 models) of bioactive silicate glass-derived scaffolds produced by foam replication. For these types of biomedical materials, the porosity dependence of elastic and shear moduli follows a second-order power-law approximation, whereas the relationship between porosity and Poisson’s ratio is well fitted by a linear equation.

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OP0009 DERIVATION AND VALIDATION OF THE SCLERODERMA LUNG 3-STAGE INDEX (SL3SI), A NEW FUNCTIONAL INDEX FOR INTERSTITIAL LUNG DISEASE WITH PROGNOSTIC IMPLICATIONS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.5788 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 7.2-7

Author(s):

A. Santaniello ◽

C. Bellocchi ◽

L. Bettolini ◽

M. Cassavia ◽

G. Montanelli ◽

...

Keyword(s):

At Risk ◽

Interstitial Lung Disease ◽

Lung Disease ◽

Disease Severity ◽

Disease Duration ◽

Functional Model ◽

Severity Scale ◽

Predictive Capability ◽

Patients At Risk ◽

Disease Severity Scale

Background:The staging of interstitial lung disease (ILD) is important to monitor disease progression and for prognostication. A disease severity scale of Systemic Sclerosis (SSc)-related lung disease has long been proposed (i.e. Medsger’s severity scale). This scale was mostly developed by discussion and consensus and stage thresholds were not computed by a data-driven approach. Hidden Markov models (HMM) are methods to estimate population quantities for chronic diseases with a staged interpretation which are diagnosed by markers measured at irregular intervals.Objectives:To build a SSc-ILD specific disease severity scale with prognostic relevance via HMM modeling.Methods:A total of 358 SSc patients at risk for or with ILD were enrolled in a discovery (207 cases, Milan1) and in a validation (151 cases, Milan2, Pavia and Rome) cohort. Patients were included if satisfied the following criteria: 1) Diagnosis of SSc according to the EULAR/ACR 2013 criteria, 2) absence of anticentromere antibodies, 3) dcSSc subset or 4) other subsets with either 4a) ILD-related antibodies (Scl70, PmScl, Ku) or 4b) evidence of ILD on HRCT, 5) disease duration < 5 years at the time of the first pulmonary function test (PFT). Serial PFTs were retrieved and the time up to the last available visit -if the patient alive-, or to death due to pulmonary complications, was recorded. HMM were used to estimate the threshold of a 3-stage model (SL3SI, Scleroderma Lung 3-Stage Index) based on PFT functional values (normal/mild, moderate, severe involvement) in the discovery cohort. Survival estimates of the SL3SI model were compared to Medsger’s severity classes estimates and their predictive capability evaluated via the explained residual variation (R2) of prediction errors (the higher the better). One-hundred random replicates were generated to simulate the prediction effort in patients with different disease duration and lung severity.Results:Patients characteristics are summarized in the Table. Fifteen-years survival estimates for Mesdger’s classes in the discovery set were: normal=0.88, mild=0.86, moderate=0.84 and severe=0.71. The SL3SI was defined by the following thresholds: normal/mild, FVC and DLco >=75%; moderate FVC or DLco 74-55%; severe, FVC or DLco <55%. SL3SI 15-yrs survival estimates were: normal/mild=0.89, moderate=0.82 and severe=0.63. Prediction analysis showed a higher R2values at 15 yrs for the SL3SI compared to Medsger’s classes, providing evidence for a better predictive capability of the former (discovery: 0.31 vs 0.25; validation: 0.28 vs 0.19).Conclusion:The SL3SI, a simplified 3-stage functional model of SSc-ILD, yields better survival estimates and long-term prognostic information than Medsger’s classes. Its reproducibility and ease of use make it a useful tool for the functional and prognostic evaluation of SSc patients at risk for or with ILD.Table:VariablesDiscovery (n=207)Replication (n=151)DcSSc62 (30%)98 (64%)Age at first PFR48.6±1249.1±14.4Disease duration at first PFR1.7±1.61.3±2.4FVC90.5±18.191.1±20.2DLco70.7±19.861.3±20.1ILD on HRCT179 (86%)125 (80%)Scl70157 (76%)153 (78%)SSA63 (30%)32 (21%)n of visits38571473Follow-up time, yrs11±5.610.6±5.7Deaths27 (13%)23 (15%)Disclosure of Interests:Alessandro Santaniello: None declared, Chiara Bellocchi: None declared, Luca Bettolini: None declared, Marcello Cassavia: None declared, Gaia Montanelli: None declared, Adriana Severino: None declared, Monica Caronni: None declared, Corrado Campochiaro Speakers bureau: Novartis, Pfizer, Roche, GSK, SOBI, Enrico De Lorenzis: None declared, Gerlando Natalello: None declared, Paolo Delvino: None declared, Claudio Tirelli: None declared, Lorenzo Cavagna: None declared, Giacomo De Luca Speakers bureau: SOBI, Novartis, Celgene, Pfizer, MSD, Silvia Laura Bosello: None declared, Lorenzo Beretta Grant/research support from: Pfizer

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