Predictive ability and economic gains from volatility forecast combinations

2019 ◽  
Vol 39 (2) ◽  
pp. 200-219 ◽  
Author(s):  
Stavroula P. Fameliti ◽  
Vasiliki D. Skintzi
Keyword(s):  
Predictive Ability ◽  
Forecast Combinations ◽  
Volatility Forecast

Validation and inter-rater reliability of inexpensive, mini, no-touch infrared surface thermometry devices as an assessment tool for prediction of wound-related deep and surrounding infection

WCET Journal ◽  
2019 ◽  
pp. 18-22
Author(s):  
Hiske Smart ◽  
Eman Al Al Jahmi ◽  
Ebrahim Buhiji ◽  
Sally-Anne Smart
Keyword(s):  
Temperature Difference ◽  
Assessment Tool ◽  
Predictive Ability ◽  
Skin Surface ◽  
Infrared Thermometry ◽  
Health Care Clinics ◽  
Primary Health ◽  
Wound Assessment ◽  
The Times ◽  
Assessment Modality

Industrial infrared thermometry devices are large and, despite being less expensive than the current gold standard Exergen Dermatemp medical infrared thermometer, are still not affordable enough to ensure unrestricted and consistent use of this assessment modality in regular wound-related day-to-day practice. An increased skin surface temperature differentiation of 3°F associated with a wound has a positive predictive ability to detect deep or surrounding wound infection. This study hypothesised that inexpensive, pen- or pocket-sized, no-touch surface infrared thermometry devices will be equal in ability to detect a 3oF increased skin temperature compared to the Exergen Dermatemp infrared device and be reliable in the hands of any wound assessor. The odds of the control and other thermometers to detect a 3oF temperature difference, irrespective of the raters, were achieved in all five of the mini thermometers tested, with a correct temperature difference prediction that occurred in 90.933% of the times (odds determined 9/10). As a result of this study mini, no-touch infrared thermometry, to detect a 3oF temperature difference in wound assessment to determine tendency, could be implemented into primary health care clinics, rural clinics, day-to-day hospital practice and standard outpatients departments at a small financial cost, regardless of which thermometer is put to use.

Quantitative Structure- Property Relationship (QSPR) Investigation of Camptothecin Drugs Derivatives

2018 ◽  
Vol 21 (7) ◽  
pp. 533-542 ◽  
Author(s):  
Neda Ahmadinejad ◽  
Fatemeh Shafiei ◽  
Tahereh Momeni Isfahani
Keyword(s):  
Thermodynamic Properties ◽  
Predictive Ability ◽  
Total Sample ◽  
Basis Sets ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Chemical Structures ◽  
Property Relationship ◽  
Structure Property Relationship

Aim and Objective: Quantitative Structure- Property Relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been developed for modeling and predicting thermodynamic properties of 76 camptothecin derivatives using molecular descriptors. Materials and Methods: Thermodynamic properties of camptothecin such as the thermal energy, entropy and heat capacity were calculated at Hartree–Fock level of theory and 3-21G basis sets by Gaussian 09. Results: The appropriate descriptors for the studied properties are computed and optimized by the genetic algorithms (GA) and multiple linear regressions (MLR) method among the descriptors derived from the Dragon software. Leave-One-Out Cross-Validation (LOOCV) is used to evaluate predictive models by partitioning the total sample into training and test sets. Conclusion: The predictive ability of the models was found to be satisfactory and could be used for predicting thermodynamic properties of camptothecin derivatives.

Hybrid Design of Isonicotinic Acid Hydrazide Derivatives: Machine Learning Studies, Synthesis and Biological Evaluation of their Antituberculosis Activity

2020 ◽  
Vol 17 (3) ◽  
pp. 365-375
Author(s):  
Vasyl Kovalishyn ◽  
Diana Hodyna ◽  
Vitaliy O. Sinenko ◽  
Volodymyr Blagodatny ◽  
Ivan Semenyuta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Isonicotinic Acid ◽  
Acid Hydrazide ◽  
Predictive Ability ◽  
Antitubercular Activity ◽  
Biological Evaluation ◽  
Starting Point ◽  
Binary Classifiers ◽  
Synthesis And Biological Evaluation

Background: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis (Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs. Objective: The goal of this work is to identify potent small molecule anti-TB agents by machine learning, synthesis and biological evaluation. Methods: The On-line Chemical Database and Modeling Environment (OCHEM) was used to build predictive machine learning models. Seven compounds were synthesized and tested in vitro for their antitubercular activity against H37Rv and resistant Mtb strains. Results: A set of predictive models was built with OCHEM based on a set of previously synthesized isoniazid (INH) derivatives containing a thiazole core and tested against Mtb. The predictive ability of the models was tested by a 5-fold cross-validation, and resulted in balanced accuracies (BA) of 61–78% for the binary classifiers. Test set validation showed that the models could be instrumental in predicting anti- TB activity with a reasonable accuracy (with BA = 67–79 %) within the applicability domain. Seven designed compounds were synthesized and demonstrated activity against both the H37Rv and multidrugresistant (MDR) Mtb strains resistant to rifampicin and isoniazid. According to the acute toxicity evaluation in Daphnia magna neonates, six compounds were classified as moderately toxic (LD50 in the range of 10−100 mg/L) and one as practically harmless (LD50 in the range of 100−1000 mg/L). Conclusion: The newly identified compounds may represent a starting point for further development of therapies against Mtb. The developed models are available online at OCHEM http://ochem.eu/article/11 1066 and can be used to virtually screen for potential compounds with anti-TB activity.

3D-QSAR Studies of S-DABO Derivatives as Non-nucleoside HIV-1 Reverse Transcriptase Inhibitors

2019 ◽  
Vol 16 (8) ◽  
pp. 868-881
Author(s):  
Yueping Wang ◽  
Jie Chang ◽  
Jiangyuan Wang ◽  
Peng Zhong ◽  
Yufang Zhang ◽  
...  
Keyword(s):  
Reverse Transcriptase ◽  
Three Dimensional ◽  
Predictive Ability ◽  
3D Qsar ◽  
Binding Mode ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Reverse Transcriptase Inhibitors ◽  
Qsar Studies ◽  
Hiv 1

Background: S-dihydro-alkyloxy-benzyl-oxopyrimidines (S-DABOs) as non-nucleoside reverse transcriptase inhibitors have received considerable attention during the last decade due to their high potency against HIV-1. Methods: In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR) of a series of 38 S-DABO analogues developed in our lab was studied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). The Docking/MMFF94s computational protocol based on the co-crystallized complex (PDB ID: 1RT2) was used to determine the most probable binding mode and to obtain reliable conformations for molecular alignment. Statistically significant CoMFA (q2=0.766 and r2=0.949) and CoMSIA (q2=0.827 and r2=0.974) models were generated using the training set of 30 compounds on the basis of hybrid docking-based and ligand-based alignment. Results: The predictive ability of CoMFA and CoMSIA models was further validated using a test set of eight compounds with predictive r2 pred values of 0.843 and 0.723, respectively. Conclusion: The information obtained from the 3D contour maps can be used in designing new SDABO derivatives with improved HIV-1 inhibitory activity.

Determination of Pyridostigmine Bromide in Presence of its Related Impurities by Four Modified Classical Least Square Based Models: A Comparative Study

2020 ◽  
Vol 17 (1) ◽  
pp. 87-94
Author(s):  
Ibrahim A. Naguib ◽  
Fatma F. Abdallah ◽  
Aml A. Emam ◽  
Eglal A. Abdelaleem
Keyword(s):  
Comparative Study ◽  
Least Squares ◽  
Orthogonal Projection ◽  
Predictive Ability ◽  
Least Square ◽  
Projection To Latent Structures ◽  
Preprocessing Method ◽  
Spectral Residual ◽  
Latent Structures

: Quantitative determination of pyridostigmine bromide in the presence of its two related substances; impurity A and impurity B was considered as a case study to construct the comparison. Introduction: Novel manipulations of the well-known classical least squares multivariate calibration model were explained in detail as a comparative analytical study in this research work. In addition to the application of plain classical least squares model, two preprocessing steps were tried, where prior to modeling with classical least squares, first derivatization and orthogonal projection to latent structures were applied to produce two novel manipulations of the classical least square-based model. Moreover, spectral residual augmented classical least squares model is included in the present comparative study. Methods: 3 factor 4 level design was implemented constructing a training set of 16 mixtures with different concentrations of the studied components. To investigate the predictive ability of the studied models; a test set consisting of 9 mixtures was constructed. Results: The key performance indicator of this comparative study was the root mean square error of prediction for the independent test set mixtures, where it was found 1.367 when classical least squares applied with no preprocessing method, 1.352 when first derivative data was implemented, 0.2100 when orthogonal projection to latent structures preprocessing method was applied and 0.2747 when spectral residual augmented classical least squares was performed. Conclusion: Coupling of classical least squares model with orthogonal projection to latent structures preprocessing method produced significant improvement of the predictive ability of it.

Earnings Informativeness and Strategic Disclosure: An Empirical Examination of “Pro Forma” Earnings

2004 ◽  
Vol 79 (3) ◽  
pp. 769-795 ◽  
Author(s):  
Barbara A. Lougee ◽  
Carol A. Marquardt
Keyword(s):  
Market Efficiency ◽  
Predictive Ability ◽  
Press Releases ◽  
Empirical Examination ◽  
Strategic Disclosure ◽  
Earnings Information ◽  
Pro Forma Earnings ◽  
Earnings Informativeness ◽  
Earnings Surprises ◽  
Pro Forma

This paper provides evidence on the characteristics of firms that include “pro forma” earnings information in their press releases, whether the usefulness of pro forma earnings to investors varies systematically with these characteristics, and whether the investor response to pro forma earnings is consistent with market efficiency or mispricing. Using a sample of 249 press releases from 1997–99, we find that firms with low GAAP earnings informativeness are more likely to disclose pro forma earnings than other firms. We also find that strategic considerations, measured using the direction of GAAP earnings surprises, are an important determinant of pro forma reporting. In addition, our examination of the relative and incremental information content of pro forma earnings shows that investors find pro forma earnings to be more useful when GAAP earnings informativeness is low or when strategic considerations are absent. Tests of the predictive ability of pro forma earnings for future profitability and returns are mixed, and we therefore cannot conclusively determine whether the investor reaction to pro forma earnings at the time of the press release is consistent with market efficiency or mispricing. The paper contributes to the growing literature on pro forma earnings and more generally to the literature on voluntary and strategic disclosure.

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