QSPR for the prediction of critical micelle concentration of different classes of surfactants using machine learning algorithms

The determination of the critical micelle concentration (CMC) is a crucial factor when evaluating surfactants, making it an essential tool in studying the properties of surfactants in various industrial fields. In this present research, we assembled a comprehensive set of 593 different classes of surfactants including, anionic, cationic, nonionic, zwitterionic, and Gemini surfactants to establish a link between their molecular structure and the negative logarithmic value of critical micelle concentration (pCMC) utilizing quantitative structure-property relationship (QSPR) methodologies. Statistical analysis revealed that a set of 14 significant Mordred descriptors (SlogP, GATS6d, nAcid, GATS8dv, GATS4dv, PEOE_VSA11, GATS8d, ATS0p, GATS1d, MATS5p, GATS3d, NdssC, GATS6dv and EState_VSA4), along with temperature, served as appropriate inputs. Different machine learning methods, such as multiple linear regression (MLR), random forest regression (RFR), artificial neural network (ANN), and support vector regression (SVM), were employed in this study to build QSPR models. According to the statistical coefficients of QSPR models, SVR with Dragonfly hyperparameter optimization (SVR-DA) was the most accurate in predicting pCMC values, achieving (R2 = 0.9740, Q2 = 0.9739, r‾m2 = 0.9627, and Δrm2 = 0.0244) for the entire dataset.

Differential Effect of Aortic Valve Replacement for Severe Aortic Stenosis on Hyperemic and Resting Epicardial Coronary Pressure Indices

BACKGROUND

Coronary pressure indices to assess coronary artery disease are currently underused in patients with aortic stenosis due to many potential physiological effects that might hinder their interpretation. Studies with varying sample sizes have provided us with conflicting results on the effect of transcatheter aortic valve replacement (TAVR) on these indices. The aim of this meta-analysis was to study immediate and long-term effects of TAVR on fractional flow reserve (FFR) and nonhyperemic pressure ratios (NHPRs).

METHODS AND RESULTS

Lesion-specific coronary pressure data were extracted from 6 studies, resulting in 147 lesions for immediate change in FFR analysis and 105 for NHPR analysis. To investigate the long-term changes, 93 lesions for FFR analysis and 68 for NHPR analysis were found. Lesion data were pooled and compared with paired t tests. Immediately after TAVR, FFR decreased significantly (-0.0130±0.0406 SD, P: 0.0002) while NHPR remained stable (0.0003±0.0675, P: 0.9675). Long-term after TAVR, FFR decreased significantly (-0.0230±0.0747, P: 0.0038) while NHPR increased nonsignificantly (0.0166±0.0699, P: 0.0543). When only borderline NHPR lesions were considered, this increase became significant (0.0249±0.0441, P: 0.0015). Sensitivity analysis confirmed our results in borderline lesions.

CONCLUSIONS

TAVR resulted in small significant, but opposite, changes in FFR and NHPR. Using the standard cut-offs in patients with severe aortic stenosis, FFR might underestimate the physiological significance of a coronary lesion while NHPRs might overestimate its significance. The described changes only play a clinically relevant role in borderline lesions. Therefore, even in patients with aortic stenosis, an overtly positive or negative physiological assessment can be trusted.

The role of gender in resting full-cycle ratio (RFR) guided coronary revascularization

BACKGROUND

Gender-based differences in clinical outcomes of patients undergoing fractional flow reserved (FFR) guided coronary revascularization is well documented. This study aimed to compare resting full-cycle ratio (RFR) values between men and women and whether this translated into difference in clinical outcomes in patients who underwent RFR-guided coronary revascularization.

METHODS

This was a retrospective single-centre study of consecutive patients who underwent RFR-guided revascularization for coronary lesions with intermediate degree of stenosis. The primary endpoint was a composite of all-cause mortality, myocardial infarction (MI), unplanned revascularization, and unstable angina requiring hospital admission at one year.

RESULTS

In 373 consecutive patients (510 lesions, 26% women) there was no statistically significant difference in RFR value between men and women (0.90 ± 10 versus 0.90 ± 11, P = 0.95). There was no statistically significant difference between men and women in the primary endpoint, even after adjustment to the imbalance between the two groups [3.7% vs. 3.0%; HR 1.43, 95% CI (0.46 to 4.43), P = 0.54]; or its individual components of death (1.1% vs 0.8%, P = 0.76), MI (1.9% vs 0.8%, P = 0.38) or unplanned revascularization, including unstable angina admissions (2.6% vs 2.3%, P = 0.82). The comparable clinical outcomes were consistent across all different subgroups, including clinical presentation, diabetes status, left ventricle systolic function, kidney function, and the interrogated coronary artery.

CONCLUSION

Our study suggests no significant gender-based difference in the value of RFR or 1-year clinical outcomes in patients undergoing resting physiology guided coronary revascularization.

Validation of resting full-cycle ratio and diastolic pressure ratio with [15O]H2O positron emission tomography myocardial perfusion

Fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) are invasive techniques used to evaluate the hemodynamic significance of coronary artery stenosis. These methods have been validated through perfusion imaging and clinical trials. New invasive pressure ratios that do not require hyperemia have recently emerged, and it is essential to confirm their diagnostic efficacy. The aim of this study was to validate the resting full-cycle ratio (RFR) and the diastolic pressure ratio (dPR), against [15O]H2O positron emission tomography (PET) imaging. A total of 129 symptomatic patients with an intermediate risk of coronary artery disease (CAD) were included. All patients underwent cardiac [15O]H2O PET with quantitative assessment of resting and hyperemic myocardial perfusion. Within a 2 week period, coronary angiography was performed. Intracoronary pressure measurements were obtained in 320 vessels and RFR, dPR, and FFR were computed. PET derived regional hyperemic myocardial blood flow (hMBF) and myocardial perfusion reserve (MPR) served as reference standards. In coronary arteries with stenoses (43%, 136 of 320), the overall diagnostic accuracies of RFR, dPR, and FFR did not differ when PET hyperemic MBF < 2.3 ml min-1 (69.9%, 70.6%, and 77.1%, respectively) and PET MPR < 2.5 (70.6%, 71.3%, and 66.9%, respectively) were considered as the reference for myocardial ischemia. Non-significant differences between the areas under the receiver operating characteristic (ROC) curve were found between the different indices. Furthermore, the integration of FFR with RFR (or dPR) does not enhance the diagnostic information already achieved by FFR in the characterization of ischemia via PET perfusion. In conclusion, the novel non-hyperemic pressure ratios, RFR and dPR, have a diagnostic performance comparable to FFR in assessing regional myocardial ischemia. These findings suggest that RFR and dPR may be considered as an FFR alternative for invasively guiding revascularization treatment in symptomatic patients with CAD.

Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by Bat algorithm

Wastewater quality modelling plays a vital role in planning and management of wastewater treatment plants (WWTP). This paper develops a new hybrid machine learning model based on extreme learning machine (ELM) optimized by Bat algorithm (ELM-Bat) for modelling five day effluent biochemical oxygen demand (BOD5). Specifically, this hybrid model combines the Bat algorithm for model parameters optimization and the standalone ELM. The proposed model was developed using historical measured effluents wastewater quality variables, i.e., the chemical oxygen demand (COD), temperature, pH, total suspended solid (TSS), specific conductance (SC) and the wastewater flow (Q). The performances of the hybrid ELM-Bat were compared with those of the multilayer perceptron neural network (MLPNN), the random forest regression (RFR), the Gaussian process regression (GPR), the random vector functional link network (RVFL), and the multiple linear regression (MLR) models. By comparing several input variables combination, the improvement achieved in the accuracy of prediction through the hybrid ELM-Bat was quantified. All models were first calibrated using training dataset and later tested using validation and based on four performances metrics namely, root mean square error (RMSE), mean absolute error (MAE), the correlation coefficient (R), and the Nash-Sutcliffe model efficiency (NSE). In all, it is concluded that the ELM-Bat is the most accurate model when all the six input were included as input variables, and it outperforms all other benchmark models in terms of predictive accuracy, exhibiting RMSE, MAE, R and NSE values of approximately, 0.885, 0.781, 2.621, and 1.989, respectively.

A Molecular Dynamics Simulation Study on Enhancement of Mechanical and Tribological Properties of Nitrile-Butadiene Rubber with Varied Contents of Acrylonitrile

The molecular models of nitrile-butadiene rubber (NBR) with varied contents of acrylonitrile (ACN) were developed and investigated to provide an understanding of the enhancement mechanisms of ACN. The investigation was conducted using molecular dynamics (MD) simulations to calculate and predict the mechanical and tribological properties of NBR through the constant strain method and the shearing model. The MD simulation results showed that the mechanical properties of NBR showed an increasing trend until the content of ACN reached 40%. The mechanism to enhance the strength of the rubber by ACN was investigated and analyzed by assessing the binding energy, radius of gyration, mean square displacement, and free volume. The abrasion rate (AR) of NBR was calculated using Fe-NBR-Fe models during the friction processes. The wear results of atomistic simulations indicated that the NBR with 40% ACN content had the best tribological properties due to the synergy among appropriate polarity, rigidity, and chain length of the NBR molecules. In addition, the random forest regression model of predicted AR, based on the dataset of feature parameters extracted by the MD models, was developed to obtain the variable importance for identifying the highly correlated parameters of AR. The torsion-bend-bend energy was obtained and used to successfully predict the AR trend on the new NBR models with other acrylonitrile contents.

Improving the accuracy of air relative humidity prediction using hybrid machine learning based on empirical mode decomposition: a comparative study

This paper proposes a hybrid air relative humidity prediction based on preprocessing signal decomposition. New modelling strategy was introduced based on the use of the empirical mode decomposition, variational mode decomposition, and the empirical wavelet transform, combined with standalone machine learning to increase their numerical performances. First, standalone models, i.e., extreme learning machine, multilayer perceptron neural network, and random forest regression, were used for predicting daily air relative humidity using various daily meteorological variables, i.e., maximal and minimal air temperatures, precipitation, solar radiation, and wind speed, measured at two meteorological stations located in Algeria. Second, meteorological variables are decomposed into several intrinsic mode functions and presented as new input variables to the hybrid models. The comparison between the models was achieved based on numerical and graphical indices, and obtained results demonstrate the superiority of the proposed hybrid models compared to the standalone models. Further analysis revealed that using standalone models, the best performances are obtained using the multilayer perceptron neural network with Pearson correlation coefficient, Nash-Sutcliffe efficiency, root-mean-square error, and mean absolute error of approximately ≈0.939, ≈0.882, ≈7.44, and ≈5.62 at Constantine station, and ≈0.943, ≈0.887, ≈7.72, and ≈5.93 at Sétif station, respectively. The hybrid models based on the empirical wavelet transform decomposition exhibited high performances with Pearson correlation coefficient, Nash-Sutcliffe efficiency, root-mean-square error, and mean absolute error of approximately ≈0.950, ≈0.902, ≈6.79, and ≈5.24, at Constantine station, and ≈0.955, ≈0.912, ≈6.82, and ≈5.29, at Sétif station. Finally, we show that the new hybrid approaches delivered high predictive accuracies of air relative humidity, and it was concluded that the contribution of the signal decomposition was demonstrated and justified.

Myocardial mass affects diagnostic performance of non-hyperemic pressure-derived indexes in the assessment of coronary stenosis

Background Several non-hyperemic pressure-derived Indexes (NHPI) have been introduced for the assessment of coronary stenosis, showing a good correlation with fractional flow reserve (FFR). Notably, either the assessment of NHPI during adenosine administration (NHPI_ADO) or the Hybrid Approach (NHPI_HA), combining NHPI with FFR, have been showed to increase the accuracy of such indexes. It remains unclear whether diagnostic performance might be affected by the extent of the subtended myocardial mass.

METHODS

We enrolled consecutive patients with an intermediate coronary stenosis assessed with NHPI and FFR. NHPI were also measured during adenosine (ADO) administration (NHPI_ADO). The amount of jeopardized myocardium was assessed using the Duke Jeopardy Score (DJS). With FFR as reference, we assessed the accuracy of NHPI, NHPI_ADO and NHPI_HA according to the extent of the subtended myocardium.

RESULTS

One-hundred-seventy stenoses from 151 patients were grouped according to the DJS as follows: A) Small Extent (SE, n = 82); B) Moderate Extent (ME, n = 53); C) Large Extent (LE, n = 35). As compared with FFR, NHPI showed a significantly different accuracy, as assessed by the Youden's index, according to the extent of the jeopardized myocardium (SE: 0.39 ± 0.05, ME: 0.68 ± 0.06, LE: 0.28 ± 0.06, p < 0.001). Conversely, both the NHPI_ADO (SE: 0.76 ± 0.02, ME: 0.88 ± 0.02, LE: 0.82 ± 0.02, p = 0.72) and NHPI_HA (SE: 0.82 ± 0.07, ME: 0.84 ± 0.02, LE: 0.88 ± 0.02, p = 0.70) allowed for a better diagnostic accuracy regardless of the amount of myocardium subtended.

CONCLUSIONS

Diagnostic performance of NHPI might be affected by the extent of myocardial territory subtended by the coronary stenosis. A hybrid approach might be useful to overcome this limitation.

Cyanobacteria blue-green algae prediction enhancement using hybrid machine learning-based gamma test variable selection and empirical wavelet transform

This study aims to evaluate the usefulness and effectiveness of four machine learning (ML) models for modelling cyanobacteria blue-green algae (CBGA) at two rivers located in the USA. The proposed modelling framework was based on establishing a link between five water quality variables and the concentration of CBGA. For this purpose, artificial neural network (ANN), extreme learning machine (ELM), random forest regression (RFR), and random vector functional link (RVFL) are developed. First, the four models were developed using only water quality variables. Second, based on the results of the first, a new modelling strategy was introduced based on preprocessing signal decomposition. Hence, the empirical mode decomposition (EMD), the variational mode decomposition (VMD), and the empirical wavelet transform (EWT) were used for decomposing the water quality variables into several subcomponents, and the obtained intrinsic mode functions (IMFs) and multiresolution analysis (MRA) components were used as new input variables for the ML models. Results of the present investigation show that (i) using single models, good predictive accuracy was obtained using the RFR model exhibiting an R and NSE values of ≈0.914 and ≈0.833 for the first station, and ≈0.944 and ≈0.884 for the second station, while the others models, i.e., ANN, RVFL, and ELM, have failed to provide a good estimation of the CBGA; (ii) the decomposition methods have contributed to a significant improvement of the individual models performances; (iii) among the thee decomposition methods, the EMD was found to be superior to the VMD and EWT; and (iv) the ANN and RFR were found to be more accurate compared to the ELM and RVFL models, exhibiting high numerical performances with R and NSE values of approximately ≈0.983, ≈0.967, and ≈0.989 and ≈0.976, respectively.

COVID-19 in Bangladesh: A Deeper Outlook into The Forecast with Prediction of Upcoming Per Day Cases Using Time Series

A global pandemic on March 11th of 2020, which was initially renowned by the World Health Organization (WHO) revealed the coronavirus (the COVID-19 epidemic). Coronavirus was flown in -December 2019 in Wuhan, Hubei region in China. Currently, the situation is enlarged by the infection in more than 200 countries all over the world. In this situation it was rising into huge forms in Bangladesh too. Modulated with a public dataset delivered by the IEDCR health authority, we have produced a sustainable prognostic method of COVID-19 outbreak in Bangladesh using a deep learning model. Throughout the research, we forecasted up to 30 days in which per day actual prediction was confirmed, death and recoveries number of people. Furthermore, we illustrated that long short-term memory (LSTM) demands the actual output trends among time series data analysis with a controversial study that exceeds random forest (RF) regression and support vector regression (SVR), which both are machine learning (ML) models. The current COVID-19 outbreak in Bangladesh has been considered in this paper. Here, a well-known recurrent neural network (RNN) model in order to referred by the LSTM network that has forecasted COVID-19 cases on per day infected scenario of Bangladesh from May 15th of 2020 till June 15th of 2020. Added with a comparative study that drives into the LSTM, SVR, RF regression which is processed by the RMSE transmission rate. In all respects, in Bangladesh the gravity of COVID-19 has become excessive nowadays so that depending on this situation public health sectors and common people need to be aware of this situation and also be able to get knowledge of how long self-lockdown will be maintained. So far, to the best of our knowledge LSTM based time series analysis forecasting infectious diseases is a well-done formula.

Applicability of machine learning in modeling of atmospheric particle pollution in Bangladesh

Atmospheric particle pollution causes acute and chronic health effects. Predicting the concentrations of PM_2.5 and PM₁₀, therefore, is a prerequisite to avoid the consequences and mitigate the complications. This research utilized the machine learning (ML) models such as linear-support vector machine (L-SVM), medium Gaussian-support vector machine (M-SVM), Gaussian process regression (GPR), artificial neural network (ANN), random forest regression (RFR), and a time series model namely PROPHET. Atmospheric NO_X, SO₂, CO, and O₃, along with meteorological variables from Dhaka, Chattogram, Rajshahi, and Sylhet for the period of 2013 to 2019, were utilized as exploratory variables. Results showed that the overall performance of GPR performed better particularly for Dhaka in predicting the concentration of both PM_2.5 and PM₁₀ while ANN performed best in case of Chattogram and Sylhet for predicting PM_2.5. However, in terms of predicting PM₁₀, M-SVM and RFR were selected respectively. Therefore, this study recommends utilizing "ensemble learning" models by combining several best models to advance application of ML in predicting pollutants' concentration in Bangladesh.