This study presents a cost-effective and environmentally friendly approach for the simultaneous determination of a veterinary binary mixture comprising doxycycline hydrochloride (DOX) and tylosin tartrate (TYZ) utiliz...
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This study presents a cost-effective and environmentally friendly approach for the simultaneous determination of a veterinary binary mixture comprising doxycycline hydrochloride (DOX) and tylosin tartrate (TYZ) utilizing UV spectroscopy alongside dimension reduction algorithms (DRAs). Seventeen DRAs were evaluated, and their performances were compared based on four metrics: mean squared error (MSE), mean absolute error (MAE), median absolute error (MedAE), and coefficient of determination (R2). Based on the performance indices, Isomap algorithm demonstrated the highest predictive capacity among all DRAs. MSE, MAE, MedAE and R2 values of (0.38, 0.28, 0.19 and 0.999) and (0.08, 0.26, 0.22 and 0.998) were obtained for calibration and test datasets, respectively across a concentration range 4.67-30 mu g mL- 1 for DOX. MSE, MAE, MedAE and R2 values of (0.54, 0.34, 0.19 and 0.994) and (0.07, 0.19, 0.07 and 0.998) were obtained for calibration and test datasets, respectively across a concentration range 3.51-24 mu g mL- 1 for TYZ. The developed method underwent validation utilizing the accuracy profile approach. An ecological impact assessment was carried out employing six greenness evaluation tools: The Green Solvent Selection Tool (GSST), National Environmental Methods Index (NEMI), the Assessment of Green Profile (AGP), carbon footprint analysis, Analytical Greenness Calculator (AGREE), and Complementary Green Analytical Procedure Index (Complex GAPI). Additionally, we applied blueness and whiteness assessments using Blue Applicability Grade Index (BAGI) and Red-Green-Blue 12 (RGB 12) algorithms, respectively. The proposed method demonstrated higher GSST scores, a more "green" profile in NEMI, a superior AGP profile, and better environmental sustainability in Complex GAPI. The calculated carbon footprint value was 0.0002 kg CO2 equivalent per sample, The AGREE score was 0.87, BAGI was assessed at 72.5, and the whiteness assessment by the RGB12 algorithm was 89.6. Statis
Reducing dimensions of hyperspectral data is very important as the removal of high-dimensional spectral variables could improve the predictive ability of the model. In the current study, four different linear dimensio...
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Reducing dimensions of hyperspectral data is very important as the removal of high-dimensional spectral variables could improve the predictive ability of the model. In the current study, four different linear dimension reduction algorithms, including principal component analysis (PCA), local preserving projections (LPP), neighborhood preserving embedding (NPE), and linear discriminant analysis (LDA), were used to reduce hyperspectral dimensions, and their classification performances on the algae concentration levels in water samples using hyperspectral imaging were compared. The LPP model showed satisfactory classification accuracy of 94.296 %, which was superior to the results based on reducing spectral dimensions with LDA (94.118 %), NPE (93.353 %), and PCA (90.588 %). The results demonstrated the potential of hyperspectral imaging coupled with dimensionreduction methods in classifying water bodies with different algae concentration levels.
Parkinson's disease (PD) is progressive and heterogeneous. Levodopa is widely prescribed to control PD, and its long-term-treatment leads to dyskinesia in a dose-dependent manner. Interpretation of clinical trials...
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Parkinson's disease (PD) is progressive and heterogeneous. Levodopa is widely prescribed to control PD, and its long-term-treatment leads to dyskinesia in a dose-dependent manner. Interpretation of clinical trials comparing different drug treatments for PD is complicated by different dose intensities employed: higher doses of levodopa produce better symptomatic control but more late complications. Thus, the dose must be recalibrated and reduced gradually. Since recommendations for gradually reducing Levodopa are currently lacking and estimation of Levodopa amount can help doctor to correctly prescribe drug amount, this study aims to predict Levodopa amount and incremental doses using Hybrid Machine Learning Systems (HMLS) and a mixture of radiomics and clinical features. We selected 264 patients from PPMI and obtained 950 features including imaging and nominating features. We generated seven datasets constructed from the dataset in years 0 and 1, which linked with outcomes, (O1) patients being on/off drug in year 1, (O2) dose amount in year 1, and (O3-8) incremental dose from 1st to 2nd, 2nd to 3rd, 3rd to 4th, 4th to 5th, 1st to 4th, 1st to 5th year. HMLSs included 10 feature extraction/9 feature selection algorithms followed by 10 prediction algorithms. To predict O1, timeless dataset + Random Forest + ReliefA had the highest accuracy similar to 88.5% +/- 2.2%, and external testing similar to 91.6%. Furthermore, to predict O2, timeless dataset + Minimum Redundancy Maximum Relevance Algorithm (MRMR) + K Nearest Neighbor Regressor (KNN-R) achieved a mean absolute error (MAE) similar to 47.5 +/- 13.6 ([30.3:850 milligram]) and external testing similar to 31.9. To predict dose increments (O3-8), HMLSs: Unsupervised Feature Selection with Ordinal Locality + KNNR, ReliefA + KNNR, ReliefA + KNNR, Local Learning-based Clustering Feature Selection + KNNR, MRMR + KNNR, and MRMR + KNNR applied to timeless datasets resulted in MAEs similar to 0.42 +/- 0.18, 0.10 +/- 0.09, 0.0
Objectives. Parkinson's disease (PD) is a complex neurodegenerative disorder, affecting 2%-3% of the elderly population. Montreal Cognitive Assessment (MoCA), a rapid nonmotor screening test, assesses different co...
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Objectives. Parkinson's disease (PD) is a complex neurodegenerative disorder, affecting 2%-3% of the elderly population. Montreal Cognitive Assessment (MoCA), a rapid nonmotor screening test, assesses different cognitive dysfunctionality aspects. Early MoCA prediction may facilitate better temporal therapy and disease control. Radiomics features (RF), in addition to clinical features (CF), are indicated to increase clinical diagnoses, etc, bridging between medical imaging procedures and personalized medicine. We investigate the effect of RFs, CFs, and conventional imaging features (CIF) to enhance prediction performance using hybrid machine learning systems (HMLS). Methods. We selected 210 patients with 981 features (CFs, CIFs, and RFs) from the Parkinson's Progression-Markers-Initiative database. We generated 4 datasets, namely using (i), (ii) year-0 (D1) or year-1 (D2) features, (iii) longitudinal data (D3, putting datasets in years 0 and 1 longitudinally next to each other), and (iv) timeless data (D4, effectively doubling dataset size by listing both datasets from years 0 and 1 separately). First, we directly applied 23 predictor algorithms (PA) to the datasets to predict year-4 MoCA, which PD patients this year have a higher dementia risk. Subsequently, HMLSs, including 14 attribute extraction and 10 feature selection algorithms followed by PAs were employed to enhance prediction performances. 80% of all datapoints were utilized to select the best model based on minimum mean absolute error (MAE) resulting from 5-fold cross-validation. Subsequently, the remaining 20% was used for hold-out testing of the selected models. Results. When applying PAs without ASAs/FEAs to datasets (MoCA outcome range: [11,30]), Adaboost achieved an MAE of 1.74 +/- 0.29 on D4 with a hold-out testing performance of 1.71. When employing HMLSs, D4 + Minimum_Redundancy_Maximum_Relevance (MRMR)+K_Nearest_Neighbor Regressor achieved the highest performance of 1.05 +/- 0.25 with a hold-out t
Mitral valve stenosis (MS) is one of the most prevalent heart valve diseases. The mitral valve orifice area (MVA) is a reliable measure for evaluating the MS severity. This measure is typically obtained by applying pl...
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ISBN:
(纸本)9781728121949
Mitral valve stenosis (MS) is one of the most prevalent heart valve diseases. The mitral valve orifice area (MVA) is a reliable measure for evaluating the MS severity. This measure is typically obtained by applying planimetry methods to the echocardiography video frames in a parasternal short axis view (PSAX). From the diagnostic perspective, the most proper frame for measuring the MVA is the mid-diastole frame. Since manual methods for localizing this frame within a sequence of recorded echocardiography frames are time-consuming and user-dependent, a novel three-stage approach is proposed. Firstly, the mitral valve orifice (MVO) region is detected automatically for each frame using the Circular Hough transform (CHT) leading to a lower computational cost and an increased accuracy. Secondly, for each of the detected MVO regions, a data dimensionality reduction (DR) method is applied to map it into a single point in a two-dimensional (2D) space. Finally, a curve is generated based on Euclidean distances between consecutive points in the 2D space. The curve analysis allows for the detection of the location of the mid-diastole frame. The proposed algorithm was validated using echocardiographic video sequences collected from 11 different participants including 7 individuals diagnosed with mitral valve stenosis and 4 control non-stenosis individuals. The mid-diastole frame location estimated by an expert cardiologist was used as the gold standard for assessing the evaluation results. The performance of different DR methods including the linear principal component analysis (LPCA) and the kernel PCA (with a polynomial or a Gaussian kernel) was evaluated. The PCA with Gaussian kernel produced the best results with the average difference between the proposed method and the gold standard equal to 0.57 frames in MS and 0.50 frames in non-MS cases.
Islanding is an unusual condition in a power system where the generating station continues to supply the local load after one or multiple transmission line outage. This study develops a new islanding detection techniq...
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Islanding is an unusual condition in a power system where the generating station continues to supply the local load after one or multiple transmission line outage. This study develops a new islanding detection technique using the artificial neural network (ANN) classifier, which is provided with synchronised phasor measurements from a nine-bus Western Electricity Coordinating Council power system. An excessive number of data frames are generated in the phasor data concentrator. Before sending these data to the classifier, multiplier-based method (MBM) and Andrews plot-based method (APBM) are applied for dimensionreduction and feature extraction. Comparisons are prepared with other dimension reduction algorithms. The accuracy of the classifier has been increased by increasing the number of hidden layers, the best accuracy is observed at a certain level for APBM. Non-detection zone (NDZ) for APBM is also evaluated. It is observed that the classification accuracy, and the detection time change when the neural network is retrained. All the results are compared and analysed statistically. This method can perform faster compared to other existing algorithms with an excellent accuracy and smaller NDZ.
Electrogram-guided ablation has been recently developed for allowing better detection and localization of abnormal atrial activity that may be the source of arrhythmogeneity. Nevertheless, no clear indication for the ...
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ISBN:
(纸本)9781479943463
Electrogram-guided ablation has been recently developed for allowing better detection and localization of abnormal atrial activity that may be the source of arrhythmogeneity. Nevertheless, no clear indication for the benefit of using electrograms guided ablation over empirical ablation was established thus far, and there is a clear need of improving the localization of cardiac arrhythmogenic targets for ablation. In this work we propose a new approach for detection and localization of irregular cardiac activity during ablation procedures that is based on dimension reduction algorithms and principal component analysis (PCA). We employ mathematical modeling and computer simulations to demonstrate the feasibility of the new approach for two well established arrhythmogenic sources for irregular conduction - spiral waves and patchy fibrosis. Our results show that the PCA method can differentiate between focal ectopic activity and spiral wave activity. Moreover, the technique allows the detection of spiral wave cores. Fibrotic patches larger than 2 mm(2) could also be visualized using the PCA method, both for quiescent atrial tissue and for tissue exhibiting spiral wave activity. We envision that this method, contingent to further numerical and experimental validation studies in more complex, realistic geometrical configurations and with clinical data, can improve existing atrial ablation mapping capabilities, thus increasing success rates and optimizing arrhythmia management.
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