EP Parameters Research Articles

An Adaptative Eps Parameter of DBSCAN Algorithm for Identifying Clusters with Heterogeneous Density

An Adaptative Eps Parameter of DBSCAN Algorithm for Identifying Clusters with Heterogeneous Density

ANDClust: An Adaptive Neighborhood Distance‐Based Clustering Algorithm to Cluster Varying Density and/or Neck‐Typed Datasets

AbstractAlthough density‐based clustering algorithms can successfully define clusters in arbitrary shapes, they encounter issues if the dataset has varying densities or neck‐typed clusters due to the requirement for precise distance parameters, such as eps parameter of DBSCAN. These approches assume that data density is homogenous, but this is rarely the case in practice. In this study, a new clustering algorithm named ANDClust (Adaptive Neighborhood Distance‐based Clustering Algorithm) is proposed to handle datasets with varying density and/or neck‐typed clusters. The algorithm consists of three parts. The first part uses Multivariate Kernel Density Estimation (MulKDE) to find the dataset's peak points, which are the start points for the Minimum Spanning Tree (MST) to construct clusters in the second part. Lastly, an Adaptive Neighborhood Distance (AND) ratio is used to weigh the distance between the data pairs. This method enables this approach to support inter‐cluster and intra‐cluster density varieties by acting as if the distance parameter differs for each data of the dataset. ANDClust is tested on synthetic and real datasets to reveal its efficiency. The algorithm shows superior clustering quality in a good run‐time compared to its competitors. Moreover, ANDClust could effectively define clusters of arbitrary shapes and process high‐dimensional, imbalanced datasets may have outliers.

Intradermal DNA vaccine delivery using vacuum-controlled, needle-free electroporation

Intradermal delivery of DNA vaccines via electroporation (ID-EP) has shown clinical promise, but the use of needle electrodes is typically required to achieve consistent results. Here, delivery of a DNA vaccine targeting the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is achieved using noninvasive intradermal vacuum-EP (ID-VEP), which functions by pulling a small volume of skin tissue into a vacuum chamber containing noninvasive electrodes to perform EP at the injection site. Gene expression and immunogenicity correlated with EP parameters and vacuum chamber geometry in guinea pigs. ID-VEP generated potent humoral and cellular immune responses across multiple studies, while vacuum (without EP) greatly enhanced localized transfection but did not improve immunogenicity. Because EP was performed noninvasively, the only treatment site reaction observed was transient redness, and ID-VEP immune responses were comparable to a clinical needle-based ID-EP device. The ID-VEP delivery procedure is straightforward and highly repeatable, without any dependence on operator technique. This work demonstrates a novel, reliable, and needle-free delivery method for DNA vaccines.

Improvement of DBSCAN Algorithm Based on K-Dist Graph for Adaptive Determining Parameters

For the shortcomings of an unstable clustering effect and low accuracy caused by the manual setting of the two parameters Eps and MinPts of the DBSCAN (density-based spatial clustering of applications with noise) algorithm, this paper proposes an adaptive determination method for DBSCAN algorithm parameters based on the K-dist graph, noted as X-DBSCAN. The algorithm uses the least squares polynomial curve fitting method to fit the curve in the K-dist graph to generate a list of candidate Eps parameters and uses the mathematical expectation method and noise reduction threshold to generate the corresponding MinPts parameter list. According to the clustering results of each group of parameters in the Eps and MinPts parameter lists, a stable range of cluster number changes is found, and the MinPts and Eps corresponding to the maximum K value in the stable range are selected as the optimal algorithm parameters. The optimality of this parameter was verified using silhouette coefficients. A variety of experiments were designed from multiple angles on the artificial dataset and the UCI real dataset. The experimental results show that the clustering accuracy of X-DBSCAN was 21.83% and 15.52% higher than that of DBSCAN on the artificial and real datasets, respectively. The X-DBSCAN algorithm was also superior to other algorithms through comprehensive evaluation and analysis of various clustering indicators. In addition, experiments on four synthetic Gaussian datasets of different dimensions showed that the average clustering indices of the proposed algorithm were above 0.999. The X-DBSCAN algorithm can select parameters adaptively in combination with the characteristics of the dataset; the clustering effect is better, and clustering process automation is realized.

Intelligent Bat Algorithm for Finding Eps Parameter of DbScan Clustering Algorithm

Clustering is an unsupervised learning method that classified data according to similarity probabilities. DBScan as a high-quality algorithm has been introduced for clustering spatial data due to its ability to remove noise (outlier) and constructing arbitrarily shapes. However, it has a problem in determining a suitable value of Eps parameter. This paper proposes a new clustering method, termed as DBScanBAT, that it optimizes DBScan algorithm by BAT algorithm. The proposed method automatically sets the DBScan parameters (Eps) and finds the optimal value for it. The results of the proposed DBScanBAT automatically generates near original number of clusters better than DBScanPSO and original DBScan. Furthermore, the proposed method has the ability to generate high quality clusters with minimum entropy [ 0.2752, 0.4291] in TR11 and TR12 datasets.

Clinical Value of Echo-Tracking in Gestational Diabetes Mellitus.

Vascular endothelial function plays an essential role in gestational diabetes mellitus (GDM) pathogenesis. Vascular elasticity is evaluated by the echo-tracking (ET) technique. In this study, we aimed to assess the clinical value of the ET technique in women with GDM and provide a basis for early evaluation and prevention of gestational diabetes. Sixty-five patients with GDM and 65 gestational week-matched normal glucose tolerance pregnant women were enrolled in this study. The endothelial function parameters of pressure-strain elasticity coefficient (EP), the common carotid stiffness index (β), arterial compliance (AC), single-point pulsed-wave velocity (PWVβ), and augment index (AI) of bilateral common carotid arteries, were compared between the GDM and control groups by using ET technique. The correlations between plasma glucose levels and ET parameters were also analyzed. Our results indicated that EP, β, PWVβ, and AI were significantly higher in the GDM group (p<0.05), whereas AC was significantly lower in patients with GDM than in the control group (p<0.001). In addition, fasting plasma glucose and plasma glucose level after 2 h oral glucose tolerance test was correlated with ET parameters of EP, β, PWVβ, AI, and AC. The application of ET technology to gestational diabetes women could provide a basis for early evaluation and prevention of gestational diabetes. It would detect early and accurate signs of diabetes in pregnant women and provide a timely and reasonable clinical treatment plan to reduce and delay the occurrence of complications.

Research on Baseline Extraction Method Based on NMR Spectrogram

This paper proposes a baseline extraction method based on a nuclear magnetic resonance atlas, which uses the DBSCAN algorithm to cluster the data extracted by the nuclear magnetic resonance spectrometer and determines the baseline of the atlas according to the clustering results. In this paper, the core parameters of the DBSCAN algorithm are improved, and the Eps parameter set is selected by combining k-dist and equal interval division. The optimal number of outliers is obtained based on this set, and the baseline value range is set. Finally, the clustering quality is tested by the silhouette coefficient. The final experiment shows that the accuracy and stability of the baseline set based on the improved DBSCAN clustering algorithm are better.

Anodic Reactivity of Alkyl S-Glucosides.

A voltammetric study of a series of alkyl and aryl S-glucosides unveiled the reactivity patterns of alkyl S-glucosides toward anodic oxidation and found noteworthy differences with the trends followed by aryl derivatives. The oxidation potential of alkyl S-glucosides, estimated herein from square-wave voltammetry peak potentials (Ep), depends on the steric properties of the aglycone. Glucosides substituted with bulky groups exhibit Ep values at voltages more positive than the values of those carrying small aglycones. This relationship, observed in all analyzed alkyl series, is evidenced by good linear correlations between Ep and Taft's steric parameters (ES) of the respective alkyl substituents. Moreover, the role of the aglycone's steric properties as a primary reactivity modulator is backed by poor correlations between Ep and the radical stabilization energies (RSEs) of the aglycone-derived thiyl radicals (RS•). In contrast, aryl glucosides' Ep values exhibit excellent correlations with the aryl substituents' Hammett parameters (σ+) and the ArS• RSEs, evidencing the inherent stability of the reactive radical intermediate as the primary factor controlling aryl glucoside's electrochemical reactivity. The reactivity differences between alkyl and aryl S-glucosides also extend to the protective group's effect on Ep. Alkyl S-glucosides' reactivity proved to be more sensitive to protective group exchange.

Abstract 14161: Drug Compounds Combination Screening Using Human Induced Pluripotent Stem Cell-Derived Cardiac Tissues

Introduction: The introduction of human-induced pluripotent stem cells derived cardiomyocytes (hiPSC-CMs) technology provided an important tool for the field of safety pharmacology (QT screening). This approach has been limited, however, mostly to the assessment of a single drug. We present a large scale EP phenotyping system comprised of hiPSC-derived cardiac cell sheets (hiPSC-CCSs), fluorescent voltage sensors and actuators, and an optical mapping system; with the aim of performing drug combination screening. Methods and Results: hIPSCs-CMs were seeded as hiPSC-CCSs in multi-well plates. Tissues were treated with varying concentrations of drug compounds’ combinations. Optical signals were acquired following loading of the hiPSC-CCSs with voltage-sensitive dyes or expression of genetically-encoded voltage indicator (ArcLight). EP parameters such as conduction velocity (CV) and action potential duration (APD) were extracted. ATX2 (late sodium current activator) and Dofetilide (IKr blocker) were chosen as representative APD prolonging reagents. Example of the effects of 16 different drug doses combinations on APD (% change) prolongation is depicted in Fig.1a. Similarly, examples of the restitution plots obtained at 4 different drug combinations are presented in Fig.1b-c. Notice that drug compound's effect on prolonging APD showed greater effect as compared to the cumulative effect of each drug alone. Figure 1: (a) Percentage of change in APD 80 15 minutes post treatment (b, c) APD 80 per condition before and after treatment, respectively Conclusion: These results highlight the potential of the hiPSC-CCS as a model for combinational drug screening. This may bring a unique value to the fields of safety pharmacology, pathophysiology testing and drug screening applications.

DBHC: A DBSCAN-based hierarchical clustering algorithm

Clustering is the process of partitioning objects of a dataset into some groups according to similarities and dissimilarities between its objects. DBSCAN is one of the most important clustering algorithms in the density based approach of clustering. In spite of the numerous advantages of the DBSCAN algorithm, it has two important input parameters, MinPts and Eps, which determining their values is still a great challenge. This problem arises because values of these parameters are heavily dependent on data distribution. To overcome this challenge, firstly features of these parameters are investigated and the data distribution are analyzed. Then a DBSCAN-based hierarchical clustering (DBHC) method is proposed in this paper in order to fix this challenge. For this purpose, DBHC first determines values of these parameters using the notion of k nearest neighbor and k-dist plot. Because most of the real world data is not distributed uniformly, it is needed to be produced several values for the Eps parameter. Then, DBHC executes the DBSCAN algorithm several times based on the number of Eps produced earlier. Finally, DBHC method merges obtained clusters if the number of produced clusters is larger than the number which has estimated by the user. To evaluate the performance of the DBHC method, several experiments were performed on some of benchmark datasets of UCI database. Obtained results were compared with other previous works. The obtained results consistently showed that the DBHC method led to better results in comparison to the other works.

A novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm

The commonly used density-based spatial clustering method (DBSCAN) connects contiguous regions with sufficiently large densities when processing datasets to efficiently discover clusters of different shapes and densities and outliers. However, the algorithm has the problem that radius of neighborhood (Eps) argument requires to be selected manually. For datasets with higher dimensionality and larger data volume, the selection of Eps parameters can be difficult thus leading to poor clustering quality. To solve the above problem, we propose a novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm (BSA-DBSCAN). We use the global search capability of the bird swarm method to select the best Eps parameter neighborhood values. We can avoid manual intervention and realize adaptive parameter optimization in the clustering process. To further explore the clustering performance of BSA-DBSCAN method, we test the synthetic datasets and the real-world datasets respectively and perform images analysis on the clustering evaluation index values. The simulation experiments show that the improved method in this paper can reasonably search the Eps parameter value and can obtain the higher accuracy of clustering.

P1421Assessment of intraatrial conduction during staged catheter ablation of persistent AF

Abstract Goal. To evaluate the electrophysiological changes in the LA myocardium between the stages of sequential RFA of persistent AF. Materials and methods. From 2015 to 2018 43 patients (27 men and 16 women) underwent sequential ablation (second intervention in a period of 3 to 6 months) of persistent AF. The routine isolation was performed in the first stage. During the second stage additional lesions were performed in the areas of the restored conduction and posterior wall isolation. All patients underwent a routine EP before each stage with measurements of the atrial myocardial refractory periods, as well as intraatrial conduction. Results. The EP parameters before the first and second stages had significantly changed: refractory periods in the areas of previously performed RFA had increased from 184 (166; 208) ms to 212 (174; 249) ms, respectively, p &amp;lt;0.001, the dispersion of refractivity decreased from 34 (18; 48) ms to 21 (16; 29) ms, p = 0.017. According to ECG, the duration of intraatrial conduction also decreased by 34 (24; 51) ms, p = 0.034. Conclusion. Electrophysiological markers of intra- and interatrial conduction show a tendency to reverse modeling of atrial functions in patients passing through the staged treatment of persistent AF.

IMPLEMENTATION OF DBSCAN CLUSTERING ALGORITHM WITHIN THE FRAMEWORK OF THE OBJECTIVE CLUSTERING INDUCTIVE TECHNOLOGY BASED ON R AND KNIME TOOLS

Context. The problem of the data clustering within the framework of the objective clustering inductive technology is considered. Practical implementation of the obtained hybrid model based on the complex use of R and KNIME tools is performed. The object of the study is the hybrid model of the data clustering based on the complex use of both DBSCAN clustering algorithm and the objective clustering inductive technology. Objective. The aim of the work is the creation of the hybrid model of the objective clustering based on DBSCAN clustering algorithm and its practical implementation on the basis of the complex use of both R and KNIME tools. Method. The inductive methods of complex systems modelling have been used as the basis to determine the optimal parameters of DBSCAN clustering algorithm within the framework of the objective clustering inductive technology. The practical implementation of this technology involves: the use of two equal power subsets, which contain the same quantity of pairwise similar objects; calculation of the internal and the external clustering quality criteria; calculation of the complex balance criterion, maximum value of which corresponds to the best clustering in terms of the used criteria. Implementation of this process involves two main stages. Firstly, the optimal values of the EPS parameter were determined at each step within the range of the minPts value changes. The charts of the complex balance criterion versus the EPS value were obtained for each minPts value as the results of this stage implementation. Then, the analysis of the obtained intermediate results was performed in order to determine the optimal solution, which corresponds to both the maximum value of the complex balance criterion on the one side and the aims of the current clustering on the other side. Results. The developed hybrid model has been implemented based on software KNIME with the use of plugins, which have been written in software R. The efficiency of the model was tasted with the use of the different data: low dimensional data of the computing school of East Finland University; Fisher’s iris; gene expression profiles of the patients, which were investigated on lung cancer. Conclusions. The results of the simulation have shown high efficiency of the proposed method. The studied objects were distributed into clusters correctly in all cases. The proposed method allows us to decrease the reproducibility error, since the solution concerning determination of the clustering algorithm optimal parameters was taken based on both the clustering results obtained on equal power subsets separately and the difference of the clustering results obtained on the two equal power subsets.

An improved density-based spatial clustering of application with noise

Although the density-based spatial clustering of application with noise algorithm can identify clusters with arbitrary shape, there is a problem that the global parameter Eps needs to be manually set. In this paper, we propose a parameter adaptive density-based spatial clustering of application with noise by using the cuckoo search algorithm, which could solve the global optimization problem quickly. According to the cuckoo search algorithm to calculate the optimal global parameter Eps, the improved algorithm avoids human intervention in the process of clustering, and achieves clustering process automation. The simulation results show that the proposed algorithm in this paper can select the reasonable Eps parameter value and get the clustering results with high accuracy.

Al-Mg-Si/SiC laminated composites: Fabrication, architectural characteristics, toughness, damage tolerance, fracture mechanisms

Abstract Different architectures of layered laminates comprising two exterior layers of Al-Mg-Si/SiC metal matrix composite and an Al1050 ductile interlayer were fabricated by means of hot roll-bonding with applying different strains of er = 39%, 51%, and 63%. For monolithics production, ceramic particulate reinforcement contents of 0, 5, 10, and 15 vol% were utilized. The aim of introducing ductile metal interlayer was to compensate the low toughness of composite layers and consequently enhancement of damage tolerance of bundled structures along with prevention of their catastrophic failure through activation of extrinsic toughening mechanism. Effects of architectural characteristics and fabrication routes on toughness and fracture behavior of materials were studied by mechanical examinations including three-point bending (3PB) and shear tests. Fracture surfaces of 3PB examination were studied by SEM while associated mechanisms and correlations with debonded area (delamination), deformability, and SiC content were disclosed and discussed. Different analyses by deriving σm were performed and the role of reinforcement content, lamination and er were defined. Deformability of fabricated materials by identifying et, ei, and ep parameters were discussed. Based on results, et trend was obeyed ep rather than ei due to more ep contribution in materials' total ductility. Toughness change by accounting initiation and propagation values were surveyed considering the combined effects of σm, elongation, and stress-bearing capacity. Eventually, it was inferred that toughness was strongly controlled by elongation alteration. In addition, the contribution of propagation was more highlighted rather than initiation for laminates. Based on fracture surfaces, in monolithic samples, fracture morphology associated with SiC concentration. Fracture morphology of exterior composite layers had not been affected by er in laminates; since interlayer fracture was strongly influenced by rolling. This was due to distinct involved failure mechanisms. Al1050 deformability was governed by delamination length as an indication of constraint level. Interfacial strength which acquired by shear test revealed that composite laminates had not been influenced by er due to the weak bondings of layers beside presence of SiC particles; however, aluminum laminates showed enhanced shear strength.

Early Diagnosis and Monitoring of Neurodegenerative Langerhans Cell Histiocytosis.

BackgroundNeurodegenerative Langerhans Cell Histiocytosis (ND-LCH) is a rare, unpredictable consequence that may devastate the quality of life of patients cured from LCH. We prospectively applied a multidisciplinary diagnostic work-up to early identify and follow-up patients with ND-LCH, with the ultimate goal of better determining the appropriate time for starting therapy.MethodsWe studied 27 children and young adults with either ND-LCH verified by structural magnetic resonance imaging (MRI) (group 1) or specific risk factors for (diabetes insipidus, craniofacial bone lesions), but no evidence of, neurodegenerative MRI changes (group 2). All patients underwent clinical, neurophysiological and MRI studies.ResultsSeventeen patients had MRI alterations typical for ND-LCH. Nine showed neurological impairment but only three were symptomatic; 11 had abnormal somatosensory evoked potentials (SEPs), and five had abnormal brainstem auditory evoked potentials (BAEPs). MR spectroscopy (MRS) showed reduced cerebellar NAA/Cr ratio in nine patients. SEPs showed sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for predicting ND-LCH of 70.6% (95%CI, 44.0%-89.7%), 100% (69.2%-100%), 100% (73.5%-100%), and 66.7% (38.4%-88.2%), respectively. Repeated investigations in group 1 revealed increasingly abnormal EP parameters, or neurological examination, or both, in nine of fifteen patients while MRI remained unchanged in all but one patient.ConclusionA targeted MRI study should be performed in all patients with risk factors for ND-LCH for early identification of demyelination. The combined use of SEPs and careful neurological evaluation may represent a valuable, low-cost, well-tolerated and easily available methodology to monitor patients from pre-symptomatic to symptomatic stages. We suggest a multidisciplinary protocol including clinical, MRS, and neurophysiological investigations to identify a population target for future therapeutic trials.

The photodynamic effect of far-red range phthalocyanines (AlPc and Pc green) supported by electropermeabilization in human gastric adenocarcinoma cells of sensitive and resistant type

IntroductionElectroporation (EP) is commonly applied for effective drug transport thorough cell membranes based on the application of electromagnetic field. When applied with cytostatics, it is called electrochemotherapy (ECT) – a quite new method of cancer treatment. A high-voltage pulse causes the formation of temporary pores in the cell membrane which create an additional way for the intracellular drug transport. In the current work, EP was effectively merged with the already known photodynamic therapy (PDT) to selective photosensitizers’ delivery to diseased tissue. The application of electroporation can reduce the dose of applied drug. Research objectiveThe aim of research was to evaluate the effectiveness of photodynamic reaction using two near infrared cyanines (AlPc and Pc green) combined with electroporation in two human gastric adenocarcinoma cell lines. Materials and methodsTwo human cell lines – EPG85-257P (parental) and EPG85-257RDB (resistant to daunorubicin) – of gastric cancer were used. The effect of two photosensitizers (aluminum 1,8,15,22-tetrakis(-phenylthio)-29H,31H-phthalocyanine chloride and Phthalocyanine green) was investigated. The efficiency of EP parameters was assessed by propidium iodide uptake. The viability assay was applied to analyse EP, PDT and EP-PDT effect. Cyanine localization was determined by confocal microscopy. Immunocytochemical evaluation of manganese superoxide dismutase and glutathione S-transferase-pi was determined after applied therapies. ResultsPDT in combination with EP affected the viability of EPG85-257P and EPG85-257RDB cells negatively while both cyanine were used. The most evident changes were observed in the following concentrations: 15, 10 and 5μM. The optimal field strength for enhanced EP-PDT was 800 and 1200V/cm. AlPc distributed selectively in the lysosomes of parental cell line. ConclusionsPDT, enhanced by EP, caused decreased viability when compared to the application of PDT alone. Both phthalocyanines found to be more effective after electroporation. Due to the low concentration of light-sensitive compounds and safety of electroporation itself, a treatment plan can be an alternative therapeutic modality against gastric adenocarcinomas.

Inducible Focal Atrial Tachycardia of Coronary Sinus Origin Masquerading as Ventricular Tachycardia

CASE: This is the case of a 35-year old male physician presenting with a 12-year history of palpitations despite oral metoprolol. He had reversible left hemiparesis with right hemispheric lacunar infarctions on computerized tomography scan. The 12-lead electrocardiogram (ECG) recorded during an episode of palpitations revealed wide QRS (0.16 s) tachycardia (heart rate=214 beats per minute) with left bundle branch block (LBBB) QRS morphology; no identifiable P waves; QS pattern in leads V1 to V4; RS pattern in leads V5 and V6; and a QRS axis at -1300. There were no AV dissociation; fusion and captured beats; and precordial QRS concordance. Electrophysiology studies (EPS) were performed. During programmed electrical stimulation (PES) at the right ventricular apex, sustained narrow QRS (0.10 s) tachycardia (168 bpm) was induced. Examination of the intracardiac atrial (A) and ventricular (V) electrograms during the tachycardia revealed a 1:1 AV relationship; closer A electrogram to the preceding A electrogram than the following V electrogram producing a short RP tachycardia (&lt;70 ms); variable VA intervals; VAAV response during ventricular overdrive maneuver, and termination of the tachycardia at the V electrogram. The above EP parameters favored a diagnosis of focal atrial tachycardia (FAT) over re-entrant supraventricular tachycardia (SVT). The earliest activation site of the induced SVT was recorded near the ostium of the coronary sinus (CS 9 and 10) which coincided with the findings on 3D mapping. DIAGNOSIS: FAT with acceleration-dependent functional LBBB based on a mechanism of triggered automaticity arising from the coronary sinus ostium. TREATMENT AND OUTCOME: Radiofrequency ablation was applied on the identified arrhythymogenic atrial focus and effectively restored sinus rhythm and rendered the FAT non-inducible.

Choosing DBSCAN Parameters Automatically using Differential Evolution

Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has been widely applied in many areas of science due to its simplicity, robustness against noise (outlier) and ability to discover clusters of arbitrary shapes. However, DBSCAN algorithm requires two initial input parameters, namely Eps (the radius of the cluster) and MinPts (the minimum data objects required inside the cluster) which both have a significant influence on the clustering results. Hence, DBSCAN is sensitive to its input parameters and it is hard to determine them a priori. This paper presents an efficient and effective hybrid clustering method, named BDE-DBSCAN, that combines Binary Differential Evolution and DBSCAN algorithm to simultaneously quickly and automatically specify appropriate parameter values for Eps and MinPts. Since the Eps parameter can largely degrades the efficiency of the DBSCAN algorithm, the combination of an analytical way for estimating Eps and Tournament Selection (TS) method is also employed. Experimental results indicate the proposed method is precise in determining appropriate input parameters of DBSCAN algorithm.

Strain effects and band parameters in MgO, ZnO, and CdO

We have derived consistent sets of band parameters (bandgaps, crystal-field splittings, effective masses, Luttinger, and EP parameters) and strain deformation potentials for MgO, ZnO, and CdO in the wurtzite phase. To overcome the limitations of density-functional theory in the local-density and generalized gradient approximations, we employ a hybrid functional as well as exact-exchange-based quasiparticle energy calculations in the G0W0 approach. We demonstrate that the band and strain parameters derived in this fashion are in very good agreement with the available experimental data and provide predictions for all parameters that have not been determined experimentally so far.