
dust mites under microscope
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Enhanced Promptmicroscopic images, dust mites, arthropod exoskeletons, microscopic scale, six-legged creatures, translucent bodies, tiny legs moving rapidly, hairy appendages, fine details, high magnification, oil immersion objective lens, transmitted light microscopy, phase contrast microscopy, differential interference contrast microscopy, diatoms, pollen grains, fungal hyphae, environmental samples, SEM imaging, backscattered electron imaging, elemental analysis, EDS spectra, X-ray microanalysis, SEM resolution up to 3 nanometers, 10-15 micrometer depth of field, soft focus effect, depth cues created by subtle gradations of brightness, gentle illumination, diffuse reflected light, no harsh shadows, detailed texture mapping, precise color representation, RGB color mode, 24-bit color depth, 3000 x 2000 pixel resolution, JPEG compression, lossless compression, TIFF format, DICOM metadata, geotagging, metadata tags, XML file format, batch processing, automated image analysis software, statistical analysis, histogram equalization, contrast stretching, sharpening filters, unsharp mask, Gaussian blur, median filter, Sobel operator, Laplacian of Gaussian, Canny edge detection, Hough transform, watershed transform, Otsu thresholding, K-means clustering, principal component analysis, PCA, feature extraction, machine learning algorithms, neural networks, supervised learning, unsupervised learning, reinforcement learning, deep learning, convolutional neural networks, CNN, autoencoders, generative adversarial networks, GANs, transfer learning, fine-tuning pre-trained models, hyperparameter tuning, grid search, random search, Bayesian optimization, cross-validation, k-fold cross-validation, stratified sampling, bootstrap resampling, permutation testing, paired t-test, two-sample t-test, one-way ANOVA, Kruskal-Wallis test, non-parametric tests, confidence intervals, hypothesis testing, statistical significance, p-value, effect size, Cohen's d, correlation coefficient, regression analysis, linear regression, logistic regression, decision trees, random forests, gradient boosting, support vector machines, SVMs, k-nearest neighbors, k-NN, naive Bayes, clustering algorithms, hierarchical clustering, DBSCAN, k-means++, expectation-maximization algorithm, spectral clustering, multi-dimensional scaling, MDS, principal coordinate analysis, PCA, multidimensional scaling, dimensionality reduction, feature selection, recursive feature elimination, mutual information, correlation-based feature selection, wrapper methods, embedded methods, filter methods, genetic algorithms, simulated annealing, particle swarm optimization, evolutionary strategies, natural evolution strategies, covariance matrix, precision matrix, Fisher information matrix, likelihood ratio test, Wald test, score test, log-likelihood ratio test, Akaike information criterion, AIC, Bayesian information criterion, BIC, deviance information criterion, DIC, Schwarz criterion, SBC, effective sample size, ESS, effective degrees of freedom, edf, Monte Carlo simulation, Markov chain Monte Carlo, MCMC, Metropolis-Hastings algorithm, Gibbs sampler, Hamiltonian Monte Carlo, HMC, NUTS algorithm, No-U-turn sampler, adaptive rejection sampling, importance sampling, variance reduction techniques, antithetic variates, control variates, stratum sampling, systematic sampling, stratified sampling, cluster sampling, multistage sampling, sequential sampling, adaptive sampling, importance resampling, residual resampling, residual sampling, leave-one-out cross-validation, LOOCV, leave-p-out cross-validation, LPOCV, bootstrapping, jackknife resampling, bagging, random subspace method,
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