Viscoelastic and Inertial Focusing of Asymmetric Cells in Spiral Channels

Explosive growth of microfluidics has triggered numerous advances in focusing, separating, ordering, and concentrating of cells Microfluidic systems capable of performing these functions are rapidly finding applications in clinical and biomedical fields. However, most microfluidic methods have been demonstrated using spherical particles in Newtonian fluids. Yet, fluids such as blood, saliva, and cytoplasm are non-Newtonian, and cells such as red blood cells and spermatozoa cells are asymmetrical in shape. These key differences can reduce effectiveness of the microfluidic separation methods. In this work, we use spiral inertial microfluidic devices to investigate migration dynamics and focusing evolution of beads in non-Newtonian, elasto-inertial flows. Coupling of Dean flow that arises from the spiral channel geometry with fluid elasticity yields complex migration behavior. The flow rate, device curvature, and medium viscosity were found to influence lateral migration of cells or particles within these channels. In addition, we used spermatozoa cells to investigate the mechanism of asymmetric shape effects on lateral migration in the spiral channel. The sperm cell migration direction and alignment were found to depend on the flow parameters, leading to differences in focusing equilibrium as compared with spherical particles. Ultimately, insights from this work offer a useful guide to microfluidic device design for improving efficiency of 3D focusing in cell sorting and cytometry applications

Comparison of predicting results.

Long non-coding RNAs (lncRNAs) have been widely studied for their important biological significance. In general, we need to distinguish them from protein coding RNAs (pcRNAs) with similar functions. Based on various strategies, algorithms and tools have been designed and developed to train and validate such classification capabilities. However, many of them lack certain scalability, versatility, and rely heavily on genome annotation. In this paper, we design a convenient and biologically meaningful classification tool "Prelnc2" using multi-scale position and frequency information of wavelet transform spectrum and generalizes the frequency statistics method. Finally, we used the extracted features and auxiliary features together to train the model and verify it with test data. PreLnc2 achieved 93.2% accuracy for animal and plant transcripts, outperforming PreLnc by 2.1% improvement and our method provides an effective alternative to the prediction of lncRNAs.</div

Prediction results on CPC2 datasets.

Long non-coding RNAs (lncRNAs) have been widely studied for their important biological significance. In general, we need to distinguish them from protein coding RNAs (pcRNAs) with similar functions. Based on various strategies, algorithms and tools have been designed and developed to train and validate such classification capabilities. However, many of them lack certain scalability, versatility, and rely heavily on genome annotation. In this paper, we design a convenient and biologically meaningful classification tool "Prelnc2" using multi-scale position and frequency information of wavelet transform spectrum and generalizes the frequency statistics method. Finally, we used the extracted features and auxiliary features together to train the model and verify it with test data. PreLnc2 achieved 93.2% accuracy for animal and plant transcripts, outperforming PreLnc by 2.1% improvement and our method provides an effective alternative to the prediction of lncRNAs.</div

Prediction on NONCODEv5.

Long non-coding RNAs (lncRNAs) have been widely studied for their important biological significance. In general, we need to distinguish them from protein coding RNAs (pcRNAs) with similar functions. Based on various strategies, algorithms and tools have been designed and developed to train and validate such classification capabilities. However, many of them lack certain scalability, versatility, and rely heavily on genome annotation. In this paper, we design a convenient and biologically meaningful classification tool "Prelnc2" using multi-scale position and frequency information of wavelet transform spectrum and generalizes the frequency statistics method. Finally, we used the extracted features and auxiliary features together to train the model and verify it with test data. PreLnc2 achieved 93.2% accuracy for animal and plant transcripts, outperforming PreLnc by 2.1% improvement and our method provides an effective alternative to the prediction of lncRNAs.</div

Morse wave shapes, time-frequency scalogram and sum of wavelet transform map.

(A) Morse wave shapes, the black line shows the normal distribution, and the remaining-colored lines show their imaginary and real components. (B) Wavelet transform map of an RNA sequence, The highlighted parts of the scalogram show that it has some high electrostatic properties, and the y-axis shows the characteristics at different levels. (C) The column sum of the time spectrum.</p

Accuracy and AUC of wavelet feature classification.

Accuracy and AUC of wavelet feature classification.</p

The first subset of eight features.

Long non-coding RNAs (lncRNAs) have been widely studied for their important biological significance. In general, we need to distinguish them from protein coding RNAs (pcRNAs) with similar functions. Based on various strategies, algorithms and tools have been designed and developed to train and validate such classification capabilities. However, many of them lack certain scalability, versatility, and rely heavily on genome annotation. In this paper, we design a convenient and biologically meaningful classification tool "Prelnc2" using multi-scale position and frequency information of wavelet transform spectrum and generalizes the frequency statistics method. Finally, we used the extracted features and auxiliary features together to train the model and verify it with test data. PreLnc2 achieved 93.2% accuracy for animal and plant transcripts, outperforming PreLnc by 2.1% improvement and our method provides an effective alternative to the prediction of lncRNAs.</div

Heterozygous dataset.

Long non-coding RNAs (lncRNAs) have been widely studied for their important biological significance. In general, we need to distinguish them from protein coding RNAs (pcRNAs) with similar functions. Based on various strategies, algorithms and tools have been designed and developed to train and validate such classification capabilities. However, many of them lack certain scalability, versatility, and rely heavily on genome annotation. In this paper, we design a convenient and biologically meaningful classification tool "Prelnc2" using multi-scale position and frequency information of wavelet transform spectrum and generalizes the frequency statistics method. Finally, we used the extracted features and auxiliary features together to train the model and verify it with test data. PreLnc2 achieved 93.2% accuracy for animal and plant transcripts, outperforming PreLnc by 2.1% improvement and our method provides an effective alternative to the prediction of lncRNAs.</div

The result of generalization prediction using a known model.

(A) The prediction results of the trained Human, Mouse, Cow models on other animal datasets, and on average the prediction results of Mouse are better. (B) The prediction results of the trained A. thaliana, O. sativa, Z. mays models in prediction of other plant datasets, on average A. thaliana prediction results are better.</p

Features extracted from wavelet transform.

Long non-coding RNAs (lncRNAs) have been widely studied for their important biological significance. In general, we need to distinguish them from protein coding RNAs (pcRNAs) with similar functions. Based on various strategies, algorithms and tools have been designed and developed to train and validate such classification capabilities. However, many of them lack certain scalability, versatility, and rely heavily on genome annotation. In this paper, we design a convenient and biologically meaningful classification tool "Prelnc2" using multi-scale position and frequency information of wavelet transform spectrum and generalizes the frequency statistics method. Finally, we used the extracted features and auxiliary features together to train the model and verify it with test data. PreLnc2 achieved 93.2% accuracy for animal and plant transcripts, outperforming PreLnc by 2.1% improvement and our method provides an effective alternative to the prediction of lncRNAs.</div