Tumorigenesis is a complex process that is driven by a combination of networks of genes and environmental factors; however, efficient approaches to identifying functional networks that are perturbed by the process of tumorigenesis are lacking. In this study, we provide a comprehensive network-based strategy for the systematic discovery of functional synergistic modules that are causal determinants of inflammation-induced tumorigenesis. Our approach prioritizes candidate genes selected by integrating clinical-based and network-based genome-wide gene prediction methods and identifies functional synergistic modules based on combinatorial CRISPR-Cas9 screening. On the basis of candidate genes inferred de novo from experimental and computational methods to be involved in inflammation and cancer, we used an existing TGFβ1-induced cellular transformation model in colonic epithelial cells and a new combinatorial CRISPR-Cas9 screening strategy to construct an inflammation-induced differential genetic interaction network. The inflammation-induced differential genetic interaction network that we generated yielded functional insights into the genes and functional module combinations, and showed varied responses to the inflammation agents as well as active traditional Chinese medicine compounds. We identified opposing differential genetic interactions of inflammation-induced tumorigenesis: synergistic promotion and suppression. The synergistic promotion state was primarily caused by deletions in the immune and metabolism modules; the synergistic suppression state was primarily induced by deletions in the proliferation and immune modules or in the proliferation and metabolism modules. These results provide insight into possible early combinational targets and biomarkers for inflammation-induced tumorigenesis and highlight the synergistic effects that occur among immune, proliferation, and metabolism modules. In conclusion, this approach deepens the understanding of the underlying mechanisms that cause inflammation to potentially increase the cancer risk of colonic epithelial cells and accelerate the translation into novel functional modules or synergistic module combinations that modulate complex disease phenotypes.
Whale vocal calls contain valuable information and abundant characteristics that are important for classification of whale sub-populations and related biological research. In this study, an effective data-driven approach based on pre-trained Convolutional Neural Networks (CNN) using multi-scale waveforms and time-frequency feature representations is developed in order to perform the classification of whale calls from a large open-source dataset recorded by sensors carried by whales. Specifically, the classification is carried out through a transfer learning approach by using pre-trained state-of-the-art CNN models in the field of computer vision. 1D raw waveforms and 2D log-mel features of the whale-call data are respectively used as the input of CNN models. For raw waveform input, windows are applied to capture multiple sketches of a whale-call clip at different time scales and stack the features from different sketches for classification. When using the log-mel features, the delta and delta-delta features are also calculated to produce a 3-channel feature representation for analysis. In the training, a 4-fold cross-validation technique is employed to reduce the overfitting effect, while the Mix-up technique is also applied to implement data augmentation in order to further improve the system performance. The results show that the proposed method can improve the accuracies by more than 20% in percentage for the classification into 16 whale pods compared with the baseline method using groups of 2D shape descriptors of spectrograms and the Fisher discriminant scores on the same dataset. Moreover, it is shown that classifications based on log-mel features have higher accuracies than those based directly on raw waveforms. The phylogeny graph is also produced to significantly illustrate the relationships among the whale sub-populations.
The rotary axis is the basis for rotational motion. Its motion errors have critical effects on the accuracy of the related equipment, such as a five-axis computer numerical control machine tool. There are several difficult problems in the implementation of high-precision and fast measurement of the multi-degree-of-freedom motion errors of a rotary axis. In this paper, a novel method for the simultaneous measurement of five-degree-of-freedom motion errors of a rotary axis is proposed, which uses a single-mode fiber-coupled laser with a full-circle measuring range. It has the advantages of high efficiency, low cost, and it requires no decoupling calculation. An experimental system was built and a series of experiments were performed. The standard deviation of stability for 60 min of the five-degree-of-freedom measurement is 0.05 arcsec, 0.06 arcsec, 0.04 μm, 0.03 μm, and 0.19 arcsec, respectively. The repeatability deviation of measuring an indexing table is ± 3.4 arcsec, ± 4.6 arcsec, ± 2.6 μm, ± 2.4 μm, and ± 3.2 arcsec. The maximum deviation of comparison is 3.9 arcsec and 3.2 arcsec. These results demonstrate the effectiveness of the proposed method; thus, a new approach of simultaneous measurement of the multi-degree-of-freedom motion errors of a rotary axis is provided.
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