Recently, many self-supervised pre-training methods have been proposed to improve the performance of deep neural networks (DNNs) for 3D point clouds processing. However, the common mechanism underlying the effectiveness of different pre-training methods remains unclear. In this paper, we use game-theoretic interactions as a unified approach to explore the common mechanism of pre-training methods. Specifically, we decompose the output score of a DNN into the sum of numerous effects of interactions, with each interaction representing a distinct 3D substructure of the input point cloud. Based on the decomposed interactions, we draw the following conclusions. (1) The common mechanism across different pre-training methods is that they enhance the strength of high-order interactions encoded by DNNs, which represent complex and global 3D structures, while reducing the strength of low-order interactions, which represent simple and local 3D structures. (2) Sufficient pre-training and adequate fine-tuning data for downstream tasks further reinforce the mechanism described above. (3) Pre-training methods carry a potential risk of reducing the transferability of features encoded by DNNs. Inspired by the observed common mechanism, we propose a new method to directly enhance the strength of high-order interactions and reduce the strength of low-order interactions encoded by DNNs, improving performance without the need for pre-training on large-scale datasets. Experiments show that our method achieves performance comparable to traditional pre-training methods.

Despite a standardized diagnostic examination, cancer of unknown primary (CUP) is a rare metastatic malignancy with an unidentified tissue of origin (TOO). Patients diagnosed with CUP are typically treated with empiric chemotherapy, although their prognosis is worse than those with metastatic cancer of a known origin. TOO identification of CUP has been employed in precision medicine, and subsequent site-specific therapy is clinically helpful. For example, molecular profiling, including genomic profiling, gene expression profiling, epigenetics and proteins, has facilitated TOO identification. Moreover, machine learning has improved identification accuracy, and non-invasive methods, such as liquid biopsy and image omics, are gaining momentum. However, the heterogeneity in prediction accuracy, sample requirements and technical fundamentals among the various techniques is noteworthy. Accordingly, we systematically reviewed the development and limitations of novel TOO identification methods, compared their pros and cons and assessed their potential clinical usefulness. Our study may help patients shift from empirical to customized care and improve their prognoses.

Robust strategies to identify patients at high risk for tumor metastasis, such as those frequently observed in intrahepatic cholangiocarcinoma (ICC), remain limited. While gene/protein expression profiling holds great potential as an approach to cancer diagnosis and prognosis, previously developed protocols using multiple diagnostic signatures for expression-based metastasis prediction have not been widely applied successfully because batch effects and different data types greatly decreased the predictive performance of gene/protein expression profile-based signatures in interlaboratory and data type dependent validation. To address this problem and assist in more precise diagnosis, we performed a genome-wide integrative proteome and transcriptome analysis and developed an ensemble machine learning-based integration algorithm for metastasis prediction (EMLI-Metastasis) and risk stratification (EMLI-Prognosis) in ICC. Based on massive proteome (216) and transcriptome (244) data sets, 132 feature (biomarker) genes were selected and used to train the EMLI-Metastasis algorithm. To accurately detect the metastasis of ICC patients, we developed a weighted ensemble machine learning method based on k-Top Scoring Pairs (k-TSP) method. This approach generates a metastasis classifier for each bootstrap aggregating training data set. Ten binary expression rank-based classifiers were generated for detection of metastasis separately. To further improve the accuracy of the method, the 10 binary metastasis classifiers were combined by weighted voting based on the score from the prediction results of each classifier. The prediction accuracy of the EMLI-Metastasis algorithm achieved 97.1% and 85.0% in proteome and transcriptome datasets, respectively. Among the 132 feature genes, 21 gene-pair signatures were developed to establish a metastasis-related prognosis risk-stratification model in ICC (EMLI-Prognosis). Based on EMLI-Prognosis algorithm, patients in the high-risk group had significantly dismal overall survival relative to the low-risk group in the clinical cohort (P-value < 0.05). Taken together, the EMLI-ICC algorithm provides a powerful and robust means for accurate metastasis prediction and risk stratification across proteome and transcriptome data types that is superior to currently used clinicopathological features in patients with ICC. Our developed algorithm could have profound implications not just in improved clinical care in cancer metastasis risk prediction, but also more broadly in machine-learning-based multi-cohort diagnosis method development. To make the EMLI-ICC algorithm easily accessible for clinical application, we established a web-based server for metastasis risk prediction (http://ibi.zju.edu.cn/EMLI/).

High cycle fatigue testing to long lives requires high frequency equipment. The working frequency of the conventional electrohydraulic vibration exciters in fatigue test system is limited to fairly narrow range by the frequency response capability of the servo valve. In response to these needs, a four-axis structural strength fatigue test system capable of frequency and amplitude controlled testing at 5∼200Hz has been developed. The four-axis structural strength fatigue test system includes four electrohydraulic vibration exciters mounted in horizontal and vertical structural frame respectively. This electrohydraulic vibration exciters include 2D valve, double acting cylinder and bias control servo valve etc. In the 2D valve, the rotary and sliding motions are independently exploited to control the frequency and the magnitude of the vibration respectively. The frequency of the 2D valve controlled vibration exciter is determined by the rotary speed, number of grooves distributed on a spool land and coupled pattern of the groove's number on the spool land and the window's number on the spool sleeve. By adjusting these factors in the 2D valve design, the proposed scheme of vibration exciter is apt to realize high-frequency vibration and to increase the working frequency of multi-axis structural strength fatigue test system. In this paper, the structure of test system is first described. Then, the principal of the electrohydaulic vibration exciter using 2D valve is discussed, which includes the control method of frequency, amplitude and phase. The dynamic characteristics of electrohydaulic vibration exciter in test system are briefly discussed. Finally, the measured vibration wave is given to validate the theoretical analysis. Four-axis structural strength fatigue test system formed by the electrohydraulic vibration exciter using 2D valve has now been put into practical operation as a standard fatigue testing device. It is classified that the vibration of the tested object can be excited synchronously in four-axis with the frequency 5∼200Hz adjustable or excited with in one-axis solely.

Flexible pneumatic bending joint is a new type of joint based on FPA, which can be adopted in multi-fingered dexterous hand design. The changing of average radius and shell thickness of bending joint during movement are analyzed, which implies that the average radius is almost invariable while the shell thickness changes obviously. The static model of bending joint is established based on principle of moment equilibrium, and was validated by experiment. Experimental result indicates that the theoretical model can basically describe the static properties of bending joint. Finally, the reasons of error between theoretical curve and experimental data are discussed