Smart filter aided domain adversarial neural network: An unsupervised domain adaptation method for fault diagnosis in noisy industrial scenarios

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, different units of a fleet or between simulated and real data. However, in real industrial scenarios, unknown levels and types of noise can amplify the difficulty of domain alignment, thus severely affecting the diagnostic performance of deep learning models. To address this issue, we propose an UDA method called Smart Filter-Aided Domain Adversarial Neural Network (SFDANN) for fault diagnosis in noisy industrial scenarios. The proposed methodology comprises two steps. In the first step, we develop a smart filter that dynamically enforces similarity between the source and target domain data in the time-frequency domain. This is achieved by combining a learnable wavelet packet transform network (LWPT) and a traditional wavelet packet transform module. In the second step, we input the data reconstructed by the smart filter into a domain adversarial neural network (DANN). To learn domain-invariant and discriminative features, the learnable modules of SFDANN are trained in a unified manner with three objectives: time-frequency feature proximity, domain alignment, and fault classification. We validate the effectiveness of the proposed SFDANN method based on two fault diagnosis cases: one involving fault diagnosis of bearings in noisy environments and another involving fault diagnosis of slab tracks in a train-track-bridge coupling vibration system, where the transfer task involves transferring from numerical simulations to field measurements. Results show that compared to other representative state of the art UDA methods, SFDANN exhibits superior performance and remarkable stability

Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection

Latest methods represent shapes with open surfaces using unsigned distance functions (UDFs). They train neural networks to learn UDFs and reconstruct surfaces with the gradients around the zero level set of the UDF. However, the differential networks struggle from learning the zero level set where the UDF is not differentiable, which leads to large errors on unsigned distances and gradients around the zero level set, resulting in highly fragmented and discontinuous surfaces. To resolve this problem, we propose to learn a more continuous zero level set in UDFs with level set projections. Our insight is to guide the learning of zero level set using the rest non-zero level sets via a projection procedure. Our idea is inspired from the observations that the non-zero level sets are much smoother and more continuous than the zero level set. We pull the non-zero level sets onto the zero level set with gradient constraints which align gradients over different level sets and correct unsigned distance errors on the zero level set, leading to a smoother and more continuous unsigned distance field. We conduct comprehensive experiments in surface reconstruction for point clouds, real scans or depth maps, and further explore the performance in unsupervised point cloud upsampling and unsupervised point normal estimation with the learned UDF, which demonstrate our non-trivial improvements over the state-of-the-art methods. Code is available at https://github.com/junshengzhou/LevelSetUDF .Comment: To appear at ICCV2023. Code is available at https://github.com/junshengzhou/LevelSetUD

Circular RNAs as a potential source of neoepitopes in cancer

Neoepitopes have attracted much attention as targets for immunotherapy against cancer. Therefore, efficient neoepitope screening technology is an essential step in the development of personalized vaccines. Circular RNAs (circRNAs) are generated by back-splicing and have a single-stranded continuous circular structure. So far, various circRNAs have been poorly characterized, though new evidence suggests that a few translated circRNAs may play a role in cancer. In the present study, circRNA was used as a source of neoepitope, a novel strategy as circRNA-derived neoepitopes have never been previously explored. The present study reports CIRC_neo (circRNA-derived neoepitope prediction pipeline), which is a comprehensive and automated bioinformatic pipeline for the prediction of circRNA-derived neoepitopes from RNA sequencing data. The computational prediction from sequencing data requires complex computational workflows to identify circRNAs, derive the resulting peptides, infer the types of human leukocyte antigens (HLA I and HLA II) in patients, and predict the neoepitopes binding to these antigens. The present study proposes a novel source of neoepitopes. The study focused on cancer-specific circRNAs, which have greatly expanded the source pool for neoepitope discovery. The statistical analysis of different features of circRNA-derived neoepitopes revealed that circRNAs could produce long proteins or truncated proteins. Because the peptides were completely foreign to the human body, they could be highly immunogenic. Importantly, circRNA-derived neoepitopes capable of binding to HLA were discovered. In the current study, circRNAs were systematically analyzed, revealing potential targets and novel research clues for cancer diagnosis, treatment, and prospective personalized vaccine research

The Herb Medicine Formula “Chong Lou Fu Fang” Increases the Cytotoxicity of Chemotherapeutic Agents and Down-Regulates the Expression of Chemotherapeutic Agent Resistance-Related Genes in Human Gastric Cancer Cells In Vitro

The herb medicine formula “Chong Lou Fu Fang” (CLFF) has efficacy in inhibiting the proliferation of human gastric cancer in vitro and in vivo. To explore the potentially useful combination of CLFF with chemotherapeutic agents commonly used in gastric cancer therapy, we assess the interaction between CLFF and these chemotherapeutic agents in both SGC-7901 cell lines and BGC-823 cell lines using a median effect analysis and apoptosis analysis, and we also investigate the influence of CLFF on chemotherapeutic agent-associated gene expression. The synergistic analysis indicated that CLFF had a synergistic effect on the cytotoxicity of 5-fluorouracil (5-FU) in a relative broad dose inhibition range (20–95% fraction affected in SGC-7901cell lines and 5–65% fraction affected in BGC-823 cell lines), while the synergistic interaction between CLFF and oxaliplatin or docetaxel only existed in a low dose inhibition range (≤50% fraction affected in both cell lines). Combination of CLFF and chemotherapeutic agents could also induce apoptosis in a synergistic manner. After 24 h, CLFF alone or CLFF combination with chemotherapeutic agents could significantly suppress the levels of expression of chemotherapeutic agent resistance related genes in gastric cancer cells. Our findings indicate that there are useful synergistic interactions between CLFF and chemotherapeutic agents in gastric cancer cells, and the possible mechanisms might be partially due to the down-regulation of chemotherapeutic agent resistance related genes and the synergistic apoptotic effect

DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding

This paper presents DavarOCR, an open-source toolbox for OCR and document understanding tasks. DavarOCR currently implements 19 advanced algorithms, covering 9 different task forms. DavarOCR provides detailed usage instructions and the trained models for each algorithm. Compared with the previous opensource OCR toolbox, DavarOCR has relatively more complete support for the sub-tasks of the cutting-edge technology of document understanding. In order to promote the development and application of OCR technology in academia and industry, we pay more attention to the use of modules that different sub-domains of technology can share. DavarOCR is publicly released at https://github.com/hikopensource/Davar-Lab-OCR.Comment: Short paper, Accept by ACM MM202

Cell-free miRNAs may indicate diagnosis and docetaxel sensitivity of tumor cells in malignant effusions

<p>Abstract</p> <p>Background</p> <p>Circulating cell-free microRNAs have been identified as potential cancer biomarkers. However, the existence and the potential application of cell-free miRNAs in effusion samples are still uncertain. In order to explore the potential role of cell-free miRNA in malignant effusions, we selected 22 miRNAs differentially expressed in the serum of lung cancer patients and studied their expression levels in body cavity effusion samples.</p> <p>Methods</p> <p>We measured the expression of 22 miRNAs using qRT-PCR in two samples, which were pooled with 18 malignant and 12 benign effusions, respectively. After discarding 9 lowly expressed miRNAs, a panel of 13 miRNAs were measured in 29 samples (benign n = 11, malignant n = 18). We also carried out a WST-8 test to evaluate the docetaxel sensitivity of tumor cells directly isolated from 15 malignant effusions.</p> <p>Results</p> <p>We compared the miRNA expression levels between benign and malignant effusions using a Mann-Whitney U test and found miR-24, miR-26a and miR-30d were expressed differently between the two groups (<it>P </it>= 0.006, 0.021 and 0.011, respectively). Cells isolated from effusions rich in cell-free miR-152 were more sensitive to docetaxel (r = 0.60, <it>P </it>= 0.016).</p> <p>Conclusions</p> <p>Collectively, our study demonstrated that cell-free miRNAs in the supernatant of effusions may aid in the diagnosis of malignancy and predict chemosensitivity to docetaxel.</p

Optimal scheduling of integrated energy systems with exergy and demand responsiveness

To fairly use demand response to regulate customer load , support the economic and environmental protection, and assess the quantity and quality of the synergistic growth of the integrated energy system, a multi-objective optimum scheduling model and a solution method considering exergy efficiency and demand response are presented. To begin with, a mathematical model of each energy gadget is created. The electricity–gas load demand response model is then built using the price elasticity matrix, while the cooling load demand response model is built taking into account the user’s comfort temperature. On this basis, a multi-objective optimal dispatching model is developed with the optimization goals of minimizing system operation costs, reducing carbon emissions, and increasing exergy efficiency. Finally, the model is solved using NSGA-II to produce the Pareto optimal frontier solution set in various situations, and the VIKOR decision procedure is utilized to identify the complete best dispatching solution. The simulation results suggest that the proposed model can match the system’s scheduling needs in terms of numerous objectives such as economy, environmental protection, and exergy efficiency while also assuring user’s comfort

Comparative studies of salinomycin-loaded nanoparticles prepared by nanoprecipitation and single emulsion method

To establish a satisfactory delivery system for the delivery of salinomycin (Sal), a novel, selective cancer stem cell inhibitor with prominent toxicity, gelatinase-responsive core-shell nanoparticles (NPs), were prepared by nanoprecipitation method (NR-NPs) and single emulsion method (SE-NPs). The gelatinase-responsive copolymer was prepared by carboxylation and double amination method. We studied the stability of NPs prepared by nanoprecipitation method with different proportions of F68 in aqueous phase to determine the best proportion used in our study. Then, the NPs were prepared by nanoprecipitation method with the best proportion of F68 and single emulsion method, and their physiochemical traits including morphology, particle size, zeta potential, drug loading content, stability, and in vitro release profiles were studied. The SE-NPs showed significant differences in particle size, drug loading content, stability, and in vitro release profiles compared to NR-NPs. The SE-NPs presented higher drug entrapment efficiency and superior stability than the NR-NPs. The drug release rate of SE-NPs was more sustainable than that of the NR-NPs, and in vivo experiment indicated that NPs could prominently reduce the toxicity of Sal. Our study demonstrates that the SE-NPs could be a satisfactory method for the preparation of gelatinase-responsive NPs for intelligent delivery of Sal

Use of Radiomics Combined With Machine Learning Method in the Recurrence Patterns After Intensity-Modulated Radiotherapy for Nasopharyngeal Carcinoma: A Preliminary Study

Objective: To analyze the recurrence patterns and reasons in patients with nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiotherapy (IMRT) and to investigate the feasibility of radiomics for analysis of radioresistance.Methods: We analyzed 306 NPC patients treated with IMRT from Jul-2009 to Aug-2016, 20 of whom developed with recurrence. For the NPCs with recurrence, CT, MR, or PET/CT images of recurrent disease were registered with the primary planning CT for dosimetry analysis. The recurrences were defined as in-field, marginal or out-of-field, according to dose-volume histogram (DVH) of the recurrence volume. To explore the predictive power of radiomics for NPCs with in-field recurrences (NPC-IFR), 16 NPCs with non-progression disease (NPC-NPD) were used for comparison. For these NPC-IFRs and NPC-NPDs, 1117 radiomic features were quantified from the tumor region using pre-treatment spectral attenuated inversion-recovery T2-weighted (SPAIR T2W) magnetic resonance imaging (MRI). Intraclass correlation coefficients (ICC) and Pearson correlation coefficient (PCC) was calculated to identify influential feature subset. Kruskal-Wallis test and receiver operating characteristic (ROC) analysis were employed to assess the capability of each feature on NPC-IFR prediction. Principal component analysis (PCA) was performed for feature reduction. Artificial neural network (ANN), k-nearest neighbor (KNN), and support vector machine (SVM) models were trained and validated by using stratified 10-fold cross validation.Results: The median follow up was 26.5 (range 8–65) months. 9/20 (45%) occurred in the primary tumor, 8/20 (40%) occurred in regional lymph nodes, and 3/20 (15%) patients developed a primary and regional failure. Dosimetric and target volume analysis of the recurrence indicated that there were 18 in-field, and 1 marginal as well as 1 out-of-field recurrence. With pre-therapeutic SPAIR T2W MRI images available, 11 NPC-IFRs (11 of 18 NPC-IFRs who had available pre-therapeutic MRI) and 16 NPC-NPDs were subsequently employed for radiomic analysis. Results showed that NPC-IFRs vs. NPC-NPDs could be differentiated by 8 features (AUCs: 0.727–0.835). The classification models showed potential in prediction of NPC-IFR with higher accuracies (ANN: 0.812, KNN: 0.775, SVM: 0.732).Conclusion: In-field and high-dose region relapse were the main recurrence patterns which may be due to the radioresistance. After integration in the clinical workflow, radiomic analysis can be served as imaging biomarkers to facilitate early salvage for NPC patients who are at risk of in-field recurrence

Reversion of pH-Induced Physiological Drug Resistance: A Novel Function of Copolymeric Nanoparticles

The extracellular pH of cancer cells is lower than the intracellular pH. Weakly basic anticancer drugs will be protonated extracellularly and display a decreased intracellular concentration. In this study, we show that copolymeric nanoparticles (NPs) are able to overcome this “pH-induced physiological drug resistance” (PIPDR) by delivering drugs to the cancer cells via endocytosis rather than passive diffussion.As a model nanoparticle, Tetradrine (Tet, Pka 7.80) was incorporated into mPEG-PCL. The effectiveness of free Tet and Tet-NPs were compared at different extracellular pHs (pH values 6.8 and 7.4, respectively) by MTT assay, morphological observation and apoptotic analysis in vitro and on a murine model by tumor volume measurement, PET-CT scanning and side effect evaluation in vivo.<0.05) when the extracellular pH decreased from 7.4 to 6.8. Meanwhile, the cytotoxicity of Tet-NPs was not significantly influenced by reduced pH. In vivo experiment also revealed that Tet-NPs reversed PIPDR more effectively than other existing methods and with much less side effects.The reversion of PIPDR is a new discovered mechanism of copolymeric NPs. This study emphasized the importance of cancer microenvironmental factors in anticancer drug resistance and revealed the superiority of nanoscale drug carrier from a different aspect