Comprehensive Evaluation Model of Bearing Transportation Protection Effect of Bogie Traction Motor under Data Imbalance

Under different transportation protection, the sample data of bogie traction motor bearings of urban rail vehicles are seriously unbalanced, and the fault diagnosis ability and generalization effect are poor, which makes it difficult to evaluate the protection effect of bearings effectively. In this paper, a multimeasure hybrid evaluation model based on compressed sensing is proposed to evaluate the effect of bearing transportation protection under data imbalance. Firstly, bearing vibration signals under different transport protection conditions were compressed and sampled, and the original high-Witt collection in time domain, frequency domain, and time-frequency domain was extracted. Then, a multimeasure mixed feature evaluation model of correlation, distance, and signal was constructed, and the optimal multimeasure combination strategy was optimized by using comprehensive sensitivity score evaluation index. Finally, an evaluation model of bearing protection effect based on unified feature index was constructed by using the best feature subset evaluated, and the unified indicator was quantified to characterize the protection effect of different protection states. The experimental results show that the model can effectively evaluate bearings under different transport protection.

The Association between Temperament, Chronotype, Depressive Symptoms, and Disease Activity among Patients with Inflammatory Bowel Disease—A Cross-Sectional Pilot Study

The psychological aspect may play an important role in ulcerative colitis (UC) and Crohn’s disease (CD). The aims of this study were to explore the differences between patients with UC and CD regarding chronotype, temperament and depression, and to assess the psychological factors mentioned as predictors of disease activity. In total, n = 37 patients with UC and n = 47 patients with CD were included in the study. They underwent a clinical assessment, including the Mayo score or Crohn Disease Activity Index (CDAI), and completed questionnaires: a sociodemographic survey, Formal Characteristics of Behavior–Temperament Inventory (FCB-TI), Chronotype Questionnaire (CQ), and the Beck Depression Index II (BDI). The Sensory Sensitivity score was higher among patients with CD than UC (p = 0.04). The emotional reactivity and endurance scores were higher among women than men with CD (p = 0.028 and p = 0.012 respectively). CQ Morningness–Eveningness (ME) correlated with Endurance (p = 0.041), Emotional Reactivity (p = 0.016), and Activity (p = 0.004). ME correlated with Rhythmicity among CD patients (p = 0.002). The Mayo score was predicted by Perseverance. The CDAI score was predicted by the BDI score. The pattern of the relationship between chronotype and temperament may differentiate patients with UC and CD. Personal disposition may play a role in the clinical assessment of patients with IBD.

The Individual Differences in the Perception of Oral Chemesthesis Are Linked to Taste Sensitivity

Chemesthesis is a part of the flavor experience of foods. Chemesthetic perception is studied to understand its effect on food-related behavior and health. Thus, the objective of this research was to study individual differences in chemesthetic perception. Our study involved sensory tests of three chemesthetic modalities (astringency, pungency, and cooling). Participants (N = 196) evaluated the intensity of samples in different concentrations using a line scale under sensory laboratory conditions. Aluminum ammonium sulfate, capsaicin, and menthol were used as the prototypic chemesthetic compounds. The participants were divided into sensitivity groups in different chemesthetic modalities by hierarchical clustering based on their intensity ratings. In addition, an oral chemesthesis sensitivity score was determined to represent the generalized chemesthesis sensitivity. The results showed that people can perceive chemesthesis on different intensity levels. There were significantly positive correlations between (1) sensitivity scores for oral chemesthesis and taste as well as (2) each chemesthesis and taste modalities. Moreover, based on the multinomial logistic regression model, significant interactions between oral chemesthesis and taste sensitivity were discovered. Our findings showed that people can be classified into different oral chemesthesis sensitivity groups. The methods and results of this study can be utilized to investigate associations with food-related behavior and health.

Model constructions of chemosensitivity and prognosis of high grade serous ovarian cancer based on evaluation of immune microenvironment and immune response

Background The prognosis of high grade serous ovarian cancer (HGSOC) patients is closely related to the immune microenvironment and immune response. Based on this, the purpose of this study was to construct a model to predict chemosensitivity and prognosis, and provide novel biomarkers for immunotherapy and prognosis evaluation of HGSOC. Methods GSE40595 (38 samples), GSE18520 (63 samples), GSE26712 (195 samples), TCGA (321 samples) and GTEx (88 samples) were integrated to screen differential expressed genes (DEGs) of HGSOC. The prognosis related DEGs (DEPGs) were screened through overall survival analysis. The DEGs-encoded protein–protein interaction network was constructed and hub genes of DEPGs (DEPHGs) were generated by STRING. Immune characteristics of the samples were judged by ssGSEA, ESTIMATE and CYBERSORT. TIMER was used to analyze the relationship between DEPHGs and tumor-infiltrating immunocytes, as well as the immune checkpoint genes, finally immune-related DEPHGs (IDEPHGs) were determined, and whose expression in 12 pairs of HGSOC tissues and tumor-adjacent tissues were analyzed by histological verification. Furthermore, the chemosensitivity genes in IDEPHGs were screened according to GSE15622 (n = 65). Finally, two prediction models of paclitaxel sensitivity score (PTX score) and carboplatin sensitivity score (CBP score) were constructed by lasso algorithm. The area under curve was calculated to estimate the accuracy of candidate gene models in evaluating chemotherapy sensitivity. Results 491 DEGs were screened and 37 DEGs were identified as DEPGs, and 11 DEPHGs were further identified. Among them, CXCL13, IDO1, PI3, SPP1 and TRIM22 were screened as IDEPHGs and verified in the human tissues. Further analysis showed that IDO1, PI3 and TRIM22 could independently affect the chemotherapy sensitivity of HGSOC patients. The PTX score was significantly better than TRIM22, PI3, SPP1, IDO1 and CXCL13 in predicting paclitaxel sensitivity, so was CBP score in predicting carboplatin sensitivity. What’s more, both of the HGSOC patients with high PTX score or high CBP score had longer survival time. Conclusions Five IDEPHGs identified through comprehensive bioinformatics analysis were closely related with the prognosis, immune microenvironment and chemotherapy sensitivity of HGSOC. Two prediction models based on IDEPHGs might have potential application of chemotherapy sensitivity and prognosis for patients with HGSOC.

Development of Hybrid Artificial Neural Network and Decision Tree Algorithm for Air Quality Prediction

With the drastic development of smart cities and technology, various pollutions occurred in an environment such as air, water and noise. More specifically, air pollution has risen due to the population and transportation and it also direct impact on human health. To suggest a useful preventive measure, proper prediction of air quality is needed. Today machine learning (ML) algorithms support intelligent data analysis and are applied in various fields like medical, Market basket analysis, Finance and weather forecasting etc. This work is used to analyze the performance of air quality prediction by using various ML algorithms. In addition, a hybrid neural network and decision tree model is proposed for accurate forecasting. Experiential results show that the proposed hybrid model achieves higher accuracy than other methods. The proposed model attained an accuracy of 99.88%, with a sensitivity score of 99% and a specificity score of 99.88%.

Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks

This paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low-memory 3D U-Net as backbone, which allows the method to process large 3D image patches, often comprising full lungs, in a single pass through the network. This makes the method simple, robust and efficient. We validated the proposed method on three datasets with very different characteristics and various airway abnormalities: (1) a dataset of pediatric patients including subjects with cystic fibrosis, (2) a subset of the Danish Lung Cancer Screening Trial, including subjects with chronic obstructive pulmonary disease, and (3) the EXACT’09 public dataset. We compared our method with other state-of-the-art airway segmentation methods, including relevant learning-based methods in the literature evaluated on the EXACT’09 data. We show that our method can extract highly complete airway trees with few false positive errors, on scans from both healthy and diseased subjects, and also that the method generalizes well across different datasets. On the EXACT’09 test set, our method achieved the second highest sensitivity score among all methods that reported good specificity.

Automatic Pectoral Muscle Removal and Microcalcification Localization in Digital Mammograms

Objectives: Breast cancer is the most common cancer diagnosed in women, and microcalcification (MCC) clusters act as an early indicator. Thus, the detection of MCCs plays an important role in diagnosing breast cancer.Methods: This paper presents a methodology for mammogram preprocessing and MCC detection. The preprocessing method employs automatic artefact deletion and pectoral muscle removal based on region-growing segmentation and polynomial contour fitting. The MCC detection method uses a convolutional neural network for region-of-interest (ROI) classification, along with morphological operations and wavelet reconstruction to reduce false positives (FPs).Results: The methodology was evaluated using the mini-MIAS and UTP datasets in terms of segmentation accuracy in the preprocessing phase, as well as sensitivity and the mean FP rate per image in the MCC detection phase. With the mini-MIAS dataset, the proposed methods achieved accuracy scores of 99% for breast segmentation and 95% for pectoral segmentation, a sensitivity score of 82% for MCC detection, and an FP rate per image of 3.27. With the UTP dataset, the methods achieved accuracy scores of 97% for breast segmentation and 91% for pectoral segmentation, a sensitivity score of 78% for MCC detection, and an FP rate per image of 0.74.Conclusions: The proposed preprocessing method outperformed the state-of-the-art methods for breast segmentation and achieved relatively good results for pectoral muscle removal. Furthermore, the MCC detection module achieved the highest test accuracy in identifying potential ROIs with MCCs compared to other methods.

The Resting-State Electroencephalogram Microstate Correlations With Empathy and Their Moderating Effect on the Relationship Between Empathy and Disgust

Humans have a natural ability to understand the emotions and feelings of others, whether one actually witnesses the situation of another, perceives it from a photograph, reads about it in a fiction book, or merely imagines it. This is the phenomenon of empathy, which requires us to mentally represent external information to experience the emotions of others. Studies have shown that individuals with high empathy have high anterior insula and adjacent frontal operculum activation when they are aware of negative emotions in others. As a negative emotion, disgust processing involves insula coupling. What are the neurophysiological characteristics for regulating the levels of empathy and disgust? To answer this question, we collected electroencephalogram microstates (EEG-ms) of 196 college students at rest and used the Disgust Scale and Interpersonal Reactivity Index. The results showed that: (1) there was a significant positive correlation between empathy and disgust sensitivity; (2) the empathy score and the intensity of transition possibility between EEG-ms C and D were significantly positively correlated; and (3) the connection strength between the transition possibility of EEG-ms C and D could adjust the relationship between the disgust sensitivity score and the empathy score. This study provides new neurophysiological characteristics for an understanding of the regulate relationship between empathy and disgust and provides a new perspective on emotion and attention.

A Machine Learning Approach as an Aid for Early COVID-19 Detection

The novel coronavirus SARS-CoV-2 that causes the disease COVID-19 has forced us to go into our homes and limit our physical interactions with others. Economies around the world have come to a halt, with non-essential businesses being forced to close in order to prevent further propagation of the virus. Developing countries are having more difficulties due to their lack of access to diagnostic resources. In this study, we present an approach for detecting COVID-19 infections exclusively on the basis of self-reported symptoms. Such an approach is of great interest because it is relatively inexpensive and easy to deploy at either an individual or population scale. Our best model delivers a sensitivity score of 0.752, a specificity score of 0.609, and an area under the curve for the receiver operating characteristic of 0.728. These are promising results that justify continuing research efforts towards a machine learning test for detecting COVID-19.

EMBR-11. SYNERGISTIC DRUG COMBINATIONS FOR THE TREATMENT OF MYC AMPLIFIED GROUP 3 MEDULLOBLASTOMA

Background Medulloblastoma (MB) is a highly aggressive brain tumour in children. Patients with Group 3 MB harbouring a MYC-amplification (subtype II) show a particularly poor outcome despite high-intensity multimodal therapy. We and others have previously shown that MYC amplified Group 3 MB cells are highly susceptible towards treatment with class I histone deacetylase (HDAC) inhibitors such as entinostat. However, in clinical trials HDACi as a monotherapy show only modest efficacy in solid tumours. We propose to increase the efficacy of class I HDACi by drug combinations. Methods To identify synergistic drug combinations (entinostat + X) for the treatment of MYC amplified MB we performed a drug screen with a library of n=75 clinically available compounds as single agents and in combination with entinostat in n=3 MYC amplified vs. n=1 MYC-non amplified cell lines. Synergistic behaviour of the six most promising drug combinations was validated by metabolic activity assays, cell count experiments and gene expression profiling. Synergy was assessed by the Loewe additivity model using a combination of ray design and checkerboard matrix. Results The drug screen revealed n=20/75 drugs that were particularly effective (drug sensitivity score ≥10) in combination with entinostat treatment in all three MYC amplified cell lines. Synergy assessment of the top n=6 drugs confirmed strong synergistic activity with entinostat for n=2 drugs (navitoclax, irinotecan). The BCL-2 family inhibitor navitoclax showed the most robust synergy with entinostat in subsequent validation experiments. Conclusion Several drugs either clinically available or currently in clinical trials, including the BCL-2/Xl/w inhibitor navitoclax, show promising effects in a combination therapy with entinostat for the treatment of MYC amplified Group 3 MB.