Semi-Supervised Learning via Swapped Prediction for Communication Signal Recognition

Deep neural networks have been widely used in communication signal recognition and achieved remarkable performance, but this superiority typically depends on using massive examples for supervised learning, whereas training a deep neural network on small datasets with few labels generally falls into overfitting, resulting in degenerated performance. To this end, we develop a semi-supervised learning (SSL) method that effectively utilizes a large collection of more readily available unlabeled signal data to improve generalization. The proposed method relies largely on a novel implementation of consistency-based regularization, termed Swapped Prediction, which leverages strong data augmentation to perturb an unlabeled sample and then encourage its corresponding model prediction to be close to its original, optimized with a scaled cross-entropy loss with swapped symmetry. Extensive experiments indicate that our proposed method can achieve a promising result for deep SSL of communication signal recognition

Mechanism Analysis and Dynamics Simulation of Assist Manipulator

In order to reduce labour intensity and improve working efficiency, a kind of assist manipulator was designed which is an auxiliary tool used for the assembly line of the marine diesel engine that can conveniently realize the delivery of parts and field assembly. Motion and force analysis of the mechanism of assist manipulator was examined with the help of MATLAB software on the base of d\u27Alembert principle, the disciplinary of displacement, velocity, acceleration, and force rules in the process of mechanism movement was obtained by mechanical analysis. Based on the kinematical analysis, the parameters of mechanism size were optimized to improve the loading state. The Creo software, ANSYS software, and RecurDyn software were used to model and analyse the rigid-flexible coupling dynamics of the manipulator, and the motion law and stress distribution of the key components was obtained

Quantitative Evaluation of Chinese Herb Medicine in the Treatment of Sialorrhea and Frequent Nighttime Urination in Patients with Parkinson’s Disease

Aims. To evaluate the efficacy of Lian-Se formula (LSF), one Chinese herb formulation for treating sialorrhea and frequent overnight urination in patients with Parkinson’s disease (PD). Methods. 96 PD patients suffering from sialorrhea and/or frequent nighttime urination were divided into two groups: an LSF group (n = 48) treated with LSF for 6 weeks and a placebo group (n = 48) treated with a placebo formula whose appearance and taste were the same as LSF for 6 weeks. All patients were treated by standard antiparkinsonism medicine according to the PD guideline of China. The changes of the quantity of saliva (QS) (mL), frequency of nighttime urination (FNU) and early sleep activity (ESA), and nocturnal activity (NA) by analyzing actigraphic records as the primary results and the total score of unified Parkinson’s disease rating scale (UPDRS) and the Epworth Sleepiness Scale (ESS) as the secondary results were used to evaluate the clinical efficacy in both groups. Results. There were no significant differences in the baseline values of QS, FNU, NA, ESA, UPDRS total score, and ESS between the two groups. At the end of week 6, the QS, FNU, NA, and ESA in the LSF group showed superior results to those of the placebo group with no differences in the total UPDRS score between the two groups during the investigation. The ESS was significantly improved at the end of week 6 compared with the baseline and the placebo group. Laboratory test results indicated there were no side effects in either group. Conclusion. The findings of LSF treatment have clear clinical effects in patients with sialorrhea and frequent overnight urination. LSF thus appears to be a potential choice as an additional drug that can improve the sialorrhea and frequent overnight urination symptoms of PD patients

Nao-Xue-Shu

Aim. To determine one traditional Chinese medicine (TCM) Nao-Xue-Shu oral liquid which protects and improves secondary brain insults (SBI) in hypertensive cerebral hemorrhage (HCH). Methods. 158 patients with HCH were divided into routine clinical medicine plus Nao-Xue-Shu oral liquid (n=78) as treatment group, and routine clinical medicine (n=80) only served as the control group. The incidence of SBI and the classification of a favorable prognosis and a bad prognosis using the Glasgow outcome scale (GOS) were assessed to evaluate the clinical effects. The changes of IL-6 and TNF-α levels were determined to study the mechanism of the effects for the TCM. Results. The incidence of SBI at the end of week 2 was 8.97% in the treatment group and 23.75% in the control group, and the difference was significant (P<0.001). The incidence of a favorable prognosis was 48.72% in the treatment group and 32.72% in the control group, and the difference was significant (P<0.01) at the end of week 2. These findings indicate clear differences for IL-6 and TNF-α at the end of week 1 and week 2 compared with before treatment for the treatment group and a marked difference at the end of week 2 between the two groups. It also shows a significant difference between the end of week 2 and before treatment for IL-6 and TNF-α for the control group, although the difference was much smaller than the treatment group. Conclusion. Nao-Xue-Shu oral liquid could protect against the occurrence of SBI and improve HCH and SBI patients. It may also decrease the damage and the mass effects of the hematoma by reducing IL-6 and TNF-α to obtain the effects, and thus it is a potentially suitable drug for HCH and SBI

Condition monitoring of helical gears using automated selection of features and sensors

The selection of most sensitive sensors and signal processing methods is essential process for the design of condition monitoring and intelligent fault diagnosis and prognostic systems. Normally, sensory data includes high level of noise and irrelevant or red undant information which makes the selection of the most sensitive sensor and signal processing method a difficult task. This paper introduces a new application of the Automated Sensor and Signal Processing Approach (ASPS), for the design of condition monitoring systems for developing an effective monitoring system for gearbox fault diagnosis. The approach is based on using Taguchi's orthogonal arrays, combined with automated selection of sensory characteristic features, to provide economically effective and optimal selection of sensors and signal processing methods with reduced experimental work. Multi-sensory signals such as acoustic emission, vibration, speed and torque are collected from the gearbox test rig under different health and operating conditions. Time and frequency domain signal processing methods are utilised to assess the suggested approach. The experiments investigate a single stage gearbox system with three level of damage in a helical gear to evaluate the proposed approach. Two different classification models are employed using neural networks to evaluate the methodology. The results have shown that the suggested approach can be applied to the design of condition monitoring systems of gearbox monitoring without the need for implementing pattern recognition tools during the design phase; where the pattern recognition can be implemented as part of decision making for diagnostics. The suggested system has a wide range of applications including industrial machinery as well as wind turbines for renewable energy applications

Analysis of the potential association between ferroptosis and immune in hepatocellular carcinoma and their relationship with prognosis

BackgroundThe development of targeted therapy and immunotherapy has enriched the treatment of hepatocellular carcinoma (HCC), however, have had poor or no reponse, or even no response. Previous research suggested that ferroptosis and tumor immune microenvironment (TIME) may have a fundamental impact on efficacy during HCC immunotherapy and targeted therapy. Therefore, there is a clinical need to develop a signature that categorizes HCC patients in order to make more accurate clinical decisions.MethodsClinical data and gene expression data of HCC patients were obtained from The Cancer Genome Atlas (TCGA) portal and International Cancer Genome Consortium (ICGC) portal. To identify ferroptosis-related immune-related genes (ferroptosis-related IRGs), Pearson correlation analysis was conducted. The ferroptosis-related IRGs prognostic signature (FIPS) was constructed using Univariate Cox and LASSO Cox algorithms. The predictive effectiveness of FIPS was evaluated using Receiver Operating Characteristic (ROC) curves and survivorship curve. The correlation ship between FIPS and TIME was evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA) and CIBERSORT. The relationship between FIPS and immunotherapy responsiveness was evaluated using immunophenoscore. The expression level of 10 ferroptosis-related IRGs in normal liver tissues and HCC tissues was compared using immunohistochemistry. Finally, we established a nomogram (based on FIPS, TNM stage, and age) for clinical application.ResultsThe FIPS was established with ten ferroptosis-related IRGs. The high-FIPS subgroup showed a poor clinical prognosis and an obviously higher proportion of HCC patients with advanced TNM stage, high WHO grade and high alpha fetoprotein(AFP) value. Analysis of TIME indicated that patients in the high-FIPS subgroup may be in immunosuppressed state. Meanwhile, we found that ferroptosis may be inhibited in the high-FIPS subgroup and this subgroup may be impervious to immunotherapy and sorafenib.ConclusionWe constructed a novel potential prognostic signature for HCC patients that predicts overall survival, ferroptosis and immune status, sorafenib sensitivity, and immunotherapy responsiveness

A Common Genetic Variant (97906C>A) of DAB2IP/AIP1 Is Associated with an Increased Risk and Early Onset of Lung Cancer in Chinese Males

DOC-2/DAB2 interactive protein (DAB2IP) is a novel identified tumor suppressor gene that inhibits cell growth and facilitates cell apoptosis. One genetic variant in DAB2IP gene was reported to be associated with an increased risk of aggressive prostate cancer recently. Since DAB2IP involves in the development of lung cancer and low expression of DAB2IP are observed in lung cancer, we hypothesized that the variations in DAB2IP gene can increase the genetic susceptibility to lung cancer. In a case-control study of 1056 lung cancer cases and 1056 sex and age frequency-matched cancer-free controls, we investigated the association between two common polymorphisms in DAB2IP gene (−1420T>G, rs7042542; 97906C>A, rs1571801) and the risk of lung cancer. We found that compared with the 97906CC genotypes, carriers of variant genotypes (97906AC+AA) had a significant increased risk of lung cancer (adjusted odds ratio [OR] = 1.33, 95%CI = 1.04–1.70, P = 0.023) and the number of variant (risk) allele worked in a dose-response manner (Ptrend = 0.0158). Further stratification analysis showed that the risk association was more pronounced in subjects aged less than 60 years old, males, non-smokers, non-drinkers, overweight groups and in those with family cancer history in first or second-degree relatives, and the 97906A interacted with overweight on lung cancer risk. We further found the number of risk alleles (97906A allele) were negatively correlated with early diagnosis age of lung cancer in male patients (P = 0.003). However, no significant association was observed on the −1420T>G polymorphism. Our data suggested that the 97906A variant genotypes are associated with the increased risk and early onset of lung cancer, particularly in males

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe