Underwater target recognition method based on t-SNE and stacked nonnegative constrained denoising autoencoder

1822-1832Underwater targets recognition is a difficult task due to the specific attributes of underwater target radiated noises, low signal to noise ratio and so on. In this paper, the input data optimization method and recognition model were researched. The underwater target radiated noise spectrum was chosen as the original feature. The t-distributed stochastic neighbor embedding (t-SNE) algorithm was used to reduce the dimensionality of the original spectrum segments divided by frequency. The optimal features can be obtained by analyzing the separability. Then the stacked nonnegative constrained denoising autoencoder (SNDAE) model was established to recognize the optimal features. The experimental signal spectra were processed by above methods. The results show that the recognition accuracy of SNDAE is higher than that of other contrastive methods. And the frequency of input band with the highest recognition accuracy is approximately the same as that with the best separability based on t-SNE, indicating that the above method can improve the recognition accuracy and efficiency

Treatment efficacy of carrelizumab in metastatic castration-resistant prostate cancer, and the significance of circulating tumor DNA fraction

Purpose: To explore the efficacy of carrelizumab in the treatment of metastatic castration-resistant prostate cancer (mCRPC), and the significance of circulating tumor DNA (ctDNA) fraction in the process.Methods: 100 mCRPC patients who were treated in the Oncology Department of Harbin Medical University Cancer Hospital in a time frame of January 2018 to January 2019 were enrolled and assigned (1:1) into control and study groups and were given a regimen consisting of a combination of docetaxel and prednisone. Prognosis of patients with high and low ctDNA fractions relative to baseline ctDNA level, was compared.Results: The study group obtained considerably higher objective response rate (ORR) in relation to the control group (p < 0.05). Serum levels of prostate-specific antigen (PSA) and testosterone (TTE) were significantly lower in the study group versus control group. Better quality of life and bladder function were witnessed in the study group when compared to control group (p < 0.05). The proportion of patients with ctDNA fraction < 2 % in the study group significantly increased, but there was no significant change in ctDNA in the control group. The clinical prognosis of patients with low ctDNA fraction was significantly better than that of patients with high fraction (p < 0.05).Conclusion: Combined use of carrelizumab and docetaxel-prednisone regimen for mCRPC patients substantially improved clinical efficacy, quality of life, and long-term prognosis, while reducing ctDNA levels. Thus, the combination regimen has promise for the treatment of mCRPC patients

Task-Oriented and Semantics-Aware 6G Networks

Upon the arrival of emerging devices, including Extended Reality (XR) and Unmanned Aerial Vehicles (UAVs), the traditional bit-oriented communication framework is approaching Shannon's physical capacity limit and fails to guarantee the massive amount of transmission within latency requirements. By jointly exploiting the context of data and its importance to the task, an emerging communication paradigm shift to semantic level and effectiveness level is envisioned to be a key revolution in Sixth Generation (6G) networks. However, an explicit and systematic communication framework incorporating both semantic level and effectiveness level has not been proposed yet. In this article, we propose a generic task-oriented and semantics-aware (TOSA) communication framework for various tasks with diverse data types, which incorporates both semantic level information and effectiveness-aware performance metrics. We first analyze the unique characteristics of all data types, and summarise the semantic information, along with corresponding extraction methods. We then propose a detailed TOSA communication framework for different time-critical and non-critical tasks. In the TOSA framework, we present the TOSA information, extraction methods, recovery methods, and effectiveness-aware performance metrics. Last but not least, we present a TOSA framework tailored for Unmanned Aerial Vehicle (UAV) control task to validate the effectiveness of the proposed TOSA communication framework

Involvement of Lysosome Membrane Permeabilization and Reactive Oxygen Species Production in the Necrosis Induced by Chlamydia muridarum Infection in L929 Cells

Chlamydiae, obligate intracellular bacteria, are associated with a variety of human diseases. The chlamydial life cycle undergoes a biphasic development: replicative reticulate bodies (RBs) phase and infectious elementary bodies (EBs) phase. At the end of the chlamydial intracellular life cycle, EBs have to be released to the surrounded cells. Therefore, the interactions between Chlamydiae and cell death pathways could greatly influence the outcomes of Chlamydia infection. However, the underlying molecular mechanisms remain elusive. Here, we investigated host cell death after Chlamydia infection in vitro, in L929 cells, and showed that Chlamydia infection induces cell necrosis, as detected by the propidium iodide (PI)-Annexin V double-staining flow-cytometric assay and Lactate dehydrogenase (LDH) release assay. The production of reactive oxygen species (ROS), an important factor in induction of necrosis, was increased after Chlamydia infection, and inhibition of ROS with specific pharmacological inhibitors, diphenylene iodonium (DPI) or butylated hydroxyanisole (BHA), led to significant suppression of necrosis. Interestingly, live-cell imaging revealed that Chlamydia infection induced lysosome membrane permeabilization (LMP). When an inhibitor upstream of LMP, CA-074-Me, was added to cells, the production of ROS was reduced with concomitant inhibition of necrosis. Taken together, our results indicate that Chlamydia infection elicits the production of ROS, which is dependent on LMP at least partially, followed by induction of host-cell necrosis. To our best knowledge, this is the first live-cell-imaging observation of LMP post Chlamydia infection and report on the link of LMP to ROS to necrosis during Chlamydia infection. </p

Machine Learning for the Preliminary Diagnosis of Dementia

Objective: The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods: We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results: Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and F-measure = 0.81) among the six classification models. Conclusion: The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia

Machine learning for the preliminary diagnosis of dementia

Objective. The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods. We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results. Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and F-measure = 0.81) among the six classification models. Conclusion. The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia