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The goal of this work was to automatically predict the dangerous states of hazardous chemicals' transport tank using multi-sensor data fusion. Eight kinds of sensors, which are gas sensor, temperature sensor, humidity sensor, pressure sensor, liquid level sensor, acceleration sensor, angle sensor and switch sensor, were used in the monitor system of a tank for LNG (liquefied natural gas) transportation on road. Data from the tank during transporting LNG in 20 days were analyzed to obtain the statistics of sensors' signals. Based on the JDL data fusion model, different means were applied to process data in different fusion levels, such as weighted average, least-squares estimation, Kalman filtering, and neural network. The data fusion system firstly automatically judges the transportation pattern of the tank with the characteristic parameters of the sensors, and then predicts the dangerous states of the tank, including leakage, traffic accident, etc., with special judge criteria under different transportation patterns. The algorithm is trial used for LNG transport tank monitoring, and results show that the predicted results were in accordance with the real states of the tank as far as now.

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Background Immunogenic cell death (ICD), which releases danger-associated molecular patterns (DAMP) that induce potent anticancer immune response, has emerged as a key component of therapy-induced anti-tumor immunity. The aim of this work was to analyze whether the carbonic anhydrase IX inhibitor S4 can elicit ICD in glioma cells. Methods The effects of S4 on glioma cell growth were evaluated using the CCK-8, clonogenic and sphere assays. Glioma cell apoptosis was determined by flow cytometry. Surface-exposed calreticulin (CRT) was inspected by confocal imaging. The supernatants of S4-treated cells were concentrated for the determination of HMGB1and HSP70/90 expression by immunoblotting. RNA-seq was performed to compare gene expression profiles between S4-treated and control cells. Pharmacological inhibition of apoptosis, autophagy, necroptosis and endoplasmic reticulum (ER) stress was achieved by inhibitors. In vivo effects of S4 were evaluated in glioma xenografts. Immunohistochemistry (IHC) was performed to stain Ki67 and CRT. Results S4 significantly decreased the viability of glioma cells and induced apoptosis and autophagy. Moreover, S4 triggered CRT exposure and the release of HMGB1 and HSP70/90. Inhibition of either apoptosis or autophagy significantly reversed S4-induced release of DAMP molecules. RNA-seq analysis indicated that the ER stress pathway was deregulated upon exposure to S4. Both PERK-eIF2α and IRE1α- XBP1 axis were activated in S4-treated cells. Furthermore, pharmacological inhibition of PERK significantly suppressed S4-triggered ICD markers and autophagy. In glioma xerografts, S4 significantly reduced tumor growth. Conclusions Altogether, these findings suggest S4 as a novel ICD inducer in glioma and might have implications for S4-based immunotherapy.

The quantification accuracy of the gas mixture recognizing is greatly dependent on the gas sensor array signal processing method. The paper reports the new hybrid architecture with two main stages for gas mixture recognition. The first stage combine the principal component analysis (PCA) and back propagation neural network (BPNN) to qualitative identify the gas mixture, and the second stage composed of the independent component analysis (ICA) and BP sub networks to quantify the gas concentrations. The hybrid architecture and three other commonly used methods of PCA+BPNN, ICA+BPNN, and ICA+BP sub networks were respectively applied in binary gas mixture quantification based on the same gas sensor array, and results show that the hybrid architecture has the lowest quantitative recognition errors and fast converge speed comparing with the other methods.

Background Immunogenic cell death (ICD), which releases danger-associated molecular patterns (DAMP) that induce potent anticancer immune response, has emerged as a key component of therapy-induced anti-tumor immunity. The aim of this work was to analyze whether the carbonic anhydrase IX inhibitor S4 can elicit ICD in glioma cells. Methods The effects of S4 on glioma cell growth were evaluated using the CCK-8, clonogenic and sphere assays. Glioma cell apoptosis was determined by flow cytometry. Surface-exposed calreticulin (CRT) was inspected by confocal imaging. The supernatants of S4-treated cells were concentrated for the determination of HMGB1and HSP70/90 expression by immunoblotting. RNA-seq was performed to compare gene expression profiles between S4-treated and control cells. Pharmacological inhibition of apoptosis, autophagy, necroptosis and endoplasmic reticulum (ER) stress was achieved by inhibitors. In vivo effects of S4 were evaluated in glioma xenografts. Immunohistochemistry (IHC) was performed to stain Ki67 and CRT. Results S4 significantly decreased the viability of glioma cells and induced apoptosis and autophagy. Moreover, S4 triggered CRT exposure and the release of HMGB1 and HSP70/90. Inhibition of either apoptosis or autophagy significantly reversed S4-induced release of DAMP molecules. RNA-seq analysis indicated that the ER stress pathway was deregulated upon exposure to S4. Both PERK-eIF2α and IRE1α- XBP1 axes were activated in S4-treated cells. Furthermore, pharmacological inhibition of PERK significantly suppressed S4-triggered ICD markers and autophagy. In glioma xenografts, S4 significantly reduced tumor growth. Conclusions Altogether, these findings suggest S4 as a novel ICD inducer in glioma and might have implications for S4-based immunotherapy.

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