Interference with NTSR1 Expression Exerts an Anti-Invasion Effect via the Jun/miR-494/SOCS6 Axis of Glioblastoma Cells

Background/Aims: Glioblastoma is the most common and aggressive brain tumor and carries a poor prognosis. Previously, we found that neurotensin receptor 1 (NTSR1) contributes to glioma progression, but the underlying mechanisms of NTSR1 in glioblastoma invasion remain to be clarified. The aim of this study was to investigate the molecular mechanisms of NTSR1 in glioblastoma invasion. Methods: Cell migration and invasion were evaluated using wound-healing and transwell assays. Cell proliferation was detected using CCK-8. The expression of NTSR1, Jun, and suppressor of cytokine signaling 6 (SOCS6) was detected using western blotting. The expression of miR-494 was detected by Quantitative real-time PCR. Chromatin immunoprecipitation assay was performed to examine the interaction between Jun and miR-494 promoter. Dual-luciferase reporter assay and western blotting were performed to identify the direct regulation of SOCS6 by miR-494. An orthotopic xenograft mouse model was conducted to assess tumor growth and invasion. Results: NTSR1 knockdown attenuated the invasion of glioblastoma cells. Jun was positively regulated by NTSR1, which promoted miR-494 expression through binding to miR-494 promoter. SOCS6 was confirmed as a direct target of miR-494, thus, NTSR1-induced miR-494 upregulation resulted in SOCS6 downregulation. Both miR-494 and SOCS6 were involved in the NTSR1-induced invasion of glioblastoma cells. In vivo, tumor invasion and growth were inhibited by NTSR1 knockdown, but were restored with miR-494 overexpression. Conclusion: NTSR1 knockdown inhibited glioblastoma invasion via the Jun/miR-494/SOCS6 axis

Heating of multi‐species upflowing ion beams observed by Cluster on March 28, 2001

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149495/1/epp320083.pd

Sciences for The 2.5-meter Wide Field Survey Telescope (WFST)

The Wide Field Survey Telescope (WFST) is a dedicated photometric survey facility under construction jointly by the University of Science and Technology of China and Purple Mountain Observatory. It is equipped with a primary mirror of 2.5m in diameter, an active optical system, and a mosaic CCD camera of 0.73 Gpix on the main focus plane to achieve high-quality imaging over a field of view of 6.5 square degrees. The installation of WFST in the Lenghu observing site is planned to happen in the summer of 2023, and the operation is scheduled to commence within three months afterward. WFST will scan the northern sky in four optical bands (u, g, r, and i) at cadences from hourly/daily to semi-weekly in the deep high-cadence survey (DHS) and the wide field survey (WFS) programs, respectively. WFS reaches a depth of 22.27, 23.32, 22.84, and 22.31 in AB magnitudes in a nominal 30-second exposure in the four bands during a photometric night, respectively, enabling us to search tremendous amount of transients in the low-z universe and systematically investigate the variability of Galactic and extragalactic objects. Intranight 90s exposures as deep as 23 and 24 mag in u and g bands via DHS provide a unique opportunity to facilitate explorations of energetic transients in demand for high sensitivity, including the electromagnetic counterparts of gravitational-wave events detected by the second/third-generation GW detectors, supernovae within a few hours of their explosions, tidal disruption events and luminous fast optical transients even beyond a redshift of 1. Meanwhile, the final 6-year co-added images, anticipated to reach g about 25.5 mag in WFS or even deeper by 1.5 mag in DHS, will be of significant value to general Galactic and extragalactic sciences. The highly uniform legacy surveys of WFST will also serve as an indispensable complement to those of LSST which monitors the southern sky.Comment: 46 pages, submitted to SCMP

Safety Impact Analysis Considering Physical Failures and Cyber-Attacks for Mechanically Pumped Loop Systems (MPLs)

As complex systems composed of physical and cyber components, mechanically pumped loop systems (MPLs) are vulnerable to both passive threats (e.g., physical failures) and active threats such as cyber-attacks launched on the network control systems. The impact of the aforementioned two threats on MPL operations is yet unknown, and there is no practical way to evaluate their severity. To assess the severity of the impact of physical failures and cyber-attacks on MPLs, a safety impact analysis framework based on Elman Neural Network (ENN) observers and the Gaussian Mixture Model (GMM) algorithm is suggested. The framework discusses three common attack and failure modes: sensor hard failure that occurs suddenly, sensor soft failure that occurs gradually over time, and denial-of-service (DoS) attacks that prevent communication between the controller and valve. Both sensor failures and DoS attacks render the system unsafe, according to simulation data. In comparison to DoS attacks, however, sensor failures, particularly soft failures, inflict the greatest harm to the MPLs. Furthermore, sensors engaged in global control, rather than those involved in local control, need additional protection

A Multi-Band LNA Covering 17–38 GHz in 45 nm CMOS SOI

This paper presents a multi-band low-noise amplifier (LNA) in the 45-nm CMOS silicon-on-insulator (SOI) process. The LNA consists of three stages, with the differential cascode amplifier as the core structure. The first stage is mainly responsible for input matching to ensure favourable noise characteristics and bandwidth, while the subsequent stages increase the gain. Moreover, the LNA utilizes baluns for input/output and interstage impedance matching. Switch capacitances are added to switch the three operating bands of the LNA, which cover 17–38 GHz overall. Measurement results show that the proposed LNA achieves a gain (S21) of 23.0 dB and a noise figure (NF) of 4.0 dB

Feature fusion improves performance and interpretability of machine learning models in identifying soil pollution of potentially contaminated sites

Owing to the rapid development of big data technology, use of machine learning methods to identify soil pollution of potentially contaminated sites (PCS) at regional scales and in different industries has become a research hot spot. However, due to the difficulty in obtaining key indexes of site pollution sources and pathways, current methods have problems such as low accuracy of model predictions and insufficient scientific basis. In this study, we collected the environmental data of 199 PCS in 6 typical industries involving heavy metal and organic pollution. Then, 21 indexes based on basic information, potential for pollution from product and raw material, pollution control level, and migration capacity of soil pollutants were used to established the soil pollution identification index system. We fused the original indexes into the new feature subset with 11 indexes through the method of consolidation calculation. The new feature subset was then used to train machine learning models of random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), and tested to determine whether it improved the accuracy and precision of soil pollination identification models. The results of correlation analysis showed that the four new indexes created by feature fusion have the correlation with soil pollution is similar to the original indexes. The accuracies and precisions of three machine learning models trained on the new feature subset were 67.4%− 72.9% and 72.0%− 74.7%, which were 2.1%− 2.5% and 0.3%− 5.7% higher than these of the models trained on original indexes, respectively. When the PCS were divided into typical heavy metal and organic pollution sites according to the enterprise industries, the accuracy of the model trained on the two datasets for identifying soil heavy metal and organic pollution were significantly improve to approximately 80%. Owing to the imbalance in positive and negative samples in the prediction of soil organic pollution, the precisions of soil organic pollution identification models were 58%− 72.5%, which were significantly lower than their accuracies. According to the factors analysis based on the model interpretability of SHAP, most of the indexes of basic information, potential for pollution from product and raw material, and pollution control level had different degrees of impact on soil pollution. However, the indexes of migration capacity of soil pollutants had the least effect in the classification task of soil pollution identification of PCS. Among the indexes, traces of soil pollution, industrial utilization years/start-up time, pollution control risk scores and enterprise scale having the greatest effects on soil pollution with the mean SHAP values of 0.17–0.36, which reflected their contribution rate on soil pollution and could help to optimize the current index scoring of the technical regulation for identifying site soil pollution. This study provides a new technical method to identify soil pollution based on big data and machine learning methods, in addition to providing a reference and scientific basis for environmental management and soil pollution control of PCS

A Multi-Band LNA Covering 17–38 GHz in 45 nm CMOS SOI

This paper presents a multi-band low-noise amplifier (LNA) in the 45-nm CMOS silicon-on-insulator (SOI) process. The LNA consists of three stages, with the differential cascode amplifier as the core structure. The first stage is mainly responsible for input matching to ensure favourable noise characteristics and bandwidth, while the subsequent stages increase the gain. Moreover, the LNA utilizes baluns for input/output and interstage impedance matching. Switch capacitances are added to switch the three operating bands of the LNA, which cover 17–38 GHz overall. Measurement results show that the proposed LNA achieves a gain (S21) of 23.0 dB and a noise figure (NF) of 4.0 dB

Mechanism Analysis and Self-Adaptive RBFNN Based Hybrid Soft Sensor Model in Energy Production Process: A Case Study

Despite hard sensors can be easily used in various condition monitoring of energy production process, soft sensors are confined to some specific scenarios due to difficulty installation requirements and complex work conditions. However, industrial process may refer to complex control and operation, the extraction of relevant information from abundant sensors data may be challenging, and description of complicated process data patterns is also becoming a hot topic in soft-sensor development. In this paper, a hybrid soft sensor model based mechanism analysis and data-driven is proposed, and ventilation sensing of coal mill in a power plant is conducted as a case study. Firstly, mechanism model of ventilation is established via mass and energy conservation law, and object-relevant features are identified as the inputs of data-driven method. Secondly, radial basis function neural network (RBFNN) is used for soft sensor modeling, and genetic algorithm (GA) is adopted for quick and accurate determination of the RBFNN hyper-parameters, thus self-adaptive RBFNN (SA-RBFNN) is proposed to improve the soft sensor performance in energy production process. Finally, effectiveness of the proposed method is verified on a real-world power plant dataset, taking coal mill ventilation soft sensing as a case study

Factors Influencing Earthworm Fauna in Parks in Megacity Beijing, China: An Application of a Synthetic and Simple Index (ESI)

Complicated factors in urban areas have been reported to impact the density, biomass, and diversity of earthworm fauna. Urban parks provide essential habitats for earthworm fauna. However, how earthworm fauna are impacted by park traits, such as construction age, distance to city center, visitor volumes, sizes of greenspaces/parks, and attractiveness, etc., still remains unknown. These traits are well characterized by the impacts of urbanization intensity and administration quality of parks in megacities. Therefore, 16 parks with gradients of construction ages and geographical locations in Beijing city were selected for investigation. Furthermore, an earthworm synthetic and simple index (ESI) for characterizing earthworm community has been developed to compensate for the lack of robustness by using single ecological indexes. The results showed that earthworm population density (38.6 ind/m2) and biomass (34.0 g/m2) in parks were comparable to those in other land use types in Beijing. Ecological groupings were dominated by disturbance-tolerant endogeic and deep soil-inhabiting anecic groups, and most of them were adults. The earthworm population was influenced by urbanization intensity, while the earthworm community composition, species biodiversity, and ESI were affected by administration quality of parks. The soil moisture and microbial biomass carbon were the key factors in shaping earthworm assemblages. ESI could be employed as an effective indicator in depicting character of earthworm fauna. This study highlighted the impacts of park traits on earthworms in urban parks. The variation in park traits that influence earthworm fauna was probably attributed to soil properties

A 19.6–39.4 GHz Broadband Low Noise Amplifier Based on Triple-Coupled Technique and T-Coil Network in 65-nm CMOS

This paper presents a differential 19.6–39.4 GHz broadband low-noise amplifier (LNA) in 65-nm CMOS technology. The LNA consists of two cascode stage and one common-source stage. To achieve a wide bandwidth and low average noise figure, inter-stage peak-gain distribution technique and transformer-based triple-coupled technique are developed. Besides, a new compact T-coil-based network is proposed to neutralize the parasitic capacitors and enlarge the gain. The measure results show that the 3-dB bandwidth is from 19.6 to 39.4 GHz, the maximum gain is 23.5 dB, and the noise figure (NF) is from 3.7 to 5.8 dB. The dc power comsumption is 46 mW with 1V supply voltage. The input P1dB is −17 dBm at 30 GHz