Combination Rules for Potential Parameters of Unlike Molecules on Exp‐Six Model

1956 ◽  
Vol 24 (6) ◽  
pp. 1275-1276 ◽  
Author(s):  
B. N. Srivastava ◽  
K. P. Srivastava
Keyword(s):  
Combination Rules ◽  
Potential Parameters

Decreased mean platelet volume is associated with microsatellite instability in colorectal cancer: A propensity score-matched analysis

2021 ◽  
pp. 1-9
Author(s):  
Wen Wang ◽  
Guangyu Wang ◽  
Shuang Fu ◽  
Beibei Zhang ◽  
Zengyao Liu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Propensity Score ◽  
Microsatellite Instability ◽  
Propensity Score Matching ◽  
Mean Platelet Volume ◽  
Innate Immune ◽  
Platelet Volume ◽  
Activated Platelets ◽  
Medical University ◽  
Potential Parameters

BACKGROUND: Patients with microsatellite instability-high (MSI-H) colorectal cancer (CRC) generally have a better prognosis and a more effective immune response than patients with microsatellite stable (MSS) CRC. Moreover, activated platelets play a crucial role in modulating innate immune cells. Mean platelet volume (MPV) is an indicator of platelet activation. This study is to examine the association between MPV and MSI status in CRC. METHODS: We collected the clinical and pathological variables of 424 CRC patients diagnosed at the Harbin Medical University Cancer Hospital from January 2018 to December 2018. Associations between MPV levels and MSI status were examined. Propensity score matching (PSM) was performed to reduce the possibility of selection bias. RESULTS: 424 CRC patients were divided into low-MPV group and high-MPV group according to the optimal cut-off value of MPV. 131 high-MPV patients were matched to low-MPV counterparts in a 1:1 ratio by propensity score matching. As MPV levels increased, the percentage of patients with MSI-H reduced. Furthermore, compared with MSS group, the MSI-H group had a significantly lower MPV levels (p= 0.003 after matching). In addition, logistic regression analysis identified reduced MPV as an independent risk factor for MSI-H in CRC patients after controlling for other potential parameters. CONCLUSION: Lower MPV is associated with MSI-H subtype of CRC. Further study on MPV in MSI-H CRC is warranted.

scholarly journals Anaerobic Degradation of Environmentally Hazardous Aquatic Plant Pistia stratiotes and Soluble Cu(II) Detoxification by Methanogenic Granular Microbial Preparation

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3849
Author(s):  
Olesia Havryliuk ◽  
Vira Hovorukha ◽  
Oleksandr Savitsky ◽  
Volodymyr Trilis ◽  
Antonina Kalinichenko ◽  
...  
Keyword(s):  
Aquatic Plants ◽  
Anaerobic Degradation ◽  
Aquatic Plant ◽  
High Efficiency ◽  
Pistia Stratiotes ◽  
Copper Compounds ◽  
Mechanical Removal ◽  
Microbial Preparation ◽  
Copper Contaminated ◽  
Potential Parameters

The aquatic plant Pistia stratiotes L. is environmentally hazardous and requires effective methods for its utilization. The harmfulness of these plants is determined by their excessive growth in water bodies and degradation of local aquatic ecosystems. Mechanical removal of these plants is widespread but requires fairly resource-intensive technology. However, these aquatic plants are polymer-containing substrates and have a great potential for conversion into bioenergy. The aim of the work was to determine the main patterns of Pistia stratiotes L. degradation via granular microbial preparation (GMP) to obtain biomethane gas while simultaneously detoxifying toxic copper compounds. The composition of the gas phase was determined via gas chromatography. The pH and redox potential parameters were determined potentiometrically, and Cu(II) concentration photocolorimetrically. Applying the preparation, high efficiency of biomethane fermentation of aquatic plants and Cu(II) detoxification were achieved. Biomethane yield reached 68.0 ± 11.1 L/kg VS of Pistia stratiotes L. biomass. The plants’ weight was decreased by 9 times. The Cu(II) was completely removed after 3 and 10 days of fermentation from initial concentrations of 100 ppm and 200 ppm, respectively. The result confirms the possibility of using the GMP to obtain biomethane from environmentally hazardous substrates and detoxify copper-contaminated fluids.

scholarly journals Road Characteristics Detection Based on Joint Convolutional Neural Networks with Adaptive Squares

2021 ◽  
Vol 10 (6) ◽  
pp. 377
Author(s):  
Chiao-Ling Kuo ◽  
Ming-Hua Tsai
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Autonomous Vehicles ◽  
Detection Accuracy ◽  
Geospatial Information ◽  
Combination Rules ◽  
And Performance ◽  
Road Characteristics ◽  
Machine Readable ◽  
Background Image

The importance of road characteristics has been highlighted, as road characteristics are fundamental structures established to support many transportation-relevant services. However, there is still huge room for improvement in terms of types and performance of road characteristics detection. With the advantage of geographically tiled maps with high update rates, remarkable accessibility, and increasing availability, this paper proposes a novel simple deep-learning-based approach, namely joint convolutional neural networks (CNNs) adopting adaptive squares with combination rules to detect road characteristics from roadmap tiles. The proposed joint CNNs are responsible for the foreground and background image classification and various types of road characteristics classification from previous foreground images, raising detection accuracy. The adaptive squares with combination rules help efficiently focus road characteristics, augmenting the ability to detect them and provide optimal detection results. Five types of road characteristics—crossroads, T-junctions, Y-junctions, corners, and curves—are exploited, and experimental results demonstrate successful outcomes with outstanding performance in reality. The information of exploited road characteristics with location and type is, thus, converted from human-readable to machine-readable, the results will benefit many applications like feature point reminders, road condition reports, or alert detection for users, drivers, and even autonomous vehicles. We believe this approach will also enable a new path for object detection and geospatial information extraction from valuable map tiles.

scholarly journals Investigating the Use of Natural and Artificial Records for Prediction of Seismic Response of Regular and Irregular RC Bridges Considering Displacement Directions

2021 ◽  
Vol 11 (3) ◽  
pp. 906
Author(s):  
Payam Tehrani ◽  
Denis Mitchell
Keyword(s):  
Seismic Response ◽  
Radial Displacement ◽  
Time History ◽  
Good Prediction ◽  
3D Analysis ◽  
Combination Rules ◽  
Principal Axes ◽  
Artificial Records ◽  
The Mean ◽  
Irregular Bridges

The seismic responses of continuous multi-span reinforced concrete (RC) bridges were predicted using inelastic time history analyses (ITHA) and incremental dynamic analysis (IDA). Some important issues in ITHA were studied in this research, including: the effects of using artificial and natural records on predictions of the mean seismic demands, effects of displacement directions on predictions of the mean seismic response, the use of 2D analysis with combination rules for prediction of the response obtained using 3D analysis, and prediction of the maximum radial displacement demands compared to the displacements obtained along the principal axes of the bridges. In addition, IDA was conducted and predictions were obtained at different damage states. These issues were investigated for the case of regular and irregular bridges using three different sets of natural and artificial records. The results indicated that the use of natural and artificial records typically resulted in similar predictions for the cases studied. The effect of displacement direction was important in predicting the mean seismic response. It was shown that 2D analyses with the combination rules resulted in good predictions of the radial displacement demands obtained from 3D analyses. The use of artificial records in IDA resulted in good prediction of the median collapse capacity.

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