Multi-step prediction of chlorophyll concentration based on Adaptive Graph-Temporal Convolutional Network with Series Decomposition

Chlorophyll concentration can well reflect the nutritional status and algal blooms of water bodies, and is an important indicator for evaluating water quality. The prediction of chlorophyll concentration change trend is of great significance to environmental protection and aquaculture. However, there is a complex and indistinguishable nonlinear relationship between many factors affecting chlorophyll concentration. In order to effectively mine the nonlinear features contained in the data. This paper proposes a time-series decomposition adaptive graph-time convolutional network ( AGTCNSD ) prediction model. Firstly, the original sequence is decomposed into trend component and periodic component by moving average method. Secondly, based on the graph convolutional neural network, the water quality parameter data is modeled, and a parameter embedding matrix is defined. The idea of matrix decomposition is used to assign weight parameters to each node. The adaptive graph convolution learns the relationship between different water quality parameters, updates the state information of each parameter, and improves the learning ability of the update relationship between nodes. Finally, time dependence is captured by time convolution to achieve multi-step prediction of chlorophyll concentration. The validity of the model is verified by the water quality data of the coastal city Beihai. The results show that the prediction effect of this method is better than other methods. It can be used as a scientific resource for environmental management decision-making.Comment: 12 pages, 10 figures, 3 tables, 45 reference

Influence of Section Orientation of Ultrasound Shear Wave Elastography on the Measurement of TI-RADS Category 4 Thyroid Nodules Stiffness

Thyroid shear wave elastography (SWE) is widely used as a noninvasive screening tool for thyroid nodules (TN) diagnosis. Herein, we assessed the effect of SWE section orientation on the stiffness measurement of TI-RADS category 4 TN. In this retrospective study, we followed up patients who had 2D ultrasound and elastography of the thyroid with pathological findings at our institution. The reliability and agreement between the aforementioned evaluations were further examined via calculation of the mean and maximum modulus values of the TN in both section orientations. As a result, there was good agreement in the measurement of the shear wave modulus of TN between the two different views, which provides relative flexibility for patients with anatomical or physiological defects

Identification of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning

BackgroundIn the pathogenesis of osteoarthritis (OA) and metabolic syndrome (MetS), the immune system plays a particularly important role. The purpose of this study was to find key diagnostic candidate genes in OA patients who also had metabolic syndrome.MethodsWe searched the Gene Expression Omnibus (GEO) database for three OA and one MetS dataset. Limma, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were used to identify and analyze the immune genes associated with OA and MetS. They were evaluated using nomograms and receiver operating characteristic (ROC) curves, and finally, immune cells dysregulated in OA were investigated using immune infiltration analysis.ResultsAfter Limma analysis, the integrated OA dataset yielded 2263 DEGs, and the MetS dataset yielded the most relevant module containing 691 genes after WGCNA, with a total of 82 intersections between the two. The immune-related genes were mostly enriched in the enrichment analysis, and the immune infiltration analysis revealed an imbalance in multiple immune cells. Further machine learning screening yielded eight core genes that were evaluated by nomogram and diagnostic value and found to have a high diagnostic value (area under the curve from 0.82 to 0.96).ConclusionEight immune-related core genes were identified (FZD7, IRAK3, KDELR3, PHC2, RHOB, RNF170, SOX13, and ZKSCAN4), and a nomogram for the diagnosis of OA and MetS was established. This research could lead to the identification of potential peripheral blood diagnostic candidate genes for MetS patients who also suffer from OA

Physicochemical characterization and antioxidant activity of polysaccharides from Chlorella sp. by microwave-assisted enzymatic extraction

Microwave-assisted enzymatic extraction (MAEE) was used for the separation of polysaccharides from micro-Chlorella. The extraction condition of MAEE was optimized by Box-Behnken design and response surface methodology. Results showed that the optimal condition for the extraction of Chlorella sp. crude polysaccharides (CSCP) was at 50°C for 2.3 h with 380 W of microwave power and 0.31% of enzyme dosage. Under the optimal extraction condition, the extraction yield of CSCP reached 0.72%. Similarly, the α-amylase modification conditions of the CSCP were also optimized, in which the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging rate was used as the response value. The scavenging rate of DPPH free radicals was 17.58% when enzyme dosage was 271 U/g at 51°C for 14 min. Moreover, the enzyme-modified CSCP presented a typical heteropolysaccharide mainly including glucose (48.84%), ribose (13.57%) and mannose (11.30%). MAEE used in this work achieved a high extraction yield of CSCP, which provides an efficient method for the extraction of CSCP from Chlorella sp

Different Perspectives of a Factory of the Future: An Overview

Digitalfactory,andCloudManufacturingaretwoapproaches that aim at addressing the Factory of the Future, i.e., to provide digital support to manufacturing factories. They find their roots in two different geographical areas, respectively Europe and China, and therefore presents some differences as well as the same goal of building the factory of the future. In this paper, we present both the digital factory and the cloud manufacturing approaches and discuss their differences