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

Effects of Socioeconomic Status, Parent–Child Relationship, and Learning Motivation on Reading Ability

Against the background of Chinese culture, we investigated the relationship between family socioeconomic status (SES) and children’s reading ability. Participants included 2294 middle-school students in grade 8. SES was measured by parents’ education level, parents’ occupational prestige, and family property, and children’s reading ability was estimated with item response theory. In addition, we adopted an 8-item parent–child relationship scale and a 22-item learning motivation scale that included four dimensions. We examined whether the parent–child relationship mediated the relationship between family SES and reading ability and whether this was moderated by learning motivation. The results indicated that the parent–child relationship played a mediating role in the relationship between SES and reading ability. This relationship was moderated by students’ learning motivation. The direct effects of SES on reading ability at high, medium, and low levels of learning motivation were 0.24, 0.32, and 0.40, respectively

Nonparametric Homogeneity Pursuit in Functional-Coefficient Models

This paper explores homogeneity of coefficient functions in nonlinear models with functional coefficients and identifies the underlying semiparametric modelling structure. With initial kernel estimates, we combine the classic hierarchical clustering method with a generalised version of the information criterion to estimate the number of clusters, each of which has a common functional coefficient, and determine the membership of each cluster. To identify a possible semi-varying coefficient modelling framework, we further introduce a penalised local least squares method to determine zero coefficients, non-zero constant coefficients and functional coefficients which vary with an index variable. Through the nonparametric kernel-based cluster analysis and the penalised approach, we can substantially reduce the number of unknown parametric and nonparametric components in the models, thereby achieving the aim of dimension reduction. Under some regularity conditions, we establish the asymptotic properties for the proposed methods including the consistency of the homogeneity pursuit. Numerical studies, including Monte-Carlo experiments and two empirical applications, are given to demonstrate the finite-sample performance of our methods

Double lung transplantation for end-stage Kartagener syndrome: A case report and literature review

Kartagener syndrome (KS) is an autosomal recessive disorder characterized by situs inversus, paranasal sinusitis and bronchiectasis. We report the successful use of double lung transplant (DLTx) to treat end-stage KS. A 49-year-old Han woman was admitted to Renmin Hospital (Wuhan University, China) in September 2017 with a ≥15 year history of chronic productive cough that had worsened during the past year. Clinical examination and imaging investigations revealed respiratory failure and situs inversus consistent with KS. The patient was successfully treated with DLTx involving bilateral bronchial anastomoses. DLTx is a feasible treatment option for end-stage KS

A novel non‑selective atypical PKC agonist could protect neuronal cell line from A β ‑oligomer induced toxicity by suppressing A β generation

Atypical protein kinase C (aPKCs) serve key functions in embryonic development by regulating apical-basal polarity. Previous studies have shed light on their roles during adulthood, especially in the development of Alzheimer\u27s disease (AD). Although the crystal structure of PKCι has been resolved, an agonist of aPKCs remains to be discovered. In the present study, by using the Discovery Studio program and LibDock methodology, a small molecule library (K66-X4436 KINA Set) of compounds were screened for potential binding to PKCι. Subsequently, the computational docking results were validated using affinity selection-mass spectrometry, before in vitro kinase activity was used to determine the function of the hit compounds. A cell-based model assay that can mimic the pathology of AD was then established and used to assess the function of these hit compounds. As a result, the aPKC agonist Z640 was identified, which could bind to PKCι in silico, in vitro and in this cell-based model. Z640 was further confirmed as a non-selective aPKC agonist that can activate the kinase activity of both PKCι and PKCζ. In the cell-based assay, Z640 was found to protect neuronal cell lines from amyloid-β (Aβ) oligomer-induced cell death by reducing reactive oxygen species production and restore mitochondrial function. In addition, Z640 could reduce Aβ40 generation in a dose-dependent manner and shift amyloid precursor protein processing towards the non-amyloid pathway. To conclude, the present study is the first, to the best of the authors\u27 knowledge to identify an aPKC agonist by combining computer-assisted drug discovery and cell-based assays. The present study also revealed that aPKC agonists have therapeutic potential for the treatment of AD

Trends and Progress in Nuclear and Hadron Physics: A Straight or Winding Road

Quantitative calculations of the properties of hadrons and nuclei, with assessed uncertainties, have emerged as competitive with experimental measurements in a number of major cases. We may well be entering an era where theoretical predictions are critical for experimental progress. Cross-fertilization between the fields of relativistic hadronic structure and non-relativistic nuclear structure is readily apparent. Non-perturbative renormalization methods such as similarity renormalization group and Okubo–Lee–Suzuki schemes as well as many-body methods such as coupled cluster, configuration interaction and lattice simulation methods are now employed and advancing in both major areas of physics. New algorithms to apply these approaches on supercomputers are shared among these areas of physics. The roads to success have intertwined with each community taking the lead at various times in the recent past. We briefly sketch these fascinating paths and comment on some symbiotic relationships. We also overview some recent results from the Hamiltonian basis light-front quantization approach

Hadron Spectra, Decays and Scattering Properties within Basis Light Front Quantization

We survey recent progress in calculating properties of the electron and hadrons within the Basis Light Front Quantization (BLFQ) approach. We include applications to electromagnetic and strong scattering processes in relativistic heavy ion collisions. We present an initial investigation into the glueball states by applying BLFQ with multigluon sectors, introducing future research possibilities on multi-quark and multi-gluon systems.Comment: Presented at LightCone 2017, Mumbai, Indi