A Deep Learning Approach for Chromium Detection and Characterization from Soil Hyperspectral Data.

High levels of chromium (Cr) in soil pose a significant threat to both humans and the environment. Laboratory-based chemical analysis methods for Cr are time consuming and expensive; thus, there is an urgent need for a more efficient method for detecting Cr in soil. In this study, a deep neural network (DNN) approach was applied to the Land Use and Cover Area frame Survey (LUCAS) dataset to develop a hyperspectral soil Cr content prediction model with good generalizability and accuracy. The optimal DNN model was constructed by optimizing the spectral preprocessing methods and DNN hyperparameters, which achieved good predictive performance for Cr detection, with a correlation coefficient value of 0.79 on the testing set. Four important hyperspectral bands with strong Cr sensitivity (400-439, 1364-1422, 1862-1934, and 2158-2499 nm) were identified by permutation importance and local interpretable model-agnostic explanations. Soil iron oxide and clay mineral content were found to be important factors influencing soil Cr content. The findings of this study provide a feasible method for rapidly determining soil Cr content from hyperspectral data, which can be further refined and applied to large-scale Cr detection in the future.

GRIK1 promotes glioblastoma malignancy and is a novel prognostic factor of poor prognosis.

Primary tumors of the central nervous system (CNS) are classified into over 100 different histological types. The most common type of glioma is derived from astrocytes, and the most invasive glioblastoma (WHO IV) accounts for over 57% of these tumors. Glioblastoma (GBM) is the most common and fatal tumor of the CNS, with strong growth and invasion capabilities, which makes complete surgical resection almost impossible. Despite various treatment methods such as surgery, radiotherapy, and chemotherapy, glioma is still an incurable disease, and the median survival time of patients with GBM is shorter than 15 months. Thus, molecular mechanisms of GBM characteristic invasive growth need to be clarified to improve the poor prognosis. Glutamate ionotropic receptor kainate type subunit 1 (GRIK1) is essential for brain function and is involved in many mental and neurological diseases. However, GRIK1's pathogenic roles and mechanisms in GBM are still unknown. Single-nuclear RNA sequencing of primary and recurrent GBM samples revealed that GRIK1 expression was noticeably higher in the recurrent samples. Moreover, immunohistochemical staining of an array of GBM samples showed that high levels of GRIK1 correlated with poor prognosis of GBM, consistent with The Cancer Genome Atlas database. Knockdown of GRIK1 retarded GBM cells growth, migration, and invasion. Taken together, these findings show that GRIK1 is a unique and important component in the development of GBM and may be considered as a biomarker for the diagnosis and therapy in individuals with GBM.

The initial stages of cement hydration at the molecular level.

Cement hydration is crucial for the strength development of cement-based materials; however, the mechanism that underlies this complex reaction remains poorly understood at the molecular level. An in-depth understanding of cement hydration is required for the development of environmentally friendly cement and consequently the reduction of carbon emissions in the cement industry. Here, we use molecular dynamics simulations with a reactive force field to investigate the initial hydration processes of tricalcium silicate (C3S) and dicalcium silicate (C2S) up to 40 ns. Our simulations provide theoretical support for the rapid initial hydration of C3S compared to C2S at the molecular level. The dissolution pathways of calcium ions in C3S and C2S are revealed, showing that, two dissolution processes are required for the complete dissolution of calcium ions in C3S. Our findings promote the understanding of the calcium dissolution stage and serve as a valuable reference for the investigation of the initial cement hydration.

Ab initio calculation of the adsorption of As, Cd, Cr, and Hg heavy metal atoms onto the illite(001) surface: Implications for soil pollution and reclamation.

Elucidating the mechanisms of heavy metal (HM) adsorption on clay minerals is key to solving HM pollution in soil. In this study, the adsorption of four HM atoms (As, Cd, Cr, and Hg) on the illite(001) surface was investigated using density functional theory calculations. Different adsorption configurations were investigated and the electronic properties (i.e., adsorption energy (Ead) and electron transfer) were analyzed. The Ead values of the four HM atoms on the illite(001) surface were found to be As > Cr > Cd > Hg. The Ead values for the most stable adsorption configurations of As, Cr, Cd, and Hg were -1.8554, -0.7982, -0.3358, and -0.2678 eV, respectively. The As atoms show effective chemisorption at all six adsorption sites, while Cd, Cr, and Hg atoms mainly exhibited physisorption. The hollow and top (O) sites were more favorable than the top (K) sites for the adsorption of HM atoms. The Gibbs free energy results show that the illite(001) surface was energetically favorable for the adsorption of As and Cr atoms under the influence of 298 K and 1 atm. After adsorption, there was a redistribution of positions and reconfiguration of the chemical bonding of the surface atoms, with a non-negligible influence around the upper surface atoms. Bader charge analysis shows electrons were transferred from the surface to the HM atoms, and a strong correlation between the valence electron variations and the adsorption energy was observed. HM atoms had a high electronic state overlap with the surface O atoms near the Fermi energy level, indicating that the surface O atoms, though not the topmost atoms around the surface, significantly influence HM adsorption. The above results show illite(001) preferentially adsorbed As among all four investigated HM atoms, indicating that soils containing a high proportion of illite might be more prone to As pollution.

Ab initio calculations of CO2 adsorption on ß-C2S(100) and M3-C3S(001) surfaces: An exploration of early CO2 sequestration pathways.

Investigating CO2 sequestration in cement-based materials is significant for achieving carbon neutrality in the cement and concrete industries. The early CO2 sequestration pathways on cement-based materials are fundamental for CO2 sequestration, which is not clear. Towards this, the adsorption behavior of CO2 on ß-C2S(100) and M3-C3S(001) was investigated at the atomic level using density functional theory calculations, which were then compared with water adsorption results. The molecular adsorption configurations of CO2 on both ß-C2S(100) and M3-C3S(001) were tilted from their initial configurations due to the influence of surface Ca and O atoms. The CO2 adsorption energy on M3-C3S(001) and ß-C2S(100) were -0.458 eV and -0.426 eV, respectively, indicating adsorption on M3-C3S(001) was more energetically favorable. After CO2 adsorption, electrons were transferred from the surface to the CO2 molecule. Furthermore, the Ca-O bond orders of ß-C2S(100) and M3-C3S(001) after CO2 adsorption were maximally decreased by 2.79% and 6.99%, respectively. A more significant adsorption influence on surfaces was found for H2O, with more negative adsorption energy, more evident electron transfer, and a greater decrease in bond order. The CO2 adsorption on ß-C2S(100) and M3-C3S(001) were still spontaneous at 298 K and 1 atm. This study provides important theoretical insights into early CO2 sequestration at the atomic level, which has practical implications for the design of efficient CO2 sequestration technologies.

In situ relative self-dependent calibration of electron cyclotron emission imaging via shape matching.

Electron Cyclotron Emission Imaging (ECEI) is a diagnostic system which measures 2-D electron temperature profiles with high spatial-temporal resolution. Usually only the normalized electron temperature fluctuations are utilized to investigate the magnetohydrodynamics modes due to the difficulties of ECEI calibration. In this paper, we developed a self-dependent calibration method for 24 × 16 channel high-resolution ECEI on the Experimental Advanced Superconducting Tokamak. The technique of shape matching is applied to solve for the matrix of the calibration coefficients. The calibrated area is further expanded to an occupation ratio of 88% observation area by utilizing the features of sawtooth crash. The result is self-consistent and consistent with calibrated 1D ECE measurement.

Diagnostic capacity of electron cyclotron emission imaging system with continuous large observation area on EAST tokamak.

Electron cyclotron emission imaging on EAST provides direct measurements of the 2-D electron temperature dynamics in a continuous large observation area with high temporal and spatial resolution. Besides the normal MHD investigation, a system with a view field large enough to cover the core plasma region has been applied to extract more plasma information, such as the plasma center location, the deposition location of auxiliary heating, and the core toroidal rotation speed. These results solely based on electron cyclotron emission imaging (ECEI) data are consistent with the results of the equilibrium fitting (EFIT), numerical code, and other diagnostics, which indicate the powerful diagnostic capacity of this ECEI system.

Pattern formation induced by internal microscopic fluctuations.

We report spatiotemporal patterns induced by microscopic fluctuations in the Gray-Scott model. In the framework of stochastic kinetics, the macroscopic effect of internal noise of the system was investigated by simulating the reaction-diffusion master equation using Gillespie's algorithm. Pattern formation at the level of stochastic description is presented in comparison with that given by deterministic equations. Complex spatiotemporal patterns, including spiral waves, Turing patterns, self-replicating spots and others, which are not captured or correctly predicted by the deterministic reaction-diffusion equations, are induced by internal reaction fluctuations. Furthermore the intrinsic noise selects and controls the pattern formation with different intensities of fluctuation.