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C60H140N20O12S4, monoclinic, P2/n (no. 13), a = 15.6996 Å, b = 8.5361 Å, c = 15.8896 Å, β = 94.722°, V = 2122.19(10) Å3, Z = 4, R gt(F) = 0.0440, wR ref(F 2) = 0.1366, T = 296(2) K.

C15H40N4O8, monoclinic, Cc (no. 9), a = 25.986(3) Å, b = 8.3450(6) Å, c = 21.1358(15) Å, β = 90.877(6)°, V = 4582.9(6) Å3, Z = 8, Rgt(F) = 0.0357, wRref(F2) = 0.0916, T = 296(2) K.

C20H25NO4, triclinic, P1̅ (no. 2), a = 8.3727(6) Å, b = 9.7374(7) Å, c = 12.3559(8) Å, α = 80.826(4)°, β = 73.432(4)°, γ = 75.425(4)°, V = 930.28(11) Å3, Z = 2, Rgt(F) = 0.0454, wRref(F2) = 0.1374, T = 296(2) K.

C9H18N4S3, monoclinic, P21/c (no. 14), a = 12.8444(3) Å, b = 15.7980(5) Å, c = 14.1803(4), β = 98.293(2)°, V = 2847.32(14) Å3, Z = 8, Rgt(F) = 0.0510, wRref(F2) = 0.1466, T = 296 K.

C11H16N2O7, orthorhombic, Pnma (no. 62), a = 13.753(3) Å, b = 6.9471(15) Å, c = 13.797(3) Å, V = 1318.2(5) Å3, Z = 4, Rgt(F) = 0.0386, wRref(F2) = 0.1650, T = 296 K.

C10H18N2O3S, orthorhombic, Pna21, a = 12.5603(19) Å, b = 11.5744(17) Å, c = 8.9054(11) Å, V = 1294.6(3) Å3, Z = 4, Rgt(F) = 0.0406, wRref(F2) = 0.1007, S = 1.025, T = 296(2) K.

We propose an accurate method for modeling acoustic wave propagation. The spatial derivatives are calculated using Fourier transform to reduce spatial numerical dispersion. The standard staggered grid is adopted to suppress the non-causal ringing artifacts as in the traditional pseudo-spectral method. Moreover, to eliminate time dispersion arising from the discretization of the time derivative, an additional time-dispersion elimination term is introduced. As a result, the present method not only retains the advantages of the conventional pseudo-spectral method such as coarser spatial sampling or higher spatial derivative approximation accuracy, but also achieves higher temporal derivative approximation accuracy due to the adoption of the additional time-dispersion elimination term. Numerical examples demonstrate that the temporal dispersion elimination process can be contaminated by spatial numerical dispersion. Thus, the temporal and spatial numerical dispersions should be handled simultaneously, as proposed in this paper, to achieve accurate acoustic simulation. [ABSTRACT FROM AUTHOR]

Estimating clean seismic signals from noisy single‐channel records is a hot research topic in the field of microseismic data processing. Due to the existence of strong random noise, the signal‐to‐noise ratio of such data is low, presenting a challenge for signal restoration. In this paper, we propose a denoising method of microseismic data based on a single‐channel phase space reconstruction and independent component analysis algorithm. Specifically, we first apply the phase space reconstruction to transform the one‐dimensional seismic signal into a high‐dimensional phase space in which signal and noise have different trajectories. We then employ independent component analysis to the reconstructed data for noise separation. Experimental results on real microseismic data verify that our proposed denoising method is useful for noise suppression and can enhance the signal‐to‐noise ratio of data, which can be further used for signal identification and event positioning in microseismic monitoring. [ABSTRACT FROM AUTHOR]

Herein, a magnetic borate‐functionalized MXene composite with multiple boronic affinity sites was fabricated by embedding Fe3O4 nanoparticles with 4‐formylphenylboronic acid functionalized Ti3C2Tx nanosheets and served as sorbent for the simultaneous extraction of catecholamines (CAs) in urine samples. The morphology and structure of the magnetic materials were investigated using scanning microscopy, vibrating sample magnetometer, X‐ray photoelectron spectrometer, and X‐ray diffraction. The introduction of polyethyleneimine can amplify the bonded boronic acid groups, thereby effectively improving the adsorption capacities for CAs based on the multiple interactions of boronic affinity, hydrogen bonding, and metal coordination. The adsorption performance was investigated using the kinetics and isotherms models, and the main parameters that influence the extraction efficiency were optimized. Under the most favorable magnetic solid‐phase extraction condition, a sensitive method for the analysis of CAs in urine samples was developed by combining magnetic solid‐phase extraction conditions with high‐performance liquid chromatography detection. The findings illustrated that the proposed approach possessed a wide linearity range of 0.05–250 ng/mL with an acceptable correlation coefficient (R2 ≥ 0.9984) and detection limits of 0.010–0.015 ng/mL for the target CAs. The research not only provides a notable composite with multiple boronic affinity sites but also offers an effective and feasible measure for the detection of CAs in biological samples. [ABSTRACT FROM AUTHOR]

Seismic velocity analysis is the basis for seismic imaging and understanding complex subsurface geological structures. Although the performance of automatic velocity analysis methods based on Common Middle Point (CMP) data or Velocity Spectra (VS) is encouraging, particularly deep learning methods. However, most methods ignore the complementarity between CMP data and VS data, and only one of them is selected for velocity modelling. We propose a multimodal neural network (MMN) that combines the advantages of CMP data details representation and simplification of VS. MMN includes multilayer convolution structures and auto-encoder structures, which are used to extract time–space amplitude information from CMP gathers and energy groups features from VS data, respectively. This paper compared MMN with the CMP single-modal network (CSN) and the velocity spectra single-modal network (VSSN). Based on synthetic data, we investigated their differences in terms of continuity, accuracy, noise resistance and generalization. The MMN prediction results makes a trade-off between the overall continuity and local details. Visualization analysis of the intermediate feature maps explains the MMN velocity prediction mechanism, that is, the multi-angle representation and complementary fusion of velocity information. Finally, the performance of the proposed method is demonstrated using the braided river deposited field data example. [ABSTRACT FROM AUTHOR]

Novel strategies for diagnosing triple-negative breast cancer (TNBC) are essential for effective clinical treatment. Vascular endothelial growth factor receptor 2 (VEGFR2) and neuropilin-1 (NRP-1) are potential targets for tumor imaging agents. We designed and synthesized [99mTc]Tc-labeled heptapeptide (A7R) and its D-type peptide (DA7R) with high affinity and specificity for VEGFR2 and NRP-1 as novel single-photon emission computed tomography (SPECT) probes for TNBC imaging. The specificities of A7R and DA7R were first evaluated in vitro using flow cytometry and confocal microscopy and ex vivo using fluorescence imaging. Subsequently, A7R and DA7R were labeled with [99mTc]Tc through 6-hydrazino nicotinamide (HYNIC), and their radiochemical purities (RCPs) and stability in vitro were assessed. The imaging performance and biodistribution of [99mTc]Tc-HYNIC-A7R and [99mTc]Tc-HYNIC-DA7R were evaluated in TNBC mouse models. A7R and DA7R exhibited good TNBC cell-targeting abilities and were readily labeled with [99mTc]Tc through HYNIC. Both [99mTc]Tc-HYNIC-A7R and [99mTc]Tc-HYNIC-DA7R had high RCPs and stability in vitro, and their accumulation in tumor tissues in the TNBC models was evident, with fast blood clearance and favorable biodistribution. More importantly, [99mTc]Tc-HYNIC-DA7R showed better tumor-to-muscle ratios and lower renal uptake than [99mTc]Tc-HYNIC-A7R. These results suggest that both [99mTc]Tc-HYNIC-DA7R and [99mTc]Tc-HYNIC-A7R have substantial potential as probes for targeted SPECT imaging of TNBC, and the former may be considered for future clinical translation owing to its superior tumor imaging performance. [ABSTRACT FROM AUTHOR]