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Integrating scientific principles into machine learning models to enhance their predictive performance and generalizability is a central challenge in the development of AI for Science. Herein, we introduce Uni-pKa, a novel framework that successfully incorporates thermodynamic principles into machine learning modeling, achieving high-precision predictions of acid dissociation constants (pKa), a crucial task in the rational design of drugs and catalysts, as well as a modeling challenge in computational physical chemistry for small organic molecules. Uni-pKautilizes a comprehensive free energy model to represent molecular protonation equilibria accurately. It features a structure enumerator that reconstructs molecular configurations from pKadata, coupled with a neural network that functions as a free energy predictor, ensuring high-throughput, data-driven prediction while preserving thermodynamic consistency. Employing a pretraining-finetuning strategy with both predicted and experimental pKadata, Uni-pKanot only achieves state-of-the-art accuracy in chemoinformatics but also shows comparable precision to quantum mechanics-based methods.

The electromagnetic oscillation treatment was introduced to enhance the fine-grain quality of the Cr–Co–Mo K477 superalloy turbine guide castings, and the effect of electromagnetic oscillation on the microstructure was investigated. The results indicate that the electromagnetic oscillation induced the large columnar grains into fine grains, with a reduction of 59.9% and 30.3% in grain sizes at the blade concave and inlet edge, respectively, and the Micro-Vickers hardness increased by 4.6%. The application of an alternating magnetic field in the melt induces Lorentz force, leading to short-range forced oscillations that break dendrites and inhibit the growth of columnar grains. On the one hand, the magnetic field penetrates the melt and decays due to the skin effect, and the decay rate of the magnetic field after penetrating the melt is related to the conductivity of the melt and the frequency of the magnetic. On the other hand, the electromagnetic oscillation device is positioned beneath the investment casting mold, causing a distribution of large magnetic field at the lower part and small at the upper end. Thus, along the height direction, the grain size gradually increases and tends to be irregular. The magnetic field cannot cover the blades entirely, exhibiting a fine grain—short columnar grain—coarse grain distribution. The refinement effects of the grain are more prominent when the height of the blades is lower and the blade structure is shorter.

Among various sensor array configurations, the L-shaped nested array offers improved performance for 2-D direction-of-arrival (DOA) estimation through co-array processing. However, conventional methods overlook the multidimensional signal structure and fail to eliminate the cross term generated from the correlated co-array signal and noise components. It leads to a significant degradation in DOA estimation performance. To deal with this problem, an iterative 2-D DOA estimation algorithm based on tensor modeling is proposed. It is capable of eliminating the cross term. Specifically, the co-array signals of virtual subarrays in orthogonal directions are derived and concatenated to construct a higher order tensor, whose factor matrices have the Vandermonde structure and preserve the interconnected azimuth and elevation information. A computationally efficient tensor decomposition method is then developed to independently estimate the azimuth and elevation angles, which are effectively paired using the spatial cross-correlation matrix. Furthermore, after investigating the cross term effect, a two-step iterative algorithm is proposed to sequentially estimate and remove the cross term based on the initial estimates obtained from the high-order tensor decomposition. Consequently, the 2-D DOA estimation with enhanced estimation accuracy, resolution, and moderate computational complexity is achieved for the L-shaped nested array. Simulation results demonstrate the superiority of the proposed algorithm over competing methods.

Direction-of-arrival (DOA) estimation using deep neural networks has shown great potential for applications in complicated environments. However, conventional matrix-based deep neural networks vectorize multi-dimensional signal statistics into an excessively long input, necessitating a large number of parameters in neural layers. These parameters require substantial computational resources for training. To address the problem, we propose a resource-efficient tensorized neural network for deep tensor two-dimensional DOA estimation. In this network, the covariance tensor corresponding to the uniform rectangular array (URA) is propagated to hidden state tensors that encapsulate essential signal features. To reduce the number of trainable parameters, the feedforward propagation is formulated as inverse Tucker decomposition, compressing the parameters into inverse Tucker factors. An effective tensorized backpropagation procedure is then designed to train the compressed parameters, and the Tucker rank sequences are tuned through Bayesian optimization to ensure satisfactory network performance. Our simulation results demonstrate the superiority of the proposed tensorized deep neural network over its matrix-based counterpart. In a scenario with a $10\times 10$ URA and $2$ sources, the proposed network reduces the number of trained parameters by more than $122,000$ times. Consequently, it achieves faster training speed and utilizes less GPU memory, while maintains comparable estimation accuracy and angular resolution even under non-ideal conditions and in varying scenarios.

The interface heat transfer coefficient (IHTC) at the different casting/shell mold interfaces during the vacuum investment casting process was investigated by measuring the temperatures of K4169 superalloy melt and mold shell during the preheating, transferring, pouring, and solidification processes. The inverse model of ProCAST was used to calculate the interface heat transfer coefficient (IHTC) based on the temperature measurement results. The inverse calculation results show that the heat transfer between the shell mold and the outside air changes from natural convection to forced convection during the transfer process, but the heat transfer between the shell mold and the inside air is consistently natural convection heat transfer. Then, the convective heat transfer coefficient gradually decreases to 0.1 W/m2·K with the vacuum increases and is finally converted to radiation heat transfer at a vacuum of 10-3Pa. In the early stages of solidification, the IHTC between the shell mold and the casting can reach 16,000 W/m2·K due to the close contact and the large temperature difference between them, and with the thermal expansion of the shell mold and cooling contraction of the casting, the formation of the gap leads to the interfacial heat transfer coefficient gradually stabilizing at 100–200 W/m2·K. The experimental validation results show that the IHTC has a great influence on the accuracy of the prediction of the location and shape of shrinkage porosities in the vacuum investment castings.

The discrete element method (DEM) is the most widely applied numerical tool to simulate triaxial test, a common geotechnical test to measure the shear strength of soil. However, the typical DEM model uses sphere clusters to approximate soil particles, which is not sufficiently accurate to simulate realistic soil particles. This paper shows the potential of using a physics engine technique as a promising alternative to typical DEM method. Originally developed for simulating realistic physical and mechanical processes in video games and computer-animated films, physics engines have developed quickly and are being applied in scientific computing. Physics engines use triangular face tesselations to represent realistic objectives, which provides higher accuracy to model realistic soil particle geometries. In this paper, physics engine is applied to simulate true triaxial tests of Monterey No. 0 sand. The numerical results agree well with experimental results. This study provides DEM modelers with the physics engine technique as another promising option to simulate realistic soil particles in geotechnical tests.

Sparse array direction-of-arrival (DOA) estimation using tensor model has been developed to handle multidimensional sub-Nyquist sampled signals. Furthermore, augmented virtual arrays can be derived for Nyquist-matched coarray tensor processing. However, the partially augmentable sparse array corresponds to a discontinuous virtual array, whereas the existing methods can only utilize its continuous part. Conventional virtual linear array interpolation techniques complete coarray covariance matrices with dispersed missing elements, but fail to complete the coarray tensor with whole missing slices. In this article, we propose a coarray tensor completion algorithm for 2-D DOA estimation, where the coarray tensor statistics can be entirely exploited. In particular, in order to impose an effective low-rank regularization on the slice-missing coarray tensor, we propose shift dimensional augmenting and coarray tensor reshaping approaches to reformulate a structured coarray tensor with sufficiently dispersed missing elements. Furthermore, the shape of the reformulated coarray tensor is optimized by maximizing the dispersion-to-percentage ratio of missing elements. As such, a coarray tensor nuclear norm minimization problem can be designed to optimize the completed coarray tensor corresponding to a filled virtual array, based on which the closed-form DOA estimation is achieved. Meanwhile, the global convergence of the coarray tensor completion is theoretically proved. Simulation results demonstrate the effectiveness of the proposed algorithm compared with other matrix-based and tensor-based methods.

The deuteration of energetic materials contributes to high signal-to-noise ratios (SNRs) in neutron diffraction, thus allowing the structures of energetic materials to be effectively investigated. This study developed the synthesis methods of deuterated energetic materials through chemical synthesis or newly developed one-pot H/D exchange. Using these methods, it synthesized nine deuterated energetic materials in a concise and low-cost manner: deuterated 1,3,5-triamino-2,4,6-trinitrobenzene (TATB-d6, 1), 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane (HMX-d8, 2), 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX-d6, 3), dihydroxylammonium 5,5′-bis(tetrazole-1-oate) (TKX-50-d8, 4), nitroguanidine (NQ-d4, 5), 1,1-diamino-2,2-dinitroethylene (FOX-7-d4, 6), 2,6-diamino-3,5-dinitropyrazine-1-oxide (LLM-105-d4, 7), trinitrotoluene (TNT-d3, 8), and 3-nitro-1,2,4-triazol-5-one (NTO-d2, 9). Furthermore, the single crystals of HMX-d8(2) and RDX-d6(3) were obtained, and the α-, β-, γ-, and δ-polymorphs of HMX-d8(2) were prepared accordingly. The deuterated energetic materials were characterized and analyzed using infrared spectroscopy (IR), nuclear magnetic resonance (NMR) spectroscopy, differential scanning calorimetry (DSC), thermogravimetry (TG), X-ray diffraction (XRD), and neutron diffraction. Besides, this study determined the decomposition activation energy (Ea), pre-exponential factor (A), decomposition rate constant (k), and critical explosion temperature (Tb) of TATB-d6(1), HMX-d8(2), and RDX-d6(3) via DSC experiments at different heating rates. The NMR and neutron diffraction data show that these deuterated energetic materials have high deuteration rates of more than 95%. The DSC and TG analyses indicate that the deuterated energetic materials exhibit slightly higher decomposition temperatures than their nondeuterated counterparts. Furthermore, neutron diffraction shows that the deuterated energetic materials feature high SNRs.

Particle shape governs the macro-mechanical behavior of high explosives. This study develops 3D computational geometry techniques to determine commonly used sphericity and roundness definitions of typical high explosive particles, including HMX (octahydro-1,3,5,7-tetranitro- 1,3,5,7-tetrazocine), RDX (1,3,5-Trinitroperhydro-1,3,5-triazine), and CL-20 (2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane). This technique can automatically determine principal dimensions, volume, surface area, minimum circumscribed sphere, maximum inscribed sphere, and the 3D convex hull of 3D particle geometries and determine commonly used 3D sphericity descriptors of high explosive particles. This technique can also automatically identify corners on the 3D particle surface, fit appropriate spheres to these corners, and compute Wadell’s roundness of high explosive particles. This study demonstrates that the qualities of 3D particle geometries affect the computational results of particle sphericity and roundness descriptors of high explosives. These descriptors display a hierarchy of resistance to the effects of low image quality. Therefore, the minimum requirements for ensuring reliable shape characterization of these parameters are established for high explosives. This study systematically compares 2D and 3D particle shape characterizations of around 15,000 particles from three high explosives (HMX, RDX, and CL-20). Results show that 2D sphericity and rounded definitions either underestimate the corresponding 3D definitions or vary within large ranges leading to uncertainties for inferring 3D particle characteristics from 2D images of high explosives.

Molecular docking, a structure-based virtual screening method, is a reliable tool to enrich potential bioactive molecules from molecular databases. With the rapid expansion of compound library sizes, the speed of existing molecular docking programs becomes less than adequate to meet the demand for screening ultralarge libraries containing tens of millions or billions of molecules. Here, we propose Uni-Dock, a GPU-accelerated molecular docking program that supports various scoring functions including vina, vinardo, and ad4. Uni-Dock achieves more than 1000-fold speedup with high accuracy compared with the AutoDock Vina running in single CPU core, outperforming reported GPU-accelerated docking programs including AutoDock-GPU and Vina-GPU based on head-to-head experiments. Uni-Dock docks molecules in batches simultaneously using concurrent threads of each molecule. The data flow between GPU and CPU is optimized to eliminate CPU hotspots and maximize GPU utility. Additionally, Uni-Dock also supports hydrogen bond biased docking for all scoring functions and can be migrated to multiple GPUs of different architectures and manufacturers. We analyzed the improved performance of Uni-Dock on the CASF-2016 and DUD-E datasets and recommend three combinations of hyperparameters corresponding to different docking scenarios. To demonstrate Uni-Dock’s capability on routinely screening ultralarge libraries, we performed hierarchical virtual screening experiments with Uni-Dock on the Enamine Diverse REAL druglike set containing 38.2 million molecules to a popular target KRAS G12D in 12 h using 100 NVIDIA V100 GPUs. To the best of our knowledge, Uni-Dock should be the fastest GPU-accelerated docking program to date.

Bacterial infections are one of the major contributing factors to human mortality, which can cause secondary damage to the injured area, such as leading to inflammation, tissue death, and even personal death. Herein, we developed a novel cyclodextrin (CD)-modified amphiphilic microgel with a 3D network nanostructure that encapsulates hydrophilic indocyanine green (ICG) as a trigger for photothermal therapy (PTT) and hydrophobic N,N′-disubstituted-butyl-N,N′-dinitro-1,4-benzenediamine (BNN6) as a heat-sensitive nitric oxide (NO) donor (CD@I–B) to cope with bacteria-infected wound therapy. This biocompatible microgel showed excellent broad-spectrum antibacterial capability under near-infrared (NIR) laser irradiation, while the photothermal conversion process promotes the deswelling of the microgel and release of NO, which synergistically accelerates wound healing. The therapy strategy by synergizing NO delivery with PTT promoted the formation of neovascularization and collagen fiber as well as the elimination of inflammation cells, thus facilitating wound healing. Our study further demonstrates the fantastic opportunities of applying high-performance microgels to provide all-in-one sites for treating wound sterilization and healing.

Augmented coarrays can be derived from spatially undersampled signals of sparse arrays for underdetermined direction-of-arrival (DOA) estimation. With the extended dimension of sparse arrays, the sampled signals can be modeled as sub-Nyquist tensors, thereby enabling coarray tensor processing to enhance the estimation performance. The existing methods, however, are not applicable to generalized multi-dimensional sparse arrays, such as sparse planar array and sparse cubic array, and have not fully exploited the achievable source identifiability. In this paper, we propose a coarray tensor DOA estimation algorithm for multi-dimensional structured sparse arrays and investigate an optimal coarray tensor structure for source identifiability enhancement. Specifically, the cross-correlation tensor between sub-Nyquist tensor signals is calculated to derive a coarray tensor. Based on the uniqueness condition for coarray tensor decomposition, the achievable source identifiability is analysed. Furthermore, to enhance the source identifiability, a dimension increment approach is proposed to embed shifting information in the coarray tensor. The shifting-embedded coarray tensor is subsequently reshaped to optimize the source identifiability. The resulting maximum number of degrees-of-freedom is theoretically proved to exceed the number of physical sensors. Hence, the optimally reshaped coarray tensor can be decomposed for underdetermined DOA estimation with closed-form solutions. Simulation results demonstrate the effectiveness of the proposed algorithm in both underdetermined and overdetermined cases.

Adaptive beamforming using sparse arrays can alleviate system burden with a sub-Nyquist sampling rate while achieving high resolution. To process multi-dimensional signals without losing structural information, tensor models can be incorporated in the beamformer design. Unfortunately, existing tensor beamformers are only suitable for uniform arrays and cannot handle ambiguous sidelobes caused by sparse sensor deployment. In this article, we propose a sub-Nyquist tensor beamformer based on a coprimality constraint. Specifically, the signals received by the sparse subarrays of a coprime planar array are modeled as two sub-Nyquist tensors. To enhance the desired component of the sub-Nyquist tensor signals, we formulate a pair of tensor beamformer weights and investigate the principle of tensorial signal filtering. A coprimality-based combined distortionless response constraint is then imposed to jointly optimize the tensor beamformer weights, which eliminates spatial aliasing. Moreover, to solve the joint coprime tensor beamformer weights optimization problem with non-convex tensor-based objective and constraint, we decompose it into interconnected bidirectional sub-beamformer optimization problems, which are further relaxed to ensure the convexity. The relaxed problems are solved by an alternating minimization approach with global convergence. Simulation results demonstrate the superiority of the proposed sub-Nyquist tensor beamformer over conventional beamformers in terms of mainlobe enhancement and sidelobe attenuation.

Macrophage-hitchhiked arsenic/AB bionic preparations were developed to improve the therapeutic effect on liver cancer by means of the tumor-targeting ability of macrophages in vivo. In vitroand in vivocellular uptake assays demonstrated that arsenic/AB, with negatively charged particles of around 100–200 nm size, could hitchhike to macrophages. Dissolution experiments of arsenic/AB showed that arsenic/AB could delay the release of arsenic and ensure the safety of macrophages during its transport. Histological examination confirmed the safety of the preparations for major organs. In vivodistribution experiment showed that the arsenic/AB bionic preparations could rapidly accumulate in tumors, and in vivotreatment experiment showed a significant tumor inhibition of arsenic/AB. The therapeutic mechanism of liver cancer might be that the arsenic/AB bionic preparations could inhibit tumor growth by reducing inflammatory response and inhibiting CSF1 secretion to block CSF1R activation to induce more differentiation of tumor-associated macrophages (TAMs) towards the anti-tumor M1 phenotype. Therefore, we concluded that the arsenic/AB bionic preparations could improve the distribution of arsenic in vivoby hitchhiking on macrophages as well as make it have tumor targeting and deep penetration abilities, thus increasing the therapeutic effect of arsenic on liver cancer with reduced side effects.

Direction-of-arrival (DOA) estimation using sub-Nyquist tensor signals benefits from enhanced performance by extracting structural angular information with multi-dimensional sparse arrays. Although convolutional neural network (CNN) has been employed to achieve efficient DOA estimation in challenging conditions, conventional methods demand excessive memory storage and computation power to process sub-Nyquist tensor statistics. In this letter, we propose a decomposed CNN for sub-Nyquist tensor-based 2-D DOA estimation, where an augmented coarray tensor is derived and used as the network input. To compress convolution kernels for efficient coarray tensor propagation, we develop a convolution kernel decomposition approach. This enables the acquisition of canonical polyadic (CP) factors containing compressed parameters. Performing decomposable convolution between the coarray tensor and the CP factors leads to resource-efficient DOA estimation. Our simulation results indicate that the proposed method conserves system resources while maintaining competitive performance.

Background: Conducting of clinical trials for rare diseases faces multiple challenges. Patients’ cognition and attitude toward clinical trials are crucial, which may affect their participation and compliance, and affect the schedule of clinical trials eventually. Objective and Method: This study aims to explore the knowledge and attitudes of clinical trials of patients with rare diseases or patients’ guardians. An anonymous cross-sectional survey was conducted from November 1, 2021, to November 30, 2021. A total of 1131 valid questionnaires were included. Among them, 417 were filled in by the patients themselves, and 714 were answered by the patients’ guardians. Results: The average score of clinical trial knowledge of the patients (8.25) was lower than that of the guardians (8.85). The willingness of the patients to participate in clinical trials was high (4.28), and the willingness of the patients’ guardians was also high for patients to participate in clinical trials (4.35). The main promoting factors of clinical trial participation were the possibility of curing the disease. The main hindering factors of participation in clinical trials were lack of access to clinical trial information and concern about the safety and effectiveness of the trial drug. Conclusions: In conclusion, most respondents had some basic knowledge of clinical trials and high willingness to participate in clinical trials. But there were some cognitive deficiencies about clinical trials and many hindering factors to participate in clinical trials. Clinical trials of rare diseases should be patient-centered and truly meet the unmet clinical, psychological, and social needs of patients with rare diseases.

Background: In order to meet the unmet needs of rare disease patients in China, importing orphan drugs is an important way. The objectives of this study were to investigate the marketing trend of orphan drugs approved by the US Food and Drug Administration (FDA) and imported by China, to examine the orphan drug lag between China and the United States. Methods: This study analyzes the orphan drugs approved by FDA and imported by China from January 2010 to December 2021. The approval lag for orphan drugs between China and the US was calculated and analyzed by approval time. Factors potentially affecting the approval lag, such as target disease, ATC classification, formulation, corporation name, drug type, and whether the indications belong to the first batch of rare diseases catalogue were investigated. Results: The number of FDA-approved orphan drugs imported by China is increasing year by year, and the approval lag of these drugs is gradually decreasing, especially in the classification of Non-L, Injections, Non-United States, and biological product. Compared with 2010–2015, the approval lag of total drugs in the study was significantly improved in 2016–2021 (1977 days) compared with 2010–2015 (3928 days). Conclusion: China’s groundbreaking regulatory reforms of drugs since 2015 had made significant progress in reducing orphan drug lags, but there is still considerable room for progress. We should more actively promote the approval of rare disease drugs in China, establish a better approval mechanism, and enable Chinese patients with rare diseases to receive drug treatment in a timely manner.

Interface engineering of zinc metal anodes is a promising remedy to relieve their inferior stability caused by dendrite growth and side reactions. Nevertheless, the low affinity and additional weight of the protective coating remain obstacles to their further implementation. Here, aroused by DFT simulation, self-assembled monolayers (SAMs) are selectively constructed to enhance the stability of zinc metal anodes in dilute aqueous electrolytes. It is found that the monolayer thiol molecules relatively prefer to selectively graft onto the unstable zinc crystal facets through strong Zn–S chemical interactions to engineer a covalent interface, enabling the uniform deposition of Zn2+onto (002) crystal facets. Therefore, dendrite-free anodes with suppressed side reactions can be achieved, proven by in situ optical visualization and differential electrochemical mass spectrometry (DEMS). In particular, the thiol endows the symmetric cells with a 4000 h ultrastable plating/stripping at a specific current density of 1.0 mA cm–2, much superior to those of bare zinc anodes. Additionally, the full battery of modified anodes enables stable cycling of 87.2% capacity retention after 3300 cycles. By selectively capping unstable crystal facets with inert molecules, this work provides a promising design strategy at the molecular level for stable metal anodes.

Renewable electricity from splitting water to produce hydrogen is a favorable technology to achieve carbon neutrality, but slow anodic oxygen evolution reaction (OER) kinetics limits its large-scale commercialization. Electron spin polarization and increasing the reaction temperature are considered as potential ways to promote alkaline OER. Here, it is reported that in the alkaline OER process under an AC magnetic field, a ferromagnetic ordered electrocatalyst can simultaneously act as a heater and a spin polarizer to achieve significant OER enhancement at a low current density. Moreover, its effect obviously precedes antiferromagnetic, ferrimagnetic, and diamagnetic electrocatalysts. In particular, the noncorrected overpotential of the ferromagnetic electrocatalyst Co at 10 mA cm–2is reduced by a maximum of 36.6% to 243 mV at 4.320 mT. It is found that the magnetic heating effect is immediate, and more importantly, it is localized and hardly affects the temperature of the entire electrolytic cell. In addition, the spin pinning effect established on the ferromagnetic/paramagnetic interface generated during the reconstruction of the ferromagnetic electrocatalyst expands the ferromagnetic order of the paramagnetic layer. Also, the introduction of an external magnetic field further increases the orderly arrangement of spins, thereby promoting OER. This work provides a reference for the design of high-performance OER electrocatalysts under a magnetic field.

Accurate prediction of protein-ligand complex structures is a crucial step in structure-based drug design. Traditional molecular docking methods exhibit limitations in terms of accuracy and sampling space, while relying on machine-learning approaches may lead to invalid conformations. In this study, we propose a novel strategy that combines molecular docking and machine learning methods. Firstly, the protein-ligand binding poses are predicted using a deep learning model. Subsequently, position-restricted docking on predicted binding poses is performed using Uni-Dock, generating physically constrained and valid binding poses. Finally, the binding poses are re-scored and ranked using machine learning scoring functions. This strategy harnesses the predictive power of machine learning and the physical constraints advantage of molecular docking. Evaluation experiments on multiple datasets demonstrate that, compared to using molecular docking or machine learning methods alone, our proposed strategy can significantly improve the success rate and accuracy of protein-ligand complex structure predictions.

Persistence of solitary wave solutions of the regularized long wave equation with small perturbations are investigated by the geometric singular perturbation theory and bifurcation theory. Two different kinds of the perturbations are considered in this paper: one is the Kuramoto–Sivashinsky perturbation, the other is the Marangoni effects. Indeed, the solitary wave persists under small perturbations. Furthermore, the different perturbations do affect the proper wave speed c ensuring the persistence of the solitary waves. Finally, numerical simulations are utilized to confirm the theoretical results. [ABSTRACT FROM AUTHOR]

ABSTRACTCircular RNAs (circRNAs) play a pivotal regulatory role in bladder cancer (BC) occurrence and progression. The expression level, role and mechanism of circ_0000326 in BC remain unknown. In the present study, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was conducted to evaluate the expressions of circ_0000326, microRNA-338-3p (miR-338-3p) and ETS Proto-Oncogene 1(ETS1) mRNA in BC tissues and cell lines. Cell counting kit-8 (CCK-8) assay, wound healing assay and flow cytometry were used to detect the impacts of circ_0000326 on BC cell growth, migration and apoptosis. Western blot was used to detect the expressions of ETS1, phospho-phosphoinositide-3 kinase (p-PI3K), phospho-AKT, PI3K and AKT protein. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to analyze the biological function of ETS1 in BC. Here, we found that circ_0000326 expression was significantly elevated in BC cell lines and tissues, and circ_0000326 could promote BC cell growth and migration, and inhibit apoptosis. Dual-luciferase reporter gene assay confirmed that circ_0000326 and ETS1 could bind directly to miR-338-3p. Furthermore, circ_0000326 sponged miR-338-3p and up-regulated ETS1 expression. ETS1 was associated with the activation of PI3K/AKT pathway. Moreover, circ_0000326 could activate PI3K/AKT pathway by miR-338-3p/ETS1 axis. Collectively, circ_0000326/miR-338-3p/ETS1/PI3K/AKT pathway is involved in regulating BC progression.

First- and second-order topological phases, capable of inherent protection against disorder of materials, have been recently experimentally demonstrated in various artificial materials through observing the topologically protected edge states. Topological phase transition represents a new class of quantum critical phenomena, which is accompanied by the changes related to the bulk topology of energy band structures instead of symmetry. However, it is still a challenge to directly observe the topological phase transitions defined in terms of bulk states. Here, we theoretically and experimentally demonstrate the direct observation of multifarious topological phase transitions with real-space indicator in a single photonic chip, which is formed by integration of 324 × 33 waveguides supporting both first- and second-order topological phases. The trivial-to-first-order, trivial-to-second-order and first-to-second-order topological phase transitions signified by the band gap closure can all be directly detected via photon evolution in the bulk. We further observe the creation and destruction of gapped topological edge states associated with these topological phase transitions. The bulk-state-based route to investigate the high-dimensional and high-order topological features, together with the platform of freely engineering topological materials by three-dimensional laser direct writing in a single photonic chip, opens up a new avenue to explore the mechanisms and applications of artificial devices.

Nanomedicine has attracted increasing attention and emerged as a safer and more effective modality in cancer treatment than conventional chemotherapy. In particular, the distinction of tumor microenvironment and normal tissues is often used in stimulus-responsive drug delivery systems for controlled release of therapeutic agents at target sites. In this study, we developed mesoporous silica nanoparticles (MSNs) coated with polyacrylic acid (PAA), and pH-sensitive lipid (PSL) for synergistic delivery and dual-pH-responsive sequential release of arsenic trioxide (ATO) and paclitaxel (PTX) (PL-PMSN-PTX/ATO). Tumor-targeting peptide F56 was used to modify MSNs, which conferred a target-specific delivery to cancer and endothelial cells under neoangiogenesis. PAA- and PSL-coated nanoparticles were characterized by TGA, TEM, FT-IR, and DLS. The drug-loaded nanoparticles displayed a dual-pH-responsive (pHe= 6.5, pHendo= 5.0) and sequential drug release profile. PTX within PSL was preferentially released at pH = 6.5, whereas ATO was mainly released at pH = 5.0. Drug-free carriers showed low cytotoxicity toward MCF-7 cells, but ATO and PTX co-delivered nanoparticles displayed a significant synergistic effect against MCF-7 cells, showing greater cell-cycle arrest in treated cells and more activation of apoptosis-related proteins than free drugs. Furthermore, the extracellular release of PTX caused an expansion of the interstitial space, allowing deeper penetration of the nanoparticles into the tumor mass through a tumor priming effect. As a result, FPL-PMSN-PTX/ATO exhibited improved in vivo circulation time, tumor-targeted delivery, and overall therapeutic efficacy.

Many-body localization (MBL) describes a quantum phase where an isolated interacting system subject to sufficient disorder displays non-ergodic behaviour, evading thermal equilibrium that occurs under its own dynamics. Previously, the thermalization–MBL transition has been largely characterized with the growth of disorder. Here, we explore a new axis, reporting on an energy-resolved MBL transition using a 19-qubit programmable superconducting processor, which enables precise control and flexibility of both disorder strength and initial state preparation. We observe that the onset of localization occurs at different disorder strengths, with distinguishable energy scales, by measuring time-evolved observables and quantities related to many-body wave functions. Our results open avenues for the experimental exploration of many-body mobility edges in MBL systems, whose existence is widely debated due to the finiteness of the system size, and where exact simulations in classical computers become unfeasible.

Modern development of flexible electronics has made use of bioelectronic materials as artificial tissue in vivo. As hydrogels are more similar to nerve tissue, functional hydrogels have become a promising candidate for bioelectronics. Meanwhile, interfacing functional hydrogels and living tissues is at the forefront of bioelectronics. The peripheral nerve injury often leads to paralysis, chronic pain, neurologic disorders, and even disability, because it has affected the bioelectrical signal transmission between the brain and the rest of body. Here, a kind of light-stimuli-responsive and stretchable conducting polymer hydrogel (CPH) is developed to explore artificial nerve. The conductivity of CPH can be enhanced when illuminated by near-infrared light, which can promote the conduction of the bioelectrical signal. When CPH is mechanically elongated, it still has high durability of conductivity and, thus, can accommodate unexpected strain of nerve tissues in motion. Thereby, CPH can better serve as an implant of the serious peripheral nerve injury in vivo, especially in the case that the length of the missing nerve exceeds 10 mm.

As an ideal hydrogen production route, electrolyzed water still faces the challenges of high cost of noble-metal electrocatalysts and low performance of non-noble-metal catalysts in scalable applications. Recently, introduction of external fields (such as magnetic fields, light fields, etc.) to improve the electrocatalytic water splitting performance of non-noble-metal catalysts has attracted great attention due to their simplicity. Here, a simple method for preparing magnetic superstructure (NiFe2O4@MOF-74) is described, and the hydrogen evolution reaction (HER) behavior of its carbonized derivative, a ferromagnetic superstructure, is revealed in a wide range of applied voltage under an AC magnetic field. The overpotential (@10 mA cm–2) required for the HER of the obtained ferromagnetic superstructure in 1 M KOH was reduced by 31 mV (7.7%) when a much small AC magnetic field (only 2.3 mT) is applied. Surprisingly, the promotion effect of the AC magnetic field is not monotonically increasing with the increase of the applied voltage or the strength of AC magnetic field, but increasing first, then weakening. This unusual behavior is believed to be mainly caused by the enhanced induced electromotive force and the additional energy by the applied AC magnetic field. This discovery provides a new idea for adjusting the performance of electrocatalytic reactions.

Aiming at the plastic shrinkage cracking of cement mortar, the constitutive equations of it in laboratory were established by testing plastic tensile strength, plastic capillary shrinkage stress and water loss evaporation rate of mortar under different composition parameters, environmental parameters, initial structural parameters and constraints condition parameters. And the early warning system of it was explored. The results showed that the seven-element constitutive equation of plain mortar and the nine-element constitutive equation of mortar with polyvinyl alcohol (PVA) fiber can effectively predict the plastic shrinkage cracking state of mortar outdoors. An early warning system for it was initially established.

A high-optical-resolution artificial retina system that accurately communicates with the optic nerve is the main challenge in the modern biological science and bionic field. Here, we developed a bionic artificial retina possessing phototransduction “cells” with measurements even smaller than that of the neural cells. Using the technique of micrometer processing, we constructed a pyramid-shape periodic microarray of a photoreceptor. Each “sensing cell” took advantage of polythiophene derivative/fullerene derivative (PCBM) as a photoelectric converter. Because folic acid played an essential role in eye growth, we particularly modified the polythiophene derivatives with folic acid tags. Therefore, the artificial retina could enlarge the contact area and even recognize the nerve cells to improve the consequence of nerve stimulation. We implanted the artificial retina into blinded rats’ eyes. Electrophysiological analysis revealed its recovery of photosensitive function 3 months after surgery. Our work provides an innovative idea for fabricating a high-resolution bionic artificial retina system. It shows great potential in artificial intelligence and biomedicine.

Graphene films grown by chemical vapour deposition have unusual physical and chemical properties that offer promise for applications such as flexible electronics and high-frequency transistors1–10. However, wrinkles invariably form during growth because of the strong coupling to the substrate, and these limit the large-scale homogeneity of the film1–4,11,12. Here we develop a proton-assisted method of chemical vapour deposition to grow ultra-flat graphene films that are wrinkle-free. Our method of proton penetration13–17and recombination to form hydrogen can also reduce the wrinkles formed during traditional chemical vapour deposition of graphene. Some of the wrinkles disappear entirely, owing to the decoupling of van der Waals interactions and possibly an increase in distance from the growth surface. The electronic band structure of the as-grown graphene films shows a V-shaped Dirac cone and a linear dispersion relation within the atomic plane or across an atomic step, confirming the decoupling from the substrate. The ultra-flat nature of the graphene films ensures that their surfaces are easy to clean after a wet transfer process. A robust quantum Hall effect appears even at room temperature in a device with a linewidth of 100 micrometres. Graphene films grown by proton-assisted chemical vapour deposition should largely retain their intrinsic performance, and our method should be easily generalizable to other nanomaterials for strain and doping engineering.

Numerous theoretical simulation works have predicted the fantastic properties of arsenene, such as a tunable band gap, topological states, and a high carrier mobility. However, the experimental synthesis of two-dimensional arsenic materials is still difficult. Herein, we report the epitaxial growth of single-crystalline few-layer gray arsenic nanoflakes with hexagonal and half-hexagonal shapes via a van der Waals epitaxy method. The gray arsenic nanoflakes can be transferred from mica to other substrates without structural damage. Moreover, a universal method for estimating the antidegradation efficiency of polymer-passivated gray arsenic nanoflakes by determining the interfacial interaction energies and geometry changes via first-principles calculations was developed. Consistent with theoretical predictions, we further experimentally confirm that functional polymer coating can effectively suppress the chemical degradation and phase transformation of gray arsenic nanoflakes for at least 50 days under ambient exposure, thus facilitating further nanodevice fabrication, preservation, and measurements. Moreover, the gray arsenic nanoflakes show metal to semiconductor transformation after long-term ambient exposure owing to the oxidation of arsenic in air.

In-situ observation and metrology of the complex phenomena is a critical step to better understanding of the fundamental physics involved in laser powder bed fusion additive manufacturing process. By using high-speed imaging, we observed and analyzed the vapor plume evolution and spatter generation during single track formation of 304 stainless steel within a board range of scan speed. The experiments were performed on both powder bed and on bare substrate under identical laser parameter sets and observation conditions, in order to identify the role of powder particle on spatter ejection and process stability. With video frames superimposed, observation results are presented and analyzed at track level. It is shown that scan speed has a significant influence on the vapor plume behavior and process stability, and the spatter generation during single track formation is shown to be well related with process stability, rather than with the energy input. Additionally, a unique type of droplet spatter was identified at the starting point of track formation as result of the coupling between the melt pool and intensive vapor flux.

The functional role of colonic epithelium in the pathogenesis of ulcerative colitis (UC) remains unclear. Here, we reveal a novel mechanism by which colonic epithelia recruit T helper-17 (Th17) cells during the onset of UC. mTOR complex 1 (mTORC1) was hyper-activated in colonic epithelia of UC mice. While colonic epithelial TSC1 (mTORC1 negative regulator) disruption induced constitutive mTORC1 activation in the colon epithelia and aggravated UC, RPTOR (essential mTORC1 component) depletion inactivated mTORC1 and ameliorated UC. TSC1 deficiency enhanced, whereas RPTOR ablation reduced the expression of cyclooxygenase 2 (COX-2), interleukin-1 (IL-1), IL-6, and IL-23, as well as Th17 infiltration in the colon. Importantly, inhibition of COX-2 reversed the elevation in the expression of these proinflammatory mediators induced by TSC1 deficiency, and subsequently reduced the symptoms and pathological characteristics of UC in mouse models. Mechanistically, mTORC1 activates COX-2 transcription via phosphorylating STAT3 and enhancing it’s binding to the COX-2 promoter. Consistently, enhanced mTORC1 activity and COX2 expression, as well as strong positive correlation between each other, were observed in colonic epithelial tissues of UC patients. Collectively, our study demonstrates an essential role of epithelial mTORC1 in UC pathogenesis and establishes a novel link between colonic epithelium, Th17 responses, and UC development.

Delivery in water markets is generally operated by agencies through channel systems, which imposes physical and institutional market constraints. Many water markets allow water users to post selling and buying requests on a board. However, water users may not be able to choose efficiently when the information (including the constraints) becomes complex. This study proposes an innovative two‐phase model to address this problem based on practical experience in China. The first phase seeks and determines the optimal assignment that maximizes the incremental improvement of the system's social welfare according to the bids and asks in the water market. The second phase sets appropriate prices under constraints. Applying this model to China's Xiying Irrigation District shows that it can improve social welfare more than the current “pool exchange” method can. Within the second phase, we evaluate three objective functions (minimum variance, threshold‐based balance, and two‐sided balance), which represent different managerial goals. The threshold‐based balance function should be preferred by most users, while the two‐sided balance should be preferred by players who post extreme prices. A two‐phase model is proposed for identifying optimal trades in water markets with physical or institutional constraintsThree objective functions for different managerial purposes are comparedExpanding a channel system's tradable range can increase social welfare, but its marginal value is diminishing

How future multiple tropical cyclone events (MTCEs) could change is crucial for effective risk management and ensuring human safety, however, it remains unclear. This study projects changes in MTCEs by 2050 in the major basins of the Northern Hemisphere using high‐resolution climate models. Results show a significant increase in the frequency and duration of MTCEs over the North Atlantic (NA), a notable decrease over the western North Pacific (WNP), and little change over the eastern North Pacific (ENP). The increase in MTCEs over the NA is concentrated in August–September, while the decrease over the WNP occurs in most months. In contrast, the ENP exhibits large yet insignificant seasonal variation, suggesting considerable uncertainty in this basin. Further analysis shows that mid‐level vertical motion dominates the MTCE changes over the WNP, while vertical wind shear contributes the most to the NA, which may be linked to future changes in tropical convection. Multiple tropical cyclone (TC) events (MTCEs), that is two or more TCs simultaneously occurring in the same basin, pose great risks to human society. This study projects future changes in the MTCEs by 2050, showing a significant increase over the North Atlantic (NA) while a robust reduction over the western North Pacific (WNP). The future MTCEs over the eastern North Pacific (ENP) show little change relative to the present climate. The increase of MTCEs over the NA is concentrated in August–September, while the decrease over the WNP occurs nearly from April to November. In contrast, there is large yet insignificant seasonal variation over the ENP, which could lead to little change in annual MTCEs. Furthermore, these changes are primarily attributed to the changes in local large‐scale dynamic conditions associated with tropical convection in future decades. The projection of future multiple tropical cyclone events (MTCEs) shows a decline in the western North Pacific (WNP) but a significant increase in the North Atlantic (NA)The mid‐level vertical motion dominates the MTCE changes over the WNP, while vertical wind shear is important for the NAFuture monthly MTCEs show large yet insignificant seasonal variation over the eastern North Pacific, causing little trend in annual MTCEs The projection of future multiple tropical cyclone events (MTCEs) shows a decline in the western North Pacific (WNP) but a significant increase in the North Atlantic (NA) The mid‐level vertical motion dominates the MTCE changes over the WNP, while vertical wind shear is important for the NA Future monthly MTCEs show large yet insignificant seasonal variation over the eastern North Pacific, causing little trend in annual MTCEs

Backgrounds: The level of job satisfaction among clinical research associates (CRAs) is of importance because it may affect CRAs’ retention, turnover, and then further affect the quality of clinical trials. Objectives: The objectives of this study were to (1) examine the level of job satisfaction among CRAs in China and (2) explore the association between CRAs’ demographic and work-related characteristics and their job satisfaction. Methods: A Chinese CRA job satisfaction questionnaire was developed to measure CRAs’ attitudes in relation to their job. The questionnaire contained 6 subcomponents, including work content, work reward, supervisors, coworkers, investigators, and organization administration. Then an anonymous cross-sectional web-based survey was conducted among CRAs in China. Results: 465 valid questionnaires were received (93% response rate). The overall job satisfaction of Chinese CRAs was moderate. As for the individual subcomponents, coworkers ranked the highest and work reward ranked the lowest. The female group was more satisfied with investigators than were the male group. Different scores with supervisors were seen among age groups. Different scores with overall satisfaction, work reward, and organizational administration were seen among geographic location groups and also among company type groups. There were no significant differences in job satisfaction based on marital status, education level, and working position of the respondents. Conclusions:Chinese CRAs were moderately satisfied with their job. They were most satisfied with coworkers and most dissatisfied with work reward. Further, Chinese CRAs’ job satisfaction levels were more related with work-related characteristics, including geographic location and company type.

A palladium-catalyzed regioselective and asymmetric allylic alkylation of azlactones with MBH carbonates has been developed with chiral oxalamide-phosphine ligands. The corresponding reaction afforded a range of optically active γ-arylidenyl glutamic acid derivatives bearing an α-chiral quaternary stereocenter in good yields with excellent linear regio- and high enantioselectivity. This protocol furnishes an alternative approach for the construction of enantio-enriched unnatural α-amino acid derivatives.

Composition optimization, structural design, and introduction of external magnetic fields into the catalytic process can remarkably improve the oxygen evolution reaction (OER) performance of a catalyst. NiFe2O4@(Ni, Fe)S/P materials with a heterogeneous core–shell structure were prepared by the sulfide/phosphorus method based on spinel-structured NiFe2O4nanomicrospheres. After the sulfide/phosphorus treatment, not only the intrinsic activity of the material and the active surface area were increased but also the charge transfer resistance was reduced due to the internal electric field. The overpotential of NiFe2O4@(Ni, Fe)P at 10 mA cm–2(iR correction), Tafel slope, and charge transfer resistance were 261 mV, 42 mV dec–1, and 3.163 Ω, respectively. With an alternating magnetic field, the overpotential of NiFe2O4@(Ni, Fe)P at 10 mA cm–2(without iR correction) declined by 45.5% from 323 mV (0 mT) to 176 mV (4.320 mT). Such enhancement of performance is primarily accounted for the enrichment of the reactive ion OH–on the electrode surface induced by the inductive electric potential derived from the Faraday induction effect of the AMF. This condition increased the electrode potential and thus the charge transfer rate on the one hand and weakened the diffusion of the active substance from the electrolyte to the electrode surface on the other hand. The OER process was dominantly controlled by the charge transfer process under low current conditions. A fast charge transfer rate boosted the OER performance of the catalyst. At high currents, diffusion exerted a significant effect on the OER process and low OH–diffusion rates would lead to a decrease in the OER performance of the catalyst.

Variable stars play a key role in understanding the Milky Way and the universe. The era of astronomical big data presents new challenges for quick identification of interesting and important variable stars. Accurately estimating the periods is the most important step to distinguish different types of variable stars. Here, we propose a new method of determining the variability periods. By combining the statistical parameters of the light curves, the colors of the variables, the window function and the Generalized Lomb-Scargle (GLS) algorithm, the aperiodic variables are excluded and the periodic variables are divided into eclipsing binaries and NEB variables (other types of periodic variable stars other than eclipsing binaries), the periods of the two main types of variables are derived. We construct a random forest classifier based on 241,154 periodic variables from the ASAS-SN and OGLE data sets of variables. The random forest classifier is trained on 17 features, among which 11 are extracted from the light curves and 6 are from the Gaia Early DR3, ALLWISE, and 2MASS catalogs. The variables are classified into 7 superclasses and 17 subclasses. In comparison with the ASAS-SN and OGLE catalogs, the classification accuracy is generally above approximately 82% and the period accuracy is 70%–99%. To further test the reliability of the new method and classifier, we compare our results with the results of Chen et al. for ZTF DR2. The classification accuracy is generally above 70%. The period accuracy of the EW and SR variables is ∼50% and 53%, respectively. And the period accuracy of other types of variables is 65%–98%.

Background: Physicians play an important role in clinical trials, but attitudes toward clinical trials among physicians with different levels of experience have seldom been studied. The objective of this study was to assess the different attitudes toward clinical trials among junior, intermediate, and senior physicians. Methods: An anonymous cross-sectional survey was conducted between April 1 and May 1, 2015. A total of 217 physicians (66 junior, 86 intermediate, and 65 senior positions) completed and submitted the validated questionnaires. Results: The surveyed physicians responded with a high degree of agreement on the necessity of conducting clinical trials. Senior and intermediate physicians considered most about helping patients with new treatments in conducting a clinical trial. For junior physicians, the opportunity to have access to clinical trial methodology and enhancing their reputation in society or in the hospital were the 2 most significant factors. In addition, physicians with lower titles perceived greater difficulties in conducting clinical trials, especially in communicating with the Good Clinical Practice (GCP) office and the trial sponsor, enrolling trial participants, and obtaining informed consent. Conclusions: Our results revealed that physicians with different positional titles perceived different benefits and difficulties in conducting clinical trials. Junior physicians need more administrative assistance in clinical trials and related training.

The brittle to ductile failure mode transition and the formation of adiabatic shear bands (ASB) of polypropylene were observed at high strain rate impact loading. The dynamic experiments were conducted using a split Hopkinson pressure bar (SHPB) set-up with hat-shaped specimens. The post-test observations of the recovered specimens were performed by polarized light microscopy. The mechanical behaviors of specimens are strongly influenced by temperature, the strength of specimen decreases with increasing temperature. Furthermore, the specimen fractures as a brittle solid at room temperature (20°C), while at a high temperature (100°C), the specimen fractures on a ductile model. At high temperature impact tests, shear bands are observed in the shear zone of the hat-shaped specimen, and the cracks formed at the corners propagate along the shear bands.

Background: The US Food and Drug Administration (FDA) is responsible for protecting public health. Issuance of a warning letter is the FDA’s principal means of achieving prompt voluntary compliance with regulations. The objective of this study is to analyze the trends of warning letters issued to medical products about current good manufacturing practices (CGMP) violations during 2007–2014, with a secondary purpose of providing suggestions to manufacturers on how to avoid warning letters. Methods: Warning letters were collected from the FDA website, and information about the date of issuance, company, country, and type of medical product was collected from the letters related to medical products about CGMP violations. Descriptive statistics were performed for the analysis. Results: A total of 997 warning letters were issued to medical products about CGMP violations during 2007–2014. It showed an upward trend in the number of warning letters from 2009 to 2011. In 2011, it reached a maximum and the FDA released 159 letters. Overall, 685 (68.7%) letters were issued to medical devices, 223 (22.4%) to finished pharmaceuticals, followed by active pharmaceutical ingredients (5.7%), biological products (including blood products) (1.8%), and pharmacy compounding (1.4%). Despite a sharp reduction in 2013, the annual number of warning letters released to medical devices was larger than others; conversely, changes in other products remained relatively stable. Conclusions: The issuance number of warning letters increased during 2009–2011. Medical device manufacturers received the largest numbers of letters. The issuance number and type of warning letters were greatly influenced by changes in FDA’s internal enforcement procedures, drug policies and regulations, as well as other departments’ regulations. Manufacturers should comply with regulations voluntarily and respond promptly to policy changes.

The recent process in the fabrication of the ordered Ge/Si quantum dots (QDs) is reviewed. The fabrication step generally started on the preparation of patterned substrate prepared in advance by using several interesting methods, such as photo lithography, focus ion beam (FIB), reactive ion etching (RIE), and extreme ultraviolet lithography (EUV-IL) et al, which are introduced briefly in this article. Here, we’d like to focus on the detailed process of nanosphere lithography (NSL) which has the advantages of less cost and higher product compared with the referred methods. The ordered Ge nanostructures always show as Hexagonal close-packed array on the patterned Si substrate and have the advantages of potential applications in electronic and optoelectronic devices.

To support the implementation of China's carbon neutrality target, this study assesses the impact of future climate change on hydroelectricity production (HP) in China. We developed a hydropower database for mainland China, which covers 92% of the installed capacity indicated by national statistics (352 GW in 2018). An HP model was applied to simulate monthly HP by 3,521 conventional plants at different levels of global warming (1°, 1.5°, 2°, and 3°C above preindustrial) using an ensemble of monthly streamflow time series computed by five global hydrological models (GHMs), where each global hydrological model was driven by four global climate models (GCMs). With a probability of 80%, limiting global warming to 1.5°C will constrain changes of national HP to the range of −1.4%–2.5% as compared to near‐present climate (1°C). In case of a 3°C world, this range more than doubles. HP is projected to likely increase in the two provinces Sichuan and Yunnan that encompass more than half of China's installed capacity. However, with a probability of about 10%, HP may decrease in a 3°C world by more than 15% in five provinces, and 40% of the installed capacity may suffer from an HP decrease of more than 5%. The uncertainty of projected HP changes is mainly due to the GCMs. We found that relative changes in annual HP and streamflow often differ in magnitude and direction as HP depends on seasonal streamflow patterns and installed capacities that determine the maximum flow through the turbines. A new hydropower database for mainland China was compliedSignificant reductions due to climate change cannot be precluded for 40% of the installed capacityChanges in annual hydroelectricity production and streamflow often differ in magnitude and direction A new hydropower database for mainland China was complied Significant reductions due to climate change cannot be precluded for 40% of the installed capacity Changes in annual hydroelectricity production and streamflow often differ in magnitude and direction

In this paper, Fe3O4 magnetic nanoparticles were synthesized by chemical co-precipitation method and their surface was modified by sodium oleate. The γ-Fe2O3 magnetic nanoparticles were achieved by thermal oxidizing of Fe3O4. The γ-Fe2O3 magnetic fluid was prepared by using silicone oil as carrier liquid and oleic acid as surface modification agent, and the saturation magnetization of prepared γ-Fe2O3 magnetic fluid hits 14.25emu/g.