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To investigate the regular patterns by wind and sand in low hilly basins, we analyzed the particle size, component end element and fractal variability of surface sediments, as well as the near-surface wind energy and sand transport potential, and determined the characteristics of their spatial differentiation in the desert west of Yinshan Mountain in China. The results showed that the regional dominant wind was mainly westerly and southwest wind, and the average annual average wind speed of sand wind was 6.56–7.62 m s –1 , the annual average drift potential of the sand wind was 359.99 VU, and the average annual value of synthetic drift potential was 204.46 VU, which classified region as a the middle-wind-energy environment with middle-wind-direction variability. Under the action of dominant wind, the particle size of dune sediment gradually refined from the northwest to the southeast and northeast, and the fractal dimension gradually increased. The sediments of Baiyinchagan Desert, Boketai Desert, and southern Yamaleike Desert dunes were fine (the average particle size was 0.191 mm), and the average fractal dimension value was 2.372; the Haili Desert and the northern Yamaleike Desert dunes was large (the average particle size was 0.212 mm), and the average fractal dimension was 2.327. At the same time, fed by the near source Gobi coarse sand, under the action of long-term wind and sand, the Haili and Yamaleike Deserts formed tall and stable crescent sand dunes and sand dune chains. The particle size end member indicated that the desert sediment was wind deposit, while the desert peripheral Gobi desert end member indicated that the type of sediment was wind deposit and river alluvial material formed under the combined action of wind and water, the heterogeneity of the Gobi outside the desert was significantly higher than the desert surface, which showed moderate spatial differentiation. The topography of low mountains and hilly basins affected near-surface sandstorm processes and the formation and evolution of sandstorm landforms.

In recent years, combined drug screening has played a very important role in modern drug discovery. Generally, synergistic drug combinations are crucial in treatment for many diseases. However, the toxic side effects of drug combinations are probably increased with the increase of drugs numbers, so the accurate prediction of toxic side effects of drug combinations is equally important. In this paper, we built a Metapath-based Aggregated Embedding Model on Single Drug–Side Effect Heterogeneous Information Network (MAEM-SSHIN), which extracts feature from a heterogeneous information network of single drug side effects, and a Graph Convolutional Network on Combinatorial drugs and Side effect Heterogeneous Information Network (GCN-CSHIN), which transforms the complex task of predicting multiple side effects between drug pairs into the more manageable prediction of relationships between combinatorial drugs and individual side effects. MAEM-SSHIN and GCN-CSHIN provided a united novel framework for predicting potential side effects in combinatorial drug therapies. This integration enhances prediction accuracy, efficiency, and scalability. Our experimental results demonstrate that this combined framework outperforms existing methodologies in predicting side effects, and marks a significant advancement in pharmaceutical research.

Highly dispersed Ag-doped Polypyrrole (PPy) spherical composites can be efficiently synthesized via oxidative polymerization of pyrrole with FeCl 3 in an aqueous Ag + -containing solution in the presence of trisodium citrate, followed by concentrated ammonia treatment. However, the formation mechanisms involved in how to control the shape and how to get the metallic Ag 0 need further investigation. In order to elucidate the formation mechanisms, the intermediates in different reaction stage were collected and investigated. Combining the experimental phenomenon and the structure characterization of the samples, it was found that citrate ions make a role of complexing Ag + to produce [Ag 3 (C 6 H 5 O 7 ) n+1 ] 3n− complexes in the early reaction stage, then mainly play a role of steric stabilizer of AgCl micelles and are responsible for the shape tailoring of PPy composite as well as the reduction of Ag + in the process of ammonia treatment. Evidently, negative-charged AgCl micelles become the main nucleation sites of pyrrole polymerization through the electrostatic attraction between the negative and positive ions. Concentrated ammonia is adopted to eliminate AgCl cores from the precursor of Ag-doped PPy composites obtained by chemical redox reaction and provides an accelerated reaction condition for reduction of Ag + by reductants (citrate ion or pyrrole monomer). Ag-containing micelles induction method is a facial chemical method to obtain uniform Ag-doped composites and can be broadened to design other Ag-containing functional materials.

Drug combination therapy, involving the use of two or more drugs, has been widely employed to treat complex diseases such as cancer. It enhances therapeutic efficacy, reduces drug resistance, and minimizes side effects. However, traditional methods to identify effective drug combinations are time-consuming, costly, and less efficient than computational methods. Therefore, developing computational approaches to predict drug combinations has become increasingly important. In this paper, we developed the Random Walk with Restart for Drug Combination (RWRDC) model to predict effective drug combinations for cancer therapy. The RWRDC model offers a quantitative mathematical method for predicting the potential effective drug combinations. Cross-validation results indicate that the RWRDC model outperforms other predictive models, particularly in breast, colorectal, and lung cancer predictions across various performance metrics. We have theoretically proven the convergence of its algorithm and provided an explanation for the algorithm's rationality. A targeted case study on breast cancer further highlights the capability of RWRDC to identify effective drug combinations. These findings highlight our model as a novel and effective tool for discovering potential effective drug combinations, offering new possibilities in therapy. Additionally, the graph-based framework of RWRDC holds potential for predicting drug combinations in other complex diseases, expanding its utility in the medical field.

This paper presents a molecular force-based deployment algorithm of charging stations according to the principle of intermolecular forces in physics to expand the flight coverage of electric-powered multi-rotor Unmanned Aerial Vehicles (UAV). With the help of this algorithm, a multi-rotor UAV can reach anywhere in the specific area by charging at the charging station several times. In this algorithm, a number of equal circles are used to cover the specific area (in a two-dimensional plane), and the center of each circle denotes a charging station. The radius of these circles is equal to the radius of action of the UAV. The number of the circles is set by the users. Under the combined effect of three virtual forces, the centers of the circles, called nodes, keep moving within the specific area, and multiple iterations are performed to adjust the location of each node. Finally, a proper deployment scheme for the charging stations is generated, which can achieve the working area maximization of the UAV by a certain number of charging stations. Simulation experiments were executed, and the results under different conditions show that the proposed algorithm can meet the expected requirements and has an advantage over three other algorithms in terms of coverage ratio. The experiment results also indicate that in the case of dense node density, the proposed algorithm has a better coverage performance than the case of sparse node density. The experimental data are available at https://figshare.com/projects/MFA/24064 . The codes will be published later.

Purpose The purpose of this study is to explore the mechanism of branch pits and tunnels formation and increase the specific surface area and capacitance of anode Al foil for high voltage electrolytic capacitor by D.C. etching in acidic solution and neutral. Design/methodology/approach Al foil was first D.C. etched in HCl-H2SO4 mixed acidic solution to form main tunnels perpendicular to the Al surface, and then D.C. etched in neutral NaCl solution including 0.5 per cent C6H8O7 and Cu(NO3)2 with different concentration to form branch tunnels normal to Al surface. Between two etching, Cu nuclei were electroless deposited on the interior surface of main tunnels by natural occluded corrosion cell effect to form micro Cu-Al galvanic local cells. The effects of electroless deposited Cu nuclei on cross-section etching morphologies and electrochemical behavior of Al foil was investigated with SEM, polarization curve and electrochemical impedance spectroscopy (EIS). Findings The results show that sub branch tunnels can form along the main tunnels owing to the formation of Cu-Al micro-batteries, in which Cu is cathode and Al is anode. With increase in Cu(NO3)2 concentration, more Cu nuclei can be electroless deposited and serve as the favorable sites for branch tunnel initiation along the whole length of main tunnels, leading to enhancement in specific capacitance of anode Al foil. Originality/value Cu nuclei were electroless deposited on the interior surface of main tunnels by natural occluded corrosion cell effect to form micro Cu-Al galvanic local cells, which can serve as the favorable sites for branch tunnel initiation along the main tunnels to enhance specific capacitance of anode Al foil.

Mesoscopic effects in ferromagnets could be different from mesoscopic effects in normal metals. While normal metals with a short mean-free-path do not exhibit classical magnetoresistance, weakly disordered ferromagnets with a similar mean-free-path display magnetoresistance including domain well resistance (DWR) and anisotropic magnetoresistance (AMR). Magnetoresistance could lead to novel mesoscopic effects because the wave function phase depends on the scattering potential. In this thesis, we present our measurements of mesoscopic resistance fluctuations in cobalt nanoparticles and study how the fluctuations with bias voltage, bias fingerprints, respond to magnetization-reversal processes. The resistance has been found to be very sensitive to the magnetic state of the sample. In particular, we observe significant wave-function phase shifts generated by domain walls, and it is explained by mistracking effect, where electron spins lag in orientation with respect to the moments inside the domain wall. Short dephasing length and dephasing time are found in our Co nanoparticles, which we attribute to the strong magnetocrystalline anisotropy.

β-Thalassemia is a global health issue, caused by mutations in the HBB gene. Among these mutations, HBB -28 （A〉G） mutations is one of the three most common mutations in China and Southeast Asia patients with β-thalassemia. Correcting this mutation in human embryos may prevent the disease being passed onto future generations and cure anemia. Here we report the first study using base editor （BE） system to correct disease mutant in human embryos. Firstly, we produced a 293T cell line with an exogenous HBB -28 （A〉G） mutant fragment for gRNAs and targeting efficiency evaluation. Then we collected primary skin fibroblast cells from a β-thalassemia patient with HBB -28 （A〉G） homozygous mutation. Data showed that base editor could precisely correct HBB -28 （A〉G） mutation in the patient＇s primary cells. To model homozygous mutation disease embryos, we consb＇ucted nuclear transfer embryos by fusing the lymphocyte or skin fibroblast cells with enucleated in vitro matured （IVM） oocytes.Notably, the gene correction efficiency was over 23.0% in these embryos by base editor. Although these embryos were still mosaic, the percentage of repaired blastomeres was over 20.0%. In addition, we found that base editor variants, with narrowed deamination window, could promote G-to-A conversion at HBB -28 site precisely in human embryos. Collectively, this study demonstrated the feasibility of curing genetic disease in human somatic cells and embryos by base editor system.

Despite remarkable advances in whole-brain imaging technologies, the lack of quantitative approaches to bridge rodent preclinical and human studies remains a critical challenge. Here we present TransBrain, a computational framework enabling bidirectional translation of brain-wide phenotypes between humans and mice. TransBrain improves human-mice homology mapping accuracy through: (1) a novel detached region-specific deep neural networks trained on integrated multi-modal human transcriptomics to improve cortical correspondence (89.5% improvement over the original transcriptome), which revealed two evolutionarily conserved gradients explaining >50% of cortical organizational variance, and (2) random walk-based graph representation learning to construct a unified cross-species latent space incorporating anatomical hierarchies and structural connectivity. We demonstrated TransBrain's utility through three cross-species applications: quantitative assessment of resting-state brain organizational features, inferring human cognitive functions from mouse optogenetic circuits, and translating molecular insights from mouse models to individual-level mechanisms in autism. TransBrain enables quantitative cross-species comparison and mechanistic investigation of both normal and pathological brain functions.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Figure 1 revised; Manuscript formatting correction

Psychiatry seeks to unravel brain dysfunction and individual differences in real-world contexts. Naturalistic stimuli, like movie watching, are increasingly recognized for eliciting complex, context-dependent neural activity with high ecological validity. Yet, current methods often rely on standard paradigms that average data across time, limiting the full potential of such stimuli. Here, we present STIM, a Topological Data Analysis-based framework designed to dynamically track how individuals integrate complex contexts in real time. Applied to large-sample fMRI data from movie watching, STIM constructs a robust low-dimensional dynamical landscape that reflects group consensus while probing individual variations at both global (spanning narratives) and local (within specific narratives) levels. At the global level, individual differences emerge along a center-periphery gradient in the dynamical landscape, which significantly predicts fluid intelligence, underscoring the importance of neural adaptability and diversity. At finer scales, local geometric features correlate with context-specific psychological traits beyond cognition. STIM also captures developmental changes in the dynamical landscape and reveals abnormalities in conditions such as autism. These findings demonstrate that STIM leverages the rich information from movie stimuli and fMRI recordings as neural probes to assess individual differences in cognition and mental health.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Figure 1 revised; Figure 3 revised;Figure 4 revised; data availability updated; Supplemental figures updated.