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Abstract Magnetic resonance imaging (MRI) is a non‐interposition imaging technique that provides rich anatomical and physiological information. Yet it is limited by the long imaging time. Recently, deep neural networks have shown potential to significantly accelerate MRI. However, most of these approaches ignore the correlation between adjacent slices in MRI image sequences. In addition, the existing deep learning‐based methods for MRI are mainly based on convolutional neural networks (CNNs). They fail to capture long‐distance dependencies due to the small receptive field. Inspired by the feature similarity in adjacent slices and impressive performance of Transformer for exploiting the long‐distance dependencies, a novel multislice texture transformer network is presented for undersampled MRI reconstruction (TTMRI). Specifically, the proposed TTMRI is consisted of four modules, namely the texture extraction, correlation calculation, texture transfer and texture synthesis. It takes three adjacent slices as inputs, in which the middle one is the target image to be reconstructed, and the other two are auxiliary images. The multiscale features are extracted by the texture extraction module and their inter‐dependencies are calculated by the correlation calculation module, respectively. Then the relevant features are transferred by the texture transfer module and fused by the texture synthesis module. By considering inter‐slice correlations and leveraging the Transformer architecture, the joint feature learning across target and adjacent slices are encouraged. Moreover, TTMRI can be stacked with multiple layers to recover more texture information at different levels. Extensive experiments demonstrate that the proposed TTMRI outperforms other state‐of‐the‐art methods in both quantitative and qualitative evaluationsions.

We reliably judge locations of static objects when we walk despite the retinal images of these objects moving with every step we take. Here, we showed our brains solve this optical illusion by adopting an allocentric spatial reference frame. We measured perceived target location after the observer walked a short distance from the home base. Supporting the allocentric coding scheme, we found the intrinsic bias , which acts as a spatial reference frame for perceiving location of a dimly lit target in the dark, remained grounded at the home base rather than traveled along with the observer. The path-integration mechanism responsible for this can utilize both active and passive (vestibular) translational motion signals, but only along the horizontal direction. This asymmetric path-integration finding in human visual space perception is reminiscent of the asymmetric spatial memory finding in desert ants, pointing to nature’s wondrous and logically simple design for terrestrial creatures.

Abstract Background Tumor-associated macrophages (TAMs) play a pivotal role in reshaping the tumor microenvironment following radiotherapy. The mechanisms underlying this reprogramming process remain to be elucidated. Methods Subcutaneous Lewis lung carcinoma (LLC) murine model was treated with hypofrationated radiotherapy (8 Gy × 3F). Single-cell RNA sequencing was utilized to identify subclusters and functions of TAMs. Multiplex assay and enzyme-linked immunosorbent assay (ELISA) were employed to measure serum chemokine levels. Bindarit was used to inhibit CCL8, CCL7, and CCL2. The infiltration of TAMs after combination treatment with hypofractionated radiotherapy and Bindarit was quantified with flow cytometry, while the influx of CD206 and CCL8 was assessed by immunostaining. Results Transcriptome analysis identified a distinct subset of M2-like macrophages characterized by elevated Ccl8 expression level following hypofractionated radiotherapy in LLC-bearing mice. Remarkbly, hypofractionated radiotherapy not only promoted CCL8high macrophages infiltration but also reprogrammed them by upregulating immunosuppressive genes, thereby fostering an immunosuppressive tumor microenvironment. Additioinally, hypofractionated radiotherapy enhanced the CCL signaling pathway, augmenting the pro-tumorigenic functions of CCL8high macrophages and boosting TAMs recruitment. The adjunctive treatment combining hypofractionated radiotherapy with Bindarit effectively reduced M2 macrophages infiltration and prolonged the duration of local tumor control. Conclusions Hypofractionated radiotherapy enhances the infiltration of CCL8high macrophages and amplifies their roles in macrophage recruitment through the CCL signaling pathway, leading to an immunosuppressive tumor microenvironment. These findings highlight the potential of targeting TAMs and introduces a novel combination to improve the efficacy of hypofractionated radiotherapy.

The controllable and safe hydrogen storage technologies are widely recognized as the main bottleneck for the accomplishment of sustainable hydrogen energy. Ammonia borane (AB) has regarded as a competitive candidate for chemical hydrogen storage. However, developing efficient yet high-performance catalysts towards hydrogen evolution from AB hydrolysis remains an enormous challenge. Herein, cobalt phosphide nanosheets are synthesized by a facile salt-assisted along with low-temperature phosphidation strategy for simultaneously modulating its morphology and electronic structure, and function as hydrogen evolution photocatalysts. Impressively, the Co2P nanosheets display extraordinary performance with a record high turnover frequency of 44.9 min−1, outperforming most of the noble-metal-free catalysts reported to date. This remarkable performance is attributed to its desired nanosheets structure, featuring with high specific surface area, abundant exposed active sites, and short charge diffusion paths. Our findings provide a novel strategy for regulating metal phosphides with desired phase structure and morphology for energy-related applications and beyond.

This paper presents an optimized preparation process for external ointment using the Definitive Screening Design (DSD) method. The ointment is a Traditional Chinese Medicine (TCM) formula developed by Professor WYH, a renowned TCM practitioner in Jiangsu Province, China, known for its proven clinical efficacy. In this study, a stepwise regression model was employed to analyze the relationship between key process factors (such as mixing speed and time) and rheological parameters. Machine learning techniques, including Monte Carlo simulation, decision tree analysis, and Gaussian process, were used for parameter optimization. Through rigorous experimentation and verification, we have successfully identified the optimal preparation process for WYH ointment. The optimized parameters included drug ratio of 24.5%, mixing time of 8 min, mixing speed of 1175 rpm, petroleum dosage of 79 g, liquid paraffin dosage of 6.7 g. The final ointment formulation was prepared using method B. This research not only contributes to the optimization of the WYH ointment preparation process but also provides valuable insights and practical guidance for designing the preparation processes of other TCM ointments. This advanced DSD method enhances the screening approach for identifying the best preparation process, thereby improving the scientific rigor and quality of TCM ointment preparation processes.

旨在为探明植物油出油方式提供技术支持，建立了一种鉴别压榨植物油和浸出植物油的方法。采用顶空-气质联用法测定植物油的残留溶剂，对顶空进样器平衡温度进行了优化，并探究植物油残留溶剂的特征性成分，通过对压榨植物油和浸出植物油样品中特征性成分的分析确定植物油出油方式的鉴别方法。结果表明：六号溶剂主要成分的出峰面积均随着平衡温度的升高而增加，优化的平衡温度为150 ℃；六号溶剂中5种主要成分在0~1.00 mg/kg范围内线性关系良好，相关系数均可达0.999以上，检出限为0.001 0~0.014 4 mg/kg；六号溶剂中不仅含有烷烃、环烷烃，还含有烯烃、芳香烃（包括多环芳烃）等，成分较为复杂，浸出植物油中溶剂残留物主要为2-甲基戊烷、3-甲基戊烷 、正己烷、甲基环戊烷和环己烷，占比达97.5%，与六号溶剂中这5种物质含量（占比达96.6%）相似，因此确定这5种物质为植物油残留溶剂的特征性成分；实际样品测定表明压榨植物油中没有溶剂残留或残留量较低，浸出植物油中均有溶剂残留且残留量较高；总结出鉴别压榨植物油和浸出植物油的方法为若同时检出4种及以上六号溶剂特征性成分或特征性成分总含量大于或等于0.50 mg/kg的样品可判定为浸出植物油，若同时检出小于或等于3种六号溶剂特征性成分且特征性成分总含量小于 0.50 mg/kg的样品可判定为压榨植物油。综上，采用顶空-气质联用法对植物油中六号溶剂的特征性成分进行分析，以特征性成分有无和含量可以鉴别压榨植物油和浸出植物油。In order to provide technical support for the exploration of vegetable oil production mode, a method for identifying pressed vegetable oil and extracted vegetable oil was established. The residual solvent in vegetable oil was determined by headspace-gas chromatography-mass spectrometry. The equilibrium temperature of headspace injection was optimized, and the characteristic components of residual solvent in vegetable oil was identified. By analyzing the characteristic components in pressed and extracted vegetable oil samples, the extraction method of vegetable oil was identified. The results showed that the peak areas of main components of No. 6 solvent increased with the increase of equilibrium temperature, and the optimal equilibrium temperature was 150 ℃. The linearity of five main components was good in the range of 0-1.00 mg/kg, the correlation coefficient was more than 0.999, and the detection limits was 0.001 0-0.014 4 mg/kg. The components of No.6 solvent were relatively complex, including not only alkane and cycloalkanes, but also olefins, aromatic hydrocarbons (including polycyclic aromatic hydrocarbons). The solvent residues in the extracted vegetable oil were mainly 2-methylpentane, 3-methylpentane, n-hexane, methylcyclopentane and cyclohexane, accounting for 97.5%, which were similar to the contents of these five substances in No. 6 solvent(accouting for 96.6%). Therefore, it was determined that these five substances were characteristic components of residual solvents in vegetable oil. The determination of practical samples showed that there was no or low solvent residue in pressed vegetable oil, while there was solvent residue in the extracted vegetable oil and the residue amount was relative high. The methods for identifying pressed vegetable oil and extracted vegetable oil were summarized as follows: if four or more characteristic components of No. 6 solvent at the same time or the total content of characteristic components greater than or equal to 0.50 mg/kg were detected, the sample could be determined as extracted vegetable oil. If less than or equal to three main components of No. 6 solvent at the same time and the total content of characteristic components less than 0.50 mg/kg were detected, the sample could be judged as pressed vegetable oil. By analyzing characteristic components of No. 6 solvent in vegetable oil using headspace-gas chromatography-mass spectrometry, the pressed vegetable oil and extracted vegetable oil can be identified based on the presence or absence and content of its characteristic components.