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N-tetradecanoyl-L-homoserine lactone (C14-HSL) is a long-chain signaling molecule belonging to acyl-homoserine lactones (AHLs), which is widely present in the quorum sensing (QS) system of Gram-negative bacteria. In this study, the effects of C14-HSL on chalcopyrite bioleaching mediated by Acidithiobacillus ferrooxidans (A. ferrooxidans) were investigated. After cultivating A. ferrooxidans with different energy substrates and exploring the potential mechanisms of signal molecule production, chalcopyrite was selected as the energy substrate for further study. Molecular docking analysis revealed that the high binding affinity between AHL and the receptor protein AfeR in A. ferrooxidans was beneficial for the activation of transcription by the AfeR-AHL complex, promoting their biological impact. The variations in the physicochemical parameters of pH, redox potential, and copper ions revealed that after adding C14-HSL, the leaching rate of chalcopyrite increased (1.15 times during the initial 12 days). Further analysis of the mechanism of extracellular polymers formation indicated that the presence of C14-HSL could promote the formation of biofilms and the adhesion of bacteria, facilitating mineral leaching rate of A. ferrooxidans. This research provides a theoretical basis for regulating the biological leaching process of chalcopyrite and metal recovery using signaling molecules, which could also be used to control environmental damage caused by acid mine/rock drainage.

N-tetradecanoyl-L-homoserine lactone (C[sub.14]-HSL) is a long-chain signaling molecule belonging to acyl-homoserine lactones (AHLs), which is widely present in the quorum sensing (QS) system of Gram-negative bacteria. In this study, the effects of C[sub.14]-HSL on chalcopyrite bioleaching mediated by Acidithiobacillus ferrooxidans (A. ferrooxidans) were investigated. After cultivating A. ferrooxidans with different energy substrates and exploring the potential mechanisms of signal molecule production, chalcopyrite was selected as the energy substrate for further study. Molecular docking analysis revealed that the high binding affinity between AHL and the receptor protein AfeR in A. ferrooxidans was beneficial for the activation of transcription by the AfeR-AHL complex, promoting their biological impact. The variations in the physicochemical parameters of pH, redox potential, and copper ions revealed that after adding C[sub.14]-HSL, the leaching rate of chalcopyrite increased (1.15 times during the initial 12 days). Further analysis of the mechanism of extracellular polymers formation indicated that the presence of C[sub.14]-HSL could promote the formation of biofilms and the adhesion of bacteria, facilitating mineral leaching rate of A. ferrooxidans. This research provides a theoretical basis for regulating the biological leaching process of chalcopyrite and metal recovery using signaling molecules, which could also be used to control environmental damage caused by acid mine/rock drainage.

This paper presents experimental studies on hydrodynamics and bed morphological characteristics under varying water and sediment discharges over a gravel channel bed with a boulder. Firstly, flow characteristics over a non-eroded bed with a mild slope were investigated. Results show that along the transect line located one diameter away from the boulder centerline, the existence of the boulder has negligible impact on the mean flow characteristics, which are similar to flows over a flat bed. At the boulder centerline, the flow is largely deflected by the boulder and turbulence characteristics in the horizontal plane are largely enhanced in the wake of the boulder. Secondly, water scour experiments were carried out over a steep slope. It could be observed that scour occurred around the boulder and bedloads were deposited downstream, forming a typical pool–riffle sequence. An analysis shows that the length scale (L/D) of geometric features associated with pool depth, riffle height and pool–riffle distance (S/D) are positively related to the boulder-related Froude number (Frb): L/D = 1.18Frb − 0.11 and S/D = 12.5Frb + 0.6; and the erosion volume (Ve) for flat bed and boulder bed is positively and negatively related to the averaged Froude number (Fr): Ve/D3 = 37.1Fr − 21.3 and Ve/D3 = −44.8Fr − 38.6, where D is the boulder diameter.

To address the issue of weak fault recognition ability of power equipment in single-spectrum images, a multi-spectral fusion recognition method based on prompt learning is proposed. A multi-spectral imaging system is used to capture images of normal and faulty power equipment, collecting multi-spectral data including visible light, infrared, and ultraviolet. The collected dataset is annotated with text labels for training the large model. The generalization ability of the large model in power equipment fault recognition is verified, and the original large model is tested on the collected dataset for device type and fault recognition. Trainable prompts based on infrared and ultraviolet images are designed for parameter updates. Throughout the training process, the parameters of the pre-trained large model remain fixed, and only the designed lightweight prompts are updated, significantly reducing the number of training parameters and alleviating the model's dependence on large-scale datasets. The proposed method is compared with several existing methods, and the results demonstrate that this approach can greatly improve the accuracy of power equipment fault recognition, achieving an accuracy of 90.14%. Ablation experiments and visual results further validate the effectiveness of the method. Additionally, the proposed method optimizes only a small number of trainable parameters, ensuring its efficiency. 针对单谱段图像在电力设备故障识别中的局限性, 提出了一种基于提示学习(prompt learning)的多谱段融合识别方法。为提升大模型对电力设备故障的识别精度, 设计了基于红外图像和紫外图像的可训练提示(prompts), 这些提示作为可训练部分用于模型的参数更新。这种策略很大程度地减少了训练所需的参数量, 且降低了大模型对下游数据量的依赖。利用集成可见光、红外和紫外等谱段的混合成像系统, 对正常和故障电力设备进行了拍摄, 并构建了相应的多谱段数据集, 该数据集经过文本标注后, 可用于大模型的训练。实验结果表明, 所提出的方法可显著提升电力设备故障识别的精度, 平均识别精度达到90.14%。消融实验和可视化结果进一步验证了所提出方法的有效性。此外, 由于所设计的方法只优化了极少数可训练参数, 确保了方法的高效性。

The E3 ligase TRIM7 has emerged as a critical player in viral infection and pathogenesis. However, the mechanism governing the TRIM7–substrate association remains to be defined. Here we report the crystal structures of TRIM7 in complex with 2C peptides of human enterovirus. Structure-guided studies reveal the C-terminal glutamine residue of 2C as the primary determinant for TRIM7 binding. Leveraged by this finding, we identify norovirus and SARS-CoV-2 proteins, and physiological proteins, as new TRIM7 substrates. Crystal structures of TRIM7 in complex with multiple peptides derived from SARS-CoV-2 proteins display the same glutamine-end recognition mode. Furthermore, TRIM7 could trigger the ubiquitination and degradation of these substrates, possibly representing a new Gln/C-degron pathway. Together, these findings unveil a common recognition mode by TRIM7, providing the foundation for further mechanistic characterization of antiviral and cellular functions of TRIM7.Using structural and biochemical methods, Liang et al. revealed a C-terminal glutamine-end recognition mechanism of TRIM7 E3 ligases, which enables identification of substrates for TRIM7 and provides insight into the versatile functions of TRIM7 in viral infection and the C-degron pathway.

In some plants, sucrose: sucrose 1-fructosyltransferase (1-SST) is the first irreversible key enzyme in fructan biosynthesis. Studies have shown that fructan accumulation enhances abiotic stress tolerance of plants. To investigate the role of 1-SST in drought stress responses, a total of 37 cotton plants expressing a 1-SST gene from Allium cepa were developed by Agrobacterium -mediated transformation. Under drought stress in the field, compared with wild-type, ectopic expression of Ac1-SST in cotton resulted in significantly higher soluble sugars (especially 1-kestose), proline and relative water contents, as well as decreased malondialdehyde content, which contributed to maintaining intracellular osmoregulation and reducing membrane damage. In addition, ectopic expression of Ac1-SST in cotton significantly improved the photosynthesis rate, performance of PSII (including Pn, Fv/Fm, WUE, ΦPSII, and PI total ) and plant growth under drought stress. Furthermore, compared with the wild-type, under the droughted field, the yield loss per square meter of transgenic cotton was reduced by an average of 20.9% over two consecutive years. Our results indicate that the Ac1-SST gene can be used to improve drought tolerance and yield of cotton varieties, and might also be a promising drought-resistant gene for improving other crop varieties.

Uncemented bipolar hemiarthroplasty (UBHA) has been widely used to treat geriatric displaced femoral neck fracture (GDFNF), which results in a high 30-day mortality rate among the elderly. To date, few studies have focused on the risk factors for mortality after UBHA for GDFNF. In this retrospective study, elderly patients (age ≥70 years) who underwent UBHA for GDFNF were studied in order to provide helpful insight into the risk factors for mortality postoperatively. This retrospective study enrolled 835 elderly patients who underwent UBHA for GDFNF from January 2010 to December 2017. The Kaplan–Meier method and Cox regression analysis were used to identify significant risk factors predicting mortality after UBHA for GDFNF. Univariate analysis showed that underweight (body mass index <18.5 kg/m2), smoking, alcohol use, hypertension, chronic kidney disease, hypoproteinemia, low activities of daily living (ADL) score (0 to 2), and postoperative delirium were identified as the potential risk factors responsible for mortality after UBHA for GDFNF. Multivariate analysis suggested that underweight, hypoproteinemia, low ADL score, and postoperative delirium were significant risk factors predicting mortality after UBHA for GDFNF. Postoperative delirium was the most robust risk factor for mortality after UBHA for GDFNF. Underweight, hypoproteinemia, and low ADL score were also closely associated with mortality after UBHA for GDFNF. [Orthopedics. 2021;44(4):e570–e576.]

To address the issue of weak fault recognition ability of power equipment in single-spectrum images, a multi-spectral fusion recognition method based on prompt learning is proposed. A multi-spectral imaging system is used to capture images of normal and faulty power equipment, collecting multi-spectral data including visible light, infrared, and ultraviolet. The collected dataset is annotated with text labels for training the large model. The generalization ability of the large model in power equipment fault recognition is verified, and the original large model is tested on the collected dataset for device type and fault recognition. Trainable prompts based on infrared and ultraviolet images are designed for parameter updates. Throughout the training process, the parameters of the pre-trained large model remain fixed, and only the designed lightweight prompts are updated, significantly reducing the number of training parameters and alleviating the model's dependence on large-scale datasets. The proposed metho

Writing competitive programming problems is exacting. Authors must: set constraints, input distributions, and edge cases that rule out shortcuts; target specific algorithms (e.g., max-flow, dynamic programming, data structures); and calibrate complexity beyond the reach of most competitors. We argue that this makes for an ideal test of general large language model capabilities and study whether they can do this reliably. We introduce AutoCode, which uses multiple rounds of validation to yield competition-grade problem statements and test cases. On held-out problems, AutoCode test suites approach 99% consistency with official judgments, a significant improvement over current state-of-the-art methods like HardTests, which achieve less than 81%. Furthermore, starting with a random seed problem, AutoCode can create novel variants with reference and brute-force solutions. By cross-verifying these generated solutions against test cases, we can further filter out malformed problems. Our system ensures high correctness, as verified by human experts. AutoCode successfully produces novel problems judged by Grandmaster-level (top 0.3%) competitive programmers to be of contest quality.

We introduce FrontierCS, a benchmark of 156 open-ended problems across diverse areas of computer science, designed and reviewed by experts, including CS PhDs and top-tier competitive programming participants and problem setters. Unlike existing benchmarks that focus on tasks with known optimal solutions, FrontierCS targets problems where the optimal solution is unknown, but the quality of a solution can be objectively evaluated. Models solve these tasks by implementing executable programs rather than outputting a direct answer. FrontierCS includes algorithmic problems, which are often NP-hard variants of competitive programming problems with objective partial scoring, and research problems with the same property. For each problem we provide an expert reference solution and an automatic evaluator. Combining open-ended design, measurable progress, and expert curation, FrontierCS provides a benchmark at the frontier of computer-science difficulty. Empirically, we find that frontier reasoning models still lag far behind human experts on both the algorithmic and research tracks, that increasing reasoning budgets alone does not close this gap, and that models often over-optimize for generating merely workable code instead of discovering high-quality algorithms and system designs.