‐symmetrical bis‐β‐amino alcohols

The syntheses of new optically active C-2-symmetrical bis-beta-amino alcohols 1-6 from (S)-2-(1-hydroxy-1,1-diphenylmethyl)-pyrrolidine are described. Especially attention is focused on bridges, which link the two beta-amino alcohol units. These new chiral ligands have been successfully applied in the catalytic enantioselective addition of diethylzinc to aldehydes to give sec-alcohols in good yields with up to 95% enantiomeric excess.Department of Applied Biology and Chemical Technolog

Research progress on high-entropy alloys for extreme loading environments

High-entropy alloys（HEAs）have attracted considerable attention from the research community as a pioneering alloy design paradigm over the past two decades. They have fundamentally challenged traditional design paradigms and exhibited exceptional mechanical properties and functional characteristics，thereby positioning themselves as promising candidates for significant engineering applications in the future. Recent advancements have unveiled several alloy systems that demonstrate exceptional performance across diverse metrics，including low-temperature fracture toughness，high-temperature strength，impact resistance，radiation tolerance，and fatigue resistance. These qualities render HEAs highly attractive materials for research with substantial application potential in critical domains such as deep space exploration，deep-sea investigations，low-temperature superconductivity，and advanced nuclear energy technologies. This paper will briefly introduce the concept and classification of HEAs，and review the experimental progress of HEAs under various extreme conditions such as extremely low temperatures，high-speed impacts，and high nuclear radiation. We also summarize the strategies for enhancing the strength and toughness of HEAs，and extract the deformation mechanisms and physical and chemical properties of HEAs under different extreme loads. It is foreseeable that the main development direction of HEAs will be to form microscopic fluctuations in chemical composition and construct multi-scalestructural ordering efficiently through fine adjustment of the selection and proportion of alloying elements and optimization of heat treatment processes. For comprehensive studies on HEAs subjected to extreme loads，it is essential to explore their microscopic deformation mechanisms further while proposing innovative strategies designed to address inherent trade-offs between strength and toughness. The integration of state-of-the-art simulation techniques combined with advanced characterization methods will be crucial for improving research efficiency while providing insights into microstructural behavior. Additionally，tailored optimization approaches should be implemented for distinct advantageous systems and phase structures，particularly those capable of activating dislocation movements，twinning，phase transformations and incorporating novel processing methodologies such as additive manufacturing. Finally，conducting more realistic simulation experiments that closely replicate extreme environments along with generating relevant engineering data are vital steps toward accelerating the practical application of HEAs in challenging settings

Mechanistic Investigation for Solidification of Pb in Fly Ash by Alkali Mineral Slag—Calcium Chloroaluminate as an Example

With the increase in municipal solid waste incineration, fly ash, its heavy metal content, and its disposal methods have attracted wide attention. This work investigates if the alkali-activated mineral slag gel solidification of heavy metals in fly ash has positive significance in promoting the harmless treatment of fly ash. This study obtained the optimal solidification conditions of fly ash from a grate incinerator, which are mineral slag content of 40%, activator content of 4%, and water content of 27.5%. Furthermore, the stability of synthesized calcium chloroaluminate is systematically investigated. The solidification effect of calcium chloroaluminate on Pb at pH = 10–13 was conducted at ambient temperatures from 15 °C to 35 °C to simulate the solidification environment of fly ash. The results show that the adsorption capacity of calcium chloroaluminate to Pb in a strongly alkaline environment is 0.1–3.5 mg/g. Pb is mainly solidified as lead-acid calcium chloroaluminate. This work provides a novel treatment strategy for fly ash

A Multi-Scale Target Detection Method Using an Improved Faster Region Convolutional Neural Network Based on Enhanced Backbone and Optimized Mechanisms

Currently, existing deep learning methods exhibit many limitations in multi-target detection, such as low accuracy and high rates of false detection and missed detections. This paper proposes an improved Faster R-CNN algorithm, aiming to enhance the algorithm’s capability in detecting multi-scale targets. This algorithm has three improvements based on Faster R-CNN. Firstly, the new algorithm uses the ResNet101 network for feature extraction of the detection image, which achieves stronger feature extraction capabilities. Secondly, the new algorithm integrates Online Hard Example Mining (OHEM), Soft non-maximum suppression (Soft-NMS), and Distance Intersection Over Union (DIOU) modules, which improves the positive and negative sample imbalance and the problem of small targets being easily missed during model training. Finally, the Region Proposal Network (RPN) is simplified to achieve a faster detection speed and a lower miss rate. The multi-scale training (MST) strategy is also used to train the improved Faster R-CNN to achieve a balance between detection accuracy and efficiency. Compared to the other detection models, the improved Faster R-CNN demonstrates significant advantages in terms of [email protected], F1-score, and Log average miss rate (LAMR). The model proposed in this paper provides valuable insights and inspiration for many fields, such as smart agriculture, medical diagnosis, and face recognition

A versatile CRISPR/Cas9 system off-target prediction tool using language model

Abstract Genome editing with the CRISPR/Cas9 system has revolutionized life and medical sciences, particularly in treating monogenic genetic diseases by enabling long-term therapeutic effects from a single intervention. However, the CRISPR/Cas9 system can tolerate mismatches and DNA/RNA bulges at target sites, leading to unintended off-target effects that pose challenges for gene-editing therapy development. Existing high-throughput detection and in silico prediction methods are often limited to specifically designed single guide RNAs (sgRNAs) and perform poorly on unseen sequences. To address these limitations, we introduce CCLMoff, a deep learning framework for off-target prediction that incorporates a pretrained RNA language model from RNAcentral. CCLMoff captures mutual sequence information between sgRNAs and target sites and is trained on a comprehensive, updated dataset. This approach enables accurate off-target identification and strong generalization across diverse NGS-based detection datasets. Model interpretation reveals the biological importance of the seed region, underscoring CCLMoff’s analytical capabilities. The development of CCLMoff lays the foundation for a comprehensive, end-to-end sgRNA design platform, enhancing both the precision and efficiency of CRISPR/Cas9-based therapeutics. CCLMoff is a versatile tool and is publicly available at github.com/duwa2/CCLMoff