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Abstract The rapid emergence of large language model (LLM) technology presents promising opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build an LLM-based reaction development framework (LLM-RDF) to handle fundamental tasks involved throughout the chemical synthesis development. LLM-RDF comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted to accomplish the designated tasks. A web application with LLM-RDF as the backend was built to allow chemist users to interact with automated experimental platforms and analyze results via natural language, thus, eliminating the need for coding skills and ensuring accessibility for all chemists. We demonstrated the capabilities of LLM-RDF in guiding the end-to-end synthesis development process for the copper/TEMPO catalyzed aerobic alcohol oxidation to aldehyde reaction, including literature search and information extraction, substrate scope and condition screening, reaction kinetics study, reaction condition optimization, reaction scale-up and product purification. Furthermore, LLM-RDF’s broader applicability and versability was validated on various synthesis tasks of three distinct reactions (SNAr reaction, photoredox C-C cross-coupling reaction, and heterogeneous photoelectrochemical reaction).

This study aimed to investigate the effects of dietary supplementation with zinc oxide nanoparticles (ZnONPs) on lactation, rumen microbiota, and metabolomics in dairy goats. Twenty Guanzhong dairy goats, with comparable milk yields and in the mid-lactation stage, were randomly divided into two groups, with 10 goats in each group. The control group was fed a standard diet, while the ZnONP group received the control diet plus 30 mg ZnONPs/kg DM. The pre-trial period lasted for 7 days, followed by a trial period of 30 days. The results showed that the addition of ZnONPs increased the milk yield and milk fat content (p

A Ti–48Al–2Cr–2Nb alloy was produced by electron beam powder bed fusion. The effect of the volume energy density parameter on the forming quality was investigated, and the tissue characteristics, hardness distribution, and mechanical properties of the alloy were analyzed. The results showed that the quality was better when the volume energy density parameter was 21.5–22.5 J/mm3. The microstructure of the alloy has the phenomenon of changing with the forming height: with an increase in the forming height, the forming part produces the phenomenon of strip microstructure along the construction direction, and the regional microstructure variability occurs under the cross section of the same height. The average hardness of the alloy reached 375.2 HV at the bottom, 381.8 HV in the middle, and 389.6 HV at the top, and the standard errors of the hardness values were 12.40, 10.49, and 7.12 HV, respectively, with the microhardness showing a gradual increase along the molding direction, and the homogeneity was gradually better. The transverse tensile strength at room temperature reached 526 MPa, the yield strength reached 461 MPa, and the elongation at break reached 1.24%, while the longitudinal tensile strength reached 486 MPa, the yield strength reached 414 MPa, and the elongation at break reached 1.02%.

The propagation rate coefficient (kp) is one of the most crucial kinetic parameters in free-radical polymerization (FRP) as it directly governs the rate of polymerization and the resulting molecular weight distribution. The kp in FRP can typically be obtained through experimental measurements or quantum chemical calculations, both of which can be time consuming and resource intensive. Herein, we developed a machine learning model based solely on the structural features of monomers involved in FRP, utilizing molecular embedding and a Lasso regression algorithm to predict kp more efficiently and accurately. The result shows that the model achieves a mean absolute percentage error (MAPE) of only 5.49% in the predictions for four new monomers, which indicates that the model exhibits strong generalization capabilities and provides reliable and robust predictions. In addition, this model can accurately predict the influence of the ester side chain length of (meth)acrylates on kp, aligning well with established scientific knowledge. This approach offers a straightforward and practical model for other researchers to rapidly obtain accurate kp values by employing monomer structural information. The model is sufficiently general to apply to a wide range of (meth)acrylate and butadiene FRP monomers, thereby supporting kinetic modeling of polymerization reactions.

Naturally occurring nontrivial protein topologies not only draw increasing attention on the relationships between topology and protein function, but also stimulate research endeavors in harnessing topology as a unique dimension for protein engineering. However, artificial design and synthesis of protein topologies is still in its infancy with successes only on a few simple topologies and its development is limited by the small number of intertwined protein motifs known to date. Herein, we report a systematic framework for the discovery of symmetric intertwined protein motifs from Protein Data Bank. The symmetric protein assemblies were screened based on their calculated Gauss linking number to assess their extent of chain intertwining. Then, the potential of such symmetric intertwined motifs in designing complex protein topologies is evaluated based on several parameters, including buried surface area, N-C terminal distances and so on, giving rise to a library of premium intertwined motifs useful for protein topology engineering. Taking C2 assemblies for example, the potential biological significances of chain intertwining in protein architectures are also discussed based on the analysis of their postulated biological functions. With an expanded toolbox of intertwined motifs, the design space of topological proteins would be greatly expanded to enable a broader scope for the application of protein topology engineering.

To decrease the consumption of energy and material resources caused by the traditional ''two-step'' process for separating propylene from the C3 crude components, including propane, propylene, methylacetylene and propadiene, this work utilizes the metal-organic frameworks (MOFs) involved ''one-step'' process to purify propylene. First, the relationship between the geometric/energy descriptors of MOFs and their performance metrics was established through univariate analysis, and results show that the top-performance MOFs can be screened by the differences of LCD, ρ, VSA and ϕ. Then, different combinations of descriptors and algorithms were used for machine learning. The combination (RF algorithm, 7 basic descriptors + PSD + MorganFPs + nodes + topologies) with the best prediction accuracy (R=0.87) for predicting the performance of MOFs was found. Finally, 4 optimal pore structures for the design of high propylene adsorption performance materials were summarized, which mainly contain cylindrical channels and spherical cavities similar in size to the target gas. The microscopic control of pore structures obtained from our bottom-up approach is useful for the development of MOFs and other nanoporous materials which can be used for ''one-step'' separation of propylene from C3 mixtures in various industrial situations.

Background: Recent studies have shown that the mRNA expression-based stemness index (mRNAsi) can accurately quantify the similarity of cancer cells to stem cells, and mRNAsi-related genes are used as biomarkers for cancer. However, mRNAsi-driven tumor heterogeneity is rarely investigated, especially whether mRNAsi can distinguish hepatocellular carcinoma (HCC) into different molecular subtypes is still largely unknown. Methods: Using OCLR machine learning algorithm, weighted gene co-expression network analysis, consistent unsupervised clustering, survival analysis and multivariate cox regression etc. to identify biomarkers and molecular subtypes related to tumor stemness in HCC. Results: We firstly demonstrate that the high mRNAsi is significantly associated with the poor survival and high disease grades in HCC. Secondly, we identify 212 mRNAsi-related genes that can divide HCC into three molecular subtypes: low cancer stemness cell phenotype (CSCP-L), moderate cancer stemness cell phenotype (CSCP-M) and high cancer stemness cell phenotype (CSCP-H), especially over-activated ribosomes, spliceosomes and nucleotide metabolism lead to the worst prognosis for the CSCP-H subtype patients, while activated amino acids, fatty acids and complement systems result in the best prognosis for the CSCP-L subtype. Thirdly, we find that three CSCP subtypes have different mutation characteristics, immune microenvironment and immune checkpoint expression, which may cause the differential prognosis for three subtypes. Finally, we identify 10 robust mRNAsi-related biomarkers that can effectively predict the survival of HCC patients. Conclusions: These novel cancer stemness-related CSCP subtypes and biomarkers in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy of HCC patients.

Liver cancer is the third leading cause of cancer-associated mortality globally, and >830,000 patients with liver cancer undergoing treatment succumbed to the disease in 2020, which indicates the urgent need to develop a more effective anti-liver cancer drug. In our previous study, nucleus-targeting hybrid peptides obtained from the fusion of LTX-315 and the rhodamine B group possessed potent anti-adherent cancer cell activity. Hybrid peptides accumulated in the cell nucleus and damaged the nuclear membrane, resulting in the transfer of reactive oxygen species (ROS) from the cytoplasm to the nucleus and the induction of apoptosis. However, the source of the high concentration of ROS within the cytoplasm is unclear. Moreover, although our previous study demonstrated that hybrid peptides possessed potent anticancer activity against adherent cancer cells, their efficacy on liver cancer remained unexplored. The current study found that the hybrid peptide NTP-217 killed liver cancer cells after 4-h treatment with a half-maximal inhibitory concentration of 14.6-45.7 μM. NTP-217 could stably accumulate in the liver tumor tissue and markedly inhibited liver tumor growth in mice. Furthermore, NTP-217 destroyed mitochondria and induced the leakage of mitochondrial contents, resulting in the generation of a substantial quantity of ROS. Unlike the apoptosis induced by 24 h of treatment by NTP-217, 4 h of treatment caused ROS-mediated necrotic cell death. These findings suggested that short-time treatment with hybrid peptides could trigger ROS-mediated rapid necrosis in liver cancer cells, and provided a basis for the future development of hybrid peptides as anti-liver cancer agents.