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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 (S N Ar reaction, photoredox C-C cross-coupling reaction, and heterogeneous photoelectrochemical reaction). The rise of large language model (LLM) technology offers new opportunities for advancing chemical synthesis. Here, the authors developed an LLM-based reaction development framework (LLM-RDF) to copilot the design and experimental tasks throughout the end-to-end chemical synthesis development.

Background: The association between a diverse array of environmental risk factors and the risk of endometriosis is contradictory. Objective: To summarize the evidence of associations between environmental risk factors and the risk of endometriosis. Methods: Databases such as PubMed, EMBASE, Web of Science, and ClinicalTrial.gov were systematically searched in June 2020. Meta-analyses of observational studies investigated any environmental exposure (non-genetic) and endometriosis risk. For each article, we estimated the summary effect size, 95% CIs, and the 95% prediction interval (PI). We also estimated the between-study heterogeneity expressed by I 2 , evidence for small-study effects, and evidence of excess significance bias. Results: About 12 eligible articles (featuring 143,422 cases and 5,112,967 participants) yielded data on 40 unique environmental risk factors, including life styles ( n = 16), reproductive factors ( n = 3), early life factors ( n = 4), and a range of other risk factors [e.g., phthalate metabolites, endocrine-disrupting chemicals, and body mass index (BMI)]. About 25 of these 40 associations (62.5%) were statistically significant ( p < 0.05) under random-effects models. Evidence for an association was indicated for alcohol intake [relative risk (RR): 1.25; 95% CI: 1.11–1.41] and the exposure to endocrine disruptor chemicals (EDCs) (RR: 1.41; 95% CI: 1.23–1.60) while 15 associations presented only weak evidence. Conclusions: Our analyses showed that alcohol intake and exposure to endocrine-disrupting chemicals may be potential risk factors for endometriosis and supported by suggestive epidemiological evidence. However, it was evident that there was substantial heterogeneity and/or bias between the different studies featured in various meta-analyses included in this review; therefore, the outcomes of our analysis should be interpreted cautiously.

This study aimed to investigate the effects of long non-coding RNA ROR (regulator of reprogramming) on cisplatin (DDP) resistance in patients with non-small-cell lung cancer by regulating PI3K/Akt/mTOR signaling pathway. Human cisplatin-resistant A549/DDP cell lines were selected and divided into control group, negative control group, si-ROR group, ROR over-expression group, Wortmannin group, and ROR over-expression + Wortmannin group. MTT assay was used to determine the optimum inhibitory concentration of DDP. Quantitative real-time polymerase chain reaction and western blotting were applied to detect expressions of long non-coding RNA ROR, PI3K, Akt, and mTOR. Colony-forming assay, scratch test, Transwell assay, and flow cytometry were conducted to detect cell proliferation, migration, invasion, and apoptosis, respectively. Tumor-formation assay was performed to detect the growth of transplanted tumors. Long non-coding RNA ROR expression was high in human A549/DDP cell lines. Compared with the control and negative control groups, the mRNA and protein expressions of PI3K, Akt, mTOR, and bcl-2 decreased, whereas the mRNA and protein expression of bax and the sensitivity of cells to DDP significantly increased. Cell proliferation, migration, and invasion abilities decreased in the si-ROR and Wortmannin groups. In comparison with control and negative control groups, the mRNA and protein expressions of PI3K, Akt, mTOR, and bcl-2 increased, whereas the mRNA and protein expressions of bax decreased, the sensitivity of cells to DDP significantly increased, and cell proliferation, migration, and invasion abilities decreased in the ROR over-expression group. For nude mice in tumor-formation assay, compared with control and negative control groups, the tumor weight was found to be lighter (1.03 ± 0.15) g, the protein expressions of PI3K, Akt, mTOR, and bcl-2 decreased, and the protein expression of bax increased in the si-ROR group. Long non-coding RNA ROR may affect the sensitivity of lung adenocarcinoma cells to DDP by targeting PI3K/Akt/mTOR signaling pathway.

Mitochondria, inherited maternally, are energy metabolism organelles that generate most of the chemical energy needed to power cellular various biochemical reactions. Deciphering mitochondrial genome (mitogenome) is important for elucidating vital activities of species. The complete chloroplast (cp) and nuclear genome sequences of Populus simonii ( P. simonii ) have been reported, but there has been little progress in its mitogenome. Here, we assemble the complete P. simonii mitogenome into three circular-mapping molecules (lengths 312.5, 283, and 186 kb) with the total length of 781.5 kb. All three molecules of the P. simonii mitogenome had protein-coding capability. Whole-genome alignment analyses of four Populus species revealed the fission of poplar mitogenome in P. simonii . Comparative repeat analyses of four Populus mitogenomes showed that there were no repeats longer than 350 bp in Populus mitogenomes, contributing to the stability of genome sizes and gene contents in the genus Populus . As the first reported multi-circular mitogenome in Populus , this study of P. simonii mitogenome are imperative for better elucidating their biological functions, replication and recombination mechanisms, and their unique evolutionary trajectories in Populus .

Background As a perennial crop, oil-Camellia possesses a long domestication history and produces high-quality seed oil that is beneficial to human health. Camellia oleifera Abel. is a sister species to the tea plant, which is extensively cultivated for edible oil production. However, the molecular mechanism of the domestication of oil-Camellia is still limited due to the lack of sufficient genomic information. Results To elucidate the genetic and genomic basis of evolution and domestication, here we report a chromosome-scale reference genome of wild oil-Camellia (2.95 Gb), together with transcriptome sequencing data of 221 cultivars. The oil-Camellia genome, assembled by an integrative approach of multiple sequencing technologies, consists of a large proportion of repetitive elements (76.1%) and high heterozygosity (2.52%). We construct a genetic map of high-density corrected markers by sequencing the controlled-pollination hybrids. Genome-wide association studies reveal a subset of artificially selected genes that are involved in the oil biosynthesis and phytohormone pathways. Particularly, we identify the elite alleles of genes encoding sugar-dependent triacylglycerol lipase 1 , β-ketoacyl-acyl carrier protein synthase III , and stearoyl-acyl carrier protein desaturases ; these alleles play important roles in enhancing the yield and quality of seed oil during oil-Camellia domestication. Conclusions We generate a chromosome-scale reference genome for oil-Camellia plants and demonstrate that the artificial selection of elite alleles of genes involved in oil biosynthesis contributes to oil-Camellia domestication.

The crosstalk between macrophages and tubular epithelial cells (TECs) actively regulates the progression of renal fibrosis. In the present study, we revealed the significance of circular RNA ACTR2 (circACTR2) in regulating macrophage inflammation, epithelial–mesenchymal transition (EMT) of TECs, and the development of renal fibrosis. Our results showed UUO-induced renal fibrosis was associated with increased inflammation and EMT, hypertrophy of contralateral kidney, up-regulations of circACTR2 and NLRP3, and the down-regulation of miR-561. CircACTR2 sufficiently and essentially promoted the activation of NLRP3 inflammasome, pyroptosis, and inflammation in macrophages, and through paracrine effect, stimulated EMT and fibrosis of TECs. Mechanistically, circACTR2 sponged miR-561 and up-regulated NLRP3 expression level to induce the secretion of IL-1β. In TECs, IL-1β induced renal fibrosis via up-regulating fascin-1. Knocking down circACTR2 or elevating miR-561 potently alleviated renal fibrosis in vivo . In summary, circACTR2, by sponging miR-561, activated NLRP3 inflammasome, promoted macrophage inflammation, and stimulated macrophage-induced EMT and fibrosis of TECs. Knocking down circACTR2 and overexpressing miR-561 may, thus, benefit the treatment of renal fibrosis. Graphical abstract

Long non-coding RNAs (lncRNAs) have been widely studied for their important biological significance. In general, we need to distinguish them from protein coding RNAs (pcRNAs) with similar functions. Based on various strategies, algorithms and tools have been designed and developed to train and validate such classification capabilities. However, many of them lack certain scalability, versatility, and rely heavily on genome annotation. In this paper, we design a convenient and biologically meaningful classification tool \"Prelnc2\" using multi-scale position and frequency information of wavelet transform spectrum and generalizes the frequency statistics method. Finally, we used the extracted features and auxiliary features together to train the model and verify it with test data. PreLnc2 achieved 93.2% accuracy for animal and plant transcripts, outperforming PreLnc by 2.1% improvement and our method provides an effective alternative to the prediction of lncRNAs.

Point cloud analysis is integral to numerous applications, including mapping and autonomous driving. However, the unstructured and disordered nature of point clouds presents significant challenges for feature extraction. While both local and non-local features are essential for effective 3D point cloud analysis, existing methods often fail to seamlessly integrate these complementary features. To address this limitation, we propose the Local-Non-Local Complementary Learning Network (LNLCL-Net), a novel framework that enhances feature extraction and representation. Leveraging partial convolution, LNLCL-Net divides the feature map into distinct local and non-local components. Local features are modeled through relative positional relationships, while non-local features capture absolute positional information. A Complementary Interactive Attention module is introduced to enable adaptive integration of these features, enriching their complementary relationship. Extensive experiments on benchmark datasets, including ModelNet40, ScanObjectNN, and ShapeNet Part, demonstrate the superiority of our approach in both quantitative and qualitative metrics, achieving state-of-the-art performance in classification and segmentation tasks.

Background Neoadjuvant chemotherapy is a promising treatment option for potential resectable gastric cancer, but patients’ responses vary. We aimed to develop and validate a radiomics score (rad_score) to predict treatment response to neoadjuvant chemotherapy and to investigate its efficacy in survival stratification. Methods A total of 106 patients with neoadjuvant chemotherapy before gastrectomy were included (training cohort: n  = 74; validation cohort: n  = 32). Radiomics features were extracted from the pre-treatment portal venous-phase CT. After feature reduction, a rad_score was established by Randomised Tree algorithm. A rad_clinical_score was constructed by integrating the rad_score with clinical variables, so was a clinical score by clinical variables only. The three scores were validated regarding their discrimination and clinical usefulness. The patients were stratified into two groups according to the score thresholds (updated with post-operative clinical variables), and their survivals were compared. Results In the validation cohort, the rad_score demonstrated a good predicting performance in treatment response to the neoadjuvant chemotherapy (AUC [95% CI] =0.82 [0.67, 0.98]), which was better than the clinical score (based on pre-operative clinical variables) without significant difference (0.62 [0.42, 0.83], P  = 0.09). The rad_clinical_score could not further improve the performance of the rad_score (0.70 [0.51, 0.88], P  = 0.16). Based on the thresholds of these scores, the high-score groups all achieved better survivals than the low-score groups in the whole cohort (all P  < 0.001). Conclusion The rad_score that we developed was effective in predicting treatment response to neoadjuvant chemotherapy and in stratifying patients with gastric cancer into different survival groups. Our proposed strategy is useful for individualised treatment planning.

Agriculture has a crucial impact on the economic, ecological, and social development of the world. More rapid and precise prevention and control work, especially for accurate classification and detection, is required due to the increasing severity of agricultural pests and diseases. However, the results of the image classification and detection are unsatisfactory because of the limitation of image data volume acquisition and the wide range of influencing factors of pests and diseases. In order to solve these problems, the vision transformer (ViT) model is improved, and a multi-scale and multi-factor ViT attention model (SFA-ViT) is proposed in this paper. Data augmentation considering multiple influencing factors is implemented in SFA-ViT to mitigate the impact of insufficient experimental data. Meanwhile, SFA-ViT optimizes the ViT model from a multi-scale perspective, and encourages the model to understand more features, from fine-grained to coarse-grained, during the classification task. Further, the detection model based on the self-attention mechanism of the multi-scale ViT is constructed to achieve the accurate localization of the pest and disease. Finally, experimental validation of the model, based on the IP102 and Plant Village dataset, is carried out. The results indicate that the various components of SFA-ViT effectively enhance the final classification and detection outcomes, and our model outperforms the current models significantly.