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Quantum chemical (QC) property prediction is crucial for computational materials and drug design, but relies on expensive electronic structure calculations like density functional theory (DFT). Recent deep learning methods accelerate this process using 1D SMILES or 2D graphs as inputs but struggle to achieve high accuracy as most QC properties depend on refined 3D molecular equilibrium conformations. We introduce Uni-Mol+, a deep learning approach that leverages 3D conformations for accurate QC property prediction. Uni-Mol+ first generates a raw 3D conformation using RDKit then iteratively refines it towards DFT equilibrium conformation using neural networks, which is finally used to predict the QC properties. To effectively learn this conformation update process, we introduce a two-track Transformer model backbone and a novel training approach. Our benchmarking results demonstrate that the proposed Uni-Mol+ significantly improves the accuracy of QC property prediction in various datasets. Quantum chemical (QC) property prediction is crucial in computational chemistry. Here, the authors introduce Uni-Mol+, a deep model that uses iterative updates of 3D molecular conformations to improves the accuracy of QC property prediction.

There is increasing interest in how sleep affects cognitive function; however, the combined impact of naps and night-time sleep on different cognitive domains is still not well understood. This study investigates the relationship between naps, night-time sleep, and cognitive function over time among middle-aged and older adults in China, as well as how this relationship may differ between rural and urban residents. A total of 2,938 community residents aged 45 and older were selected from the China Health and Retirement Longitudinal Study (CHARLS, conducted in 2013, 2015, and 2018). The study examined the relationship between napping, night-time sleep, and cognitive function using fixed-effects analysis over a period of five years. Sleeping 6-8 hours/ night and napping for less than 30 minutes/ day were associated with better cognitive function (β = 0.383, 95% CI: 0.198, 0.567) and memory (β = 0.304, 95% CI: 0.155, 0.451) across the entire sample. In contrast, sleeping more than 8 hours/ night and napping more than 90 minutes/ day were associated with poor mental status. Specifically, sleeping 6-8 hours/ night was significantly associated with better cognitive function (β = 0.501, 95% CI: 0.252, 0.750) and memory (β = 0.372, 95% CI: 0.173, 0.572) in rural respondents. Sleeping more than 8 hours/ night was associated with poorer mental status among urban respondents (β = -0.291, 95% CI: -0.551, -0.032). Rural respondents who napped less than 90 minutes/ day had improved cognitive function. Napping for more than 90 minutes/ day was significantly correlated with cognitive function and mental status, which was primarily observed among urban respondents. Considerable differences were observed between rural and urban areas regarding the relationship between napping, night-time sleep, and cognitive function. When designing interventions to enhance cognitive function, it's essential to take into account cultural context, geographical factors, and individual differences.

CRISPR-Cas is a powerful genome editing technology and has a great potential for in vivo gene therapy. Successful translational application of CRISPR-Cas to biomedicine still faces many safety concerns, including off-target side effect, cell fitness problem after CRISPR-Cas treatment, and on-target genome editing side effect in undesired tissues. To solve these issues, it is needed to design sgRNA with high cell-specific efficacy and specificity. Existing single-guide RNA (sgRNA) design tools mainly depend on a sgRNA sequence and the local information of the targeted genome, thus are not sufficient to account for the difference in the cellular response of the same gene in different cell types. To incorporate cell-specific information into the sgRNA design, we develop novel interpretable machine learning models, which integrate features learned from advanced transformer-based deep neural network with cell-specific gene property derived from biological network and gene expression profile, for the prediction of CRISPR-Cas9 and CRISPR-Cas12a efficacy and specificity. In benchmark studies, our models significantly outperform state-of-the-art algorithms. Furthermore, we find that the network-based gene property is critical for the prediction of cell-specific post-treatment cellular response. Our results suggest that the design of efficient and safe CRISPR-Cas needs to consider cell-specific information of genes. Our findings may bolster developing more accurate predictive models of CRISPR-Cas across a broad spectrum of biological conditions as well as provide new insight into developing efficient and safe CRISPR-based gene therapy.

Background Previous studies indicated that obesity would accelerate frailty progression. However, obesity is heterogeneous by different metabolic status. The associations of metabolic heterogeneity of obesity with frailty progression remain unclear. Methods A total of 6730 participants from the China Health and Retirement Longitudinal Study (CHARLS) and 4713 from the English Longitudinal Study of Ageing (ELSA) were included at baseline. Metabolic heterogeneity of obesity was evaluated based on four obesity and metabolic phenotypes as metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obesity (MHOO), and metabolically unhealthy overweight/obesity (MUOO). Frailty status was assessed by the frailty index (FI) ranging from 0 to 100 and frailty was defined as FI ≥ 25. Linear mixed‐effect models were used to analyse the associations of metabolic heterogeneity of obesity with frailty progression. Results In the CHARLS, MUOO and MUNW presented the accelerated FI progression with additional annual increases of 0.284 (95% CI: 0.155 to 0.413, P < 0.001) and 0.169 (95% CI: 0.035 to 0.303, P = 0.013) as compared with MHNW. MHOO presented no accelerated FI progression (β: ‐0.011, 95% CI: −0.196 to 0.173, P = 0.904) as compared with MHNW. In the ELSA, the accelerated FI progression was marginally significant for MUOO (β: 0.103, 95% CI: −0.005 to 0.210, P = 0.061) and MUNW (β: 0.157, 95% CI: −0.011 to 0.324, P = 0.066), but not for MHOO (β: ‐0.047, 95% CI: −0.157 to 0.062, P = 0.396) in comparison with MHNW. The associations of MUOO and MUNW with the accelerated FI progression were stronger after excluding the baseline frail participants in both cohorts. The metabolic status changed over time. When compared with stable MHNW, participants who changed from MHNW to MUNW presented the accelerated FI progression with additional annual increases of 0.356 (95% CI: 0.113 to 0.599, P = 0.004) and 0.255 (95% CI: 0.033 to 0.477, P = 0.024) in the CHARLS and ELSA, respectively. The accelerated FI progression was also found in MHOO participants who transitioned to MUOO (CHARLS, β: 0.358, 95% CI: 0.053 to 0.663, P = 0.022; ELSA, β: 0.210, 95% CI: 0.049 to 0.370, P = 0.011). Conclusions Metabolically unhealthy overweight/obesity and normal weight, but not metabolically healthy overweight/obesity, accelerated frailty progression as compared with metabolically healthy normal weight. Regardless of obesity status, transitions from healthy metabolic status to unhealthy metabolic status accelerated frailty progression as compared with stable metabolically healthy normal weight. Our findings highlight the important role of metabolic status in frailty progression and recommend the stratified management of obesity based on metabolic status.

Background Although cisplatin-based chemotherapy has been used as the first-line treatment for ovarian cancer (OC), tumor cells develop resistance to cisplatin during treatment, causing poor prognosis in OC patients. Studies have demonstrated that overactivation of the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) pathway is involved in tumor chemoresistance and that overexpression of microRNA-497 (miR497) may overcome OC chemotherapy resistance by inhibiting the mTOR pathway. However, the low transcriptional efficiency and unstable chemical properties of miR497 limit its clinical application. Additionally, triptolide (TP) was confirmed to possess a superior killing effect on cisplatin-resistant cell lines, partially through inhibiting the mTOR pathway. Even so, the clinical applications of TP are restricted by serious systemic toxicity and weak water solubility. Results Herein, whether the combined application of miR497 and TP could further overcome OC chemoresistance by synergically suppressing the mTOR signaling pathway was investigated. Bioinspired hybrid nanoparticles formed by the fusion of CD47-expressing tumor exosomes and cRGD-modified liposomes (miR497/TP-HENPs) were prepared to codeliver miR497 and TP. In vitro results indicated that the nanoparticles were efficiently taken up by tumor cells, thus significantly enhancing tumor cell apoptosis. Similarly, the hybrid nanoparticles were effectively enriched in the tumor areas and exerted significant anticancer activity without any negative effects in vivo. Mechanistically, they promoted dephosphorylation of the overactivated PI3K/AKT/mTOR signaling pathway, boosted reactive oxygen species (ROS) generation and upregulated the polarization of macrophages from M2 to M1 macrophages. Conclusion Overall, our findings may provide a translational strategy to overcome cisplatin-resistant OC and offer a potential solution for the treatment of other cisplatin-resistant tumors. Graphical Abstract

Anthocyanins spatiotemporally accumulate in certain tissues of particular species in the banana plant, and MYB transcription factors (TFs) serve as their primary regulators. However, the precise regulatory mechanism in banana remains to be determined. Here, we report the identification and characterization of MaMYB4 , an R2R3-MYB repressor TF, characterized by the presence of EAR (ethylene-responsive element binding factor–associated amphiphilic repression) and TLLLFR motifs. MaMYB4 expression was induced by the accumulation of anthocyanins. Transgenic banana plants overexpressing MaMYB4 displayed a significant reduction in anthocyanin compared to wild type. Consistent with the above results, metabolome results showed that there was a decrease in all three identified cyanidins and one delphinidin, the main anthocyanins that determine the color of banana leaves, whereas both transcriptome and reverse transcription–quantitative polymerase chain reaction analysis showed that many key anthocyanin synthesis structural genes and TF regulators were downregulated in MaMYB4 overexpressors. Furthermore, dual-luciferase assays showed that MaMYB4 was able to bind to the CHS , ANS , DFR , and bHLH promoters, leading to inhibition of their expression. Yeast two-hybrid analysis verified that MaMYB4 did not interact with bHLH, which ruled out the possibility that MaMYB4 could be incorporated into the MYB-bHLH-WD40 complex. Our results indicated that MaMYB4 acts as a repressor of anthocyanin biosynthesis in banana, likely due to a two-level repression mechanism that consists of reduced expression of anthocyanin synthesis structural genes and the parallel downregulation of bHLH to interfere with the proper assembly of the MYB-bHLH-WD40 activation complex. To the best of our knowledge, this is the first MYB TF that regulates anthocyanin synthesis that was identified by genetic methods in bananas, which will be helpful for manipulating anthocyanin coloration in banana programs in the future.

Wheat growth, development, and grain yield are affected by global climate warming. The general consensus is that global warming shortens the overall length of wheat growing period and reduces global wheat yield. Here, focusing on China, the largest wheat producer in the world, we show that warming increases wheat yield in most winter wheat growing regions in China. We collated data from field experiments under stress-free conditions and artificial warming from 12 locations over China to assess the impact of warming on wheat yield. The data cover 14 wheat cultivars, 27 site-years, and a range of growing season temperatures from 7.5 °C to 17.2 °C. Our results indicate that warming up to +3 °C increased winter wheat yield by 5.8% per °C (change rate of yield/average of yield), while reduced spring wheat yield by 16.1% per °C. Although artificial warming reduced the total growth duration, warming-induced longer early developmental phases and grain filling duration, and subsequently more and larger grains contributed to the yield increase of winter wheat. The yield decline of spring wheat was due to the opposite changes of those key processes in response to temperature rise.

SII and SIRI are two novel systemic inflammation indexes that were suggested in predicting poor outcomes in cancers. However, no studies have examined their effect on cardiovascular diseases (CVDs) and all-cause mortality. Thus, this study aims to investigate associations between SII, SIRI, and the risks for CVDs and all-cause mortality. A total of 85,154 participants from the Kailuan cohort were included and followed up for incidents of CVDs (including MI, stroke) and all-cause death for 10 years. Multiple Cox regression was used to calculate the adjusted hazard ratios (HRs). During the follow-up period, 4262 stroke events, 1233 MI events, and 7225 all-cause deaths were identified, respectively. Compared with the lowest quantile (Q1) of SII or SIRI, after adjusted for most cardiovascular risk factors, both indexes showed positive associations with the risk for stroke (adjusted HRs in Q4 were 1.264 (95% CI: 1.157,1.382) for SII, 1.194 (95% CI: 1.087,1.313) for SIRI), and all-cause death (adjusted HRs in Q4 were 1.246 (95% CI: 1.165,1.331) for SII, 1.393 (95% CI: 1.296,1.498) for SIRI). Additionally, higher SII and SIRI are also associated with increased risk of hemorrhagic stroke and ischemic stroke. Higher SIRI but not SII exhibited a higher MI risk, the adjusted HR in Q4 was 1.204 (1.013,1.431). The significant association remained after additional adjustment for CRP. Subgroup analysis and sensitivity analysis displayed consistent results except for SIRI with MI, where the association did not arrive at significance in subjects aged ≥60. Elevated SII and SIRI increased the risk of stroke, two stroke subtypes, and all-cause death. Higher SIRI, but not SII associated with increased MI incidence, and the association of SIRI was only significant in subjects aged <60.

Within the sustainability framework, technological innovation’s impact is acknowledged. However, the environmental implications of institutional innovation, a crucial component of the innovation system, remain unclear, necessitating further research. This paper focuses on financial innovation as a representative of institutional innovation, exploring its relationship with carbon emissions. Utilizing panel data from 30 Chinese provinces spanning 2011 to 2022, we establish a spatial Durbin model and a mediating effects model to delve into the intricate relationships among financial innovation, industrial structure upgrading, and carbon emissions. Our findings reveal that: (1) Financial innovation significantly contributes to the upgrading of industrial structures both locally and in neighboring regions; (2) Both financial innovation and industrial structure upgrading effectively mitigate carbon emissions, with the latter playing a mediating role; (3) All three studied factors exhibit spatial clustering effects; (4) The suppressive effect of financial innovation on carbon emissions exhibits a notable spatial spillover. Compared to recent studies, this work innovatively explores the mediating impact mechanism of financial innovation suppressing carbon emissions, particularly demonstrating the spatial spillover characteristics of the mediating effect among the three variables. As China is a major carbon emitter and emerging economy, these insights offer valuable insights for global carbon governance.