Direct single-molecule dynamic detection of chemical reactions.

Single-molecule detection can reveal time trajectories and reaction pathways of individual intermediates/transition states in chemical reactions and biological processes, which is of fundamental importance to elucidate their intrinsic mechanisms. We present a reliable, label-free single-molecule approach that allows us to directly explore the dynamic process of basic chemical reactions at the single-event level by using stable graphene-molecule single-molecule junctions. These junctions are constructed by covalently connecting a single molecule with a 9-fluorenone center to nanogapped graphene electrodes. For the first time, real-time single-molecule electrical measurements unambiguously show reproducible large-amplitude two-level fluctuations that are highly dependent on solvent environments in a nucleophilic addition reaction of hydroxylamine to a carbonyl group. Both theoretical simulations and ensemble experiments prove that this observation originates from the reversible transition between the reactant and a new intermediate state within a time scale of a few microseconds. These investigations open up a new route that is able to be immediately applied to probe fast single-molecule physics or biophysics with high time resolution, making an important contribution to broad fields beyond reaction chemistry

Effects of different cooling methods on the carbon footprint of cooked rice

peer-reviewedGlobal warming has become a serious problem facing the international community. All countries strive to reduce greenhouse gas (GHG) emissions. The food system produces a large amount of GHGs, and thus study of the carbon footprint (CF) in the food industry has attracted the attention of researchers. Based on the lifecycle assessment (LCA) method, the present study calculated CFs of cooling of cooked rice, as a unit operation under different operational conditions. The results showed that the carbon footprints for cooling 200 g cooked rice were 54.36 ± 1.07 gCO2eq for refrigerator cooling at 0 °C, 66.05 ± 2.00 g CO2eq for refrigerator cooling at 8 °C, 741.55 ± 27.26 g CO2eq for vacuum cooling, 1914.10 ± 141.24 g CO2eq for air blast cooling at 0 °C, 2463.61 ± 221.21 g CO2eq for air blast cooling at 3 °C, and 3916.54 ± 202.28 g CO2eq for air blast cooling at 8 °C. In addition, the CF for the cooling process was positively correlated with the output power of equipment and the cooling time. The carbon emissions arising from electricity consumption contributed to most of the CF for the cooling process. Sensitivity analysis of the parameters for the CF for the cooling process revealed that the CF of cooling process was stable for the applied equipment emission factor, but sensitive to the efficiency of electricity use and the extent of load. Improving the efficiency of electricity use and increasing cooling load could reduce the final CF of a product

Revealing quantum mechanical effects in enzyme catalysis with large-scale electronic structure simulation

Enzymes have evolved to facilitate challenging reactions at ambient conditions with specificity seldom matched by other catalysts. Computational modeling provides valuable insight into catalytic mechanism, and the large size of enzymes mandates multi-scale, quantum mechanical-molecular mechanical (QM/MM) simulations. Although QM/MM plays an essential role in balancing simulation cost to enable sampling with the full QM treatment needed to understand electronic structure in enzyme active sites, the relative importance of these two strategies for understanding enzyme mechanism is not well known. We explore challenges in QM/MM for studying the reactivity and stability of three diverse enzymes: i) Mg[supercript 2+]-dependent catechol O-methyltransferase (COMT), ii) radical enzyme choline trimethylamine lyase (CutC), and iii) DNA methyltransferase (DNMT1), which has structural Zn[superscript 2+] binding sites. In COMT, strong non-covalent interactions lead to long range coupling of electronic structure properties across the active site, but the more isolated nature of the metallocofactor in DNMT1 leads to faster convergence of some properties. We quantify these effects in COMT by computing covariance matrices of by-residue electronic structure properties during dynamics and along the reaction coordinate. In CutC, we observe spontaneous bond cleavage following initiation events, highlighting the importance of sampling and dynamics. We use electronic structure analysis to quantify the relative importance of CHO and OHO non-covalent interactions in imparting reactivity. These three diverse cases enable us to provide some general recommendations regarding QM/MM simulation of enzymes.NEC CorporationNational Institute of Environmental Health Sciences (Grant P30-ES002109)Burroughs Wellcome Fund (Career Award at the Scientific Interface)United States. Department of Energy (Computational Science Graduate Fellowship

New insights into the cortex-to-stele ratio show it to effectively indicate inter- and intraspecific function in the absorptive roots of temperate trees

The cortex-to-stele ratio (CSR), as it increases from thin- to thick-root species in angiosperms, is theorised to effectively reflect a compensation for the ‘lag’ of absorption behind transportation. But it is still not known if this compensatory effect exists in gymnosperm species or governs root structure and function within species. Here, anatomical, morphological, and tissue chemical traits of absorptive roots were measured in three temperate angiosperm and three gymnosperm species. Differences in the CSR and the above functional traits, as well as their intraspecific associations, were analyzed and then compared between angiosperms and gymnosperms. At the intraspecific level, the CSR decreased with increasing root order for all species. The expected functional indication of the CSR was consistent with decreases in specific root length (SRL) and N concentration and increases in the C to N ratio (C:N ratio) and the number of and total cross-sectional area of conduits with increasing root order, demonstrating that the CSR indicates the strength of absorption and transportation at the intraspecific level, but intraspecific changes are due to root development rather than the compensatory effect. These trends resulted in significant intraspecific associations between the CSR and SRL (R2 = 0.36 ~ 0.80), N concentration (R2 = 0.48 ~ 0.93), the C:N ratio (R2 = 0.47 ~ 0.91), and the number of (R2 = 0.21 ~ 0.78) and total cross-sectional area (R2 = 0.29 ~ 0.72) of conduits in each species (p< 0.05). The overall mean CSR of absorptive roots in angiosperms was four times greater than in gymnosperms, and in angiosperms, the CSR was significantly higher in thick- than in thin-rooted species, whereas in gymnosperms, the interspecific differences were not significant (p > 0.05). This suggests that the compensation for the lag of absorption via cortex thickness regulation was stronger in three angiosperm species than in three gymnosperm species. In addition, there was poor concordance between angiosperms and gymnosperms in the relationships between CSRs and anatomical, morphological, and tissue chemical traits. However, these gymnosperm species show a more stable intraspecific functional association compared to three angiosperm species. In general, absorptive root CSRs could manifest complex strategies in resource acquisition for trees at both intra- and interspecific levels

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Objectives: To develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN. Methods: A total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model. Results: The best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density ($p$ = 0.015), circumference ($p$ = 0.009), circularity ($p$ = 0.010), and orientation ($p$ = 0.012). Conclusion: Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC. The codes and dataset are available at https://github.com/bupt-ai-cz/BALNMPComment: Update Table 1 and corresponding description

LncRNAs: the bridge linking RNA and colorectal cancer.

Long noncoding RNAs (lncRNAs) are transcribed by genomic regions (exceeding 200 nucleotides in length) that do not encode proteins. While the exquisite regulation of lncRNA transcription can provide signals of malignant transformation, lncRNAs control pleiotropic cancer phenotypes through interactions with other cellular molecules including DNA, protein, and RNA. Recent studies have demonstrated that dysregulation of lncRNAs is influential in proliferation, angiogenesis, metastasis, invasion, apoptosis, stemness, and genome instability in colorectal cancer (CRC), with consequent clinical implications. In this review, we explicate the roles of different lncRNAs in CRC, and the potential implications for their clinical application

Molecular characterization of immunogenic cell death indicates prognosis and tumor microenvironment infiltration in osteosarcoma

IntroductionOsteosarcoma (OS) is a highly aggressive bone malignancy with a poor prognosis, mainly in children and adolescents. Immunogenic cell death (ICD) is classified as a type of programmed cell death associated with the tumor immune microenvironment, prognosis, and immunotherapy. However, the feature of the ICD molecular subtype and the related tumor microenvironment (TME) and immune cell infiltration has not been carefully investigated in OS.MethodsThe ICD-related genes were extracted from previous studies, and the RNA expression profiles and corresponding data of OS were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus database. The ICD-related molecular subtypes were classed by the "ConsensusclusterPlus" package and the construction of ICD-related signatures through univariate regression analysis. ROC curves, independent analysis, and internal validation were used to evaluate signature performance. Moreover, a series of bioinformatic analyses were used for Immunotherapy efficacy, tumor immune microenvironments, and chemotherapeutic drug sensitivity between the high- and low-risk groups.ResultsHerein, we identified two ICD-related subtypes and found significant heterogeneity in clinical prognosis, TME, and immune response signaling among distinct ICD subtypes. Subsequently, a novel ICD-related prognostic signature was developed to determine its predictive performance in OS. Also, a highly accurate nomogram was then constructed to improve the clinical applicability of the novel ICD-related signature. Furthermore, we observed significant correlations between ICD risk score and TME, immunotherapy response, and chemotherapeutic drug sensitivity. Notably, the in vitro experiments further verified that high GALNT14 expression is closely associated with poor prognosis and malignant progress of OS.DiscussionHence, we identified and validated that the novel ICD-related signature could serve as a promising biomarker for the OS's prognosis, chemotherapy, and immunotherapy response prediction, providing guidance for personalized and accurate immunotherapy strategies for OS

Code of CIE-GUI.zip

The Code of CIE-GUI includes M.file, GUI interface, Fig of CIE1931xy and CIE1976UCS