Comprehensive pan-cancer analysis: essential role of ABCB family genes in cancer.

Background: The adenosine triphosphate-binding-cassette (ABC) transporter orchestrates the transmembrane transport of diverse substrates with the aid of ATP as an energy source. ABC transporter constitutes a widespread superfamily of transporters prominently present on the cellular membrane of organisms. Advancements in understanding have unveiled additional roles beyond mere intracellular or extracellular transport functions for the ABC protein family, encompassing involvement in DNA repair, protein translation, and gene expression regulation. Yet its role in tumors is still unknown. Methods: This study drew support from multiple databases, including Gene Expression Omnibus (GEO), European Genome-phenome Archive (EGA), The Cancer Genome Atlas (TCGA), and employed multidimensional bioinformatics analyses, incorporating online databases and the R-project. Through a comprehensive analysis, we seek to discern transcriptional-level disparities among genes and their consequential impacts on prognosis, tumor microenvironment (TME), stemness score, immune subtypes, clinical characteristics, and drug sensitivity across human cancers. Results: ABC transporter subfamily B (ABCB) family genes exhibited heightened expression across diverse tumors, demonstrating a significant correlation with overall prognosis in pan-cancer contexts. Notably, gene expression levels manifested substantial associations with TME, stemness score, immune subtypes, clinical characteristics, and drug sensitivity in specific cancers, including kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), and pancreatic adenocarcinoma (PAAD). Within this subset, transporter associated with antigen processing 1 (TAP1), TAP2, and ABCB6 emerged as noteworthy oncogenes. Conclusions: The outcomes of this study contribute to a comprehensive understanding of the implications of ABCB family genes in tumor progression, offering insights into potential therapeutic targets for cancer. Notably, the identification of ABCB6 as a significant oncogene suggests promising avenues for targeted therapies in KIRP, LIHC, and PAAD.

Combination of DCE-MRI and NME-DWI via Deep Neural Network for Predicting Breast Cancer Molecular Subtypes.

BACKGROUND: To explore whether the combination of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and nonmono-exponential (NME) model-based diffusion-weighted imaging (DWI) via deep neural network (DNN) can improve the prediction of breast cancer molecular subtypes compared to either imaging technique used alone. PATIENTS AND METHODS: This prospective study examined 480 breast cancers in 475 patients undergoing DCE-MRI and NME-DWI at 3.0 T. Breast cancers were classified as follows: human epidermal growth factor receptor 2 enriched (HER2-enriched), luminal A, luminal B (HER2-), luminal B (HER2+), and triple-negative subtypes. A total of 20% cases were withheld as an independent test dataset, and the remaining cases were used to train DNN with an 80% to 20% training-validation split and 5-fold cross-validation. The diagnostic accuracies of DNN in 5-way subtype classification between the DCE-MRI, NME-DWI, and their combined multiparametric-MRI datasets were compared using analysis of variance with least significant difference posthoc test. Areas under the receiver-operating characteristic curves were calculated to assess the performances of DNN in binary subtype classification between the 3 datasets. RESULTS: The 5-way classification accuracies of DNN on both DCE-MRI (0.71) and NME-DWI (0.64) were significantly lower (P < .05) than on multiparametric-MRI (0.76), while on DCE-MRI was significantly higher (P < .05) than on NME-DWI. The comparative results of binary classification between the 3 datasets were consistent with the 5-way classification. CONCLUSION: The combination of DCE-MRI and NME-DWI via DNN achieved a significant improvement in breast cancer molecular subtype prediction compared to either imaging technique used alone. Additionally, DCE-MRI outperformed NME-DWI in differentiating subtypes.

Dbl family RhoGEFs in cancer: different roles and targeting strategies.

Small Ras homologous guanosine triphosphatase (Rho GTPase) family proteins are highly associated with tumorigenesis and development. As intrinsic exchange activity regulators of Rho GTPases, Rho guanine nucleotide exchange factors (RhoGEFs) have been demonstrated to be closely involved in tumor development and received increasing attention. They mainly contain two families: the diffuse B-cell lymphoma (Dbl) family and the dedicator of cytokinesis (Dock) family. More and more emphasis has been paid to the Dbl family members for their abnormally high expression in various cancers and their correlation to poor prognosis. In this review, the common and distinctive structures of Dbl family members are discussed, and their roles in cancer are summarized with a focus on Ect2, Tiam1/2, P-Rex1/2, Vav1/2/3, Trio, KALRN, and LARG. Significantly, the strategies targeting Dbl family RhoGEFs are highlighted as novel therapeutic opportunities for cancer.

Chiral Bis-phosphate Macrocycles for Catalytic, Efficient, and Enantioselective Electrophilic Fluorination.

Incorporation of privileged catalytic scaffolds into a macrocyclic skeleton represents an attractive strategy to furnish supramolecular catalysis systems with enzyme-mimetic cavity and multi-site cooperation. Herein we reported the synthesis, structure, binding properties and catalytic application of a series of chiral bis-phosphate macrocycles toward the challenging asymmetric electrophilic fluorination. With a large, integrated chiral cavity and two cooperative phosphate sites, these macrocycles exhibited good inclusion toward 1,4-diazabicyclo[2.2.2]octane (DABCO) dicationic ammoniums through complementary ion-pair and C-H⋅⋅⋅O interactions, as confirmed by crystallographic and solution binding studies. In fluorocyclization of tryptamines with Selectfluor reagent which has a similar DABCO-based dicationic structure, only 2 mol% macrocycle catalyst afforded the desired pyrroloindoline products in moderate yields and up to 91 % ee. For comparison, the acyclic mono-phosphate analogue gave obviously lower reactivity and enantioselectivity (<20 % ee), suggesting a remarkable macrocyclic effect. The high catalytic efficiency and superior stereocontrol were ascribed to the tight ion-pair binding and cavity-directed noncovalent interaction cooperation.

Benefits of Hypothermic Oxygenated Perfusion Versus Static Cold Storage in Liver Transplant: A Comprehensive Systematic Review and Meta-analysis.

Background: The magnitude of potential benefits that hypothermic oxygenated perfusion (HOPE) may provide for liver transplantation (LT) patients compared to static cold storage (SCS) remains uncertain. In this systematic review and meta-analysis, we aimed to investigate the therapeutic effect that HOPE can offer LT recipients relative to SCS by synthesizing available evidence. Methods: A literature search was conducted in Embase, Medline, Web of Science, and the Cochrane database up to 1 June, 2023. The included studies were pooled for meta-analysis to synthesize their findings. Subgroup analysis was performed to investigate potential differences between HOPE and SCS for specific subgroups. Results: A total of 11 studies comprising 1765 patients were included. Compared with SCS, HOPE was associated with a significant reduction in the incidence of early allograft dysfunction (EAD) (OR: 0.36, 95% CI: 0.26-0.50), as well as a noteworthy decrease in graft loss rate within one year (OR: 0.57, 95% CI: 0.33-0.97) and a lower occurrence of Clavien-Dindo grade IIIa or higher complications (OR: 0.62, 95% CI: 0.43-0.89). Subgroup analysis revealed that HOPE significantly reduced the one-year mortality rate, any biliary complications incidence, and acute rejection of transplanted liver rate in patients who received organs from donation after cardiac death (DCD). Conclusions: HOPE has demonstrated efficacy in reducing the incidence of EAD after LT and shows some potential in diminishing postoperative complications such as biliary complications and acute rejection. This ultimately leads to improved patient prognosis, particularly among those receiving DCD grafts.

MS-TCNet: An effective Transformer-CNN combined network using multi-scale feature learning for 3D medical image segmentation.

Medical image segmentation is a fundamental research problem in the field of medical image processing. Recently, the Transformer have achieved highly competitive performance in computer vision. Therefore, many methods combining Transformer with convolutional neural networks (CNNs) have emerged for segmenting medical images. However, these methods cannot effectively capture the multi-scale features in medical images, even though texture and contextual information embedded in the multi-scale features are extremely beneficial for segmentation. To alleviate this limitation, we propose a novel Transformer-CNN combined network using multi-scale feature learning for three-dimensional (3D) medical image segmentation, which is called MS-TCNet. The proposed model utilizes a shunted Transformer and CNN to construct an encoder and pyramid decoder, allowing six different scale levels of feature learning. It captures multi-scale features with refinement at each scale level. Additionally, we propose a novel lightweight multi-scale feature fusion (MSFF) module that can fully fuse the different-scale semantic features generated by the pyramid decoder for each segmentation class, resulting in a more accurate segmentation output. We conducted experiments on three widely used 3D medical image segmentation datasets. The experimental results indicated that our method outperformed state-of-the-art medical image segmentation methods, suggesting its effectiveness, robustness, and superiority. Meanwhile, our model has a smaller number of parameters and lower computational complexity than conventional 3D segmentation networks. The results confirmed that the model is capable of effective multi-scale feature learning and that the learned multi-scale features are useful for improving segmentation performance. We open-sourced our code, which can be found at https://github.com/AustinYuAo/MS-TCNet.

Mimicking the Shape and Function of the ClC Chloride Channel Selective Pore by Combining a Molecular Hourglass Shape with Anion-π Interactions.

ClC is the main family of natural chloride channel proteins that transport Cl- across the cell membrane with high selectivity. The chloride transport and selectivity are determined by the hourglass-shaped pore and the filter located in the central and narrow region of the pore. Artificial unimolecular channel that mimics both the shape and function of the ClC selective pore is attractive, because it could provide simple molecular model to probe the intriguing mechanism and structure-function relevance of ClC. Here we elaborated upon the concept of molecular hourglass plus anion-π interactions for this purpose. The concept was validated by experimental results of molecular hourglasses using shape-persistent 1,3-alternate tetraoxacalix[2]arene[2]triazine as the central macrocyclic skeleton to control the conductance and selectivity, and anion-π interactions as the driving force to facilitate the chloride dehydration and movement along the channel.

Chiral Benzene Triimide (BTI) Radical Anions for Probing the Interplay of Unpaired Electron Spin and Chirality.

Herein a series of chiral BTI radical anions bearing different chiral substituents were efficiently prepared by chemical reduction. X-ray crystallography revealed finely-tuned packing and helix assemblies of the radicals by the size of chiral substituents in crystalline state. In accordance with the crystalline-state packing, the powder ESR spectra indicate that 4 a- ⋅CoCp2 + and 4 c- ⋅CoCp2 + π-dimers exhibit thermally excited triplet states arising from strong spin-spin interactions, while discrete 4 b- ⋅CoCp2 + shows a broad doublet-state signal reflecting weak spin-spin interactions. The interplay between the unpaired electron spin and chiral substituents was studied by UV-Vis-NIR spectra, electronic circular dichroism (ECD) and TD DFT calculations. Different NIR absorptions of the radicals attributing to isolated SOMO→LUMO+1 (~889 nm) transitions were recorded. The emergence of Cotton effects (CEs) at the NIR region for 4 c- ⋅CoCp2 + radical enantiomers suggest the interplay between chirality and unpaired electron spin. The origin of the different circularly polarized light absorptions regarding SOMO derived transitions (around 880 nm) was attributed to chiral substitutes regulated electric and magnetic transition dipole moments of the unpaired electron participated transition.

Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity.

Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.

Reversing the ion transport selectivity through arm modification of an artificial molecular hourglass.

An arm modification strategy, by replacing relatively rigid, electron-deficient side arms with flexible ether chain arms and linking them onto a tetraoxacalix[2]arene[2]triazine skeleton, was utilized to design an artificial molecular hourglass. The planar bilayer experiments confirmed the unimolecular channel mechanism and suggested reversed ion selectivity from the previously reported anion selectivity to weak cation selectivity.

Comparison of different adjuvant therapy regimen efficacies in patients with high risk of recurrence after radical resection of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) has a high recurrence rate even after radical surgery. Postoperative adjuvant transhepatic arterial chemoembolization (PA-TACE), postoperative adjuvant hepatic arterial infusion chemotherapy (PA-HAIC), postoperative adjuvant radiotherapy (PA-RT), and postoperative adjuvant molecular targeted therapy have been demonstrated to be effective in reducing the postoperative recurrence rate. The present network meta-analysis was conducted to compare the effects of PA-TACE, PA-HAIC, PA-RT and postoperative adjuvant molecular targeted therapy on the overall survival (OS) and disease-free survival (DFS) in HCC patients after radical resection and to determine the optimal treatment strategy. METHODS: Network meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, Embase, Cochrane Library, and Web of Science were used to collect eligible studies up to December 25, 2022. Studies related to PA-TACE, PA-HAIC, and postoperative adjuvant molecular targeted therapy after radical HCC resection was included. The endpoints were OS and DFS, and the effect size was determined using hazard ratio with a 95% confidence interval. R software and "gemtc" package were employed to analyze the results. RESULTS: A total of 38 studies involving 7079 patients with HCC after radical resection were ultimately enrolled to be analyzed. Four postoperative adjuvant therapy measures and two oncology indicators were evaluated. In this study, OS-related investigations validated that PA-Sorafenib and PA-RT markedly enhanced the OS rates in patients after radical resection when compared to PA-TACE and PA-HAIC. However, statistical analysis revealed no significant difference between PA-Sorafenib and PA-RT, as well as PA-TACE and PA-HAIC. In the DFS-related investigations, PA-RT demonstrated superior efficacy over PA-Sorafenib, PA-TACE, and PA-HAIC. Additionally, PA-Sorafenib displayed better efficacy than PA-TACE. Nevertheless, there was no statistical significance between PA-Sorafenib and PA-HAIC, as well as PA-TACE and PA-HAIC. We also performed a subgroup analysis of studies focusing on HCC complicated by microvascular invasion after radical resection. In terms of OS, both PA-RT and PA-Sorafenib demonstrated a noteworthy improvement over PA-TACE, whereas no statistical significance was detected between PA-RT and PA-Sorafenib. Likewise, for DFS, both PA-Sorafenib and PA-RT exhibited superior efficacy compared to PA-TACE. CONCLUSION: In patients with HCC after radical resection and a high risk of recurrence, both PA-Sorafenib and PA-RT significantly improved OS and DFS when compared to PA-TACE and PA-HAIC. Notably, PA-RT exhibited superior efficacy over PA-Sorafenib, PA-TACE, and PA-HAIC in terms of DFS. Similarly, PA-Sorafenib appeared to be more effective than PA-TACE for DFS.

Prognostic and immune implications of a novel 7-methylguanosine-related microRNA signature in breast invasive carcinoma: from exploration to validation.

OBJECTIVES: This study aims to develop and validate a prognostic signature based on 7-methylguanosine-related (M7G-related) miRNAs for predicting prognosis and immune implications in breast invasive carcinoma (BRCA). MATERIALS AND METHODS: M7G-related miRNA data of BRCA were obtained from The Cancer Genome Atlas (TCGA). Least absolute shrinkage and selection operator (LASSO)-penalized, univariate, and multivariate Cox regression analyses were used to construct the prognostic signature. Furthermore, the predictive validity was verified using Kaplan-Meier (KM) survival risk and receiver operating characteristic (ROC) plots. Internal random sampling verification was used to simplify and validate the signature. RT-qPCR was used to quantify the expression level of transcriptional profiles. The independent prognostic role of the risk score was validated using univariate and multivariate regression. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used for functional and immune enrichment analysis. RESULTS: A total of 18 M7G-related miRNAs were identified to construct the prognostic signature in BRCA. The low-risk group exhibited significantly higher overall survival than the high-risk group in the KM survival plot (P < 0.001). The area under the curve (AUC) for 1-, 3-, and 5-year survivals in the ROC curve were 0.737, 0.724, and 0.702, respectively. The survival significance in the training and testing cohorts was confirmed by random sampling verification. The most prominent miRNAs in the signature were the miR-7, miR-139, miR-10b, and miR-4728. Furthermore, immune scores for B, mast, and Th1 cells varied between risk groups. Our research demonstrated that CD52 was the most positively correlated gene with immune cells and functions in BRCA. CONCLUSION: Our study presents a comprehensive and systematic analysis of M7G-related miRNAs to construct a prognostic signature in BRCA. The signature demonstrated excellent prognostic validity, with the risk score as an independent prognostic factor. These results provide critical evidence for further investigation of M7G miRNAs and offer new insights for BRCA patients in the context of effective immunotherapy.

An Artificial Single Molecular Channel Showing High Chloride Transport Selectivity and pH-Responsive Conductance.

Inspired by the unique structure and function of the natural chloride channel (ClC) selectivity filter, we present herein the design of a ClC-type single channel molecule. This channel displays high ion transport activity with half-maximal effective concentration, EC50 , of 0.10 µM, or 0.075 mol % (channel molecule to lipid ratio), as determined by fluorescent analysis using lucigenin-encapsulated vesicles. Planar bilayer lipid membrane conductance measurements indicated an excellent Cl- /K+ selectivity with a permeability ratio P Cl - ${{_{{\rm Cl}{^{- }}}}}$ /P K + ${{_{{\rm K}{^{+}}}}}$ up to 12.31, which is comparable with the chloride selectivity of natural ClC proteins. Moreover, high anion/anion selectivity (P Cl - ${{_{{\rm Cl}{^{- }}}}}$ /P Br - ${{_{{\rm Br}{^{- }}}}}$ =66.21) and pH-dependent conductance and ion selectivity of the channel molecule were revealed. The ClC-like transport behavior is contributed by the cooperation of hydrogen bonding and anion-π interactions in the central macrocyclic skeleton, and by the existence of pH-responsive terminal phenylalanine residues.

Structure- and Data-Driven Protein Engineering of Transaminases for Improving Activity and Stereoselectivity.

Amine transaminases (ATAs) are powerful biocatalysts for the stereoselective synthesis of chiral amines. Machine learning provides a promising approach for protein engineering, but activity prediction models for ATAs remain elusive due to the difficulty of obtaining high-quality training data. Thus, we first created variants of the ATA from Ruegeria sp. (3FCR) with improved catalytic activity (up to 2000-fold) as well as reversed stereoselectivity by a structure-dependent rational design and collected a high-quality dataset in this process. Subsequently, we designed a modified one-hot code to describe steric and electronic effects of substrates and residues within ATAs. Finally, we built a gradient boosting regression tree predictor for catalytic activity and stereoselectivity, and applied this for the data-driven design of optimized variants which then showed improved activity (up to 3-fold compared to the best variants previously identified). We also demonstrated that the model can predict the catalytic activity for ATA variants of another origin by retraining with a small set of additional data.

Cryptococcus tetragattii Meningitis Associated with Travel, Taiwan.

Meningitis caused by Cryptococcus tetragattii fungus is rare and has been found in specific geographic regions. We report a case of meningitis caused by C. tetragattii (molecular type VGIV) in an immunocompetent patient in Taiwan. The patient had traveled to Egypt and was positive for granulocyte-macrophage colony-stimulating factor autoantibody.

Comparison of Dynamic Contrast-Enhanced MRI and Non-Mono-Exponential Model-Based Diffusion-Weighted Imaging for the Prediction of Prognostic Biomarkers and Molecular Subtypes of Breast Cancer Based on Radiomics.

BACKGROUND: Dynamic contrast-enhanced (DCE) MRI and non-mono-exponential model-based diffusion-weighted imaging (NME-DWI) that does not require contrast agent can both characterize breast cancer. However, which technique is superior remains unclear. PURPOSE: To compare the performances of DCE-MRI, NME-DWI and their combination as multiparametric MRI (MP-MRI) in the prediction of breast cancer prognostic biomarkers and molecular subtypes based on radiomics. STUDY TYPE: Prospective. POPULATION: A total of 477 female patients with 483 breast cancers (5-fold cross-validation: training/validation, 80%/20%). FIELD STRENGTH/SEQUENCE: A 3.0 T/DCE-MRI (6 dynamic frames) and NME-DWI (13 b values). ASSESSMENT: After data preprocessing, high-throughput features were extracted from each tumor volume of interest, and optimal features were selected using recursive feature elimination method. To identify ER+ vs. ER-, PR+ vs. PR-, HER2+ vs. HER2-, Ki-67+ vs. Ki-67-, luminal A/B vs. nonluminal A/B, and triple negative (TN) vs. non-TN, the following models were implemented: random forest, adaptive boosting, support vector machine, linear discriminant analysis, and logistic regression. STATISTICAL TESTS: Student's t, chi-square, and Fisher's exact tests were applied on clinical characteristics to confirm whether significant differences exist between different statuses (±) of prognostic biomarkers or molecular subtypes. The model performances were compared between the DCE-MRI, NME-DWI, and MP-MRI datasets using the area under the receiver-operating characteristic curve (AUC) and the DeLong test. P < 0.05 was considered significant. RESULTS: With few exceptions, no significant differences (P = 0.062-0.984) were observed in the AUCs of models for six classification tasks between the DCE-MRI (AUC = 0.62-0.87) and NME-DWI (AUC = 0.62-0.91) datasets, while the model performances on the two imaging datasets were significantly poorer than on the MP-MRI dataset (AUC = 0.68-0.93). Additionally, the random forest and adaptive boosting models (AUC = 0.62-0.93) outperformed other three models (AUC = 0.62-0.90). DATA CONCLUSION: NME-DWI was comparable with DCE-MRI in predictive performance and could be used as an alternative technique. Besides, MP-MRI demonstrated significantly higher AUCs than either DCE-MRI or NME-DWI. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 2.

Intermolecular n→π* Interactions Based on a Tailored Multicarbonyl-Containing Macrocycle.

Towards unexplored intermolecular n→π* interactions, presented herein are the synthesis, structure, self-assembly and function of a multicarbonyl-containing macrocycle calix[2]arene[2]barbiturate 1. X-ray single crystal diffraction reveals the presence of Cl⋅⋅⋅C=O interactions in CH2 Cl2 ⊂1 host-guest complex and multiple intermolecular C=O⋅⋅⋅C=O interactions between molecules 1 in crystalline state. The intermolecular C=O⋅⋅⋅C=O interactions as attractive driving force led to unprecedented self-assembly of nanotube with diameter around 1.4 nm and inner surface engineered by aromatic rings. SEM and TEM images of the self-assembly of 1 demonstrated temperature-dependent morphologies which allows the observation of spheres at 25 °C and rods at 0 °C, respectively. XRD analysis indicated consistent hexagonal patterns in the self-assembly and single crystal lattice, indicating the nanotubes driven by C=O⋅⋅⋅C=O interactions constitute the basic structural architectures of both aggregates. The nanoscopic tubes (pores) formed in the rodlike single crystal engendering the separation of moving dyes were preliminarily investigated by a single-crystal chromatography and crystal-packed column chromatography.

Shifting the pH Optima of (R)-Selective Transaminases by Protein Engineering.

Amine transaminases (ATAs) are powerful biocatalysts for the stereoselective synthesis of chiral amines. However, wild-type ATAs usually show pH optima at slightly alkaline values and exhibit low catalytic activity under physiological conditions. For efficient asymmetric synthesis ATAs are commonly used in combination with lactate dehydrogenase (LDH, optimal pH: 7.5) and glucose dehydrogenase (GDH, optimal pH: 7.75) to shift the equilibrium towards the synthesis of the target chiral amine and hence their pH optima should fit to each other. Based on a protein structure alignment, variants of (R)-selective transaminases were rationally designed, produced in E. coli, purified and subjected to biochemical characterization. This resulted in the discovery of the variant E49Q of the ATA from Aspergillus fumigatus, for which the pH optimum was successfully shifted from pH 8.5 to 7.5 and this variant furthermore had a two times higher specific activity than the wild-type protein at pH 7.5. A possible mechanism for this shift of the optimal pH is proposed. Asymmetric synthesis of (R)-1-phenylethylamine from acetophenone in combination with LDH and GDH confirmed that the variant E49Q shows superior performance at pH 7.5 compared to the wild-type enzyme.

Xenon Induces Its Own Preferred Heterochiral Host from Exclusive Homochiral Assembly.

Xenon binding represents a formidable challenge, and efficient hosts remain rare. Here we report our findings that while enantiomeric bis(urea)-bis(thiourea) macrocycles form exclusive homochiral dimeric assemblies, xenon is able to overcome the narcissism and induces an otherwise-nonobservable heterochiral assembly as its preferred host. An experimental approach and fitting model were developed to obtain binding constants associated with the invisible assembly species. The determined xenon binding affinity with the heterochiral capsule reaches 1600 M-1, which is 15 times higher than that with the homochiral capsule and represents the highest record for an assembled host. The origin of the large difference in xenon affinity between the two subtle diastereotopic assemblies was revealed by single-crystal analysis. In the heterochiral capsule with S4 symmetry, the xenon atom is more tightly enclosed by van der Waals surroundings of the four thiourea groups arranged in a spherical cross-array, superior to the antiparallel array in the homochiral capsule with D2 symmetry.

[Main hypotheses on mechanisms underlying plant invasion: A review.] / æ¤ç‰©å ¥ä¾µæœºåˆ¶çš„ä¸»è¦å‡è¯´.

Plant invasion is one of the most serious global problems, destroying ecosystem structure and function. With the severity of plant invasion, it is particularly important to understand the mechanisms of plant invasion in order to control and solve the problem. We summarized different mechanisms of plant invasion and the synergy among them, expounded the allelopathy, the plant-soil feedbacks, the reciprocal symbiosis, the effects of plant functional traits and phenotype plasticity in the process of plant invasion, and comprehensively analyzed the synergy of multiple mechanisms on plant invasion trajectory. According to the results, the invasion trajectory of alien plants in the invasive site was divided into four stages: introduction, colonization, establishment, and invasion. Integrating all kinds of obstacles and promoting factors encountered into it and putting forward the invasion curve of plants would contribute to the future research and management of invasive plants. We further highlighted the current research deficiencies and future research directions and objectives based on analyzing current research methods of plant invasion.