Genetics of cell-type-specific post-transcriptional gene regulation during human neurogenesis.

The function of some genetic variants associated with brain-relevant traits has been explained through colocalization with expression quantitative trait loci (eQTL) conducted in bulk postmortem adult brain tissue. However, many brain-trait associated loci have unknown cellular or molecular function. These genetic variants may exert context-specific function on different molecular phenotypes including post-transcriptional changes. Here, we identified genetic regulation of RNA editing and alternative polyadenylation (APA) within a cell-type-specific population of human neural progenitors and neurons. More RNA editing and isoforms utilizing longer polyadenylation sequences were observed in neurons, likely due to higher expression of genes encoding the proteins mediating these post-transcriptional events. We also detected hundreds of cell-type-specific editing quantitative trait loci (edQTLs) and alternative polyadenylation QTLs (apaQTLs). We found colocalizations of a neuron edQTL in CCDC88A with educational attainment and a progenitor apaQTL in EP300 with schizophrenia, suggesting that genetically mediated post-transcriptional regulation during brain development leads to differences in brain function.

HIV-1 p17 matrix protein enhances type I interferon responses through the p17-OLA1-STING axis.

Stimulator of IFN genes (STING; also known as STING1) is an important adaptor protein for detecting cytosolic double-stranded DNA, which can come from HIV infection. Several HIV proteins, such as p6, Vpx and Vif, can influence STING-mediated innate immunity, but the function of p17 is still unknown. In this study, we find that HIV-1 p17, but not HIV-2 p17 or SIV p17, promotes STING signaling induced by cyclic GMP-AMP (cGAMP) treatment. Mechanistically, HIV-1 p17 binds to Obg-like ATPase 1 (OLA1) and inhibits the regulation of STING by OLA1. Here, OLA1 interacts with STING and inhibits the translocation and phosphorylation of STING upon cGAMP stimulation. Furthermore, compared with HIV-2 and SIV, the ATPase and GTPase activities of OLA1 are only promoted by HIV-1 p17. Our study shows that the p17 of HIV-1, but not HIV-2 or SIV, promotes STING-mediated innate immunity by interfering the interaction between OLA1 and STING, thus providing a new clue for specific immune activation of HIV-1.

IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

Neuron-specific transcriptomic signatures indicate neuroinflammation and altered neuronal activity in ASD temporal cortex.

Autism spectrum disorder (ASD) is a highly heterogeneous disorder, yet transcriptomic profiling of bulk brain tissue has identified substantial convergence among dysregulated genes and pathways in ASD. However, this approach lacks cell-specific resolution. We performed comprehensive transcriptomic analyses on bulk tissue and laser-capture microdissected (LCM) neurons from 59 postmortem human brains (27 ASD and 32 controls) in the superior temporal gyrus (STG) of individuals ranging from 2 to 73 years of age. In bulk tissue, synaptic signaling, heat shock protein-related pathways, and RNA splicing were significantly altered in ASD. There was age-dependent dysregulation of genes involved in gamma aminobutyric acid (GABA) (GAD1 and GAD2) and glutamate (SLC38A1) signaling pathways. In LCM neurons, AP-1-mediated neuroinflammation and insulin/IGF-1 signaling pathways were upregulated in ASD, while mitochondrial function, ribosome, and spliceosome components were downregulated. GABA synthesizing enzymes GAD1 and GAD2 were both downregulated in ASD neurons. Mechanistic modeling suggested a direct link between inflammation and ASD in neurons, and prioritized inflammation-associated genes for future study. Alterations in small nucleolar RNAs (snoRNAs) associated with splicing events suggested interplay between snoRNA dysregulation and splicing disruption in neurons of individuals with ASD. Our findings supported the fundamental hypothesis of altered neuronal communication in ASD, demonstrated that inflammation was elevated at least in part in ASD neurons, and may reveal windows of opportunity for biotherapeutics to target the trajectory of gene expression and clinical manifestation of ASD throughout the human lifespan.

A class of anti-inflammatory lipids decrease with aging in the central nervous system.

Lipids contribute to the structure, development, and function of healthy brains. Dysregulated lipid metabolism is linked to aging and diseased brains. However, our understanding of lipid metabolism in aging brains remains limited. Here we examined the brain lipidome of mice across their lifespan using untargeted lipidomics. Co-expression network analysis highlighted a progressive decrease in 3-sulfogalactosyl diacylglycerols (SGDGs) and SGDG pathway members, including the potential degradation products lyso-SGDGs. SGDGs show an age-related decline specifically in the central nervous system and are associated with myelination. We also found that an SGDG dramatically suppresses LPS-induced gene expression and release of pro-inflammatory cytokines from macrophages and microglia by acting on the NF-κB pathway. The detection of SGDGs in human and macaque brains establishes their evolutionary conservation. This work enhances interest in SGDGs regarding their roles in aging and inflammatory diseases and highlights the complexity of the brain lipidome and potential biological functions in aging.

Hypoxia-inducible factor prolyl hydroxylase inhibitor alleviates heatstroke-induced acute kidney injury by activating BNIP3-mediated mitophagy.

Hypoxia-induced inflammation and apoptosis are important pathophysiological features of heat stroke-induced acute kidney injury (HS-AKI). Hypoxia-inducible factor (HIF) is a key protein that regulates cell adaptation to hypoxia. HIF-prolyl hydroxylase inhibitor (HIF-PHI) stabilizes HIF to increase cell adaptation to hypoxia. Herein, we reported that HIF-PHI pretreatment significantly improved renal function, enhanced thermotolerance, and increased the survival rate of mice in the context of HS. Moreover, HIF-PHI could alleviate HS-induced mitochondrial damage, inflammation, and apoptosis in renal tubular epithelial cells (RTECs) by enhancing mitophagy in vitro and in vivo. By contrast, mitophagy inhibitors Mdivi-1, 3-MA, and Baf-A1 reversed the renoprotective effects of HIF-PHI. Mechanistically, HIF-PHI protects RTECs from inflammation and apoptosis by enhancing Bcl-2 adenovirus E18 19-kDa-interacting protein 3 (BNIP3)-mediated mitophagy, while genetic ablation of BNIP3 attenuated HIF-PHI-induced mitophagy and abolished HIF-PHI-mediated renal protection. Thus, our results indicated that HIF-PHI protects renal function by upregulating BNIP3-mediated mitophagy to improve HS-induced inflammation and apoptosis of RTECs, suggesting HIF-PHI as a promising therapeutic agent to treat HS-AKI.

Artificial Funnel Nanochannel Device Emulates Synaptic Behavior.

Creating artificial synapses that can interact with biological neural systems is critical for developing advanced intelligent systems. However, there are still many difficulties, including device morphology and fluid selection. Based on Micro-Electro-Mechanical System technologies, we utilized two immiscible electrolytes to form a liquid/liquid interface at the tip of a funnel nanochannel, effectively enabling a wafer-level fabrication, interactions between multiple information carriers, and electron-to-chemical signal transitions. The distinctive ionic transport properties successfully achieved a hysteresis in ionic transport, resulting in adjustable multistage conductance gradient and synaptic functions. Notably, the device is similar to biological systems in terms of structure and signal carriers, especially for the low operating voltage (200 mV), which matches the biological neural potential (∼110 mV). This work lays the foundation for realizing the function of iontronics neuromorphic computing at ultralow operating voltages and in-memory computing, which can break the limits of information barriers for brain-machine interfaces.

Genome-wide analysis and expression pattern of the ZoPP2C gene family in Zingiber officinale Roscoe.

BACKGROUND: Protein phosphatases type 2C (PP2C) are heavily involved in plant growth and development, hormone-related signaling pathways and the response of various biotic and abiotic stresses. However, a comprehensive report identifying the genome-scale of PP2C gene family in ginger is yet to be published. RESULTS: In this study, 97 ZoPP2C genes were identified based on the ginger genome. These genes were classified into 15 branches (A-O) according to the phylogenetic analysis and distributed unevenly on 11 ginger chromosomes. The proteins mainly functioned in the nucleus. Similar motif patterns and exon/intron arrangement structures were identified in the same subfamily of ZoPP2Cs. Collinearity analysis indicated that ZoPP2Cs had 33 pairs of fragment duplicated events uniformly distributed on the corresponding chromosomes. Furthermore, ZoPP2Cs showed greater evolutionary proximity to banana's PP2Cs. The forecast of cis-regulatory elements and transcription factor binding sites demonstrated that ZoPP2Cs participate in ginger growth, development, and responses to hormones and stresses. ZoERFs have plenty of binding sites of ZoPP2Cs, suggesting a potential synergistic contribution between ZoERFs and ZoPP2Cs towards regulating growth/development and adverse conditions. The protein-protein interaction network displayed that five ZoPP2Cs (9/23/26/49/92) proteins have robust interaction relationship and potential function as hub proteins. Furthermore, the RNA-Seq and qRT-PCR analyses have shown that ZoPP2Cs exhibit various expression patterns during ginger maturation and responses to environmental stresses such as chilling, drought, flooding, salt, and Fusarium solani. Notably, exogenous application of melatonin led to notable up-regulation of ZoPP2Cs (17/59/11/72/43) under chilling stress. CONCLUSIONS: Taken together, our investigation provides significant insights of the ginger PP2C gene family and establishes the groundwork for its functional validation and genetic engineering applications.

Atomic-Scale Cryo-TEM Studies of the Electrochemistry of Redox Mediator in Li-O2 Batteries.

Rechargeable aprotic lithium (Li)-oxygen battery (LOB) is a potential next-generation energy storage technology because of its high theoretical specific energy. However, the role of redox mediator on the oxide electrochemistry remains unclear. This is partly due to the intrinsic complexity of the battery chemistry and the lack of in-depth studies of oxygen electrodes at the atomic level by reliable techniques. Herein, cryo-transmission electron microscopy (cryo-TEM) is used to study how the redox mediator LiI affects the oxygen electrochemistry in LOBs. It is revealed that with or without LiI in the electrolyte, the discharge products are plate-like LiOH or toroidal Li2O2, respectively. The I2 assists the decomposition of LiOH via the formation of LiIO3 in the charge process. In addition, a LiI protective layer is formed on the Li anode surface by the shuttle of I3 -, which inhibits the parasitic Li/electrolyte reaction and improves the cycle performance of the LOBs. The LOBs returned to 2e- oxygen reduction reaction (ORR) to produce Li2O2 after the LiI in the electrolyte is consumed. This work provides new insight on the role of redox mediator on the complex electrochemistry in LOBs which may aid the design LOBs for practical applications.

Versatile CYP98A enzymes catalyse meta-hydroxylation reveals diversity of salvianolic acids biosynthesis.

Salvianolic acids (SA), such as rosmarinic acid (RA), danshensu (DSS), and their derivative salvianolic acid B (SAB), etc. widely existed in Lamiaceae and Boraginaceae families, are of interest due to medicinal properties in the pharmaceutical industries. Hundreds of studies in past decades described that 4-coumaroyl-CoA and 4-hydroxyphenyllactic acid (4-HPL) are common substrates to biosynthesize SA with participation of rosmarinic acid synthase (RAS) and cytochrome P450 98A (CYP98A) subfamily enzymes in different plants. However, in our recent study, several acyl donors and acceptors included DSS as well as their ester-forming products all were determined in SA-rich plants, which indicated that previous recognition to SA biosynthesis is insufficient. Here, we used Salvia miltiorrhiza, a representative important medicinal plant rich in SA, to elucidate the diversity of SA biosynthesis. Various acyl donors as well as acceptors are catalysed by SmRAS to form precursors of RA and two SmCYP98A family members, SmCYP98A14 and SmCYP98A75, are responsible for different positions' meta-hydroxylation of these precursors. SmCYP98A75 preferentially catalyses C-3' hydroxylation, and SmCYP98A14 preferentially catalyses C-3 hydroxylation in RA generation. In addition, relative to C-3' hydroxylation of the acyl acceptor moiety in RA biosynthesis, SmCYP98A75 has been verified as the first enzyme that participates in DSS formation. Furthermore, SmCYP98A enzymes knockout resulted in the decrease and overexpression leaded to dramatic increase of SA accumlation. Our study provides new insights into SA biosynthesis diversity in SA-abundant species and versatility of CYP98A enzymes catalytic preference in meta-hydroxylation reactions. Moreover, CYP98A enzymes are ideal metabolic engineering targets to elevate SA content.

DeepCoVDR: deep transfer learning with graph transformer and cross-attention for predicting COVID-19 drug response.

MOTIVATION: The coronavirus disease 2019 (COVID-19) remains a global public health emergency. Although people, especially those with underlying health conditions, could benefit from several approved COVID-19 therapeutics, the development of effective antiviral COVID-19 drugs is still a very urgent problem. Accurate and robust drug response prediction to a new chemical compound is critical for discovering safe and effective COVID-19 therapeutics. RESULTS: In this study, we propose DeepCoVDR, a novel COVID-19 drug response prediction method based on deep transfer learning with graph transformer and cross-attention. First, we adopt a graph transformer and feed-forward neural network to mine the drug and cell line information. Then, we use a cross-attention module that calculates the interaction between the drug and cell line. After that, DeepCoVDR combines drug and cell line representation and their interaction features to predict drug response. To solve the problem of SARS-CoV-2 data scarcity, we apply transfer learning and use the SARS-CoV-2 dataset to fine-tune the model pretrained on the cancer dataset. The experiments of regression and classification show that DeepCoVDR outperforms baseline methods. We also evaluate DeepCoVDR on the cancer dataset, and the results indicate that our approach has high performance compared with other state-of-the-art methods. Moreover, we use DeepCoVDR to predict COVID-19 drugs from FDA-approved drugs and demonstrate the effectiveness of DeepCoVDR in identifying novel COVID-19 drugs. AVAILABILITY AND IMPLEMENTATION: https://github.com/Hhhzj-7/DeepCoVDR.

Altered gut mycobiome in patients with end-stage renal disease and its correlations with serum and fecal metabolomes.

BACKGROUND: The relationship between the gut mycobiome and end-stage renal disease (ESRD) remains largely unexplored. METHODS: In this study, we compared the gut fungal populations of 223 ESRD patients and 69 healthy controls (HCs) based on shotgun metagenomic sequencing data, and analyzed their associations with host serum and fecal metabolites. RESULTS: Our findings revealed that ESRD patients had a higher diversity in the gut mycobiome compared to HCs. Dysbiosis of the gut mycobiome in ESRD patients was characterized by a decrease of Saccharomyces cerevisiae and an increase in various opportunistic pathogens, such as Aspergillus fumigatus, Cladophialophora immunda, Exophiala spinifera, Hortaea werneckii, Trichophyton rubrum, and others. Through multi-omics analysis, we observed a substantial contribution of the gut mycobiome to host serum and fecal metabolomes. The opportunistic pathogens enriched in ESRD patients were frequently and positively correlated with the levels of creatinine, homocysteine, and phenylacetylglycine in the serum. The populations of Saccharomyces, including the HC-enriched Saccharomyces cerevisiae, were frequently and negatively correlated with the levels of various toxic metabolites in the feces. CONCLUSIONS: Our results provided a comprehensive understanding of the associations between the gut mycobiome and the development of ESRD, which had important implications for guiding future therapeutic studies in this field.

Metagenomic analysis of the gut virome in patients with irritable bowel syndrome.

Irritable bowel syndrome (IBS), a chronic functional gastrointestinal disorder, is recognized for its association with alterations in the gut microbiome and metabolome. This study delves into the largely unexplored domain of the gut virome in IBS patients. We conducted a comprehensive analysis of the fecal metagenomic data set from 277 IBS patients and 84 healthy controls to characterize the gut viral community. Our findings revealed a distinct gut virome in IBS patients compared to healthy individuals, marked by significant variances in between-sample diversity and altered abundances of 127 viral operational taxonomic units (vOTUs). Specifically, 111 vOTUs, predominantly belonging to crAss-like, Siphoviridae, Myoviridae, and Quimbyviridae families, were more abundant in IBS patients, whereas the healthy control group exhibited enrichment of 16 vOTUs from multiple families. We also investigated the interplay between the gut virome and bacteriome, identifying a correlation between IBS-enriched bacteria like Klebsiella pneumoniae, Fusobacterium varium, and Ruminococcus gnavus, and the IBS-associated vOTUs. Furthermore, we assessed the potential of gut viral signatures in predicting IBS, achieving a notable area under the receiver operator characteristic curve (AUC) of 0.834. These findings highlight significant shifts in the viral diversity, taxonomic distribution, and functional composition of the gut virome in IBS patients, suggesting the potential role of the gut virome in IBS pathogenesis and opening new avenues for diagnostic and therapeutic strategies targeting the gut virome in IBS management.

Metagenomic analysis reveals altered gut virome and diagnostic potential in pancreatic cancer.

Pancreatic cancer (PC) is a highly aggressive malignancy with a poor prognosis, making early diagnosis crucial for improving patient outcomes. While the gut microbiome, including bacteria and viruses, is believed to be essential in cancer pathogenicity, the potential contribution of the gut virome to PC remains largely unexplored. In this study, we conducted a comparative analysis of the gut viral compositional and functional profiles between PC patients and healthy controls, based on fecal metagenomes from two publicly available data sets comprising a total of 101 patients and 82 healthy controls. Our results revealed a decreasing trend in the gut virome diversity of PC patients with disease severity. We identified significant alterations in the overall viral structure of PC patients, with a meta-analysis revealing 219 viral operational taxonomic units (vOTUs) showing significant differences in relative abundance between patients and healthy controls. Among these, 65 vOTUs were enriched in PC patients, and 154 were reduced. Host prediction revealed that PC-enriched vOTUs preferentially infected bacterial members of Veillonellaceae, Enterobacteriaceae, Fusobacteriaceae, and Streptococcaceae, while PC-reduced vOTUs were more likely to infect Ruminococcaceae, Lachnospiraceae, Clostridiaceae, Oscillospiraceae, and Peptostreptococcaceae. Furthermore, we constructed random forest models based on the PC-associated vOTUs, achieving an optimal average area under the curve (AUC) of up to 0.879 for distinguishing patients from controls. Through additional 10 public cohorts, we demonstrated the reproducibility and high specificity of these viral signatures. Our study suggests that the gut virome may play a role in PC development and could serve as a promising target for PC diagnosis and therapeutic intervention. Future studies should further explore the underlying mechanisms of gut virus-bacteria interactions and validate the diagnostic models in larger and more diverse populations.

Efficient automated high dynamic range 3D measurement via deep reinforcement learning.

High dynamic range 3D measurement technology, utilizing multiple exposures, is pivotal in industrial metrology. However, selecting the optimal exposure sequence to balance measurement efficiency and quality remains challenging. This study reinterprets this challenge as a Markov decision problem and presents an innovative exposure selection method rooted in deep reinforcement learning. Our approach's foundation is the exposure image prediction network (EIPN), designed to predict images under specific exposures, thereby simulating a virtual environment. Concurrently, we establish a reward function that amalgamates considerations of exposure number, exposure time, coverage, and accuracy, providing a comprehensive task definition and precise feedback. Building upon these foundational elements, the exposure selection network (ESN) emerges as the centerpiece of our strategy, acting decisively as an agent to derive the optimal exposure sequence selection. Experiments prove that the proposed method can obtain similar coverage (0.997 vs. 1) and precision (0.0263 mm vs. 0.0230 mm) with fewer exposures (generally 4) compared to the results of 20 exposures.

Tensor Network Message Passing.

When studying interacting systems, computing their statistical properties is a fundamental problem in various fields such as physics, applied mathematics, and machine learning. However, this task can be quite challenging due to the exponential growth of the state space as the system size increases. Many standard methods have significant weaknesses. For instance, message-passing algorithms can be inaccurate and even fail to converge due to short loops, while tensor network methods can have exponential computational complexity in large graphs due to long loops. In this Letter, we propose a new method called "tensor network message passing." This approach allows us to compute local observables like marginal probabilities and correlations by combining the strengths of tensor networks in contracting small subgraphs with many short loops and the strengths of message-passing methods in globally sparse graphs, thus addressing the crucial weaknesses of both approaches. Our algorithm is exact for systems that are globally treelike and locally dense-connected when the dense local graphs have a limited tree width. We have conducted numerical experiments on synthetic and real-world graphs to compute magnetizations of Ising models and spin glasses, and have demonstrated the superiority of our approach over standard belief propagation and the recently proposed loopy message-passing algorithm. In addition, we discuss the potential applications of our method in inference problems in networks, combinatorial optimization problems, and decoding problems in quantum error correction.

Rh(I)-Catalyzed [4+3]/[4+1] Cycloaddition of Diene-Vinylcyclopropanes and Carbon Monoxide to Access Angular 5/7/5 Tricycles.

Report here is a Rh-catalyzed [4+3]/[4+1] cycloaddition of diene-vinylcyclopropanes (diene-VCPs) and carbon monoxide to access compounds with angular 5/7/5 tricyclic skeleton found in natural products. The reaction has broad scope and further transformation of the [4+3]/[4+1] cycloadduct was also investigated. How this [4+3]/[4+1] reaction occurs and why its competing [4+3] reaction is disfavored have been investigated computationally.

Predictors and prognosis of early neurological outcomes on patients with Vertebrobasilar artery occlusion undergoing endovascular treatment.

BACKGROUND: This research explored the factors influencing early neurological outcomes (ENO) in patients who had vertebrobasilar artery occlusion (VBAO) and received endovascular treatment (EVT), as well as examining the causal influence of ENO on the prognosis of VBAO patients. METHODS: A retrospective review was carried out on patients from 65 Chinese stroke centers, all within 24 hours of the estimated occlusion time. ENO includes early neurological improvement (ENI) and early neurological deterioration (END), defined as a decrease or an increase of at least 4 points in NIHSS score between baseline and 24 hours after EVT. Death within 24 hours after EVT also consider as END. END was further divided into explainable END and unexplainable END (unEND). Independent predictors of ENO and the association between ENO and outcomes in patients with VBAO were determined using center-adjusted analyses. The study developed a multivariate logistic regression model to examine the comparative risk of unEND versus explainable END on the clinical outcomes in VBAO patients. RESULTS: A total of 2257 patients were included. Glasgow Coma Scale (GCS) (OR 1.16, 95% CI 1.03-1.30) and successful reperfusion (OR 1.15, 95% CI 1.02-1.30) were associated with ENI. Baseline NIHSS (OR 0.60, 95% CI 0.53-0.68), successful reperfusion (OR 0.79, 95% CI 0.71-0.89) and puncture to reperfusion time (OR 1.17, 95% CI 1.03-1.33) were associated with END. When examining three-month prognostic indexes, both END and ENI were found to be linked to the three-month outcomes, but in opposite directions. A subgroup analysis of END suggested that unexplained END typically demonstrated a more favorable prognosis compared to explained END, although the prognosis remained generally unfavorable. CONCLUSIONS: ENO, whether they manifested as early improvement or deterioration, were linked to the prognosis of VBAO patients undergoing EVT. The outcomes after unEND were more favorable than those following explained END.

Dynamically or Kinetically Controlled? Computational Study of the Mechanisms of Electrophilic Aminoalkenylation of Heteroaromatics with Keteniminium Ions.

Quantum chemical calculations and molecular dynamics simulations were applied to study the electrophilic aminoalkenylation of heteroaromatics with keniminium ions. Post-transition state bifurcation (PTSB) was found in the electrophilic addition step for the aminoalkenylation of pyrroles and indoles, and the selectivity for these reactions was dynamically controlled. However, the aminoalkenylation of furan was kinetically controlled because no apparent PTSB was found in the electrophilic addition step. The substituents on the keteniminium ions can also affect the dynamic results for the aminoalkenylations to pyrroles: the C2-aminoalkenylated product is much more favored over the C3-aminoalkenylated product for keteniminium ions with electron-donating substituents, while the product ratio (C2 product/C3 product) decreased when stronger electron-withdrawing substituents were applied.

UiO-66/PIM-1 Mixed-Matrix Membrane for Hexane Isomer Separation.

The separation of high-octane dibranched alkanes from naphtha is critical in the refining of gasoline. To date, research on the membrane-based separation of alkane isomers has been limited, with a particular paucity of investigations into mixed-matrix membranes. Herein, the continuous and dense UiO-66/PIM-1 mixed-matrix membrane, which was prepared through precise control of the interfacial structure, was first applied to the differentiation of C6 alkane isomers. Due to the synergistic combination of UiO-66 with differential adsorption capabilities for alkanes and PIM-1 that possesses a cross-linkable structure, the resulting UiO-66/PIM-1-(20) membrane demonstrated remarkable separation performance and high stability. Pervaporation measurements showed that the mass fraction of 2,2-dimethylbutane in the feed side was increased from 50.0 to 75.8 wt % while an excellent flux of 1700 g m-2 h-1 was maintained over a continuous 40 h period. The UiO-66/PIM-1-(20) membrane, characterized by its facile replication and processing, shows potential for large-scale fabrication. This study offers a new approach to the membrane separation of alkane isomers.