Chemoenzymatic Installation of Site-Specific Chemical Groups on DNA Enhances the Catalytic Activity.

Functional DNAs are valuable molecular tools in chemical biology and analytical chemistry but suffer from low activities due to their limited chemical functionalities. Here, we present a chemoenzymatic method for site-specific installation of diverse functional groups on DNA, and showcase the application of this method to enhance the catalytic activity of a DNA catalyst. Through chemoenzymatic introduction of distinct chemical groups, such as hydroxyl, carboxyl, and benzyl, at specific positions, we achieve significant enhancements in the catalytic activity of the RNA-cleaving deoxyribozyme 10-23. A single carboxyl modification results in a 100-fold increase, while dual modifications (carboxyl and benzyl) yield an approximately 700-fold increase in activity when an RNA cleavage reaction is catalyzed on a DNA-RNA chimeric substrate. The resulting dually modified DNA catalyst, CaBn, exhibits a kobs of 3.76 min-1 in the presence of 1 mM Mg2+ and can be employed for fluorescent imaging of intracellular magnesium ions. Molecular dynamics simulations reveal the superior capability of CaBn to recruit magnesium ions to metal-ion-binding site 2 and adopt a catalytically competent conformation. Our work provides a broadly accessible strategy for DNA functionalization with diverse chemical modifications, and CaBn offers a highly active DNA catalyst with immense potential in chemistry and biotechnology.

A novel untrained SSVEP-EEG feature enhancement method using canonical correlation analysis and underdamped second-order stochastic resonance.

Objective: Compared with the light-flashing paradigm, the ring-shaped motion checkerboard patterns avoid uncomfortable flicker or brightness modulation, improving the practical interactivity of brain-computer interface (BCI) applications. However, due to fewer harmonic responses and more concentrated frequency energy elicited by the ring-shaped checkerboard patterns, the mainstream untrained algorithms such as canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods have poor recognition performance and low information transmission rate (ITR). Methods: To address this issue, a novel untrained SSVEP-EEG feature enhancement method using CCA and underdamped second-order stochastic resonance (USSR) is proposed to extract electroencephalogram (EEG) features. Results: In contrast to typical unsupervised dimensionality reduction methods such as common average reference (CAR), principal component analysis (PCA), multidimensional scaling (MDS), and locally linear embedding (LLE), CCA exhibits higher adaptability for SSVEP rhythm components. Conclusion: This study recruits 42 subjects to evaluate the proposed method and experimental results show that the untrained method can achieve higher detection accuracy and robustness. Significance: This untrained method provides the possibility of applying a nonlinear model from one-dimensional signals to multi-dimensional signals.

A portable and intelligent logic detector for simultaneous and in-situ detection of Al3+ and fluoride in groundwater.

To develop a convenient and intelligent detector for simultaneous and in-situ detection of Al3+ and F- in groundwater, a novel organic probe called RBP has been prepared. With the increase of Al3+, RBP showed a significant fluorescence enhancement at 588 nm, and the detection limit was 0.130 mg/L. After combining with fluorescent internal standard CDs, the fluorescence of RBP-Al-CDs at 588 nm was quenched due to the replace of F- for Al3+, while the CDs at 460 nm remained unchanged, and the detection limit was 0.0186 mg/L. For convenient and intelligent detection, an RBP-based logic detector has been developed for simultaneous detection of Al3+ and F-. Within the ultra-trace, low concentration, and high concentration range of Al3+ and F-, the logic detector can achieve rapid feedback on their concentration levels ("U", "L" and "H") through different output modes of the signal lamps. The development of logical detector is of great significance for studying the in-situ chemical behavior of Al3+ and F- and for daily household detection.

Construction and Application of a Big Data System for Regional Lakes in Coalbed Methane Development.

With the rapid development and widespread application of big data and artificial intelligence, the upgrading of digital and intelligent industries has been rapidly popularized in the oil and gas industry. First, based on the theory of ″regional data lake″, the digital nature of the CBM governance system is analyzed, and the optimization model of CBM governance for different data types is established. Second, considering the geological characteristics and development mode of the CBM reservoir, the regional data lake expansion model is established. Third, a theoretical model of coupling ″on-site data, laboratory data, management data, and data management system″ has been established. The research shows the following: (a) The CBM governance system based on the regional data lake can be divided into four parts: basic support, data life cycle, core governance areas, and governance strategy support. (b) The coupling of the coalbed methane governance model with the BP neural network model in this article has good application results. (c) The computational efficiency of this model has been improved by 12%, which has broad application prospects.

Copper-Catalyzed Enantioconvergent Radical C(sp3 )-N Cross-Coupling: Access to α,α-Disubstituted Amino Acids.

Transition-metal catalyzed enantioconvergent cross-coupling of tertiary alkyl halides with ammonia offers a rapid avenue to chiral unnatural α,α-disubstituted amino acids. However, the construction of chiral C-N bonds between tertiary-carbon electrophiles and nitrogen nucleophiles presented a great challenge owing to steric congestion. We report a copper-catalyzed enantioconvergent radical C-N cross-coupling of alkyl halides with sulfoximines (as ammonia surrogates) under mild conditions by employing a chiral anionic N,N,N-ligand with a long spreading side arm. An array of α,α-disubstituted amino acid derivatives were obtained with good efficiency and enantioselectivity. The synthetic utility of the strategy has been showcased by the elaboration of the coupling products into different chiral α-fully substituted amine building blocks.

Co-infections of Klebsiella pneumoniae and Elizabethkingia miricola in black-spotted frogs (Pelophylax nigromaculatus).

Pelophylax nigromaculatus is a common commercial specie of frogs that generally cultured throughout China. With the application of high-density culture, P. nigromaculatus can be co-infected by two or more pathogens, which thereby induce synergistic influence on the virulence of the infection. In this study, two bacterial strains were simultaneously isolated from diseased frogs by incubating on Luria-Bertani (LB) agar. Isolates were identified as Klebsiella pneumoniae and Elizabethkingia miricola by morphological, physiological and biochemical features, as well as 16S rRNA sequencing and phylogenetic analysis. The whole genome of K. pneumoniae and E. miricola isolates consist single circular chromosome of 5,419,557 bp and 4,215,349 bp, respectively. The genomic sequence analysis further indicated that K. pneumoniae isolate conserved 172 virulent and 349 antibiotic-resistance genes, whereas E. miricola contained 24 virulent and 168 antibiotic resistance genes. In LB broth, both isolates could grow well at 0%-1% NaCl concentration and pH 5-7. Antibiotic susceptibility testing revealed that both K. pneumoniae and E. miricola were resistant to kanamycin, neomycin, ampicillin, piperacillin, carbenicillin, enrofloxacin, norfloxacin and sulfisoxazole. Histopathological studies showed that co-infection caused considerable lesions in the tissues of brain, eye, muscle, spleen, kidney and liver, including cell degeneration, necrosis, hemorrhage and inflammatory cell infiltration. The LD50 of K. pneumoniae and E. miricola isolates were 6.31 × 105 CFU/g and 3.98 × 105 CFU/g frog weight, respectively. Moreover, experimentally infected frogs exhibited quick and higher mortality under coinfection with K. pneumoniae and E. miricola than those single challenge of each bacterium. To date, no natural co-infection by these two bacteria has been reported from frogs and even amphibians. The results will not only shed light on the feature and pathogenesis of K. pneumoniae and E. miricola, but also highlight that co-infection of these two pathogen is a potential threat to black-spotted frog farming.

Fibroblast-Derived Extracellular Vesicle-Packaged Long Noncoding RNA Upregulated in Diabetic Skin Enhances Keratinocyte MMP-9 Expression and Delays Diabetic Wound Healing.

Accurate communication between fibroblasts and keratinocytes is crucial for diabetic wound healing. Extracellular vesicles are being explored as essential mediators of intercellular communication in the skin. However, the mechanisms underlying wound healing mediated by fibroblast-derived extracellular vesicles (Fib-EVs) remain unclear. The present study evaluated the role of long noncoding RNA upregulated in diabetic skin (lnc-URIDS) packed in Fib-EVs in the wound healing of streptozotocin-induced diabetes and the potential mechanisms of the effects. We demonstrated that high glucose induced the enrichment of lnc-URIDS in Fib-EVs, facilitated the transfer of lnc-URIDS to primary rat epidermal keratinocytes, and increased the expression of matrix metalloproteinase-9. Mechanistically, the binding of lnc-URIDS to YTH domain family protein-2 enhanced the degradation of YTH domain family protein-2 in the lysosomes, which increased the translational activity of the messenger RNA of matrix metalloproteinase-9 and ultimately induced the degradation of collagen for wound healing. The results provided an insight into the crosstalk and cooperation between fibroblasts and keratinocytes in collagen homeostasis in diabetic wounds and clarified the mechanism by which lnc-URIDS degrades collagen for diabetic wound healing.

Maleic hydrazide prompting growth and delaying senescence of mother frond in S. Polyrriza 7498.

The effect and function mechanism of maleic hydrazide on the growth of mature leaves is unclear. Duckweed is widely used as a model plant to study the effect of compounds on plant growth. The observation of section and ultrastructure of the fronds, the comparation of SOD enzyme activity and related-gene transcriptional expression level showed that 75 µg/mL maleic hydrazide could prompt the growth of the mother fronds in S. Polyrriza 7498. The half-mother fronds (without meristematic tissue, cut from the mother fronds) with little meristematic tissue could repair themselves and delay their senescence by 75 µg/mL MH. The mother fronds turned more greener with 50 µg/mL MH and exogenous 0.1 µmol/L 6-BA (a kind of cytokinin) treatment, as well as with the increasing of fresh and dry weight in S. Polyrriza 7498. RNA-Seq data found that the happy growth of the mother fronds caused by MH, was probably resulted from up-regulating the expression of gene related to the synthesis and signaling transduction of cytokinin in S. Polyrriza 7498. Which are responsible for the maintaining membrane system integrate and transport protein function. The work gives lights to the study of function mechanism of MH prompting mature leaves growth and delaying mature leaves senescence in plant. And it provides a strategy to increase biomass with the application of low concentration MH and 6-BA in the same time in agriculture.

Association between adiponectin and newly diagnosed type 2 diabetes in population with the clustering of obesity, dyslipidaemia and hypertension: a cross-sectional study.

OBJECTIVES: Adiponectin is closely related to glucose metabolism and traditional diabetes risk factors (obesity, hypertension and dyslipidaemia). We aimed to explore the association between adiponectin levels and newly diagnosed type 2 diabetes mellitus (T2DM) and pre-diabetes in subgroups classified according to T2DM risk factors. SETTING: Sun Yat-sen Memorial Hospital of Sun Yat-sen University. PARTICIPANTS: 3680 individuals (1753 men and 1927 women) aged 18-70 years from Guangzhou and Dongguan, China, were enrolled from December 2018 to October 2019. PRIMARY AND SECONDARY OUTCOME MEASURES: T2DM was defined as fasting plasma glucose (FPG)≥7.0 mmol/L or HbA1c≥6.5%, and pre-diabetes was defined as 6.1 mmol/L≤FPG<7.0 mmol/L or 5.7≤HbA1c<6.5%. RESULTS: With the increasing number of T2DM risk factors, the proportion of the population with high-quartile adiponectin levels gradually decreased (p<0.001). A low level of adiponectin was significantly associated with diabetes and pre-diabetes in a population with ≥1 T2DM risk factor, whereas its association was not consistently significant in the population with all three T2DM risk factors. For instance, participants were more likely to have diabetes or prediabetes with low levels of adiponectin when they had ≥ one T2DM risk factor (quartile 2 vs. 1: OR 0.71 [95%CI: 0.56-0.89]; P=0.003; quartile 3 vs. 1: OR 0.57 [95%CIs: 0.44-0.72]; P<0.001; and quartile 4 vs. 1: OR 0.52 [95%CIs: 0.40-0.67]; P<0.001). CONCLUSION: Adiponectin was negatively associated with diabetes and pre-diabetes in a population with few T2DM risk factors, while their relationship gradually attenuated with the accumulation of T2DM risk factors, especially in a population with coexisting diseases such as obesity, hypertension and dyslipidaemia.

Different computational relations in language are captured by distinct brain systems.

A critical way for humans to acquire information is through language, yet whether and how language experience drives specific neural semantic representations is still poorly understood. We considered statistical properties captured by 3 different computational principles of language (simple co-occurrence, network-(graph)-topological relations, and neural-network-vector-embedding relations) and tested the extent to which they can explain the neural patterns of semantic representations, measured by 2 functional magnetic resonance imaging experiments that shared common semantic processes. Distinct graph-topological word relations, and not simple co-occurrence or neural-network-vector-embedding relations, had unique explanatory power for the neural patterns in the anterior temporal lobe (capturing graph-common-neighbors), inferior frontal gyrus, and posterior middle/inferior temporal gyrus (capturing graph-shortest-path). These results were relatively specific to language: they were not explained by sensory-motor similarities and the same computational relations of visual objects (based on visual image database) showed effects in the visual cortex in the picture naming experiment. That is, different topological properties within language and the same topological computations (common-neighbors) for language and visual inputs are captured by different brain regions. These findings reveal the specific neural semantic representations along graph-topological properties of language, highlighting the information type-specific and statistical property-specific manner of semantic representations in the human brain.