Synthesis of L-aspartic acid-based bimetallic hybrid nanoflowers to immobilize snailase for the production of rare ginsenoside compound K.

The conversion of the common ginsenoside Rb1 to the rare ginsenoside compound K (CK) using snailase (Sna) is an efficient method for industrial production. In order to improve the stability and recoverability of Sna during the catalytic conversion of ginsenosides, the cage-like immobilised Sna material ZIF-ZnCo-Sna and the hybrid nanoflower-based immobilised Sna material Asp@ZIF-ZnCo-Sna modified with L-aspartic acid (Asp) were synthesised using a one-step method. The addition of Asp provides a richer ligand pattern and the morphology of the material changed from a cage to a hybrid nanoflower. The modified hybrid nanoflower Asp@ZIF-ZnCo-Sna has a larger specific surface area, resulting in an enzyme loading of 142.57 mg g-1. The more abundant mesopores allowed the enzyme to maintain a good conformation and the enzyme activity was 79.8% of that of the free Sna. In addition, the total conversion rate of Asp@ZIF-ZnCo-Sna to ginsenoside Rb1 was as high as 88.35%, whereas that of ZIF-ZnCo-Sna was 79.12%. Moreover, after 6 cycles, the catalytic conversion of ZIF-ZnCo-Sna and Asp@ZIF-ZnCo-Sna and the crystalline shape remained the same, indicating that both composites have good stability and catalytic properties. This new approach of improving the MOF morphology and enzymatic activity by a one-step addition of small biological molecules provides a simple, rapid, and effective strategy for biocatalysis. It also provides a certain reference value for the immobilized Sna to produce rare ginsenoside CK.

Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus.

BACKGROUND: Regulation of gene expression plays an essential role in controlling the phenotypes of plants. Brassica napus (B. napus) is an important source for the vegetable oil in the world, and the seed oil content is an important trait of B. napus. RESULTS: We perform a comprehensive analysis of the transcriptional variability in the seeds of B. napus at two developmental stages, 20 and 40 days after flowering (DAF). We detect 53,759 and 53,550 independent expression quantitative trait loci (eQTLs) for 79,605 and 76,713 expressed genes at 20 and 40 DAF, respectively. Among them, the local eQTLs are mapped to the adjacent genes more frequently. The adjacent gene pairs are regulated by local eQTLs with the same open chromatin state and show a stronger mode of expression piggybacking. Inter-subgenomic analysis indicates that there is a feedback regulation for the homoeologous gene pairs to maintain partial expression dosage. We also identify 141 eQTL hotspots and find that hotspot87-88 co-localizes with a QTL for the seed oil content. To further resolve the regulatory network of this eQTL hotspot, we construct the XGBoost model using 856 RNA-seq datasets and the Basenji model using 59 ATAC-seq datasets. Using these two models, we predict the mechanisms affecting the seed oil content regulated by hotspot87-88 and experimentally validate that the transcription factors, NAC13 and SCL31, positively regulate the seed oil content. CONCLUSIONS: We comprehensively characterize the gene regulatory features in the seeds of B. napus and reveal the gene networks regulating the seed oil content of B. napus.

PlantDeepSEA, a deep learning-based web service to predict the regulatory effects of genomic variants in plants.

Characterizing regulatory effects of genomic variants in plants remains a challenge. Although several tools based on deep-learning models and large-scale chromatin-profiling data have been available to predict regulatory elements and variant effects, no dedicated tools or web services have been reported in plants. Here, we present PlantDeepSEA as a deep learning-based web service to predict regulatory effects of genomic variants in multiple tissues of six plant species (including four crops). PlantDeepSEA provides two main functions. One is called Variant Effector, which aims to predict the effects of sequence variants on chromatin accessibility. Another is Sequence Profiler, a utility that performs 'in silico saturated mutagenesis' analysis to discover high-impact sites (e.g., cis-regulatory elements) within a sequence. When validated on independent test sets, the area under receiver operating characteristic curve of deep learning models in PlantDeepSEA ranges from 0.93 to 0.99. We demonstrate the usability of the web service with two examples. PlantDeepSEA could help to prioritize regulatory causal variants and might improve our understanding of their mechanisms of action in different tissues in plants. PlantDeepSEA is available at http://plantdeepsea.ncpgr.cn/.

Self-enhancement of CO reversible absorption accompanied by phase transition in protic chlorocuprate ionic liquids for effective CO separation from N2.

An efficient strategy for the high-capacity capture of CO is reported, and a phase change in protic chlorocuprate ionic liquids (PCILs) from liquid to solid is found during CO absorption. The highest CO capacity is 0.96 molCO molIL-1, being at least 150 times higher than that in [BMIM][PF6]. Both absorption and membrane permeation reveal that the PCILs are potential for the selective separation of CO from N2.

Factors influencing clinical outcomes of acute ischemic stroke treated with intravenous recombinant tissue plasminogen activator / 中华医学杂志(英文版)

<p><b>BACKGROUND</b>Thrombolysis with recombinant tissue plasminogen activator (rt-PA) has gained international recognition, clinical outcomes following this thrombolytic therapy varied from patient to patient. Factors affecting clinical outcomes have not been well understood yet, so this retrospective case-control study aimed to investigate factors that may influence clinical outcomes of acute ischemic stroke treated with intravenous rt-PA.</p><p><b>METHODS</b>One hundred and one patients with acute ischemic stroke who received intravenous rt-PA thrombolysis within 4.5 hours from disease onset were included. Patients were divided into good or poor outcome group according to modified Rankin Scale (mRS) score, good outcome group: mRS score of 0-1; poor outcome group: mRS of 2-6. Stroke characteristics were compared between the two groups. Factors for stroke outcomes were analyzed via univariate analysis and Logistic regression.</p><p><b>RESULTS</b>Of the 101 patients studied, patients in good outcome group (n = 55) were significantly younger than patients in poor outcome group (n = 46, (62.82 ± 14.25) vs. (68.81 ± 9.85) years, P = 0.029). Good outcome group had fewer patients with diabetic history (9.09% vs. 28.26%, P = 0.012), fewer patients with leukoaraiosis (7.27% vs. 28.26%, P = 0.005) and presented with lower blood glucose level ((5.72 ± 1.76) vs. (6.72 ± 1.32) mmol/L, P = 0.012), lower systolic blood pressure level ((135.45 ± 19.36) vs. (148.78 ± 19.39) mmHg, P = 0.003), lower baseline NIHSS score (12.02 ± 5.26 vs. 15.78 ± 4.98, P = 0.002) and shorter onset-to-treatment time (OTT) ((2.38 ± 1.21) vs. (2.57 ± 1.03) hours, P = 0.044) than poor outcome group. Logistic regression analysis showed that absence of diabetic history (odds ratio (OR) 0.968 (95% CI 0.941-0.996)), absence of leukoaraiosis (OR 0.835 (95% CI 0.712-0.980)), lower baseline NIHSS score (OR 0.885 (95% CI 0.793-0.989)), lower pre-thrombolysis systolic blood pressure (OR 0.962 (95% CI 0.929-0.997)), and lower blood glucose level (OR 0.699 (95% CI 0.491-0.994)) before thrombolysis were significantly associated with better outcome.</p><p><b>CONCLUSION</b>Patients with no history of diabetes, no leukoaraiosis, low blood glucose level, low systolic blood pressure level and low baseline NIHSS score before thrombolysis have a better outcome.</p>