Solid acid catalyst was prepared from Kraft lignin by chemical activation with phosphoric acid, pyrolysis and sulfuric acid. This catalyst had high acid density as characterized by scanning electron microscope (SEM), energy-dispersive x-ray spectrometry (EDX) and Brunauer, Emmett, and Teller (BET) method analyses. It was further used to catalyze the esterification of oleic acid and one-step conversion of non-pretreated Jatropha oil to biodiesel. The effects of catalyst loading, reaction temperature and oil-to-methanol molar ratio, on the catalytic activity of the esterification were investigated.
In metabolomics, identification of metabolic pathways altered by disease, genetics, or environmental perturbations is crucial to uncover the underlying biological mechanisms. A number of pathway analysis methods are currently available, which are generally based on equal-probability, topological-centrality, or model-separability methods. In brief, prior identification of significant metabolites is needed for the first two types of methods, while each pathway is modeled separately in the model-separability-based methods. In these methods, interactions between metabolic pathways are not taken into consideration. The current study aims to develop a novel metabolic pathway identification method based on multi-block partial least squares (MB-PLS) analysis by including all pathways into a global model to facilitate biological interpretation. The detected metabolites are first assigned to pathway blocks based on their roles in metabolism as defined by the KEGG pathway database. The metabolite intensity or concentration data matrix is then reconstructed as data blocks according to the metabolite subsets. Then, a MB-PLS model is built on these data blocks. A new metric, named the pathway importance in projection (PIP), is proposed for evaluation of the significance of each metabolic pathway for group separation. A simulated dataset was generated by imposing artificial perturbation on four pre-defined pathways of the healthy control group of a colorectal cancer study. Performance of the proposed method was evaluated and compared with seven other commonly used methods using both an actual metabolomics dataset and the simulated dataset. For the real metabolomics dataset, most of the significant pathways identified by the proposed method were found to be consistent with the published literature. For the simulated dataset, the significant pathways identified by the proposed method are highly consistent with the pre-defined pathways. The experimental results demonstrate that the proposed method is effective for identification of significant metabolic pathways, which may facilitate biological interpretation of metabolomics data.
Herein, a rapid and sensitive current-volt measurement was developed for identifying the IS6110 DNA sequence to diagnose Mycobacterium tuberculosis (TB). An aminated capture probe was immobilized on a 1,1'-carbonyldiimidazole-functionalized interdigitated electrode (IDE) silica substrate, and the target sequence was detected by complementation. It was found that all tested concentrations displayed a higher response in current changes than the control, and the limit of detection was 10 fM. The sensitivity ranged from 1 to 10 fM. The control sequences with single-, triple-mismatch and noncomplementary sequences showed great discrimination. This rapid and easy DNA detection method helps to identify M. tuberculosis for early-stage diagnosis of TB.
A protocol for the construction of an angular tricyclic benzofuran skeleton based on the C-H activation strategy has been established. Different phthalide lactones on this skeleton can be easily assembled with various side chains by using C-H activation with aldehydes and subsequent reduction. This skeleton provides a versatile and crucial motif for the total synthesis of naturally occurring angular tricyclic benzofurans and their derivatives. Based on this protocol, the improved total syntheses of daldinin A and annullatin D were achieved in yields of 17.3 and 7.6%, respectively.
Hemoglobinopathies are among the most common autosomal-recessive disorders worldwide. A comprehensive next-generation sequencing (NGS) test would greatly facilitate screening and diagnosis of these disorders. An NGS panel targeting the coding regions of hemoglobin genes and four modifier genes was designed. We validated the assay by using 2522 subjects affected with hemoglobinopathies and applied it to carrier testing in a cohort of 10,111 couples who were also screened through traditional methods. In the clinical genotyping analysis of 1182 β-thalassemia subjects, we identified a group of additional variants that can be used for accurate diagnosis. In the molecular screening analysis of the 10,111 couples, we detected 4180 individuals in total who carried 4840 mutant alleles, and identified 186 couples at risk of having affected offspring. 12.1% of the pathogenic or likely pathogenic variants identified by our NGS assay, which were undetectable by traditional methods. Compared with the traditional methods, our assay identified an additional at-risk 35 couples. We describe a comprehensive NGS-based test that offers advantages over the traditional screening/molecular testing methods. To our knowledge, this is among the first large-scale population study to systematically evaluate the application of an NGS technique in carrier screening and molecular diagnosis of hemoglobinopathies.
The first evidence for the Higgs boson decay to a Z boson and a photon is presented, with a statistical significance of 3.4 standard deviations. The result is derived from a combined analysis of the searches performed by the ATLAS and CMS Collaborations with proton-proton collision datasets collected at the CERN Large Hadron Collider (LHC) from 2015 to 2018. These correspond to integrated luminosities of around 140 fb^{-1} for each experiment, at a center-of-mass energy of 13 TeV. The measured signal yield is 2.2±0.7 times the standard model prediction, and agrees with the theoretical expectation within 1.9 standard deviations.