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graminearum. The promoters of the three genes contain the binding sites for the transcription factor ZmDOF and ZmHSF, which are also induced by the two pathogens. The results imply that the three 2OGDs and the two transcription factors might be involved in the resistance to the two pathogens. This study provided a comprehensive understanding of the 2OGD superfamily in maize and laid the foundation for the further functional analysis of their roles in maize resistance to eat rot and stalk rot.Membrane proteins are gatekeepers to the cell and essential for determination of the function of cells. Identification of the types of membrane proteins is an essential problem in cell biology. It is time-consuming and expensive to identify the type of membrane proteins with traditional experimental methods. The alternative way is to design effective computational methods, which can provide quick and reliable predictions. To date, several computational methods have been proposed in this regard. Several of them used the features extracted from the sequence information of individual proteins. Recently, networks are more and more popular to tackle different protein-related problems, which can organize proteins in a system level and give an overview of all proteins. However, such form weakens the essential properties of proteins, such as their sequence information. Sodium Bicarbonate cost In this study, a novel feature fusion scheme was proposed, which integrated the information of protein sequences and protein-protein interaction network. The fused features of a protein were defined as the linear combination of sequence features of all proteins in the network, where the combination coefficients were the probabilities yielded by the random walk with restart algorithm with the protein as the seed node. Several models with such fused features and different classification algorithms were built and evaluated. Their performance for predicting the type of membrane proteins was improved compared with the models only with the sequence features or network information.Four new guaiane-type sesquiterpenes, chamaejasmins A-D (1-4), were isolated from the root of Stellera camaejasme L. collected in Nepal, together with two known terpenes, stelleraguaianone B (5) and 1α,7α,10αH-guaia-4,11-dien-3-one (6). The structures of 1-4 including their absolute configurations were determined by extensive 2D NMR analyses, mass spectroscopy, and TDDFT calculations of their 13C chemical shifts and ECD spectra. Chamaejasmin A (1) showed cytotoxicity against HeLa cells with an IC50 value of 6.3 μM.

Cannflavins are a group of prenylflavonoids derived from Cannabis sativa L.. Cannflavin A (CFL-A), B (CFL-B) and C (CFL-C) have been heralded for their anti-inflammatory properties in pre-clinical evaluations. This scoping review aims to synthesise the evidence base on cannflavins to provide an overview of the current research landscape to inform research strategies to aid clinical translation.

A scoping review was conducted of EMBASE, MEDLINE, Pubmed, CENTRAL and Google Scholar databases up to 26th February 2020. All studies describing original research on cannflavins and their isomers were included for review.

26 full text articles were included. CFL-A and CFL-B demonstrated potent anti-inflammatory activity via inhibition of 12-o-tetradecanoylphorbol 13-acetate induced PGE2 release (CFL-A half maximal inhibitory concentration (IC50) 0.7μM; CFL-B IC50 0.7μM) and microsomal prostaglandin E synthase-1 (CFL-A IC50 1.8μM; CFL-B IC50 3.7μM). Outcomes were also described in preclinical models of anti-oxidatnvestigations. Identification of cannflavin-rich chemovars, novel extraction techniques and recent identification of a biosynthetic pathway will hopefully allow research to be scaled appropriately. In order to fully evaluate the therapeutic properties of cannflavins focused research now needs to be embedded within institutions with a track-record of clinical translation.Three novel pterosin dimmers, named as obtupterosin A (1), B (2) and C (3), together with eight known pterosins (4-11) were isolated from Pteris obtusiloba. Their structures were elucidated on the basis of ESI-MS, 1D and 2D NMR spectral data, CD, X-ray and literature comparisons. Compounds 1 and 2 were a pair of isomers. Compounds 1 and 3 were the novel type of pterosin dimer. The new compounds (1-3) were assessed for their cytotoxic activities and their α-glucosidase inhibition activity. Compounds 1-3 exhibited cytotoxic activity against HCT-116 cells with IC50 value of 27.5 μM, 30.6 μM and 12.8 μM, respectively. However, all were found to be inactive at 200 μM for α-glucosidase inhibition.This paper contributes to the pursuit of leveraging unstructured medical notes to structured clinical decision making. In particular, we present a pipeline for clinical information extraction from medical notes related to preterm birth, and discuss the main challenges as well as its potential for clinical practice. A large collection of medical notes, created by staff during hospitalizations of patients who were at risk of delivering preterm, was gathered and analyzed. Based on an annotated collection of notes, we trained and evaluated information extraction components to discover clinical entities such as symptoms, events, anatomical sites and procedures, as well as attributes linked to these clinical entities. In a retrospective study, we show that these are highly informative for clinical decision support models that are trained to predict whether delivery is likely to occur within specific time windows, in combination with structured information from electronic health records.Heart disease remains one of the significantcauses ofmortality and morbidity amongst the world's population. Predicting heart disease is considered as one of the vital issues in clinical data analysis. Since the number of data is rising gradually, it is muchcomplicatedforanalyzing and processing, and especially, it becomes difficult to maintain the e-healthcare data. Moreover, the prediction model under machine learning seems to be anessentialfacet in this research area. In this scenario, this paper aims to propose a new heart disease prediction model with the inclusion of specificprocesses like Feature Extraction, Record, Attribute minimization, and Classification. Initially, both statistical and higher-order statistical features are extracted under feature extraction. Subsequently, the record and attribute minimization carried out, where Component Analysis PCA plays its major role in solving the "curse of dimensionality."Finally, the prediction process takes place by the Neural Network (NN) model that intake the dimensionally reduced features.