Morphological, Biochemical, and Molecular Diversity Assessment of Egyptian Bottle Gourd Cultivars.

The genetic variability and relationships between ten bottle gourd cultivars were evaluated based on morphological, biochemical, and molecular parameters. The results displayed high variability among selected cultivars in terms of photosynthetic pigments, total free amino acids, total phenol content, isozymes pattern, and protein electrophoresis. Furthermore, differences in molecular markers were revealed by the SCoT technique. The peroxidase (POD) and polyphenyl oxidase (PPO) isozymes patterns did not detect significant differences in bands among cultivars. The protein patterns revealed seventeen bands ranging from 126 to 9 kDa and five polymorphic bands representing 29.41%. On the other hand, eight SCoT primers were used to evaluate the genetic variability and relationships between the ten Egyptian bottle gourd cultivars. The results of SCoT analysis detected 44 amplicons with 50% polymorphism. In addition, the results of the phylogenetic tree that is constructed based on the similarity coefficient revealed by SCoT analysis confirm the results of biochemical analysis indicating a genetic relationship between the most efficient bottle gourd cultivars (S1 and S2 cultivars). In addition, there is a genetic relationship among the less efficient bottle gourd cultivars (S4 and S5 cultivars). These results could be beneficial to distinguish among bottle gourd cultivars in the plant breeding programs.

DeepMC-iNABP: Deep learning for multiclass identification and classification of nucleic acid-binding proteins.

Nucleic acid-binding proteins (NABPs), including DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs), play vital roles in gene expression. Accurate identification of these proteins is crucial. However, there are two existing challenges: one is the problem of ignoring DNA- and RNA-binding proteins (DRBPs), and the other is a cross-predicting problem referring to DBP predictors predicting DBPs as RBPs, and vice versa. In this study, we proposed a computational predictor, called DeepMC-iNABP, with the goal of solving these difficulties by utilizing a multiclass classification strategy and deep learning approaches. DBPs, RBPs, DRBPs and non-NABPs as separate classes of data were used for training the DeepMC-iNABP model. The results on test data collected in this study and two independent test datasets showed that DeepMC-iNABP has a strong advantage in identifying the DRBPs and has the ability to alleviate the cross-prediction problem to a certain extent. The web-server of DeepMC-iNABP is freely available at http://www.deepmc-inabp.net/. The datasets used in this research can also be downloaded from the website.

iLncDA-LTR: Identification of lncRNA-disease associations by learning to rank.

Identifying the associations between lncRNAs and diseases is helpful for the treatment and diagnosis of complex diseases. The existing computational methods mainly focus on the identification of associations between known lncRNAs and known diseases. However, with the application of high-throughput sequencing in lncRNA research, more and more lncRNAs have been detected. Predicting diseases related with newly detected lncRNAs has not been fully explored. Therefore, there is an urgent need for developing powerful computational methods to predict diseases related with newly detected lncRNAs. In this paper, we propose a Learning to Rank (LTR)-based method called iLncDA-LTR to predict diseases related with newly detected lncRNAs. iLncDA-LTR treats this task as an information retrieval task. The newly detected lncRNAs and diseases are considered as queries and documents, respectively. For a given newly detected lncRNA (query), iLncDA-LTR integrates multiple relevant information into LTR for predicting candidate diseases associated with query lncRNA. Experimental results show that iLncDA-LTR outperforms the other exiting state-of-the-art predictors on independent dataset. The corresponding web server of iLncDA-LTR has been constructed as well (http://bliulab.net/iLncDA-LTR/).

Use of PCR-denaturing gradient gel electrophoresis for the discrimination of Candida species isolated from natural habitats.

Candida species are opportunistic microbes that cause chronic infections for a human being. Therefore, the exact identification of Candida species is extremely important for improved therapeutic strategy against these species. Identification based on conventional methods cannot differentiate between some of yeasts species, hence PCR based molecular techniques and sequencing could be an alternative tool for the yeasts identification. A quick molecular method based on the polymerase chain reaction (PCR) and Denaturing Gradient Gel Electrophoresis (DGGE) was applied for distinguishing strains belonging to the Candida species. Six different species designated as AH-20, AH-21, AH-22, AH-23, AH-24 and AH-25 were isolated from soil samples, and their exact identification was detected based on the D1/D2 domain of the 26S rRNA gene amplification and sequence determination. Alignment results and the comparison of 26S rRNA gene sequences of the isolates to 26S rRNA gene sequences available in the GenBank database, as well as the phylogenetic analysis, confirmed the accurate position of the isolates as Candida intermedia strain AH-20, Candida boidinii strain AH-21, Candida tropicalis strain AH-22, Candida mengyuniae strain AH-23, Candida maltosa strain AH-24 and Candida maltosa strain AH-25. Fragments of the D1/D2 domain of 26S rRNA gene were amplified using NL1-GC/LS2 primers and separated by the DGGE. Results showed that all Candida species reported in this study were well discriminated by a distinct band in the DGGE profile. Our results demonstrated that DGGE technique using NL1-GC/LS2 primers could use for the rapid discrimination of yeast strains belonging to the same genera.

Antibacterial activity of Miswak Salvadora persica extracts against isolated and genetically identified oral cavity pathogens.

BACKGROUND: Despite medical progress worldwide, dental caries are still widespread. Miswak is derived from the plant Arak (Salvadora persica). It is used by Muslim peoples as a natural product for the cleansing of teeth, to ensure oral and dental hygiene. OBJECTIVE: This study was conducted to evaluate the antimicrobial effects of ethanol, methanol, and ethanol/methanol extracts of Miswak against three bacterial pathogens of the oral cavity. METHODS: The pathogens were isolated from the oral cavity of volunteers/patients and were identified on the basis of 16S rRNA gene amplification data. Sequence comparisons were made with 16S rRNA gene sequences available in the GenBank database. RESULTS: The results of sequence alignment and phylogenetic analysis identified the three pathogens as being Staphylococcus aureus strain KKU-020, Enterococcus faecalis strain KKU-021 and Klebsiella pneumoniae strain KKU-022. All Miswak extracts showed powerful antimicrobial activity against the three pathogens. The maximum zone of inhibition (40.67 ± 0.88 mm) was observed against E. faecalis KKU-021 with ethanolic extracts whilst methanolic extracts showed the minimum zone of inhibition (10.33 ± 0.88 mm) against K. pneumonia KKU-022. CONCLUSION: Based upon the significant effects of the Miswak extracts, against the oral cavity pathogens in our study, we recommend that Miswak could be used as a dental hygiene method to prevent tooth caries.

Application of PCR-DGGE to analyse the yeast population dynamics in slurry reactors during degradation of polycyclic aromatic hydrocarbons in weathered oil.

Slurry-phase reactors have been used to investigate the biodegradation feasibility of polycyclic aromatic hydrocarbons (PAHs) in weathered crude oil, by mixed culture containing five PAHs-degrading yeast strains. Yeasts were isolated from the oily soil by enrichment culture, using phenanthrene as a sole carbon source, and identified based on the 26S ribosomal DNA (rDNA) sequence. Yeast strains belonged to the genera Candida, Pichia, Rhodotorula and Sporidiobolus. The experiment was carried out for a period of 6 weeks at room temperature with a solid : liquid ratio of 50% w/w. The results showed that high removal efficiency was obtained for all PAHs, including low molecular weight (LMW) and high molecular weight (HMW) compounds (89.3-98.6% and 66.3-89.4% within 6 weeks, respectively). The higher removal efficiency for HMW-PAHs obtained in this work suggested that yeast strains mixture could play an important role to reclaim oil-contaminated sites. Denaturing gradient gel electrophoresis (DGGE) of polymerase chain reaction (PCR)-amplified 26S rRNA genes was used to follow the changes of yeast populations during the slurry reactor process. The results of DGGE indicated that Candida maltosa-like and Pichia guilliermondii were the dominant species but Rhodotorula dairenensis appeared as a weak band and Sporidiobolus salmonicolor and Pichia anomala disappeared during the study. Moreover, the results showed that all of the five strains, including the two belonging to the same genus, could be differentiated from each other in the DGGE profile.