ABSTRACT
Objective@#To study the effect of artificial intelligence in the pathological diagnosis of periapical cysts and to explore the application of artificial intelligence in the field of oral pathology.@*Methods@#Pathological images of eighty-seven periapical cysts were selected as subjects to read, and a neural network with a U-net structure was constructed. The 87 HE images and labeled images of periapical cysts were divided into a training set (72 images) and a test set (15 images), which were used in the training model and test model, respectively. Finally, the target level index F1 score, pixel level index Dice coefficient and receiver operating characteristic (ROC) curve were used to evaluate the ability of the U-net model to recognize periapical cyst epithelium.@*Results @# The F1 score of the U-net network model for recognizing periapical cyst epithelium was 0.75, and the Dice index and the areas under the ROC curve were 0.685 and 0.878, respectively.@*Conclusion@#The U-net network model constructed by artificial intelligence has a good segmentation result in identifying periapical cyst epithelium, which can be preliminarily applied in the pathological diagnosis of periapical cysts and is expected to be gradually popularized in clinical practice after further verification with large samples.
ABSTRACT
Aim: Development of commercial hybrid of sunflower on basis of best inbred combination remains a key challenge to sunflower breeders. In the current investigation, heterosis of F1 hybrids, parental genetic diversity and correlation between genetic distance and level of heterosis were estimated. Methodology: Thirty five parental genotypes (3 CMS A lines and 32 R lines) and their hybrids were assessed for physio-morphological, yield and quality traits. Heterosis was measured as mid-parent and better parent heterosis. Among parents, SSR marker based genetic distances were calculated using DARwin software. Correlation between heterosis and genetic distances was carried out by Karl Pearson’s simple correlation method. Results: Range of genetic distances, based on SSR marker analysis, varied from 0.32-0.73. Genetic distance had significant positive correlation with the heterosis for oil content (r = 0.22 p<0.05) and linoleic acid (r = 0.32 p<0.05), but negative correlation was observed for days to maturity, test weight, volume weight, stearic acid and oleic acid. There was no significant correlation between genetic distance and heterosis for seed yield and other agronomic traits. Interpretation: Although, genetic distance is poor predictor of heterosis, dependence of oil content on genetic distance among parental lines may be used for designing an effective breeding program for sunflower.
ABSTRACT
@#<p><strong>Background and Objective:</strong> Microorganisms, including bacteria, serve as major players in various processes affecting both the quality of aquatic sediment as well as the fate of pollutants released into such matrix. This study, evaluated the similarity in bacterial community structure between sediments collected from aquaculture and non aquaculture sites of a tropical lake. Describing and comparing the bacterial community present in each site may provide clues on the impact of aquaculture practices on aquatic ecosystems.<br /><strong>Methodology:</strong> Microbial DNA was extracted using PowerSoil® DNA Isolation Kit for all sediment samples. DNA isolates were used as template in the analysis of the hypervariable region of 16S rDNA through nested polymerase chain reaction (PCR) and denaturing gradient gel electrophoresis (DGGE). Excised representative 16S rDNA DGGE bands were sequenced and identified through BLAST analysis.<br /><strong>Results:</strong> Based on the generated mean Dice similarity coefficient of 57.77%, the bacterial community structure between aquaculture and non-aquaculture sediments was highly similar but certain taxa were found unique for each site. Bacteria belonging to Proteobacteria and Firmicutes dominated the aquaculture sediments while Proteobacteria, Firmicutes, and Chloroflexi dominated the non-aquaculture sediments. Certain physicochemical parameters operating in the two sites may have influenced the shift in representative microbes. Shewanella baltica and Trichococcus sp. were found only in aquaculture sediment owing to their ability to tolerate quantities of ammonia and high organic matter from their environment.<br /><strong>Conclusions:</strong> This study described the applicability of 16S rDNA PCR-DGGE as a culture-independent technique for describing and comparing the similarity between bacterial communities in sediment. Based on the generated similarity index, the bacterial community between aquaculture and non-aquaculture sediments of Taal Lake was highly similar but interestingly, harbored unique bacterial populations as seen in the DGGE profiles. The shift in dominant taxa and unique representatives per site may have been influenced by certain differences between each site's physico-chemical parameters.</p>