Treatment of denture stomatitis using iron nanoparticles green-synthesized by Silybum marianum extract

Stomatitis or generalized inflammation of the mouth includes inflammation or pain in the mouth. Natural compounds are one of the best options for stomatitis treatment. Silybum marianum has many medicinal properties in traditional medicine. In recent research, iron nanoparticles were formulated by S. marianum. The research aim was to determine the Fe nanoparticles’ (FeNPs) efficacy in denture stomatitis treatment. FeNPs were characterized by TEM, FE-SEM, and UV-Visible. This was a clinical trial study with 60 patients who received FeNPs. The patients in 14 days were suggested to use mouthwash 1 time per 6 h each time 15–20 drops for 120–180 s and after that they should avoid drinking and eating for 0.5 h; patients in the two groups were offered to apply the drug. At each visit, mycological samples were taken for culture from the palatal mucosa. Inflammation amount and erythema were determined in each session and determined with a graded blade and recorded. The erythema surface of the palatal was significantly decreased in the group at follow-up visits compared to the pretreatment condition

The Practice of Deep Learning Methods in Biodiversity Information Collection

Deep learning is one machine learning method based on the layers used in artificial neural networks. The breakthrough of deep learning in classification led to its rapid application in speech recognition, natural language understanding, and image processing and recognition. In the field of biodiversity informatics, deep learning efforts are being applied in rapid species identification and counts of individuals identified based on image, audio, video, and other data types. However, deep learning methods hold great potential for application in all aspects of biodiversity informatics. We present a case study illustrating how to increase data collection quality and efficiency using well-established technology such as optical character recognition (OCR) and some image classification. Our goal is to image data from the scanned documents of various butterfly atlases, add species, specimens, collections, photographs and other relevant information, and build a database of butterfly images. Information collection involves image annotation and text-based descritpion input. Although the work of image annotation is simple, this process can be accelerated by deep learning-based target segmentation to make the selection process easier, such as changing box select to a double click. The process of information collection is complicated, involving input of species names, specimen collection, specimen description, and other information. Generally, there are many images in atlases, the text layout is rather messy, and overall OCR success is poor. Therefore, the measures we take are as follows: Step A: select the screenshot of the text and then call the OCR interface to generate the text material; Step B: proceed with NLP- (natural language processing) related processing; Step C: perform manual operations on the results, and introduce the NLP function again to this process; Step D: submit the result. The deep learning applications we integrated in our client tool include: target segmentation of the annotated image for automatic positioning and background removal, etc. to improve the quality of the image used for identification; making a preliminary judgment on various attributes of the labeled image and using the results to assist the automatic filling of relevant information in step B, including species information, specimen attributes (specimen image, nature photo, hand drawing pictures, etc.), insect stage (egg, adult, etc.); OCR in step A. Some simple machine learning methods such as k-nearest neighbor can be used to automatically determine gender, pose, and so on. While complex information such as collection place, time, and collector can be analyzed by deep learning-based NLP methods in the future. In our infomation collection process, ten fields are required to submit one single record. Of those, about 4-5 input fields can be dealt with the AI-assistant. It can thus be seen from the above process that deep learning has reduced the workload of manual information annotation by at least 30%. With improvements in accuracy, the era of using automatic information extraction robots to replace manual information annotation and collection is just around the corner

Comparative Analysis of the Mitochondrial Genomes of Callitettixini Spittlebugs (Hemiptera: Cercopidae) Confirms the Overall High Evolutionary Speed of the AT-Rich Region but Reveals the Presence of Short Conservative Elements at the Tribal Level

<div><p>The present study compares the mitochondrial genomes of five species of the spittlebug tribe Callitettixini (Hemiptera: Cercopoidea: Cercopidae) from eastern Asia. All genomes of the five species sequenced are circular double-stranded DNA molecules and range from 15,222 to 15,637 bp in length. They contain 22 tRNA genes, 13 protein coding genes (PCGs) and 2 rRNA genes and share the putative ancestral gene arrangement of insects. The PCGs show an extreme bias of nucleotide and amino acid composition. Significant differences of the substitution rates among the different genes as well as the different codon position of each PCG are revealed by the comparative evolutionary analyses. The substitution speeds of the first and second codon position of different PCGs are negatively correlated with their GC content. Among the five species, the AT-rich region features great differences in length and pattern and generally shows a 2–5 times higher substitution rate than the fastest PCG in the mitochondrial genome, <i>atp8</i>. Despite the significant variability in length, short conservative segments were identified in the AT-rich region within Callitettixini, although absent from the other groups of the spittlebug superfamily Cercopoidea.</p></div

The sequences of the conserved area of the AT-rich regions of the five Callitettixini species.

<p>Numbers at the left-top corners are corresponding to the numbered conserved areas marked in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109140#pone-0109140-g004" target="_blank">figure 4</a>.</p

Relationships inferred from bayesian interface based on a 15071 nucleotide matrix (−LnL = 80211.79).

<p>Maximum likelihood (−LnL = −83131.48) and maximum parsimony analyses yield the same topology (tree length  = 16509, 6495 sites constant and 5058 sites informative). The numbers near the nodes give Bayesian posterior probability, the bootstrap values from pseudoreplicates of the maximum likelihood and maximum parsimony analyses.</p

Schematic models of the AT-rich region and the relative position of the conserved segments.

<p>The black blocks indicate the conserved parts. The thick lines show the position of the repeat regions. The empty blocks indicate the none-conserved parts. The gray block represents the poly(N) area.</p

Nucleotide compositions of the codon positions of the PCGs.

<p>Nucleotide compositions of the codon positions of the PCGs.</p

Pairwise distance (K2p) of the AT-rich region, <i>cox1</i> and <i>atp8.</i>

<p>Pairwise distance (K2p) of the AT-rich region, <i>cox1</i> and <i>atp8.</i></p

Average nucleotide composition and biases of the coding genes and AT-rich region of the mitogenomes of the five Callitettixini species.

<p>*: represented with the J strand.</p>#<p>: AT-skew  =  (A-T)/(A+T), GC-skew  =  (G - C)/(G+C).</p><p>Average nucleotide composition and biases of the coding genes and AT-rich region of the mitogenomes of the five Callitettixini species.</p

Average Amino acid frequencies of proteins coded by the mitogenomes of the five Callitettixini species given in percentage.

<p>Average Amino acid frequencies of proteins coded by the mitogenomes of the five Callitettixini species given in percentage.</p