Solar-activated and hydrothermally synthesized effective rGO/Ag2S composites for the destruction of naphthol green B dye and antibacterial applications.

Graphene-based nanocomposites are developing as a new class of materials with several uses. The varied weight percentages of rGO on Ag2S catalysts were synthesized using a simple hydrothermal process and employed for the decomposition of anionic dye naphthol green B (NGB) under solar light. The reduced graphene oxide-based silver sulfide (rGO/Ag2S) nanoparticles were then examined using XRD, SEM, EDS, HR-TEM, XPS, UV-DRS, and PL analysis. Using solar light, the photocatalytic activity of the produced catalyst was examined for the degradation of naphthol green B (NGB) in an aqueous solution. At pH 9, rGO/Ag2S is discovered to be more effective than the other catalysts for the NGB dye mineralization. Analyses have been conducted on the influence of operational parameters on the photo-mineralization of NGB, including the initial pH, initial dye concentration, and catalyst dosage. The dye concentration increased; the efficiency of photocatalytic degradation tended to decrease. Chemical oxygen demand (COD) studies have verified the NGB dye mineralization. Active species trapping revealed that holes, hydroxyl radicals, and superoxide radicals all played major roles in the photocatalytic deterioration of NGB processes. Additionally, a potential mechanism of NGB dye degradation by rGO/Ag2S catalyst is presented. The synthesized compound was further evaluated for antibacterial activity, and the results indicated that rGO/Ag2S were potentially effective antibacterial agents.

Transfer Learning Based Automatic Human Identification using Dental Traits- An Aid to Forensic Odontology.

Forensic Odontology deals with identifying humans based on their dental traits because of their robust nature. Classical methods of human identification require more manual effort and are difficult to use for large number of Images. A Novel way of automating the process of human identification by using deep learning approaches is proposed in this paper. Transfer learning using AlexNet is applied in three stages: In the first stage, the features of the query tooth image are extracted and its location is identified as either in the upper or lower Jaw. In the second stage of transfer learning, the tooth is then classified into any of the four classes namely Molar, Premolar, Canine or Incisor. In the last stage, the classified tooth is then numbered according to the universal numbering system and finally the candidate identification is made by using distance as metrics. These three stage transfer learning approach proposed in this work helps in reducing the search space in the process of candidate matching. Also, instead of making the network classify all the 32 teeth into 32 different classes, this approach reduces the number of classes assigned to the classification layer in each stage thereby increasing the performance of the network. This work outperforms the classical approaches in terms of both accuracy and precision. The hit rate in human identification is also higher compared to the other state-of-art methods.

NGS meta data analysis for identification of SNP and INDEL patterns in human airway transcriptome: A preliminary indicator for lung cancer.

High-throughput sequencing of RNA (RNA-Seq) was developed primarily to analyze global gene expression in different tissues. It is also an efficient way to discover coding SNPs and when multiple individuals with different genetic backgrounds were used, RNA-Seq is very effective for the identification of SNPs. The objective of this study was to perform SNP and INDEL discoveries in human airway transcriptome of healthy never smokers, healthy current smokers, smokers without lung cancer and smokers with lung cancer. By preliminary comparative analysis of these four data sets, it is expected to get SNP and INDEL patterns responsible for lung cancer. A total of 85,028 SNPs and 5738 INDELs in healthy never smokers, 32,671 SNPs and 1561 INDELs in healthy current smokers, 50,205 SNPs and 3008 INDELs in smokers without lung cancer and 51,299 SNPs and 3138 INDELs in smokers with lung cancer were identified. The analysis of the SNPs and INDELs in genes that were reported earlier as differentially expressed was also performed. It has been found that a smoking person has SNPs at position 62,186,542 and 62,190,293 in SCGB1A1 gene and 180,017,251, 180,017,252, and 180,017,597 in SCGB3A1 gene and INDELs at position 35,871,168 in NFKBIA gene and 180,017,797 in SCGB3A1 gene. The SNPs identified in this study provides a resource for genetic studies in smokers and shall contribute to the development of a personalized medicine. This study is only a preliminary kind and more vigorous data analysis and wet lab validation are required.

Elongated styloid process: A study.

The styloid process (SP) on the temporal bone is a highly variable formation. The normal length of the SP ranges from 20 to 30 mm. In spite of its being normally distributed in the population, SPs could be divided into two groups - short SPs with >20 mm and long SPs with <20 mm in length. The SP is often denoted as elongated when it is longer than 30 mm or 33 mm. These dimensions, based on early reports, do not respect the natural variation of the SP. The aim of this study is to investigate the natural variation of the length of the SP.