Spectrographic analysis of trace elements in archaeological skeletal material from Florida: a preliminary report

Technological advances have resulted in the development of an additional approach to determining the dietary practices of prehistoric populations. Bones are known to retain certain trace elements, the analysis of which should yield valuable clues to subsistence. Since there has never been a study of this nature attempted on Indian remains in Florida, a qualitative trace element analysis, using an optical emission spectrograph, was made of bone samples from indigenous populations representing both inland and coastal environments in this region. Results of this study showed that magnesium was present in all samples, copper was present in nearly half the samples, and manganese and zinc were not detected. Thus, although the diets of these groups may have varied, the trace element content did not reflect significant differences as have been reported for other types of analyses on Indian populations from diverse geographic regions.     

Biosynthesis of silver nanoparticles using Malva parviflora and their antifungal activity

Cheeseweed mallow (Malva parviflora L.) was used to biosynthesize silver nanoparticles. The biosynthesized silver nanoparticles were classified by UV–vis Spectroscopy and Fourier-Transform Infrared Spectroscopy (FT-IR). The shape and size distribution were visualized by Transmission Electron Microscopy (TEM), Field Emission Scanning Electron Microscopy (FE-SEM), and Zeta potential analysis. The chemical composition of M. parviflora leaf extract was identified by Gas Chromatography and Mass Spectroscopy (GC/MS). Finally, in vitro antifungal assay was done to assess the potential of biosynthesized silver nanoparticles and crude leaf extract of M. parviflora for inhibiting the mycelial growth of phytopathogenic fungi. The UV–vis analysis manifests the formation of silver nanoparticles. FTIR analysis established that chemicals of the leaf extract stabilized the biosynthesized silver nanoparticles by binding with the free silver ions. The TEM, FE-SEM and zeta potential analyzer confirmed that the biosynthesized silver nanoparticles were mostly spherical with an average diameter of 50.6 nm. The biosynthesized silver nanoparticles and leaf extract of M. parviflora effectively mitigate the mycelial growth of Helminthosporium rostratum, Fusarium solani, Fusarium oxysporum, and Alternaria alternata. The maximum reduction in mycelial growth by biosynthesized nanoparticles was observed against H. rostratum (88.6%). Whereas, the leaf extract of M. parviflora was most effective against F. solani (65.3%). Thus, the biosynthesis of nanoparticle assisted by M. parviflora is a feasible and eco-friendly method for the synthesis of silver nanoparticles. Further the silver nanoparticles and leaf extract of M. parviflora could be explored for the development of the fungicide.     

Validation of low-dose lung cancer PET-CT protocol and PET image improvement using machine learning

PurposeTo conduct a simplified lesion-detection task of a low-dose (LD) PET-CT protocol for frequent lung screening using 30% of the effective PETCT dose and to investigate the feasibility of increasing clinical value of low-statistics scans using machine learning.MethodsWe acquired 33 SD PET images, of which 13 had actual LD (ALD) PET, and simulated LD (SLD) PET images at seven different count levels from the SD PET scans. We employed image quality transfer (IQT), a machine learning algorithm that performs patch-regression to map parameters from low-quality to high-quality images. At each count level, patches extracted from 23 pairs of SD/SLD PET images were used to train three IQT models – global linear, single tree, and random forest regressions with cubic patch sizes of 3 and 5 voxels. The models were then used to estimate SD images from LD images at each count level for 10 unseen subjects. Lesion-detection task was carried out on matched lesion-present and lesion-absent images.ResultsLD PET-CT protocol yielded lesion detectability with sensitivity of 0.98 and specificity of 1. Random forest algorithm with cubic patch size of 5 allowed further 11.7% reduction in the effective PETCT dose without compromising lesion detectability, but underestimated SUV by 30%.ConclusionLD PET-CT protocol was validated for lesion detection using ALD PET scans. Substantial image quality improvement or additional dose reduction while preserving clinical values can be achieved using machine learning methods though SUV quantification may be biased and adjustment of our research protocol is required for clinical use.