Paper Foxing Stains on a Historic Manuscript from the Early Qajar Era: Abiotic or Biotic Foxing?

The aim of this study was to identify the nature and cause of foxing spots in a historical manuscript. This manuscript was a Holy Quran from the beginning of the Qajar period and the end of the 18th century. Samples were incubated for 14 days and were evaluated for the presence of fungal activity. UV fluorescence photography, micro X-ray fluorescence spectroscopy and Fourier transform infrared spectroscopy were also used to investigate the characteristics and causes of foxing spots. The results showed that there was no fungal activity in the foxing spots of this manuscript. Based on the morphology of the stain in UV fluorescence photography, these foxing stains are of the Bullseye type, usually associated with metal ions. µXRF spectroscopy also showed a high accumulation of iron and copper at the site of these spots. This indicates abiotic foxing in this manuscript. Based on FTIR spectroscopy and peak deconvolution and fitting by Gaussian function, abiotic foxing increases the cellulose oxidation rate. Intensification of cellulose oxidation in foxing stains can be considered as one of the reasons for paper discoloration.

An algorithms to improve the energy resolution of two-step cascade spectrum

The present paper proposes an algorithm to improve the energy resolution of two-step cascade spectrum. The energy resolution plays an important role in the domain of gamma spectrum analysis. The better the energy resolution is, the better the ability of peak resolving is. The algorithm is constructed based on an analyze of energy resolution of the summation amplitude of coincident pulses spectrometer using the analogue technique. The algorithm proposed has been tested on some two-step cascade spectra of 164Dy nucleus obtained from the (n, ) reaction experiment using the gamma – gamma coincidence spectrometer at Dalat Nuclear Research Institute. Two-step cascade spectra corresponding to the cascade decays from the compound state to final states whose energies are 0, 74, and 242 keV have been evaluated. The results obtained show that the energy resolution of the two-step cascade spectrum has been reduced by 1.05 to 2.04 times within the energy range of 586 to 6830 keV. Our algorithm can therefore be applied to improve the ability of peak deconvolution, the accuracy, and the realibility in analyzing two-step cascade spectra.

A Bayesian approach to accurate and robust signature detection on LINCS L1000 data

Motivation LINCS L1000 dataset contains numerous cellular expression data induced by large sets of perturbagens. Although it provides invaluable resources for drug discovery as well as understanding of disease mechanisms, the existing peak deconvolution algorithms cannot recover the accurate expression level of genes in many cases, inducing severe noise in the dataset and limiting its applications in biomedical studies. Results Here, we present a novel Bayesian-based peak deconvolution algorithm that gives unbiased likelihood estimations for peak locations and characterize the peaks with probability based z-scores. Based on the above algorithm, we build a pipeline to process raw data from L1000 assay into signatures that represent the features of perturbagen. The performance of the proposed pipeline is evaluated using similarity between the signatures of bio-replicates and the drugs with shared targets, and the results show that signatures derived from our pipeline gives a substantially more reliable and informative representation for perturbagens than existing methods. Thus, the new pipeline may significantly boost the performance of L1000 data in the downstream applications such as drug repurposing, disease modeling and gene function prediction. Availability and implementation The code and the precomputed data for LINCS L1000 Phase II (GSE 70138) are available at https://github.com/njpipeorgan/L1000-bayesian. Supplementary information Supplementary data are available at Bioinformatics online.

A Bayesian approach to accurate and robust signature detection on LINCS L1000 data

LINCS L1000 dataset produced by L1000 assay contains numerous cellular expression data induced by large sets of perturbagens. Although it provides invaluable resources for drug discovery as well as understanding of disease mechanisms, severe noise in the dataset makes the detection of reliable gene expression signals difficult. Existing methods for the peak deconvolution, either k-means based or Gaussian mixture model, cannot reliably recover the accurate expression level of genes in many cases, thereby limiting their robust applications in biomedical studies. Here, we have developed a novel Bayes’ theory based deconvolution algorithm that gives unbiased likelihood estimations for peak positions and characterizes the peak with a probability based z-scores. Based on above algorithm, a pipeline is built to process raw data from L1000 assay into signatures that represent the features of perturbagen. The performance of the proposed new pipeline is rigorously evaluated using the similarity between bio-replicates and between drugs with shared targets. The results show that the new signature derived from the proposed algorithm gives a substantially more reliable and informative representation for perturbagens than existing methods. Thus, our new Bayesian-based peak deconvolution and z-score calculation method may significantly boost the performance of invaluable L1000 data in the down-stream applications such as drug repurposing, disease modeling, and gene function prediction.