Raman spectroscopy and structural study of baryte-hashemite solid solution from pyrometamorphic rocks of the Hatrurim Complex, Israel

The Investigation of the Subtle Structural Discrepancies between Oryza Sativa Recombinant and Plasma-Derived Human Serum Albumins to Design a Novel Nanoparticle as a Taxane Delivery System

To solve the large size faultiness of Oryza sativa recombinant human serum albumin nanoparticle (OsrHSA NP), the structural discrepancies between OsrHSA and plasma-derived human serum albumin (pdHSA) were analyzed deeply in this research. It demonstrated that there were some subtle structural discrepancies located in subdomain IA and IIA between OsrHSA and pdHSA, which included peptide backbone, disulphide bridge and some amino acids. Firstly, the structural discrepancies were investigated through literature comparison, it inferred that the structural discrepancies resulted from the fatty acid (FA) binding to OsrHSA at site 2 of subdomain IA and IIA. To form a cavity for accommodation of FA molecule in OsrHSA, the peptide backbone structure of subdomain IA and IIA would change, accompanied by the conformational transition of disulphide bridges and side chain structure change of some amino acids in subdomain IA and IIA. These alterations induced the exposure of tryptophan (Trp) and tyrosine (Tyr) residues in subdomain IA and IIA and the decrease of net negative charges of molecular surface. The former would promote more OsrHSA molecules aggregate, and the latter would weaken the electrostatic repulsion. As a result, the size of OsrHSA NP was more extensive than that of pdHSA NP (175.84 ± 15.63 nm vs. 31.67 ± 1.31 nm) when the concentration of Dimethyl Sulphoxide (DMSO) was 30% (v/v). In this study, the experimental scheme of OsrHSA NP preparation was improved. There were two changes in the enhanced preparation scheme: pH 8.2 PBS buffer and 63% DMSO. It indicated that the improved OsrHSA NP carrier was comparable to the pdHSA NP carrier. The size and drug loading of paclitaxel-loaded improved OsrHSA NP were 53.57 ± 3.63 nm and 7.25 ± 0.46% (w/w), and those of docetaxel-loaded improved OsrHSA NP were 44.75 ± 2.26 nm and 8.43 ± 0.74% (w/w). Moreover, both NPs exhibited good stability for 168 h at 7.4 pH values. It is established that the improved OsrHSA NP is comparable to the pdHSA NP as a taxane delivery system.

RamanCMP: A Raman spectral classification acceleration method based on lightweight model and model compression techniques

In recent years, Raman spectroscopy combined with deep learning techniques has been widely used in various fields such as medical, chemical, and geological. However, there is still room for optimization of deep learning techniques and model compression algorithms for processing Raman spectral data. To further optimize deep learning models applied to Raman spectroscopy, in this study time, accuracy, sensitivity, specificity and floating point operations numbers(FLOPs) are used as evaluation metrics to optimize the model, which is named RamanCompact(RamanCMP). The experimental data used in this research are selected from the RRUFF public dataset, which consists of 723 Raman spectroscopy data samples from 10 different mineral categories. In this paper, 1D-EfficientNet adapted to the spectral data as well as 1D-DRSN are proposed to improve the model classification accuracy. To achieve better classification accuracy while optimizing the time parameters, three model compression methods are designed: knowledge distillation using 1D-EfficientNet model as a teacher model to train convolutional neural networks(CNN), proposing a channel conversion method to optimize 1D-DRSN model, and using 1D-DRSN model as a feature extractor in combination with linear discriminant analysis(LDA) model for classification. Compared with the traditional LDA and CNN models, the accuracy of 1D-EfficientNet and 1D-DRSN is improved by more than 20%. The time of the distilled model is reduced by 9680.9s compared with the teacher model 1D-EfficientNet under the condition of losing 2.07% accuracy. The accuracy of the distilled model is improved by 20% compared to the CNN student model while keeping inference efficiency constant. The 1D-DRSN optimized with channel conversion method saves 60% inference time of the original 1D-DRSN model. Feature extraction reduces the inference time of 1D-DRSN model by 93% with 94.48% accuracy. This study innovatively combines lightweight models and model compression algorithms to improve the classification speed of deep learning models in the field of Raman spectroscopy, forming a complete set of analysis methods and laying the foundation for future research.

Mineralogical Diversity of Ca2SiO4-Bearing Combustion Metamorphic Rocks in the Hatrurim Basin: Implications for Storage and Partitioning of Elements in Oil Shale Clinkering

This is the first attempt to provide a general mineralogical and geochemical survey of natural Ca2SiO4-bearing combustion metamorphic (CM) rocks produced by annealing and decarbonation of bioproductive Maastrichtian oil shales in the Hatrurim Basin (Negev Desert, Israel). We present a synthesis of data collected for fifteen years on thirty nine minerals existing as fairly large grains suitable for analytical examination. The Hatrurim Ca2SiO4-bearing CM rocks, which are natural analogs of industrial cement clinker, have been studied comprehensively, with a focus on several key issues: major- and trace-element compositions of the rocks and their sedimentary precursors; mineral chemistry of rock-forming phases; accessory mineralogy; incorporation of heavy metals and other trace elements into different phases of clinker-like natural assemblages; role of trace elements in stabilization/destabilization of Ca2SiO4 polymorphic modifications; mineralogical diversity of Ca2SiO4-bearing CM rocks and trace element partitioning during high-temperature–low-pressure anhydrous sintering. The reported results have implications for mineral formation and element partitioning during high-temperature–low-pressure combustion metamorphism of trace element-loaded bituminous marine chalky sediments (“oil shales”) as well as for the joint effect of multiple elements on the properties and hydration behavior of crystalline phases in industrial cement clinkers.