The study of the extensive data sets generated by spectrometers, which are of the type commonly referred to as big data, plays a crucial role in extracting valuable information on mineral composition in various fields, such as chemistry, geology, archaeology, pharmacy and anthropology. The analysis of these spectroscopic data falls into the category of big data, which requires the application of advanced statistical methods such as principal component analysis and cluster analysis. However, the large amount of data (big data) recorded by spectrometers makes it very difficult to obtain reliable results from raw data. The usual method is to carry out different mathematical transformations of the raw data. Here, we propose to use the affine transformation for highlight the underlying features for each sample. Finally, an application to spectroscopic data collected from minerals or rocks recorded by NASA's Jet Propulsion Laboratory is performed. An illustrative example has been included by analysing three mineral samples, which have different diageneses and parageneses and belong to different mineralogical groups.
Hepatocellular carcinoma (HCC) is one of the most common malignancies. The currently available clinical drugs for HCC frequently cause serious side effects and the treatment outcomes are unsatisfactory. It is urgent to develop effective drugs with high selectivity and low adverse effects for HCC.
Nucleocapsid protein (N) of SARS-CoV-2 is a multivalent protein, which is responsible for viral replication, assembly, packaging and modulates host immune response. In this study, we report conformational measurements of N protein at different pH by observing transition in secondary and tertiary structural contents by biophysical and computational approaches. Spectroscopic measurements revealed that N protein loses its secondary and tertiary structure at extreme acidic pH while maintaining its native conformation at mild acidic and alkaline pH.
The fact that the photoabsorption spectrum of a material contains information about the atomic structure, commonly understood in terms of multiple scattering theory, is the basis of the popular extended X-ray absorption spectroscopy (EXAFS) technique. How much of the same structural information is present in other complementary spectroscopic signals is not obvious. Here we use a machine learning approach to demonstrate that within theoretical models that accurately predict the EXAFS signal, the extended near-edge region does indeed contain the EXAFS-accessible structural information.
In Nature, the four-electron reduction of O is catalyzed at preorganized multimetallic active sites. These complex active sites often feature low-coordinate, redox-active metal centers precisely positioned to facilitate rapid O activation processes that obviate the generation of toxic, partially reduced oxygen species. Very few biomimetic constructs simultaneously recapitulate the complexity and reactivity of these biological cofactors.
Electrical bioimpedance is based on the opposition exerted by body tissues to the passage of an electrical current. This characteristic allows the assessment of the individual's body composition, nutritional status, and hydration status. Electrical bioimpedance can be used to estimate body composition, health-related markers, general health status, diagnosis and prognostic of diseases, evaluation of treatment progress, and others.
