PRODUCTION AND CHARACTERIZATION OF FISH OIL METHYL ESTER

Limited availability of edible oils prevents it from being used as viable source of Biodiesel.  Low cost and abundantly found fish oil produced from soap stock could be a better option for biodiesel processing. Such type of fish oil contains a higher amount of moisture and FFA and requires a pre-treatment prior to biodiesel production. In this study, refining of raw oil, optimization of process variables of transesterification, qualitative as well as quantitative aspects of fish oil methyl ester (FOME) has been evaluated. With optimized production process, it was found that almost complete conversion of fish oil to methyl ester has taken place. The fuel properties of FOME were found to be in accordance with the ASTM, IS and EN standards

Advancing magnetic material discovery through machine learning: Unveiling new manganese-based materials

Magnetic materials are used in a variety of applications, such as electric generators, speakers, hard drives, MRI machines, etc. Discovery of new magnetic materials with desirable properties is essential for advancement in these applications. In this research article, we describe the development and validation of a machine-learning model to discover new manganese-based stable magnetic materials. The machine learning model is trained on the input data from the Materials Project database to predict the magnetization and formation energy of the materials. New hypothetical structures are made using the substitution method, and the properties are predicted using the machine learning model to select the materials with desired properties. Harnessing the power of machine learning allows us to intelligently narrow down the vast pool of potential candidates. By doing so, we deftly reduce the number of materials that warrant in-depth examination using density functional theory, rendering the task more manageable and efficient. The selected materials, seemingly promising with their magnetic potential, undergo a meticulous validation process using the Vienna Ab initio Simulation Package, grounded in density functional theory. Our results underscore the paramount significance of input data in the efficacy of the machine learning model. Particularly in the realm of magnetic materials, the proper initialization of atomic magnetic spins holds the key to converging upon the true magnetic state of each material

Mapping T Cell Responses to Native and Neo-Islet Antigen Epitopes in at Risk and Type 1 Diabetes Subjects

AIMS: Recent studies highlight the potentially important role of neoepitopes in breaking immune tolerance in type 1 diabetes. T cell reactivity to these neoepitopes has been reported, but how this response compares quantitatively and phenotypically with previous reports on native epitopes is not known. Thus, an understanding of the relationship between native and neoepitopes and their role as tolerance breakers or disease drivers in type 1 diabetes is required. We set out to compare T cell reactivity and phenotype against a panel of neo- and native islet autoantigenic epitopes to examine how this relates to stages of type 1 diabetes development. METHODS: Fifty-four subjects comprising patients with T1D, and autoantibody-positive unaffected family members were tested against a panel of neo- and native epitopes by ELISPOT (IFN-γ, IL-10, and IL-17). A further subset of two patients was analyzed by Single Cell Immune Profiling (RNAseq and TCR α/β) after stimulation with pools of native and neoepitope peptides. RESULTS: T cell responses to native and neoepitopes were present in patients with type 1 diabetes and at-risk subjects, and overall, there were no significant differences in the frequency, magnitude, or phenotype between the two sets of peptide stimuli. Single cell RNAseq on responder T cells revealed a similar profile in T1D patients stimulated with either neo- or native epitopes. A pro-inflammatory gene expression profile (TNF-α, IFN-γ) was dominant in both native and neoepitope stimulated T cells. TCRs with identical clonotypes were found in T cell responding to both native and neoepitopes. CONCLUSION/INTERPRETATION: These data suggest that in peripheral blood, T cell responses to both native and neoepitopes are similar in terms of frequency and phenotype in patients with type 1 diabetes and high-risk unaffected family members. Furthermore, using a combination of transcriptomic and clonotypic analyses, albeit using a limited panel of peptides, we show that neoepitopes are comparable to native epitopes currently in use for immune-monitoring studies