APPLICATIONS OF DIACYLGLYCEROL (DAG) RICH MICROENCAPSULATED VITAMIN-C IN FOOD PROCESSING -A REVIEW

Oils enriched with fat soluble vitamins are of plenty in market but oils with less calorie and enriched with water soluble vitamins and their use in food processing are still not in a practice. Water-soluble vitamins such as vitamin C can also be added to oils through the process of microencapsulation.The present study deals with DAG rich microencapsulated Vitamin-C oil. DAG (diacylglycerol) is devoid of one fatty acid than TAG(triacylglycerol) gives less calories in foods. On the other hand, vitamin–C is an well known antioxidant because of its ability to stabilise free radicals. Rice bran oil also contains oryzanol naturally which prevents cholesterol absorption. Therefore, combination of duo can serve as a duel nutraceutical in food industry and use of such oils (vitamin-C enriched DAG oil ) in fortification of various food products like butter, yogurt, Nutella mix will be very beneficial (less calorie containing vitamin enriched foods) in our day to day life

Autonomously revealing hidden local structures in supercooled liquids

Few questions in condensed matter science have proven as difficult to unravel as the interplay between structure and dynamics in supercooled liquids and glasses. The conundrum: close to the glass transition, the dynamics slow down dramatically and become heterogeneous while the structure appears largely unperturbed. Largely unperturbed, however, is not the same as unperturbed, and many studies have attempted to identify "slow" local structures by exploiting dynamical information. Nonetheless, the question remains open: is the key to the slow dynamics imprinted in purely structural information? And if so, is there a way to determine the relevant structures without any dynamical information? Here, we use a newly developed unsupervised machine learning (UML) algorithm to identify structural heterogeneities in three archetypical glass formers. In each system, the UML approach autonomously designs an order parameter based purely on structural variation within a single snapshot. Impressively, this order parameter strongly correlates with the dynamical heterogeneity. Moreover, the structural characteristics linked to slow particles disappear as we move away from the glass transition. Our results demonstrate the power of machine learning techniques to detect structural patterns even in disordered systems, and provide a new way forward for unraveling the structural origins of the slow dynamics of glassy materials

Modifications structurelles des verres sous déformation périodique par cisaillement

In this thesis we have investigated structural changes in glasses undergoing periodic deformation. We have used molecular dynamics for sampling equilibrated liquid configurations of binary glass forming models and then sheared the system using athermal quasi static protocol with strain amplitudes γ_max. At a certain value of γ_max=γ_y the system yields, identified by a transition from absorbing to diffusive state. In diffusive state the system consists a shear band which is a high strain localized subvolume of the system. The purpose of the thesis has been to examine changes in structural properties in steady states of cyclic shear across this yielding transition. Long range structural feature is characterised by "Hyperuniformity", which describes suppression of density fluctuations. Our results show that in absorbing states the glass is hyperuniform and above yielding the system splits into two hyperuniform phases separated by an interface which coincides with the boundary of the shear band. Therefore, in the sub-volumes inside and outside the band we have hyperuniformity but in the direction perpendicular to the band, the hyperuniformity is lost. Local structural order was examined by computing per particle two-body excess entropy S₂ and tetrahedrality in local structure n_tet . It was found that absorbing states have higher mean local order. Additionally, in steady state particles participating in larger plastic rearrangements on the average have lower local structural order. Specially particles with n_tet=12, a number associated to icosahedral clustering, prefer to remain immobile. As we analysed the system in presence of shear band, we find that outside shear band almost 30% of the particles have local icosahedral clustering whereas inside shear band this percentage is very low (below 5 %) This result marks the different structural arrangements inside and outside shear band.Dans cette thèse, nous avons étudié les changements structurels des verres subissant des déformations périodiques. Nous avons utilisé la dynamique moléculaire pour échantillonner des configurations liquides équilibrées de modèles de formation de verre binaire, puis cisaillé le système en utilisant un protocole quasi statique athermique avec des amplitudes maximales γ_max. À une certaine valeur γ_max=γ_y, le système cède, ce qui se manifeste par une transition de l’état absorbant à l’état diffusif. A l’état diffusif, le système consiste en une bande de cisaillement qui est un sous-volume localisé de déformation élevée du système. Le but de la thèse était d’examiner les changements dans les propriétés structurelles dans les états stationnaires de cisaillement cyclique à travers cette yielding transition. La caractéristique structure à longue portée est caractérisée par "l’hyperuniformité", qui décrit la suppression des fluctuations de densité. Nos résultats montrent que dans les états absorbants, le verre est hyperuniforme et au-dessus, le système se divise en deux phases hyperuniformes séparées par une interface qui coïncide avec la limite de la bande de cisaillement. Par conséquent, dans lessous-volumes à l’intérieur et à l’extérieur de la bande, nous avons une hyperuniformité mais dans la direction perpendiculaire à la bande, l’hyperuniformité est perdue. L’ordre structurel local a été examiné en calculant l’entropie excédentaire à deux corps par particule S₂ et la tétraédricité dans la structure local n_tet. Il a été constaté que les états absorbants ont un ordre local moyen plus élevé. De plus, à l’état stationnaire, les particules participant à des réarrangements plastiques plus importants ont en moyenne un ordre structurel local inférieur. En particulier, les particules engagées dans 12 tétraèdres (n_tet=12), associées à un empilement icosaédrique, tendent à rester immobile. En analysant le système en présence d’une bande de cisaillement, nous constatons que près de 30% des particules sont impliquées dans un agencement icosaédrique local hors de la bande de cisaillement alors que cette fraction tombe à <5 % dans la bande de cisaillement. Ce résultat marque la différence dispositions structurelles à l’intérieur et à l’extérieur de la bande de cisaillement

Anticancer activity of novel surface functionalized metal doped hydroxyapatite

In this manuscript it has been described a novel synthesis of mercury doped hydroxyapatite (Hap) and its application on human liver carcinoma cell line (Hep G2) and human lung fibroblast carcinoma cell line (MRC 5). Nano-sized hydroxyapatite doped with Hg2+ was synthesized by a solution based chemical method along with mercury ion. The surface of nanoparticle of mercury doped hydroxyapatite (MHAp) was functionalized by using phosphonomethyl iminodiacetic acid (PMIDA) for making it stable as dispersed phase with negative zeta potential. Surface functionalization was confirmed by FTIR measurements. Crystalline nature, morphology and surface topology were studied by powder XRD, FESEM and AFM measurements. Particle size of the well dispersed sample was obtained by HRTEM image. The studies on cell viability of Hep G2 and MRC 5 cell in presence of mercury doped HAp nanoparticle (MHAp) were determined through WST assay. It was observed that nanocomposite exhibited a site specific action towards MRC 5 cell lines along with reduction of toxicity toward normal cells

Relating the physical properties of aqueous solutions of ionic liquids with their chemical structures

The physical properties of an aqueous solution of a macromolecule primarily depend on its chemical structure and the mesoscopic aggregates formed by many of such molecules. Ionic liquids (ILs) are the macromolecules that have caught significant research interests for their enormous industrial and biomedical applications. In the present paper, the physical properties, such as density, viscosity, ionic conductivity of aqueous solutions of various ILs, have been investigated. These properties are found to systematically depend on the shape and size of the anion and the cation along with the solution concentration. The ionic conductivity and viscosity behavior of the solutions do not strictly follow the Walden rule that relates the conductivity to the viscosity of the solution. However, the modified Walden rule could explain the behavior. A simple calculation based on the geometry of a given molecule could shed the light on the observed results

Autonomously revealing hidden local structures in supercooled liquids

Few questions in condensed matter science have proven as difficult to unravel as the interplay between structure and dynamics in supercooled liquids. To explore this link, much research has been devoted to pinpointing local structures and order parameters that correlate strongly with dynamics. Here we use an unsupervised machine learning algorithm to identify structural heterogeneities in three archetypical glass formers—without using any dynamical information. In each system, the unsupervised machine learning approach autonomously designs a purely structural order parameter within a single snapshot. Comparing the structural order parameter with the dynamics, we find strong correlations with the dynamical heterogeneities. Moreover, the structural characteristics linked to slow particles disappear further away from the glass transition. Our results demonstrate the power of machine learning techniques to detect structural patterns even in disordered systems, and provide a new way forward for unraveling the structural origins of the slow dynamics of glassy materials

Autonomously revealing hidden local structures in supercooled liquids

Few questions in condensed matter science have proven as difficult to unravel as the interplay between structure and dynamics in supercooled liquids. To explore this link, much research has been devoted to pinpointing local structures and order parameters that correlate strongly with dynamics. Here we use an unsupervised machine learning algorithm to identify structural heterogeneities in three archetypical glass formers—without using any dynamical information. In each system, the unsupervised machine learning approach autonomously designs a purely structural order parameter within a single snapshot. Comparing the structural order parameter with the dynamics, we find strong correlations with the dynamical heterogeneities. Moreover, the structural characteristics linked to slow particles disappear further away from the glass transition. Our results demonstrate the power of machine learning techniques to detect structural patterns even in disordered systems, and provide a new way forward for unraveling the structural origins of the slow dynamics of glassy materials

Novel Helical Trp‐ and Arg‐Rich Antimicrobial Peptides Locate Near Membrane Surfaces and Rigidify Lipid Model Membranes

Antibiotics are losing effectiveness as bacteria become resistant to conventional drugs. To find new alternatives, antimicrobial peptides (AMPs) are rationally designed with different lengths, charges, hydrophobicities (H), and hydrophobic moments (μH), containing only three types of amino acids: arginine, tryptophan, and valine. Six AMPs with low minimum inhibitory concentrations (MICs) and gram‐positive (G(+))> Euk33 (eukaryotic with 33 mol% cholesterol). The two most effective peptides, E2‐35 (16 amino acid [AA] residues) and E2‐05 (22 AAs), are predominantly helical in G(–) IM and G(+) LMMs. AMP/membrane interactions such as membrane elasticity, chain order parameter, and location of the peptides in the membrane are investigated by low‐angle and wide‐angle X‐ray diffuse scattering (XDS). It is found that headgroup location correlates with efficacy and toxicity. The membrane bending modulus KC displays nonmonotonic changes due to increasing concentrations of E2‐35 and E2‐05 in G(–) and G(+) LMMs, suggesting a bacterial killing mechanism where domain formation causes ion and water leakage

Applying artificial intelligence to accelerate and de-risk antibody discovery

As in all sectors of science and industry, artificial intelligence (AI) is meant to have a high impact in the discovery of antibodies in the coming years. Antibody discovery was traditionally conducted through a succession of experimental steps: animal immunization, screening of relevant clones, in vitro testing, affinity maturation, in vivo testing in animal models, then different steps of humanization and maturation generating the candidate that will be tested in clinical trials. This scheme suffers from different flaws, rendering the whole process very risky, with an attrition rate over 95%. The rise of in silico methods, among which AI, has been gradually proven to reliably guide different experimental steps with more robust processes. They are now capable of covering the whole discovery process. Amongst the players in this new field, the company MAbSilico proposes an in silico pipeline allowing to design antibody sequences in a few days, already humanized and optimized for affinity and developability, considerably de-risking and accelerating the discovery process

Copper Nanoparticle (CuNP) Nanochain Arrays with a Reduced Toxicity Response: A Biophysical and Biochemical Outlook on Vigna radiata

Copper deficiency or toxicity in agricultural soil circumscribes a plant’s growth and physiology, hampering photochemical and biochemical networks within the system. So far, copper sulfate (CS) has been used widely despite its toxic effect. To get around this long-standing problem, copper nanoparticles (CuNPs) have been synthesized, characterized, and tested on mung bean plants along with commercially available salt CS, to observe morphological abnormalities enforced if any. CuNPs enhanced photosynthetic activity by modulating fluorescence emission, photophosphorylation, electron transport chain (ETC), and carbon assimilatory pathway under controlled laboratory conditions, as revealed from biochemical and biophysical studies on treated isolated mung bean chloroplast. CuNPs at the recommended dose worked better than CS in plants in terms of basic morphology, pigment contents, and antioxidative activities. CuNPs showed elevated nitrogen assimilation compared to CS. At higher doses CS was found to be toxic to the plant system, whereas CuNP did not impart any toxicity to the system including morphological and/or physiological alterations. This newly synthesized polymer-encapsulated CuNPs can be utilized as nutritional amendment to balance the nutritional disparity enforced by copper imbalance