Identification and molecular mechanisms of novel antioxidative peptides from fermented camel milk (Kachchi breed,India) with anti-inflammatory activity in raw macrophages cell lines

The investigation was aimed at assessing anti-inflammatory and antioxidative activities along with the release of peptides with antioxidative properties during the fermentation of camel milk by Lacticaseibacillus casei (NK9). Reverse-phase high-performance liquid chromatography (RP-HPLC) was used to separate the bioactive peptides of 3 and 10 kDa (permeates and retentates). Reverse-phase liquid chromatography–mass spectrometry (RPLC/MS) was used to identify and characterise the pure bioactive peptides, and the effect of fermented camel milk on inflammation produced by lipopolysaccharide (LPS)/endotoxin in RAW 264.7 (Ralph and William's cell line) was also examined. Furthermore, docking revealed that peptides (LLNEK and IYTFPQPQSL) were predicted to inhibit myeloperoxidase (nMPO) activity by engaging with different residues in and around the human myeloperoxidase (hMPO) active site.     

An Unsupervised Writer Identification Based on Generating ClusterableEmbeddings

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems. Due to its importance, numerous studies have been conducted in various languages. Researchers have established several learning methods for writer identification including supervised and unsupervised learning. However, supervised methods require a large amount of annotation data, which is impossible in most scenarios. On the other hand, unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted. This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features. A pairwise architecture-based Autoembedder was applied to generate clusterable embeddings for handwritten text images. Furthermore, the trained baseline architecture generates the embedding of the data image, and the K-means algorithm is used to distinguish the embedding of individual writers. The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks. In addition, traditional evaluation metrics are used in the proposed model. Finally, the proposed model is compared with a few unsupervised models, and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.     

Filler Wetting in Miscible ESBR/SSBR Blends and Its Effect on Mechanical Properties

The selective wetting behavior of silica in emulsion styrene butadiene rubber (ESBR)/solution styrene butadiene rubber (SSBR) blends is characterized by the wetting concept, which is further developed for filled blends based on miscible rubbers. It is found that not only the chemical rubber–filler affinity but also the topology of the filler surface significantly influences the selective filler wetting in rubber blends. The nanopore structure of the silica surface has been recognized as the main reason for the difference in the wetting behavior of the branched ESBR molecules and linear SSBR molecules. However, the effect of nanopore structure becomes more significant in the presence of silane. It is discussed that the adsorption of silane on silica surface constricts the nanopore to some extent that hinders effectively the space filling of the nanopores by the branched ESBR molecules but not by the linear SSBR molecules. As a result, in silanized ESBR/SSBR blends the dominant wetting of silica surface by the tightly bonded layer of SSBR molecules causes a low‐energy dissipation in the rubber–filler interphase. That imparts the low rolling resistance to the blends similar to that of a silica‐filled SSBR compound, while the ESBR‐rich matrix warrants the good tensile behavior, i.e., good abrasion and wear resistance of the blends.

     

Numerical modelling of isothermal release and distribution of helium and hydrogen gases inside the AIHMS cylindrical enclosure

Hydrogen is a highly flammable gas and accidental release in confined space can pose serious combustion hazards. Numerical studies are required to assess the formation of flammable hydrogen cloud within confined spaces. In the present study, numerical investigations on the release of helium and hydrogen gases as high-velocity jets and their subsequent distribution inside an unventilated cylindrical enclosure (AIHMS facility) has been carried out as a first step towards numerical studies on hydrogen distribution in confined spaces for safety assessments. Experimental data for jet release of helium at volume Richardson number 0.1 and subsequent distribution has been used as benchmark data. Sensitivity studies on the influence of grid sizes, time-steps and turbulence models are performed. The performance of the validated numerical model is evaluated using statistical performance parameters. Similarity relations are used to determine input parameters for hydrogen jet for corresponding experimental data with helium jets. Finally, the mixing and flammability aspects of hydrogen distribution inside the enclosure are studied using four numerical indices that quantify mixing and deflagration potential of a distribution. It is concluded that the helium experiments can be used for validation of numerical models for hydrogen safety studies and any one of the similarity relationships, viz., equal buoyancy, equal volumetric flow, or equal concentration can be used for assessing the behaviour of hydrogen release and distribution within confined spaces.     

Poly(N-vinylpyrrolidone)-stabilized colloidal graphene-reinforced poly(ethylene-co-methyl acrylate) to mitigate electromagnetic radiation pollution

Polymer Bulletin - Electrically conducting flexible polymeric nanocomposite has been fabricated through wet mixing method where conducting inclusion was acoustically exfoliated pristine graphene...     

Ubiquitin C-Terminal Hydrolase L1: Biochemical and Cellular Characterization of a Covalent Cyanopyrrolidine-Based Inhibitor

The deubiquitinase (DUB) ubiquitin C-terminal hydrolase L1 (UCHL1) is expressed primarily in the central nervous system under normal physiological conditions. However, UCHL1 is overexpressed in various aggressive forms of cancer with strong evidence supporting UCHL1 as an oncogene in lung, glioma, and blood cancers. In particular, the level of UCHL1 expression in these cancers correlates with increased invasiveness and metastatic behavior, as well as poor patient prognosis. Although UCHL1 is considered an oncogene with potential as a therapeutic target, there remains a significant lack of useful small-molecule probes to pharmacologically validate in vivo targeting of the enzyme. Herein, we describe the characterization of a new covalent cyanopyrrolidine-based UCHL1 inhibitory scaffold in biochemical and cellular studies to better understand the utility of this inhibitor in elucidating the role of UCHL1 in cancer biology.     

Impact of adhesive ingredients on adhesion between rubber and brass-plated steel wire in tire

Proficiency on underlying mechanism of rubber-metal adhesion has been increased significantly in the last few decades. Researchers have investigated the effect of various ingredients, such as hexamethoxymethyl melamine, resorcinol, cobalt stearate, and silica, on rubber-metal interface. The role of each ingredient on rubber-metal interfacial adhesion is still a subject of scrutiny. In this article, a typical belt skim compound of truck radial tire is selected and the effect of each adhesive ingredient on adhesion strength is explored. Out of these ingredients, the effect of cobalt stearate is found noteworthy. It has improved adhesion strength by 12% (without aging) and by 11% (humid-aged), respectively, over control compound. For detailed understanding of the effect of cobalt stearate on adhesion, scanning electron microscopy and energy dispersive spectroscopy are utilized to ascertain the rubber coverage and distribution of elements. X-ray photoelectron spectroscopy results helped us to understand the impact of Cu_XS layer depth on rubber-metal adhesion. The depth profile of the Cu_XS layer was found to be one of the dominant factors of rubber-metal adhesion retention. Thus, this study has made an attempt to find the impact of different adhesive ingredients on the formation of Cu_XS layer depth at rubber-metal interface and establish a correlation with adhesion strength simultaneously.     

Hierarchical syntactic models for human activity recognition through mobility traces

Recognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for