Effect of Germination and Extrusion on Physicochemical Properties and Nutritional Qualities of Extrudates and Tortilla from Wheat

Wheat is the most common grain in the temperate region. Modifying its constituent through food processing improves its functionality and nutrient access. In this study, the combined effect of germination and extrusion on physicochemical properties and nutritional qualities of extrudates and tortilla from wheat was evaluated. Results showed that germination significantly increased (P <0.05) the γ‐aminobutyric acid content in germinated whole wheat (GW) and extruded germinated whole wheat (EGW) as compared to the control of whole wheat (WW). Germination also significantly increased the protein content, reducing sugar and total soluble sugar content in GW, while extrusion had much increasing impact on reducing sugar content in extruded samples. Specific mechanical energy during extrusion was reduced as feed moisture content increased from 20 to 30%. Higher extruder screw speed (350 rpm) led to better expansion ratio at low moisture content (20%) as compared to low screw speed (200 rpm). Extrusion significantly increased the starch digestibility but decreased the protein digestibility in extrudates. Tortilla made from 100% WW had about the same physical characteristics, namely color and rollability, with tortilla made from 85% WW with 15% GW, 85% WW with 15% extruded whole wheat (EW), and 85% WW with 15% EGW. Tortilla made from 85% WW with 15% GW showed the largest diameter, thinnest thickness and least extensibility. A 15% extruded germinated wheat (350 rpm) addition in 85% WW showed significant increase of γ‐aminobutyric acid content in tortilla compared to the control (100% WW).     

Spectroscopic and Molecular Docking Investigation on the Interaction of Cumin Components with Plasma Protein: Assessment of the Comparative Interactions of Aldehyde and Alcohol with Human Serum Albumin

The interaction of the important plasma protein, human serum albumin (HSA), with two monoterpenes found in cumin oil, i.e., cuminaldehyde (4-isopropylbenzaldehyde) and cuminol (4-isopropylbenzyl alcohol), was studied in this paper. Both experimental and computational methods were utilized to understand the mechanism of binding. The UV absorption profile of HSA changes in the presence of both cuminaldehyde and cuminol, due to the interaction between HSA with both monoterpenes. The intrinsic fluorescence intensity of HSA was also quenched on the sequential addition of both ligands, due to change in the microenvironment of the fluorophore present in the former. Quenching of HSA by cuminaldehyde was much higher in comparison to that in the presence of cuminol. Fluorescence quenching data were analyzed using modified Stern-Volmer and Lineweaver-Burk methods, which suggested that the binding mechanism was of a static type for both ligands. In both cases, the binding was favored by the domination of hydrophobic as well as hydrogen bonding/Van der Waals forces. Both ligands partially unfolded the secondary structure of HSA, although the effect of cuminaldehyde was more pronounced, as compared to cuminol. The preferred binding site of cuminaldehyde and cuminol inside HSA was also the same; namely, drug binding site 1, located in subdomain IIA. The study showed that cuminaldehyde binds strongly with albumin as compared to its alcohol counterpart, which is due to the more hydrophobic nature of the former.     

An Inexact Two-stage Fuzzy-stochastic Programming Model for Water Resources Management

An inexact two-stage fuzzy-stochastic programming (ITFSP) method is developed for water resources management under uncertainty. Fuzzy sets theory is introduced to represent various punishment policies under different water availability conditions. As an extension of conventional two-stage stochastic programming (TSP) method, two special characteristics of the proposed approach make it unique compared with existing approaches. One is it could handle flexible penalty rates, which are much reasonable for both of the authorities and users, and have seldom been considered in the TSP framework. The other is uncertain information expressed as discrete intervals and probability distribution functions can be effectively reflected in the optimization processes and solutions. After formulating the model, a hypothetical case is employed for demonstrating its applicability under two scenarios, where the inflow is divided into four and eight intervals, respectively. The results indicate that reasonable solutions have been obtained. They provide desired allocation patterns with maximized system benefit under two feasibility levels. The solutions present as stable intervals with different risk levels in violating the water demands, and can be used for generating decision alternatives. Comparisons of the solution from the ITFSP with that from the ITSP (inexact two-stage stochastic programming) and TSP approach are also undertaken. It shows that the ITFSP could produce more system benefit than existing methods and deal with flexible penalty policies for better water management and utilization.     

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.     

Enc-Unet: A novel method for Glioma segmentation

The diagnosis' treatment planning, follow-up and prognostication of Gliomas is significantly enhanced on Magnetic Resonance Imaging. In the present research, deep learning-based variant of convolutional neural network methodology is proposed for glioma segmentation where pretrained autoencoder acts as backbone to the 3D-Unet which performs the segmentation task as well as image restoration. Further, Unet accepts input as the combination of three non-native MR images (T2, T1CE, and FLAIR) to extract maximum and superior features for segmenting tumor regions. Further, weighted dice loss employed, focusses on segregating tumor region into three regions of interest namely whole tumor with oedema (WT), enhancing tumor (ET), and tumor core (TC). The optimizer preferred in the proposed methodology is Adam and the learning rate is initially set to $1e-4$, progressively reduced by a cosine decay after 50 epochs. The learning parameters are reduced to a larger extent (up to 9.8 M as compared to 27 M). The experimental results show that the proposed model achieved Dice similarity coefficients: 0.77, 0.92, and 0.84; sensitivity: 0.90, 0.95, and 0.89; specificity: 0.97, 0.99, and 0.99; Hausdorff95: 5.74, 4.89, and 6.00, in the three regions including ET, WT, TC. This proposed Glioma segmentation method is efficient for segregation of tumors.     

Variable chemical process and radiative nonlinear impact on magnetohydrodynamics Cross nanofluid: An approach toward controlling global warming

Solar energy is the basic source of renewable energy, and it is being used for controlling global pollution/warming. As the Cross nanofluid is very useful for cooling solar devices, in this paper analysis of the global warming effect is investigated by incorporating the nonlinear thermal radiation over the exponentially extendable surface because it plays a major role related to solar energy absorption of nanofluid. Furthermore, the mathematical modeling of Cross nanofluid involving magnetic effect and diffusion is discussed by using the fact of chemical reaction. Chemical reaction finds astonishing applications in pollution studies, chemical processing equipment, and polymer production. As a result of this study, it is noticed that more magnetized conducting fluid controls the motion of fluids for both cases of shear thinning and shear thickening. Brownian motion parameter Nb affects the rate of the random motion of nanoparticles. Increased Nb temperature also increases due to these random movements of nanoparticles. That is the reason why pollutant nanoparticles spread in air as a result of global warming increase.     

Membrane Rigidity-Tunable Fusogenic Nanosensor for High Throughput Detection of Fusion-Competent Influenza A Virus

The emergence of fatal viruses that pose continuous threats to global health has fueled the intense effort to develop direct, accurate, and high-throughput virus detection platforms. Current diagnostic methods, including qPCR and rapid antigen tests, indicate how much of the virus is present, whether small fragments or whole viruses. However, these methods do not indicate the probability of the virus to be active, capable of interacting with host cells and initiating the infection cycle. Herein, a sialic acid-presenting fusogenic liposome (sLipo–Chol) nanosensor with purposefully modulated membrane rigidity to rapidly detect the fusion-competent influenza A virus (IAV) is developed. This nanosensor possesses virus-specific features, including hemagglutinin (HA) binding and HA-mediated membrane fusion. It is explored how the fusogenic capability of sLipo–Chol with different membrane rigidities impacts their sensing performance by integrating Förster resonance energy transfer (FRET) pairs into the bilayers. The addition of an intact virus led to instant FRET signal changes, thus enabling the direct detection of diverse IAV subtypes—even in avian fecal samples—within an hour at room temperature. Therefore, the sensing approach, with an understanding of the cellular pathogenesis of influenza viruses, will aid in developing bioinspired nanomaterials for evolution into nanosystems to detect infection-competent viruses.     

Special issue on towards advancements in machine learning for exploiting large-scale and heterogeneous repositories

Neural Computing and Applications -     

Energy optimization in a smart community grid system using genetic algorithm

A smart community grid is an electrical network, which connects several producers, consumers, and prosumers to share energy in an intelligent and secure way. The main challenges of smart community grid are demand response, demand bidding, dynamic electricity tariffs, demand-side management, and prosumers handling. The current state-of-the-art smart grid decision making is focused on consumers and producers behavior while the aim of this research is to achieve prosumer's different goals in an optimized and intelligent way. A genetic algorithm (GA)-based solution is proposed to share energy in an optimized way without affecting the prosumers' preferences. Six prosumers smart community grids data sets are used to validate the performance of the proposed system. The results show that the proposed method significantly improves the loss of energy sharing without compromising the user's preferences.     

Effect of type of polymerization catalyst system on the degradation and stabilization of polyethylenes in the melt state—Part 4: Comparative antioxidant effectiveness on organoleptic extractables

Several polyethylene resins using Ziegler, metallocene, and Phillips catalyst technologies were examined to obtain more detailed information about the effect of different polymerization catalyst systems on the production of extractable thermo-oxidative degradation products formed during melt processing cycles. This produces volatile organoleptic components (VOCs and extractable) such as hydrocarbons, alcohols, aldehydes, ketones, and carboxylic acids. Although some of the oxidation products are in-chain bound, many are produced as free, easily extractable entities or volatile components. The purpose of this study is to identify the nature of the products by gas chromatography–mass spectrometry (GC–MS) and FTIR analysis. The identity of the VOCs formed is necessary to modify the product's quality or establish which are toxic and/or leachable with food products. The results show that the evolution of carbonyl products, nature, and quantity is influenced significantly by the polymer type and catalyst used. Over 300 organoleptics low molar mass degradation products, such as alkane, alkene, carbonyl, and alcohol functionalities were detected by GC–MS analysis coupled with FTIR analysis on hexane extractables. Certain stabilizers can control the generation of certain functionalities and inhibit others. Of importance was the discovery of the relationship between additive activity and structure and inhibition of the formation of specific types of oxidation functionalities to a particular catalyst system.