Contributions to Diffusion in Complex Materials Quantified with Machine Learning.

Using machine learning with a variational formula for diffusivity, we recast diffusion as a sum of individual contributions to diffusion-called "kinosons"-and compute their statistical distribution to model a complex multicomponent alloy. Calculating kinosons is orders of magnitude more efficient than computing whole trajectories, and it elucidates kinetic mechanisms for diffusion. The density of kinosons with temperature leads to new accurate analytic models for macroscale diffusivity. This combination of machine learning with diffusion theory promises insight into other complex materials.

Therapeutic potential of lipid nanosystems for the treatment of Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disorder. The degeneration of dopaminergic neurons in the midbrain is primarily responsible for the onset of the disease. The major challenge faced in the treatment of PD is the blood-brain barrier (BBB), which impedes the delivery of therapeutics to targeted locations. To address this issue, lipid nanosystems have been used for the precise delivery of therapeutic compounds in anti-PD therapy. In this review, we will discuss the application and clinical significance of lipid nanosystem in delivering therapeutic compounds for anti-PD treatment. These medicinal compounds include ropinirole, apomorphine, bromocriptine, astaxanthin, resveratrol, dopamine, glyceryl monooleate, levodopa, N-3,4-bis(pivaloyloxy)- dopamine and fibroblast growth factor, which have significant potential to treat PD in the early stage. This review, in a nutshell, will pave the way for researchers to develop diagnostic and potential therapeutic approaches using nanomedicine to overcome the challenges posed by the BBB in delivering therapeutic compounds for PD.

Antimicrobial Nanoparticles Mediated Prevention and Control of Membrane Biofouling in Water and Wastewater Treatment: Current Trends and Future Perspectives.

Global water scarcity and water pollution necessitate wastewater reclamation for further use. As an alternative to conventional techniques, membrane technology is extensively used as an advanced method for water purification and wastewater treatment due to its selectivity, permeability, and efficient removal of pollutants. However, microbial biofouling is a major threat that deteriorates membrane performance and imparts operational challenges. It is a natural phenomenon caused by the undesirable colonization of microbes on membrane surfaces. The economic penalties associated with this menace are enormous. The traditional preventive measures are dominated by biocides, toxic chemicals, cleaners and antifouling surfaces, which are costly and often cause secondary pollution. Recent focus is thus being directed to promote inputs from nanotechnology to control and mitigate this major threat. Different anti-microbial nanomaterials can be effectively used to prevent the adhesion of microbes onto the membrane surfaces and eliminate microbial biofilms, to provide an economical and eco-friendly solution to biofouling. This review addresses the formation of microbial biofilms and biofouling in membrane operations. The potential of nanocomposite membranes in alleviating this problem and the challenges in commercialization are discussed. The antifouling mechanisms are also highlighted, which are not widely elucidated.

Techno-economic analysis of production and purification of lipase from Bacillus subtilis (NCIM 2193).

Lipase is one of the essential enzymes from the hydrolase family, which can be produced from multiple sources like bacteria, fungi, plants, and animals. Due to the various industrial applications, it is necessary to produce and purify lipase cost-effectively. The present study is concerned with the techno-economic analysis of the production and purification of lipase using Bacillus subtilis. In the lab experiment, a purification fold of 1347.5 was achieved with 50% recovery upon purification. The experimental data fit into a model, simulate, and economically assess a more extensive industrial setup Using SuperPro designer. Annual production of 64 batches with 26.4 kg of lipase produced per batch, and a total yearly operating cost of $16,021,000, with a payback time of around 1.37 years, were retrieved upon simulating experimental data. This study indicates the potential of the used bacteria for industrial lipase production with its techno-economic feasibility.

The evaluation of the performance of rice husk and rice straw as potential matrix to obtain the best lipase immobilized system: creating wealth from wastes.

India generates 126.6 and 42 million tons of Rice straw (RS) and Rice husk (RH) annually, respectively. These agro-processing wastes feedstock are dumped in landfills or burnt, releasing toxic gases and particulate matter into the environment. This paper explores the valorization of these wastes feedstock into sustainable, economic products. We compare these wastes as matrices for lipase immobilization. These matrices were characterized, different parameters (pH, temperature, ionic strength, and metal ion cofactors) were checked, and the selected matrix was analyzed for reusability and hydrolysis of vegetable oils. Lipase immobilized Rice straw (LIRS) showed the highest activity with 72.84% protein loading. Field emission scanning electron microscopy (FESEM) demonstrated morphological changes after enzyme immobilization. FTIR showed no new bond formation, and immobilization data was fitted to Freundlich adsorption isotherm (with K = 12.18 mg/g, nF = 4.5). The highest activity with protein loading, 91.05%, was observed at pH 8, 37 °C temperature, 50 mM ionic strength, and lipase activity doubled in the presence of Mg2+ ions. The LIRS retained 75% of its initial activity up to five cycles and efficiently hydrolyzed different oils. The results reflected that the LIRS system performs better and can be used to degrade oily waste.

MTRRE-Net: A deep learning model for detection of breast cancer from histopathological images.

Histopathological image classification has become one of the most challenging tasks among researchers due to the fine-grained variability of the disease. However, the rapid development of deep learning-based models such as the Convolutional Neural Network (CNN) has propelled much attentiveness to the classification of complex biomedical images. In this work, we propose a novel end-to-end deep learning model, named Multi-scale Dual Residual Recurrent Network (MTRRE-Net), for breast cancer classification from histopathological images. This model introduces a contrasting approach of dual residual block combined with the recurrent network to overcome the vanishing gradient problem even if the network is significantly deep. The proposed model has been evaluated on a publicly available standard dataset, namely BreaKHis, and achieved impressive accuracy in overcoming state-of-the-art models on all the images considered at various magnification levels.

The emergence of metal oxide nanoparticles (NPs) as a phytomedicine: A two-facet role in plant growth, nano-toxicity and anti-phyto-microbial activity.

Anti-microbial resistance (AMR) has recently emerged as an area of high interest owing to the rapid surge of AMR phenotypes. Metal oxide NPs (MeONPs) have been identified as novel phytomedicine and have recently peaked a lot of interest due to their potential applications in combating phytopathogens, besides enhancing plant growth and yields. Numerous MeONPs (Ti2O, MgO, CuO, Ag2O, SiO2, ZnO, and CaO) have been synthesized and tested to validate their antimicrobial roles without causing toxicity to the cells. This review discusses the application of the MeONPs with special emphasis on anti-microbial activities in agriculture and enlists how cellular toxicity caused through reactive oxygen species (ROS) production affects plant growth, morphology, and viability. This review further highlights the two-facet role of silver and copper oxide NPs including their anti-microbial applications and toxicities. Furthermore, the factor modulating nanotoxicity and immunomodulation for cytokine production has also been discussed. Thus, this article will not only provide the researchers with the potential bottlenecks but also emphasizes a comprehensive outline of breakthroughs in the applicability of MeONPs in agriculture.

Challenges and opportunities of using immobilized lipase as biosensor.

Over the years, the science of biosensors has evolved significantly. The first or earliest generation of biosensors only detected either the decrease or increase of product or reactant-based natural mediators as the pathway for electron transfer. The subsequent second-generation biosensors were biomolecule based and used artificial redox mediators, such as organic dyes to detect and to increase the reproducibility and sensitivity of the result. However, the recent generation of biosensors work mostly on the principle of electron mobility, with different criteria, such as selectivity, precision, sensitivity, etc., can be used to quantify, efficiently. This review deals with exploring the scope and applications of Immobilized lipase biosensors. Generally, Triglycerides or TG molecules are either detected using Gas Chromatography or, using a chemical or an enzymatic assay. Immobilization of lipase on solid supports has led to increased stability and reusability of the enzyme in non-aqueous solvents. With better enzyme performance, efficient product recovery, and separation from the reaction, immobilized lipase biosensors are garnering increasing interest worldwide. Along with so many advantages including but not limiting to ones mentioned earlier, immobilized lipase-based biosensors come with their own set of challenges, such as the partitioning of the analyte with aqueous medium, slower reaction rate, etc., they have been discussed in the following review. Alongside, we also review the development of a new generation of biosensors and bioelectronic devices based on nanotechnology.

AltWOA: Altruistic Whale Optimization Algorithm for feature selection on microarray datasets.

The data-driven modern era has enabled the collection of large amounts of biomedical and clinical data. DNA microarray gene expression datasets have mainly gained significant attention to the research community owing to their ability to identify diseases through the "bio-markers" or specific alterations in the gene sequence that represent that particular disease (for example, different types of cancer). However, gene expression datasets are very high-dimensional, while only a few of those are "bio-markers". Meta-heuristic-based feature selection effectively filters out only the relevant genes from a large set of attributes efficiently to reduce data storage and computation requirements. To this end, in this paper, we propose an Altruistic Whale Optimization Algorithm (AltWOA) for the feature selection problem in high-dimensional microarray data. AltWOA is an improvement on the basic Whale Optimization Algorithm. We embed the concept of altruism in the whale population to help efficient propagation of candidate solutions that can reach the global optima over the iterations. Evaluation of the proposed method on eight high dimensional microarray datasets reveals the superiority of AltWOA compared to popular and classical techniques in the literature on the same datasets both in terms of accuracy and the final number of features selected. The relevant codes for the proposed approach are available publicly at https://github.com/Rohit-Kundu/AltWOA.

DRDA-Net: Dense residual dual-shuffle attention network for breast cancer classification using histopathological images.

Breast cancer is caused by the uncontrolled growth and division of cells in the breast, whereby a mass of tissue called a tumor is created. Early detection of breast cancer can save many lives. Hence, many researchers worldwide have invested considerable effort in developing robust computer-aided tools for the classification of breast cancer using histopathological images. For this purpose, in this study we designed a dual-shuffle attention-guided deep learning model, called the dense residual dual-shuffle attention network (DRDA-Net). Inspired by the bottleneck unit of the ShuffleNet architecture, in our proposed model we incorporate a channel attention mechanism, which enhances the model's ability to learn the complex patterns of images. Moreover, the model's densely connected blocks address both the overfitting and the vanishing gradient problem, although the model is trained on a substantially small dataset. We have evaluated our proposed model on the publicly available BreaKHis dataset and achieved classification accuracies of 95.72%, 94.41%, 97.43% and 98.1% on four different magnification levels i.e., 40x, 1000x, 200x, 400x respectively which proves the supremacy of the proposed model. The relevant code of the proposed DRDA-Net model can be foundt at: https://github.com/SohamChattopadhyayEE/DRDA-Net.

A mechanical-thermodynamic model for understanding endocytosis of COVID-19 virus SARS-CoV-2.

We analyze the endocytosis process of COVID-19 (coronavirus disease 2019) virus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) using a mechanical-thermodynamic model. The virus particle is designed to interface with the cell membrane as a hard sphere. The role of cytoplasmic BAR (Bin/Amphiphysin/RVs) proteins is considered in the endocytosis. Interestingly, the Endophilin N-BAR cytoplasmic proteins show resistance in participating endocytosis, whereas F-BAR, Arfaptin BAR, Amphiphysin N-BAR, and PX-BAR proteins participate in endocytosis. The increase in membrane tension, concentrated force between the cell membrane receptor, and spike glycoprotein present on the surface of virus particle promote the endocytosis. Also, the increase in the bending modulus of membrane leads to the two-phase solution of BAR protein concentration on the interior of cell membrane surface. We observe an unstable region of protein concentration, which may help one to retard the endocytosis process and thus the viral infection. Though the present study is focused on SARS-CoV-2, it can be extended to understand any other viral infections, involving endocytosis process.

MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features.

COVID-19 is a respiratory disease that causes infection in both lungs and the upper respiratory tract. The World Health Organization (WHO) has declared it a global pandemic because of its rapid spread across the globe. The most common way for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (RT-PCR) which takes a significant amount of time to get the result. Computer based medical image analysis is more beneficial for the diagnosis of such disease as it can give better results in less time. Computed Tomography (CT) scans are used to monitor lung diseases including COVID-19. In this work, a hybrid model for COVID-19 detection has developed which has two key stages. In the first stage, we have fine-tuned the parameters of the pre-trained convolutional neural networks (CNNs) to extract some features from the COVID-19 affected lungs. As pre-trained CNNs, we have used two standard CNNs namely, GoogleNet and ResNet18. Then, we have proposed a hybrid meta-heuristic feature selection (FS) algorithm, named as Manta Ray Foraging based Golden Ratio Optimizer (MRFGRO) to select the most significant feature subset. The proposed model is implemented over three publicly available datasets, namely, COVID-CT dataset, SARS-COV-2 dataset, and MOSMED dataset, and attains state-of-the-art classification accuracies of 99.15%, 99.42% and 95.57% respectively. Obtained results confirm that the proposed approach is quite efficient when compared to the local texture descriptors used for COVID-19 detection from chest CT-scan images.

Study of lipase producing gene in wheat - an in silico approach.

BACKGROUND: Lipases (EC 3.1.1.3) catalyze the hydrolysis of oil into free fatty acids and glycerol forming the 3rd largest group of commercialized enzymes. Plant lipases grab attention recently because of their specificity, less production and purified cost, and easy availability. In silico approach is the first step to identify different genes coding for lipase in a most common indigenous plant, wheat, to explore the possibility of this plant as an alternative source for commercial lipase production. As the hierarchy organization of genes reflects an ancient process of gene duplication and divergence, many of the theoretical and analytical tools of the phylogenetic systematics can be utilized for comparative genomic studies. Also, in addition to experimental identification and characterization of genes, for computational genomic analysis, Arabidopsis has become a popular strategy to identify crop genes which are economically important, as Arabidopsis genes had been well identified and characterized for lipase. A number of articles had been reported in which genes of wheat have shown strong homology with Arabidopsis. The complete genome sequences of rice and Arabidopsis constitute a valuable resource for comparative genome analysis as they are representatives of the two major evolutionary lineages within the angiosperms. Here, in this in silico approach, Arabidopsis and Oryza sativa serve as models for dicotyledonous and monocotyledonous species, respectively, and the genomic sequence data available was used to identify the lipase genes in wheat. RESULTS: In this present study, Ensembl Plants database was explored for lipase producing gene present in wheat genome and 21 genes were screened down as they contain specific domain and motif for lipase (GXSXG). According to the evolutionary analysis, it was found that the gene TraesCS5B02G157100, located in 5B chromosome, has 58.35% sequence similarity with the reported lipase gene of Arabidopsis thaliana and gene TraesCS3A02G463500 located in the 3A chromosome has 51.74% sequence similarity with the reported lipase gene of Oryza sativa. Homology modeling was performed using protein sequences coded by aforementioned genes and optimized by molecular dynamic simulations. Further with the help of molecular docking of modeled structures with tributyrin, binding efficiency was checked, and the difference in energies (DE) was -9.83 kcal/mol and -6.67 kcal/mol, respectively. CONCLUSIONS: The present work provides a basic understanding of the gene-encoding lipase in wheat, which could be easily accessible and used as a potent industrial enzyme. The study enlightens another direction which can be used further to explore plant lipases.

COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer.

The COVID-19 virus is spreading across the world very rapidly. The World Health Organization (WHO) declared it a global pandemic on 11 March 2020. Early detection of this virus is necessary because of the unavailability of any specific drug. The researchers have developed different techniques for COVID-19 detection, but only a few of them have achieved satisfactory results. There are three ways for COVID-19 detection to date, those are real-time reverse transcription-polymerize chain reaction (RT-PCR), Computed Tomography (CT), and X-ray plays. In this work, we have proposed a less expensive computational model for automatic COVID-19 detection from Chest X-ray and CT-scan images. Our paper has a two-fold contribution. Initially, we have extracted deep features from the image dataset and then introduced a completely novel meta-heuristic feature selection approach, named Clustering-based Golden Ratio Optimizer (CGRO). The model has been implemented on three publicly available datasets, namely the COVID CT-dataset, SARS-Cov-2 dataset, and Chest X-Ray dataset, and attained state-of-the-art accuracies of 99.31%, 98.65%, and 99.44%, respectively.

Real-time assessment and characterization of immobilized lipase onto a natural matrix and qualitative reaction kinetic studies using swept-source optical coherence tomography.

The current study presents a method based on Optical Coherence Tomography (OCT) for non-destructive, real-time analysis and portrayal of immobilization efficacy for lipase on a natural matrix namely, eggshell. Subsequently, qualitative biochemical reaction kinetics of immobilized lipase was also studied. Successful immobilization of lipase on eggshell was confirmed by the presence of a clear peak in 'A' scan of OCT image. From immobilization kinetics it is clearly observed that the thickness of the highest peak of the A-scan increases significantly and peak intensity saturated after 90 min of incubation. Hydrolysis of oil using immobilized lipase indicated that the release of free fatty acids increased up to 8 h during reaction and the result was in accordance with the 'B' scan data of the OCT system. Changes in scattering coefficient-based analysis were performed with respect to incubation time to showcase the immobilization process and hydrolysis reaction of lipase. Scanning electron microscope analysis with smoother surface indicated presence of lipase on eggshell matrices, with no further change after oil hydrolysis.

Metal Immiscibility Route to Synthesis of Ultrathin Carbides, Borides, and Nitrides.

Ultrathin ceramic coatings are of high interest as protective coatings from aviation to biomedical applications. Here, a generic approach of making scalable ultrathin transition metal-carbide/boride/nitride using immiscibility of two metals is demonstrated. Ultrathin tantalum carbide, nitride, and boride are grown using chemical vapor deposition by heating a tantalum-copper bilayer with corresponding precursor (C2 H2 , B powder, and NH3 ). The ultrathin crystals are found on the copper surface (opposite of the metal-metal junction). A detailed microscopy analysis followed by density functional theory based calculation demonstrates the migration mechanism, where Ta atoms prefer to stay in clusters in the Cu matrix. These ultrathin materials have good interface attachment with Cu, improving the scratch resistance and oxidation resistance of Cu. This metal-metal immiscibility system can be extended to other metals to synthesize metal carbide, boride, and nitride coatings.

Development of a novel integrated continuous reactor system for biocatalytic production of biodiesel.

A novel integrated immobilized enzyme-reactor system involving a continuous stirred tank reactor with two packed bed reactors in series was developed for the continuous production of biodiesel. The problem of methanol solubility into oil was solved by introducing a stirred tank reactor to dissolve methanol into partially converted oil. This step made the process perfectly continuous without requiring any organic solvent and intermittent methanol addition in the process. The substrate feeding rate of 0.74 mL/min and enzyme loading of 0.75 g per reactor were determined to be optimum for maximum biodiesel yield. The integrated continuous process was stable up to 45 cycles with biodiesel productivity of 137.2 g/L/h, which was approximately 5 times higher than solvent free batch process. In comparison with the processes reported in literature using expensive Novozyme 435 and hazardous organic solvent, the present process is completely green and perfectly continuous with economic and environmental advantages.