Vellore Institute of Technology University

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

One of the major environmental problems today is hydrocarbon contamination resulting from the activities related to the petrochemical industry. Accidental releases of petroleum products are of particular concern in the environment. Hydrocarbon components have been known to belong to the family of carcinogens and neurotoxic organic pollutants. Currently accepted disposal methods of incineration or burial insecure landfills can become prohibitively expensive when amounts of contaminants are large. Mechanical and chemical methods generally used to remove hydrocarbons from contaminated sites have limited effectiveness and can be expensive. Bioremediation is the promising technology for the treatment of these contaminated sites since it is cost-effective and will lead to complete mineralization. Bioremediation functions basically on biodegradation, which may refer to complete mineralization of organic contaminants into carbon dioxide, water, inorganic compounds, and cell protein or transformation of complex organic contaminants to other simpler organic compounds by biological agents like microorganisms. Many indigenous microorganisms in water and soil are capable of degrading hydrocarbon contaminants. This paper presents an updated overview of petroleum hydrocarbon degradation by microorganisms under different ecosystems.

Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

and NaOH. The products were characterized by x-ray diffraction (XRD) analysis, transmission electron microscopy (TEM) and photoluminescence (PL) spectroscopy. Bacteriological tests such as minimum inhibitory concentration (MIC) and disk diffusion were performed in Luria-Bertani and nutrient agar media on solid agar plates and in liquid broth systems using different concentrations of ZnO by a standard microbial method for the first time. Our bacteriological study showed the enhanced biocidal activity of ZnO nanoparticles compared with bulk ZnO in repeated experiments. This demonstrated that the bactericidal efficacy of ZnO nanoparticles increases with decreasing particle size. It is proposed that both the abrasiveness and the surface oxygen species of ZnO nanoparticles promote the biocidal properties of ZnO nanoparticles.

Nanotechnology deals with the production and usage of material with nanoscale dimension. Nanoscale dimension provides nanoparticles a large surface area to volume ratio and thus very specific properties. Zinc oxide nanoparticles (ZnO NPs) had been in recent studies due to its large bandwidth and high exciton binding energy and it has potential applications like antibacterial, antifungal, anti-diabetic, anti-inflammatory, wound healing, antioxidant and optic properties. Due to the large rate of toxic chemicals and extreme environment employed in the physical and chemical production of these NPs, green methods employing the use of plants, fungus, bacteria, and algae have been adopted. This review is a comprehensive study of the synthesis and characterization methods used for the green synthesis of ZnO NPs using different biological sources. Keywords: Zinc oxide nanoparticles, Biosynthesis, Plant, Microbes, Algae

As per ISO and ASTM standards, nanoparticles are particles of sizes ranging from 1 to 100nm with one or more dimensions. The nanoparticles are generally classified into the organic, inorganic and carbon based particles in nanometric scale that has improved properties compared to larger sizes of respective materials. The nanoparticles show enhanced properties such as high reactivity, strength, surface area, sensitivity, stability, etc. because of their small size. The nanoparticles are synthesised by various methods for research and commercial uses that are classified into three main types namely physical, chemical and mechanical processes that has seen a vast improvement over time. This paper presents a review on nanoparticles, their types, properties, synthesis methods and its applications in the field of environment.

Microalgae has been consumed in human diet for thousands of years. It is an under-exploited crop for production of dietary foods. Microalgae cultivation does not compete with land and resources required for traditional crops and has a superior yield compared to terrestrial crops. Its high protein content has exhibited a huge potential to meet the dietary requirements of growing population. Apart from being a source of protein, presence of various bio-active components in microalgae provide an added health benefit. This review describes various microalgal sources of proteins and other bio-active components. One of the heavily studied group of bio-active components are pigments due to their anticarcenogenic, antioxidative and antihypertensive properties. Compared to various plant and floral species, microalgae contain higher amounts of pigments. Microalgal derived proteins have complete Essential Amino Acids (EAA) profiles and their protein content is higher than conventional sources such as meat, poultry and dairy products. However, microalgal based functional foods have not flooded the market. The lack of awareness coupled with scarce incentives for producers result in under-exploitation of microalgal potential. Application of microalgal derived components as dietary and nutraceutical supplements is discussed comprehensively. Keywords: Health, Human, Microalgae, Protein, Supplement

Due to digitization, a huge volume of data is being generated across several sectors such as healthcare, production, sales, IoT devices, Web, organizations. Machine learning algorithms are used to uncover patterns among the attributes of this data. Hence, they can be used to make predictions that can be used by medical practitioners and people at managerial level to make executive decisions. Not all the attributes in the datasets generated are important for training the machine learning algorithms. Some attributes might be irrelevant and some might not affect the outcome of the prediction. Ignoring or removing these irrelevant or less important attributes reduces the burden on machine learning algorithms. In this work two of the prominent dimensionality reduction techniques, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are investigated on four popular Machine Learning (ML) algorithms, Decision Tree Induction, Support Vector Machine (SVM), Naive Bayes Classifier and Random Forest Classifier using publicly available Cardiotocography (CTG) dataset from University of California and Irvine Machine Learning Repository. The experimentation results prove that PCA outperforms LDA in all the measures. Also, the performance of the classifiers, Decision Tree, Random Forest examined is not affected much by using PCA and LDA.To further analyze the performance of PCA and LDA the eperimentation is carried out on Diabetic Retinopathy (DR) and Intrusion Detection System (IDS) datasets. Experimentation results prove that ML algorithms with PCA produce better results when dimensionality of the datasets is high. When dimensionality of datasets is low it is observed that the ML algorithms without dimensionality reduction yields better results.

Fungi are an understudied, biotechnologically valuable group of organisms. Due to the immense range of habitats that fungi inhabit, and the consequent need to compete against a diverse array of other fungi, bacteria, and animals, fungi have developed numerous survival mechanisms. The unique attributes of fungi thus herald great promise for their application in biotechnology and industry. Moreover, fungi can be grown with relative ease, making production at scale viable. The search for fungal biodiversity, and the construction of a living fungi collection, both have incredible economic potential in locating organisms with novel industrial uses that will lead to novel products. This manuscript reviews fifty ways in which fungi can potentially be utilized as biotechnology. We provide notes and examples for each potential exploitation and give examples from our own work and the work of other notable researchers. We also provide a flow chart that can be used to convince funding bodies of the importance of fungi for biotechnological research and as potential products. Fungi have provided the world with penicillin, lovastatin, and other globally significant medicines, and they remain an untapped resource with enormous industrial potential.

Accurate prediction of traffic flow is an integral component in most of the Intelligent Transportation Systems (ITS) applications. The data driven approach using Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models reported in most studies demands sound database for model building. Hence, the applicability of these models remains a question in places where the data availability could be an issue. The present study tries to overcome the above issue by proposing a prediction scheme using Seasonal ARIMA (SARIMA) model for short term prediction of traffic flow using only limited input data. A 3-lane arterial roadway in Chennai, India was selected as the study stretch and limited flow data from only three consecutive days was used for the model development using SARIMA. After necessary differencing to make the input time series a stationary one, the autocorrelation function (ACF) and partial autocorrelation function (PACF) were plotted to identify the suitable order of the SARIMA model. The model parameters were found using maximum likelihood method in R. The developed model was validated by performing 24 hrs. ahead forecast and the predicted flows were compared with the actual flow values. A comparison of the proposed model with historic average and naive method was also attempted. The effect of increase in sample size of input data on prediction results was studied. Short term prediction of traffic flow during morning and evening peak periods was also attempted using both historic and real time data. The mean absolute percentage error (MAPE) between actual and predicted flow was found to be in the range of 4–10, which is acceptable in most of the ITS applications. The prediction scheme proposed in this study for traffic flow prediction could be considered in situations where database is a major constraint during model development using ARIMA.

In the area of materials science, corrosion of biomaterials is of paramount importance as biomaterials are required for the survival of the human beings suffering from acute heart diseases, arthritis, osteoporosis and other joint complications.The present article discusses various issues associated with biological corrosion of different kinds of implants used as cardio stents, orthopedic and dental implants.As the materials used for these implants are manifold starting from metallic materials such as stainless steel (SS), cobalt chromium, titanium and its alloys, bioceramics, composites and polymers are in constant contact with the aggressive body fluid, they often fail and finally fracture due to corrosion.The corrosion behavior of various implants and the role of the surface oxide film and the corrosion products on the failure of implants are discussed.Surface modification of implants, which is considered to be the best solution to combat corrosion and to enhance the life span of the implants and longevity of the human beings is dealt in detail and the recent advances in the coating techniques which make use of the superior properties of nanomaterials that lead to better mechanical properties and improved biocompatibility are also presented.

Since Facebook officially changed its name to Meta in Oct. 2021, the metaverse has become a new norm of social networks and three-dimensional (3D) virtual worlds. The metaverse aims to bring 3D immersive and personalized experiences to users by leveraging many pertinent technologies. Despite great attention and benefits, a natural question in the metaverse is how to secure its users’ digital content and data. In this regard, blockchain is a promising solution owing to its distinct features of decentralization, immutability, and transparency. To better understand the role of blockchain in the metaverse, we aim to provide an extensive survey on the applications of blockchain for the metaverse. We first present a preliminary to blockchain and the metaverse and highlight the motivations behind the use of blockchain for the metaverse. Next, we extensively discuss blockchain-based methods for the metaverse from technical perspectives, such as data acquisition, data storage, data sharing, data interoperability, and data privacy preservation. For each perspective, we first discuss the technical challenges of the metaverse and then highlight how blockchain can help. Moreover, we investigate the impact of blockchain on key-enabling technologies in the metaverse, including Internet-of-Things, digital twins, multi-sensory and immersive applications, artificial intelligence, and big data. We also present some major projects to showcase the role of blockchain in metaverse applications and services. Finally, we present some promising directions to drive further research innovations and developments toward the use of blockchain in the metaverse in the future.

Lead is a prevalent heavy metal that pollutes the environment and accumulates in the human body via absorption, bioavailability, bioconcentration, and biomagnification disrupts the neurological, skeletal, reproductive, hematopoietic, renal, and cardiovascular systems. Lead's distinctive physical and chemical characteristics make it ideal for a variety of uses. It has been linked to human activities for ages and is harmful to health. This review article examines the long-term health consequences of lead exposure in humans. Acute and chronic symptoms of lead poisoning include kidney, brain, reproductive organ, and CNS/PNS damage. Toxic metals have a long half-life in the bone matrix and brain (2–3 years), causing neurological problems and bone loss. The article also shows the problems of high BPb in both men and women during pregnancy. Renal system blood lead levels of 30–60 g/dL may cause kidney failure in severe circumstances. The oxidative stress that occurs in human cells has also been explored. Finally, lead poisoning and lead buildup prevention and therapy have been reviewed. The use of micronutrients and antioxidants has demonstrated a reduction in harmful effects. Adults with BPb >45 g/dL should have chelation, whereas children should receive succimer.

In the past few years, therapeutic microRNA (miRNA) and small interfering RNA (siRNA) are some of the most important biopharmaceuticals that are in commercial space as future medicines. This review summarizes the patents of miRNA- and siRNA-based new drugs, and also provides a snapshot about significant biopharmaceutical companies that are investing for the therapeutic development of miRNA and siRNA molecules. An insightful view about individual siRNA and miRNA drugs has been depicted with their present status, which is gaining attention in the therapeutic landscape. The efforts of the biopharmaceuticals are discussed with the status of their preclinical and/or clinical trials. Here, some of the setbacks have been highlighted during the biopharmaceutical development of miRNA and siRNA as individual therapeutics. Finally, a snapshot is illustrated about pharmacokinetics, pharmacodynamics with absorption, distribution, metabolism, and excretion (ADME), which is the fundamental development process of these therapeutics, as well as the delivery system for miRNA- and siRNA-based drugs.

Recent scientific studies have established a relationship between the consumption of phytochemicals such as carotenoids, polyphenols, isoprenoids, phytosterols, saponins, dietary fibers, polysaccharides, etc., with health benefits such as prevention of diabetes, obesity, cancer, cardiovascular diseases, etc. This has led to the popularization of phytochemicals. Nowadays, foods containing phytochemicals as a constituent (functional foods) and the concentrated form of phytochemicals (nutraceuticals) are used as a preventive measure or cure for many diseases. The health benefits of these phytochemicals depend on their purity and structural stability. The yield, purity, and structural stability of extracted phytochemicals depend on the matrix in which the phytochemical is present, the method of extraction, the solvent used, the temperature, and the time of extraction.

Imperative utilization of biosensors has acquired paramount importance in the field of drug discovery, biomedicine, food safety standards, defense, security, and environmental monitoring. This has led to the invention of precise and powerful analytical tools using biological sensing element as biosensor. Glucometers utilizing the strategy of electrochemical detection of oxygen or hydrogen peroxide using immobilized glucose oxidase electrode seeded the discovery of biosensors. Recent advances in biological techniques and instrumentation involving fluorescence tag to nanomaterials have increased the sensitive limit of biosensors. Use of aptamers or nucleotides, affibodies, peptide arrays, and molecule imprinted polymers provide tools to develop innovative biosensors over classical methods. Integrated approaches provided a better perspective for developing specific and sensitive biosensors with high regenerative potentials. Various biosensors ranging from nanomaterials, polymers to microbes have wider potential applications. It is quite important to integrate multifaceted approaches to design biosensors that have the potential for diverse usage. In light of this, this review provides an overview of different types of biosensors being used ranging from electrochemical, fluorescence tagged, nanomaterials, silica or quartz, and microbes for various biomedical and environmental applications with future outlook of biosensor technology.

A systematic and complete antibacterial study on well-designed and well-characterized microparticle (micro), nanoparticle (nano), and capped nano ZnO has been carried out in both dark and light conditions with the objective of arriving at the mechanism of the antibacterial activity of ZnO, particularly in the dark. The present systematic study has conclusively proved that reactive oxygen species (ROS) such as (•)OH, (•)O2(-), and H2O2 are significantly produced from aqueous suspension of ZnO even in the dark and are mainly responsible for the activity in the dark up to 17%, rather than Zn(2+) ion leaching as proposed earlier. This work further confirms that surface defects play a major role in the production of ROS both in the presence and absence of light. In the dark, superoxide ((•)O2(-)) radical mediated ROS generation through singly ionized oxygen vacancy is proposed for the first time, and it is confirmed by EPR and scavenger studies. ROS such as (•)O2(-), H2O2, and (•)OH have been estimated by UV-visible spectroscopy using nitro blue tetrazolium (NBT), KMnO4 titrations, and fluorescence spectroscopy, respectively. These are correlated to the antibacterial activity of ZnO in the dark and light. The activity is found to be highest for nano ZnO and least for micro ZnO, with capped ZnO between the two, highlighting the important role of surface defects in generation of ROS. The surface charge density of ZnO in dark and light has been estimated for the first time to the best of our knowledge, and it can influence antibacterial activity. Our work proposes a new mechanism mediated by superoxide species, for antibacterial activity of ZnO especially in the dark.

This work investigates the role of oxidation state in the antibacterial activity of copper oxide nanoparticles (NPs).

Abstract Nowadays, being in digital era the data generated by various applications are increasing drastically both row-wise and column wise; this creates a bottleneck for analytics and also increases the burden of machine learning algorithms that work for pattern recognition. This cause of dimensionality can be handled through reduction techniques. The Dimensionality Reduction (DR) can be handled in two ways namely Feature Selection (FS) and Feature Extraction (FE). This paper focuses on a survey of feature selection methods, from this extensive survey we can conclude that most of the FS methods use static data. However, after the emergence of IoT and web-based applications, the data are generated dynamically and grow in a fast rate, so it is likely to have noisy data, it also hinders the performance of the algorithm. With the increase in the size of the data set, the scalability of the FS methods becomes jeopardized. So the existing DR algorithms do not address the issues with the dynamic data. Using FS methods not only reduces the burden of the data but also avoids overfitting of the model.

Plants extract from Ocimum tenuiflorum, Solanum tricobatum, Syzygium cumini, Centella asiatica and Citrus sinensis was used for the synthesis of silver nanoparticles (Ag NPs) from silver nitrate solution. Ag NPs were characterized by UV–vis spectrophotometer, X-ray diffractometer (XRD), atomic force microscope (AFM) and scanning electron microscope (SEM). The formation and stability of the reduced silver nanoparticles in the colloidal solution were monitored by UV–vis spectrophotometer analysis. The mean particle diameter of silver nanoparticles was calculated from the XRD pattern according to the line width of the plane, refraction peak using the Scherrer’s equation. AFM showed the formation of silver nanoparticle with an average size of 28 nm, 26.5 nm, 65 nm, 22.3 nm and 28.4 nm corresponding to O. tenuiflorum, S. cumini, C. sinensis, S. tricobatum and C. asiatica, respectively. SEM determination of the brown color stable samples showed the formation of silver nanoparticles and well dispersed nanoparticles could be seen in the samples treated with silver nitrate. Antimicrobial activity of the silver bio-nanoparticles was performed by well diffusion method against Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli and Klebsiella pneumoniae. The highest antimicrobial activity of silver nanoparticles synthesized by S. tricobatum, O. tenuiflorum extracts was found against S. aureus (30 mm) and E. coli (30 mm) respectively. The Ag NPs synthesized in this process has the efficient antimicrobial activity against pathogenic bacteria. Of these, silver nanoparticles are playing a major role in the field of nanotechnology and nanomedicine.