Amrita Vishwa Vidyapeetham

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.

Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are occurring in very large volumes requiring a scalable solution. There are different malware datasets available publicly for further research by cyber security community. However, no existing study has shown the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware datasets available publicly are to be updated systematically and benchmarked. In this paper, a deep neural network (DNN), a type of deep learning model, is explored to develop a flexible and effective IDS to detect and classify unforeseen and unpredictable cyberattacks. The continuous change in network behavior and rapid evolution of attacks makes it necessary to evaluate various datasets which are generated over the years through static and dynamic approaches. This type of study facilitates to identify the best algorithm which can effectively work in detecting future cyberattacks. A comprehensive evaluation of experiments of DNNs and other classical machine learning classifiers are shown on various publicly available benchmark malware datasets. The optimal network parameters and network topologies for DNNs are chosen through the following hyperparameter selection methods with KDDCup 99 dataset. All the experiments of DNNs are run till 1,000 epochs with the learning rate varying in the range [0.01-0.5]. The DNN model which performed well on KDDCup 99 is applied on other datasets, such as NSL-KDD, UNSW-NB15, Kyoto, WSN-DS, and CICIDS 2017, to conduct the benchmark. Our DNN model learns the abstract and high-dimensional feature representation of the IDS data by passing them into many hidden layers. Through a rigorous experimental testing, it is confirmed that DNNs perform well in comparison with the classical machine learning classifiers. Finally, we propose a highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks.

Stock market or equity market have a profound impact in today's economy. A rise or fall in the share price has an important role in determining the investor's gain. The existing forecasting methods make use of both linear (AR, MA, ARIMA) and non-linear algorithms (ARCH, GARCH, Neural Networks), but they focus on predicting the stock index movement or price forecasting for a single company using the daily closing price. The proposed method is a model independent approach. Here we are not fitting the data to a specific model, rather we are identifying the latent dynamics existing in the data using deep learning architectures. In this work we use three different deep learning architectures for the price prediction of NSE listed companies and compares their performance. We are applying a sliding window approach for predicting future values on a short term basis. The performance of the models were quantified using percentage error.

Current wound dressings have disadvantages such as less flexibility, poor mechanical strength, lack of porosity, and a tendency for dressings to adhere onto the wound surface; in addition, a majority of the dressings did not possess antibacterial activity. Hydrogel-based wound dressings would be helpful to provide a cooling sensation and a moisture environment, as well as act as a barrier to microbes. To overcome these hassles, we have developed flexible and microporous chitosan hydrogel/nano zinc oxide composite bandages (CZBs) via the incorporation of zinc oxide nanoparticles (nZnO) into chitosan hydrogel. The prepared nanocomposite bandages were characterized using Fourier transform infrared spectroscopy (FT-IR), X-ray diffractometry (XRD), and scanning electron microscopy (SEM). In addition, swelling, degradation, blood clotting, antibacterial, cytocompatibility, cell attachment on the material, and cell infiltration into the composite bandages were evaluated. The nanocomposite bandage showed enhanced swelling, blood clotting, and antibacterial activity. Cytocompatibility of the composite bandage has been analyzed in normal human dermal fibroblast cells. Cell attachment and infiltration studies showed that the cells were found attached to the nanocomposite bandages and penetrated into the interior. Furthermore, the in vivo evaluations in Sprague-Dawley rats revealed that these nanocomposite bandages enhanced the wound healing and helped for faster re-epithelialization and collagen deposition. The obtained data strongly encourage the use of these composite bandages for burn wounds, chronic wounds, and diabetic foot ulcers.

The success of traditional methods for solving computer vision problems heavily depends on the feature extraction process. But Convolutional Neural Networks (CNN) have provided an alternative for automatically learning the domain specific features. Now every problem in the broader domain of computer vision is re-examined from the perspective of this new methodology. Therefore it is essential to figure-out the type of network specific to a problem. In this work, we have done a thorough literature survey of Convolutional Neural Networks which is the widely used framework of deep learning. With AlexNet as the base CNN model, we have reviewed all the variations emerged over time to suit various applications and a small discussion on the available frameworks for the implementation of the same. We hope this piece of article will really serve as a guide for any neophyte in the area.

The neural network, one of the intelligent data mining technique that has been used by researchers in various areas for the past 10 years. Prediction and analysis of stock market data have got an important role in today’s economy. The various algorithms used for forecasting can be categorized into linear (AR, MA, ARIMA, ARMA) and non-linear models (ARCH, GARCH, Neural Network). In this paper, we are using four types of deep learning architectures i.e Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for predicting the stock price of a company based on the historical prices available. Here we are using day-wise closing price of two different stock markets, National Stock Exchange (NSE) of India and New York Stock Exchange (NYSE). The network was trained with the stock price of a single company from NSE and predicted for five different companies from both NSE and NYSE. It has been observed that CNN is outperforming the other models. The network was able to predict for NYSE even though it was trained with NSE data. This was possible because both the stock markets share some common inner dynamics. The results obtained were com- pared with ARIMA model and it has been observed that the neural networks are outperforming the existing linear model (ARIMA).

Data Mining is all about data analysis techniques. It is useful for extracting hidden and interesting patterns from large datasets. Clustering techniques are important when it comes to extracting knowledge from large amount of spatial data collected from various applications including GIS, satellite images, X-ray crystallography, remote sensing and environmental assessment and planning etc. To extract useful pattern from these complex data sources several popular spatial data clustering techniques have been proposed. DBSCAN (Density Based Spatial Clustering of Applications with Noise) is a pioneer density based algorithm. It can discover clusters of any arbitrary shape and size in databases containing even noise and outliers. DBSCAN however are known to have a number of problems such as: (a) it requires user's input to specify parameter values for executing the algorithm; (b) it is prone to dilemma in deciding meaningful clusters from datasets with varying densities; (c) and it incurs certain computational complexity. Many researchers attempted to enhance the basic DBSCAN algorithm, in order to overcome these drawbacks, such as VDBSCAN, FDBSCAN, DD_DBSCAN, and IDBSCAN. In this study, we survey over different variations of DBSCAN algorithms that were proposed so far. These variations are critically evaluated and their limitations are also listed.

Driverless cars with artificial intelligence (AI) and automated supermarkets run by collaborative robots (cobots) working without human supervision have sparked off new debates: what will be the impacts of extreme automation, turbocharged by the Internet of Things (IoT), AI, and the Industry 4.0, on Big Data and omics implementation science? The IoT builds on (1) broadband wireless internet connectivity, (2) miniaturized sensors embedded in animate and inanimate objects ranging from the house cat to the milk carton in your smart fridge, and (3) AI and cobots making sense of Big Data collected by sensors. Industry 4.0 is a high-tech strategy for manufacturing automation that employs the IoT, thus creating the Smart Factory. Extreme automation until "everything is connected to everything else" poses, however, vulnerabilities that have been little considered to date. First, highly integrated systems are vulnerable to systemic risks such as total network collapse in the event of failure of one of its parts, for example, by hacking or Internet viruses that can fully invade integrated systems. Second, extreme connectivity creates new social and political power structures. If left unchecked, they might lead to authoritarian governance by one person in total control of network power, directly or through her/his connected surrogates. We propose Industry 5.0 that can democratize knowledge coproduction from Big Data, building on the new concept of symmetrical innovation. Industry 5.0 utilizes IoT, but differs from predecessor automation systems by having three-dimensional (3D) symmetry in innovation ecosystem design: (1) a built-in safe exit strategy in case of demise of hyperconnected entrenched digital knowledge networks. Importantly, such safe exists are orthogonal-in that they allow "digital detox" by employing pathways unrelated/unaffected by automated networks, for example, electronic patient records versus material/article trails on vital medical information; (2) equal emphasis on both acceleration and deceleration of innovation if diminishing returns become apparent; and (3) next generation social science and humanities (SSH) research for global governance of emerging technologies: "Post-ELSI Technology Evaluation Research" (PETER). Importantly, PETER considers the technology opportunity costs, ethics, ethics-of-ethics, framings (epistemology), independence, and reflexivity of SSH research in technology policymaking. Industry 5.0 is poised to harness extreme automation and Big Data with safety, innovative technology policy, and responsible implementation science, enabled by 3D symmetry in innovation ecosystem design.

Smart polymers have enormous potential in various applications. In particular, smart polymeric drug delivery systems have been explored as "intelligent" delivery systems able to release, at the appropriate time and site of action, entrapped drugs in response to specific physiological triggers. These polymers exhibit a non-linear response to a small stimulus leading to a macroscopic alteration in their structure/properties. The responses vary widely from swelling/contraction to disintegration. Synthesis of new polymers and crosslinkers with greater biocompatibility and better biodegradability would increase and enhance current applications. The most fascinating features of the smart polymers arise from their versatility and tunable sensitivity. The most significant weakness of all these external stimuli-sensitive polymers is slow response time. The versatility of polymer sources and their combinatorial synthesis make it possible to tune polymer sensitivity to a given stimulus within a narrow range. Development of smart polymer systems may lead to more accurate and programmable drug delivery. In this review, we discuss various mechanisms by which polymer systems are assembled in situ to form implanted devices for sustained release of therapeutic macromolecules, and we highlight various applications in the field of advanced drug delivery.

Background: Candida auris has emerged globally as an MDR nosocomial pathogen in ICU patients. Objectives: We studied the antifungal susceptibility of C. auris isolates (n = 350) from 10 hospitals in India collected over a period of 8 years. To investigate azole resistance, ERG11 gene sequencing and expression profiling was conducted. In addition, echinocandin resistance linked to mutations in the C. auris FKS1 gene was analysed. Methods: CLSI antifungal susceptibility testing of six azoles, amphotericin B, three echinocandins, terbinafine, 5-flucytosine and nystatin was conducted. Screening for amino acid substitutions in ERG11 and FKS1 was performed. Results: Overall, 90% of C. auris were fluconazole resistant (MICs 32 to ≥64 mg/L) and 2% and 8% were resistant to echinocandins (≥8 mg/L) and amphotericin B (≥2 mg/L), respectively. ERG11 sequences of C. auris exhibited amino acid substitutions Y132 and K143 in 77% (n = 34/44) of strains that were fluconazole resistant whereas WT genotypes, i.e. without substitutions at these positions, were observed in isolates with low fluconazole MICs (1-2 mg/L) suggesting that these substitutions confer a phenotype of resistance to fluconazole similar to that described for Candida albicans. No significant expression of ERG11 was observed, although expression was inducible in vitro with fluconazole exposure. Echinocandin resistance was linked to a novel mutation S639F in FKS1 hot spot region I. Conclusions: Overall, 25% and 13% of isolates were MDR and multi-azole resistant, respectively. The most common resistance combination was azoles and 5-flucytosine in 14% followed by azoles and amphotericin B in 7% and azoles and echinocandins in 2% of isolates.

Security breaches due to attacks by malicious software (malware) continue to escalate posing a major security concern in this digital age. With many computer users, corporations, and governments affected due to an exponential growth in malware attacks, malware detection continues to be a hot research topic. Current malware detection solutions that adopt the static and dynamic analysis of malware signatures and behavior patterns are time consuming and have proven to be ineffective in identifying unknown malwares in real-time. Recent malwares use polymorphic, metamorphic, and other evasive techniques to change the malware behaviors quickly and to generate a large number of new malwares. Such new malwares are predominantly variants of existing malwares, and machine learning algorithms (MLAs) are being employed recently to conduct an effective malware analysis. However, such approaches are time consuming as they require extensive feature engineering, feature learning, and feature representation. By using the advanced MLAs such as deep learning, the feature engineering phase can be completely avoided. Recently reported research studies in this direction show the performance of their algorithms with a biased training data, which limits their practical use in real-time situations. There is a compelling need to mitigate bias and evaluate these methods independently in order to arrive at a new enhanced method for effective zero-day malware detection. To fill the gap in the literature, this paper, first, evaluates the classical MLAs and deep learning architectures for malware detection, classification, and categorization using different public and private datasets. Second, we remove all the dataset bias removed in the experimental analysis by having different splits of the public and private datasets to train and test the model in a disjoint way using different timescales. Third, our major contribution is in proposing a novel image processing technique with optimal parameters for MLAs and deep learning architectures to arrive at an effective zero-day malware detection model. A comprehensive comparative study of our model demonstrates that our proposed deep learning architectures outperform classical MLAs. Our novelty in combining visualization and deep learning architectures for static, dynamic, and image processing-based hybrid approach applied in a big data environment is the first of its kind toward achieving robust intelligent zero-day malware detection. Overall, this paper paves way for an effective visual detection of malware using a scalable and hybrid deep learning framework for real-time deployments.

Nanotechnology is the science of manipulating atoms and molecules in the nanoscale - 80,000 times smaller than the width of a human hair. The world market for products that contain nanomaterials is expected to reach $2.6 trillion by 2015. The use of nanotechnology has stretched across various streams of science, from electronics to medicine and has now found applications in the field of cosmetics by taking the name of nanocosmetics. This widespread influence of nanotechnology in the cosmetic industries is due to the enhanced properties attained by the particles at the nano level including color, transparency, solubility etc. The different types of nanomaterials employed in cosmetics include nanosomes, liposomes, fullerenes, solid lipid nanoparticles etc. Recently, concerns over the safety of such nanocosmetics are raised and have forced the cosmetic industries to limit the use of nanotechnology in cosmetics and for enforcing laws to undergo a full-fledged safety assessment before they enter into the market. In this review, emphasis is made on the types of nanomaterials used in cosmetics by the various cosmetic brands, the potential risks caused by them both to human life and also to the environment and what all regulations have been undertaken or can be taken to overcome them.

Recently, Convolutional neural network (CNN) architectures in deep learning have achieved significant results in the field of computer vision. To transform this performance toward the task of intrusion detection (ID) in cyber security, this paper models network traffic as time-series, particularly transmission control protocol / internet protocol (TCP/IP) packets in a predefined time range with supervised learning methods such as multi-layer perceptron (MLP), CNN, CNN-recurrent neural network (CNN-RNN), CNN-long short-term memory (CNN-LSTM) and CNN-gated recurrent unit (GRU), using millions of known good and bad network connections. To measure the efficacy of these approaches we evaluate on the most important synthetic ID data set such as KDDCup 99. To select the optimal network architecture, comprehensive analysis of various MLP, CNN, CNN-RNN, CNN-LSTM and CNN-GRU with its topologies, network parameters and network structures is used. The models in each experiment are run up to 1000 epochs with learning rate in the range [0.01-05]. CNN and its variant architectures have significantly performed well in comparison to the classical machine learning classifiers. This is mainly due to the reason that CNN have capability to extract high level feature representations that represents the abstract form of low level feature sets of network traffic connections.

Self-cleaning and multifunctional materials are used in applications such as windows, solar panels, cements, paints, and textiles. This state-of-the-art review summarizes the materials involved in self-cleaning and multifunctional coatings.

Blockchain is a distributed, transparent, immutable ledger. Consensus protocol forms the core of blockchain. They decide how a blockchain works. With the advent of new possibilities in blockchain technology, researchers are keen to find a well-optimized Byzantine fault tolerant consensus protocol. Creating a global consensus protocol or tailoring a cross-platform plug and play software application for implementation of various consensus protocols are ideas of huge interest. Stellar Consensus Protocol (SCP) is considered to be a global consensus protocol and promises to be Byzantine Fault Tolerant (BFT) by bringing with it the concept of quorum slices and federated byzantine fault tolerance. This consensus's working and its comparison with other protocols that were earlier proposed are analyzed here. Also, hyperledger an open-source project by Linux Foundation which includes implementing the concept of practical byzantine fault tolerance and also a platform where various other consensus protocols and blockchain applications can be deployed in a plug and play manner is also being discussed here. This paper focuses on analyzing these consensus protocols already proposed and their feasibility and efficiency in meeting the characteristics they propose to provide.

We report the effect of carboxyl functionalization of graphene in pacifying its strong hydrophobic interaction with cells and associated toxic effects. Pristine graphene was found to accumulate on the cell membrane causing high oxidative stress leading to apoptosis, whereas carboxyl functionalized hydrophilic graphene was internalized by the cells without causing any toxicity.

The increase in cancer incidence and mortality is challenging current cancer care delivery globally, disproportionally affecting low- and middle-income countries (LMICs) when it comes to receiving evidence-based cancer prevention, treatment, and palliative and survivorship care. Patients in LMICs often rely on traditional, complementary, and integrative medicine (TCIM) that is more familiar, less costly, and widely available. However, spheres of influence and tensions between conventional medicine and TCIM can further disrupt efforts in evidence-based cancer care. Integrative oncology provides a framework to research and integrate safe, effective TCIM alongside conventional cancer treatment and can help bridge health care gaps in delivering evidence-informed, patient-centered care. This growing field uses lifestyle modifications, mind and body therapies (eg, acupuncture, massage, meditation, and yoga), and natural products to improve symptom management and quality of life among patients with cancer. On the basis of this review of the global challenges of cancer control and the current status of integrative oncology, the authors recommend: 1) educating and integrating TCIM providers into the cancer control workforce to promote risk reduction and culturally salient healthy life styles; 2) developing and testing TCIM interventions to address cancer symptoms or treatment-related adverse effects (eg, pain, insomnia, fatigue); and 3) disseminating and implementing evidence-based TCIM interventions as part of comprehensive palliative and survivorship care so patients from all cultures can live with or beyond cancer with respect, dignity, and vitality. With conventional medicine and TCIM united under a cohesive framework, integrative oncology may provide citizens of the world with access to safe, effective, evidence-informed, and culturally sensitive cancer care.

Caveolae are invaginations of the plasma membrane involved in many cellular processes, including clathrin-independent endocytosis, cholesterol transport, and signal transduction. They are characterized by the presence of caveolin proteins. Mutations that cause deficiency in caveolin-3, which is expressed exclusively in skeletal and cardiac muscle, have been linked to muscular dystrophy. Polymerase I and transcript release factor (PTRF; also known as cavin) is a caveolar-associated protein suggested to play an essential role in the formation of caveolae and the stabilization of caveolins. Here, we identified PTRF mutations in 5 nonconsanguineous patients who presented with both generalized lipodystrophy and muscular dystrophy. Muscle hypertrophy, muscle mounding, mild metabolic complications, and elevated serum creatine kinase levels were observed in these patients. Skeletal muscle biopsies revealed chronic dystrophic changes, deficiency and mislocalization of all 3 caveolin family members, and reduction of caveolae structure. We generated expression constructs recapitulating the human mutations; upon overexpression in myoblasts, these mutations resulted in PTRF mislocalization and disrupted physical interaction with caveolins. Our data confirm that PTRF is essential for formation of caveolae and proper localization of caveolins in human cells and suggest that clinical features observed in the patients with PTRF mutations are associated with a secondary deficiency of caveolins.

Various strategies have been explored to overcome critically sized bone defects via bone tissue engineering approaches that incorporate biomimetic scaffolds. Biomimetic scaffolds may provide a novel platform for phenotypically stable tissue formation and stem cell differentiation. In recent years, osteoinductive and inorganic biomimetic scaffold materials have been optimized to offer an osteo-friendly microenvironment for the osteogenic commitment of stem cells. Furthermore, scaffold structures with a microarchitecture design similar to native bone tissue are necessary for successful bone tissue regeneration. For this reason, various methods for fabricating 3D porous structures have been developed. Innovative techniques, such as 3D printing methods, are currently being utilized for optimal host stem cell infiltration, vascularization, nutrient transfer, and stem cell differentiation. In this progress report, biomimetic materials and fabrication approaches that are currently being utilized for biomimetic scaffold design are reviewed.

The advances in genomics, proteomics, and bioinformatics have directed the development of new anticancer agents to reduce drug abuse and increase safe and specific drug treatment. Theranostics, combining therapy and diagnosis, is an appealing approach for chemotherapy in medicine which exhibits improved biodistribution, selective cancer targeting ability, reduced toxicity, masked drug efficacy, and minimum side effects. The role of diagnosis tools in theranostics is to collect the information of the diseased state before and after specific treatment. Magnetic particle-, mesoporous silica-, various carbon allotrope-, and polymer nanoparticle-based theranostic systems are well accepted and clinically significant. Currently, small conjugate-based systems have received much attention for cancer treatment and diagnosis. The structural architecture of these systems is relatively simple, compact, biocompatible, and unidirectional. In this tutorial review, we summarize the latest developments on small conjugate based theranostic agents for tumor treatment and diagnosis using fluorescence and magnetic resonance imaging (MRI).