S. Sachin Kumar

Scope of algae as third generation biofuels

An initiative has been taken to develop different solid, liquid, and gaseous biofuels as the alternative energy resources. The current research and technology based on the third generation biofuels derived from algal biomass have been considered as the best alternative bioresource that avoids the disadvantages of first and second generation biofuels. Algal biomass has been investigated for the implementation of economic conversion processes producing different biofuels such as biodiesel, bioethanol, biogas, biohydrogen, and other valuable co-products. In the present review, the recent findings and advance developments in algal biomass for improved biofuel production have been explored. This review discusses about the importance of the algal cell contents, various strategies for product formation through various conversion technologies, and its future scope as an energy security.

MicroRNA-195 inhibits proliferation, invasion and metastasis in breast cancer cells by targeting FASN, HMGCR, ACACA and CYP27B1

De novo lipogenesis, a hallmark for cancers is required for cellular transformation. Further it is believed that resistance to apoptosis and epithelial-to-mesenchymal-transition(EMT) facilitates metastasis via over-expression of anti-apoptotic Bcl-2. Previously we demonstrated that hsa-miR-195 targets BCL2, induces apoptosis and augmented the effect of etoposide in breast cancer cells. However, the mechanism behind its function remains elusive. Herein gene expression profiling was done in presence/absence of hsa-miR-195 in Breast cancer cells. IPA revealed mitochondrial dysfunction, fatty acid metabolism and xenobiotic metabolism signalling among the top processes being affected. For the first time we herein identified ACACA, FASN (the key enzymes of de novo fatty acid synthesis), HMGCR (the key enzyme of de novo cholesterol synthesis) and CYP27B1 as direct targets of hsa-miR-195. We further showed that ectopic expression of hsa-miR-195 in MCF-7 and MDA-MB-231 cells not only altered cellular cholesterol and triglyceride levels significantly but also resulted in reduced proliferation, invasion and migration. We further demonstrated that over expression of hsa-miR-195 decreased the Mesenchymal markers expression and enhanced Epithelial markers. In conclusion we say that hsa-miR-195 targets the genes of de novo lipogenesis, inhibits cell proliferation, migration, and invasion which potentially opens new avenues for the treatment of breast cancer.

Deep Model for Classification of Hyperspectral image using Restricted Boltzmann Machine

This paper presents an improved classification of hyperspectral images using deep learning, by extracting meaningful representations at higher levels. Deep learning is a set of algorithm in machine learning that attempt to model high level abstraction of data by using architectures composed of multiple non-linear transformation. It allows artificial systems to discover re-usable features that capture structure in an environment. The ability of undirected graphical models like Restricted Boltzmann Machine, to capture distribution among pixels at the hidden level is utilized here to extract features for each band in the hyperspectral image. To enhance the quality of image a band-by-band non-linear diffusion is introduced as a preprocessing step which ensures increased class separability and noise reduction. After preprocessing, a powerful regenerative model Restricted Boltzmann Machine (RBM) is used for the feature extraction. The generated feature vectors is feed as input to different classifiers for the classification. A statistical comparison of accuracies, obtained with RBM under different conditions illustrates the effectiveness of proposed method. Hyperspectral dataset acquired by Airborne Visible/Infrared imaging Spectrometer is used for experimentation.

A new search for thermotolerant yeasts, its characterization and optimization using response surface methodology for ethanol production

The progressive rise in energy crisis followed by green house gas (GHG) emissions is serving as the driving force for bioethanol production from renewable resources. Current bioethanol research focuses on lignocellulosic feedstocks as these are abundantly available, renewable, sustainable and exhibit no competition between the crops for food and fuel. However, the technologies in use have some drawbacks including incapability of pentose fermentation, reduced tolerance to products formed, costly processes, etc. Therefore, the present study was carried out with the objective of isolating hexose and pentose fermenting thermophilic/thermotolerant ethanologens with acceptable product yield. Two thermotolerant isolates, NIRE-K1 and NIRE-K3 were screened for fermenting both glucose and xylose and identified as Kluyveromyces marxianus NIRE-K1 and K. marxianus NIRE-K3. After optimization using Face-centered Central Composite Design (FCCD), the growth parameters like temperature and pH were found to be 45.17°C and 5.49, respectively for K. marxianus NIRE-K1 and 45.41°C and 5.24, respectively for K. marxianus NIRE-K3. Further, batch fermentations were carried out under optimized conditions, where K. marxianus NIRE-K3 was found to be superior over K. marxianus NIRE-K1. Ethanol yield (Yx∕s), sugar to ethanol conversion rate (%), microbial biomass concentration (X) and volumetric product productivity (Qp) obtained by K. marxianus NIRE-K3 were found to be 9.3, 9.55, 14.63, and 31.94% higher than that of K. marxianus NIRE-K1, respectively. This study revealed the promising potential of both the screened thermotolerant isolates for bioethanol production.

A robust watermarking method based on Compressed Sensing and Arnold scrambling

Watermarking is a technique for information hiding, which is used to identify the authentication and copyright protection. In this paper, a new method of watermarking scheme is proposed, which uses both Compressed Sensing and Arnold scrambling method for efficient data compression and encryption. Compressive sensing technique aims at the reconstruction of sparse signal using a small number of linear measurements. Compressed measurements are then encrypted using Arnold transform. The proposed encryption scheme is computationally more secure against investigated attacks on digital multimedia signals.

Bioprospecting thermostable cellulosomes for efficient biofuel production from lignocellulosic biomass

The adverse climatic conditions due to continuous use of fossil-derived fuels are the driving factors for the development of renewable sources of energy. Current biofuel research focuses mainly on lignocellulosic biomass (LCB) such as agricultural, industrial and municipal solid wastes due to their abundance and renewability. Although many mesophilic cellulolytic microorganisms have been reported, efficient and economical bioconversion to simple sugars is still a challenge. Thermostable cellulolytic enzymes play an indispensible role in degradation of the complex polymeric structure of LCB into fermentable sugar stream due to their higher flexibility with respect to process configurations and better specific activity than the mesophilic enzymes. In some anaerobic thermophilic/thermotolerant microorganisms, few cellulases are organized as unique multifunctional enzyme complex, called the cellulosome. The use of cellulosomal multienzyme complexes for saccharification seems to be a promising and cost-effective alternative for complete breakdown of cellulosic biomass. This paper aims to explore and review the important findings in cellulosomics and forward the path for new cutting-edge opportunities in the success of biorefineries. Herein, we summarize the protein structure, regulatory mechanisms and their expression in the host cells. Furthermore, we discuss the recent advances in specific strategies used to design new multifunctional cellulosomal enzymes, which can function as lignocellulosic biocatalysts and evaluate the roadblocks in the yield and stability of such designer thermozymes with overall progress in lignocellulose-based biorefinery.

Novel SVD Based Character Recognition Approach for Malayalam Language Script

The research on character recognition for Malayalam script dates back to 1990’s. Compared to other Indian languages the research and developments on OCR reported for Malayalam script is very less. The character level and word level accuracy of the existing OCR tools for Indian languages can be improved by implementing robust character recognition and post-processing algorithms. In this paper, we are proposing a character recognition procedure based on Singular Value Decomposition (SVD) and k- Nearest Neighbor classifier (k-NN). The proposed character recognition scheme tested with the dataset created from Malayalam literature books and it could classify 94% of character images accurately.

Convolutional Neural Networks for the Recognition of Malayalam Characters

Optical Character Recognition (OCR) has an important role in information retrieval which converts scanned documents into machine editable and searchable text formats. This work is focussing on the recognition part of OCR. LeNet-5, a Convolutional Neural Network (CNN) trained with gradient based learning and backpropagation algorithm is used for classification of Malayalam character images. Result obtained for multi-class classifier shows that CNN performance is dropping down when the number of classes exceeds range of 40. Accuracy is improved by grouping misclassified characters together. Without grouping, CNN is giving an average accuracy of 75% and after grouping the performance is improved upto 92%. Inner level classification is done using multi-class SVM which is giving an average accuracy in the range of 99-100%.

A VMD based approach for speech enhancement

This paper proposes a Variational Mode Decomposition (VMD) based approach for enhancement of speech signals distorted by white Gaussian noise. VMD is a data adaptive method which decomposes the signal into intrinsic mode functions (IMFs) by using the Alternating Direction Method of Multipliers (ADMM). Each IMF or mode will contain a center frequency and its harmonics. This paper tries to explore VMD as a Speech enhancement technique. In the proposed method, the noisy speech signal is decomposed into IMFs using VMD. The noisy IMFs are enhanced using two methods; VMD based wavelet shrinkage (VMD-WS) and VMD based MMSE log STSA (VMD-MMSE). The speech signal distorted with different noise levels are enhanced using the VMD based methods. The level of noise reduction and speech signal quality are measured using the objective quality measures.

Condition Monitoring in Roller Bearings using Cyclostationary Features

Proper machine condition monitoring is really crucial for any industrial and mechanical systems. The efficiency of mechanical systems greatly relies on rotating components like shaft, bearing and rotor. This paper focuses on detecting different fault in the roller bearings by casting the problem as machine learning based pattern classification problem. The different bearing fault conditions considered are, bearing-good condition, bearing with inner race fault, bearing with outer race fault and bearing with inner and outer race fault. Earlier the statistical features of the vibration signals were used for the classification task. In this paper, the cyclostationary behavior of the vibration signals is exploited for the purpose. In the feature space the vibration signals are represented by cyclostationary feature vectors extracted from it. The features thus extracted were trained and tested using pattern classification algorithms like decision tree J48, Sequential Minimum Optimization (SMO) and Regularized Least Square (RLS) based classification and provides a comparison on accuracies of each method in detecting faults.