Spatio-temporal regulation of gene expression lies at the core of many biological phenomena like memory, self-organization, and growth and differentiation. Sequencing of the human and other genomes collectively showed that although only a fraction of the genome codes for proteins, it is pervasively transcribed. The expression profile of thousands of RNA transcripts, protein-coding, or otherwise, provides invaluable information about the identity, fate, and potential of tissues. However, homogenization of a large number of cells to collect RNA for transcriptomics studies masks rare cells and underplays the role of spatial organization. A new set of techniques, collectively called spatial transcriptomics, aims to detect thousands of gene transcripts at high resolution in tissues and cells. Here, we discuss the principle used in two major types of spatial transcriptomics techniques. Further we describe how spatial transcriptomics is contributing to the growth of key areas of biology.

Clustering is an unsupervised method of classifying data objects into similar groups based on some features or properties usually known as similarity or dissimilarity measures. K-Means is one of the most popular clustering methods that come under the hard clustering group. In this clustering method, any data object can belong to a single cluster. On the other hand, in soft clustering methods (e.g., fuzzy c-means clustering), the data object can be clustered in more than one cluster with some degree which is specified by the membership value with the limitation imposed as the summation of these membership values should be equal to individual. While the clustering method of K-Means is a comparatively old technique, it still has tremendous popularity in terms of being used in applications for data grouping and machine learning. In this article, K-Means approach with five different distance measures such as Euclidean, Squared Euclidean, Half Squared Euclidean, Cosine, and City Block distance has been explored. A comparative study is made based on the performance of these similarity criteria on real-time Edible oil dataset acquired using MIR spectroscopy. In addition, it attempts to investigate the measure of similarity for a specific collection of unique patterns carrying data. In the MATLAB R2015b environment given by Mathworks, the K-Means algorithm with different similarity and dissimilarity measures were formulated and implemented.

Low field \(^{1} \)H Nuclear Magnetic Resonance (LF NMR) Spectroscopy is an efficient tool to capture a research sample’s content and purity. LF NMR is employed for the qualitative analysis of Edible oils that are available in Indian Market. Edible oils used for the study include Coconut, Groundnut, Olive, Mustard, Rice bran, and Soyabean oil. Principal Component Analysis (PCA) is used to build a model initially that could classify the oils based on their chemical composition. This model built using PCA could capture 96% of total variance in data. Linear Discriminant Analysis (LDA) was used to build a model that could classify with 100% accuracy. The results provide successful proof for detecting adulteration and further classification of edible oils using Low field \(^{1} \)H NMR Spectroscopy in conjunction with Multivariate Statistical methods such as PCA and LDA.

Various honey samples and possible adulterants has been characterized using Fourier Transform Infrared (FT-IR) spectroscopy integrated with ATR sampling. Spectral data of twelve varieties of samples including mono-floral honey, multi-floral honey and different variety of adulterants has been collected in Mid-IR region (4000cm -1 -400cm -1 ). Spectral Mid-IR data has been corrected using baseline correction method and preprocessed using 2nd order derivative & Standard Normal Variate (SNV) methods for removal of any additive & multiplicative scattering effects. The principal component analysis (PCA) has been used for dimension reduction in the data set and exploratory data analysis of the honey and its adulterant samples. K-means, K-medians and Fuzzy C Means based classification model has been developed for the classification of pure honey samples and adulterants. Developed model has been cross-validated using external samples.

Eisenia fetida, the common vermicomposting earthworm, shows robust regeneration of posterior segments removed by amputation. During the period of regeneration, the newly formed tissue initially contains only undifferentiated cells but subsequently differentiates into a variety of cell types including muscle, nerve and vasculature. Transcriptomics analysis, reported previously, provided a number of candidate non-coding RNAs that were induced during regeneration. We found that one such long non-coding RNA (lncRNA) is expressed in the skin, only at the base of newly formed chaetae. The spatial organization and precise arrangement of the regenerating chaetae and the cells expressing the lncRNA on the ventral side clearly support a model wherein the regenerating tissue contains a zone of growth and cell division at the tip and a zone of differentiation at the site of amputation. The temporal expression pattern of the lncRNA, named Neev, closely resembled the pattern of chitin synthase genes, implicated in chaetae formation. We found that the lncRNA has 49 sites for binding a set of four microRNAs (miRNAs) while the chitin synthase 8 mRNA has 478 sites. The over-representation of shared miRNA sites suggests that lncRNA Neev may act as a miRNA sponge to transiently de-repress chitin synthase 8 during formation of new chaetae in the regenerating segments of Eisenia fetida.

Earthworms show a wide spectrum of regenerative potential with certain species like Eisenia fetida capable of regenerating more than two-thirds of their body while other closely related species, such as Paranais litoralis seem to have lost this ability. Earthworms belong to the phylum Annelida, in which the genomes of the marine oligochaete Capitella telata and the freshwater leech Helobdella robusta have been sequenced and studied. Herein, we report the transcriptomic changes in Eisenia fetida (Indian isolate) during regeneration. Following injury, E. fetida regenerates the posterior segments in a time spanning several weeks. We analyzed gene expression changes both in the newly regenerating cells and in the adjacent tissue, at early (15days post amputation), intermediate (20days post amputation) and late (30 days post amputation) by RNAseq based de novo assembly and comparison of transcriptomes. We also generated a draft genome sequence of this terrestrial red worm using short reads and mate-pair reads. An in-depth analysis of the miRNome of the worm showed that many miRNA gene families have undergone extensive duplications. Sox4, a master regulator of TGF-beta mediated epithelial-mesenchymal transition was induced in the newly regenerated tissue. Genes for several proteins such as sialidases and neurotrophins were identified amongst the differentially expressed transcripts. The regeneration of the ventral nerve cord was also accompanied by the induction of nerve growth factor and neurofilament genes. We identified 315 novel differentially expressed transcripts in the transcriptome, that have no homolog in any other species. Surprisingly, 82% of these novel differentially expressed transcripts showed poor potential for coding proteins, suggesting that novel ncRNAs may play a critical role in regeneration of earthworm.

When dealing with IR spectroscopy, the preprocessing of spectral data is considered as one of the most important parts of chemometrics modeling. Due to any uncontrollable physical variations may lead to an additive, multiplicative and wavelength-dependent scattering effects in the recorded spectra. Pre-processing techniques basically are required to remove these scattering effects from the spectra and subsequently improve the further quantitative and qualitative analysis. Most popular pre-processing techniques are; baseline correction, smoothing of the spectra, normalization, scattering correction and spectral derivatives. This paper begins with the theoretical and mathematical foundation of various pre-processing techniques used for IR spectroscopy. Then a qualitative analysis is performed by applying these techniques to the spectral data collected using various samples of ghee. The comparison of various preprocessing is obtained by modeling of the data using Principle Component Analysis (PCA) and then the k-means clustering algorithm.

An accurate forecasting of Institutional Electricity load can proved to be useful asset for efficient utilization of the infrastructure available in terms of future demand and supply. Time series method of forecasting has got very wide applications like sales forecasting, yield prediction and Supply Chain Monitoring (SCM) system etc. This paper presents a classical time series models available for predicting the future demand. The classical models used to predict the future load and demand assumes the linear relationship between input and output but in the real world this doesn’t seems to be practical. The intelligent and self-learning models like neural network has the lead to approximate any kind of non-linear function and can fit into these situations. Classification and prediction capabilities of Neural Network have also shown a great potential in forecasting. A neural network based time series forecasting model is also developed for electricity load forecasting. Behavioral pattern and trend of the experimental data are being studied and analyzed for accurate forecasting of electricity load.

Annelids form a connecting link between segmented and non-segmented organisms. In other words, phylogenetically, the segmented body pattern starts from Annelida, a phylum that consists of thousands of species, including marine worms, freshwater leeches and earthworms that inhabit deep layers of soil to environmental niches in forests and cultivated land. We are using Eisenia fetida (Indian isolate) a top dwelling, vermicomposting worm due to its ability to regenerate its posterior after damage, injury or complete removal. On average, Eisenia fetida has 100-110 segments. We separated the anterior (upto 55-60th segment) and posterior of the worm, and allowed it to regenerate. In this model, only the posterior could be regenerated after injury. We isolated RNA from the regenerated tissue and the immediate adjacent old tissue at 15 days, 20 days and 30 days during regeneration. We carried out transcriptome sequencing and analysis. With the aim of identifying specific factors which promote nerve regeneration, we have annotated the differentially expressed genes. In all organisms which possess a segmented body, the expression pattern of the Hox cluster is conserved. Hox gene expression, a conserved developmental phenomenon in establishment of body plan has been studied by comparative genomics of other annelids like the marine worm Capitella telleta, the leech Helobdella robusta. We have used a combination of high-throughput sequencing based techniques and validation through cell and molecular biology to identify key aspects of the gene expression program of regeneration in this worm. Besides the transcriptome, we have also done whole genome sequencing, miRnome and metagenome sequencing of this terrestrial annelid.

Earthworms show a wide spectrum of regenerative potential with certain species like Eisenia fetida capable of regenerating more than two-thirds of their body while other closely related species, such as Paranais litoralis seem to have lost this ability. Earthworms belong to the phylum annelida, in which the genomes of the marine oligochaete Capitella telata , and the freshwater leech Helobdella robusta have been sequenced and studied. The terrestrial annelids, in spite of their ecological relevance and unique biochemical repertoire, are represented by a single rough genome draft of Eisenia fetida (North American isolate), which suggested that extensive duplications have led to a large number of HOX genes in this annelid. Herein, we report the draft genome sequence of Eisenia fetida (Indian isolate), a terrestrial redworm widely used for vermicomposting assembled using short reads and mate-pair reads. An in-depth analysis of the miRNome of the worm, showed that many miRNA gene families have also undergone extensive duplications. Genes for several important proteins such as sialidases and neurotrophins were identified by RNA sequencing of tissue samples. We also used de novo assembled RNA-Seq data to identify genes that are differentially expressed during regeneration, both in the newly regenerating cells and in the adjacent tissue. Sox4, a master regulator of TGF-beta induced epithelial-mesenchymal transition was induced in the newly regenerated tissue. The regeneration of the ventral nerve cord was also accompanied by the induction of nerve growth factor and neurofilament genes. The metagenome of the worm, characterized using 16S rRNA sequencing, revealed the identity of several bacterial species that reside in the nephridia of the worm. Comparison of the bodywall and cocoon metagenomes showed exclusion of hereditary symbionts in the regenerated tissue. In summary, we present extensive genome, transcriptome and metagenome data to establish the transcriptome and metagenome dynamics during regeneration.

This paper presents a practical implementation model for simulating the pitching and yawing motions of an autonomous underwater vehicle (AUV) and its consequent control using an ARM based SBC Friendly ARM mini2440. A rotary encoder and adxl303 triple axis accelerometer has been used to measure the position and acceleration respectively. The emphasis of this paper is given on the interfacing of the sensors with Friendly ARM mini2440 and the resultant control algorithm implemented on the embedded platform to control the designed testbench. The control algorithm implemented is PID control. It is to be noted that the entire project was done to study the movements of an underwater vehicle and the ensuing control strategies and the prototype developed fulfills those criteria.

Now-a-days; brushless DC (BLDC) motors are becoming very popular in the field of underwater robotic propulsion systems. Since the thrusters which are being used in underwater robotic vehicles has incorporated Brushless DC motors for its propulsion system. Hence; it is very important to precise control of the speed of brushless DC motor for the underwater robotic application. It is difficult to derive the transfer function of BLDC motor because it is 3 phase non linear system. Hence; modeling and simulation of the BLDC motor is developed in Simulink environment and tested using the embedded dsPIC controller and inverter driver. It is very important to give proper sequences of commutation to run BLDC motor smoothly.

Gimbal system for surveillance applications are often needed moving camera system. Surveillance camera units mounted on moving vehicles create apparent movement in image plane, which causes difficulties in pointing and tracking of a specific featured object. This paper describes the development and control of the robotic system having 3-revolutionary joints, (RRR/3-R) robotic manipulator platform using orthogonal servo motors, and image based object tracking and camera stabilizing scheme controlled with fuzzy logic to neutralize the movement of object. Stabilizing platform moves in pan-tilt way using orthogonal servos to keep the movable object image in center of image frame. For back tracking of the yaw and pitch angles a MEMS based IMU unit is mounted on camera to analyze the object tracking by monitoring the servo motor angles, that indicates the validity and precision of applied control logic. To compensate the yaw and pitch of the system, two independent fuzzy logic control loop are designed to control the position of pan and tilt servo motor.

This paper presents a control strategy using fuzzy logic based approach coupled with classical gain compensator based on pole placement for the analysis of a third order model developed for the yaw plane dynamics of an Autonomous underwater vehicle. However, the first principle based mathematical models formulated for AUV are based on variety of assumptions and uses estimated coefficients to represent the dynamics and uncertain oceanic conditions, may not be the true representation of the actual system. In order to take care of the above unknown disturbances, a fuzzy logic control scheme with state feedback gain compensator based on heuristic knowledge is utilized to compensate model parameter uncertainties. The obvious benefit of the scheme over other conventional methods lies in its simplicity and emulation of common human logic in the design process. It provides good performance objectives such as minimal overshoot, fast rise and settling time and less transient phase oscillations under variety of disturbances encountered in deep-sea environment. The controller formulated is self-adjusting and adaptive in the sense that once it is tuned and customized for a given input domain, it ensures the stable control excursion under variety of operating conditions. Its response and performance are compared with stand-alone fuzzy logic controller and classical state feedback controller designed for yaw dynamics of the system.

This paper presents a robust H-infinity based control methodology for an Autonomous Underwater Vehicle(AUV). The kinematics and dynamics of an AUV is described using six degree of freedom differential equations of motion using body and earth-fixed frame of references. Due to hydrodynamic forces, these equations are highly coupled and non-linear. From the practical point of view it is essential to consider a reduced order model for efficient controller design. Hence the system is commonly subdivided into smaller subsystems, like depth, steering (or yawing) and speed subsystems, which are considered to be mutually non-interactive from the controller design perspective. In this study a reduced order model was derived using the depth plane dynamics of the vehicle. The working environment of an AUV is vastly uncertain due to varying environmental conditions, thereby demanding a robust controller which has the ability to adapt to these uncertainties and provide stabilizing effect irrespective of the change in the surrounding conditions. The proposed H-infinity controller takes into account the uncertainties in the hydrodynamic parameters which arise due to changing operating conditions and provides suitable control action for desired set point tracking as well as disturbance rejection. The altitude of the vehicle is strongly dependent on the pitch angle, and the controller presented here takes care of both the pitch and depth plane dynamics. The mixed sensitivity approach for H-infinity controller design is followed, and the efficacy of the controller is compared with Linear Quadratic Gaussian(LQG) controller and the Mixed H2/H-infinity controller. The controller design and simulation has been done in Matlab, and the simulated results provide satisfactory results, for disturbance rejection and set point tracking for the H-infinity controller in presence of hydrodynamic parametric uncertainties.