A Novel DNA Sequence Compression Method Based on Chaos Game Representation

Unique signature images derived out of Chaos Game Representation of bio-sequences is an area of research that has been confined to pattern recognition applications. In this paper we pose and answer an interesting question – can we reproduce a bio-sequence in a lossless way given the co-ordinates of the final point in its CGR image? We show that it is possible in principle, but would need enormous resolution for representation of coordinates, roughly corresponding to the information content of direct binary coding of the sequence. We go on to show that we can code nucleotide codon triplets using this method in which 16 codons can be coded using 4 bits, the remaining 48 using 6 bits. Theoretically up to 11% compression is possible with this method. However, algorithm overheads reduce this to very nominal compression percentage of less than 4% for human genome and 9% for bacterial genome. We report the results on a subset of standard test sequences and also an independent wider data set

Discretized Bayesian pursuit – A new scheme for reinforcement learning

The success of Learning Automata (LA)-based estimator algorithms over the classical, Linear Reward-Inaction ( L RI )-like schemes, can be explained by their ability to pursue the actions with the highest reward probability estimates. Without access to reward probability estimates, it makes sense for schemes like the L RI to first make large exploring steps, and then to gradually turn exploration into exploitation by making progressively smaller learning steps. However, this behavior becomes counter-intuitive when pursuing actions based on their estimated reward probabilities. Learning should then ideally proceed in progressively larger steps, as the reward probability estimates turn more accurate. This paper introduces a new estimator algorithm, the Discretized Bayesian Pursuit Algorithm (DBPA), that achieves this. The DBPA is implemented by linearly discretizing the action probability space of the Bayesian Pursuit Algorithm (BPA) [1]. The key innovation is that the linear discrete updating rules mitigate the counter-intuitive behavior of the corresponding linear continuous updating rules, by augmenting them with the reward probability estimates. Extensive experimental results show the superiority of DBPA over previous estimator algorithms. Indeed, the DBPA is probably the fastest reported LA to date

Role of nutritional deficiency in the development of autism spectrum disorders

Autism spectrum disorders (ASD) are characterized by behavioural abnormalities and impaired communication skills. Both genetic and environmental factors have been attributed as causative factors. It has been reported that there are alterations in the organization of functional networks in brain as well as in the balance between structural and functional net-works in brain in children and adolescents with ASD when compared to normal children. Various studies have shown that lower levels of micronutrients like magnesium, selenium, Vitamin A, Vitamin D and Vitamin E, Folic acid and iron are found in children with ASD. This narrative review was undertaken to highlight the role of nutritional deficiency in the development of ASD in children relevant literature was collected from Google scholar, Pubmed, Cross Ref and Scopus. This review also takes into consideration how nutritional deficiency during pregnancy, infancy and childhood can have a role in the development of ASD in children

The association of the serotonin-sensitive aryl acylamidase with acetylcholinesterase in the monkey brain

The serotonin-sensitive aryl acylamidase was partially purified from monkey brain. The aryl acylamidase activity was inhibited by serotonin (Ki = 0.425 mM) and tryptamine (Ki = 3.6 mM) but not by a number of other amines. It was also inhibited by acetylcholine (Ki = 2 mM) and its analogues and homologues. The relationship of aryl acylamidase to acetylcholinesterase was examined. The ratios of specific activities of aryl acylamidase and acetylcholinesterase in the different steps of purification were approximately constant and the percentage recoveries of both enzyme activities were comparable. Elution profiles of both enzyme activities from concanavalin-A-Sepharose, Sephadex G-200 and DEAE-Sephadex A-25 columns were similar. Both enzyme activities migrated in a similar fashion on gel electrophoresis in different percentage gels for different time intervals. Both enzymes showed similar distribution in the various anatomical regions and in the different subcellular fractions of monkey brain. Eserine and neostigmine, potent competitive inhibitors of acetylcholinesterase also potently inhibited aryl acylamidase in a non-competitive manner. Inhibition of both enzymes was 100% at 10 μM of both the inhibitors. Tetraisopropylpyrophosphoramide, a selective inhibitor of pseudo-cholinesterase, did not inhibit either the brain acetylcholinesterase or aryl acylamidase at 10μM. Serotonin inhibited acetylcholinesterase only at concentrations above 10 mM. Dixon plots of one inhibitor in the presence of additional inhibitors indicated that serotonin, butyrylcholine, acetylcholine, neostigmine and eserine (all non-competitive inhibitors of aryl acylamidase) acted at the same site as inhibitors of aryl acylamidase. The serotonin-insensitive aryl acylamidase of monkey liver was also insensitive to acetylcholine and eserine and was not found associated with acetylcholinesterase [A. Oommen and A. S. Balasubramanian (1978) Biochem. Pharmacol. 27, 891-895]. Erythrocyte membrane known to contain true cholinesterase had aryl acylamidase activity sensitive to serotonin, acetylcholine and eserine. All these considerations suggest that the serotonin-sensitive aryl acylamidase activity is a property of true cholinesterase. Although some of the experimental results would suggest that in the monkey brain the two activities may be associated with the same protein with two different active sites further experiments are needed to confirm this

SUCCESSIVE SOLVENT EXTRACTION AND HPTLC OF STEM BARK OF ASOKA - SARACA ASOCA (ROXB.) DE WILDE

Asoka – Saraca asoca (Roxb.) de Wilde, is a medium sized evergreen tree growing in tropical regions. It has been used for various medicinal purposes from the time immemorial. Ample citations about its usage can be elicited from Veda’s, Puranas and Samhitas. Owing to extensive use, lack of cultivation and irrational collection practices it became an endangered drug. It’s one among the five endangered plants listed by NMPB. This scarcity of drug in the market eventually led to adulteration. It is one of the severely adulterated drug next to Bala – Sida species. Various pharmacognostical and phytochemical techniques are evolved from time to time to check the adulteration. Due to the sophisticated methodologies used by medicinal plant dealers, these methods fail to check adulteration. Pharmacognostical analysis of sample drug and its powder microscopy serves as an effective method to check adulteration. But it won’t serve fruitful when the drug gets adulterated with exhausted samples. In such cases, effective marker compounds of the drug need to be analysed. This can be achieved by analysing successive solvent extractives of test drug and by HPTLC analysis. Here an attempt has been done to analyse the successive solvent extraction and HPTLC of stem bark of Asoka – Saraca asoca (Roxb.) de Wilde. as an effective methodology to ensure the purity. The successive solvent extraction revealed 1.78%, 0.4%, 13.63% & 27.69% of extractives respectively in petroleum ether, cyclohexane, acetone and methyl alcohol. The qualitative analysis also showed significance difference in the steroids, alkaloids, phenols and flavonoids in each solvent. The results are promising and suggestive of considering these experiments as an effective method to ensure the quality and purity of drug sample

A study of Angstrom's turbidity parameters from solar radiation measurements in India

Values of Angstrom's turbidity coefficient β and the wavelength exponent α have been computed for a number of stations in India from pyrheliometric measurements of direct solar radiation, for the whole spectrum and for specified spectral regions using filters OG1, RG2 and RG8. Large seasonal variations are noticed in β, with high values in summer and low values in winter at all stations. Rainout and washout are effective in the removal of aerosols from the atmosphere and a marked fall is noticed in β after thunderstorms and after the onset of the monsoon at all stations. Values of at stations in the southern half of the subcontinent remain more or less constant throughout the year with a mean value of about 1.0, indicating that smaller haze particles predominate and the size distribution remains the same despite the large increase of turbidity in summer. In the northern half of the sub-continent, α shows seasonal variations with low values, sometimes becoming zero or negative in summer and the normally accepted values in winter. Over north and central India therefore, while smaller particles are more numerous in winter, large particles predominate in summer

Challenges of modeling rainfall triggered landslides in a data-sparse region: A case study from the Western Ghats, India

Accurate rainfall estimates are required to forecast the spatio-temporal distribution of rain-triggered landslides. In this study, a comparison between rain gauge and satellite rainfall data for assessing landslide distribution in a data-sparse region, the mountainous district of Idukki, along the Western Ghats of southwestern India, is carried out. Global Precipitation Mission Integrated Multi-satellitE Retrievals for GPM-Late (GPM IMERG-L) rainfall products were compared with rain gauge measurements, and it was found that the satellite rainfall observations were underpredicting the actual rainfall. A conditional merging algorithm was applied to develop a product that combines the accuracy of rain gauges and the spatial variability of satellite precipitation data. Correlation Coefficient (CC) and Root Mean Squared Error (RMSE) were used to check the performance of the conditional merging process. An example from a station with the least favorable statistics shows the CC increasing from 0.589 to 0.974 and the RMSE decreasing from 65.22 to 20.01. A case scenario was considered that evaluated the performance of a landslide prediction model by relying solely on a sparse rain gauge network. Rainfall thresholds computed from both the conditionally merged GPM IMERG-L and the rain gauge data were compared and the differences indicated that relying solely on a discrete, sparse rain gauge network would create false predictions. A total of 18.7% of landslide predictions only were identified as true positives, while 60.7% was the overall false-negative rate, and the remaining were false-positives. This pointed towards the need of having a continuous data that is both accurate in measurement and efficient in capturing spatial variability of rainfall

On merging the fields of neural networks and adaptive data structures to yield new pattern recognition methodologies

The aim of this talk is to explain a pioneering exploratory research endeavour that attempts to merge two completely different fields in Computer Science so as to yield very fascinating results. These are the well-established fields of Neural Networks (NNs) and Adaptive Data Structures (ADS) respectively. The field of NNs deals with the training and learning capabilities of a large number of neurons, each possessing minimal computational properties. On the other hand, the field of ADS concerns designing, implementing and analyzing data structures which adaptively change with time so as to optimize some access criteria. In this talk, we shall demonstrate how these fields can be merged, so that the neural elements are themselves linked together using a data structure. This structure can be a singly-linked or doubly-linked list, or even a Binary Search Tree (BST). While the results themselves are quite generic, in particular, we shall, as a prima facie case, present the results in which a Self-Organizing Map (SOM) with an underlying BST structure can be adaptively re-structured using conditional rotations. These rotations on the nodes of the tree are local and are performed in constant time, guaranteeing a decrease in the Weighted Path Length of the entire tree. As a result, the algorithm, referred to as the Tree-based Topology-Oriented SOM with Conditional Rotations (TTO-CONROT), converges in such a manner that the neurons are ultimately placed in the input space so as to represent its stochastic distribution. Besides, the neighborhood properties of the neurons suit the best BST that represents the data