Analysis of expressed sequence tags derived from inflorescence shoot of ,i>Tectona grandis (teak)

Teak Inflorescence Shoot Stage 4 (TIS4) shoots bearing the floral meristems were used to construct a cDNA librariy with insert size range of 1500 - 5000 bp. The titer of the library was 7.5 x 105 pfu/ml(primary) and 4.5 x 109 pfu/ml (amplified). EST generation and analysis were performed using the cDNA library where a total of 1384 plaques were randomly picked and their inserts PCR-amplified using T3and T7 universal primers. Only 1125 plaques generated single amplified fragments, each which were purified and sequenced using the SK universal primer. The generated raw 5’ ESTs were filtered and clustered. A total of 674 nonredundants (69 consensus sequences and 605 singletons) were generated and their identities searched through BLASTX. Of the 674 nonredundants, 107 of them (15.9%) showed no hits or no identity. All the 567 nonredundants identified through BLASTX were categorized into theirfunctional categories and were further analysed using InterProScan to detect their protein signatures and to assign their GO numbers. From all the sequences analysed, only 186 (32.8%) sequences were given the GO numbers and grouped into the three GO main categories namely biological process, cellular component and molecular function. Several important ESTs were highlighted based on their functional categories. There were five sequences found to be related to flowering and light induction

Geological Rating for D-Slope

The purpose of this paper is to present the development of Geological Rating (GR) to carry out slope assessment. In this study, the D-5lope has been developed using geological, hydrological and geotechnical data to evaluate the potential failure of slopes. The geological complexity, the scale of the instability phenomena and the high number of interacting factors complicate most the natural and cut slope analysis. In order to be able to have a structured approach to such complexity, a comprehensive method based on the Geological Rating (GR) is proposed. A total of fourteen (14) parameters (12 geological parameters and 2 hydrological parameters) relating to the slopes have been considered. The slopes are divided into four categories: (I) Not Dangerous Slope (NOS), (II) Slightly Dangerous Slope (SDS), (III) Moderately Dangerous Slope (MOS) , and (IV) Highly Dangerous Slope (HDS). The definitions of these categories are discussed

Pairwise classification using combination of statistical descriptors with spectral analysis features for recognizing walking activities

The advancement of sensor technology has provided valuable information for evaluating functional abilities in various application domains. Human activity recognition (HAR) has gained high demand from the researchers to undergo their exploration in activity recognition system by utilizing Micro-machine Electromechanical (MEMs) sensor technology. Tri-axial accelerometer sensor is utilized to record various kinds of activities signal placed at selected areas of the human bodies. The presence of high inter-class similarities between two or more different activities is considered as a recent challenge in HAR. The nt of incorrectly classified instances involving various types of walking activities could degrade the average accuracy performance. Hence, pairwise classification learning methods are proposed to tackle the problem of differentiating between very similar activities. Several machine learning classifier models are applied using hold out validation approach to evaluate the proposed method

Mining web navigation profiles for recommendation systems

This study explores web usage mining, for which many data mining techniques such as clustering, classification and pattern discovery have been applied to web server logs. The output is a set of discovered patterns which form the main input to the recommendation systems which in return predict the next web navigations. Most of the recommendation systems are user-centered which make a prediction list to the users based on their long term navigation history, user’s databases or full user’s profiles. Companies wish to attract anonymous users, directed them at the early stages of their visits and get them involved with their websites. Learning and mining the web navigation profiles followed by enhanced classification to the similar activities of previous users will provide an appropriate model to recommend to the current anonymous active user with short term navigation. Using CTI dataset, the experimental results show better prediction accuracy than the previous works. An adaptive profiling to save time is a key factor for future works

ONE-AGAINST-ALL BINARIZATION CLASSIFICATION STRATEGY TO RECOGNIZE INTERCLASS SIMILARITIES ACTIVITIES FROM SEVERAL SENSOR POSITIONS

Prior knowledge in pervasive computing recently has garnered a great deal of attention due to the high demand in most applications in order to fulfil the human needs. Human Activity Recognition (HAR) has considered every bit unitary of the applications that are widely explored to provide the valuable information to the human. Small in size within the various smartphones, accelerometer sensor has utilized to undergo the HAR research. Current HAR is not only covered the simple daily activities but also, broadly covered the complex activities. Nevertheless, the existence of high interclass similarities activities tends to increase the level of incorrectly classified instances. Hence, this study demonstrates the binarization classification strategy to tackle the abovementioned issue for the activities with a high degree of similarities. Acceleration signal in the time domain is transformed into frequency terms for separating the signals between gravitational and body acceleration. Two different groups of features; statistical, and frequency analysis are extracted in order to increase the diversity in differentiating between stationary and locomotion activities. The problem complexity is simplified using the binarization strategy before the extracted subset is evaluated. One-Against-All (OAA) classification strategy is introduced to tackle the challenge in improving the accuracy for very similar activity. The proposed work significantly resulted with high accuracy performance, particularly in differentiating between the various high interclass similarities activities using two physical activity datasets; WISDM and PSRG

Pairwise Classification using Combination of Statistical Descriptors with Spectral Analysis Features for Recognizing Walking Activities

The advancement of sensor technology has provided valuable information for evaluating functional abilities in various application domains. Human activity recognition (HAR) has gained high demand from the researchers to undergo their exploration in activity recognition system by utilizing Micro-machine Electromechanical (MEMs) sensor technology. Tri-axial accelerometer sensor is utilized to record various kinds of activities signal placed at selected areas of the human bodies. The presence of high inter-class similarities between two or more different activities is considered as a recent challenge in HAR. The nt of incorrectly classified instances involving various types of walking activities could degrade the average accuracy performance. Hence, pairwise classification learning methods are proposed to tackle the problem of differentiating between very similar activities. Several machine learning classifier models are applied using hold out validation approach to evaluate the proposed method

Utilization of sugarcane bagasse ash in concrete as partial replacement of cement

This research addresses the suitability of sugarcane bagasse ash (SCBA) in concrete used as partial cement replacement. Two grades of concrete M15 and M20 were used for the experimental analysis. The cement was partially replaced by SCBA at 0%, 5%, and 10%, by weight in normal strength concrete (NSC). The innovative part of this study is to consider two grades of concrete mixes to evaluate the performance of concrete while cement is replaced by sugarcane bagasse ash. The cylindrical specimens having size 150 mm x 300 mm were used and tested after curing period of 7, 14 and 28 days. It was observed through the experimental work that the compressive strength increases with incorporating SCBA in concrete. Results indicated that the use of SCBA in concrete (M20) at 5% increased the average amount of compressive strength by 12% as compared to the normal strength concrete. The outcome of this work indicates that maximum strength of concrete could be attained at 5% replacement of cement with SCBA. Furthermore, the SCBA also gives compatible slump values which increase the workability of concrete

Isolation and characterization of LHY homolog gene expressed in flowering tissues of Tectona grandis (teak)

Floral initiation of teak through molecular biology approach is being studied for better understanding of teak flower development. Through PCR subtractive hybridization method, LHY homolog gene has beenisolated from teak flowering tissues. The full-length cDNA of the gene was 2948 base pair (bp) and potentially encoded for 768 amino acids. It was named Tectona grandis LHY (Tg-LHY), as the gene wassimilar to the LHY gene of some species. Amino acid sequence alignment revealed that Tg-LHY was similar to LHY of Castanea sativa, LHY of Phaseolus vulgaris and LHY of Arabidopsis thaliana. The highly conserved region found in Tg-LHY was the MYB protein, which is the DNA-binding protein responsible in negative feedback loop reaction of central oscillator in plant circadian clock system. The level of gene expression was found to be high four hours after dawn in flowering shoots and flower.This paper reported the isolation and characterization of the gene