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

A Decision Tree Based on Spatial Relationships for Predicting Hotspots in Peatlands

Predicting hotspot occurrence as an indicator of forest and land fires is essential in developing an early warning system for fire prevention.  This work applied a spatial decision tree algorithm on spatial data of forest fires. The algorithm is the improvement of the conventional decision tree algorithm in which the distance and topological relationships are included to grow up spatial decision trees. Spatial data consist of a target layer and ten explanatory layers representing physical, weather, socio-economic and peatland characteristics in the study area Rokan Hilir District, Indonesia. Target objects are hotspots of 2008 and non-hotspot points.  The result is a pruned spatial decision tree with 122 leaves and the accuracy of 71.66%.  The spatial tree has produces higher accuracy than the non-spatial trees that were created using the ID3 and C4.5 algorithm. The ID3 decision tree has accuracy of 49.02% while the accuracy of C4.5 decision tree reaches 65.24%

Burn Area Processing to Generate False Alarm Data for Hotspot Prediction Models

Developing hotspot prediction models using decision tree algorithms require target classes to which objects in a dataset are classified.  In modeling hotspots occurrence, target classes are the true class representing hotspots occurrence and the false class indicating non hotspots occurrence.  This paper presents the results of satellite image processing in order to determine the radius of a hotspot such that random points are generated outside a hotspot buffer as false alarm data.  Clustering and majority filtering were performed on the Landsat TM image to extract burn scars in the study area i.e. Rokan Hilir, Riau Province Indonesia.  Calculation on burn areas and FIRMS MODIS fire/hotspots in 2006 results the radius of a hotspot 0.90737 km.  Therefore, non-hotspots were randomly generated in areas that are located 0.90737 km away from a hotspot. Three decision tree algorithms i.e. ID3, C4.5 and extended spatial ID3 have been applied on a dataset containing 235 objects that have the true class and 326 objects that have the false class. The results are decision trees for modeling hotspots occurrence which have the accuracy of 49.02% for the ID3 decision tree, 65.24% for the C4.5 decision tree, and 71.66% for the extended spatial ID3 decision tree

Integration of EFQM excellence model and information systems criterion

Higher Education Institutions (HEIs) have become key institutions in the knowledge-based economy. Over the past decade, the Malaysian government has placed greater emphasis on improved efficiency and productivity in the HEI as an engine for promoting quality human capital for a knowledge-based economy. Importantly, the government raised the share of research and development in GDP from 1.5% in the Eighth Malaysia Plan (2000-2005) to 4.9% in the Ninth Malaysia Plan (2006-2010) for HEIs. As a result, there is a need to monitor the quality performance of HEIs to see if the governments objectives are being met. The European Foundation for Quality Management (EFQM) excellence model was introduced at the beginning of 1992 as the framework for assessing organizations for the European Quality Award. In fact, this model has been claimed to be the most widely used model of the national excellence awards in the European countries. However, it does not have Information Systems (IS) as a single criterion. The purpose of this paper is to evaluate the interrelationships between the EFQM excellence model and information systems criterion of Malcolm Baldrige National Quality Award (MBNQA) model in the HEIs of Malaysia. The paper identified ten (10) criteria from the research model: leadership; policy and strategy; people; partnership and resources; information systems; processes; people results; student results; society results and key performance results. We obtained 118 valid responses from person in charge of quality management in Malaysian HEIs. Structural equation model (SEM) is used to analyse the data and results indicate that the relationships among the research model followed the Information Systems-Quality Management theory and TQM theory

K-means clustering to improve the accuracy of decision tree response classification.

The use of deep generation with statistical-based surface generation merits from response utterances readily available from corpus. Representation and quality of the instance data are the foremost factors that affect classification accuracy of the statistical-based method. Thus, in classification task, any irrelevant or unreliable tagging of response classes represented will result in low accuracy. This study focused on improving dialogue act classification of a user utterance into a response class by clustering the semantic and pragmatic features extracted from each user utterance. A Decision tree approach is used to classify 64 mixed-initiative, transaction dialogue corpus in theater domain. The experiment shows that by using clustering technique in pre-processing stage for re-tagging response classes, the Decision tree is able to achieve 97.5% recognition accuracy in classification, better than the 81.95% recognition accuracy when using Decision tree alone

Improving accuracy of intention-based response classification using decision tree.

This study focused on improving the dialogue act classification to classify a user utterance into a response class using a decision tree approach. Decision tree classifier is tested on 64 mixed-initiative, transaction dialogue corpus in theater domain. The result from the comparative experiment show that decision tree able to achieve 81.95% recognition accuracy in classification better than the 73.9% obtained using Bayesian networks and 71.3% achieved by using Maximum likelihood estimation. This result showed that the performance of decision tree as classifier is well suited for these tasks

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

Aquilaria malaccensis polyploids as improved planting materials

Aquilaria malaccensis is an agarwood-producing timber species used in many traditional remedies and modern therapeutic treatments and perfume industries. In this study, we aimed to enhance A. malaccensis phytochemical content through in-vitro polyploidisation. Shoot tip and nodal segment from 8-week-old in-vitro A. malaccensis plantlets were treated with different concentrations of colchicine and trifluralin at various exposure times to obtain polyploids. Tetraploid plantlets (10%) was obtained using nodal segment explants treated with 0.1 mM trifluralin at 120 hours. Chemical profiling of diploid and tetraploid samples (leaf, stem and root) was evaluated separately using headspace-solid phase microextraction (HS-SPME) combined with gas chromatograph mass spectrometry (GCMS). Phytochemical content increased in tetraploid, particularly in stem whereby the total phytochemical contents were 43.19% in tetraploid compared with 5.87% in diploid. The HS-SPME-GCMS analyses showed that tetraploid stem contained high levels of sesquiterpenoids found in agarwood oil such as α-eudesmol (18.3%), α-gurjunene (8.61%) and γ-gurjunene (6.22%). On the other hand, aromadendrene (2.49%) and α-humulene (3.38%) were detected in diploid samples. Tetraploid leaf samples were observed to contain α-humulene (3.79%) while diploid only contained (2E) tridecenol (19%). There were no significant differences between diploid and tetraploid in terms of total phytochemical content in root samples. Nevertheless, high sesquiterpenoid content, γ-gurjunene (14.0%), was detected in tetraploid sample while γ-muurolene (2.96%), in diploid. α-Guaiene content was higher in root samples of diploid (6.49%) than tetraploid (1.09%). These results demonstrated that tetraploid plantlets led to higher yield of total phytochemical content and might facilitate production of high quality A. malaccensis clones

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

Comparison of hidden Markov Model and Naïve Bayes algorithms among events in smart home environment

The smart home environment consists of numerous subsystems which are heterogeneous in nature. Smart home environment are configured in such a way that it comfort driven as well as achieving optimized security and task-oriented without human intervention inside the home. The subsystems, due to their diversified nature develop difficulties as the events communicate making the smart home uncomfortable. The complexity of decision making in handling events stands at the bottleneck in ensuring various tasks executed jointly among diversified systems in smart home environment. In this paper, we propose Hidden Markov Model (HMM) and Naïve Bayes (NB) to test the accuracy and response time of the home data and to compare between the two algorithms. The result experimented shows that the HMM algorithm stands at higher accuracy and better response time than the NB. The implementation has been carried out in such a way that quality information is acquired among the systems to demonstrate the effectiveness of decision making among events in the smart home environment