Objective: This study investigates functional specialisation in, and effective connectivity between the
precentral gyrus (PCG) and supplementary motor area (SMA) in seven right handed female subjects.
Methods: Unimanual (UNIright and UNIleft) and bimanual (BIM) self-paced tapping of hand fingers were
performed by the subjects to activate PCG and SMA. Brain activations and effective connectivity were
analysed using statistical parametric mapping (SPM), dynamic causal modeling (DCM) and Bayesian
model selection (BMS) and were reported based on group fixed (FFX) and random (RFX) effects
analyses. Results: Group results showed that the observed brain activation for UNIright and UNIleft fulfill contralateral behavior of motor coordination with a larger activation area for UNIright. The activation for BIM occurs in both hemispheres with BIMright showing higher extent of activation as compared to BIMleft. Region of interest (ROI) analyses reveal that the number of activated voxel (NOV) and percentage of signal change (PSC) on average is higher in PCG than SMA for all tapping conditions. However, comparing between hemispheres for both UNI and BIM, higher PSC is observed in the right PCG and the left SMA. DCM and BMS results indicate that most subjects prefer PCG as the intrinsic input for UNIright and UNIleft. The input was later found to be bi-directionally connected to SMA for UNIright. The bi-directional model was then used for BIM in the left and right hemispheres. The model was in favour of six out of seven subjects. DCM results for BIM indicate the existence of interhemispheric connectivity between the right and left hemisphere PCG. Conclusion: The findings strongly support the existence of functional specialisation and integration i.e. effective connectivity in human brain during finger tapping and can be used as baselines in determining the probable motor coordination pathways and their connection strength in a population of subjects.
Many of the conventional similarity methods assume that molecular fragments that do not relate to biological activity carry the same weight as the important ones. One possible approach to this problem is to use the Bayesian inference network (BIN), which models molecules and reference structures as probabilistic inference networks. The relationships between molecules and reference structures in the Bayesian network are encoded using a set of conditional probability distributions, which can be estimated by the fragment weighting function, a function of the frequencies of the fragments in the molecule or the reference structure as well as throughout the collection. The weighting function combines one or more fragment weighting schemes. In this paper, we have investigated five different weighting functions and present a new fragment weighting scheme. Later on, these functions were modified to combine the new weighting scheme. Simulated virtual screening experiments with the MDL Drug Data Report (23) and maximum unbiased validation data sets show that the use of new weighting scheme can provide significantly more effective screening when compared with the use of current weighting schemes.
Foot-and-mouth disease (FMD) is endemic in the countries of mainland Southeast Asia where it represents a major obstacle to the development of productive animal industries. The aim of this study was to use genetic data to determine the distribution of FMD virus (FMDV) lineages in the Southeast Asia region, and in particular identify possible sources of FMDV causing outbreaks in Malaysia. Complete VP1 sequences, obtained from 214 samples collected between 2000 and 2009, from FMD outbreaks in six Southeast Asian countries, were compared with sequences previously reported. Phylogenetic analysis of these sequences showed that there were two patterns of FMDV distribution in Malaysia. Firstly, for some lineages (O/SEA/Mya98 and serotype A), outbreaks occurred every year in the country and did not appear to persist, suggesting that these incursions were quickly eradicated. Furthermore, for these lineages FMD viruses in Malaysia were closely related to those from neighbouring countries, demonstrating the close epidemiological links between countries in the region. In contrast, for O/ME-SA/PanAsia lineage, viruses were introduced and remained to cause outbreaks in subsequent years. In particular, the recent incursion and maintenance of the PanAsia-2 sublineage into Malaysia appears to be unique and independent from other outbreaks in the region. This study is the first characterisation of FMDV in Malaysia and provides evidence for different epidemiological sources of virus introduction into the country.
Southeast Asian populations of the fruit fly Drosophila melanogaster differ from ancestral African and derived European populations by several morphological characteristics. It has been argued that this morphological differentiation could be the result of an early colonization of Southeast Asia that predated the migration of D. melanogaster to Europe after the last glacial period (around 10,000 years ago). To investigate the colonization process of Southeast Asia, we collected nucleotide polymorphism data for more than 200 X-linked fragments and 50 autosomal loci from a population of Malaysia. We analyzed this new single nucleotide polymorphism data set jointly with already existing data from an African and a European population by employing an Approximate Bayesian Computation approach. By contrasting different demographic models of these three populations, we do not find any evidence for an early divergence between the African and the Asian populations. Rather, we show that Asian and European populations of D. melanogaster share a non-African most recent common ancestor that existed about 2,500 years ago.
Sundaland has a dynamic geographic history because its landmasses were periodically interconnected when sea levels fell during glacial periods. Superimposed on this geographic dynamism were environmental changes related to climatic oscillations. To investigate how tropical taxa responded to such changes, we studied the divergence and demographic history of two co-distributed rainforest passerine species, Arachnothera longirostra and Malacocincla malaccensis. We sampled birds primarily from Borneo and the Malay Peninsula, which straddle the now-submerged Sunda shelf, and analysed multilocus DNA data with a variety of coalescent and gene genealogy methods. Cross-shelf divergence in both species occurred well before the last glacial maximum, i.e., before the most recent land connection. However, post-divergence gene flow occurred, and it was more pronounced in A. longirostra (a highly vagile nectarivore/insectivore) than in M. malaccensis (an understory insectivore). Despite current habitat continuity on Borneo, the population of M. malaccensis in northeastern Borneo is substantially divergent from that on the rest of the island. The NE population experienced dramatic demographic fluctuations, probably because of competition with the other population, which expanded from western Borneo after the mid-Pleistocene. In contrast, the Borneo population of A. longirostra has little structure and appears to have experienced demographic expansion 16 kya, long after it had diverged from the Malay Peninsula population (630-690 kya). Malay Peninsula populations of both species have remained relatively stable. Overall, the most recent glacial period was not the chief determinant of the evolutionary dynamics of our study species, and in this respect, they are different from temperate species.
A new genus of trypanorhynch cestode is described from two species of sharks, the sliteye shark Loxodon macrorhinus Müller & Henle and the straight-tooth weasel shark Paragaleus tengi (Chen) collected in the Makassar Strait (off Indonesian Borneo) and Sulu Sea (off Malaysian Borneo). Ancipirhynchus afossalis n. g., n. sp. possesses two bothria and a heteroacanthous, heteromorphous tentacular armature with three distinctive files of hooks on the external tentacle surface but lacks prebulbar organs and gland cells within the tentacular bulbs. The hook arrangement of alternating files on the external surface of the tentacle resembles that seen in the superfamily Otobothrioidea Dollfus, 1942 in the genus Fossobothrium Beveridge & Campbell, 2005. However, the new species lacks the defining characteristic of this group, i.e. the paired bothrial pits. A Bayesian inference (BI) analysis of 37 LSU sequences of trypanorhynchs from three superfamilies provided evidence supporting the taxonomic placement of Ancipirhynchus afossalis n. g., n. sp. within the Otobothrioidea.
Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets.
Plastid trnL-trnF and nuclear ribosomal ITS sequences were obtained from selected wild-type individuals of Polygonum minus Huds. in Peninsular Malaysia. The 380 bp trnL-trnF sequences of the Polygonum minus accessions were identical. Therefore, the trnL-trnF failed to distinguish between the Polygonum minus accessions. However, the divergence of ITS sequences (650 bp) among the Polygonum minus accessions was 1%, indicating that these accessions could be distinguished by the ITS sequences. A phylogenetic relationship based on the ITS sequences was inferred using neighbor-joining, maximum parsimony and Bayesian inference. All of the tree topologies indicated that Polygonum minus from Peninsular Malaysia is unique and different from the synonymous Persicaria minor (Huds.) Opiz and Polygonum kawagoeanum Makino.
Kajian ini bertujuan untuk memetakan kes kemortalan bayi mengikut daerah di Semenanjung Malaysia bagi tahun 1991 hingga 2000. Penganggaran risiko relatif berdasarkan kaedah Bayes empirik telah digunakan dalam kajian ini. Tiga kaedah penganggaran parameter dihuraikan iaitu kaedah momen, kaedah kebolehjadian maksimum dan kaedah penganggaran gabungan momen dan kebolehjadian maksimum. Keteguhan anggaran parameter yang diperoleh diuji menggunakan kaedah Bootstrap. Hasil kajian mendapati jurang antara kawasan berisiko rendah dengan kawasan berisiko tinggi adalah lebih besar pada awal dekad 2000 berbanding pada awal dekad 1990-an walaupun pada dasarnya kadar mortaliti bayi secara keseluruhannya adalah semakin berkurangan pada peringkat nasional. Kawasan pantai timur Semenanjung Malaysia masih pada takuk yang sama iaitu masih berada dalam kategori berisiko tinggi sepanjang tempoh yang dikaji. Seterusnya, gambaran terdapatnya tompokan risiko juga turut terpapar dalam peta yang dihasilkan. Berdasarkan kaedah Bootstrap, parameter-parameter yang dianggarkan dalam kajian ini adalah teguh.
In the present study, we employed microsatellite DNA markers to analyze the genetic diversity and differentiation between and within cultured stocks and wild populations of the orange-spotted grouper originating from the South China Sea and Southeast Asia. Compared to wild populations, genetic changes including reduced genetic diversity and significant differentiation have taken place in cultured grouper stocks, as shown by allele richness and heterozygosity studies, pairwise F(st), structure, molecular variance analysis, as well as multidimensional scaling analysis. Although two geographically adjacent orange-spotted grouper populations in China showed negligible genetic divergence, significant population differentiation was observed in wild grouper populations distributed in a wide geographical area from China, through Malaysia to Indonesia. However, the Mantel test rejected the isolation-by-distance model of genetic structure, which indicated the genetic differentiation among the populations could result from the co-effects of various factors, such as historical dispersal, local environment, ocean currents, river flows and island blocks. Our results demonstrated that microsatellite markers could be suitable not only for genetic monitoring cultured stocks but also for revealing the population structuring of wild orange-spotted grouper populations. Meanwhile, our study provided important information for breeding programs, management of cultured stocks and conservation of wild populations of the orange-spotted grouper.
Query expansion is the process of reformulating an original query to improve retrieval performance in information retrieval systems. Relevance feedback is one of the most useful query modification techniques in information retrieval systems. In this paper, we introduce query expansion into ligand-based virtual screening (LBVS) using the relevance feedback technique. In this approach, a few high-ranking molecules of unknown activity are filtered from the outputs of a Bayesian inference network based on a single ligand molecule to form a set of ligand molecules. This set of ligand molecules is used to form a new ligand molecule. Simulated virtual screening experiments with the MDL Drug Data Report and maximum unbiased validation data sets show that the use of ligand expansion provides a very simple way of improving the LBVS, especially when the active molecules being sought have a high degree of structural heterogeneity. However, the effectiveness of the ligand expansion is slightly less when structurally-homogeneous sets of actives are being sought.
Cardiovascular disease (CVD) is the major cause of death globally. More people die of CVDs each year than from any other disease. Over 80% of CVD deaths occur in low and middle income countries and occur almost equally in male and female. In this paper, different computational models based on Bayesian Networks, Multilayer Perceptron,Radial Basis Function and Logistic Regression methods are presented to predict early risk detection of the cardiovascular event. A total of 929 (626 male and 303 female) heart attack data are used to construct the models.The models are tested using combined as well as separate male and female data. Among the models used, it is found that the Multilayer Perceptron model yields the best accuracy result.
Understanding the mechanisms of gene regulation during breast cancer is one of the most difficult problems among oncologists because this regulation is likely comprised of complex genetic interactions. Given this complexity, a computational study using the Bayesian network technique has been employed to construct a gene regulatory network from microarray data. Although the Bayesian network has been notified as a prominent method to infer gene regulatory processes, learning the Bayesian network structure is NP hard and computationally intricate. Therefore, we propose a novel inference method based on low-order conditional independence that extends to the case of the Bayesian network to deal with a large number of genes and an insufficient sample size. This method has been evaluated and compared with full-order conditional independence and different prognostic indices on a publicly available breast cancer data set. Our results suggest that the low-order conditional independence method will be able to handle a large number of genes in a small sample size with the least mean square error. In addition, this proposed method performs significantly better than other methods, including the full-order conditional independence and the St. Gallen consensus criteria. The proposed method achieved an area under the ROC curve of 0.79203, whereas the full-order conditional independence and the St. Gallen consensus criteria obtained 0.76438 and 0.73810, respectively. Furthermore, our empirical evaluation using the low-order conditional independence method has demonstrated a promising relationship between six gene regulators and two regulated genes and will be further investigated as potential breast cancer metastasis prognostic markers.
Many of the similarity-based virtual screening approaches assume that molecular fragments that are not related to the biological activity carry the same weight as the important ones. This was the reason that led to the use of Bayesian networks as an alternative to existing tools for similarity-based virtual screening. In our recent work, the retrieval performance of the Bayesian inference network (BIN) was observed to improve significantly when molecular fragments were reweighted using the relevance feedback information. In this paper, a set of active reference structures were used to reweight the fragments in the reference structure. In this approach, higher weights were assigned to those fragments that occur more frequently in the set of active reference structures while others were penalized. Simulated virtual screening experiments with MDL Drug Data Report datasets showed that the proposed approach significantly improved the retrieval effectiveness of ligand-based virtual screening, especially when the active molecules being sought had a high degree of structural heterogeneity.
The output of state-of-the-art reverse-engineering methods for biological networks is often based on the fitting of a mathematical model to the data. Typically, different datasets do not give single consistent network predictions but rather an ensemble of inconsistent networks inferred under the same reverse-engineering method that are only consistent with the specific experimentally measured data. Here, we focus on an alternative approach for combining the information contained within such an ensemble of inconsistent gene networks called meta-analysis, to make more accurate predictions and to estimate the reliability of these predictions. We review two existing meta-analysis approaches; the Fisher transformation combined coefficient test (FTCCT) and Fisher's inverse combined probability test (FICPT); and compare their performance with five well-known methods, ARACNe, Context Likelihood or Relatedness network (CLR), Maximum Relevance Minimum Redundancy (MRNET), Relevance Network (RN) and Bayesian Network (BN). We conducted in-depth numerical ensemble simulations and demonstrated for biological expression data that the meta-analysis approaches consistently outperformed the best gene regulatory network inference (GRNI) methods in the literature. Furthermore, the meta-analysis approaches have a low computational complexity. We conclude that the meta-analysis approaches are a powerful tool for integrating different datasets to give more accurate and reliable predictions for biological networks.
The warp ikat method of making decorated textiles is one of the most geographically widespread in southeast Asia, being used by Austronesian peoples in Indonesia, Malaysia and the Philippines, and Daic peoples on the Asian mainland. In this study a dataset consisting of the decorative characters of 36 of these warp ikat weaving traditions is investigated using Bayesian and Neighbornet techniques, and the results are used to construct a phylogenetic tree and taxonomy for warp ikat weaving in southeast Asia. The results and analysis show that these diverse traditions have a common ancestor amongst neolithic cultures the Asian mainland, and parallels exist between the patterns of textile weaving descent and linguistic phylogeny for the Austronesian group. Ancestral state analysis is used to reconstruct some of the features of the ancestral weaving tradition. The widely held theory that weaving motifs originated in the late Bronze Age Dong-Son culture is shown to be inconsistent with the data.
In video analytics, robust observation detection is very important as the content of the videos varies a lot, especially for tracking implementation. Contrary to the image processing field, the problems of blurring, moderate deformation, low illumination surroundings, illumination change and homogenous texture are normally encountered in video analytics. Patch-Based Observation Detection (PBOD) is developed to improve detection robustness to complex scenes by fusing both feature- and template-based recognition methods. While we believe that feature-based detectors are more distinctive,however, for finding the matching between the frames are best achieved by a collection of points as in template-based detectors. Two methods of PBOD-the deterministic and probabilistic approaches-have been tested to find the best mode of detection. Both algorithms start by building comparison vectors at each detected points of interest. The vectors are matched to build candidate patches based on their respective coordination. For the deterministic method, patch matching is done in 2-level test where threshold-based position and size smoothing are applied to the patch with the highest correlation value. Forthe second approach, patch matching is done probabilistically by modelling the histograms of the patches by Poisson distributions for both RGB and HSV colour models. Then,maximum likelihood is applied for position smoothing while a Bayesian approach is appliedfor size smoothing. The result showed that probabilistic PBOD outperforms the deterministic approach with average distance error of 10.03% compared with 21.03%. This algorithm is best implemented as a complement to other simpler detection methods due to heavy processing requirement.
Non-parametric modeling is a method which relies heavily on data and motivated by the smoothness properties in estimating a function which involves spline and non-spline approaches. Spline approach consists of regression spline and smoothing spline. Regression spline with Bayesian approach is considered in the first step of a two-step method in estimating the structural parameters for stochastic differential equation (SDE). The selection of knot and order of spline can be done heuristically based on the scatter plot. To overcome the subjective and tedious process of selecting the optimal knot and order of spline, an algorithm was proposed. A single optimal knot is selected out of all the points with exception of the first and the last data which gives the least value of Generalized Cross Validation (GCV) for each order of spline. The use is illustrated using observed data of opening share prices of Petronas Gas Bhd. The results showed that the Mean Square Errors (MSE) for stochastic model with parameters estimated using optimal knot for 1,000, 5,000 and 10,000 runs of Brownian motions are smaller than the SDE models with estimated parameters using knot selected heuristically. This verified the viability of the two-step method in the estimation of the drift and diffusion parameters of SDE with an improvement of a single knot selection.
Statistical modeling of extreme rainfall is essential since the results can often facilitate civil engineers and planners to estimate the ability of building structures to survive under the utmost extreme conditions. Data comprising of annual maximum series (AMS) of extreme rainfall in Alor Setar were fitted to Generalized Extreme Value (GEV) distribution using method of maximum likelihood (ML) and Bayesian Markov Chain Monte Carlo (MCMC) simulations. The weakness of ML method in handling small sample is hoped to be tackled by means of Bayesian MCMC simulations in this study. In order to obtain the posterior densities, non-informative and independent priors were employed. Performances of parameter estimations were verified by conducting several goodness-of-fit tests. The results showed that Bayesian MCMC method was slightly better than ML method in estimating GEV parameters.
Transferrin is a protein super-family involved in iron transport, a central process in cellular homeostasis. Throughout the evolution of vertebrates, transferrin members have diversified into distinct subfamilies including serotransferrin, ovotransferrin, lactoferrin, melanotransferrin, the inhibitor of carbonic anhydrase, pacifastin, and the major yolk protein in sea urchin. Previous phylogenetic analyses have established the branching order of the diverse transferrin subfamilies but were mostly focused on the transferrin repertoire present in mammals. Here, we conduct a comprehensive phylogenetic analysis of transferrin protein sequences in sequenced vertebrates, placing a special focus on the less-studied nonmammalian vertebrates. Our analyses uncover a novel transferrin clade present across fish, sauropsid, and amphibian genomes but strikingly absent from mammals. Our reconstructed scenario implies that this novel class emerged through a duplication event at the vertebrate ancestor, and that it was subsequently lost in the lineage leading to mammals. We detect footprints of accelerated evolution following the duplication event, which suggest positive selection and early functional divergence of this novel clade. Interestingly, the loss of this novel class of transferrin in mammals coincided with the divergence by duplication of lactoferrin and serotransferrin in this lineage. Altogether, our results provide novel insights on the evolution of iron-binding proteins in the various vertebrate groups.