Direct transport of recombinant protein from cytosol to extracellular medium offers great advantages, such as high specific activity and a simple purification step. This work presents an investigation on the potential of an ABC (ATP-binding cassette) transporter system, the hemolysin transport system, for efficient protein secretion in Escherichia coli (E. coli). A higher secretory production of recombinant cyclodextrin glucanotransferase (CGTase) was achieved by a new plasmid design and subsequently by optimization of culture conditions via central composite design. An improvement of at least fourfold extracellular recombinant CGTase was obtained using the new plasmid design. The optimization process consisted of 20 experiments involving six star points and six replicates at the central point. The predicted optimum culture conditions for maximum recombinant CGTase secretion were found to be 25.76 μM IPTG, 1.0% (w/v) arabinose and 34.7°C post-induction temperature, with a predicted extracellular CGTase activity of 68.76 U/ml. Validation of the model gave an extracellular CGTase activity of 69.15 ± 0.71 U/ml, resulting in a 3.45-fold increase compared to the initial conditions. This corresponded to an extracellular CGTase yield of about 0.58 mg/l. We showed that a synergistic balance of transported protein and secretory pathway is important for efficient protein transport. In addition, we also demonstrated the first successful removal of the C-terminal secretion signal from the transported fusion protein by thrombin proteolytic cleavage.
This study aimed to identify the significant factors that give large effects on the efficiency of Cu(II) extraction from aqueous solutions by soybean oil-based organic solvents using fractional factorial design. Six factors (mixing time (t), di-2-ethylhexylphosphoric acid concentration ([D2EHPA]), organic to aqueous phase ratio (O:A), sodium sulfate concentration ([Na(2)SO(4)]), equilibrium pH (pH(eq)) and tributylphosphate concentration ([TBP])) affecting the percentage extraction (%E) of Cu(II) were investigated. A 2(6-1) fractional factorial design was applied and the results were analyzed statistically. The results show that only [D2EHPA], pH(eq) and their second-order interaction ([D2EHPA] × pH(eq)) influenced the %E significantly. Regression models for %E were developed and the adequacy of the reduced model was examined. The results of this study indicate that fractional factorial design is a useful tool for screening a large number of variables and reducing the number of experiments.
It has recently been shown that ancestors of New Guineans and Bougainville Islanders have inherited a proportion of their ancestry from Denisovans, an archaic hominin group from Siberia. However, only a sparse sampling of populations from Southeast Asia and Oceania were analyzed. Here, we quantify Denisova admixture in 33 additional populations from Asia and Oceania. Aboriginal Australians, Near Oceanians, Polynesians, Fijians, east Indonesians, and Mamanwa (a "Negrito" group from the Philippines) have all inherited genetic material from Denisovans, but mainland East Asians, western Indonesians, Jehai (a Negrito group from Malaysia), and Onge (a Negrito group from the Andaman Islands) have not. These results indicate that Denisova gene flow occurred into the common ancestors of New Guineans, Australians, and Mamanwa but not into the ancestors of the Jehai and Onge and suggest that relatives of present-day East Asians were not in Southeast Asia when the Denisova gene flow occurred. Our finding that descendants of the earliest inhabitants of Southeast Asia do not all harbor Denisova admixture is inconsistent with a history in which the Denisova interbreeding occurred in mainland Asia and then spread over Southeast Asia, leading to all its earliest modern human inhabitants. Instead, the data can be most parsimoniously explained if the Denisova gene flow occurred in Southeast Asia itself. Thus, archaic Denisovans must have lived over an extraordinarily broad geographic and ecological range, from Siberia to tropical Asia.
Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an improved (modified) binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce particles' speed for giving the rate at which a particle changes its position, and we propose a rule for updating particle's positions. By performing experiments on ten different gene expression datasets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.
The catalytic cracking of waste cooking palm oil to biofuel was studied over different types of nano-crystalline zeolite catalysts in a fixed bed reactor. The effect of reaction temperature (400-500 °C), catalyst-to-oil ratio (6-14) and catalyst pore size of different nanocrystalline zeolites (0.54-0.80 nm) were studied over the conversion of waste cooking palm oil, yields of Organic Liquid Product (OLP) and gasoline fraction in the OLP following central composite design (CCD). The response surface methodology was used to determine the optimum value of the operating variables for maximum conversion as well as maximum yield of OLP and gasoline fraction, respectively. The optimum reaction temperature of 458 °C with oil/catalyst ratio=6 over the nanocrystalline zeolite Y with pore size of 0.67 nm gave 86.4 wt% oil conversion, 46.5 wt% OLP yield and 33.5 wt% gasoline fraction yield, respectively. The experimental results were in agreement with the simulated values within an experimental error of less than 5%.
In Malaysia, most municipal wastes currently are disposed into poorly managed 'controlled tipping' systems with little or no pollution protection measures. This study was undertaken to assist the relevant governmental bodies and service providers to identify an improved waste disposal management strategy. The study applied the choice experiment technique to estimate the nonmarket values for a number of waste disposal technologies. Implicit prices for environmental attributes such as psychological fear, land use, air pollution, and river water quality were estimated. Compensating surplus estimates incorporating distance from the residences of the respondents to the proposed disposal facility were calculated for a number of generic and technology-specific choice sets. The resulting estimates were higher for technology-specific options, and the distance factor was a significant determinant in setting an equitable solid waste management fee.
In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was investigated using a wavelet neural network model. The experimental results with error less than 0.0965 (in terms of MSE) were produced, and were then compared with those obtained from the response surface methodology. Performance assessment indicated that the neural network model possessed superior predictive ability than the polynomial model, since a very close agreement between the experimental and the predicted values was obtained.
The annual disease incident worldwide is desirable to be predicted for taking appropriate policy to prevent disease outbreak. This chapter considers the performance of different forecasting method to predict the future number of disease incidence, especially for seasonal disease. Six forecasting methods, namely linear regression, moving average, decomposition, Holt-Winter's, ARIMA, and artificial neural network (ANN), were used for disease forecasting on tuberculosis monthly data. The model derived met the requirement of time series with seasonality pattern and downward trend. The forecasting performance was compared using similar error measure in the base of the last 5 years forecast result. The findings indicate that ARIMA model was the most appropriate model since it obtained the less relatively error than the other model.
This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR) filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end reference signal will then be a whitened sequence to a cascaded adaptive FIR filter in the beamforming structure. By using an inverse kepstrum filter as a whitening filter with the use of a delay filter, the cascaded adaptive FIR filter estimates only the numerator of the polynomial part from the ratio of overall combined transfer functions. The test results have shown that the adaptive FIR filter is more effective in beamforming structure than an adaptive noise cancelling (ANC) structure in terms of signal distortion in the desired signal and noise reduction in noise with nonminimum phase components. In addition, the inverse kepstrum method shows almost the same convergence level in estimate of noise statistics with the use of a smaller amount of adaptive FIR filter weights than the kepstrum method, hence it could provide better computational simplicity in processing. Furthermore, the rear-end inverse kepstrum method in beamforming structure has shown less signal distortion in the desired signal than the front-end kepstrum method and the front-end inverse kepstrum method in beamforming structure.
The process involved in the local scour below pipelines is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This paper describes the use of artificial neural networks (ANN) to estimate the pipeline scour depth. The data sets of laboratory measurements were collected from published works and used to train the network or evolve the program. The developed networks were validated by using the observations that were not involved in training. The performance of ANN was found to be more effective when compared with the results of regression equations in predicting the scour depth around pipelines.
Central composite design (CCD) and response surface methodology (RSM) were employed to optimize four important variables, i.e. amounts of oil, bacterial inoculum, nitrogen and phosphorus, for the removal of selected n-alkanes during bioremediation of weathered crude oil in coastal sediments using laboratory bioreactors over a 60 day experimentation period. The reactors contained 1 kg soil with different oil, microorganisms and nutrients concentrations. The F Value of 26.89 and the probability value (P < 0.0001) demonstrated significance of the regression model. For crude oil concentration of 2, 16 and 30 g per kg sediments and under optimized conditions, n-alkanes removal was 97.38, 93.14 and 90.21% respectively. Natural attenuation removed 30.07, 25.92 and 23.09% n-alkanes from 2, 16 and 30 g oil/kg sediments respectively. Excessive nutrients addition was found to inhibit bioremediation.
The utilization of natural mica as a biocatalyst support in kinetic investigations is first described in this study. The formation of lactose caprate from lactose sugar and capric acid, using free lipase (free-CRL) and lipase immobilized on nanoporous mica (NER-CRL) as a biocatalyst, was evaluated through a kinetic study. The apparent kinetic parameters, K(m) and V(max), were determined by means of the Michaelis-Menten kinetic model. The Ping-Pong Bi-Bi mechanism with single substrate inhibition was adopted as it best explains the experimental findings. The kinetic results show lower K(m) values with NER-CRL than with free-CRL, indicating the higher affinity of NER-CRL towards both substrates at the maximum reaction velocity (V(max,app)>V(max)). The kinetic parameters deduced from this model were used to simulate reaction rate data which were in close agreement with the experimental values.
Response surface methodology (RSM) was used to optimize the concentrations of chitosan and glycerol for coating Berangan banana (Musa sapientum cv. Berangan). The effects of main edible coating components, chitosan (0.5-2.5%, w/w) and glycerol (0-2%, w/w) on weight loss, firmness, total colour difference, total soluble solids content (TSS) and titratable acidity (TA) of coated banana were studied during 10 days of storage at 26±2°C and 40-50% relative humidity. Results showed that the experimental data could be adequately fitted into a second-order polynomial model with coefficient of determination (R 2 ) ranging from 0.745 to 0.930 for all the variables studied. In general, the chitosan concentration appeared to be the most significant (P< 0.1) factor influencing all variables except for TSS. The optimum concentration of chitosan and glycerol were predicted to be 2.02% and 0.18%, respectively. Statistical assessment showed insignificant difference between experimental and predicted values.
Industrial statistics is an important part of the management system in any industry that strives to continuously improve quality and increase productivity and efficiency. That system covers supply chain management, production design and prototyping, production process and marketing. Industrial statisticians, industrial engineers and industrial leaders should work together hand in hand, in the same language, to ensure that the process and products are as expected. The system itself is never complete. Thus, the usefulness, manageability and reliability of all statistical models used in the system are to be considered as first priority, but those skills are not sufficient. Industrial statisticians should also, of course, be able to come and go between the two poles: statistics and industry. This requirement needs a good understanding about the culture of these poles and how to conduct a mutual symbiosis. One of the principal bridges between these cultures is statistical process control (SPC). This paper is to show that modern industry cannot escape from SPC, especially in a multivariate setting. This setting, which characterizes modern industry, consists of two philosophical problems: how to order data and how to measure process variability. Our recent research results sponsored by the Government of Malaysia will be presented to illustrate the challenging statistical problems in modern industry.
This paper focuses on measuring risk due to extreme events going beyond the multivariate normal distribution of joint returns. The concept of tail dependence has been found useful as a tool to describe dependence between extreme data in finance. Specifically, we adopted a multivariate Copula-EGARCH approach in order to investigate the presence of conditional dependence between international financial markets. In addition, we proposed a mixed Clayton-Gumbel copula with estimators for measuring both, the upper and lower tail dependence. The results showed significant dependence for Singapore and Malaysia as well as for Singapore and US, while the dependence for Malaysia and US was relatively weak
In two experiments, we investigated age-related changes in how prosodic pitch accents affect memory. Participants listened to recorded discourses that contained two contrasts between pairs of items (e.g., one story contrasted British scientists with French scientists and Malaysia with Indonesia). The end of each discourse referred to one item from each pair; these references received a pitch accent that either denoted contrast (L + H* in the ToBI system) or did not (H*). A contrastive accent on a particular pair improved later recognition memory equally for young and older adults. However, older adults showed decreased memory if the other pair received a contrastive accent (Experiment 1). Young adults with low working memory performance also showed this penalty (Experiment 2). These results suggest that pitch accents guide processing resources to important information for both older and younger adults but diminish memory for less important information in groups with reduced resources, including older adults.
The goal of this paper is to discuss and compare three feature extraction methods: Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC) and Weighted Linear Prediction Cepstral Coefficients (WLPCC) for recognizing the stuttered events. Speech samples from the University College London Archive of Stuttered Speech (UCLASS) were used for our analysis. The stuttered events were identified through manual segmentation and were used for feature extraction. Two simple classifiers namely, k-nearest neighbour (kNN) and Linear Discriminant Analysis (LDA) were employed for speech dysfluencies classification. Conventional validation method was used for testing the reliability of the classifier results. The study on the effect of different frame length, percentage of overlapping, value of ã in a first order pre-emphasizer and different order p were discussed. The speech dysfluencies classification accuracy was found to be improved by applying statistical normalization before feature extraction. The experimental investigation elucidated LPC, LPCC and WLPCC features can be used for identifying the stuttered events and WLPCC features slightly outperforms LPCC features and LPC features.
To investigate the allele and genotype frequencies and associated risk of interleukin (IL)-8 -251T>A polymorphism on colorectal cancer (CRC) susceptibility risk.
Experimental findings indicate that in-situ chondrocytes die readily following impact loading, but remain essentially unaffected at low (non-impact) strain rates. This study was aimed at identifying possible causes for cell death in impact loading by quantifying chondrocyte mechanics when cartilage was subjected to a 5% nominal tissue strain at different strain rates. Multi-scale modelling techniques were used to simulate cartilage tissue and the corresponding chondrocytes residing in the tissue. Chondrocytes were modelled by accounting for the cell membrane, pericellular matrix and pericellular capsule. The results suggest that cell deformations, cell fluid pressures and fluid flow velocity through cells are highest at the highest (impact) strain rate, but they do not reach damaging levels. Tangential strain rates of the cell membrane were highest at the highest strain rate and were observed primarily in superficial tissue cells. Since cell death following impact loading occurs primarily in superficial zone cells, we speculate that cell death in impact loading is caused by the high tangential strain rates in the membrane of superficial zone cells causing membrane rupture and loss of cell content and integrity.
This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases.