The identification of novel Mycobacterium tuberculosis DHFR inhibitors and the investigation of their binding preferences by using molecular modelling

It is an urgent need to develop new drugs for Mycobacterium tuberculosis (Mtb), and the enzyme, dihydrofolate reductase (DHFR) is a recognised drug target. The crystal structures of methotrexate binding to mt- and h-DHFR separately indicate that the glycerol (GOL) binding site is likely to be critical for the function of mt-DHFR selective inhibitors. We have used in silico methods to screen NCI small molecule database and a group of related compounds were obtained that inhibit mt-DHFR activity and showed bactericidal effects against a test Mtb strain. The binding poses were then analysed and the influence of GOL binding site was studied by using molecular modelling. By comparing the chemical structures, 4 compounds that might be able to occupy the GOL binding site were identified. However, these compounds contain large hydrophobic side chains. As the GOL binding site is more hydrophilic, molecular modelling indicated that these compounds were failed to occupy the GOL site. The most potent inhibitor (compound 6) demonstrated limited selectivity for mt-DHFR, but did contain a novel central core (7H-pyrrolo[3,2-f]quinazoline-1,3-diamine), which may significantly expand the chemical space of novel mt-DHFR inhibitors. Collectively, these observations will inform future medicinal chemistry efforts to improve the selectivity of compounds against mt-DHFR

Critical remarks on some applications of digital image analysis with emphasis on statistical methodology

The results of Rijal and Noor [1] and [2] regarding the use of statistical methods and sources of uncertainty associated with making inferences when using digital images provide the motivation for this study. In this paper three other examples are presented with the purpose of providing an overview of applications (possibly statistical) of image analysis, and general issues highlighted. One conclusion from this study is that statistical/mathematical methodology should be emphasized in digital image analysis

Astatistical performance in dicator in some image processing problems / Chang Yun Fah

The ability to compare or relate two digital images may be useful in developing performance evaluation algorithms. This thesis investigates the use of a particular correlation measure, 2 p R developed from the multidimensional unreplicated linear functional relationship (MULFR) model with single slope, as a measure or indicator of performance. This MULFR model is an extended version of the ULFR model introduced by Adcock in 1877. A literature survey was carried out showing that 2 p R has not been used before. The coefficient 2 p R was investigated in its ability to handle the issues of non-perfect reference image, multiple image attributes and combining image local-global information simultaneously. This survey is followed with the maximum likelihood estimation of parameters and a brief discussion of some theoretical properties of 2 p R . To investigate robust properties of 2 p R , an extensive simulation exercise was then carried out. Promising results, thus far, motivate the use of 2 p R in two image analysis problems; firstly a character recognition problem and secondly a particular data compression problem. In a handwritten Chinese character recognition problem, the 2 p R achieved the highest recognition rates even the pre-processing stage is removed from the recognition system. A substantial reduction of processing time, approximately 40.36% to 75.31%, was achieved using 2 p R . In JPEG compression problem, 2 p R is used as a measure of image quality which in turn indicates the performance of the compression method. It is shown that 2 p R performs well and satisfies the monotonicity, accuracy and consistency properties when perfect reference image was used. 2 p R was also shown to perform better than some frequently used similarity measures when imperfect reference image was used

Multidimensional unreplicated linear functional relationship model with single slope and its coefficient of determination

Multidimensional unreplicated linear functional relationship model (MULFR) with single slope is considered where p-dimensional measurement errors are introduced. When the ratio of error variances is known, the parameters’ estimation can be considered as a generalization of the unreplicated linear functional relationship model. However, investigation on unbiased property of the estimators are not strict-forward. Taylor approximation is applied to show the intercept and slope estimators are approximately unbiased. The consistency property is discussed using Fisher Information Matrix. The coefficient of determination for MULFR model and its properties are also studied. A simulation study is carried out to evaluate the proposed estimators of the intercept and slope, and the coefficient of determination. This coefficient of determination provides a useful analysis tool for many image processing applications. A numerical example for JPEG compressed image quality assessment is explained

Exploring the role of correlations for analyzing the Malaysia road accident problem

The belief or misconception that correlation may mean causation has seen widespread use of various types of correlations in the analysis of numerical data. The role of correlation, in particular partial correlations, in specific areas of data analysis; approach of problem, interrelationships of variables and modeling will be considered in a particular case study, namely the Malaysian road accident proble

A Study of an Architecture Design Learning Process Based on Social Learning, Course Teaching, Interaction, and Analogical Thinking

The students in the vocational education of architecture design in Taiwan often face many learning obstacles, such as no problem solving ability and lack of creativity. Therefore, this study used a social learning model as a learning strategy in the architecture design learning process to solve related learning difficulties. Firstly, this study used cognitive development teaching activities and a learning process based on analogical thinking and analogical reasoning to build the social learning model. Secondly, the social learning model of this study was implemented in the teaching of a required course of architecture design for 120 freshmen in China University of Technology. The questionnaire survey results were then statically analyzed and compared to measure the differences in the students’ knowledge about architecture designs before and after the teaching in this study. In this study, the social learning model is proven helpful in inspiring the students’ creativity by converting new knowledge of architecture design into schemas and hence retaining the new knowledge for future application. The social learning model can be applied in the teaching of architecture design in other schools, while more research can be conducted in the future to further confirm its feasibility to promote effective learning

Efficiency analysis by combination of frontier methods: Evidence from unreplicated linear functional relationship model

This study proposes a new efficiency measurement technique CDS as combination of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) and compares the CDS efficiency score with the DEA and SFA efficiency scores. The financial companies listed in Malaysian Stock Exchange for the period 2007-2016 are used to estimate the different types of efficiency score. Besides, linear regression analysis and ULFR (unreplicated linear functional relationship) analysis are used to analyze the performance of this CDS technique with the DEA and SFA techniques. The result suggests that the most efficient model is CDS which has a significant positive correlation with profit risk. Among the CDS, DEA and SFA techniques, the recommended technique (CDS) shows higher coefficient of determination values for both ULFR (0.9994) and linear regression (0.292) analysis. Also, based on the results of CDS, this study postulates that the most efficient firm is ACSM (Aeon Credit Service (M) Bhd) and the least efficient firm is MAY (Malayan Banking Bhd)

Enhanced conflict monitoring via a short-duration, video-assisted deep breathing in healthy young adults: an event-related potential approach through the Go/NoGo paradigm

Objectives Practitioners of mindfulness are reported to have greater cognitive control especially in conflict monitoring, response inhibition and sustained attention. However, due to the various existing methods in each mindfulness practices and also, the high commitment factor, a barrier still exists for an individual to pick up the practices. Therefore, the effect of short duration deep breathing on the cognitive control is investigated here. Methods Short duration guided deep breathing videos consisting of 5, 7 and 9 min respectively were created and used on subjects training. The effect on cognitive control was assessed using a Go/NoGo task along with event-related potential (ERP) measurements at Fz, Cz, and Pz. Results From the study, the significant outcome showed at the follow-up session in which participants engaged for 5 min deep breathing group showed a profound NoGo N2 amplitude increment as compared to the control group, indicating an enhanced conflict monitoring ability. An inverse relationship between the NoGo N2 amplitude and the breathing duration is observed as well at the follow-up session. Conclusion These results indicated the possibility of performing short duration deep breathing guided by a video to achieve an enhanced conflict monitoring as an alternative to other mindfulness practices and 5 min is found to be the optimum practice duration. Significant This study is the first to establish a relationship between deep breathing and conflict monitoring through ERP. The study population of young adults taken from the same environment reduces the variance in ERP results due to age and environment. Limitation A larger sample size would provide a greater statistical power. A longer duration of deep breathing should be investigated to further clarify the relationship between the practice duration and the NoGo N2 amplitude. The result can be split by gender and analyzed separately due to the different brain structure of males and females

Raw EEG data for 'Enhanced conflict monitoring via deep breathing'

Included here are the raw EEG signals and the behavioral data from the Go/NoGo task. The ERP was extracted using EEGLAB and ERPLAB using the included eventlist files