Mannopyranoside Glycolipids Inhibit Mycobacterial and Biofilm Growth and Potentiate Isoniazid Inhibition Activities in M. smegmatis

Lipomannan and lipoarabinomannan are integral components of the mycobacterial cell wall. Earlier studies demonstrated that synthetic arabinan and arabinomannan glycolipids acted as inhibitors of mycobacterial growth, in addition to exhibiting inhibitory activities of mycobacterial biofilm. Herein, it is demonstrated that synthetic mannan glycolipids are better inhibitors of mycobacterial growth, whereas lipoarabinomannan has a higher inhibition efficiency to biofilm. Syntheses of mannan glycolipids with a graded number of mannan moieties and an arabinomannan glycolipid are conducted by chemical methods and subsequent mycobacterial growth and biofilm inhibition studies are conducted on Mycobacterium smegmatis. Growth inhibition of (73±3) % is observed with a mannose trisaccharide containing a glycolipid, whereas this glycolipid did not promote biofilm inhibition activity better than that of arabinomannan glycolipid. The antibiotic supplementation activities of glycolipids on growth and biofilm inhibitions are evaluated. Increases in growth and biofilm inhibitions are observed if the antibiotic is supplemented with glycolipids, which leads to a significant reduction of inhibition concentrations of the antibiotic.     

Construction of a radial basis function network using an evolutionary algorithm for grade estimation in a placer gold deposit

This paper presents a study highlighting the predictive performance of a radial basis function (RBF) network in estimating the grade of an offshore placer gold deposit. In applying the radial basis function network to grade estimation of the deposit, several pertinent issues regarding RBF model construction are addressed in this study. One of the issues is the selection of the RBF network along with its center and width parameters. Selection was done by an evolutionary algorithm that utilizes the concept of cooperative coevolutions of the RBFs and the associated network. Furthermore, the problem of data division, which arose during the creation of the training, calibration and validation of data sets for the RBF model development, was resolved with the help of an integrated approach of data segmentation and genetic algorithms (GA). A simulation study conducted showed that nearly 27% of the time, a bad data division would result if random data divisions were adopted in this study. In addition, the efficacy of the RBF network was tested against a feed-forward network and geostatistical techniques. The outcome of this comparative study indicated that the RBF model performed decisively better than the feed-forward network and the ordinary kriging (OK).     

Artificial neural networks and genetic algorithm for bearing fault detection

A study is presented to compare the performance of three types of artificial neural network (ANN), namely, multi layer perceptron (MLP), radial basis function (RBF) network and probabilistic neural network (PNN), for bearing fault detection. Features are extracted from time domain vibration signals, without and with preprocessing, of a rotating machine with normal and defective bearings. The extracted features are used as inputs to all three ANN classifiers: MLP, RBF and PNN for two- class (normal or fault) recognition. Genetic algorithms (GAs) have been used to select the characteristic parameters of the classifiers and the input features. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine. The roles of different vibration signals and preprocessing techniques are investigated. The results show the effectiveness of the features and the classifiers in detection of machine condition.     

Favorable influence of ssDNA-functionalized SWCNT on the navigation pattern of C. elegans

Microsystem Technologies - Single-Walled Carbon Nanotubes (SWCNTs) are widely used as potential carriers in drug delivery systems. The objective of this work was to observe the effects of pristine,...     

Generating vegetation leaf area index Earth system data record from multiple sensors. Part 2: Implementation,analysis and validation

The evaluation of a new global monthly leaf area index (LAI) data set for the period July 1981 to December 2006 derived from AVHRR Normalized Difference Vegetation Index (NDVI) data is described. The physically based algorithm is detailed in the first of the two part series. Here, the implementation, production and evaluation of the data set are described. The data set is evaluated both by direct comparisons to ground data and indirectly through inter-comparisons with similar data sets. This indirect validation showed satisfactory agreement with existing LAI products, importantly MODIS, at a range of spatial scales, and significant correlations with key climate variables in areas where temperature and precipitation limit plant growth. The data set successfully reproduced well-documented spatio-temporal trends and inter-annual variations in vegetation activity in the northern latitudes and semi-arid tropics. Comparison with plot scale field measurements over homogeneous vegetation patches indicated a 7% underestimation when all major vegetation types are taken into account. The error in mean values obtained from distributions of AVHRR LAI and high-resolution field LAI maps for different biomes is within 0.5 LAI for six out of the ten selected sites. These validation exercises though limited by the amount of field data, and thus less than comprehensive, indicated satisfactory agreement between the LAI product and field measurements. Overall, the inter-comparison with short-term LAI data sets, evaluation of long term trends with known variations in climate variables, and validation with field measurements together build confidence in the utility of this new 26 year LAI record for long term vegetation monitoring and modeling studies.     

Inventory model with two rates of production for deteriorating items with permissible delay in payments

Some decentralized smoothing problems are solved by applying a forward-pass fixed-interval smoother formula in discrete-time systems. It is assumed that a simple estimation structure consists of a global processor and of two local processors. Two cases are considered for the problems of decentralized smoothing and smoothing update: when the local backward-pass information filtered estimates are available, and when the local-smoothed estimates are available. Some features of present algorithms are discussed from the point of view of data transmissions and numerical computations, etc.     

Multiresolution approach for multiple human detection using moments and local binary patterns

Human detection is a central problem in development of any surveillance application. In this study, we present a simple and efficient, multi-resolution gray scale invariant approach for multiple human detection. The multiresolution is important for objects of different size and gray scale invariance is important due to uneven illumination and within-class variability. The proposed method is based on integration of central moments upon multi-resolution gray scale invariant local binary patterns operator. Since, the local binary patterns operator is invariant against different resolutions of space scale and monotonic change in gray scale, therefore the proposed method is robust in terms of variations in space scale as well as gray scale. Another advantage is high computational accuracy of the method due to use of moment operator which enhances the efficiency of the proposed method. Moreover, the proposed method is simple, as these operations can be performed within a few steps in a small neighborhood and a lookup table. The proposed method is tested on multiple human images and experimentally found appropriate for multiple human detection. The proposed method has been evaluated over two datasets, one is our own created dataset and the other is standard INRIA human detection dataset. Experimental results obtained from the proposed method demonstrate that better discrimination can be achieved for human and non-human objects in real scenes.     

Artificial neural network based system identification and model predictive control of a flotation column

The paper describes the design of a neural network based model predictive controller for controlling the interface level in a flotation column. For the system identification, the tailings valve opening is subjected to a pseudo-random ternary signal and response of the interface level is recorded over a period of time. The data so generated is used to develop a dynamic feed forward neural network model. The model uses two past values and one present value of the tailings valve opening as well as interface level as inputs and predicts the future interface level. This model is used for the design of a model predictive controller to control the interface level. The controller was tested both for liquid–gas system as well as liquid–gas–solid system and was found to perform very satisfactorily. The performance of the controller was compared with that of a conventional PI controller for a two-phase system and was found to be better.     

The efficiency of private and public schools in urban and rural areas: moving beyond the development goals

Data from the Young Lives study are used to evaluate the efficiency of education systems in four low and middle income countries: Ethiopia, India, Peru and Vietnam. A meta‐frontier variant of data envelopment analysis is used to assess the relative performance of each country's system, and, within each country, to evaluate the impact of public and private schooling, and of urban and rural location. Comparisons are drawn between the four countries; the results indicate that in no country does the educational system perform uniformly badly or well. Conditioning on the inputs available, rural areas are often indicative of higher levels of efficiency, thus suggesting a number of implications for policy.