Structure‐Guided Design of Thiazolidine Derivatives as Mycobacterium tuberculosis Pantothenate Synthetase Inhibitors

The pantothenate biosynthetic pathway is essential for the persistent growth and virulence of Mycobacterium tuberculosis (Mtb) and one of the enzymes in the pathway, pantothenate synthetase (PS, EC: 6.3.2.1), encoded by the panC gene, has become an appropriate target for new therapeutics to treat tuberculosis. Herein, we report nanomolar thiazolidine inhibitors of Mtb PS developed by a rational inhibitor design approach. The thiazolidine compounds were discovered by using energy‐based pharmacophore modelling and subsequent in vitro screening, which resulted in compounds with a half maximal inhibitory concentration (IC₅₀) value of (1.12±0.12) μM . These compounds were subsequently optimised by a combination of modelling and synthetic chemistry. Hit expansion of the lead by chemical synthesis led to an improved inhibitor with an IC₅₀ value of 350 nM and an Mtb minimum inhibitory concentration (MIC) of 1.55 μM . Some of these compounds also showed good activity against dormant Mtb cells.     

Investigation on Maximizing Packet Delivery Rate in WSN Using Cluster Approach

Wireless Personal Communications - Wireless Sensor Network plays a vital role and part of real time communication applications. Location of unknown node is difficult to find in the presence of...     

Topology Driven Cooperative Self Scheduling for Improved Lifetime Maximization in WSN

In Wireless Sensor Network (WSN), scheduling is one of the important issues that impacts the lifetime of entire WSN. Various scheduling schemes have been proposed earlier to increase the lifetime of the network. Still, the results from such methods are compromised in terms of achieving high lifetime. With this objective to increase the lifetime of network, an Efficient Topology driven Cooperative Self-Scheduling (TDCSS) model is recommended in this study. Instead of scheduling the network nodes in a centralized manner, a combined approach is proposed. Based on the situation, the proposed TDCSS approach performs scheduling in both the ways. By sharing the node statistics in a periodic manner, the overhead during the transmission of control packets gets reduced. This in turn impacts the lifetime of all the nodes. Further, this also reduces the number of idle conditions of each sensor node which is required for every cycle. The proposed method enables every sensor to schedule its own conditions according to duty cycle and topology constraints. Central scheduler monitors the network conditions whereas total transmissions occurs at every cycle. According to this, the source can infer the possible routes in a cycle and approximate the available routes. Further, based on the statistics of previous transmissions, the routes towards the sink are identified. Among the routes found, a single optimal route with energy efficiency is selected to perform data transmission. This cooperative approach improves the lifetime of entire network with high throughput performance.     

An efficient chaos based image encryption algorithm using enhanced thorp shuffle and chaotic convolution function

A Unique chaotic image encryption on the basis of Enhanced Thorp shuffle and Zig-zag Scan based Convolution (ETS-ZSC) is proposed in this paper. A one-dimensional chaotic map is utilized for both shuffling the plain image and producing the critical grid for the convolution activity. The substitution operation is performed in two ways: forward substitution and reverse substitution with zigzag scan. The original seed of the logistic map is created from the hyper chaotic system by matching with the plain image to overcome the differential attacks. The security analyzes are held for the proposed method to prove safe against the chosen plain text/known plain text attack. From the simulations results, it is observed that the proposed methodology is having enough high key sensitivity, key space, good randomness, and equal sharing of pixels in cipher image.

     

Oil sorption behavior of acetylated nettle fiber

Abstract

In this work, acetylation of raw nettle fibers was performed to improve the oil sorption capacity. Raw nettle fibers were acetylated with acetic anhydride using N-Bromosuccinimide (NBS) as a catalyst. Box–Behnken experimental design was used to study the effect of some selected parameters such as reaction time, reaction temperature and catalyst concentration (%) on weight percent gain (WPG) and oil sorption behavior. Highest WPG and oil sorption were achieved at 90?min reaction time, 120?°C and 2% catalyst. The oil sorption of acetylated nettle was 23.21?g/g and 18.75?g/g against diesel engine oil and crude oil, respectively. Nettle fibers were also characterized by Fourier transform infrared spectroscopy, thermal gravimetric analysis and Scanning electron microscopy. Oil sorption capacity of acetylated nettle were higher than that of commercial polypropylene sorbent. Hence, these acetylated nettle fibers can be used in the place of synthetic sorbents for oil spill cleanup applications.     

Effect of different solvents on volatile and non-volatile constituents of red bell pepper (<Emphasis Type="Italic">Capsicum annuum</Emphasis> L.) and their in vitro antioxidant activity

The fresh fruit of Capsicum annuum L. (red bell pepper) was successively extracted using n-hexane, chloroform, ethyl acetate, ethanol and their percentage yield was calculated. The effectiveness of each extract on chemical composition and antioxidant activity was studied. The qualitative phytochemical evaluation of each extract of bell pepper was done by HPTLC and Gas chromatography–mass spectroscopy (GC-MS) analysis. The total content of phenols, flavonoids and carotenoids were estimated by standard chemical methods. Further, the antioxidant potential of each extract was measured via DPPH and reducing power assays. Gas chromatography–mass spectroscopy analysis showed that the majority of compounds were related to phenols and flavonoids. Further analysis of the extract by HPTLC verified the presence of different types of phenolic compounds in addition to flavonoids and carotenoids. Among the different solvent extracts analyzed, total phenolic content was higher in ethanol extract (7.136?±?0.03%, w/w) whereas ethyl acetate extract showed the presence of higher flavonoid content (4.0521?±?0.03%, w/w). The ethanol and ethyl acetate extracts of the fruit of C. annuum exhibited the highest radical scavenging activity with inhibition percentage of 53.66 and 49.55% at a concentration of 254 µg/ml. Based on the biochemical analysis and phytochemical screening, we conclude that C. annuum possess potent antioxidant potential and this ability of the extract is attributed to the presence of rich polyphenolic compounds.     

Energy and electromagnetic pollution considerations in ARoF-based multi-operator multi-service systems

Energy-efficient system design with controlled levels of electromagnetic pollution, without compromising on the user quality of experience and operator revenue, are essential considerations in the design of next-generation wireless communication networks. Integration of multiple services and multiple operator transmissions onto a common shared infrastructure is the solution forward to satisfy the contradictory subscriber and operator requirements and also to derive a future-proof fronthaul to support new techniques like opportunistic, co-operative and cognitive communications. This paper reports the link budget analysis, energy efficiency estimation and electromagnetic radiation calculation for a Multi-Operator Multi-Service Analog Radio over Fiber fronthaul for a small cell configuration. The BER analysis and the link budget analysis are presented for the central base station to remote antenna unit (RAU) link as well as the RAU to user equipment link. The estimated minimum transmission power sufficient to establish and maintain a quality connection suggests the possibility of eliminating the energy consuming power amplifiers in the transceivers and also achieve a reduced electromagnetic pollution in the environment.     

Bridging semantic gap between high-level and low-level features in content-based video retrieval using multi-stage ESN–SVM classifier

Content-based video retrieval system aims at assisting a user to retrieve targeted video sequence in a large database. Most of the search engines use textual annotations to retrieve videos. These types of engines offer a low-level abstraction while the user seeks high-level semantics. Bridging this type of semantic gap in video retrieval remains an important challenge. In this paper, colour, texture and shapes are considered to be low-level features and motion is a high-level feature. Colour histograms convert the RGB colour space into YcbCr and extract hue and saturation values from frames. After colour extraction, filter mask is applied and gradient value is computed. Gradient and threshold values are compared to draw the edge map. Edges are smoothed for sharpening to remove the unnecessary connected components. These diverse shapes are then extracted and stored in shape feature vectors. Finally, an SVM classifier is used for classification of low-level features. For high-level features, depth images are extracted for motion feature identification and classification is done via echo state neural networks (ESN). ESN are a supervised learning technique and follow the principle of recurrent neural networks. ESN are well known for time series classification and also proved their effective performance in gesture detection. By combining the existing algorithms, a high-performance multimedia event detection system is constructed. The effectiveness and efficiency of proposed event detection mechanism is validated using MSR 3D action pair dataset. Experimental results show that the detection accuracy of proposed combination is better than those of other algorithms     

Speech based automatic personality perception using spectral features

Automatic personality perception is the prediction of personality that others attribute to a person in a given situation. The aim of automatic personality perception is to forecast the behaviour of the speaker perceived by the listener from nonverbal behavior. Extroversion, Conscientiousness, Agreeableness, Neuroticism, and Openness are the speaker traits used for personality assessment. In this work, a speaker trait prediction approach for automatic personality assessment is proposed. This approach is based on modeling the relationship between speech signal and personality traits using spectral features. The experiments are achieved over the SSPNet Personality Corpus. The Frequency Domain Linear Prediction and Mel Frequency Cepstral Coefficient features are extracted for the prediction of speaker traits. The classification is done using Instance based k-Nearest neighbor and Support Vector Machine (SVM) classifiers. The experimental results show that k-Nearest Neighbor classifier outperforms SVM classifier. The classification accuracy is between 90 and 100%.