Increasing use of therapeutic peptides for treating cancer has received considerable attention of the scientific community in the recent years. The present study describes the in silico model developed for predicting and designing anticancer peptides (ACPs). ACPs residue composition analysis show the preference of A, F, K, L and W. Positional preference analysis revealed that residues A, F and K are favored at N-terminus and residues L and K are preferred at C-terminus. Motif analysis revealed the presence of motifs like LAKLA, AKLAK, FAKL and LAKL in ACPs. Machine learning models were developed using various input features and implementing different machine learning classifiers on two datasets main and alternate dataset. In the case of main dataset, dipeptide composition based ETree classifier model achieved maximum Matthews correlation coefficient (MCC) of 0.51 and 0.83 area under receiver operating characteristics (AUROC) on the training dataset. In the case of alternate dataset, amino acid composition based ETree classifier performed best and achieved the highest MCC of 0.80 and AUROC of 0.97 on the training dataset. Five-fold cross-validation technique was implemented for model training and testing, and their performance was also evaluated on the validation dataset. Best models were implemented in the webserver AntiCP 2.0, which is freely available at https://webs.iiitd.edu.in/raghava/anticp2/. The webserver is compatible with multiple screens such as iPhone, iPad, laptop and android phones. The standalone version of the software is available at GitHub; docker-based container also developed.

BACKGROUND: Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD. RESULT: Firstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 Å. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 Å in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 Å and 2.88 Å for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 Å and 2.09 Å for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively. CONCLUSION: The study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of re-docking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench ( http://webs.iiitd.edu.in/raghava/ppdbench/ ).

In the 2.4 GHz unlicensed spectrum, the coexistence of WiFi, Bluetooth and IEEE 802.15.4 devices generates increased channel contention. Notably, low-power wireless networks experience packet loss and delays due to interference. To improve the performance of low-power wireless networks under interference, we propose a data driven proactive approach based on interference modeling for white space prediction. We leverage statistical analysis of real-world traces from two indoor environments characterized by varying channel conditions to identify interference patterns. We characterize interference in terms of Inter-Arrival Time (IAT) and number of interfering signals and use a Gaussian Mixture Model (GMM) to accurately estimate the interference distribution as observed by the low-power wireless nodes. Then, we use a Hidden Markov Model (HMM) for white space prediction. Our validation w.r.t. real-world traces from two environments show that our GMM model can estimate interference with an accuracy higher than 94:7%. Moreover, the white space prediction evaluation shows an average accuracy of 97:7% and 89:5% across the two environments.

Cross Technology Interference (CTI) is a prevalent phenomenon in the 2.4 GHz unlicensed spectrum causing packet losses and increased channel contention. In particular, WiFi interference is a severe problem for low-power wireless networks causing a significant degradation of the overall performance. We propose here a proactive approach based on WiFi interference modeling for accurately predicting transmission opportunities for low-power wireless networks. We leverage statistical analysis of real-world WiFi traces to learn aggregated traffic characteristics in terms of Inter-Arrival Time (IAT) that, once captured into a specific 2nd order Markov Modulated Poisson Process (MMPP(2)) model, enable accurate estimation of interference. We further use a hidden Markov model (HMM) for channeloccupancy prediction. We evaluated the performance of: i) the MMPP(2) traffic model w. r. t. real-world traces and an existing Pareto model for accurately characterizing the WiFi traffic and, ii) compared the HMM based white space prediction to random channel access. We report encouraging results for using interference modeling for white space prediction.

The conventional approach for designing vaccine against a particular disease involves stimulation of the immune system using the whole pathogen responsible for the disease. In the post-genomic era, a major challenge is to identify antigenic regions or epitopes that can stimulate different arms of the immune system. In the past two decades, numerous methods and databases have been developed for designing vaccine or immunotherapy against various pathogen-causing diseases. This review describes various computational resources important for designing subunit vaccines or epitope-based immunotherapy. First, different immunological databases are described that maintain epitopes, antigens and vaccine targets. This is followed by in silico tools used for predicting linear and conformational B-cell epitopes required for activating humoral immunity. Finally, information on T-cell epitope prediction methods is provided that includes indirect methods like prediction of Major Histocompatibility Complex and transporter-associated protein binders. Different studies for validating the predicted epitopes are also examined critically. This review enlists novel in silico resources and tools available for predicting humoral and cell-mediated immune potential. These predicted epitopes could be used for designing epitope-based vaccines or immunotherapy as they may activate the adaptive immunity. Authors emphasized the need to develop tools for the prediction of adjuvants to activate innate and adaptive immune system simultaneously. In addition, attention has also been given to novel prediction methods to predict general therapeutic properties of peptides like half-life, cytotoxicity and immune toxicity.

We present a technique to identify transmission timing for IEEE 802.15.4 based Wireless Sensor Networks (WSNs) in the presence of WiFi interference. Our technique is based on modeling WiFi traffic with a Modulated Markov Poisson Process (MMPP) model in order to enable us to predict when WiFi transmissions take place and avoid them. We have evaluated the accuracy of our model in a small test-bed. Results are promising and suggest that our approach can increase the reliability of IEEE802.15.4 transmissions.

Wireless sensor networks (WSNs) are being increasingly used for monitoring and control in modern process industries, which until a few years ago were almost exclusively done by cables. In order to successfully integrate WSNs in process industries, proper understanding of the radio channel characteristics is necessary. Predominantly, in control applications, the sensors are static, and variations in the channel are caused by the objects in the region, which move to different locations every now and then. Hence, over a period of several hours, the radio channel will have completely different properties, which will have a significant impact on network performance. In this paper, we conduct and analyze channel measurements in a fully functional factory over a much longer time horizon of 20 hours, which, to the best of our knowledge, have not been undertaken before. A key observation from our analysis is that the radio channel, over a longer time scale, shows a more complicated behavior than over a relatively shorter time period. We also show that if the long-term channel variations are not effectively characterized, then we run the risk of transmitting at a rate, as low as, 94% less than what the channel could actually support.

One of the challenges for a successful use of wireless sensor networks in process industries is to design networks with energy efficient transmission, to increase the lifetime of the deployed network while maintaining the required latency and bit-error rate. The design of such transmission schemes depend on the radio channel characteristics of the region. This paper presents an investigation of the statistical properties of the radio channel in a typical process industry, particularly when the network is meant to be deployed for a long time duration, e.g., days, weeks, and even months. Using 17-20-h-long extensive measurement campaigns in a rolling mill and a paper mill, we highlight the non-stationarity in the environment and quantify the ability of various distributions, given in the literature, to describe the variations on the links. Finally, we analyze the design of an optimal received signal-to-noise ratio (SNR) for the deployed nodes and show that improper selection of the distribution for modeling of the variations in the channel can lead to an overuse of energy by a factor of four or even higher.

Tracking of people via active badges is important for location-aware computing and for security applications. However, the human body has a major effect on the antenna gain pattern of the device that the person is wearing. In this paper, the gain pattern due to the effect of the human body is experimentally measured and represented by a first-order directional gain pattern model. A method is presented to estimate the model parameters from multiple received signal strength (RSS) measurements. An alternating gain and position estimation (AGAPE) algorithm is proposed to jointly estimate the orientation and the position of the badge using RSS measurements at known-position anchor nodes. Lower bounds on mean squared error (MSE) and experimental results are presented that both show that the accuracy of position estimates can be greatly improved by including orientation estimates in the localization system. Next, we propose a new tracking filter that accepts orientation estimates as input, which we call the orientation-enhanced extended Kalman filter (OE-EKF), which improves tracking accuracy in active RFID tracking systems.

High-spatial-resolution videos offer the possibility of viewing an arbitrary region-of-interest (RoI) interactively. In this paper, we focus on a real-time video streaming system with interactive RoI functionality. Real-time video encoding poses a bottleneck for developing such a system. We present some solutions for speeding up the encoding process at the server so it can be performed in real-time. First, we encode only those video slices that intersect the users' RoIs during a given frame-interval. The server load can be further reduced by skipping the downsampling of the static background in the scene. We also make use of the modes and motion vectors information available in the original high-definition bitstream. Experimental results show that the encoding time at the server can be reduced significantly (more than 90% in some cases) without affecting the video quality or increasing the transmission bit-rate.

We consider the problem of rate allocation among multiple simultaneous video streams sharing multiple heterogeneous access networks. We develop and evaluate an analytical framework for optimal rate allocation based on observed available bit rate (ABR) and round-trip time (RTT) over each access network and video distortion-rate (DR) characteristics. The rate allocation is formulated as a convex optimization problem that minimizes the total expected distortion of all video streams. We present a distributed approximation of its solution and compare its performance against Hinfin-optimal control and two heuristic schemes based on TCP-style additive-increase-multiplicative-decrease (AIMD) principles. The various rate allocation schemes are evaluated in simulations of multiple high-definition (HD) video streams sharing multiple access networks. Our results demonstrate that, in comparison with heuristic AIMD-based schemes, both media-aware allocation and Hinfin-optimal control benefit from proactive congestion avoidance and reduce the average packet loss rate from 45% to below 2%. Improvement in average received video quality ranges between 1.5 to 10.7 dB in PSNR for various background traffic loads and video playout deadlines. Media-aware allocation further exploits its knowledge of the video DR characteristics to achieve a more balanced video quality among all streams.

Accurate representation of the physical layer is required for analysis and simulation of multi-hop networking in sensor, ad hoc, and mesh networks. Radio links that are geographically proximate often experience similar environmental shadowing effects and thus have correlated shadowing. This paper presents and analyzes a non-site-specific statistical propagation model which accounts for the correlations that exist in shadow fading between links in multi-hop networks. We describe two measurement campaigns to measure a large number of multi-hop networks in an ensemble of environments. The measurements show statistically significant correlations among shadowing experienced on different links in the network, with correlation coefficients up to 0.33. Finally, we analyze multi-hop paths in three and four node networks using both correlated and independent shadowing models and show that independent shadowing models can underestimate the probability of route failure by a factor of two or greater.

Accurate analysis and simulation in multi-hop (sensor, ad hoc, and mesh) networks requires accurate representation of physical layer fading processes. Current models either ignore fading or assume independent fading on links. In reality, the shadowing losses on two geographically proximate links are correlated by the common environment through which the radio waves travel. In this paper we propose a network shadowing (NeSh) model which connects shadowing on all links in a network to a model for the physical environment in which the network operates, thus explaining shadowing correlations between links. The NeSh model is then used to analyze connectivity in a simple multi-hop network.

Unlike current models for radio channel shadowing indicate, real-world shadowing losses on different links in a network are not independent. The correlations have both detrimental and beneficial impacts on sensor, ad hoc, and mesh networks. First, the probability of network connectivity reduces when link shadowing correlations are considered. Next, the variance bounds for sensor self-localization change, and provide the insight that algorithms must infer localization information from link correlations in order to avoid significant degradation from correlated shadowing. Finally, a major benefit is that shadowing correlations between links enable the tomographic imaging of an environment from pairwise RSS measurements. This paper applies measurement-based models, and measurements themselves, to analyze and to verify both the benefits and drawbacks of correlated link shadowing.

RFID is widely used to track the movement of goods through a supply chain. In this paper, we extend the domain of RFID by presenting SixthSense, a platform for RFID-based enterprise intelligence systems. We consider an enterprise setting where people (or rather their employee badges) and their personal objects such as books and mobiles are tagged with cheap, passive RFID tags, and there is good coverage of RFID readers in the workplace. SixthSense combines mobility information obtained from RFID-based sensing with information from enterprise systems such as calendar and presence, to automatically draw inferences about the association and interaction amongst people, objects, and workspaces. For instance, SixthSense is able to automatically distinguish between people and objects, learn the identities of people, and infer the ownership of objects by people.

This paper proposes a simple yet efficient localization algorithm for GPS-free sensor nodes in wireless sensor networks (WSN). Our approach is hybrid in the sense that it utilizes a combination of GPS-enabled and localized GPS-free nodes. It relaxes the need to equip a large number of nodes with GPS interfaces, to have high radio ranges for all nodes, and to have a uniform deployment of GPS-enabled nodes. The proposed strategy performs well, extends the lifetime of WSN (GPS and high radio ranges drain energy faster), and reduces cost (GPS devices increase the cost of sensors considerably).

Contemporary wireless devices integrate multiple networking technologies, such as cellular, WiMax and IEEE 802.11a/b/g, as alternative means of accessing the Internet. Efficient utilization of available bandwidth over heterogeneous access networks is important, especially for media streaming applications with high data rates and stringent delay requirements. In this work we consider the problem of rate allocation among multiple video streaming sessions sharing multiple access networks. We develop and evaluate an analytical framework for optimal video rate allocation, based on observed available bit rate (ABR) and round trip time (RTT) over each access network, as well as the video distortion-rate (DR) characteristics. The rate allocation is formulated as a convex optimization problem that minimizes the sum of expected distortion of all video streams. We then present a distributed approximation of the optimization, which enables autonomous rate allocation at each device in a media- and network-aware fashion. Performance of the proposed allocation scheme is compared against robust rate control based on H∞ optimal control and two heuristic schemes employing TCP style additive-increase-multiplicative-decrease (AIMD) principles. Wesimulate in NS-2 [1] simultaneous streaming of multiple high-definition(HD) video streams over multiple access networks, using ABR and RTT traces collected on Ethernet, IEEE 802.11g, and IEEE 802.11b networks deployed in a corporate environment. In comparison with heuristic AIMD-based schemes, rate allocation from both the media-aware convex optimization scheme and H∞ optimal control benefit from proactive avoidance of network congestion, and can reduce the average packet loss ratio from 27% to below 2%, while improving the average received video quality by 3.3 - 4.5 dB in PSNR.

We consider a scenario where devices with multiple networking capabilities access networks with heterogeneous characteristics. In such a setting, we address the problem of efficient utilization of multiple access networks (wireless and/or wireline) by devices via optimal assignment of traffic flows with given utilities to different networks. We develop and analyze a device middleware functionality that monitors network characteristics and employs a Markov Decision Process (MDP) based control scheme that in conjunction with stochastic characterization of the available bit rate and delay of the networks generates an optimal policy for allocation of flows to different networks. The optimal policy maximizes, under available bit rate and delay constraints on the access networks, a discounted reward which is a function of the flow utilities. The flow assignment policy is periodically updated and is consulted by the flows to dynamically perform network selection during their lifetimes. We perform measurement tests to collect traces of available bit rate and delay characteristics on Ethernet and WLAN networks on a work day in a corporate work environment. We implement our flow assignment framework in ns-2 and simulate the system performance for a set of elastic video-like flows using the collected traces. We demonstrate that the MDP based flow assignment policy leads to significant enhancement in the QoS provisioning (lower packet delays and packet loss rates) for the flows, as compared to policies which do not perform dynamic flow assignment but statically allocate flows to different networks using heuristics like average available bit rate on the networks.