AMAP : Hierarchical multi-label prediction of biologically active and antimicrobial peptides

Due to increase in antibiotic resistance in recent years, development of efficient and accurate techniques for discovery and design of biologically active peptides such as antimicrobial peptides (AMPs) has become essential. The screening of natural and synthetic AMPs in the wet lab is a challenge due to time and cost involved in such experiments. Bioinformatics methods can be used to speed up discovery and design of antimicrobial peptides by limiting the wet-lab search to promising peptide sequences. However, most such tools are typically limited to the prediction of whether a peptide exhibits antimicrobial activity or not and they do not identify the exact type of the biological activities of these peptides. In this work, we have designed a machine learning based model called AMAP for predicting biological activity of peptides with a specialized focus on antimicrobial activity prediction. AMAP used multi-label classification to predict 14 different types of biological functions of a given peptide sequence with improved accuracy in comparison to existing state of the art techniques. We have performed stringent performance analyses of the proposed method. In addition to cross-validation and performance comparison with existing AMP predictors, AMAP has also been benchmarked on recently published experimentally verified peptides that were not a part of our training set. We have also analyzed features used in this work and our analysis shows that the proposed predictor can generalize well in predicting biological activity of novel peptide sequences. A webserver of the proposed method is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#AMA

Combined Effect of Nicotine and Caffeine on Orthodontic Tooth Movement in Rats

Background: The individual effects of nicotine and caffeine have been reported in previous studies but their combined effect on tooth movement needs to be elucidated. The objective of this study was to evaluate the combined effect of nicotine and caffeine on the magnitude of orthodontic tooth movement (OTM) in rats. Material and Methods: This experimental study was conducted on Sprague-Dawley rats (Animal House and Pathology Laboratory; Post Graduate Medical Institute, Lahore) in the department of Orthodontics, de’Montmorency College of Dentistry, Lahore from 8th July 2014 to 8th January 2015. Forty male Sprague-Dawley rats were divided into four equal groups: Control group (CR), nicotine group (NT), caffeine group (CF) and combined nicotine and caffeine group (CNC). Closed coil nickel titanium (NiTi) spring was placed between incisor and maxillary molar. Nicotine group (NT) was treated by intraperitoneal injections of nicotine. Caffeine was given to caffeine group and Combined nicotine and caffeine group (CNC) was treated in the same way as individual nicotine and caffeine groups daily for 14 days. All the rats were sacrificed on 15th day. Magnitude of the orthodontic tooth movement was measured using digital Vernier caliper. Means and standard deviation were calculated for orthodontic tooth movement. One-way ANOVA was used to determine the mean difference in OTM. Post hoc Tukey test was used for multiple comparisons among the groups. Results: The mean orthodontic tooth movement (OTM) was 0.32 mm ± 0.05 in control group, 0.56 mm ± 0.04 in nicotine group, 0.52 mm ± 0.034 in caffeine group and 0.8 mm ± 0.06 in combined NC group, respectively. The difference between mean OTM among the groups was statistically significant (P-value <0.001). The mean OTM in CNC group was significantly higher as compared to other groups (CR, NT, CF, NT) (P-value <0.001). Conclusions: In rats, the combined use of nicotine and caffeine results in greater orthodontic tooth movement as compared to their individual use. Key words: Bone remodeling, Caffeine, Nicotine, Orthodontic tooth movemen

A blockchain-based Fog-oriented lightweight framework for smart public vehicular transportation systems

Rapid urbanization is putting a strain on the transport systems of cities worldwide. The effects of this trend include prolonged traffic jams and increasing environmental pollution from rising CO 2 emissions. As city planning requires innovative ways of dealing with the rapid urbanization trend, technological solutions were proposed such as cloud computing, smart vehicles, and Vehicular Ad hoc NETwork (VANET). In this paper, we take advantage of next-generation network technologies to propose a responsive and lightweight framework for smart transportation system which employs blockchain for authentication using fog computing's improvement over cloud computing for distributed applications to provide an efficient and secure transportation system. We take into account the future technologies of 5G and Beyond 5G (B5G) and argue that the integration of B5G technologies, federated learning, blockchain, and edge computing provides the perfect platform necessary for a smart transportation system The evaluation of the proposed framework is done by comparing it to the current cloud-based approach in iFogSim, a popular simulation tool for fog computing research. The evaluation of blockchain-based authentication was done using a customized implementation of blockchain executed in an experimental setup. The simulation results showed that the proposed framework provides superior performance in terms of security, latency, and energy consumption of the system.</p